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Counting the number of negative values in Pandas DataFrame
schedule Aug 12, 2023
Last updated local_offer
Tags Python●Pandas
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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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