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# Getting rows where column value contains any substring in a list in Pandas DataFrame

schedule Aug 12, 2023
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local_offer
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Consider the following DataFrame:

``` df = pd.DataFrame({"A":["abc","de","fg"]})df A0 abc1 de2 fg ```

# Solution

To get all rows where the string in column `A` contains either the substring `"ab"` or `"fg"`:

``` substrings = ["ab","fg"]mask = df["A"].str.contains("|".join(substrings))df[mask] A0 abc2 fg ```

# Explanation

We begin by constructing our regular expression:

``` substrings = ["ab","fg"]"|".join(substrings) 'ab|fg' ```

Here, the pipeline character `|` represents an `OR` in regular expression.

We then use the Series' `str.contains(~)` method to obtain a Series of booleans where `True` indicates a string that contains either the substring `"ab"` or `"fg"`:

``` mask = df["A"].str.contains("|".join(substrings))mask 0 True1 False2 TrueName: A, dtype: bool ```

Finally, we use the `[]` syntax to extract all rows corresponding to `True` from `df`:

``` df[mask] A0 abc2 fg ```
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