Resampling with groupby for time data in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'date' column to datetime format.

Pandas

df['date'] = pd.to_datetime(df['[1]'])

Drag options to blanks, or click blank then click option'

Atime

Bdate

Ctimestamp

Ddatetime

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong column name like 'time' or 'timestamp'.

Not converting the column to datetime before resampling.

We convert the 'date' column to datetime using pd.to_datetime with the column name 'date'.

2fill in blank

medium

Complete the code to set the 'date' column as the DataFrame index.

Pandas

df = df.set_index('[1]')

Drag options to blanks, or click blank then click option'

Adate

Btime

Cindex

Dtimestamp

Attempts:

3 left

💡 Hint

Common Mistakes

Setting a non-datetime column as index.

Not setting any index before resampling.

Setting the 'date' column as index allows us to use time-based resampling.

3fill in blank

hard

Fix the error in the code to resample the data by month and calculate the mean.

Pandas

monthly_mean = df.resample('[1]').mean()

Drag options to blanks, or click blank then click option'

AM

BD

CW

DY

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'W' or 'D' instead of 'M' for monthly resampling.

Not calling the aggregation function after resampling.

The code uses 'M' to resample by calendar month and then calculates the mean.

4fill in blank

hard

Fill both blanks to group data by 'category' and resample by week, calculating the sum.

Pandas

weekly_sum = df.groupby('[1]').resample('[2]').sum()

Drag options to blanks, or click blank then click option'

Acategory

Bdate

CW

DM

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'date' instead of 'category' for grouping.

Using 'M' instead of 'W' for weekly resampling.

We group by 'category' and resample by week ('W') to get weekly sums per category.

5fill in blank

hard

Fill all three blanks to group by 'region', resample by day, and calculate the maximum value.

Pandas

daily_max = df.groupby('[1]').resample('[2]').[3]()

Drag options to blanks, or click blank then click option'

Aregion

BD

Cmax

Dsum

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sum' instead of 'max' for aggregation.

Using wrong group or resample parameters.

Grouping by 'region', resampling daily ('D'), and applying max() finds the daily max per region.

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