Python Pandas 用另一列下一行的值替换一列中的 NaN

Question

name	ID	gender
John	123	male
Scot	na	na
124	male	na
Jill	231	female

I want to cut the missing values for "Scot" from the below and paste them instead of the "nan" values so the new dataframe will be thus:

name	ID	gender
John	123	male
Scot	124	male
Jill	231	female

Answer 1

I think you're looking for bfill .

Here's example: https://www.geeksforgeeks.org/python-pandas-series-bfill/

So this should do it:

df['ID'] = df['ID'].bfill()
df['gender'] = df['gender'].bfill()

or, if you don't need to be selective, you can run it on the entire dataframe:

df = df.bfill()

Answer 2

It might be easier to fix that, by changeing the way you originally load the data, because it seems you have a linebreak there. However you could do something like this:

Test data:

import pandas as pd
import numpy as np

df = pd.DataFrame({'name': {0: 'John', 1: 'Scot', 2: '124', 3: 'Jill'},
 'ID': {0: '123', 1: np.nan, 2: 'male', 3: '231'},
 'gender': {0: 'male', 1: np.nan, 2: np.nan, 3: 'female'}})

Code:

# find out which rows are valid (m) and which contain the offset data (m2)
m = df['ID'].isna()
m2 = m.shift(fill_value=False)

# create a separate dataframe, only containing the relevant row and columns for filling nan values
df2 = df[df.columns[:-1]][m2].copy()

# harmonize the index and column names so it fits the original dataframe
df2.columns = df.columns[1:]
df2.index = df2.index-1

# fill empty values by using the newly created dataframe values
df.fillna(df2)[~m2]

Output:

#    name   ID  gender
# 0  John  123    male
# 1  Scot  124    male
# 3  Jill  231  female