使用pandas在csv文件的同一行上填充下一列值的行中的空值

Question

I have this type of DataFrame

name     surname       middle

Frank    Doe           NaN
John     Nan           Wood
Jack     Putt          Nan
Frank    Nan           Joyce

I want to move "middle" values on NaN same rows values on "surname" column. How can i do this? I tried to use the fillna method but got no results. Here is my code:

import os
from pandas.io.parsers import read_csv


for csvFilename in os.listdir('.'):
   if not csvFilename.endswith('.csv'):
      continue
data=read_csv(csvFilename)
filtered_data["surname"].fillna(filtered_data["middle"].mean(),inplace=True)
filtered_data.to_csv('output.csv' , index=False)

Answer 1

Conditional column flipping

Using pd.isnull() , columns can be rearranged conditionally.

import pandas as pd
from cStringIO import StringIO

# Create fake DataFrame... you can read this in however you like
df = pd.read_table(StringIO('''
name     surname       middle
Frank    Doe           NaN
John     NaN           Wood
Jack     Putt          NaN
Frank    NaN           Joyce'''), sep='\s+')

print 'Original DataFrame:'
print df
print

# Assign the middle name to any surname with a NaN
df.loc[pd.isnull(df['surname']), 'surname'] = df[pd.isnull(df['surname'])]['middle']

print 'Manipulated DataFrame:'
print df
print

---

Original DataFrame:
    name surname middle
0  Frank     Doe    NaN
1   John     NaN   Wood
2   Jack    Putt    NaN
3  Frank     NaN  Joyce

Manipulated DataFrame:
    name surname middle
0  Frank     Doe    NaN
1   John    Wood   Wood
2   Jack    Putt    NaN
3  Frank   Joyce  Joyce

Answer 2

I think there is an easier way to do that:

df['surname'] = df['middle'].combine_first(df['surname'])
print(df)

Output:

    name surname middle
0  Frank     Doe    NaN
1   John    Wood   Wood
2   Jack    Putt    NaN
3  Frank   Joyce  Joyce