Class DataFrame (0.26.0)

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

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

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

Properties

at

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

Returns
TypeDescription
bigframes.core.indexers.AtDataFrameIndexerIndexers object.

axes

Return a list representing the axes of the DataFrame.

It has the row axis labels and column axis labels as the only members. They are returned in that order.

Examples:

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

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

columns

The column labels of the DataFrame.

Examples:

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

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

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

You can also set new labels for columns.

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

dtypes

Return the dtypes in the DataFrame.

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

empty

Indicates whether Series/DataFrame is empty.

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

Returns
TypeDescription
boolIf Series/DataFrame is empty, return True, if not return False.

iat

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

Returns
TypeDescription
bigframes.core.indexers.IatDataFrameIndexerIndexers object.

iloc

Purely integer-location based indexing for selection by position.

Returns
TypeDescription
bigframes.core.indexers.ILocDataFrameIndexerPurely integer-location Indexers.

index

The index (row labels) of the DataFrame.

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

Examples:

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

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

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

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')],
   name='Name')
>>> df1.index.values
array([('Alice', 'Seattle'), ('Bob', 'New York'), ('Aritra', 'Kona')],
    dtype=object)
Returns
TypeDescription
IndexThe index object of the DataFrame.

loc

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

Returns
TypeDescription
bigframes.core.indexers.ILocDataFrameIndexerIndexers object.

ndim

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

Returns
TypeDescription
intReturn 1 if Series. Otherwise return 2 if DataFrame.

plot

Make plots of Dataframes.

Returns
TypeDescription
bigframes.operations.plotting.PlotAccessorAn accessor making plots.

query_job

BigQuery job metadata for the most recent query.

shape

Return a tuple representing the dimensionality of the DataFrame.

Examples:

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

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

size

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

Examples:

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

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.size
4
Returns
TypeDescription
intReturn the number of rows if Series. Otherwise return the number of rows times number of columns if DataFrame.

sql

Compiles this DataFrame's expression tree to SQL.

values

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

Examples:

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

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

Methods

__array_ufunc__

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

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

__getitem__

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

Gets the specified column(s) from the DataFrame.

__repr__

__repr__() -> str

Converts a DataFrame to a string. Calls to_pandas.

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

__setitem__

__setitem__(
    key: str, value: typing.Union[bigframes.series.Series, int, float, typing.Callable]
)

Modify or insert a column into the DataFrame.

Note: This does not modify the original table the DataFrame was derived from.

abs

abs() -> bigframes.dataframe.DataFrame

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

This function only applies to elements that are all numeric.

add

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

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

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

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

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator +:

>>> df['A'] + (df['B'])
0    5
1    7
2    9
dtype: Int64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

add_prefix

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

Prefix labels with string prefix.

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

Parameters
NameDescription
prefix str

The string to add before each label.

axis int or str or None, default None

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

add_suffix

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

Suffix labels with string suffix.

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

agg

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

Aggregate using one or more operations over columns.

Examples:

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

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

Using a single function:

>>> df.agg('sum')
A    6.0
B    6.0
dtype: Float64

Using a list of functions:

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

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

Returns
TypeDescription
DataFrame or bigframes.series.SeriesAggregated results.

aggregate

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

API documentation for aggregate method.

align

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

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

Join method is specified for each axis Index.

Parameters
NameDescription
join {{'outer', 'inner', 'left', 'right'}}, default 'outer'

Type of alignment to be performed. left: use only keys from left frame, preserve key order. right: use only keys from right frame, preserve key order. outer: use union of keys from both frames, sort keys lexicographically. inner: use intersection of keys from both frames, preserve the order of the left keys.

axis allowed axis of the other object, default None

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

Returns
TypeDescription
tuple of (DataFrame, type of other)Aligned objects.

all

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

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

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

Examples:

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

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

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

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

Checking across rows to see if all values are True:

>>> df.all(axis=1)
0    False
1    False
dtype: boolean
Parameters
NameDescription
axis {index (0), columns (1)}

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

bool_only bool. default False

Include only boolean columns.

Returns
TypeDescription
bigframes.series.SeriesSeries indicating if all elements are True per column.

any

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

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

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

Examples:

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

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

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

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

Checking if each row contains at least one True element:

>>> df.any(axis=1)
0    True
1    True
dtype: boolean
Parameters
NameDescription
axis {index (0), columns (1)}

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

bool_only bool. default False

Include only boolean columns.

Returns
TypeDescription
bigframes.series.SeriesSeries indicating if any element is True per column.

apply

apply(func, *, 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) the final return type is inferred from the return type of the applied function.

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]

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

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

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

func function

Function to apply to each column or row.

Returns
TypeDescription
pandas.Series or bigframes.DataFrameResult of applying func along the given axis of the DataFrame.

applymap

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

Apply a function to a Dataframe elementwise.

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

Examples:

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

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

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

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

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

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

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

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

na_action Optional[str], default None

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

Returns
TypeDescription
bigframes.dataframe.DataFrameTransformed DataFrame.

assign

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

Assign new columns to a DataFrame.

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

Returns
TypeDescription
bigframes.dataframe.DataFrameA new DataFrame with the new columns in addition to all the existing columns.

astype

astype(
    dtype: typing.Union[
        typing.Literal[
            "boolean",
            "Float64",
            "Int64",
            "int64[pyarrow]",
            "string",
            "string[pyarrow]",
            "timestamp[us, tz=UTC][pyarrow]",
            "timestamp[us][pyarrow]",
            "date32[day][pyarrow]",
            "time64[us][pyarrow]",
            "decimal128(38, 9)[pyarrow]",
            "decimal256(76, 38)[pyarrow]",
            "binary[pyarrow]",
        ],
        pandas.core.arrays.boolean.BooleanDtype,
        pandas.core.arrays.floating.Float64Dtype,
        pandas.core.arrays.integer.Int64Dtype,
        pandas.core.arrays.string_.StringDtype,
        pandas.core.dtypes.dtypes.ArrowDtype,
        geopandas.array.GeometryDtype,
    ]
) -> 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([1, 2], dtype='Int64')
>>> ser
0    1
1    2
dtype: Int64

Convert to Float64 type:

>>> ser.astype('Float64')
0    1.0
1    2.0
dtype: Float64
Parameter
NameDescription
dtype str or pandas.ExtensionDtype

A dtype supported by BigQuery DataFrame include 'boolean','Float64','Int64', 'string', 'string[pyarrow]','timestamp[us, tz=UTC][pyarrow]', 'timestampus][pyarrow]','date32day][pyarrow]','time64us][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")).

bfill

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

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

Returns
TypeDescription
Series/DataFrame or NoneObject with missing values filled.

combine

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

Perform column-wise combine with another DataFrame.

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

Examples:

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

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

The DataFrame to merge column-wise.

func function

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

fill_value scalar value, default None

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

overwrite bool, default True

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

Returns
TypeDescription
DataFrameCombination of the provided DataFrames.

combine_first

combine_first(other: bigframes.dataframe.DataFrame)

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

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

Examples:

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

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

Provided DataFrame to use to fill null values.

Returns
TypeDescription
DataFrameThe result of combining the provided DataFrame with the other object.

copy

copy() -> bigframes.dataframe.DataFrame

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

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

Examples:

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

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

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

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

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

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

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

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

>>> df.loc[df["b"] == 2, "b"] = 22
>>> df
   a     b
0  1  22.0
1  3   4.0
<BLANKLINE>
[2 rows x 2 columns]
>>> df_copy
   a  b
0  1  2
1  3  4
<BLANKLINE>
[2 rows x 2 columns]

corr

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

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

Examples:

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

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

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

min_periods int, default None

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

numeric_only bool, default False

Include only float, int, boolean, decimal data.

Returns
TypeDescription
DataFrameCorrelation matrix.

count

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

Count non-NA cells for each column.

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

Examples:

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

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

Counting non-NA values for each column:

>>> df.count()
A    4.0
B    5.0
C    3.0
dtype: Float64
Parameter
NameDescription
numeric_only bool, default False

Include only float, int or boolean data.

Returns
TypeDescription
bigframes.series.SeriesFor each column/row the number of non-NA/null entries. If level is specified returns a DataFrame.

cummax

cummax() -> bigframes.dataframe.DataFrame

Return cumulative maximum over columns.

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

Examples:

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

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

>>> df.cummax()
    A       B
0   3       1
1   3       2
2   3       3
<BLANKLINE>
[3 rows x 2 columns]
Returns
TypeDescription
bigframes.dataframe.DataFrameReturn cumulative maximum of DataFrame.

cummin

cummin() -> bigframes.dataframe.DataFrame

Return cumulative minimum over columns.

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

Examples:

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

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

>>> df.cummin()
    A       B
0   3       1
1   1       1
2   1       1
<BLANKLINE>
[3 rows x 2 columns]
Returns
TypeDescription
bigframes.dataframe.DataFrameReturn cumulative minimum of DataFrame.

cumprod

cumprod() -> bigframes.dataframe.DataFrame

Return cumulative product over columns.

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

Examples:

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

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

>>> df.cumprod()
    A       B
0   3       1
1   3       2
2   6       6
<BLANKLINE>
[3 rows x 2 columns]
Returns
TypeDescription
bigframes.dataframe.DataFrameReturn cumulative product of DataFrame.

cumsum

cumsum()

Return cumulative sum over columns.

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

Examples:

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

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

>>> df.cumsum()
    A       B
0   3       1
1   4       3
2   6       6
<BLANKLINE>
[3 rows x 2 columns]
Returns
TypeDescription
bigframes.dataframe.DataFrameReturn cumulative sum of DataFrame.

describe

describe() -> bigframes.dataframe.DataFrame

Generate descriptive statistics.

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

Only supports numeric columns.

Examples:

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

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

>>> df.describe()
              A               B
count       3.0             3.0
mean        2.0        3.333333
std         1.0        4.163332
min         1.0             0.0
25%         1.0             0.0
50%         2.0             2.0
75%         3.0             8.0
max         3.0             8.0
<BLANKLINE>
[8 rows x 2 columns]
Returns
TypeDescription
bigframes.dataframe.DataFrameSummary statistics of the Series or Dataframe provided.

diff

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

First discrete difference of element.

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

Examples:

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

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

Calculating difference with default periods=1:

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

Calculating difference with periods=-1:

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

Periods to shift for calculating difference, accepts negative values.

Returns
TypeDescription
bigframes.dataframe.DataFrameFirst differences of the Series.

div

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

API documentation for div method.

divide

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

API documentation for divide method.

dot

dot(other: _DataFrameOrSeries) -> _DataFrameOrSeries

Compute the matrix multiplication between the DataFrame and other.

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

It can also be called using self @ other.

The dot method for Series computes the inner product, instead of the matrix product here.

Examples:

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

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

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

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

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

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

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

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

The other object to compute the matrix product with.

Returns
TypeDescription
Series or DataFrameIf other is a Series, return the matrix product between self and other as a Series. If other is a DataFrame, return the matrix product of self and other in a DataFrame.

drop

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

Drop specified labels from columns.

Remove columns by directly specifying column names.

Examples:

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

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

Drop columns:

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

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

Drop a row by index:

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

Drop columns and/or rows of MultiIndex DataFrame:

>>> import pandas as pd
>>> midx = pd.MultiIndex(levels=[['llama', 'cow', 'falcon'],
...                              ['speed', 'weight', 'length']],
...                      codes=[[0, 0, 0, 1, 1, 1, 2, 2, 2],
...                             [0, 1, 2, 0, 1, 2, 0, 1, 2]])
>>> df = bpd.DataFrame(index=midx, columns=['big', 'small'],
...                    data=[[45, 30], [200, 100], [1.5, 1], [30, 20],
...                          [250, 150], [1.5, 0.8], [320, 250],
...                          [1, 0.8], [0.3, 0.2]])
>>> df
                 big  small
llama  speed    45.0   30.0
       weight  200.0  100.0
       length    1.5    1.0
cow    speed    30.0   20.0
       weight  250.0  150.0
       length    1.5    0.8
falcon speed   320.0  250.0
       weight    1.0    0.8
       length    0.3    0.2
<BLANKLINE>
[9 rows x 2 columns]

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

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

>>> df.drop(index='length', level=1)
                 big  small
llama  speed    45.0   30.0
       weight  200.0  100.0
cow    speed    30.0   20.0
       weight  250.0  150.0
falcon speed   320.0  250.0
       weight    1.0    0.8
<BLANKLINE>
[6 rows x 2 columns]
Exceptions
TypeDescription
KeyErrorIf any of the labels is not found in the selected axis.
Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame without the removed column labels.

drop_duplicates

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

Return DataFrame with duplicate rows removed.

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

Parameters
NameDescription
subset column label or sequence of labels, optional

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

keep {'first', 'last', False}, default 'first'

Determines which duplicates (if any) to keep. - 'first' : Drop duplicates except for the first occurrence. - 'last' : Drop duplicates except for the last occurrence. - False : Drop all duplicates.

Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame with duplicates removed

droplevel

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

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

Parameters
NameDescription
level int, str, or list-like

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

axis {0 or 'index', 1 or 'columns'}, default 0

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

Returns
TypeDescription
DataFrameDataFrame with requested index / column level(s) removed.

dropna

dropna(
    *, axis: int | str = 0, inplace: bool = False, how: str = "any", 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]
Parameters
NameDescription
axis {0 or 'index', 1 or 'columns'}, default 'columns'

Determine if rows or columns which contain missing values are removed. * 0, or 'index' : Drop rows which contain missing values. * 1, or 'columns' : Drop columns which contain missing value.

how {'any', 'all'}, default 'any'

Determine if row or column is removed from DataFrame, when we have at least one NA or all NA. * 'any' : If any NA values are present, drop that row or column. * 'all' : If all values are NA, drop that row or column.

ignore_index bool, default False

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

Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame with NA entries dropped from it.

duplicated

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

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

Parameters
NameDescription
subset column label or sequence of labels, optional

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

keep {'first', 'last', False}, default 'first'

Determines which duplicates (if any) to mark. - first : Mark duplicates as True except for the first occurrence. - last : Mark duplicates as True except for the last occurrence. - False : Mark all duplicates as True.

Returns
TypeDescription
bigframes.series.SeriesBoolean series for each duplicated rows.

eq

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

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

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator ==:

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

Parameters
NameDescription
other scalar, sequence, Series, or DataFrame

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

axis {0 or 'index', 1 or 'columns'}, default 'columns'

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

Returns
TypeDescription
DataFrameResult of the comparison.

equals

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

Test whether two objects contain the same elements.

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

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

Parameter
NameDescription
other Series or DataFrame

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

Returns
TypeDescription
boolTrue if all elements are the same in both objects, False otherwise.

expanding

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

Provide expanding window calculations.

Parameter
NameDescription
min_periods int, default 1

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

Returns
TypeDescription
bigframes.core.window.WindowExpanding subclass.

ffill

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

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

Examples:

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

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

Fill NA/NaN values in DataFrames:

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

Fill NA/NaN values in Series:

>>> series = bpd.Series([1, np.nan, 2, 3])
>>> series.ffill()
0    1.0
1    1.0
2    2.0
3    3.0
dtype: Float64
Returns
TypeDescription
Series/DataFrame or NoneObject with missing values filled.

fillna

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

Fill NA/NaN values using the specified method.

Examples:

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

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

Replace all NA elements with 0s.

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

You can use fill values from another DataFrame:

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

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

Returns
TypeDescription
DataFrameObject with missing values filled

filter

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

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

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

Parameters
NameDescription
items list-like

Keep labels from axis which are in items.

like str

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

regex str (regular expression)

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

axis {0 or 'index', 1 or 'columns', None}, default None

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

first_valid_index

first_valid_index()

API documentation for first_valid_index method.

floordiv

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

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

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

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

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator //:

>>> df['A'] // (df['B'])
0    0
1    0
2    0
dtype: Int64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

from_dict

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

Construct DataFrame from dict of array-like or dicts.

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

Parameters
NameDescription
data dict

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

orient {'columns', 'index', 'tight'}, default 'columns'

The "orientation" of the data. If the keys of the passed dict should be the columns of the resulting DataFrame, pass 'columns' (default). Otherwise if the keys should be rows, pass 'index'. If 'tight', assume a dict with keys ['index', 'columns', 'data', 'index_names', 'column_names'].

dtype dtype, default None

Data type to force after DataFrame construction, otherwise infer.

columns list, default None

Column labels to use when orient='index'. Raises a ValueError if used with orient='columns' or orient='tight'.

Returns
TypeDescription
DataFrameDataFrame.

from_records

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

Convert structured or record ndarray to DataFrame.

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

Parameters
NameDescription
data structured ndarray, sequence of tuples or dicts

Structured input data.

index str, list of fields, array-like

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

exclude sequence, default None

Columns or fields to exclude.

columns sequence, default None

Column names to use. If the passed data do not have names associated with them, this argument provides names for the columns. Otherwise this argument indicates the order of the columns in the result (any names not found in the data will become all-NA columns).

coerce_float bool, default False

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

nrows int, default None

Number of rows to read if data is an iterator.

Returns
TypeDescription
DataFrameDataFrame.

ge

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

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

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator >=:

>>> df["degrees"] >= 360
circle        True
triangle     False
rectangle     True
Name: degrees, dtype: boolean
Parameters
NameDescription
other scalar, sequence, Series, or DataFrame

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

axis {0 or 'index', 1 or 'columns'}, default 'columns'

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

Returns
TypeDescription
DataFrameDataFrame of bool. The result of the comparison.

get

get(key, default=None)

Get item from object for given key (ex: DataFrame column).

Returns default value if not found.

Examples:

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

>>> df = bpd.DataFrame(
...     [
...         [24.3, 75.7, "high"],
...         [31, 87.8, "high"],
...         [22, 71.6, "medium"],
...         [35, 95, "medium"],
...     ],
...     columns=["temp_celsius", "temp_fahrenheit", "windspeed"],
...     index=["2014-02-12", "2014-02-13", "2014-02-14", "2014-02-15"],
... )
>>> df
            temp_celsius  temp_fahrenheit windspeed
2014-02-12          24.3             75.7      high
2014-02-13          31.0             87.8      high
2014-02-14          22.0             71.6    medium
2014-02-15          35.0             95.0    medium
<BLANKLINE>
[4 rows x 3 columns]

>>> df.get(["temp_celsius", "windspeed"])
            temp_celsius windspeed
2014-02-12          24.3      high
2014-02-13          31.0      high
2014-02-14          22.0    medium
2014-02-15          35.0    medium
<BLANKLINE>
[4 rows x 2 columns]

>>> ser = df['windspeed']
>>> ser
2014-02-12      high
2014-02-13      high
2014-02-14    medium
2014-02-15    medium
Name: windspeed, dtype: string
>>> ser.get('2014-02-13')
'high'

If the key is not found, the default value will be used.

>>> df.get(["temp_celsius", "temp_kelvin"])
>>> df.get(["temp_celsius", "temp_kelvin"], default="default_value")
'default_value'

groupby

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

Group DataFrame by columns.

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

Examples:

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

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

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

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

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

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

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

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

A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.

level int, level name, or sequence of such, default None

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

as_index bool, default True

Default True. Return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively "SQL-style" grouped output. This argument has no effect on filtrations such as head(), tail(), nth() and in transformations.

dropna bool, default True

Default True. If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.

Returns
TypeDescription
bigframes.core.groupby.SeriesGroupByA groupby object that contains information about the groups.

gt

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

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

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

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

Examples:

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

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

You can also use arithmetic operator >:

>>> df["degrees"] > 360
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean
Parameters
NameDescription
other scalar, sequence, Series, or DataFrame

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

axis {0 or 'index', 1 or 'columns'}, default 'columns'

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

Returns
TypeDescription
DataFrameDataFrame of bool: The result of the comparison.

head

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

Return the first n rows.

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

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

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

Examples:

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

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

Viewing the first 5 lines:

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

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

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

For negative values of n:

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

Default 5. Number of rows to select.

Returns
TypeDescription
same type as callerThe first n rows of the caller object.

idxmax

idxmax() -> bigframes.series.Series

Return index of first occurrence of maximum over columns.

NA/null values are excluded.

Examples:

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

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

>>> df.idxmax()
A    0
B    2
dtype: Int64
Returns
TypeDescription
SeriesIndexes of maxima along the columns.

idxmin

idxmin() -> bigframes.series.Series

Return index of first occurrence of minimum over columns.

NA/null values are excluded.

Examples:

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

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

>>> df.idxmin()
A    1
B    0
dtype: Int64
Returns
TypeDescription
SeriesIndexes of minima along the columns.

info

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

Print a concise summary of a DataFrame.

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

Parameters
NameDescription
verbose bool, optional

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

buf writable buffer, defaults to sys.stdout

Where to send the output. By default, the output is printed to sys.stdout. Pass a writable buffer if you need to further process the output.

max_cols int, optional

When to switch from the verbose to the truncated output. If the DataFrame has more than max_cols columns, the truncated output is used. By default, the setting in pandas.options.display.max_info_columns is used.

memory_usage bool, optional

Specifies whether total memory usage of the DataFrame elements (including the index) should be displayed. By default, this follows the pandas.options.display.memory_usage setting. True always show memory usage. False never shows memory usage. Memory estimation is made based in column dtype and number of rows assuming values consume the same memory amount for corresponding dtypes.

show_counts bool, optional

Whether to show the non-null counts. By default, this is shown only if the DataFrame is smaller than pandas.options.display.max_info_rows and pandas.options.display.max_info_columns. A value of True always shows the counts, and False never shows the counts.

Returns
TypeDescription
NoneThis method prints a summary of a DataFrame and returns None.

interpolate

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

Fill NaN values using an interpolation method.

Examples:

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

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

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

Returns
TypeDescription
DataFrameReturns the same object type as the caller, interpolated at some or all NaN values

isin

isin(values) -> bigframes.dataframe.DataFrame

Whether each element in the DataFrame is contained in values.

Examples:

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

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

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

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

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

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

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

Returns
TypeDescription
DataFrameDataFrame of booleans showing whether each element in the DataFrame is contained in values.

isna

isna() -> bigframes.dataframe.DataFrame

Detect missing values.

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

Examples:

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

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

Show which entries in a DataFrame are NA:

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

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

Show which entries in a Series are NA:

>>> ser = bpd.Series([5, None, 6, np.nan, bpd.NA])
>>> ser
0     5.0
1    <NA>
2     6.0
3    <NA>
4    <NA>
dtype: Float64

>>> 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

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     5.0
1    <NA>
2     6.0
3    <NA>
4    <NA>
dtype: Float64

>>> 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

items

items()

Iterate over (column name, Series) pairs.

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

Examples:

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

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

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

iterrows

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

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

:Yields: a tuple (index, data) where data contains row values as a Series

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
0
>>> row
A    1
B    4
Name: 0, dtype: object

itertuples

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

Iterate over DataFrame rows as namedtuples.

Parameters
NameDescription
index bool, default True

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

name str or None, default "Pandas"

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

Returns
TypeDescription
iterator **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=0, A=1, B=4)An object to iterate over namedtuples for each row in the DataFrame with the first field possibly being the index and following fields being the column values.

join

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

Join columns of another DataFrame.

Join columns with other DataFrame on index

Examples:

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

Join two DataFrames by specifying how to handle the operation:

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

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

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

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

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

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

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

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

>>> df1.join(df2, on="col1", how="right")
      col1  col2 col3  col4
<NA>    11  <NA>  foo     3
<NA>    22  <NA>  baz     4
<BLANKLINE>
[2 rows x 4 columns]
Parameter
NameDescription
how {'left', 'right', 'outer', 'inner'}, default 'left'`

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

Returns
TypeDescription
bigframes.dataframe.DataFrameA dataframe containing columns from both the caller and other.

keys

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

Get the 'info axis'.

This is index for Series, columns for DataFrame.

Returns
TypeDescription
Index **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')Info axis.

kurt

kurt(*, numeric_only: bool = False)

Return unbiased kurtosis over columns.

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

Examples:

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

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

Calculating the kurtosis value of each column:

>>> df.kurt()
A        -1.2
B   -0.177515
C         5.0
dtype: Float64
Parameter
NameDescription
numeric_only bool, default False

Include only float, int, boolean columns.

Returns
TypeDescription
SeriesSeries.

kurtosis

kurtosis(*, numeric_only: bool = False)

API documentation for kurtosis method.

le

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

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

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator <=:

>>> df["degrees"] <= 180
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean
Parameters
NameDescription
other scalar, sequence, Series, or DataFrame

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

axis {0 or 'index', 1 or 'columns'}, default 'columns'

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

Returns
TypeDescription
DataFrameDataFrame of bool. The result of the comparison.

lt

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

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

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator <:

>>> df["degrees"] < 180
circle       False
triangle     False
rectangle    False
Name: degrees, dtype: boolean
Parameters
NameDescription
other scalar, sequence, Series, or DataFrame

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

axis {0 or 'index', 1 or 'columns'}, default 'columns'

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

Returns
TypeDescription
DataFrameDataFrame of bool. The result of the comparison.

map

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

Apply a function to a Dataframe elementwise.

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

Examples:

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

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([int], float, reuse=False)
... def minutes_to_hours(x):
...     return x/60

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

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

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

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

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

na_action Optional[str], default None

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

Returns
TypeDescription
bigframes.dataframe.DataFrameTransformed DataFrame.

max

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

Return the maximum of the values over the requested axis.

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

Examples:

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

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

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

>>> df.max()
A    3.0
B    4.0
dtype: Float64

Finding the maximum value in each row.

>>> df.max(axis=1)
0    2.0
1    4.0
dtype: Float64
Parameters
NameDescription
axis {index (0), columns (1)}

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

numeric_only bool. default False

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

Returns
TypeDescription
bigframes.series.SeriesSeries after the maximum of values.

mean

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

Return the mean of the values over the requested axis.

Examples:

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

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

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

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

Calculating the mean of each row.

>>> df.mean(axis=1)
0    1.5
1    3.5
dtype: Float64
Parameters
NameDescription
axis {index (0), columns (1)}

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

numeric_only bool. default False

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

Returns
TypeDescription
bigframes.series.SeriesSeries with the mean of values.

median

median(
    *, numeric_only: bool = False, exact: bool = False
) -> 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    1.0
B    2.0
dtype: Float64
Parameters
NameDescription
numeric_only bool. default False

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

exact bool. default False

Default False. Get the exact median instead of an approximate one. Note: exact=True not yet supported.

Returns
TypeDescription
bigframes.series.SeriesSeries with the median of values.

melt

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

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

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

Examples:

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

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

Using melt without optional arguments:

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

Using melt with id_vars and value_vars:

>>> df.melt(id_vars='A', value_vars=['B', 'C'])
       A    variable        value
0    1.0           B            1
1   <NA>           B            2
2    3.0           B            3
3    4.0           B            4
4    5.0           B            5
5    1.0           C         <NA>
6    <NA>          C            3
7    3.0           C         <NA>
8    4.0           C            4
9    5.0           C            5
<BLANKLINE>
[10 rows x 3 columns]
Parameters
NameDescription
id_vars tuple, list, or ndarray, optional

Column(s) to use as identifier variables.

value_vars tuple, list, or ndarray, optional

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

var_name scalar

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

value_name scalar, default 'value'

Name to use for the 'value' column.

Returns
TypeDescription
DataFrameUnpivoted DataFrame.

memory_usage

memory_usage(index: bool = True)

Return the memory usage of each column in bytes.

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

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

Parameter
NameDescription
index bool, default True

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

Returns
TypeDescription
SeriesA Series whose index is the original column names and whose values is the memory usage of each column in bytes.

merge

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

Merge DataFrame objects with a database-style join.

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

Examples:

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

Merge DataFrames df1 and df2 by specifiying type of merge:

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

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

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

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

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

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

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

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

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

left_on label or list of labels

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

right_on label or list of labels

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

Returns
TypeDescription
bigframes.dataframe.DataFrameA DataFrame of the two merged objects.

min

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

Return the minimum of the values over the requested axis.

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

Examples:

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

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

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

>>> df.min()
A    1.0
B    2.0
dtype: Float64

Finding the minimum value in each row.

>>> df.min(axis=1)
0    1.0
1    3.0
dtype: Float64
Parameters
NameDescription
axis {index (0), columns (1)}

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

numeric_only bool, default False

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

Returns
TypeDescription
bigframes.series.SeriesSeries with the minimum of the values.

mod

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

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

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

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

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator %:

>>> df['A'] % (df['B'])
0    1
1    2
2    3
dtype: Int64
Parameter
NameDescription
axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

mul

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

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

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

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

Examples:

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

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

You can use method name:

>>> df['A'].mul(df['B'])
0     4
1    10
2    18
dtype: Int64

You can also use arithmetic operator *:

>>> df['A'] * (df['B'])
0     4
1    10
2    18
dtype: Int64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

multiply

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

API documentation for multiply method.

ne

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

Get not equal to of DataFrame and other, element-wise (binary operator ne).

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator !=:

>>> df["degrees"] != 360
circle       False
triangle      True
rectangle    False
Name: degrees, dtype: boolean
Parameters
NameDescription
other scalar, sequence, Series, or DataFrame

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

axis {0 or 'index', 1 or 'columns'}, default 'columns'

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

Returns
TypeDescription
DataFrameResult of the comparison.

nlargest

nlargest(
    n: int,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    keep: str = "first",
) -> bigframes.dataframe.DataFrame

Return the first n rows ordered by columns in descending order.

Return the first n rows with the largest values in columns, in descending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=False).head(n), but more performant.

Examples:

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

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

Returns rows with the largest value in 'A', including all ties:

>>> df.nlargest(1, 'A', keep = "all")
    A       B       C
4   5       1       a
5   5       2       b
<BLANKLINE>
[2 rows x 3 columns]

Returns the first row with the largest value in 'A', default behavior in case of ties:

>>> df.nlargest(1, 'A')
    A       B       C
4   5       1       a
<BLANKLINE>
[1 rows x 3 columns]

Returns the last row with the largest value in 'A' in case of ties:

>>> df.nlargest(1, 'A', keep = "last")
    A       B       C
5   5       2       b
<BLANKLINE>
[1 rows x 3 columns]

Returns the row with the largest combined values in both 'A' and 'C':

>>> df.nlargest(1, ['A', 'C'])
    A       B       C
5   5       2       b
<BLANKLINE>
[1 rows x 3 columns]
Parameters
NameDescription
n int

Number of rows to return.

columns label or list of labels

Column label(s) to order by.

keep {'first', 'last', 'all'}, default 'first'

Where there are duplicate values: - first : prioritize the first occurrence(s) - last : prioritize the last occurrence(s) - all : do not drop any duplicates, even it means selecting more than n items.

Returns
TypeDescription
DataFrameThe first n rows ordered by the given columns in descending order.

notna

notna() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

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

Returns
TypeDescription
NDFrameMask of bool values for each element that indicates whether an element is not an NA value.

notnull

notnull() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

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

Returns
TypeDescription
NDFrameMask of bool values for each element that indicates whether an element is not an NA value.

nsmallest

nsmallest(
    n: int,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    keep: str = "first",
) -> bigframes.dataframe.DataFrame

Return the first n rows ordered by columns in ascending order.

Return the first n rows with the smallest values in columns, in ascending order. The columns that are not specified are returned as well, but not used for ordering.

This method is equivalent to df.sort_values(columns, ascending=True).head(n), but more performant.

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

Returns rows with the smallest value in 'A', including all ties:

>>> df.nsmallest(1, 'A', keep = "all")
    A       B       C
0   1       5       a
1   1       6       b
<BLANKLINE>
[2 rows x 3 columns]

Returns the first row with the smallest value in 'A', default behavior in case of ties:

>>> df.nsmallest(1, 'A')
    A       B       C
0   1       5       a
<BLANKLINE>
[1 rows x 3 columns]

Returns the last row with the smallest value in 'A' in case of ties:

>>> df.nsmallest(1, 'A', keep = "last")
    A       B       C
1   1       6       b
<BLANKLINE>
[1 rows x 3 columns]

Returns rows with the smallest values in 'A' and 'C'

>>> df.nsmallest(1, ['A', 'C'])
    A       B       C
0   1       5       a
<BLANKLINE>
[1 rows x 3 columns]
Parameters
NameDescription
n int

Number of rows to return.

columns label or list of labels

Column label(s) to order by.

keep {'first', 'last', 'all'}, default 'first'

Where there are duplicate values: - first : prioritize the first occurrence(s) - last : prioritize the last occurrence(s) - all : do not drop any duplicates, even it means selecting more than n items.

Returns
TypeDescription
DataFrameThe first n rows ordered by the given columns in ascending order.

nunique

nunique() -> bigframes.series.Series

Count number of distinct elements in each column.

Examples:

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

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

>>> df.nunique()
A    3.0
B    2.0
dtype: Float64
Returns
TypeDescription
bigframes.series.SeriesSeries with number of distinct elements.

pct_change

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

Fractional change between the current and a prior element.

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

Parameter
NameDescription
periods int, default 1

Periods to shift for forming percent change.

Returns
TypeDescription
Series or DataFrameThe same type as the calling object.

peek

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

Preview n arbitrary rows from the dataframe. No guarantees about row selection or ordering. DataFrame.peek(force=False) will always be very fast, but will not succeed if data requires full data scanning. Using force=True will always succeed, but may be perform queries. Query results will be cached so that future steps will benefit from these queries.

Parameters
NameDescription
n int, default 5

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

force bool, default True

If the data cannot be peeked efficiently, the dataframe will instead be fully materialized as part of the operation if force=True. If force=False, the operation will throw a ValueError.

Exceptions
TypeDescription
ValueErrorIf force=False and data cannot be efficiently peeked.
Returns
TypeDescription
pandas.DataFrameA pandas DataFrame with n rows.

pipe

pipe(func: Callable[..., T] | tuple[Callable[..., T], str], *args, **kwargs) -> T

Apply chainable functions that expect Series or DataFrames.

Examples:

Constructing a income DataFrame from a dictionary.

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

>>> data = [[8000, 1000], [9500, np.nan], [5000, 2000]]
>>> df = bpd.DataFrame(data, columns=['Salary', 'Others'])
>>> df
   Salary  Others
0    8000  1000.0
1    9500    <NA>
2    5000  2000.0
<BLANKLINE>
[3 rows x 2 columns]

Functions that perform tax reductions on an income DataFrame.

>>> def subtract_federal_tax(df):
...     return df * 0.9
>>> def subtract_state_tax(df, rate):
...     return df * (1 - rate)
>>> def subtract_national_insurance(df, rate, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)

Instead of writing

>>> subtract_national_insurance(
...     subtract_state_tax(subtract_federal_tax(df), rate=0.12),
...     rate=0.05,
...     rate_increase=0.02)  # doctest: +SKIP

You can write

>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(subtract_national_insurance, rate=0.05, rate_increase=0.02)
... )
    Salary   Others
0  5892.48   736.56
1  6997.32     <NA>
2   3682.8  1473.12
<BLANKLINE>
[3 rows x 2 columns]

If you have a function that takes the data as (say) the second argument, pass a tuple indicating which keyword expects the data. For example, suppose national_insurance takes its data as df in the second argument:

>>> def subtract_national_insurance(rate, df, rate_increase):
...     new_rate = rate + rate_increase
...     return df * (1 - new_rate)
>>> (
...     df.pipe(subtract_federal_tax)
...     .pipe(subtract_state_tax, rate=0.12)
...     .pipe(
...         (subtract_national_insurance, 'df'),
...         rate=0.05,
...         rate_increase=0.02
...     )
... )
    Salary   Others
0  5892.48   736.56
1  6997.32     <NA>
2   3682.8  1473.12
<BLANKLINE>
[3 rows x 2 columns]
Parameters
NameDescription
args iterable, optional

Positional arguments passed into func.

kwargs mapping, optional

A dictionary of keyword arguments passed into func.

func function

Function to apply to this object. args, and kwargs are passed into func. Alternatively a (callable, data_keyword) tuple where data_keyword is a string indicating the keyword of callable that expects this object.

pivot

pivot(
    *,
    columns: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    index: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    values: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None
) -> bigframes.dataframe.DataFrame

Return reshaped DataFrame organized by given index / column values.

Reshape data (produce a "pivot" table) based on column values. Uses unique values from specified index / columns to form axes of the resulting DataFrame. This function does not support data aggregation, multiple values will result in a MultiIndex in the columns.

Examples:

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

>>> df = bpd.DataFrame({
...     "foo": ["one", "one", "one", "two", "two"],
...     "bar": ["A", "B", "C", "A", "B"],
...     "baz": [1, 2, 3, 4, 5],
...     "zoo": ['x', 'y', 'z', 'q', 'w']
... })

>>> df
    foo     bar     baz     zoo
0   one       A       1       x
1   one       B       2       y
2   one       C       3       z
3   two       A       4       q
4   two       B       5       w
<BLANKLINE>
[5 rows x 4 columns]

Using pivot without optional arguments:

>>> df.pivot(columns='foo')
        bar             baz             zoo
foo  one     two     one     two     one     two
0      A    <NA>       1    <NA>       x    <NA>
1      B    <NA>       2    <NA>       y    <NA>
2      C    <NA>       3    <NA>       z    <NA>
3   <NA>       A    <NA>       4    <NA>       q
4   <NA>       B    <NA>       5    <NA>       w
<BLANKLINE>
[5 rows x 6 columns]

Using pivot with index and values:

>>> df.pivot(columns='foo', index='bar', values='baz')
foo     one     two
bar
A       1         4
B       2         5
C       3      <NA>
<BLANKLINE>
[3 rows x 2 columns]
Parameters
NameDescription
columns str or object or a list of str

Column to use to make new frame's columns.

index str or object or a list of str, optional

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

values str, object or a list of the previous, optional

Column(s) to use for populating new frame's values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.

Returns
TypeDescription
DataFrameReturns reshaped DataFrame.

pow

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

Get Exponential power of dataframe and other, element-wise (binary operator **).

Equivalent to dataframe ** other, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, rpow.

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

Examples:

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

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

You can use method name:

>>> df['A'].pow(df['B'])
0      1
1     32
2    729
dtype: Int64

You can also use arithmetic operator **:

>>> df['A'] ** (df['B'])
0      1
1     32
2    729
dtype: Int64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

prod

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

Return the product of the values over the requested axis.

Examples:

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

>>> df = bpd.DataFrame({"A": [1, 2, 3], "B": [4.5, 5.5, 6.5]})
>>> df
    A    B
0   1  4.5
1   2  5.5
2   3  6.5
<BLANKLINE>
[3 rows x 2 columns]

Calculating the product of each column(the default behavior without an explicit axis parameter):

>>> df.prod()
A        6.0
B    160.875
dtype: Float64

Calculating the product of each row:

>>> df.prod(axis=1)
0     4.5
1    11.0
2    19.5
dtype: Float64
Parameters
NameDescription
axis {index (0), columns (1)}

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

numeric_only bool. default False

Include only float, int, boolean columns.

Returns
TypeDescription
bigframes.series.SeriesSeries with the product of the values.

product

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

API documentation for product method.

radd

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

API documentation for radd method.

rank

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

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

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

Parameters
NameDescription
method {'average', 'min', 'max', 'first', 'dense'}, default 'average'

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

numeric_only bool, default False

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

na_option {'keep', 'top', 'bottom'}, default 'keep'

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

ascending bool, default True

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

Returns
TypeDescription
same type as callerReturn a Series or DataFrame with data ranks as values.

rdiv

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

API documentation for rdiv method.

reindex

reindex(
    labels=None,
    *,
    index=None,
    columns=None,
    axis: typing.Optional[typing.Union[str, int]] = None,
    validate: typing.Optional[bool] = None
)

Conform DataFrame to new index with optional filling logic.

Places NA in locations having no value in the previous index. A new object is produced.

Parameters
NameDescription
labels array-like, optional

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

index array-like, optional

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

columns array-like, optional

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

axis int or str, optional

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

Returns
TypeDescription
DataFrameDataFrame with changed index.

reindex_like

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

Return an object with matching indices as other object.

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

Parameter
NameDescription
other Object of the same data type

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

Returns
TypeDescription
Series or DataFrameSame type as caller, but with changed indices on each axis.

rename

rename(
    *, columns: typing.Mapping[typing.Hashable, typing.Hashable]
) -> bigframes.dataframe.DataFrame

Rename columns.

Dict values must be unique (1-to-1). Labels not contained in a dict will be left as-is. Extra labels listed don't throw an error.

Examples:

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

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

Rename columns using a mapping:

>>> df.rename(columns={"A": "col1", "B": "col2"})
   col1  col2
0     1     4
1     2     5
2     3     6
<BLANKLINE>
[3 rows x 2 columns]
Parameter
NameDescription
columns Mapping

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

Exceptions
TypeDescription
KeyErrorIf any of the labels is not found.
Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame with the renamed axis labels.

rename_axis

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

Set the name of the axis for the index.

Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame with the new index name

reorder_levels

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

Rearrange index levels using input order. May not drop or duplicate levels.

Parameters
NameDescription
order list of int or list of str

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

axis {0 or 'index', 1 or 'columns'}, default 0

Where to reorder levels.

Returns
TypeDescription
DataFrameDataFrame of rearranged index.

replace

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

Replace values given in to_replace with value.

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

Examples:

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

>>> df = bpd.DataFrame({
...     'int_col': [1, 1, 2, 3],
...     'string_col': ["a", "b", "c", "b"],
...     })

Using scalar to_replace and value:

>>> df.replace("b", "e")
   int_col string_col
0        1          a
1        1          e
2        2          c
3        3          e
<BLANKLINE>
[4 rows x 2 columns]

Using dictionary:

>>> df.replace({"a": "e", 2: 5})
   int_col string_col
0        1          e
1        1          b
2        5          c
3        3          b
<BLANKLINE>
[4 rows x 2 columns]

Using regex:

>>> df.replace("[ab]", "e", regex=True)
   int_col string_col
0        1          e
1        1          e
2        2          c
3        3          e
<BLANKLINE>
[4 rows x 2 columns]
Parameters
NameDescription
to_replace str, regex, list, int, float or None

How to find the values that will be replaced. numeric: numeric values equal to to_replace will be replaced with value str: string exactly matching to_replace will be replaced with value regex: regexs matching to_replace will be replaced 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 scalar, default None

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

regex bool, default False

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

Returns
TypeDescription
Series/DataFrameObject after replacement.

reset_index

reset_index(*, drop: bool = False) -> bigframes.dataframe.DataFrame

Reset the index.

Reset the index of the DataFrame, and use the default one instead.

Examples:

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

>>> import numpy as np
>>> df = bpd.DataFrame([('bird', 389.0),
...                     ('bird', 24.0),
...                     ('mammal', 80.5),
...                     ('mammal', np.nan)],
...                    index=['falcon', 'parrot', 'lion', 'monkey'],
...                    columns=('class', 'max_speed'))
>>> df
         class  max_speed
falcon    bird      389.0
parrot    bird       24.0
lion    mammal       80.5
monkey  mammal       <NA>
<BLANKLINE>
[4 rows x 2 columns]

When we reset the index, the old index is added as a column, and a new sequential index is used:

>>> df.reset_index()
    index   class  max_speed
0  falcon    bird      389.0
1  parrot    bird       24.0
2    lion  mammal       80.5
3  monkey  mammal       <NA>
<BLANKLINE>
[4 rows x 3 columns]

We can use the drop parameter to avoid the old index being added as a column:

>>> df.reset_index(drop=True)
    class  max_speed
0    bird      389.0
1    bird       24.0
2  mammal       80.5
3  mammal       <NA>
<BLANKLINE>
[4 rows x 2 columns]

You can also use reset_index with MultiIndex.

>>> import pandas as pd
>>> index = pd.MultiIndex.from_tuples([('bird', 'falcon'),
...                                    ('bird', 'parrot'),
...                                    ('mammal', 'lion'),
...                                    ('mammal', 'monkey')],
...                                   names=['class', 'name'])
>>> columns = ['speed', 'max']
>>> df = bpd.DataFrame([(389.0, 'fly'),
...                     (24.0, 'fly'),
...                     (80.5, 'run'),
...                     (np.nan, 'jump')],
...                    index=index,
...                    columns=columns)
>>> df
               speed   max
class  name
bird   falcon  389.0   fly
       parrot   24.0   fly
mammal lion     80.5   run
       monkey   <NA>  jump
<BLANKLINE>
[4 rows x 2 columns]

>>> df.reset_index()
    class    name  speed   max
0    bird  falcon  389.0   fly
1    bird  parrot   24.0   fly
2  mammal    lion   80.5   run
3  mammal  monkey   <NA>  jump
<BLANKLINE>
[4 rows x 4 columns]

>>> df.reset_index(drop=True)
   speed   max
0  389.0   fly
1   24.0   fly
2   80.5   run
3   <NA>  jump
<BLANKLINE>
[4 rows x 2 columns]
Parameter
NameDescription
drop bool, default False

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

Returns
TypeDescription
bigframes.dataframe.DataFrameDataFrame with the new index.

rfloordiv

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

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

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

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

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rfloordiv(df['B'])
0    4
1    2
2    2
dtype: Int64

It's equivalent to using arithmetic operator: //:

>>> df['B'] // (df['A'])
0    4
1    2
2    2
dtype: Int64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

rmod

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

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

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

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

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rmod(df['B'])
0    0
1    1
2    0
dtype: Int64

It's equivalent to using arithmetic operator: %:

>>> df['B'] % (df['A'])
0    0
1    1
2    0
dtype: Int64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

rmul

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

API documentation for rmul method.

rolling

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

Provide rolling window calculations.

Parameters
NameDescription
window int, timedelta, str, offset, or BaseIndexer subclass

Size of the moving window. If an integer, the fixed number of observations used for each window. If a timedelta, str, or offset, the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetime-like indexes. To learn more about the offsets & frequency strings, please see this link https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases__. If a BaseIndexer subclass, the window boundaries based on the defined get_window_bounds method. Additional rolling keyword arguments, namely min_periods, center, closed and step will be passed to get_window_bounds.

min_periods int, default None

Minimum number of observations in window required to have a value; otherwise, result is np.nan. For a window that is specified by an offset, min_periods will default to 1. For a window that is specified by an integer, min_periods will default to the size of the window.

Returns
TypeDescription
bigframes.core.window.WindowWindow subclass if a win_type is passed. Rolling subclass if win_type is not passed.

rpow

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

Get Exponential power of dataframe and other, element-wise (binary operator rpow).

Equivalent to other ** dataframe, but with support to substitute a fill_value for missing data in one of the inputs. With reverse version, pow.

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

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rpow(df['B'])
0      4
1     25
2    216
dtype: Int64

It's equivalent to using arithmetic operator: **:

>>> df['B'] ** (df['A'])
0      4
1     25
2    216
dtype: Int64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

rsub

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

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

Equivalent to other - dataframe. With reverse version, sub.

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

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rsub(df['B'])
0    3
1    3
2    3
dtype: Int64

It's equivalent to using arithmetic operator: -:

>>> df['B'] - (df['A'])
0    3
1    3
2    3
dtype: Int64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

rtruediv

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

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

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

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

Examples:

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

>>> df = bpd.DataFrame({
...     'A': [1, 2, 3],
...     'B': [4, 5, 6],
...     })
>>> df['A'].rtruediv(df['B'])
0    4.0
1    2.5
2    2.0
dtype: Float64

It's equivalent to using arithmetic operator: /:

>>> df['B'] / (df['A'])
0    4.0
1    2.5
2    2.0
dtype: Float64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

sample

sample(
    n: typing.Optional[int] = None,
    frac: typing.Optional[float] = None,
    *,
    random_state: typing.Optional[int] = None
) -> bigframes.dataframe.DataFrame

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

You can use random_state for reproducibility.

Examples:

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

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

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

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

A random 50% sample of the DataFrame:

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

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

>>> s = df['num_legs']
>>> s.sample(n=3, random_state=1)
dog       4
fish      0
spider    8
Name: num_legs, dtype: Int64
Parameters
NameDescription
n Optional[int], default None

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

frac Optional[float], default None

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

random_state Optional[int], default None

Seed for random number generator.

select_dtypes

select_dtypes(include=None, exclude=None) -> bigframes.dataframe.DataFrame

Return a subset of the DataFrame's columns based on the column dtypes.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': ["hello", "world"], 'col3': [True, False]})
>>> df.select_dtypes(include=['Int64'])
   col1
0     1
1     2
<BLANKLINE>
[2 rows x 1 columns]

>>> df.select_dtypes(exclude=['Int64'])
    col2   col3
0  hello   True
1  world  False
<BLANKLINE>
[2 rows x 2 columns]
Parameters
NameDescription
include scalar or list-like

A selection of dtypes or strings to be included.

exclude scalar or list-like

A selection of dtypes or strings to be excluded.

Returns
TypeDescription
DataFrameThe subset of the frame including the dtypes in include and excluding the dtypes in exclude.

set_index

set_index(
    keys: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]],
    append: bool = False,
    drop: bool = True,
) -> bigframes.dataframe.DataFrame

Set the DataFrame index using existing columns.

Set the DataFrame index (row labels) using one existing column. The index can replace the existing index.

Examples:

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

>>> df = bpd.DataFrame({'month': [1, 4, 7, 10],
...                     'year': [2012, 2014, 2013, 2014],
...                     'sale': [55, 40, 84, 31]})
>>> df
   month  year  sale
0      1  2012    55
1      4  2014    40
2      7  2013    84
3     10  2014    31
<BLANKLINE>
[4 rows x 3 columns]

Set the 'month' column to become the index:

>>> df.set_index('month')
       year  sale
month
1      2012    55
4      2014    40
7      2013    84
10     2014    31
<BLANKLINE>
[4 rows x 2 columns]

Create a MultiIndex using columns 'year' and 'month':

>>> df.set_index(['year', 'month'])
            sale
year month
2012 1        55
2014 4        40
2013 7        84
2014 10       31
<BLANKLINE>
[4 rows x 1 columns]
Returns
TypeDescription
DataFrameChanged row labels.

shift

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

Shift index by desired number of periods.

Shifts the index without realigning the data.

Returns
TypeDescription
NDFrameCopy of input object, shifted.

skew

skew(*, numeric_only: bool = False)

Return unbiased skew over columns.

Normalized by N-1.

Examples:

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

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

Calculating the skewness of each column.

>>> df.skew()
A         0.0
B         0.0
C    2.236068
dtype: Float64
Parameter
NameDescription
numeric_only bool, default False

Include only float, int, boolean columns.

Returns
TypeDescription
SeriesSeries.

sort_index

sort_index(
    ascending: bool = True, na_position: typing.Literal["first", "last"] = "last"
) -> bigframes.dataframe.DataFrame

Sort object by labels (along an axis).

Returns
TypeDescription
DataFrameThe original DataFrame sorted by the labels.

sort_values

sort_values(
    by: typing.Union[str, typing.Sequence[str]],
    *,
    ascending: typing.Union[bool, typing.Sequence[bool]] = True,
    kind: str = "quicksort",
    na_position: typing.Literal["first", "last"] = "last"
) -> bigframes.dataframe.DataFrame

Sort by the values along row axis.

Examples:

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

>>> df = bpd.DataFrame({
...     'col1': ['A', 'A', 'B', bpd.NA, 'D', 'C'],
...     'col2': [2, 1, 9, 8, 7, 4],
...     'col3': [0, 1, 9, 4, 2, 3],
...     'col4': ['a', 'B', 'c', 'D', 'e', 'F']
... })
>>> df
   col1  col2  col3 col4
0     A     2     0    a
1     A     1     1    B
2     B     9     9    c
3  <NA>     8     4    D
4     D     7     2    e
5     C     4     3    F
<BLANKLINE>
[6 rows x 4 columns]

Sort by col1:

>>> df.sort_values(by=['col1'])
   col1  col2  col3 col4
0     A     2     0    a
1     A     1     1    B
2     B     9     9    c
5     C     4     3    F
4     D     7     2    e
3  <NA>     8     4    D
<BLANKLINE>
[6 rows x 4 columns]

Sort by multiple columns:

>>> df.sort_values(by=['col1', 'col2'])
   col1  col2  col3 col4
1     A     1     1    B
0     A     2     0    a
2     B     9     9    c
5     C     4     3    F
4     D     7     2    e
3  <NA>     8     4    D
<BLANKLINE>
[6 rows x 4 columns]

Sort Descending:

>>> df.sort_values(by='col1', ascending=False)
   col1  col2  col3 col4
4     D     7     2    e
5     C     4     3    F
2     B     9     9    c
0     A     2     0    a
1     A     1     1    B
3  <NA>     8     4    D
<BLANKLINE>
[6 rows x 4 columns]

Putting NAs first:

>>> df.sort_values(by='col1', ascending=False, na_position='first')
   col1  col2  col3 col4
3  <NA>     8     4    D
4     D     7     2    e
5     C     4     3    F
2     B     9     9    c
0     A     2     0    a
1     A     1     1    B
<BLANKLINE>
[6 rows x 4 columns]
Parameters
NameDescription
by str or Sequence[str]

Name or list of names to sort by.

ascending bool or Sequence[bool], default True

Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.

kind str, default 'quicksort'

Choice of sorting algorithm. Accepts 'quicksort', 'mergesort', 'heapsort', 'stable'. Ignored except when determining whether to sort stably. 'mergesort' or 'stable' will result in stable reorder.

na_position {'first', 'last'}, default last

{'first', 'last'}, default 'last' Puts NaNs at the beginning if first; last puts NaNs at the end.

Returns
TypeDescription
DataFrameDataFrame with sorted values.

stack

stack(level: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = -1)

Stack the prescribed level(s) from columns to index.

Return a reshaped DataFrame or Series having a multi-level index with one or more new inner-most levels compared to the current DataFrame. The new inner-most levels are created by pivoting the columns of the current dataframe:

  • if the columns have a single level, the output is a Series;
  • if the columns have multiple levels, the new index level(s) is (are) taken from the prescribed level(s) and the output is a DataFrame.

Examples:

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

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

>>> df.stack()
foo  A    1
     B    2
bar  A    3
     B    4
dtype: Int64
Parameter
NameDescription
level int, str, or list of these, default -1 (last level)

Level(s) to stack from the column axis onto the index axis.

Returns
TypeDescription
DataFrame or SeriesStacked dataframe or series.

std

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

Return sample standard deviation over columns.

Normalized by N-1 by default.

Examples:

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

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

Calculating the standard deviation of each column:

>>> df.std()
A    1.581139
B    1.140175
C    0.447214
dtype: Float64
Parameter
NameDescription
numeric_only bool. default False

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

Returns
TypeDescription
bigframes.series.SeriesSeries with sample standard deviation.

sub

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

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

Equivalent to dataframe - other. With reverse version, rsub.

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

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator -:

>>> df['A'] - (df['B'])
0    -3
1    -3
2    -3
dtype: Int64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

subtract

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

API documentation for subtract method.

sum

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

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

Examples:

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

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

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

>>> df.sum()
A    4.0
B    6.0
dtype: Float64

Calculating the sum of each row.

>>> df.sum(axis=1)
0    3.0
1    7.0
dtype: Float64
Parameters
NameDescription
axis {index (0), columns (1)}

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

numeric_only bool. default False

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

Returns
TypeDescription
bigframes.series.SeriesSeries with the sum of values.

swaplevel

swaplevel(i: int = -2, j: int = -1, axis: int | str = 0)

Swap levels i and j in a MultiIndex.

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

Parameters
NameDescription
i int or str

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

j int or str

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

axis {0 or 'index', 1 or 'columns'}, default 0

The axis to swap levels on. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.

Returns
TypeDescription
DataFrameDataFrame with levels swapped in MultiIndex.

tail

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

Return the last n rows.

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

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

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

Parameter
NameDescription
n int, default 5

Number of rows to select.

to_csv

to_csv(
    path_or_buf: str, sep=",", *, header: bool = True, index: bool = True
) -> None

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

Parameters
NameDescription
path_or_buf str

A destination URI of Cloud Storage files(s) to store the extracted dataframe in format of gs://<bucket_name>/<object_name_or_glob>. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies. None, file-like objects or local file paths not yet supported.

index bool, default True

If True, write row names (index).

Returns
TypeDescription
NoneString output not yet supported.

to_dict

to_dict(orient: typing.Literal['dict', 'list', 'series', 'split', 'tight', 'records', 'index'] = 'dict', into: type[dict] = <class 'dict'>, **kwargs) -> dict | list[dict]

Convert the DataFrame to a dictionary.

The type of the key-value pairs can be customized with the parameters (see below).

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_dict()
{'col1': {0: 1, 1: 2}, 'col2': {0: 3, 1: 4}}

You can specify the return orientation.

>>> df.to_dict('series')
{'col1': 0    1
1    2
Name: col1, dtype: Int64,
'col2': 0    3
1    4
Name: col2, dtype: Int64}

>>> df.to_dict('split')
{'index': [0, 1], 'columns': ['col1', 'col2'], 'data': [[1, 3], [2, 4]]}

>>> df.to_dict("tight")
{'index': [0, 1],
'columns': ['col1', 'col2'],
'data': [[1, 3], [2, 4]],
'index_names': [None],
'column_names': [None]}
Parameters
NameDescription
orient str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'}

Determines the type of the values of the dictionary. 'dict' (default) : dict like {column -> {index -> value}}. 'list' : dict like {column -> [values]}. 'series' : dict like {column -> Series(values)}. split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}. 'tight' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values], 'index_names' -> [index.names], 'column_names' -> [column.names]}. 'records' : list like [{column -> value}, ... , {column -> value}]. 'index' : dict like {index -> {column -> value}}.

into class, default dict

The collections.abc.Mapping subclass used for all Mappings in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.

index bool, default True

Whether to include the index item (and index_names item if orient is 'tight') in the returned dictionary. Can only be False when orient is 'split' or 'tight'.

Returns
TypeDescription
dict or list of dictReturn a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the orient parameter.

to_excel

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

Write DataFrame to an Excel sheet.

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

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

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_excel(tempfile.TemporaryFile())
Parameters
NameDescription
excel_writer path-like, file-like, or ExcelWriter object

File path or existing ExcelWriter.

sheet_name str, default 'Sheet1'

Name of sheet which will contain DataFrame.

to_gbq

to_gbq(
    destination_table: typing.Optional[str] = None,
    *,
    if_exists: typing.Optional[typing.Literal["fail", "replace", "append"]] = None,
    index: bool = True,
    ordering_id: typing.Optional[str] = None,
    clustering_columns: typing.Union[
        pandas.core.indexes.base.Index, typing.Iterable[typing.Hashable]
    ] = ()
) -> str

Write a DataFrame to a BigQuery table.

Examples:

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

Write a DataFrame to a BigQuery table.

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> # destination_table = PROJECT_ID + "." + DATASET_ID + "." + TABLE_NAME
>>> df.to_gbq("bigframes-dev.birds.test-numbers", if_exists="replace")
'bigframes-dev.birds.test-numbers'

Write a DataFrame to a temporary BigQuery table in the anonymous dataset.

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> destination = df.to_gbq(ordering_id="ordering_id")
>>> # The table created can be read outside of the current session.
>>> bpd.close_session()  # For demonstration, only.
>>> bpd.read_gbq(destination, index_col="ordering_id")
             col1  col2
ordering_id
0               1     3
1               2     4
<BLANKLINE>
[2 rows x 2 columns]

Write a DataFrame to a BigQuery table with clustering columns:

df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4], 'col3': [5, 6]}) clustering_cols = ['col1', 'col3'] df.to_gbq( ... "bigframes-dev.birds.test-clusters", ... if_exists="replace", ... clustering_columns=clustering_cols, ... ) 'bigframes-dev.birds.test-clusters'

Parameters
NameDescription
destination_table Optional[str]

Name of table to be written, in the form dataset.tablename or project.dataset.tablename. If no destination_table is set, a new temporary table is created in the BigQuery anonymous dataset.

if_exists Optional[str]

Behavior when the destination table exists. When destination_table is set, this defaults to 'fail'. When destination_table is not set, this field is not applicable. A new table is always created. Value can be one of: 'fail' If table exists raise pandas_gbq.gbq.TableCreationError. 'replace' If table exists, drop it, recreate it, and insert data. 'append' If table exists, insert data. Create if does not exist.

index bool. default True

whether write row names (index) or not.

ordering_id Optional[str], default None

If set, write the ordering of the DataFrame as a column in the result table with this name.

clustering_columns Union[pd.Index, Iterable[Hashable]], default ()

Specifies the columns for clustering in the BigQuery table. The order of columns in this list is significant for clustering hierarchy. Index columns may be included in clustering if the index parameter is set to True, and their names are specified in this. These index columns, if included, precede DataFrame columns in the clustering order. The clustering order within the Index/DataFrame columns follows the order specified in clustering_columns.

Returns
TypeDescription
strThe fully-qualified ID for the written table, in the form project.dataset.tablename.

to_html

to_html(
    buf=None,
    columns: typing.Optional[typing.Sequence[str]] = None,
    col_space=None,
    header: bool = True,
    index: bool = True,
    na_rep: str = "NaN",
    formatters=None,
    float_format=None,
    sparsify: bool | None = None,
    index_names: bool = True,
    justify: str | None = None,
    max_rows: int | None = None,
    max_cols: int | None = None,
    show_dimensions: bool = False,
    decimal: str = ".",
    bold_rows: bool = True,
    classes: str | list | tuple | None = None,
    escape: bool = True,
    notebook: bool = False,
    border: int | None = None,
    table_id: str | None = None,
    render_links: bool = False,
    encoding: str | None = None,
) -> str

Render a DataFrame as an HTML table.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> print(df.to_html())
<table border="1" class="dataframe">
<thead>
    <tr style="text-align: right;">
    <th></th>
    <th>col1</th>
    <th>col2</th>
    </tr>
</thead>
<tbody>
    <tr>
    <th>0</th>
    <td>1</td>
    <td>3</td>
    </tr>
    <tr>
    <th>1</th>
    <td>2</td>
    <td>4</td>
    </tr>
</tbody>
</table>
Parameters
NameDescription
buf str, Path or StringIO-like, optional, default None

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

columns sequence, optional, default None

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

col_space str or int, list or dict of int or str, optional

The minimum width of each column in CSS length units. An int is assumed to be px units.

header bool, optional

Whether to print column labels, default True.

index bool, optional, default True

Whether to print index (row) labels.

na_rep str, optional, default 'NaN'

String representation of NAN to use.

formatters list, tuple or dict of one-param. functions, optional

Formatter functions to apply to columns' elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.

float_format one-parameter function, optional, default None

Formatter function to apply to columns' elements if they are floats. This function must return a unicode string and will be applied only to the non-NaN elements, with NaN being handled by na_rep.

sparsify bool, optional, default True

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

index_names bool, optional, default True

Prints the names of the indexes.

justify str, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), 'right' out of the box. Valid values are, 'left', 'right', 'center', 'justify', 'justify-all', 'start', 'end', 'inherit', 'match-parent', 'initial', 'unset'.

max_rows int, optional

Maximum number of rows to display in the console.

max_cols int, optional

Maximum number of columns to display in the console.

show_dimensions bool, default False

Display DataFrame dimensions (number of rows by number of columns).

decimal str, default '.'

Character recognized as decimal separator, e.g. ',' in Europe.

bold_rows bool, default True

Make the row labels bold in the output.

classes str or list or tuple, default None

CSS class(es) to apply to the resulting html table.

escape bool, default True

Convert the characters <, >, and & to HTML-safe sequences.

notebook bool, default False

Whether the generated HTML is for IPython Notebook.

border int

A border=border attribute is included in the opening

tag. Default pd.options.display.html.border.

table_id str, optional

A css id is included in the opening

tag if specified.

render_links bool, default False

Convert URLs to HTML links.

encoding str, default "utf-8"

Set character encoding.

Returns
TypeDescription
str or NoneIf buf is None, returns the result as a string. Otherwise returns None.

to_json

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

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

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

Parameters
NameDescription
path_or_buf str

A destination URI of Cloud Storage files(s) to store the extracted dataframe in format of gs://<bucket_name>/<object_name_or_glob>. Must contain a wildcard * character. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies. None, file-like objects or local file paths not yet supported.

orient {split, records, index, columns, values, table}, default 'columns

Indication of expected JSON string format. * Series: - default is 'index' - allowed values are: {{'split', 'records', 'index', 'table'}}. * DataFrame: - default is 'columns' - allowed values are: {{'split', 'records', 'index', 'columns', 'values', 'table'}}. * The format of the JSON string: - 'split' : dict like {{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}} - 'records' : list like [{{column -> value}}, ... , {{column -> value}}] - 'index' : dict like {{index -> {{column -> value}}}} - 'columns' : dict like {{column -> {{index -> value}}}} - 'values' : just the values array - 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}} Describing the data, where data component is like orient='records'.

index bool, default True

If True, write row names (index).

lines bool, default False

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

Returns
TypeDescription
NoneString output not yet supported.

to_latex

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

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

Requires \usepackage{{booktabs}}. The output can be copy/pasted into a main LaTeX document or read from an external file with \input{{table.tex}}.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> print(df.to_latex())
\begin{tabular}{lrr}
\toprule
& col1 & col2 \\
\midrule
0 & 1 & 3 \\
1 & 2 & 4 \\
\bottomrule
\end{tabular}
<BLANKLINE>
Parameters
NameDescription
buf str, Path or StringIO-like, optional, default None

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

columns list of label, optional

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

header bool or list of str, default True

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

index bool, default True

Write row names (index).

to_markdown

to_markdown(buf=None, mode: str = "wt", index: bool = True, **kwargs) -> str | None

Print DataFrame in Markdown-friendly format.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> print(df.to_markdown())
|    |   col1 |   col2 |
|---:|-------:|-------:|
|  0 |      1 |      3 |
|  1 |      2 |      4 |
Parameters
NameDescription
buf str, Path or StringIO-like, optional, default None

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

mode str, optional

Mode in which file is opened.

index bool, optional, default True

Add index (row) labels.

Returns
TypeDescription
DataFrameDataFrame in Markdown-friendly format.

to_numpy

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

Convert the DataFrame to a NumPy array.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_numpy()
array([[1, 3],
       [2, 4]], dtype=object)
Parameters
NameDescription
dtype None

The dtype to pass to numpy.asarray().

copy bool, default None

Whether to ensure that the returned value is not a view on another array.

na_value Any, default None

The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

Returns
TypeDescription
numpy.ndarrayThe converted NumPy array.

to_orc

to_orc(path=None, **kwargs) -> bytes | None

Write a DataFrame to the ORC format.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> import tempfile
>>> df.to_orc(tempfile.TemporaryFile())
Parameter
NameDescription
path str, file-like object or None, default None

If a string, it will be used as Root Directory path when writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned.

to_pandas

to_pandas(
    max_download_size: typing.Optional[int] = None,
    sampling_method: typing.Optional[str] = None,
    random_state: typing.Optional[int] = None,
    *,
    ordered: bool = True
) -> pandas.core.frame.DataFrame

Write DataFrame to pandas DataFrame.

Parameters
NameDescription
max_download_size int, default None

Download size threshold in MB. If max_download_size is exceeded when downloading data (e.g., to_pandas()), the data will be downsampled if bigframes.options.sampling.enable_downsampling is True, otherwise, an error will be raised. If set to a value other than None, this will supersede the global config.

sampling_method str, default None

Downsampling algorithms to be chosen from, the choices are: "head": This algorithm returns a portion of the data from the beginning. It is fast and requires minimal computations to perform the downsampling; "uniform": This algorithm returns uniform random samples of the data. If set to a value other than None, this will supersede the global config.

random_state int, default None

The seed for the uniform downsampling algorithm. If provided, the uniform method may take longer to execute and require more computation. If set to a value other than None, this will supersede the global config.

ordered bool, default True

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

Returns
TypeDescription
pandas.DataFrameA pandas DataFrame with all rows and columns of this DataFrame if the data_sampling_threshold_mb is not exceeded; otherwise, a pandas DataFrame with downsampled rows and all columns of this DataFrame.

to_pandas_batches

to_pandas_batches() -> typing.Iterable[pandas.core.frame.DataFrame]

Stream DataFrame results to an iterable of pandas DataFrame

to_parquet

to_parquet(
    path: str,
    *,
    compression: typing.Optional[typing.Literal["snappy", "gzip"]] = "snappy",
    index: bool = True
) -> None

Write a DataFrame to the binary Parquet format.

This function writes the dataframe as a parquet file <https://parquet.apache.org/>_ to Cloud Storage.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> gcs_bucket = "gs://bigframes-dev-testing/sample_parquet*.parquet"
>>> df.to_parquet(path=gcs_bucket)
Parameters
NameDescription
path str

Destination URI(s) of Cloud Storage files(s) to store the extracted dataframe in format of gs://<bucket_name>/<object_name_or_glob>. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies.

compression str, default 'snappy'

Name of the compression to use. Use None for no compression. Supported options: 'gzip', 'snappy'.

index bool, default True

If True, include the dataframe's index(es) in the file output. If False, they will not be written to the file.

to_pickle

to_pickle(path, **kwargs) -> None

Pickle (serialize) object to file.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> gcs_bucket = "gs://bigframes-dev-testing/sample_pickle_gcs.pkl"
>>> df.to_pickle(path=gcs_bucket)
Parameter
NameDescription
path str

File path where the pickled object will be stored.

to_records

to_records(
    index: bool = True, column_dtypes=None, index_dtypes=None
) -> numpy.recarray

Convert DataFrame to a NumPy record array.

Index will be included as the first field of the record array if requested.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> df.to_records()
rec.array([(0, 1, 3), (1, 2, 4)],
          dtype=[('index', 'O'), ('col1', 'O'), ('col2', 'O')])
Parameters
NameDescription
index bool, default True

Include index in resulting record array, stored in 'index' field or using the index label, if set.

column_dtypes str, type, dict, default None

If a string or type, the data type to store all columns. If a dictionary, a mapping of column names and indices (zero-indexed) to specific data types.

index_dtypes str, type, dict, default None

If a string or type, the data type to store all index levels. If a dictionary, a mapping of index level names and indices (zero-indexed) to specific data types. This mapping is applied only if index=True.

Returns
TypeDescription
np.recarrayNumPy ndarray with the DataFrame labels as fields and each row of the DataFrame as entries.

to_string

to_string(
    buf=None,
    columns: typing.Optional[typing.Sequence[str]] = None,
    col_space=None,
    header: typing.Union[bool, typing.Sequence[str]] = True,
    index: bool = True,
    na_rep: str = "NaN",
    formatters=None,
    float_format=None,
    sparsify: bool | None = None,
    index_names: bool = True,
    justify: str | None = None,
    max_rows: int | None = None,
    max_cols: int | None = None,
    show_dimensions: bool = False,
    decimal: str = ".",
    line_width: int | None = None,
    min_rows: int | None = None,
    max_colwidth: int | None = None,
    encoding: str | None = None,
) -> str | None

Render a DataFrame to a console-friendly tabular output.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> print(df.to_string())
   col1  col2
0     1     3
1     2     4
Parameters
NameDescription
buf str, Path or StringIO-like, optional, default None

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

columns sequence, optional, default None

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

col_space int, list or dict of int, optional

The minimum width of each column.

header bool or sequence, optional

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

index bool, optional, default True

Whether to print index (row) labels.

na_rep str, optional, default 'NaN'

String representation of NAN to use.

formatters list, tuple or dict of one-param. functions, optional

Formatter functions to apply to columns' elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.

float_format one-parameter function, optional, default None

Formatter function to apply to columns' elements if they are floats. The result of this function must be a unicode string.

sparsify bool, optional, default True

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

index_names bool, optional, default True

Prints the names of the indexes.

justify str, default None

How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), 'right' out of the box. Valid values are, 'left', 'right', 'center', 'justify', 'justify-all', 'start', 'end', 'inherit', 'match-parent', 'initial', 'unset'.

max_rows int, optional

Maximum number of rows to display in the console.

min_rows int, optional

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

max_cols int, optional

Maximum number of columns to display in the console.

show_dimensions bool, default False

Display DataFrame dimensions (number of rows by number of columns).

decimal str, default '.'

Character recognized as decimal separator, e.g. ',' in Europe.

line_width int, optional

Width to wrap a line in characters.

max_colwidth int, optional

Max width to truncate each column in characters. By default, no limit.

encoding str, default "utf-8"

Set character encoding.

Returns
TypeDescription
str or NoneIf buf is None, returns the result as a string. Otherwise returns None.

truediv

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

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

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

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

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator /:

>>> df['A'] / (df['B'])
0    0.25
1     0.4
2     0.5
dtype: Float64
Parameters
NameDescription
other float, int, or Series

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

axis {0 or 'index', 1 or 'columns'}

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

Returns
TypeDescription
DataFrameDataFrame result of the arithmetic operation.

unstack

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

Pivot a level of the (necessarily hierarchical) index labels.

Returns a DataFrame having a new level of column labels whose inner-most level consists of the pivoted index labels.

If the index is not a MultiIndex, the output will be a Series (the analogue of stack when the columns are not a MultiIndex).

Examples:

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

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

>>> df.unstack()
A   foo    1
    bar    3
B   foo    2
    bar    4
dtype: Int64
Parameter
NameDescription
level int, str, or list of these, default -1 (last level)

Level(s) of index to unstack, can pass level name.

Returns
TypeDescription
DataFrame or SeriesDataFrame or Series.

update

update(other, join: str = "left", overwrite=True, filter_func=None)

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Examples:

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

>>> df = bpd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = bpd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6
<BLANKLINE>
[3 rows x 2 columns]
Parameters
NameDescription
other DataFrame, or object coercible into a DataFrame

Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.

join {'left'}, default 'left'

Only left join is implemented, keeping the index and columns of the original object.

overwrite bool, default True

How to handle non-NA values for overlapping keys: True: overwrite original DataFrame's values with values from other. False: only update values that are NA in the original DataFrame.

filter_func callable(1d-array) -> bool 1d-array, optional

Can choose to replace values other than NA. Return True for values that should be updated.

Returns
TypeDescription
NoneThis method directly changes calling object.

value_counts

value_counts(
    subset: typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]] = None,
    normalize: bool = False,
    sort: bool = True,
    ascending: bool = False,
    dropna: bool = True,
)

Return a Series containing counts of unique rows in the DataFrame.

Examples:

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

>>> df = bpd.DataFrame({'num_legs': [2, 4, 4, 6, 7],
...                     'num_wings': [2, 0, 0, 0, bpd.NA]},
...                    index=['falcon', 'dog', 'cat', 'ant', 'octopus'],
...                    dtype='Int64')
>>> df
         num_legs  num_wings
falcon          2          2
dog             4          0
cat             4          0
ant             6          0
octopus         7       <NA>
<BLANKLINE>
[5 rows x 2 columns]

value_counts sorts the result by counts in a descending order by default:

>>> df.value_counts()
num_legs  num_wings
4         0          2
2         2          1
6         0          1
Name: count, dtype: Int64

You can normalize the counts to return relative frequencies by setting normalize=True:

>>> df.value_counts(normalize=True)
num_legs  num_wings
4         0             0.5
2         2            0.25
6         0            0.25
Name: proportion, dtype: Float64

You can get the rows in the ascending order of the counts by setting ascending=True:

>>> df.value_counts(ascending=True)
num_legs  num_wings
2         2          1
6         0          1
4         0          2
Name: count, dtype: Int64

You can include the counts of the rows with NA values by setting dropna=False:

>>> df.value_counts(dropna=False)
num_legs  num_wings
4         0            2
2         2            1
6         0            1
7         <NA>         1
Name: count, dtype: Int64
Parameters
NameDescription
subset label or list of labels, optional

Columns to use when counting unique combinations.

normalize bool, default False

Return proportions rather than frequencies.

sort bool, default True

Sort by frequencies.

ascending bool, default False

Sort in ascending order.

dropna bool, default True

Don’t include counts of rows that contain NA values.

Returns
TypeDescription
SeriesSeries containing counts of unique rows in the DataFrame

var

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

Return unbiased variance over requested axis.

Normalized by N-1 by default.

Examples:

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

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

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

>>> df.var()
A    2.0
B    2.0
dtype: Float64

Calculating the variance of each row.

>>> df.var(axis=1)
0    0.5
1    0.5
dtype: Float64
Parameters
NameDescription
axis {index (0), columns (1)}

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

numeric_only bool. default False

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

Returns
TypeDescription
bigframes.series.SeriesSeries with unbiased variance over requested axis.