Pandas read csv dateint columns to datetime

Question

I\'m new to both StackOverflow and pandas. I am trying to read in a large CSV file with stock market bin data in the following format:

date,time,open,high,low,cl         


        

Answer 1

There are quite a few ways to do this. One way to do it during read_csv would be to use the parse_dates and date_parser arguments, telling parse_dates to combine the date and time columns and defining an inline function to parse the dates:

>>> df = pd.read_csv("bindat.csv", parse_dates=[["date", "time"]],
date_parser=lambda x: pd.to_datetime(x, format="%Y%m%d %H%M"), 
index_col="date_time")
>>> df
                        open     high      low    close    volume  splits  earnings  dividends  sym
date_time                                                                                          
2013-06-25 07:15:00  49.2634  49.2634  49.2634  49.2634   156.293       1         0          0  JPM
2013-06-25 07:30:00  49.2730  49.2730  49.2730  49.2730   208.390       1         0          0  JPM
2013-06-25 07:40:00  49.1866  49.1866  49.1866  49.1866   224.019       1         0          0  JPM
2013-06-25 07:45:00  49.3210  49.3210  49.3210  49.3210   208.390       1         0          0  JPM
2013-06-25 07:50:00  49.3306  49.3690  49.3306  49.3690  4583.540       1         0          0  JPM
2013-06-25 07:55:00  49.3690  49.3690  49.3690  49.3690   416.780       1         0          0  JPM
2013-06-25 08:00:00  49.3690  49.3690  49.3594  49.3594  1715.050       1         0          0  JPM
2013-06-25 08:05:00  49.3690  49.3690  49.3306  49.3306  1333.700       1         0          0  JPM
2013-06-25 08:10:00  49.3306  49.3786  49.3306  49.3786  1567.090       1         0          0  JPM
2013-06-25 16:10:00  49.3306  49.3786  49.3306  49.3786  1567.090       1         0          0  JPM

where I added an extra row at the end to make sure that hours were behaving.