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Calculating the percentage of each value in each group in Pandas

schedule Aug 12, 2023
Last updated
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PythonPandas
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Consider the following DataFrame:

df = pd.DataFrame({"A":[2,3,4],"B":[6,7,8],"group":["a","a","b"]})
df
A B group
0 2 6 a
1 3 7 a
2 4 8 b

To compute the percentage of each value in each distinct group:

df.groupby("group").apply(lambda my_df: my_df / my_df.sum())
A B
0 0.4 0.461538
1 0.6 0.538462
2 1.0 1.000000

Note the following:

  • the function defined in apply(~) is called twice in this case - once for each group.

  • the argument (my_df) passed to this function is a DataFrame representing a single group.

  • the my_df.sum() returns a Series containing the sum of each column of my_df. In this case, for group a, my_df.sum() would evaluate to a Series holding values [5,13].

  • dividing my_df by this Series involves dividing values in column A by 5, and dividing values in column B by 13.

  • the return type of argument function is a DataFrame.

* * *

To compute the percentage of a specific column instead of all numeric columns:

df.groupby("group").apply(lambda my_df: my_df["A"] / my_df["A"].sum())
group
a 0 0.4
1 0.6
b 2 1.0
Name: A, dtype: float64
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Published by Isshin Inada
Edited by 0 others
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