DataFrame structure (index, columns, values) in Data Analysis Python - Mini Project: Build & Apply

Understanding DataFrame Structure: Index, Columns, and Values

📖 Scenario: You are working with a small dataset of fruit sales in a store. You want to organize this data in a table format to easily see the fruit names, their quantities sold, and prices.

🎯 Goal: Build a simple DataFrame using pandas that shows the fruit names as the index, and columns for quantity sold and price. Learn how to set up the index, columns, and access the values.

📋 What You'll Learn

Create a pandas DataFrame with specific data

Set the fruit names as the index

Define columns for quantity and price

Access the DataFrame's index, columns, and values attributes

💡 Why This Matters

🌍 Real World

Organizing sales data in tables helps store managers quickly find and analyze product information.

💼 Career

Data analysts and scientists often use DataFrames to clean, organize, and analyze data efficiently.

1

Create the initial DataFrame

Import pandas as pd and create a DataFrame called df with this data: fruits 'Apple', 'Banana', 'Cherry'; quantities 10, 20, 15; prices 0.5, 0.3, 0.8. Use a dictionary with keys 'Fruit', 'Quantity', and 'Price'.

Data Analysis Python

import pandas as pd
# Create the DataFrame df with the given data
# Your code here

Hint

Use pd.DataFrame() with a dictionary to create the table.

2

Set the index to the fruit names

Set the Fruit column as the index of the DataFrame df using set_index method with inplace=True.

Data Analysis Python

import pandas as pd
data = {'Fruit': ['Apple', 'Banana', 'Cherry'], 'Quantity': [10, 20, 15], 'Price': [0.5, 0.3, 0.8]}
df = pd.DataFrame(data)
# Set the 'Fruit' column as the index of df
# Your code here

Hint

Use df.set_index('Fruit', inplace=True) to change the index.

3

Access the DataFrame's index, columns, and values

Create three variables: index to store df.index, columns to store df.columns, and values to store df.values.

Data Analysis Python

import pandas as pd
data = {'Fruit': ['Apple', 'Banana', 'Cherry'], 'Quantity': [10, 20, 15], 'Price': [0.5, 0.3, 0.8]}
df = pd.DataFrame(data)
df.set_index('Fruit', inplace=True)
# Assign df.index to index, df.columns to columns, and df.values to values
# Your code here

Hint

Use df.index, df.columns, and df.values to get these parts.

4

Complete the DataFrame structure setup

Add a comment above the DataFrame creation that says # Fruit sales data to label the data clearly.

Data Analysis Python

# Fruit sales data
import pandas as pd
data = {'Fruit': ['Apple', 'Banana', 'Cherry'], 'Quantity': [10, 20, 15], 'Price': [0.5, 0.3, 0.8]}
df = pd.DataFrame(data)
df.set_index('Fruit', inplace=True)
index = df.index
columns = df.columns
values = df.values
# Your code here

Hint

Comments start with # in Python. Add it above the data.

Practice

1. Which part of a DataFrame represents the labels for the rows?

easy

A. Values

B. Columns

C. Index

D. Cells

Solution

1. Step 1: Understand DataFrame parts

   A DataFrame has three main parts: index (row labels), columns (column names), and values (data).

2. Step 2: Identify row labels

   The row labels are called the index, which helps identify each row uniquely.

3. Final Answer:

   Index -> Option C

4. Quick Check:

   Row labels = Index [OK]

Hint: Rows are labeled by the index in a DataFrame [OK]

Common Mistakes:

• Confusing columns with index
• Thinking values are labels
• Mixing cells with index

2. Which of the following is the correct way to get the column names of a DataFrame named df?

easy

A. df.columns

B. df.index

C. df.values

D. df.names

Solution

1. Step 1: Recall DataFrame attributes

   To get column names, use the columns attribute of the DataFrame.

2. Step 2: Match correct syntax

   The correct syntax is df.columns. Other options refer to rows or data.

3. Final Answer:

   df.columns -> Option A

4. Quick Check:

   Columns = df.columns [OK]

Hint: Use df.columns to get column names quickly [OK]

Common Mistakes:

• Using df.index for columns
• Trying df.names which doesn't exist
• Using df.values for columns

3. Given the DataFrame df below:

   A  B  C
0  1  4  7
1  2  5  8
2  3  6  9

What will df.values[1, 2] return?

medium

A. 5

B. 2

C. 6

D. 8

Solution

1. Step 1: Understand DataFrame values and indexing

   df.values returns the data as a 2D array. The first index is the row, the second is the column.

2. Step 2: Locate the value at row 1, column 2

   Row 1 is the second row: [2, 5, 8]. Column 2 is the third column, value 8.

3. Final Answer:

   8 -> Option D

4. Quick Check:

   df.values[1, 2] = 8 [OK]

Hint: Remember indexing starts at 0 for rows and columns [OK]

Common Mistakes:

• Mixing row and column indexes
• Using df.iloc instead of df.values
• Off-by-one errors in indexing

4. What is wrong with this code to get the row labels of a DataFrame df?

row_labels = df.values

medium

A. It returns an empty list

B. It returns the data values, not the row labels

C. It returns the column names instead

D. It causes a syntax error

Solution

1. Step 1: Understand what df.values returns

   df.values returns the data inside the DataFrame as a numpy array, not the row labels.

2. Step 2: Identify correct attribute for row labels

   Row labels are stored in df.index, not df.values.

3. Final Answer:

   It returns the data values, not the row labels -> Option B

4. Quick Check:

   df.values ≠ row labels [OK]

Hint: Use df.index for rows, not df.values [OK]

Common Mistakes:

• Confusing df.values with df.index
• Expecting df.values to give labels
• Using wrong attribute names

5. You have a DataFrame df with custom row labels ["a", "b", "c"] and columns ["X", "Y"]. Which code correctly extracts the data values as a 2D array?

hard

A. df.values

B. df.index

C. df.columns

D. df.data

Solution

1. Step 1: Recall DataFrame parts

   Index holds row labels, columns hold column names, and values hold the actual data.

2. Step 2: Identify correct attribute for data values

   To get the data as a 2D array, use df.values. Other options do not return data values.

3. Final Answer:

   df.values -> Option A

4. Quick Check:

   Data values = df.values [OK]

Hint: Use df.values to get data regardless of labels [OK]

Common Mistakes:

• Using df.index or df.columns for data
• Trying df.data which doesn't exist
• Confusing labels with values