Python中Pandas的相关使用介绍（三）

本篇介绍以下几个知识点：
（1）利用pandas读取文件
（2）利用concat合并文件
（3）利用append合并文件
（4）利用append合并DataFrame与Series
（5）利用merge合并DataFrame
（6）merge处理重叠区域，handle overlapping

（1）利用pandas读取文件
1.1 没有指定文件的路径

import pandas as pd
import numpy as np

data = pd.read_csv('zuobiao.csv') #read_csv可以读取txt文件，也可以读取excel的csv格式。
print(data)

1.2 指定文件的具体路径

import pandas as pd
import numpy as np

data = pd.read_csv('F:/Python/poem.txt') #指定了文件的具体路径，注意斜杠的形式。
print(data)

（2）利用pandas的concat合并文件
2.1 concat的ignore_index元素

import numpy as np
import pandas as pd

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'])

print(df1,"\n",df2,'\n',df3)

#合并
res1 = pd.concat([df1,df2,df3],axis = 0) #axis = 1是横向合并
print(res1)

res2 = pd.concat([df1,df2,df3],axis = 0,ignore_index = True) #ignore_index = True意思为：索引按顺序来
print(res2)

2.2 concat的join元素

import numpy as np
import pandas as pd

df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'],index = [1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1,columns = ['b','c','d','e'],index = [2,3,4])


print(df1)
print("\n"*2)
print(df2)
res = pd.concat([df1,df2],join = 'inner',ignore_index = True) #outer
print(res)

2.3 concat的join_axes元素

import numpy as np
import pandas as pd

df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'],index = [1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1,columns = ['b','c','d','e'],index = [2,3,4])
res = pd.concat([df1,df2],axis = 1,join_axes = [df1.index])##不要join_axes

print(df1)
print(df2)
print("\n"*2)
print(res)

（3）利用pandas的append合并文件

import numpy as np
import pandas as pd

df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'],index = [1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1,columns = ['b','c','d','e'],index = [2,3,4])
res = df1.append(df2,ignore_index = True)

print(df1)
print(df2)
print("\n"*2)
print(res)

append直接在第一个的结尾附加第二个

（4）利用append合并DataFrame与Series

import numpy as np
import pandas as pd

df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'])
s1 = pd.Series([1,2,3,4],index = ['a','b','c','d'])
res = df1.append(s1,ignore_index = True)

print(df1)
print(s1)
print("\n"*2)
print(res)

（5）利用merge合并DataFrame

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']})
res = pd.merge(left,right,on = 'key')

print(left)
print(right)
print("\n"*2)
print(res)

import numpy as np
import pandas as pd

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']})
res = pd.merge(left,right,on = 'key')

##res = pd.merge(left,right,on = ['key1','key2']) ##只考虑相同的值，交集
##print(res)

##res = pd.merge(left,right,on = ['key1','key2'],how = 'outer') ##不论相同与否,合并两个key，并集。不同的用nan
##print(res)

##res = pd.merge(left,right,on = ['key1','key2'],how = 'right') #基于right
##print(res)

print(left)
print(right)
print("\n"*2)
print(res)

merge的indicator元素

import numpy as np
import pandas as pd

df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']})
df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
res = pd.merge(df1,df2,on = 'col1',how = 'outer',indicator = 'df')#indicator = True

print(df1)
print(df2)
print("\n"*2)
print(res)

merged by index

import numpy as np
import pandas as pd

left = pd.DataFrame({'A':['A0','A1','A2'],
                     'B':['B0','B1','B2']},
                    index = ['K0','K1','K2'])
right = pd.DataFrame({'C':['C0','C1','C2'],
                     'D':['D0','D1','D2']},
                    index = ['K0','K1','K2'])

res = pd.merge(left,right,left_index = True,right_index = True,how = 'outer')
#res = pd.merge(left,right,left_index = True,right_index = True,how = 'inner')

print(left)
print(right)
print("\n"*2)
print(res)

（6）处理重叠区域，handle overlapping

import numpy as np
import pandas as pd

#handle overlapping
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') #outer
#两个都有k，合并后有一个公共的k

print(boys)
print(girls)
print("\n"*2)
print(res)

附加本次学习的所有原始代码：

'''
import pandas as pd
import numpy as np

data = pd.read_csv('zuobiao.csv')
print(data)

data.to_pickle('student.pickle')


#合并DataFrame,concatenaing
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'])

#print(df1,"\n",df2,'\n',df3)

#上下合并
res = pd.concat([df1,df2,df3],axis = 0) #1是横向
##print(res)
##
##res2 = pd.concat([df1,df2,df3],axis = 0,ignore_index = True) #0是横向
##print(res2)


# join,['inner','outer']

##df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'],index = [1,2,3])
##df2 = pd.DataFrame(np.ones((3,4))*1,columns = ['b','c','d','e'],index = [2,3,4])
##res = pd.concat([df1,df2],join = 'inner',ignore_index = True) #outer
##print(res)


## join_axes
##df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'],index = [1,2,3])
##df2 = pd.DataFrame(np.ones((3,4))*1,columns = ['b','c','d','e'],index = [2,3,4])
##res = pd.concat([df1,df2],axis = 1,join_axes = [df1.index])##不要join_axes
##print(res)


##append
df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'],index = [1,2,3])
df2 = pd.DataFrame(np.ones((3,4))*1,columns = ['b','c','d','e'],index = [2,3,4])
res = df1.append(df2,ignore_index = True)
print(res)


##DataFrame加Series
df1 = pd.DataFrame(np.ones((3,4))*0,columns = ['a','b','c','d'])
s1 = pd.Series([1,2,3,4],index = ['a','b','c','d'])
res = df1.append(s1,ignore_index = True)
print(res)

'''





# 合并merge
import pandas as pd
import numpy as np

'''
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)

print("\n"*3)

res = pd.merge(left,right,on = 'key')
print(res)
'''




##有两个key
##left = pd.DataFrame({'key1':['K0','K1','K2','K3'],
##                     'key2':['K0','K1','K0','K1'],
##                     'A':['A0','A1','A2','A3'],
##                     'B':['B0','B1','B2','B3']})
##
##right = pd.DataFrame({'key1':['K0','K1','K2','K3'],
##                      'key2':['K0','K0','K0','K0'],
##                     'C':['C0','C1','C2','C3'],
##                     'D':['D0','D1','D2','D3']})
##
##print(left)
##print(right)
##print('\n'*3)

##res = pd.merge(left,right,on = ['key1','key2']) ##只考虑相同的值，交集
##print(res)

##res = pd.merge(left,right,on = ['key1','key2'],how = 'outer') ##,基于两个的key，并集。不同的用nan
##print(res)

##res = pd.merge(left,right,on = ['key1','key2'],how = 'right') #基于right
##print(res)



###indicator参数
##df1 = pd.DataFrame({'col1':[0,1],'col_left':['a','b']})
##df2 = pd.DataFrame({'col1':[1,2,2],'col_right':[2,2,2]})
##print(df1)
##print(df2)
##print('\n'*3)
##
##res = pd.merge(df1,df2,on = 'col1',how = 'outer',indicator = 'df')#indicator = True
##print(res )




#merged by index

##left = pd.DataFrame({'A':['A0','A1','A2'],
##                     'B':['B0','B1','B2']},
##                    index = ['K0','K1','K2'])
##right = pd.DataFrame({'C':['C0','C1','C2'],
##                     'D':['D0','D1','D2']},
##                    index = ['K0','K1','K2'])
##
##print(left)
##print(right)
##print("\n"*3)
##res = pd.merge(left,right,left_index = True,right_index = True,how = 'outer')
###res = pd.merge(left,right,left_index = True,right_index = True,how = 'inner')
##print(res)



#handle overlapping
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') #outer

print(res)


#join功能

#plt模块画图