How to drop NaN from one column according to another columns specific value

user_13961615 0
Viewed 192

Can't figure out how to drop NaN values from specific column according to another column specific value. Part of DataFrame(df):

            vol.            group
1186      10,448,898          1
1187      nan                 0
1188      35,047,520          1
          ...   
8329      130,703             0
8330      241,489             1
8332      nan                 1
8333      101,142             0
8334      nan                 1

I need to drop nan values from vol. but only when according value in group is 1.

I tried:

df.loc[df['group'] == 1,'vol.'].dropna(inplace=True)

But df still have all values as dropna takes no effect.

drop pandas dataframe nan python
2 Answers

You can change logic - select all values without 1 with nans in boolean indexing:

#if necessary convert strings nan to missing values `NaN`s
df['vol.'] = df['vol.'].replace('nan', np.nan)


df = df[(df['group'] != 1) | df['vol.'].notna()]
print (df)
            vol.  group
1186  10,448,898      1
1187         NaN      0
1188  35,047,520      1
8329     130,703      0
8330     241,489      1
8333     101,142      0
user_29010020

use this:

df = df.loc[df['group'] == 1,'vol.']
df.dropna(inplace=True)
user_22193790
Related