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    Class Index (2.11.0) 

      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.Series bigframes.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

    __setitem__

      __setitem__ 
 ( 
 key 
 , 
 value 
 ) 
 - 
> None

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

    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.

    item

      item 
 ()

    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'
    Exceptions
    Type
    Description
    ValueError
    If the data is not length = 1.
    Returns
    Type
    Description
    scalar
    The first element of Index.

    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

    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')
    Parameters
    Name
    Description
    name
    label or list of labels

    Name(s) to set.

    inplace
    bool

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

    Exceptions
    Type
    Description
    ValueError
    If name is not the same length as levels.
    Returns
    Type
    Description
    bigframes.pandas.Index None
    The same type as the caller or None if inplace=True .

    sort_values

      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')
    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.

    unique

      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')
    Parameter
    Name
    Description
    level
    int or hashable, optional

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

    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.
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