在DataFrame中为NaN行应用映射，Python3.6

import pandas as pd

# 创建一个包含NaN值的DataFrame
data = {'A': [1, 2, None, 4, 5],
        'B': [None, 6, 7, 8, None],
        'C': [9, 10, None, None, 13]}
df = pd.DataFrame(data)

# 使用fillna()方法填充NaN行为指定值
df_filled = df.fillna({'A': 0, 'B': 0, 'C': 0})

print(df_filled)

     A    B     C
0  1.0  0.0   9.0
1  2.0  6.0  10.0
2  0.0  7.0   0.0
3  4.0  8.0   0.0
4  5.0  0.0  13.0

import pandas as pd
import numpy as np

# 创建一个包含NaN值的DataFrame
data = {'A': [1, 2, None, 4, 5],
        'B': [None, 6, 7, 8, None],
        'C': [9, 10, None, None, 13]}
df = pd.DataFrame(data)

# 自定义函数，将NaN行应用映射为指定的值
def map_nan(row):
    if np.isnan(row['A']):
        row['A'] = 0
    if np.isnan(row['B']):
        row['B'] = 0
    if np.isnan(row['C']):
        row['C'] = 0
    return row

# 使用apply()方法应用自定义函数
df_mapped = df.apply(map_nan, axis=1)

print(df_mapped)

     A    B     C
0  1.0  0.0   9.0
1  2.0  6.0  10.0
2  0.0  7.0   0.0
3  4.0  8.0   0.0
4  5.0  0.0  13.0