Descriptive statistics review in Data Analysis Python

Introduction

Descriptive statistics help us understand data by summarizing it with simple numbers and charts.

You want to know the average score of a class test.

You need to find the most common color of cars in a parking lot.

You want to see how spread out people's heights are in a group.

You want to quickly describe sales numbers for a month.

You want to check if data has any unusual values.

Syntax

Data Analysis Python

import pandas as pd

# Calculate mean, median, mode, std, min, max
mean_value = data['column'].mean()
median_value = data['column'].median()
mode_value = data['column'].mode()[0]
std_dev = data['column'].std()
minimum = data['column'].min()
maximum = data['column'].max()

# Or get all at once
summary = data['column'].describe()

Use describe() to get many stats quickly.

Mode may return multiple values; use the first one with [0].

Examples

Finds the average age in the data.

Data Analysis Python

mean_age = data['age'].mean()

Finds the middle income value when sorted.

Data Analysis Python

median_income = data['income'].median()

Finds the most common color.

Data Analysis Python

mode_color = data['color'].mode()[0]

Gets count, mean, std, min, max, and quartiles for scores.

Data Analysis Python

summary_stats = data['score'].describe()

Sample Program

This code creates a small dataset and calculates basic descriptive statistics like mean, median, mode, and a summary for age.

Data Analysis Python

import pandas as pd

# Create sample data
data = pd.DataFrame({
    'age': [23, 45, 31, 35, 23, 40, 29],
    'income': [50000, 64000, 58000, 60000, 52000, 62000, 58000],
    'color': ['red', 'blue', 'red', 'green', 'blue', 'blue', 'red']
})

# Calculate descriptive statistics
mean_age = data['age'].mean()
median_income = data['income'].median()
mode_color = data['color'].mode()[0]
summary_age = data['age'].describe()

print(f"Mean age: {mean_age}")
print(f"Median income: {median_income}")
print(f"Most common color: {mode_color}")
print("Age summary stats:\n", summary_age)

Important Notes

Descriptive statistics give a quick snapshot but do not explain why data looks a certain way.

Always check for missing data before calculating statistics.

Use visualizations alongside stats for better understanding.

Summary

Descriptive statistics summarize data with simple numbers.

Common stats include mean, median, mode, standard deviation, min, and max.

Use describe() in pandas for a quick summary.

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