to_datetime() for date parsing in Pandas - Mini Project: Build & Apply

Parsing Dates with pandas to_datetime()

📖 Scenario: You work in a small business that keeps sales records in text files. The dates are written as strings, but you want to analyze sales by date. To do this, you need to convert these date strings into real date objects that Python can understand.

🎯 Goal: Learn how to use pandas to_datetime() to convert a list of date strings into a pandas Series of datetime objects. This will help you prepare data for time-based analysis.

📋 What You'll Learn

Create a pandas Series with date strings

Create a format string for the date format

Use pandas to_datetime() with the format string to parse dates

Print the resulting Series of datetime objects

💡 Why This Matters

🌍 Real World

Businesses often receive data with dates as text. Converting these to datetime objects allows sorting, filtering, and time-based analysis.

💼 Career

Data analysts and scientists frequently clean and prepare date data for reports, dashboards, and machine learning models.

1

Create a pandas Series with date strings

Import pandas as pd and create a pandas Series called date_strings with these exact values: '2023-01-15', '2023-02-20', '2023-03-25'.

Pandas

import pandas as pd
# Create the pandas Series called date_strings with the given date strings
# Your code here

Need a hint?

Use pd.Series() to create a Series from a list of strings.

2

Create a date format string

Create a variable called date_format and set it to the string '%Y-%m-%d' which matches the format of the date strings.

Pandas

import pandas as pd
date_strings = pd.Series(['2023-01-15', '2023-02-20', '2023-03-25'])
# Create the date_format variable with the string '%Y-%m-%d'
# Your code here

Need a hint?

The format '%Y-%m-%d' means 4-digit year, 2-digit month, 2-digit day separated by dashes.

3

Convert the date strings to datetime objects

Use pd.to_datetime() with the date_strings Series and the format=date_format argument. Save the result in a variable called dates.

Pandas

import pandas as pd
date_strings = pd.Series(['2023-01-15', '2023-02-20', '2023-03-25'])
date_format = '%Y-%m-%d'
# Convert date_strings to datetime using pd.to_datetime with format=date_format
# Your code here

Need a hint?

Use pd.to_datetime() with the format parameter to parse the dates correctly.

4

Print the parsed datetime Series

Print the dates variable to see the converted datetime objects.

Pandas

import pandas as pd
date_strings = pd.Series(['2023-01-15', '2023-02-20', '2023-03-25'])
date_format = '%Y-%m-%d'
dates = pd.to_datetime(date_strings, format=date_format)
# Print the dates variable
# Your code here

Need a hint?

Use print(dates) to display the Series of datetime objects.