Python: drop value=0 row in specific columns [duplicate]

Question

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

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 — 蔡嚴毅
– 蔡嚴毅, 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. — sediq khan
– sediq khan, Commented Apr 14, 2023 at 18:27

jpp · Accepted Answer · 2018-04-15 13:31:56Z

15

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.

edited Apr 15, 2018 at 13:31

answered Apr 15, 2018 at 12:40

jpp

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