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# Counting the number of negative values in Pandas DataFrame

*schedule*Aug 12, 2023

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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.

Published by Isshin Inada

Edited by 0 others

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