Descriptive statistics review in Data Analysis Python - Mini Project: Build & Apply

Descriptive statistics review

📖 Scenario: You work as a data analyst for a small retail store. You have collected daily sales data for a week. Your manager wants you to summarize this data to understand the sales performance.

🎯 Goal: You will create a list of daily sales, set a threshold for high sales, filter the sales above this threshold, and then calculate basic descriptive statistics like mean and median for these high sales.

📋 What You'll Learn

Create a list called daily_sales with exact values for 7 days

Create a variable called high_sales_threshold with the value 200

Use a list comprehension to create a list called high_sales containing sales greater than high_sales_threshold

Calculate the mean and median of high_sales using the statistics module

Print the mean and median values exactly as shown

💡 Why This Matters

🌍 Real World

Retail stores and many businesses collect daily sales data. Summarizing this data helps managers understand performance and make decisions.

💼 Career

Data analysts often need to clean, filter, and summarize data using descriptive statistics to provide insights to stakeholders.

1

Create the daily sales data

Create a list called daily_sales with these exact values: [150, 220, 180, 300, 250, 90, 400]

Data Analysis Python

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

Hint

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

2

Set the high sales threshold

Create a variable called high_sales_threshold and set it to 200

Data Analysis Python

daily_sales = [150, 220, 180, 300, 250, 90, 400]
# Create the variable high_sales_threshold and set it to 200
# Your code here

Hint

Just assign the number 200 to the variable high_sales_threshold.

3

Filter high sales using list comprehension

Use a list comprehension to create a list called high_sales that contains only the sales from daily_sales greater than high_sales_threshold

Data Analysis Python

daily_sales = [150, 220, 180, 300, 250, 90, 400]
high_sales_threshold = 200
# Create the list high_sales using list comprehension
# Your code here

Hint

Use the syntax: [item for item in list if condition].

4

Calculate and print mean and median of high sales

Import the statistics module. Calculate the mean and median of high_sales using statistics.mean() and statistics.median(). Then print the results exactly as: Mean: <mean_value> and Median: <median_value>

Data Analysis Python

daily_sales = [150, 220, 180, 300, 250, 90, 400]
high_sales_threshold = 200
high_sales = [sale for sale in daily_sales if sale > high_sales_threshold]
# Import statistics and calculate mean and median of high_sales
# Print the mean and median values
# Your code here

Hint

Use import statistics at the top. Then use statistics.mean(high_sales) and statistics.median(high_sales). Use f-strings to print.

Practice

1. Which of the following best describes the median in a dataset?

easy

A. The middle value when data is sorted from smallest to largest

B. The most frequently occurring value in the data

C. The average of all values in the data

D. The difference between the largest and smallest values

Solution

1. Step 1: Understand what median means

   The median is the value that lies in the middle of a sorted list of numbers.

2. Step 2: Compare with other options

   Mode is the most frequent value, mean is the average, and range is max-min.

3. Final Answer:

   The middle value when data is sorted from smallest to largest -> Option A

4. Quick Check:

   Median = middle value [OK]

Hint: Median splits sorted data in half [OK]

Common Mistakes:

• Confusing median with mean
• Thinking median is the most frequent value
• Mixing median with range

2. Which of the following is the correct way to get descriptive statistics of a pandas DataFrame df?

easy

A. describe(df)

B. df.summary()

C. df.describe()

D. df.stats()

Solution

1. Step 1: Recall pandas method for summary

   In pandas, describe() is the method to get descriptive stats.

2. Step 2: Check other options

   Methods like summary(), stats() do not exist in pandas, and describe(df) is not valid syntax.

3. Final Answer:

   df.describe() -> Option C

4. Quick Check:

   Use df.describe() for stats [OK]

Hint: Use df.describe() for quick stats [OK]

Common Mistakes:

• Using non-existent methods like summary()
• Calling describe as a function with df argument
• Confusing with other libraries

3. Given the following code, what will be the output of print(df.mean())?

import pandas as pd
data = {'A': [1, 2, 3], 'B': [4, 5, 6]}
df = pd.DataFrame(data)

medium

A. A 2.0
B 5.0
dtype: float64

B. A 3.0
B 6.0
dtype: int64

C. A 1.5
B 4.5
dtype: float64

D. Error: mean() not defined

Solution

1. Step 1: Calculate mean for each column

   Column A values: 1, 2, 3; mean = (1+2+3)/3 = 2.0. Column B values: 4, 5, 6; mean = (4+5+6)/3 = 5.0.

2. Step 2: Understand pandas output format

   Printing df.mean() shows means per column with dtype float64.

3. Final Answer:

   A 2.0 B 5.0 dtype: float64 -> Option A

4. Quick Check:

   Mean = sum/number of values [OK]

Hint: Mean = sum of values divided by count [OK]

Common Mistakes:

• Calculating median instead of mean
• Mixing up column values
• Expecting integer dtype instead of float

4. The following code is intended to calculate the standard deviation of column 'X' in DataFrame df. What is the error?

import pandas as pd
data = {'X': [10, 20, 30, 40]}
df = pd.DataFrame(data)
std_dev = df.std('X')
print(std_dev)

medium

A. DataFrame std() method requires axis=1 argument

B. The std() method does not take any arguments; column must be selected first

C. The code should use df['X'].variance() instead

D. No error; code runs correctly

Solution

1. Step 1: Check std() method usage

   In pandas, std() calculates standard deviation for all numeric columns or Series. It does not accept a column name as argument.

2. Step 2: Correct way to get std of column 'X'

   We must select the column first: df['X'].std().

3. Final Answer:

   The std() method does not take any arguments; column must be selected first -> Option B

4. Quick Check:

   Use df['X'].std() to get column std [OK]

Hint: Select column before std() call [OK]

Common Mistakes:

• Passing column name as argument to std()
• Using variance() instead of std()
• Assuming std() needs axis=1

5. You have a DataFrame df with columns 'Age' and 'Salary'. You want to find the average salary for people older than 30. Which code correctly does this?

hard

A. df['Salary'].mean(df['Age'] > 30)

B. df['Salary'].mean().filter(df['Age'] > 30)

C. df.mean()['Salary'][df['Age'] > 30]

D. df[df['Age'] > 30]['Salary'].mean()

Solution

1. Step 1: Filter rows where Age > 30

   Use boolean indexing: df[df['Age'] > 30] selects rows with Age greater than 30.

2. Step 2: Select 'Salary' column and calculate mean

   From filtered rows, select 'Salary' column and call mean() to get average salary.

3. Final Answer:

   df[df['Age'] > 30]['Salary'].mean() -> Option D

4. Quick Check:

   Filter then mean for conditional average [OK]

Hint: Filter rows first, then calculate mean [OK]

Common Mistakes:

• Passing condition inside mean()
• Trying to filter after mean()
• Using mean() on entire DataFrame