How to map different column values to one column - pandas

Question

I have a data frame below:
import pandas as pd
df = pd.DataFrame({"SK":["EYF","EYF","RMK","MB","RMK","GYF","RMK","MYF"],
"SA":["a","b","tm","tmb","tm","cd","tms","alb"],
"C":["","11","12","13","","15","16","17"]})
df
I want to map some values of "SK","SA" and "C" to a new column:
df["D"]= df["SK"].map({"EYF":1,"MB":2,"GYF":3})
df
df["D"]= df["SA"].map({"tm":4})
df
df["D"]= df["C"].map({"16":5,"17":6})
df
But when I run the next map function, "D" column values mapped by previous map function turn to NaN.
I wanna get df below:
Any help will be appreciated.

Answer 1

You can create 3 Series and then replace misisng values from previous Series by Series.fillna or Series.combine_first:
a = df["SK"].map({"EYF":1,"MB":2,"GYF":3})
b = df["SA"].map({"tm":4})
c = df["C"].map({"16":5,"17":6})
df["D"] = a.fillna(b).fillna(c)
#alternative
df["D"] = a.combine_first(b).combine_first(c)
print (df)
SK SA C D
0 EYF a 1.0
1 EYF b 11 1.0
2 RMK tm 12 4.0
3 MB tmb 13 2.0
4 RMK tm 4.0
5 GYF cd 15 3.0
6 RMK tms 16 5.0
7 MYF alb 17 6.0
Order is important for priority if same match for some value:
df = pd.DataFrame({"SK":["EYF","EYF"],
"SA":["a","tm"],
"C":["16","17"]})
a = df["SK"].map({"EYF":1,"MB":2,"GYF":3})
b = df["SA"].map({"tm":4})
c = df["C"].map({"16":5,"17":6})
df["D1"] = a.fillna(b).fillna(c)
df["D2"] = b.fillna(a).fillna(c)
df["D3"] = c.fillna(b).fillna(a)
print (df)
SK SA C D1 D2 D3
0 EYF a 16 1 1.0 5
1 EYF tm 17 1 4.0 6