How to Convert String Dates to Datetime Objects and Filter by Date Range in Pandas?

Converting Strings to Datetime Format in Pandas

Problem:

Given a Pandas dataframe with a column containing string date values, how do you convert these values to the datetime format? Additionally, how can you filter rows based on a specified range of dates?

Solution:

Conversion to Datetime Format:

To convert strings to datetime format in Pandas, use the to_datetime function. In general, the parser can automatically determine the format of the input string, eliminating the need for explicit format specification.

import pandas as pd

df = pd.DataFrame({'I_DATE': ['28-03-2012 2:15:00 PM', '28-03-2012 2:17:28 PM', '28-03-2012 2:50:50 PM']})

df['I_DATE'] = pd.to_datetime(df['I_DATE'])

This code converts the I_DATE column to datetime format. The output looks like this:

   I_DATE
0 2012-03-28 14:15:00
1 2012-03-28 14:17:28
2 2012-03-28 14:50:50

Accessing Date Components:

After conversion, you can access individual date components, such as the date, time, or day, using the dt accessor. For example:

# Get the date only
df['I_DATE'].dt.date

# Get the time only
df['I_DATE'].dt.time

Filtering Rows Based on Date Range:

To filter rows based on a range of dates, use the following syntax:

df[(df['date'] > 'start_date') & (df['date'] < 'end_date')]

For instance, to filter rows where the dates are between '2015-02-04' and '2015-02-10':

df[(df['date'] > '2015-02-04') & (df['date'] < '2015-02-10')]

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