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Class DataFrame (2.9.0) | Python client library

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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.

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 the accessor for AI operators.

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)

BigQuery REST API Client the DataFrame uses for operations.

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')

Return the dtypes in the DataFrame.

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

Examples:

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

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

Indicates whether Series/DataFrame is empty.

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

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

Examples:

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

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

Get value at specified row/column pair

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

Get value within a series

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

Purely integer-location based indexing for selection by position.

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)

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

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

Make plots of Dataframes.

BigQuery job metadata for the most recent query.

API documentation for semantics property.

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)

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

Compiles this DataFrame's expression tree to SQL.

API documentation for struct property.

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)

__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]

Object to be added to the DataFrame.

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

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

Object to bitwise AND with the DataFrame.

__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)

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

Whether to copy the data, False is not supported.

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

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

__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]

Object to be compared to the DataFrame for equality.

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]

Object to divide the DataFrame by.

__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]

Object to be compared to the DataFrame.

__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]

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

__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]

Object to be compared to the DataFrame.

__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]

__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]

Object to be compared to the DataFrame.

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__(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]

Object to be compared to the DataFrame.

__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

Object to be matrix multiplied with the DataFrame.

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]

Object to modulo the DataFrame by.

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]

Object to multiply with the DataFrame.

__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]

Object to be compared to the DataFrame for inequality.

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

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

Object to bitwise OR with the DataFrame.

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]

Object to exponentiate the DataFrame with.

__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]

Object to be added to the DataFrame.

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

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

Object to bitwise AND with the DataFrame.

Converts a DataFrame to a string. Calls to_pandas.

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

Get integer divison of other by DataFrame.

Equivalent to DataFrame.rfloordiv(other).

Object to divide by the DataFrame.

Get integer divison of other by DataFrame.

Equivalent to DataFrame.rmod(other).

Object to modulo by the DataFrame.

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]

Object to multiply the DataFrame with.

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

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

Object to bitwise OR with the DataFrame.

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

Equivalent to DataFrame.rpow(other).

Object to exponentiate with the DataFrame.

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

Equivalent to DataFrame.rsub(other).

Object to subtract the DataFrame from.

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

Equivalent to DataFrame.rtruediv(other).

Object to divide by the DataFrame.

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

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

Object to bitwise XOR with the DataFrame.

__setitem__(key: str, value: SingleItemValue)

Modify or insert a column into the DataFrame.

>>> 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]

It can be a new column to be inserted, or an existing column to be modified.

Value to be assigned to the column

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]

Object to subtract from the DataFrame.

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]

Object to divide the DataFrame by.

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

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

Object to bitwise XOR with the DataFrame.

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.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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.

The string to add before each label.

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

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

Suffix labels with string suffix.

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

agg(
    func: typing.Union[
        str,
        typing.Sequence[str],
        typing.Mapping[typing.Hashable, typing.Union[typing.Sequence[str], 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]

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

aggregate(
    func: typing.Union[
        str,
        typing.Sequence[str],
        typing.Mapping[typing.Hashable, typing.Union[typing.Sequence[str], 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]

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

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.

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.

Align on index (0), columns (1), or both (None).

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

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Include only boolean columns.

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

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Include only boolean columns.

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.

>>> 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, cloud_function_service_account="default")
... 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, cloud_function_service_account="default")
... 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, cloud_function_service_account="default")
... 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

Positional arguments to pass to func in addition to the array/series.

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 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.

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.

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

Python function wrapped by remote_function decorator, returns a single value from a single value.

{None, 'ignore'}, default None. If ignore, propagate NaN values, without passing them to func.

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

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

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

Examples:

Draw an area plot based on basic business metrics:

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

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

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

Draw an area plot for a single column:

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

Draw with a different x:

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

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

Column to plot. By default uses all columns.

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

Number of random items for plotting.

Seed for random number generator.

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.

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

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

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

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

Draw a vertical bar plot.

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

Examples:

Basic plot.

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

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

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

Plot stacked bar charts for the DataFrame.

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

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

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

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

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

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

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

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.

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]

The DataFrame to merge column-wise.

Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.

The value to fill NaNs with prior to passing any column to the merge func.

If True, columns in self that do not exist in other will be overwritten with NaNs.

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]

Provided DataFrame to use to fill null values.

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]

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]

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

The minimum number of observations needed to return a result. Non-default values are not yet supported, so a result will be returned for at least two observations.

Include only float, int, boolean, decimal data.

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

Object with which to compute correlations.

Include only float, int or boolean data.

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

Include only float, int or boolean data.

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]

Include only float, int, boolean, decimal data.

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]

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]

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]

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]

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.

If "all": All columns of the input will be included in the output. If None: The result will include all numeric columns.

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]

Periods to shift for calculating difference, accepts negative values.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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.

>>> 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]

The other object to compute the matrix product with.

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]

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.

Only consider certain columns for identifying duplicates, by default use all of the columns.

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.

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

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

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

Axis along which the level(s) is removed: * 0 or 'index': remove level(s) in column. * 1 or 'columns': remove level(s) in row.

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]

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.

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.

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.

Not supported.

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

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

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Only consider certain columns for identifying duplicates, by default use all of the columns.

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.

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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

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

Test whether two objects contain the same elements.

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

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

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

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]

The expression string to evaluate.

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

Provide expanding window calculations.

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

explode(
    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
>>> import numpy as np
>>> 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]

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.

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

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

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]

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.

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.

Keep labels from axis which are in items.

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

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

The axis to filter on, expressed either as an index (int) or axis name (str). By default this is the info axis, 'columns' for DataFrame. For Series this parameter is unused and defaults to None.

API documentation for first_valid_index method.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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.

Of the form {field : array-like} or {field : dict}.

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'].

Data type to force after DataFrame construction, otherwise infer.

Column labels to use when orient='index'.

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.

Structured input data.

Field of array to use as the index, alternately a specific set of input labels to use.

Columns or fields to exclude.

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).

Attempt to convert values of non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets.

Number of rows to read if data is an iterator.

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.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

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.dataframe_group_by.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]

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.

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

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.

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.

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.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

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]

Default 5. Number of rows to select.

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

Draw one histogram of the DataFrame’s columns.

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

Examples:

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

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

Number of histogram bins to be used.

idxmax() -> 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

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

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.

Whether to print the full summary. By default, the setting in pandas.options.display.max_info_columns is followed.

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.

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.

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.

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.

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]

Insertion index. Must verify 0 <= loc <= len(columns).

Label of the inserted column.

Content of the inserted column.

Allow duplicate column labels to be created.

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]

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

isin(values) -> 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]

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.

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

isnull() -> 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

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

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

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))

If True, return the index as the first element of the tuple.

The name of the returned namedtuples or None to return regular tuples.

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]

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.

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')

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

Include only float, int, boolean columns.

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

Include only float, int, boolean columns.

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.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

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

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

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

Examples:

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

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

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

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

Number of random items for plotting.

Seed for random number generator.

lt(other: 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.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

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.

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

Python function wrapped by remote_function decorator, returns a single value from a single value.

{None, 'ignore'}, default None. If ignore, propagate NaN values, without passing them to func.

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.

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).

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).

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

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Default False. Include only float, int, boolean columns.

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

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Default False. Include only float, int, boolean columns.

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

Default False. Include only float, int, boolean columns.

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

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]

Column(s) to use as identifier variables.

Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

Name to use for the 'variable' column. If None it uses frame.columns.name or 'variable'.

Name to use for the 'value' column.

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.

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.

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.

>>> 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]

Columns to join on. It must be found in both DataFrames. Either on or left_on + right_on must be passed in.

Columns to join on in the left DataFrame. Either on or left_on + right_on must be passed in.

Columns to join on in the right DataFrame. Either on or left_on + right_on must be passed in.

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

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Default False. Include only float, int, boolean columns.

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: +, -, *, /, //, %, **.

>>> 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

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns (1 or 'columns').

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.

>>> 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]

Number of rows to return.

Column label(s) to order by.

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.

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.

notnull() -> 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.

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.

>>> 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]

Number of rows to return.

Column label(s) to order by.

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.

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

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.

Periods to shift for forming percent change.

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.

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

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.

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

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.

>>> 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]

Column to use to make new frame's columns.

Column to use to make new frame's index. If not given, uses existing index.

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.

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]

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.

Column to use to make new frame's index. If not given, uses existing index.

Column to use to make new frame's columns.

Aggregation function name to compute summary statistics (e.g., 'sum', 'mean').

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Include only float, int, boolean columns.

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

Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

Include only float, int, boolean columns.

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]

Value between 0 <= q <= 1, the quantile(s) to compute.

Include only float, int or boolean data.

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]

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.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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.

How to rank the group of records that have the same value (i.e. ties): average: average rank of the group, min: lowest rank in the group max: highest rank in the group, first: ranks assigned in order they appear in the array, dense`: like 'min', but rank always increases by 1 between groups.

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

How to rank NaN values: keep: assign NaN rank to NaN values, , top: assign lowest rank to NaN values, bottom: assign highest rank to NaN values.

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

rdiv(
    other: float | int | bigframes.series.Series | bigframes.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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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.

New labels / index to conform the axis specified by 'axis' to.

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

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

Axis to target. Can be either the axis name ('index', 'columns') or number (0, 1).

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.

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

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]

Dict-like from old column labels to new column labels.

Default False. Whether to modify the DataFrame rather than creating a new one.

Set the name of the axis for the index.

Value to set the axis name attribute.

Default False. Modifies the object directly, instead of creating a new Series or DataFrame.

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.

List representing new level order. Reference level by number (position) or by key (label).

Where to reorder levels.

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]

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 withvalue list of str, regex, or numeric: First, if to_replace and value are both lists, they must be the same length. Second, if regex=True then all of the strings in both lists will be interpreted as regexs otherwise they will match directly. This doesn't matter much for value since there are only a few possible substitution regexes you can use. str, regex and numeric rules apply as above.

Value to replace any values matching to_replace with. For a DataFrame a dict of values can be used to specify which value to use for each column (columns not in the dict will not be filled). Regular expressions, strings and lists or dicts of such objects are also allowed.

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

reset_index(*, 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]

Do not try to insert index into dataframe columns. This resets the index to the default integer index.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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

Provide rolling window calculations.

Examples:

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

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

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

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

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

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

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

round(
    decimals: typing.Union[int, dict[typing.Hashable, int]] = 0,
) -> bigframes.dataframe.DataFrame

Round a DataFrame to a variable number of decimal places.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame([(.21, .32), (.01, .67), (.66, .03), (.21, .18)],
...                   columns=['dogs', 'cats'])
>>> df
   dogs  cats
0  0.21  0.32
1  0.01  0.67
2  0.66  0.03
3  0.21  0.18
<BLANKLINE>
[4 rows x 2 columns]

By providing an integer each column is rounded to the same number
of decimal places

>>> df.round(1)
    dogs  cats
0   0.2   0.3
1   0.0   0.7
2   0.7   0.0
3   0.2   0.2
<BLANKLINE>
[4 rows x 2 columns]

With a dict, the number of places for specific columns can be
specified with the column names as key and the number of decimal
places as value

>>> df.round({'dogs': 1, 'cats': 0})
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0
<BLANKLINE>
[4 rows x 2 columns]

Using a Series, the number of places for specific columns can be
specified with the column names as index and the number of
decimal places as value

>>> decimals = pd.Series([0, 1], index=['cats', 'dogs'])
>>> df.round(decimals)
    dogs  cats
0   0.2   0.0
1   0.0   1.0
2   0.7   0.0
3   0.2   0.0
<BLANKLINE>
[4 rows x 2 columns]

Number of decimal places to round each column to. If an int is given, round each column to the same number of places. Otherwise dict and Series round to variable numbers of places. Column names should be in the keys if decimals is a dict-like, or in the index if decimals is a Series. Any columns not included in decimals will be left as is. Elements of decimals which are not columns of the input will be ignored.

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: +, -, *, /, //, %, **.

>>> 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

Any single or multiple element data structure, or list-like object.

Whether to compare by the index (0 or 'index') or columns. (1 or 'columns'). For Series input, axis to match Series index on.

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