I want to replace some values in my dataframe that meets a condition. I tried to write the code but does not seem working
dfa = df.copy()
for value in df['Clean Company Name']:
if value=="NaN":
dfa['Clean Company Name'].replace(df['Company Name'])
dfa.head()
As you can see, the NaN values are not replaced by the 'Company Name'
How do I achieve that result?
If need replace NaN values need functions combine_first or fillna:
df['Clean Company Name'].combine_first(df['Company Name'])
Or:
df['Clean Company Name'].fillna(df['Company Name'])
Sample:
df = pd.DataFrame({'Company Name':['s','d','f'], 'Clean Company Name': [np.nan, 'r', 't']})
print (df)
Clean Company Name Company Name
0 NaN s
1 r d
2 t f
#if need check NaNs
print (df['Clean Company Name'].isnull())
0 True
1 False
2 False
Name: Clean Company Name, dtype: bool
df['Clean Company Name'] = df['Clean Company Name'].combine_first(df['Company Name'])
print (df)
Clean Company Name Company Name
0 s s
1 r d
2 t f
More about missing data.
EDIT:
For replace data by condition is possible use loc with boolean mask:
print (df['Company Name'] == 'd')
0 False
1 True
2 False
Name: Company Name, dtype: bool
df.loc[df['Company Name'] == 'd', 'Clean Company Name'] = 'sss'
print (df)
Clean Company Name Company Name
0 NaN s
1 sss d
2 t f
