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Class DataFrame (0.12.0) | Python client library

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

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

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

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

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')]

The column labels of the DataFrame.

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.

Indicates whether Series/DataFrame is empty.

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

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

Purely integer-location based indexing for selection by position.

The index (row labels) of the DataFrame.

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

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).

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

BigQuery job metadata for the most recent query.

Return a tuple representing the dimensionality of the DataFrame.

Examples:

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

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

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

Compiles this DataFrame's expression tree to SQL.

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)

__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__(
    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.

Converts a DataFrame to a string. Calls to_pandas.

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

__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() -> bigframes.dataframe.DataFrame

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

This function only applies to elements that are all numeric.

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator +:

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

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

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

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

Prefix labels with string prefix.

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

The string to add before each label.

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

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

Suffix labels with string suffix.

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

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

Aggregate using one or more operations over the specified axis.

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

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

Aggregate using one or more operations over the specified axis.

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

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

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

Join method is specified for each axis Index.

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

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

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

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

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

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

Include only boolean columns.

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

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

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

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

Include only boolean columns.

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.

Function to apply to each column or row.

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

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.

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

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

Assign new columns to a DataFrame.

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

astype(
    dtype: typing.Union[
        typing.Literal[
            "boolean",
            "Float64",
            "Int64",
            "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.

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

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

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

Perform column-wise combine with another DataFrame.

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

Examples:

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

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

The DataFrame to merge column-wise.

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

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

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

combine_first(other: bigframes.dataframe.DataFrame)

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

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

Examples:

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

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

Provided DataFrame to use to fill null values.

copy() -> bigframes.dataframe.DataFrame

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

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

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.

Include only float, int or boolean data.

cummax() -> bigframes.dataframe.DataFrame

Return cumulative maximum over a DataFrame axis.

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

cummin() -> bigframes.dataframe.DataFrame

Return cumulative minimum over a DataFrame axis.

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

cumprod() -> bigframes.dataframe.DataFrame

Return cumulative product over a DataFrame axis.

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

Return cumulative sum over a DataFrame axis.

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

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.

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).

Periods to shift for calculating difference, accepts negative values.

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator /:

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

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

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

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator /:

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

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

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

dot(other: _DataFrameOrSeries) -> _DataFrameOrSeries

Compute the matrix multiplication between the DataFrame and other.

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

It can also be called using self @ other.

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

The other object to compute the matrix product with.

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[str, int]] = None
) -> bigframes.dataframe.DataFrame

Drop specified labels from columns.

Remove columns by directly specifying column names.

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.

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

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

droplevel(level: LevelsType, axis: int | str = 0)

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

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

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

dropna(
    *, axis: int | str = 0, inplace: bool = False, how: str = "any", ignore_index=False
) -> DataFrame

Remove missing values.

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

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

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

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

Return boolean Series denoting duplicate rows.

Considering certain columns is optional.

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

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

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

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

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

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

Test whether two objects contain the same elements.

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

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

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

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

Provide expanding window calculations.

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

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

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

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

Fill NA/NaN values using the specified method.

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

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

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

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

Keep labels from axis which are in items.

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

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

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

API documentation for first_valid_index method.

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator //:

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

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

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator >=:

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

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

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

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

Returns default value if not found.

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.

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

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

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

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

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

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

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

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

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

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

You can also use arithmetic operator >:

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

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

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

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

Return the first n rows.

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

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.

Default 5. Number of rows to select.

idxmax() -> bigframes.series.Series

Return index of first occurrence of maximum over requested axis.

NA/null values are excluded.

idxmin() -> bigframes.series.Series

Return index of first occurrence of minimum over requested axis.

NA/null values are excluded.

isin(values) -> bigframes.dataframe.DataFrame

Whether each element in the DataFrame is contained in values.

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

isna() -> bigframes.dataframe.DataFrame

Detect missing values.

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

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.

Iterate over (column name, Series) pairs.

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

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

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.

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.

Include only float, int, boolean columns.

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.

Include only float, int, boolean columns.

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator <=:

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

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

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator <:

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

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

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

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

Apply a function to a Dataframe elementwise.

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

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

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.

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

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

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

Return the mean of the values over the requested axis.

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

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

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

Return the median of the values over the requested axis.

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

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

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(
    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.

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

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

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

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

Return the minimum of the values over the requested axis.

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

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

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

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator %:

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

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

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator *:

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

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

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

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator *:

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

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

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

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

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

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

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

Examples:

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

You can use method name:

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

You can also use arithmetic operator !=:

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

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

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

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

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

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

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

Number of rows to return.

Column label(s) to order by.

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

notna() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

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

notnull() -> bigframes.dataframe.DataFrame

Detect existing (non-missing) values.

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

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

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

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

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

Number of rows to return.

Column label(s) to order by.

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

nunique() -> bigframes.series.Series

Count number of distinct elements in specified axis.

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

Fractional change between the current and a prior element.

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

Periods to shift for forming percent change.

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.

Column to use to make new frame's columns.

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

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

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator **:

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

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

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

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

Return the product of the values over the requested axis.

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

Include only float, int, boolean columns.

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

Return the product of the values over the requested axis.

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

Include only float, int, boolean columns.

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator +:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

It's equivalent to using arithmetic operator: /:

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

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

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

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

Conform DataFrame to new index with optional filling logic.

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

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

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

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

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

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

Return an object with matching indices as other object.

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

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

rename(
    *, columns: 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.

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

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

Set the name of the axis for the index.

reorder_levels(order: LevelsType, axis: int | str = 0)

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

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

Where to reorder levels.

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

Reset the index.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

It's equivalent to using arithmetic operator: %:

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

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

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

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator *:

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

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

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

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

Provide rolling window calculations.

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.

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.

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

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

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

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

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

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

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

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

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

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

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

>>> 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

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

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

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

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

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

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

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

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

It's equivalent to using arithmetic operator: /:

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

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

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

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.

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

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

Seed for random number generator.

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.

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

Shift index by desired number of periods.

Shifts the index without realigning the data.

skew(*, numeric_only: bool = False)

Return unbiased skew over requested axis.

Normalized by N-1.

Include only float, int, boolean columns.

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

Sort object by labels (along an axis).

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

Sort by the values along row axis.

Name or list of names to sort by.

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

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.

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

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.

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.

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

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

>>> 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

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

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

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

>>> 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

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

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

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.

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

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

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.

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

The axis to swap levels on. 0 or 'index' for row-wise, 1 or 'columns' for column-wise.

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.

Number of rows to select.

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.

A destination URI of Cloud Storage files(s) to store the extracted dataframe in format of gs://<bucket_name>/<object_name_or_glob>. If the data size is more than 1GB, you must use a wildcard to export the data into multiple files and the size of the files varies. None, file-like objects or local file paths not yet supported.

If True, write row names (index).

to_dict(orient: 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]}

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}}.

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.

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'.

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())

File path or existing ExcelWriter.

Name of sheet which will contain DataFrame.

to_gbq(
    destination_table: str,
    *,
    if_exists: typing.Optional[typing.Literal["fail", "replace", "append"]] = "fail",
    index: bool = True,
    ordering_id: typing.Optional[str] = None
) -> None

Write a DataFrame to a BigQuery table.

Examples:

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

>>> 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")

Name of table to be written, in the form dataset.tablename or project.dataset.tablename.

Behavior when the destination table exists. 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.

whether write row names (index) or not.

If set, write the ordering of the DataFrame as a column in the result table with this name.

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.

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.

Indication of expected JSON string format. * Series: - default is 'index' - allowed values are: {{'split', 'records', 'index', 'table'}}. * DataFrame: - default is 'columns' - allowed values are: {{'split', 'records', 'index', 'columns', 'values', 'table'}}. * The format of the JSON string: - 'split' : dict like {{'index' -> [index], 'columns' -> [columns], 'data' -> [values]}} - 'records' : list like [{{column -> value}}, ... , {{column -> value}}] - 'index' : dict like {{index -> {{column -> value}}}} - 'columns' : dict like {{column -> {{index -> value}}}} - 'values' : just the values array - 'table' : dict like {{'schema': {{schema}}, 'data': {{data}}}} Describing the data, where data component is like orient='records'.

If True, write row names (index).

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

to_latex(
    buf=None,
    columns: Sequence | None = None,
    header: bool | 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>

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

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

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

Write row names (index).

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 |

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

Mode in which file is opened.

Add index (row) labels.

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)

The dtype to pass to numpy.asarray().

Whether to ensure that the returned value is not a view on another array.

The value to use for missing values. The default value depends on dtype and the dtypes of the DataFrame columns.

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())

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(
    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.

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.

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.

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.

to_pandas_batches() -> typing.Iterable[pandas.core.frame.DataFrame]

Stream DataFrame results to an iterable of pandas DataFrame

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)

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.

Name of the compression to use. Use None for no compression. Supported options: 'gzip', 'snappy'.

If True, include the dataframe's index(es) in the file output. If False, they will not be written to the file.

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)

File path where the pickled object will be stored.

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')])

Include index in resulting record array, stored in 'index' field or using the index label, if set.

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.

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.

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

Render a DataFrame to a console-friendly tabular output.

Examples:

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

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

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

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

The minimum width of each column.

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

Whether to print index (row) labels.

String representation of NAN to use.

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.

Formatter function to apply to columns' elements if they are floats. The result of this function must be a unicode string.

Set to False for a DataFrame with a hierarchical index to print every multiindex key at each row.

Prints the names of the indexes.

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'.

Maximum number of rows to display in the console.

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

Maximum number of columns to display in the console.

Display DataFrame dimensions (number of rows by number of columns).

Character recognized as decimal separator, e.g. ',' in Europe.

Width to wrap a line in characters.

Max width to truncate each column in characters. By default, no limit.

Set character encoding.

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

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

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

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

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

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

You can use method name:

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

You can also use arithmetic operator /:

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

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

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

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(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]

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.

Only left join is implemented, keeping the index and columns of the original object.

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.

Can choose to replace values other than NA. Return True for values that should be updated.

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.

Columns to use when counting unique combinations.

Return proportions rather than frequencies.

Sort by frequencies.

Sort in ascending order.

Don’t include counts of rows that contain NA values.

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.

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

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

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

Last updated 2025-08-12 UTC.

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