Series Cookbook

Appending values to a Series Applying a function to Series Binning values in a Series Changing data type of Series Checking if a value is NaN in Pandas Series Checking if all values are NaN in Series Checking if all values in a Series are unique Checking if Series has missing values Converting Python list to Series Converting Series of lists into DataFrame Converting Series to a Numpy array Converting Series to Python list Counting frequency of values in Series Creating a Series of zeroes Creating a Series with constant value Filtering strings based on length in Series Filtering values of a Series Getting frequency counts of values in intervals Getting index of largest value Getting index of smallest value Getting index of value in Series Getting integer index of largest value Getting integer index of smallest value Getting integer index of value in Series Getting intersection of Series Getting length of each string in Series Getting list of integer indices where value is boolean True in Series Getting the index of the nth value in Series Getting the most frequent value in Series Getting value of Series using integer index Grouping Series by its values Handling error - "Truth value of a Series is ambiguous"Inverting a Series of booleans Removing missing values from a Series Removing substrings from strings in a Series Removing values from Series Resetting index of Series Sorting values in a Series Splitting strings based on space Stripping leading and trailing whitespace Taking the floor or ceiling of values in Series Using index.get_loc(~) for multiple values

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Grouping Series by its values in Pandas

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Series Cookbook

Mar 13, 2022

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To group the values of a Series, use the groupby(~) method:


        
        
            
                
                
                    import pandas as pd

s = pd.Series([3,4,3,5])
grouped_numbers = s.groupby(s)

# Do something with the grouped values
grouped_numbers.sum()   # Returns a Series
                
            
            3    6
4    4
5    5
dtype: int64

Here, note the following:

we see the value 6 corresponding to the index 3 because we are summing up all occurrences of the value of 3 (i.e. 3+3=6).
the syntax s.groupby(s) is slightly awkward - but we need to pass in s as the argument since the groupby(~) groups by the index instead of values by default.
the groupby(~) returns a SeriesGroupBy object, which has aggregate functions such as sum().

Published by Isshin Inada

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