What tool should I use to create new column in dataframe based on multiple condition

Question

I have column (Column1) in dataframe that contain information about work centers and values that I need to assign names of these work centers in empty column (Column2), example:

Input:

Column1:	Column2:
WC1	NaN
123	NaN
456	NaN
789	NaN
abc	NaN
fgh	NaN
WC2	NaN
123	NaN
abc	NaN
123	NaN

Output:

Column1:	Column2:
WC1	WC1
123	WC1
456	WC1
789	WC1
abc	WC1
fgh	WC1
WC2	WC2
123	WC2
abc	WC2
123	WC2

I tried many things and read famous DON'T https://stackoverflow.com/a/55557758 but still can't understand what should I use because I want loop to go through every row one by one to assign WC1 only to top values and when loop will find WC2 it will stop assigning WC1 to values

Answer 1

Use Series.where with filter rows with WC value by Series.str.contains and forward non missing values by Series.ffill:

df['Column2'] = df['Column1'].where(df['Column1'].str.contains('WC')).ffill()
print (df)
  Column1 Column2
0     WC1     WC1
1     123     WC1
2     456     WC1
3     789     WC1
4     abc     WC1
5     fgh     WC1
6     WC2     WC2
7     123     WC2
8     abc     WC2
9     123     WC2

How it working:

print (df['Column1'].str.contains('WC'))
0     True
1    False
2    False
3    False
4    False
5    False
6     True
7    False
8    False
9    False
Name: Column1, dtype: bool

print (df['Column1'].where(df['Column1'].str.contains('WC')))
0    WC1
1    NaN
2    NaN
3    NaN
4    NaN
5    NaN
6    WC2
7    NaN
8    NaN
9    NaN
Name: Column1, dtype: object

---

print (df['Column1'].where(df['Column1'].str.contains('WC')).ffill())
0    WC1
1    WC1
2    WC1
3    WC1
4    WC1
5    WC1
6    WC2
7    WC2
8    WC2
9    WC2
Name: Column1, dtype: object