五、Pandas —— 合并 concat

Bertha 。 2023-08-22 06:37 2227阅读 0赞

import pandas as pd
    import numpy as np
    # concatenating
    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)
    #      a    b    c    d
    # 0  0.0  0.0  0.0  0.0
    # 1  0.0  0.0  0.0  0.0
    # 2  0.0  0.0  0.0  0.0
    print(df2)
    #      a    b    c    d
    # 0  1.0  1.0  1.0  1.0
    # 1  1.0  1.0  1.0  1.0
    # 2  1.0  1.0  1.0  1.0
    print(df3)
    #      a    b    c    d
    # 0  2.0  2.0  2.0  2.0
    # 1  2.0  2.0  2.0  2.0
    # 2  2.0  2.0  2.0  2.0
    print("**********concat 上下(左右)合并**********")
    # axis = 0 指定行，表示 上下合并
    # axis = 1 指定列，表示 左右合并
    # 指定ignore_index = True，表示忽略原有行号，生成新行号
    res = pd.concat([df1, df2, df3], axis=0, ignore_index=True)
    print(res)
    #      a    b    c    d
    # 0  0.0  0.0  0.0  0.0
    # 1  0.0  0.0  0.0  0.0
    # 2  0.0  0.0  0.0  0.0
    # 0  1.0  1.0  1.0  1.0
    # 1  1.0  1.0  1.0  1.0
    # 2  1.0  1.0  1.0  1.0
    # 0  2.0  2.0  2.0  2.0
    # 1  2.0  2.0  2.0  2.0
    # 2  2.0  2.0  2.0  2.0
    print("**********concat的参数 join 合并**********")
    # axis = 0 指定行，表示 上下合并
    # axis = 1 指定列，表示 左右合并
    # join = {'inner', 'outer'}
    df4 = pd.DataFrame(np.ones((3, 4))*0, columns=['a', 'b', 'c', 'd'])
    df5 = pd.DataFrame(np.ones((3, 4))*1, columns=['b', 'c', 'd', 'e'])
    print(df4)
    #      a    b    c    d
    # 0  0.0  0.0  0.0  0.0
    # 1  0.0  0.0  0.0  0.0
    # 2  0.0  0.0  0.0  0.0
    print(df5)
    #      b    c    d    e
    # 0  1.0  1.0  1.0  1.0
    # 1  1.0  1.0  1.0  1.0
    # 2  1.0  1.0  1.0  1.0
    print("=======参数join的 outer上下（左右）合并方式=========")
    # 会报警告,Python 3.7需要加上参数sort=False，就不会报警 默认是 join='outer' 链接,
    res = pd.concat([df4, df5], join='outer',  sort=False,ignore_index=True)
    print(res)
    #      a    b    c    d    e
    # 0  0.0  0.0  0.0  0.0  NaN
    # 1  0.0  0.0  0.0  0.0  NaN
    # 2  0.0  0.0  0.0  0.0  NaN
    # 3  NaN  1.0  1.0  1.0  1.0
    # 4  NaN  1.0  1.0  1.0  1.0
    # 5  NaN  1.0  1.0  1.0  1.0
    print("=======参数join的 inner 合并方式=========")
    res = pd.concat([df4, df5], join='inner',ignore_index=True)
    print(res)
    #      b    c    d
    # 0  0.0  0.0  0.0
    # 1  0.0  0.0  0.0
    # 2  0.0  0.0  0.0
    # 3  1.0  1.0  1.0
    # 4  1.0  1.0  1.0
    # 5  1.0  1.0  1.0
    print("**********concat的参数 join_axes 合并**********")
    # axis = 0 指定行，表示 上下合并
    # axis = 1 指定列，表示 左右合并
    df6 = pd.DataFrame(np.ones((3, 4))*0, columns=['a', 'b', 'c', 'd'],index=[1, 2, 3])
    df7 = pd.DataFrame(np.ones((3, 4))*1, columns=['b', 'c', 'd', 'e'],index=[2, 3, 4])
    print("===========左右合并时，行号不一致============")
    # join_axes （指定哪个df为左表）指定按照哪一个df的行号进行合并
    # 指定参数 join_axes时，类似于按照指定的左表进行左连接
    res = pd.concat([df6, df7], axis=1, join_axes=[df7.index])
    print(res)
    #      a    b    c    d    b    c    d    e
    # 1  0.0  0.0  0.0  0.0  NaN  NaN  NaN  NaN
    # 2  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0
    # 3  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0
    
    # 不指定参数 join_axes时，类似于全连接
    res = pd.concat([df6, df7], axis=1)
    print(res)
    #      a    b    c    d    b    c    d    e
    # 1  0.0  0.0  0.0  0.0  NaN  NaN  NaN  NaN
    # 2  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0
    # 3  0.0  0.0  0.0  0.0  1.0  1.0  1.0  1.0
    # 4  NaN  NaN  NaN  NaN  1.0  1.0  1.0  1.0
    
    print("**********concat的参数 append 合并**********")
    # axis = 0 指定行，表示 上下合并
    # axis = 1 指定列，表示 左右合并
    df8 = pd.DataFrame(np.ones((3, 4))*0, columns=['a', 'b', 'c', 'd'])
    df9 = pd.DataFrame(np.ones((3, 4))*1, columns=['a', 'b', 'c', 'd'])
    df10 = pd.DataFrame(np.ones((3, 4))*3, columns=['a', 'b', 'c', 'd'])
    print(df8)
    #      a    b    c    d
    # 0  0.0  0.0  0.0  0.0
    # 1  0.0  0.0  0.0  0.0
    # 2  0.0  0.0  0.0  0.0
    print(df9)
    #      b    c    d    e
    # 0  1.0  1.0  1.0  1.0
    # 1  1.0  1.0  1.0  1.0
    # 2  1.0  1.0  1.0  1.0
    print(df10)
    #      a    b    c    d
    # 0  3.0  3.0  3.0  3.0
    # 1  3.0  3.0  3.0  3.0
    # 2  3.0  3.0  3.0  3.0
    print("==========连接两个 df=============")
    # 不指定参数 sort=False 时会报警
    # df9 追加到 df8 后面
    res = df8.append(df9, ignore_index=True, sort=False)
    print(res)
    #      a    b    c    d
    # 0  0.0  0.0  0.0  0.0
    # 1  0.0  0.0  0.0  0.0
    # 2  0.0  0.0  0.0  0.0
    # 3  1.0  1.0  1.0  1.0
    # 4  1.0  1.0  1.0  1.0
    # 5  1.0  1.0  1.0  1.0
    print("============连接多个df============")
    res = df8.append([df9, df10], ignore_index=True, sort=False)
    print(res)
    #      a    b    c    d
    # 0  0.0  0.0  0.0  0.0
    # 1  0.0  0.0  0.0  0.0
    # 2  0.0  0.0  0.0  0.0
    # 3  1.0  1.0  1.0  1.0
    # 4  1.0  1.0  1.0  1.0
    # 5  1.0  1.0  1.0  1.0
    # 6  3.0  3.0  3.0  3.0
    # 7  3.0  3.0  3.0  3.0
    # 8  3.0  3.0  3.0  3.0
    print("===========append添加Series=============")
    s1 = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])
    res = df8.append(s1, ignore_index=True)
    print(res)
    #      a    b    c    d
    # 0  0.0  0.0  0.0  0.0
    # 1  0.0  0.0  0.0  0.0
    # 2  0.0  0.0  0.0  0.0
    # 3  1.0  2.0  3.0  4.0