3.6，3.7pandas合并concat&merge

頭文件：

import pandas as pd import numpy as np

concat基礎(chǔ)合并用法

df1= pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d']) df2= pd.DataFrame(np.ones((3,4))*1,columns = ['a','b','c','d']) df3= pd.DataFrame(np.ones((3,4))*2,columns = ['a','b','c','d'])res = pd.concat([df1,df2,df3],axis=0,ignore_index=True) #axis=0 豎向合并 ignore_index是讓索引重新排序df1= pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d']) df2= pd.DataFrame(np.ones((3,4))*1,columns = ['b','c','d','e'])

concat中join用法

#join='outer'可以對標(biāo)簽不一樣的部分用NAN進(jìn)行填充 #join = 'inter'時(shí)對標(biāo)簽不一樣的部分去掉 res = pd.concat([df1,df2],join='outer',ignore_index=True)

join_axes (新版本刪除了)

join_axes= [df1.index]設(shè)置合并后按照df1的索引進(jìn)行保留

# res = pd.concat([df1,df2],axis=1,join_axes = [df1.index])

append添加數(shù)據(jù)

#添加整個(gè)數(shù)據(jù) df1= pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d']) df2= pd.DataFrame(np.ones((3,4))*1,columns = ['a','b','c','d']) df3= pd.DataFrame(np.ones((3,4))*2,columns = ['a','b','c','d']) res = df1.append([df2,df3],ignore_index=True) #上下結(jié)合#添加一行數(shù)據(jù) s1 = pd.Series([1,2,3,4],index=['a','b','c','d']) res = df1.append(s1,ignore_index=True)

merge按照key合并

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'] }) #按照key合并 res = pd.merge(left,right,on='key') print(res)

merge考慮兩個(gè)key,根據(jù)index進(jìn)行數(shù)據(jù)合并

#若考慮兩個(gè)key,根據(jù)index進(jìn)行數(shù)據(jù)合并 res = pd.merge(left,right,on=['key1','ke2']) #默認(rèn)是'inner'：只考慮相同部分 res = pd.merge(left,right,on=['key1','ke2'],how='outer') #outer：全部考慮，不存在的用nan填充

merge中indicator

indicator=True時(shí)，能展示合并后，哪部分是有某標(biāo)簽數(shù)據(jù)，哪部分是沒有該標(biāo)簽數(shù)據(jù)的

res = pd.merge(left,right,on=['key1','ke2'],how='outer',indicator=True)

merge中l(wèi)eft_index和right_index

根據(jù)數(shù)據(jù)索引進(jìn)行結(jié)合
res = pd.merge(left,right,left_index=True,right_index=True,how=‘outer’)

merge中suffixes

suffixes來區(qū)分標(biāo)簽相同但是數(shù)值不同的數(shù)據(jù)

boys = pd.DataFrame({'K':['K0','K1','K2'],'age':[1,2,3]}) girls = pd.DataFrame({'K':['K0','K1','K2'],'age':[4,5,6]})res = pd.merge(boys,girls,on='K',suffixes=['_boy','_girl'],how='inner')

3.8pandas plot畫圖

頭文件：

import pandas as pd import numpy as np import matplotlib.pyplot as plt

Series的數(shù)據(jù)（類似數(shù)組）

data = pd.Series(np.random.randn(1000),index=np.arange(1000)) data = data.cumsum() #對生成的1000個(gè)數(shù)據(jù)進(jìn)行累加 data.plot() plt.show()

DataFrame數(shù)據(jù)（類似矩陣）

折線圖：

data = pd.DataFrame(np.random.randn(1000,4),index = np.arange(1000),columns = list("ABCD")) data = data.cumsum() data.plot() plt.show()

散點(diǎn)圖

ax = data.plot.scatter(x='A',y='B',color='DarkBlue',label = 'Class 1') data.plot.scatter(x = 'A',y = 'C',color = 'DarkGreen',label='Class 2',ax=ax) plt.show() 創(chuàng)作挑戰(zhàn)賽新人創(chuàng)作獎(jiǎng)勵(lì)來咯，堅(jiān)持創(chuàng)作打卡瓜分現(xiàn)金大獎(jiǎng)

總結(jié)

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