Python数据分析（5）数据处理：数据合并、拼接和组合

快来打我* 2022-05-23 13:14 198阅读 0赞

#coding=gbk
    #数据处理
    import numpy as np
    import pandas as pd
    #合并操作
    frame1=pd.DataFrame({'id':['pencil','pen','book','smug'],
                         'price':[12,14,23.5,66]})
    print(frame1)
    #        id  price
    # 0  pencil   12.0
    # 1     pen   14.0
    # 2    book   23.5
    # 3    smug   66.0
    frame2=pd.DataFrame({'id':['pencil','pencil','book','ball'],
                         'color':['red','blue','white','yellow']})
    print(frame2)
    #     color      id
    # 0     red  pencil
    # 1    blue  pencil
    # 2   white    book
    # 3  yellow    ball
    frame3=pd.merge(frame1,frame2,on='id')  #合并操作merge（），将其共同的部分进行合并,on指定列进行合并
    print(frame3)
    #        id  price  color
    # 0  pencil   12.0    red
    # 1  pencil   12.0   blue
    # 2    book   23.5  white
    #2个DataFrame 基准列不同情况下
    f1=pd.DataFrame({'sid':['pencil','pen','book','smug'],
                         'number':[12,14,23.5,66]})
    print(f1)
    print(pd.merge(f1,frame2,left_on='sid',right_on='id'))  #在 id 和 sid 具有相同元素是，合并
    #    number     sid  color      id
    # 0    12.0  pencil    red  pencil
    # 1    12.0  pencil   blue  pencil
    # 2    23.5    book  white    book
    #上述都是内连接，设置选项可以使用how ，有左连接，右连接 和 外连接，其原理与数据库的连接相同
    f2=pd.merge(f1,frame2,left_on='sid',right_on='id',how='left')
    print(f2)
    #    number     sid  color      id    z左连接，选取左边f1的全部元素，右边frame2没有的使用nan表示。
    # 0    12.0  pencil    red  pencil
    # 1    12.0  pencil   blue  pencil
    # 2    14.0     pen    NaN     NaN
    # 3    23.5    book  white    book
    # 4    66.0    smug    NaN     NaN
    print(pd.merge(f1,frame2,left_on='sid',right_on='id',how='outer'))  #外连接
    #    number     sid   color      id
    # 0    12.0  pencil     red  pencil
    # 1    12.0  pencil    blue  pencil
    # 2    14.0     pen     NaN     NaN
    # 3    23.5    book   white    book
    # 4    66.0    smug     NaN     NaN
    # 5     NaN     NaN  yellow    ball
    #根据索引进行合并
    print(pd.merge(frame1,frame2,left_index=True,right_index=True))
    #     id_x  price   color    id_y
    # 0  pencil   12.0     red  pencil
    # 1     pen   14.0    blue  pencil
    # 2    book   23.5   white    book
    # 3    smug   66.0  yellow    ball
    #print(frame1.join(frame2))    不能使用，因为有相同的列名称

#2拼接，numpy的concatenate（） 函数 进行数组的拼接
    array1=np.arange(9).reshape(3,3)
    array2=np.arange(9).reshape(3,3)+12
    print(np.concatenate([array1,array2],axis=1))#在列上增加
    # [[ 0  1  2 12 13 14]
    #  [ 3  4  5 15 16 17]
    #  [ 6  7  8 18 19 20]]
    print(np.concatenate([array1,array2],axis=0))
    # [[ 0  1  2]
    #  [ 3  4  5]
    #  [ 6  7  8]
    #  [12 13 14]
    #  [15 16 17]
    #  [18 19 20]]
    #pandas库的concat（）函数
    s1=pd.Series(np.random.rand(4),index=[1,2,3,4])
    print(s1)
    # 1    0.991280
    # 2    0.637284
    # 3    0.563105
    # 4    0.840878
    # dtype: float64
    s2=pd.Series(np.random.rand(4),index=[5,6,7,8])
    print(s2)
    # 5    0.955175
    # 6    0.236703
    # 7    0.867245
    # 8    0.779975
    # dtype: float64
    print(pd.concat([s1,s2]))
    # 1    0.598666
    # 2    0.630031
    # 3    0.688623
    # 4    0.020529
    # 5    0.956814
    # 6    0.654709
    # 7    0.390657
    # 8    0.531092
    # dtype: float64
    print(pd.concat([s1,s2],axis=1))
    #           0         1
    # 1  0.101299       NaN
    # 2  0.403274       NaN
    # 3  0.893143       NaN
    # 4  0.412365       NaN
    # 5       NaN  0.909780
    # 6       NaN  0.601977
    # 7       NaN  0.227269
    # 8       NaN  0.755501
    print(pd.concat([s1,s2],axis=1,keys=['part1','part2'])) #key设置拼接的部分
    #       part1     part2
    # 1  0.961096       NaN
    # 2  0.513365       NaN
    # 3  0.781232       NaN
    # 4  0.289688       NaN
    # 5       NaN  0.807915
    # 6       NaN  0.981938
    # 7       NaN  0.366429
    # 8       NaN  0.678213

#组合：2个数据集索引部分或者全部重合的情况下     combine_first()
    s3=pd.Series(np.random.rand(5),index=[1,2,3,4,5])
    print(s3)
    # 1    0.165086
    # 2    0.344045
    # 3    0.354699
    # 4    0.283886
    # 5    0.084317
    # dtype: float64
    s4=pd.Series(np.random.rand(4),index=[1,3,4,6])
    print(s4)
    # 1    0.122579
    # 3    0.401560
    # 4    0.948493
    # 6    0.976377
    # dtype: float64
    print(s3.combine_first(s4)) #相同的索引以s3的值填充
    # 1    0.087640
    # 2    0.386778
    # 3    0.817685
    # 4    0.217306
    # 5    0.067610
    # 6    0.276078
    # dtype: float64
    print(s4.combine_first(s3))
    # 1    0.326173
    # 2    0.386778
    # 3    0.615864
    # 4    0.805372
    # 5    0.067610
    # 6    0.276078
    # dtype: float64

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