Why datetime handling matters in Pandas - See It in Action

Why datetime handling matters

📖 Scenario: Imagine you work for a small online store. You have a list of orders with dates and times when each order was placed. You want to understand how many orders came in each day to plan your stock better.

🎯 Goal: You will create a small program that reads order dates, converts them to a proper date format, counts how many orders happened each day, and shows the result.

📋 What You'll Learn

Create a pandas DataFrame with order dates as strings

Convert the order date strings to datetime objects

Count the number of orders per day

Print the daily order counts

💡 Why This Matters

🌍 Real World

Handling dates and times correctly helps businesses understand trends over time, like daily sales or website visits.

💼 Career

Data scientists often work with timestamps to analyze patterns, forecast demand, and make data-driven decisions.

1

Create the orders data

Create a pandas DataFrame called orders with one column named order_date containing these exact string dates: '2024-06-01 10:15', '2024-06-01 12:30', '2024-06-02 09:00', '2024-06-02 17:45', '2024-06-03 14:00'.

Pandas

# Create the orders DataFrame with the given order_date strings
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary where the key is 'order_date' and the value is the list of date strings.

2

Convert strings to datetime

Create a new column in orders called order_datetime by converting the order_date strings to datetime objects using pd.to_datetime().

Pandas

import pandas as pd

orders = pd.DataFrame({
    'order_date': ['2024-06-01 10:15', '2024-06-01 12:30', '2024-06-02 09:00', '2024-06-02 17:45', '2024-06-03 14:00']
})
# Convert order_date strings to datetime and store in order_datetime
# Your code here

Need a hint?

Use pd.to_datetime() on the order_date column and assign it to a new column order_datetime.

3

Count orders per day

Create a new variable called daily_counts that counts how many orders happened each day. Use the dt.date attribute on order_datetime to get the date part, then use value_counts() and sort the result by date.

Pandas

import pandas as pd

orders = pd.DataFrame({
    'order_date': ['2024-06-01 10:15', '2024-06-01 12:30', '2024-06-02 09:00', '2024-06-02 17:45', '2024-06-03 14:00']
})

orders['order_datetime'] = pd.to_datetime(orders['order_date'])
# Count orders per day and store in daily_counts
# Your code here

Need a hint?

Use orders['order_datetime'].dt.date to get dates, then value_counts() to count, and sort_index() to order by date.

4

Print the daily order counts

Print the daily_counts variable to show how many orders happened on each day.

Pandas

import pandas as pd

orders = pd.DataFrame({
    'order_date': ['2024-06-01 10:15', '2024-06-01 12:30', '2024-06-02 09:00', '2024-06-02 17:45', '2024-06-03 14:00']
})

orders['order_datetime'] = pd.to_datetime(orders['order_date'])

daily_counts = orders['order_datetime'].dt.date.value_counts().sort_index()
# Print the daily_counts
# Your code here

Need a hint?

Use print(daily_counts) to show the counts.