Pandas changing data format od a long column - pandas

Question

I'm polishing my code. In one point I want to convert date given as string to another string that holds the same date but shows it in different format.
After each date there is code, always same code for given date.
Here is my df:
import pandas as pd
data = ['2012-06-29 A','2012-08-29 B','2012-10-29 X','2012-10-15 A']*50000
data.sort()
df = pd.DataFrame({'A':data})
A
2012-06-29 A
2012-06-29 A
2012-06-29 A
2012-06-29 A
2012-06-29 A
And here is how I'm doing it now:
df['A'] = df['A'].apply(lambda x: pd.to_datetime(x.split(' ')[0]).strftime('%d %b %Y ') + x.split(' ')[1])
A
29 Jun 2012 A
29 Jun 2012 A
29 Jun 2012 A
29 Jun 2012 A
29 Jun 2012 A
It works fine but it seems to create bottleneck (not really it is only part of data preparation).
Can it be done better/faster?
In total I have about 15 dates like this for 1 df (and many dfs). I wonder if creation of dict or temporary support_df from unique dates and applying those somehow (how?) through lambda to avoid multiple conversions.
Additional info (maybe useful): Column A, later on, becomes part of MultiIndex.

Answer 1

IIUC, my first attempt would be this method:
No need to apply on the dataframe:
(pd.to_datetime(df['A'].str.split().str[0]).dt.strftime('%d %b %Y') + ' '
+ df['A'].str.split().str[1])
Second attempt using list comprehension instead of .str accessor:
(pd.to_datetime(pd.Series([i.split()[0] for i in df.A])).dt.strftime('%d %b %Y')
+ ' ' + pd.Series([i.split()[1] for i in df.A]))
Third attempt:
ls = [i.split() for i in df.A]
i,j = zip(*ls)
pd.Series(pd.to_datetime(i).strftime('%d %b %Y')) + ' ' + pd.Series(j)