數據處理——數據合併

# 同樣,數據處理就先給導入pandas先
import pandas as pd

# df1==df2
df1 = pd.DataFrame({'一班':[90,80,66,75,99,55,76,78,98,None,90],
                   '二班':[75,98,100,None,77,45,None,66,56,80,57],
                   '三班':[45,89,77,67,65,100,None,75,64,88,99]})
df2 = pd.DataFrame({'一班':[90,80,66,75,99,55,76,78,98,None,90],
                  '二班':[75,98,100,None,77,45,None,66,56,80,57],
                  '三班':[45,89,77,67,65,100,None,75,64,88,99]})

 

1數據堆疊

  數據堆疊分爲如下兩種:app

    • 行堆疊
    • 列堆疊

  pd.concat(objs, axis=0)函數

  • objs:參與合併的多個DataFrame。無默認
  • axis:表示軸向,axis=0表示行合併,axis=1表示列合併
pd.concat([df1, df2, df3], axis=1)
  一班 三班 二班 一班 三班 二班 一班 三班 二班
0 90.0 45.0 75.0 90.0 45.0 75.0 90.0 45.0 75.0
1 80.0 89.0 98.0 80.0 89.0 98.0 80.0 89.0 98.0
2 66.0 77.0 100.0 66.0 77.0 100.0 66.0 77.0 100.0
3 75.0 67.0 NaN 75.0 67.0 NaN 75.0 67.0 NaN
4 99.0 65.0 77.0 99.0 65.0 77.0 99.0 65.0 77.0
5 55.0 100.0 45.0 55.0 100.0 45.0 55.0 100.0 45.0
6 76.0 NaN NaN 76.0 NaN NaN 76.0 NaN NaN
7 78.0 75.0 66.0 78.0 75.0 66.0 78.0 75.0 66.0
8 98.0 64.0 56.0 98.0 64.0 56.0 98.0 64.0 56.0
9 NaN 88.0 80.0 NaN 88.0 80.0 NaN 88.0 80.0
10 90.0 99.0 57.0 90.0 99.0 57.0 90.0 99.0 57.0

 

  固然,若是axis=0(行堆疊)時,也能夠使用append函數spa

# append 直接在末尾追加,注意特徵數目相同,而且數據類型相同
df1.append(df2)
  一班 三班 二班
0 90.0 45.0 75.0
1 80.0 89.0 98.0
2 66.0 77.0 100.0
3 75.0 67.0 NaN
4 99.0 65.0 77.0
5 55.0 100.0 45.0
6 76.0 NaN NaN
7 78.0 75.0 66.0
8 98.0 64.0 56.0
9 NaN 88.0 80.0
10 90.0 99.0 57.0
0 90.0 45.0 75.0
1 80.0 89.0 98.0
2 66.0 77.0 100.0
3 75.0 67.0 NaN
4 99.0 65.0 77.0
5 55.0 100.0 45.0
6 76.0 NaN NaN
7 78.0 75.0 66.0
8 98.0 64.0 56.0
9 NaN 88.0 80.0
10 90.0 99.0 57.0

 

2主鍵合併

  主鍵合併大概是應用最關的合併方式了,也是我最喜歡的方式。code

 

pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None, suffixes=('_x', '_y'))blog

  • left:表示進行合併的左邊的DataFrame。無默認。
  • right:表示進行合併的右邊的DataFrame。無默認。
  • how:表示合併的方法。默認爲'inner'。可取'left'(左鏈接),'right'(右鏈接),'inner'(內鏈接),'outer'(外鏈接)。
  • on:表示合併的主鍵。默認爲空。
  • left_on:表示左邊的合併主鍵。默認爲空。
  • right_on:表示右邊的合併主鍵。默認爲空。
  • suffixes:表示列名相同的時候的後綴。默認爲('_x', '_y')

 

# 合併數據
pd.merge(df1, df2, on='一班')
  一班 三班_x 二班_x 三班_y 二班_y
0 90.0 45.0 75.0 45.0 75.0
1 90.0 45.0 75.0 99.0 57.0
2 90.0 99.0 57.0 45.0 75.0
3 90.0 99.0 57.0 99.0 57.0
4 80.0 89.0 98.0 89.0 98.0
5 66.0 77.0 100.0 77.0 100.0
6 75.0 67.0 NaN 67.0 NaN
7 99.0 65.0 77.0 65.0 77.0
8 55.0 100.0 45.0 100.0 45.0
9 76.0 NaN NaN NaN NaN
10 78.0 75.0 66.0 75.0 66.0
11 98.0 64.0 56.0 64.0 56.0
12 NaN 88.0 80.0 88.0 80.0

 

pd.merge(df1, df2, left_on='一班', right_on='二班', suffixes=('_1', '_2))
  一班_1 三班_1 二班_1 一班_2 三班_2 二班_2
0 80.0 89.0 98.0 NaN 88.0 80.0
1 66.0 77.0 100.0 78.0 75.0 66.0
2 75.0 67.0 NaN 90.0 45.0 75.0
3 98.0 64.0 56.0 80.0 89.0 98.0
4 NaN 88.0 80.0 75.0 67.0 NaN
5 NaN 88.0 80.0 76.0 NaN NaN

 

3重疊合並

  不是特別建議,畢竟重疊合並沒什麼依據,並且浪費數據資源。資源

 

  DataFrame.combine_first(other) 重疊合並,當二者皆有之前者爲準,爲空時,則使用後者的補上。pandas

df1['一班'].combine_first(df1['二班'])
0     90.0
1     80.0
2     66.0
3     75.0
4     99.0
5     55.0
6     76.0
7     78.0
8     98.0
9     80.0
10    90.0
Name: 一班, dtype: float64
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