根据字典设置 Pandas 值

Question

我想用字典中的值替换数据框的值。用简单的英语来说：如果存在与Column C字典键匹配的值，则替换Column D为字典中与该特定键对应的值。

import pandas as pd
import numpy as np
dfp = pd.DataFrame({'A' : [np.NaN,np.NaN,3,4,5,5,3,1,5,np.NaN], 
                    'B' : [1,0,3,5,0,0,np.NaN,9,0,0], 
                    'C' : ['AA1233445','A9875', 'rmacy','Idaho Rx','Ab123455','TV192837','RX','Ohio Drugs','RX12345','USA Pharma'], 
                    'D' : [123456,123456,1234567,12345678,12345,12345,12345678,123456789,1234567,np.NaN],
                    'E' : ['Assign','Unassign','Assign','Ugly','Appreciate','Undo','Assign','Unicycle','Assign','Unicorn',]})
print(dfp)

z = {'rmacy': 999}
dfp.loc[dfp['C'].isin(z.keys()), 'D' ] = z.values() # <--- code to change

Output: 
     A    B           C            D           E
0  NaN  1.0   AA1233445       123456      Assign
1  NaN  0.0       A9875       123456    Unassign
2  3.0  3.0       rmacy        (999)      Assign #<--- Worked with paranthesis
3  4.0  5.0    Idaho Rx  1.23457e+07        Ugly
4  5.0  0.0    Ab123455        12345  Appreciate
5  5.0  0.0    TV192837        12345        Undo
6  3.0  NaN          RX  1.23457e+07      Assign
7  1.0  9.0  Ohio Drugs  1.23457e+08    Unicycle
8  5.0  0.0     RX12345  1.23457e+06      Assign
9  NaN  0.0  USA Pharma          NaN     Unicorn

上面的代码可以工作（除了将值放入括号中。但是如果字典大于一个键，它会将两个值放入，Column D因为该列中有两个匹配项。

     A    B           C            D           E
0  NaN  1.0   AA1233445       123456      Assign
1  NaN  0.0       A9875       123456    Unassign
2  3.0  3.0       rmacy   (999, 333)      Assign
3  4.0  5.0    Idaho Rx  1.23457e+07        Ugly
4  5.0  0.0    Ab123455        12345  Appreciate
5  5.0  0.0    TV192837        12345        Undo
6  3.0  NaN          RX   (999, 333)      Assign
7  1.0  9.0  Ohio Drugs  1.23457e+08    Unicycle
8  5.0  0.0     RX12345  1.23457e+06      Assign
9  NaN  0.0  USA Pharma          NaN     Unicorn

如何解决这个问题呢？

Answer 1

使用map和fillna

dfp.assign(D=dfp.C.map(z).fillna(dfp.D))

     A    B           C            D           E
0  NaN  1.0   AA1233445     123456.0      Assign
1  NaN  0.0       A9875     123456.0    Unassign
2  3.0  3.0       rmacy        999.0      Assign
3  4.0  5.0    Idaho Rx   12345678.0        Ugly
4  5.0  0.0    Ab123455      12345.0  Appreciate
5  5.0  0.0    TV192837      12345.0        Undo
6  3.0  NaN          RX   12345678.0      Assign
7  1.0  9.0  Ohio Drugs  123456789.0    Unicycle
8  5.0  0.0     RX12345    1234567.0      Assign
9  NaN  0.0  USA Pharma          NaN     Unicorn