Cofeinnie Bonda Cofeinnie Bonda - 4 months ago 24
Python Question

The fastest way to parse dates in Python when reading .csv file?

I have a .csv file that has 2 separate columns for

'Date'
and
' Time'
. I read the file like this:

data1 = pd.read_csv('filename.csv', parse_dates=['Date', 'Time'])


But it seems that only the
' Date'
column is in time format while the
'Time'
column is still string or in a format other than time format.

When I do the following:

data0 = pd.read_csv('filename.csv')
data0['Date'] = pd.to_datetime(data0['Date'])
data0['Time'] = pd.to_datetime(data0['Time'])


It gives a dataframe I want, but takes quite some time.
So what's the fastest way to read in the file and convert the date and time from a string format?

The .csv file is like this:

Date Time Open High Low Close
0 2004-04-12 8:31 AM 1139.870 1140.860 1139.870 1140.860
1 2005-04-12 10:31 AM 1141.219 1141.960 1141.219 1141.960
2 2006-04-12 12:33 PM 1142.069 1142.290 1142.069 1142.120
3 2007-04-12 3:24 PM 1142.240 1143.140 1142.240 1143.140
4 2008-04-12 5:32 PM 1143.350 1143.589 1143.350 1143.589


Thanks!

Answer

Here, In your case 'Time' is in AM/PM format which take more time to parse.

You can add format to increase speed of to_datetime() method.

data0=pd.read_csv('filename.csv')

# %Y - year including the century
# %m - month (01 to 12)
# %d - day of the month (01 to 31)
data0['Date']=pd.to_datetime(data0['Date'], format="%Y/%m/%d")

# %I - hour, using a -hour clock (01 to 12)
# %M - minute
# %p - either am or pm according to the given time value
# data0['Time']=pd.to_datetime(data0['Time'], format="%I:%M %p") -> around 1 sec
data0['Time']=pd.datetools.to_time(data0['Time'], format="%I:%M %p")

For more methods info : Pandas Tools

For more format options check - datetime format directives.

For 500K rows it improved speed from around 60 seconds -> 0.01 seconds in my system.

You can also use :

# Combine date & time directly from string format
pd.Timestamp(data0['Date'][0] + " " + data0['Time'][0])