Python:Pandas GroupBy

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StevenSwiniarski
Published Jun 11, 2022
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The GroupBy object is returned by calls to .groupby() on a Series or DataFrame. The .groupby() function reassembles the data into distinct groups, often for aggregation.

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Example

The following example produces a GroupBy object from a DataFrame and uses it to produce some aggregate results.

import pandas as pd


df = pd.DataFrame({'Key' : ['A', 'A', 'A', 'B', 'B', 'C'],
                   'Value' : [15., 23., 17., 5., 8., 12.]})
print(df, end='\n\n')


group = df.groupby(['Key'], as_index=False)


print(group.count(), end='\n\n')


print(group.sum(), end='\n\n')


print(group.mean())

This produces the following output:

  Key  Value
0   A   15.0
1   A   23.0
2   A   17.0
3   B    5.0
4   B    8.0
5   C   12.0


  Key  Value
0   A      3
1   B      2
2   C      1


  Key  Value
0   A   55.0
1   B   13.0
2   C   12.0


  Key      Value
0   A  18.333333
1   B   6.500000
2   C  12.000000

Selected methods of the GroupBy object are listed below:

GroupBy

.agg()
Applies one or more aggregation functions to grouped data in a Pandas DataFrame.
.count()
Produces a new Series or DataFrame with counts of the values for each group in a GroupBy object.
.last()
Returns the last value in each group of a Pandas Series or DataFrame.
.max()
Produces a new Series or DataFrame with maximum values for the groups in a GroupBy object.
.mean()
Produces a new Series or DataFrame with aggregate mean values for the groups in a GroupBy object.
.median()
Returns a Series or DataFrame containing the median of each group in a GroupBy object.
.min()
Produces a new Series or DataFrame with minimum values for the groups in a GroupBy object.
.prod()
Produces a new Series or DataFrame by computing the product of the values within the group.
.sum()
Produces a new Series or DataFrame with aggregate sums for the groups in a GroupBy object.
first()
Returns the first non-null value from each group.
size()
Returns a Series containing the size (row count) of each group.

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    Machine Learning Data Scientists solve problems at scale, make predictions, find patterns, and more! They use Python, SQL, and algorithms.
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    Learn Python 3

    Learn the basics of Python 3.12, one of the most powerful, versatile, and in-demand programming languages today.
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