通過使用另一個Pandas DataFrame在Pandas DataFrame中填寫NA值

Question

import pandas as pd


df1 = pd.DataFrame({
                  'value1': ["a","a","a","b","b","b","c","c"],
                  'value2': [1,2,3,4,4,4,5,5],
                    'value3': [1,2,3, None , None, None, None, None],
                    'value4': [1,2,3,None , None, None, None, None],
                    'value5': [1,2,3,None , None, None, None, None]})

df2 = pd.DataFrame({
                  'value1': ["k","j","l","m","x","y"],
                  'value2': [2, 2, 1, 3, 4, 5],
                  'value3': [2, 2, 2, 3, 4, 5],
                  'value4': [3, 2, 2, 3, 4, 5],
                  'value5': [2, 1, 2, 3, 4, 5]})

df1 = 
  value1  value2  value3  value4  value5
0      a       1     1.0     1.0     1.0
1      a       2     2.0     2.0     2.0
2      a       3     3.0     3.0     3.0
3      b       4     NaN     NaN     NaN
4      b       4     NaN     NaN     NaN
5      b       4     NaN     NaN     NaN
6      c       5     NaN     NaN     NaN
7      c       5     NaN     NaN     NaN

df2 = 
  value1  value2  value3  value4  value5
0      k       2       2       3       2
1      j       2       2       2       1
2      l       1       2       2       2
3      m       3       3       3       3
4      x       4       4       4       4
5      y       5       5       5       5

我想從df2中的值填充df1中的NaN

所以df1的結果看起來像

df1 = 
  value1  value2  value3  value4  value5
0      a       1     1.0     1.0     1.0
1      a       2     2.0     2.0     2.0
2      a       3     3.0     3.0     3.0
3      b       4     2       2       1
4      b       4     2       2       2
5      b       4     3       3       3
6      c       5     4       4       4
7      c       5     5       5       5

我用下面的代碼。

tmp1 = df1[df1.value1 == 'b'].iloc[:, 2:]
tmp2 = df2.iloc[1:, 2:]

tmp1 = tmp2可以更新tmp1中的值，但是當我使用以下命令時

df1[df1.value1 == 'b'].iloc[:, 2:]= tmp2

如下所示，它不會更新df1中的值。

  value1  value2  value3  value4  value5
0      a       1     1.0     1.0     1.0
1      a       2     2.0     2.0     2.0
2      a       3     3.0     3.0     3.0
3      b       4     NaN     NaN     NaN
4      b       4     NaN     NaN     NaN
5      b       4     NaN     NaN     NaN
6      c       5     NaN     NaN     NaN
7      c       5     NaN     NaN     NaN

為什么會發生，如何解決這個問題？

謝謝。

Answer 1

該行不執行您認為的操作：

tmp1 = df1[df1.value1 == 'b'].iloc[:, 2:]

方法被順序地施加，因此df1[df1.value1 == 'b']只保留行3, 4, 5從df1 。 但這不是您想要的，您想要從滿足條件的第一個實例開始更新所有行 。

而是，首先找到所需的索引。

idx = df1['value1'].eq('b').values.argmax()

然后，您需要顯式分配 df2的最后n行：

df1.iloc[idx:, 2:] = df2.iloc[-(len(df1.index)-idx):, 2:].values

print(df1)

  value1  value2  value3  value4  value5
0      a       1     1.0     1.0     1.0
1      a       2     2.0     2.0     2.0
2      a       3     3.0     3.0     3.0
3      b       4     2.0     2.0     1.0
4      b       4     2.0     2.0     2.0
5      b       4     3.0     3.0     3.0
6      c       5     4.0     4.0     4.0
7      c       5     5.0     5.0     5.0

Answer 2

如果要使用索引對齊替換nan值，請使用pandas fillna

df1.fillna(df2)

如果要更新df1，請添加就位

df1.fillna(df2, inplace=True)

-

• 編輯沒有對齊索引的情況：

如果目標值和替換值的索引未對齊，則可以對齊它們，以便可以使用數據框fillna方法。

要對齊索引，請獲取要替換的df1中包含nans的行的索引，過濾df2以包括替換值，然后將df1中的替換索引分配為df2的索引。 然后使用fillna將值從df2傳輸到df1。

# in this case, find index values when df1.value1 is greater than or equal to 'b'
# (alternately could be indexes of rows containing nans)
idx = df1.index[df1.value1 >= 'b']
# get the section of df2 which will provide replacement values
# limit length to length of idx
align_df = df2[1:len(idx) + 1]
# set the index to match the nan rows from df1
align_df.index = idx
# use auto-alignment with fillna to transfer values from align_df(df2) to df1
df1.fillna(align_df)

# or can use df1.combine_first(align_df) because of the matching target and replacement indexes