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# Removing rows with all zeros in Pandas DataFrame

schedule Aug 11, 2023
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Consider the following DataFrame:

``` df = pd.DataFrame({"A":[0,0,4],"B":[5,0,7]}, index=["a","b","c"])df A Ba 0 5b 0 0c 4 7 ```

# Solution

To remove rows with all zeroes in `df`:

``` df[~(df == 0).all(axis=1)] A Ba 0 5c 4 7 ```

# Explanation

We first check for the presence of zeroes:

``` (df == 0) A Ba True Falseb True Truec False False ```

We then call `all(axis=1)`, which returns `True` if all values are `True` for each row:

``` (df == 0).all(axis=1) a Falseb Truec Falsedtype: bool ```

This tell us that the second row (`b`) has all zeros. Since we want the rows that are not all zeros, we must invert the booleans using `~`:

``` ~(df == 0).all(axis=1) a Trueb Falsec Truedtype: bool ```

Finally, we pass this boolean mask into `df[~]` to fetch all the rows corresponding to `True` in the mask:

``` df[~(df == 0).all(axis=1)] A Ba 0 5c 4 7 ```
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