Class DataFrame (1.40.0)

DataFrame(
    data=None,
    index: vendored_pandas_typing.Axes | None = None,
    columns: vendored_pandas_typing.Axes | None = None,
    dtype: typing.Optional[
        bigframes.dtypes.DtypeString | bigframes.dtypes.Dtype
    ] = None,
    copy: typing.Optional[bool] = None,
    *,
    session: typing.Optional[bigframes.session.Session] = None
)

Two-dimensional, size-mutable, potentially heterogeneous tabular data.

Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure.

Properties

T

The transpose of the DataFrame.

All columns must be the same dtype (numerics can be coerced to a common supertype).

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df
   col1  col2
0     1     3
1     2     4
<BLANKLINE>
[2 rows x 2 columns]

>>> df.T
      0  1
col1  1  2
col2  3  4
<BLANKLINE>
[2 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame`	The transposed DataFrame.

at

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 within a series

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

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

bqclient

BigQuery REST API Client the DataFrame uses for operations.

columns

The column labels of the DataFrame.

Examples:

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

You can access the column labels of a DataFrame via columns property.

>>> df = bpd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
...                     'Age': [25, 30, 35],
...                     'Location': ['Seattle', 'New York', 'Kona']},
...                    index=([10, 20, 30]))
>>> df
      Name  Age  Location
10   Alice   25   Seattle
20     Bob   30  New York
30  Aritra   35      Kona
<BLANKLINE>
[3 rows x 3 columns]
>>> df.columns
Index(['Name', 'Age', 'Location'], dtype='object')

You can also set new labels for columns.

>>> df.columns = ["NewName", "NewAge", "NewLocation"]
>>> df
   NewName  NewAge NewLocation
10   Alice      25     Seattle
20     Bob      30    New York
30  Aritra      35        Kona
<BLANKLINE>
[3 rows x 3 columns]
>>> df.columns
Index(['NewName', 'NewAge', 'NewLocation'], dtype='object')

dtypes

Return the dtypes in the DataFrame.

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

Examples:

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

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

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

empty

Indicates whether Series/DataFrame is empty.

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

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

iat

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)

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

iloc

Purely integer-location based indexing for selection by position.

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

index

The index (row labels) of the DataFrame.

The index of a DataFrame is a series of labels that identify each row. The labels can be integers, strings, or any other hashable type. The index is used for label-based access and alignment, and can be accessed or modified using this attribute.

Examples:

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

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

>>> df = bpd.DataFrame({'Name': ['Alice', 'Bob', 'Aritra'],
...                     'Age': [25, 30, 35],
...                     'Location': ['Seattle', 'New York', 'Kona']},
...                    index=([10, 20, 30]))
>>> df
      Name  Age  Location
10   Alice   25   Seattle
20     Bob   30  New York
30  Aritra   35      Kona
<BLANKLINE>
[3 rows x 3 columns]
>>> df.index # doctest: +ELLIPSIS
Index([10, 20, 30], dtype='Int64')
>>> df.index.values
array([10, 20, 30])

Let's try setting a new index for the dataframe and see that reflect via

index property.

>>> df1 = df.set_index(["Name", "Location"])
>>> df1
                 Age
Name   Location
Alice  Seattle    25
Bob    New York   30
Aritra Kona       35
<BLANKLINE>
[3 rows x 1 columns]
>>> df1.index # doctest: +ELLIPSIS
MultiIndex([( 'Alice',  'Seattle'),
    (   'Bob', 'New York'),
    ('Aritra',     'Kona')],
   names=['Name', 'Location'])
>>> df1.index.values
array([('Alice', 'Seattle'), ('Bob', 'New York'), ('Aritra', 'Kona')],
    dtype=object)

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

loc

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

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

ndim

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

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

plot

Make plots of Dataframes.

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

query_job

BigQuery job metadata for the most recent query.

Returns
Type	Description
`None or google.cloud.bigquery.QueryJob`	The most recent `QueryJob`_.

semantics

API documentation for semantics property.

shape

Return a tuple representing the dimensionality of the DataFrame.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2, 3],
...                     'col2': [4, 5, 6]})
>>> df.shape
(3, 2)

Returns
Type	Description
`Tuple[int, int]`	Tuple of array dimensions.

size

Return an int representing the number of elements in this object.

Examples:

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

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.size
4

Returns
Type	Description
`int`	Return the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame.

sql

Compiles this DataFrame's expression tree to SQL.

Returns
Type	Description
`str`	string representing the compiled SQL.

struct

API documentation for struct property.

values

Return the values of DataFrame in the form of a NumPy array.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.values
array([[1, 3],
       [2, 4]], dtype=object)

Returns
Type	Description
`numpy.ndarray`	The values of the DataFrame.

Methods

add

__add__(other) -> bigframes.dataframe.DataFrame

Get addition of DataFrame and other, column-wise, using arithmetic operator +.

Equivalent to DataFrame.add(other).

Examples:

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

>>> df = bpd.DataFrame({
...         'height': [1.5, 2.6],
...         'weight': [500, 800]
...     },
...     index=['elk', 'moose'])
>>> df
       height  weight
elk       1.5     500
moose     2.6     800
<BLANKLINE>
[2 rows x 2 columns]

Adding a scalar affects all rows and columns.

>>> df + 1.5
       height  weight
elk       3.0   501.5
moose     4.1   801.5
<BLANKLINE>
[2 rows x 2 columns]

You can add another DataFrame with index and columns aligned.

>>> delta = bpd.DataFrame({
...         'height': [0.5, 0.9],
...         'weight': [50, 80]
...     },
...     index=['elk', 'moose'])
>>> df + delta
       height  weight
elk       2.0     550
moose     3.5     880
<BLANKLINE>
[2 rows x 2 columns]

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

>>> delta = bpd.DataFrame({
...         'depth': [0.5, 0.9, 1.0],
...         'weight': [50, 80, 100]
...     },
...     index=['elk', 'moose', 'bison'])
>>> df + delta
       depth  height  weight
elk     <NA>    <NA>     550
moose   <NA>    <NA>     880
bison   <NA>    <NA>    <NA>
<BLANKLINE>
[3 rows x 3 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of adding `other` to DataFrame.

and

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

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

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

Returns
Type	Description
`bigframes.dataframe.DataFrame`	The result of the operation.

array

__array__(dtype=None, copy: typing.Optional[bool] = None) -> numpy.ndarray

Returns the rows as NumPy array.

Equivalent to DataFrame.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

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [11, 22, 33]})

>>> np.array(df)
array([[1, 11],
    [2, 22],
    [3, 33]], dtype=object)

>>> np.asarray(df)
array([[1, 11],
    [2, 22],
    [3, 33]], dtype=object)

Parameters
Name	Description
`dtype`	`str or numpy.dtype, optional` The dtype to use for the resulting NumPy array. By default, the dtype is inferred from the data.
`copy`	`bool or None, optional` Whether to copy the data, False is not supported.

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

__array_ufunc__

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

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

eq

__eq__(other) -> bigframes.dataframe.DataFrame

Check equality of DataFrame and other, element-wise, using logical operator ==.

Equivalent to DataFrame.eq(other).

Examples:

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

>>> df = bpd.DataFrame({
...         'a': [0, 3, 4],
...         'b': [360, 0, 180]
...      })
>>> df == 0
       a      b
0   True  False
1  False   True
2  False  False
<BLANKLINE>
[3 rows x 2 columns]

Parameter
Name	Description
`other`	`scalar or DataFrame` Object to be compared to the DataFrame for equality.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of comparing `other` to DataFrame.

floordiv

__floordiv__(other)

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

Equivalent to DataFrame.floordiv(other).

Examples:

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

You can divide by a scalar:

>>> df = bpd.DataFrame({"a": [15, 15, 15], "b": [30, 30, 30]})
>>> df // 2
   a   b
0  7  15
1  7  15
2  7  15
<BLANKLINE>
[3 rows x 2 columns]

You can also divide by another DataFrame with index and column labels aligned:

>>> divisor = bpd.DataFrame({"a": [2, 3, 4], "b": [5, 6, 7]})
>>> df // divisor
   a  b
0  7  6
1  5  5
2  3  4
<BLANKLINE>
[3 rows x 2 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the integer divison.

ge

__ge__(other) -> bigframes.dataframe.DataFrame

Check whether DataFrame is greater than or equal to other, element-wise, using logical operator >=.

Equivalent to DataFrame.ge(other).

Examples:

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

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

Parameter
Name	Description
`other`	`scalar or DataFrame` Object to be compared to the DataFrame.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of comparing `other` to DataFrame.

getitem

__getitem__(
    key: typing.Union[
        typing.Hashable,
        typing.Sequence[typing.Hashable],
        pandas.core.indexes.base.Index,
        bigframes.series.Series,
    ],
)

Gets the specified column(s) from the DataFrame.

Examples:

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

>>> df = bpd.DataFrame({
...     "name" : ["alpha", "beta", "gamma"],
...     "age": [20, 30, 40],
...     "location": ["WA", "NY", "CA"]
... })
>>> df
    name  age location
0  alpha   20       WA
1   beta   30       NY
2  gamma   40       CA
<BLANKLINE>
[3 rows x 3 columns]

You can specify a column label to retrieve the corresponding Series.

>>> df["name"]
0    alpha
1     beta
2    gamma
Name: name, dtype: string

You can specify a list of column labels to retrieve a Dataframe.

>>> df[["name", "age"]]
    name  age
0  alpha   20
1   beta   30
2  gamma   40
<BLANKLINE>
[3 rows x 2 columns]

You can specify a condition as a series of booleans to retrieve matching rows.

>>> df[df["age"] > 25]
    name  age location
1   beta   30       NY
2  gamma   40       CA
<BLANKLINE>
[2 rows x 3 columns]

You can specify a pandas Index with desired column labels.

>>> import pandas as pd
>>> df[pd.Index(["age", "location"])]
   age location
0   20       WA
1   30       NY
2   40       CA
<BLANKLINE>
[3 rows x 2 columns]

Parameter
Name	Description
`key`	`index` Index or list of indices. It can be a column label, a list of column labels, a Series of booleans or a pandas Index of desired column labels

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

gt

__gt__(other) -> bigframes.dataframe.DataFrame

Check whether DataFrame is greater than other, element-wise, using logical operator >.

Equivalent to DataFrame.gt(other).

Examples:

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

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

Parameter
Name	Description
`other`	`scalar or DataFrame` Object to be compared to the DataFrame.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of comparing `other` to DataFrame.

invert

__invert__() -> bigframes.dataframe.DataFrame

Returns the bitwise inversion of the DataFrame, element-wise using operator ````.

Examples:

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

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of inverting elements in the input.

le

__le__(other) -> bigframes.dataframe.DataFrame

Check whether DataFrame is less than or equal to other, element-wise, using logical operator <=.

Equivalent to DataFrame.le(other).

Examples:

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

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

Parameter
Name	Description
`other`	`scalar or DataFrame` Object to be compared to the DataFrame.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of comparing `other` to DataFrame.

len

__len__()

Returns number of rows in the DataFrame, serves len operator.

Examples:

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

>>> df = bpd.DataFrame({
...         'a': [0, 1, 2],
...         'b': [3, 4, 5]
...      })
>>> len(df)
3

lt

__lt__(other) -> bigframes.dataframe.DataFrame

Check whether DataFrame is less than other, element-wise, using logical operator <.

Equivalent to DataFrame.lt(other).

Examples:

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

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

Parameter
Name	Description
`other`	`scalar or DataFrame` Object to be compared to the DataFrame.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of comparing `other` to DataFrame.

matmul

__matmul__(other) -> bigframes.dataframe.DataFrame

Compute the matrix multiplication between the DataFrame and other, using operator @.

Equivalent to DataFrame.dot(other).

Examples:

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

>>> left = bpd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> left
   0  1   2   3
0  0  1  -2  -1
1  1  1   1   1
<BLANKLINE>
[2 rows x 4 columns]
>>> right = bpd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> right
    0   1
0   0   1
1   1   2
2  -1  -1
3   2   0
<BLANKLINE>
[4 rows x 2 columns]
>>> left @ right
   0  1
0  1  4
1  2  2
<BLANKLINE>
[2 rows x 2 columns]

The operand can be a Series, in which case the result will also be a Series:

>>> right = bpd.Series([1, 2, -1,0])
>>> left @ right
0    4
1    2
dtype: Int64

Parameter
Name	Description
`other`	`DataFrame or Series` Object to be matrix multiplied with the DataFrame.

Returns
Type	Description
`DataFrame or Series`	The result of the matrix multiplication.

mod

__mod__(other)

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

Equivalent to DataFrame.mod(other).

Examples:

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

You can modulo with a scalar:

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df % 3
   a  b
0  1  1
1  2  2
2  0  0
<BLANKLINE>
[3 rows x 2 columns]

You can also modulo with another DataFrame with index and column labels aligned:

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

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the modulo.

mul

__mul__(other)

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

Equivalent to DataFrame.mul(other).

Examples:

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

You can multiply with a scalar:

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df * 3
   a   b
0  3  12
1  6  15
2  9  18
<BLANKLINE>
[3 rows x 2 columns]

You can also multiply with another DataFrame with index and column labels aligned:

>>> df1 = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df * df1
   a   b
0  2  12
1  4  15
2  6  18
<BLANKLINE>
[3 rows x 2 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the multiplication.

ne

__ne__(other) -> bigframes.dataframe.DataFrame

Check inequality of DataFrame and other, element-wise, using logical operator !=.

Equivalent to DataFrame.ne(other).

Examples:

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

>>> df = bpd.DataFrame({
...         'a': [0, 3, 4],
...         'b': [360, 0, 180]
...      })
>>> df != 0
       a      b
0  False   True
1   True  False
2   True   True
<BLANKLINE>
[3 rows x 2 columns]

Parameter
Name	Description
`other`	`scalar or DataFrame` Object to be compared to the DataFrame for inequality.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of comparing `other` to DataFrame.

or

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

Get bitwise OR of DataFrame and other, element-wise, using operator |.

Parameter
Name	Description
`other`	`scalar, Series or DataFrame` Object to bitwise OR with the DataFrame.

Returns
Type	Description
`bigframes.dataframe.DataFrame`	The result of the operation.

pow

__pow__(other)

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

Equivalent to DataFrame.pow(other).

Examples:

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

You can exponentiate with a scalar:

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df ** 2
   a   b
0  1  16
1  4  25
2  9  36
<BLANKLINE>
[3 rows x 2 columns]

You can also exponentiate with another DataFrame with index and column labels aligned:

>>> exponent = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df ** exponent
   a    b
0  1   64
1  4  125
2  9  216
<BLANKLINE>
[3 rows x 2 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the exponentiation.

radd

__radd__(other) -> bigframes.dataframe.DataFrame

Get addition of DataFrame and other, column-wise, using arithmetic operator +.

Equivalent to DataFrame.add(other).

Examples:

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

>>> df = bpd.DataFrame({
...         'height': [1.5, 2.6],
...         'weight': [500, 800]
...     },
...     index=['elk', 'moose'])
>>> df
       height  weight
elk       1.5     500
moose     2.6     800
<BLANKLINE>
[2 rows x 2 columns]

Adding a scalar affects all rows and columns.

>>> df + 1.5
       height  weight
elk       3.0   501.5
moose     4.1   801.5
<BLANKLINE>
[2 rows x 2 columns]

You can add another DataFrame with index and columns aligned.

>>> delta = bpd.DataFrame({
...         'height': [0.5, 0.9],
...         'weight': [50, 80]
...     },
...     index=['elk', 'moose'])
>>> df + delta
       height  weight
elk       2.0     550
moose     3.5     880
<BLANKLINE>
[2 rows x 2 columns]

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

>>> delta = bpd.DataFrame({
...         'depth': [0.5, 0.9, 1.0],
...         'weight': [50, 80, 100]
...     },
...     index=['elk', 'moose', 'bison'])
>>> df + delta
       depth  height  weight
elk     <NA>    <NA>     550
moose   <NA>    <NA>     880
bison   <NA>    <NA>    <NA>
<BLANKLINE>
[3 rows x 3 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of adding `other` to DataFrame.

rand

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

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

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

Returns
Type	Description
`bigframes.dataframe.DataFrame`	The result of the operation.

repr

__repr__() -> str

Converts a DataFrame to a string. Calls to_pandas.

Only represents the first <xref uid="bigframes.options">bigframes.options</xref>.display.max_rows.

rfloordiv

__rfloordiv__(other)

Get integer divison of other by DataFrame.

Equivalent to DataFrame.rfloordiv(other).

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the integer divison.

rmod

__rmod__(other)

Get integer divison of other by DataFrame.

Equivalent to DataFrame.rmod(other).

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the modulo.

rmul

__rmul__(other)

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

Equivalent to DataFrame.rmul(other).

Examples:

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

You can multiply with a scalar:

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df * 3
   a   b
0  3  12
1  6  15
2  9  18
<BLANKLINE>
[3 rows x 2 columns]

You can also multiply with another DataFrame with index and column labels aligned:

>>> df1 = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df * df1
   a   b
0  2  12
1  4  15
2  6  18
<BLANKLINE>
[3 rows x 2 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the multiplication.

ror

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

Get bitwise OR of DataFrame and other, element-wise, using operator |.

Parameter
Name	Description
`other`	`scalar, Series or DataFrame` Object to bitwise OR with the DataFrame.

Returns
Type	Description
`bigframes.dataframe.DataFrame`	The result of the operation.

rpow

__rpow__(other)

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

Equivalent to DataFrame.rpow(other).

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

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

rsub

__rsub__(other)

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

Equivalent to DataFrame.rsub(other).

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the subtraction.

rtruediv

__rtruediv__(other)

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

Equivalent to DataFrame.rtruediv(other).

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the division.

rxor

__rxor__(
    other: bool | int | bigframes.series.Series,
) -> bigframes.dataframe.DataFrame

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

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

Returns
Type	Description
`bigframes.dataframe.DataFrame`	The result of the operation.

setitem

__setitem__(key: str, value: SingleItemValue)

Modify or insert a column into the DataFrame.

Examples:

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

>>> df = bpd.DataFrame({
...     "name" : ["alpha", "beta", "gamma"],
...     "age": [20, 30, 40],
...     "location": ["WA", "NY", "CA"]
... })
>>> df
    name  age location
0  alpha   20       WA
1   beta   30       NY
2  gamma   40       CA
<BLANKLINE>
[3 rows x 3 columns]

You can add assign a constant to a new column.

>>> df["country"] = "USA"
>>> df
    name  age location country
0  alpha   20       WA     USA
1   beta   30       NY     USA
2  gamma   40       CA     USA
<BLANKLINE>
[3 rows x 4 columns]

You can assign a Series to a new column.

>>> df["new_age"] = df["age"] + 5
>>> df
    name  age location country  new_age
0  alpha   20       WA     USA       25
1   beta   30       NY     USA       35
2  gamma   40       CA     USA       45
<BLANKLINE>
[3 rows x 5 columns]

You can assign a Series to an existing column.

>>> df["new_age"] = bpd.Series([29, 39, 19], index=[1, 2, 0])
>>> df
    name  age location country  new_age
0  alpha   20       WA     USA       19
1   beta   30       NY     USA       29
2  gamma   40       CA     USA       39
<BLANKLINE>
[3 rows x 5 columns]

Parameters
Name	Description
`key`	`column index` It can be a new column to be inserted, or an existing column to be modified.
`value`	`scalar or Series` Value to be assigned to the column

sub

__sub__(other)

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

Equivalent to DataFrame.sub(other).

Examples:

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

You can subtract a scalar:

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df - 2
    a  b
0  -1  2
1   0  3
2   1  4
<BLANKLINE>
[3 rows x 2 columns]

You can also subtract another DataFrame with index and column labels aligned:

>>> df1 = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df - df1
    a  b
0  -1  1
1   0  2
2   1  3
<BLANKLINE>
[3 rows x 2 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the subtraction.

truediv

__truediv__(other)

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

Equivalent to DataFrame.truediv(other).

Examples:

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

You can multiply with a scalar:

>>> df = bpd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> df / 2
     a    b
0  0.5  2.0
1  1.0  2.5
2  1.5  3.0
<BLANKLINE>
[3 rows x 2 columns]

You can also multiply with another DataFrame with index and column labels aligned:

>>> denominator = bpd.DataFrame({"a": [2, 2, 2], "b": [3, 3, 3]})
>>> df / denominator
    a         b
0  0.5  1.333333
1  1.0  1.666667
2  1.5       2.0
<BLANKLINE>
[3 rows x 2 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of the division.

xor

__xor__(
    other: bool | int | bigframes.series.Series,
) -> bigframes.dataframe.DataFrame

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

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

Returns
Type	Description
`bigframes.dataframe.DataFrame`	The result of the operation.

abs

abs() -> bigframes.dataframe.DataFrame

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

This function only applies to elements that are all numeric.

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

add

add(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get addition of DataFrame and other, element-wise (binary operator +).

Equivalent to dataframe + other. With reverse version, radd.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].add(df['B'])
0    5
1    7
2    9
dtype: Int64

You can also use arithmetic operator +:

>>> df['A'] + df['B']
0    5
1    7
2    9
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

add_prefix

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

Prefix labels with string prefix.

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

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

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

add_suffix

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

Suffix labels with string suffix.

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

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

agg

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

Aggregate using one or more operations over columns.

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

Using a single function:

>>> df.agg('sum')
A    6
B    6
dtype: Int64

Using a list of functions:

>>> df.agg(['sum', 'mean'])
          A   B
sum     6.0 6.0
mean        2.0     2.0
<BLANKLINE>
[2 rows x 2 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Aggregated results.

aggregate

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

Aggregate using one or more operations over columns.

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

Using a single function:

>>> df.agg('sum')
A    6
B    6
dtype: Int64

Using a list of functions:

>>> df.agg(['sum', 'mean'])
          A   B
sum     6.0 6.0
mean        2.0     2.0
<BLANKLINE>
[2 rows x 2 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Aggregated results.

align

align(
    other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    join: str = "outer",
    axis: typing.Optional[typing.Union[str, int]] = None,
) -> typing.Tuple[
    typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
]

Align two objects on their axes with the specified join method.

Join method is specified for each axis Index.

Parameters
Name	Description
`join`	`{'outer', 'inner', 'left', 'right'}, default 'outer'` Type of alignment to be performed. left: use only keys from left frame, preserve key order. right: use only keys from right frame, preserve key order. outer: use union of keys from both frames, sort keys lexicographically. inner: use intersection of keys from both frames, preserve the order of the left keys.
`axis`	`allowed axis of the other object, default None` Align on index (0), columns (1), or both (None).

Returns
Type	Description
`Tuple[bigframes.pandas.DataFrame or bigframes.pandas.Series, type of other]`	Aligned objects.

all

all(
    axis: typing.Union[str, int] = 0, *, bool_only: bool = False
) -> bigframes.series.Series

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).

Examples:

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

>>> df = bpd.DataFrame({"A": [True, True], "B": [False, False]})
>>> df
        A        B
0    True    False
1    True    False
<BLANKLINE>
[2 rows x 2 columns]

Checking if all values in each column are True(the default behavior without an explicit axis parameter):

>>> df.all()
A     True
B    False
dtype: boolean

Checking across rows to see if all values are True:

>>> df.all(axis=1)
0    False
1    False
dtype: boolean

Parameters
Name	Description
`axis`	`{index (0), columns (1)}` Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
`bool_only`	`bool. default False` Include only boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series indicating if all elements are True per column.

any

any(
    *, axis: typing.Union[str, int] = 0, bool_only: bool = False
) -> bigframes.series.Series

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
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({"A": [True, True], "B": [False, False]})
>>> df
        A        B
0    True    False
1    True    False
<BLANKLINE>
[2 rows x 2 columns]

Checking if each column contains at least one True element(the default behavior without an explicit axis parameter):

>>> df.any()
A     True
B    False
dtype: boolean

Checking if each row contains at least one True element:

>>> df.any(axis=1)
0    True
1    True
dtype: boolean

Parameters
Name	Description
`axis`	`{index (0), columns (1)}` Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
`bool_only`	`bool. default False` Include only boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series indicating if any element is True per column.

apply

apply(func, *, axis=0, args: typing.Tuple = (), **kwargs)

Apply a function along an axis of the DataFrame.

Objects passed to the function are Series objects whose index is the DataFrame's index (axis=0) or the DataFrame's columns (axis=1). The final return type is inferred from the return type of the applied function.

Note: axis=1 scenario is in preview.

Examples:

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

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

>>> def square(x):
...     return x * x

>>> df.apply(square)
   col1  col2
0     1     9
1     4    16
<BLANKLINE>
[2 rows x 2 columns]

You could apply a user defined function to every row of the DataFrame by creating a remote function out of it, and using it with axis=1. Within the function, each row is passed as a pandas.Series. It is recommended to select only the necessary columns before calling apply(). Note: This feature is currently in preview.

>>> @bpd.remote_function(reuse=False)
... def foo(row: pd.Series) -> int:
...     result = 1
...     result += row["col1"]
...     result += row["col2"]*row["col2"]
...     return result

>>> df[["col1", "col2"]].apply(foo, axis=1)
0    11
1    19
dtype: Int64

You could return an array output for every input row from the remote function.

>>> @bpd.remote_function(reuse=False)
... def marks_analyzer(marks: pd.Series) -> list[float]:
...     import statistics
...     average = marks.mean()
...     median = marks.median()
...     gemetric_mean = statistics.geometric_mean(marks.values)
...     harmonic_mean = statistics.harmonic_mean(marks.values)
...     return [
...         round(stat, 2) for stat in
...         (average, median, gemetric_mean, harmonic_mean)
...     ]

>>> df = bpd.DataFrame({
...     "physics": [67, 80, 75],
...     "chemistry": [88, 56, 72],
...     "algebra": [78, 91, 79]
... }, index=["Alice", "Bob", "Charlie"])
>>> stats = df.apply(marks_analyzer, axis=1)
>>> stats
Alice      [77.67 78.   77.19 76.71]
Bob        [75.67 80.   74.15 72.56]
Charlie    [75.33 75.   75.28 75.22]
dtype: list<item: double>[pyarrow]

You could also apply a remote function which accepts multiple parameters to every row of a DataFrame by using it with axis=1 if the DataFrame has matching number of columns and data types. Note: This feature is currently in preview.

>>> df = bpd.DataFrame({
...     'col1': [1, 2],
...     'col2': [3, 4],
...     'col3': [5, 5]
... })
>>> df
   col1  col2  col3
0     1     3     5
1     2     4     5
<BLANKLINE>
[2 rows x 3 columns]

>>> @bpd.remote_function(reuse=False)
... def foo(x: int, y: int, z: int) -> float:
...     result = 1
...     result += x
...     result += y/z
...     return result

>>> df.apply(foo, axis=1)
0    2.6
1    3.8
dtype: Float64

Parameters
Name	Description
`args`	`tuple` Positional arguments to pass to `func` in addition to the array/series.
`func`	`function` Function to apply to each column or row. To apply to each row (i.e. when `axis=1` is specified) the function can be of one of the two types: (1). It accepts a single input parameter of type `Series`, in which case each row is delivered to the function as a pandas Series. (2). It accept one or more parameters, in which case column values are delivered to the function as separate arguments (mapping to those parameters) for each row. For this to work the `DataFrame` must have same number of columns and matching data types.
`axis`	`{index (0), columns (1)}` Axis along which the function is applied. Specify 0 or 'index' to apply function to each column. Specify 1 or 'columns' to apply function to each row.

Exceptions
Type	Description
`ValueError`	If a remote function is not provided when `axis=1` is specified.
`ValueError`	If number or input params in the remote function are not the same as the number of columns in the dataframe.
`ValueError`	If the dtypes of the columns in the dataframe are not compatible with the data types of the remote function input params.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Result of applying `func` along the given axis of the DataFrame.

applymap

applymap(
    func, na_action: typing.Optional[str] = None
) -> bigframes.dataframe.DataFrame

Apply a function to a Dataframe elementwise.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Examples:

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

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

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

>>> df_minutes = bpd.DataFrame(
...     {"system_minutes" : [0, 30, 60, 90, 120],
...      "user_minutes" : [0, 15, 75, 90, 6]})
>>> df_minutes
system_minutes  user_minutes
0               0             0
1              30            15
2              60            75
3              90            90
4             120             6
<BLANKLINE>
[5 rows x 2 columns]

>>> df_hours = df_minutes.map(minutes_to_hours)
>>> df_hours
system_minutes  user_minutes
0             0.0           0.0
1             0.5          0.25
2             1.0          1.25
3             1.5           1.5
4             2.0           0.1
<BLANKLINE>
[5 rows x 2 columns]

If there are NA/None values in the data, you can ignore applying the remote function on such values by specifying na_action='ignore'.

>>> df_minutes = bpd.DataFrame(
...     {
...         "system_minutes" : [0, 30, 60, None, 90, 120, bpd.NA],
...         "user_minutes" : [0, 15, 75, 90, 6, None, bpd.NA]
...     }, dtype="Int64")
>>> df_hours = df_minutes.map(minutes_to_hours, na_action='ignore')
>>> df_hours
system_minutes  user_minutes
0             0.0           0.0
1             0.5          0.25
2             1.0          1.25
3            <NA>           1.5
4             1.5           0.1
5             2.0          <NA>
6            <NA>          <NA>
<BLANKLINE>
[7 rows x 2 columns]

Parameters
Name	Description
`func`	`function` Python function wrapped by `remote_function` decorator, returns a single value from a single value.
`na_action`	`Optional[str], default None` `{None, 'ignore'}`, default None. If `ignore`, propagate NaN values, without passing them to func.

Exceptions
Type	Description
`TypeError`	If value provided for `func` is not callable.
`ValueError`	If value provided for `na_action` is not `None` or `ignore`.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Transformed DataFrame.

area

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')

Parameters
Name	Description
`x`	`label or position, optional` Coordinates for the X axis. By default uses the index.
`y`	`label or position, optional` Column to plot. By default uses all columns.
`stacked`	`bool, default True` Area plots are stacked by default. Set to False to create a unstacked plot.
`sampling_n`	`int, default 100` Number of random items for plotting.
`sampling_random_state`	`int, default 0` Seed for random number generator.

Returns
Type	Description
`matplotlib.axes.Axes or numpy.ndarray`	Area plot, or array of area plots if subplots is True.

assign

assign(**kwargs) -> bigframes.dataframe.DataFrame

Assign new columns to a DataFrame.

Returns a new object with all original columns in addition to new ones. Existing columns that are re-assigned will be overwritten.

Note: Assigning multiple columns within the same assign is possible. Later items in '**kwargs' may refer to newly created or modified columns in 'df'; items are computed and assigned into 'df' in order.

Returns
Type	Description
`bigframes.pandas.DataFrame`	A new DataFrame with the new columns in addition to all the existing columns.

astype

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

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

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

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

bar

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

Draw a vertical bar plot.

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"}
... )

Parameters
Name	Description
`x`	`label or position, optional` Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.
`y`	`label or position, optional` Allows plotting of one column versus another. If not specified, all numerical columns are used.

Returns
Type	Description
`matplotlib.axes.Axes or numpy.ndarray`	Area plot, or array of area plots if subplots is True.

bfill

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

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

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

cache

cache()

Materializes the DataFrame to a temporary table.

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame

combine

combine(
    other: bigframes.dataframe.DataFrame,
    func: typing.Callable[
        [bigframes.series.Series, bigframes.series.Series], bigframes.series.Series
    ],
    fill_value=None,
    overwrite: bool = True,
    *,
    how: str = "outer"
) -> bigframes.dataframe.DataFrame

Perform column-wise combine with another DataFrame.

Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

Examples:

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

>>> df1 = bpd.DataFrame({'A': [0, 0], 'B': [4, 4]})
>>> df2 = bpd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
>>> df1.combine(df2, take_smaller)
   A  B
0  0  3
1  0  3
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
`other`	`DataFrame` The DataFrame to merge column-wise.
`func`	`function` Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.
`fill_value`	`scalar value, default None` The value to fill NaNs with prior to passing any column to the merge func.
`overwrite`	`bool, default True` If True, columns in `self` that do not exist in `other` will be overwritten with NaNs.

Exceptions
Type	Description
`ValueError`	If `func` return value is not Series.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Combination of the provided DataFrames.

combine_first

combine_first(other: bigframes.dataframe.DataFrame)

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

Combine two DataFrame objects by filling null values in one DataFrame with non-null values from other DataFrame. The row and column indexes of the resulting DataFrame will be the union of the two. The resulting dataframe contains the 'first' dataframe values and overrides the second one values where both first.loc[index, col] and second.loc[index, col] are not missing values, upon calling first.combine_first(second).

Examples:

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

>>> df1 = bpd.DataFrame({'A': [None, 0], 'B': [None, 4]})
>>> df2 = bpd.DataFrame({'A': [1, 1], 'B': [3, 3]})
>>> df1.combine_first(df2)
     A    B
0  1.0  3.0
1  0.0  4.0
<BLANKLINE>
[2 rows x 2 columns]

Parameter
Name	Description
`other`	`DataFrame` Provided DataFrame to use to fill null values.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The result of combining the provided DataFrame with the other object.

copy

copy() -> bigframes.dataframe.DataFrame

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

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

Examples:

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

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

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

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

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

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

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

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

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

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

corr

corr(
    method="pearson", min_periods=None, numeric_only=False
) -> bigframes.dataframe.DataFrame

Compute pairwise correlation of columns, excluding NA/null values.

Examples:

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

>>> df = bpd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600],
...                    'C': [0.8, 0.4, 0.9]})
>>> df.corr(numeric_only=True)
          A         B         C
A       1.0       1.0  0.188982
B       1.0       1.0  0.188982
C  0.188982  0.188982       1.0
<BLANKLINE>
[3 rows x 3 columns]

Parameters
Name	Description
`method`	`string, default "pearson"` Correlation method to use - currently only "pearson" is supported.
`min_periods`	`int, default None` The minimum number of observations needed to return a result. Non-default values are not yet supported, so a result will be returned for at least two observations.
`numeric_only`	`bool, default False` Include only float, int, boolean, decimal data.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Correlation matrix.

corrwith

corrwith(
    other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    numeric_only: bool = False
)

Compute pairwise correlation.

Pairwise correlation is computed between rows or columns of DataFrame with rows or columns of Series or DataFrame. DataFrames are first aligned along both axes before computing the correlations.

Examples:

import bigframes.pandas as bpd bpd.options.display.progress_bar = None
index = ["a", "b", "c", "d", "e"] columns = ["one", "two", "three", "four"] df1 = bpd.DataFrame(np.arange(20).reshape(5, 4), index=index, columns=columns) df2 = bpd.DataFrame(np.arange(16).reshape(4, 4), index=index[:4], columns=columns) df1.corrwith(df2) one 1.0 two 1.0 three 1.0 four 1.0 dtype: Float64

Parameters
Name	Description
`other`	`DataFrame, Series` Object with which to compute correlations.
`numeric_only`	`bool, default False` Include only `float`, `int` or `boolean` data.

Returns
Type	Description
`bigframes.pandas.Series`	Pairwise correlations.

count

count(*, numeric_only: bool = False) -> bigframes.series.Series

Count non-NA cells for each column.

The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Examples:

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

>>> df = bpd.DataFrame({"A": [1, None, 3, 4, 5],
...                     "B": [1, 2, 3, 4, 5],
...                     "C": [None, 3.5, None, 4.5, 5.0]})
>>> df
       A    B          C
0    1.0    1       <NA>
1   <NA>    2        3.5
2    3.0    3       <NA>
3    4.0    4        4.5
4    5.0    5        5.0
<BLANKLINE>
[5 rows x 3 columns]

Counting non-NA values for each column:

>>> df.count()
A    4
B    5
C    3
dtype: Int64

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only `float`, `int` or `boolean` data.

Returns
Type	Description
`bigframes.pandas.Series`	For each column/row the number of non-NA/null entries. If `level` is specified returns a `DataFrame`.

cov

cov(*, numeric_only: bool = False) -> bigframes.dataframe.DataFrame

Compute pairwise covariance of columns, excluding NA/null values.

Examples:

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

>>> df = bpd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600],
...                    'C': [0.8, 0.4, 0.9]})
>>> df.cov(numeric_only=True)
       A        B     C
A    1.0    100.0  0.05
B  100.0  10000.0   5.0
C   0.05      5.0  0.07
<BLANKLINE>
[3 rows x 3 columns]

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean, decimal data.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The covariance matrix of the series of the DataFrame.

cummax

cummax() -> bigframes.dataframe.DataFrame

Return cumulative maximum over columns.

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

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.cummax()
    A       B
0   3       1
1   3       2
2   3       3
<BLANKLINE>
[3 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame`	Return cumulative maximum of DataFrame.

cummin

cummin() -> bigframes.dataframe.DataFrame

Return cumulative minimum over columns.

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

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.cummin()
    A       B
0   3       1
1   1       1
2   1       1
<BLANKLINE>
[3 rows x 2 columns]

Returns
Type	Description
`bigframes.pandas.DataFrame`	Return cumulative minimum of DataFrame.

cumprod

cumprod() -> bigframes.dataframe.DataFrame

Return cumulative product over columns.

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

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.cumprod()
     A    B
0  3.0  1.0
1  3.0  2.0
2  6.0  6.0
<BLANKLINE>
[3 rows x 2 columns]

Exceptions
Type	Description
`ValueError`	If values are not of numeric type.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Return cumulative product of DataFrame.

cumsum

cumsum()

Return cumulative sum over columns.

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

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.cumsum()
    A       B
0   3       1
1   4       3
2   6       6
<BLANKLINE>
[3 rows x 2 columns]

Exceptions
Type	Description
`ValueError`	If values are not of numeric type.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Return cumulative sum of DataFrame.

describe

describe(
    include: typing.Union[None, typing.Literal["all"]] = None,
) -> bigframes.dataframe.DataFrame

Generate descriptive statistics.

Descriptive statistics include those that summarize the central tendency, dispersion and shape of a dataset's distribution, excluding NaN values.

Only supports numeric columns.

Note: For numeric data, the result's index will include count, mean, std, min, max as well as lower, 50 and upper percentiles. By default the lower percentile is 25 and the upper percentile is 75. The 50 percentile is the same as the median.

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [0, 2, 8]})
>>> df
    A       B
0   3       0
1   1       2
2   2       8
<BLANKLINE>
[3 rows x 2 columns]

>>> df.describe()
              A               B
count       3.0             3.0
mean        2.0        3.333333
std         1.0        4.163332
min         1.0             0.0
25%         1.0             0.0
50%         2.0             2.0
75%         3.0             8.0
max         3.0             8.0
<BLANKLINE>
[8 rows x 2 columns]

Exceptions
Type	Description
`ValueError`	If unsupported `include` type is provided.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Summary statistics of the Series or Dataframe provided.

diff

diff(periods: int = 1) -> bigframes.dataframe.DataFrame

First discrete difference of element.

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

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

Calculating difference with default periods=1:

>>> df.diff()
       A       B
0   <NA>    <NA>
1     -2       1
2      1       1
<BLANKLINE>
[3 rows x 2 columns]

Calculating difference with periods=-1:

>>> df.diff(periods=-1)
       A       B
0      2      -1
1     -1      -1
2   <NA>    <NA>
<BLANKLINE>
[3 rows x 2 columns]

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	First differences of the Series.

div

div(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get floating division of DataFrame and other, element-wise (binary operator /).

Equivalent to dataframe / other. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].truediv(df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

You can also use arithmetic operator /:

>>> df['A'] / (df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

divide

divide(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get floating division of DataFrame and other, element-wise (binary operator /).

Equivalent to dataframe / other. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].truediv(df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

You can also use arithmetic operator /:

>>> df['A'] / (df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

dot

dot(other: _DataFrameOrSeries) -> _DataFrameOrSeries

Compute the matrix multiplication between the DataFrame and other.

This method computes the matrix product between the DataFrame and the values of an other Series or DataFrame.

It can also be called using self @ other.

Examples:

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

>>> left = bpd.DataFrame([[0, 1, -2, -1], [1, 1, 1, 1]])
>>> left
   0  1   2   3
0  0  1  -2  -1
1  1  1   1   1
<BLANKLINE>
[2 rows x 4 columns]
>>> right = bpd.DataFrame([[0, 1], [1, 2], [-1, -1], [2, 0]])
>>> right
    0   1
0   0   1
1   1   2
2  -1  -1
3   2   0
<BLANKLINE>
[4 rows x 2 columns]
>>> left.dot(right)
   0  1
0  1  4
1  2  2
<BLANKLINE>
[2 rows x 2 columns]

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

>>> left @ right
   0  1
0  1  4
1  2  2
<BLANKLINE>
[2 rows x 2 columns]

The right input can be a Series, in which case the result will also be a Series:

>>> right = bpd.Series([1, 2, -1,0])
>>> left @ right
0    4
1    2
dtype: Int64

Any user defined index of the left matrix and columns of the right matrix will reflect in the result.

>>> left = bpd.DataFrame([[1, 2, 3], [2, 5, 7]], index=["alpha", "beta"])
>>> left
       0  1  2
alpha  1  2  3
beta   2  5  7
<BLANKLINE>
[2 rows x 3 columns]
>>> right = bpd.DataFrame([[2, 4, 8], [1, 5, 10], [3, 6, 9]], columns=["red", "green", "blue"])
>>> right
   red  green  blue
0    2      4     8
1    1      5    10
2    3      6     9
<BLANKLINE>
[3 rows x 3 columns]
>>> left.dot(right)
       red  green  blue
alpha   13     32    55
beta    30     75   129
<BLANKLINE>
[2 rows x 3 columns]

Parameter
Name	Description
`other`	`Series or DataFrame` The other object to compute the matrix product with.

Exceptions
Type	Description
`RuntimeError`	If unable to construct all columns.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	If `other` is a Series, return the matrix product between self and other as a Series. If other is a DataFrame, return the matrix product of self and other in a DataFrame.

drop

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

Drop specified labels from columns.

Remove columns by directly specifying column names.

Examples:

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

>>> df = bpd.DataFrame(np.arange(12).reshape(3, 4),
...                    columns=['A', 'B', 'C', 'D'])
>>> df
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
<BLANKLINE>
[3 rows x 4 columns]

Drop columns:

>>> df.drop(['B', 'C'], axis=1)
   A   D
0  0   3
1  4   7
2  8  11
<BLANKLINE>
[3 rows x 2 columns]

>>> df.drop(columns=['B', 'C'])
   A   D
0  0   3
1  4   7
2  8  11
<BLANKLINE>
[3 rows x 2 columns]

Drop a row by index:

>>> df.drop([0, 1])
   A  B   C   D
2  8  9  10  11
<BLANKLINE>
[1 rows x 4 columns]

Drop columns and/or rows of MultiIndex DataFrame:

>>> 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]])
>>> df = bpd.DataFrame(index=midx, columns=['big', 'small'],
...                    data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                          [250, 150], [1.5, 0.8], [320, 250],
...                          [1, 0.8], [0.3, 0.2]])
>>> df
                 big  small
llama  speed    45.0   30.0
       weight  200.0  100.0
       length    1.5    1.0
cow    speed    30.0   20.0
       weight  250.0  150.0
       length    1.5    0.8
falcon speed   320.0  250.0
       weight    1.0    0.8
       length    0.3    0.2
<BLANKLINE>
[9 rows x 2 columns]

Drop a specific index and column combination from the MultiIndex DataFrame, i.e., drop the index 'cow' and column 'small':

>>> df.drop(index='cow', columns='small')
                 big
llama  speed    45.0
       weight  200.0
       length    1.5
falcon speed   320.0
       weight    1.0
       length    0.3
<BLANKLINE>
[6 rows x 1 columns]

>>> df.drop(index='length', level=1)
                 big  small
llama  speed    45.0   30.0
       weight  200.0  100.0
cow    speed    30.0   20.0
       weight  250.0  150.0
falcon speed   320.0  250.0
       weight    1.0    0.8
<BLANKLINE>
[6 rows x 2 columns]

Exceptions
Type	Description
`KeyError`	If any of the labels is not found in the selected axis.
`ValueError`	If values for both `labels` and `index`/`columns` are provided.
`ValueError`	If a multi-index tuple is provided as `level`.
`ValueError`	If either `labels` or `index`/`columns` is not provided.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame without the removed column labels.

drop_duplicates

drop_duplicates(
    subset: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    *,
    keep: str = "first"
) -> bigframes.dataframe.DataFrame

Return DataFrame with duplicate rows removed.

Considering certain columns is optional. Indexes, including time indexes are ignored.

Parameters
Name	Description
`subset`	`column label or sequence of labels, optional` Only consider certain columns for identifying duplicates, by default use all of the columns.
`keep`	`{'first', 'last', False}, default 'first'` Determines which duplicates (if any) to keep. - 'first' : Drop duplicates except for the first occurrence. - 'last' : Drop duplicates except for the last occurrence. - `False` : Drop all duplicates.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame with duplicates removed

droplevel

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

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

Parameters
Name	Description
`level`	`int, str, or list-like` If a string is given, must be the name of a level If list-like, elements must be names or positional indexes of levels.
`axis`	`{0 or 'index', 1 or 'columns'}, default 0` Axis along which the level(s) is removed: * 0 or 'index': remove level(s) in column. * 1 or 'columns': remove level(s) in row.

Exceptions
Type	Description
`ValueError`	If columns are not multi-index

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame with requested index / column level(s) removed.

dropna

dropna(
    *,
    axis: int | str = 0,
    how: str = "any",
    subset: typing.Union[
        None, typing.Hashable, typing.Sequence[typing.Hashable]
    ] = None,
    inplace: bool = False,
    ignore_index=False
) -> bigframes.dataframe.DataFrame

Remove missing values.

Examples:

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

>>> df = bpd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
...                     "toy": [np.nan, 'Batmobile', 'Bullwhip'],
...                     "born": [bpd.NA, "1940-04-25", bpd.NA]})
>>> df
       name        toy        born
0    Alfred       <NA>        <NA>
1    Batman  Batmobile  1940-04-25
2  Catwoman   Bullwhip        <NA>
<BLANKLINE>
[3 rows x 3 columns]

Drop the rows where at least one element is missing:

>>> df.dropna()
     name        toy        born
1  Batman  Batmobile  1940-04-25
<BLANKLINE>
[1 rows x 3 columns]

Drop the columns where at least one element is missing.

>>> df.dropna(axis='columns')
       name
0    Alfred
1    Batman
2  Catwoman
<BLANKLINE>
[3 rows x 1 columns]

Drop the rows where all elements are missing:

>>> df.dropna(how='all')
       name        toy        born
0    Alfred       <NA>        <NA>
1    Batman  Batmobile  1940-04-25
2  Catwoman   Bullwhip        <NA>
<BLANKLINE>
[3 rows x 3 columns]

Define in which columns to look for missing values.

>>> df.dropna(subset=['name', 'toy'])
       name        toy        born
1    Batman  Batmobile  1940-04-25
2  Catwoman   Bullwhip        <NA>
<BLANKLINE>
[2 rows x 3 columns]

Parameters
Name	Description
`axis`	`{0 or 'index', 1 or 'columns'}, default 'columns'` Determine if rows or columns which contain missing values are removed. * 0, or 'index' : Drop rows which contain missing values. * 1, or 'columns' : Drop columns which contain missing value.
`how`	`{'any', 'all'}, default 'any'` Determine if row or column is removed from DataFrame, when we have at least one NA or all NA. * 'any' : If any NA values are present, drop that row or column. * 'all' : If all values are NA, drop that row or column.
`subset`	`column label or sequence of labels, optional` Labels along other axis to consider, e.g. if you are dropping rows these would be a list of columns to include. Only supports axis=0.
`inplace`	`bool, default False` Not supported.
`ignore_index`	`bool, default False` If `True`, the resulting axis will be labeled 0, 1, …, n - 1.

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame with NA entries dropped from it.

duplicated

duplicated(subset=None, keep: str = "first") -> bigframes.series.Series

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters
Name	Description
`subset`	`column label or sequence of labels, optional` Only consider certain columns for identifying duplicates, by default use all of the columns.
`keep`	`{'first', 'last', False}, default 'first'` Determines which duplicates (if any) to mark. - `first` : Mark duplicates as `True` except for the first occurrence. - `last` : Mark duplicates as `True` except for the last occurrence. - False : Mark all duplicates as `True`.

Returns
Type	Description
`bigframes.pandas.Series`	Boolean series for each duplicated rows.

eq

eq(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

Get equal to of DataFrame and other, element-wise (binary operator eq).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Examples:

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

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].eq(360)
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean

You can also use logical operator ==:

>>> df["degrees"] == 360
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean

Parameters
Name	Description
`other`	`scalar, sequence, Series, or DataFrame` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}, default 'columns'` Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

Returns
Type	Description
`bigframes.pandas.DataFrame`	Result of the comparison.

equals

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

Test whether two objects contain the same elements.

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

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

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

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

eval

eval(expr: str) -> bigframes.dataframe.DataFrame

Evaluate a string describing operations on DataFrame columns.

Operates on columns only, not specific rows or elements. This allows eval to run arbitrary code, which can make you vulnerable to code injection if you pass user input to this function.

Examples:

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

>>> df = bpd.DataFrame({'A': range(1, 6), 'B': range(10, 0, -2)})
>>> df
A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
<BLANKLINE>
[5 rows x 2 columns]
>>> df.eval('A + B')
0    11
1    10
2     9
3     8
4     7
dtype: Int64

Assignment is allowed though by default the original DataFrame is not modified.

>>> df.eval('C = A + B')
   A   B   C
0  1  10  11
1  2   8  10
2  3   6   9
3  4   4   8
4  5   2   7
<BLANKLINE>
[5 rows x 3 columns]
>>> df
   A   B
0  1  10
1  2   8
2  3   6
3  4   4
4  5   2
<BLANKLINE>
[5 rows x 2 columns]

Multiple columns can be assigned to using multi-line expressions:

>>> df.eval(
...     '''
... C = A + B
... D = A - B
... '''
... )
   A   B   C  D
0  1  10  11 -9
1  2   8  10 -6
2  3   6   9 -3
3  4   4   8  0
4  5   2   7  3
<BLANKLINE>
[5 rows x 4 columns]

Parameter
Name	Description
`expr`	`str` The expression string to evaluate.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result after the operation.

expanding

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

Provide expanding window calculations.

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

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

explode

explode(
    column: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    *,
    ignore_index: typing.Optional[bool] = False
) -> bigframes.dataframe.DataFrame

Transform each element of an array to a row, replicating index values.

Examples:

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

>>> df = bpd.DataFrame({'A': [[0, 1, 2], [], [], [3, 4]],
...                     'B': 1,
...                     'C': [['a', 'b', 'c'], np.nan, [], ['d', 'e']]})
>>> df.explode('A')
    A  B              C
0     0  1  ['a' 'b' 'c']
0     1  1  ['a' 'b' 'c']
0     2  1  ['a' 'b' 'c']
1  <NA>  1             []
2  <NA>  1             []
3     3  1      ['d' 'e']
3     4  1      ['d' 'e']
<BLANKLINE>
[7 rows x 3 columns]
>>> df.explode(list('AC'))
    A  B     C
0     0  1     a
0     1  1     b
0     2  1     c
1  <NA>  1  <NA>
2  <NA>  1  <NA>
3     3  1     d
3     4  1     e
<BLANKLINE>
[7 rows x 3 columns]

Parameters
Name	Description
`column`	`str, Sequence[str]` Column(s) to explode. For multiple columns, specify a non-empty list with each element be str or tuple, and all specified columns their list-like data on same row of the frame must have matching length.
`ignore_index`	`bool, default False` If True, the resulting index will be labeled 0, 1, …, n - 1.

Exceptions
Type	Description
`ValueError`	* If columns of the frame are not unique. * If specified columns to explode is empty list. * If specified columns to explode have not matching count of elements rowwise in the frame.
`KeyError`	If incorrect column names are provided

Returns
Type	Description
`bigframes.pandas.DataFrame`	Exploded lists to rows of the subset columns; index will be duplicated for these rows.

ffill

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

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

Examples:

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

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

Fill NA/NaN values in DataFrames:

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

Fill NA/NaN values in Series:

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

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

fillna

fillna(value=None) -> bigframes.dataframe.DataFrame

Fill NA/NaN values using the specified method.

Examples:

>>> import bigframes.pandas as bpd
>>> 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]

Replace all NA elements with 0s.

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

You can use fill values from another DataFrame:

>>> df_fill = bpd.DataFrame(np.arange(12).reshape(3, 4),
...                         columns=['A', 'B', 'C', 'D'])
>>> df_fill
   A  B   C   D
0  0  1   2   3
1  4  5   6   7
2  8  9  10  11
<BLANKLINE>
[3 rows x 4 columns]
>>> df.fillna(df_fill)
    A    B     C     D
0   0.0  2.0   2.0   0.0
1   3.0  4.0   6.0   1.0
2   8.0  9.0  10.0  11.0
3  <NA>  3.0  <NA>   4.0
<BLANKLINE>
[4 rows x 4 columns]

Parameter
Name	Description
`value`	`scalar, Series` Value to use to fill holes (e.g. 0), alternately a Series of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the Series will not be filled. This value cannot be a list.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Object with missing values filled

filter

filter(
    items: typing.Optional[typing.Iterable] = None,
    like: typing.Optional[str] = None,
    regex: typing.Optional[str] = None,
    axis: int | str | None = None,
) -> bigframes.dataframe.DataFrame

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

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

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

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

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

first_valid_index

first_valid_index()

API documentation for first_valid_index method.

floordiv

floordiv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get integer division of DataFrame and other, element-wise (binary operator //).

Equivalent to dataframe // other. With reverse version, rfloordiv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].floordiv(df['B'])
0    0
1    0
2    0
dtype: Int64

You can also use arithmetic operator //:

>>> df['A'] // (df['B'])
0    0
1    0
2    0
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

from_dict

from_dict(
    data: dict, orient: str = "columns", dtype=None, columns=None
) -> bigframes.dataframe.DataFrame

Construct DataFrame from dict of array-like or dicts.

Creates DataFrame object from dictionary by columns or by index allowing dtype specification.

Parameters
Name	Description
`data`	`dict` Of the form {field : array-like} or {field : dict}.
`orient`	`{'columns', 'index', 'tight'}, default 'columns'` The "orientation" of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass 'columns' (default). Otherwise if the keys should be rows, pass 'index'. If 'tight', assume a dict with keys ['index', 'columns', 'data', 'index_names', 'column_names'].
`dtype`	`dtype, default None` Data type to force after DataFrame construction, otherwise infer.
`columns`	`list, default None` Column labels to use when `orient='index'`.

Exceptions
Type	Description
`ValueError`	If used with `orient='columns'` or `orient='tight'`.

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

from_records

from_records(
    data,
    index=None,
    exclude=None,
    columns=None,
    coerce_float: bool = False,
    nrows: typing.Optional[int] = None,
) -> bigframes.dataframe.DataFrame

Convert structured or record ndarray to DataFrame.

Creates a DataFrame object from a structured ndarray, sequence of tuples or dicts, or DataFrame.

Parameters
Name	Description
`data`	`structured ndarray, sequence of tuples or dicts` Structured input data.
`index`	`str, list of fields, array-like` Field of array to use as the index, alternately a specific set of input labels to use.
`exclude`	`sequence, default None` Columns or fields to exclude.
`columns`	`sequence, default None` Column names to use. If the passed data do not have names associated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns).
`coerce_float`	`bool, default False` Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets.
`nrows`	`int, default None` Number of rows to read if data is an iterator.

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

ge

ge(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

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

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Note: Mismatched indices will be unioned together. NaN values in floating point columns are considered different (i.e. NaN != NaN).

Examples:

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

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].ge(360)
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean

You can also use arithmetic operator >=:

>>> df["degrees"] >= 360
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean

Parameters
Name	Description
`other`	`scalar, sequence, Series, or DataFrame` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}, default 'columns'` Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame of bool. The result of the comparison.

groupby

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

Group DataFrame by 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

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

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

We can also choose to include NA in group keys or not by setting dropna:

>>> df = bpd.DataFrame([[1, 2, 3],[1, None, 4], [2, 1, 3], [1, 2, 2]],
...                    columns=["a", "b", "c"])
>>> df.groupby(by=["b"]).sum()
     a  c
b
1.0  2  3
2.0  2  5
<BLANKLINE>
[2 rows x 2 columns]

>>> df.groupby(by=["b"], dropna=False).sum()
      a  c
b
1.0   2  3
2.0   2  5
<NA>  1  4
<BLANKLINE>
[3 rows x 2 columns]

We can also choose to return object with group labels or not by setting as_index:

>>> df.groupby(by=["b"], as_index=False).sum()
     b  a  c
0  1.0  2  3
1  2.0  2  5
<BLANKLINE>
[2 rows x 3 columns]

Parameters
Name	Description
`by`	`str, Sequence[str]` 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.
`level`	`int, level name, or sequence of such, default None` If the axis is a MultiIndex (hierarchical), group by a particular level or levels. Do not specify both `by` and `level`.
`as_index`	`bool, default True` Default True. Return object with group labels as the index. Only relevant for DataFrame input. `as_index=False` is effectively "SQL-style" grouped output. This argument has no effect on filtrations such as `head()`, `tail()`, `nth()` and in transformations.
`dropna`	`bool, default True` Default True. If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

Exceptions
Type	Description
`ValueError`	If both `by` and `level` are specified.
`TypeError`	If one of `by` or `level` is not specified.

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

gt

gt(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

Get 'greater than' of DataFrame and other, element-wise (binary operator >).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Note: Mismatched indices will be unioned together. NaN values in floating point columns are considered different (i.e. NaN != NaN).

Examples:

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

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].gt(360)
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean

You can also use arithmetic operator >:

>>> df["degrees"] > 360
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean

Parameters
Name	Description
`other`	`scalar, sequence, Series, or DataFrame` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}, default 'columns'` Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame of bool: The result of the comparison.

head

head(n: int = 5) -> bigframes.dataframe.DataFrame

Return the first n rows.

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

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

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

Examples:

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

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

Viewing the first 5 lines:

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

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

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

For negative values of n:

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

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

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

hist

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)

Parameters
Name	Description
`by`	`str or sequence, optional` Column in the DataFrame to group by. It is not supported yet.
`bins`	`int, default 10` Number of histogram bins to be used.

Returns
Type	Description
`class`	`matplotlib.AxesSubplot`: A histogram plot.

idxmax

idxmax() -> bigframes.series.Series

Return index of first occurrence of maximum over columns.

NA/null values are excluded.

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.idxmax()
A    0
B    2
dtype: Int64

Returns
Type	Description
`bigframes.pandas.Series`	Indexes of maxima along the columns.

idxmin

idxmin() -> bigframes.series.Series

Return index of first occurrence of minimum over columns.

NA/null values are excluded.

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 3]})
>>> df
    A       B
0   3       1
1   1       2
2   2       3
<BLANKLINE>
[3 rows x 2 columns]

>>> df.idxmin()
A    1
B    0
dtype: Int64

Returns
Type	Description
`bigframes.pandas.Series`	Indexes of minima along the columns.

info

info(
    verbose: typing.Optional[bool] = None,
    buf=None,
    max_cols: typing.Optional[int] = None,
    memory_usage: typing.Optional[bool] = None,
    show_counts: typing.Optional[bool] = None,
)

Print a concise summary of a DataFrame.

This method prints information about a DataFrame including the index dtypeand columns, non-null values and memory usage.

Parameters
Name	Description
`verbose`	`bool, optional` Whether to print the full summary. By default, the setting in `pandas.options.display.max_info_columns` is followed.
`buf`	`writable buffer, defaults to sys.stdout` Where to send the output. By default, the output is printed to sys.stdout. Pass a writable buffer if you need to further process the output.
`max_cols`	`int, optional` When to switch from the verbose to the truncated output. If the DataFrame has more than `max_cols` columns, the truncated output is used. By default, the setting in `pandas.options.display.max_info_columns` is used.
`memory_usage`	`bool, optional` Specifies whether total memory usage of the DataFrame elements (including the index) should be displayed. By default, this follows the `pandas.options.display.memory_usage` setting. True always show memory usage. False never shows memory usage. Memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes.
`show_counts`	`bool, optional` Whether to show the non-null counts. By default, this is shown only if the DataFrame is smaller than `pandas.options.display.max_info_rows` and `pandas.options.display.max_info_columns`. A value of True always shows the counts, and False never shows the counts.

Returns
Type	Description
`None`	This method prints a summary of a DataFrame and returns None.

insert

insert(
    loc: int,
    column: blocks.Label,
    value: SingleItemValue,
    allow_duplicates: bool = False,
)

Insert column into DataFrame at specified location.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})

Insert a new column named 'col3' between 'col1' and 'col2' with all entries set to 5.

>>> df.insert(1, 'col3', 5)
>>> df
   col1  col3  col2
0     1     5     3
1     2     5     4
<BLANKLINE>
[2 rows x 3 columns]

Insert another column named 'col2' at the beginning of the DataFrame with values [5, 6]

>>> df.insert(0, 'col2', [5, 6], allow_duplicates=True)
>>> df
   col2  col1  col3  col2
0     5     1     5     3
1     6     2     5     4
<BLANKLINE>
[2 rows x 4 columns]

Parameters
Name	Description
`loc`	`int` Insertion index. Must verify 0 <= loc <= len(columns).
`column`	`str, number, or hashable object` Label of the inserted column.
`value`	`Scalar, Series, or array-like` Content of the inserted column.
`allow_duplicates`	`bool, default False` Allow duplicate column labels to be created.

Exceptions
Type	Description
`IndexError`	If `column` index is out of bounds with the total count of columns.
`ValueError`	If `column` is already contained in the DataFrame, unless `allow_duplicates` is set to True.

interpolate

interpolate(method: str = "linear") -> bigframes.dataframe.DataFrame

Fill NaN values using an interpolation method.

Examples:

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

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

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	Returns the same object type as the caller, interpolated at some or all `NaN` values

isin

isin(values) -> bigframes.dataframe.DataFrame

Whether each element in the DataFrame is contained in values.

Examples:

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

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

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings).

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True
<BLANKLINE>
[2 rows x 2 columns]

When values is a dict, we can pass it to check for each column separately:

>>> df.isin({'num_wings': [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True
<BLANKLINE>
[2 rows x 2 columns]

Parameter
Name	Description
`values`	`iterable, or dict` The result will only be true at a location if all the labels match. If `values` is a dict, the keys must be the column names, which must match.

Exceptions
Type	Description
`TypeError`	If values provided are not list-like objects.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame of booleans showing whether each element in the DataFrame is contained in values.

isna

isna() -> bigframes.dataframe.DataFrame

Detect missing values.

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

Examples:

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

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

Show which entries in a DataFrame are NA:

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

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

Show which entries in a Series are NA:

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

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

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

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

isnull

isnull() -> bigframes.dataframe.DataFrame

Detect missing values.

Examples:

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

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

Show which entries in a DataFrame are NA:

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

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

Show which entries in a Series are NA:

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

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

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

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

items

items()

Iterate over (column name, Series) pairs.

Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series.

Examples:

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

>>> df = bpd.DataFrame({'species': ['bear', 'bear', 'marsupial'],
...                     'population': [1864, 22000, 80000]},
...                    index=['panda', 'polar', 'koala'])
>>> df
         species  population
panda       bear        1864
polar       bear       22000
koala  marsupial       80000
<BLANKLINE>
[3 rows x 2 columns]

>>> for label, content in df.items():
...     print(f'--> label: {label}')
...     print(f'--> content:\n{content}')
...
--> label: species
--> content:
panda         bear
polar         bear
koala    marsupial
Name: species, dtype: string
--> label: population
--> content:
panda     1864
polar    22000
koala    80000
Name: population, dtype: Int64

Returns
Type	Description
`Iterator`	Iterator of label, Series for each column.

iterrows

iterrows() -> typing.Iterable[tuple[typing.Any, pandas.core.series.Series]]

Iterate over DataFrame rows as (index, Series) pairs.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> index, row = next(df.iterrows())
>>> index
np.int64(0)
>>> row
A    1
B    4
Name: 0, dtype: object

Returns
Type	Description
`Iterable[Tuple]`	A tuple where data contains row values as a Series

itertuples

itertuples(
    index: bool = True, name: typing.Optional[str] = "Pandas"
) -> typing.Iterable[tuple[typing.Any, ...]]

Iterate over DataFrame rows as namedtuples.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> next(df.itertuples(name="Pair"))
Pair(Index=np.int64(0), A=np.int64(1), B=np.int64(4))

Parameters
Name	Description
`index`	`bool, default True` If True, return the index as the first element of the tuple.
`name`	`str or None, default "Pandas"` The name of the returned namedtuples or None to return regular tuples.

Returns
Type	Description
`Iterable[Tuple]`	An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values.

join

join(
    other: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    *,
    on: typing.Optional[str] = None,
    how: str = "left"
) -> bigframes.dataframe.DataFrame

Join columns of another DataFrame.

Join columns with other DataFrame on index

Examples:

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

Join two DataFrames by specifying how to handle the operation:

>>> df1 = bpd.DataFrame({'col1': ['foo', 'bar'], 'col2': [1, 2]}, index=[10, 11])
>>> df1
   col1  col2
10  foo     1
11  bar     2
<BLANKLINE>
[2 rows x 2 columns]

>>> df2 = bpd.DataFrame({'col3': ['foo', 'baz'], 'col4': [3, 4]}, index=[11, 22])
>>> df2
   col3  col4
11  foo     3
22  baz     4
<BLANKLINE>
[2 rows x 2 columns]

>>> df1.join(df2)
   col1  col2  col3  col4
10  foo     1  <NA>  <NA>
11  bar     2   foo     3
<BLANKLINE>
[2 rows x 4 columns]

>>> df1.join(df2, how="left")
   col1  col2  col3  col4
10  foo     1  <NA>  <NA>
11  bar     2   foo     3
<BLANKLINE>
[2 rows x 4 columns]

>>> df1.join(df2, how="right")
    col1  col2 col3  col4
11  bar      2  foo     3
22  <NA>  <NA>  baz     4
<BLANKLINE>
[2 rows x 4 columns]

>>> df1.join(df2, how="outer")
    col1  col2  col3  col4
10   foo     1  <NA>  <NA>
11   bar     2   foo     3
22  <NA>  <NA>   baz     4
<BLANKLINE>
[3 rows x 4 columns]

>>> df1.join(df2, how="inner")
   col1  col2 col3  col4
11  bar     2  foo     3
<BLANKLINE>
[1 rows x 4 columns]

Another option to join using the key columns is to use the on parameter:

>>> df1.join(df2, on="col1", how="right")
      col1  col2 col3  col4
<NA>    11  <NA>  foo     3
<NA>    22  <NA>  baz     4
<BLANKLINE>
[2 rows x 4 columns]

Parameter

Name Description

how

{'left', 'right', 'outer', 'inner'}, default 'left'

How to handle the operation of the two objects. left: use calling frame's index (or column if on is specified) right: use other's index. outer: form union of calling frame's index (or column if on is specified) with other's index, and sort it lexicographically. inner: form intersection of calling frame's index (or column if on is specified) with other's index, preserving the order of the calling's one. cross: creates the cartesian product from both frames, preserves the order of the left keys.

Exceptions
Type	Description
`ValueError`	If value for `on` is specified for cross join.
`ValueError`	If join on columns does not match the index level of the other DataFrame. Join on columns with multi-index is not supported.
`ValueError`	If left index to join on does not have the same number of levels as the right index.

Returns
Type	Description
`bigframes.pandas.DataFrame`	A dataframe containing columns from both the caller and `other`.

keys

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

Get the 'info axis'.

This is index for Series, columns for DataFrame.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df.keys()
Index(['A', 'B'], dtype='object')

Returns
Type	Description
`pandas.Index`	Info axis.

kurt

kurt(*, numeric_only: bool = False)

Return unbiased kurtosis over columns.

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

>>> df = bpd.DataFrame({"A": [1, 2, 3, 4, 5],
...                     "B": [3, 4, 3, 2, 1],
...                     "C": [2, 2, 3, 2, 2]})
>>> df
    A       B       C
0   1       3       2
1   2       4       2
2   3       3       3
3   4       2       2
4   5       1       2
<BLANKLINE>
[5 rows x 3 columns]

Calculating the kurtosis value of each column:

>>> df.kurt()
A        -1.2
B   -0.177515
C         5.0
dtype: Float64

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series.

kurtosis

kurtosis(*, numeric_only: bool = False)

Return unbiased kurtosis over columns.

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

>>> df = bpd.DataFrame({"A": [1, 2, 3, 4, 5],
...                     "B": [3, 4, 3, 2, 1],
...                     "C": [2, 2, 3, 2, 2]})
>>> df
    A       B       C
0   1       3       2
1   2       4       2
2   3       3       3
3   4       2       2
4   5       1       2
<BLANKLINE>
[5 rows x 3 columns]

Calculating the kurtosis value of each column:

>>> df.kurt()
A        -1.2
B   -0.177515
C         5.0
dtype: Float64

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series.

le

le(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

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

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Note: Mismatched indices will be unioned together. NaN values in floating point columns are considered different (i.e. NaN != NaN).

Examples:

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

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].le(180)
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean

You can also use arithmetic operator <=:

>>> df["degrees"] <= 180
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean

Parameters
Name	Description
`other`	`scalar, sequence, Series, or DataFrame` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}, default 'columns'` Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame of bool. The result of the comparison.

line

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.

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')

Parameters
Name	Description
`x`	`label or position, optional` Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.
`y`	`label or position, optional` Allows plotting of one column versus another. If not specified, all numerical columns are used.
`color`	`str, array-like, or dict, optional` 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.
`sampling_n`	`int, default 100` Number of random items for plotting.
`sampling_random_state`	`int, default 0` Seed for random number generator.

Returns
Type	Description
`matplotlib.axes.Axes or np.ndarray of them`	An ndarray is returned with one `matplotlib.axes.Axes` per column when `subplots=True`.

lt

lt(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

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

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Note: Mismatched indices will be unioned together. NaN values in floating point columns are considered different (i.e. NaN != NaN).

Examples:

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

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].lt(180)
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean

You can also use arithmetic operator <:

>>> df["degrees"] < 180
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean

Parameters
Name	Description
`other`	`scalar, sequence, Series, or DataFrame` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}, default 'columns'` Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame of bool. The result of the comparison.

map

map(func, na_action: typing.Optional[str] = None) -> bigframes.dataframe.DataFrame

Apply a function to a Dataframe elementwise.

This method applies a function that accepts and returns a scalar to every element of a DataFrame.

Examples:

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

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

>>> df_minutes = bpd.DataFrame(
...     {"system_minutes" : [0, 30, 60, 90, 120],
...      "user_minutes" : [0, 15, 75, 90, 6]})
>>> df_minutes
system_minutes  user_minutes
0               0             0
1              30            15
2              60            75
3              90            90
4             120             6
<BLANKLINE>
[5 rows x 2 columns]

>>> df_hours = df_minutes.map(minutes_to_hours)
>>> df_hours
system_minutes  user_minutes
0             0.0           0.0
1             0.5          0.25
2             1.0          1.25
3             1.5           1.5
4             2.0           0.1
<BLANKLINE>
[5 rows x 2 columns]

If there are NA/None values in the data, you can ignore applying the remote function on such values by specifying na_action='ignore'.

>>> df_minutes = bpd.DataFrame(
...     {
...         "system_minutes" : [0, 30, 60, None, 90, 120, bpd.NA],
...         "user_minutes" : [0, 15, 75, 90, 6, None, bpd.NA]
...     }, dtype="Int64")
>>> df_hours = df_minutes.map(minutes_to_hours, na_action='ignore')
>>> df_hours
system_minutes  user_minutes
0             0.0           0.0
1             0.5          0.25
2             1.0          1.25
3            <NA>           1.5
4             1.5           0.1
5             2.0          <NA>
6            <NA>          <NA>
<BLANKLINE>
[7 rows x 2 columns]

Parameters
Name	Description
`func`	`function` Python function wrapped by `remote_function` decorator, returns a single value from a single value.
`na_action`	`Optional[str], default None` `{None, 'ignore'}`, default None. If `ignore`, propagate NaN values, without passing them to func.

Exceptions
Type	Description
`TypeError`	If value provided for `func` is not callable.
`ValueError`	If value provided for `na_action` is not `None` or `ignore`.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Transformed DataFrame.

mask

mask(cond, other=None)

Replace values where the condition is False.

Examples:

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

>>> df = bpd.DataFrame({'a': [20, 10, 0], 'b': [0, 10, 20]})
>>> df
    a   b
0  20   0
1  10  10
2   0  20
<BLANKLINE>
[3 rows x 2 columns]

You can filter the values in the dataframe based on a condition. The values matching the condition would be kept, and not matching would be replaced. The default replacement value is NA. For example, when the condition is a dataframe:

>>> df.mask(df > 0)
      a     b
0  <NA>     0
1  <NA>  <NA>
2     0  <NA>
<BLANKLINE>
[3 rows x 2 columns]

You can specify a custom replacement value for non-matching values.

>>> df.mask(df > 0, -1)
    a   b
0  -1   0
1  -1  -1
2   0  -1
<BLANKLINE>
[3 rows x 2 columns]

Besides dataframe, the condition can be a series too. For example:

>>> df.mask(df['a'] > 10, -1)
    a   b
0  -1  -1
1  10  10
2   0  20
<BLANKLINE>
[3 rows x 2 columns]

As for the replacement, it can be a dataframe too. For example:

>>> df.mask(df > 10, -df)
      a     b
0   -20     0
1    10    10
2     0   -20
<BLANKLINE>
[3 rows x 2 columns]

>>> df.mask(df['a'] > 10, -df)
      a     b
0   -20     0
1    10    10
2     0    20
<BLANKLINE>
[3 rows x 2 columns]

Please note, replacement doesn't support Series for now. In pandas, when specifying a Series as replacement, the axis value should be specified at the same time, which is not supported in bigframes DataFrame.

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

Returns
Type	Description
`DataFrame`	DataFrame after the replacement.

max

max(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

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

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

Finding the maximum value in each column (the default behavior without an explicit axis parameter).

>>> df.max()
A    3
B    4
dtype: Int64

Finding the maximum value in each row.

>>> df.max(axis=1)
0    2
1    4
dtype: Int64

Parameters
Name	Description
`axis`	`{index (0), columns (1)}` Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
`numeric_only`	`bool. default False` Default False. Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series after the maximum of values.

mean

mean(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return the mean of the values over the requested axis.

Examples:

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

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

Calculating the mean of each column (the default behavior without an explicit axis parameter).

>>> df.mean()
A    2.0
B    3.0
dtype: Float64

Calculating the mean of each row.

>>> df.mean(axis=1)
0    1.5
1    3.5
dtype: Float64

Parameters
Name	Description
`axis`	`{index (0), columns (1)}` Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
`numeric_only`	`bool. default False` Default False. Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series with the mean of values.

median

median(
    *, numeric_only: bool = False, exact: bool = True
) -> bigframes.series.Series

Return the median of the values over colunms.

Examples:

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

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

Finding the median value of each column.

>>> df.median()
A    2.0
B    3.0
dtype: Float64

Parameters
Name	Description
`numeric_only`	`bool. default False` Default False. Include only float, int, boolean columns.
`exact`	`bool. default True` Default True. Get the exact median instead of an approximate one.

Returns
Type	Description
`bigframes.pandas.Series`	Series with the median of values.

melt

melt(
    id_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None,
    value_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None,
    var_name: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    value_name: typing.Hashable = "value",
)

Unpivot a DataFrame from wide to long format, optionally leaving identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (id_vars), while all other columns, considered measured variables (value_vars), are "unpivoted" to the row axis, leaving just two non-identifier columns, 'variable' and 'value'.

Examples:

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

>>> df = bpd.DataFrame({"A": [1, None, 3, 4, 5],
...                     "B": [1, 2, 3, 4, 5],
...                     "C": [None, 3.5, None, 4.5, 5.0]})
>>> df
        A       B      C
0     1.0       1   <NA>
1    <NA>       2    3.5
2     3.0       3   <NA>
3     4.0       4    4.5
4     5.0       5    5.0
<BLANKLINE>
[5 rows x 3 columns]

Using melt without optional arguments:

>>> df.melt()
    variable    value
0          A      1.0
1          A     <NA>
2          A      3.0
3          A      4.0
4          A      5.0
5          B      1.0
6          B      2.0
7          B      3.0
8          B      4.0
9          B      5.0
10         C     <NA>
11         C      3.5
12         C     <NA>
13         C      4.5
14         C      5.0
<BLANKLINE>
[15 rows x 2 columns]

Using melt with id_vars and value_vars:

>>> df.melt(id_vars='A', value_vars=['B', 'C'])
      A variable  value
0   1.0        B    1.0
1  <NA>        B    2.0
2   3.0        B    3.0
3   4.0        B    4.0
4   5.0        B    5.0
5   1.0        C   <NA>
6  <NA>        C    3.5
7   3.0        C   <NA>
8   4.0        C    4.5
9   5.0        C    5.0
<BLANKLINE>
[10 rows x 3 columns]

Parameters
Name	Description
`id_vars`	`tuple, list, or ndarray, optional` Column(s) to use as identifier variables.
`value_vars`	`tuple, list, or ndarray, optional` Column(s) to unpivot. If not specified, uses all columns that are not set as `id_vars`.
`var_name`	`scalar` Name to use for the 'variable' column. If None it uses `frame.columns.name` or 'variable'.
`value_name`	`scalar, default 'value'` Name to use for the 'value' column.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Unpivoted DataFrame.

memory_usage

memory_usage(index: bool = True)

Return the memory usage of each column in bytes.

The memory usage can optionally include the contribution of the index and elements of object dtype.

This value is displayed in DataFrame.info by default. This can be suppressed by setting pandas.options.display.memory_usage to False.

Parameter
Name	Description
`index`	`bool, default True` Specifies whether to include the memory usage of the DataFrame's index in returned Series. If `index=True`, the memory usage of the index is the first item in the output.

Returns
Type	Description
`bigframes.pandas.Series`	A Series whose index is the original column names and whose values is the memory usage of each column in bytes.

merge

merge(
    right: bigframes.dataframe.DataFrame,
    how: typing.Literal["inner", "left", "outer", "right", "cross"] = "inner",
    on: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    *,
    left_on: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    right_on: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    sort: bool = False,
    suffixes: tuple[str, str] = ("_x", "_y")
) -> bigframes.dataframe.DataFrame

Merge DataFrame objects with a database-style join.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Examples:

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

Merge DataFrames df1 and df2 by specifying type of merge:

>>> df1 = bpd.DataFrame({'a': ['foo', 'bar'], 'b': [1, 2]})
>>> df1
     a  b
0  foo  1
1  bar  2
<BLANKLINE>
[2 rows x 2 columns]

>>> df2 = bpd.DataFrame({'a': ['foo', 'baz'], 'c': [3, 4]})
>>> df2
     a  c
0  foo  3
1  baz  4
<BLANKLINE>
[2 rows x 2 columns]

>>> df1.merge(df2, how="inner", on="a")
     a  b  c
0  foo  1  3
<BLANKLINE>
[1 rows x 3 columns]

>>> df1.merge(df2, how='left', on='a')
     a  b     c
0  foo  1     3
1  bar  2  <NA>
<BLANKLINE>
[2 rows x 3 columns]

Merge df1 and df2 on the lkey and rkey columns. The value columns have the default suffixes, _x and _y, appended.

>>> df1 = bpd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [1, 2, 3, 5]})
>>> df1
  lkey  value
0  foo      1
1  bar      2
2  baz      3
3  foo      5
<BLANKLINE>
[4 rows x 2 columns]

>>> df2 = bpd.DataFrame({'rkey': ['foo', 'bar', 'baz', 'foo'],
...                     'value': [5, 6, 7, 8]})
>>> df2
  rkey  value
0  foo      5
1  bar      6
2  baz      7
3  foo      8
<BLANKLINE>
[4 rows x 2 columns]

>>> df1.merge(df2, left_on='lkey', right_on='rkey')
  lkey  value_x rkey  value_y
0  foo        1  foo        5
1  foo        1  foo        8
2  bar        2  bar        6
3  baz        3  baz        7
4  foo        5  foo        5
5  foo        5  foo        8
<BLANKLINE>
[6 rows x 4 columns]

Parameters
Name	Description
`on`	`label or list of labels` Columns to join on. It must be found in both DataFrames. Either on or left_on + right_on must be passed in.
`left_on`	`label or list of labels` Columns to join on in the left DataFrame. Either on or left_on + right_on must be passed in.
`right_on`	`label or list of labels` Columns to join on in the right DataFrame. Either on or left_on + right_on must be passed in.

Exceptions
Type	Description
`ValueError`	If value for `on` is specified for cross join.
`ValueError`	If `on` or `left_on` + `right_on` are not specified when `on` is `None`.
`ValueError`	If `on` and `left_on` + `right_on` are specified when `on` is not `None`.
`ValueError`	If no column with the provided label is found in `self` for left join.
`ValueError`	If no column with the provided label is found in `self` for right join.

Returns
Type	Description
`bigframes.pandas.DataFrame`	A DataFrame of the two merged objects.

min

min(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return the minimum 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

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

Finding the minimum value in each column (the default behavior without an explicit axis parameter).

>>> df.min()
A    1
B    2
dtype: Int64

Finding the minimum value in each row.

>>> df.min(axis=1)
0    1
1    3
dtype: Int64

Parameters
Name	Description
`axis`	`{index (0), columns (1)}` Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
`numeric_only`	`bool, default False` Default False. Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series with the minimum of the values.

mod

mod(
    other: int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get modulo of DataFrame and other, element-wise (binary operator %).

Equivalent to dataframe % other. With reverse version, rmod.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].mod(df['B'])
0    1
1    2
2    3
dtype: Int64

You can also use arithmetic operator %:

>>> df['A'] % (df['B'])
0    1
1    2
2    3
dtype: Int64

Parameter
Name	Description
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

mul

mul(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get multiplication of DataFrame and other, element-wise (binary operator *).

Equivalent to dataframe * other. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].mul(df['B'])
0     4
1    10
2    18
dtype: Int64

You can also use arithmetic operator *:

>>> df['A'] * (df['B'])
0     4
1    10
2    18
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

multiply

multiply(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get multiplication of DataFrame and other, element-wise (binary operator *).

Equivalent to dataframe * other. With reverse version, rmul.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].mul(df['B'])
0     4
1    10
2    18
dtype: Int64

You can also use arithmetic operator *:

>>> df['A'] * (df['B'])
0     4
1    10
2    18
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

ne

ne(other: typing.Any, axis: str | int = "columns") -> bigframes.dataframe.DataFrame

Get not equal to of DataFrame and other, element-wise (binary operator ne).

Among flexible wrappers (eq, ne, le, lt, ge, gt) to comparison operators.

Equivalent to ==, !=, <=, <, >=, > with support to choose axis (rows or columns) and level for comparison.

Examples:

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

You can use method name:

>>> df = bpd.DataFrame({'angles': [0, 3, 4],
...        'degrees': [360, 180, 360]},
...       index=['circle', 'triangle', 'rectangle'])
>>> df["degrees"].ne(360)
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean

You can also use arithmetic operator !=:

>>> df["degrees"] != 360
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean

Parameters
Name	Description
`other`	`scalar, sequence, Series, or DataFrame` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}, default 'columns'` Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

Returns
Type	Description
`bigframes.pandas.DataFrame`	Result of the comparison.

nlargest

nlargest(
    n: int,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    keep: str = "first",
) -> bigframes.dataframe.DataFrame

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Note: This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples:

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

>>> df = bpd.DataFrame({"A": [1, 1, 3, 3, 5, 5],
...                     "B": [5, 6, 3, 4, 1, 2],
...                     "C": ['a', 'b', 'a', 'b', 'a', 'b']})
>>> df
    A       B       C
0   1       5       a
1   1       6       b
2   3       3       a
3   3       4       b
4   5       1       a
5   5       2       b
<BLANKLINE>
[6 rows x 3 columns]

Returns rows with the largest value in 'A', including all ties:

>>> df.nlargest(1, 'A', keep = "all")
    A       B       C
4   5       1       a
5   5       2       b
<BLANKLINE>
[2 rows x 3 columns]

Returns the first row with the largest value in 'A', default behavior in case of ties:

>>> df.nlargest(1, 'A')
    A       B       C
4   5       1       a
<BLANKLINE>
[1 rows x 3 columns]

Returns the last row with the largest value in 'A' in case of ties:

>>> df.nlargest(1, 'A', keep = "last")
    A       B       C
5   5       2       b
<BLANKLINE>
[1 rows x 3 columns]

Returns the row with the largest combined values in both 'A' and 'C':

>>> df.nlargest(1, ['A', 'C'])
    A       B       C
5   5       2       b
<BLANKLINE>
[1 rows x 3 columns]

Parameters
Name	Description
`n`	`int` Number of rows to return.
`columns`	`label or list of labels` Column label(s) to order by.
`keep`	`{'first', 'last', 'all'}, default 'first'` Where there are duplicate values: - `first` : prioritize the first occurrence(s) - `last` : prioritize the last occurrence(s) - `all` : do not drop any duplicates, even it means selecting more than `n` items.

Exceptions
Type	Description
`ValueError`	If value of `keep` is not `first`, `last`, or `all`.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The first `n` rows ordered by the given columns in descending order.

notna

notna() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

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

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

notnull

notnull() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

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

nsmallest

nsmallest(
    n: int,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    keep: str = "first",
) -> bigframes.dataframe.DataFrame

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

Note: This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.

Examples:

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

>>> df = bpd.DataFrame({"A": [1, 1, 3, 3, 5, 5],
...                     "B": [5, 6, 3, 4, 1, 2],
...                     "C": ['a', 'b', 'a', 'b', 'a', 'b']})
>>> df
    A       B       C
0   1       5       a
1   1       6       b
2   3       3       a
3   3       4       b
4   5       1       a
5   5       2       b
<BLANKLINE>
[6 rows x 3 columns]

Returns rows with the smallest value in 'A', including all ties:

>>> df.nsmallest(1, 'A', keep = "all")
    A       B       C
0   1       5       a
1   1       6       b
<BLANKLINE>
[2 rows x 3 columns]

Returns the first row with the smallest value in 'A', default behavior in case of ties:

>>> df.nsmallest(1, 'A')
    A       B       C
0   1       5       a
<BLANKLINE>
[1 rows x 3 columns]

Returns the last row with the smallest value in 'A' in case of ties:

>>> df.nsmallest(1, 'A', keep = "last")
    A       B       C
1   1       6       b
<BLANKLINE>
[1 rows x 3 columns]

Returns rows with the smallest values in 'A' and 'C'

>>> df.nsmallest(1, ['A', 'C'])
    A       B       C
0   1       5       a
<BLANKLINE>
[1 rows x 3 columns]

Parameters
Name	Description
`n`	`int` Number of rows to return.
`columns`	`label or list of labels` Column label(s) to order by.
`keep`	`{'first', 'last', 'all'}, default 'first'` Where there are duplicate values: - `first` : prioritize the first occurrence(s) - `last` : prioritize the last occurrence(s) - `all` : do not drop any duplicates, even it means selecting more than `n` items.

Exceptions
Type	Description
`ValueError`	If value of `keep` is not `first`, `last`, or `all`.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The first `n` rows ordered by the given columns in ascending order.

nunique

nunique() -> bigframes.series.Series

Count number of distinct elements in each column.

Examples:

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

>>> df = bpd.DataFrame({"A": [3, 1, 2], "B": [1, 2, 2]})
>>> df
    A       B
0   3       1
1   1       2
2   2       2
<BLANKLINE>
[3 rows x 2 columns]

>>> df.nunique()
A    3
B    2
dtype: Int64

Returns
Type	Description
`bigframes.pandas.Series`	Series with number of distinct elements.

pct_change

pct_change(periods: int = 1) -> bigframes.dataframe.DataFrame

Fractional change between the current and a prior element.

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

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

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

peek

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

Preview n arbitrary rows from the dataframe. No guarantees about row selection or ordering. DataFrame.peek(force=False) will always be very fast, but will not succeed if data requires full data scanning. Using force=True will always succeed, but may be perform queries. Query results will be cached so that future steps will benefit from these queries.

Parameters
Name	Description
`n`	`int, default 5` The number of rows to select from the dataframe. Which N rows are returned is non-deterministic.
`force`	`bool, default True` If the data cannot be peeked efficiently, the dataframe will instead be fully materialized as part of the operation if `force=True`. If `force=False`, the operation will throw a ValueError.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

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

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

pivot

pivot(
    *,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    index: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    values: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None
) -> bigframes.dataframe.DataFrame

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a "pivot" table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns.

Examples:

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

>>> df = bpd.DataFrame({
...     "foo": ["one", "one", "one", "two", "two"],
...     "bar": ["A", "B", "C", "A", "B"],
...     "baz": [1, 2, 3, 4, 5],
...     "zoo": ['x', 'y', 'z', 'q', 'w']
... })

>>> df
    foo     bar     baz     zoo
0   one       A       1       x
1   one       B       2       y
2   one       C       3       z
3   two       A       4       q
4   two       B       5       w
<BLANKLINE>
[5 rows x 4 columns]

Using pivot without optional arguments:

>>> df.pivot(columns='foo')
        bar             baz             zoo
foo  one     two     one     two     one     two
0      A    <NA>       1    <NA>       x    <NA>
1      B    <NA>       2    <NA>       y    <NA>
2      C    <NA>       3    <NA>       z    <NA>
3   <NA>       A    <NA>       4    <NA>       q
4   <NA>       B    <NA>       5    <NA>       w
<BLANKLINE>
[5 rows x 6 columns]

Using pivot with index and values:

>>> df.pivot(columns='foo', index='bar', values='baz')
foo     one     two
bar
A       1         4
B       2         5
C       3      <NA>
<BLANKLINE>
[3 rows x 2 columns]

Parameters
Name	Description
`columns`	`str or object or a list of str` Column to use to make new frame's columns.
`index`	`str or object or a list of str, optional` Column to use to make new frame's index. If not given, uses existing index.
`values`	`str, object or a list of the previous, optional` Column(s) to use for populating new frame's values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Returns reshaped DataFrame.

pivot_table

pivot_table(
    values: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    index: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    aggfunc: str = "mean",
) -> bigframes.dataframe.DataFrame

Create a spreadsheet-style pivot table as a DataFrame.

The levels in the pivot table will be stored in MultiIndex objects (hierarchical indexes) on the index and columns of the result DataFrame.

Examples:

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

>>> df = bpd.DataFrame({
...     'Product': ['Product A', 'Product B', 'Product A', 'Product B', 'Product A', 'Product B'],
...     'Region': ['East', 'West', 'East', 'West', 'West', 'East'],
...     'Sales': [100, 200, 150, 100, 200, 150],
...     'Rating': [3, 5, 4, 3, 3, 5]
... })
>>> df
     Product Region  Sales  Rating
0  Product A   East    100       3
1  Product B   West    200       5
2  Product A   East    150       4
3  Product B   West    100       3
4  Product A   West    200       3
5  Product B   East    150       5
<BLANKLINE>
[6 rows x 4 columns]

Using pivot_table with default aggfunc "mean":

>>> pivot_table = df.pivot_table(
...     values=['Sales', 'Rating'],
...     index='Product',
...     columns='Region'
... )
>>> pivot_table
          Rating       Sales
Region      East West   East   West
Product
Product A    3.5  3.0  125.0  200.0
Product B    5.0  4.0  150.0  150.0
<BLANKLINE>
[2 rows x 4 columns]

Using pivot_table with specified aggfunc "max":

>>> pivot_table = df.pivot_table(
...     values=['Sales', 'Rating'],
...     index='Product',
...     columns='Region',
...     aggfunc="max"
... )
>>> pivot_table
          Rating      Sales
Region      East West  East West
Product
Product A      4    3   150  200
Product B      5    5   150  200
<BLANKLINE>
[2 rows x 4 columns]

Parameters
Name	Description
`values`	`str, object or a list of the previous, optional` Column(s) to use for populating new frame's values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.
`index`	`str or object or a list of str, optional` Column to use to make new frame's index. If not given, uses existing index.
`columns`	`str or object or a list of str` Column to use to make new frame's columns.
`aggfunc`	`str, default "mean"` Aggregation function name to compute summary statistics (e.g., 'sum', 'mean').

Returns
Type	Description
`bigframes.pandas.DataFrame`	An Excel style pivot table.

pow

pow(
    other: int | bigframes.series.Series, axis: str | int = "columns"
) -> bigframes.dataframe.DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator **).

Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rpow.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].pow(df['B'])
0      1
1     32
2    729
dtype: Int64

You can also use arithmetic operator **:

>>> df['A'] ** (df['B'])
0      1
1     32
2    729
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

prod

prod(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return the product of the values over the requested axis.

Examples:

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

>>> df = bpd.DataFrame({"A": [1, 2, 3], "B": [4.5, 5.5, 6.5]})
>>> df
    A    B
0   1  4.5
1   2  5.5
2   3  6.5
<BLANKLINE>
[3 rows x 2 columns]

Calculating the product of each column(the default behavior without an explicit axis parameter):

>>> df.prod()
A        6.0
B    160.875
dtype: Float64

Calculating the product of each row:

>>> df.prod(axis=1)
0     4.5
1    11.0
2    19.5
dtype: Float64

Parameters
Name	Description
`axis`	`{index (0), columns (1)}` Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
`numeric_only`	`bool. default False` Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series with the product of the values.

product

product(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return the product of the values over the requested axis.

Examples:

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

>>> df = bpd.DataFrame({"A": [1, 2, 3], "B": [4.5, 5.5, 6.5]})
>>> df
    A    B
0   1  4.5
1   2  5.5
2   3  6.5
<BLANKLINE>
[3 rows x 2 columns]

Calculating the product of each column(the default behavior without an explicit axis parameter):

>>> df.prod()
A        6.0
B    160.875
dtype: Float64

Calculating the product of each row:

>>> df.prod(axis=1)
0     4.5
1    11.0
2    19.5
dtype: Float64

Parameters
Name	Description
`axis`	`{index (0), columns (1)}` Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
`numeric_only`	`bool. default False` Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series with the product of the values.

quantile

quantile(
    q: typing.Union[float, typing.Sequence[float]] = 0.5, *, numeric_only: bool = False
)

Return values at the given quantile over requested axis.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]),
...                   columns=['a', 'b'])
>>> df.quantile(.1)
a    1.3
b    3.7
Name: 0.1, dtype: Float64
>>> df.quantile([.1, .5])
       a     b
0.1  1.3   3.7
0.5  2.5  55.0
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
`q`	`float or array-like, default 0.5 (50% quantile)` Value between 0 <= q <= 1, the quantile(s) to compute.
`numeric_only`	`bool, default False` Include only `float`, `int` or `boolean` data.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	If `q` is an array, a DataFrame will be returned where the index is `q`, the columns are the columns of self, and the values are the quantiles. If `q` is a float, a Series will be returned where the index is the columns of self and the values are the quantiles.

query

query(expr: str) -> bigframes.dataframe.DataFrame

Query the columns of a DataFrame with a boolean expression.

Examples:

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

>>> df = bpd.DataFrame({'A': range(1, 6),
...                    'B': range(10, 0, -2),
...                    'C C': range(10, 5, -1)})
>>> df
A   B  C C
0  1  10   10
1  2   8    9
2  3   6    8
3  4   4    7
4  5   2    6
<BLANKLINE>
[5 rows x 3 columns]
>>> df.query('A > B')
A  B  C C
4  5  2    6
<BLANKLINE>
[1 rows x 3 columns]

The previous expression is equivalent to

>>> df[df.A > df.B]
A  B  C C
4  5  2    6
<BLANKLINE>
[1 rows x 3 columns]

For columns with spaces in their name, you can use backtick quoting.

>>> df.query('B == `C C`')
A   B  C C
0  1  10   10
<BLANKLINE>
[1 rows x 3 columns]

The previous expression is equivalent to

>>> df[df.B == df['C C']]
A   B  C C
0  1  10   10
<BLANKLINE>
[1 rows x 3 columns]

Parameter

Name Description

expr

str

The query string to evaluate. You can refer to variables in the environment by prefixing them with an '@' character like @a + b. You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuations (besides underscores) or starting with digits must be surrounded by backticks. (For example, a column named "Area (cm^2)" would be referenced as <code>Area (cm^2)</code>`<code>). Column names which are Python keywords (like "list", "for", "import", etc) cannot be used. For example, if one of your columns is called </code>a a<code> and you want to sum it with </code><code>b</code><code>, your query should be </code>`<code>a a</code> + b.

Returns
Type	Description
`None or bigframes.pandas.DataFrame`	DataFrame result after the query operation, otherwise None.

radd

radd(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get addition of DataFrame and other, element-wise (binary operator +).

Equivalent to other + dataframe. With reverse version, add.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].radd(df['B'])
0    5
1    7
2    9
dtype: Int64

You can also use arithmetic operator +:

>>> df['A'] + df['B']
0    5
1    7
2    9
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

rank

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

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

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

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

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

rdiv

rdiv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get floating division of DataFrame and other, element-wise (binary operator /).

Equivalent to other / dataframe. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rtruediv(df['B'])
0    4.0
1    2.5
2    2.0
dtype: Float64

It's equivalent to using arithmetic operator: /:

>>> df['B'] / (df['A'])
0    4.0
1    2.5
2    2.0
dtype: Float64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

reindex

reindex(
    labels=None,
    *,
    index=None,
    columns=None,
    axis: typing.Optional[typing.Union[str, int]] = None,
    validate: typing.Optional[bool] = None
)

Conform DataFrame to new index with optional filling logic.

Places NA in locations having no value in the previous index. A new object is produced.

Parameters
Name	Description
`labels`	`array-like, optional` New labels / index to conform the axis specified by 'axis' to.
`index`	`array-like, optional` New labels for the index. Preferably an Index object to avoid duplicating data.
`columns`	`array-like, optional` New labels for the columns. Preferably an Index object to avoid duplicating data.
`axis`	`int or str, optional` Axis to target. Can be either the axis name ('index', 'columns') or number (0, 1).

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame with changed index.

reindex_like

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

Return an object with matching indices as other object.

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

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

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

rename

rename(
    *, columns: typing.Mapping[typing.Hashable, typing.Hashable]
) -> bigframes.dataframe.DataFrame

Rename columns.

Dict values must be unique (1-to-1). Labels not contained in a dict will be left as-is. Extra labels listed don't throw an error.

Examples:

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

>>> df = bpd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]})
>>> df
   A  B
0  1  4
1  2  5
2  3  6
<BLANKLINE>
[3 rows x 2 columns]

Rename columns using a mapping:

>>> df.rename(columns={"A": "col1", "B": "col2"})
   col1  col2
0     1     4
1     2     5
2     3     6
<BLANKLINE>
[3 rows x 2 columns]

Parameter
Name	Description
`columns`	`Mapping` Dict-like from old column labels to new column labels.

Exceptions
Type	Description
`KeyError`	If any of the labels is not found.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame with the renamed axis labels.

rename_axis

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

Set the name of the axis for the index.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame with the new index name

reorder_levels

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

Rearrange index levels using input order. May not drop or duplicate levels.

Parameters
Name	Description
`order`	`list of int or list of str` List representing new level order. Reference level by number (position) or by key (label).
`axis`	`{0 or 'index', 1 or 'columns'}, default 0` Where to reorder levels.

Exceptions
Type	Description
`ValueError`	If columns are not multi-index.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame of rearranged index.

replace

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

Replace values given in to_replace with value.

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

Examples:

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

>>> df = bpd.DataFrame({
...     'int_col': [1, 1, 2, 3],
...     'string_col': ["a", "b", "c", "b"],
...     })

Using scalar to_replace and value:

>>> df.replace("b", "e")
   int_col string_col
0        1          a
1        1          e
2        2          c
3        3          e
<BLANKLINE>
[4 rows x 2 columns]

Using dictionary:

>>> df.replace({"a": "e", 2: 5})
   int_col string_col
0        1          e
1        1          b
2        5          c
3        3          b
<BLANKLINE>
[4 rows x 2 columns]

Using regex:

>>> df.replace("[ab]", "e", regex=True)
   int_col string_col
0        1          e
1        1          e
2        2          c
3        3          e
<BLANKLINE>
[4 rows x 2 columns]

Parameters
Name	Description
`to_replace`	`str, regex, list, int, float or None` How to find the values that will be replaced. numeric: numeric values equal to `to_replace` will be replaced with `value` str: string exactly matching `to_replace` will be replaced with `value` regex: regexs matching `to_replace` will be replaced with`value` list of str, regex, or numeric: First, if `to_replace` and `value` are both lists, they must be the same length. Second, if `regex=True` then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn't matter much for `value` since there are only a few possible substitution regexes you can use. str, regex and numeric rules apply as above.
`value`	`scalar, default None` Value to replace any values matching `to_replace` with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.
`regex`	`bool, default False` Whether to interpret `to_replace` and/or `value` as regular expressions. If this is `True` then `to_replace` must be a string.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Object after replacement.

reset_index

reset_index(*, drop: bool = False) -> bigframes.dataframe.DataFrame

Reset the index.

Reset the index of the DataFrame, and use the default one instead.

Examples:

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

>>> import numpy as np
>>> df = bpd.DataFrame([('bird', 389.0),
...                     ('bird', 24.0),
...                     ('mammal', 80.5),
...                     ('mammal', np.nan)],
...                    index=['falcon', 'parrot', 'lion', 'monkey'],
...                    columns=('class', 'max_speed'))
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal       <NA>
<BLANKLINE>
[4 rows x 2 columns]

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal       <NA>
<BLANKLINE>
[4 rows x 3 columns]

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal       <NA>
<BLANKLINE>
[4 rows x 2 columns]

You can also use reset_index with MultiIndex.

>>> import pandas as pd
>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
>>> columns = ['speed', 'max']
>>> df = bpd.DataFrame([(389.0, 'fly'),
...                     (24.0, 'fly'),
...                     (80.5, 'run'),
...                     (np.nan, 'jump')],
...                    index=index,
...                    columns=columns)
>>> df
               speed   max
class  name
bird   falcon  389.0   fly
       parrot   24.0   fly
mammal lion     80.5   run
       monkey   <NA>  jump
<BLANKLINE>
[4 rows x 2 columns]

>>> df.reset_index()
    class    name  speed   max
0    bird  falcon  389.0   fly
1    bird  parrot   24.0   fly
2  mammal    lion   80.5   run
3  mammal  monkey   <NA>  jump
<BLANKLINE>
[4 rows x 4 columns]

>>> df.reset_index(drop=True)
   speed   max
0  389.0   fly
1   24.0   fly
2   80.5   run
3   <NA>  jump
<BLANKLINE>
[4 rows x 2 columns]

Parameter
Name	Description
`drop`	`bool, default False` Do not try to insert index into dataframe columns. This resets the index to the default integer index.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame with the new index.

rfloordiv

rfloordiv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get integer division of DataFrame and other, element-wise (binary operator //).

Equivalent to other // dataframe. With reverse version, rfloordiv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rfloordiv(df['B'])
0    4
1    2
2    2
dtype: Int64

It's equivalent to using arithmetic operator: //:

>>> df['B'] // (df['A'])
0    4
1    2
2    2
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

rmod

rmod(
    other: int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get modulo of DataFrame and other, element-wise (binary operator %).

Equivalent to other % dataframe. With reverse version, mod.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rmod(df['B'])
0    0
1    1
2    0
dtype: Int64

It's equivalent to using arithmetic operator: %:

>>> df['B'] % (df['A'])
0    0
1    1
2    0
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

rmul

rmul(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get multiplication of DataFrame and other, element-wise (binary operator *).

Equivalent to other * dataframe. With reverse version, mul.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].rmul(df['B'])
0     4
1    10
2    18
dtype: Int64

You can also use arithmetic operator *:

>>> df['A'] * (df['B'])
0     4
1    10
2    18
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

rolling

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

Provide rolling window calculations.

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

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

rpow

rpow(
    other: int | bigframes.series.Series, axis: str | int = "columns"
) -> bigframes.dataframe.DataFrame

Get Exponential power of dataframe and other, element-wise (binary operator rpow).

Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rpow(df['B'])
0      4
1     25
2    216
dtype: Int64

It's equivalent to using arithmetic operator: **:

>>> df['B'] ** (df['A'])
0      4
1     25
2    216
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

rsub

rsub(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get subtraction of DataFrame and other, element-wise (binary operator -).

Equivalent to other - dataframe. With reverse version, sub.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rsub(df['B'])
0    3
1    3
2    3
dtype: Int64

It's equivalent to using arithmetic operator: -:

>>> df['B'] - (df['A'])
0    3
1    3
2    3
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

rtruediv

rtruediv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get floating division of DataFrame and other, element-wise (binary operator /).

Equivalent to other / dataframe. With reverse version, truediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rtruediv(df['B'])
0    4.0
1    2.5
2    2.0
dtype: Float64

It's equivalent to using arithmetic operator: /:

>>> df['B'] / (df['A'])
0    4.0
1    2.5
2    2.0
dtype: Float64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

sample

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

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

You can use random_state for reproducibility.

Examples:

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

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

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

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

A random 50% sample of the DataFrame:

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

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

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

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

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

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

scatter

scatter(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    s: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    c: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    **kwargs
)

Create a scatter plot with varying marker point size and color.

Examples:

Let's see how to draw a scatter plot using coordinates from the values in a DataFrame's columns.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
...                   columns=['length', 'width', 'species'])
>>> ax1 = df.plot.scatter(x='length',
...                       y='width',
...                       c='DarkBlue')

And now with the color determined by a column as well.

>>> ax2 = df.plot.scatter(x='length',
...                       y='width',
...                       c='species',
...                       colormap='viridis')

Parameters
Name	Description
`x`	`int or str` The column name or column position to be used as horizontal coordinates for each point.
`y`	`int or str` The column name or column position to be used as vertical coordinates for each point.
`s`	`str, scalar or array-like, optional` The size of each point. Possible values are: - A string with the name of the column to be used for marker's size. - A single scalar so all points have the same size.
`c`	`str, int or array-like, optional` The color of each point. Possible values are: - A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19'. - A column name or position whose values will be used to color the marker points according to a colormap.
`sampling_n`	`int, default 100` Number of random items for plotting.
`sampling_random_state`	`int, default 0` Seed for random number generator.

Returns
Type	Description
`matplotlib.axes.Axes or np.ndarray of them`	An ndarray is returned with one `matplotlib.axes.Axes` per column when `subplots=True`.

select_dtypes

select_dtypes(include=None, exclude=None) -> bigframes.dataframe.DataFrame

Return a subset of the DataFrame's columns based on the column dtypes.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': ["hello", "world"], 'col3': [True, False]})
>>> df.select_dtypes(include=['Int64'])
   col1
0     1
1     2
<BLANKLINE>
[2 rows x 1 columns]

>>> df.select_dtypes(exclude=['Int64'])
    col2   col3
0  hello   True
1  world  False
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
`include`	`scalar or list-like` A selection of dtypes or strings to be included.
`exclude`	`scalar or list-like` A selection of dtypes or strings to be excluded.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The subset of the frame including the dtypes in `include` and excluding the dtypes in `exclude`.

set_index

set_index(
    keys: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    append: bool = False,
    drop: bool = True,
) -> bigframes.dataframe.DataFrame

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one existing column. The index can replace the existing index.

Examples:

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

>>> df = bpd.DataFrame({'month': [1, 4, 7, 10],
...                     'year': [2012, 2014, 2013, 2014],
...                     'sale': [55, 40, 84, 31]})
>>> df
   month  year  sale
0      1  2012    55
1      4  2014    40
2      7  2013    84
3     10  2014    31
<BLANKLINE>
[4 rows x 3 columns]

Set the 'month' column to become the index:

>>> df.set_index('month')
       year  sale
month
1      2012    55
4      2014    40
7      2013    84
10     2014    31
<BLANKLINE>
[4 rows x 2 columns]

Create a MultiIndex using columns 'year' and 'month':

>>> df.set_index(['year', 'month'])
            sale
year month
2012 1        55
2014 4        40
2013 7        84
2014 10       31
<BLANKLINE>
[4 rows x 1 columns]

Exceptions
Type	Description
`KeyError`	If key(s) are not in the columns.

Returns
Type	Description
`bigframes.pandas.DataFrame`	Changed row labels.

shift

shift(periods: int = 1) -> bigframes.dataframe.DataFrame

Shift index by desired number of periods.

Shifts the index without realigning the data.

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

skew

skew(*, numeric_only: bool = False)

Return unbiased skew over columns.

Normalized by N-1.

Examples:

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

>>> df = bpd.DataFrame({'A': [1, 2, 3, 4, 5],
...                    'B': [5, 4, 3, 2, 1],
...                    'C': [2, 2, 3, 2, 2]})
>>> df
    A       B       C
0   1       5       2
1   2       4       2
2   3       3       3
3   4       2       2
4   5       1       2
<BLANKLINE>
[5 rows x 3 columns]

Calculating the skewness of each column.

>>> df.skew()
A         0.0
B         0.0
C    2.236068
dtype: Float64

Parameter
Name	Description
`numeric_only`	`bool, default False` Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series.

sort_index

sort_index(
    ascending: bool = True, na_position: typing.Literal["first", "last"] = "last"
) -> bigframes.dataframe.DataFrame

Sort object by labels (along an axis).

Exceptions
Type	Description
`ValueError`	If value of `na_position` is not one of `first` or `last`.
`ValueError`	If length of `ascending` dose not equal length of `by`.

Returns
Type	Description
`bigframes.pandas.DataFrame`	The original DataFrame sorted by the labels.

sort_values

sort_values(
    by: typing.Union[str, typing.Sequence[str]],
    *,
    ascending: typing.Union[bool, typing.Sequence[bool]] = True,
    kind: str = "quicksort",
    na_position: typing.Literal["first", "last"] = "last"
) -> bigframes.dataframe.DataFrame

Sort by the values along row axis.

Examples:

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

>>> df = bpd.DataFrame({
...     'col1': ['A', 'A', 'B', bpd.NA, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
   col1  col2  col3 col4
0     A     2     0    a
1     A     1     1    B
2     B     9     9    c
3  <NA>     8     4    D
4     D     7     2    e
5     C     4     3    F
<BLANKLINE>
[6 rows x 4 columns]

Sort by col1:

>>> df.sort_values(by=['col1'])
   col1  col2  col3 col4
0     A     2     0    a
1     A     1     1    B
2     B     9     9    c
5     C     4     3    F
4     D     7     2    e
3  <NA>     8     4    D
<BLANKLINE>
[6 rows x 4 columns]

Sort by multiple columns:

>>> df.sort_values(by=['col1', 'col2'])
   col1  col2  col3 col4
1     A     1     1    B
0     A     2     0    a
2     B     9     9    c
5     C     4     3    F
4     D     7     2    e
3  <NA>     8     4    D
<BLANKLINE>
[6 rows x 4 columns]

Sort Descending:

>>> df.sort_values(by='col1', ascending=False)
   col1  col2  col3 col4
4     D     7     2    e
5     C     4     3    F
2     B     9     9    c
0     A     2     0    a
1     A     1     1    B
3  <NA>     8     4    D
<BLANKLINE>
[6 rows x 4 columns]

Putting NAs first:

>>> df.sort_values(by='col1', ascending=False, na_position='first')
   col1  col2  col3 col4
3  <NA>     8     4    D
4     D     7     2    e
5     C     4     3    F
2     B     9     9    c
0     A     2     0    a
1     A     1     1    B
<BLANKLINE>
[6 rows x 4 columns]

Parameters
Name	Description
`by`	`str or Sequence[str]` Name or list of names to sort by.
`ascending`	`bool or Sequence[bool], default True` Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.
`kind`	`str, default 'quicksort'` Choice of sorting algorithm. Accepts 'quicksort', 'mergesort', 'heapsort', 'stable'. Ignored except when determining whether to sort stably. 'mergesort' or 'stable' will result in stable reorder.
`na_position`	`{'first', 'last'}, default last` `{'first', 'last'}`, default 'last' Puts NaNs at the beginning if `first`; `last` puts NaNs at the end.

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

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame with sorted values.

stack

stack(level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = -1)

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

if the columns have a single level, the output is a Series;
if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.

Examples:

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

>>> df = bpd.DataFrame({'A': [1, 3], 'B': [2, 4]}, index=['foo', 'bar'])
>>> df
        A   B
foo     1   2
bar     3   4
<BLANKLINE>
[2 rows x 2 columns]

>>> df.stack()
foo  A    1
     B    2
bar  A    3
     B    4
dtype: Int64

Parameter
Name	Description
`level`	`int, str, or list of these, default -1 (last level)` Level(s) to stack from the column axis onto the index axis.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	Stacked dataframe or series.

std

std(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return sample standard deviation over columns.

Normalized by N-1 by default.

Examples:

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

>>> df = bpd.DataFrame({"A": [1, 2, 3, 4, 5],
...                     "B": [3, 4, 3, 2, 1],
...                     "C": [2, 2, 3, 2, 2]})
>>> df
    A       B       C
0   1       3       2
1   2       4       2
2   3       3       3
3   4       2       2
4   5       1       2
<BLANKLINE>
[5 rows x 3 columns]

Calculating the standard deviation of each column:

>>> df.std()
A    1.581139
B    1.140175
C    0.447214
dtype: Float64

Parameter
Name	Description
`numeric_only`	`bool. default False` Default False. Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series with sample standard deviation.

sub

sub(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get subtraction of DataFrame and other, element-wise (binary operator -).

Equivalent to dataframe - other. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].sub(df['B'])
0    -3
1    -3
2    -3
dtype: Int64

You can also use arithmetic operator -:

>>> df['A'] - (df['B'])
0    -3
1    -3
2    -3
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

subtract

subtract(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get subtraction of DataFrame and other, element-wise (binary operator -).

Equivalent to dataframe - other. With reverse version, rsub.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].sub(df['B'])
0    -3
1    -3
2    -3
dtype: Int64

You can also use arithmetic operator -:

>>> df['A'] - (df['B'])
0    -3
1    -3
2    -3
dtype: Int64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

sum

sum(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

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

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

Calculating the sum of each column (the default behavior without an explicit axis parameter).

>>> df.sum()
A    4
B    6
dtype: Int64

Calculating the sum of each row.

>>> df.sum(axis=1)
0    3
1    7
dtype: Int64

Parameters
Name	Description
`axis`	`{index (0), columns (1)}` Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
`numeric_only`	`bool. default False` Default False. Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series with the sum of values.

swaplevel

swaplevel(i: int = -2, j: int = -1, axis: int | str = 0)

Swap levels i and j in a MultiIndex.

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

Parameters
Name	Description
`i`	`int or str` Levels of the indices to be swapped. Can pass level name as string.
`j`	`int or str` Levels of the indices to be swapped. Can pass level name as string.
`axis`	`{0 or 'index', 1 or 'columns'}, default 0` The axis to swap levels on. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.

Exceptions
Type	Description
`ValueError`	If columns are not multi-index.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame with levels swapped in MultiIndex.

tail

tail(n: int = 5) -> bigframes.dataframe.DataFrame

Return the last n rows.

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

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

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

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

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

to_arrow

to_arrow(
    *, ordered: bool = True, allow_large_results: typing.Optional[bool] = None
) -> pyarrow.lib.Table

Write DataFrame to an Arrow table / record batch.

Parameters
Name	Description
`ordered`	`bool, default True` Determines whether the resulting Arrow table will be ordered. In some cases, unordered may result in a faster-executing query.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

Returns
Type	Description
`pyarrow.Table`	A pyarrow Table with all rows and columns of this DataFrame.

to_csv

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.

Parameters
Name	Description
`path_or_buf`	`str, path object, file-like object, or None, default None` String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. If a non-binary file object is passed, it should be opened with `newline=''`, disabling universal newlines. If a binary file object is passed, `mode` might need to contain a `'b'`. Alternatively, a destination URI of Cloud Storage files(s) to store the extracted dataframe in format of `gs://<bucket_name>/<object_name_or_glob>`. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies. None, file-like objects or local file paths not yet supported.
`index`	`bool, default True` If True, write row names (index).
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB. This parameter has no effect when results are saved to Google Cloud Storage (GCS).

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

to_dict

to_dict(orient: typing.Literal['dict', 'list', 'series', 'split', 'tight', 'records', 'index'] = 'dict', into: type[dict] = <class 'dict'>, *, allow_large_results: typing.Optional[bool] = None, **kwargs) -> dict | list[dict]

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters (see below).

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_dict()
{'col1': {np.int64(0): 1, np.int64(1): 2}, 'col2': {np.int64(0): 3, np.int64(1): 4}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': 0    1
1    2
Name: col1, dtype: Int64,
'col2': 0    3
1    4
Name: col2, dtype: Int64}

>>> df.to_dict('split')
{'index': [0, 1], 'columns': ['col1', 'col2'], 'data': [[1, 3], [2, 4]]}

>>> df.to_dict("tight")
{'index': [0, 1],
'columns': ['col1', 'col2'],
'data': [[1, 3], [2, 4]],
'index_names': [None],
'column_names': [None]}

Parameters
Name	Description
`orient`	`str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}` Determines the type of the values of the dictionary. 'dict' (default) : dict like {column -> {index -> value}}. 'list' : dict like {column -> [values]}. 'series' : dict like {column -> Series(values)}. split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}. 'tight' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values], 'index_names' -> [index.names], 'column_names' -> [column.names]}. 'records' : list like [{column -> value}, ... , {column -> value}]. 'index' : dict like {index -> {column -> value}}.
`into`	`class, default dict` The collections.abc.Mapping subclass used for all Mappings in the return value. 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.
`index`	`bool, default True` Whether to include the index item (and index_names item if `orient` is 'tight') in the returned dictionary. Can only be `False` when `orient` is 'split' or 'tight'.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

Returns
Type	Description
`dict or list of dict`	Return a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the `orient` parameter.

to_excel

to_excel(
    excel_writer,
    sheet_name: str = "Sheet1",
    *,
    allow_large_results: typing.Optional[bool] = None,
    **kwargs
) -> None

Write DataFrame to an Excel sheet.

To write a single DataFrame 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.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_excel(tempfile.TemporaryFile())

Parameters
Name	Description
`excel_writer`	`path-like, file-like, or ExcelWriter object` File path or existing ExcelWriter.
`sheet_name`	`str, default 'Sheet1'` Name of sheet which will contain DataFrame.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

to_gbq

to_gbq(
    destination_table: typing.Optional[str] = None,
    *,
    if_exists: typing.Optional[typing.Literal["fail", "replace", "append"]] = None,
    index: bool = True,
    ordering_id: typing.Optional[str] = None,
    clustering_columns: typing.Union[
        pandas.core.indexes.base.Index, typing.Iterable[typing.Hashable]
    ] = (),
    labels: dict[str, str] = {}
) -> str

Write a DataFrame to a BigQuery table.

Examples:

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

Write a DataFrame to a BigQuery table.

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> # destination_table = PROJECT_ID + "." + DATASET_ID + "." + TABLE_NAME
>>> df.to_gbq("bigframes-dev.birds.test-numbers", if_exists="replace")
'bigframes-dev.birds.test-numbers'

Write a DataFrame to a temporary BigQuery table in the anonymous dataset.

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> destination = df.to_gbq(ordering_id="ordering_id")
>>> # The table created can be read outside of the current session.
>>> bpd.close_session()  # Optional, to demonstrate a new session.
>>> bpd.read_gbq(destination, index_col="ordering_id")
             col1  col2
ordering_id
0               1     3
1               2     4
<BLANKLINE>
[2 rows x 2 columns]

Write a DataFrame to a BigQuery table with clustering columns:

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4], 'col3': [5, 6]})
>>> clustering_cols = ['col1', 'col3']
>>> df.to_gbq(
...             "bigframes-dev.birds.test-clusters",
...             if_exists="replace",
...             clustering_columns=clustering_cols,
...           )
'bigframes-dev.birds.test-clusters'

Parameters
Name	Description
`destination_table`	`Optional[str]` Name of table to be written, in the form `dataset.tablename` or `project.dataset.tablename`. If no `destination_table` is set, a new temporary table is created in the BigQuery anonymous dataset.
`if_exists`	`Optional[str]` Behavior when the destination table exists. When `destination_table` is set, this defaults to `'fail'`. When `destination_table` is not set, this field is not applicable. A new table is always created. Value can be one of: `'fail'` If table exists raise pandas_gbq.gbq.TableCreationError. `'replace'` If table exists, drop it, recreate it, and insert data. `'append'` If table exists, insert data. Create if does not exist.
`index`	`bool. default True` whether write row names (index) or not.
`ordering_id`	`Optional[str], default None` If set, write the ordering of the DataFrame as a column in the result table with this name.
`clustering_columns`	`Union[pd.Index, Iterable[Hashable]], default ()` Specifies the columns for clustering in the BigQuery table. The order of columns in this list is significant for clustering hierarchy. Index columns may be included in clustering if the `index` parameter is set to True, and their names are specified in this. These index columns, if included, precede DataFrame columns in the clustering order. The clustering order within the Index/DataFrame columns follows the order specified in `clustering_columns`.
`labels`	`dict[str, str], default None` Specifies table labels within BigQuery

Exceptions
Type	Description
`ValueError`	If an invalid value is provided for `if_exists` when `destination_table` is `None`. `None` or `replace` are the only valid values for `if_exists`.
`ValueError`	If an invalid value is provided for `destination_table` that is not one of `datasetID.tableId` or `projectId.datasetId.tableId`.
`ValueError`	If an invalid value is provided for `if_exists` that is not one of `fail`, `replace`, or `append`.

Returns
Type	Description
`str`	The fully-qualified ID for the written table, in the form `project.dataset.tablename`.

to_html

to_html(
    buf=None,
    columns: typing.Optional[typing.Sequence[str]] = None,
    col_space=None,
    header: bool = True,
    index: bool = True,
    na_rep: str = "NaN",
    formatters=None,
    float_format=None,
    sparsify: bool | None = None,
    index_names: bool = True,
    justify: str | None = None,
    max_rows: int | None = None,
    max_cols: int | None = None,
    show_dimensions: bool = False,
    decimal: str = ".",
    bold_rows: bool = True,
    classes: str | list | tuple | None = None,
    escape: bool = True,
    notebook: bool = False,
    border: int | None = None,
    table_id: str | None = None,
    render_links: bool = False,
    encoding: str | None = None,
    *,
    allow_large_results: bool | None = None
) -> str

Render a DataFrame as an HTML table.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> print(df.to_html())
<table border="1" class="dataframe">
<thead>
    <tr style="text-align: right;">
    <th></th>
    <th>col1</th>
    <th>col2</th>
    </tr>
</thead>
<tbody>
    <tr>
    <th>0</th>
    <td>1</td>
    <td>3</td>
    </tr>
    <tr>
    <th>1</th>
    <td>2</td>
    <td>4</td>
    </tr>
</tbody>
</table>

Parameters

Name Description

buf

str, Path or StringIO-like, optional, default None

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

columns

sequence, optional, default None

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

col_space

str or int, list or dict of int or str, optional

The minimum width of each column in CSS length units. An int is assumed to be px units.

header

bool, optional

Whether to print column labels, default True.

index

bool, optional, default True

Whether to print index (row) labels.

na_rep

str, optional, default 'NaN'

String representation of NAN to use.

formatters

list, tuple or dict of one-param. functions, optional

Formatter functions to apply to columns' elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.

float_format

one-parameter function, optional, default None

Formatter function to apply to columns' elements if they are floats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.

sparsify

bool, optional, default True

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

index_names

bool, optional, default True

Prints the names of the indexes.

justify

str, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), 'right' out of the box. Valid values are, 'left', 'right', 'center', 'justify', 'justify-all', 'start', 'end', 'inherit', 'match-parent', 'initial', 'unset'.

max_rows

int, optional

Maximum number of rows to display in the console.

max_cols

int, optional

Maximum number of columns to display in the console.

show_dimensions

bool, default False

Display DataFrame dimensions (number of rows by number of columns).

decimal

str, default '.'

Character recognized as decimal separator, e.g. ',' in Europe.

bold_rows

bool, default True

Make the row labels bold in the output.

classes

str or list or tuple, default None

CSS class(es) to apply to the resulting html table.

escape

bool, default True

Convert the characters <, >, and & to HTML-safe sequences.

notebook

bool, default False

Whether the generated HTML is for IPython Notebook.

border

int

A border=border attribute is included in the opening

tag. Default pd.options.display.html.border.

table_id

str, optional

A css id is included in the opening

tag if specified.

render_links

bool, default False

Convert URLs to HTML links.

encoding

str, default "utf-8"

Set character encoding.

allow_large_results

bool, default None

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

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

to_json

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

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

Parameters
Name	Description
`path_or_buf`	`str, path object, file-like object, or None, default None` String, path object (implementing os.PathLike[str]), or file-like object implementing a write() function. If None, the result is returned as a string. Can be a destination URI of Cloud Storage files(s) to store the extracted dataframe in format of `gs://<bucket_name>/<object_name_or_glob>`. Must contain a wildcard `*` character. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies.
`orient`	`{split, records, index, columns, values, table}, default 'columns` Indication of expected JSON string format. * Series: - default is 'index' - allowed values are: {{'split', 'records', 'index', 'table'}}. * DataFrame: - default is 'columns' - allowed values are: {{'split', 'records', 'index', 'columns', 'values', 'table'}}. * The format of the JSON string: - 'split' : dict like {{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}} - 'records' : list like [{{column -> value}}, ... , {{column -> value}}] - 'index' : dict like {{index -> {{column -> value}}}} - 'columns' : dict like {{column -> {{index -> value}}}} - 'values' : just the values array - 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}} Describing the data, where data component is like `orient='records'`.
`index`	`bool, default True` If True, write row names (index).
`lines`	`bool, default False` If 'orient' is 'records' write out line-delimited json format. Will throw ValueError if incorrect 'orient' since others are not list-like.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB. This parameter has no effect when results are saved to Google Cloud Storage (GCS).

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

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

to_latex

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

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

Requires \usepackage{{booktabs}}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{{table.tex}}.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> print(df.to_latex())
\begin{tabular}{lrr}
\toprule
& col1 & col2 \\
\midrule
0 & 1 & 3 \\
1 & 2 & 4 \\
\bottomrule
\end{tabular}
<BLANKLINE>

Parameters
Name	Description
`buf`	`str, Path or StringIO-like, optional, default None` Buffer to write to. If None, the output is returned as a string.
`columns`	`list of label, optional` The subset of columns to write. Writes all columns by default.
`header`	`bool or list of str, default True` Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
`index`	`bool, default True` Write row names (index).
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

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

to_markdown

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

Print DataFrame in Markdown-friendly format.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> print(df.to_markdown())
|    |   col1 |   col2 |
|---:|-------:|-------:|
|  0 |      1 |      3 |
|  1 |      2 |      4 |

Parameters
Name	Description
`buf`	`str, Path or StringIO-like, optional, default None` Buffer to write to. If None, the output is returned as a string.
`mode`	`str, optional` Mode in which file is opened.
`index`	`bool, optional, default True` Add index (row) labels.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

Returns
Type	Description
`str`	DataFrame in Markdown-friendly format.

to_numpy

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

Convert the DataFrame to a NumPy array.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_numpy()
array([[1, 3],
       [2, 4]], dtype=object)

Parameters
Name	Description
`dtype`	`None` The dtype to pass to `numpy.asarray()`.
`copy`	`bool, default None` Whether to ensure that the returned value is not a view on another array.
`na_value`	`Any, default None` The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

Returns
Type	Description
`numpy.ndarray`	The converted NumPy array.

to_orc

to_orc(path=None, *, allow_large_results=None, **kwargs) -> bytes | None

Write a DataFrame to the ORC format.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> import tempfile
>>> df.to_orc(tempfile.TemporaryFile())

Parameters
Name	Description
`path`	`str, file-like object or None, default None` If a string, it will be used as Root Directory path when writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

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

to_pandas

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

Write DataFrame to pandas DataFrame.

Parameters
Name	Description
`max_download_size`	`int, default None` Download size threshold in MB. If max_download_size is exceeded when downloading data (e.g., to_pandas()), the data will be downsampled if bigframes.options.sampling.enable_downsampling is True, otherwise, an error will be raised. If set to a value other than None, this will supersede the global config.
`sampling_method`	`str, default None` Downsampling algorithms to be chosen from, the choices are: "head": This algorithm returns a portion of the data from the beginning. It is fast and requires minimal computations to perform the downsampling; "uniform": This algorithm returns uniform random samples of the data. If set to a value other than None, this will supersede the global config.
`random_state`	`int, default None` The seed for the uniform downsampling algorithm. If provided, the uniform method may take longer to execute and require more computation. If set to a value other than None, this will supersede the global config.
`ordered`	`bool, default True` Determines whether the resulting pandas dataframe will be ordered. In some cases, unordered may result in a faster-executing query.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

Returns
Type	Description
`pandas.DataFrame`	A pandas DataFrame with all rows and columns of this DataFrame if the data_sampling_threshold_mb is not exceeded; otherwise, a pandas DataFrame with downsampled rows and all columns of this DataFrame.

to_pandas_batches

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.frame.DataFrame]

Stream DataFrame results to an iterable of pandas DataFrame.

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

Parameters
Name	Description
`page_size`	`int, default None` The size of each batch.
`max_results`	`int, default None` If given, only download this many rows at maximum.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

Returns
Type	Description
`Iterable[pandas.DataFrame]`	An iterable of smaller dataframes which combine to form the original dataframe. Results stream from bigquery, see https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.table.RowIterator#google_cloud_bigquery_table_RowIterator_to_arrow_iterable

to_parquet

to_parquet(
    path=None,
    *,
    compression: typing.Optional[typing.Literal["snappy", "gzip"]] = "snappy",
    index: bool = True,
    allow_large_results: typing.Optional[bool] = None
) -> typing.Optional[bytes]

Write a DataFrame to the binary Parquet format.

This function writes the dataframe as a parquet file <https://parquet.apache.org/>_ to Cloud Storage.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> gcs_bucket = "gs://bigframes-dev-testing/sample_parquet*.parquet"
>>> df.to_parquet(path=gcs_bucket)

Parameters
Name	Description
`path`	`str, path object, file-like object, or None, default None` String, path object (implementing `os.PathLike[str]`), or file-like object implementing a binary `write()` function. If None, the result is returned as bytes. If a string or path, it will be used as Root Directory path when writing a partitioned dataset. Destination URI(s) of Cloud Storage files(s) to store the extracted dataframe should be formatted `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.
`compression`	`str, default 'snappy'` Name of the compression to use. Use `None` for no compression. Supported options: `'gzip'`, `'snappy'`.
`index`	`bool, default True` If `True`, include the dataframe's index(es) in the file output. If `False`, they will not be written to the file.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB. This parameter has no effect when results are saved to Google Cloud Storage (GCS).

Exceptions
Type	Description
`ValueError`	If an invalid value provided for `compression` that is not one of `None`, `snappy`, or `gzip`.

Returns
Type	Description
`None or bytes`	bytes if no path argument is provided else None

to_pickle

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> gcs_bucket = "gs://bigframes-dev-testing/sample_pickle_gcs.pkl"
>>> df.to_pickle(path=gcs_bucket)

Parameters
Name	Description
`path`	`str` File path where the pickled object will be stored.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

to_records

to_records(
    index: bool = True,
    column_dtypes=None,
    index_dtypes=None,
    *,
    allow_large_results=None
) -> numpy.rec.recarray

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_records()
rec.array([(0, 1, 3), (1, 2, 4)],
          dtype=[('index', '<i8'), ('col1', '<i8'), ('col2', '<i8')])

Parameters
Name	Description
`index`	`bool, default True` Include index in resulting record array, stored in 'index' field or using the index label, if set.
`column_dtypes`	`str, type, dict, default None` If a string or type, the data type to store all columns. If a dictionary, a mapping of column names and indices (zero-indexed) to specific data types.
`index_dtypes`	`str, type, dict, default None` If a string or type, the data type to store all index levels. If a dictionary, a mapping of index level names and indices (zero-indexed) to specific data types.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB. This mapping is applied only if `index=True`.

Returns
Type	Description
`np.recarray`	NumPy ndarray with the DataFrame labels as fields and each row of the DataFrame as entries.

to_string

to_string(
    buf=None,
    columns: typing.Optional[typing.Sequence[str]] = None,
    col_space=None,
    header: typing.Union[bool, typing.Sequence[str]] = True,
    index: bool = True,
    na_rep: str = "NaN",
    formatters=None,
    float_format=None,
    sparsify: bool | None = None,
    index_names: bool = True,
    justify: str | None = None,
    max_rows: int | None = None,
    max_cols: int | None = None,
    show_dimensions: bool = False,
    decimal: str = ".",
    line_width: int | None = None,
    min_rows: int | None = None,
    max_colwidth: int | None = None,
    encoding: str | None = None,
    *,
    allow_large_results: typing.Optional[bool] = None
) -> str | None

Render a DataFrame to a console-friendly tabular output.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> print(df.to_string())
   col1  col2
0     1     3
1     2     4

Parameters
Name	Description
`buf`	`str, Path or StringIO-like, optional, default None` Buffer to write to. If None, the output is returned as a string.
`columns`	`sequence, optional, default None` The subset of columns to write. Writes all columns by default.
`col_space`	`int, list or dict of int, optional` The minimum width of each column.
`header`	`bool or sequence, optional` Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
`index`	`bool, optional, default True` Whether to print index (row) labels.
`na_rep`	`str, optional, default 'NaN'` String representation of NAN to use.
`formatters`	`list, tuple or dict of one-param. functions, optional` Formatter functions to apply to columns' elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
`float_format`	`one-parameter function, optional, default None` Formatter function to apply to columns' elements if they are floats. The result of this function must be a unicode string.
`sparsify`	`bool, optional, default True` Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.
`index_names`	`bool, optional, default True` Prints the names of the indexes.
`justify`	`str, default None` How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), 'right' out of the box. Valid values are, 'left', 'right', 'center', 'justify', 'justify-all', 'start', 'end', 'inherit', 'match-parent', 'initial', 'unset'.
`max_rows`	`int, optional` Maximum number of rows to display in the console.
`min_rows`	`int, optional` The number of rows to display in the console in a truncated repr (when number of rows is above `max_rows`).
`max_cols`	`int, optional` Maximum number of columns to display in the console.
`show_dimensions`	`bool, default False` Display DataFrame dimensions (number of rows by number of columns).
`decimal`	`str, default '.'` Character recognized as decimal separator, e.g. ',' in Europe.
`line_width`	`int, optional` Width to wrap a line in characters.
`max_colwidth`	`int, optional` Max width to truncate each column in characters. By default, no limit.
`encoding`	`str, default "utf-8"` Set character encoding.
`allow_large_results`	`bool, default None` If not None, overrides the global setting to allow or disallow large query results over the default size limit of 10 GB.

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

transpose

transpose() -> bigframes.dataframe.DataFrame

Transpose index and columns.

Reflect the DataFrame over its main diagonal by writing rows as columns and vice-versa. The property .T is an accessor to the method transpose.

All columns must be the same dtype (numerics can be coerced to a common supertype).

Examples:

**Square DataFrame with homogeneous dtype**

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

>>> d1 = {'col1': [1, 2], 'col2': [3, 4]}
>>> df1 = bpd.DataFrame(data=d1)
>>> df1
   col1  col2
0     1     3
1     2     4
<BLANKLINE>
[2 rows x 2 columns]

>>> df1_transposed = df1.T  # or df1.transpose()
>>> df1_transposed
      0  1
col1  1  2
col2  3  4
<BLANKLINE>
[2 rows x 2 columns]

When the dtype is homogeneous in the original DataFrame, we get a
transposed DataFrame with the same dtype:

>>> df1.dtypes
col1    Int64
col2    Int64
dtype: object
>>> df1_transposed.dtypes
0    Int64
1    Int64
dtype: object

Returns
Type	Description
`bigframes.pandas.DataFrame`	The transposed DataFrame.

truediv

truediv(
    other: float | int | bigframes.series.Series | bigframes.dataframe.DataFrame,
    axis: str | int = "columns",
) -> bigframes.dataframe.DataFrame

Get floating division of DataFrame and other, element-wise (binary operator /).

Equivalent to dataframe / other. With reverse version, rtruediv.

Among flexible wrappers (add, sub, mul, div, mod, pow) to arithmetic operators: +, -, *, /, //, %, **.

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })

You can use method name:

>>> df['A'].truediv(df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

You can also use arithmetic operator /:

>>> df['A'] / (df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64

Parameters
Name	Description
`other`	`float, int, or Series` Any single or multiple element data structure, or list-like object.
`axis`	`{0 or 'index', 1 or 'columns'}` Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

Returns
Type	Description
`bigframes.pandas.DataFrame`	DataFrame result of the arithmetic operation.

unstack

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

Pivot a level of the (necessarily hierarchical) index labels.

Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.

If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).

Examples:

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

>>> df = bpd.DataFrame({'A': [1, 3], 'B': [2, 4]}, index=['foo', 'bar'])
>>> df
        A   B
foo     1   2
bar     3   4
<BLANKLINE>
[2 rows x 2 columns]

>>> df.unstack()
A   foo    1
    bar    3
B   foo    2
    bar    4
dtype: Int64

Parameter
Name	Description
`level`	`int, str, or list of these, default -1 (last level)` Level(s) of index to unstack, can pass level name.

Returns
Type	Description
`bigframes.pandas.DataFrame or bigframes.pandas.Series`	DataFrame or Series.

update

update(other, join: str = "left", overwrite=True, filter_func=None)

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Examples:

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

>>> df = bpd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = bpd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6
<BLANKLINE>
[3 rows x 2 columns]

Parameters
Name	Description
`other`	`DataFrame, or object coercible into a DataFrame` Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.
`join`	`{'left'}, default 'left'` Only left join is implemented, keeping the index and columns of the original object.
`overwrite`	`bool, default True` How to handle non-NA values for overlapping keys: True: overwrite original DataFrame's values with values from `other`. False: only update values that are NA in the original DataFrame.
`filter_func`	`callable(1d-array) -> bool 1d-array, optional` Can choose to replace values other than NA. Return True for values that should be updated.

Exceptions
Type	Description
`ValueError`	If a type of join other than `left` is provided as an argument.

Returns
Type	Description
`None`	This method directly changes calling object.

value_counts

value_counts(
    subset: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    normalize: bool = False,
    sort: bool = True,
    ascending: bool = False,
    dropna: bool = True,
)

Return a Series containing counts of unique rows in the DataFrame.

Examples:

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

>>> df = bpd.DataFrame({'num_legs': [2, 4, 4, 6, 7],
...                     'num_wings': [2, 0, 0, 0, bpd.NA]},
...                    index=['falcon', 'dog', 'cat', 'ant', 'octopus'],
...                    dtype='Int64')
>>> df
         num_legs  num_wings
falcon          2          2
dog             4          0
cat             4          0
ant             6          0
octopus         7       <NA>
<BLANKLINE>
[5 rows x 2 columns]

value_counts sorts the result by counts in a descending order by default:

>>> df.value_counts()
num_legs  num_wings
4         0          2
2         2          1
6         0          1
Name: count, dtype: Int64

You can normalize the counts to return relative frequencies by setting normalize=True:

>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0             0.5
2         2            0.25
6         0            0.25
Name: proportion, dtype: Float64

You can get the rows in the ascending order of the counts by setting ascending=True:

>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2          1
6         0          1
4         0          2
Name: count, dtype: Int64

You can include the counts of the rows with NA values by setting dropna=False:

>>> df.value_counts(dropna=False)
num_legs  num_wings
4         0            2
2         2            1
6         0            1
7         <NA>         1
Name: count, dtype: Int64

Parameters
Name	Description
`subset`	`label or list of labels, optional` Columns to use when counting unique combinations.
`normalize`	`bool, default False` Return proportions rather than frequencies.
`sort`	`bool, default True` Sort by frequencies.
`ascending`	`bool, default False` Sort in ascending order.
`dropna`	`bool, default True` Don’t include counts of rows that contain NA values.

Returns
Type	Description
`bigframes.pandas.Series`	Series containing counts of unique rows in the DataFrame

var

var(
    axis: typing.Union[str, int] = 0, *, numeric_only: bool = False
) -> bigframes.series.Series

Return unbiased variance over requested axis.

Normalized by N-1 by default.

Examples:

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

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

Calculating the variance of each column (the default behavior without an explicit axis parameter).

>>> df.var()
A    2.0
B    2.0
dtype: Float64

Calculating the variance of each row.

>>> df.var(axis=1)
0    0.5
1    0.5
dtype: Float64

Parameters
Name	Description
`axis`	`{index (0), columns (1)}` Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.
`numeric_only`	`bool. default False` Default False. Include only float, int, boolean columns.

Returns
Type	Description
`bigframes.pandas.Series`	Series with unbiased variance over requested axis.

where

where(cond, other=None)

Replace values where the condition is False.

Examples:

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

>>> df = bpd.DataFrame({'a': [20, 10, 0], 'b': [0, 10, 20]})
>>> df
    a   b
0  20   0
1  10  10
2   0  20
<BLANKLINE>
[3 rows x 2 columns]

>>> df.where(df > 0)
      a     b
0    20  <NA>
1    10    10
2  <NA>    20
<BLANKLINE>
[3 rows x 2 columns]

You can specify a custom replacement value for non-matching values.

>>> df.where(df > 0, -1)
      a     b
0    20    -1
1    10    10
2    -1    20
<BLANKLINE>
[3 rows x 2 columns]

Besides dataframe, the condition can be a series too. For example:

>>> df.where(df['a'] > 10, -1)
      a     b
0    20     0
1    -1    -1
2    -1    -1
<BLANKLINE>
[3 rows x 2 columns]

As for the replacement, it can be a dataframe too. For example:

>>> df.where(df > 10, -df)
      a     b
0    20     0
1   -10   -10
2     0    20
<BLANKLINE>
[3 rows x 2 columns]

>>> df.where(df['a'] > 10, -df)
      a     b
0    20     0
1   -10   -10
2     0   -20
<BLANKLINE>
[3 rows x 2 columns]

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

Returns
Type	Description
`DataFrame`	DataFrame after the replacement.

Class DataFrame (1.40.0)Stay organized with collections Save and categorize content based on your preferences.

Properties

T

at

bqclient

columns

dtypes

empty

iat

iloc

index

loc

ndim

plot

query_job

semantics

shape

size

sql

struct

values

Methods

__add__

__and__

__array__

__array_ufunc__

__eq__

__floordiv__

__ge__

__getitem__

__gt__

__invert__

__le__

__len__

__lt__

__matmul__

__mod__

__mul__

__ne__

__or__

__pow__

__radd__

__rand__

__repr__

__rfloordiv__

__rmod__

__rmul__

__ror__

__rpow__

__rsub__

__rtruediv__

__rxor__

__setitem__

__sub__

__truediv__

__xor__

abs

add

add_prefix

add_suffix

agg

aggregate

align

all

any

apply

applymap

area

assign

astype

bar

bfill

cache

combine

combine_first

copy

corr

corrwith

count

cov

Class DataFrame (1.40.0)

add

and

array

eq

floordiv

ge

getitem

gt

invert

le

len

lt

matmul

mod

mul

ne

or

pow

radd

rand

repr

rfloordiv

rmod

rmul

ror

rpow

rsub

rtruediv

rxor

setitem

sub

truediv

xor