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