Class Series (1.26.0)

Series(*args, **kwargs)

N-dimensional analogue of DataFrame. Store multi-dimensional in a size-mutable, labeled data structure

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

at

Access a single value for a row/column label pair.

Examples:

>>> import bigframes.pandas as bpd
>>> s = bpd.Series([1, 2, 3], index=['A', 'B', 'C'])
>>> bpd.options.display.progress_bar = None
>>> s
A    1
B    2
C    3
dtype: Int64

Get value at specified row label

>>> s.at['B']
np.int64(2)

Returns
Type	Description
`bigframes.core.indexers.AtSeriesIndexer`	Indexers object.

dt

Accessor object for datetime-like properties of the Series values.

Returns
Type	Description
`bigframes.operations.datetimes.DatetimeMethods`	An accessor containing datetime methods.

dtype

Return the dtype object of the underlying data.

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s.dtype
Int64Dtype()

dtypes

Return the dtypes in the DataFrame.

This returns a Series with the data type of each column. The result's index is the original DataFrame's columns. Columns with mixed types aren't supported yet in BigQuery DataFrames.

Examples:

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

>>> df = bpd.DataFrame({'float': [1.0], 'int': [1], 'string': ['foo']})
>>> df.dtypes
float             Float64
int                 Int64
string    string[pyarrow]
dtype: object

Returns
Type	Description
`pandas.Series`	A pandas Series with the data type of each column.

empty

Indicates whether Series/DataFrame is empty.

True if Series/DataFrame is entirely empty (no items), meaning any of the axes are of length 0.

Returns
Type	Description
`bool`	If Series/DataFrame is empty, return True, if not return False.

geo

Accessor object for geography properties of the Series values.

Returns
Type	Description
`bigframes.geopandas.geoseries.GeoSeries`	An accessor containing geography methods.

hasnans

Return True if there are any NaNs.

Examples:

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

>>> s = bpd.Series([1, 2, 3, None])
>>> s
0     1.0
1     2.0
2     3.0
3    <NA>
dtype: Float64
>>> s.hasnans
np.True_

iat

Access a single value for a row/column pair by integer position.

Examples:

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

Get value at specified row number

>>> s.iat[1]
np.int64(2)

Returns
Type	Description
`bigframes.core.indexers.IatSeriesIndexer`	Indexers object.

iloc

Purely integer-location based indexing for selection by position.

Returns
Type	Description
`bigframes.core.indexers.IlocSeriesIndexer`	Purely integer-location Indexers.

index

The index (axis labels) of the Series.

The index of a Series is used to label and identify each element of the underlying data. The index can be thought of as an immutable ordered set (technically a multi-set, as it may contain duplicate labels), and is used to index and align data.

Examples:

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

You can access the index of a Series via index property.

>>> df = bpd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
...                     'Age': [25, 30, 35],
...                     'Location': ['Seattle', 'New York', 'Kona']},
...                    index=([10, 20, 30]))
>>> s = df["Age"]
>>> s
10    25
20    30
30    35
Name: Age, dtype: Int64
>>> s.index # doctest: +ELLIPSIS
Index([10, 20, 30], dtype='Int64')
>>> s.index.values
array([10, 20, 30])

Let's try setting a multi-index case reflect via index property.

>>> df1 = df.set_index(["Name", "Location"])
>>> s1 = df1["Age"]
>>> s1
Name    Location
Alice   Seattle     25
Bob     New York    30
Aritra  Kona        35
Name: Age, dtype: Int64
>>> s1.index # doctest: +ELLIPSIS
MultiIndex([( 'Alice',  'Seattle'),
    (   'Bob', 'New York'),
    ('Aritra',     'Kona')],
   names=['Name', 'Location'])
>>> s1.index.values
array([('Alice', 'Seattle'), ('Bob', 'New York'), ('Aritra', 'Kona')],
    dtype=object)

Returns
Type	Description
`Index`	The index object of the Series.

is_monotonic_decreasing

Return boolean if values in the object are monotonically decreasing.

Examples:

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

>>> s = bpd.Series([3, 2, 2, 1])
>>> s.is_monotonic_decreasing
np.True_

>>> s = bpd.Series([1, 2, 3])
>>> s.is_monotonic_decreasing
np.False_

Returns
Type	Description
`bool`	Boolean.

is_monotonic_increasing

Return boolean if values in the object are monotonically increasing.

Examples:

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

>>> s = bpd.Series([1, 2, 2])
>>> s.is_monotonic_increasing
np.True_

>>> s = bpd.Series([3, 2, 1])
>>> s.is_monotonic_increasing
np.False_

Returns
Type	Description
`bool`	Boolean.

list

API documentation for list property.

loc

Access a group of rows and columns by label(s) or a boolean array.

Returns
Type	Description
`bigframes.core.indexers.LocSeriesIndexer`	Indexers object.

name

Return the name of the Series.

The name of a Series becomes its index or column name if it is used to form a DataFrame. It is also used whenever displaying the Series using the interpreter.

Examples:

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

For a Series:

>>> s = bpd.Series([1, 2, 3], dtype="Int64", name='Numbers')
>>> s
0    1
1    2
2    3
Name: Numbers, dtype: Int64
>>> s.name
'Numbers'

If the Series is part of a DataFrame:

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df
   col1  col2
0     1     3
1     2     4
<BLANKLINE>
[2 rows x 2 columns]
>>> s = df["col1"]
>>> s.name
'col1'

Returns
Type	Description
`hashable object`	The name of the Series, also the column name if part of a DataFrame.

ndim

Return an int representing the number of axes / array dimensions.

Returns
Type	Description
`int`	Return 1 if Series. Otherwise return 2 if DataFrame.

plot

Make plots of Series.

Returns
Type	Description
`bigframes.operations.plotting.PlotAccessor`	An accessor making plots.

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

>>> s = bpd.Series([1, 4, 9, 16])
>>> s.shape
(4,)
>>> s = bpd.Series(['Alice', 'Bob', bpd.NA])
>>> s.shape
(3,)

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({'a': 1, 'b': 2, 'c': 3})
>>> s.size
3

For Index:

>>> idx = bpd.Index(bpd.Series([1, 2, 3]))
>>> idx.size
3

Returns
Type	Description
`int`	Return the number of elements in the underlying data.

str

Vectorized string functions for Series and Index.

NAs stay NA unless handled otherwise by a particular method. Patterned after Python’s string methods, with some inspiration from R’s stringr package.

Examples:

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

>>> s = bpd.Series(["A_Str_Series"])
>>> s
0    A_Str_Series
dtype: string

>>> s.str.lower()
0    a_str_series
dtype: string

>>> s.str.replace("_", "")
0    AStrSeries
dtype: string

Returns
Type	Description
`bigframes.operations.strings.StringMethods`	An accessor containing string methods.

struct

Accessor object for struct properties of the Series values.

Returns
Type	Description
`bigframes.operations.structs.StructAccessor`	An accessor containing struct methods.

values

Return Series as ndarray or ndarray-like depending on the dtype.

Examples:

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

>>> bpd.Series([1, 2, 3]).values
array([1, 2, 3])

>>> bpd.Series(list('aabc')).values
array(['a', 'a', 'b', 'c'], dtype=object)

Returns
Type	Description
`numpy.ndarray or ndarray-like`	Values in the Series.

Methods

add

__add__(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Get addition of Series and other, element-wise, using operator +.

Equivalent to Series.add(other).

Examples:

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

>>> s = bpd.Series([1.5, 2.6], index=['elk', 'moose'])
>>> s
elk      1.5
moose    2.6
dtype: Float64

You can add a scalar.

>>> s + 1.5
elk      3.0
moose    4.1
dtype: Float64

You can add another Series with index aligned.

>>> delta = bpd.Series([1.5, 2.6], index=['elk', 'moose'])
>>> s + delta
elk      3.0
moose    5.2
dtype: Float64

Adding any mis-aligned index will result in invalid values.

>>> delta = bpd.Series([1.5, 2.6], index=['moose', 'bison'])
>>> s + delta
elk      <NA>
moose     4.1
bison    <NA>
dtype: Float64

Parameter
Name	Description
`other`	`scalar or Series` Object to be added to the Series.

Returns
Type	Description
`Series`	The result of adding `other` to Series.

and

__and__(other: bool | int | bigframes.series.Series) -> bigframes.series.Series

Get bitwise AND of Series and other, element-wise, using operator &.

Examples:

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

>>> s = bpd.Series([0, 1, 2, 3])

You can operate with a scalar.

>>> s & 6
0    0
1    0
2    2
3    2
dtype: Int64

You can operate with another Series.

>>> s1 = bpd.Series([5, 6, 7, 8])
>>> s & s1
0    0
1    0
2    2
3    0
dtype: Int64

Parameter
Name	Description
`other`	`scalar or Series` Object to bitwise AND with the Series.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

array

__array__(dtype=None) -> numpy.ndarray

Returns the values as NumPy array.

Equivalent to Series.to_numpy(dtype).

Users should not call this directly. Rather, it is invoked by numpy.array and numpy.asarray.

Examples:

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

>>> ser = bpd.Series([1, 2, 3])

>>> np.asarray(ser)
array([1, 2, 3])

Parameter
Name	Description
`dtype`	`str or numpy.dtype, optional` The dtype to use for the resulting NumPy array. By default, the dtype is inferred from the data.

Returns
Type	Description
`numpy.ndarray`	The values in the series converted to a `numpy.ndarray` with the specified dtype.

__array_ufunc__

__array_ufunc__(
    ufunc: numpy.ufunc, method: str, *inputs, **kwargs
) -> bigframes.series.Series

Used to support numpy ufuncs. See: https://numpy.org/doc/stable/reference/ufuncs.html

floordiv

__floordiv__(
    other: float | int | bigframes.series.Series,
) -> bigframes.series.Series

Get integer division of Series by other, using arithmetic operator //.

Equivalent to Series.floordiv(other).

Examples:

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

You can divide by a scalar:

>>> s = bpd.Series([15, 30, 45])
>>> s // 2
0     7
1    15
2    22
dtype: Int64

You can also divide by another DataFrame:

>>> divisor = bpd.Series([3, 4, 4])
>>> s // divisor
0     5
1     7
2    11
dtype: Int64

Parameter
Name	Description
`other`	`scalar or Series` Object to divide the Series by.

Returns
Type	Description
`Series`	The result of the integer divison.

getitem

__getitem__(indexer)

Gets the specified index from the Series.

Examples:

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

>>> s = bpd.Series([15, 30, 45])
>>> s[1]
np.int64(30)
>>> s[0:2]
0    15
1    30
dtype: Int64

Parameter
Name	Description
`indexer`	`int or slice` Index or slice of indices.

Returns
Type	Description
`Series or Value`	Value(s) at the requested index(es).

invert

__invert__() -> bigframes.series.Series

Returns the logical inversion (binary NOT) of the Series, element-wise using operator ````.

Examples:

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

>>> ser = bpd.Series([True, False, True])
>>> `ser`
0    False
1     True
2    False
dtype: boolean

Returns
Type	Description
`Series`	The inverted values in the series.

len

__len__()

Returns number of values in the Series, serves len operator.

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> len(s)
3

matmul

__matmul__(other)

Matrix multiplication using binary @ operator.

mod

__mod__(other) -> bigframes.series.Series

Get modulo of Series with other, element-wise, using operator %.

Equivalent to Series.mod(other).

Examples:

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

You can modulo with a scalar:

>>> s = bpd.Series([1, 2, 3])
>>> s % 3
0    1
1    2
2    0
dtype: Int64

You can also modulo with another Series:

>>> modulo = bpd.Series([3, 3, 3])
>>> s % modulo
0    1
1    2
2    0
dtype: Int64

Parameter
Name	Description
`other`	`scalar or Series` Object to modulo the Series by.

Returns
Type	Description
`Series`	The result of the modulo.

mul

__mul__(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Get multiplication of Series with other, element-wise, using operator *.

Equivalent to Series.mul(other).

Examples:

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

You can multiply with a scalar:

>>> s = bpd.Series([1, 2, 3])
>>> s * 3
0    3
1    6
2    9
dtype: Int64

You can also multiply with another Series:

>>> s1 = bpd.Series([2, 3, 4])
>>> s * s1
0     2
1     6
2    12
dtype: Int64

Parameter
Name	Description
`other`	`scalar or Series` Object to multiply with the Series.

Returns
Type	Description
`Series`	The result of the multiplication.

or

__or__(other: bool | int | bigframes.series.Series) -> bigframes.series.Series

Get bitwise XOR of Series and other, element-wise, using operator ^.

Examples:

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

>>> s = bpd.Series([0, 1, 2, 3])

You can operate with a scalar.

>>> s ^ 6
0    6
1    7
2    4
3    5
dtype: Int64

You can operate with another Series.

>>> s1 = bpd.Series([5, 6, 7, 8])
>>> s ^ s1
0     5
1     7
2     5
3    11
dtype: Int64

Parameter
Name	Description
`other`	`scalar or Series` Object to bitwise XOR with the Series.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

pow

__pow__(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Get exponentiation of Series with other, element-wise, using operator **.

Equivalent to Series.pow(other).

Examples:

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

You can exponentiate with a scalar:

>>> s = bpd.Series([1, 2, 3])
>>> s ** 2
0    1
1    4
2    9
dtype: Int64

You can also exponentiate with another Series:

>>> exponent = bpd.Series([3, 2, 1])
>>> s ** exponent
0    1
1    4
2    3
dtype: Int64

Parameter
Name	Description
`other`	`scalar or Series` Object to exponentiate the Series with.

Returns
Type	Description
`Series`	The result of the exponentiation.

radd

__radd__(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Get addition of Series and other, element-wise, using operator +.

Equivalent to Series.radd(other).

Parameter
Name	Description
`other`	`scalar or Series` Object to which Series should be added.

Returns
Type	Description
`Series`	The result of adding Series to `other`.

rand

__rand__(other: bool | int | bigframes.series.Series) -> bigframes.series.Series

Get bitwise AND of Series and other, element-wise, using operator &.

Examples:

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

>>> s = bpd.Series([0, 1, 2, 3])

You can operate with a scalar.

>>> s & 6
0    0
1    0
2    2
3    2
dtype: Int64

You can operate with another Series.

>>> s1 = bpd.Series([5, 6, 7, 8])
>>> s & s1
0    0
1    0
2    2
3    0
dtype: Int64

Parameter
Name	Description
`other`	`scalar or Series` Object to bitwise AND with the Series.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

rfloordiv

__rfloordiv__(
    other: float | int | bigframes.series.Series,
) -> bigframes.series.Series

Get integer division of other by Series, using arithmetic operator //.

Equivalent to Series.rfloordiv(other).

Parameter
Name	Description
`other`	`scalar or Series` Object to divide by the Series.

Returns
Type	Description
`Series`	The result of the integer division.

rmatmul

__rmatmul__(other)

Matrix multiplication using binary @ operator.

rmod

__rmod__(other) -> bigframes.series.Series

Get modulo of other with Series, element-wise, using operator %.

Equivalent to Series.rmod(other).

Parameter
Name	Description
`other`	`scalar or Series` Object to modulo by the Series.

Returns
Type	Description
`Series`	The result of the modulo.

rmul

__rmul__(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Get multiplication of other with Series, element-wise, using operator *.

Equivalent to Series.rmul(other).

Parameter
Name	Description
`other`	`scalar or Series` Object to multiply the Series with.

Returns
Type	Description
`Series`	The result of the multiplication.

ror

__ror__(other: bool | int | bigframes.series.Series) -> bigframes.series.Series

Get bitwise XOR of Series and other, element-wise, using operator ^.

Examples:

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

>>> s = bpd.Series([0, 1, 2, 3])

You can operate with a scalar.

>>> s ^ 6
0    6
1    7
2    4
3    5
dtype: Int64

You can operate with another Series.

>>> s1 = bpd.Series([5, 6, 7, 8])
>>> s ^ s1
0     5
1     7
2     5
3    11
dtype: Int64

Parameter
Name	Description
`other`	`scalar or Series` Object to bitwise XOR with the Series.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

rpow

__rpow__(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Get exponentiation of other with Series, element-wise, using operator **.

Equivalent to Series.rpow(other).

Parameter
Name	Description
`other`	`scalar or Series` Object to exponentiate with the Series.

Returns
Type	Description
`Series`	The result of the exponentiation.

rsub

__rsub__(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Get subtraction of Series from other, element-wise, using operator -.

Equivalent to Series.rsub(other).

Parameter
Name	Description
`other`	`scalar or Series` Object to subtract the Series from.

Returns
Type	Description
`Series`	The result of subtraction.

rtruediv

__rtruediv__(
    other: float | int | bigframes.series.Series,
) -> bigframes.series.Series

Get division of other by Series, element-wise, using operator /.

Equivalent to Series.rtruediv(other).

Parameter
Name	Description
`other`	`scalar or Series` Object to divide by the Series.

Returns
Type	Description
`Series`	The result of the division.

sub

__sub__(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Get subtraction of other from Series, element-wise, using operator -.

Equivalent to Series.sub(other).

Examples:

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

>>> s = bpd.Series([1.5, 2.6], index=['elk', 'moose'])
>>> s
elk      1.5
moose    2.6
dtype: Float64

You can subtract a scalar.

>>> s - 1.5
elk      0.0
moose    1.1
dtype: Float64

You can subtract another Series with index aligned.

>>> delta = bpd.Series([0.5, 1.0], index=['elk', 'moose'])
>>> s - delta
elk      1.0
moose    1.6
dtype: Float64

Adding any mis-aligned index will result in invalid values.

>>> delta = bpd.Series([0.5, 1.0], index=['moose', 'bison'])
>>> s - delta
elk      <NA>
moose     2.1
bison    <NA>
dtype: Float64

Parameter
Name	Description
`other`	`scalar or Series` Object to subtract from the Series.

Returns
Type	Description
`Series`	The result of subtraction.

truediv

__truediv__(
    other: float | int | bigframes.series.Series,
) -> bigframes.series.Series

Get division of Series by other, element-wise, using operator /.

Equivalent to Series.truediv(other).

Examples:

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

You can multiply with a scalar:

>>> s = bpd.Series([1, 2, 3])
>>> s / 2
0    0.5
1    1.0
2    1.5
dtype: Float64

You can also multiply with another Series:

>>> denominator = bpd.Series([2, 3, 4])
>>> s / denominator
0         0.5
1    0.666667
2        0.75
dtype: Float64

Parameter
Name	Description
`other`	`scalar or Series` Object to divide the Series by.

Returns
Type	Description
`Series`	The result of the division.

abs

abs() -> bigframes.series.Series

Return a Series/DataFrame with absolute numeric value of each element.

This function only applies to elements that are all numeric.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	A Series or DataFrame containing the absolute value of each element.

add

add(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return addition of Series and other, element-wise (binary operator add).

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

Examples:

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

>>> a = bpd.Series([1, 2, 3, bpd.NA])
>>> a
0       1
1       2
2       3
3    <NA>
dtype: Int64

>>> b = bpd.Series([10, 20, 30, 40])
>>> b
0     10
1     20
2     30
3     40
dtype: Int64

>>> a.add(b)
0      11
1      22
2      33
3    <NA>
dtype: Int64

You can also use the mathematical operator +:

>>> a + b
0      11
1      22
2      33
3    <NA>
dtype: Int64

Adding two Series with explicit indexes:

>>> a = bpd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
>>> b = bpd.Series([10, 20, 30, 40], index=['a', 'b', 'd', 'e'])
>>> a.add(b)
a      11
b      22
c    <NA>
d      34
e    <NA>
dtype: Int64

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

add_prefix

add_prefix(prefix: str, axis: int | str | None = None) -> bigframes.series.Series

Prefix labels with string prefix.

For Series, the row labels are prefixed. For DataFrame, the column labels are prefixed.

Parameters
Name	Description
`prefix`	`str` The string to add before each label.
`axis`	`int or str or None, default None` `{{0 or 'index', 1 or 'columns', None}}`, default None. Axis to add prefix on.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	New Series or DataFrame with updated labels.

add_suffix

add_suffix(suffix: str, axis: int | str | None = None) -> bigframes.series.Series

Suffix labels with string suffix.

For Series, the row labels are suffixed. For DataFrame, the column labels are suffixed.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	New Series or DataFrame with updated labels.

agg

agg(
    func: typing.Union[str, typing.Sequence[str]]
) -> typing.Union[typing.Any, bigframes.series.Series]

Aggregate using one or more operations over the specified axis.

Examples:

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

>>> s = bpd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: Int64

>>> s.agg('min')
np.int64(1)

>>> s.agg(['min', 'max'])
min    1
max    4
dtype: Int64

Parameter
Name	Description
`func`	`function` Function to use for aggregating the data. Accepted combinations are: string function name, list of function names, e.g. `['sum', 'mean']`.

Returns
Type	Description
`scalar or Series`	Aggregated results

aggregate

aggregate(
    func: typing.Union[str, typing.Sequence[str]]
) -> typing.Union[typing.Any, bigframes.series.Series]

Aggregate using one or more operations over the specified axis.

Examples:

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

>>> s = bpd.Series([1, 2, 3, 4])
>>> s
0    1
1    2
2    3
3    4
dtype: Int64

>>> s.agg('min')
np.int64(1)

>>> s.agg(['min', 'max'])
min    1
max    4
dtype: Int64

Parameter
Name	Description
`func`	`function` Function to use for aggregating the data. Accepted combinations are: string function name, list of function names, e.g. `['sum', 'mean']`.

Returns
Type	Description
`scalar or Series`	Aggregated results

all

all() -> bool

Return whether all elements are True, potentially over an axis.

Returns True unless there at least one element within a Series or along a DataFrame axis that is False or equivalent (e.g. zero or empty).

Returns
Type	Description
`scalar or Series`	If level is specified, then, Series is returned; otherwise, scalar is returned.

any

any() -> bool

Return whether any element is True, potentially over an axis.

Returns False unless there is at least one element within a series or along a Dataframe axis that is True or equivalent (e.g. non-zero or non-empty).

Returns
Type	Description
`scalar or Series`	If level is specified, then, Series is returned; otherwise, scalar is returned.

apply

apply(
    func, by_row: typing.Union[typing.Literal["compat"], bool] = "compat"
) -> bigframes.series.Series

Invoke function on values of a Series.

Can be ufunc (a NumPy function that applies to the entire Series) or a Python function that only works on single values. If it is an arbitrary python function then converting it into a remote_function is recommended.

Examples:

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

For applying arbitrary python function a remote_function is recommended. Let's use reuse=False flag to make sure a new remote_function is created every time we run the following code, but you can skip it to potentially reuse a previously deployed remote_function from the same user defined function.

>>> @bpd.remote_function(reuse=False)
... def minutes_to_hours(x: int) -> float:
...     return x/60

>>> minutes = bpd.Series([0, 30, 60, 90, 120])
>>> minutes
0      0
1     30
2     60
3     90
4    120
dtype: Int64

>>> hours = minutes.apply(minutes_to_hours)
>>> hours
0    0.0
1    0.5
2    1.0
3    1.5
4    2.0
dtype: Float64

To turn a user defined function with external package dependencies into a remote_function, you would provide the names of the packages via packages param.

>>> @bpd.remote_function(
...     reuse=False,
...     packages=["cryptography"],
... )
... def get_hash(input: str) -> str:
...     from cryptography.fernet import Fernet
...
...     # handle missing value
...     if input is None:
...         input = ""
...
...     key = Fernet.generate_key()
...     f = Fernet(key)
...     return f.encrypt(input.encode()).decode()

>>> names = bpd.Series(["Alice", "Bob"])
>>> hashes = names.apply(get_hash)

Simple vectorized functions, lambdas or ufuncs can be applied directly with by_row=False.

>>> nums = bpd.Series([1, 2, 3, 4])
>>> nums
0    1
1    2
2    3
3    4
dtype: Int64
>>> nums.apply(lambda x: x*x + 2*x + 1, by_row=False)
0     4
1     9
2    16
3    25
dtype: Int64

>>> def is_odd(num):
...     return num % 2 == 1
>>> nums.apply(is_odd, by_row=False)
0     True
1    False
2     True
3    False
dtype: boolean

>>> nums.apply(np.log, by_row=False)
0         0.0
1    0.693147
2    1.098612
3    1.386294
dtype: Float64

Parameters
Name	Description
`func`	`function` BigFrames DataFrames `remote_function` to apply. The function should take a scalar and return a scalar. It will be applied to every element in the `Series`.
`by_row`	`False or "compat", default "compat"` If `"compat"` , func must be a remote function which will be passed each element of the Series, like `Series.map`. If False, the func will be passed the whole Series at once.

Returns
Type	Description
`bigframes.series.Series`	A new Series with values representing the return value of the `func` applied to each element of the original Series.

argmax

argmax() -> 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

>>> s.argmax()
np.int64(2)

>>> s.argmin()
np.int64(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
`Series`	Row position of the maximum value.

argmin

argmin() -> 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:

>>> 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

>>> s.argmax()
np.int64(2)

>>> s.argmin()
np.int64(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
`Series`	Row position of the minimum value.

astype

astype(
    dtype: typing.Union[
        typing.Literal[
            "boolean",
            "Float64",
            "Int64",
            "int64[pyarrow]",
            "string",
            "string[pyarrow]",
            "timestamp[us, tz=UTC][pyarrow]",
            "timestamp[us][pyarrow]",
            "date32[day][pyarrow]",
            "time64[us][pyarrow]",
            "decimal128(38, 9)[pyarrow]",
            "decimal256(76, 38)[pyarrow]",
            "binary[pyarrow]",
        ],
        pandas.core.arrays.boolean.BooleanDtype,
        pandas.core.arrays.floating.Float64Dtype,
        pandas.core.arrays.integer.Int64Dtype,
        pandas.core.arrays.string_.StringDtype,
        pandas.core.dtypes.dtypes.ArrowDtype,
        geopandas.array.GeometryDtype,
    ]
) -> bigframes.series.Series

Cast a pandas object to a specified dtype dtype.

Examples:

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

Create a DataFrame:

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> df = bpd.DataFrame(data=d)
>>> df.dtypes
col1    Int64
col2    Int64
dtype: object

Cast all columns to Float64:

>>> df.astype('Float64').dtypes
col1    Float64
col2    Float64
dtype: object

Create a series of type Int64:

>>> ser = bpd.Series([2023010000246789, 1624123244123101, 1054834234120101], dtype='Int64')
>>> ser
0    2023010000246789
1    1624123244123101
2    1054834234120101
dtype: Int64

Convert to Float64 type:

>>> ser.astype('Float64')
0    2023010000246789.0
1    1624123244123101.0
2    1054834234120101.0
dtype: Float64

Convert to pd.ArrowDtype(pa.timestamp("us", tz="UTC")) type:

>>> ser.astype("timestamp[us, tz=UTC][pyarrow]")
0    2034-02-08 11:13:20.246789+00:00
1    2021-06-19 17:20:44.123101+00:00
2    2003-06-05 17:30:34.120101+00:00
dtype: timestamp[us, tz=UTC][pyarrow]

Note that this is equivalent of using to_datetime with unit='us':

>>> bpd.to_datetime(ser, unit='us', utc=True)
0    2034-02-08 11:13:20.246789+00:00
1    2021-06-19 17:20:44.123101+00:00
2    2003-06-05 17:30:34.120101+00:00
dtype: timestamp[us, tz=UTC][pyarrow]

Convert pd.ArrowDtype(pa.timestamp("us", tz="UTC")) type to Int64 type:

>>> timestamp_ser = ser.astype("timestamp[us, tz=UTC][pyarrow]")
>>> timestamp_ser.astype('Int64')
0    2023010000246789
1    1624123244123101
2    1054834234120101
dtype: Int64

Parameter

Name Description

dtype

str 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")).

Returns
Type	Description
`bigframes.pandas.DataFrame`	A BigQuery DataFrame.

autocorr

autocorr(lag: int = 1) -> float

Compute the lag-N autocorrelation.

This method computes the Pearson correlation between the Series and its shifted self.

Examples:

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

>>> s = bpd.Series([0.25, 0.5, 0.2, -0.05])
>>> s.autocorr()  # doctest: +ELLIPSIS
np.float64(0.10355263309024067)
>>> s.autocorr(lag=2)
np.float64(-1.0)

If the Pearson correlation is not well defined, then 'NaN' is returned.

>>> s = bpd.Series([1, 0, 0, 0])
>>> s.autocorr()
np.float64(nan)

Parameter
Name	Description
`lag`	`int, default 1` Number of lags to apply before performing autocorrelation.

Returns
Type	Description
`float`	The Pearson correlation between self and self.shift(lag).

between

between(left, right, inclusive="both")

Return boolean Series equivalent to left <= series <= right.

This function returns a boolean vector containing True wherever the corresponding Series element is between the boundary values left and right. NA values are treated as False.

Examples:

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

Boundary values are included by default:

>>> s = bpd.Series([2, 0, 4, 8, np.nan])
>>> s.between(1, 4)
0     True
1    False
2     True
3    False
4     <NA>
dtype: boolean

With inclusive set to "neither" boundary values are excluded:

>>> s.between(1, 4, inclusive="neither")
0     True
1    False
2    False
3    False
4     <NA>
dtype: boolean

left and right can be any scalar value:

>>> s = bpd.Series(['Alice', 'Bob', 'Carol', 'Eve'])
>>> s.between('Anna', 'Daniel')
0    False
1     True
2     True
3    False
dtype: boolean

Parameters
Name	Description
`left`	`scalar or list-like` Left boundary.
`right`	`scalar or list-like` Right boundary.
`inclusive`	`{"both", "neither", "left", "right"}` Include boundaries. Whether to set each bound as closed or open.

Returns
Type	Description
`Series`	Series representing whether each element is between left and right (inclusive).

bfill

bfill(*, limit: typing.Optional[int] = None) -> bigframes.series.Series

Fill NA/NaN values by using the next valid observation to fill the gap.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series or None`	Object with missing values filled.

cache

cache()

Materializes the Series to a temporary table.

Useful if the series will be used multiple times, as this will avoid recomputating the shared intermediate value.

Returns
Type	Description
`Series`	Self

case_when

case_when(caselist) -> bigframes.series.Series

Replace values where the conditions are True.

Examples:

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

>>> c = bpd.Series([6, 7, 8, 9], name="c")
>>> a = bpd.Series([0, 0, 1, 2])
>>> b = bpd.Series([0, 3, 4, 5])

>>> c.case_when(
...     caselist=[
...         (a.gt(0), a),  # condition, replacement
...         (b.gt(0), b),
...     ]
... )
0    6
1    3
2    1
3    2
Name: c, dtype: Int64

See also:

bigframes.series.Series.mask : Replace values where the condition is True.

clip

clip(lower, upper)

Trim values at input threshold(s).

Assigns values outside boundary to boundary values. Thresholds can be singular values or array like, and in the latter case the clipping is performed element-wise in the specified axis.

Parameters
Name	Description
`lower`	`float or array-like, default None` Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.
`upper`	`float or array-like, default None` Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.

Returns
Type	Description
`Series`	Series.

combine

combine(other, func) -> bigframes.series.Series

Combine the Series with a Series or scalar according to func.

Combine the Series and other using func to perform elementwise selection for combined Series. fill_value is assumed when value is missing at some index from one of the two objects being combined.

Examples:

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

Consider 2 Datasets `s1` and `s2` containing
highest clocked speeds of different birds.

>>> s1 = bpd.Series({'falcon': 330.0, 'eagle': 160.0})
>>> s1
falcon    330.0
eagle     160.0
dtype: Float64
>>> s2 = bpd.Series({'falcon': 345.0, 'eagle': 200.0, 'duck': 30.0})
>>> s2
falcon    345.0
eagle     200.0
duck       30.0
dtype: Float64

Now, to combine the two datasets and view the highest speeds
of the birds across the two datasets

>>> s1.combine(s2, np.maximum)
falcon    345.0
eagle     200.0
duck       <NA>
dtype: Float64

Parameters
Name	Description
`other`	`Series or scalar` The value(s) to be combined with the `Series`.
`func`	`function` BigFrames DataFrames `remote_function` to apply. Takes two scalars as inputs and returns an element. Also accepts some numpy binary functions.

Returns
Type	Description
`Series`	The result of combining the Series with the other object.

combine_first

combine_first(other: bigframes.series.Series) -> bigframes.series.Series

Update null elements with value in the same location in 'other'.

Combine two Series objects by filling null values in one Series with non-null values from the other Series. Result index will be the union of the two indexes.

Examples:

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

>>> s1 = bpd.Series([1, np.nan])
>>> s2 = bpd.Series([3, 4, 5])
>>> s1.combine_first(s2)
0    1.0
1    4.0
2    5.0
dtype: Float64

Null values still persist if the location of that null value
does not exist in `other`

>>> s1 = bpd.Series({'falcon': np.nan, 'eagle': 160.0})
>>> s2 = bpd.Series({'eagle': 200.0, 'duck': 30.0})
>>> s1.combine_first(s2)
falcon     <NA>
eagle     160.0
duck       30.0
dtype: Float64

Parameter
Name	Description
`other`	`Series` The value(s) to be used for filling null values.

Returns
Type	Description
`Series`	The result of combining the provided Series with the other object.

copy

copy() -> bigframes.series.Series

Make a copy of this object's indices and data.

A new object will be created with a copy of the calling object's data and indices. Modifications to the data or indices of the copy will not be reflected in the original object.

Examples:

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

Modification in the original Series will not affect the copy Series:

>>> s = bpd.Series([1, 2], index=["a", "b"])
>>> s
a    1
b    2
dtype: Int64

>>> s_copy = s.copy()
>>> s_copy
a    1
b    2
dtype: Int64

>>> s.loc['b'] = 22
>>> s
a     1
b    22
dtype: Int64
>>> s_copy
a    1
b    2
dtype: Int64

Modification in the original DataFrame will not affect the copy DataFrame:

>>> df = bpd.DataFrame({'a': [1, 3], 'b': [2, 4]})
>>> df
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

>>> df_copy = df.copy()
>>> df_copy
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

>>> df.loc[df["b"] == 2, "b"] = 22
>>> df
   a   b
0  1  22
1  3   4
<BLANKLINE>
[2 rows x 2 columns]
>>> df_copy
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Object type matches caller.

corr

corr(other: bigframes.series.Series, method="pearson", min_periods=None) -> float

Compute the correlation with the other Series. Non-number values are ignored in the computation.

Uses the "Pearson" method of correlation. Numbers are converted to float before calculation, so the result may be unstable.

Examples:

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

>>> s1 = bpd.Series([.2, .0, .6, .2])
>>> s2 = bpd.Series([.3, .6, .0, .1])
>>> s1.corr(s2)
np.float64(-0.8510644963469901)

>>> s1 = bpd.Series([1, 2, 3], index=[0, 1, 2])
>>> s2 = bpd.Series([1, 2, 3], index=[2, 1, 0])
>>> s1.corr(s2)
np.float64(-1.0)

Parameters
Name	Description
`other`	`Series` The series with which this is to be correlated.
`method`	`string, default "pearson"` Correlation method to use - currently only "pearson" is supported.
`min_periods`	`int, default None` The minimum number of observations needed to return a result. Non-default values are not yet supported, so a result will be returned for at least two observations.

Returns
Type	Description
`float`	Will return NaN if there are fewer than two numeric pairs, either series has a variance or covariance of zero, or any input value is infinite.

count

count() -> int

Return number of non-NA/null observations in the Series.

Examples:

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

>>> s = bpd.Series([0.0, 1.0, bpd.NA])
>>> s
0     0.0
1     1.0
2    <NA>
dtype: Float64
>>> s.count()
np.int64(2)

Returns
Type	Description
`int or Series (if level specified)`	Number of non-null values in the Series.

cov

cov(other: bigframes.series.Series) -> float

Compute covariance with Series, excluding missing values.

The two Series objects are not required to be the same length and will be aligned internally before the covariance is calculated.

Parameter
Name	Description
`other`	`Series` Series with which to compute the covariance.

Returns
Type	Description
`float`	Covariance between Series and other normalized by N-1 (unbiased estimator).

cummax

cummax() -> bigframes.series.Series

Return cumulative maximum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative maximum.

Parameter
Name	Description
`axis`	`{{0 or 'index', 1 or 'columns'}}, default 0` The index or the name of the axis. 0 is equivalent to None or 'index'. For `Series` this parameter is unused and defaults to 0.

Returns
Type	Description
`bigframes.series.Series`	Return cumulative maximum of scalar or Series.

cummin

cummin() -> bigframes.series.Series

Return cumulative minimum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative minimum.

Parameters
Name	Description
`axis`	`{0 or 'index', 1 or 'columns'}, default 0` The index or the name of the axis. 0 is equivalent to None or 'index'. For `Series` this parameter is unused and defaults to 0.
`skipna`	`bool, default True` Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
Type	Description
`bigframes.series.Series`	Return cumulative minimum of scalar or Series.

cumprod

cumprod() -> bigframes.series.Series

Return cumulative product over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative product.

Examples:

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

>>> s = bpd.Series([2, np.nan, 5, -1, 0])
>>> s
0     2.0
1    <NA>
2     5.0
3    -1.0
4     0.0
dtype: Float64

By default, NA values are ignored.

>>> s.cumprod()
0     2.0
1    <NA>
2    10.0
3   -10.0
4     0.0
dtype: Float64

Returns
Type	Description
`bigframes.series.Series`	Return cumulative sum of scalar or Series.

cumsum

cumsum() -> bigframes.series.Series

Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

Parameter
Name	Description
`axis`	`{0 or 'index', 1 or 'columns'}, default 0` The index or the name of the axis. 0 is equivalent to None or 'index'. For `Series` this parameter is unused and defaults to 0.

Returns
Type	Description
`scalar or Series`	Return cumulative sum of scalar or Series.

diff

diff(periods: int = 1) -> bigframes.series.Series

First discrete difference of element.

Calculates the difference of a {klass} element compared with another element in the {klass} (default is element in previous row).

Parameter
Name	Description
`periods`	`int, default 1` Periods to shift for calculating difference, accepts negative values.

Returns
Type	Description
`Series`	First differences of the Series.

div

div(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return floating division of Series and other, element-wise (binary operator truediv).

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

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

divide

divide(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return floating division of Series and other, element-wise (binary operator truediv).

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

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

divmod

divmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]

Return integer division and modulo of Series and other, element-wise (binary operator divmod).

Equivalent to divmod(series, other).

Returns
Type	Description
`2-Tuple of Series`	The result of the operation. The result is always consistent with (floordiv, mod) (though pandas may not).

dot

dot(other)

Compute the dot product between the Series and the columns of other.

This method computes the dot product between the Series and another one, or the Series and each columns of a DataFrame, or the Series and each columns of an array.

It can also be called using self @ other in Python >= 3.5.

Examples:

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

>>> s = bpd.Series([0, 1, 2, 3])
>>> other = bpd.Series([-1, 2, -3, 4])
>>> s.dot(other)
np.int64(8)

You can also use the operator @ for the dot product:

>>> s @ other
np.int64(8)

Parameter
Name	Description
`other`	`Series` The other object to compute the dot product with its columns.

Returns
Type	Description
`scalar, Series or numpy.ndarray`	Return the dot product of the Series and other if other is a Series, the Series of the dot product of Series and each rows of other if other is a DataFrame or a numpy.ndarray between the Series and each columns of the numpy array.

drop

drop(
    labels: typing.Any = None,
    *,
    axis: typing.Union[int, str] = 0,
    index: typing.Any = None,
    columns: typing.Union[typing.Hashable, typing.Iterable[typing.Hashable]] = None,
    level: typing.Optional[typing.Union[str, int]] = None
) -> bigframes.series.Series

Return Series with specified index labels removed.

Remove elements of a Series based on specifying the index labels. When using a multi-index, labels on different levels can be removed by specifying the level.

Examples:

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

>>> s = bpd.Series(data=np.arange(3), index=['A', 'B', 'C'])
>>> s
A    0
B    1
C    2
dtype: Int64

Drop labels B and C:

>>> s.drop(labels=['B', 'C'])
A    0
dtype: Int64

Drop 2nd level label in MultiIndex Series:

>>> import pandas as pd
>>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])

>>> s = bpd.Series([45, 200, 1.2, 30, 250, 1.5, 320, 1, 0.3],
...               index=midx)
>>> s
llama   speed      45.0
        weight    200.0
        length      1.2
cow     speed      30.0
        weight    250.0
        length      1.5
falcon  speed     320.0
        weight      1.0
        length      0.3
dtype: Float64

>>> s.drop(labels='weight', level=1)
llama   speed      45.0
        length      1.2
cow     speed      30.0
        length      1.5
falcon  speed     320.0
        length      0.3
dtype: Float64

Parameter
Name	Description
`labels`	`single label or list-like` Index labels to drop.

Exceptions
Type	Description
`KeyError`	If none of the labels are found in the index.

Returns
Type	Description
`bigframes.series.Series`	Series with specified index labels removed or None if `inplace=True`.

drop_duplicates

drop_duplicates(*, keep: str = "first") -> bigframes.series.Series

Return Series with duplicate values removed.

Parameter
Name	Description
`keep`	`{'first', 'last', False}, default 'first'` Method to handle dropping duplicates: 'first' : Drop duplicates except for the first occurrence. 'last' : Drop duplicates except for the last occurrence. `False` : Drop all duplicates.

Returns
Type	Description
`bigframes.series.Series`	Series with duplicates dropped or None if `inplace=True`.

droplevel

droplevel(
    level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]],
    axis: int | str = 0,
)

Return Series with requested index / column level(s) removed.

Parameters
Name	Description
`level`	`int, str, or list-like` If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.
`axis`	`{0 or 'index', 1 or 'columns'}, default 0` For `Series` this parameter is unused and defaults to 0.

dropna

dropna(
    *,
    axis: int = 0,
    inplace: bool = False,
    how: typing.Optional[str] = None,
    ignore_index: bool = False
) -> bigframes.series.Series

Return a new Series with missing values removed.

Examples:

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

Drop NA values from a Series:

>>> ser = bpd.Series([1., 2., np.nan])
>>> ser
0     1.0
1     2.0
2    <NA>
dtype: Float64

>>> ser.dropna()
0    1.0
1    2.0
dtype: Float64

Empty strings are not considered NA values. None is considered an NA value.

>>> ser = bpd.Series(['2', bpd.NA, '', None, 'I stay'], dtype='object')
>>> ser
0         2
1      <NA>
2
3      <NA>
4    I stay
dtype: string

>>> ser.dropna()
0         2
2
4    I stay
dtype: string

Parameters
Name	Description
`axis`	`0 or 'index'` Unused. Parameter needed for compatibility with DataFrame.
`inplace`	`bool, default False` Unsupported, do not set.
`how`	`str, optional` Not in use. Kept for compatibility.

Returns
Type	Description
`Series`	Series with NA entries dropped from it.

duplicated

duplicated(keep: str = "first") -> bigframes.series.Series

Indicate duplicate Series values.

Duplicated values are indicated as True values in the resulting Series. Either all duplicates, all except the first or all except the last occurrence of duplicates can be indicated.

Parameter
Name	Description
`keep`	`{'first', 'last', False}, default 'first'` Method to handle dropping duplicates: '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`.

Returns
Type	Description
`bigframes.series.Series`	Series indicating whether each value has occurred in the preceding values.

eq

eq(other: object) -> bigframes.series.Series

Return equal of Series and other, element-wise (binary operator eq).

Equivalent to other == series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`Series`	The result of the operation.

equals

equals(
    other: typing.Union[bigframes.series.Series, bigframes.dataframe.DataFrame]
) -> bool

Test whether two objects contain the same elements.

This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.

The row/column index do not need to have the same type, as long as the values are considered equal. Corresponding columns must be of the same dtype.

Parameter
Name	Description
`other`	`Series or DataFrame` The other Series or DataFrame to be compared with the first.

Returns
Type	Description
`bool`	True if all elements are the same in both objects, False otherwise.

expanding

expanding(min_periods: int = 1) -> bigframes.core.window.Window

Provide expanding window calculations.

Parameter
Name	Description
`min_periods`	`int, default 1` Minimum number of observations in window required to have a value; otherwise, result is `np.nan`.

Returns
Type	Description
`bigframes.core.window.Window`	`Expanding` subclass.

explode

explode(*, ignore_index: typing.Optional[bool] = False) -> bigframes.series.Series

Transform each element of a list-like to a row.

Examples:

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

>>> s = bpd.Series([[1, 2, 3], [], [3, 4]])
>>> s.explode()
0       1
0       2
0       3
1    <NA>
2       3
2       4
dtype: Int64

Parameter
Name	Description
`ignore_index`	`bool, default False` If True, the resulting index will be labeled 0, 1, …, n - 1.

Returns
Type	Description
`bigframes.series.Series`	Exploded lists to rows; index will be duplicated for these rows.

ffill

ffill(*, limit: typing.Optional[int] = None) -> bigframes.series.Series

Fill NA/NaN values by propagating the last valid observation to next valid.

Examples:

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

>>> df = bpd.DataFrame([[np.nan, 2, np.nan, 0],
...                     [3, 4, np.nan, 1],
...                     [np.nan, np.nan, np.nan, np.nan],
...                     [np.nan, 3, np.nan, 4]],
...                    columns=list("ABCD")).astype("Float64")
>>> df
      A     B     C     D
0  <NA>   2.0  <NA>   0.0
1   3.0   4.0  <NA>   1.0
2  <NA>  <NA>  <NA>  <NA>
3  <NA>   3.0  <NA>   4.0
<BLANKLINE>
[4 rows x 4 columns]

Fill NA/NaN values in DataFrames:

>>> df.ffill()
      A    B     C    D
0  <NA>  2.0  <NA>  0.0
1   3.0  4.0  <NA>  1.0
2   3.0  4.0  <NA>  1.0
3   3.0  3.0  <NA>  4.0
<BLANKLINE>
[4 rows x 4 columns]

Fill NA/NaN values in Series:

>>> series = bpd.Series([1, np.nan, 2, 3])
>>> series.ffill()
0    1.0
1    1.0
2    2.0
3    3.0
dtype: Float64

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series or None`	Object with missing values filled.

fillna

fillna(value=None) -> bigframes.series.Series

Fill NA/NaN values using the specified method.

Examples:

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

>>> s = bpd.Series([np.nan, 2, np.nan, -1])
>>> s
0    <NA>
1     2.0
2    <NA>
3    -1.0
dtype: Float64

Replace all NA elements with 0s.

>>> s.fillna(0)
0    0.0
1    2.0
2    0.0
3   -1.0
dtype: Float64

You can use fill values from another Series:

>>> s_fill = bpd.Series([11, 22, 33])
>>> s.fillna(s_fill)
0    11.0
1     2.0
2    33.0
3    -1.0
dtype: Float64

Parameter
Name	Description
`value`	`scalar, dict, Series, or DataFrame, default None` Value to use to fill holes (e.g. 0).

Returns
Type	Description
`Series or None`	Object with missing values filled or None.

filter

filter(
    items: typing.Optional[typing.Iterable] = None,
    like: typing.Optional[str] = None,
    regex: typing.Optional[str] = None,
    axis: typing.Optional[typing.Union[str, int]] = None,
) -> bigframes.series.Series

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

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

Parameters
Name	Description
`items`	`list-like` Keep labels from axis which are in items.
`like`	`str` Keep labels from axis for which "like in label == True".
`regex`	`str (regular expression)` Keep labels from axis for which re.search(regex, label) == True.
`axis`	`{0 or 'index', 1 or 'columns', None}, default None` The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, 'columns' for DataFrame. For `Series` this parameter is unused and defaults to `None`.

Exceptions
Type	Description
`ValueError`	If value provided is not exactly one of `items`, `like`, or `regex`.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Same type as input object.

floordiv

floordiv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return integer division of Series and other, element-wise (binary operator floordiv).

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

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

ge

ge(other) -> bigframes.series.Series

Get 'greater than or equal to' of Series and other, element-wise (binary operator >=).

Equivalent to series >= other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

groupby

groupby(
    by: typing.Union[
        typing.Hashable,
        bigframes.series.Series,
        typing.Sequence[typing.Union[typing.Hashable, bigframes.series.Series]],
    ] = None,
    axis=0,
    level: typing.Optional[
        typing.Union[int, str, typing.Sequence[int], typing.Sequence[str]]
    ] = None,
    as_index: bool = True,
    *,
    dropna: bool = True
) -> bigframes.core.groupby.SeriesGroupBy

Group Series using a mapper or by a Series of columns.

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

Examples:

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

You can group by a named index level.

>>> s = bpd.Series([380, 370., 24., 26.],
...                index=["Falcon", "Falcon", "Parrot", "Parrot"],
...                name="Max Speed")
>>> s.index.name="Animal"
>>> s
Animal
Falcon    380.0
Falcon    370.0
Parrot     24.0
Parrot     26.0
Name: Max Speed, dtype: Float64
>>> s.groupby("Animal").mean()
Animal
Falcon    375.0
Parrot     25.0
Name: Max Speed, dtype: Float64

You can also group by more than one index levels.

>>> import pandas as pd
>>> s = bpd.Series([380, 370., 24., 26.],
...                index=pd.MultiIndex.from_tuples(
...                    [("Falcon", "Clear"),
...                     ("Falcon", "Cloudy"),
...                     ("Parrot", "Clear"),
...                     ("Parrot", "Clear")],
...                    names=["Animal", "Sky"]),
...                name="Max Speed")
>>> s
Animal    Sky
Falcon  Clear     380.0
        Cloudy    370.0
Parrot  Clear      24.0
        Clear      26.0
Name: Max Speed, dtype: Float64

>>> s.groupby("Animal").mean()
Animal
Falcon    375.0
Parrot     25.0
Name: Max Speed, dtype: Float64

>>> s.groupby("Sky").mean()
Sky
Clear     143.333333
Cloudy         370.0
Name: Max Speed, dtype: Float64

>>> s.groupby(["Animal", "Sky"]).mean()
Animal  Sky
Falcon  Clear     380.0
        Cloudy    370.0
Parrot  Clear      25.0
Name: Max Speed, dtype: Float64

You can also group by values in a Series provided the index matches with the original series.

>>> df = bpd.DataFrame({'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'],
...                     'Max Speed': [380., 370., 24., 26.],
...                     'Age': [10., 20., 4., 6.]})
>>> df
Animal  Max Speed   Age
0  Falcon      380.0  10.0
1  Falcon      370.0  20.0
2  Parrot       24.0   4.0
3  Parrot       26.0   6.0
<BLANKLINE>
[4 rows x 3 columns]

>>> df['Max Speed'].groupby(df['Animal']).mean()
Animal
Falcon    375.0
Parrot     25.0
Name: Max Speed, dtype: Float64

>>> df['Age'].groupby(df['Animal']).max()
Animal
Falcon    20.0
Parrot     6.0
Name: Age, dtype: Float64

Parameters
Name	Description
`by`	`mapping, function, label, pd.Grouper or list of such, default None` 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 a list or ndarray of length equal to the selected axis is passed (see the `groupby user guide https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#splitting-an-object-into-groups`_), the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in `self`. Notice that a tuple is interpreted as a (single) key.
`axis`	`{0 or 'index', 1 or 'columns'}, default 0` Split along rows (0) or columns (1). For `Series` this parameter is unused and defaults to 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. Do not specify both `by` and `level`.
`as_index`	`bool, default True` Return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output. This argument has no effect on filtrations (see the "filtrations in the user guide" `https://pandas.pydata.org/docs/dev/user_guide/groupby.html#filtration`), such as `head()`, `tail()`, `nth()` and in transformations (see the "transformations in the user guide" `https://pandas.pydata.org/docs/dev/user_guide/groupby.html#transformation`).

Returns
Type	Description
`bigframes.core.groupby.SeriesGroupBy`	Returns a groupby object that contains information about the groups.

gt

gt(other) -> bigframes.series.Series

Get 'less than or equal to' of Series and other, element-wise (binary operator <=).

Equivalent to series <= other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

head

head(n: int = 5) -> bigframes.series.Series

Return the first n rows.

This function returns the first n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it.

For negative values of n, this function returns all rows except the last |n| rows, equivalent to df[:n].

If n is larger than the number of rows, this function returns all rows.

Examples:

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

>>> df = bpd.DataFrame({'animal': ['alligator', 'bee', 'falcon', 'lion',
...                     'monkey', 'parrot', 'shark', 'whale', 'zebra']})
>>> df
    animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
6      shark
7      whale
8      zebra
<BLANKLINE>
[9 rows x 1 columns]

Viewing the first 5 lines:

>>> df.head()
    animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
<BLANKLINE>
[5 rows x 1 columns]

Viewing the first n lines (three in this case):

>>> df.head(3)
    animal
0  alligator
1        bee
2     falcon
<BLANKLINE>
[3 rows x 1 columns]

For negative values of n:

>>> df.head(-3)
    animal
0  alligator
1        bee
2     falcon
3       lion
4     monkey
5     parrot
<BLANKLINE>
[6 rows x 1 columns]

Parameter
Name	Description
`n`	`int, default 5` Default 5. Number of rows to select.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	The first `n` rows of the caller object.

idxmax

idxmax() -> typing.Hashable

Return the row label of the maximum value.

If multiple values equal the maximum, the first row label with that value is returned.

Returns
Type	Description
`Index`	Label of the maximum value.

idxmin

idxmin() -> typing.Hashable

Return the row label of the minimum value.

If multiple values equal the minimum, the first row label with that value is returned.

Returns
Type	Description
`Index`	Label of the minimum value.

interpolate

interpolate(method: str = "linear") -> bigframes.series.Series

Fill NaN values using an interpolation method.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3, None, None, 6],
...     'B': [None, 6, None, 2, None, 3],
...     }, index=[0, 0.1, 0.3, 0.7, 0.9, 1.0])
>>> df.interpolate()
       A     B
0.0  1.0  <NA>
0.1  2.0   6.0
0.3  3.0   4.0
0.7  4.0   2.0
0.9  5.0   2.5
1.0  6.0   3.0
<BLANKLINE>
[6 rows x 2 columns]
>>> df.interpolate(method="values")
            A         B
0.0       1.0      <NA>
0.1       2.0       6.0
0.3       3.0  4.666667
0.7  4.714286       2.0
0.9  5.571429  2.666667
1.0       6.0       3.0
<BLANKLINE>
[6 rows x 2 columns]

Parameter
Name	Description
`method`	`str, default 'linear'` Interpolation technique to use. Only 'linear' supported. 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes. 'index', 'values': use the actual numerical values of the index. 'pad': Fill in NaNs using existing values. 'nearest', 'zero', 'slinear': Emulates `scipy.interpolate.interp1d`

Returns
Type	Description
`Series`	Returns the same object type as the caller, interpolated at some or all `NaN` values

isin

isin(values) -> "Series" | None

Whether elements in Series are contained in values.

Return a boolean Series showing whether each element in the Series matches an element in the passed sequence of values exactly.

Examples:

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

>>> s = bpd.Series(['llama', 'cow', 'llama', 'beetle', 'llama',
...                 'hippo'], name='animal')
>>> s
0     llama
1       cow
2     llama
3    beetle
4     llama
5     hippo
Name: animal, dtype: string

>>> s.isin(['cow', 'llama'])
0     True
1     True
2     True
3    False
4     True
5    False
Name: animal, dtype: boolean

Strings and integers are distinct and are therefore not comparable:

>>> bpd.Series([1]).isin(['1'])
0    False
dtype: boolean
>>> bpd.Series([1.1]).isin(['1.1'])
0    False
dtype: boolean

Parameter
Name	Description
`values`	`list-like` The sequence of values to test. Passing in a single string will raise a TypeError. Instead, turn a single string into a list of one element.

Exceptions
Type	Description
`TypeError`	If input is not list-like.

Returns
Type	Description
`bigframes.series.Series`	Series of booleans indicating if each element is in values.

isna

isna() -> bigframes.series.Series

Detect missing values.

Return a boolean same-sized object indicating if the values are NA. NA values get mapped to True values. Everything else gets mapped to False values. Characters such as empty strings '' or numpy.inf are not considered NA values.

Examples:

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

>>> df = bpd.DataFrame(dict(
...         age=[5, 6, np.nan],
...         born=[bpd.NA, "1940-04-25", "1940-04-25"],
...         name=['Alfred', 'Batman', ''],
...         toy=[None, 'Batmobile', 'Joker'],
... ))
>>> df
    age        born    name        toy
0   5.0        <NA>  Alfred       <NA>
1   6.0  1940-04-25  Batman  Batmobile
2  <NA>  1940-04-25              Joker
<BLANKLINE>
[3 rows x 4 columns]

Show which entries in a DataFrame are NA:

>>> df.isna()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

>>> df.isnull()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

Show which entries in a Series are NA:

>>> ser = bpd.Series([5, None, 6, np.nan, bpd.NA])
>>> ser
0       5
1    <NA>
2       6
3    <NA>
4    <NA>
dtype: Int64

>>> ser.isna()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

>>> ser.isnull()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Mask of bool values for each element that indicates whether an element is an NA value.

isnull

isnull() -> bigframes.series.Series

Detect missing values.

Examples:

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

>>> df = bpd.DataFrame(dict(
...         age=[5, 6, np.nan],
...         born=[bpd.NA, "1940-04-25", "1940-04-25"],
...         name=['Alfred', 'Batman', ''],
...         toy=[None, 'Batmobile', 'Joker'],
... ))
>>> df
    age        born    name        toy
0   5.0        <NA>  Alfred       <NA>
1   6.0  1940-04-25  Batman  Batmobile
2  <NA>  1940-04-25              Joker
<BLANKLINE>
[3 rows x 4 columns]

Show which entries in a DataFrame are NA:

>>> df.isna()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

>>> df.isnull()
    age   born   name    toy
0  False   True  False   True
1  False  False  False  False
2   True  False  False  False
<BLANKLINE>
[3 rows x 4 columns]

Show which entries in a Series are NA:

>>> ser = bpd.Series([5, None, 6, np.nan, bpd.NA])
>>> ser
0       5
1    <NA>
2       6
3    <NA>
4    <NA>
dtype: Int64

>>> ser.isna()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

>>> ser.isnull()
0    False
1     True
2    False
3     True
4     True
dtype: boolean

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Mask of bool values for each element that indicates whether an element is an NA value.

items

items()

Iterate over (index, value) pairs of a Series.

Iterates over the Series contents, returning a tuple with the index and the value of a Series.

Examples:

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

>>> s = bpd.Series(['bear', 'bear', 'marsupial'],
...                    index=['panda', 'polar', 'koala'])
>>> s
panda       bear
polar       bear
koala  marsupial
dtype: string

>>> for index, value in s.items():
...     print(f'--> index: {index}')
...     print(f'--> value: {value}')
...
--> index: panda
--> value: bear
--> index: polar
--> value: bear
--> index: koala
--> value: marsupial

Returns
Type	Description
`Iterator`	Iterator of index, value for each content of the Series.

kurt

kurt()

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Returns
Type	Description
`scalar or scalar`	Unbiased kurtosis over requested axis.

kurtosis

kurtosis()

Return unbiased kurtosis over requested axis.

Kurtosis obtained using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Returns
Type	Description
`scalar or scalar`	Unbiased kurtosis over requested axis.

le

le(other) -> bigframes.series.Series

Get 'less than or equal to' of Series and other, element-wise (binary operator <=).

Equivalent to series <= other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the comparison.

lt

lt(other) -> bigframes.series.Series

Get 'less than' of Series and other, element-wise (binary operator <).

Equivalent to series < other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

map

map(
    arg: typing.Union[typing.Mapping, bigframes.series.Series],
    na_action: typing.Optional[str] = None,
    *,
    verify_integrity: bool = False
) -> bigframes.series.Series

Map values of Series according to an input mapping or function.

Used for substituting each value in a Series with another value, that may be derived from a remote function, dict, or a Series.

If arg is a remote function, the overhead for remote functions applies. If mapping with a dict, fully deferred computation is possible. If mapping with a Series, fully deferred computation is only possible if verify_integrity=False.

Note: Bigframes does not yet support dict subclasses that define __missing__ (i.e. provide a method for default values). These are treated the same as dict.

Examples:

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

>>> s = bpd.Series(['cat', 'dog', bpd.NA, 'rabbit'])
>>> s
0       cat
1       dog
2      <NA>
3    rabbit
dtype: string

map can accepts a dict. Values that are not found in the dict are converted to NA:

>>> s.map({'cat': 'kitten', 'dog': 'puppy'})
0    kitten
1     puppy
2      <NA>
3      <NA>
dtype: string

It also accepts a remote function:

>>> @bpd.remote_function()
... def my_mapper(val: str) -> str:
...     vowels = ["a", "e", "i", "o", "u"]
...     if val:
...         return "".join([
...             ch.upper() if ch in vowels else ch for ch in val
...         ])
...     return "N/A"

>>> s.map(my_mapper)
0       cAt
1       dOg
2       N/A
3    rAbbIt
dtype: string

Parameter
Name	Description
`arg`	`function, Mapping, Series` remote function, collections.abc.Mapping subclass or Series Mapping correspondence.

Returns
Type	Description
`Series`	Same index as caller.

mask

mask(cond, other=None) -> bigframes.series.Series

Replace values where the condition is True.

Examples:

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

>>> s = bpd.Series([10, 11, 12, 13, 14])
>>> s
0    10
1    11
2    12
3    13
4    14
dtype: Int64

You can mask the values in the Series based on a condition. The values matching the condition would be masked. The condition can be provided in formm of a Series.

>>> s.mask(s % 2 == 0)
0    <NA>
1      11
2    <NA>
3      13
4    <NA>
dtype: Int64

You can specify a custom mask value.

>>> s.mask(s % 2 == 0, -1)
0    -1
1    11
2    -1
3    13
4    -1
dtype: Int64
>>> s.mask(s % 2 == 0, 100*s)
0    1000
1      11
2    1200
3      13
4    1400
dtype: Int64

You can also use a remote function to evaluate the mask condition. This is useful in situation such as the following, where the mask condition is evaluated based on a complicated business logic which cannot be expressed in form of a Series.

>>> @bpd.remote_function(reuse=False)
... def should_mask(name: str) -> bool:
...     hash = 0
...     for char_ in name:
...         hash += ord(char_)
...     return hash % 2 == 0

>>> s = bpd.Series(["Alice", "Bob", "Caroline"])
>>> s
0       Alice
1         Bob
2    Caroline
dtype: string
>>> s.mask(should_mask)
0        <NA>
1         Bob
2    Caroline
dtype: string
>>> s.mask(should_mask, "REDACTED")
0    REDACTED
1         Bob
2    Caroline
dtype: string

Simple vectorized (i.e. they only perform operations supported on a Series) lambdas or python functions can be used directly.

>>> nums = bpd.Series([1, 2, 3, 4], name="nums")
>>> nums
0    1
1    2
2    3
3    4
Name: nums, dtype: Int64
>>> nums.mask(lambda x: (x+1) % 2 == 1)
0        1
1     <NA>
2        3
3     <NA>
Name: nums, dtype: Int64

>>> def is_odd(num):
...     return num % 2 == 1
>>> nums.mask(is_odd)
0     <NA>
1        2
2     <NA>
3        4
Name: nums, dtype: Int64

Parameters
Name	Description
`cond`	`bool Series/DataFrame, 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 Series/DataFrame and should return boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).
`other`	`scalar, Series/DataFrame, or callable` Entries where cond is True are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and should return scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).

Returns
Type	Description
`bigframes.series.Series`	Series after the replacement.

max

max() -> typing.Any

Return the maximum of the values over the requested axis.

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

Examples:

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

Calculating the max of a Series:

>>> s = bpd.Series([1, 3])
>>> s
0    1
1    3
dtype: Int64
>>> s.max()
np.int64(3)

Calculating the max of a Series containing NA values:

>>> s = bpd.Series([1, 3, bpd.NA])
>>> s
0       1
1       3
2    <NA>
dtype: Int64
>>> s.max()
np.int64(3)

Returns
Type	Description
`scalar`	Scalar.

mean

mean() -> float

Return the mean of the values over the requested axis.

Examples:

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

Calculating the mean of a Series:

>>> s = bpd.Series([1, 3])
>>> s
0    1
1    3
dtype: Int64
>>> s.mean()
np.float64(2.0)

Calculating the mean of a Series containing NA values:

>>> s = bpd.Series([1, 3, bpd.NA])
>>> s
0       1
1       3
2    <NA>
dtype: Int64
>>> s.mean()
np.float64(2.0)

Returns
Type	Description
`scalar`	Scalar.

median

median(*, exact: bool = True) -> float

Return the median of the values over the requested axis.

Parameter
Name	Description
`exact`	`bool. default True` Default True. Get the exact median instead of an approximate one.

Returns
Type	Description
`scalar`	Scalar.

min

min() -> typing.Any

Return the maximum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Examples:

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

Calculating the min of a Series:

>>> s = bpd.Series([1, 3])
>>> s
0    1
1    3
dtype: Int64
>>> s.min()
np.int64(1)

Calculating the min of a Series containing NA values:

>>> s = bpd.Series([1, 3, bpd.NA])
>>> s
0       1
1       3
2    <NA>
dtype: Int64
>>> s.min()
np.int64(1)

Returns
Type	Description
`scalar`	Scalar.

mod

mod(other) -> bigframes.series.Series

Return modulo of Series and other, element-wise (binary operator mod).

Equivalent to series % other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

mode

mode() -> bigframes.series.Series

Return the mode(s) of the Series.

The mode is the value that appears most often. There can be multiple modes.

Always returns Series even if only one value is returned.

Returns
Type	Description
`bigframes.series.Series`	Modes of the Series in sorted order.

mul

mul(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return multiplication of Series and other, element-wise (binary operator mul).

Equivalent to other * series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

multiply

multiply(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return multiplication of Series and other, element-wise (binary operator mul).

Equivalent to other * series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

ne

ne(other: object) -> bigframes.series.Series

Return not equal of Series and other, element-wise (binary operator ne).

Equivalent to other != series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

nlargest

nlargest(n: int = 5, keep: str = "first") -> bigframes.series.Series

Return the largest n elements.

Parameters
Name	Description
`n`	`int, default 5` Return this many descending sorted values.
`keep`	`{'first', 'last', 'all'}, default 'first'` When there are duplicate values that cannot all fit in a Series of `n` elements: `first` : return the first `n` occurrences in order of appearance. `last` : return the last `n` occurrences in reverse order of appearance. `all` : keep all occurrences. This can result in a Series of size larger than `n`.

Returns
Type	Description
`bigframes.series.Series`	The `n` largest values in the Series, sorted in decreasing order.

notna

notna() -> bigframes.series.Series

Detect existing (non-missing) values.

Return a boolean same-sized object indicating if the values are not NA. Non-missing values get mapped to True. Characters such as empty strings '' or numpy.inf are not considered NA values. NA values get mapped to False values.

Returns
Type	Description
`NDFrame`	Mask of bool values for each element that indicates whether an element is not an NA value.

notnull

notnull() -> bigframes.series.Series

Detect existing (non-missing) values.

Returns
Type	Description
`NDFrame`	Mask of bool values for each element that indicates whether an element is not an NA value.

nsmallest

nsmallest(n: int = 5, keep: str = "first") -> bigframes.series.Series

Return the smallest n elements.

Parameters
Name	Description
`n`	`int, default 5` Return this many ascending sorted values.
`keep`	`{'first', 'last', 'all'}, default 'first'` When there are duplicate values that cannot all fit in a Series of `n` elements: `first` : return the first `n` occurrences in order of appearance. `last` : return the last `n` occurrences in reverse order of appearance. `all` : keep all occurrences. This can result in a Series of size larger than `n`.

Returns
Type	Description
`bigframes.series.Series`	The `n` smallest values in the Series, sorted in increasing order.

nunique

nunique() -> int

Return number of unique elements in the object.

Excludes NA values by default.

Returns
Type	Description
`int`	number of unique elements in the object.

pad

pad(*, limit: typing.Optional[int] = None) -> bigframes.series.Series

Fill NA/NaN values by propagating the last valid observation to next valid.

Examples:

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

>>> df = bpd.DataFrame([[np.nan, 2, np.nan, 0],
...                     [3, 4, np.nan, 1],
...                     [np.nan, np.nan, np.nan, np.nan],
...                     [np.nan, 3, np.nan, 4]],
...                    columns=list("ABCD")).astype("Float64")
>>> df
      A     B     C     D
0  <NA>   2.0  <NA>   0.0
1   3.0   4.0  <NA>   1.0
2  <NA>  <NA>  <NA>  <NA>
3  <NA>   3.0  <NA>   4.0
<BLANKLINE>
[4 rows x 4 columns]

Fill NA/NaN values in DataFrames:

>>> df.ffill()
      A    B     C    D
0  <NA>  2.0  <NA>  0.0
1   3.0  4.0  <NA>  1.0
2   3.0  4.0  <NA>  1.0
3   3.0  3.0  <NA>  4.0
<BLANKLINE>
[4 rows x 4 columns]

Fill NA/NaN values in Series:

>>> series = bpd.Series([1, np.nan, 2, 3])
>>> series.ffill()
0    1.0
1    1.0
2    2.0
3    3.0
dtype: Float64

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series or None`	Object with missing values filled.

pct_change

pct_change(periods: int = 1) -> bigframes.series.Series

Fractional change between the current and a prior element.

Computes the fractional change from the immediately previous row by default. This is useful in comparing the fraction of change in a time series of elements.

Parameter
Name	Description
`periods`	`int, default 1` Periods to shift for forming percent change.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	The same type as the calling object.

peek

peek(n: int = 5, *, force: bool = True) -> pandas.core.series.Series

Preview n arbitrary elements from the series without guarantees about row selection or ordering.

Series.peek(force=False) will always be very fast, but will not succeed if data requires full data scanning. Using force=True will always succeed, but may be perform queries. Query results will be cached so that future steps will benefit from these queries.

Parameters
Name	Description
`n`	`int, default 5` The number of rows to select from the series. Which N rows are returned is non-deterministic.
`force`	`bool, default True` If the data cannot be peeked efficiently, the series will instead be fully materialized as part of the operation if `force=True`. If `force=False`, the operation will throw a ValueError.

Exceptions
Type	Description
`ValueError`	If force=False and data cannot be efficiently peeked.

Returns
Type	Description
`pandas.Series`	A pandas Series with n rows.

pow

pow(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return Exponential power of series and other, element-wise (binary operator pow).

Equivalent to series ** other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

prod

prod() -> float

Return the product of the values over the requested axis.

Returns
Type	Description
`scalar`	Scalar.

product

product() -> float

Return the product of the values over the requested axis.

Returns
Type	Description
`scalar`	Scalar.

quantile

quantile(
    q: typing.Union[float, typing.Sequence[float]] = 0.5
) -> typing.Union[bigframes.series.Series, float]

Return value at the given quantile.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> s = bpd.Series([1, 2, 3, 4])
>>> s.quantile(.5)
np.float64(2.5)
>>> s.quantile([.25, .5, .75])
0.25    1.75
0.5      2.5
0.75    3.25
dtype: Float64

Parameter
Name	Description
`q`	`float or array-like, default 0.5 (50% quantile)` The quantile(s) to compute, which can lie in range: 0 <= q <= 1.

Returns
Type	Description
`float or Series`	If `q` is an array, a Series will be returned where the index is `q` and the values are the quantiles, otherwise a float will be returned.

radd

radd(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return addition of Series and other, element-wise (binary operator radd).

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

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

rank

rank(
    axis=0,
    method: str = "average",
    numeric_only=False,
    na_option: str = "keep",
    ascending: bool = True,
) -> bigframes.series.Series

Compute numerical data ranks (1 through n) along axis.

By default, equal values are assigned a rank that is the average of the ranks of those values.

Parameters
Name	Description
`method`	`{'average', 'min', 'max', 'first', 'dense'}, default 'average'` How to rank the group of records that have the same value (i.e. ties): `average`: average rank of the group, `min`: lowest rank in the group max`: highest rank in the group,` first`: ranks assigned in order they appear in the array,` dense`: like 'min', but rank always increases by 1 between groups.
`numeric_only`	`bool, default False` For DataFrame objects, rank only numeric columns if set to True.
`na_option`	`{'keep', 'top', 'bottom'}, default 'keep'` How to rank NaN values: `keep`: assign NaN rank to NaN values, , `top`: assign lowest rank to NaN values, `bottom`: assign highest rank to NaN values.
`ascending`	`bool, default True` Whether or not the elements should be ranked in ascending order.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Return a Series or DataFrame with data ranks as values.

rdiv

rdiv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return floating division of Series and other, element-wise (binary operator rtruediv).

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

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

rdivmod

rdivmod(other) -> typing.Tuple[bigframes.series.Series, bigframes.series.Series]

Return integer division and modulo of Series and other, element-wise (binary operator rdivmod).

Equivalent to other divmod series.

Returns
Type	Description
`2-Tuple of Series`	The result of the operation. The result is always consistent with (rfloordiv, rmod) (though pandas may not).

reindex

reindex(index=None, *, validate: typing.Optional[bool] = None)

Conform Series to new index with optional filling logic.

Places 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.

Parameter
Name	Description
`index`	`array-like, optional` New labels for the index. Preferably an Index object to avoid duplicating data.

Returns
Type	Description
`Series`	Series with changed index.

reindex_like

reindex_like(
    other: bigframes.series.Series, *, validate: typing.Optional[bool] = None
)

Return an object with matching indices as other object.

Conform the object to the same index on all axes. Optional filling logic, placing Null in locations having no value in the previous index.

Parameter
Name	Description
`other`	`Object of the same data type` Its row and column indices are used to define the new indices of this object.

Returns
Type	Description
`Series or DataFrame`	Same type as caller, but with changed indices on each axis.

rename

rename(
    index: typing.Union[typing.Hashable, typing.Mapping[typing.Any, typing.Any]] = None,
    **kwargs
) -> bigframes.series.Series

Alter Series index labels or name.

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.

Alternatively, change Series.name with a scalar value.

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: Int64

You can changes the Series name by specifying a string scalar:

>>> s.rename("my_name")
0    1
1    2
2    3
Name: my_name, dtype: Int64

You can change the labels by specifying a mapping:

>>> s.rename({1: 3, 2: 5})
0    1
3    2
5    3
dtype: Int64

Parameter
Name	Description
`index`	`scalar, hashable sequence, dict-like or function optional` Functions or dict-like are transformations to apply to the index. Scalar or hashable sequence-like will alter the `Series.name` attribute.

Returns
Type	Description
`bigframes.series.Series`	Series with index labels.

rename_axis

rename_axis(
    mapper: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]], **kwargs
) -> bigframes.series.Series

Set the name of the axis for the index or columns.

Parameter
Name	Description
`mapper`	`scalar, list-like, optional` Value to set the axis name attribute.

Returns
Type	Description
`bigframes.series.Series`	Series with the name of the axis set.

reorder_levels

reorder_levels(
    order: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]],
    axis: int | str = 0,
)

Rearrange index levels using input order.

May not drop or duplicate levels.

Parameters
Name	Description
`order`	`list of int representing new level order` Reference level by number or key.
`axis`	`{0 or 'index', 1 or 'columns'}, default 0` For `Series` this parameter is unused and defaults to 0.

replace

replace(to_replace: typing.Any, value: typing.Any = None, *, regex: bool = False)

Replace values given in to_replace with value.

Values of the Series/DataFrame are replaced with other values dynamically. This differs from updating with .loc or .iloc, which require you to specify a location to update with some value.

Examples:

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

>>> s = bpd.Series([1, 2, 3, 4, 5])
>>> s
0    1
1    2
2    3
3    4
4    5
dtype: Int64

>>> s.replace(1, 5)
0    5
1    2
2    3
3    4
4    5
dtype: Int64

You can replace a list of values:

>>> s.replace([1, 3, 5], -1)
0    -1
1     2
2    -1
3     4
4    -1
dtype: Int64

You can use a replacement mapping:

>>> s.replace({1: 5, 3: 10})
0     5
1     2
2    10
3     4
4     5
dtype: Int64

With a string Series you can use a simple string replacement or a regex replacement:

>>> s = bpd.Series(["Hello", "Another Hello"])
>>> s.replace("Hello", "Hi")
0               Hi
1    Another Hello
dtype: string

>>> s.replace("Hello", "Hi", regex=True)
0            Hi
1    Another Hi
dtype: string

>>> s.replace("^Hello", "Hi", regex=True)
0               Hi
1    Another Hello
dtype: string

>>> s.replace("Hello$", "Hi", regex=True)
0            Hi
1    Another Hi
dtype: string

>>> s.replace("[Hh]e", "__", regex=True)
0            __llo
1    Anot__r __llo
dtype: string

Parameters
Name	Description
`to_replace`	`str, regex, list, int, float or None` How to find the values that will be replaced. * numeric, str or regex: - numeric: numeric values equal to `to_replace` will be replaced with `value` - 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, regex and numeric rules apply as above.
`value`	`scalar, default None` Value to replace any values matching `to_replace` with. For a DataFrame a dict of values can be used to specify 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.
`regex`	`bool, default False` Whether to interpret `to_replace` and/or `value` as regular expressions. If this is `True` then `to_replace` must be a string.

Exceptions

Type Description

TypeError * If to_replace is not a scalar, array-like, dict, or None * 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. * When replacing multiple bool or datetime64 objects and the arguments to to_replace does not match the type of the value being replaced

Returns
Type	Description
`Series/DataFrame`	Object after replacement.

reset_index

reset_index(
    *, name: typing.Optional[str] = None, drop: bool = False
) -> bigframes.dataframe.DataFrame | bigframes.series.Series

Generate a new DataFrame or Series with the index reset.

This is useful when the index needs to be treated as a column, or when the index is meaningless and needs to be reset to the default before another operation.

Examples:

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

>>> s = bpd.Series([1, 2, 3, 4], name='foo',
...                index=['a', 'b', 'c', 'd'])
>>> s.index.name = "idx"
>>> s
idx
a    1
b    2
c    3
d    4
Name: foo, dtype: Int64

Generate a DataFrame with default index.

>>> s.reset_index()
    idx  foo
0     a    1
1     b    2
2     c    3
3     d    4
<BLANKLINE>
[4 rows x 2 columns]

To specify the name of the new column use name param.

>>> s.reset_index(name="bar")
    idx   bar
0     a    1
1     b    2
2     c    3
3     d    4
<BLANKLINE>
[4 rows x 2 columns]

To generate a new Series with the default index set param drop=True.

>>> s.reset_index(drop=True)
0    1
1    2
2    3
3    4
Name: foo, dtype: Int64

Parameters
Name	Description
`drop`	`bool, default False` Just reset the index, without inserting it as a column in the new DataFrame.
`name`	`object, optional` The name to use for the column containing the original Series values. Uses `self.name` by default. This argument is ignored when `drop` is True.

rfloordiv

rfloordiv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return integer division of Series and other, element-wise (binary operator rfloordiv).

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

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

rmod

rmod(other) -> bigframes.series.Series

Return modulo of Series and other, element-wise (binary operator mod).

Equivalent to series % other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

rmul

rmul(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return multiplication of Series and other, element-wise (binary operator mul).

Equivalent to series * others, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`Series`	The result of the operation.

rolling

rolling(window: int, min_periods=None) -> bigframes.core.window.Window

Provide rolling window calculations.

Parameters
Name	Description
`window`	`int, timedelta, str, offset, or BaseIndexer subclass` Size of the moving window. If an integer, the fixed number of observations used for each window. If a timedelta, str, or offset, 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 datetime-like indexes. To learn more about the offsets & frequency strings, please see `this link https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases`__. If a BaseIndexer subclass, the window boundaries based on the defined `get_window_bounds` method. Additional rolling keyword arguments, namely `min_periods`, `center`, `closed` and `step` will be passed to `get_window_bounds`.
`min_periods`	`int, default None` Minimum number of observations in window required to have a value; otherwise, result is `np.nan`. For a window that is specified by an offset, `min_periods` will default to 1. For a window that is specified by an integer, `min_periods` will default to the size of the window.

Returns
Type	Description
`bigframes.core.window.Window`	`Window` subclass if a `win_type` is passed. `Rolling` subclass if `win_type` is not passed.

round

round(decimals=0) -> bigframes.series.Series

Round each value in a Series to the given number of decimals.

Examples:

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

>>> s = bpd.Series([0.1, 1.3, 2.7])
>>> s.round()
0    0.0
1    1.0
2    3.0
dtype: Float64

>>> s = bpd.Series([0.123, 1.345, 2.789])
>>> s.round(decimals=2)
0    0.12
1    1.34
2    2.79
dtype: Float64

Parameter
Name	Description
`decimals`	`int, default 0` Number of decimal places to round to. If decimals is negative, it specifies the number of positions to the left of the decimal point.

Returns
Type	Description
`bigframes.series.Series`	Rounded values of the Series.

rpow

rpow(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return Exponential power of series and other, element-wise (binary operator rpow).

Equivalent to other ** series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

rsub

rsub(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return subtraction of Series and other, element-wise (binary operator rsub).

Equivalent to other - series, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

rtruediv

rtruediv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return floating division of Series and other, element-wise (binary operator rtruediv).

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

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

sample

sample(
    n: typing.Optional[int] = None,
    frac: typing.Optional[float] = None,
    *,
    random_state: typing.Optional[int] = None,
    sort: typing.Optional[typing.Union[bool, typing.Literal["random"]]] = "random"
) -> bigframes.series.Series

Return a random sample of items from an axis of object.

You can use random_state for reproducibility.

Examples:

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

>>> df = bpd.DataFrame({'num_legs': [2, 4, 8, 0],
...                     'num_wings': [2, 0, 0, 0],
...                     'num_specimen_seen': [10, 2, 1, 8]},
...                    index=['falcon', 'dog', 'spider', 'fish'])
>>> df
        num_legs  num_wings  num_specimen_seen
falcon         2          2                 10
dog            4          0                  2
spider         8          0                  1
fish           0          0                  8
<BLANKLINE>
[4 rows x 3 columns]

Fetch one random row from the DataFrame (Note that we use random_state to ensure reproducibility of the examples):

>>> df.sample(random_state=1)
     num_legs  num_wings  num_specimen_seen
dog         4          0                  2
<BLANKLINE>
[1 rows x 3 columns]

A random 50% sample of the DataFrame:

>>> df.sample(frac=0.5, random_state=1)
      num_legs  num_wings  num_specimen_seen
dog          4          0                  2
fish         0          0                  8
<BLANKLINE>
[2 rows x 3 columns]

Extract 3 random elements from the Series df['num_legs']:

>>> s = df['num_legs']
>>> s.sample(n=3, random_state=1)
dog       4
fish      0
spider    8
Name: num_legs, dtype: Int64

Parameters
Name	Description
`n`	`Optional[int], default None` Number of items from axis to return. Cannot be used with `frac`. Default = 1 if `frac` = None.
`frac`	`Optional[float], default None` Fraction of axis items to return. Cannot be used with `n`.
`random_state`	`Optional[int], default None` Seed for random number generator.
`sort`	`Optional[bool\|Literal["random"]], default "random"` 'random' (default): No specific ordering will be applied after sampling. - 'True' : Index columns will determine the sample's order. - 'False': The sample will retain the original object's order.

Exceptions
Type	Description
`ValueError`	If both `n` and `frac` are specified.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	A new object of same type as caller containing `n` items randomly sampled from the caller object.

shift

shift(periods: int = 1) -> bigframes.series.Series

Shift index by desired number of periods.

Shifts the index without realigning the data.

Returns
Type	Description
`NDFrame`	Copy of input object, shifted.

skew

skew()

Return unbiased skew over requested axis.

Normalized by N-1.

Returns
Type	Description
`scalar`	Scalar.

sort_index

sort_index(
    *, axis=0, ascending=True, na_position="last"
) -> bigframes.series.Series

Sort Series by index labels.

Returns a new Series sorted by label if inplace argument is False, otherwise updates the original series and returns None.

Parameters
Name	Description
`axis`	`{0 or 'index'}` Unused. Parameter needed for compatibility with DataFrame.
`ascending`	`bool or list-like of bools, default True` Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.
`na_position`	`{'first', 'last'}, default 'last'` If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Not implemented for MultiIndex.

Returns
Type	Description
`bigframes.series.Series`	The original Series sorted by the labels or None if `inplace=True`.

sort_values

sort_values(
    *, axis=0, ascending=True, kind: str = "quicksort", na_position="last"
) -> bigframes.series.Series

Sort by the values.

Sort a Series in ascending or descending order by some criterion.

Examples:

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

>>> s = bpd.Series([np.nan, 1, 3, 10, 5])
>>> s
0    <NA>
1     1.0
2     3.0
3    10.0
4     5.0
dtype: Float64

Sort values ascending order (default behaviour):

>>> s.sort_values(ascending=True)
1     1.0
2     3.0
4     5.0
3    10.0
0    <NA>
dtype: Float64

Sort values descending order:

>>> s.sort_values(ascending=False)
3    10.0
4     5.0
2     3.0
1     1.0
0    <NA>
dtype: Float64

Sort values putting NAs first:

>>> s.sort_values(na_position='first')
0    <NA>
1     1.0
2     3.0
4     5.0
3    10.0
dtype: Float64

Sort a series of strings:

>>> s = bpd.Series(['z', 'b', 'd', 'a', 'c'])
>>> s
0    z
1    b
2    d
3    a
4    c
dtype: string

>>> s.sort_values()
3    a
1    b
4    c
2    d
0    z
dtype: string

Parameters
Name	Description
`axis`	`0 or 'index'` Unused. Parameter needed for compatibility with DataFrame.
`ascending`	`bool or list of bools, default True` If True, sort values in ascending order, otherwise descending.
`kind`	`str, default to 'quicksort'` Choice of sorting algorithm. Accepts 'quicksort’, ‘mergesort’, ‘heapsort’, ‘stable’. Ignored except when determining whether to sort stably. 'mergesort' or 'stable' will result in stable reorder
`na_position`	`{'first' or 'last'}, default 'last'` Argument 'first' puts NaNs at the beginning, 'last' puts NaNs at the end.

Returns
Type	Description
`bigframes.series.Series`	Series ordered by values or None if `inplace=True`.

std

std() -> float

Return sample standard deviation over requested axis.

Normalized by N-1 by default.

Examples:

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

>>> df = bpd.DataFrame({'person_id': [0, 1, 2, 3],
...                     'age': [21, 25, 62, 43],
...                     'height': [1.61, 1.87, 1.49, 2.01]}
...                   ).set_index('person_id')
>>> df
           age  height
person_id
0           21    1.61
1           25    1.87
2           62    1.49
3           43    2.01
<BLANKLINE>
[4 rows x 2 columns]

>>> df.std()
age       18.786076
height     0.237417
dtype: Float64

sub

sub(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return subtraction of Series and other, element-wise (binary operator sub).

Equivalent to series - other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

subtract

subtract(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return subtraction of Series and other, element-wise (binary operator sub).

Equivalent to series - other, but with support to substitute a fill_value for missing data in either one of the inputs.

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

sum

sum() -> float

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Examples:

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

Calculating the sum of a Series:

>>> s = bpd.Series([1, 3])
>>> s
0    1
1    3
dtype: Int64
>>> s.sum()
np.int64(4)

Calculating the sum of a Series containing NA values:

>>> s = bpd.Series([1, 3, bpd.NA])
>>> s
0       1
1       3
2    <NA>
dtype: Int64
>>> s.sum()
np.int64(4)

Returns
Type	Description
`scalar`	Scalar.

swaplevel

swaplevel(i: int = -2, j: int = -1)

Swap levels i and j in a MultiIndex.

Default is to swap the two innermost levels of the index.

Parameters
Name	Description
`i`	`int or str` Levels of the indices to be swapped. Can pass level name as string.
`j`	`int or str` Levels of the indices to be swapped. Can pass level name as string.

Returns
Type	Description
`Series`	Series with levels swapped in MultiIndex

tail

tail(n: int = 5) -> bigframes.series.Series

Return the last n rows.

This function returns last n rows from the object based on position. It is useful for quickly verifying data, for example, after sorting or appending rows.

For negative values of n, this function returns all rows except the first |n| rows, equivalent to df[|n|:].

If n is larger than the number of rows, this function returns all rows.

Parameter
Name	Description
`n`	`int, default 5` Number of rows to select.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The last `n` rows of the caller object.

to_csv

to_csv(
    path_or_buf=None, sep=",", *, header: bool = True, index: bool = True
) -> typing.Optional[str]

Write object to a comma-separated values (csv) file on Cloud Storage.

Parameters
Name	Description
`path_or_buf`	`str, path object, file-like object, or None, default None` String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. If a non-binary file object is passed, it should be opened with `newline=''`, disabling universal newlines. If a binary file object is passed, `mode` might need to contain a `'b'`. Alternatively, a destination URI of Cloud Storage files(s) to store the extracted dataframe in format of `gs://<bucket_name>/<object_name_or_glob>`. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies. None, file-like objects or local file paths not yet supported.
`index`	`bool, default True` If True, write row names (index).

Returns
Type	Description
`None or str`	If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.

to_dict

to_dict(into: type[dict] = <class 'dict'>) -> typing.Mapping

Convert Series to {label -> value} dict or dict-like object.

Parameter
Name	Description
`into`	`class, default dict` The collections.abc.Mapping subclass to use as the return object. 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.

Returns
Type	Description
`collections.abc.Mapping`	Key-value representation of Series.

to_excel

to_excel(excel_writer, sheet_name="Sheet1", **kwargs) -> None

Write Series to an Excel sheet.

To write a single Series to an Excel .xlsx file it is only necessary to specify a target file name. To write to multiple sheets it is necessary to create an ExcelWriter object with a target file name, and specify a sheet in the file to write to.

Multiple sheets may be written to by specifying unique sheet_name. With all data written to the file it is necessary to save the changes. Note that creating an ExcelWriter object with a file name that already exists will result in the contents of the existing file being erased.

Parameters
Name	Description
`excel_writer`	`path-like, file-like, or ExcelWriter object` File path or existing ExcelWriter.
`sheet_name`	`str, default 'Sheet1'` Name of sheet to contain Series.

to_frame

to_frame(name: typing.Hashable = None) -> bigframes.dataframe.DataFrame

Convert Series to DataFrame.

The column in the new dataframe will be named name (the keyword parameter) if the name parameter is provided and not None.

Returns
Type	Description
`bigframes.dataframe.DataFrame`	DataFrame representation of Series.

to_json

to_json(
    path_or_buf=None,
    orient: typing.Optional[
        typing.Literal["split", "records", "index", "columns", "values", "table"]
    ] = None,
    *,
    lines: bool = False,
    index: bool = True
) -> typing.Optional[str]

Convert the object to a JSON string, written to Cloud Storage.

Note NaN's and None will be converted to null and datetime objects will be converted to UNIX timestamps.

Note: Only orient='records' and lines=True is supported so far.

Parameters
Name	Description
`path_or_buf`	`str, path object, file-like object, or None, default None` String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. Can be a destination URI of Cloud Storage files(s) to store the extracted dataframe in format of `gs://<bucket_name>/<object_name_or_glob>`. Must contain a wildcard `*` character. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies.
`orient`	`{split, records, index, columns, values, table}, default 'columns` Indication of expected JSON string format. * Series: - default is 'index' - allowed values are: {{'split', 'records', 'index', 'table'}}. * DataFrame: - default is 'columns' - allowed values are: {{'split', 'records', 'index', 'columns', 'values', 'table'}}. * 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, where data component is like `orient='records'`.
`index`	`bool, default True` If True, write row names (index).
`lines`	`bool, default False` If 'orient' is 'records' write out line-delimited json format. Will throw ValueError if incorrect 'orient' since others are not list-like.

Exceptions
Type	Description
`ValueError`	If `lines` is True but `records` is not provided as value for `orient`.

Returns
Type	Description
`None or str`	If path_or_buf is None, returns the resulting json format as a string. Otherwise returns None.

to_latex

to_latex(
    buf=None, columns=None, header=True, index=True, **kwargs
) -> typing.Optional[str]

Render object to a LaTeX tabular, longtable, or nested table.

Parameters
Name	Description
`buf`	`str, Path or StringIO-like, optional, default None` Buffer to write to. If None, the output is returned as a string.
`columns`	`list of label, optional` The subset of columns to write. Writes all columns by default.
`header`	`bool or list of str, 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`	`bool, default True` Write row names (index).

Returns
Type	Description
`str or None`	If buf is None, returns the result as a string. Otherwise returns None.

to_list

to_list() -> list

Return a list of the values.

These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period).

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: Int64

>>> s.to_list()
[1, 2, 3]

Returns
Type	Description
`list`	list of the values

to_markdown

to_markdown(
    buf: typing.Optional[typing.IO[str]] = None,
    mode: str = "wt",
    index: bool = True,
    **kwargs
) -> typing.Optional[str]

Print {klass} in Markdown-friendly format.

Parameters
Name	Description
`buf`	`str, Path or StringIO-like, optional, default None` Buffer to write to. If None, the output is returned as a string.
`mode`	`str, optional` Mode in which file is opened, "wt" by default.
`index`	`bool, optional, default True` Add index (row) labels.

Returns
Type	Description
`str`	{klass} in Markdown-friendly format.

to_numpy

to_numpy(dtype=None, copy=False, na_value=None, **kwargs) -> numpy.ndarray

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

Parameters
Name	Description
`dtype`	`str or numpy.dtype, optional` The dtype to pass to `numpy.asarray`.
`copy`	`bool, default False` Whether to ensure that the returned value is not a view on another array. Note that `copy=False` does not ensure that `to_numpy()` is no-copy. Rather, `copy=True` ensure that a copy is made, even if not strictly necessary.
`na_value`	`Any, optional` The value to use for missing values. The default value depends on `dtype` and the type of the array.

Returns
Type	Description
`numpy.ndarray`	A NumPy ndarray representing the values in this Series or Index.

to_pandas

to_pandas(
    max_download_size: typing.Optional[int] = None,
    sampling_method: typing.Optional[str] = None,
    random_state: typing.Optional[int] = None,
    *,
    ordered: bool = True
) -> pandas.core.series.Series

Writes Series to pandas Series.

Parameters
Name	Description
`max_download_size`	`int, default None` Download size threshold in MB. If max_download_size is exceeded when downloading data (e.g., to_pandas()), the data will be downsampled if bigframes.options.sampling.enable_downsampling is True, otherwise, an error will be raised. If set to a value other than None, this will supersede the global config.
`sampling_method`	`str, default None` Downsampling algorithms to be chosen from, the choices are: "head": This algorithm returns a portion of the data from the beginning. It is fast and requires minimal computations to perform the downsampling; "uniform": This algorithm returns uniform random samples of the data. If set to a value other than None, this will supersede the global config.
`random_state`	`int, default None` The seed for the uniform downsampling algorithm. If provided, the uniform method may take longer to execute and require more computation. If set to a value other than None, this will supersede the global config.
`ordered`	`bool, default True` Determines whether the resulting pandas series will be ordered. In some cases, unordered may result in a faster-executing query.

Returns
Type	Description
`pandas.Series`	A pandas Series with all rows of this Series if the data_sampling_threshold_mb is not exceeded; otherwise, a pandas Series with downsampled rows of the DataFrame.

to_pickle

to_pickle(path, **kwargs) -> None

Pickle (serialize) object to file.

Parameter
Name	Description
`path`	`str, path object, or file-like object` String, path object (implementing `os.PathLike[str]`), or file-like object implementing a binary `write()` function. File path where the pickled object will be stored.

to_string

to_string(
    buf=None,
    na_rep="NaN",
    float_format=None,
    header=True,
    index=True,
    length=False,
    dtype=False,
    name=False,
    max_rows=None,
    min_rows=None,
) -> typing.Optional[str]

Render a string representation of the Series.

Parameters
Name	Description
`buf`	`StringIO-like, optional` Buffer to write to.
`na_rep`	`str, optional` String representation of NaN to use, default 'NaN'.
`float_format`	`one-parameter function, optional` Formatter function to apply to columns' elements if they are floats, default None.
`header`	`bool, default True` Add the Series header (index name).
`index`	`bool, optional` Add index (row) labels, default True.
`length`	`bool, default False` Add the Series length.
`dtype`	`bool, default False` Add the Series dtype.
`name`	`bool, default False` Add the Series name if not None.
`max_rows`	`int, optional` Maximum number of rows to show before truncating. If None, show all.
`min_rows`	`int, optional` The number of rows to display in a truncated repr (when number of rows is above `max_rows`).

Returns
Type	Description
`str or None`	String representation of Series if `buf=None`, otherwise None.

to_xarray

to_xarray()

Return an xarray object from the pandas object.

Returns
Type	Description
`xarray.DataArray or xarray.Dataset`	Data in the pandas structure converted to Dataset if the object is a DataFrame, or a DataArray if the object is a Series.

tolist

tolist() -> list

Return a list of the values.

These are each a scalar type, which is a Python scalar (for str, int, float) or a pandas scalar (for Timestamp/Timedelta/Interval/Period).

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s
0    1
1    2
2    3
dtype: Int64

>>> s.to_list()
[1, 2, 3]

Returns
Type	Description
`list`	list of the values

transpose

transpose() -> bigframes.series.Series

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.transpose()
0     Ant
1    Bear
2     Cow
dtype: string

Returns
Type	Description
`Series`	Series.

truediv

truediv(other: float | int | bigframes.series.Series) -> bigframes.series.Series

Return floating division of Series and other, element-wise (binary operator truediv).

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

Returns
Type	Description
`bigframes.series.Series`	The result of the operation.

unique

unique(keep_order=True) -> bigframes.series.Series

Return unique values of Series object.

By default, uniques are returned in order of appearance. Hash table-based unique, therefore does NOT sort.

Parameter

Name Description

keep_order

bool, default True **Examples:** >>> import bigframes.pandas as bpd >>> bpd.options.display.progress_bar = None >>> s = bpd.Series([2, 1, 3, 3], name='A') >>> s 0 2 1 1 2 3 3 3 Name: A, dtype: Int64 Example with order preservation: Slower, but keeps order >>> s.unique() 0 2 1 1 2 3 Name: A, dtype: Int64 Example without order preservation: Faster, but loses original order >>> s.unique(keep_order=False) 0 1 1 2 2 3 Name: A, dtype: Int64

If True, preserves the order of the first appearance of each unique value. If False, returns the elements in ascending order, which can be faster.

Returns
Type	Description
`Series`	The unique values returned as a Series.

unstack

unstack(
    level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] = -1
)

Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.

Parameter
Name	Description
`level`	`int, str, or list of these, default last level` Level(s) to unstack, can pass level name.

Returns
Type	Description
`DataFrame`	Unstacked Series.

update

update(
    other: typing.Union[bigframes.series.Series, typing.Sequence, typing.Mapping]
) -> None

Modify Series in place using values from passed Series.

Uses non-NA values from passed Series to make updates. Aligns on index.

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s.update(bpd.Series([4, 5, 6]))
>>> s
0    4
1    5
2    6
dtype: Int64

>>> s = bpd.Series(['a', 'b', 'c'])
>>> s.update(bpd.Series(['d', 'e'], index=[0, 2]))
>>> s
0    d
1    b
2    e
dtype: string

>>> s = bpd.Series([1, 2, 3])
>>> s.update(bpd.Series([4, 5, 6, 7, 8]))
>>> s
0    4
1    5
2    6
dtype: Int64

If `other` contains NaNs the corresponding values are not updated
in the original Series.

>>> s = bpd.Series([1, 2, 3])
>>> s.update(bpd.Series([4, np.nan, 6], dtype=pd.Int64Dtype()))
>>> s
0    4
1    2
2    6
dtype: Int64

`other` can also be a non-Series object type
that is coercible into a Series

>>> s = bpd.Series([1, 2, 3])
>>> s.update([4, np.nan, 6])
>>> s
0    4.0
1    2.0
2    6.0
dtype: Float64

>>> s = bpd.Series([1, 2, 3])
>>> s.update({1: 9})
>>> s
0    1
1    9
2    3
dtype: Int64

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
>>> bpd.options.display.progress_bar = None

>>> s = bpd.Series([3, 1, 2, 3, 4, bpd.NA], dtype="Int64")

>>> s
0       3
1       1
2       2
3       3
4       4
5    <NA>
dtype: Int64

value_counts sorts the result by counts in a descending order by default:

>>> s.value_counts()
3      2
1      1
2      1
4      1
Name: count, dtype: Int64

You can normalize the counts to return relative frequencies by setting normalize=True:

>>> s.value_counts(normalize=True)
3    0.4
1    0.2
2    0.2
4    0.2
Name: proportion, dtype: Float64

You can get the values in the ascending order of the counts by setting ascending=True:

>>> s.value_counts(ascending=True)
1    1
2    1
4    1
3    2
Name: count, dtype: Int64

You can include the counts of the NA values by setting dropna=False:

>>> s.value_counts(dropna=False)
3       2
1       1
2       1
4       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.

Returns
Type	Description
`Series`	Series containing counts of unique values.

var

var() -> float

Return unbiased variance over requested axis.

Normalized by N-1 by default.

Returns
Type	Description
`scalar or Series (if level specified)`	Variance.

where

where(cond, other=None)

Replace values where the condition is False.

Examples:

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

>>> s = bpd.Series([10, 11, 12, 13, 14])
>>> s
0    10
1    11
2    12
3    13
4    14
dtype: Int64

You can filter the values in the Series based on a condition. The values matching the condition would be kept, and not matching would be replaced. The default replacement value is NA.

>>> s.where(s % 2 == 0)
0      10
1    <NA>
2      12
3    <NA>
4      14
dtype: Int64

You can specify a custom replacement value for non-matching values.

>>> s.where(s % 2 == 0, -1)
0    10
1    -1
2    12
3    -1
4    14
dtype: Int64
>>> s.where(s % 2 == 0, 100*s)
0      10
1    1100
2      12
3    1300
4      14
dtype: Int64

Parameters
Name	Description
`cond`	`bool Series/DataFrame, 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 Series/DataFrame and returns boolean Series/DataFrame or array. The callable must not change input Series/DataFrame (though pandas doesn’t check it).
`other`	`scalar, Series/DataFrame, or callable` Entries where cond is False are replaced with corresponding value from other. If other is callable, it is computed on the Series/DataFrame and returns scalar or Series/DataFrame. The callable must not change input Series/DataFrame (though pandas doesn’t check it). If not specified, entries will be filled with the corresponding NULL value (np.nan for numpy dtypes, pd.NA for extension dtypes).

Returns
Type	Description
`bigframes.series.Series`	Series after the replacement.