Pandas Automates Date Parsing in CSV Files

Pandas simplifies data retrieval from CSV files with its capability to automatically infer data types, including dates. However, it occasionally fails to recognize specific date formats, such as those presented as "2013-6-4."

Solution: Specify 'parse_dates' Argument

To overcome this challenge, utilize the 'parse_dates' argument. For instance, to designate a column with dates in the "YYYY-MM-DD" format as 'datetime' objects, execute the following:

df = pandas.read_csv('test.dat', parse_dates=['datetime'], delimiter=r"\s ", names=['col1','col2','col3'])

This will convert the relevant column into 'datetime' objects.

Advanced Customization: Date Parser Functions

For more intricate date formats, employ date parser functions. These grant greater flexibility in specifying custom parsing logic. For instance, consider a 'datetime' column with the format "YYYY-MM-DD HH:MM:SS":

from datetime import datetime
dateparse = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
df = pd.read_csv(infile, parse_dates=['datetime'], date_parser=dateparse)

You can even merge multiple date-related columns into a single 'datetime' column:

dateparse = lambda x: datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
df = pd.read_csv(infile, parse_dates={'datetime': ['date', 'time']}, date_parser=dateparse)

Browse the documentation for 'strptime' for directives representing various datetime formats.