Python pandas: create a dataframe of dates from existing columns

Question

I have a pandas dataframe with a column for years and one for months. How can I create a new date column based on these two (I can assume day = 15).

I tried the following:

import pandas as pd
import numpy as np
import datetime  

df = pd.DataFrame()
df['year'] = np.arange(2000,2010)

df['mydate']= datetime.date( df['year'].apply(lambda x: int(x)) , 1 , 1)

but I get this error message:

df['mydate']= datetime.date( df['year'].apply(lambda x: int(x)) , 1 , 1)   File "C:\Anaconda\lib\site-packages\pandas\core\series.py",
line 77, in wrapper "cannot convert the series to {0}".format(str(converter))) TypeError: cannot convert the series to

which I don't understand because I explictly convert x to int.

Thanks!

Dov Grobgeld · Accepted Answer · 2015-07-02 19:34:01Z

2

You can build another column based on the existing columns by using df.apply(fnc, axis=1).

In your case this becomes:

df = pd.DataFrame()
df['year'] = np.arange(2000,2010)
df['month'] = 6

df['date_time']= df.apply(lambda row :
                          datetime.date(row.year,row.month,15), 
                          axis=1)

answered Jul 2, 2015 at 19:34

Dov Grobgeld

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2 Comments

Pythonista anonymous Over a year ago

Thanks! Was the error with my code because datetime.date works on a single row, and with functions working on rows one at a time I hvae to do apply( fun() ) rather than fun ( apply() ) ?

Dov Grobgeld Over a year ago

You were supplying a DataFrame as an argument to datetime.date(), which doesn't make sense. As date() doesn't know what to do with it, it apparently tries to convert it to an int. In my example you call datetime on three integers. I.e. the lambda receives a row (a Series) as input, and outputs a date object, which is then aggregated by df.apply to a Series which is returned.

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