Parse dates when YYYYMMDD and HH are in separate columns using pandas in Python

I have a simple question related with csv files and parsing datetime.

I have a csv file that look like this:

20110101,  1,   10
20110101,  2,   20
20110101,  3,   30

I would like to read it using pandas (read_csv) and have it in a dataframe indexed by the datetime. So far I've tried to implement the following:

import pandas as pnd
pnd.read_csv("..\\file.csv",  parse_dates = True, index_col = [0,1])

and the result I get is:

YYYYMMDD    HH            
2011-01-01 2012-07-01   10
           2012-07-02   20
           2012-07-03   30

As you see the parse_dates in converting the HH into a different date.

Is there a simple and efficient way to combine properly the column "YYYYMMDD" with the column "HH" in order to have something like this? :

2011-01-01 01:00:00  10
2011-01-01 02:00:00  20
2011-01-01 03:00:00  30

Thanks in advance for the help.


If you pass a list to index_col, it means you want to create a hierarchical index out of the columns in the list.

In addition, the parse_dates keyword can be set to either True or a list/dict. If True, then it tries to parse individual columns as dates, otherwise it combines columns to parse a single date column.

In summary, what you want to do is:

from datetime import datetime
import pandas as pd
parse = lambda x: datetime.strptime(x, '%Y%m%d %H')
pd.read_csv("..\\file.csv",  parse_dates = [['YYYYMMDD', 'HH']], 
            index_col = 0, 

I am doing this all the time, so I tested different ways for speed. The fastest I found is the following, approx. 3 times faster than Chang She's solution, at least in my case, when taking the total time of file parsing and date parsing into account:

First, parse the data file using pd.read_csv withOUT parsing dates. I find that it is slowing down the file-reading quite a lot. Make sure that the columns of the CSV file are now columns in the dataframe df. Then:

format = "%Y%m%d %H"
times = pd.to_datetime(df.YYYYMMDD + ' ' + df.HH, format=format)
df.set_index(times, inplace=True)
# and maybe for cleanup
df = df.drop(['YYYYMMDD','HH'], axis=1)

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