Pandas ersetzen Nan
data["Gender"].fillna("No Gender", inplace = True)
Anxious Armadillo
data["Gender"].fillna("No Gender", inplace = True) df.fillna('', inplace=True)df.fillna('()').applymap(ast.literal_eval)from ast import literal_eval
from io import StringIO
# replicate csv file
x = StringIO("""A,B
,"('t1', 't2')"
"('t3', 't4')",""")
def literal_converter(val):
# replace first val with '' or some other null identifier if required
return val if val == '' else literal_eval(val)
df = pd.read_csv(x, delimiter=',', converters=dict.fromkeys('AB', literal_converter))
print(df)
A B
0 (t1, t2)
1 (t3, t4)