I have a dataframe like this
| A | B | C | |-------|---|---| | ['1'] | 1 | 1 | |['1,2']| 2 | | | ['2'] | 3 | 0 | |['1,3']| 2 | | if the value of B is equal to A within the quotes then C is 1. if not present in A it will be 0. Expected output is:
| A | B | C | |-------|---|---| | ['1'] | 1 | 1 | |['1,2']| 2 | 1 | | ['2'] | 3 | 0 | |['1,3']| 2 | 0 | Like this I want to get the dataframe for multiple rows. How do I write in python to get this kind of data frame?
If values in A are strings use:
print (df.A.tolist()) ["['1']", "['1,2']", "['2']", "['1,3']"] df['C'] = [int(str(b) in a.strip("[]'").split(',')) for a, b in zip(df.A, df.B)] print (df) A B C 0 ['1'] 1 1 1 ['1,2'] 2 1 2 ['2'] 3 0 3 ['1,3'] 2 0 Or if values are one element lists use:
print (df.A.tolist()) [['1'], ['1,2'], ['2'], ['1,3']] df['C'] = [int(str(b) in a[0].split(',')) for a, b in zip(df.A, df.B)] print (df) A B C 0 [1] 1 1 1 [1,2] 2 1 2 [2] 3 0 3 [1,3] 2 0 My code:
df = pd.read_clipboard() df ''' A B 0 ['1'] 1 1 ['1,2'] 2 2 ['2'] 3 3 ['1,3'] 2 ''' ( df.assign(A=df.A.str.replace("'",'').map(eval)) .assign(C=lambda d: d.apply(lambda s: s.B in s.A, axis=1)) .assign(C=lambda d: d.C.astype(int)) ) ''' A B C 0 [1] 1 1 1 [1, 2] 2 1 2 [2] 3 0 3 [1, 3] 2 0 ''' 
df['C'] = np.where(df['B'].astype(str).isin(df.A), 1,0) basically you need to transform column b to string since column A is string. then seek for column B inside columnA.
result will be as you are defined.
print (df.A.head().tolist())?