I have a Pandas data frame like this one
String = ["".join(np.random.choice(list("PQRSTUVXYZ"), size=7)) for _ in range(7)]
Position = np.random.randint(2,7, size = 7)
df=pd.DataFrame((String,Position)).T
I would like to apply the lower() function JUST in the letter which the index is column Position.
I've tried:
df = df[0][df[1]].str.lower()
But it is lowering the whole string.
Thanks for your help!
here is a list comprehension with slice and slice_replace:
df['new'] = ([df[0].str.slice_replace(
i,i+1,df[0].str.slice(i,i+1).iloc[e].lower()).iloc[e]
for e,i in enumerate(df[1])])
0 1 new
0 TZPVTRT 2 TZpVTRT
1 VSSXYUP 3 VSSxYUP
2 YUTXTQS 2 YUtXTQS
3 SZRURSU 5 SZRURsU
4 XRXQVUP 3 XRXqVUP
5 PSQZZVV 6 PSQZZVv
6 XYYXPYV 3 XYYxPYV
note if your index starts from 1 and not 0 , replace df[0].str.slice(i,i+1) with df[0].str.slice(i-1,i)
You can index the strings. Index up to the lower character, turn that character to lower, and then add everything left over. Really nothing too different from using Series.str.slice_replace, but perhaps a bit more readable.
df['new_str'] = [s[:i] + s[i].lower() + s[i+1:] for s,i in zip(df[0], df[1])]
0 1 new_str
0 TTTQTYT 3 TTTqTYT
1 USRRUZS 2 USrRUZS
2 PPXQZZT 6 PPXQZZt
3 UZZVQRQ 3 UZZvQRQ
4 ZVUQPUV 6 ZVUQPUv
5 TRPPSVU 5 TRPPSvU
6 YYZPURQ 5 YYZPUrQ
