Datetime type in Pandas - Mini Project: Build & Apply

Working with Datetime Type in pandas

📖 Scenario: You work in a small company that tracks employee attendance. You have a list of dates when employees checked in. You want to analyze these dates using pandas to understand attendance patterns.

🎯 Goal: You will create a pandas DataFrame with date strings, convert these strings to datetime type, filter dates after a certain day, and finally print the filtered dates.

📋 What You'll Learn

Create a pandas DataFrame with a column of date strings

Create a variable with a date threshold as a datetime object

Filter the DataFrame to keep only dates after the threshold

Print the filtered dates

💡 Why This Matters

🌍 Real World

Datetime data is everywhere: tracking attendance, sales dates, sensor logs, and more. Being able to convert and filter dates helps analyze trends over time.

💼 Career

Data scientists and analysts often work with time series data. Knowing how to handle datetime types in pandas is a key skill for cleaning and analyzing such data.

1

Create a pandas DataFrame with date strings

Import pandas as pd. Create a DataFrame called df with one column named 'check_in' containing these exact date strings: '2024-01-10', '2024-02-15', '2024-03-20', '2024-04-25', '2024-05-30'.

Pandas

# Import pandas as pd
# Create DataFrame df with column 'check_in' and the given date strings
# Your code here

Need a hint?

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

2

Create a datetime threshold variable

Create a variable called threshold_date by converting the string '2024-03-01' to a pandas datetime object using pd.to_datetime().

Pandas

import pandas as pd

df = pd.DataFrame({
    'check_in': ['2024-01-10', '2024-02-15', '2024-03-20', '2024-04-25', '2024-05-30']
})

# Create threshold_date by converting '2024-03-01' to datetime
# Your code here

Need a hint?

Use pd.to_datetime('2024-03-01') to convert the string to datetime.

3

Convert 'check_in' column to datetime and filter dates

Convert the 'check_in' column in df to datetime type using pd.to_datetime(). Then create a new DataFrame called filtered_df that contains only rows where 'check_in' is after threshold_date.

Pandas

import pandas as pd

df = pd.DataFrame({
    'check_in': ['2024-01-10', '2024-02-15', '2024-03-20', '2024-04-25', '2024-05-30']
})

threshold_date = pd.to_datetime('2024-03-01')

# Convert 'check_in' column to datetime
# Create filtered_df with rows where 'check_in' > threshold_date
# Your code here

Need a hint?

Use df['check_in'] = pd.to_datetime(df['check_in']) to convert the column. Then filter with df[df['check_in'] > threshold_date].

4

Print the filtered dates

Print the filtered_df DataFrame to show only the dates after threshold_date.

Pandas

import pandas as pd

df = pd.DataFrame({
    'check_in': ['2024-01-10', '2024-02-15', '2024-03-20', '2024-04-25', '2024-05-30']
})

threshold_date = pd.to_datetime('2024-03-01')

df['check_in'] = pd.to_datetime(df['check_in'])
filtered_df = df[df['check_in'] > threshold_date]

# Print filtered_df
# Your code here

Need a hint?

Use print(filtered_df) to display the filtered DataFrame.