Resampling with groupby for time data in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of resampling with groupby on time data

What is the output of this code snippet that groups data by 'category' and resamples the time series to daily frequency, summing the values?

Pandas

import pandas as pd

data = {
    'category': ['A', 'A', 'B', 'B', 'A', 'B'],
    'value': [10, 20, 30, 40, 50, 60],
    'date': pd.to_datetime(['2024-01-01', '2024-01-02', '2024-01-01', '2024-01-03', '2024-01-03', '2024-01-02'])
}
df = pd.DataFrame(data)
df.set_index('date', inplace=True)
result = df.groupby('category').resample('D').sum()
print(result)

A

category  date      value
category date             
A        2024-01-01     10
         2024-01-02     20
         2024-01-03     50
B        2024-01-01     30
         2024-01-02     60
         2024-01-03     40

B

category  date      value
A         2024-01-01     10
          2024-01-02     20
          2024-01-03     0
B         2024-01-01     30
          2024-01-02     60
          2024-01-03     40

C

category  date      value
A         2024-01-01     10
          2024-01-02     20
          2024-01-03     50
B         2024-01-01     30
          2024-01-02      0
          2024-01-03     40

D

category  date      value
A         2024-01-01     10
          2024-01-02     20
          2024-01-03     50
B         2024-01-01     30
          2024-01-02     60
          2024-01-03      0

💡 Hint

Remember that resample fills missing dates with zeros only if you use .sum() after resampling on groups.

The groupby groups by 'category', then resample creates daily frequency for each group. The sum aggregates values on those dates. Missing dates for a group get zero sum, but since all dates exist for each group, sums are as expected.

❓ data_output

intermediate

Number of rows after groupby and resample

Given a DataFrame with 3 categories and dates spanning 4 days, what is the number of rows in the result after grouping by 'category' and resampling daily?

Pandas

import pandas as pd
import numpy as np

np.random.seed(0)
categories = ['X', 'Y', 'Z']
dates = pd.date_range('2024-01-01', periods=4)
data = {'category': np.repeat(categories, 4), 'value': np.random.randint(1, 10, 12), 'date': list(dates)*3}
df = pd.DataFrame(data).set_index('date')
result = df.groupby('category').resample('D').sum()

A 9

B 15

C 12

D 16

💡 Hint

Each category has 4 days, so total rows = categories * days.

There are 3 categories and 4 days each, so 3 * 4 = 12 rows after resampling.

🔧 Debug

advanced

Identify the error in resampling after groupby

What error does this code produce when trying to resample after grouping by 'category'?

Pandas

import pandas as pd

data = {'category': ['A', 'A'], 'value': [1, 2], 'date': ['2024-01-01', '2024-01-02']}
df = pd.DataFrame(data)
df.set_index('date', inplace=True)
df.groupby('category').resample('D').mean()

A AttributeError: 'DataFrameGroupBy' object has no attribute 'resample'

B KeyError: 'date'

C No error, returns mean values

D TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'

💡 Hint

Check the index type after setting it.

The index is string type, not datetime. Resample requires a datetime-like index, so it raises a TypeError.

🚀 Application

advanced

Calculate weekly average sales per store

You have sales data with columns 'store', 'sales', and 'date'. How do you calculate the weekly average sales per store using groupby and resample?

Pandas

import pandas as pd

data = {'store': ['S1', 'S1', 'S2', 'S2', 'S1', 'S2'],
        'sales': [100, 150, 200, 250, 130, 300],
        'date': pd.to_datetime(['2024-01-01', '2024-01-08', '2024-01-01', '2024-01-08', '2024-01-15', '2024-01-15'])}
df = pd.DataFrame(data).set_index('date')

A df.groupby('store').resample('W').mean()

B df.resample('W').groupby('store').mean()

C df.groupby('store').resample('W').sum()

D df.groupby('store').resample('D').mean()

💡 Hint

Resample should be called after groupby to apply on each group separately.

Grouping by 'store' then resampling weekly ('W') and taking mean calculates weekly average sales per store.

🧠 Conceptual

expert

Why use groupby before resample for time series data?

Why is it important to use groupby before resample when working with time series data that has multiple categories?

A Because resample automatically groups data by all columns if groupby is not used.

B Because resample only works on the index, so grouping first allows resampling within each category's time series separately.

C Because resample can only be applied to columns, so groupby moves the index to columns.

D Because groupby changes the data type of the index to datetime, enabling resample to work.

💡 Hint

Think about how resample works on time indexes and how grouping affects that.

Resample works on the time index. Grouping first splits data by category, so resample applies separately to each category's time series.