In Pandas, how to replace list elements in Series?-CodePudding

I have a following series and would like to replace a1 with a, b1 with b and c1 with c.

data = pd.Series([['a1', 'b1', 'c1'], ['b1', 'a1', 'c1'], ['c1', 'a1' ,'b1']])

Out[132]: 
0    [a1, b1, c1]
1    [b1, a1, c1]
2    [c1, a1, b1]
dtype: object

The expected results is as below.

0    [a, b, c]
1    [b, a, c]
2    [c, a, b]
dtype: object

The following code does what I am trying to do, but it does not seem to be a nice way to do.

for i, s in enumerate(data):
    temp = ['a' if x == 'a1' else x for x in s]
    temp = ['b' if x == 'b1' else x for x in temp]
    temp = ['c' if x == 'c1' else x for x in temp]
    data.iloc[i] = temp

Is there a better way of doing this? I assume pandas have a built-in function for this.

I tried it with replace, but it does not help.

data.replace['a1', 'a']
data.replace['b1', 'c']
data.replace['c1', 'c']

Thank you for any comment in advance.

CodePudding user response：

Create dictionary for replace and use list comprehension with get - second parameter y is if not exist key get original:

d = {'a1':'a', 'b1':'b', 'c1':'c'}

data = data.apply(lambda x: [d.get(y,y) for y in x])
#alternative solution
#data = data.map(lambda x: [d.get(y,y) for y in x])
print (data)
0    [a, b, c]
1    [b, a, c]
2    [c, a, b]
dtype: object

Or:

data = pd.Series([[d.get(y,y) for y in x] for x in data], index=data.index)

If performance not important:

data = data.explode().replace(d).groupby(level=0).agg(list)
print (data)
0    [a, b, c]
1    [b, a, c]
2    [c, a, b]
dtype: object