Why advanced grouping matters in Pandas - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of groupby with multiple aggregation functions

What is the output of this code snippet using pandas grouping and aggregation?

Pandas

import pandas as pd

data = {'Team': ['A', 'A', 'B', 'B', 'C', 'C'],
        'Points': [10, 15, 10, 20, 10, 30],
        'Assists': [5, 7, 8, 6, 7, 9]}
df = pd.DataFrame(data)

result = df.groupby('Team').agg({'Points': ['sum', 'mean'], 'Assists': 'max'})
print(result)

A

       Points        Assists
          sum  mean      max
Team                          
A          25  12.5        7
B          30  15.0        8
C          40  20.0        9

B

       Points        Assists
          mean  sum      max
Team                          
A          12.5  25        7
B          15.0  30        8
C          20.0  40        9

C

       Points        Assists
          sum  mean      min
Team                          
A          25  12.5        5
B          30  15.0        6
C          40  20.0        7

D

       Points        Assists
          sum  mean      max
Team                          
A          25  12.5        5
B          30  15.0        6
C          40  20.0        7

💡 Hint

Look carefully at the aggregation functions applied to each column.

The code groups by 'Team' and calculates sum and mean for 'Points' and max for 'Assists'. The max values for 'Assists' are the highest in each group.

❓ data_output

intermediate

Number of groups after grouping by multiple columns

Given this DataFrame, how many groups will result from grouping by both 'Category' and 'Subcategory'?

Pandas

import pandas as pd

data = {'Category': ['Fruit', 'Fruit', 'Vegetable', 'Vegetable', 'Fruit'],
        'Subcategory': ['Citrus', 'Berry', 'Root', 'Leafy', 'Berry'],
        'Quantity': [10, 15, 7, 8, 5]}
df = pd.DataFrame(data)

groups = df.groupby(['Category', 'Subcategory'])
print(len(groups))

A 4

B 5

C 3

D 2

💡 Hint

Count unique pairs of Category and Subcategory.

There are 4 unique pairs: (Fruit, Citrus), (Fruit, Berry), (Vegetable, Root), (Vegetable, Leafy).

🔧 Debug

advanced

Identify the error in this advanced grouping code

What error does this code raise when trying to group and filter a DataFrame?

Pandas

import pandas as pd

data = {'Team': ['X', 'X', 'Y', 'Y'], 'Score': [10, 20, 15, 25]}
df = pd.DataFrame(data)

filtered = df.groupby('Team').filter(lambda x: x['Score'].mean() > 15)
print(filtered)

A KeyError: 'Score' because 'Score' is not accessible inside lambda

B No error, prints rows where Team's average Score > 15

C TypeError: 'filter' object is not callable

D ValueError: The truth value of a DataFrame is ambiguous

💡 Hint

Check how groupby filter works with lambda functions.

The code correctly filters groups where the mean Score is greater than 15, so no error occurs.

❓ visualization

advanced

Interpreting a grouped bar chart from grouped data

You have grouped sales data by 'Region' and 'Product' and plotted a grouped bar chart showing total sales. Which statement best describes what the chart shows?

A Bars show sales trends over time for each product in all regions.

B Bars represent average sales per region stacked by product categories.

C The chart shows total sales per region with products combined into one bar each.

D Each group of bars shows total sales per product within each region side by side.

💡 Hint

Grouped bar charts display categories side by side for comparison.

Grouped bar charts place bars for each product side by side within each region group, showing total sales per product per region.

🚀 Application

expert

Choosing the right grouping method for time series data

You have a DataFrame with timestamps and sales data. You want to analyze weekly sales totals. Which pandas method and parameters will correctly group the data by week?

Pandas

import pandas as pd

# df has columns 'Date' (datetime) and 'Sales'

# Which code correctly groups by week and sums sales?

A df.groupby('Date').sum().resample('W')

B df.groupby('Date').resample('W').sum()

C df.groupby(pd.Grouper(key='Date', freq='W')).sum()

D df.resample('W').groupby('Date').sum()

💡 Hint

Use pd.Grouper with freq='W' to group by week on a datetime column.

Option C uses pd.Grouper with key='Date' and freq='W' to group by week and sum sales correctly. Other options misuse resample or groupby order.