Class DataFrame (0.13.0)

Version 0.13.0

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

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

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

Properties

at

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

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.

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.

Note: If Series/DataFrame contains only NA values, it is still not considered empty.

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

iat

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

iloc

Purely integer-location based indexing for selection by position.

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.

loc

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

.loc[] is primarily label based, but may also be used with a boolean array.

Allowed inputs are:

• A single label, e.g. 5 or 'a', (note that 5 is interpreted as a label of the index, and never as an integer position along the index).
• A list of labels, e.g. ['a', 'b', 'c'].
• A boolean series of the same length as the axis being sliced, e.g. [True, False, True].
• An alignable Index. The index of the returned selection will be the input.
• Not supported yet An alignable boolean Series. The index of the key will be aligned before masking.
• Not supported yet A slice object with labels, e.g. 'a':'f'. Note: contrary to usual python slices, both the start and the stop are included.
• Not supported yet A callable function with one argument (the calling Series or DataFrame) that returns valid output for indexing (one of the above).

Exceptions
Type	Description
NotImplementError	if the inputs are not supported.

ndim

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

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

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.

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

sql

Compiles this DataFrame's expression tree to SQL.

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator +:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

add_prefix

add_prefix(
    prefix: str, axis: typing.Optional[typing.Union[int, str]] = None
) -> bigframes.dataframe.DataFrame

Prefix labels with string prefix.

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

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

add_suffix

add_suffix(
    suffix: str, axis: typing.Optional[typing.Union[int, str]] = 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 the specified axis.

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

Returns
Type	Description
DataFrame or bigframes.series.Series	Aggregated results.

aggregate

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

Aggregate using one or more operations over the specified axis.

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

Returns
Type	Description
DataFrame or bigframes.series.Series	Aggregated results.

align

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

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

Join method is specified for each axis Index.

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

Returns
Type	Description
tuple 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).

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

Returns
Type	Description
bigframes.series.Series	Series if all elements are True.

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).

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

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.

Parameters
Name	Description
func	function Function to apply to each column or row.
args	tuple Positional arguments to pass to func in addition to the array/series.

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

applymap

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

Apply a function to a Dataframe elementwise.

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

Note: In pandas 2.1.0, DataFrame.applymap is deprecated and renamed to DataFrame.map.

Parameter
Name	Description
na_action	Optional[str], default None {None, 'ignore'}, default None. If ‘ignore’, propagate NaN values, without passing them to func.

Returns
Type	Description
bigframes.dataframe.DataFrame	Transformed 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.

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

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

astype

astype(
    dtype: typing.Union[
        typing.Literal[
            "boolean",
            "Float64",
            "Int64",
            "string",
            "string[pyarrow]",
            "timestamp[us, tz=UTC][pyarrow]",
            "timestamp[us][pyarrow]",
            "date32[day][pyarrow]",
            "time64[us][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,
    ]
) -> bigframes.dataframe.DataFrame

Cast a pandas object to a specified dtype dtype.

Parameter
Name	Description
dtype	str or pandas.ExtensionDtype A dtype supported by BigQuery DataFrame include 'boolean','Float64','Int64', 'string', 'tring[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
Type	Description
Series/DataFrame or None	Object 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
Name	Description
other	DataFrame The DataFrame to merge column-wise.
func	function Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.
fill_value	scalar value, default None The value to fill NaNs with prior to passing any column to the merge func.
overwrite	bool, default True If True, columns in self that do not exist in other will be overwritten with NaNs.

Returns
Type	Description
DataFrame	Combination of the provided DataFrames.

combine_first

combine_first(other: bigframes.dataframe.DataFrame)

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

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

Examples:

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

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

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

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

copy

copy() -> bigframes.dataframe.DataFrame

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

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

count

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

Count non-NA cells for each column or row.

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

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

Returns
Type	Description
bigframes.series.Series	For 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 a DataFrame axis.

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

Returns
Type	Description
bigframes.dataframe.DataFrame	Return cumulative maximum of DataFrame.

cummin

cummin() -> bigframes.dataframe.DataFrame

Return cumulative minimum over a DataFrame axis.

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

Returns
Type	Description
bigframes.dataframe.DataFrame	Return cumulative minimum of DataFrame.

cumprod

cumprod() -> bigframes.dataframe.DataFrame

Return cumulative product over a DataFrame axis.

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

Returns
Type	Description
bigframes.dataframe.DataFrame	Return cumulative product of DataFrame.

cumsum

cumsum()

Return cumulative sum over a DataFrame axis.

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

Returns
Type	Description
bigframes.dataframe.DataFrame	Return 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.

Note: Percentile values are approximates only.

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

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

diff

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

First discrete difference of element.

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

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

Returns
Type	Description
bigframes.dataframe.DataFrame	First differences of the Series.

div

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator /:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

divide

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator /:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

dot

dot(other: _DataFrameOrSeries) -> _DataFrameOrSeries

Compute the matrix multiplication between the DataFrame and other.

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

It can also be called using self @ other.

Note: The dimensions of DataFrame and other must be compatible in order to compute the matrix multiplication. In addition, the column names of DataFrame and the index of other must contain the same values, as they will be aligned prior to the multiplication.

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

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

drop

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

Drop specified labels from columns.

Remove columns by directly specifying column names.

Exceptions
Type	Description
KeyError	If any of the labels is not found in the selected axis.

Returns
Type	Description
bigframes.dataframe.DataFrame	DataFrame without the removed column labels.

drop_duplicates

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

Return DataFrame with duplicate rows removed.

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

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

Returns
Type	Description
bigframes.dataframe.DataFrame	DataFrame with duplicates removed

droplevel

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

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

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

Returns
Type	Description
DataFrame	DataFrame 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.

Parameters
Name	Description
axis	{0 or 'index', 1 or 'columns'}, default 'columns' Determine if rows or columns which contain missing values are removed. * 0, or 'index' : Drop rows which contain missing values. * 1, or 'columns' : Drop columns which contain missing value.
how	{'any', 'all'}, default 'any' Determine if row or column is removed from DataFrame, when we have at least one NA or all NA. * 'any' : If any NA values are present, drop that row or column. * 'all' : If all values are NA, drop that row or column.
ignore_index	bool, default False If True, the resulting axis will be labeled 0, 1, …, n - 1.

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

duplicated

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

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

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

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

eq

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

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

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator ==:

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

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

equals

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

Test whether two objects contain the same elements.

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

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

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

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

expanding

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

Provide expanding window calculations.

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

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

ffill

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

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

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

fillna

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

Fill NA/NaN values using the specified method.

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

Returns
Type	Description
DataFrame	Object with missing values filled

filter

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

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator //:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

ge

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

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

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

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator >=:

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

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

Returns
Type	Description
DataFrame	DataFrame 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.

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[str, int, typing.Sequence[typing.Union[str, int]]]
    ] = 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.

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

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

gt

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

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

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

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

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

Examples:

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

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

You can also use arithmetic operator >:

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

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

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

head

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

Return the first n rows.

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

Not yet supported 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.

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

idxmax

idxmax() -> bigframes.series.Series

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

Returns
Type	Description
Series	Indexes of maxima along the specified axis.

idxmin

idxmin() -> bigframes.series.Series

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

Returns
Type	Description
Series	Indexes of minima along the specified axis.

interpolate

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

Fill NaN values using an interpolation method.

Parameter
Name	Description
method	str, default 'linear' Interpolation technique to use. Only 'linear' supported. 'linear': Ignore the index and treat the values as equally spaced. This is the only method supported on MultiIndexes.

Returns
Type	Description
DataFrame **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], ... }) >>> df.interpolate() A B 0 1.0	Returns the same object type as the caller, interpolated at some or all NaN values

isin

isin(values) -> bigframes.dataframe.DataFrame

Whether each element in the DataFrame is contained in values.

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

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

isna

isna() -> bigframes.dataframe.DataFrame

Detect missing values.

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

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.

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.

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

iterrows

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

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

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

Returns
Type	Description
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

Parameter
Name	Description
how	{'left', 'right', 'outer', 'inner'}, default 'left'` How to handle the operation of the two objects. left: use calling frame's index (or column if on is specified) right: use other's index. outer: form union of calling frame's index (or column if on is specified) with other's index, and sort it lexicographically. inner: form intersection of calling frame's index (or column if on is specified) with other's index, preserving the order of the calling's one.

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

keys

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

Get the 'info axis'.

This is index for Series, columns for DataFrame.

Returns
Type	Description
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 requested axis.

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

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

kurtosis

kurtosis(*, numeric_only: bool = False)

Return unbiased kurtosis over requested axis.

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

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

le

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

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

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

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator <=:

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

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

Returns
Type	Description
DataFrame	DataFrame 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.

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator <:

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

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

Returns
Type	Description
DataFrame	DataFrame of bool. The result of the comparison.

map

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

Apply a function to a Dataframe elementwise.

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

Note: In pandas 2.1.0, DataFrame.applymap is deprecated and renamed to DataFrame.map.

Parameter
Name	Description
na_action	Optional[str], default None {None, 'ignore'}, default None. If ‘ignore’, propagate NaN values, without passing them to func.

Returns
Type	Description
bigframes.dataframe.DataFrame	Transformed 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.

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

Returns
Type	Description
bigframes.series.Series	Series after the maximum of values.

mean

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

Return the mean of the values over the requested axis.

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

Returns
Type	Description
bigframes.series.Series	Series with the mean of values.

median

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

Return the median of the values over the requested axis.

Parameters
Name	Description
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
Type	Description
bigframes.series.Series	Series with the median of values.

melt

melt(
    id_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None,
    value_vars: typing.Optional[typing.Iterable[typing.Hashable]] = None,
    var_name: typing.Optional[
        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'.

merge

merge(
    right: bigframes.dataframe.DataFrame,
    how: typing.Literal["inner", "left", "outer", "right"] = "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.

Warning: If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.

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

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

min

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

Return the minimum of the values over the requested axis.

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

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

Returns
Type	Description
bigframes.series.Series	Series with the minimum of the values.

mod

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator %:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

mul

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator *:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

multiply

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator *:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

ne

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

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

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator !=:

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

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

Returns
Type	Description
DataFrame	Result of the comparison.

nlargest

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

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

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

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

Parameters
Name	Description
n	int Number of rows to return.
columns	label or list of labels Column label(s) to order by.
keep	{'first', 'last', 'all'}, default 'first' Where there are duplicate values: - first : prioritize the first occurrence(s) - last : prioritize the last occurrence(s) - all : do not drop any duplicates, even it means selecting more than n items.

Returns
Type	Description
DataFrame .. note:: This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.	The first n rows ordered by the given columns in descending order.

notna

notna() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

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

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

notnull

notnull() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

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

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

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.

Parameters
Name	Description
n	int Number of rows to return.
columns	label or list of labels Column label(s) to order by.
keep	{'first', 'last', 'all'}, default 'first' Where there are duplicate values: - first : prioritize the first occurrence(s) - last : prioritize the last occurrence(s) - all : do not drop any duplicates, even it means selecting more than n items.

Returns
Type	Description
DataFrame .. note:: This function cannot be used with all column types. For example, when specifying columns with object or category dtypes, TypeError is raised.	The first n rows ordered by the given columns in ascending order.

nunique

nunique() -> bigframes.series.Series

Count number of distinct elements in specified axis.

Returns
Type	Description
bigframes.series.Series	Series with number of distinct elements.

pct_change

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

Fractional change between the current and a prior element.

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

Note: Despite the name of this method, it calculates fractional change (also known as per unit change or relative change) and not percentage change. If you need the percentage change, multiply these values by 100.

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

Returns
Type	Description
Series or DataFrame	The same type as the calling 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.

Note: BigQuery supports up to 10000 columns. Pivot operations on columns with too many unique values will fail if they would exceed this limit.

Note: The validity of the pivot operation is not checked. If columns and index do not together uniquely identify input rows, the output will be silently non-deterministic.

Parameters
Name	Description
columns	str or object or a list of str Column to use to make new frame's columns.
index	str or object or a list of str, optional Column to use to make new frame's index. If not given, uses existing index.
values	str, object or a list of the previous, optional Column(s) to use for populating new frame's values. If not specified, all remaining columns will be used and the result will have hierarchically indexed columns.

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator **:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

prod

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

Return the product of the values over the requested axis.

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

Returns
Type	Description
bigframes.series.Series	Series with the product of the values.

product

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

Return the product of the values over the requested axis.

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

Returns
Type	Description
bigframes.series.Series	Series with the product of the values.

radd

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator +:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

rank

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

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

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

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

Returns
Type	Description
same type as caller	Return a Series or DataFrame with data ranks as values.

rdiv

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

It's equivalent to using arithmetic operator: /:

>>> df['B'] / (df['A'])
0    4.0
1    2.5
2    2.0
dtype: Float64

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

reindex

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

Conform DataFrame to new index with optional filling logic.

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

Parameters
Name	Description
labels	array-like, optional New labels / index to conform the axis specified by 'axis' to.
index	array-like, optional New labels for the index. Preferably an Index object to avoid duplicating data.
columns	array-like, optional New labels for the columns. Preferably an Index object to avoid duplicating data.
axis	int or str, optional Axis to target. Can be either the axis name ('index', 'columns') or number (0, 1).

Returns
Type	Description
DataFrame	DataFrame with changed index.

reindex_like

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

Return an object with matching indices as other object.

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

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

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

rename

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

Rename columns.

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

Parameter
Name	Description
columns	Mapping Dict-like from old column labels to new column labels.

Exceptions
Type	Description
KeyError	If any of the labels is not found.

Returns
Type	Description
bigframes.dataframe.DataFrame	DataFrame with the renamed axis labels.

rename_axis

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

Set the name of the axis for the index.

Note: Currently only accepts a single string parameter (the new name of the index).

Returns
Type	Description
bigframes.dataframe.DataFrame	DataFrame with the new index name

reorder_levels

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

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

Parameters
Name	Description
order	list of int 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
Type	Description
DataFrame	DataFrame of rearranged index.

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.

Parameter
Name	Description
drop	bool, default False Do not try to insert index into dataframe columns. This resets the index to the default integer index.

Returns
Type	Description
bigframes.dataframe.DataFrame	DataFrame with the new index.

rfloordiv

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

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

>>> df['B'] // (df['A'])
0    4
1    2
2    2
dtype: Int64

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

rmod

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

It's equivalent to using arithmetic operator: %:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

rmul

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator *:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

rolling

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

Provide rolling window calculations.

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

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

rpow

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

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

>>> df['B'] ** (df['A'])
0      4
1     25
2    216
dtype: Int64

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

rsub

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

It's equivalent to using arithmetic operator: -:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

rtruediv

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

It's equivalent to using arithmetic operator: /:

>>> df['B'] / (df['A'])
0    4.0
1    2.5
2    2.0
dtype: Float64

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

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.

Parameters
Name	Description
n	Optional[int], default None Number of items from axis to return. Cannot be used with frac. Default = 1 if frac = None.
frac	Optional[float], default None Fraction of axis items to return. Cannot be used with n.
random_state	Optional[int], default None Seed for random number generator.

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.

Returns
Type	Description
DataFrame	Changed row labels.

shift

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

Shift index by desired number of periods.

Shifts the index without realigning the data.

Returns
Type	Description
NDFrame	Copy of input object, shifted.

skew

skew(*, numeric_only: bool = False)

Return unbiased skew over requested axis.

Normalized by N-1.

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

sort_index

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

Sort object by labels (along an axis).

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.

Parameters
Name	Description
by	str or Sequence[str] Name or list of names to sort by.
ascending	bool or Sequence[bool], default True Sort ascending vs. descending. Specify list for multiple sort orders. If this is a list of bools, must match the length of the by.
kind	str, default 'quicksort' Choice of sorting algorithm. Accepts 'quicksort', 'mergesort', 'heapsort', 'stable'. Ignored except when determining whether to sort stably. 'mergesort' or 'stable' will result in stable reorder.
na_position	{'first', 'last'}, default last {'first', 'last'}, default 'last' Puts NaNs at the beginning if first; last puts NaNs at the end.

stack

stack(level: typing.Union[str, int, typing.Sequence[typing.Union[str, int]]] = -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.

Note: BigQuery DataFrames does not support stack operations that would combine columns of different dtypes.

Returns
Type	Description
DataFrame or Series	Stacked dataframe or series.

std

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

Return sample standard deviation over requested axis.

Normalized by N-1 by default.

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

Returns
Type	Description
bigframes.series.Series	Series with sample standard deviation.

sub

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator -:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

subtract

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

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

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

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

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator -:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

sum

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

Return the sum of the values over the requested axis.

This is equivalent to the method numpy.sum.

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

Returns
Type	Description
bigframes.series.Series	Series with the sum of values.

swaplevel

swaplevel(i: int = -2, j: int = -1, axis: int | str = 0)

Swap levels i and j in a MultiIndex.

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

Parameters
Name	Description
i	int or str Levels of the indices to be swapped. Can pass level name as string.
j	int or str Levels of the indices to be swapped. Can pass level name as string.
axis	{0 or 'index', 1 or 'columns'}, default 0 The axis to swap levels on. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.

Returns
Type	Description
DataFrame	DataFrame with levels swapped in MultiIndex.

tail

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

Return the last n rows.

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

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

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

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

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
Name	Description
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
Type	Description
None	String 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
Name	Description
orient	str {'dict', 'list', 'series', 'split', 'tight', 'records', 'index'} Determines the type of the values of the dictionary. 'dict' (default) : dict like {column -> {index -> value}}. 'list' : dict like {column -> [values]}. 'series' : dict like {column -> Series(values)}. split' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values]}. 'tight' : dict like {'index' -> [index], 'columns' -> [columns], 'data' -> [values], 'index_names' -> [index.names], 'column_names' -> [column.names]}. 'records' : list like [{column -> value}, ... , {column -> value}]. 'index' : dict like {index -> {column -> value}}.
into	class, default dict The collections.abc.Mapping subclass used for all Mappings in the return value. Can be the actual class or an empty instance of the mapping type you want. If you want a collections.defaultdict, you must pass it initialized.
index	bool, default True Whether to include the index item (and index_names item if orient is 'tight') in the returned dictionary. Can only be False when orient is 'split' or 'tight'.

Returns
Type	Description
dict or list of dict	Return a collections.abc.Mapping object representing the DataFrame. The resulting transformation depends on the orient parameter.

to_excel

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

Write DataFrame to an Excel sheet.

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

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

Examples:

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

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

Parameters
Name	Description
excel_writer	path-like, file-like, or ExcelWriter object File path or existing ExcelWriter.
sheet_name	str, default 'Sheet1' Name of sheet which will contain DataFrame.

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
) -> 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]

Parameters
Name	Description
destination_table	Optional[str] Name of table to be written, in the form dataset.tablename or project.dataset.tablename. If no destination_table is set, a new temporary table is created in the BigQuery anonymous dataset.
if_exists	Optional[str] Behavior when the destination table exists. When destination_table is set, this defaults to 'fail'. When destination_table is not set, this field is not applicable. A new table is always created. Value can be one of: 'fail' If table exists raise pandas_gbq.gbq.TableCreationError. 'replace' If table exists, drop it, recreate it, and insert data. 'append' If table exists, insert data. Create if does not exist.
index	bool. default True whether write row names (index) or not.
ordering_id	Optional[str], default None If set, write the ordering of the DataFrame as a column in the result table with this name.

Returns
Type	Description
str	The fully-qualified ID for the written table, in the form project.dataset.tablename.

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.

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

Parameters
Name	Description
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
Type	Description
None	String 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
Name	Description
buf	str, Path or StringIO-like, optional, default None Buffer to write to. If None, the output is returned as a string.
columns	list of label, optional The subset of columns to write. Writes all columns by default.
header	bool or list of str, default True Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
index	bool, default True Write row names (index).

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

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
Name	Description
dtype	None The dtype to pass to numpy.asarray().
copy	bool, default None Whether to ensure that the returned value is not a view on another array.
na_value	Any, default None The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

Returns
Type	Description
numpy.ndarray	The 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
Name	Description
path	str, file-like object or None, default None If a string, it will be used as Root Directory path when writing a partitioned dataset. By file-like object, we refer to objects with a write() method, such as a file handle (e.g. via builtin open function). If path is None, a bytes object is returned.

to_pandas

to_pandas(
    max_download_size: typing.Optional[int] = None,
    sampling_method: typing.Optional[str] = None,
    random_state: typing.Optional[int] = None,
) -> pandas.core.frame.DataFrame

Write DataFrame to pandas DataFrame.

Parameters
Name	Description
max_download_size	int, default None Download size threshold in MB. If max_download_size is exceeded when downloading data (e.g., to_pandas()), the data will be downsampled if bigframes.options.sampling.enable_downsampling is True, otherwise, an error will be raised. If set to a value other than None, this will supersede the global config.
sampling_method	str, default None Downsampling algorithms to be chosen from, the choices are: "head": This algorithm returns a portion of the data from the beginning. It is fast and requires minimal computations to perform the downsampling; "uniform": This algorithm returns uniform random samples of the data. If set to a value other than None, this will supersede the global config.
random_state	int, default None The seed for the uniform downsampling algorithm. If provided, the uniform method may take longer to execute and require more computation. If set to a value other than None, this will supersede the global config.

Returns
Type	Description
pandas.DataFrame	A pandas DataFrame with all rows and columns of this DataFrame if the data_sampling_threshold_mb is not exceeded; otherwise, a pandas DataFrame with downsampled rows and all columns of this DataFrame.

to_pandas_batches

to_pandas_batches() -> 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
Name	Description
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
Name	Description
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
Name	Description
index	bool, default True Include index in resulting record array, stored in 'index' field or using the index label, if set.
column_dtypes	str, type, dict, default None If a string or type, the data type to store all columns. If a dictionary, a mapping of column names and indices (zero-indexed) to specific data types.
index_dtypes	str, type, dict, default None If a string or type, the data type to store all index levels. If a dictionary, a mapping of index level names and indices (zero-indexed) to specific data types. This mapping is applied only if index=True.

Returns
Type	Description
np.recarray	NumPy ndarray with the DataFrame labels as fields and each row of the DataFrame as entries.

to_string

to_string(
    buf=None,
    columns: typing.Optional[typing.Sequence[str]] = None,
    col_space=None,
    header: typing.Union[bool, typing.Sequence[str]] = True,
    index: bool = True,
    na_rep: str = "NaN",
    formatters=None,
    float_format=None,
    sparsify: typing.Optional[bool] = None,
    index_names: bool = True,
    justify: typing.Optional[str] = None,
    max_rows: typing.Optional[int] = None,
    max_cols: typing.Optional[int] = None,
    show_dimensions: bool = False,
    decimal: str = ".",
    line_width: typing.Optional[int] = None,
    min_rows: typing.Optional[int] = None,
    max_colwidth: typing.Optional[int] = None,
    encoding: typing.Optional[str] = None,
) -> str | None

Render a DataFrame to a console-friendly tabular output.

Examples:

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

>>> df = bpd.DataFrame({'col1': [1, 2], 'col2': [3, 4]})
>>> print(df.to_string())
   col1  col2
0     1     3
1     2     4

Parameters
Name	Description
buf	str, Path or StringIO-like, optional, default None Buffer to write to. If None, the output is returned as a string.
columns	sequence, optional, default None The subset of columns to write. Writes all columns by default.
col_space	int, list or dict of int, optional The minimum width of each column.
header	bool or sequence, optional Write out the column names. If a list of strings is given, it is assumed to be aliases for the column names.
index	bool, optional, default True Whether to print index (row) labels.
na_rep	str, optional, default 'NaN' String representation of NAN to use.
formatters	list, tuple or dict of one-param. functions, optional Formatter functions to apply to columns' elements by position or name. The result of each function must be a unicode string. List/tuple must be of length equal to the number of columns.
float_format	one-parameter function, optional, default None Formatter function to apply to columns' elements if they are floats. The result of this function must be a unicode string.
sparsify	bool, optional, default True Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.
index_names	bool, optional, default True Prints the names of the indexes.
justify	str, default None How to justify the column labels. If None uses the option from the print configuration (controlled by set_option), 'right' out of the box. Valid values are, 'left', 'right', 'center', 'justify', 'justify-all', 'start', 'end', 'inherit', 'match-parent', 'initial', 'unset'.
max_rows	int, optional Maximum number of rows to display in the console.
min_rows	int, optional The number of rows to display in the console in a truncated repr (when number of rows is above max_rows).
max_cols	int, optional Maximum number of columns to display in the console.
show_dimensions	bool, default False Display DataFrame dimensions (number of rows by number of columns).
decimal	str, default '.' Character recognized as decimal separator, e.g. ',' in Europe.
line_width	int, optional Width to wrap a line in characters.
max_colwidth	int, optional Max width to truncate each column in characters. By default, no limit.
encoding	str, default "utf-8" Set character encoding.

Returns
Type	Description
str or None	If 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: +, -, *, /, //, %, **.

Note: Mismatched indices will be unioned together.

Examples:

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

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

You can use method name:

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

You can also use arithmetic operator /:

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

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

Returns
Type	Description
DataFrame	DataFrame result of the arithmetic operation.

unstack

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

update

update(other, join: str = "left", overwrite=True, filter_func=None)

Modify in place using non-NA values from another DataFrame.

Aligns on indices. There is no return value.

Examples:

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

>>> df = bpd.DataFrame({'A': [1, 2, 3],
...                    'B': [400, 500, 600]})
>>> new_df = bpd.DataFrame({'B': [4, 5, 6],
...                        'C': [7, 8, 9]})
>>> df.update(new_df)
>>> df
   A  B
0  1  4
1  2  5
2  3  6
<BLANKLINE>
[3 rows x 2 columns]

Parameters
Name	Description
other	DataFrame, or object coercible into a DataFrame Should have at least one matching index/column label with the original DataFrame. If a Series is passed, its name attribute must be set, and that will be used as the column name to align with the original DataFrame.
join	{'left'}, default 'left' Only left join is implemented, keeping the index and columns of the original object.
overwrite	bool, default True How to handle non-NA values for overlapping keys: True: overwrite original DataFrame's values with values from other. False: only update values that are NA in the original DataFrame.
filter_func	callable(1d-array) -> bool 1d-array, optional Can choose to replace values other than NA. Return True for values that should be updated.

Returns
Type	Description
None	This method directly changes calling object.

value_counts

value_counts(
    subset: typing.Optional[
        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.

Parameters
Name	Description
subset	label or list of labels, optional Columns to use when counting unique combinations.
normalize	bool, default False Return proportions rather than frequencies.
sort	bool, default True Sort by frequencies.
ascending	bool, default False Sort in ascending order.
dropna	bool, default True Don’t include counts of rows that contain NA values.

Returns
Type	Description
Series	Series containing counts of unique rows in the DataFrame

var

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

Return unbiased variance over requested axis.

Normalized by N-1 by default.

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

Returns
Type	Description
bigframes.series.Series	Series with unbiased variance over requested axis.