Descriptive statistics (describe) in SciPy - Mini Project: Build & Apply

Descriptive statistics (describe)

📖 Scenario: You work as a data analyst for a small online store. You have collected daily sales data for a week. You want to understand the basic statistics of these sales numbers to see how the store is performing.

🎯 Goal: Build a small program that uses the scipy.stats.describe function to get descriptive statistics of daily sales data.

📋 What You'll Learn

Create a list called daily_sales with exact sales numbers for 7 days

Import the describe function from scipy.stats

Use describe on the daily_sales list to get statistics

Print the result of the describe function

💡 Why This Matters

🌍 Real World

Descriptive statistics help businesses quickly understand their data, like sales trends or customer behavior.

💼 Career

Data analysts and scientists use descriptive statistics as the first step to explore and summarize data before deeper analysis.

1

Create the daily sales data list

Create a list called daily_sales with these exact values: 150, 200, 170, 220, 180, 210, 190.

SciPy

# Create the daily_sales list with the exact values
# Your code here

Hint

Use square brackets [] to create a list and separate numbers with commas.

2

Import the describe function

Import the describe function from the scipy.stats module.

SciPy

daily_sales = [150, 200, 170, 220, 180, 210, 190]
# Import describe from scipy.stats
# Your code here

Hint

Use the from ... import ... syntax to import only the describe function.

3

Get descriptive statistics

Use the describe function on the daily_sales list and save the result in a variable called stats.

SciPy

daily_sales = [150, 200, 170, 220, 180, 210, 190]
from scipy.stats import describe
# Use describe on daily_sales and save to stats
# Your code here

Hint

Call describe with daily_sales inside the parentheses and assign it to stats.

4

Print the descriptive statistics

Print the variable stats to display the descriptive statistics.

SciPy

daily_sales = [150, 200, 170, 220, 180, 210, 190]
from scipy.stats import describe
stats = describe(daily_sales)
# Print the stats variable
# Your code here

Hint

Use print(stats) to show the statistics on the screen.

Practice

1. What does the scipy.stats.describe function provide when applied to a dataset?

easy

A. Summary statistics like count, mean, variance, min, max, skewness, and kurtosis

B. Only the mean and median of the dataset

C. A plot showing data distribution

D. A list of unique values in the dataset

Solution

1. Step 1: Understand the purpose of scipy.stats.describe

   This function is designed to give a quick summary of key statistics about the data.

2. Step 2: Identify the statistics returned

   It returns count, mean, variance, minimum, maximum, skewness, and kurtosis, which describe data shape and spread.

3. Final Answer:

   Summary statistics like count, mean, variance, min, max, skewness, and kurtosis -> Option A

4. Quick Check:

   Descriptive stats = count, mean, variance, min, max, skewness, kurtosis [OK]

Hint: Remember: describe summarizes data with key stats [OK]

Common Mistakes:

• Thinking it returns only mean and median
• Confusing it with plotting functions
• Expecting a list of unique values

2. Which of the following is the correct way to import and use describe from scipy.stats?

easy

A. from scipy.stats import describe; result = describe(data)

B. import scipy.stats; result = describe(data)

C. from scipy import describe; result = describe(data)

D. import describe from scipy.stats; result = describe(data)

Solution

1. Step 1: Check correct import syntax for a function

   Functions are imported using from module import function syntax in Python.

2. Step 2: Verify usage of describe after import

   After importing, call describe(data) directly.

3. Final Answer:

   from scipy.stats import describe; result = describe(data) -> Option A

4. Quick Check:

   Correct import and call = from scipy.stats import describe; result = describe(data) [OK]

Hint: Use 'from scipy.stats import describe' to call directly [OK]

Common Mistakes:

• Using wrong import syntax like 'import describe from ...'
• Importing scipy.stats but not calling with module prefix
• Importing from wrong module

3. Given the code below, what will be the output for the mean and min values?

import numpy as np
from scipy.stats import describe

data = np.array([2, 4, 6, 8, 10])
result = describe(data)
print(result.mean, result.minmax[0])

medium

A. 6.0 0

B. 5.0 2

C. 6.0 2

D. 5.0 0

Solution

1. Step 1: Calculate the mean of the data array

   The data is [2, 4, 6, 8, 10]. Mean = (2+4+6+8+10)/5 = 30/5 = 6.0.

2. Step 2: Identify the minimum value from minmax

   minmax returns (min, max). The minimum is 2.

3. Final Answer:

   6.0 2 -> Option C

4. Quick Check:

   Mean = 6.0, Min = 2 [OK]

Hint: Mean is average; min is smallest value [OK]

Common Mistakes:

• Calculating mean incorrectly as 5.0
• Confusing minmax tuple order
• Assuming min is 0 without checking data

4. What is wrong with the following code snippet?

from scipy.stats import describe
data = [1, 2, 3, 4, 5]
result = describe(data, axis=1)
print(result)

medium

A. The print statement is missing parentheses

B. The data list should be converted to a numpy array first

C. The describe function requires a second argument for axis

D. Axis=1 is invalid for 1D data, causing an error

Solution

1. Step 1: Understand the data shape and axis parameter

   The data is a 1D list. Axis=1 means operate along columns of 2D or higher arrays.

2. Step 2: Identify the error caused by axis=1 on 1D data

   Using axis=1 on 1D data causes an AxisError because axis 1 does not exist.

3. Final Answer:

   Axis=1 is invalid for 1D data, causing an error -> Option D

4. Quick Check:

   Axis must exist in data shape [OK]

Hint: Check data dimensions before setting axis [OK]

Common Mistakes:

• Assuming axis=1 works for 1D data
• Thinking data must be numpy array for describe
• Ignoring Python 3 print syntax

5. You have a dataset with some zero and negative values: data = [0, -1, 2, 3, 4]. Using scipy.stats.describe, which statistic helps you understand if the data distribution is skewed?

hard

A. Variance

B. Skewness

C. Count

D. Minimum

Solution

1. Step 1: Recall what skewness measures

   Skewness shows if data is symmetric or leans left/right (skewed).

2. Step 2: Identify which statistic indicates skewness

   Among the options, only skewness directly measures data asymmetry.

3. Final Answer:

   Skewness -> Option B

4. Quick Check:

   Skewness = data asymmetry [OK]

Hint: Skewness shows data leaning direction [OK]

Common Mistakes:

• Confusing variance with skewness
• Thinking count or minimum show skewness
• Ignoring negative and zero values impact