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I want to drop rows with zero value in specific columns

>>> df salary age gender 0 10000 23 1 1 15000 34 0 2 23000 21 1 3 0 20 0 4 28500 0 1 5 35000 37 1

some data in columns salary and age are missing and the third column, gender is a binary variables, which 1 means male 0 means female. And 0 here is not a missing data, I want to drop the row in either salary or age is missing so I can get

>>> df salary age gender 0 10000 23 1 1 15000 34 0 2 23000 21 1 3 35000 37 1
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    df = df[(df['salary'] > 0) & (df['age'] > 0)] Commented Apr 15, 2018 at 12:41
  • thanks you for editing the format for me Commented Apr 15, 2018 at 12:45
  • this is my first time asking a question on this forum after I posted this question I found the format is terrible And you edited it before I did Thanks alot Commented Apr 15, 2018 at 12:46
  • df = df[(df['Patient_Sub_Market'] != '0') & (df['Patient_Zip_and_City'] != '0')] I got rid of the rows that had zeros using the above statement. Commented Apr 14, 2023 at 18:27

1 Answer 1

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Option 1

You can filter your dataframe using pd.DataFrame.loc:

df = df.loc[~((df['salary'] == 0) | (df['age'] == 0))]

Option 2

Or a smarter way to implement your logic:

df = df.loc[df['salary'] * df['age'] != 0]

This works because if either salary or age are 0, their product will also be 0.

Option 3

The following method can be easily extended to several columns:

df.loc[(df[['a', 'b']] != 0).all(axis=1)]

Explanation

  • In all 3 cases, Boolean arrays are generated which are used to index your dataframe.
  • All these methods can be further optimised by using numpy representation, e.g. df['salary'].values.
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