Is it possible to add 3 new columns to this little DataFrame in one map?
import datetime as dt
import pandas as pd
from pandas import *
df = pd.DataFrame({'myDate':['2006-02-12'
,'2007-07-20'
,'2009-05-19']})
def convert_date(val):
d, m, y = val.split('-')
return int(d), int(y), int(m)
df[['day', 'year','month']] = df.myDate.map(convert_date)
I think you can convert column myDate to_datetime and then use dt.year, dt.month and dt.day:
df['myDate'] = pd.to_datetime(df.myDate)
df['year'] = df.myDate.dt.year
df['month'] = df.myDate.dt.month
df['day'] = df.myDate.dt.day
print (df)
myDate year month day
0 2006-02-12 2006 2 12
1 2007-07-20 2007 7 20
2 2009-05-19 2009 5 19
If want use your approach, you need add pd.Series, because else you return tuples. And change map to apply:
def convert_date(val):
d, m, y = val.split('-')
return pd.Series([int(d), int(y), int(m)])
df[['day', 'year','month']] = df.myDate.apply(convert_date)
print (df)
myDate day year month
0 2006-02-12 2006 12 2
1 2007-07-20 2007 20 7
2 2009-05-19 2009 19 5
I try use map, but result is:
def convert_date(val):
d, m, y = val.split('-')
return int(d), int(y), int(m)
df['a'], df['b'], df['c'] = df.myDate.map(convert_date)
print (df)
myDate a b c
0 2006-02-12 2006 2007 2009
1 2007-07-20 12 20 19
2 2009-05-19 2 7 5
you can use .str.split():
In [11]: df[['day', 'year','month']] = df.myDate.str.split('-', expand=True).astype(int)
In [12]: df
Out[12]:
myDate day year month
0 2006-02-12 2006 2 12
1 2007-07-20 2007 7 20
2 2009-05-19 2009 5 19
or using .str.extract()
In [21]: df.myDate.str.extract(r'(?P<year>\d{4})-(?P<month>\d{2})-(?P<day>\d{2})', expand=True).astype(int)
Out[21]:
year month day
0 2006 2 12
1 2007 7 20
2 2009 5 19
