describe() for statistical summary in Pandas - Mini Project: Build & Apply

Using describe() for Statistical Summary in pandas

📖 Scenario: You work as a data analyst for a small online store. You have collected sales data for the last week, including the number of items sold and the price of each item. You want to quickly understand the basic statistics of this data to see how sales are doing.

🎯 Goal: Build a simple pandas DataFrame with sales data and use the describe() method to get a statistical summary of the data.

📋 What You'll Learn

Create a pandas DataFrame with exact sales data

Add a variable to select numeric columns

Use the describe() method on the DataFrame

Print the statistical summary output

💡 Why This Matters

🌍 Real World

Statistical summaries help quickly understand data trends and spot unusual values in sales, finance, or any numeric data.

💼 Career

Data analysts and scientists use describe() to get fast insights before deeper analysis or reporting.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'item': ['apple', 'banana', 'orange', 'apple', 'banana'],
'quantity': [10, 5, 8, 7, 3],
'price': [0.5, 0.3, 0.7, 0.5, 0.3]

Pandas

import pandas as pd
# Create the sales_data DataFrame with the exact data
# Your code here

Hint

Use pd.DataFrame and pass a dictionary with keys as column names and values as lists of data.

2

Select numeric columns for summary

Create a variable called numeric_cols that selects only the numeric columns from sales_data using select_dtypes(include=['number'])

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'item': ['apple', 'banana', 'orange', 'apple', 'banana'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.7, 0.5, 0.3]
})
# Create numeric_cols selecting numeric columns from sales_data
# Your code here

Hint

Use select_dtypes(include=['number']) on the DataFrame to get numeric columns.

3

Get statistical summary using describe()

Use the describe() method on numeric_cols and save the result in a variable called summary

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'item': ['apple', 'banana', 'orange', 'apple', 'banana'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.7, 0.5, 0.3]
})
numeric_cols = sales_data.select_dtypes(include=['number'])
# Use describe() on numeric_cols and save to summary
# Your code here

Hint

Call describe() on the numeric columns DataFrame and assign it to summary.

4

Print the statistical summary

Print the variable summary to display the statistical summary of the numeric sales data

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'item': ['apple', 'banana', 'orange', 'apple', 'banana'],
    'quantity': [10, 5, 8, 7, 3],
    'price': [0.5, 0.3, 0.7, 0.5, 0.3]
})
numeric_cols = sales_data.select_dtypes(include=['number'])
summary = numeric_cols.describe()
# Print the summary variable
# Your code here

Hint

Use print(summary) to show the statistical summary.

Practice

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

easy

A. A statistical summary including count, mean, std, min, and max

B. A list of all column names in the DataFrame

C. A plot of the data distribution

D. A sorted version of the DataFrame

Solution

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

   The describe() function gives a quick statistical summary of numerical columns by default.

2. Step 2: Identify what the summary includes

   This summary includes count, mean, standard deviation, minimum, maximum, and quartiles.

3. Final Answer:

   A statistical summary including count, mean, std, min, and max -> Option A

4. Quick Check:

   describe() = statistical summary [OK]

Hint: Remember: describe() shows stats, not column names or plots [OK]

Common Mistakes:

• Thinking describe() lists column names
• Confusing describe() with plotting functions
• Assuming describe() sorts data

2. Which of the following is the correct way to get a statistical summary of a pandas DataFrame named df?

easy

A. df.summary()

B. df.describe()

C. describe(df)

D. df.stats()

Solution

1. Step 1: Recall pandas method syntax

   In pandas, methods are called on DataFrame objects using dot notation.

2. Step 2: Identify the correct method name

   The method to get a statistical summary is describe(), so df.describe() is correct.

3. Final Answer:

   df.describe() -> Option B

4. Quick Check:

   Method call on DataFrame = df.describe() [OK]

Hint: Use dot notation: df.describe() for summary [OK]

Common Mistakes:

• Using non-existent methods like summary() or stats()
• Calling describe() as a standalone function
• Missing parentheses after describe

3. Given the DataFrame:

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

What is the output for the mean of column A?

medium

A. 1

B. 2.5

C. 15

D. 3.0

Solution

1. Step 1: Calculate the mean of column A

   Column A values are [1, 2, 3, 4, 5]. The mean is (1+2+3+4+5)/5 = 15/5 = 3.0.

2. Step 2: Confirm output from describe()

   The describe() method prints the mean as 3.0 for column A.

3. Final Answer:

   3.0 -> Option D

4. Quick Check:

   Mean of [1,2,3,4,5] = 3.0 [OK]

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

Common Mistakes:

• Confusing sum with mean
• Using median instead of mean
• Misreading the output values

4. What is wrong with this code snippet to get a statistical summary of a DataFrame df?

summary = df.describe

medium

A. Missing parentheses to call the method

B. DataFrame does not have describe method

C. describe() requires arguments

D. Variable name 'summary' is invalid

Solution

1. Step 1: Check method call syntax

   The code assigns df.describe without parentheses, so it assigns the method itself, not the result.

2. Step 2: Identify the fix

   Adding parentheses df.describe() calls the method and returns the summary.

3. Final Answer:

   Missing parentheses to call the method -> Option A

4. Quick Check:

   Method call needs () to execute [OK]

Hint: Always add () to call methods like describe() [OK]

Common Mistakes:

• Forgetting parentheses after method names
• Thinking describe needs arguments
• Confusing method with variable name errors

5. You have a DataFrame df with both numeric and categorical columns. How can you use describe() to get a summary that includes statistics for all columns?

hard

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

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

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

D. df.describe(include=None)

Solution

1. Step 1: Understand default behavior of describe()

   By default, describe() summarizes only numeric columns.

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

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

3. Final Answer:

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

4. Quick Check:

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

Hint: Use include='all' to summarize all column types [OK]

Common Mistakes:

• Using include='numeric' which is default
• Using include='categorical' which is invalid
• Passing include=None which defaults to numeric only