describe() for statistics in Data Analysis Python - Mini Project: Build & Apply

Using describe() for Statistics

📖 Scenario: You work in a small bakery. You have sales data for different types of bread sold each day. You want to understand the basic statistics of daily sales to plan better.

🎯 Goal: Learn how to use the describe() method in pandas to get quick statistics like mean, median, min, max, and quartiles for your sales data.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data

Use the describe() method to get summary statistics

Print the summary statistics

💡 Why This Matters

🌍 Real World

Businesses often need to quickly understand their sales or production data to make decisions. Using describe() helps get a quick overview of key statistics.

💼 Career

Data analysts and scientists use describe() to summarize datasets before deeper analysis or reporting.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values:
'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
'Bread': [30, 45, 50, 40, 60],
'Pastry': [20, 25, 30, 22, 28]

Data Analysis Python

# Import pandas and create the sales_data DataFrame
# Your code here

Hint

Use pd.DataFrame() with a dictionary where keys are column names and values are lists of data.

2

Set the Day column as index

Create a new DataFrame called sales_indexed by setting the 'Day' column as the index of sales_data using the set_index() method.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread': [30, 45, 50, 40, 60],
    'Pastry': [20, 25, 30, 22, 28]
})
# Set 'Day' as index and save to sales_indexed
# Your code here

Hint

Use set_index('Day') on the DataFrame to make 'Day' the index.

3

Get summary statistics with describe()

Use the describe() method on sales_indexed and save the result to a variable called summary_stats.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread': [30, 45, 50, 40, 60],
    'Pastry': [20, 25, 30, 22, 28]
})
sales_indexed = sales_data.set_index('Day')
# Use describe() on sales_indexed and save to summary_stats
# Your code here

Hint

Call describe() on the DataFrame to get statistics like mean, min, max, and quartiles.

4

Print the summary statistics

Print the variable summary_stats to display the statistics of the sales data.

Data Analysis Python

import pandas as pd
sales_data = pd.DataFrame({
    'Day': ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday'],
    'Bread': [30, 45, 50, 40, 60],
    'Pastry': [20, 25, 30, 22, 28]
})
sales_indexed = sales_data.set_index('Day')
summary_stats = sales_indexed.describe()
# Print summary_stats
# Your code here

Hint

Use print(summary_stats) to show the statistics.

Practice

1. What does the describe() function in pandas primarily provide for a DataFrame?

easy

A. A sorted version of the DataFrame

B. A plot of the data distribution

C. A list of all column names

D. A summary of main statistics like mean, median, and count

Solution

1. Step 1: Understand the purpose of describe()

   The describe() function summarizes key statistics such as count, mean, std, min, max, and quartiles for numeric data.

2. Step 2: Compare with other options

   Options B, C, and D describe different functions or outputs unrelated to describe().

3. Final Answer:

   A summary of main statistics like mean, median, and count -> Option D

4. Quick Check:

   describe() = summary statistics [OK]

Hint: Remember: describe() = quick stats summary [OK]

Common Mistakes:

• Thinking describe() creates plots
• Confusing describe() with listing columns
• Assuming describe() sorts data

2. Which of the following is the correct way to call describe() on a pandas DataFrame named df?

easy

A. df.describe()

B. describe(df)

C. df.describe

D. df.describe[]

Solution

1. Step 1: Recall pandas method syntax

   Methods are called with parentheses after the method name, so df.describe() is correct.

2. Step 2: Check other options for syntax errors

   df.describe[] uses invalid square brackets; describe(df) incorrectly calls it as a standalone function; df.describe misses parentheses and prints the method object.

3. Final Answer:

   df.describe() -> Option A

4. Quick Check:

   Method call needs () = df.describe() [OK]

Hint: Call methods with parentheses: df.describe() [OK]

Common Mistakes:

• Forgetting parentheses when calling methods
• Using square brackets instead of parentheses
• Trying to call describe as a standalone function

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]})
print(df.describe())

What is the output for the mean of column A?

medium

A. 3.0

B. 2.5

C. 15.0

D. 10.0

Solution

1. Step 1: Calculate mean of column A

   Column A has values [1, 2, 3, 4, 5]. Mean = (1+2+3+4+5)/5 = 15/5 = 3.0.

2. Step 2: Verify other options

   Options B, C, and D do not match the calculated mean.

3. Final Answer:

   3.0 -> Option A

4. Quick Check:

   Mean of A = 3.0 [OK]

Hint: Sum values and divide by count for mean [OK]

Common Mistakes:

• Mixing mean with median or mode
• Calculating sum instead of mean
• Using wrong column values

4. You run the code below but get unexpected output:

import pandas as pd
df = pd.DataFrame({'X': [5, 10, 15]})
print(df.describe)

What is the problem and how to fix it?

medium

A. DataFrame missing columns; add more columns

B. describe is not a pandas function; import missing

C. Missing parentheses after describe; fix with df.describe()

D. Syntax error in DataFrame creation

Solution

1. Step 1: Identify the problem

   The code references df.describe without parentheses, so it prints the method object, not the result.

2. Step 2: Fix the code

   Add parentheses to call the method: df.describe().

3. Final Answer:

   Missing parentheses after describe; fix with df.describe() -> Option C

4. Quick Check:

   Method call needs () = df.describe() [OK]

Hint: Always use () to call methods [OK]

Common Mistakes:

• Forgetting parentheses on method calls
• Assuming describe is not a method
• Thinking DataFrame needs more columns

5. You have a DataFrame df with numeric and categorical columns. You want describe() to include statistics for all columns, not just numeric ones. Which code will achieve this?

hard

A. df.describe(exclude='all')

B. df.describe(include='all')

C. df.describe(numeric_only=True)

D. df.describe(include='numeric')

Solution

1. Step 1: Understand describe() default behavior

   By default, describe() shows stats only for numeric columns.

2. Step 2: Use include='all' to get all columns

   Passing include='all' makes describe() summarize numeric and categorical columns.

3. Step 3: Check other options

   exclude='all' excludes all columns (invalid result); numeric_only=True limits to numeric only; include='numeric' matches default.

4. Final Answer:

   df.describe(include='all') -> Option B

5. Quick Check:

   include='all' = all columns summary [OK]

Hint: Use include='all' to describe all columns [OK]

Common Mistakes:

• Using numeric_only=True to get all columns
• Confusing include and exclude parameters
• Passing invalid values to include