Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Welcome To Ask or Share your Answers For Others

Categories

0 votes
333 views
in Technique[技术] by (71.8m points)

python - Remove NaN 'Cells' without dropping the entire ROW (Pandas,Python3)

Right now I have a DF like this

 Word       Word2          Word3
 Hello      NaN            NaN
 My         My Name        NaN
 Yellow     Yellow Bee     Yellow Bee Hive
 Golden     Golden Gates   NaN
 Yellow     NaN            NaN

What I was hoping for was to remove all of the NaN cells from my data frame. So in the end, it would look like this, where 'Yellow Bee Hive' has moved to row 1 (similarly to what happens when you delete cells from a column in excel) :

   Word       Word2             Word3
1  Hello      My Name        Yellow Bee Hive
2  My         Yellow Bee       
3  Yellow     Golden Gates             
4  Golden       
5  Yellow    

Unfortunately, neither of these work because they delete the Entire ROW!

 df = df[pd.notnull(df['Word','Word2','Word3'])]

or

 df = df.dropna() 

Anyone have any suggestions? Should I reindex the table?

See Question&Answers more detail:os

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
Welcome To Ask or Share your Answers For Others

1 Reply

0 votes
by (71.8m points)

I think you can use this:

df = df.apply(lambda x: pd.Series(x.dropna().values))

For example:

import pandas as pd
import numpy as np

df = pd.DataFrame({
    'Word':['Hello', 'My', 'Yellow', 'Golden', 'Yellow'],
    'Word2':[np.nan, 'My Name', 'Yellow Bee', 'Golden Gates', np.nan],
    'Word3':[np.nan, np.nan, 'Yellow Bee Hive', np.nan, np.nan]
})

print(df)

Initial dataframe:

     Word         Word2            Word3
0   Hello           NaN              NaN
1      My       My Name              NaN
2  Yellow    Yellow Bee  Yellow Bee Hive
3  Golden  Golden Gates              NaN
4  Yellow           NaN              NaN

and applying this lambda function:

df = df.apply(lambda x: pd.Series(x.dropna().values))

print(df)

gives:

     Word         Word2            Word3
0   Hello       My Name  Yellow Bee Hive
1      My    Yellow Bee              NaN
2  Yellow  Golden Gates              NaN
3  Golden           NaN              NaN
4  Yellow           NaN              NaN

Then you can fill NaN values with empty strings:

df = df.fillna('')

print(df)

     Word         Word2            Word3
0   Hello       My Name  Yellow Bee Hive
1      My    Yellow Bee                 
2  Yellow  Golden Gates                 
3  Golden                               
4  Yellow    

与恶龙缠斗过久,自身亦成为恶龙;凝视深渊过久,深渊将回以凝视…
OGeek|极客中国-欢迎来到极客的世界,一个免费开放的程序员编程交流平台!开放,进步,分享!让技术改变生活,让极客改变未来! Welcome to OGeek Q&A Community for programmer and developer-Open, Learning and Share
Click Here to Ask a Question

...