8.Pandas数据合并concat

# coding: utf-8

import pandas as pd
import numpy as np

arr1=np.random.randint(0,10,(3,4))
arr2=np.random.randint(0,10,(3,4))
print arr1
print arr2

# * numpy中的合并函数concatenate()

# 按照列合并
np.concatenate([arr1,arr2])

# 按照行合并
np.concatenate([arr1,arr2],axis=1)

# * Pandas中的concat()函数
#
# * index索引不重复

ser_obj1 = pd.Series(np.random.randint(0,10,5),index=range(0,5))
ser_obj2 = pd.Series(np.random.randint(0,10,4),index=range(5,9))
ser_obj3 = pd.Series(np.random.randint(0,10,3),index=range(9,12))
print ser_obj1
print ser_obj2
print ser_obj3

# 默认是按照列合并
pd.concat([ser_obj1,ser_obj2,ser_obj3])

pd.concat([ser_obj1,ser_obj2,ser_obj3],axis=1)

# * index索引有重复的情况

ser_obj1 = pd.Series(np.random.randint(0,10,5),index=range(5))
ser_obj2 = pd.Series(np.random.randint(0,10,4),index=range(4))
ser_obj3 = pd.Series(np.random.randint(0,10,3),index=range(3))
print ser_obj1
print ser_obj2
print ser_obj3

pd.concat([ser_obj1,ser_obj2,ser_obj3],axis=1,join = 'inner')

pd.concat([ser_obj1,ser_obj2,ser_obj3])

# * DataFrame对象的concat()

df_obj1 = pd.DataFrame(np.random.randint(0,10,(3,2)),
index=['a','b','c'],columns=['A','B'])
df_obj2 = pd.DataFrame(np.random.randint(0,10,(2,2)),
index=['a','b'],columns=['C','D'])
print df_obj1
print df_obj2

pd.concat([df_obj1,df_obj2])

pd.concat([df_obj1,df_obj2],axis=1)