DataFrame as labeled two-dimensional table in Pandas - Mini Project: Build & Apply

DataFrame as labeled two-dimensional table

📖 Scenario: You work in a small bookstore. You want to organize your book sales data in a clear table format to analyze it easily.

🎯 Goal: Create a pandas DataFrame to represent book sales with labels for rows and columns. Then display the table.

📋 What You'll Learn

Use pandas to create a DataFrame

Include book titles as row labels

Include sales data as columns

Print the DataFrame to show the table

💡 Why This Matters

🌍 Real World

Organizing sales or inventory data in tables helps businesses track performance clearly.

💼 Career

Data analysts and scientists often use pandas DataFrames to manage and analyze labeled data efficiently.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Title' as a list of 'Book A', 'Book B', 'Book C'; 'Copies Sold' as a list of 150, 200, 120; and 'Revenue' as a list of 3000, 5000, 2400.

Pandas

# Create the sales_data dictionary with book titles, copies sold, and revenue
# Your code here

Hint

Use a dictionary with keys 'Title', 'Copies Sold', and 'Revenue'. Each key should have a list of values.

2

Import pandas and create DataFrame

Import pandas as pd. Then create a DataFrame called df from the sales_data dictionary.

Pandas

sales_data = {
    'Title': ['Book A', 'Book B', 'Book C'],
    'Copies Sold': [150, 200, 120],
    'Revenue': [3000, 5000, 2400]
}

# Import pandas and create DataFrame df from sales_data
# Your code here

Hint

Use import pandas as pd and then pd.DataFrame() with the dictionary.

3

Set the 'Title' column as row labels

Use the set_index method on df to set the 'Title' column as the index (row labels). Assign the result back to df.

Pandas

import pandas as pd

sales_data = {
    'Title': ['Book A', 'Book B', 'Book C'],
    'Copies Sold': [150, 200, 120],
    'Revenue': [3000, 5000, 2400]
}

df = pd.DataFrame(sales_data)

# Set 'Title' as the index of df
# Your code here

Hint

Use df.set_index('Title') and assign it back to df.

4

Print the DataFrame

Print the DataFrame df to display the labeled two-dimensional table.

Pandas

import pandas as pd

sales_data = {
    'Title': ['Book A', 'Book B', 'Book C'],
    'Copies Sold': [150, 200, 120],
    'Revenue': [3000, 5000, 2400]
}

df = pd.DataFrame(sales_data)

df = df.set_index('Title')

# Print the DataFrame df
# Your code here

Hint

Use print(df) to show the table.

Practice

1.

What is a pandas DataFrame best described as?

easy

A. A one-dimensional list of numbers

B. A type of database server

C. A function to plot graphs

D. A labeled two-dimensional table with rows and columns

Solution

1. Step 1: Understand the structure of a DataFrame

   A DataFrame organizes data in rows and columns with labels for both, like a spreadsheet.

2. Step 2: Compare options to definition

   Only "A labeled two-dimensional table with rows and columns" describes a labeled two-dimensional table, matching the DataFrame concept.

3. Final Answer:

   A labeled two-dimensional table with rows and columns -> Option D

4. Quick Check:

   DataFrame = labeled 2D table [OK]

Hint: Remember: DataFrame = table with row and column labels [OK]

Common Mistakes:

• Confusing DataFrame with a list or array
• Thinking DataFrame is a plotting tool
• Mixing DataFrame with database servers

2.

Which of the following is the correct way to create a pandas DataFrame from a dictionary?

import pandas as pd

# Which line correctly creates a DataFrame?

easy

A. df = pd.DataFrame({'A' = [1, 2], 'B' = [3, 4]})

B. df = pd.DataFrame['A': [1, 2], 'B': [3, 4]]

C. df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]})

D. df = pd.DataFrame({'A': 1, 2, 'B': 3, 4})

Solution

1. Step 1: Check dictionary syntax for DataFrame creation

   The dictionary keys are column names and values are lists of column data. Correct syntax uses curly braces and colons.

2. Step 2: Identify correct pandas DataFrame constructor usage

   df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}) uses pd.DataFrame with a dictionary correctly. Others have syntax errors or wrong brackets.

3. Final Answer:

   df = pd.DataFrame({'A': [1, 2], 'B': [3, 4]}) -> Option C

4. Quick Check:

   Use pd.DataFrame with dict and colons [OK]

Hint: Use curly braces and colons inside pd.DataFrame() [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Using '=' instead of ':' in dictionary
• Passing values without list brackets

3.

What will be the output of the following code?

import pandas as pd

data = {'Name': ['Anna', 'Bob'], 'Age': [25, 30]}
df = pd.DataFrame(data)
print(df.loc[1, 'Name'])

medium

A. Bob

B. Anna

C. 25

D. KeyError

Solution

1. Step 1: Understand DataFrame creation and indexing

   The DataFrame has two rows with index 0 and 1. 'Name' column has ['Anna', 'Bob'].

2. Step 2: Access value at row index 1 and column 'Name'

   df.loc[1, 'Name'] selects row 1, column 'Name', which is 'Bob'.

3. Final Answer:

   Bob -> Option A

4. Quick Check:

   df.loc[1, 'Name'] = Bob [OK]

Hint: loc uses row label and column name to get value [OK]

Common Mistakes:

• Confusing loc with iloc (positional index)
• Expecting row 1 to be 'Anna'
• Mistaking column name for index

4.

Identify the error in this code snippet that tries to create a DataFrame:

import pandas as pd

data = {'X': [1, 2], 'Y': [3]}
df = pd.DataFrame(data)
print(df)

medium

A. The value for key 'Y' has the wrong length (must match 'X')

B. The dictionary keys must be integers

C. pd.DataFrame cannot be created from a dictionary

D. Missing parentheses in print statement

Solution

1. Step 1: Check dictionary values for DataFrame columns

   Each column value must be a list or array of equal length. 'Y': [3] has length 1, 'X' has length 2, which is invalid.

2. Step 2: Identify correct error cause

   The value for key 'Y' has the wrong length (must match 'X') correctly points out the length mismatch.

3. Final Answer:

   The value for key 'Y' has the wrong length (must match 'X') -> Option A

4. Quick Check:

   All column values must have equal length [OK]

Hint: All DataFrame columns need list-like values of equal length [OK]

Common Mistakes:

• Using lists of unequal lengths for columns
• Thinking dictionary keys must be numbers
• Assuming print needs parentheses in Python 3

5.

You have this nested dictionary representing sales data:

sales = {
  'Store1': {'Jan': 100, 'Feb': 120},
  'Store2': {'Jan': 90, 'Feb': 110}
}

How can you create a DataFrame where rows are months and columns are stores?

hard

A. df = pd.DataFrame(sales).T

B. df = pd.DataFrame.from_dict(sales, orient='index').T

C. df = pd.DataFrame(sales, orient='index')

D. df = pd.DataFrame.from_dict(sales, orient='index')

Solution

1. Step 1: Understand the nested dictionary structure

   Keys are stores, values are dicts of months and sales. We want months as rows, stores as columns.

2. Step 2: Use from_dict with orient='index' and transpose

   from_dict with orient='index' treats outer keys as rows. Transpose (T) swaps rows and columns to get months as rows.

3. Final Answer:

   df = pd.DataFrame.from_dict(sales, orient='index').T -> Option B

4. Quick Check:

   Use from_dict with orient='index' + transpose [OK]

Hint: Use from_dict with orient='index' then transpose [OK]

Common Mistakes:

• Not transposing after from_dict
• Using wrong orient parameter
• Assuming pd.DataFrame(sales) arranges data correctly