Class DataFrameGroupBy (1.26.0) 

DataFrameGroupBy(
    block: bigframes.core.blocks.Block,
    by_col_ids: typing.Sequence[str],
    *,
    selected_cols: typing.Optional[typing.Sequence[str]] = None,
    dropna: bool = True,
    as_index: bool = True
)

Class for grouping and aggregating relational data.

Methods

agg

agg(
    func=None, **kwargs
) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

Aggregate using one or more operations.

Examples:

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

>>> data = {"A": [1, 1, 2, 2],
...         "B": [1, 2, 3, 4],
...         "C": [0.362838, 0.227877, 1.267767, -0.562860]}
>>> df = bpd.DataFrame(data)

The aggregation is for each column.

>>> df.groupby('A').agg('min')
    B         C
A
1  1  0.227877
2  3  -0.56286
<BLANKLINE>
[2 rows x 2 columns]

Multiple aggregations

>>> df.groupby('A').agg(['min', 'max'])
    B             C
       min max       min       max
A
1        1   2  0.227877  0.362838
2        3   4  -0.56286  1.267767
<BLANKLINE>
[2 rows x 4 columns]
Parameter
Name Description
func function, str, list, dict or None

Function to use for aggregating the data. Accepted combinations are: - string function name - list of function names, e.g. ['sum', 'mean'] - dict of axis labels -> function names or list of such. - None, in which case kwargs are used with Named Aggregation. Here the output has one column for each element in kwargs. The name of the column is keyword, whereas the value determines the aggregation used to compute the values in the column.
Returns
Type Description
bigframes.pandas.DataFrame A BigQuery DataFrame.

aggregate

aggregate(
    func=None, **kwargs
) -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

Aggregate using one or more operations.

Examples:

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

>>> data = {"A": [1, 1, 2, 2],
...         "B": [1, 2, 3, 4],
...         "C": [0.362838, 0.227877, 1.267767, -0.562860]}
>>> df = bpd.DataFrame(data)

The aggregation is for each column.

>>> df.groupby('A').aggregate('min')
    B         C
A
1  1  0.227877
2  3  -0.56286
<BLANKLINE>
[2 rows x 2 columns]

Multiple aggregations

>>> df.groupby('A').agg(['min', 'max'])
    B             C
       min max       min       max
A
1        1   2  0.227877  0.362838
2        3   4  -0.56286  1.267767
<BLANKLINE>
[2 rows x 4 columns]
Parameter
Name Description
func function, str, list, dict or None

Function to use for aggregating the data. Accepted combinations are: - string function name - list of function names, e.g. ['sum', 'mean'] - dict of axis labels -> function names or list of such. - None, in which case kwargs are used with Named Aggregation. Here the output has one column for each element in kwargs. The name of the column is keyword, whereas the value determines the aggregation used to compute the values in the column.
Returns
Type Description
bigframes.pandas.DataFrame A BigQuery DataFrame.

all

all() -> bigframes.dataframe.DataFrame

Return True if all values in the group are true, else False.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([1, 2, 0], index=lst)
>>> ser.groupby(level=0).all()
a     True
b    False
dtype: boolean

For DataFrameGroupBy:

>>> data = [[1, 0, 3], [1, 5, 6], [7, 8, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["ostrich", "penguin", "parrot"])
>>> df.groupby(by=["a"]).all()
        b       c
a
1   False    True
7   True    True
<BLANKLINE>
[2 rows x 2 columns]
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series DataFrame or Series of boolean values, where a value is True if all elements are True within its respective group; otherwise False.

any

any() -> bigframes.dataframe.DataFrame

Return True if any value in the group is true, else False.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([1, 2, 0], index=lst)
>>> ser.groupby(level=0).any()
a     True
b    False
dtype: boolean

For DataFrameGroupBy:

>>> data = [[1, 0, 3], [1, 0, 6], [7, 1, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["ostrich", "penguin", "parrot"])
>>> df.groupby(by=["a"]).any()
        b       c
a
1   False    True
7   True    True
<BLANKLINE>
[2 rows x 2 columns]
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series DataFrame or Series of boolean values, where a value is True if any element is True within its respective group; otherwise False.

count

count() -> bigframes.dataframe.DataFrame

Compute count of group, excluding missing values.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([1, 2, np.nan], index=lst)
>>> ser.groupby(level=0).count()
a     2
b     0
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, np.nan, 3], [1, np.nan, 6], [7, 8, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["cow", "horse", "bull"])
>>> df.groupby(by=["a"]).count()
   b  c
a
1  0  2
7  1  1
<BLANKLINE>
[2 rows x 2 columns]
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Count of values within each group.

cumcount

cumcount(ascending: bool = True)

Number each item in each group from 0 to the length of that group - 1. (DataFrameGroupBy functionality is not yet available.)

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b', 'b', 'c']
>>> ser = bpd.Series([5, 1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).cumcount()
a    0
a    1
b    0
b    1
c    0
dtype: Int64
>>> ser.groupby(level=0).cumcount(ascending=False)
a    0
a    1
b    0
b    1
c    0
dtype: Int64
Parameter
Name Description
ascending bool, default True

If False, number in reverse, from length of group - 1 to 0.
Returns
Type Description
bigframes.pandas.Series Sequence number of each element within each group.

cummax

cummax(
    *args, numeric_only: bool = False, **kwargs
) -> bigframes.dataframe.DataFrame

Cumulative max for each group.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([6, 2, 0], index=lst)
>>> ser.groupby(level=0).cummax()
a    6
a    6
b    0
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["fox", "gorilla", "lion"])
>>> df.groupby("a").cummax()
         b  c
fox      8  2
gorilla  8  5
lion     6  9
<BLANKLINE>
[3 rows x 2 columns]
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Cumulative max for each group.

cummin

cummin(
    *args, numeric_only: bool = False, **kwargs
) -> bigframes.dataframe.DataFrame

Cumulative min for each group.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([6, 2, 0], index=lst)
>>> ser.groupby(level=0).cummin()
a    6
a    2
b    0
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["fox", "gorilla", "lion"])
>>> df.groupby("a").cummin()
         b  c
fox      8  2
gorilla  2  2
lion     6  9
<BLANKLINE>
[3 rows x 2 columns]
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Cumulative min for each group.

cumprod

cumprod(*args, **kwargs) -> bigframes.dataframe.DataFrame

Cumulative product for each group.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([6, 2, 0], index=lst)
>>> ser.groupby(level=0).cumprod()
a     6.0
a    12.0
b     0.0
dtype: Float64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["cow", "horse", "bull"])
>>> df.groupby("a").cumprod()
          b     c
cow     8.0   2.0
horse  16.0  10.0
bull    6.0   9.0
<BLANKLINE>
[3 rows x 2 columns]
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Cumulative product for each group.

cumsum

cumsum(
    *args, numeric_only: bool = False, **kwargs
) -> bigframes.dataframe.DataFrame

Cumulative sum for each group.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([6, 2, 0], index=lst)
>>> ser.groupby(level=0).cumsum()
a    6
a    8
b    0
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["fox", "gorilla", "lion"])
>>> df.groupby("a").cumsum()
          b  c
fox       8  2
gorilla  10  7
lion      6  9
<BLANKLINE>
[3 rows x 2 columns]
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Cumulative sum for each group.

diff

diff(periods=1) -> bigframes.series.Series

First discrete difference of element. Calculates the difference of each element compared with another element in the group (default is element in previous row).

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser.groupby(level=0).diff()
a    <NA>
a      -5
a       6
b    <NA>
b      -1
b       0
dtype: Int64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog',
...                   'mouse', 'mouse', 'mouse', 'mouse'])
>>> df.groupby(level=0).diff()
          a     b
dog    <NA>  <NA>
dog       2     3
dog       2     4
mouse  <NA>  <NA>
mouse     0     0
mouse     1    -2
mouse    -5    -1
<BLANKLINE>
[7 rows x 2 columns]
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series First differences.

expanding

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

Provides expanding functionality.

Examples:

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

>>> lst = ['a', 'a', 'c', 'c', 'e']
>>> ser = bpd.Series([1, 0, -2, -1, 2], index=lst)
>>> ser.groupby(level=0).expanding().min()
index  index
a      a         1
       a         0
c      c        -2
       c        -2
e      e         2
dtype: Int64
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series An expanding grouper, providing expanding functionality per group.

head

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

API documentation for head method.

kurt

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

Return unbiased kurtosis over requested axis.

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

Examples:

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

>>> lst = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b']
>>> ser = bpd.Series([0, 1, 1, 0, 0, 1, 2, 4, 5], index=lst)
>>> ser.groupby(level=0).kurt()
a        -6.0
b   -1.963223
dtype: Float64
Parameter
Name Description
numeric_only bool, default False

Include only float, int or boolean data.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Variance of values within each group.

kurtosis

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

Return unbiased kurtosis over requested axis.

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

Examples:

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

>>> lst = ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b', 'b']
>>> ser = bpd.Series([0, 1, 1, 0, 0, 1, 2, 4, 5], index=lst)
>>> ser.groupby(level=0).kurtosis()
a        -6.0
b   -1.963223
dtype: Float64
Parameter
Name Description
numeric_only bool, default False

Include only float, int or boolean data.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Variance of values within each group.

max

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

Compute max of group values.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).max()
a     2
b     4
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["tiger", "leopard", "cheetah", "lion"])
>>> df.groupby(by=["a"]).max()
   b  c
a
1  8  5
2  6  9
<BLANKLINE>
[2 rows x 2 columns]
Parameters
Name Description
numeric_only bool, default False

Include only float, int, boolean columns.
min_count int, default 0

The required number of valid values to perform the operation. If fewer than min_count and non-NA values are present, the result will be NA.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Computed max of values within each group.

mean

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

Compute mean of groups, excluding missing values.

Examples:

>>> import bigframes.pandas as bpd
>>> import numpy as np
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({'A': [1, 1, 2, 1, 2],
...                    'B': [np.nan, 2, 3, 4, 5],
...                    'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C'])

Groupby one column and return the mean of the remaining columns in each group.

>>> df.groupby('A').mean()
    B         C
A
1  3.0  1.333333
2  4.0       1.5
<BLANKLINE>
[2 rows x 2 columns]

Groupby two columns and return the mean of the remaining column.

>>> df.groupby(['A', 'B']).mean()
         C
A B
1 2.0  2.0
  4.0  1.0
2 3.0  1.0
  5.0  2.0
<BLANKLINE>
[4 rows x 1 columns]

Groupby one column and return the mean of only particular column in the group.

>>> df.groupby('A')['B'].mean()
A
1    3.0
2    4.0
Name: B, dtype: Float64
Parameter
Name Description
numeric_only bool, default False

Include only float, int, boolean columns.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Mean of groups.

median

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

Compute median of groups, excluding missing values.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser.groupby(level=0).median()
a    7.0
b    3.0
dtype: Float64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog',
...                    'mouse', 'mouse', 'mouse', 'mouse'])
>>> df.groupby(level=0).median()
        a    b
dog    3.0  4.0
mouse  7.0  3.0
<BLANKLINE>
[2 rows x 2 columns]
Parameters
Name Description
numeric_only bool, default False

Include only float, int, boolean columns.
exact bool, default True

Calculate the exact median instead of an approximation.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Median of groups.

min

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

Compute min of group values.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).min()
a     1
b     3
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                    index=["tiger", "leopard", "cheetah", "lion"])
>>> df.groupby(by=["a"]).min()
   b  c
a
1  2  2
2  5  8
<BLANKLINE>
[2 rows x 2 columns]
Parameters
Name Description
numeric_only bool, default False

Include only float, int, boolean columns.
min_count int, default 0

The required number of valid values to perform the operation. If fewer than min_count and non-NA values are present, the result will be NA.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Computed min of values within each group.

nunique

nunique() -> bigframes.dataframe.DataFrame

Return DataFrame with counts of unique elements in each position.

Examples:

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

>>> df = bpd.DataFrame({'id': ['spam', 'egg', 'egg', 'spam',
...                           'ham', 'ham'],
...                    'value1': [1, 5, 5, 2, 5, 5],
...                    'value2': list('abbaxy')})
>>> df.groupby('id').nunique()
      value1  value2
id
egg        1       1
ham        1       2
spam       2       1
<BLANKLINE>
[3 rows x 2 columns]
Returns
Type Description
bigframes.pandas.DataFrame Number of unique values within a BigQuery DataFrame.

prod

prod(numeric_only: bool = False, min_count: int = 0)

Compute prod of group values. (DataFrameGroupBy functionality is not yet available.)

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).prod()
a     2.0
b    12.0
dtype: Float64
Parameters
Name Description
numeric_only bool, default False

Include only float, int, boolean columns.
min_count int, default 0

The required number of valid values to perform the operation. If fewer than min_count and non-NA values are present, the result will be NA.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Computed prod of values within each group.

quantile

quantile(
    q: typing.Union[float, typing.Sequence[float]] = 0.5, *, numeric_only: bool = False
) -> bigframes.dataframe.DataFrame

Return group values at the given quantile, a la numpy.percentile.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame([
...     ['a', 1], ['a', 2], ['a', 3],
...     ['b', 1], ['b', 3], ['b', 5]
... ], columns=['key', 'val'])
>>> df.groupby('key').quantile()
     val
key
a    2.0
b    3.0
<BLANKLINE>
[2 rows x 1 columns]
Parameters
Name Description
q float or array-like, default 0.5 (50% quantile)

Value(s) between 0 and 1 providing the quantile(s) to compute.
numeric_only bool, default False

Include only float, int or boolean data.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Return type determined by caller of GroupBy object.

rolling

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

Returns a rolling grouper, providing rolling functionality per group.

Examples:

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

>>> lst = ['a', 'a', 'a', 'a', 'e']
>>> ser = bpd.Series([1, 0, -2, -1, 2], index=lst)
>>> ser.groupby(level=0).rolling(2).min()
index  index
a      a        <NA>
    a           0
    a          -2
    a          -2
e      e        <NA>
dtype: Int64
Parameter
Name Description
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.pandas.DataFrame or bigframes.pandas.Series Return a new grouper with our rolling appended.

shift

shift(periods=1) -> bigframes.series.Series

Shift each group by periods observations.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).shift(1)
a    <NA>
a       1
b    <NA>
b       3
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["tuna", "salmon", "catfish", "goldfish"])
>>> df.groupby("a").shift(1)
             b     c
tuna      <NA>  <NA>
salmon       2     3
catfish   <NA>  <NA>
goldfish     5     8
<BLANKLINE>
[4 rows x 2 columns]
Parameter
Name Description
periods int, default 1

Number of periods to shift.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Object shifted within each group.

size

size() -> typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

API documentation for size method.

skew

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

Return unbiased skew within groups.

Normalized by N-1.

Examples:

For SeriesGroupBy:

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

>>> ser = bpd.Series([390., 350., 357., np.nan, 22., 20., 30.],
...                  index=['Falcon', 'Falcon', 'Falcon', 'Falcon',
...                         'Parrot', 'Parrot', 'Parrot'],
...                  name="Max Speed")
>>> ser.groupby(level=0).skew()
Falcon    1.525174
Parrot    1.457863
Name: Max Speed, dtype: Float64
Parameter
Name Description
numeric_only bool, default False

Include only float, int or boolean data.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Variance of values within each group.

std

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

Compute standard deviation of groups, excluding missing values.

For multiple groupings, the result index will be a MultiIndex.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser.groupby(level=0).std()
a     3.21455
b     0.57735
dtype: Float64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog',
...                    'mouse', 'mouse', 'mouse', 'mouse'])
>>> df.groupby(level=0).std()
              a         b
dog         2.0  3.511885
mouse  2.217356       1.5
<BLANKLINE>
[2 rows x 2 columns]
Parameter
Name Description
numeric_only bool, default False

Include only float, int or boolean data.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Standard deviation of values within each group.

sum

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

Compute sum of group values.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = bpd.Series([1, 2, 3, 4], index=lst)
>>> ser.groupby(level=0).sum()
a     3
b     7
dtype: Int64

For DataFrameGroupBy:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["tiger", "leopard", "cheetah", "lion"])
>>> df.groupby("a").sum()
    b   c
a
1  10   7
2  11  17
<BLANKLINE>
[2 rows x 2 columns]
Parameters
Name Description
numeric_only bool, default False

Include only float, int, boolean columns.
min_count int, default 0

The required number of valid values to perform the operation. If fewer than min_count and non-NA values are present, the result will be NA.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Computed sum of values within each group.

var

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

Compute variance of groups, excluding missing values.

For multiple groupings, the result index will be a MultiIndex.

Examples:

For SeriesGroupBy:

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

>>> lst = ['a', 'a', 'a', 'b', 'b', 'b']
>>> ser = bpd.Series([7, 2, 8, 4, 3, 3], index=lst)
>>> ser.groupby(level=0).var()
a   10.333333
b    0.333333
dtype: Float64

For DataFrameGroupBy:

>>> data = {'a': [1, 3, 5, 7, 7, 8, 3], 'b': [1, 4, 8, 4, 4, 2, 1]}
>>> df = bpd.DataFrame(data, index=['dog', 'dog', 'dog',
...                    'mouse', 'mouse', 'mouse', 'mouse'])
>>> df.groupby(level=0).var()
              a          b
dog         4.0  12.333333
mouse  4.916667       2.25
<BLANKLINE>
[2 rows x 2 columns]
Parameter
Name Description
numeric_only bool, default False

Include only float, int or boolean data.
Returns
Type Description
bigframes.pandas.DataFrame or bigframes.pandas.Series Variance of values within each group.