# 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]})
數據堆疊分爲如下兩種:app
pd.concat(objs, axis=0)函數
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 |
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
# 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 |
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