How Do I Convert a Pandas Column of String Dates to DateTime Format for Easier Date-Based Filtering?

Transforming Pandas Column into DateTime Format

Suppose you have a Pandas DataFrame containing a column with string values representing dates. To convert this column into a datetime column and subsequently perform date-based filtering, adhere to the following steps:

1. Import the Pandas library:

import pandas as pd

2. Apply the pd.to_datetime() function to the specified column:

df['Mycol'] = pd.to_datetime(df['Mycol'], format='%d%b%Y:%H:%M:%S.%f')

In this example, it is assumed that your column has dates in the format of 05SEP2014:00:00:00.000, where %d represents the day, %b the month, %Y the year, %H the hour, %M the minute, %S the second, and %f the microsecond.

3. Once converted to datetime, you can easily filter the data based on date using methods such as:

df = df[df['Mycol'] >= '2014-09-05']

This example will filter the DataFrame to only include rows where the Mycol column is on or after September 5, 2014.

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