Resampling with groupby for time data in Pandas - Mini Project: Build & Apply

Resampling with groupby for time data

📖 Scenario: You work for a bike rental company. You have a dataset of bike rentals recorded every minute. You want to analyze the total rentals per hour for each bike station.

🎯 Goal: Build a program that groups bike rental data by station and resamples the data to show total rentals per hour for each station.

📋 What You'll Learn

Create a pandas DataFrame with datetime index and rental counts for each station

Create a variable for the resampling frequency

Use groupby on the station column and resample the data by the frequency

Print the final resampled DataFrame showing total rentals per hour per station

💡 Why This Matters

🌍 Real World

Bike rental companies analyze rental data by time and location to optimize bike availability and station management.

💼 Career

Data analysts and data scientists often resample time series data grouped by categories to find trends and summaries for business decisions.

1

Create the bike rental data

Create a pandas DataFrame called df with these columns: 'station', 'rentals'. Use the datetime index with these exact timestamps: '2024-06-01 08:00', '2024-06-01 08:01', '2024-06-01 08:02', '2024-06-01 09:00', '2024-06-01 09:01'. The data should have these rows exactly:
station: 'A', 'A', 'B', 'A', 'B'
rentals: 5, 3, 2, 4, 1

Pandas

# Import pandas and create the DataFrame df with the exact data
# Your code here

Need a hint?

Use pd.to_datetime to create the datetime index. Then create the DataFrame df with the given data and index.

2

Set the resampling frequency

Create a variable called freq and set it to the string 'H' to represent hourly resampling.

Pandas

import pandas as pd

data = {
    'station': ['A', 'A', 'B', 'A', 'B'],
    'rentals': [5, 3, 2, 4, 1]
}
index = pd.to_datetime([
    '2024-06-01 08:00',
    '2024-06-01 08:01',
    '2024-06-01 08:02',
    '2024-06-01 09:00',
    '2024-06-01 09:01'
])
df = pd.DataFrame(data, index=index)

# Create the variable freq with value 'H'
# Your code here

Need a hint?

Set freq to the string 'H' for hourly resampling.

3

Group by station and resample hourly

Create a new DataFrame called hourly_rentals by grouping df by the 'station' column and resampling the 'rentals' column by the frequency freq. Use sum() to add rentals in each hour.

Pandas

import pandas as pd

data = {
    'station': ['A', 'A', 'B', 'A', 'B'],
    'rentals': [5, 3, 2, 4, 1]
}
index = pd.to_datetime([
    '2024-06-01 08:00',
    '2024-06-01 08:01',
    '2024-06-01 08:02',
    '2024-06-01 09:00',
    '2024-06-01 09:01'
])
df = pd.DataFrame(data, index=index)

freq = 'H'

# Group df by 'station' and resample 'rentals' by freq, summing the values
# Your code here

Need a hint?

Use df.groupby('station').resample(freq).sum() to get total rentals per hour per station.

4

Print the hourly rentals result

Print the hourly_rentals DataFrame to display the total rentals per hour for each station.

Pandas

import pandas as pd

data = {
    'station': ['A', 'A', 'B', 'A', 'B'],
    'rentals': [5, 3, 2, 4, 1]
}
index = pd.to_datetime([
    '2024-06-01 08:00',
    '2024-06-01 08:01',
    '2024-06-01 08:02',
    '2024-06-01 09:00',
    '2024-06-01 09:01'
])
df = pd.DataFrame(data, index=index)

freq = 'H'

hourly_rentals = df.groupby('station').resample(freq).sum()

# Print the hourly_rentals DataFrame
# Your code here

Need a hint?

Use print(hourly_rentals) to show the grouped and resampled data.