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  • pandas-赋值操作

    1，pandas操作主要有对指定位置的赋值，如上一篇中的数据选择一样，根据loc，iloc，ix选择指定位置，直接赋值

    2，插入，insert方法，插入行和列

    3，添加

    4，删除 drop方法

    5，弹出 pop方法

    In [1]:

    import pandas as pd
import numpy as np

    In [53]:

    dates = np.arange(20190809,20190815)
df1 = pd.DataFrame(np.arange(24).reshape(6,4),index=dates,columns=["A","B","C","D"])
df1

    Out[53]:

    A B C D
    20190809 0 1 2 3
    20190810 4 5 6 7
    20190811 8 9 10 11
    20190812 12 13 14 15
    20190813 16 17 18 19
    20190814 20 21 22 23

    In [20]:

    df1.iloc[2,2]

    Out[20]:

    10

    In [44]:

    df1.iloc[2,2] = 100
df1

    Out[44]:

    A B C D
    20190809 0 1 2 3
    20190810 4 5 6 7
    20190811 8 9 100 11
    20190812 12 13 14 15
    20190813 16 17 18 19
    20190814 20 21 22 23

    In [40]:

    df1.loc[20190810,"B"]=200
df1

    Out[40]:

    A B C D
    20190809 0 1 2 3
    20190810 4 200 6 7
    20190811 8 9 10 11
    20190812 12 13 14 15
    20190813 16 17 18 19
    20190814 20 21 22 23

    In [54]:

    df1[df1.A>10]=0
df1

    Out[54]:

    A B C D
    20190809 0 1 2 3
    20190810 4 5 6 7
    20190811 8 9 10 11
    20190812 0 0 0 0
    20190813 0 0 0 0
    20190814 0 0 0 0

    In [55]:

    df1.A[df1.A==0]=100
df1

    Out[55]:

    A B C D
    20190809 100 1 2 3
    20190810 4 5 6 7
    20190811 8 9 10 11
    20190812 100 0 0 0
    20190813 100 0 0 0
    20190814 100 0 0 0

    In [56]:

    #插入一列
df1["E"]=10
df1

    Out[56]:

    A B C D E
    20190809 100 1 2 3 10
    20190810 4 5 6 7 10
    20190811 8 9 10 11 10
    20190812 100 0 0 0 10
    20190813 100 0 0 0 10
    20190814 100 0 0 0 10

    In [59]:

    df1["F"]=pd.Series([1,2,3,4,5,6],index=dates)
df1

    Out[59]:

    A B C D E F
    20190809 100 1 2 3 10 1
    20190810 4 5 6 7 10 2
    20190811 8 9 10 11 10 3
    20190812 100 0 0 0 10 4
    20190813 100 0 0 0 10 5
    20190814 100 0 0 0 10 6

    In [62]:

    #添加一行
df1.loc[20190815,["A","B","C"]]=[5,6,8]
df1

    Out[62]:

    A B C D E F
    20190809 100.0 1.0 2.0 3.0 10.0 1.0
    20190810 4.0 5.0 6.0 7.0 10.0 2.0
    20190811 8.0 9.0 10.0 11.0 10.0 3.0
    20190812 100.0 0.0 0.0 0.0 10.0 4.0
    20190813 100.0 0.0 0.0 0.0 10.0 5.0
    20190814 100.0 0.0 0.0 0.0 10.0 6.0
    20190815 5.0 6.0 8.0 NaN NaN NaN

    In [65]:

    s1=pd.Series([1,2,3,4,5,6],index=["A","B","C","D","E","F"])
s1.name="S1"
df2 = df1.append(s1)
df2

    Out[65]:

    A B C D E F
    20190809 100.0 1.0 2.0 3.0 10.0 1.0
    20190810 4.0 5.0 6.0 7.0 10.0 2.0
    20190811 8.0 9.0 10.0 11.0 10.0 3.0
    20190812 100.0 0.0 0.0 0.0 10.0 4.0
    20190813 100.0 0.0 0.0 0.0 10.0 5.0
    20190814 100.0 0.0 0.0 0.0 10.0 6.0
    20190815 5.0 6.0 8.0 NaN NaN NaN
    S1 1.0 2.0 3.0 4.0 5.0 6.0

    In [67]:

    #插入一列
df1.insert(1,"G",df2["E"])
df1

    Out[67]:

    A G B C D E F
    20190809 100.0 10.0 1.0 2.0 3.0 10.0 1.0
    20190810 4.0 10.0 5.0 6.0 7.0 10.0 2.0
    20190811 8.0 10.0 9.0 10.0 11.0 10.0 3.0
    20190812 100.0 10.0 0.0 0.0 0.0 10.0 4.0
    20190813 100.0 10.0 0.0 0.0 0.0 10.0 5.0
    20190814 100.0 10.0 0.0 0.0 0.0 10.0 6.0
    20190815 5.0 NaN 6.0 8.0 NaN NaN NaN

    In [68]:

    g=df1.pop("G")
df1.insert(6,"G",g)
df1

    Out[68]:

    A B C D E F G
    20190809 100.0 1.0 2.0 3.0 10.0 1.0 10.0
    20190810 4.0 5.0 6.0 7.0 10.0 2.0 10.0
    20190811 8.0 9.0 10.0 11.0 10.0 3.0 10.0
    20190812 100.0 0.0 0.0 0.0 10.0 4.0 10.0
    20190813 100.0 0.0 0.0 0.0 10.0 5.0 10.0
    20190814 100.0 0.0 0.0 0.0 10.0 6.0 10.0
    20190815 5.0 6.0 8.0 NaN NaN NaN NaN

    In [69]:

    #删除列
del df1["G"]
df1

    Out[69]:

    A B C D E F
    20190809 100.0 1.0 2.0 3.0 10.0 1.0
    20190810 4.0 5.0 6.0 7.0 10.0 2.0
    20190811 8.0 9.0 10.0 11.0 10.0 3.0
    20190812 100.0 0.0 0.0 0.0 10.0 4.0
    20190813 100.0 0.0 0.0 0.0 10.0 5.0
    20190814 100.0 0.0 0.0 0.0 10.0 6.0
    20190815 5.0 6.0 8.0 NaN NaN NaN

    In [70]:

    df2 = df1.drop(["A","B"],axis=1)
df1

    Out[70]:

    A B C D E F
    20190809 100.0 1.0 2.0 3.0 10.0 1.0
    20190810 4.0 5.0 6.0 7.0 10.0 2.0
    20190811 8.0 9.0 10.0 11.0 10.0 3.0
    20190812 100.0 0.0 0.0 0.0 10.0 4.0
    20190813 100.0 0.0 0.0 0.0 10.0 5.0
    20190814 100.0 0.0 0.0 0.0 10.0 6.0
    20190815 5.0 6.0 8.0 NaN NaN NaN

    In [71]:

    df2

    Out[71]:

    C D E F
    20190809 2.0 3.0 10.0 1.0
    20190810 6.0 7.0 10.0 2.0
    20190811 10.0 11.0 10.0 3.0
    20190812 0.0 0.0 10.0 4.0
    20190813 0.0 0.0 10.0 5.0
    20190814 0.0 0.0 10.0 6.0
    20190815 8.0 NaN NaN NaN

    In [73]:

    #删除行
df2=df1.drop([20190810,20190812],axis=0)
df1

    Out[73]:

    A B C D E F
    20190809 100.0 1.0 2.0 3.0 10.0 1.0
    20190810 4.0 5.0 6.0 7.0 10.0 2.0
    20190811 8.0 9.0 10.0 11.0 10.0 3.0
    20190812 100.0 0.0 0.0 0.0 10.0 4.0
    20190813 100.0 0.0 0.0 0.0 10.0 5.0
    20190814 100.0 0.0 0.0 0.0 10.0 6.0
    20190815 5.0 6.0 8.0 NaN NaN NaN

    In [74]:

    df2

    Out[74]:

    A B C D E F
    20190809 100.0 1.0 2.0 3.0 10.0 1.0
    20190811 8.0 9.0 10.0 11.0 10.0 3.0
    20190813 100.0 0.0 0.0 0.0 10.0 5.0
    20190814 100.0 0.0 0.0 0.0 10.0 6.0
    20190815 5.0 6.0 8.0 NaN NaN NaN
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  • 原文地址：https://www.cnblogs.com/mrwuzs/p/11325205.html
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