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# Checking if a value exists in a DataFrame in Pandas

schedule Aug 12, 2023
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# Checking if a value exists in a DataFrame

Consider the following DataFrame:

``` import pandas as pddf = pd.DataFrame({"A":[1,2,3],"B":[4,5,6]}) A B0 1 41 2 52 3 6 ```

To check if a value exists in the DataFrame, use the built-in `in` operator against the DataFrame's `values` property:

``` 2 in df.values5 in df.values10 in df.values TrueTrueFalse ```

Here:

• the `values` property returns a NumPy array holding all the values in the DataFrame.

• we are checking if the value `2`, `5`, `10` exists in the DataFrame.

# Checking if a value exists in a certain column in the DataFrame

Consider the following DataFrame:

``` df = pd.DataFrame({"C":[7,8,9],"D":[10,11,12]})df C D0 7 101 8 112 9 12 ```

## Solution

To check if a DataFrame column contains some values in Pandas:

``` df["C"].isin([8,11]).any() True ```

Here, we’re checking if column `C` contains either the value `8` or `11`.

## Explanation

We first fetch column `C` as a Series using `df["C"]`, and then we use `isin(~)` to obtain a boolean mask where True represents the presence of a value in the given list:

``` df["C"].isin([8,11]) # returns a Series of booleans 0 False1 True2 FalseName: C, dtype: bool ```

Here, we get `True` for row `1` since it holds the value `8`.

Finally, we use the Series’ `any()` method that returns `True` if there is at least one `True` in the Series:

``` df["C"].isin([8,11]).any() True ```
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