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SeriesGroupBy
(
block
:
bigframes
.
core
.
blocks
.
Block
,
value_column
:
str
,
by_col_ids
:
typing
.
Sequence
[
str
],
value_name
:
typing
.
Hashable
=
None
,
dropna
=
True
,
)
Class for grouping and aggregating relational data.
Methods
agg
agg
(
func
=
None
,
)
-
> 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
>>> s = bpd.Series([1, 2, 3, 4], index=[1, 1, 2, 2])
>>> s.groupby(level=0).agg(['min', 'max'])
min max
1 1 2
2 3 4
<BLANKLINE>
[2 rows x 2 columns]
aggregate
aggregate
(
func
=
None
,
)
-
> 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
>>> s = bpd.Series([1, 2, 3, 4], index=[1, 1, 2, 2])
>>> s.groupby(level=0).aggregate(['min', 'max'])
min max
1 1 2
2 3 4
<BLANKLINE>
[2 rows x 2 columns]
all
all
()
-
> bigframes
.
series
.
Series
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]
any
any
()
-
> bigframes
.
series
.
Series
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]
count
count
()
-
> bigframes
.
series
.
Series
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]
cumcount
cumcount
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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
ascending
bool, default True
If False, number in reverse, from length of group - 1 to 0.
cummax
cummax
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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]
cummin
cummin
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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]
cumprod
cumprod
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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]
cumsum
cumsum
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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]
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]
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
head
head
(
n
:
int
=
5
)
-
> bigframes
.
series
.
Series
Return last first n rows of each group
Examples:
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame([[1, 2], [1, 4], [5, 6]],
... columns=['A', 'B'])
>>> df.groupby('A').head(1)
A B
0 1 2
2 5 6
[2 rows x 2 columns]
n
int
If positive: number of entries to include from start of each group. If negative: number of entries to exclude from end of each group.
kurt
kurt
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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
numeric_only
bool, default False
Include only float
, int
or boolean
data.
kurtosis
kurtosis
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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
numeric_only
bool, default False
Include only float
, int
or boolean
data.
max
max
(
*
args
)
-
> bigframes
.
series
.
Series
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]
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.
mean
mean
(
*
args
)
-
> bigframes
.
series
.
Series
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
numeric_only
bool, default False
Include only float, int, boolean columns.
median
median
(
*
args
,
exact
:
bool
=
True
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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]
numeric_only
bool, default False
Include only float, int, boolean columns.
exact
bool, default True
Calculate the exact median instead of an approximation.
min
min
(
*
args
)
-
> bigframes
.
series
.
Series
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]
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.
nunique
nunique
()
-
> bigframes
.
series
.
Series
Return number of unique elements in the group.
Examples:
>>> 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, 3], index=lst)
>>> ser.groupby(level=0).nunique()
a 2
b 1
dtype: Int64
prod
prod
(
*
args
)
-
> bigframes
.
series
.
Series
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
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.
quantile
quantile
(
q
:
typing
.
Union
[
float
,
typing
.
Sequence
[
float
]]
=
0.5
,
*
,
numeric_only
:
bool
=
False
)
-
> bigframes
.
series
.
Series
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]
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.
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
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.
shift
shift
(
periods
=
1
)
-
> bigframes
.
series
.
Series
Shift index by desired number of periods.
size
size
()
-
> bigframes
.
series
.
Series
Compute group sizes.
Examples:
For SeriesGroupBy:
>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> lst = ['a', 'a', 'b']
>>> ser = bpd.Series([1, 2, 3], index=lst)
>>> ser
a 1
a 2
b 3
dtype: Int64
>>> ser.groupby(level=0).size()
a 2
b 1
dtype: Int64
For DataFrameGroupBy:
>>> data = [[1, 2, 3], [1, 5, 6], [7, 8, 9]]
>>> df = bpd.DataFrame(data, columns=["a", "b", "c"],
... index=["owl", "toucan", "eagle"])
>>> df
a b c
owl 1 2 3
toucan 1 5 6
eagle 7 8 9
[3 rows x 3 columns]
>>> df.groupby("a").size()
a
1 2
7 1
dtype: Int64
skew
skew
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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
numeric_only
bool, default False
Include only float
, int
or boolean
data.
std
std
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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]
numeric_only
bool, default False
Include only float
, int
or boolean
data.
sum
sum
(
*
args
)
-
> bigframes
.
series
.
Series
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]
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.
var
var
(
*
args
,
**
kwargs
)
-
> bigframes
.
series
.
Series
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]
numeric_only
bool, default False
Include only float
, int
or boolean
data.
