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# Calculating the percentage of each value in each group in Pandas

schedule Aug 12, 2023
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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 group0 2 6 a1 3 7 a2 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 B0 0.4 0.4615381 0.6 0.5384622 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 0.6b 2 1.0Name: A, dtype: float64 ```
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