Why reshaping data matters in Pandas - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output shape after pivoting?

Given the DataFrame df below, what will be the shape of the DataFrame after applying pivot?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Date': ['2024-01-01', '2024-01-01', '2024-01-02', '2024-01-02'],
    'City': ['NY', 'LA', 'NY', 'LA'],
    'Temperature': [30, 60, 28, 65]
})

pivoted = df.pivot(index='Date', columns='City', values='Temperature')
print(pivoted.shape)

A (4, 1)

B (2, 4)

C (1, 4)

D (2, 2)

💡 Hint

Think about how many unique dates and cities there are.

The pivot creates a table with unique dates as rows and unique cities as columns. There are 2 unique dates and 2 unique cities, so the shape is (2, 2).

❓ data_output

intermediate

Result of melting a DataFrame

What is the resulting DataFrame after melting the following data?

Pandas

import pandas as pd

df = pd.DataFrame({
    'ID': [1, 2],
    'Math': [90, 80],
    'Science': [85, 95]
})
melted = pd.melt(df, id_vars=['ID'], var_name='Subject', value_name='Score')
print(melted)

A [{'ID':1,'Subject':'Math','Score':90},{'ID':2,'Subject':'Science','Score':95}]

B [{'ID':1,'Subject':'Math','Score':90},{'ID':1,'Subject':'Science','Score':85},{'ID':2,'Subject':'Math','Score':80},{'ID':2,'Subject':'Science','Score':95}]

C [{'Subject':'Math','Score':[90,80]},{'Subject':'Science','Score':[85,95]}]

D [{'ID':1,'Math':90,'Science':85},{'ID':2,'Math':80,'Science':95}]

💡 Hint

Melt turns columns into rows keeping the id_vars fixed.

The melt function converts columns 'Math' and 'Science' into rows under 'Subject' and their values under 'Score', repeating the 'ID' for each.

❓ visualization

advanced

Visualizing reshaped data for comparison

You have sales data for two products across three months in wide format. Which plot best shows the monthly sales comparison after reshaping the data to long format?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Month': ['Jan', 'Feb', 'Mar'],
    'Product_A': [100, 120, 130],
    'Product_B': [90, 110, 115]
})

long_df = pd.melt(df, id_vars=['Month'], var_name='Product', value_name='Sales')

plt.figure(figsize=(6,4))
for product in long_df['Product'].unique():
    subset = long_df[long_df['Product'] == product]
    plt.plot(subset['Month'], subset['Sales'], marker='o', label=product)
plt.legend()
plt.title('Monthly Sales Comparison')
plt.xlabel('Month')
plt.ylabel('Sales')
plt.tight_layout()
plt.show()

A Line plot with months on x-axis and sales on y-axis, separate lines for each product

B Pie chart showing total sales per product

C Bar chart with months on x-axis and sales stacked by product

D Scatter plot with sales on x-axis and months on y-axis

💡 Hint

Think about how to compare trends over time for multiple products.

The line plot clearly shows sales trends over months for each product after reshaping data to long format.

🔧 Debug

advanced

Identify the error in reshaping code

What error will this code raise?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Date': ['2024-01-01', '2024-01-02'],
    'City': ['NY', 'LA'],
    'Temperature': [30, 60]
})

pivoted = df.pivot(index='Date', columns='Temperature', values='City')
print(pivoted)

A No error, prints pivoted DataFrame

B KeyError: 'Temperature'

C ValueError: Index contains duplicate entries, cannot reshape

D TypeError: unhashable type: 'list'

💡 Hint

Check if the index and columns have unique values.

Since 'Date' and 'Temperature' columns have unique values, pivot works without error and prints the reshaped DataFrame.

🚀 Application

expert

Choosing reshaping method for time series analysis

You have a DataFrame with daily sales data for multiple stores in wide format, where each store is a column. You want to prepare the data for time series analysis that requires a single sales column and a store identifier column. Which reshaping method should you use?

A Use <code>groupby</code> to aggregate sales by store

B Use <code>pivot</code> to convert the data to long format

C Use <code>melt</code> to convert the data to long format

D Use <code>concat</code> to join all store columns vertically

💡 Hint

Think about turning columns into rows while keeping identifiers.

melt is used to convert wide data into long format by turning columns into rows, which is needed for time series analysis with a single sales column and store identifier.