Let's say I have a dataframe that looks like this:
![df['GlobalName'][df['GlobalName']==''] = df['IsPerson'].apply(lambda x: x if x==True else '')](https://www.lemona.fr/i.sstatic.net/I2pfg.png)
How can I apply lambda on the dataframe to make FullName = FirstName + ' ' + LastName? As far as I know lambda in dataframes has 1 input only? Thanks!
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1 Answer
I think apply here is not neccesary, only join columns together with +:
df['FullName'] = df.FirstName + ' ' + df.LastName
Or use Series.str.cat:
df['FullName'] = df.FirstName.str.cat(df.LastName, sep=' ')
Solution with lambda is possible, but slow:
df['FullName'] = df.apply(lambda x: x.FirstName + ' ' + x.LastName, axis=1)
9 Comments
Chadee Fouad
Perfect! That worked :-) unfortunately I must use lambda as the problem I have is more complex (has if statement). I've posted that example for simplicity. A follow up question regarding speed...is lambda slow in general or in this specific case (when applied on dataframes)?
jezrael
@ShadyMBA - It depends of formula. But generally is use
apply it is slow, because loops under the hood.jezrael
@ShadyMBA - Can you test
df['GlobalName'] = np.where((df['GlobalName']=='') & (df['IsPerson']), df['CleanName'], '') ?Chadee Fouad
3 solutions:
%timeit df['GlobalName'][df['GlobalName']==''] = df.apply(lambda x: x['CleanName'] if x['IsPerson'] == True else '', axis = 1) %timeit df['GlobalName'][df['GlobalName']==''] = np.where(df['IsPerson']==True, df['CleanName'], '') %timeit df['GlobalName'] = np.where((df['IsPerson']==True) & (df['GlobalName']==''), df['CleanName'], df['GlobalName']) 39.6 ms 40.6 ms 1.21 ms The 3rd solution is super fast compared to the other 2. |