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# Selecting rows based on a condition in Pandas

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schedule Jul 1, 2022
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Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4,5], "B":[6,7,8]}, index=["a","b","c"])df A Ba 3 6b 4 7c 5 8 ```

Here's our boolean mask:

``` mask = [False, True, True] ```

To extract rows from `df` that correspond to `True` in the mask, simply use the `[~]` notation:

``` df[mask] A Bb 4 7c 5 8 ```

You could also use the `loc` property like so:

``` mask = [False, True, True]df.loc[mask] A Bb 4 7c 5 8 ```

# Creating a boolean mask based on conditions

Consider the same DataFrame as above:

``` df = pd.DataFrame({"A":[3,4,5], "B":[6,7,8]}, index=["a","b","c"])df A Ba 3 6b 4 7c 5 8 ```

Create a mask based on condition column `A == 4`:

``` mask = df["A"] == 4print(mask) a Falseb Truec FalseName: A, dtype: bool ```

``` df[mask] #same as df[df["A"] == 4] A Bb 4 7 ```

Notice the resulting DataFrame only returns the row where the value in column A is 4 (where the condition is met).

## isin method

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4,5], "B":[6,7,8]}, index=["a","b","c"]) ```

To return rows where the column value of A is either 1, 2 or 3:

``` df.loc[df["A"].isin([1,2,3])] A Ba 3 6 ```

## Multiple conditions

Consider the following DataFrame:

``` df = pd.DataFrame({"A":[3,4,5], "B":[6,7,8]}, index=["a","b","c"])df A Ba 3 6b 4 7c 5 8 ```

To retrieve rows where `"A" >= 4` and `"B" > 7`:

``` mask = (df["A"] >= 4) & (df["B"] > 7)df.loc[mask] A Bc 5 8 ```
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