Why Pandas for data analysis - Challenge Your Understanding

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why is Pandas preferred for handling tabular data?

Pandas is widely used for data analysis. Which reason best explains why Pandas is preferred for handling tabular data?

A It provides easy-to-use data structures like DataFrame and Series that allow fast data manipulation and analysis.

B It is a tool for writing machine learning models without any data preprocessing.

C It is a database management system optimized for large-scale data storage.

D It is mainly designed for creating visualizations and charts from data.

💡 Hint

Think about what makes working with rows and columns easier in Pandas.

Pandas offers DataFrame and Series, which are like tables and columns. These structures make it simple to select, filter, and transform data quickly.

❓ Predict Output

intermediate

Output of Pandas DataFrame filtering

What is the output of this code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]})
filtered = df[df['A'] > 2]
print(filtered)

A

   A  B
0  1  5
1  2  6
2  3  7
3  4  8

B

   A  B
0  1  5
1  2  6

C

   A  B
2  3  7
3  4  8

D

Empty DataFrame
Columns: [A, B]
Index: []

💡 Hint

Look at the condition used to filter rows.

The code filters rows where column 'A' has values greater than 2, so only rows with A=3 and A=4 remain.

❓ data_output

advanced

Result of groupby and aggregation in Pandas

Given the DataFrame below, what is the result of grouping by 'Category' and calculating the sum of 'Value'?

Pandas

import pandas as pd

df = pd.DataFrame({'Category': ['A', 'B', 'A', 'B', 'C'], 'Value': [10, 20, 30, 40, 50]})
grouped = df.groupby('Category').sum()
print(grouped)

A

          Value
Category       
A            40
B            60
C            50

B

          Value
Category       
A            30
B            40
C            50

C

          Value
Category       
A            10
B            20
C            50

D

          Value
Category       
A            70
B            60
C            50

💡 Hint

Sum the 'Value' for each unique 'Category'.

Category 'A' has values 10 and 30 (sum 40), 'B' has 20 and 40 (sum 60), 'C' has 50.

🔧 Debug

advanced

Identify the error in this Pandas code

What error does this code produce?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [1, 2, 3], 'Y': [4, 5, 6]})
result = df['Z'] + 1
print(result)

A TypeError: unsupported operand type(s) for +: 'int' and 'str'

B ValueError: operands could not be broadcast together

C No error, prints a Series with values [2, 3, 4]

D KeyError: 'Z'

💡 Hint

Check if the column 'Z' exists in the DataFrame.

The DataFrame has columns 'X' and 'Y' only. Accessing 'Z' raises a KeyError.

🚀 Application

expert

Pandas operation to combine and summarize data

You have two DataFrames: one with sales data and one with product info. Which Pandas operation correctly merges these and calculates total sales per product?

Pandas

import pandas as pd

sales = pd.DataFrame({'ProductID': [1, 2, 1, 3], 'Quantity': [5, 3, 2, 4]})
products = pd.DataFrame({'ProductID': [1, 2, 3], 'Name': ['Pen', 'Pencil', 'Eraser']})

A

merged = sales.merge(products)
total = merged.groupby('ProductID')['Quantity'].count()
print(total)

B

merged = pd.merge(sales, products, on='ProductID')
total = merged.groupby('Name')['Quantity'].sum()
print(total)

C

merged = pd.concat([sales, products], axis=1)
total = merged.groupby('ProductID')['Quantity'].sum()
print(total)

D

merged = sales.join(products, on='ProductID')
total = merged.groupby('ProductID')['Quantity'].mean()
print(total)

💡 Hint

Think about joining on a common column and then grouping by product name.

Option B merges on 'ProductID', then groups by 'Name' to sum quantities, giving total sales per product.