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 |
