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Class Index (2.14.0) | Python client library

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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.

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')

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()

Return the dtypes as a Series for the underlying MultiIndex.

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

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

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

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

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 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 the names of the Index.

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

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

BigQuery job metadata for the most recent query.

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,)

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')

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])

__setitem__(key, value) -> None

Index objects are immutable. Use Index constructor to create modified Index.

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

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

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.

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.

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')

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")).

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

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

Set name for new object.

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')

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')

One of: 'first' : Drop duplicates except for the first occurrence. 'last' : Drop duplicates except for the last occurrence. False : Drop all duplicates.

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')

If the Index is a MultiIndex, drop the value when any or all levels are NaN.

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')

Scalar value to use to fill holes (e.g. 0). This value cannot be a list-likes.

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')

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')

It is either the integer position or the name of the level.

get_loc(key) -> typing.Union[int, slice, bigframes.series.Series]

Get integer location, slice or boolean mask for requested label.

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'])

Sought values.

Return the first element of the underlying data as a Python scalar.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> s = bpd.Series([1], index=['a'])
>>> s.index.item()
'a'

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'

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'

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

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')

Name(s) to set.

Default False. Modifies the object directly, instead of creating a new Index or MultiIndex.

sort_values(
    *, inplace: bool = False, ascending: bool = True, na_position: str = "last"
) -> bigframes.core.indexes.base.Index

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')

Should the index values be sorted in an ascending order.

Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end.

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

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

If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

Gets the Index as a pandas Index.

If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

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.

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

Index of resulting Series. If None, defaults to original index.

Name of resulting Series. If None, defaults to name of original index.

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

Return the transpose, which is by definition self.

unique(
    level: typing.Optional[typing.Union[typing.Hashable, int]] = None,
) -> bigframes.core.indexes.base.Index

Returns unique values in the index.

Examples:

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

Only return values from specified level (for MultiIndex). If int, gets the level by integer position, else by level name.

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

If True, then the object returned will contain the relative frequencies of the unique values.

Sort by frequencies.

Sort in ascending order.

Don't include counts of NaN.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-12 UTC.

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