to_datetime() conversion in Data Analysis Python - Mini Project: Build & Apply

Convert Strings to Dates with to_datetime()

📖 Scenario: You work in a small business that keeps sales records as text. You want to analyze sales by date, but the dates are stored as words, not real dates. You need to change these text dates into real date values so you can sort and filter them easily.

🎯 Goal: Convert a list of sales dates stored as strings into real date objects using pandas.to_datetime(). This will help you work with dates correctly in your analysis.

📋 What You'll Learn

Create a pandas DataFrame with a column of date strings

Create a variable to hold the date format string

Use pandas to_datetime() with the format string to convert the date strings

Print the DataFrame with the converted dates

💡 Why This Matters

🌍 Real World

Businesses often get dates as text from reports or user input. Converting these to real dates lets them sort, filter, and analyze data by time.

💼 Career

Data analysts and scientists frequently convert date strings to datetime objects to prepare data for time series analysis, reporting, and visualization.

1

Create a DataFrame with date strings

Create a pandas DataFrame called sales_data with one column named 'date' containing these exact strings: '2023-01-15', '2023-02-20', '2023-03-25'.

Data Analysis Python

import pandas as pd
# Create the DataFrame sales_data with the date strings
# Your code here

Hint

Use pd.DataFrame and pass a dictionary with key 'date' and the list of strings as value.

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.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'date': ['2023-01-15', '2023-02-20', '2023-03-25']
})
# Create the date_format variable with '%Y-%m-%d'
# Your code here

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 pandas.to_datetime() with the sales_data['date'] column and the date_format variable to convert the strings to datetime objects. Save the result back to sales_data['date'].

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'date': ['2023-01-15', '2023-02-20', '2023-03-25']
})
date_format = '%Y-%m-%d'
# Convert sales_data['date'] to datetime using to_datetime and date_format
# Your code here

Hint

Use pd.to_datetime() with the format parameter set to date_format.

4

Print the DataFrame with converted dates

Print the sales_data DataFrame to show the dates as datetime objects.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'date': ['2023-01-15', '2023-02-20', '2023-03-25']
})
date_format = '%Y-%m-%d'
sales_data['date'] = pd.to_datetime(sales_data['date'], format=date_format)
# Print the sales_data DataFrame
# Your code here

Hint

Use print(sales_data) to display the DataFrame.