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Class Series (2.4.0) | Python client library

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

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

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

Examples:

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

>>> df = bpd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
...                    index=[4, 5, 6], columns=['A', 'B', 'C'])
>>> df
    A   B   C
4   0   2   3
5   0   4   1
6  10  20  30
<BLANKLINE>
[3 rows x 3 columns]

Get value at specified row/column pair

>>> df.at[4, 'B']
np.int64(2)

Get value at specified row label

>>> df.loc[5].at['B']
np.int64(4)

API documentation for blob property.

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

Examples:

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

>>> seconds_series = bpd.Series(pd.date_range("2000-01-01", periods=3, freq="s"))
>>> seconds_series
0    2000-01-01 00:00:00
1    2000-01-01 00:00:01
2    2000-01-01 00:00:02
dtype: timestamp`us][pyarrow]`

>>> seconds_series.dt.second
0    0
1    1
2    2
dtype: Int64

>>> hours_series = bpd.Series(pd.date_range("2000-01-01", periods=3, freq="h"))
>>> hours_series
0    2000-01-01 00:00:00
1    2000-01-01 01:00:00
2    2000-01-01 02:00:00
dtype: timestamp`us][pyarrow]`

>>> hours_series.dt.hour
0    0
1    1
2    2
dtype: Int64

>>> quarters_series = bpd.Series(pd.date_range("2000-01-01", periods=3, freq="QE"))
>>> quarters_series
0    2000-03-31 00:00:00
1    2000-06-30 00:00:00
2    2000-09-30 00:00:00
dtype: timestamp`us][pyarrow]`

>>> quarters_series.dt.quarter
0    1
1    2
2    3
dtype: Int64

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()

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

Indicates whether Series/DataFrame is empty.

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

Accessor object for geography properties of the Series values.

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_

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

Examples:

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

>>> df = bpd.DataFrame([[0, 2, 3], [0, 4, 1], [10, 20, 30]],
...                    columns=['A', 'B', 'C'])
>>> df
    A       B       C
0   0       2       3
1   0       4       1
2   10      20      30
<BLANKLINE>
[3 rows x 3 columns]

Get value at specified row/column pair

>>> df.iat[1, 2]
np.int64(1)

Get value within a series

>>> df.loc[0].iat[1]
np.int64(2)

Purely integer-location based indexing for selection by position.

Examples:

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

>>> mydict = [{'a': 1, 'b': 2, 'c': 3, 'd': 4},
...               {'a': 100, 'b': 200, 'c': 300, 'd': 400},
...               {'a': 1000, 'b': 2000, 'c': 3000, 'd': 4000}]
>>> df = bpd.DataFrame(mydict)
>>> df
      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000
<BLANKLINE>
[3 rows x 4 columns]

Indexing just the rows

With a scalar integer.

>>> type(df.iloc[0])
<class 'pandas.core.series.Series'>

>>> df.iloc[0]
a    1
b    2
c    3
d    4
Name: 0, dtype: Int64

With a list of integers.

>>> df.iloc[0]
a    1
b    2
c    3
d    4
Name: 0, dtype: Int64

>>> type(df.iloc[[0]])
<class 'bigframes.dataframe.DataFrame'>

>>> df.iloc[[0, 1]]
    a    b    c    d
0    1    2    3    4
1  100  200  300  400
<BLANKLINE>
[2 rows x 4 columns]

With a slice object.

>>> df.iloc[:3]
      a     b     c     d
0     1     2     3     4
1   100   200   300   400
2  1000  2000  3000  4000
<BLANKLINE>
[3 rows x 4 columns]

Indexing both axes

You can mix the indexer types for the index and columns. Use : to select the entire axis.

With scalar integers.

>>> df.iloc[0, 1]
np.int64(2)

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)

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_

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_

API documentation for list property.

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

Examples:

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

>>> df = bpd.DataFrame([[1, 2], [4, 5], [7, 8]],
...                    index=['cobra', 'viper', 'sidewinder'],
...                    columns=['max_speed', 'shield'])
>>> df
            max_speed  shield
cobra               1       2
viper               4       5
sidewinder          7       8
<BLANKLINE>
[3 rows x 2 columns]

Single label. Note this returns the row as a Series.

>>> df.loc['viper']
max_speed    4
shield       5
Name: viper, dtype: Int64

List of labels. Note using [[]] returns a DataFrame.

>>> df.loc[['viper', 'sidewinder']]
            max_speed  shield
viper               4       5
sidewinder          7       8
<BLANKLINE>
[2 rows x 2 columns]

Slice with labels for row and single label for column. As mentioned

above, note that both the start and stop of the slice are included.

>>> df.loc['cobra', 'shield']
np.int64(2)

Index (same behavior as df.reindex)

>>> df.loc[bpd.Index(["cobra", "viper"], name="foo")]
      max_speed  shield
cobra          1       2
viper          4       5
<BLANKLINE>
[2 rows x 2 columns]

Conditional that returns a boolean Series with column labels specified

>>> df.loc[df['shield'] > 6, ['max_speed']]
            max_speed
sidewinder          7
<BLANKLINE>
[1 rows x 1 columns]

Multiple conditional using | that returns a boolean Series

>>> df.loc[(df['max_speed'] > 4) | (df['shield'] < 5)]
            max_speed  shield
cobra               1       2
sidewinder          7       8
<BLANKLINE>
[2 rows x 2 columns]

Please ensure that each condition is wrapped in parentheses ().

Set value for an entire column

>>> df.loc[:, 'max_speed'] = 30
>>> df
            max_speed  shield
cobra              30       2
viper              30       5
sidewinder         30       8
<BLANKLINE>
[3 rows x 2 columns]

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'

>>> s.name = "Integers"
>>> s
0    1
1    2
2    3
Name: Integers, dtype: Int64

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'

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

Make plots of Series.

Examples:

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

>>> ser = bpd.Series([1, 2, 3, 3])
>>> plot = ser.plot(kind='hist', title="My plot")
>>> plot
<Axes: title={'center': 'My plot'}, ylabel='Frequency'>

BigQuery job metadata for the most recent query.

Return a tuple of the shape of the underlying data.

Examples:

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

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

Return the number of elements in the underlying data.

Examples:

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

For Series:

>>> s = bpd.Series(['Ant', 'Bear', 'Cow'])
>>> s
0     Ant
1    Bear
2     Cow
dtype: string
>>> s.size
3

For Index:

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

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

Accessor object for struct properties of the Series 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)

__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

Object to be added to the Series.

__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

Object to bitwise AND with the Series.

__array__(dtype=None, copy: typing.Optional[bool] = 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])

The dtype to use for the resulting NumPy array. By default, the dtype is inferred from the data.

Whether to copy the data, False is not supported.

__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__(
    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

Object to divide the Series by.

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

Index or slice of indices.

__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 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

Matrix multiplication using binary @ operator.

__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

Object to modulo the Series by.

__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

Object to multiply with the Series.

__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

Object to bitwise XOR with the Series.

__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

Object to exponentiate the Series with.

__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).

Object to which Series should be added.

__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

Object to bitwise AND with the Series.

__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).

Object to divide by the Series.

Matrix multiplication using binary @ operator.

__rmod__(other) -> bigframes.series.Series

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

Equivalent to Series.rmod(other).

Object to modulo by the Series.

__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).

Object to multiply the Series with.

__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

Object to bitwise XOR with the Series.

__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).

Object to exponentiate with the Series.

__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).

Object to subtract the Series from.

__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).

Object to divide by the Series.

__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

Object to subtract from the Series.

__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

Object to divide the Series by.

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.

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

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.

The string to add before each label.

{{0 or 'index', 1 or 'columns', None}}, default None. Axis to add prefix on.

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.

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

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

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

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

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).

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).

Examples:

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

For Series input, the output is a scalar indicating whether any element is True.

>>> bpd.Series([False, False]).any()
np.False_

>>> bpd.Series([True, False]).any()
np.True_

>>> bpd.Series([], dtype="float64").any()
np.False_

>>> bpd.Series([np.nan]).any()
np.False_

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, cloud_function_service_account="default")
... 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"],
...     cloud_function_service_account="default"
... )
... 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)

You could return an array output from the remote function.

>>> @bpd.remote_function(reuse=False, cloud_function_service_account="default")
... def text_analyzer(text: str) -> list[int]:
...     words = text.count(" ") + 1
...     periods = text.count(".")
...     exclamations = text.count("!")
...     questions = text.count("?")
...     return [words, periods, exclamations, questions]

>>> texts = bpd.Series([
...     "The quick brown fox jumps over the lazy dog.",
...     "I love this product! It's amazing.",
...     "Hungry? Wanna eat? Lets go!"
... ])
>>> features = texts.apply(text_analyzer)
>>> features
0    [9 1 0 0]
1    [6 1 1 0]
2    [5 0 1 2]
dtype: list<item: int64>[pyarrow]

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

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.

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.

area(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    stacked: bool = True,
    **kwargs
)

Draw a stacked area plot. An area plot displays quantitative data visually.

This function calls pandas.plot to generate a plot with a random sample of items. For consistent results, the random sampling is reproducible. Use the sampling_random_state parameter to modify the sampling seed.

Examples:

Draw an area plot based on basic business metrics:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame(
...     {
...         'sales': [3, 2, 3, 9, 10, 6],
...         'signups': [5, 5, 6, 12, 14, 13],
...         'visits': [20, 42, 28, 62, 81, 50],
...     },
...     index=["01-31", "02-28", "03-31", "04-30", "05-31", "06-30"]
... )
>>> ax = df.plot.area()

Area plots are stacked by default. To produce an unstacked plot, pass stacked=False:

>>> ax = df.plot.area(stacked=False)

Draw an area plot for a single column:

>>> ax = df.plot.area(y='sales')

Draw with a different x:

>>> df = bpd.DataFrame({
...     'sales': [3, 2, 3],
...     'visits': [20, 42, 28],
...     'day': [1, 2, 3],
... })
>>> ax = df.plot.area(x='day')

Coordinates for the X axis. By default uses the index.

Column to plot. By default uses all columns.

Area plots are stacked by default. Set to False to create a unstacked plot.

Number of random items for plotting.

Seed for random number generator.

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.

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.

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,
    ],
    *,
    errors: typing.Literal["raise", "null"] = "raise"
) -> 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

A dtype supported by BigQuery DataFrame include 'boolean', 'Float64', 'Int64', 'int64[pyarrow]', 'string', 'string[pyarrow]', 'timestamp[us, tz=UTC][pyarrow]', 'timestamp[us][pyarrow]', 'date32[day][pyarrow]', 'time64[us][pyarrow]'. A pandas.ExtensionDtype include pandas.BooleanDtype(), pandas.Float64Dtype(), pandas.Int64Dtype(), pandas.StringDtype(storage="pyarrow"), pd.ArrowDtype(pa.date32()), pd.ArrowDtype(pa.time64("us")), pd.ArrowDtype(pa.timestamp("us")), pd.ArrowDtype(pa.timestamp("us", tz="UTC")).

Control raising of exceptions on invalid data for provided dtype. If 'raise', allow exceptions to be raised if any value fails cast If 'null', will assign null value if value fails cast

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)

Number of lags to apply before performing autocorrelation.

bar(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    **kwargs
)

Draw a vertical bar plot.

This function calls pandas.plot to generate a plot with a random sample of items. For consistent results, the random sampling is reproducible. Use the sampling_random_state parameter to modify the sampling seed.

Examples:

Basic plot.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
>>> ax = df.plot.bar(x='lab', y='val', rot=0)

Plot a whole dataframe to a bar plot. Each column is assigned a distinct color, and each row is nested in a group along the horizontal axis.

>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
...          'rabbit', 'giraffe', 'coyote', 'horse']
>>> df = bpd.DataFrame({'speed': speed, 'lifespan': lifespan}, index=index)
>>> ax = df.plot.bar(rot=0)

Plot stacked bar charts for the DataFrame.

>>> ax = df.plot.bar(stacked=True)

If you don’t like the default colours, you can specify how you’d like each column to be colored.

>>> axes = df.plot.bar(
...     rot=0, subplots=True, color={"speed": "red", "lifespan": "green"}
... )

Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.

Allows plotting of one column versus another. If not specified, all numerical columns are used.

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
>>> import numpy as np
>>> 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

Left boundary.

Right boundary.

Include boundaries. Whether to set each bound as closed or open.

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

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

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.

case_when(caselist) -> bigframes.series.Series

Replace values where the conditions are True.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> 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

If you'd like to change the type, add a case with the condition True at the end of the case list

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

See also:

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

Takes the form: (condition0, replacement0), (condition1, replacement1), ... . condition should be a 1-D boolean array-like object or a callable. If condition is a callable, it is computed on the Series and should return a boolean Series or array. The callable must not change the input Series (though pandas doesnt check it). replacement should be a 1-D array-like object, a scalar or a callable. If replacement is a callable, it is computed on the Series and should return a scalar or Series. The callable must not change the input Series (though pandas doesnt check it).

clip(lower=None, upper=None)

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.

Minimum threshold value. All values below this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.

Maximum threshold value. All values above this threshold will be set to it. A missing threshold (e.g NA) will not clip the value.

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

The value(s) to be combined with the Series.

BigFrames DataFrames remote_function to apply. Takes two scalars as inputs and returns an element. Also accepts some numpy binary functions.

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

The value(s) to be used for filling null values.

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]

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)

The series with which this is to be correlated.

Correlation method to use - currently only "pearson" is supported.

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.

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)

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.

Examples:

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

>>> s1 = bpd.Series([0.90010907, 0.13484424, 0.62036035])
>>> s2 = bpd.Series([0.12528585, 0.26962463, 0.51111198])
>>> s1.cov(s2)
np.float64(-0.01685762652715874)

Series with which to compute the covariance.

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.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> 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.cummax()
0     2.0
1    <NA>
2     5.0
3     5.0
4     5.0
dtype: Float64

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.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> 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.cummin()
0     2.0
1    <NA>
2     2.0
3    -1.0
4    -1.0
dtype: Float64

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
>>> import numpy as np
>>> 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

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.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> 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.cumsum()
0     2.0
1    <NA>
2     7.0
3     6.0
4     6.0
dtype: Float64

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.

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

First discrete difference of element.

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

Examples:

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

Difference with previous row

>>> s = bpd.Series([1, 1, 2, 3, 5, 8])
>>> s.diff()
0    <NA>
1       0
2       1
3       1
4       2
5       3
dtype: Int64

Difference with 3rd previous row

>>> s.diff(periods=3)
0    <NA>
1    <NA>
2    <NA>
3       2
4       4
5       6
dtype: Int64

Difference with following row

>>> s.diff(periods=-1)
0       0
1      -1
2      -1
3      -2
4      -3
5    <NA>
dtype: Int64

Periods to shift for calculating difference, accepts negative values.

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.divide(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.divide(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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).

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.divmod(b)
(a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64,
a     0.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64)

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.

>>> 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)

The other object to compute the dot product with its columns.

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

Index labels to drop.

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

Return Series with duplicate values removed.

Examples:

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

Generate a Series with duplicated entries.

>>> 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

With the 'keep' parameter, the selection behaviour of duplicated values can be changed. The value 'first' keeps the first occurrence for each set of duplicated entries. The default value of keep is 'first'.

>>> s.drop_duplicates()
0     llama
1       cow
3    beetle
5     hippo
Name: animal, dtype: string

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

>>> s.drop_duplicates(keep='last')
1       cow
3    beetle
4     llama
5     hippo
Name: animal, dtype: string

The value False for parameter 'keep' discards all sets of duplicated entries.

>>> s.drop_duplicates(keep=False)
1       cow
3    beetle
5     hippo
Name: animal, dtype: string

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.

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.

If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.

For Series this parameter is unused and defaults to 0.

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
>>> import numpy as np
>>> 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

Unused. Parameter needed for compatibility with DataFrame.

Unsupported, do not set.

Not in use. Kept for compatibility.

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.

Examples:

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

By default, for each set of duplicated values, the first occurrence is set on False and all others on True:

>>> animals = bpd.Series(['llama', 'cow', 'llama', 'beetle', 'llama'])
>>> animals.duplicated()
0    False
1    False
2     True
3    False
4     True
dtype: boolean

which is equivalent to

>>> animals.duplicated(keep='first')
0    False
1    False
2     True
3    False
4     True
dtype: boolean

By using 'last', the last occurrence of each set of duplicated values is set on False and all others on True:

>>> animals.duplicated(keep='last')
0     True
1    False
2     True
3    False
4    False
dtype: boolean

By setting keep on False, all duplicates are True:

>>> animals.duplicated(keep=False)
0     True
1    False
2     True
3    False
4     True
dtype: boolean

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.

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.eq(b)
a    True
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: boolean

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.

The other Series or DataFrame to be compared with the first.

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

Provide expanding window calculations.

Minimum number of observations in window required to have a value; otherwise, result is np.nan.

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
0    [1 2 3]
1         []
2      [3 4]
dtype: list<item: int64>[pyarrow]

>>> s.explode()
0       1
0       2
0       3
1    <NA>
2       3
2       4
dtype: Int64

If True, the resulting index will be labeled 0, 1, …, n - 1.

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

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

Fill NA/NaN values using the specified method.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> 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

Value to use to fill holes (e.g. 0).

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.

Keep labels from axis which are in items.

Keep labels from axis for which "like in label == True".

Keep labels from axis for which re.search(regex, label) == True.

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.

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.floordiv(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

ge(other) -> bigframes.series.Series

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

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.ge(b)
a    True
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: boolean

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.series_group_by.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

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.

Split along rows (0) or columns (1). For Series this parameter is unused and defaults to 0.

If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both by and level.

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).

gt(other) -> bigframes.series.Series

Return Greater than of series and other, element-wise (binary operator gt).

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.gt(b)
a    False
b     <NA>
c     <NA>
d     <NA>
e     <NA>
dtype: boolean

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]

Default 5. Number of rows to select.

hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)

Draw one histogram of the DataFrame’s columns.

A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one matplotlib.axes.Axes. This is useful when the DataFrame's Series are in a similar scale.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> import numpy as np
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame(np.random.randint(1, 7, 6000), columns=['one'])
>>> df['two'] = np.random.randint(1, 7, 6000) + np.random.randint(1, 7, 6000)
>>> ax = df.plot.hist(bins=12, alpha=0.5)

Column in the DataFrame to group by. It is not supported yet.

Number of histogram bins to be used.

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.

Examples:

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

>>> s = bpd.Series(data=[1, None, 4, 3, 4],
...                index=['A', 'B', 'C', 'D', 'E'])
>>> s
A     1.0
B    <NA>
C     4.0
D     3.0
E     4.0
dtype: Float64

>>> s.idxmax()
'C'

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.

Examples:

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

>>> s = bpd.Series(data=[1, None, 4, 1],
...                index=['A', 'B', 'C', 'D'])
>>> s
A     1.0
B    <NA>
C     4.0
D     1.0
dtype: Float64

>>> s.idxmin()
'A'

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

Fill NaN values using an interpolation method.

Examples:

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

Filling in NaN in a Series via linear interpolation.

>>> s = bpd.Series([0, 1, np.nan, 3])
>>> s
0     0.0
1     1.0
2    <NA>
3     3.0
dtype: Float64

>>> s.interpolate()
0    0.0
1    1.0
2    2.0
3    3.0
dtype: Float64

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

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.

>>> 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

To invert the boolean values, use the ` operator:

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

Passing a single string as s.isin('llama') will raise an error. Use a list of one element instead:

>>> s.isin(['llama'])
0     True
1    False
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

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.

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

isnull() -> 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

Lazily iterate over (index, value) tuples.

This method returns an iterable tuple (index, value). This is convenient if you want to create a lazy iterator.

Examples:

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

>>> s = bpd.Series(['A', 'B', 'C'])
>>> for index, value in s.items():
...     print(f"Index : {index}, Value : {value}")
Index : 0, Value : A
Index : 1, Value : B
Index : 2, Value : C

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

Return alias for index.

Examples:

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

>>> s = bpd.Series([1, 2, 3], index=[0, 1, 2])
>>> s.keys()
Index([0, 1, 2], dtype='Int64')

Return unbiased kurtosis over requested axis.

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

Examples:

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

>>> s = bpd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
>>> s
cat      1
dog      2
dog      2
mouse    3
dtype: Int64

>>> s.kurt()
np.float64(1.5)

With a DataFrame

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

>>> df.kurt()
a    1.5
b    4.0
dtype: Float64

Return unbiased kurtosis over requested axis.

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

Examples:

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

>>> s = bpd.Series([1, 2, 2, 3], index=['cat', 'dog', 'dog', 'mouse'])
>>> s
cat      1
dog      2
dog      2
mouse    3
dtype: Int64

>>> s.kurt()
np.float64(1.5)

With a DataFrame

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

>>> df.kurt()
a    1.5
b    4.0
dtype: Float64

le(other) -> bigframes.series.Series

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

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.le(b)
a    True
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: boolean

line(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    **kwargs
)

Plot Series or DataFrame as lines. This function is useful to plot lines using DataFrame's values as coordinates.

This function calls pandas.plot to generate a plot with a random sample of items. For consistent results, the random sampling is reproducible. Use the sampling_random_state parameter to modify the sampling seed.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame(
...     {
...         'one': [1, 2, 3, 4],
...         'three': [3, 6, 9, 12],
...         'reverse_ten': [40, 30, 20, 10],
...     }
... )
>>> ax = df.plot.line(x='one')

Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.

Allows plotting of one column versus another. If not specified, all numerical columns are used.

The color for each of the DataFrame's columns. Possible values are: - A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19'. - A sequence of color strings referred to by name, RGB or RGBA code, which will be used for each column recursively. For instance ['green','yellow'] each column's %(kind)s will be filled in green or yellow, alternatively. If there is only a single column to be plotted, then only the first color from the color list will be used. - A dict of the form {column name : color}, so that each column will be colored accordingly. For example, if your columns are called a and b, then passing {'a': 'green', 'b': 'red'} will color %(kind)ss for column a in green and %(kind)ss for column b in red.

Number of random items for plotting.

Seed for random number generator.

lt(other) -> bigframes.series.Series

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

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.lt(b)
a    False
b     <NA>
c     <NA>
d     <NA>
e     <NA>
dtype: boolean

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.

>>> 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(cloud_function_service_account="default")
... 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

remote function, collections.abc.Mapping subclass or Series Mapping correspondence.

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, cloud_function_service_account="default")
... 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

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).

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).

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)

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)

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

Return the median of the values over the requested axis.

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s.median()
np.float64(2.0)

With a DataFrame

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

>>> df.median()
a    1.5
b    2.5
dtype: Float64

Default True. Get the exact median instead of an approximate one.

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)

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.mod(b)
a     0.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

Examples:

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

>>> s = bpd.Series([2, 4, 8, 2, 4, None])
>>> s.mode()
0    2.0
1    4.0
dtype: Float64

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.multiply(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.multiply(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.ne(b)
a    False
b     <NA>
c     <NA>
d     <NA>
e     <NA>
dtype: boolean

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

Return the largest n elements.

Examples:

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

>>> countries_population = {"Italy": 59000000, "France": 65000000,
...                          "Malta": 434000, "Maldives": 434000,
...                          "Brunei": 434000, "Iceland": 337000,
...                          "Nauru": 11300, "Tuvalu": 11300,
...                          "Anguilla": 11300, "Montserrat": 5200}
>>> s = bpd.Series(countries_population)
>>> s
Italy         59000000
France        65000000
Malta           434000
Maldives        434000
Brunei          434000
Iceland         337000
Nauru            11300
Tuvalu           11300
Anguilla         11300
Montserrat        5200
dtype: Int64

The n largest elements where n=5 by default.

>>> s.nlargest()
France      65000000
Italy       59000000
Malta         434000
Maldives      434000
Brunei        434000
dtype: Int64

The n largest elements where n=3. Default keep value is first so Malta will be kept.

>>> s.nlargest(3)
France    65000000
Italy     59000000
Malta       434000
dtype: Int64

The n largest elements where n=3 and keeping the last duplicates. Brunei will be kept since it is the last with value 434000 based on the index order.

>>> s.nlargest(3, keep='last')
France    65000000
Italy     59000000
Brunei      434000
dtype: Int64

The n largest elements where n=3 with all duplicates kept. Note that the returned Series has five elements due to the three duplicates.

>>> s.nlargest(3, keep='all')
France      65000000
Italy       59000000
Malta         434000
Maldives      434000
Brunei        434000
dtype: Int64

Return this many descending sorted values.

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.

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.

notnull() -> 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.

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

Return the smallest n elements.

Examples:

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

>>> countries_population = {"Italy": 59000000, "France": 65000000,
...                          "Malta": 434000, "Maldives": 434000,
...                          "Brunei": 434000, "Iceland": 337000,
...                          "Nauru": 11300, "Tuvalu": 11300,
...                          "Anguilla": 11300, "Montserrat": 5200}
>>> s = bpd.Series(countries_population)
>>> s
Italy         59000000
France        65000000
Malta           434000
Maldives        434000
Brunei          434000
Iceland         337000
Nauru            11300
Tuvalu           11300
Anguilla         11300
Montserrat        5200
dtype: Int64

The n smallest elements where n=5 by default.

>>> s.nsmallest()
Montserrat      5200
Nauru          11300
Tuvalu         11300
Anguilla       11300
Iceland       337000
dtype: Int64

The n smallest elements where n=3. Default keep value is first so Nauru and Tuvalu will be kept.

>>> s.nsmallest(3)
Montserrat     5200
Nauru         11300
Tuvalu        11300
dtype: Int64

The n smallest elements where n=3 with all duplicates kept. Note that the returned Series has four elements due to the three duplicates.

>>> s.nsmallest(3, keep='all')
Montserrat     5200
Nauru         11300
Tuvalu        11300
Anguilla      11300
dtype: Int64

Return this many ascending sorted values.

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.

Return number of unique elements in the object.

Excludes NA values by default.

Examples:

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

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

>>> s.nunique()
np.int64(4)

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

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.

Periods to shift for forming percent change.

peek(
    n: int = 5, *, force: bool = True, allow_large_results=None
) -> 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.

The number of rows to select from the series. Which N rows are returned is non-deterministic.

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.

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

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.pow(b)
a     1.0
b     1.0
c     1.0
d    <NA>
e    <NA>
dtype: Float64

Return the product of the values over the requested axis.

Return the product of the values over the requested axis.

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

The quantile(s) to compute, which can lie in range: 0 <= q <= 1.

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.add(b)
a     2.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

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.

For DataFrame objects, rank only numeric columns if set to True.

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.

Whether or not the elements should be ranked in ascending order.

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.divide(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.divmod(b)
(a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64,
a     0.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64)

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.

New labels for the index. Preferably an Index object to avoid duplicating data.

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.

Its row and column indices are used to define the new indices of this object.

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

Functions or dict-like are transformations to apply to the index. Scalar or hashable sequence-like will alter the Series.name attribute.

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.

Value to set the axis name attribute.

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.

Reference level by number or key.

For Series this parameter is unused and defaults to 0.

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

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 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.

Whether to interpret to_replace and/or value as regular expressions. If this is True then to_replace must be a string.

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
>>> import pandas as pd
>>> 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

>>> arrays = [np.array(['bar', 'bar', 'baz', 'baz']),
...           np.array(['one', 'two', 'one', 'two'])]
>>> s2 = bpd.Series(
...     range(4), name='foo',
...     index=pd.MultiIndex.from_arrays(arrays,
...                                     names=['a', 'b']))

If level is not set, all levels are removed from the Index.

>>> s2.reset_index()
     a    b  foo
0  bar  one    0
1  bar  two    1
2  baz  one    2
3  baz  two    3
<BLANKLINE>
[4 rows x 3 columns]

Just reset the index, without inserting it as a column in the new DataFrame.

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(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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.floordiv(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.mod(b)
a     0.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.multiply(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

rolling(
    window: (
        int
        | pandas._libs.tslibs.timedeltas.Timedelta
        | numpy.timedelta64
        | datetime.timedelta
        | str
    ),
    min_periods: int | None = None,
    closed: typing.Literal["right", "left", "both", "neither"] = "right",
) -> bigframes.core.window.rolling.Window

Provide rolling window calculations.

Examples:

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

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

>>> df = bpd.DataFrame({'A': [0,1,2,3], 'B': [0,2,4,6]})
>>> df.rolling(window=2, on='A', closed='both').sum()
A     B
0  0  <NA>
1  1     2
2  2     6
3  3    12
<BLANKLINE>
[4 rows x 2 columns]

Size of the moving window. If an integer, the fixed number of observations used for each window. If a string, the timedelta representation in string. This string must be parsable by pandas.Timedelta(). Otherwise, the time range for each window.

Minimum number of observations in window required to have a value; otherwise, result is np.nan. For a window that is specified by an integer, min_periods will default to the size of the window. For a window that is not spicified by an interger, min_periods will default to 1.

For a DataFrame, a column label on which to calculate the rolling window, rather than the DataFrame’s index.

If 'right', the first point in the window is excluded from calculations. If 'left', the last point in the window is excluded from calculations. If 'both', the no points in the window are excluded from calculations. If 'neither', the first and last points in the window are excluded from calculations.

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

Number of decimal places to round to. If decimals is negative, it specifies the number of positions to the left of the decimal point.

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.pow(b)
a     1.0
b     1.0
c     1.0
d    <NA>
e    <NA>
dtype: Float64

rsub(other) -> 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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.subtract(b)
a     0.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.divide(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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

Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.

Fraction of axis items to return. Cannot be used with n.

Seed for random number generator.

• '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.

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

Shift index by desired number of periods.

Shifts the index without realigning the data.

Return unbiased skew over requested axis.

Normalized by N-1.

Examples:

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

>>> s = bpd.Series([1, 2, 3])
>>> s.skew()
np.float64(0.0)

With a DataFrame

>>> df = bpd.DataFrame({'a': [1, 2, 3], 'b': [2, 3, 4], 'c': [1, 3, 5]},
...                    index=['tiger', 'zebra', 'cow'])
>>> df
        a   b   c
tiger   1   2   1
zebra   2   3   3
cow     3   4   5
<BLANKLINE>
[3 rows x 3 columns]

>>> df.skew()
a   0.0
b   0.0
c   0.0
dtype: Float64

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.

Examples:

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

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

Sort Descending

>>> s.sort_index(ascending=False)
4    d
3    a
2    b
1    c
dtype: string

By default NaNs are put at the end, but use na_position to place them at the beginning

>>> s = bpd.Series(['a', 'b', 'c', 'd'], index=[3, 2, 1, np.nan])
>>> s.sort_index(na_position='first')
<NA>    d
1.0     c
2.0     b
3.0     a
dtype: string

Unused. Parameter needed for compatibility with DataFrame.

Whether to modify the Series rather than creating a new one.

Sort ascending vs. descending. When the index is a MultiIndex the sort direction can be controlled for each level individually.

If 'first' puts NaNs at the beginning, 'last' puts NaNs at the end. Not implemented for MultiIndex.

Sort by the values.

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

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> 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

Unused. Parameter needed for compatibility with DataFrame.

Whether to modify the Series rather than creating a new one.

If True, sort values in ascending order, otherwise descending.

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

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

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(other) -> 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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.subtract(b)
a     0.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

subtract(other) -> 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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.subtract(b)
a     0.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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)

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.

Levels of the indices to be swapped. Can pass level name as string.

Levels of the indices to be swapped. Can pass level name as string.

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.

Number of rows to select.

take(
    indices: typing.Sequence[int], axis: int | str | None = 0, **kwargs
) -> bigframes.series.Series

Return the elements in the given positional indices along an axis.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

An array of ints indicating which positions to take.

The axis on which to select elements. 0 means that we are selecting rows, 1 means that we are selecting columns. For Series this parameter is unused and defaults to 0.

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

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

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.

If True, write row names (index).

If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB. This parameter has no effect when results are saved to Google Cloud Storage (GCS).

to_dict(into: type[dict] = <class 'dict'>, *, allow_large_results: typing.Optional[bool] = None) -> typing.Mapping

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

Examples:

>>> import bigframes.pandas as bpd
>>> from collections import OrderedDict, defaultdict
>>> bpd.options.display.progress_bar = None

>>> s = bpd.Series([1, 2, 3, 4])
>>> s.to_dict()
{np.int64(0): 1, np.int64(1): 2, np.int64(2): 3, np.int64(3): 4}

>>> s.to_dict(into=OrderedDict)
OrderedDict({np.int64(0): 1, np.int64(1): 2, np.int64(2): 3, np.int64(3): 4})

>>> dd = defaultdict(list)
>>> s.to_dict(into=dd)
defaultdict(<class 'list'>, {np.int64(0): 1, np.int64(1): 2, np.int64(2): 3, np.int64(3): 4})

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.

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

to_excel(
    excel_writer, sheet_name="Sheet1", *, allow_large_results=None, **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.

File path or existing ExcelWriter.

Name of sheet to contain Series.

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

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.

Examples:

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

>>> s = bpd.Series(["a", "b", "c"],
...                name="vals")
>>> s.to_frame()
  vals
0    a
1    b
2    c
<BLANKLINE>
[3 rows x 1 columns]

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

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.

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'.

If True, write row names (index).

If 'orient' is 'records' write out line-delimited json format. Will throw ValueError if incorrect 'orient' since others are not list-like.

If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB. This parameter has no effect when results are saved to Google Cloud Storage (GCS).

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

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

Buffer to write to. If None, the output is returned as a string.

The subset of columns to write. Writes all columns by default.

Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.

Write row names (index).

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

to_list(*, allow_large_results: typing.Optional[bool] = None) -> 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]

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

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

Print Series in Markdown-friendly format.

Examples:

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

>>> s = bpd.Series(["elk", "pig", "dog", "quetzal"], name="animal")
>>> print(s.to_markdown())
|    | animal   |
|---:|:---------|
|  0 | elk      |
|  1 | pig      |
|  2 | dog      |
|  3 | quetzal  |

Output markdown with a tabulate option.

>>> print(s.to_markdown(tablefmt="grid"))
+----+----------+
|    | animal   |
+====+==========+
|  0 | elk      |
+----+----------+
|  1 | pig      |
+----+----------+
|  2 | dog      |
+----+----------+
|  3 | quetzal  |
+----+----------+

Buffer to write to. If None, the output is returned as a string.

Mode in which file is opened, "wt" by default.

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

Add index (row) labels.

to_numpy(
    dtype=None,
    copy=False,
    na_value=_NoDefault.no_default,
    *,
    allow_large_results=None,
    **kwargs
) -> numpy.ndarray

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

Examples:

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

>>> ser = bpd.Series(pd.Categorical(['a', 'b', 'a']))
>>> ser.to_numpy()
array(['a', 'b', 'a'], dtype=object)

Specify the dtype to control how datetime-aware data is represented. Use dtype=object to return an ndarray of pandas Timestamp objects, each with the correct tz.

>>> ser = bpd.Series(pd.date_range('2000', periods=2, tz="CET"))
>>> ser.to_numpy(dtype=object)
array([Timestamp('1999-12-31 23:00:00+0000', tz='UTC'),
       Timestamp('2000-01-01 23:00:00+0000', tz='UTC')], dtype=object)

Or dtype=datetime64[ns] to return an ndarray of native datetime64 values. The values are converted to UTC and the timezone info is dropped.

>>> ser.to_numpy(dtype="datetime64[ns]")
array(['1999-12-31T23:00:00.000000000', '2000-01-01T23:00:00.000000000'],
      dtype='datetime64[ns]')

The dtype to pass to numpy.asarray.

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.

The value to use for missing values. The default value depends on dtype and the type of the array.

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

to_pandas(
    max_download_size: typing.Optional[int] = None,
    sampling_method: typing.Optional[str] = None,
    random_state: typing.Optional[int] = None,
    *,
    ordered: bool = True,
    dry_run: bool = False,
    allow_large_results: typing.Optional[bool] = None
) -> pandas.core.series.Series

Writes Series to pandas Series.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> s = bpd.Series([4, 3, 2])

Download the data from BigQuery and convert it into an in-memory pandas Series.

>>> s.to_pandas()
0    4
1    3
2    2
dtype: Int64

Estimate job statistics without processing or downloading data by using dry_run=True.

>>> s.to_pandas(dry_run=True) # doctest: +SKIP
columnCount                                                            1
columnDtypes                                               {None: Int64}
indexLevel                                                             1
indexDtypes                                                      [Int64]
projectId                                                  bigframes-dev
location                                                              US
jobType                                                            QUERY
destinationTable       {'projectId': 'bigframes-dev', 'datasetId': '_...
useLegacySql                                                       False
referencedTables                                                    None
totalBytesProcessed                                                    0
cacheHit                                                           False
statementType                                                     SELECT
creationTime                            2025-04-03 18:54:59.219000+00:00
dtype: object

.. deprecated:: 2.0.0 max_download_size parameter is deprecated. Please use to_pandas_batches() method instead. Download size threshold in MB. If max_download_size is exceeded when downloading data, 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.

.. deprecated:: 2.0.0 sampling_method parameter is deprecated. Please use sample() method instead. 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.

.. deprecated:: 2.0.0 random_state parameter is deprecated. Please use sample() method instead. 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.

Determines whether the resulting pandas series will be ordered. In some cases, unordered may result in a faster-executing query.

If this argument is true, this method will not process the data. Instead, it returns a Pandas Series containing dry run job statistics

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

to_pandas_batches(
    page_size: typing.Optional[int] = None,
    max_results: typing.Optional[int] = None,
    *,
    allow_large_results: typing.Optional[bool] = None
) -> typing.Iterable[pandas.core.series.Series]

Stream Series results to an iterable of pandas Series.

page_size and max_results determine the size and number of batches, see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob#google_cloud_bigquery_job_QueryJob_result

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> s = bpd.Series([4, 3, 2, 2, 3])

Iterate through the results in batches, limiting the total rows yielded across all batches via max_results:

>>> for s_batch in s.to_pandas_batches(max_results=3):
...     print(s_batch)
0    4
1    3
2    2
dtype: Int64

Alternatively, control the approximate size of each batch using page_size and fetch batches manually using next():

>>> it = s.to_pandas_batches(page_size=2)
>>> next(it)
0    4
1    3
dtype: Int64
>>> next(it)
2    2
3    2
dtype: Int64

The maximum number of rows of each batch. Non-positive values are ignored.

The maximum total number of rows of all batches.

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

to_pickle(path, *, allow_large_results=None, **kwargs) -> None

Pickle (serialize) object to file.

Examples:

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

>>> original_df = bpd.DataFrame({"foo": range(5), "bar": range(5, 10)})
>>> original_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
<BLANKLINE>
[5 rows x 2 columns]

>>> original_df.to_pickle("./dummy.pkl")

>>> unpickled_df = bpd.read_pickle("./dummy.pkl")
>>> unpickled_df
   foo  bar
0    0    5
1    1    6
2    2    7
3    3    8
4    4    9
<BLANKLINE>
[5 rows x 2 columns]

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.

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

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,
    *,
    allow_large_results=None
) -> typing.Optional[str]

Render a string representation of the Series.

Buffer to write to.

String representation of NaN to use, default 'NaN'.

Formatter function to apply to columns' elements if they are floats, default None.

Add the Series header (index name).

Add index (row) labels, default True.

Add the Series length.

Add the Series dtype.

Add the Series name if not None.

Maximum number of rows to show before truncating. If None, show all.

The number of rows to display in a truncated repr (when number of rows is above max_rows).

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

to_xarray(*, allow_large_results: typing.Optional[bool] = None)

Return an xarray object from the pandas object.

tolist(*, allow_large_results: typing.Optional[bool] = None) -> 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]

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

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

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.

Examples:

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

>>> a = bpd.Series([1, 1, 1, np.nan], index=['a', 'b', 'c', 'd'])
>>> a
a     1.0
b     1.0
c     1.0
d    <NA>
dtype: Float64

>>> b = bpd.Series([1, np.nan, 1, np.nan], index=['a', 'b', 'd', 'e'])
>>> b
a     1.0
b    <NA>
d     1.0
e    <NA>
dtype: Float64

>>> a.divide(b)
a     1.0
b    <NA>
c    <NA>
d    <NA>
e    <NA>
dtype: Float64

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.

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.

unstack(
    level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] = -1,
)

Unstack, also known as pivot, Series with MultiIndex to produce DataFrame.

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(
    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

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

Sort by frequencies.

Sort in ascending order.

Don't include counts of NaN.

Return unbiased variance over requested axis.

Normalized by N-1 by default.

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

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).

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).

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

Last updated 2025-08-12 UTC.

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