Class Index (2.0.0)
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Index(data=None,dtype=None,*,name=None,session=None)

Immutable sequence used for indexing and alignment.

The basic object storing axis labels for all objects.

Parameters
Name	Description
`data`	`pandas.Series pandas.Index bigframes.series.Seriesbigframes.core.indexes.base.Index` Labels (1-dimensional).
`session`	`Optional[bigframes.session.Session]` BigQuery DataFrames session where queries are run. If not set, a default session is used.

Properties

T

Return the transpose, which is by definition self.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> s = bpd.Series(['Ant', 'Bear', 'Cow']) >>> s 0 Ant 1 Bear 2 Cow dtype: string >>> s.T 0 Ant 1 Bear 2 Cow dtype: string

For Index:

>>> idx = bpd.Index([1, 2, 3]) >>> idx.T Index([1, 2, 3], dtype='Int64')

Returns
Type	Description
`bigframes.pandas.Index`	Index

dtype

Return the dtype object of the underlying data.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='Int64') >>> idx.dtype Int64Dtype()

dtypes

Return the dtypes as a Series for the underlying MultiIndex.

Returns
Type	Description
`Pandas.Series`	Pandas.Series of the MultiIndex dtypes.

empty

Returns True if the Index is empty, otherwise returns False.

has_duplicates

Check if the Index has duplicate values.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([1, 5, 7, 7]) >>> bool(idx.has_duplicates) True >>> idx = bpd.Index([1, 5, 7]) >>> bool(idx.has_duplicates) False

Returns
Type	Description
`bool`	Whether or not the Index has duplicate values.

is_monotonic_decreasing

Return a boolean if the values are equal or decreasing.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> bool(bpd.Index([3, 2, 1]).is_monotonic_decreasing) True >>> bool(bpd.Index([3, 2, 2]).is_monotonic_decreasing) True >>> bool(bpd.Index([3, 1, 2]).is_monotonic_decreasing) False

Returns
Type	Description
`bool`	True, if the values monotonically decreasing, otherwise False.

is_monotonic_increasing

Return a boolean if the values are equal or increasing.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> bool(bpd.Index([1, 2, 3]).is_monotonic_increasing) True >>> bool(bpd.Index([1, 2, 2]).is_monotonic_increasing) True >>> bool(bpd.Index([1, 3, 2]).is_monotonic_increasing) False

Returns
Type	Description
`bool`	True, if the values monotonically increasing, otherwise False.

is_unique

Return if the index has unique values.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([1, 5, 7, 7]) >>> idx.is_unique False >>> idx = bpd.Index([1, 5, 7]) >>> idx.is_unique True

Returns
Type	Description
`bool`	True if the index has unique values, otherwise False.

name

Returns Index name.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([1, 2, 3], name='x') >>> idx Index([1, 2, 3], dtype='Int64', name='x') >>> idx.name 'x'

Returns
Type	Description
`blocks.Label`	Index or MultiIndex name

names

Returns the names of the Index.

Returns
Type	Description
`Sequence[blocks.Label]`	A Sequence of Index or MultiIndex name

ndim

Number of dimensions of the underlying data, by definition 1.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> s = bpd.Series(['Ant', 'Bear', 'Cow']) >>> s 0 Ant 1 Bear 2 Cow dtype: string >>> s.ndim 1

For Index:

>>> idx = bpd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='Int64') >>> idx.ndim 1

Returns
Type	Description
`int`	Number or dimensions.

nlevels

Integer number of levels in this MultiIndex

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> mi = bpd.MultiIndex.from_arrays([['a'], ['b'], ['c']]) >>> mi MultiIndex([('a', 'b', 'c')], ) >>> mi.nlevels 3

Returns
Type	Description
`int`	Number of levels.

query_job

BigQuery job metadata for the most recent query.

shape

Return a tuple of the shape of the underlying data.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='Int64') >>> idx.shape (3,)

Returns
Type	Description
`Tuple[int]`	A Tuple of integers representing the shape.

size

Return the number of elements in the underlying data.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None

For Series:

>>> s = bpd.Series(['Ant', 'Bear', 'Cow']) >>> s 0 Ant 1 Bear 2 Cow dtype: string

For Index:

>>> idx = bpd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='Int64')

Returns
Type	Description
`int`	Number of elements

values

Return an array representing the data in the Index.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='Int64') >>> idx.values array([1, 2, 3])

Returns
Type	Description
`array`	Numpy.ndarray or ExtensionArray

Methods

all

all()-> bool

Return whether all elements are Truthy.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None

True, because nonzero integers are considered True.

>>> bool(bpd.Index([1, 2, 3]).all()) True False, because 0 is considered False. >>> bool(bpd.Index([0, 1, 2]).all()) False

Exceptions
Type	Description
`TypeError`	MultiIndex with more than 1 level does not support `all`.

Returns
Type	Description
`bool`	A single element array-like may be converted to bool.

any

any()-> bool

Return whether any element is Truthy.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> index = bpd.Index([0, 1, 2]) >>> bool(index.any()) True >>> index = bpd.Index([0, 0, 0]) >>> bool(index.any()) False

Exceptions
Type	Description
`TypeError`	MultiIndex with more than 1 level does not support `any`.

Returns
Type	Description
`bool`	A single element array-like may be converted to bool.

argmax

argmax()-> int

Return int position of the largest value in the Series.

If the maximum is achieved in multiple locations, the first row position is returned.

Examples:

Consider dataset containing cereal calories

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> s = bpd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0, ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}) >>> s Corn Flakes 100.0 Almond Delight 110.0 Cinnamon Toast Crunch 120.0 Cocoa Puff 110.0 dtype: Float64 >>> int(s.argmax()) 2 >>> int(s.argmin()) 0

The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed.

Returns
Type	Description
`int`	Row position of the maximum value.

argmin

argmin()-> int

Return int position of the smallest value in the series.

If the minimum is achieved in multiple locations, the first row position is returned.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None

Consider dataset containing cereal calories

>>> s = bpd.Series({'Corn Flakes': 100.0, 'Almond Delight': 110.0, ... 'Cinnamon Toast Crunch': 120.0, 'Cocoa Puff': 110.0}) >>> s Corn Flakes 100.0 Almond Delight 110.0 Cinnamon Toast Crunch 120.0 Cocoa Puff 110.0 dtype: Float64 >>> int(s.argmax()) 2 >>> int(s.argmin()) 0

The maximum cereal calories is the third element and the minimum cereal calories is the first element, since series is zero-indexed.

Returns
Type	Description
`int`	Row position of the minimum value.

astype

astype(dtype,*,errors:typing.Literal["raise","null"]="raise")-> bigframes.core.indexes.base.Index

Create an Index with values cast to dtypes.

The class of a new Index is determined by dtype. When conversion is impossible, a TypeError exception is raised.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([1, 2, 3]) >>> idx Index([1, 2, 3], dtype='Int64')

Parameters
Name	Description
`dtype`	`str, data type, or pandas.ExtensionDtype` A dtype supported by BigQuery DataFrame include `'boolean'`, `'Float64'`, `'Int64'`, `'int64[pyarrow]'`, `'string'`, `'string[pyarrow]'`, `'timestamp[us, tz=UTC][pyarrow]'`, `'timestamp[us][pyarrow]'`, `'date32[day][pyarrow]'`, `'time64[us][pyarrow]'`. A pandas.ExtensionDtype include `pandas.BooleanDtype()`, `pandas.Float64Dtype()`, `pandas.Int64Dtype()`, `pandas.StringDtype(storage="pyarrow")`, `pd.ArrowDtype(pa.date32())`, `pd.ArrowDtype(pa.time64("us"))`, `pd.ArrowDtype(pa.timestamp("us"))`, `pd.ArrowDtype(pa.timestamp("us", tz="UTC"))`.
`errors`	`{'raise', 'null'}, default 'raise'` Control raising of exceptions on invalid data for provided dtype. If 'raise', allow exceptions to be raised if any value fails cast If 'null', will assign null value if value fails cast

Exceptions
Type	Description
`ValueError`	If `errors` is not one of `raise`.
`TypeError`	MultiIndex with more than 1 level does not support `astype`.

Returns
Type	Description
`bigframes.pandas.Index`	Index with values cast to specified dtype.

copy

copy(name:typing.Optional[typing.Hashable]=None)

Make a copy of this object.

Name is set on the new object.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index(['a', 'b', 'c']) >>> new_idx = idx.copy() >>> idx is new_idx False

Parameter
Name	Description
`name`	`Label, optional` Set name for new object.

Returns
Type	Description
`bigframes.pandas.Index`	Index reference to new object, which is a copy of this object.

drop

drop(labels:typing.Any)-> bigframes.core.indexes.base.Index

Make new Index with passed list of labels deleted.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index(['a', 'b', 'c']) >>> idx.drop(['a']) Index(['b', 'c'], dtype='string')

Returns
Type	Description
`bigframes.pandas.Index`	Will be same type as self.

drop_duplicates

drop_duplicates(*,keep:str="first")-> bigframes.core.indexes.base.Index

Return Index with duplicate values removed.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None Generate an pandas.Index with duplicate values. >>> idx = bpd.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])

The keep parameter controls which duplicate values are removed. The value first keeps the first occurrence for each set of duplicated entries. The default value of keep is first.

>>> idx.drop_duplicates(keep='first') Index(['lama', 'cow', 'beetle', 'hippo'], dtype='string')

The value last keeps the last occurrence for each set of duplicated entries.

>>> idx.drop_duplicates(keep='last') Index(['cow', 'beetle', 'lama', 'hippo'], dtype='string')

The value False discards all sets of duplicated entries.

>>> idx.drop_duplicates(keep=False) Index(['cow', 'beetle', 'hippo'], dtype='string')

Parameter
Name	Description
`keep`	`{'first', 'last', False}, default 'first'` One of: 'first' : Drop duplicates except for the first occurrence. 'last' : Drop duplicates except for the last occurrence. `False` : Drop all duplicates.

dropna

dropna(how:typing.Literal["all","any"]="any",)-> bigframes.core.indexes.base.Index

Return Index without NA/NaN values.

Examples:

>>> import bigframes.pandas as bpd >>> import numpy as np >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([1, np.nan, 3]) >>> idx.dropna() Index([1.0, 3.0], dtype='Float64')

Parameter
Name	Description
`how`	`{'any', 'all'}, default 'any'` If the Index is a MultiIndex, drop the value when any or all levels are NaN.

Exceptions
Type	Description
`ValueError`	If `how` is not `any` or `all`

fillna

fillna(value=None)-> bigframes.core.indexes.base.Index

Fill NA/NaN values with the specified value.

Examples:

>>> import bigframes.pandas as bpd >>> import numpy as np >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([np.nan, np.nan, 3]) >>> idx.fillna(0) Index([0.0, 0.0, 3.0], dtype='Float64')

Parameter
Name	Description
`value`	`scalar` Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes.

Exceptions
Type	Description
`TypeError`	MultiIndex with more than 1 level does not support `fillna`.

from_frame

from_frame(frame:typing.Union[bigframes.series.Series,bigframes.dataframe.DataFrame],)-> bigframes.core.indexes.base.Index

Make a MultiIndex from a DataFrame.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> df = bpd.DataFrame([['HI', 'Temp'], ['HI', 'Precip'], ... ['NJ', 'Temp'], ['NJ', 'Precip']], ... columns=['a', 'b']) >>> df a b 0 HI Temp 1 HI Precip 2 NJ Temp 3 NJ Precip <BLANKLINE> [4 rows x 2 columns] >>> bpd.MultiIndex.from_frame(df) Index([0, 1, 2, 3], dtype='Int64')

Parameter
Name	Description
`frame`	`Union[bigframes.pandas.Series, bigframes.pandas.DataFrame]` bigframes.pandas.Series or bigframes.pandas.DataFrame to convert to bigframes.pandas.Index.

Exceptions
Type	Description
`bigframes.exceptions.NullIndexError`	If Index is Null.

Returns
Type	Description
`bigframes.pandas.Index`	The Index representation of the given Series or DataFrame.

get_level_values

get_level_values(level)-> bigframes.core.indexes.base.Index

Return an Index of values for requested level.

This is primarily useful to get an individual level of values from a MultiIndex, but is provided on Index as well for compatibility.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index(list('abc')) >>> idx Index(['a', 'b', 'c'], dtype='string')

Get level values by supplying level as integer:

>>> idx.get_level_values(0) Index(['a', 'b', 'c'], dtype='string')

Parameter
Name	Description
`level`	`int or str` It is either the integer position or the name of the level.

Returns
Type	Description
`bigframes.pandas.Index`	Calling object, as there is only one level in the Index.

isin

isin(values)-> bigframes.core.indexes.base.Index

Return a boolean array where the index values are in values.

Compute boolean array to check whether each index value is found in the passed set of values. The length of the returned boolean array matches the length of the index.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([1,2,3]) >>> idx Index([1, 2, 3], dtype='Int64')

Check whether each index value in a list of values.

>>> idx.isin([1, 4]) Index([True, False, False], dtype='boolean') >>> midx = bpd.MultiIndex.from_arrays([[1,2,3], ... ['red', 'blue', 'green']], ... names=('number', 'color')) >>> midx MultiIndex([(1, 'red'), (2, 'blue'), (3, 'green')], names=['number', 'color'])

Parameter
Name	Description
`values`	`set or list-like` Sought values.

Exceptions
Type	Description
`TypeError`	If object passed to `isin()` is not a list-like

Returns
Type	Description
`bigframes.pandas.Series`	Series of boolean values.

max

max()-> typing.Any

Return the maximum value of the Index.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([3, 2, 1]) >>> int(idx.max()) 3 >>> idx = bpd.Index(['c', 'b', 'a']) >>> idx.max() 'c'

Returns
Type	Description
`scalar`	Maximum value.

min

min()-> typing.Any

Return the minimum value of the Index.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([3, 2, 1]) >>> int(idx.min()) 1 >>> idx = bpd.Index(['c', 'b', 'a']) >>> idx.min() 'a'

Returns
Type	Description
`scalar`	Minimum value.

nunique

nunique()-> int

Return number of unique elements in the object.

Excludes NA values by default.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> s = bpd.Series([1, 3, 5, 7, 7]) >>> s 0 1 1 3 2 5 3 7 4 7 dtype: Int64 >>> int(s.nunique()) 4

Returns
Type	Description
`int`	Number of unique elements

rename

rename(name:typing.Union[str,typing.Sequence[str]],)-> bigframes.core.indexes.base.Index

Alter Index or MultiIndex name.

Able to set new names without level. Defaults to returning new index. Length of names must match number of levels in MultiIndex.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index(['A', 'C', 'A', 'B'], name='score') >>> idx.rename('grade') Index(['A', 'C', 'A', 'B'], dtype='string', name='grade')

Parameter
Name	Description
`name`	`label or list of labels` Name(s) to set.

Exceptions
Type	Description
`ValueError`	If `name` is not the same length as levels.

Returns
Type	Description
`bigframes.pandas.Index`	The same type as the caller.

sort_values

sort_values(*,ascending:bool=True,na_position:str="last")

Return a sorted copy of the index.

Return a sorted copy of the index, and optionally return the indices that sorted the index itself.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index([10, 100, 1, 1000]) >>> idx Index([10, 100, 1, 1000], dtype='Int64')

Sort values in ascending order (default behavior).

>>> idx.sort_values() Index([1, 10, 100, 1000], dtype='Int64')

Parameters
Name	Description
`ascending`	`bool, default True` Should the index values be sorted in an ascending order.
`na_position`	`{'first' or 'last'}, default 'last'` Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end.

Exceptions
Type	Description
`ValueError`	If `no_position` is not one of `first` or `last`.

Returns
Type	Description
`pandas.Index`	Sorted copy of the index.

to_numpy

to_numpy(dtype=None,*,allow_large_results=None,**kwargs)-> numpy.ndarray

A NumPy ndarray representing the values in this Series or Index.

Parameter
Name	Description
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

to_pandas

Gets the Index as a pandas Index.

Parameters
Name	Description
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.
`dry_run`	`bool, default False` If this argument is true, this method will not process the data. Instead, it returns a Pandas series containing dtype and the amount of bytes to be processed.

Returns
Type	Description
`pandas.Index pandas.Series`	A pandas Index with all of the labels from this Index. If dry run is set to True, returns a Series containing dry run statistics.

to_series

to_series(index:typing.Optional[bigframes.core.indexes.base.Index]=None,name:typing.Optional[typing.Hashable]=None,)-> bigframes.series.Series

Create a Series with both index and values equal to the index keys.

Useful with map for returning an indexer based on an index.

Examples:

>>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> idx = bpd.Index(['Ant', 'Bear', 'Cow'], name='animal')

By default, the original index and original name is reused.

>>> idx.to_series() animal Ant Ant Bear Bear Cow Cow Name: animal, dtype: string

To enforce a new index, specify new labels to index:

>>> idx.to_series(index=[0, 1, 2]) 0 Ant 1 Bear 2 Cow Name: animal, dtype: string

To override the name of the resulting column, specify name:

>>> idx.to_series(name='zoo') animal Ant Ant Bear Bear Cow Cow Name: zoo, dtype: string

Parameters
Name	Description
`index`	`Index, optional` Index of resulting Series. If None, defaults to original index.
`name`	`str, optional` Name of resulting Series. If None, defaults to name of original index.

Returns
Type	Description
`bigframes.pandas.Series`	The dtype will be based on the type of the Index values.

transpose

transpose()-> bigframes.core.indexes.base.Index

Return the transpose, which is by definition self.

value_counts

value_counts(normalize:bool=False,sort:bool=True,ascending:bool=False,*,dropna:bool=True)

Return a Series containing counts of unique values.

The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.

Examples:

>>> import bigframes.pandas as bpd >>> import numpy as np >>> bpd.options.display.progress_bar = None >>> index = bpd.Index([3, 1, 2, 3, 4, np.nan]) >>> index.value_counts() 3.0 2 1.0 1 2.0 1 4.0 1 Name: count, dtype: Int64

With normalize set to True, returns the relative frequency by dividing all values by the sum of values.

>>> s = bpd.Series([3, 1, 2, 3, 4, np.nan]) >>> s.value_counts(normalize=True) 3.0 0.4 1.0 0.2 2.0 0.2 4.0 0.2 Name: proportion, dtype: Float64

dropna

With dropna set to False we can also see NaN index values.

>>> s.value_counts(dropna=False) 3.0 2 1.0 1 2.0 1 4.0 1 <NA> 1 Name: count, dtype: Int64

Parameters
Name	Description
`normalize`	`bool, default False` If True, then the object returned will contain the relative frequencies of the unique values.
`sort`	`bool, default True` Sort by frequencies.
`ascending`	`bool, default False` Sort in ascending order.
`dropna`	`bool, default True` Don't include counts of NaN.

Class Index (2.0.0) Stay organized with collections Save and categorize content based on your preferences.

Parameters

Properties

T

dtype

dtypes

empty

has_duplicates

is_monotonic_decreasing

is_monotonic_increasing

is_unique

name

names

ndim

nlevels

query_job

shape

size

values

Methods

all

any

argmax

argmin

astype

copy

drop

drop_duplicates

dropna

fillna

from_frame

get_level_values

isin

max

min

nunique

rename

sort_values

to_numpy

to_pandas

to_series

transpose

value_counts

Class Index (2.0.0)
Stay organized with collections Save and categorize content based on your preferences.