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# Counting the number of negative values in Pandas DataFrame

schedule Aug 12, 2023
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local_offer
PythonPandas
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Consider the following DataFrame:

``` df = pd.DataFrame({"A":[-3,4],"B":[5,-6]})df A B0 -3 51 4 -6 ```

# Solution

To count the total number of negative values in this DataFrame:

``` (df < 0).sum().sum() 2 ```

# Explanation

Here, we are first checking for the presence of negative values:

``` (df < 0) A B0 True False1 False True ```

`True` indicates an entry that is negative. We then call `sum()`, which computes the sum of each column by default:

``` (df < 0).sum() A 1B 1dtype: int64 ```

Note that boolean `True` is internally represented as a `1`, while `False` as a `0`. What we actually want is to compute the sum of all the values of the DataFrame, yet `sum()` only allows summation either row-wise or column-wise. Therefore, we must call `sum()` twice to get the total count.

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