I have the following dataframe 'df':

df = pd.DataFrame({'a':[0,2,3],'b':[1,3,4],'c':[4,1,2]})

Now I want to drop such rows such that that row has 0 value for column'a' and non zero value for column 'b'. How can I do that?

I tried this, df = df[df['a'] == 0 and df['b'] != 0], but got error.

CodePudding user response：

You're almost there. You need to use & instead of and when chaining conditions inside a Pandas .loc.

df = pd.DataFrame({'a':[0,2,3],'b':[1,3,4],'c':[4,1,2]})

df = df[(df['a'] == 0) & (df['b'] != 0)]

Result:

    a   b   c
0   0   1   4

CodePudding user response：

import pandas library

import pandas as pd
df = pd.DataFrame({'a':[0,2,3],'b':[1,3,4],'c':[4,1,2]})
df ## data of dataframe

take any rows which are having 0 in any columns

df = df[(df['a'] == 0) | (df['b'] == 0)]

CodePudding user response：

For combining conditions in pandas you need to use the bitwise & operator. e.g:

condtion_a = df.a == 0
condtion_b = df.b != 0
condition = condtion_a & condtion_b
df = df[condition]

Or in a one liner:

df = df[(df.a == 0) & (df.b != 0)]

Bonus: here is a nice explanation