Python學習在pandas（五）合併資料merge、視覺化資料plt
阿新 • • 發佈：2018-12-14
1、依據一組key合併
import pandas as pd #定義資料集並打印出 left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'], 'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3']}) right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'], 'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3']}) print(left) print(right) res = pd.merge(left, right, on='key')#合併key列 print(res)
結果：
2、依據key1與key2 columns進行合併，並打印出四種結果['left', 'right', 'outer', 'inner']
import pandas as pd #定義資料集並打印出 left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'], 'key2': ['K0', 'K1', 'K0', 'K1'], 'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3']}) right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'], 'key2': ['K0', 'K0', 'K0', 'K0'], 'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3']}) print(left) print(right) #依據key1與key2 columns進行合併，並打印出四種結果['left', 'right', 'outer', 'inner'] res = pd.merge(left, right, on=['key1', 'key2'], how='inner') print(res) res = pd.merge(left, right, on=['key1', 'key2'], how='outer') print(res) res = pd.merge(left, right, on=['key1', 'key2'], how='left') print(res) res = pd.merge(left, right, on=['key1', 'key2'], how='right') print(res)
結果：
key1 key2 A B 0 K0 K0 A0 B0 1 K0 K1 A1 B1 2 K1 K0 A2 B2 3 K2 K1 A3 B3 key1 key2 C D 0 K0 K0 C0 D0 1 K1 K0 C1 D1 2 K1 K0 C2 D2 3 K2 K0 C3 D3 #相同的擷取 key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K1 K0 A2 B2 C1 D1 2 K1 K0 A2 B2 C2 D2 key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K0 K1 A1 B1 NaN NaN 2 K1 K0 A2 B2 C1 D1 3 K1 K0 A2 B2 C2 D2 4 K2 K1 A3 B3 NaN NaN 5 K2 K0 NaN NaN C3 D3 key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K0 K1 A1 B1 NaN NaN 2 K1 K0 A2 B2 C1 D1 3 K1 K0 A2 B2 C2 D2 4 K2 K1 A3 B3 NaN NaN key1 key2 A B C D 0 K0 K0 A0 B0 C0 D0 1 K1 K0 A2 B2 C1 D1 2 K1 K0 A2 B2 C2 D2 3 K2 K0 NaN NaN C3 D3
3、關於merge我不太明白，具體在這裡有介紹：merge
4、視覺化
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = pd.DataFrame(
np.random.randn(1000,4),
index=np.arange(1000),
columns=list("ABCD")
)
#data.cumsum()
data.plot()#類似於matplotlib中的plt.plot
plt.show()
結果：
程式碼：
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
data = pd.DataFrame(
np.random.randn(1000,4),
index=np.arange(1000),
columns=list("ABCD")
)#生成資料
data.cumsum()#用於累加
ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label='Class1')
# 將之下這個 data 畫在上一個 ax 上面
data.plot.scatter(x='A',y='C',color='LightGreen',label='Class2',ax=ax)#ax = ax表在第一張圖片上繼續畫圖，把data附在ax中
plt.show()結果：
