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

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

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

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

Hint

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

Practice

1. What is the main purpose of using resample in pandas when working with time series data?

easy

A. To change the frequency of time-based data, like converting daily data to monthly data.

B. To sort data alphabetically by column values.

C. To merge two DataFrames based on a common key.

D. To filter rows based on a condition.

Solution

1. Step 1: Understand the function of resample

   Resample is used to change the time frequency of data, such as converting daily data into monthly or weekly data.

2. Step 2: Compare with other options

   Sorting, merging, and filtering are different pandas operations unrelated to resampling.

3. Final Answer:

   To change the frequency of time-based data, like converting daily data to monthly data. -> Option A

4. Quick Check:

   Resample changes time frequency = A [OK]

Hint: Resample changes time intervals, not sorting or filtering [OK]

Common Mistakes:

• Confusing resample with sort or filter
• Thinking resample merges data
• Using resample without datetime index

2. Which of the following is the correct way to resample a DataFrame df by month after grouping by a column 'category' and assuming 'date' is the datetime index?

easy

A. df.resample('M').groupby('category').mean()

B. df.groupby('category').resample('M')

C. df.groupby('category').resample('M').mean()

D. df.resample('M').mean().groupby('category')

Solution

1. Step 1: Understand groupby and resample order

   To resample within each group, first group by the column, then resample the datetime index inside each group.

2. Step 2: Check syntax correctness

   df.groupby('category').resample('M').mean() correctly uses df.groupby('category').resample('M').mean(). Other options have wrong order or missing aggregation.

3. Final Answer:

   df.groupby('category').resample('M').mean() -> Option C

4. Quick Check:

   Group then resample = B [OK]

Hint: Group first, then resample on datetime index [OK]

Common Mistakes:

• Resampling before grouping
• Missing aggregation after resample
• Not setting datetime as index before resampling

3. Given the following code, what will be the output?

import pandas as pd

data = {'category': ['A', 'A', 'B', 'B'],
        'date': pd.to_datetime(['2023-01-01', '2023-01-15', '2023-01-01', '2023-01-20']),
        'value': [10, 20, 30, 40]}
df = pd.DataFrame(data)
df = df.set_index('date')
result = df.groupby('category').resample('M').sum()
print(result)

medium

A. An error because resample cannot be used after groupby.

B. A DataFrame with daily sums of 'value' for each category.

C. A DataFrame grouped by category only, ignoring dates.

D. A DataFrame with monthly sums of 'value' for each category, indexed by category and month end date.

Solution

1. Step 1: Understand the code flow

   The DataFrame is grouped by 'category', then resampled monthly on the datetime index, summing 'value' within each month and category.

2. Step 2: Analyze the output structure

   The result will have a MultiIndex with 'category' and the resampled month-end dates, showing sums per group per month.

3. Final Answer:

   A DataFrame with monthly sums of 'value' for each category, indexed by category and month end date. -> Option D

4. Quick Check:

   Group + monthly resample sum = C [OK]

Hint: Group then resample sums per month per group [OK]

Common Mistakes:

• Expecting daily sums instead of monthly
• Thinking resample causes error after groupby
• Ignoring the datetime index requirement

4. You run this code but get an error:

df.groupby('category').resample('W').mean()

What is the most likely cause of the error?

medium

A. The 'category' column contains null values.

B. The 'date' column is not set as the DataFrame index before resampling.

C. The resample frequency 'W' is invalid.

D. You cannot use mean() after resample.

Solution

1. Step 1: Check resample requirements

   Resample requires a datetime index. If 'date' is a column but not index, resample fails.

2. Step 2: Analyze error cause

   Most common error is forgetting to set 'date' as index before resampling.

3. Final Answer:

   The 'date' column is not set as the DataFrame index before resampling. -> Option B

4. Quick Check:

   Datetime index missing causes resample error = A [OK]

Hint: Set datetime column as index before resample [OK]

Common Mistakes:

• Not setting datetime index
• Assuming resample frequency is wrong
• Thinking aggregation like mean() is invalid

5. You have a DataFrame df with columns 'store', 'date' (datetime), and 'sales'. You want to find the total weekly sales per store, but only for weeks where the total sales exceed 1000. Which code snippet correctly does this?

hard

A. df.set_index('date').groupby('store').resample('W').sum().query('sales > 1000')

B. df.groupby('store').resample('W').sum().query('sales > 1000')

C. df.groupby('store').resample('W').sum().filter(lambda x: x['sales'] > 1000)

D. df.set_index('date').resample('W').groupby('store').sum().loc[lambda x: x['sales'] > 1000]

Solution

1. Step 1: Set datetime index before resampling

   Resample requires the datetime column as index, so df.set_index('date') is needed.

2. Step 2: Group by 'store' then resample weekly and sum sales

   Use groupby('store').resample('W').sum() to get weekly sales per store.

3. Step 3: Filter weeks where sales exceed 1000

   Use query('sales > 1000') to keep only those weeks.

4. Final Answer:

   df.set_index('date').groupby('store').resample('W').sum().query('sales > 1000') -> Option A

5. Quick Check:

   Set index, group, resample, filter sales > 1000 = D [OK]

Hint: Set datetime index, group, resample, then filter [OK]

Common Mistakes:

• Not setting datetime as index
• Wrong order of groupby and resample
• Using filter incorrectly instead of query