arcgis.features module

The arcgis.features module contains types and functions for working with features and feature layers in the GIS.

Entities located in space with a geometrical representation (such as points, lines or polygons) and a set of properties can be represented as features. The arcgis.features module is used for working with feature data, feature layers and collections of feature layers in the GIS. It also contains the spatial analysis functions which operate against feature data.

In the GIS, entities located in space with a set of properties can be represented as features. Features are stored as feature classes, which represent a set of features located using a single spatial type (point, line, polygon) and a common set of properties. This is the geographic extension of the classic tabular or relational representation for entities - a set of entities is modelled as rows in a table. Tables represent entity classes with uniform properties. In addition to working with entities with location as features, the system can also work with non-spatial entities as rows in tables. The system can also model relationships between entities using properties which act as primary and foreign keys. A collection of feature classes and tables, with the associated relationships among the entities, is a feature layer collection. FeatureLayerCollections are one of the dataset types contained in a Datastore. Finally, features are not simply entities in a dataset. Features have a visual representation and user experience - on a map, in a 3D scene, as entities with a property sheet or popups.

arcgis.features.Feature

class arcgis.features.Feature(geometry=None, attributes=None)

Entities located in space with a set of properties can be represented as features.

as_dict

returns the feature as a dictionary

as_row

converts a feature to a list for insertion into an insert cursor Output:

[row items], [field names] returns a list of fields and the row object

attributes

returns the feature attributes

fields

returns a list of feature fields

classmethod from_dict(feature)

returns a feature from a dict

classmethod from_json(json_str)

returns a feature from a JSON string

geometry

returns the feature geometry

geometry_type

returns the feature’s geometry type

get_value(field_name)

returns a value for a given field name

set_value(field_name, value)

sets an attribute value for a given field name

arcgis.features.FeatureLayer

class arcgis.features.FeatureLayer(url, gis=None, container=None, dynamic_layer=None)

The feature layer is the primary concept for working with features in a GIS.

Users create, import, export, analyze, edit, and visualize features, i.e. entities in space as feature layers.

Feature layers can be added to and visualized using maps. They act as inputs to and outputs from feature analysis tools.

Feature layers are created by publishing feature data to a GIS, and are exposed as a broader resource (Item) in the GIS. Feature layer objects can be obtained through the layers attribute on feature layer Items in the GIS.

calculate(where, calc_expression, sql_format='standard')

The calculate operation is performed on a feature layer resource. It updates the values of one or more fields in an existing feature service layer based on SQL expressions or scalar values. The calculate operation can only be used if the supportsCalculate property of the layer is true. Neither the Shape field nor system fields can be updated using calculate. System fields include ObjectId and GlobalId. See Calculate a field for more information on supported expressions

Inputs:

where - A where clause can be used to limit the updated records.

Any legal SQL where clause operating on the fields in the layer is allowed.

calcExpression - The array of field/value info objects that

contain the field or fields to update and their scalar values or SQL expression. Allowed types are dictionary and list. List must be a list of dictionary objects. Calculation Format is as follows:

{“field” : “<field name>”, “value” : “<value>”}

sqlFormat - The SQL format for the calcExpression. It can be

either standard SQL92 (standard) or native SQL (native). The default is standard. Values: standard, native

Output:

JSON as string

Usage: >>>print(fl.calculate(where=”OBJECTID < 2”,

calcExpression={“field”: “ZONE”,

“value” : “R1”}))

{‘updatedFeatureCount’: 1, ‘success’: True}

container

The feature layer collection to which this layer belongs.

delete_features(deletes=None, where=None, geometry_filter=None, gdb_version=None, rollback_on_failure=True)

This operation deletes features in a feature layer or table Inputs:

deletes - string of OIDs to remove from service where - A where clause for the query filter.

Any legal SQL where clause operating on the fields in the layer is allowed. Features conforming to the specified where clause will be deleted.

geometry_filter - spatial filter from arcgis.geometry.filters module to filter results by a

spatial relationship with another geometry

gdb_version - Geodatabase version to apply the edits. rollback_on_failure - Optional parameter to specify if the

edits should be applied only if all submitted edits succeed. If false, the server will apply the edits that succeed even if some of the submitted edits fail. If true, the server will apply the edits only if all edits succeed. The default value is true.

Output:

dictionary of messages

edit_features(adds=None, updates=None, deletes=None, gdb_version=None, use_global_ids=False, rollback_on_failure=True)

This operation adds, updates, and deletes features to the associated feature layer or table in a single call. Inputs:

adds - The array of features to be added. updates - The array of features to be updateded. deletes - string of OIDs to remove from service gdbVersion - Geodatabase version to apply the edits. useGlobalIds - instead of referencing the default Object ID

field, the service will look at a GUID field to track changes. This means the GUIDs will be passed instead of OIDs for delete, update or add features.

rollbackOnFailure - Optional parameter to specify if the

edits should be applied only if all submitted edits succeed. If false, the server will apply the edits that succeed even if some of the submitted edits fail. If true, the server will apply the edits only if all edits succeed. The default value is true.

Output:

dictionary of messages

classmethod fromitem(item, layer_id=0)

Creates a feature layer from a GIS Item. The type of item should be a ‘Feature Service’ that represents a FeatureLayerCollection. The layer_id is the id of the layer in feature layer collection (feature service).

generate_renderer(definition, where=None)

This operation groups data using the supplied definition (classification definition) and an optional where clause. The result is a renderer object. Use baseSymbol and colorRamp to define the symbols assigned to each class. If the operation is performed on a table, the result is a renderer object containing the data classes and no symbols.

Argument	Description
definition	required dict. The definition using the renderer that is generated. Use either class breaks or unique value classificatoin definitions. See: https://resources.arcgis.com/en/help/rest/apiref/ms_classification.html
where	optional string. A where clause for which the data needs to be classified. Any legal SQL where clause operating on the fields in the dynamic layer/table is allowed.

Returns:dictionary

get_html_popup(oid)

The htmlPopup resource provides details about the HTML pop-up authored by the user using ArcGIS for Desktop. Input:

oid - object id of the feature where the HTML pop-up

Output:

manager

Helper object to manage the feature layer, update it’s definition, etc

properties

The properties of this object

query(where='1=1', out_fields='*', time_filter=None, geometry_filter=None, return_geometry=True, return_count_only=False, return_ids_only=False, return_distinct_values=False, return_extent_only=False, group_by_fields_for_statistics=None, statistic_filter=None, result_offset=None, result_record_count=None, object_ids=None, distance=None, units=None, max_allowable_offset=None, out_sr=None, geometry_precision=None, gdb_version=None, order_by_fields=None, out_statistics=None, return_z=False, return_m=False, multipatch_option=None, quanitization_parameters=None, return_centroid=False, return_all_records=True, **kwargs)

queries a feature layer based on a sql statement Inputs:

where - the selection sql statement out_fields - the attribute fields to return object_ids - The object IDs of this layer or table to be

queried.

distance - The buffer distance for the input geometries.

The distance unit is specified by units. For example, if the distance is 100, the query geometry is a point, units is set to meters, and all points within 100 meters of the point are returned.

units - The unit for calculating the buffer distance. If

unit is not specified, the unit is derived from the geometry spatial reference. If the geometry spatial reference is not specified, the unit is derived from the feature service data spatial reference. This parameter only applies if supportsQueryWithDistance is true. Values: esriSRUnit_Meter | esriSRUnit_StatuteMile | esriSRUnit_Foot | esriSRUnit_Kilometer | esriSRUnit_NauticalMile | esriSRUnit_USNauticalMile

time_filter - a TimeFilter object where either the start time

or start and end time are defined to limit the search results for a given time. The values in the timeFilter should be as UTC timestampes in milliseconds. No checking occurs to see if they are in the right format.

geometry_filter - spatial filter from arcgis.geometry.filters module to filter results by a

spatial relationship with another geometry

max_allowable_offset - This option can be used to specify the

maxAllowableOffset to be used for generalizing geometries returned by the query operation. The maxAllowableOffset is in the units of outSR. If outSR is not specified, maxAllowableOffset is assumed to be in the unit of the spatial reference of the map.

out_sr - The spatial reference of the returned geometry. geometry_precision - This option can be used to specify the

number of decimal places in the response geometries returned by the Query operation.

gdb_version - Geodatabase version to query return_geometry - If true, geometry is returned with the query. Default is true. return_distinct_values - If true, it returns distinct values

based on the fields specified in outFields. This parameter applies only if the supportsAdvancedQueries property of the layer is true.

return_ids_only - If true, the response only includes an

array of object IDs. Otherwise, the response is a feature set. The default is false.

return_count_only - If true, the response only includes the

count (number of features/records) that would be returned by a query. Otherwise, the response is a feature set. The default is false. This option supersedes the returnIdsOnly parameter. If returnCountOnly = true, the response will return both the count and the extent.

return_extent_only - If true, the response only includes the

extent of the features that would be returned by the query. If returnCountOnly=true, the response will return both the count and the extent. The default is false. This parameter applies only if the supportsReturningQueryExtent property of the layer is true.

order_by_fields - One or more field names on which the

features/records need to be ordered. Use ASC or DESC for ascending or descending, respectively, following every field to control the ordering.

group_by_fields_for_statistics - One or more field names on

which the values need to be grouped for calculating the statistics.

out_statistics - The definitions for one or more field-based

statistics to be calculated.

return_z - If true, Z values are included in the results if

the features have Z values. Otherwise, Z values are not returned. The default is false.

return_m - If true, M values are included in the results if

the features have M values. Otherwise, M values are not returned. The default is false.

multipatch_option - This option dictates how the geometry of

a multipatch feature will be returned.

result_offset - This option can be used for fetching query

results by skipping the specified number of records and starting from the next record (that is, resultOffset + 1th). This option is ignored if return_all_records is True (i.e. by default).

result_record_count - This option can be used for fetching

query results up to the result_record_count specified. When result_offset is specified but this parameter is not, the map service defaults it to max_record_count. The maximum value for this parameter is the value of the layer’s max_record_count property. This option is ignored if return_all_records is True (i.e. by default).

quantization_parameters - Used to project the geometry onto

a virtual grid, likely representing pixels on the screen.

return_centroid - Used to return the geometry centroid

associated with each feature returned. If true, the result includes the geometry centroid. The default is false.

return_all_records - When True, the query operation will call

the service until all records that satisfy the where_clause are returned. Note: result_offset and result_record_count will be ignored if return_all_records is True. Also, if return_count_only, return_ids_only, or return_extent_only are True, this parameter will be ignored.

kwargs - optional parameters that can be passed to the Query

function. This will allow users to pass additional parameters not explicitly implemented on the function. A complete list of functions available is documented on the Query REST API.

Output:

A FeatureSet containing the features matching the query unless another return type is specified, such as count

query_related_records(object_ids, relationship_id, out_fields='*', definition_expression=None, return_geometry=True, max_allowable_offset=None, geometry_precision=None, out_wkid=None, gdb_version=None, return_z=False, return_m=False)

The Query operation is performed on a feature service layer resource. The result of this operation are feature sets grouped by source layer/table object IDs. Each feature set contains Feature objects including the values for the fields requested by the user. For related layers, if you request geometry information, the geometry of each feature is also returned in the feature set. For related tables, the feature set does not include geometries. Inputs:

objectIds - the object IDs of the table/layer to be queried relationshipId - The ID of the relationship to be queried. outFields - the list of fields from the related table/layer

to be included in the returned feature set. This list is a comma delimited list of field names. If you specify the shape field in the list of return fields, it is ignored. To request geometry, set returnGeometry to true. You can also specify the wildcard “*” as the value of this parameter. In this case, the result s will include all the field values.

definitionExpression - The definition expression to be

applied to the related table/layer. From the list of objectIds, only those records that conform to this expression are queried for related records.

returnGeometry - If true, the feature set includes the

geometry associated with each feature. The default is true.

maxAllowableOffset - This option can be used to specify the

maxAllowableOffset to be used for generalizing geometries returned by the query operation. The maxAllowableOffset is in the units of the outSR. If outSR is not specified, then maxAllowableOffset is assumed to be in the unit of the spatial reference of the map.

geometryPrecision - This option can be used to specify the

number of decimal places in the response geometries.

outWKID - The spatial reference of the returned geometry. gdbVersion - The geodatabase version to query. This parameter

applies only if the isDataVersioned property of the layer queried is true.

returnZ - If true, Z values are included in the results if

the features have Z values. Otherwise, Z values are not returned. The default is false.

returnM - If true, M values are included in the results if

the features have M values. Otherwise, M values are not returned. The default is false.

validate_sql(sql, sql_type='where')

The validate_sql operation validates an SQL-92 expression or WHERE clause. The validate_sql operation ensures that an SQL-92 expression, such as one written by a user through a user interface, is correct before performing another operation that uses the expression. For example, validateSQL can be used to validate information that is subsequently passed in as part of the where parameter of the calculate operation. validate_sql also prevents SQL injection. In addition, all table and field names used in the SQL expression or WHERE clause are validated to ensure they are valid tables and fields.

Parameters:

sql:

the SQL expression of WHERE clause to validate Example: “Population > 300000”

sql_type:

Three SQL types are supported in validate_sql - where (default) - Represents the custom WHERE clause the user

can compose when querying a layer or using calculate.

• expression - Represents an SQL-92 expression. Currently, expression is used as a default value expression when adding a new field or using the calculate API.
• statement - Represents the full SQL-92 statement that can be passed directly to the database. No current ArcGIS REST API resource or operation supports using the full SQL-92 SELECT statement directly. It has been added to the validateSQL for completeness. Values: where | expression | statement

arcgis.features.Table

class arcgis.features.Table(url, gis=None, container=None, dynamic_layer=None)

Tables represent entity classes with uniform properties. In addition to working with “entities with location” as features, the GIS can also work with non-spatial entities as rows in tables.

Working with tables is similar to working with feature layers, except that the rows (Features) in a table do not have a geometry, and tables ignore any geometry related operation.

calculate(where, calc_expression, sql_format='standard')

The calculate operation is performed on a feature layer resource. It updates the values of one or more fields in an existing feature service layer based on SQL expressions or scalar values. The calculate operation can only be used if the supportsCalculate property of the layer is true. Neither the Shape field nor system fields can be updated using calculate. System fields include ObjectId and GlobalId. See Calculate a field for more information on supported expressions

Inputs:

where - A where clause can be used to limit the updated records.

Any legal SQL where clause operating on the fields in the layer is allowed.

calcExpression - The array of field/value info objects that

contain the field or fields to update and their scalar values or SQL expression. Allowed types are dictionary and list. List must be a list of dictionary objects. Calculation Format is as follows:

{“field” : “<field name>”, “value” : “<value>”}

sqlFormat - The SQL format for the calcExpression. It can be

either standard SQL92 (standard) or native SQL (native). The default is standard. Values: standard, native

Output:

JSON as string

Usage: >>>print(fl.calculate(where=”OBJECTID < 2”,

calcExpression={“field”: “ZONE”,

“value” : “R1”}))

{‘updatedFeatureCount’: 1, ‘success’: True}

container

The feature layer collection to which this layer belongs.

delete_features(deletes=None, where=None, geometry_filter=None, gdb_version=None, rollback_on_failure=True)

This operation deletes features in a feature layer or table Inputs:

deletes - string of OIDs to remove from service where - A where clause for the query filter.

Any legal SQL where clause operating on the fields in the layer is allowed. Features conforming to the specified where clause will be deleted.

geometry_filter - spatial filter from arcgis.geometry.filters module to filter results by a

spatial relationship with another geometry

gdb_version - Geodatabase version to apply the edits. rollback_on_failure - Optional parameter to specify if the

edits should be applied only if all submitted edits succeed. If false, the server will apply the edits that succeed even if some of the submitted edits fail. If true, the server will apply the edits only if all edits succeed. The default value is true.

Output:

dictionary of messages

edit_features(adds=None, updates=None, deletes=None, gdb_version=None, use_global_ids=False, rollback_on_failure=True)

This operation adds, updates, and deletes features to the associated feature layer or table in a single call. Inputs:

adds - The array of features to be added. updates - The array of features to be updateded. deletes - string of OIDs to remove from service gdbVersion - Geodatabase version to apply the edits. useGlobalIds - instead of referencing the default Object ID

field, the service will look at a GUID field to track changes. This means the GUIDs will be passed instead of OIDs for delete, update or add features.

rollbackOnFailure - Optional parameter to specify if the

edits should be applied only if all submitted edits succeed. If false, the server will apply the edits that succeed even if some of the submitted edits fail. If true, the server will apply the edits only if all edits succeed. The default value is true.

Output:

dictionary of messages

fromitem(item, layer_id=0)

Creates a feature layer from a GIS Item. The type of item should be a ‘Feature Service’ that represents a FeatureLayerCollection. The layer_id is the id of the layer in feature layer collection (feature service).

generate_renderer(definition, where=None)

This operation groups data using the supplied definition (classification definition) and an optional where clause. The result is a renderer object. Use baseSymbol and colorRamp to define the symbols assigned to each class. If the operation is performed on a table, the result is a renderer object containing the data classes and no symbols.

Argument	Description
definition	required dict. The definition using the renderer that is generated. Use either class breaks or unique value classificatoin definitions. See: https://resources.arcgis.com/en/help/rest/apiref/ms_classification.html
where	optional string. A where clause for which the data needs to be classified. Any legal SQL where clause operating on the fields in the dynamic layer/table is allowed.

Returns:dictionary

get_html_popup(oid)

The htmlPopup resource provides details about the HTML pop-up authored by the user using ArcGIS for Desktop. Input:

oid - object id of the feature where the HTML pop-up

Output:

manager

Helper object to manage the feature layer, update it’s definition, etc

properties

The properties of this object

query(where='1=1', out_fields='*', time_filter=None, geometry_filter=None, return_geometry=True, return_count_only=False, return_ids_only=False, return_distinct_values=False, return_extent_only=False, group_by_fields_for_statistics=None, statistic_filter=None, result_offset=None, result_record_count=None, object_ids=None, distance=None, units=None, max_allowable_offset=None, out_sr=None, geometry_precision=None, gdb_version=None, order_by_fields=None, out_statistics=None, return_z=False, return_m=False, multipatch_option=None, quanitization_parameters=None, return_centroid=False, return_all_records=True, **kwargs)

queries a feature layer based on a sql statement Inputs:

where - the selection sql statement out_fields - the attribute fields to return object_ids - The object IDs of this layer or table to be

queried.

distance - The buffer distance for the input geometries.

The distance unit is specified by units. For example, if the distance is 100, the query geometry is a point, units is set to meters, and all points within 100 meters of the point are returned.

units - The unit for calculating the buffer distance. If

unit is not specified, the unit is derived from the geometry spatial reference. If the geometry spatial reference is not specified, the unit is derived from the feature service data spatial reference. This parameter only applies if supportsQueryWithDistance is true. Values: esriSRUnit_Meter | esriSRUnit_StatuteMile | esriSRUnit_Foot | esriSRUnit_Kilometer | esriSRUnit_NauticalMile | esriSRUnit_USNauticalMile

time_filter - a TimeFilter object where either the start time

or start and end time are defined to limit the search results for a given time. The values in the timeFilter should be as UTC timestampes in milliseconds. No checking occurs to see if they are in the right format.

geometry_filter - spatial filter from arcgis.geometry.filters module to filter results by a

spatial relationship with another geometry

max_allowable_offset - This option can be used to specify the

maxAllowableOffset to be used for generalizing geometries returned by the query operation. The maxAllowableOffset is in the units of outSR. If outSR is not specified, maxAllowableOffset is assumed to be in the unit of the spatial reference of the map.

out_sr - The spatial reference of the returned geometry. geometry_precision - This option can be used to specify the

number of decimal places in the response geometries returned by the Query operation.

gdb_version - Geodatabase version to query return_geometry - If true, geometry is returned with the query. Default is true. return_distinct_values - If true, it returns distinct values

based on the fields specified in outFields. This parameter applies only if the supportsAdvancedQueries property of the layer is true.

return_ids_only - If true, the response only includes an

array of object IDs. Otherwise, the response is a feature set. The default is false.

return_count_only - If true, the response only includes the

count (number of features/records) that would be returned by a query. Otherwise, the response is a feature set. The default is false. This option supersedes the returnIdsOnly parameter. If returnCountOnly = true, the response will return both the count and the extent.

return_extent_only - If true, the response only includes the

extent of the features that would be returned by the query. If returnCountOnly=true, the response will return both the count and the extent. The default is false. This parameter applies only if the supportsReturningQueryExtent property of the layer is true.

order_by_fields - One or more field names on which the

features/records need to be ordered. Use ASC or DESC for ascending or descending, respectively, following every field to control the ordering.

group_by_fields_for_statistics - One or more field names on

which the values need to be grouped for calculating the statistics.

out_statistics - The definitions for one or more field-based

statistics to be calculated.

return_z - If true, Z values are included in the results if

the features have Z values. Otherwise, Z values are not returned. The default is false.

return_m - If true, M values are included in the results if

the features have M values. Otherwise, M values are not returned. The default is false.

multipatch_option - This option dictates how the geometry of

a multipatch feature will be returned.

result_offset - This option can be used for fetching query

results by skipping the specified number of records and starting from the next record (that is, resultOffset + 1th). This option is ignored if return_all_records is True (i.e. by default).

result_record_count - This option can be used for fetching

query results up to the result_record_count specified. When result_offset is specified but this parameter is not, the map service defaults it to max_record_count. The maximum value for this parameter is the value of the layer’s max_record_count property. This option is ignored if return_all_records is True (i.e. by default).

quantization_parameters - Used to project the geometry onto

a virtual grid, likely representing pixels on the screen.

return_centroid - Used to return the geometry centroid

associated with each feature returned. If true, the result includes the geometry centroid. The default is false.

return_all_records - When True, the query operation will call

the service until all records that satisfy the where_clause are returned. Note: result_offset and result_record_count will be ignored if return_all_records is True. Also, if return_count_only, return_ids_only, or return_extent_only are True, this parameter will be ignored.

kwargs - optional parameters that can be passed to the Query

function. This will allow users to pass additional parameters not explicitly implemented on the function. A complete list of functions available is documented on the Query REST API.

Output:

A FeatureSet containing the features matching the query unless another return type is specified, such as count

query_related_records(object_ids, relationship_id, out_fields='*', definition_expression=None, return_geometry=True, max_allowable_offset=None, geometry_precision=None, out_wkid=None, gdb_version=None, return_z=False, return_m=False)

The Query operation is performed on a feature service layer resource. The result of this operation are feature sets grouped by source layer/table object IDs. Each feature set contains Feature objects including the values for the fields requested by the user. For related layers, if you request geometry information, the geometry of each feature is also returned in the feature set. For related tables, the feature set does not include geometries. Inputs:

objectIds - the object IDs of the table/layer to be queried relationshipId - The ID of the relationship to be queried. outFields - the list of fields from the related table/layer

to be included in the returned feature set. This list is a comma delimited list of field names. If you specify the shape field in the list of return fields, it is ignored. To request geometry, set returnGeometry to true. You can also specify the wildcard “*” as the value of this parameter. In this case, the result s will include all the field values.

definitionExpression - The definition expression to be

applied to the related table/layer. From the list of objectIds, only those records that conform to this expression are queried for related records.

returnGeometry - If true, the feature set includes the

geometry associated with each feature. The default is true.

maxAllowableOffset - This option can be used to specify the

maxAllowableOffset to be used for generalizing geometries returned by the query operation. The maxAllowableOffset is in the units of the outSR. If outSR is not specified, then maxAllowableOffset is assumed to be in the unit of the spatial reference of the map.

geometryPrecision - This option can be used to specify the

number of decimal places in the response geometries.

outWKID - The spatial reference of the returned geometry. gdbVersion - The geodatabase version to query. This parameter

applies only if the isDataVersioned property of the layer queried is true.

returnZ - If true, Z values are included in the results if

the features have Z values. Otherwise, Z values are not returned. The default is false.

returnM - If true, M values are included in the results if

the features have M values. Otherwise, M values are not returned. The default is false.

validate_sql(sql, sql_type='where')

The validate_sql operation validates an SQL-92 expression or WHERE clause. The validate_sql operation ensures that an SQL-92 expression, such as one written by a user through a user interface, is correct before performing another operation that uses the expression. For example, validateSQL can be used to validate information that is subsequently passed in as part of the where parameter of the calculate operation. validate_sql also prevents SQL injection. In addition, all table and field names used in the SQL expression or WHERE clause are validated to ensure they are valid tables and fields.

Parameters:

sql:

the SQL expression of WHERE clause to validate Example: “Population > 300000”

sql_type:

Three SQL types are supported in validate_sql - where (default) - Represents the custom WHERE clause the user

can compose when querying a layer or using calculate.

• expression - Represents an SQL-92 expression. Currently, expression is used as a default value expression when adding a new field or using the calculate API.
• statement - Represents the full SQL-92 statement that can be passed directly to the database. No current ArcGIS REST API resource or operation supports using the full SQL-92 SELECT statement directly. It has been added to the validateSQL for completeness. Values: where | expression | statement

arcgis.features.FeatureLayerCollection

class arcgis.features.FeatureLayerCollection(url, gis=None)

A FeatureLayerCollection is a collection of feature layers and tables, with the associated relationships among the entities.

In a web GIS, a feature layer collection is exposed as a feature service with multiple feature layers.

Instances of FeatureDatasets can be obtained from feature service Items in the GIS using FeatureLayerCollection.fromitem(item), from feature service endpoints using the constructor, or by accessing the dataset attribute of feature layer objects.

FeatureDatasets can be configured and managed using their manager helper object.

If the dataset supports the sync operation, the replicas helper object allows management and synchronization of replicas for disconnected editing of the feature layer collection.

Note: You can use the layers and tables property to get to the individual layers and tables in this feature layer collection.

fromitem(item)

manager

helper object to manage the feature layer collection, update it’s definition, etc

properties

The properties of this object

query(layer_defs_filter=None, geometry_filter=None, time_filter=None, return_geometry=True, return_ids_only=False, return_count_only=False, return_z=False, return_m=False, out_sr=None)

queries the feature layer collection

query_related_records(object_ids, relationship_id, out_fields='*', definition_expression=None, return_geometry=True, max_allowable_offset=None, geometry_precision=None, out_wkid=None, gdb_version=None, return_z=False, return_m=False)

The Query operation is performed on a feature service layer resource. The result of this operation are feature sets grouped by source layer/table object IDs. Each feature set contains Feature objects including the values for the fields requested by the user. For related layers, if you request geometry information, the geometry of each feature is also returned in the feature set. For related tables, the feature set does not include geometries. Inputs:

objectIds - the object IDs of the table/layer to be queried relationshipId - The ID of the relationship to be queried. outFields - the list of fields from the related table/layer

to be included in the returned feature set. This list is a comma delimited list of field names. If you specify the shape field in the list of return fields, it is ignored. To request geometry, set returnGeometry to true. You can also specify the wildcard “*” as the value of this parameter. In this case, the result s will include all the field values.

definitionExpression - The definition expression to be

applied to the related table/layer. From the list of objectIds, only those records that conform to this expression are queried for related records.

returnGeometry - If true, the feature set includes the

geometry associated with each feature. The default is true.

maxAllowableOffset - This option can be used to specify the

maxAllowableOffset to be used for generalizing geometries returned by the query operation. The maxAllowableOffset is in the units of the outSR. If outSR is not specified, then maxAllowableOffset is assumed to be in the unit of the spatial reference of the map.

geometryPrecision - This option can be used to specify the

number of decimal places in the response geometries.

outWKID - The spatial reference of the returned geometry. gdbVersion - The geodatabase version to query. This parameter

applies only if the isDataVersioned property of the layer queried is true.

returnZ - If true, Z values are included in the results if

the features have Z values. Otherwise, Z values are not returned. The default is false.

returnM - If true, M values are included in the results if

the features have M values. Otherwise, M values are not returned. The default is false.

upload(path, description=None)

Uploads a new item to the server. Once the operation is completed successfully, the JSON structure of the uploaded item is returned.

Parameters:

path:path of the file to upload description:optional descriptive text for the upload item

arcgis.features.FeatureSet

class arcgis.features.FeatureSet(features, fields=None, has_z=False, has_m=False, geometry_type=None, spatial_reference=None, display_field_name=None, object_id_field_name=None, global_id_field_name=None)

A set of features with information about their fields, field aliases, geometry type, spatial reference etc.

FeatureSets are commonly used as input/output with several Geoprocessing Tools, and can be the obtained through the query() methods of feature layers. A FeatureSet can be combined with a layer definition to compose a FeatureCollection.

FeatureSet contains Feature objects, including the values for the fields requested by the user. For layers, if you request geometry information, the geometry of each feature is also returned in the FeatureSet. For tables, the FeatureSet does not include geometries.

If a Spatial Reference is not specified at the FeatureSet level, the FeatureSet will assume the SpatialReference of its first feature. If the SpatialReference of the first feature is also not specified, the spatial reference will be UnknownCoordinateSystem.

df

converts the FeatureSet to a Pandas dataframe. Requires pandas

display_field_name

gets/sets the displayFieldName

features

gets the features in the FeatureSet

fields

gets the fields in the FeatureSet

static from_dataframe(df)

returns a featureset from a Pandas’ Data or Spatial DataFrame

static from_dict(featureset_dict)

returns a featureset from a dict

static from_geojson(geojson)

Converts a GeoJSON Feature Collection into a FeatureSet

static from_json(json_str)

returns a featureset from a JSON string

geometry_type

gets/sets the geometry Type

global_id_field_name

gets/sets the globalIdFieldName

has_m

gets/set the M-property

has_z

gets/sets the Z-property

object_id_field_name

gets/sets the object id field

save(save_location, out_name, encoding=None)

Saves a featureset object to a feature class Input:

saveLocation - output location of the data outName - name of the table the data will be saved to

Types:

*.csv - CSV file returned *.json - text file with json * If no extension, a shapefile if the path is a

folder, a featureclass if the path is a GDB

encoding - character encoding is used to represent a repertoire

of characters by some kind of encoding system. The default is None.

spatial_reference

gets the featureset’s spatial reference

to_dict()

converts the object to Python dictionary

to_geojson

converts the object to GeoJSON

to_json

converts the object to JSON

value

returns object as dictionary

arcgis.features.FeatureCollection

class arcgis.features.FeatureCollection(dictdata)

FeatureCollection is an object with a layer definition and a feature set.

It is an in-memory collection of features with rendering information.

Feature Collections can be stored as Items in the GIS, added as layers to a map or scene, passed as inputs to feature analysis tools, and returned as results from feature analysis tools if an output name for a feature layer is not specified when calling the tool.

static from_featureset(fset, symbol=None)

Create a FeatureCollection object from a FeatureSet object.

Returns:A FeatureCollection object.

query()

Returns the data in this feature collection as a FeatureSet. Filtering by where clause is not supported for feature collections

arcgis.features.SpatialDataFrame

class arcgis.features.SpatialDataFrame(*args, **kwargs)

A Spatial Dataframe is an object to manipulate, manage and translate data into new forms of information for users.

Required Parameters:

None

Optional:

param data:panda’s dataframe containing attribute information param geometry:list/array/geoseries of arcgis.geometry objects param sr:spatial reference of the dataframe. This can be the factory code, WKT string, arcpy.SpatialReference object, or arcgis.SpatailReference object. param gis:passing a gis.GIS object set to Pro will ensure arcpy is installed and a full swatch of functionality is available to the end user.

JSON

Returns an Esri JSON representation of the geometry as a string.

T

Transpose index and columns

WKB

Returns the well-known binary (WKB) representation for OGC geometry. It provides a portable representation of a geometry value as a contiguous stream of bytes.

WKT

Returns the well-known text (WKT) representation for OGC geometry. It provides a portable representation of a geometry value as a text string.

abs()

Return an object with absolute value taken–only applicable to objects that are all numeric.

abs: type of caller

add(other, axis='columns', level=None, fill_value=None)

Addition of dataframe and other, element-wise (binary operator add).

Equivalent to dataframe + other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.radd

add_prefix(prefix)

Concatenate prefix string with panel items names.

prefix : string

with_prefix : type of caller

add_suffix(suffix)

Concatenate suffix string with panel items names.

suffix : string

with_suffix : type of caller

agg(func, axis=0, *args, **kwargs)

Aggregate using callable, string, dict, or list of string/callables

New in version 0.20.0.

func : callable, string, dictionary, or list of string/callables

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. For a DataFrame, can pass a dict, if the keys are DataFrame column names.

Accepted Combinations are:

• string function name
• function
• list of functions
• dict of column names -> functions (or list of functions)

Numpy functions mean/median/prod/sum/std/var are special cased so the default behavior is applying the function along axis=0 (e.g., np.mean(arr_2d, axis=0)) as opposed to mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).

agg is an alias for aggregate. Use the alias.

aggregated : DataFrame

>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
...                   index=pd.date_range('1/1/2000', periods=10))
>>> df.iloc[3:7] = np.nan

Aggregate these functions across all columns

>>> df.agg(['sum', 'min'])
            A         B         C
sum -0.182253 -0.614014 -2.909534
min -1.916563 -1.460076 -1.568297

Different aggregations per column

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
            A         B
max       NaN  1.514318
min -1.916563 -1.460076
sum -0.182253       NaN

pandas.DataFrame.apply pandas.DataFrame.transform pandas.DataFrame.groupby.aggregate pandas.DataFrame.resample.aggregate pandas.DataFrame.rolling.aggregate

aggregate(func, axis=0, *args, **kwargs)

Aggregate using callable, string, dict, or list of string/callables

New in version 0.20.0.

func : callable, string, dictionary, or list of string/callables

Function to use for aggregating the data. If a function, must either work when passed a DataFrame or when passed to DataFrame.apply. For a DataFrame, can pass a dict, if the keys are DataFrame column names.

Accepted Combinations are:

• string function name
• function
• list of functions
• dict of column names -> functions (or list of functions)

Numpy functions mean/median/prod/sum/std/var are special cased so the default behavior is applying the function along axis=0 (e.g., np.mean(arr_2d, axis=0)) as opposed to mimicking the default Numpy behavior (e.g., np.mean(arr_2d)).

agg is an alias for aggregate. Use the alias.

aggregated : DataFrame

>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
...                   index=pd.date_range('1/1/2000', periods=10))
>>> df.iloc[3:7] = np.nan

Aggregate these functions across all columns

>>> df.agg(['sum', 'min'])
            A         B         C
sum -0.182253 -0.614014 -2.909534
min -1.916563 -1.460076 -1.568297

Different aggregations per column

>>> df.agg({'A' : ['sum', 'min'], 'B' : ['min', 'max']})
            A         B
max       NaN  1.514318
min -1.916563 -1.460076
sum -0.182253       NaN

pandas.DataFrame.apply pandas.DataFrame.transform pandas.DataFrame.groupby.aggregate pandas.DataFrame.resample.aggregate pandas.DataFrame.rolling.aggregate

align(other, join='outer', axis=None, level=None, copy=True, fill_value=None, method=None, limit=None, fill_axis=0, broadcast_axis=None)

Align two objects on their axes with the specified join method for each axis Index

other : DataFrame or Series join : {‘outer’, ‘inner’, ‘left’, ‘right’}, default ‘outer’ axis : allowed axis of the other object, default None

Align on index (0), columns (1), or both (None)

level : int or level name, default None

Broadcast across a level, matching Index values on the passed MultiIndex level

copy : boolean, default True

Always returns new objects. If copy=False and no reindexing is required then original objects are returned.

fill_value : scalar, default np.NaN

Value to use for missing values. Defaults to NaN, but can be any “compatible” value

method : str, default None limit : int, default None fill_axis : {0 or ‘index’, 1 or ‘columns’}, default 0

Filling axis, method and limit

broadcast_axis : {0 or ‘index’, 1 or ‘columns’}, default None

Broadcast values along this axis, if aligning two objects of different dimensions

New in version 0.17.0.

(left, right) : (DataFrame, type of other)

Aligned objects

all(axis=None, bool_only=None, skipna=None, level=None, **kwargs)

Return whether all elements are True over requested axis

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

bool_only : boolean, default None

Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.

all : Series or DataFrame (if level specified)

angle_distance_to(second_geometry, method='GEODESIC')

Returns a tuple of angle and distance to another point using a measurement type.

Paramters:

second_geometry:  

• a second geometry

method:

• PLANAR measurements reflect the projection of geographic

data onto the 2D surface (in other words, they will not take into account the curvature of the earth). GEODESIC, GREAT_ELLIPTIC, LOXODROME, and PRESERVE_SHAPE measurement types may be chosen as an alternative, if desired.

any(axis=None, bool_only=None, skipna=None, level=None, **kwargs)

Return whether any element is True over requested axis

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

bool_only : boolean, default None

Include only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.

any : Series or DataFrame (if level specified)

append(other, ignore_index=False, verify_integrity=False)

Append rows of other to the end of this frame, returning a new object. Columns not in this frame are added as new columns.

other : DataFrame or Series/dict-like object, or list of these

The data to append.

ignore_index : boolean, default False

If True, do not use the index labels.

verify_integrity : boolean, default False

If True, raise ValueError on creating index with duplicates.

appended : DataFrame

If a list of dict/series is passed and the keys are all contained in the DataFrame’s index, the order of the columns in the resulting DataFrame will be unchanged.

Iteratively appending rows to a DataFrame can be more computationally intensive than a single concatenate. A better solution is to append those rows to a list and then concatenate the list with the original DataFrame all at once.

pandas.concat : General function to concatenate DataFrame, Series

or Panel objects

>>> df = pd.DataFrame([[1, 2], [3, 4]], columns=list('AB'))
>>> df
   A  B
0  1  2
1  3  4
>>> df2 = pd.DataFrame([[5, 6], [7, 8]], columns=list('AB'))
>>> df.append(df2)
   A  B
0  1  2
1  3  4
0  5  6
1  7  8

With ignore_index set to True:

>>> df.append(df2, ignore_index=True)
   A  B
0  1  2
1  3  4
2  5  6
3  7  8

The following, while not recommended methods for generating DataFrames, show two ways to generate a DataFrame from multiple data sources.

Less efficient:

>>> df = pd.DataFrame(columns=['A'])
>>> for i in range(5):
...     df = df.append({'A'}: i}, ignore_index=True)
>>> df
   A
0  0
1  1
2  2
3  3
4  4

More efficient:

>>> pd.concat([pd.DataFrame([i], columns=['A']) for i in range(5)],
...           ignore_index=True)
   A
0  0
1  1
2  2
3  3
4  4

apply(func, axis=0, broadcast=False, raw=False, reduce=None, args=(), **kwds)

Applies function along input axis of DataFrame.

Objects passed to functions are Series objects having index either the DataFrame’s index (axis=0) or the columns (axis=1). Return type depends on whether passed function aggregates, or the reduce argument if the DataFrame is empty.

func : function

Function to apply to each column/row

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

• 0 or ‘index’: apply function to each column
• 1 or ‘columns’: apply function to each row

broadcast : boolean, default False

For aggregation functions, return object of same size with values propagated

raw : boolean, default False

If False, convert each row or column into a Series. If raw=True the passed function will receive ndarray objects instead. If you are just applying a NumPy reduction function this will achieve much better performance

reduce : boolean or None, default None

Try to apply reduction procedures. If the DataFrame is empty, apply will use reduce to determine whether the result should be a Series or a DataFrame. If reduce is None (the default), apply’s return value will be guessed by calling func an empty Series (note: while guessing, exceptions raised by func will be ignored). If reduce is True a Series will always be returned, and if False a DataFrame will always be returned.

args : tuple

Positional arguments to pass to function in addition to the array/series

Additional keyword arguments will be passed as keywords to the function

In the current implementation apply calls func twice on the first column/row to decide whether it can take a fast or slow code path. This can lead to unexpected behavior if func has side-effects, as they will take effect twice for the first column/row.

>>> df.apply(numpy.sqrt) # returns DataFrame
>>> df.apply(numpy.sum, axis=0) # equiv to df.sum(0)
>>> df.apply(numpy.sum, axis=1) # equiv to df.sum(1)

DataFrame.applymap: For elementwise operations DataFrame.aggregate: only perform aggregating type operations DataFrame.transform: only perform transformating type operations

applied : Series or DataFrame

applymap(func)

Apply a function to a DataFrame that is intended to operate elementwise, i.e. like doing map(func, series) for each series in the DataFrame

func : function

Python function, returns a single value from a single value

>>> df = pd.DataFrame(np.random.randn(3, 3))
>>> df
    0         1          2
0  -0.029638  1.081563   1.280300
1   0.647747  0.831136  -1.549481
2   0.513416 -0.884417   0.195343
>>> df = df.applymap(lambda x: '%.2f' % x)
>>> df
    0         1          2
0  -0.03      1.08       1.28
1   0.65      0.83      -1.55
2   0.51     -0.88       0.20

applied : DataFrame

DataFrame.apply : For operations on rows/columns

area

The area of a polygon feature. Empty for all other feature types.

as_arcpy

Returns an Esri JSON representation of the geometry as a string.

as_blocks(copy=True)

Convert the frame to a dict of dtype -> Constructor Types that each has a homogeneous dtype.

Deprecated since version 0.21.0.

NOTE: the dtypes of the blocks WILL BE PRESERVED HERE (unlike in

as_matrix)

copy : boolean, default True

values : a dict of dtype -> Constructor Types

as_matrix(columns=None)

Convert the frame to its Numpy-array representation.

columns: list, optional, default:None

If None, return all columns, otherwise, returns specified columns.

values : ndarray

If the caller is heterogeneous and contains booleans or objects, the result will be of dtype=object. See Notes.

Return is NOT a Numpy-matrix, rather, a Numpy-array.

The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks.

e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcase to int32. By numpy.find_common_type convention, mixing int64 and uint64 will result in a flot64 dtype.

This method is provided for backwards compatibility. Generally, it is recommended to use ‘.values’.

pandas.DataFrame.values

asfreq(freq, method=None, how=None, normalize=False, fill_value=None)

Convert TimeSeries to specified frequency.

Optionally provide filling method to pad/backfill missing values.

Returns the original data conformed to a new index with the specified frequency. resample is more appropriate if an operation, such as summarization, is necessary to represent the data at the new frequency.

freq : DateOffset object, or string method : {‘backfill’/’bfill’, ‘pad’/’ffill’}, default None

Method to use for filling holes in reindexed Series (note this does not fill NaNs that already were present):

• ‘pad’ / ‘ffill’: propagate last valid observation forward to next valid
• ‘backfill’ / ‘bfill’: use NEXT valid observation to fill

how : {‘start’, ‘end’}, default end

For PeriodIndex only, see PeriodIndex.asfreq

normalize : bool, default False

Whether to reset output index to midnight

fill_value: scalar, optional

Value to use for missing values, applied during upsampling (note this does not fill NaNs that already were present).

New in version 0.20.0.

converted : type of caller

Start by creating a series with 4 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=4, freq='T')
>>> series = pd.Series([0.0, None, 2.0, 3.0], index=index)
>>> df = pd.DataFrame({'s':series})
>>> df
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:01:00    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:03:00    3.0

Upsample the series into 30 second bins.

>>> df.asfreq(freq='30S')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    NaN
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    NaN
2000-01-01 00:03:00    3.0

Upsample again, providing a fill value.

>>> df.asfreq(freq='30S', fill_value=9.0)
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    9.0
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    9.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    9.0
2000-01-01 00:03:00    3.0

Upsample again, providing a method.

>>> df.asfreq(freq='30S', method='bfill')
                       s
2000-01-01 00:00:00    0.0
2000-01-01 00:00:30    NaN
2000-01-01 00:01:00    NaN
2000-01-01 00:01:30    2.0
2000-01-01 00:02:00    2.0
2000-01-01 00:02:30    3.0
2000-01-01 00:03:00    3.0

reindex

To learn more about the frequency strings, please see this link.

asof(where, subset=None)

The last row without any NaN is taken (or the last row without NaN considering only the subset of columns in the case of a DataFrame)

New in version 0.19.0: For DataFrame

If there is no good value, NaN is returned for a Series a Series of NaN values for a DataFrame

where : date or array of dates subset : string or list of strings, default None

if not None use these columns for NaN propagation

Dates are assumed to be sorted Raises if this is not the case

where is scalar

• value or NaN if input is Series
• Series if input is DataFrame

where is Index: same shape object as input

merge_asof

assign(**kwargs)

Assign new columns to a DataFrame, returning a new object (a copy) with all the original columns in addition to the new ones.

kwargs : keyword, value pairs

keywords are the column names. If the values are callable, they are computed on the DataFrame and assigned to the new columns. The callable must not change input DataFrame (though pandas doesn’t check it). If the values are not callable, (e.g. a Series, scalar, or array), they are simply assigned.

df : DataFrame

A new DataFrame with the new columns in addition to all the existing columns.

For python 3.6 and above, the columns are inserted in the order of **kwargs. For python 3.5 and earlier, since **kwargs is unordered, the columns are inserted in alphabetical order at the end of your DataFrame. Assigning multiple columns within the same assign is possible, but you cannot reference other columns created within the same assign call.

>>> df = DataFrame({'A': range(1, 11), 'B': np.random.randn(10)})

Where the value is a callable, evaluated on df:

>>> df.assign(ln_A = lambda x: np.log(x.A))
    A         B      ln_A
0   1  0.426905  0.000000
1   2 -0.780949  0.693147
2   3 -0.418711  1.098612
3   4 -0.269708  1.386294
4   5 -0.274002  1.609438
5   6 -0.500792  1.791759
6   7  1.649697  1.945910
7   8 -1.495604  2.079442
8   9  0.549296  2.197225
9  10 -0.758542  2.302585

Where the value already exists and is inserted:

>>> newcol = np.log(df['A'])
>>> df.assign(ln_A=newcol)
    A         B      ln_A
0   1  0.426905  0.000000
1   2 -0.780949  0.693147
2   3 -0.418711  1.098612
3   4 -0.269708  1.386294
4   5 -0.274002  1.609438
5   6 -0.500792  1.791759
6   7  1.649697  1.945910
7   8 -1.495604  2.079442
8   9  0.549296  2.197225
9  10 -0.758542  2.302585

astype(dtype, copy=True, errors='raise', **kwargs)

Cast a pandas object to a specified dtype dtype.

dtype : data type, or dict of column name -> data type

Use a numpy.dtype or Python type to cast entire pandas object to the same type. Alternatively, use {col: dtype, …}, where col is a column label and dtype is a numpy.dtype or Python type to cast one or more of the DataFrame’s columns to column-specific types.

copy : bool, default True.

Return a copy when copy=True (be very careful setting copy=False as changes to values then may propagate to other pandas objects).

errors : {‘raise’, ‘ignore’}, default ‘raise’.

Control raising of exceptions on invalid data for provided dtype.

• raise : allow exceptions to be raised
• ignore : suppress exceptions. On error return original object

New in version 0.20.0.

raise_on_error : raise on invalid input

Deprecated since version 0.20.0: Use errors instead

kwargs : keyword arguments to pass on to the constructor

casted : type of caller

>>> ser = pd.Series([1, 2], dtype='int32')
>>> ser
0    1
1    2
dtype: int32
>>> ser.astype('int64')
0    1
1    2
dtype: int64

Convert to categorical type:

>>> ser.astype('category')
0    1
1    2
dtype: category
Categories (2, int64): [1, 2]

Convert to ordered categorical type with custom ordering:

>>> ser.astype('category', ordered=True, categories=[2, 1])
0    1
1    2
dtype: category
Categories (2, int64): [2 < 1]

Note that using copy=False and changing data on a new pandas object may propagate changes:

>>> s1 = pd.Series([1,2])
>>> s2 = s1.astype('int', copy=False)
>>> s2[0] = 10
>>> s1  # note that s1[0] has changed too
0    10
1     2
dtype: int64

pandas.to_datetime : Convert argument to datetime. pandas.to_timedelta : Convert argument to timedelta. pandas.to_numeric : Convert argument to a numeric type. numpy.ndarray.astype : Cast a numpy array to a specified type.

at

Fast label-based scalar accessor

Similarly to loc, at provides label based scalar lookups. You can also set using these indexers.

at_time(time, asof=False)

Select values at particular time of day (e.g. 9:30AM).

time : datetime.time or string

values_at_time : type of caller

axes

Return a list with the row axis labels and column axis labels as the only members. They are returned in that order.

between_time(start_time, end_time, include_start=True, include_end=True)

Select values between particular times of the day (e.g., 9:00-9:30 AM).

start_time : datetime.time or string end_time : datetime.time or string include_start : boolean, default True include_end : boolean, default True

values_between_time : type of caller

bfill(axis=None, inplace=False, limit=None, downcast=None)

Synonym for DataFrame.fillna(method='bfill')

blocks

Internal property, property synonym for as_blocks()

Deprecated since version 0.21.0.

bool()

Return the bool of a single element PandasObject.

This must be a boolean scalar value, either True or False. Raise a ValueError if the PandasObject does not have exactly 1 element, or that element is not boolean

boundary()

Constructs the boundary of the geometry.

bounds

Return a DataFrame of minx, miny, maxx, maxy values of geometry objects

boxplot(column=None, by=None, ax=None, fontsize=None, rot=0, grid=True, figsize=None, layout=None, return_type=None, **kwds)

Make a box plot from DataFrame column optionally grouped by some columns or other inputs

data : the pandas object holding the data column : column name or list of names, or vector

Can be any valid input to groupby

by : string or sequence

Column in the DataFrame to group by

ax : Matplotlib axes object, optional fontsize : int or string rot : label rotation angle figsize : A tuple (width, height) in inches grid : Setting this to True will show the grid layout : tuple (optional)

(rows, columns) for the layout of the plot

return_type : {None, ‘axes’, ‘dict’, ‘both’}, default None

The kind of object to return. The default is axes ‘axes’ returns the matplotlib axes the boxplot is drawn on; ‘dict’ returns a dictionary whose values are the matplotlib Lines of the boxplot; ‘both’ returns a namedtuple with the axes and dict.

When grouping with by, a Series mapping columns to return_type is returned, unless return_type is None, in which case a NumPy array of axes is returned with the same shape as layout. See the prose documentation for more.

kwds : other plotting keyword arguments to be passed to matplotlib boxplot

function

lines : dict ax : matplotlib Axes (ax, lines): namedtuple

Use return_type='dict' when you want to tweak the appearance of the lines after plotting. In this case a dict containing the Lines making up the boxes, caps, fliers, medians, and whiskers is returned.

buffer(distance)

Constructs a polygon at a specified distance from the geometry.

Parameters:

distance:

• length in current projection. Only polygon accept

negative values.

centroid

The true centroid if it is within or on the feature; otherwise, the label point is returned. Returns a point object.

clip(envelope)

Constructs the intersection of the geometry and the specified extent.

Parameters:

envelope:

• arcpy.Extent object

clip_lower(threshold, axis=None, inplace=False)

Return copy of the input with values below given value(s) truncated.

threshold : float or array_like axis : int or string axis name, optional

Align object with threshold along the given axis.

inplace : boolean, default False

Whether to perform the operation in place on the data

New in version 0.21.0.

clip

clipped : same type as input

clip_upper(threshold, axis=None, inplace=False)

Return copy of input with values above given value(s) truncated.

threshold : float or array_like axis : int or string axis name, optional

Align object with threshold along the given axis.

inplace : boolean, default False

Whether to perform the operation in place on the data

New in version 0.21.0.

clip

clipped : same type as input

combine(other, func, fill_value=None, overwrite=True)

Add two DataFrame objects and do not propagate NaN values, so if for a (column, time) one frame is missing a value, it will default to the other frame’s value (which might be NaN as well)

other : DataFrame func : function fill_value : scalar value overwrite : boolean, default True

If True then overwrite values for common keys in the calling frame

result : DataFrame

combine_first(other)

Combine two DataFrame objects and default to non-null values in frame calling the method. Result index columns will be the union of the respective indexes and columns

other : DataFrame

a’s values prioritized, use values from b to fill holes:

>>> a.combine_first(b)

combined : DataFrame

compound(axis=None, skipna=None, level=None)

Return the compound percentage of the values for the requested axis

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

compounded : Series or DataFrame (if level specified)

consolidate(inplace=False)

DEPRECATED: consolidate will be an internal implementation only.

contains(second_geometry, relation=None)

Indicates if the base geometry contains the comparison geometry.

Paramters:

second_geometry:  

• a second geometry

convert_objects(convert_dates=True, convert_numeric=False, convert_timedeltas=True, copy=True)

Deprecated. Attempt to infer better dtype for object columns

convert_dates : boolean, default True

If True, convert to date where possible. If ‘coerce’, force conversion, with unconvertible values becoming NaT.

convert_numeric : boolean, default False

If True, attempt to coerce to numbers (including strings), with unconvertible values becoming NaN.

convert_timedeltas : boolean, default True

If True, convert to timedelta where possible. If ‘coerce’, force conversion, with unconvertible values becoming NaT.

copy : boolean, default True

If True, return a copy even if no copy is necessary (e.g. no conversion was done). Note: This is meant for internal use, and should not be confused with inplace.

pandas.to_datetime : Convert argument to datetime. pandas.to_timedelta : Convert argument to timedelta. pandas.to_numeric : Return a fixed frequency timedelta index,

with day as the default.

converted : same as input object

convex_hull()

Constructs the geometry that is the minimal bounding polygon such that all outer angles are convex.

coordinates()

returns the point coordinates of the geometry as a np.array object

copy(deep=True)

Make a copy of this SpatialDataFrame object Parameters:

Deep:boolean, default True Make a deep copy, i.e. also copy data

Returns:

copy:of SpatialDataFrame

corr(method='pearson', min_periods=1)

Compute pairwise correlation of columns, excluding NA/null values

method : {‘pearson’, ‘kendall’, ‘spearman’}

• pearson : standard correlation coefficient
• kendall : Kendall Tau correlation coefficient
• spearman : Spearman rank correlation

min_periods : int, optional

Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson and spearman correlation

y : DataFrame

corrwith(other, axis=0, drop=False)

Compute pairwise correlation between rows or columns of two DataFrame objects.

other : DataFrame axis : {0 or ‘index’, 1 or ‘columns’}, default 0

0 or ‘index’ to compute column-wise, 1 or ‘columns’ for row-wise

drop : boolean, default False

Drop missing indices from result, default returns union of all

correls : Series

count(axis=0, level=None, numeric_only=False)

Return Series with number of non-NA/null observations over requested axis. Works with non-floating point data as well (detects NaN and None)

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame

numeric_only : boolean, default False

Include only float, int, boolean data

count : Series (or DataFrame if level specified)

cov(min_periods=None)

Compute pairwise covariance of columns, excluding NA/null values

min_periods : int, optional

Minimum number of observations required per pair of columns to have a valid result.

y : DataFrame

y contains the covariance matrix of the DataFrame’s time series. The covariance is normalized by N-1 (unbiased estimator).

crosses(second_geometry)

Indicates if the two geometries intersect in a geometry of a lesser shape type.

Paramters:

second_geometry:  

• a second geometry

cummax(axis=None, skipna=True, *args, **kwargs)

Return cumulative max over requested axis.

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

cummax : Series

pandas.core.window.Expanding.max : Similar functionality

but ignores NaN values.

cummin(axis=None, skipna=True, *args, **kwargs)

Return cumulative minimum over requested axis.

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

cummin : Series

pandas.core.window.Expanding.min : Similar functionality

but ignores NaN values.

cumprod(axis=None, skipna=True, *args, **kwargs)

Return cumulative product over requested axis.

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

cumprod : Series

pandas.core.window.Expanding.prod : Similar functionality

but ignores NaN values.

cumsum(axis=None, skipna=True, *args, **kwargs)

Return cumulative sum over requested axis.

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

cumsum : Series

pandas.core.window.Expanding.sum : Similar functionality

but ignores NaN values.

cut(cutter)

Splits this geometry into a part left of the cutting polyline, and a part right of it.

Parameters:

cutter:

• The cutting polyline geometry.

densify(method, distance, deviation)

Creates a new geometry with added vertices

Parameters:

method:

• The type of densification, DISTANCE, ANGLE, or GEODESIC

distance:

• The maximum distance between vertices. The actual

distance between vertices will usually be less than the maximum distance as new vertices will be evenly distributed along the original segment. If using a type of DISTANCE or ANGLE, the distance is measured in the units of the geometry’s spatial reference. If using a type of GEODESIC, the distance is measured in meters.

deviation:

• Densify uses straight lines to approximate curves.

You use deviation to control the accuracy of this approximation. The deviation is the maximum distance between the new segment and the original curve. The smaller its value, the more segments will be required to approximate the curve.

describe(percentiles=None, include=None, exclude=None)

Generates descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution, excluding NaN values.

Analyzes both numeric and object series, as well as DataFrame column sets of mixed data types. The output will vary depending on what is provided. Refer to the notes below for more detail.

percentiles : list-like of numbers, optional

The percentiles to include in the output. All should fall between 0 and 1. The default is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.

include : ‘all’, list-like of dtypes or None (default), optional

A white list of data types to include in the result. Ignored for Series. Here are the options:

• ‘all’ : All columns of the input will be included in the output.
• A list-like of dtypes : Limits the results to the provided data types. To limit the result to numeric types submit numpy.number. To limit it instead to object columns submit the numpy.object data type. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To select pandas categorical columns, use 'category'
• None (default) : The result will include all numeric columns.

exclude : list-like of dtypes or None (default), optional,

A black list of data types to omit from the result. Ignored for Series. Here are the options:

• A list-like of dtypes : Excludes the provided data types from the result. To exclude numeric types submit numpy.number. To exclude object columns submit the data type numpy.object. Strings can also be used in the style of select_dtypes (e.g. df.describe(include=['O'])). To exclude pandas categorical columns, use 'category'
• None (default) : The result will exclude nothing.

summary: Series/DataFrame of summary statistics

For numeric data, the result’s index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

For object data (e.g. strings or timestamps), the result’s index will include count, unique, top, and freq. The top is the most common value. The freq is the most common value’s frequency. Timestamps also include the first and last items.

If multiple object values have the highest count, then the count and top results will be arbitrarily chosen from among those with the highest count.

For mixed data types provided via a DataFrame, the default is to return only an analysis of numeric columns. If the dataframe consists only of object and categorical data without any numeric columns, the default is to return an analysis of both the object and categorical columns. If include='all' is provided as an option, the result will include a union of attributes of each type.

The include and exclude parameters can be used to limit which columns in a DataFrame are analyzed for the output. The parameters are ignored when analyzing a Series.

Describing a numeric Series.

>>> s = pd.Series([1, 2, 3])
>>> s.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0

Describing a categorical Series.

>>> s = pd.Series(['a', 'a', 'b', 'c'])
>>> s.describe()
count     4
unique    3
top       a
freq      2
dtype: object

Describing a timestamp Series.

>>> s = pd.Series([
...   np.datetime64("2000-01-01"),
...   np.datetime64("2010-01-01"),
...   np.datetime64("2010-01-01")
... ])
>>> s.describe()
count                       3
unique                      2
top       2010-01-01 00:00:00
freq                        2
first     2000-01-01 00:00:00
last      2010-01-01 00:00:00
dtype: object

Describing a DataFrame. By default only numeric fields are returned.

>>> df = pd.DataFrame({ 'object': ['a', 'b', 'c'],
...                     'numeric': [1, 2, 3],
...                     'categorical': pd.Categorical(['d','e','f'])
...                   })
>>> df.describe()
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Describing all columns of a DataFrame regardless of data type.

>>> df.describe(include='all')
        categorical  numeric object
count            3      3.0      3
unique           3      NaN      3
top              f      NaN      c
freq             1      NaN      1
mean           NaN      2.0    NaN
std            NaN      1.0    NaN
min            NaN      1.0    NaN
25%            NaN      1.5    NaN
50%            NaN      2.0    NaN
75%            NaN      2.5    NaN
max            NaN      3.0    NaN

Describing a column from a DataFrame by accessing it as an attribute.

>>> df.numeric.describe()
count    3.0
mean     2.0
std      1.0
min      1.0
25%      1.5
50%      2.0
75%      2.5
max      3.0
Name: numeric, dtype: float64

Including only numeric columns in a DataFrame description.

>>> df.describe(include=[np.number])
       numeric
count      3.0
mean       2.0
std        1.0
min        1.0
25%        1.5
50%        2.0
75%        2.5
max        3.0

Including only string columns in a DataFrame description.

>>> df.describe(include=[np.object])
       object
count       3
unique      3
top         c
freq        1

Including only categorical columns from a DataFrame description.

>>> df.describe(include=['category'])
       categorical
count            3
unique           3
top              f
freq             1

Excluding numeric columns from a DataFrame description.

>>> df.describe(exclude=[np.number])
       categorical object
count            3      3
unique           3      3
top              f      c
freq             1      1

Excluding object columns from a DataFrame description.

>>> df.describe(exclude=[np.object])
        categorical  numeric
count            3      3.0
unique           3      NaN
top              f      NaN
freq             1      NaN
mean           NaN      2.0
std            NaN      1.0
min            NaN      1.0
25%            NaN      1.5
50%            NaN      2.0
75%            NaN      2.5
max            NaN      3.0

DataFrame.count DataFrame.max DataFrame.min DataFrame.mean DataFrame.std DataFrame.select_dtypes

diff(periods=1, axis=0)

1st discrete difference of object

periods : int, default 1

Periods to shift for forming difference

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

Take difference over rows (0) or columns (1).

diffed : DataFrame

difference(second_geometry)

Constructs the geometry that is composed only of the region unique to the base geometry but not part of the other geometry. The following illustration shows the results when the red polygon is the source geometry.

Paramters:

second_geometry:  

• a second geometry

disjoint(second_geometry)

Indicates if the base and comparison geometries share no points in common.

Paramters:

second_geometry:  

• a second geometry

distance_to(second_geometry)

Returns the minimum distance between two geometries. If the geometries intersect, the minimum distance is 0. Both geometries must have the same projection.

Paramters:

second_geometry:  

• a second geometry

div(other, axis='columns', level=None, fill_value=None)

Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rtruediv

divide(other, axis='columns', level=None, fill_value=None)

Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rtruediv

dot(other)

Matrix multiplication with DataFrame or Series objects

other : DataFrame or Series

dot_product : DataFrame or Series

drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors='raise')

Return new object with labels in requested axis removed.

labels : single label or list-like

Index or column labels to drop.

axis : int or axis name

Whether to drop labels from the index (0 / ‘index’) or columns (1 / ‘columns’).

index, columns : single label or list-like

Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).

New in version 0.21.0.

level : int or level name, default None

For MultiIndex

inplace : bool, default False

If True, do operation inplace and return None.

errors : {‘ignore’, ‘raise’}, default ‘raise’

If ‘ignore’, suppress error and existing labels are dropped.

dropped : type of caller

>>> df = pd.DataFrame(np.arange(12).reshape(3,4),
                      columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11

Drop columns

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11

>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11

Drop a row by index

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11

Specifying both labels and index or columns will raise a ValueError.

drop_duplicates(subset=None, keep='first', inplace=False)

Return DataFrame with duplicate rows removed, optionally only considering certain columns

subset : column label or sequence of labels, optional

Only consider certain columns for identifying duplicates, by default use all of the columns

keep : {‘first’, ‘last’, False}, default ‘first’

• first : Drop duplicates except for the first occurrence.
• last : Drop duplicates except for the last occurrence.
• False : Drop all duplicates.

inplace : boolean, default False

Whether to drop duplicates in place or to return a copy

deduplicated : DataFrame

dropna(axis=0, how='any', thresh=None, subset=None, inplace=False)

Return object with labels on given axis omitted where alternately any or all of the data are missing

axis : {0 or ‘index’, 1 or ‘columns’}, or tuple/list thereof

Pass tuple or list to drop on multiple axes

how : {‘any’, ‘all’}

• any : if any NA values are present, drop that label
• all : if all values are NA, drop that label

thresh : int, default None

int value : require that many non-NA values

subset : array-like

Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include

inplace : boolean, default False

If True, do operation inplace and return None.

dropped : DataFrame

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0], [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, 5]],
...                   columns=list('ABCD'))
>>> df
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5

Drop the columns where all elements are nan:

>>> df.dropna(axis=1, how='all')
     A    B  D
0  NaN  2.0  0
1  3.0  4.0  1
2  NaN  NaN  5

Drop the columns where any of the elements is nan

>>> df.dropna(axis=1, how='any')
   D
0  0
1  1
2  5

Drop the rows where all of the elements are nan (there is no row to drop, so df stays the same):

>>> df.dropna(axis=0, how='all')
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5

Keep only the rows with at least 2 non-na values:

>>> df.dropna(thresh=2)
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1

dtypes

Return the dtypes in this object.

duplicated(subset=None, keep='first')

Return boolean Series denoting duplicate rows, optionally only considering certain columns

subset : column label or sequence of labels, optional

Only consider certain columns for identifying duplicates, by default use all of the columns

keep : {‘first’, ‘last’, False}, default ‘first’

• first : Mark duplicates as True except for the first occurrence.
• last : Mark duplicates as True except for the last occurrence.
• False : Mark all duplicates as True.

duplicated : Series

empty

True if NDFrame is entirely empty [no items], meaning any of the axes are of length 0.

If NDFrame contains only NaNs, it is still not considered empty. See the example below.

An example of an actual empty DataFrame. Notice the index is empty:

>>> df_empty = pd.DataFrame({'A' : []})
>>> df_empty
Empty DataFrame
Columns: [A]
Index: []
>>> df_empty.empty
True

If we only have NaNs in our DataFrame, it is not considered empty! We will need to drop the NaNs to make the DataFrame empty:

>>> df = pd.DataFrame({'A' : [np.nan]})
>>> df
    A
0 NaN
>>> df.empty
False
>>> df.dropna().empty
True

pandas.Series.dropna pandas.DataFrame.dropna

eq(other, axis='columns', level=None)

Wrapper for flexible comparison methods eq

equals(second_geometry)

Indicates if the base and comparison geometries are of the same shape type and define the same set of points in the plane. This is a 2D comparison only; M and Z values are ignored. Paramters:

second_geometry:  

• a second geometry

erase(other, inplace=False)

Erases

eval(expr, inplace=False, **kwargs)

Evaluate an expression in the context of the calling DataFrame instance.

expr : string

The expression string to evaluate.

inplace : bool, default False

If the expression contains an assignment, whether to perform the operation inplace and mutate the existing DataFrame. Otherwise, a new DataFrame is returned.

New in version 0.18.0.

kwargs : dict

See the documentation for eval() for complete details on the keyword arguments accepted by query().

ret : ndarray, scalar, or pandas object

pandas.DataFrame.query pandas.DataFrame.assign pandas.eval

For more details see the API documentation for eval(). For detailed examples see enhancing performance with eval.

>>> from numpy.random import randn
>>> from pandas import DataFrame
>>> df = DataFrame(randn(10, 2), columns=list('ab'))
>>> df.eval('a + b')
>>> df.eval('c = a + b')

ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, freq=None, adjust=True, ignore_na=False, axis=0)

Provides exponential weighted functions

New in version 0.18.0.

com : float, optional

Specify decay in terms of center of mass,

span : float, optional

Specify decay in terms of span,

halflife : float, optional

Specify decay in terms of half-life,

alpha : float, optional

Specify smoothing factor directly,

New in version 0.18.0.

min_periods : int, default 0

Minimum number of observations in window required to have a value (otherwise result is NA).

freq : None or string alias / date offset object, default=None

Deprecated since version 0.18.0: Frequency to conform to before computing statistic

adjust : boolean, default True

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average)

ignore_na : boolean, default False

Ignore missing values when calculating weights; specify True to reproduce pre-0.15.0 behavior

a Window sub-classed for the particular operation

>>> df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

>>> df.ewm(com=0.5).mean()
          B
0  0.000000
1  0.750000
2  1.615385
3  1.615385
4  3.670213

Exactly one of center of mass, span, half-life, and alpha must be provided. Allowed values and relationship between the parameters are specified in the parameter descriptions above; see the link at the end of this section for a detailed explanation.

The freq keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of resample() (i.e. using the mean).

When adjust is True (default), weighted averages are calculated using weights (1-alpha)**(n-1), (1-alpha)**(n-2), …, 1-alpha, 1.

When adjust is False, weighted averages are calculated recursively as:

weighted_average[0] = arg[0]; weighted_average[i] = (1-alpha)*weighted_average[i-1] + alpha*arg[i].

When ignore_na is False (default), weights are based on absolute positions. For example, the weights of x and y used in calculating the final weighted average of [x, None, y] are (1-alpha)**2 and 1 (if adjust is True), and (1-alpha)**2 and alpha (if adjust is False).

When ignore_na is True (reproducing pre-0.15.0 behavior), weights are based on relative positions. For example, the weights of x and y used in calculating the final weighted average of [x, None, y] are 1-alpha and 1 (if adjust is True), and 1-alpha and alpha (if adjust is False).

More details can be found at http://pandas.pydata.org/pandas-docs/stable/computation.html#exponentially-weighted-windows

expanding(min_periods=1, freq=None, center=False, axis=0)

Provides expanding transformations.

New in version 0.18.0.

min_periods : int, default None

Minimum number of observations in window required to have a value (otherwise result is NA).

freq : string or DateOffset object, optional (default None)

Deprecated since version 0.18.0: Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object.

center : boolean, default False

Set the labels at the center of the window.

axis : int or string, default 0

a Window sub-classed for the particular operation

>>> df = DataFrame({'B': [0, 1, 2, np.nan, 4]})
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

>>> df.expanding(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  3.0
4  7.0

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

The freq keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of resample() (i.e. using the mean).

extent

the extent of the geometry

ffill(axis=None, inplace=False, limit=None, downcast=None)

Synonym for DataFrame.fillna(method='ffill')

fillna(value=None, method=None, axis=None, inplace=False, limit=None, downcast=None, **kwargs)

Fill NA/NaN values using the specified method

value : scalar, dict, Series, or DataFrame

Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). (values not in the dict/Series/DataFrame will not be filled). This value cannot be a list.

method : {‘backfill’, ‘bfill’, ‘pad’, ‘ffill’, None}, default None

Method to use for filling holes in reindexed Series pad / ffill: propagate last valid observation forward to next valid backfill / bfill: use NEXT valid observation to fill gap

axis : {0 or ‘index’, 1 or ‘columns’} inplace : boolean, default False

If True, fill in place. Note: this will modify any other views on this object, (e.g. a no-copy slice for a column in a DataFrame).

limit : int, default None

If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

downcast : dict, default is None

a dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible)

reindex, asfreq

filled : DataFrame

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, 5],
...                    [np.nan, 3, np.nan, 4]],
...                    columns=list('ABCD'))
>>> df
     A    B   C  D
0  NaN  2.0 NaN  0
1  3.0  4.0 NaN  1
2  NaN  NaN NaN  5
3  NaN  3.0 NaN  4

Replace all NaN elements with 0s.

>>> df.fillna(0)
    A   B   C   D
0   0.0 2.0 0.0 0
1   3.0 4.0 0.0 1
2   0.0 0.0 0.0 5
3   0.0 3.0 0.0 4

We can also propagate non-null values forward or backward.

>>> df.fillna(method='ffill')
    A   B   C   D
0   NaN 2.0 NaN 0
1   3.0 4.0 NaN 1
2   3.0 4.0 NaN 5
3   3.0 3.0 NaN 4

Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.

>>> values = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
>>> df.fillna(value=values)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 2.0 1
2   0.0 1.0 2.0 5
3   0.0 3.0 2.0 4

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
    A   B   C   D
0   0.0 2.0 2.0 0
1   3.0 4.0 NaN 1
2   NaN 1.0 NaN 5
3   NaN 3.0 NaN 4

filter(items=None, like=None, regex=None, axis=None)

Subset rows or columns of dataframe according to labels in the specified index.

Note that this routine does not filter a dataframe on its contents. The filter is applied to the labels of the index.

items : list-like

List of info axis to restrict to (must not all be present)

like : string

Keep info axis where “arg in col == True”

regex : string (regular expression)

Keep info axis with re.search(regex, col) == True

axis : int or string axis name

The axis to filter on. By default this is the info axis, ‘index’ for Series, ‘columns’ for DataFrame

same type as input object

>>> df
one  two  three
mouse     1    2      3
rabbit    4    5      6

>>> # select columns by name
>>> df.filter(items=['one', 'three'])
one  three
mouse     1      3
rabbit    4      6

>>> # select columns by regular expression
>>> df.filter(regex='e$', axis=1)
one  three
mouse     1      3
rabbit    4      6

>>> # select rows containing 'bbi'
>>> df.filter(like='bbi', axis=0)
one  two  three
rabbit    4    5      6

pandas.DataFrame.loc

The items, like, and regex parameters are enforced to be mutually exclusive.

axis defaults to the info axis that is used when indexing with [].

first(offset)

Convenience method for subsetting initial periods of time series data based on a date offset.

offset : string, DateOffset, dateutil.relativedelta

ts.first(‘10D’) -> First 10 days

subset : type of caller

first_point

The first coordinate point of the geometry.

first_valid_index()

Return index for first non-NA/null value.

If all elements are non-NA/null, returns None. Also returns None for empty DataFrame.

scalar : type of index

floordiv(other, axis='columns', level=None, fill_value=None)

Integer division of dataframe and other, element-wise (binary operator floordiv).

Equivalent to dataframe // other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rfloordiv

from_csv(path, header=0, sep=', ', index_col=0, parse_dates=True, encoding=None, tupleize_cols=None, infer_datetime_format=False)

Read CSV file (DEPRECATED, please use pandas.read_csv() instead).

It is preferable to use the more powerful pandas.read_csv() for most general purposes, but from_csv makes for an easy roundtrip to and from a file (the exact counterpart of to_csv), especially with a DataFrame of time series data.

This method only differs from the preferred pandas.read_csv() in some defaults:

• index_col is 0 instead of None (take first column as index by default)
• parse_dates is True instead of False (try parsing the index as datetime by default)

So a pd.DataFrame.from_csv(path) can be replaced by pd.read_csv(path, index_col=0, parse_dates=True).

path : string file path or file handle / StringIO header : int, default 0

Row to use as header (skip prior rows)

sep : string, default ‘,’

Field delimiter

index_col : int or sequence, default 0

Column to use for index. If a sequence is given, a MultiIndex is used. Different default from read_table

parse_dates : boolean, default True

Parse dates. Different default from read_table

tupleize_cols : boolean, default False

write multi_index columns as a list of tuples (if True) or new (expanded format) if False)

infer_datetime_format: boolean, default False

If True and parse_dates is True for a column, try to infer the datetime format based on the first datetime string. If the format can be inferred, there often will be a large parsing speed-up.

pandas.read_csv

y : DataFrame

static from_df(df, address_column='address', geocoder=None)

Returns a SpatialDataFrame from a dataframe with an address column. Inputs:

df: Pandas dataframe with an address column

Optional Parameters:

address_column: string, default “address”. This is the name of a

column in the specified dataframe that contains addresses (as strings). The addresses are batch geocoded using the GIS’s first configured geocoder and their locations used as the geometry of the spatial dataframe. Ignored if the ‘geometry’ parameter is also specified.

geocoder: the geocoder to be used. If not specified,

the active GIS’s first geocoder is used.

NOTE: Credits will be consumed for batch_geocoding, from the GIS to which the geocoder belongs.

from_dict(data, orient='columns', dtype=None)

Construct DataFrame from dict of array-like or dicts

data : dict

{field : array-like} or {field : dict}

orient : {‘columns’, ‘index’}, default ‘columns’

The “orientation” of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass ‘columns’ (default). Otherwise if the keys should be rows, pass ‘index’.

dtype : dtype, default None

Data type to force, otherwise infer

DataFrame

static from_featureclass(filename, **kwargs)

Returns a SpatialDataFrame from a feature class. Inputs:

filename: full path to the feature class

Optional Parameters:

sql_clause: sql clause to parse data down where_clause: where statement sr: spatial reference object

static from_hdf(path_or_buf, key=None, **kwargs)

read from the store, close it if we opened it

Retrieve pandas object stored in file, optionally based on where criteria

path_or_buf : path (string), buffer, or path object (pathlib.Path or

py._path.local.LocalPath) to read from

New in version 0.19.0: support for pathlib, py.path.

key : group identifier in the store. Can be omitted if the HDF file

contains a single pandas object.

where : list of Term (or convertable) objects, optional start : optional, integer (defaults to None), row number to start

selection

stop : optional, integer (defaults to None), row number to stop

selection

columns : optional, a list of columns that if not None, will limit the

return columns

iterator : optional, boolean, return an iterator, default False chunksize : optional, nrows to include in iteration, return an iterator

The selected object

from_items(items, columns=None, orient='columns')

Convert (key, value) pairs to DataFrame. The keys will be the axis index (usually the columns, but depends on the specified orientation). The values should be arrays or Series.

items : sequence of (key, value) pairs

Values should be arrays or Series.

columns : sequence of column labels, optional

Must be passed if orient=’index’.

orient : {‘columns’, ‘index’}, default ‘columns’

The “orientation” of the data. If the keys of the input correspond to column labels, pass ‘columns’ (default). Otherwise if the keys correspond to the index, pass ‘index’.

frame : DataFrame

static from_layer(layer, **kwargs)

Returns a SpatialDataFrame from a FeatureLayer or Table object. Inputs:

param layer:FeatureLayer or Table param gis:GIS object

Returns a SpatialDataFrame for services with geometry and Panda’s Dataframe for table services.

from_records(data, index=None, exclude=None, columns=None, coerce_float=False, nrows=None)

Convert structured or record ndarray to DataFrame

data : ndarray (structured dtype), list of tuples, dict, or DataFrame index : string, list of fields, array-like

Field of array to use as the index, alternately a specific set of input labels to use

exclude : sequence, default None

Columns or fields to exclude

columns : sequence, default None

Column names to use. If the passed data do not have names associated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns)

coerce_float : boolean, default False

Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets

df : DataFrame

ftypes

Return the ftypes (indication of sparse/dense and dtype) in this object.

ge(other, axis='columns', level=None)

Wrapper for flexible comparison methods ge

generalize(max_offset)

Creates a new simplified geometry using a specified maximum offset tolerance.

Parameters:

max_offset:

• The maximum offset tolerance.

geoextent

returns the extent of the spatial dataframe

geometry

Get/Set the geometry data for SpatialDataFrame

geometry_type

The geometry type: polygon, polyline, point, multipoint, multipatch, dimension, or annotation

get(key, default=None)

Get item from object for given key (DataFrame column, Panel slice, etc.). Returns default value if not found.

key : object

value : type of items contained in object

get_area(method, units=None)

Returns the area of the feature using a measurement type.

Parameters:

method:

• PLANAR measurements reflect the projection of

geographic data onto the 2D surface (in other words, they will not take into account the curvature of the earth). GEODESIC, GREAT_ELLIPTIC, LOXODROME, and PRESERVE_SHAPE measurement types may be chosen as an alternative, if desired.

units:

• Areal unit of measure keywords: ACRES | ARES | HECTARES

SQUARECENTIMETERS | SQUAREDECIMETERS | SQUAREINCHES | SQUAREFEET

SQUAREKILOMETERS | SQUAREMETERS | SQUAREMILES |

SQUAREMILLIMETERS | SQUAREYARDS

get_dtype_counts()

Return the counts of dtypes in this object.

get_ftype_counts()

Return the counts of ftypes in this object.

get_length(method, units)

Returns the length of the feature using a measurement type.

Parameters:

method:

• PLANAR measurements reflect the projection of

geographic data onto the 2D surface (in other words, they will not take into account the curvature of the earth). GEODESIC, GREAT_ELLIPTIC, LOXODROME, and PRESERVE_SHAPE measurement types may be chosen as an alternative, if desired.

units:

• Linear unit of measure keywords: CENTIMETERS |

DECIMETERS | FEET | INCHES | KILOMETERS | METERS | MILES | MILLIMETERS | NAUTICALMILES | YARDS

get_part(index=None)

Returns an array of point objects for a particular part of geometry or an array containing a number of arrays, one for each part.

Parameters:

index:

• The index position of the geometry.

get_value(index, col, takeable=False)

Quickly retrieve single value at passed column and index

Deprecated since version 0.21.0.

Please use .at[] or .iat[] accessors.

index : row label col : column label takeable : interpret the index/col as indexers, default False

value : scalar value

get_values()

same as values (but handles sparseness conversions)

groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, squeeze=False, **kwargs)

Group series using mapper (dict or key function, apply given function to group, return result as series) or by a series of columns.

by : mapping, function, str, or iterable

Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If an ndarray is passed, the values are used as-is determine the groups. A str or list of strs may be passed to group by the columns in self

axis : int, default 0 level : int, level name, or sequence of such, default None

If the axis is a MultiIndex (hierarchical), group by a particular level or levels

as_index : boolean, default True

For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output

sort : boolean, default True

Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. groupby preserves the order of rows within each group.

group_keys : boolean, default True

When calling apply, add group keys to index to identify pieces

squeeze : boolean, default False

reduce the dimensionality of the return type if possible, otherwise return a consistent type

DataFrame results

>>> data.groupby(func, axis=0).mean()
>>> data.groupby(['col1', 'col2'])['col3'].mean()

DataFrame with hierarchical index

>>> data.groupby(['col1', 'col2']).mean()

GroupBy object

gt(other, axis='columns', level=None)

Wrapper for flexible comparison methods gt

head(n=5)

Return the first n rows.

n : int, default 5

Number of rows to select.

obj_head : type of caller

The first n rows of the caller object.

hist(data, column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwds)

Draw histogram of the DataFrame’s series using matplotlib / pylab.

data : DataFrame column : string or sequence

If passed, will be used to limit data to a subset of columns

by : object, optional

If passed, then used to form histograms for separate groups

grid : boolean, default True

Whether to show axis grid lines

xlabelsize : int, default None

If specified changes the x-axis label size

xrot : float, default None

rotation of x axis labels

ylabelsize : int, default None

If specified changes the y-axis label size

yrot : float, default None

rotation of y axis labels

ax : matplotlib axes object, default None sharex : boolean, default True if ax is None else False

In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in; Be aware, that passing in both an ax and sharex=True will alter all x axis labels for all subplots in a figure!

sharey : boolean, default False

In case subplots=True, share y axis and set some y axis labels to invisible

figsize : tuple

The size of the figure to create in inches by default

layout : tuple, optional

Tuple of (rows, columns) for the layout of the histograms

bins : integer, default 10

Number of histogram bins to be used

kwds : other plotting keyword arguments

To be passed to hist function

hull_rectangle

A space-delimited string of the coordinate pairs of the convex hull rectangle.

iat

Fast integer location scalar accessor.

Similarly to iloc, iat provides integer based lookups. You can also set using these indexers.

idxmax(axis=0, skipna=True)

Return index of first occurrence of maximum over requested axis. NA/null values are excluded.

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise

skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be first index.

idxmax : Series

This method is the DataFrame version of ndarray.argmax.

Series.idxmax

idxmin(axis=0, skipna=True)

Return index of first occurrence of minimum over requested axis. NA/null values are excluded.

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise

skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

idxmin : Series

This method is the DataFrame version of ndarray.argmin.

Series.idxmin

iloc

Purely integer-location based indexing for selection by position.

.iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.

Allowed inputs are:

• An integer, e.g. 5.
• A list or array of integers, e.g. [4, 3, 0].
• A slice object with ints, e.g. 1:7.
• A boolean array.
• A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above)

.iloc will raise IndexError if a requested indexer is out-of-bounds, except slice indexers which allow out-of-bounds indexing (this conforms with python/numpy slice semantics).

See more at Selection by Position

infer_objects()

Attempt to infer better dtypes for object columns.

Attempts soft conversion of object-dtyped columns, leaving non-object and unconvertible columns unchanged. The inference rules are the same as during normal Series/DataFrame construction.

New in version 0.21.0.

pandas.to_datetime : Convert argument to datetime. pandas.to_timedelta : Convert argument to timedelta. pandas.to_numeric : Convert argument to numeric typeR

converted : same type as input object

>>> df = pd.DataFrame({"A": ["a", 1, 2, 3]})
>>> df = df.iloc[1:]
>>> df
   A
1  1
2  2
3  3

>>> df.dtypes
A    object
dtype: object

>>> df.infer_objects().dtypes
A    int64
dtype: object

info(verbose=None, buf=None, max_cols=None, memory_usage=None, null_counts=None)

Concise summary of a DataFrame.

verbose : {None, True, False}, optional

Whether to print the full summary. None follows the display.max_info_columns setting. True or False overrides the display.max_info_columns setting.

buf : writable buffer, defaults to sys.stdout max_cols : int, default None

Determines whether full summary or short summary is printed. None follows the display.max_info_columns setting.

memory_usage : boolean/string, default None

Specifies whether total memory usage of the DataFrame elements (including index) should be displayed. None follows the display.memory_usage setting. True or False overrides the display.memory_usage setting. A value of ‘deep’ is equivalent of True, with deep introspection. Memory usage is shown in human-readable units (base-2 representation).

null_counts : boolean, default None

Whether to show the non-null counts

• If None, then only show if the frame is smaller than max_info_rows and max_info_columns.
• If True, always show counts.
• If False, never show counts.

insert(loc, column, value, allow_duplicates=False)

Insert column into DataFrame at specified location.

Raises a ValueError if column is already contained in the DataFrame, unless allow_duplicates is set to True.

loc : int

Insertion index. Must verify 0 <= loc <= len(columns)

column : string, number, or hashable object

label of the inserted column

value : int, Series, or array-like allow_duplicates : bool, optional

interpolate(method='linear', axis=0, limit=None, inplace=False, limit_direction='forward', downcast=None, **kwargs)

Interpolate values according to different methods.

Please note that only method='linear' is supported for DataFrames/Series with a MultiIndex.

method : {‘linear’, ‘time’, ‘index’, ‘values’, ‘nearest’, ‘zero’,

‘slinear’, ‘quadratic’, ‘cubic’, ‘barycentric’, ‘krogh’, ‘polynomial’, ‘spline’, ‘piecewise_polynomial’, ‘from_derivatives’, ‘pchip’, ‘akima’}

• ‘linear’: ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. default
• ‘time’: interpolation works on daily and higher resolution data to interpolate given length of interval
• ‘index’, ‘values’: use the actual numerical values of the index
• ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘barycentric’, ‘polynomial’ is passed to scipy.interpolate.interp1d. Both ‘polynomial’ and ‘spline’ require that you also specify an order (int), e.g. df.interpolate(method=’polynomial’, order=4). These use the actual numerical values of the index.
• ‘krogh’, ‘piecewise_polynomial’, ‘spline’, ‘pchip’ and ‘akima’ are all wrappers around the scipy interpolation methods of similar names. These use the actual numerical values of the index. For more information on their behavior, see the scipy documentation and tutorial documentation
• ‘from_derivatives’ refers to BPoly.from_derivatives which replaces ‘piecewise_polynomial’ interpolation method in scipy 0.18

New in version 0.18.1: Added support for the ‘akima’ method Added interpolate method ‘from_derivatives’ which replaces ‘piecewise_polynomial’ in scipy 0.18; backwards-compatible with scipy < 0.18

axis : {0, 1}, default 0

• 0: fill column-by-column
• 1: fill row-by-row

limit : int, default None.

Maximum number of consecutive NaNs to fill. Must be greater than 0.

limit_direction : {‘forward’, ‘backward’, ‘both’}, default ‘forward’

If limit is specified, consecutive NaNs will be filled in this direction.

New in version 0.17.0.

inplace : bool, default False

Update the NDFrame in place if possible.

downcast : optional, ‘infer’ or None, defaults to None

Downcast dtypes if possible.

kwargs : keyword arguments to pass on to the interpolating function.

Series or DataFrame of same shape interpolated at the NaNs

reindex, replace, fillna

Filling in NaNs

>>> s = pd.Series([0, 1, np.nan, 3])
>>> s.interpolate()
0    0
1    1
2    2
3    3
dtype: float64

intersect(second_geometry, dimension)

Constructs a geometry that is the geometric intersection of the two input geometries. Different dimension values can be used to create different shape types. The intersection of two geometries of the same shape type is a geometry containing only the regions of overlap between the original geometries.

Paramters:

second_geometry:  

• a second geometry

dimension:

• The topological dimension (shape type) of the

resulting geometry.

1 -A zero-dimensional geometry (point or multipoint). 2 -A one-dimensional geometry (polyline). 4 -A two-dimensional geometry (polygon).

is_copy = None

is_empty

Return True for each empty geometry, False for non-empty

is_multipart

True, if the number of parts for the geometry is more than 1

isin(values)

Return boolean DataFrame showing whether each element in the DataFrame is contained in values.

values : iterable, Series, DataFrame or dictionary

The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dictionary, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.

DataFrame of booleans

When values is a list:

>>> df = DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']})
>>> df.isin([1, 3, 12, 'a'])
       A      B
0   True   True
1  False  False
2   True  False

When values is a dict:

>>> df = DataFrame({'A': [1, 2, 3], 'B': [1, 4, 7]})
>>> df.isin({'A': [1, 3], 'B': [4, 7, 12]})
       A      B
0   True  False  # Note that B didn't match the 1 here.
1  False   True
2   True   True

When values is a Series or DataFrame:

>>> df = DataFrame({'A': [1, 2, 3], 'B': ['a', 'b', 'f']})
>>> other = DataFrame({'A': [1, 3, 3, 2], 'B': ['e', 'f', 'f', 'e']})
>>> df.isin(other)
       A      B
0   True  False
1  False  False  # Column A in `other` has a 3, but not at index 1.
2   True   True

isna()

Return a boolean same-sized object indicating if the values are NA.

DataFrame.notna : boolean inverse of isna DataFrame.isnull : alias of isna isna : top-level isna

isnull()

Return a boolean same-sized object indicating if the values are NA.

DataFrame.notna : boolean inverse of isna DataFrame.isnull : alias of isna isna : top-level isna

items()

Iterator over (column name, Series) pairs.

iterrows : Iterate over DataFrame rows as (index, Series) pairs. itertuples : Iterate over DataFrame rows as namedtuples of the values.

iteritems()

Iterator over (column name, Series) pairs.

iterrows : Iterate over DataFrame rows as (index, Series) pairs. itertuples : Iterate over DataFrame rows as namedtuples of the values.

iterrows()

Iterate over DataFrame rows as (index, Series) pairs.

1. Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). For example,

   >>> df = pd.DataFrame([[1, 1.5]], columns=['int', 'float'])
   >>> row = next(df.iterrows())[1]
   >>> row
   int      1.0
   float    1.5
   Name: 0, dtype: float64
   >>> print(row['int'].dtype)
   float64
   >>> print(df['int'].dtype)
   int64
   

   To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally faster than iterrows.

2. You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.

it : generator

A generator that iterates over the rows of the frame.

itertuples : Iterate over DataFrame rows as namedtuples of the values. iteritems : Iterate over (column name, Series) pairs.

itertuples(index=True, name='Pandas')

Iterate over DataFrame rows as namedtuples, with index value as first element of the tuple.

index : boolean, default True

If True, return the index as the first element of the tuple.

name : string, default “Pandas”

The name of the returned namedtuples or None to return regular tuples.

The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. With a large number of columns (>255), regular tuples are returned.

iterrows : Iterate over DataFrame rows as (index, Series) pairs. iteritems : Iterate over (column name, Series) pairs.

>>> df = pd.DataFrame({'col1': [1, 2], 'col2': [0.1, 0.2]},
                      index=['a', 'b'])
>>> df
   col1  col2
a     1   0.1
b     2   0.2
>>> for row in df.itertuples():
...     print(row)
...
Pandas(Index='a', col1=1, col2=0.10000000000000001)
Pandas(Index='b', col1=2, col2=0.20000000000000001)

ix

A primarily label-location based indexer, with integer position fallback.

.ix[] supports mixed integer and label based access. It is primarily label based, but will fall back to integer positional access unless the corresponding axis is of integer type.

.ix is the most general indexer and will support any of the inputs in .loc and .iloc. .ix also supports floating point label schemes. .ix is exceptionally useful when dealing with mixed positional and label based hierachical indexes.

However, when an axis is integer based, ONLY label based access and not positional access is supported. Thus, in such cases, it’s usually better to be explicit and use .iloc or .loc.

See more at Advanced Indexing.

join(other, on=None, how='left', lsuffix='', rsuffix='', sort=False)

Join columns with other DataFrame either on index or on a key column. Efficiently Join multiple DataFrame objects by index at once by passing a list.

other : DataFrame, Series with name field set, or list of DataFrame

Index should be similar to one of the columns in this one. If a Series is passed, its name attribute must be set, and that will be used as the column name in the resulting joined DataFrame

on : column name, tuple/list of column names, or array-like

Column(s) in the caller to join on the index in other, otherwise joins index-on-index. If multiples columns given, the passed DataFrame must have a MultiIndex. Can pass an array as the join key if not already contained in the calling DataFrame. Like an Excel VLOOKUP operation

how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default: ‘left’

How to handle the operation of the two objects.

• left: use calling frame’s index (or column if on is specified)
• right: use other frame’s index
• outer: form union of calling frame’s index (or column if on is specified) with other frame’s index, and sort it lexicographically
• inner: form intersection of calling frame’s index (or column if on is specified) with other frame’s index, preserving the order of the calling’s one

lsuffix : string

Suffix to use from left frame’s overlapping columns

rsuffix : string

Suffix to use from right frame’s overlapping columns

sort : boolean, default False

Order result DataFrame lexicographically by the join key. If False, the order of the join key depends on the join type (how keyword)

on, lsuffix, and rsuffix options are not supported when passing a list of DataFrame objects

>>> caller = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3', 'K4', 'K5'],
...                        'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})

>>> caller
    A key
0  A0  K0
1  A1  K1
2  A2  K2
3  A3  K3
4  A4  K4
5  A5  K5

>>> other = pd.DataFrame({'key': ['K0', 'K1', 'K2'],
...                       'B': ['B0', 'B1', 'B2']})

>>> other
    B key
0  B0  K0
1  B1  K1
2  B2  K2

Join DataFrames using their indexes.

>>> caller.join(other, lsuffix='_caller', rsuffix='_other')

>>>     A key_caller    B key_other
    0  A0         K0   B0        K0
    1  A1         K1   B1        K1
    2  A2         K2   B2        K2
    3  A3         K3  NaN       NaN
    4  A4         K4  NaN       NaN
    5  A5         K5  NaN       NaN

If we want to join using the key columns, we need to set key to be the index in both caller and other. The joined DataFrame will have key as its index.

>>> caller.set_index('key').join(other.set_index('key'))

>>>      A    B
    key
    K0   A0   B0
    K1   A1   B1
    K2   A2   B2
    K3   A3  NaN
    K4   A4  NaN
    K5   A5  NaN

Another option to join using the key columns is to use the on parameter. DataFrame.join always uses other’s index but we can use any column in the caller. This method preserves the original caller’s index in the result.

>>> caller.join(other.set_index('key'), on='key')

>>>     A key    B
    0  A0  K0   B0
    1  A1  K1   B1
    2  A2  K2   B2
    3  A3  K3  NaN
    4  A4  K4  NaN
    5  A5  K5  NaN

DataFrame.merge : For column(s)-on-columns(s) operations

joined : DataFrame

keys()

Get the ‘info axis’ (see Indexing for more)

This is index for Series, columns for DataFrame and major_axis for Panel.

kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

kurt : Series or DataFrame (if level specified)

kurtosis(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

kurt : Series or DataFrame (if level specified)

label_point

The point at which the label is located. The labelPoint is always located within or on a feature.

last(offset)

Convenience method for subsetting final periods of time series data based on a date offset.

offset : string, DateOffset, dateutil.relativedelta

ts.last(‘5M’) -> Last 5 months

subset : type of caller

last_point

The last coordinate of the feature.

last_valid_index()

Return index for first non-NA/null value.

If all elements are non-NA/null, returns None. Also returns None for empty DataFrame.

scalar : type of index

le(other, axis='columns', level=None)

Wrapper for flexible comparison methods le

length

The length of the linear feature. Zero for point and multipoint feature types.

length3D

The 3D length of the linear feature. Zero for point and multipoint feature types.

loc

Purely label-location based indexer for selection by label.

.loc[] is primarily label based, but may also be used with a boolean array.

Allowed inputs are:

• A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
• A list or array of labels, e.g. ['a', 'b', 'c'].
• A slice object with labels, e.g. 'a':'f' (note that contrary to usual python slices, both the start and the stop are included!).
• A boolean array.
• A callable function with one argument (the calling Series, DataFrame or Panel) and that returns valid output for indexing (one of the above)

.loc will raise a KeyError when the items are not found.

See more at Selection by Label

lookup(row_labels, col_labels)

Label-based “fancy indexing” function for DataFrame. Given equal-length arrays of row and column labels, return an array of the values corresponding to each (row, col) pair.

row_labels : sequence

The row labels to use for lookup

col_labels : sequence

The column labels to use for lookup

Akin to:

result = []
for row, col in zip(row_labels, col_labels):
    result.append(df.get_value(row, col))

values : ndarray

The found values

lt(other, axis='columns', level=None)

Wrapper for flexible comparison methods lt

mad(axis=None, skipna=None, level=None)

Return the mean absolute deviation of the values for the requested axis

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

mad : Series or DataFrame (if level specified)

mask(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False, raise_on_error=None)

Return an object of same shape as self and whose corresponding entries are from self where cond is False and otherwise are from other.

cond : boolean NDFrame, array-like, or callable

Where cond is False, keep the original value. Where True, replace with corresponding value from other. If cond is callable, it is computed on the NDFrame and should return boolean NDFrame or array. The callable must not change input NDFrame (though pandas doesn’t check it).

New in version 0.18.1: A callable can be used as cond.

other : scalar, NDFrame, or callable

Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the NDFrame and should return scalar or NDFrame. The callable must not change input NDFrame (though pandas doesn’t check it).

New in version 0.18.1: A callable can be used as other.

inplace : boolean, default False

Whether to perform the operation in place on the data

axis : alignment axis if needed, default None level : alignment level if needed, default None errors : str, {‘raise’, ‘ignore’}, default ‘raise’

• raise : allow exceptions to be raised
• ignore : suppress exceptions. On error return original object

Note that currently this parameter won’t affect the results and will always coerce to a suitable dtype.

try_cast : boolean, default False

try to cast the result back to the input type (if possible),

raise_on_error : boolean, default True

Whether to raise on invalid data types (e.g. trying to where on strings)

Deprecated since version 0.21.0.

wh : same type as caller

The mask method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is False the element is used; otherwise the corresponding element from the DataFrame other is used.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the mask documentation in indexing.

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0

>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN

>>> s.where(s > 1, 10)
0    10.0
1    10.0
2    2.0
3    3.0
4    4.0

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

DataFrame.where()

max(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

This method returns the maximum of the values in the object.

If you want the index of the maximum, use idxmax. This is the equivalent of the numpy.ndarray method argmax.

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

max : Series or DataFrame (if level specified)

mean(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the mean of the values for the requested axis

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

mean : Series or DataFrame (if level specified)

measure_on_line(second_geometry, as_percentage=False)

Returns a measure from the start point of this line to the in_point.

Paramters:

second_geometry:  

• a second geometry

as_percentage:

• If False, the measure will be returned as a

distance; if True, the measure will be returned as a percentage.

median(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the median of the values for the requested axis

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

median : Series or DataFrame (if level specified)

melt(id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)

“Unpivots” a DataFrame from wide format to long format, optionally leaving identifier variables set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are “unpivoted” to the row axis, leaving just two non-identifier columns, ‘variable’ and ‘value’.

New in version 0.20.0.

frame : DataFrame id_vars : tuple, list, or ndarray, optional

Column(s) to use as identifier variables.

value_vars : tuple, list, or ndarray, optional

Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

var_name : scalar

Name to use for the ‘variable’ column. If None it uses frame.columns.name or ‘variable’.

value_name : scalar, default ‘value’

Name to use for the ‘value’ column.

col_level : int or string, optional

If columns are a MultiIndex then use this level to melt.

melt pivot_table DataFrame.pivot

>>> import pandas as pd
>>> df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
...                    'B': {0: 1, 1: 3, 2: 5},
...                    'C': {0: 2, 1: 4, 2: 6}})
>>> df
   A  B  C
0  a  1  2
1  b  3  4
2  c  5  6

>>> df.melt(id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> df.melt(id_vars=['A'], value_vars=['B', 'C'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5
3  a        C      2
4  b        C      4
5  c        C      6

The names of ‘variable’ and ‘value’ columns can be customized:

>>> df.melt(id_vars=['A'], value_vars=['B'],
...         var_name='myVarname', value_name='myValname')
   A myVarname  myValname
0  a         B          1
1  b         B          3
2  c         B          5

If you have multi-index columns:

>>> df.columns = [list('ABC'), list('DEF')]
>>> df
   A  B  C
   D  E  F
0  a  1  2
1  b  3  4
2  c  5  6

>>> df.melt(col_level=0, id_vars=['A'], value_vars=['B'])
   A variable  value
0  a        B      1
1  b        B      3
2  c        B      5

>>> df.melt(id_vars=[('A', 'D')], value_vars=[('B', 'E')])
  (A, D) variable_0 variable_1  value
0      a          B          E      1
1      b          B          E      3
2      c          B          E      5

memory_usage(index=True, deep=False)

Memory usage of DataFrame columns.

index : bool

Specifies whether to include memory usage of DataFrame’s index in returned Series. If index=True (default is False) the first index of the Series is Index.

deep : bool

Introspect the data deeply, interrogate object dtypes for system-level memory consumption

sizes : Series

A series with column names as index and memory usage of columns with units of bytes.

Memory usage does not include memory consumed by elements that are not components of the array if deep=False

numpy.ndarray.nbytes

merge(right, how='inner', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False, validate=None)

Merge DataFrame objects by performing a database-style join operation by columns or indexes.

If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on.

right : DataFrame how : {‘left’, ‘right’, ‘outer’, ‘inner’}, default ‘inner’

• left: use only keys from left frame, similar to a SQL left outer join; preserve key order
• right: use only keys from right frame, similar to a SQL right outer join; preserve key order
• outer: use union of keys from both frames, similar to a SQL full outer join; sort keys lexicographically
• inner: use intersection of keys from both frames, similar to a SQL inner join; preserve the order of the left keys

on : label or list

Field names to join on. Must be found in both DataFrames. If on is None and not merging on indexes, then it merges on the intersection of the columns by default.

left_on : label or list, or array-like

Field names to join on in left DataFrame. Can be a vector or list of vectors of the length of the DataFrame to use a particular vector as the join key instead of columns

right_on : label or list, or array-like

Field names to join on in right DataFrame or vector/list of vectors per left_on docs

left_index : boolean, default False

Use the index from the left DataFrame as the join key(s). If it is a MultiIndex, the number of keys in the other DataFrame (either the index or a number of columns) must match the number of levels

right_index : boolean, default False

Use the index from the right DataFrame as the join key. Same caveats as left_index

sort : boolean, default False

Sort the join keys lexicographically in the result DataFrame. If False, the order of the join keys depends on the join type (how keyword)

suffixes : 2-length sequence (tuple, list, …)

Suffix to apply to overlapping column names in the left and right side, respectively

copy : boolean, default True

If False, do not copy data unnecessarily

indicator : boolean or string, default False

If True, adds a column to output DataFrame called “_merge” with information on the source of each row. If string, column with information on source of each row will be added to output DataFrame, and column will be named value of string. Information column is Categorical-type and takes on a value of “left_only” for observations whose merge key only appears in ‘left’ DataFrame, “right_only” for observations whose merge key only appears in ‘right’ DataFrame, and “both” if the observation’s merge key is found in both.

New in version 0.17.0.

validate : string, default None

If specified, checks if merge is of specified type.

• “one_to_one” or “1:1”: check if merge keys are unique in both left and right datasets.
• “one_to_many” or “1:m”: check if merge keys are unique in left dataset.
• “many_to_one” or “m:1”: check if merge keys are unique in right dataset.
• “many_to_many” or “m:m”: allowed, but does not result in checks.

New in version 0.21.0.

>>> A              >>> B
    lkey value         rkey value
0   foo  1         0   foo  5
1   bar  2         1   bar  6
2   baz  3         2   qux  7
3   foo  4         3   bar  8

>>> A.merge(B, left_on='lkey', right_on='rkey', how='outer')
   lkey  value_x  rkey  value_y
0  foo   1        foo   5
1  foo   4        foo   5
2  bar   2        bar   6
3  bar   2        bar   8
4  baz   3        NaN   NaN
5  NaN   NaN      qux   7

merged : DataFrame

The output type will the be same as ‘left’, if it is a subclass of DataFrame.

merge_ordered merge_asof

merge_datasets(other)

This operation combines two dataframes into one new DataFrame. If the operation is combining two SpatialDataFrames, the geometry_type must match.

min(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

This method returns the minimum of the values in the object.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

min : Series or DataFrame (if level specified)

mod(other, axis='columns', level=None, fill_value=None)

Modulo of dataframe and other, element-wise (binary operator mod).

Equivalent to dataframe % other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rmod

mode(axis=0, numeric_only=False)

Gets the mode(s) of each element along the axis selected. Adds a row for each mode per label, fills in gaps with nan.

Note that there could be multiple values returned for the selected axis (when more than one item share the maximum frequency), which is the reason why a dataframe is returned. If you want to impute missing values with the mode in a dataframe df, you can just do this: df.fillna(df.mode().iloc[0])

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

• 0 or ‘index’ : get mode of each column
• 1 or ‘columns’ : get mode of each row

numeric_only : boolean, default False

if True, only apply to numeric columns

modes : DataFrame (sorted)

>>> df = pd.DataFrame({'A': [1, 2, 1, 2, 1, 2, 3]})
>>> df.mode()
   A
0  1
1  2

mul(other, axis='columns', level=None, fill_value=None)

Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rmul

multiply(other, axis='columns', level=None, fill_value=None)

Multiplication of dataframe and other, element-wise (binary operator mul).

Equivalent to dataframe * other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rmul

ndim

Number of axes / array dimensions

ne(other, axis='columns', level=None)

Wrapper for flexible comparison methods ne

nlargest(n, columns, keep='first')

Get the rows of a DataFrame sorted by the n largest values of columns.

New in version 0.17.0.

n : int

Number of items to retrieve

columns : list or str

Column name or names to order by

keep : {‘first’, ‘last’, False}, default ‘first’

Where there are duplicate values: - first : take the first occurrence. - last : take the last occurrence.

DataFrame

>>> df = DataFrame({'a': [1, 10, 8, 11, -1],
...                 'b': list('abdce'),
...                 'c': [1.0, 2.0, np.nan, 3.0, 4.0]})
>>> df.nlargest(3, 'a')
    a  b   c
3  11  c   3
1  10  b   2
2   8  d NaN

notna()

Return a boolean same-sized object indicating if the values are not NA.

DataFrame.isna : boolean inverse of notna DataFrame.notnull : alias of notna notna : top-level notna

notnull()

Return a boolean same-sized object indicating if the values are not NA.

DataFrame.isna : boolean inverse of notna DataFrame.notnull : alias of notna notna : top-level notna

nsmallest(n, columns, keep='first')

Get the rows of a DataFrame sorted by the n smallest values of columns.

New in version 0.17.0.

n : int

Number of items to retrieve

columns : list or str

Column name or names to order by

keep : {‘first’, ‘last’, False}, default ‘first’

Where there are duplicate values: - first : take the first occurrence. - last : take the last occurrence.

DataFrame

>>> df = DataFrame({'a': [1, 10, 8, 11, -1],
...                 'b': list('abdce'),
...                 'c': [1.0, 2.0, np.nan, 3.0, 4.0]})
>>> df.nsmallest(3, 'a')
   a  b   c
4 -1  e   4
0  1  a   1
2  8  d NaN

nunique(axis=0, dropna=True)

Return Series with number of distinct observations over requested axis.

New in version 0.20.0.

axis : {0 or ‘index’, 1 or ‘columns’}, default 0 dropna : boolean, default True

Don’t include NaN in the counts.

nunique : Series

>>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [1, 1, 1]})
>>> df.nunique()
A    3
B    1

>>> df.nunique(axis=1)
0    1
1    2
2    2

overlaps(second_geometry)

Indicates if the intersection of the two geometries has the same shape type as one of the input geometries and is not equivalent to either of the input geometries.

Paramters:

second_geometry:  

• a second geometry

part_count

The number of geometry parts for the feature.

pct_change(periods=1, fill_method='pad', limit=None, freq=None, **kwargs)

Percent change over given number of periods.

periods : int, default 1

Periods to shift for forming percent change

fill_method : str, default ‘pad’

How to handle NAs before computing percent changes

limit : int, default None

The number of consecutive NAs to fill before stopping

freq : DateOffset, timedelta, or offset alias string, optional

Increment to use from time series API (e.g. ‘M’ or BDay())

chg : NDFrame

By default, the percentage change is calculated along the stat axis: 0, or Index, for DataFrame and 1, or minor for Panel. You can change this with the axis keyword argument.

pipe(func, *args, **kwargs)

Apply func(self, *args, **kwargs)

func : function

function to apply to the NDFrame. args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects the NDFrame.

args : iterable, optional

positional arguments passed into func.

kwargs : mapping, optional

a dictionary of keyword arguments passed into func.

object : the return type of func.

Use .pipe when chaining together functions that expect Series, DataFrames or GroupBy objects. Instead of writing

>>> f(g(h(df), arg1=a), arg2=b, arg3=c)

You can write

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe(f, arg2=b, arg3=c)
... )

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose f takes its data as arg2:

>>> (df.pipe(h)
...    .pipe(g, arg1=a)
...    .pipe((f, 'arg2'), arg1=a, arg3=c)
...  )

pandas.DataFrame.apply pandas.DataFrame.applymap pandas.Series.map

pivot(index=None, columns=None, values=None)

Reshape data (produce a “pivot” table) based on column values. Uses unique values from index / columns to form axes of the resulting DataFrame.

index : string or object, optional

Column name to use to make new frame’s index. If None, uses existing index.

columns : string or object

Column name to use to make new frame’s columns

values : string or object, optional

Column name to use for populating new frame’s values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns

pivoted : DataFrame

DataFrame.pivot_table : generalization of pivot that can handle

duplicate values for one index/column pair

DataFrame.unstack : pivot based on the index values instead of a

column

For finer-tuned control, see hierarchical indexing documentation along with the related stack/unstack methods

>>> df = pd.DataFrame({'foo': ['one','one','one','two','two','two'],
                       'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
                       'baz': [1, 2, 3, 4, 5, 6]})
>>> df
    foo   bar  baz
0   one   A    1
1   one   B    2
2   one   C    3
3   two   A    4
4   two   B    5
5   two   C    6

>>> df.pivot(index='foo', columns='bar', values='baz')
     A   B   C
one  1   2   3
two  4   5   6

>>> df.pivot(index='foo', columns='bar')['baz']
     A   B   C
one  1   2   3
two  4   5   6

pivot_table(values=None, index=None, columns=None, aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All')

Create a spreadsheet-style pivot table as a DataFrame. The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame

values : column to aggregate, optional index : column, Grouper, array, or list of the previous

If an array is passed, it must be the same length as the data. The list can contain any of the other types (except list). Keys to group by on the pivot table index. If an array is passed, it is being used as the same manner as column values.

columns : column, Grouper, array, or list of the previous

If an array is passed, it must be the same length as the data. The list can contain any of the other types (except list). Keys to group by on the pivot table column. If an array is passed, it is being used as the same manner as column values.

aggfunc : function or list of functions, default numpy.mean

If list of functions passed, the resulting pivot table will have hierarchical columns whose top level are the function names (inferred from the function objects themselves)

fill_value : scalar, default None

Value to replace missing values with

margins : boolean, default False

Add all row / columns (e.g. for subtotal / grand totals)

dropna : boolean, default True

Do not include columns whose entries are all NaN

margins_name : string, default ‘All’

Name of the row / column that will contain the totals when margins is True.

>>> df = pd.DataFrame({"A": ["foo", "foo", "foo", "foo", "foo",
...                          "bar", "bar", "bar", "bar"],
...                    "B": ["one", "one", "one", "two", "two",
...                          "one", "one", "two", "two"],
...                    "C": ["small", "large", "large", "small",
...                          "small", "large", "small", "small",
...                          "large"],
...                    "D": [1, 2, 2, 3, 3, 4, 5, 6, 7]})
>>> df
     A    B      C  D
0  foo  one  small  1
1  foo  one  large  2
2  foo  one  large  2
3  foo  two  small  3
4  foo  two  small  3
5  bar  one  large  4
6  bar  one  small  5
7  bar  two  small  6
8  bar  two  large  7

>>> table = pivot_table(df, values='D', index=['A', 'B'],
...                     columns=['C'], aggfunc=np.sum)
>>> table
... 
C        large  small
A   B
bar one    4.0    5.0
    two    7.0    6.0
foo one    4.0    1.0
    two    NaN    6.0

table : DataFrame

DataFrame.pivot : pivot without aggregation that can handle

non-numeric data

plot(*args, **kwargs)

writes the spatial dataframe to a map

point_count

The total number of points for the feature.

point_from_angle_and_distance(angle, distance, method='GEODESCIC')

Returns a point at a given angle and distance in degrees and meters using the specified measurement type.

Parameters:

angle:

• The angle in degrees to the returned point.

distance:

• The distance in meters to the returned point.

method:

• PLANAR measurements reflect the projection of geographic

data onto the 2D surface (in other words, they will not take into account the curvature of the earth). GEODESIC, GREAT_ELLIPTIC, LOXODROME, and PRESERVE_SHAPE measurement types may be chosen as an alternative, if desired.

pop(item)

Return item and drop from frame. Raise KeyError if not found.

item : str

Column label to be popped

popped : Series

>>> df = pd.DataFrame([('falcon', 'bird',    389.0),
...                    ('parrot', 'bird',     24.0),
...                    ('lion',   'mammal',   80.5),
...                    ('monkey', 'mammal', np.nan)],
...                   columns=('name', 'class', 'max_speed'))
>>> df
     name   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

>>> df.pop('class')
0      bird
1      bird
2    mammal
3    mammal
Name: class, dtype: object

>>> df
     name  max_speed
0  falcon      389.0
1  parrot       24.0
2    lion       80.5
3  monkey        NaN

position_along_line(value, use_percentage=False)

Returns a point on a line at a specified distance from the beginning of the line.

Parameters:

value:

• The distance along the line.

use_percentage:

• The distance may be specified as a fixed unit

of measure or a ratio of the length of the line. If True, value is used as a percentage; if False, value is used as a distance. For percentages, the value should be expressed as a double from 0.0 (0%) to 1.0 (100%).

pow(other, axis='columns', level=None, fill_value=None)

Exponential power of dataframe and other, element-wise (binary operator pow).

Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rpow

prod(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the product of the values for the requested axis

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

prod : Series or DataFrame (if level specified)

product(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the product of the values for the requested axis

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

prod : Series or DataFrame (if level specified)

project_as(spatial_reference, transformation_name=None)

Projects a geometry and optionally applies a geotransformation.

Parameter:

spatial_reference:  

• The new spatial reference. This can be a

SpatialReference object or the coordinate system name.

transformation_name:  

• The geotransformation name.

quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

Return values at the given quantile over requested axis, a la numpy.percentile.

q : float or array-like, default 0.5 (50% quantile)

0 <= q <= 1, the quantile(s) to compute

axis : {0, 1, ‘index’, ‘columns’} (default 0)

0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise

interpolation : {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}

New in version 0.18.0.

This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j:

• linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
• lower: i.
• higher: j.
• nearest: i or j whichever is nearest.
• midpoint: (i + j) / 2.

quantiles : Series or DataFrame

• If q is an array, a DataFrame will be returned where the index is q, the columns are the columns of self, and the values are the quantiles.
• If q is a float, a Series will be returned where the index is the columns of self and the values are the quantiles.

>>> df = DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
                   columns=['a', 'b'])
>>> df.quantile(.1)
a    1.3
b    3.7
dtype: float64
>>> df.quantile([.1, .5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0

query(expr, inplace=False, **kwargs)

Query the columns of a frame with a boolean expression.

expr : string

The query string to evaluate. You can refer to variables in the environment by prefixing them with an ‘@’ character like @a + b.

inplace : bool

Whether the query should modify the data in place or return a modified copy

New in version 0.18.0.

kwargs : dict

See the documentation for pandas.eval() for complete details on the keyword arguments accepted by DataFrame.query().

q : DataFrame

The result of the evaluation of this expression is first passed to DataFrame.loc and if that fails because of a multidimensional key (e.g., a DataFrame) then the result will be passed to DataFrame.__getitem__().

This method uses the top-level pandas.eval() function to evaluate the passed query.

The query() method uses a slightly modified Python syntax by default. For example, the & and | (bitwise) operators have the precedence of their boolean cousins, and and or. This is syntactically valid Python, however the semantics are different.

You can change the semantics of the expression by passing the keyword argument parser='python'. This enforces the same semantics as evaluation in Python space. Likewise, you can pass engine='python' to evaluate an expression using Python itself as a backend. This is not recommended as it is inefficient compared to using numexpr as the engine.

The DataFrame.index and DataFrame.columns attributes of the DataFrame instance are placed in the query namespace by default, which allows you to treat both the index and columns of the frame as a column in the frame. The identifier index is used for the frame index; you can also use the name of the index to identify it in a query.

For further details and examples see the query documentation in indexing.

pandas.eval DataFrame.eval

>>> from numpy.random import randn
>>> from pandas import DataFrame
>>> df = DataFrame(randn(10, 2), columns=list('ab'))
>>> df.query('a > b')
>>> df[df.a > df.b]  # same result as the previous expression

query_point_and_distance(second_geometry, use_percentage=False)

Finds the point on the polyline nearest to the in_point and the distance between those points. Also returns information about the side of the line the in_point is on as well as the distance along the line where the nearest point occurs.

Paramters:

second_geometry:  

• a second geometry

as_percentage:

• if False, the measure will be returned as

distance, True, measure will be a percentage

radd(other, axis='columns', level=None, fill_value=None)

Addition of dataframe and other, element-wise (binary operator radd).

Equivalent to other + dataframe, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.add

rank(axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)

Compute numerical data ranks (1 through n) along axis. Equal values are assigned a rank that is the average of the ranks of those values

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

index to direct ranking

method : {‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}

• average: average rank of group
• min: lowest rank in group
• max: highest rank in group
• first: ranks assigned in order they appear in the array
• dense: like ‘min’, but rank always increases by 1 between groups

numeric_only : boolean, default None

Include only float, int, boolean data. Valid only for DataFrame or Panel objects

na_option : {‘keep’, ‘top’, ‘bottom’}

• keep: leave NA values where they are
• top: smallest rank if ascending
• bottom: smallest rank if descending

ascending : boolean, default True

False for ranks by high (1) to low (N)

pct : boolean, default False

Computes percentage rank of data

ranks : same type as caller

rdiv(other, axis='columns', level=None, fill_value=None)

Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.truediv

reindex(labels=None, index=None, columns=None, axis=None, method=None, copy=True, level=None, fill_value=nan, limit=None, tolerance=None)

Conform DataFrame to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False

labels : array-like, optional

New labels / index to conform the axis specified by ‘axis’ to.

index, columns : array-like, optional (should be specified using keywords)

New labels / index to conform to. Preferably an Index object to avoid duplicating data

axis : int or str, optional

Axis to target. Can be either the axis name (‘index’, ‘columns’) or number (0, 1).

method : {None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}, optional

method to use for filling holes in reindexed DataFrame. Please note: this is only applicable to DataFrames/Series with a monotonically increasing/decreasing index.

• default: don’t fill gaps
• pad / ffill: propagate last valid observation forward to next valid
• backfill / bfill: use next valid observation to fill gap
• nearest: use nearest valid observations to fill gap

copy : boolean, default True

Return a new object, even if the passed indexes are the same

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

fill_value : scalar, default np.NaN

Value to use for missing values. Defaults to NaN, but can be any “compatible” value

limit : int, default None

Maximum number of consecutive elements to forward or backward fill

tolerance : optional

Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation abs(index[indexer] - target) <= tolerance.

Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

New in version 0.17.0.

New in version 0.21.0: (list-like tolerance)

DataFrame.reindex supports two calling conventions

• (index=index_labels, columns=column_labels, ...)
• (labels, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

Create a dataframe with some fictional data.

>>> index = ['Firefox', 'Chrome', 'Safari', 'IE10', 'Konqueror']
>>> df = pd.DataFrame({
...      'http_status': [200,200,404,404,301],
...      'response_time': [0.04, 0.02, 0.07, 0.08, 1.0]},
...       index=index)
>>> df
           http_status  response_time
Firefox            200           0.04
Chrome             200           0.02
Safari             404           0.07
IE10               404           0.08
Konqueror          301           1.00

Create a new index and reindex the dataframe. By default values in the new index that do not have corresponding records in the dataframe are assigned NaN.

>>> new_index= ['Safari', 'Iceweasel', 'Comodo Dragon', 'IE10',
...             'Chrome']
>>> df.reindex(new_index)
               http_status  response_time
Safari               404.0           0.07
Iceweasel              NaN            NaN
Comodo Dragon          NaN            NaN
IE10                 404.0           0.08
Chrome               200.0           0.02

We can fill in the missing values by passing a value to the keyword fill_value. Because the index is not monotonically increasing or decreasing, we cannot use arguments to the keyword method to fill the NaN values.

>>> df.reindex(new_index, fill_value=0)
               http_status  response_time
Safari                 404           0.07
Iceweasel                0           0.00
Comodo Dragon            0           0.00
IE10                   404           0.08
Chrome                 200           0.02

>>> df.reindex(new_index, fill_value='missing')
              http_status response_time
Safari                404          0.07
Iceweasel         missing       missing
Comodo Dragon     missing       missing
IE10                  404          0.08
Chrome                200          0.02

We can also reindex the columns.

>>> df.reindex(columns=['http_status', 'user_agent'])
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

Or we can use “axis-style” keyword arguments

>>> df.reindex(['http_status', 'user_agent'], axis="columns")
           http_status  user_agent
Firefox            200         NaN
Chrome             200         NaN
Safari             404         NaN
IE10               404         NaN
Konqueror          301         NaN

To further illustrate the filling functionality in reindex, we will create a dataframe with a monotonically increasing index (for example, a sequence of dates).

>>> date_index = pd.date_range('1/1/2010', periods=6, freq='D')
>>> df2 = pd.DataFrame({"prices": [100, 101, np.nan, 100, 89, 88]},
...                    index=date_index)
>>> df2
            prices
2010-01-01     100
2010-01-02     101
2010-01-03     NaN
2010-01-04     100
2010-01-05      89
2010-01-06      88

Suppose we decide to expand the dataframe to cover a wider date range.

>>> date_index2 = pd.date_range('12/29/2009', periods=10, freq='D')
>>> df2.reindex(date_index2)
            prices
2009-12-29     NaN
2009-12-30     NaN
2009-12-31     NaN
2010-01-01     100
2010-01-02     101
2010-01-03     NaN
2010-01-04     100
2010-01-05      89
2010-01-06      88
2010-01-07     NaN

The index entries that did not have a value in the original data frame (for example, ‘2009-12-29’) are by default filled with NaN. If desired, we can fill in the missing values using one of several options.

For example, to backpropagate the last valid value to fill the NaN values, pass bfill as an argument to the method keyword.

>>> df2.reindex(date_index2, method='bfill')
            prices
2009-12-29     100
2009-12-30     100
2009-12-31     100
2010-01-01     100
2010-01-02     101
2010-01-03     NaN
2010-01-04     100
2010-01-05      89
2010-01-06      88
2010-01-07     NaN

Please note that the NaN value present in the original dataframe (at index value 2010-01-03) will not be filled by any of the value propagation schemes. This is because filling while reindexing does not look at dataframe values, but only compares the original and desired indexes. If you do want to fill in the NaN values present in the original dataframe, use the fillna() method.

See the user guide for more.

reindexed : DataFrame

reindex_axis(labels, axis=0, method=None, level=None, copy=True, limit=None, fill_value=nan)

Conform input object to new index with optional filling logic, placing NA/NaN in locations having no value in the previous index. A new object is produced unless the new index is equivalent to the current one and copy=False

labels : array-like

New labels / index to conform to. Preferably an Index object to avoid duplicating data

axis : {0 or ‘index’, 1 or ‘columns’} method : {None, ‘backfill’/’bfill’, ‘pad’/’ffill’, ‘nearest’}, optional

Method to use for filling holes in reindexed DataFrame:

• default: don’t fill gaps
• pad / ffill: propagate last valid observation forward to next valid
• backfill / bfill: use next valid observation to fill gap
• nearest: use nearest valid observations to fill gap

copy : boolean, default True

Return a new object, even if the passed indexes are the same

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

limit : int, default None

Maximum number of consecutive elements to forward or backward fill

tolerance : optional

Maximum distance between original and new labels for inexact matches. The values of the index at the matching locations most satisfy the equation abs(index[indexer] - target) <= tolerance.

Tolerance may be a scalar value, which applies the same tolerance to all values, or list-like, which applies variable tolerance per element. List-like includes list, tuple, array, Series, and must be the same size as the index and its dtype must exactly match the index’s type.

New in version 0.17.0.

New in version 0.21.0: (list-like tolerance)

>>> df.reindex_axis(['A', 'B', 'C'], axis=1)

reindex, reindex_like

reindexed : DataFrame

reindex_like(other, method=None, copy=True, limit=None, tolerance=None)

Return an object with matching indices to myself.

other : Object method : string or None copy : boolean, default True limit : int, default None

Maximum number of consecutive labels to fill for inexact matches.

tolerance : optional

Maximum distance between labels of the other object and this object for inexact matches. Can be list-like.

New in version 0.17.0.

New in version 0.21.0: (list-like tolerance)

Like calling s.reindex(index=other.index, columns=other.columns,

method=…)

reindexed : same as input

rename(mapper=None, index=None, columns=None, axis=None, copy=True, inplace=False, level=None)

Alter axes labels.

Function / dict values must be unique (1-to-1). Labels not contained in a dict / Series will be left as-is. Extra labels listed don’t throw an error.

See the user guide for more.

mapper, index, columns : dict-like or function, optional

dict-like or functions transformations to apply to that axis’ values. Use either mapper and axis to specify the axis to target with mapper, or index and columns.

axis : int or str, optional

Axis to target with mapper. Can be either the axis name (‘index’, ‘columns’) or number (0, 1). The default is ‘index’.

copy : boolean, default True

Also copy underlying data

inplace : boolean, default False

Whether to return a new %(klass)s. If True then value of copy is ignored.

level : int or level name, default None

In case of a MultiIndex, only rename labels in the specified level.

renamed : DataFrame

pandas.DataFrame.rename_axis

DataFrame.rename supports two calling conventions

• (index=index_mapper, columns=columns_mapper, ...)
• (mapper, axis={'index', 'columns'}, ...)

We highly recommend using keyword arguments to clarify your intent.

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename(index=str, columns={"A": "a", "B": "c"})
   a  c
0  1  4
1  2  5
2  3  6

>>> df.rename(index=str, columns={"A": "a", "C": "c"})
   a  B
0  1  4
1  2  5
2  3  6

Using axis-style parameters

>>> df.rename(str.lower, axis='columns')
   a  b
0  1  4
1  2  5
2  3  6

>>> df.rename({1: 2, 2: 4}, axis='index')
   A  B
0  1  4
2  2  5
4  3  6

rename_axis(mapper, axis=0, copy=True, inplace=False)

Alter the name of the index or columns.

mapper : scalar, list-like, optional

Value to set the axis name attribute.

axis : int or string, default 0 copy : boolean, default True

Also copy underlying data

inplace : boolean, default False

renamed : type of caller or None if inplace=True

Prior to version 0.21.0, rename_axis could also be used to change the axis labels by passing a mapping or scalar. This behavior is deprecated and will be removed in a future version. Use rename instead.

pandas.Series.rename, pandas.DataFrame.rename pandas.Index.rename

>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.rename_axis("foo")
     A  B
foo
0    1  4
1    2  5
2    3  6

>>> df.rename_axis("bar", axis="columns")
bar  A  B
0    1  4
1    2  5
2    3  6

reorder_levels(order, axis=0)

Rearrange index levels using input order. May not drop or duplicate levels

order : list of int or list of str

List representing new level order. Reference level by number (position) or by key (label).

axis : int

Where to reorder levels.

type of caller (new object)

replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad', axis=None)

Replace values given in ‘to_replace’ with ‘value’.

to_replace : str, regex, list, dict, Series, numeric, or None

• str or regex:

  • str: string exactly matching to_replace will be replaced with value
  • regex: regexs matching to_replace will be replaced with value

• list of str, regex, or numeric:

  • First, if to_replace and value are both lists, they must be the same length.
  • Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn’t matter much for value since there are only a few possible substitution regexes you can use.
  • str and regex rules apply as above.

• dict:

  • Nested dictionaries, e.g., {‘a’: {‘b’: nan}}, are read as follows: look in column ‘a’ for the value ‘b’ and replace it with nan. You can nest regular expressions as well. Note that column names (the top-level dictionary keys in a nested dictionary) cannot be regular expressions.
  • Keys map to column names and values map to substitution values. You can treat this as a special case of passing two lists except that you are specifying the column to search in.

• None:

  • This means that the regex argument must be a string, compiled regular expression, or list, dict, ndarray or Series of such elements. If value is also None then this must be a nested dictionary or Series.

See the examples section for examples of each of these.

value : scalar, dict, list, str, regex, default None

Value to use to fill holes (e.g. 0), alternately a dict of values specifying which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.

inplace : boolean, default False

If True, in place. Note: this will modify any other views on this object (e.g. a column from a DataFrame). Returns the caller if this is True.

limit : int, default None

Maximum size gap to forward or backward fill

regex : bool or same types as to_replace, default False

Whether to interpret to_replace and/or value as regular expressions. If this is True then to_replace must be a string. Otherwise, to_replace must be None because this parameter will be interpreted as a regular expression or a list, dict, or array of regular expressions.

method : string, optional, {‘pad’, ‘ffill’, ‘bfill’}

The method to use when for replacement, when to_replace is a list.

NDFrame.reindex NDFrame.asfreq NDFrame.fillna

filled : NDFrame

AssertionError

• If regex is not a bool and to_replace is not None.

TypeError

• If to_replace is a dict and value is not a list, dict, ndarray, or Series
• If to_replace is None and regex is not compilable into a regular expression or is a list, dict, ndarray, or Series.

ValueError

• If to_replace and value are list s or ndarray s, but they are not the same length.

• Regex substitution is performed under the hood with re.sub. The rules for substitution for re.sub are the same.
• Regular expressions will only substitute on strings, meaning you cannot provide, for example, a regular expression matching floating point numbers and expect the columns in your frame that have a numeric dtype to be matched. However, if those floating point numbers are strings, then you can do this.
• This method has a lot of options. You are encouraged to experiment and play with this method to gain intuition about how it works.

reproject(spatial_reference, transformation=None, inplace=False)

Reprojects a given dataframe into a new coordinate system.

resample(rule, how=None, axis=0, fill_method=None, closed=None, label=None, convention='start', kind=None, loffset=None, limit=None, base=0, on=None, level=None)

Convenience method for frequency conversion and resampling of time series. Object must have a datetime-like index (DatetimeIndex, PeriodIndex, or TimedeltaIndex), or pass datetime-like values to the on or level keyword.

rule : string

the offset string or object representing target conversion

axis : int, optional, default 0 closed : {‘right’, ‘left’}

Which side of bin interval is closed. The default is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.

label : {‘right’, ‘left’}

Which bin edge label to label bucket with. The default is ‘left’ for all frequency offsets except for ‘M’, ‘A’, ‘Q’, ‘BM’, ‘BA’, ‘BQ’, and ‘W’ which all have a default of ‘right’.

convention : {‘start’, ‘end’, ‘s’, ‘e’}

For PeriodIndex only, controls whether to use the start or end of rule

loffset : timedelta

Adjust the resampled time labels

base : int, default 0

For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. For example, for ‘5min’ frequency, base could range from 0 through 4. Defaults to 0

on : string, optional

For a DataFrame, column to use instead of index for resampling. Column must be datetime-like.

New in version 0.19.0.

level : string or int, optional

For a MultiIndex, level (name or number) to use for resampling. Level must be datetime-like.

New in version 0.19.0.

To learn more about the offset strings, please see this link.

Start by creating a series with 9 one minute timestamps.

>>> index = pd.date_range('1/1/2000', periods=9, freq='T')
>>> series = pd.Series(range(9), index=index)
>>> series
2000-01-01 00:00:00    0
2000-01-01 00:01:00    1
2000-01-01 00:02:00    2
2000-01-01 00:03:00    3
2000-01-01 00:04:00    4
2000-01-01 00:05:00    5
2000-01-01 00:06:00    6
2000-01-01 00:07:00    7
2000-01-01 00:08:00    8
Freq: T, dtype: int64

Downsample the series into 3 minute bins and sum the values of the timestamps falling into a bin.

>>> series.resample('3T').sum()
2000-01-01 00:00:00     3
2000-01-01 00:03:00    12
2000-01-01 00:06:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but label each bin using the right edge instead of the left. Please note that the value in the bucket used as the label is not included in the bucket, which it labels. For example, in the original series the bucket 2000-01-01 00:03:00 contains the value 3, but the summed value in the resampled bucket with the label 2000-01-01 00:03:00 does not include 3 (if it did, the summed value would be 6, not 3). To include this value close the right side of the bin interval as illustrated in the example below this one.

>>> series.resample('3T', label='right').sum()
2000-01-01 00:03:00     3
2000-01-01 00:06:00    12
2000-01-01 00:09:00    21
Freq: 3T, dtype: int64

Downsample the series into 3 minute bins as above, but close the right side of the bin interval.

>>> series.resample('3T', label='right', closed='right').sum()
2000-01-01 00:00:00     0
2000-01-01 00:03:00     6
2000-01-01 00:06:00    15
2000-01-01 00:09:00    15
Freq: 3T, dtype: int64

Upsample the series into 30 second bins.

>>> series.resample('30S').asfreq()[0:5] #select first 5 rows
2000-01-01 00:00:00   0.0
2000-01-01 00:00:30   NaN
2000-01-01 00:01:00   1.0
2000-01-01 00:01:30   NaN
2000-01-01 00:02:00   2.0
Freq: 30S, dtype: float64

Upsample the series into 30 second bins and fill the NaN values using the pad method.

>>> series.resample('30S').pad()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    0
2000-01-01 00:01:00    1
2000-01-01 00:01:30    1
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Upsample the series into 30 second bins and fill the NaN values using the bfill method.

>>> series.resample('30S').bfill()[0:5]
2000-01-01 00:00:00    0
2000-01-01 00:00:30    1
2000-01-01 00:01:00    1
2000-01-01 00:01:30    2
2000-01-01 00:02:00    2
Freq: 30S, dtype: int64

Pass a custom function via apply

>>> def custom_resampler(array_like):
...     return np.sum(array_like)+5

>>> series.resample('3T').apply(custom_resampler)
2000-01-01 00:00:00     8
2000-01-01 00:03:00    17
2000-01-01 00:06:00    26
Freq: 3T, dtype: int64

For a Series with a PeriodIndex, the keyword convention can be used to control whether to use the start or end of rule.

>>> s = pd.Series([1, 2], index=pd.period_range('2012-01-01',
                                                freq='A',
                                                periods=2))
>>> s
2012    1
2013    2
Freq: A-DEC, dtype: int64

Resample by month using ‘start’ convention. Values are assigned to the first month of the period.

>>> s.resample('M', convention='start').asfreq().head()
2012-01    1.0
2012-02    NaN
2012-03    NaN
2012-04    NaN
2012-05    NaN
Freq: M, dtype: float64

Resample by month using ‘end’ convention. Values are assigned to the last month of the period.

>>> s.resample('M', convention='end').asfreq()
2012-12    1.0
2013-01    NaN
2013-02    NaN
2013-03    NaN
2013-04    NaN
2013-05    NaN
2013-06    NaN
2013-07    NaN
2013-08    NaN
2013-09    NaN
2013-10    NaN
2013-11    NaN
2013-12    2.0
Freq: M, dtype: float64

For DataFrame objects, the keyword on can be used to specify the column instead of the index for resampling.

>>> df = pd.DataFrame(data=9*[range(4)], columns=['a', 'b', 'c', 'd'])
>>> df['time'] = pd.date_range('1/1/2000', periods=9, freq='T')
>>> df.resample('3T', on='time').sum()
                     a  b  c  d
time
2000-01-01 00:00:00  0  3  6  9
2000-01-01 00:03:00  0  3  6  9
2000-01-01 00:06:00  0  3  6  9

For a DataFrame with MultiIndex, the keyword level can be used to specify on level the resampling needs to take place.

>>> time = pd.date_range('1/1/2000', periods=5, freq='T')
>>> df2 = pd.DataFrame(data=10*[range(4)],
                       columns=['a', 'b', 'c', 'd'],
                       index=pd.MultiIndex.from_product([time, [1, 2]])
                       )
>>> df2.resample('3T', level=0).sum()
                     a  b   c   d
2000-01-01 00:00:00  0  6  12  18
2000-01-01 00:03:00  0  4   8  12

reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')

For DataFrame with multi-level index, return new DataFrame with labeling information in the columns under the index names, defaulting to ‘level_0’, ‘level_1’, etc. if any are None. For a standard index, the index name will be used (if set), otherwise a default ‘index’ or ‘level_0’ (if ‘index’ is already taken) will be used.

level : int, str, tuple, or list, default None

Only remove the given levels from the index. Removes all levels by default

drop : boolean, default False

Do not try to insert index into dataframe columns. This resets the index to the default integer index.

inplace : boolean, default False

Modify the DataFrame in place (do not create a new object)

col_level : int or str, default 0

If the columns have multiple levels, determines which level the labels are inserted into. By default it is inserted into the first level.

col_fill : object, default ‘’

If the columns have multiple levels, determines how the other levels are named. If None then the index name is repeated.

resetted : DataFrame

>>> df = pd.DataFrame([('bird',    389.0),
...                    ('bird',     24.0),
...                    ('mammal',   80.5),
...                    ('mammal', np.nan)],
...                   index=['falcon', 'parrot', 'lion', 'monkey'],
...                   columns=('class', 'max_speed'))
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal        NaN

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal        NaN

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal        NaN

You can also use reset_index with MultiIndex.

>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
>>> columns = pd.MultiIndex.from_tuples([('speed', 'max'),
...                                      ('species', 'type')])
>>> df = pd.DataFrame([(389.0, 'fly'),
...                    ( 24.0, 'fly'),
...                    ( 80.5, 'run'),
...                    (np.nan, 'jump')],
...                   index=index,
...                   columns=columns)
>>> df
               speed species
                 max    type
class  name
bird   falcon  389.0     fly
       parrot   24.0     fly
mammal lion     80.5     run
       monkey    NaN    jump

If the index has multiple levels, we can reset a subset of them:

>>> df.reset_index(level='class')
         class  speed species
                  max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

If we are not dropping the index, by default, it is placed in the top level. We can place it in another level:

>>> df.reset_index(level='class', col_level=1)
                speed species
         class    max    type
name
falcon    bird  389.0     fly
parrot    bird   24.0     fly
lion    mammal   80.5     run
monkey  mammal    NaN    jump

When the index is inserted under another level, we can specify under which one with the parameter col_fill:

>>> df.reset_index(level='class', col_level=1, col_fill='species')
              species  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

If we specify a nonexistent level for col_fill, it is created:

>>> df.reset_index(level='class', col_level=1, col_fill='genus')
                genus  speed species
                class    max    type
name
falcon           bird  389.0     fly
parrot           bird   24.0     fly
lion           mammal   80.5     run
monkey         mammal    NaN    jump

rfloordiv(other, axis='columns', level=None, fill_value=None)

Integer division of dataframe and other, element-wise (binary operator rfloordiv).

Equivalent to other // dataframe, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.floordiv

rmod(other, axis='columns', level=None, fill_value=None)

Modulo of dataframe and other, element-wise (binary operator rmod).

Equivalent to other % dataframe, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.mod

rmul(other, axis='columns', level=None, fill_value=None)

Multiplication of dataframe and other, element-wise (binary operator rmul).

Equivalent to other * dataframe, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.mul

rolling(window, min_periods=None, freq=None, center=False, win_type=None, on=None, axis=0, closed=None)

Provides rolling window calculations.

New in version 0.18.0.

window : int, or offset

Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size.

If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. This is new in 0.19.0

min_periods : int, default None

Minimum number of observations in window required to have a value (otherwise result is NA). For a window that is specified by an offset, this will default to 1.

freq : string or DateOffset object, optional (default None)

Deprecated since version 0.18.0: Frequency to conform the data to before computing the statistic. Specified as a frequency string or DateOffset object.

center : boolean, default False

Set the labels at the center of the window.

win_type : string, default None

Provide a window type. See the notes below.

on : string, optional

For a DataFrame, column on which to calculate the rolling window, rather than the index

closed : string, default None

Make the interval closed on the ‘right’, ‘left’, ‘both’ or ‘neither’ endpoints. For offset-based windows, it defaults to ‘right’. For fixed windows, defaults to ‘both’. Remaining cases not implemented for fixed windows.

New in version 0.20.0.

axis : int or string, default 0

a Window or Rolling sub-classed for the particular operation

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

Rolling sum with a window length of 2, using the ‘triang’ window type.

>>> df.rolling(2, win_type='triang').sum()
     B
0  NaN
1  1.0
2  2.5
3  NaN
4  NaN

Rolling sum with a window length of 2, min_periods defaults to the window length.

>>> df.rolling(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  NaN
4  NaN

Same as above, but explicity set the min_periods

>>> df.rolling(2, min_periods=1).sum()
     B
0  0.0
1  1.0
2  3.0
3  2.0
4  4.0

A ragged (meaning not-a-regular frequency), time-indexed DataFrame

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
....:                 index = [pd.Timestamp('20130101 09:00:00'),
....:                          pd.Timestamp('20130101 09:00:02'),
....:                          pd.Timestamp('20130101 09:00:03'),
....:                          pd.Timestamp('20130101 09:00:05'),
....:                          pd.Timestamp('20130101 09:00:06')])

>>> df
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

Contrasting to an integer rolling window, this will roll a variable length window corresponding to the time period. The default for min_periods is 1.

>>> df.rolling('2s').sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

The freq keyword is used to conform time series data to a specified frequency by resampling the data. This is done with the default parameters of resample() (i.e. using the mean).

To learn more about the offsets & frequency strings, please see this link.

The recognized win_types are:

• boxcar
• triang
• blackman
• hamming
• bartlett
• parzen
• bohman
• blackmanharris
• nuttall
• barthann
• kaiser (needs beta)
• gaussian (needs std)
• general_gaussian (needs power, width)
• slepian (needs width).

round(decimals=0, *args, **kwargs)

Round a DataFrame to a variable number of decimal places.

New in version 0.17.0.

decimals : int, dict, Series

Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.

>>> df = pd.DataFrame(np.random.random([3, 3]),
...     columns=['A', 'B', 'C'], index=['first', 'second', 'third'])
>>> df
               A         B         C
first   0.028208  0.992815  0.173891
second  0.038683  0.645646  0.577595
third   0.877076  0.149370  0.491027
>>> df.round(2)
           A     B     C
first   0.03  0.99  0.17
second  0.04  0.65  0.58
third   0.88  0.15  0.49
>>> df.round({'A': 1, 'C': 2})
          A         B     C
first   0.0  0.992815  0.17
second  0.0  0.645646  0.58
third   0.9  0.149370  0.49
>>> decimals = pd.Series([1, 0, 2], index=['A', 'B', 'C'])
>>> df.round(decimals)
          A  B     C
first   0.0  1  0.17
second  0.0  1  0.58
third   0.9  0  0.49

DataFrame object

numpy.around Series.round

rpow(other, axis='columns', level=None, fill_value=None)

Exponential power of dataframe and other, element-wise (binary operator rpow).

Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.pow

rsub(other, axis='columns', level=None, fill_value=None)

Subtraction of dataframe and other, element-wise (binary operator rsub).

Equivalent to other - dataframe, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.sub

rtruediv(other, axis='columns', level=None, fill_value=None)

Floating division of dataframe and other, element-wise (binary operator rtruediv).

Equivalent to other / dataframe, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.truediv

sample(n=None, frac=None, replace=False, weights=None, random_state=None, axis=None)

Returns a random sample of items from an axis of object.

n : int, optional

Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.

frac : float, optional

Fraction of axis items to return. Cannot be used with n.

replace : boolean, optional

Sample with or without replacement. Default = False.

weights : str or ndarray-like, optional

Default ‘None’ results in equal probability weighting. If passed a Series, will align with target object on index. Index values in weights not found in sampled object will be ignored and index values in sampled object not in weights will be assigned weights of zero. If called on a DataFrame, will accept the name of a column when axis = 0. Unless weights are a Series, weights must be same length as axis being sampled. If weights do not sum to 1, they will be normalized to sum to 1. Missing values in the weights column will be treated as zero. inf and -inf values not allowed.

random_state : int or numpy.random.RandomState, optional

Seed for the random number generator (if int), or numpy RandomState object.

axis : int or string, optional

Axis to sample. Accepts axis number or name. Default is stat axis for given data type (0 for Series and DataFrames, 1 for Panels).

A new object of same type as caller.

Generate an example Series and DataFrame:

>>> s = pd.Series(np.random.randn(50))
>>> s.head()
0   -0.038497
1    1.820773
2   -0.972766
3   -1.598270
4   -1.095526
dtype: float64
>>> df = pd.DataFrame(np.random.randn(50, 4), columns=list('ABCD'))
>>> df.head()
          A         B         C         D
0  0.016443 -2.318952 -0.566372 -1.028078
1 -1.051921  0.438836  0.658280 -0.175797
2 -1.243569 -0.364626 -0.215065  0.057736
3  1.768216  0.404512 -0.385604 -1.457834
4  1.072446 -1.137172  0.314194 -0.046661

Next extract a random sample from both of these objects…

3 random elements from the Series:

>>> s.sample(n=3)
27   -0.994689
55   -1.049016
67   -0.224565
dtype: float64

And a random 10% of the DataFrame with replacement:

>>> df.sample(frac=0.1, replace=True)
           A         B         C         D
35  1.981780  0.142106  1.817165 -0.290805
49 -1.336199 -0.448634 -0.789640  0.217116
40  0.823173 -0.078816  1.009536  1.015108
15  1.421154 -0.055301 -1.922594 -0.019696
6  -0.148339  0.832938  1.787600 -1.383767

segment_along_line(start_measure, end_measure, use_percentage=False)

Returns a Polyline between start and end measures. Similar to Polyline.positionAlongLine but will return a polyline segment between two points on the polyline instead of a single point.

Parameters:

start_measure:

• The starting distance from the beginning of the

line.

end_measure:

• The ending distance from the beginning of the

line.

use_percentage:

• The start and end measures may be specified as

fixed units or as a ratio. If True, start_measure and end_measure are used as a percentage; if False, start_measure and end_measure are used as a distance. For percentages, the measures should be expressed as a double from 0.0 (0 percent) to 1.0 (100 percent).

select(crit, axis=0)

Return data corresponding to axis labels matching criteria

DEPRECATED: use df.loc[df.index.map(crit)] to select via labels

crit : function

To be called on each index (label). Should return True or False

axis : int

selection : type of caller

select_by_location(other, matches_only=True)

Selects all rows in a given SpatialDataFrame based on a given geometry

Inputs:

other: arcpy.Geometry object matches_only: boolean value, if true, only matched records will be

returned, else a field called ‘select_by_location’ will be added to the dataframe with the results of the select by location.

select_dtypes(include=None, exclude=None)

Return a subset of a DataFrame including/excluding columns based on their dtype.

include, exclude : scalar or list-like

A selection of dtypes or strings to be included/excluded. At least one of these parameters must be supplied.

ValueError

• If both of include and exclude are empty
• If include and exclude have overlapping elements
• If any kind of string dtype is passed in.

subset : DataFrame

The subset of the frame including the dtypes in include and excluding the dtypes in exclude.

• To select all numeric types use the numpy dtype numpy.number
• To select strings you must use the object dtype, but note that this will return all object dtype columns
• See the numpy dtype hierarchy
• To select datetimes, use np.datetime64, ‘datetime’ or ‘datetime64’
• To select timedeltas, use np.timedelta64, ‘timedelta’ or ‘timedelta64’
• To select Pandas categorical dtypes, use ‘category’
• To select Pandas datetimetz dtypes, use ‘datetimetz’ (new in 0.20.0), or a ‘datetime64[ns, tz]’ string

>>> df = pd.DataFrame({'a': np.random.randn(6).astype('f4'),
...                    'b': [True, False] * 3,
...                    'c': [1.0, 2.0] * 3})
>>> df
        a      b  c
0  0.3962   True  1
1  0.1459  False  2
2  0.2623   True  1
3  0.0764  False  2
4 -0.9703   True  1
5 -1.2094  False  2
>>> df.select_dtypes(include='bool')
   c
0  True
1  False
2  True
3  False
4  True
5  False
>>> df.select_dtypes(include=['float64'])
   c
0  1
1  2
2  1
3  2
4  1
5  2
>>> df.select_dtypes(exclude=['floating'])
       b
0   True
1  False
2   True
3  False
4   True
5  False

sem(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased standard error of the mean over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

ddof : int, default 1

degrees of freedom

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

sem : Series or DataFrame (if level specified)

series_extent

Return a single bounding box (xmin, ymin, xmax, ymax) for all geometries

This is a shortcut for calculating the min/max x and y bounds individually.

set_axis(labels, axis=0, inplace=None)

Assign desired index to given axis

labels: list-like or Index

The values for the new index

axis : int or string, default 0 inplace : boolean, default None

Whether to return a new NDFrame instance.

WARNING: inplace=None currently falls back to to True, but in a future version, will default to False. Use inplace=True explicitly rather than relying on the default.

New in version 0.21.0: The signature is make consistent to the rest of the API. Previously, the “axis” and “labels” arguments were respectively the first and second positional arguments.

renamed : NDFrame or None

An object of same type as caller if inplace=False, None otherwise.

pandas.NDFrame.rename

>>> s = pd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: int64
>>> s.set_axis(['a', 'b', 'c'], axis=0, inplace=False)
a    1
b    2
c    3
dtype: int64
>>> df = pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df.set_axis(['a', 'b', 'c'], axis=0, inplace=False)
   A  B
a  1  4
b  2  5
c  3  6
>>> df.set_axis(['I', 'II'], axis=1, inplace=False)
   I  II
0  1   4
1  2   5
2  3   6
>>> df.set_axis(['i', 'ii'], axis=1, inplace=True)
>>> df
   i  ii
0  1   4
1  2   5
2  3   6

set_geometry(col, drop=False, inplace=False, sr=None)

Set the SpatialDataFrame geometry using either an existing column or the specified input. By default yields a new object.

The original geometry column is replaced with the input.

Parameters: col: column label or array drop: boolean, default True

Delete column to be used as the new geometry

inplace: boolean, default False

Modify the SpatialDataFrame in place (do not create a new object)

sr : str/integer the wkid value

Coordinate system to use. If passed, overrides both DataFrame and col’s sr. Otherwise, tries to get sr from passed col values or DataFrame.

Returns: SpatialDataFrame

set_index(keys, drop=True, append=False, inplace=False, verify_integrity=False)

Set the DataFrame index (row labels) using one or more existing columns. By default yields a new object.

keys : column label or list of column labels / arrays drop : boolean, default True

Delete columns to be used as the new index

append : boolean, default False

Whether to append columns to existing index

inplace : boolean, default False

Modify the DataFrame in place (do not create a new object)

verify_integrity : boolean, default False

Check the new index for duplicates. Otherwise defer the check until necessary. Setting to False will improve the performance of this method

>>> df = pd.DataFrame({'month': [1, 4, 7, 10],
...                    'year': [2012, 2014, 2013, 2014],
...                    'sale':[55, 40, 84, 31]})
   month  sale  year
0  1      55    2012
1  4      40    2014
2  7      84    2013
3  10     31    2014

Set the index to become the ‘month’ column:

>>> df.set_index('month')
       sale  year
month
1      55    2012
4      40    2014
7      84    2013
10     31    2014

Create a multi-index using columns ‘year’ and ‘month’:

>>> df.set_index(['year', 'month'])
            sale
year  month
2012  1     55
2014  4     40
2013  7     84
2014  10    31

Create a multi-index using a set of values and a column:

>>> df.set_index([[1, 2, 3, 4], 'year'])
         month  sale
   year
1  2012  1      55
2  2014  4      40
3  2013  7      84
4  2014  10     31

dataframe : DataFrame

set_value(index, col, value, takeable=False)

Put single value at passed column and index

Deprecated since version 0.21.0.

Please use .at[] or .iat[] accessors.

index : row label col : column label value : scalar value takeable : interpret the index/col as indexers, default False

frame : DataFrame

If label pair is contained, will be reference to calling DataFrame, otherwise a new object

shape

Return a tuple representing the dimensionality of the DataFrame.

shift(periods=1, freq=None, axis=0)

Shift index by desired number of periods with an optional time freq

periods : int

Number of periods to move, can be positive or negative

freq : DateOffset, timedelta, or time rule string, optional

Increment to use from the tseries module or time rule (e.g. ‘EOM’). See Notes.

axis : {0 or ‘index’, 1 or ‘columns’}

If freq is specified then the index values are shifted but the data is not realigned. That is, use freq if you would like to extend the index when shifting and preserve the original data.

shifted : DataFrame

sindex

size

number of elements in the NDFrame

skew(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return unbiased skew over requested axis Normalized by N-1

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

skew : Series or DataFrame (if level specified)

slice_shift(periods=1, axis=0)

Equivalent to shift without copying data. The shifted data will not include the dropped periods and the shifted axis will be smaller than the original.

periods : int

Number of periods to move, can be positive or negative

While the slice_shift is faster than shift, you may pay for it later during alignment.

shifted : same type as caller

snap_to_line(second_geometry)

Returns a new point based on in_point snapped to this geometry.

Paramters:

second_geometry:  

• a second geometry

sort_index(axis=0, level=None, ascending=True, inplace=False, kind='quicksort', na_position='last', sort_remaining=True, by=None)

Sort object by labels (along an axis)

axis : index, columns to direct sorting level : int or level name or list of ints or list of level names

if not None, sort on values in specified index level(s)

ascending : boolean, default True

Sort ascending vs. descending

inplace : bool, default False

if True, perform operation in-place

kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, default ‘quicksort’

Choice of sorting algorithm. See also ndarray.np.sort for more information. mergesort is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label.

na_position : {‘first’, ‘last’}, default ‘last’

first puts NaNs at the beginning, last puts NaNs at the end. Not implemented for MultiIndex.

sort_remaining : bool, default True

if true and sorting by level and index is multilevel, sort by other levels too (in order) after sorting by specified level

sorted_obj : DataFrame

sort_values(by, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')

Sort by the values along either axis

New in version 0.17.0.

by : str or list of str

Name or list of names which refer to the axis items.

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

Axis to direct sorting

ascending : bool or list of bool, default True

Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.

inplace : bool, default False

if True, perform operation in-place

kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, default ‘quicksort’

Choice of sorting algorithm. See also ndarray.np.sort for more information. mergesort is the only stable algorithm. For DataFrames, this option is only applied when sorting on a single column or label.

na_position : {‘first’, ‘last’}, default ‘last’

first puts NaNs at the beginning, last puts NaNs at the end

sorted_obj : DataFrame

>>> df = pd.DataFrame({
...     'col1' : ['A', 'A', 'B', np.nan, 'D', 'C'],
...     'col2' : [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
... })
>>> df
    col1 col2 col3
0   A    2    0
1   A    1    1
2   B    9    9
3   NaN  8    4
4   D    7    2
5   C    4    3

Sort by col1

>>> df.sort_values(by=['col1'])
    col1 col2 col3
0   A    2    0
1   A    1    1
2   B    9    9
5   C    4    3
4   D    7    2
3   NaN  8    4

Sort by multiple columns

>>> df.sort_values(by=['col1', 'col2'])
    col1 col2 col3
1   A    1    1
0   A    2    0
2   B    9    9
5   C    4    3
4   D    7    2
3   NaN  8    4

Sort Descending

>>> df.sort_values(by='col1', ascending=False)
    col1 col2 col3
4   D    7    2
5   C    4    3
2   B    9    9
0   A    2    0
1   A    1    1
3   NaN  8    4

Putting NAs first

>>> df.sort_values(by='col1', ascending=False, na_position='first')
    col1 col2 col3
3   NaN  8    4
4   D    7    2
5   C    4    3
2   B    9    9
0   A    2    0
1   A    1    1

sortlevel(level=0, axis=0, ascending=True, inplace=False, sort_remaining=True)

DEPRECATED: use DataFrame.sort_index()

Sort multilevel index by chosen axis and primary level. Data will be lexicographically sorted by the chosen level followed by the other levels (in order)

level : int axis : {0 or ‘index’, 1 or ‘columns’}, default 0 ascending : boolean, default True inplace : boolean, default False

Sort the DataFrame without creating a new instance

sort_remaining : boolean, default True

Sort by the other levels too.

sorted : DataFrame

DataFrame.sort_index(level=…)

spatial_reference

The spatial reference of the geometry.

squeeze(axis=None)

Squeeze length 1 dimensions.

axis : None, integer or string axis name, optional

The axis to squeeze if 1-sized.

New in version 0.20.0.

scalar if 1-sized, else original object

stack(level=-1, dropna=True)

Pivot a level of the (possibly hierarchical) column labels, returning a DataFrame (or Series in the case of an object with a single level of column labels) having a hierarchical index with a new inner-most level of row labels. The level involved will automatically get sorted.

level : int, string, or list of these, default last level

Level(s) to stack, can pass level name

dropna : boolean, default True

Whether to drop rows in the resulting Frame/Series with no valid values

>>> s
     a   b
one  1.  2.
two  3.  4.

>>> s.stack()
one a    1
    b    2
two a    3
    b    4

stacked : DataFrame or Series

std(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return sample standard deviation over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

ddof : int, default 1

degrees of freedom

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

std : Series or DataFrame (if level specified)

style

Property returning a Styler object containing methods for building a styled HTML representation fo the DataFrame.

pandas.io.formats.style.Styler

sub(other, axis='columns', level=None, fill_value=None)

Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rsub

subtract(other, axis='columns', level=None, fill_value=None)

Subtraction of dataframe and other, element-wise (binary operator sub).

Equivalent to dataframe - other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rsub

sum(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Return the sum of the values for the requested axis

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA or empty, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

sum : Series or DataFrame (if level specified)

swapaxes(axis1, axis2, copy=True)

Interchange axes and swap values axes appropriately

y : same as input

swaplevel(i=-2, j=-1, axis=0)

Swap levels i and j in a MultiIndex on a particular axis

i, j : int, string (can be mixed)

Level of index to be swapped. Can pass level name as string.

swapped : type of caller (new object)

Changed in version 0.18.1: The indexes i and j are now optional, and default to the two innermost levels of the index.

symmetric_difference(second_geometry)

Constructs the geometry that is the union of two geometries minus the instersection of those geometries. The two input geometries must be the same shape type. Parameters:

second_geometry:  

• a second geometry

tail(n=5)

Return the last n rows.

n : int, default 5

Number of rows to select.

obj_tail : type of caller

The last n rows of the caller object.

take(indices, axis=0, convert=None, is_copy=True, **kwargs)

Return the elements in the given positional indices along an axis.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

indices : array-like

An array of ints indicating which positions to take.

axis : int, default 0

The axis on which to select elements. “0” means that we are selecting rows, “1” means that we are selecting columns, etc.

convert : bool, default True

Deprecated since version 0.21.0: In the future, negative indices will always be converted.

Whether to convert negative indices into positive ones. For example, -1 would map to the len(axis) - 1. The conversions are similar to the behavior of indexing a regular Python list.

is_copy : bool, default True

Whether to return a copy of the original object or not.

>>> df = pd.DataFrame([('falcon', 'bird',    389.0),
                       ('parrot', 'bird',     24.0),
                       ('lion',   'mammal',   80.5),
                       ('monkey', 'mammal', np.nan)],
                      columns=('name', 'class', 'max_speed'),
                      index=[0, 2, 3, 1])
>>> df
     name   class  max_speed
0  falcon    bird      389.0
2  parrot    bird       24.0
3    lion  mammal       80.5
1  monkey  mammal        NaN

Take elements at positions 0 and 3 along the axis 0 (default).

Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That’s because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.

>>> df.take([0, 3])
0  falcon    bird      389.0
1  monkey  mammal        NaN

Take elements at indices 1 and 2 along the axis 1 (column selection).

>>> df.take([1, 2], axis=1)
    class  max_speed
0    bird      389.0
2    bird       24.0
3  mammal       80.5
1  mammal        NaN

We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.

>>> df.take([-1, -2])
     name   class  max_speed
1  monkey  mammal        NaN
3    lion  mammal       80.5

taken : type of caller

An array-like containing the elements taken from the object.

numpy.ndarray.take numpy.take

to_clipboard(excel=None, sep=None, **kwargs)

Attempt to write text representation of object to the system clipboard This can be pasted into Excel, for example.

excel : boolean, defaults to True

if True, use the provided separator, writing in a csv format for allowing easy pasting into excel. if False, write a string representation of the object to the clipboard

sep : optional, defaults to tab other keywords are passed to to_csv

Requirements for your platform

• Linux: xclip, or xsel (with gtk or PyQt4 modules)
• Windows: none
• OS X: none

to_csv(path_or_buf=None, sep=', ', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, mode='w', encoding=None, compression=None, quoting=None, quotechar='"', line_terminator='\n', chunksize=None, tupleize_cols=None, date_format=None, doublequote=True, escapechar=None, decimal='.')

Write DataFrame to a comma-separated values (csv) file

path_or_buf : string or file handle, default None

File path or object, if None is provided the result is returned as a string.

sep : character, default ‘,’

Field delimiter for the output file.

na_rep : string, default ‘’

Missing data representation

float_format : string, default None

Format string for floating point numbers

columns : sequence, optional

Columns to write

header : boolean or list of string, default True

Write out the column names. If a list of strings is given it is assumed to be aliases for the column names

index : boolean, default True

Write row names (index)

index_label : string or sequence, or False, default None

Column label for index column(s) if desired. If None is given, and header and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex. If False do not print fields for index names. Use index_label=False for easier importing in R

mode : str

Python write mode, default ‘w’

encoding : string, optional

A string representing the encoding to use in the output file, defaults to ‘ascii’ on Python 2 and ‘utf-8’ on Python 3.

compression : string, optional

a string representing the compression to use in the output file, allowed values are ‘gzip’, ‘bz2’, ‘xz’, only used when the first argument is a filename

line_terminator : string, default '\n'

The newline character or character sequence to use in the output file

quoting : optional constant from csv module

defaults to csv.QUOTE_MINIMAL. If you have set a float_format then floats are converted to strings and thus csv.QUOTE_NONNUMERIC will treat them as non-numeric

quotechar : string (length 1), default ‘"’

character used to quote fields

doublequote : boolean, default True

Control quoting of quotechar inside a field

escapechar : string (length 1), default None

character used to escape sep and quotechar when appropriate

chunksize : int or None

rows to write at a time

tupleize_cols : boolean, default False

Deprecated since version 0.21.0: This argument will be removed and will always write each row of the multi-index as a separate row in the CSV file.

Write MultiIndex columns as a list of tuples (if True) or in the new, expanded format, where each MultiIndex column is a row in the CSV (if False).

date_format : string, default None

Format string for datetime objects

decimal: string, default ‘.’

Character recognized as decimal separator. E.g. use ‘,’ for European data

to_dense()

Return dense representation of NDFrame (as opposed to sparse)

to_dict(orient='dict', into=<class 'dict'>)

Convert DataFrame to dictionary.

orient : str {‘dict’, ‘list’, ‘series’, ‘split’, ‘records’, ‘index’}

Determines the type of the values of the dictionary.

• dict (default) : dict like {column -> {index -> value}}

• list : dict like {column -> [values]}

• series : dict like {column -> Series(values)}

• split : dict like {index -> [index], columns -> [columns], data -> [values]}

• records : list like [{column -> value}, … , {column -> value}]

• index : dict like {index -> {column -> value}}

  New in version 0.17.0.

Abbreviations are allowed. s indicates series and sp indicates split.

into : class, default dict

The collections.Mapping subclass used for all Mappings in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.

New in version 0.21.0.

result : collections.Mapping like {column -> {index -> value}}

>>> df = pd.DataFrame(
        {'col1': [1, 2], 'col2': [0.5, 0.75]}, index=['a', 'b'])
>>> df
   col1  col2
a     1   0.1
b     2   0.2
>>> df.to_dict()
{'col1': {'a': 1, 'b': 2}, 'col2': {'a': 0.5, 'b': 0.75}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': a    1
b    2
Name: col1, dtype: int64, 'col2': a    0.50
b    0.75
Name: col2, dtype: float64}
>>> df.to_dict('split')
{'columns': ['col1', 'col2'],
'data': [[1.0, 0.5], [2.0, 0.75]],
'index': ['a', 'b']}
>>> df.to_dict('records')
[{'col1': 1.0, 'col2': 0.5}, {'col1': 2.0, 'col2': 0.75}]
>>> df.to_dict('index')
{'a': {'col1': 1.0, 'col2': 0.5}, 'b': {'col1': 2.0, 'col2': 0.75}}

You can also specify the mapping type.

>>> from collections import OrderedDict, defaultdict
>>> df.to_dict(into=OrderedDict)
OrderedDict([('col1', OrderedDict([('a', 1), ('b', 2)])),
           ('col2', OrderedDict([('a', 0.5), ('b', 0.75)]))])

If you want a defaultdict, you need to initialize it:

>>> dd = defaultdict(list)
>>> df.to_dict('records', into=dd)
[defaultdict(<type 'list'>, {'col2': 0.5, 'col1': 1.0}),
defaultdict(<type 'list'>, {'col2': 0.75, 'col1': 2.0})]

to_excel(excel_writer, sheet_name='Sheet1', na_rep='', float_format=None, columns=None, header=True, index=True, index_label=None, startrow=0, startcol=0, engine=None, merge_cells=True, encoding=None, inf_rep='inf', verbose=True, freeze_panes=None)

Write DataFrame to an excel sheet

excel_writer : string or ExcelWriter object

File path or existing ExcelWriter

sheet_name : string, default ‘Sheet1’

Name of sheet which will contain DataFrame

na_rep : string, default ‘’

Missing data representation

float_format : string, default None

Format string for floating point numbers

columns : sequence, optional

Columns to write

header : boolean or list of string, default True

Write out the column names. If a list of strings is given it is assumed to be aliases for the column names

index : boolean, default True

Write row names (index)

index_label : string or sequence, default None

Column label for index column(s) if desired. If None is given, and header and index are True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.

startrow :

upper left cell row to dump data frame

startcol :

upper left cell column to dump data frame

engine : string, default None

write engine to use - you can also set this via the options io.excel.xlsx.writer, io.excel.xls.writer, and io.excel.xlsm.writer.

merge_cells : boolean, default True

Write MultiIndex and Hierarchical Rows as merged cells.

encoding: string, default None

encoding of the resulting excel file. Only necessary for xlwt, other writers support unicode natively.

inf_rep : string, default ‘inf’

Representation for infinity (there is no native representation for infinity in Excel)

freeze_panes : tuple of integer (length 2), default None

Specifies the one-based bottommost row and rightmost column that is to be frozen

New in version 0.20.0.

If passing an existing ExcelWriter object, then the sheet will be added to the existing workbook. This can be used to save different DataFrames to one workbook:

>>> writer = pd.ExcelWriter('output.xlsx')
>>> df1.to_excel(writer,'Sheet1')
>>> df2.to_excel(writer,'Sheet2')
>>> writer.save()

For compatibility with to_csv, to_excel serializes lists and dicts to strings before writing.

to_feather(fname)

write out the binary feather-format for DataFrames

New in version 0.20.0.

fname : str

string file path

to_feature_collection(name=None, drawing_info=None, extent=None, global_id_field=None)

converts a Spatial DataFrame to a Feature Collection

optional argument	Description
name	optional string. Name of the Feature Collection
drawing_info	Optional dictionary. This is the rendering information for a Feature Collection. Rendering information is a dictionary with the symbology, labelling and other properties defined. See: http://resources.arcgis.com/en/help/arcgis-rest-api/index.html#/Renderer_objects/02r30000019t000000/
extent	Optional dictionary. If desired, a custom extent can be provided to set where the map starts up when showing the data. The default is the full extent of the dataset in the Spatial DataFrame.
global_id_field	Optional string. The Global ID field of the dataset.

Returns:FeatureCollection object

to_featureclass(out_location, out_name, overwrite=True, skip_invalid=True)

converts a SpatialDataFrame to a feature class

Parameters:

out_location:save location workspace out_name:name of the feature class to save as overwrite:boolean. True means to erase and replace value, false means to append skip_invalids:if True, any bad rows will be ignored.

Output:

tuple of feature class path and list of bad rows by index number.

to_featurelayer(title, gis=None, tags=None)

publishes a spatial dataframe to a new feature layer

to_featureset()

Converts a spatial dataframe to a feature set object

to_gbq(destination_table, project_id, chunksize=10000, verbose=True, reauth=False, if_exists='fail', private_key=None)

Write a DataFrame to a Google BigQuery table.

The main method a user calls to export pandas DataFrame contents to Google BigQuery table.

Google BigQuery API Client Library v2 for Python is used. Documentation is available here

Authentication to the Google BigQuery service is via OAuth 2.0.

• If “private_key” is not provided:

  By default “application default credentials” are used.

  If default application credentials are not found or are restrictive, user account credentials are used. In this case, you will be asked to grant permissions for product name ‘pandas GBQ’.

• If “private_key” is provided:

  Service account credentials will be used to authenticate.

dataframe : DataFrame

DataFrame to be written

destination_table : string

Name of table to be written, in the form ‘dataset.tablename’

project_id : str

Google BigQuery Account project ID.

chunksize : int (default 10000)

Number of rows to be inserted in each chunk from the dataframe.

verbose : boolean (default True)

Show percentage complete

reauth : boolean (default False)

Force Google BigQuery to reauthenticate the user. This is useful if multiple accounts are used.

if_exists : {‘fail’, ‘replace’, ‘append’}, default ‘fail’

‘fail’: If table exists, do nothing. ‘replace’: If table exists, drop it, recreate it, and insert data. ‘append’: If table exists, insert data. Create if does not exist.

private_key : str (optional)

Service account private key in JSON format. Can be file path or string contents. This is useful for remote server authentication (eg. jupyter iPython notebook on remote host)

to_hdf(path_or_buf, key, **kwargs)

Write the contained data to an HDF5 file using HDFStore.

path_or_buf : the path (string) or HDFStore object key : string

indentifier for the group in the store

mode : optional, {‘a’, ‘w’, ‘r+’}, default ‘a’

'w'

Write; a new file is created (an existing file with the same name would be deleted).

'a'

Append; an existing file is opened for reading and writing, and if the file does not exist it is created.

'r+'

It is similar to 'a', but the file must already exist.

format : ‘fixed(f)|table(t)’, default is ‘fixed’

fixed(f) : Fixed format

Fast writing/reading. Not-appendable, nor searchable

table(t) : Table format

Write as a PyTables Table structure which may perform worse but allow more flexible operations like searching / selecting subsets of the data

append : boolean, default False

For Table formats, append the input data to the existing

data_columns : list of columns, or True, default None

List of columns to create as indexed data columns for on-disk queries, or True to use all columns. By default only the axes of the object are indexed. See here.

Applicable only to format=’table’.

complevel : int, 1-9, default 0

If a complib is specified compression will be applied where possible

complib : {‘zlib’, ‘bzip2’, ‘lzo’, ‘blosc’, None}, default None

If complevel is > 0 apply compression to objects written in the store wherever possible

fletcher32 : bool, default False

If applying compression use the fletcher32 checksum

dropna : boolean, default False.

If true, ALL nan rows will not be written to store.

to_html(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, bold_rows=True, classes=None, escape=True, max_rows=None, max_cols=None, show_dimensions=False, notebook=False, decimal='.', border=None)

Render a DataFrame as an HTML table.

to_html-specific options:

bold_rows : boolean, default True

Make the row labels bold in the output

classes : str or list or tuple, default None

CSS class(es) to apply to the resulting html table

escape : boolean, default True

Convert the characters <, >, and & to HTML-safe sequences.=

max_rows : int, optional

Maximum number of rows to show before truncating. If None, show all.

max_cols : int, optional

Maximum number of columns to show before truncating. If None, show all.

decimal : string, default ‘.’

Character recognized as decimal separator, e.g. ‘,’ in Europe

New in version 0.18.0.

border : int

A border=border attribute is included in the opening <table> tag. Default pd.options.html.border.

New in version 0.19.0.

buf : StringIO-like, optional

buffer to write to

columns : sequence, optional

the subset of columns to write; default None writes all columns

col_space : int, optional

the minimum width of each column

header : bool, optional

whether to print column labels, default True

index : bool, optional

whether to print index (row) labels, default True

na_rep : string, optional

string representation of NAN to use, default ‘NaN’

formatters : list or dict of one-parameter functions, optional

formatter functions to apply to columns’ elements by position or name, default None. The result of each function must be a unicode string. List must be of length equal to the number of columns.

float_format : one-parameter function, optional

formatter function to apply to columns’ elements if they are floats, default None. The result of this function must be a unicode string.

sparsify : bool, optional

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row, default True

index_names : bool, optional

Prints the names of the indexes, default True

line_width : int, optional

Width to wrap a line in characters, default no wrap

justify : {‘left’, ‘right’, ‘center’, ‘justify’,

‘justify-all’, ‘start’, ‘end’, ‘inherit’, ‘match-parent’, ‘initial’, ‘unset’}, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box.

formatted : string (or unicode, depending on data and options)

to_json(path_or_buf=None, orient=None, date_format=None, double_precision=10, force_ascii=True, date_unit='ms', default_handler=None, lines=False, compression=None)

Convert the object to a JSON string.

Note NaN’s and None will be converted to null and datetime objects will be converted to UNIX timestamps.

path_or_buf : the path or buffer to write the result string

if this is None, return the converted string

orient : string

• Series

  • default is ‘index’
  • allowed values are: {‘split’,’records’,’index’}

• DataFrame

  • default is ‘columns’
  • allowed values are: {‘split’,’records’,’index’,’columns’,’values’}

• The format of the JSON string

  • split : dict like {index -> [index], columns -> [columns], data -> [values]}

  • records : list like [{column -> value}, … , {column -> value}]

  • index : dict like {index -> {column -> value}}

  • columns : dict like {column -> {index -> value}}

  • values : just the values array

  • table : dict like {‘schema’: {schema}, ‘data’: {data}} describing the data, and the data component is like orient='records'.

    Changed in version 0.20.0.

date_format : {None, ‘epoch’, ‘iso’}

Type of date conversion. epoch = epoch milliseconds, iso = ISO8601. The default depends on the orient. For orient=’table’, the default is ‘iso’. For all other orients, the default is ‘epoch’.

double_precision : The number of decimal places to use when encoding

floating point values, default 10.

force_ascii : force encoded string to be ASCII, default True. date_unit : string, default ‘ms’ (milliseconds)

The time unit to encode to, governs timestamp and ISO8601 precision. One of ‘s’, ‘ms’, ‘us’, ‘ns’ for second, millisecond, microsecond, and nanosecond respectively.

default_handler : callable, default None

Handler to call if object cannot otherwise be converted to a suitable format for JSON. Should receive a single argument which is the object to convert and return a serialisable object.

lines : boolean, default False

If ‘orient’ is ‘records’ write out line delimited json format. Will throw ValueError if incorrect ‘orient’ since others are not list like.

New in version 0.19.0.

compression : {None, ‘gzip’, ‘bz2’, ‘xz’}

A string representing the compression to use in the output file, only used when the first argument is a filename

New in version 0.21.0.

same type as input object with filtered info axis

pd.read_json

>>> df = pd.DataFrame([['a', 'b'], ['c', 'd']],
...                   index=['row 1', 'row 2'],
...                   columns=['col 1', 'col 2'])
>>> df.to_json(orient='split')
'{"columns":["col 1","col 2"],
  "index":["row 1","row 2"],
  "data":[["a","b"],["c","d"]]}'

Encoding/decoding a Dataframe using 'index' formatted JSON:

>>> df.to_json(orient='index')
'{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}'

Encoding/decoding a Dataframe using 'records' formatted JSON. Note that index labels are not preserved with this encoding.

>>> df.to_json(orient='records')
'[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]'

Encoding with Table Schema

>>> df.to_json(orient='table')
'{"schema": {"fields": [{"name": "index", "type": "string"},
                        {"name": "col 1", "type": "string"},
                        {"name": "col 2", "type": "string"}],
             "primaryKey": "index",
             "pandas_version": "0.20.0"},
  "data": [{"index": "row 1", "col 1": "a", "col 2": "b"},
           {"index": "row 2", "col 1": "c", "col 2": "d"}]}'

to_latex(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, bold_rows=False, column_format=None, longtable=None, escape=None, encoding=None, decimal='.', multicolumn=None, multicolumn_format=None, multirow=None)

Render an object to a tabular environment table. You can splice this into a LaTeX document. Requires \usepackage{booktabs}.

Changed in version 0.20.2: Added to Series

to_latex-specific options:

bold_rows : boolean, default False

Make the row labels bold in the output

column_format : str, default None

The columns format as specified in LaTeX table format e.g ‘rcl’ for 3 columns

longtable : boolean, default will be read from the pandas config module

Default: False. Use a longtable environment instead of tabular. Requires adding a \usepackage{longtable} to your LaTeX preamble.

escape : boolean, default will be read from the pandas config module

Default: True. When set to False prevents from escaping latex special characters in column names.

encoding : str, default None

A string representing the encoding to use in the output file, defaults to ‘ascii’ on Python 2 and ‘utf-8’ on Python 3.

decimal : string, default ‘.’

Character recognized as decimal separator, e.g. ‘,’ in Europe.

New in version 0.18.0.

multicolumn : boolean, default True

Use multicolumn to enhance MultiIndex columns. The default will be read from the config module.

New in version 0.20.0.

multicolumn_format : str, default ‘l’

The alignment for multicolumns, similar to column_format The default will be read from the config module.

New in version 0.20.0.

multirow : boolean, default False

Use multirow to enhance MultiIndex rows. Requires adding a \usepackage{multirow} to your LaTeX preamble. Will print centered labels (instead of top-aligned) across the contained rows, separating groups via clines. The default will be read from the pandas config module.

New in version 0.20.0.

to_msgpack(path_or_buf=None, encoding='utf-8', **kwargs)

msgpack (serialize) object to input file path

THIS IS AN EXPERIMENTAL LIBRARY and the storage format may not be stable until a future release.

path : string File path, buffer-like, or None

if None, return generated string

append : boolean whether to append to an existing msgpack

(default is False)

compress : type of compressor (zlib or blosc), default to None (no

compression)

to_panel()

Transform long (stacked) format (DataFrame) into wide (3D, Panel) format.

Currently the index of the DataFrame must be a 2-level MultiIndex. This may be generalized later

panel : Panel

to_parquet(fname, engine='auto', compression='snappy', **kwargs)

Write a DataFrame to the binary parquet format.

New in version 0.21.0.

fname : str

string file path

engine : {‘auto’, ‘pyarrow’, ‘fastparquet’}, default ‘auto’

Parquet reader library to use. If ‘auto’, then the option ‘io.parquet.engine’ is used. If ‘auto’, then the first library to be installed is used.

compression : str, optional, default ‘snappy’

compression method, includes {‘gzip’, ‘snappy’, ‘brotli’}

kwargs

Additional keyword arguments passed to the engine

to_period(freq=None, axis=0, copy=True)

Convert DataFrame from DatetimeIndex to PeriodIndex with desired frequency (inferred from index if not passed)

freq : string, default axis : {0 or ‘index’, 1 or ‘columns’}, default 0

The axis to convert (the index by default)

copy : boolean, default True

If False then underlying input data is not copied

ts : TimeSeries with PeriodIndex

to_pickle(path, compression='infer', protocol=4)

Pickle (serialize) object to input file path.

path : string

File path

compression : {‘infer’, ‘gzip’, ‘bz2’, ‘xz’, None}, default ‘infer’

a string representing the compression to use in the output file

New in version 0.20.0.

protocol : int

Int which indicates which protocol should be used by the pickler, default HIGHEST_PROTOCOL (see [1], paragraph 12.1.2). The possible values for this parameter depend on the version of Python. For Python 2.x, possible values are 0, 1, 2. For Python>=3.0, 3 is a valid value. For Python >= 3.4, 4 is a valid value.A negative value for the protocol parameter is equivalent to setting its value to HIGHEST_PROTOCOL.

[1]	https://docs.python.org/3/library/pickle.html

New in version 0.21.0.

to_records(index=True, convert_datetime64=True)

Convert DataFrame to record array. Index will be put in the ‘index’ field of the record array if requested

index : boolean, default True

Include index in resulting record array, stored in ‘index’ field

convert_datetime64 : boolean, default True

Whether to convert the index to datetime.datetime if it is a DatetimeIndex

y : recarray

to_sparse(fill_value=None, kind='block')

Convert to SparseDataFrame

fill_value : float, default NaN kind : {‘block’, ‘integer’}

y : SparseDataFrame

to_sql(name, con, flavor=None, schema=None, if_exists='fail', index=True, index_label=None, chunksize=None, dtype=None)

Write records stored in a DataFrame to a SQL database.

name : string

Name of SQL table

con : SQLAlchemy engine or DBAPI2 connection (legacy mode)

Using SQLAlchemy makes it possible to use any DB supported by that library. If a DBAPI2 object, only sqlite3 is supported.

flavor : ‘sqlite’, default None

Deprecated since version 0.19.0: ‘sqlite’ is the only supported option if SQLAlchemy is not used.

schema : string, default None

Specify the schema (if database flavor supports this). If None, use default schema.

if_exists : {‘fail’, ‘replace’, ‘append’}, default ‘fail’

• fail: If table exists, do nothing.
• replace: If table exists, drop it, recreate it, and insert data.
• append: If table exists, insert data. Create if does not exist.

index : boolean, default True

Write DataFrame index as a column.

index_label : string or sequence, default None

Column label for index column(s). If None is given (default) and index is True, then the index names are used. A sequence should be given if the DataFrame uses MultiIndex.

chunksize : int, default None

If not None, then rows will be written in batches of this size at a time. If None, all rows will be written at once.

dtype : dict of column name to SQL type, default None

Optional specifying the datatype for columns. The SQL type should be a SQLAlchemy type, or a string for sqlite3 fallback connection.

to_stata(fname, convert_dates=None, write_index=True, encoding='latin-1', byteorder=None, time_stamp=None, data_label=None, variable_labels=None)

A class for writing Stata binary dta files from array-like objects

fname : str or buffer

String path of file-like object

convert_dates : dict

Dictionary mapping columns containing datetime types to stata internal format to use when wirting the dates. Options are ‘tc’, ‘td’, ‘tm’, ‘tw’, ‘th’, ‘tq’, ‘ty’. Column can be either an integer or a name. Datetime columns that do not have a conversion type specified will be converted to ‘tc’. Raises NotImplementedError if a datetime column has timezone information

write_index : bool

Write the index to Stata dataset.

encoding : str

Default is latin-1. Unicode is not supported

byteorder : str

Can be “>”, “<”, “little”, or “big”. default is sys.byteorder

time_stamp : datetime

A datetime to use as file creation date. Default is the current time.

dataset_label : str

A label for the data set. Must be 80 characters or smaller.

variable_labels : dict

Dictionary containing columns as keys and variable labels as values. Each label must be 80 characters or smaller.

New in version 0.19.0.

NotImplementedError

• If datetimes contain timezone information
• Column dtype is not representable in Stata

ValueError

• Columns listed in convert_dates are noth either datetime64[ns] or datetime.datetime
• Column listed in convert_dates is not in DataFrame
• Categorical label contains more than 32,000 characters

New in version 0.19.0.

>>> writer = StataWriter('./data_file.dta', data)
>>> writer.write_file()

Or with dates

>>> writer = StataWriter('./date_data_file.dta', data, {2 : 'tw'})
>>> writer.write_file()

to_string(buf=None, columns=None, col_space=None, header=True, index=True, na_rep='NaN', formatters=None, float_format=None, sparsify=None, index_names=True, justify=None, line_width=None, max_rows=None, max_cols=None, show_dimensions=False)

Render a DataFrame to a console-friendly tabular output.

buf : StringIO-like, optional

buffer to write to

columns : sequence, optional

the subset of columns to write; default None writes all columns

col_space : int, optional

the minimum width of each column

header : bool, optional

Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names

index : bool, optional

whether to print index (row) labels, default True

na_rep : string, optional

string representation of NAN to use, default ‘NaN’

formatters : list or dict of one-parameter functions, optional

formatter functions to apply to columns’ elements by position or name, default None. The result of each function must be a unicode string. List must be of length equal to the number of columns.

float_format : one-parameter function, optional

formatter function to apply to columns’ elements if they are floats, default None. The result of this function must be a unicode string.

sparsify : bool, optional

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row, default True

index_names : bool, optional

Prints the names of the indexes, default True

line_width : int, optional

Width to wrap a line in characters, default no wrap

justify : {‘left’, ‘right’, ‘center’, ‘justify’,

‘justify-all’, ‘start’, ‘end’, ‘inherit’, ‘match-parent’, ‘initial’, ‘unset’}, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), ‘right’ out of the box.

formatted : string (or unicode, depending on data and options)

to_timestamp(freq=None, how='start', axis=0, copy=True)

Cast to DatetimeIndex of timestamps, at beginning of period

freq : string, default frequency of PeriodIndex

Desired frequency

how : {‘s’, ‘e’, ‘start’, ‘end’}

Convention for converting period to timestamp; start of period vs. end

axis : {0 or ‘index’, 1 or ‘columns’}, default 0

The axis to convert (the index by default)

copy : boolean, default True

If false then underlying input data is not copied

df : DataFrame with DatetimeIndex

to_xarray()

Return an xarray object from the pandas object.

a DataArray for a Series a Dataset for a DataFrame a DataArray for higher dims

>>> df = pd.DataFrame({'A' : [1, 1, 2],
                       'B' : ['foo', 'bar', 'foo'],
                       'C' : np.arange(4.,7)})
>>> df
   A    B    C
0  1  foo  4.0
1  1  bar  5.0
2  2  foo  6.0

>>> df.to_xarray()
<xarray.Dataset>
Dimensions:  (index: 3)
Coordinates:
  * index    (index) int64 0 1 2
Data variables:
    A        (index) int64 1 1 2
    B        (index) object 'foo' 'bar' 'foo'
    C        (index) float64 4.0 5.0 6.0

>>> df = pd.DataFrame({'A' : [1, 1, 2],
                       'B' : ['foo', 'bar', 'foo'],
                       'C' : np.arange(4.,7)}
                     ).set_index(['B','A'])
>>> df
         C
B   A
foo 1  4.0
bar 1  5.0
foo 2  6.0

>>> df.to_xarray()
<xarray.Dataset>
Dimensions:  (A: 2, B: 2)
Coordinates:
  * B        (B) object 'bar' 'foo'
  * A        (A) int64 1 2
Data variables:
    C        (B, A) float64 5.0 nan 4.0 6.0

>>> p = pd.Panel(np.arange(24).reshape(4,3,2),
                 items=list('ABCD'),
                 major_axis=pd.date_range('20130101', periods=3),
                 minor_axis=['first', 'second'])
>>> p
<class 'pandas.core.panel.Panel'>
Dimensions: 4 (items) x 3 (major_axis) x 2 (minor_axis)
Items axis: A to D
Major_axis axis: 2013-01-01 00:00:00 to 2013-01-03 00:00:00
Minor_axis axis: first to second

>>> p.to_xarray()
<xarray.DataArray (items: 4, major_axis: 3, minor_axis: 2)>
array([[[ 0,  1],
        [ 2,  3],
        [ 4,  5]],
       [[ 6,  7],
        [ 8,  9],
        [10, 11]],
       [[12, 13],
        [14, 15],
        [16, 17]],
       [[18, 19],
        [20, 21],
        [22, 23]]])
Coordinates:
  * items       (items) object 'A' 'B' 'C' 'D'
  * major_axis  (major_axis) datetime64[ns] 2013-01-01 2013-01-02 2013-01-03  # noqa
  * minor_axis  (minor_axis) object 'first' 'second'

See the xarray docs

touches(second_geometry)

Indicates if the boundaries of the geometries intersect.

Paramters:

second_geometry:  

• a second geometry

transform(func, *args, **kwargs)

Call function producing a like-indexed NDFrame and return a NDFrame with the transformed values

New in version 0.20.0.

func : callable, string, dictionary, or list of string/callables

To apply to column

Accepted Combinations are:

• string function name
• function
• list of functions
• dict of column names -> functions (or list of functions)

transformed : NDFrame

>>> df = pd.DataFrame(np.random.randn(10, 3), columns=['A', 'B', 'C'],
...                   index=pd.date_range('1/1/2000', periods=10))
df.iloc[3:7] = np.nan

>>> df.transform(lambda x: (x - x.mean()) / x.std())
                   A         B         C
2000-01-01  0.579457  1.236184  0.123424
2000-01-02  0.370357 -0.605875 -1.231325
2000-01-03  1.455756 -0.277446  0.288967
2000-01-04       NaN       NaN       NaN
2000-01-05       NaN       NaN       NaN
2000-01-06       NaN       NaN       NaN
2000-01-07       NaN       NaN       NaN
2000-01-08 -0.498658  1.274522  1.642524
2000-01-09 -0.540524 -1.012676 -0.828968
2000-01-10 -1.366388 -0.614710  0.005378

pandas.NDFrame.aggregate pandas.NDFrame.apply

transpose(*args, **kwargs)

Transpose index and columns

true_centroid

The center of gravity for a feature.

truediv(other, axis='columns', level=None, fill_value=None)

Floating division of dataframe and other, element-wise (binary operator truediv).

Equivalent to dataframe / other, but with support to substitute a fill_value for missing data in one of the inputs.

other : Series, DataFrame, or constant axis : {0, 1, ‘index’, ‘columns’}

For Series input, axis to match Series index on

fill_value : None or float value, default None

Fill missing (NaN) values with this value. If both DataFrame locations are missing, the result will be missing

level : int or name

Broadcast across a level, matching Index values on the passed MultiIndex level

Mismatched indices will be unioned together

result : DataFrame

DataFrame.rtruediv

truncate(before=None, after=None, axis=None, copy=True)

Truncates a sorted DataFrame/Series before and/or after some particular index value. If the axis contains only datetime values, before/after parameters are converted to datetime values.

before : date, string, int

Truncate all rows before this index value

after : date, string, int

Truncate all rows after this index value

axis : {0 or ‘index’, 1 or ‘columns’}

• 0 or ‘index’: apply truncation to rows
• 1 or ‘columns’: apply truncation to columns

Default is stat axis for given data type (0 for Series and DataFrames, 1 for Panels)

copy : boolean, default is True,

return a copy of the truncated section

truncated : type of caller

>>> df = pd.DataFrame({'A': ['a', 'b', 'c', 'd', 'e'],
...                    'B': ['f', 'g', 'h', 'i', 'j'],
...                    'C': ['k', 'l', 'm', 'n', 'o']},
...                    index=[1, 2, 3, 4, 5])
>>> df.truncate(before=2, after=4)
   A  B  C
2  b  g  l
3  c  h  m
4  d  i  n
>>> df = pd.DataFrame({'A': [1, 2, 3, 4, 5],
...                    'B': [6, 7, 8, 9, 10],
...                    'C': [11, 12, 13, 14, 15]},
...                    index=['a', 'b', 'c', 'd', 'e'])
>>> df.truncate(before='b', after='d')
   A  B   C
b  2  7  12
c  3  8  13
d  4  9  14

The index values in truncate can be datetimes or string dates. Note that truncate assumes a 0 value for any unspecified date component in a DatetimeIndex in contrast to slicing which returns any partially matching dates.

>>> dates = pd.date_range('2016-01-01', '2016-02-01', freq='s')
>>> df = pd.DataFrame(index=dates, data={'A': 1})
>>> df.truncate('2016-01-05', '2016-01-10').tail()
                     A
2016-01-09 23:59:56  1
2016-01-09 23:59:57  1
2016-01-09 23:59:58  1
2016-01-09 23:59:59  1
2016-01-10 00:00:00  1
>>> df.loc['2016-01-05':'2016-01-10', :].tail()
                     A
2016-01-10 23:59:55  1
2016-01-10 23:59:56  1
2016-01-10 23:59:57  1
2016-01-10 23:59:58  1
2016-01-10 23:59:59  1

tshift(periods=1, freq=None, axis=0)

Shift the time index, using the index’s frequency if available.

periods : int

Number of periods to move, can be positive or negative

freq : DateOffset, timedelta, or time rule string, default None

Increment to use from the tseries module or time rule (e.g. ‘EOM’)

axis : int or basestring

Corresponds to the axis that contains the Index

If freq is not specified then tries to use the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown

shifted : NDFrame

tz_convert(tz, axis=0, level=None, copy=True)

Convert tz-aware axis to target time zone.

tz : string or pytz.timezone object axis : the axis to convert level : int, str, default None

If axis ia a MultiIndex, convert a specific level. Otherwise must be None

copy : boolean, default True

Also make a copy of the underlying data

TypeError

If the axis is tz-naive.

tz_localize(tz, axis=0, level=None, copy=True, ambiguous='raise')

Localize tz-naive TimeSeries to target time zone.

tz : string or pytz.timezone object axis : the axis to localize level : int, str, default None

If axis ia a MultiIndex, localize a specific level. Otherwise must be None

copy : boolean, default True

Also make a copy of the underlying data

ambiguous : ‘infer’, bool-ndarray, ‘NaT’, default ‘raise’

• ‘infer’ will attempt to infer fall dst-transition hours based on order
• bool-ndarray where True signifies a DST time, False designates a non-DST time (note that this flag is only applicable for ambiguous times)
• ‘NaT’ will return NaT where there are ambiguous times
• ‘raise’ will raise an AmbiguousTimeError if there are ambiguous times

infer_dst : boolean, default False

Deprecated since version 0.15.0: Attempt to infer fall dst-transition hours based on order

TypeError

If the TimeSeries is tz-aware and tz is not None.

union(second_geometry)

Constructs the geometry that is the set-theoretic union of the input geometries.

Paramters:

second_geometry:  

• a second geometry

unstack(level=-1, fill_value=None)

Pivot a level of the (necessarily hierarchical) index labels, returning a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels. If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex). The level involved will automatically get sorted.

level : int, string, or list of these, default -1 (last level)

Level(s) of index to unstack, can pass level name

fill_value : replace NaN with this value if the unstack produces

missing values

DataFrame.pivot : Pivot a table based on column values. DataFrame.stack : Pivot a level of the column labels (inverse operation

from unstack).

>>> index = pd.MultiIndex.from_tuples([('one', 'a'), ('one', 'b'),
...                                    ('two', 'a'), ('two', 'b')])
>>> s = pd.Series(np.arange(1.0, 5.0), index=index)
>>> s
one  a   1.0
     b   2.0
two  a   3.0
     b   4.0
dtype: float64

>>> s.unstack(level=-1)
     a   b
one  1.0  2.0
two  3.0  4.0

>>> s.unstack(level=0)
   one  two
a  1.0   3.0
b  2.0   4.0

>>> df = s.unstack(level=0)
>>> df.unstack()
one  a  1.0
     b  2.0
two  a  3.0
     b  4.0
dtype: float64

unstacked : DataFrame or Series

update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False)

Modify DataFrame in place using non-NA values from passed DataFrame. Aligns on indices

other : DataFrame, or object coercible into a DataFrame join : {‘left’}, default ‘left’ overwrite : boolean, default True

If True then overwrite values for common keys in the calling frame

filter_func : callable(1d-array) -> 1d-array<boolean>, default None

Can choose to replace values other than NA. Return True for values that should be updated

raise_conflict : boolean

If True, will raise an error if the DataFrame and other both contain data in the same place.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e', 'f', 'g', 'h', 'i']})
>>> df.update(new_df)
>>> df
   A  B
0  a  d
1  b  e
2  c  f

>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_column = pd.Series(['d', 'e'], name='B', index=[0, 2])
>>> df.update(new_column)
>>> df
   A  B
0  a  d
1  b  y
2  c  e
>>> df = pd.DataFrame({'A': ['a', 'b', 'c'],
...                    'B': ['x', 'y', 'z']})
>>> new_df = pd.DataFrame({'B': ['d', 'e']}, index=[1, 2])
>>> df.update(new_df)
>>> df
   A  B
0  a  x
1  b  d
2  c  e

If other contains NaNs the corresponding values are not updated in the original dataframe.

>>> df = pd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = pd.DataFrame({'B': [4, np.nan, 6]})
>>> df.update(new_df)
>>> df
   A      B
0  1    4.0
1  2  500.0
2  3    6.0

values

Numpy representation of NDFrame

The dtype will be a lower-common-denominator dtype (implicit upcasting); that is to say if the dtypes (even of numeric types) are mixed, the one that accommodates all will be chosen. Use this with care if you are not dealing with the blocks.

e.g. If the dtypes are float16 and float32, dtype will be upcast to float32. If dtypes are int32 and uint8, dtype will be upcast to int32. By numpy.find_common_type convention, mixing int64 and uint64 will result in a flot64 dtype.

var(axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)

Return unbiased variance over requested axis.

Normalized by N-1 by default. This can be changed using the ddof argument

axis : {index (0), columns (1)} skipna : boolean, default True

Exclude NA/null values. If an entire row/column is NA, the result will be NA

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series

ddof : int, default 1

degrees of freedom

numeric_only : boolean, default None

Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

var : Series or DataFrame (if level specified)

where(cond, other=nan, inplace=False, axis=None, level=None, errors='raise', try_cast=False, raise_on_error=None)

Return an object of same shape as self and whose corresponding entries are from self where cond is True and otherwise are from other.

cond : boolean NDFrame, array-like, or callable

Where cond is True, keep the original value. Where False, replace with corresponding value from other. If cond is callable, it is computed on the NDFrame and should return boolean NDFrame or array. The callable must not change input NDFrame (though pandas doesn’t check it).

New in version 0.18.1: A callable can be used as cond.

other : scalar, NDFrame, or callable

Entries where cond is False are replaced with corresponding value from other. If other is callable, it is computed on the NDFrame and should return scalar or NDFrame. The callable must not change input NDFrame (though pandas doesn’t check it).

New in version 0.18.1: A callable can be used as other.

inplace : boolean, default False

Whether to perform the operation in place on the data

axis : alignment axis if needed, default None level : alignment level if needed, default None errors : str, {‘raise’, ‘ignore’}, default ‘raise’

• raise : allow exceptions to be raised
• ignore : suppress exceptions. On error return original object

Note that currently this parameter won’t affect the results and will always coerce to a suitable dtype.

try_cast : boolean, default False

try to cast the result back to the input type (if possible),

raise_on_error : boolean, default True

Whether to raise on invalid data types (e.g. trying to where on strings)

Deprecated since version 0.21.0.

wh : same type as caller

The where method is an application of the if-then idiom. For each element in the calling DataFrame, if cond is True the element is used; otherwise the corresponding element from the DataFrame other is used.

The signature for DataFrame.where() differs from numpy.where(). Roughly df1.where(m, df2) is equivalent to np.where(m, df1, df2).

For further details and examples see the where documentation in indexing.

>>> s = pd.Series(range(5))
>>> s.where(s > 0)
0    NaN
1    1.0
2    2.0
3    3.0
4    4.0

>>> s.mask(s > 0)
0    0.0
1    NaN
2    NaN
3    NaN
4    NaN

>>> s.where(s > 1, 10)
0    10.0
1    10.0
2    2.0
3    3.0
4    4.0

>>> df = pd.DataFrame(np.arange(10).reshape(-1, 2), columns=['A', 'B'])
>>> m = df % 3 == 0
>>> df.where(m, -df)
   A  B
0  0 -1
1 -2  3
2 -4 -5
3  6 -7
4 -8  9
>>> df.where(m, -df) == np.where(m, df, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True
>>> df.where(m, -df) == df.mask(~m, -df)
      A     B
0  True  True
1  True  True
2  True  True
3  True  True
4  True  True

DataFrame.mask()

within(second_geometry, relation=None)

Indicates if the base geometry is within the comparison geometry. Paramters:

second_geometry:  

• a second geometry

relation:

• The spatial relationship type.

BOUNDARY - Relationship has no restrictions for interiors or boundaries. CLEMENTINI - Interiors of geometries must intersect. Specifying

CLEMENTINI is equivalent to specifying None. This is the default.

PROPER - Boundaries of geometries must not intersect.

xs(key, axis=0, level=None, drop_level=True)

Returns a cross-section (row(s) or column(s)) from the Series/DataFrame. Defaults to cross-section on the rows (axis=0).

key : object

Some label contained in the index, or partially in a MultiIndex

axis : int, default 0

Axis to retrieve cross-section on

level : object, defaults to first n levels (n=1 or len(key))

In case of a key partially contained in a MultiIndex, indicate which levels are used. Levels can be referred by label or position.

drop_level : boolean, default True

If False, returns object with same levels as self.

>>> df
   A  B  C
a  4  5  2
b  4  0  9
c  9  7  3
>>> df.xs('a')
A    4
B    5
C    2
Name: a
>>> df.xs('C', axis=1)
a    2
b    9
c    3
Name: C

>>> df
                    A  B  C  D
first second third
bar   one    1      4  1  8  9
      two    1      7  5  5  0
baz   one    1      6  6  8  0
      three  2      5  3  5  3
>>> df.xs(('baz', 'three'))
       A  B  C  D
third
2      5  3  5  3
>>> df.xs('one', level=1)
             A  B  C  D
first third
bar   1      4  1  8  9
baz   1      6  6  8  0
>>> df.xs(('baz', 2), level=[0, 'third'])
        A  B  C  D
second
three   5  3  5  3

xs : Series or DataFrame

xs is only for getting, not setting values.

MultiIndex Slicers is a generic way to get/set values on any level or levels. It is a superset of xs functionality, see MultiIndex Slicers