Exploratory Data Analysis (EDA) template in Data Analysis Python - Mini Project: Build & Apply

Exploratory Data Analysis (EDA) template

📖 Scenario: You have a small dataset about daily sales in a store. You want to understand the data better by exploring it step-by-step.

🎯 Goal: Build a simple Exploratory Data Analysis (EDA) template in Python that loads data, sets a configuration, applies core analysis, and prints the results.

📋 What You'll Learn

Create a dictionary with sales data for 5 days

Add a threshold variable to filter days with sales above it

Use a loop to select days with sales above the threshold

Print the filtered days and their sales

💡 Why This Matters

🌍 Real World

Exploratory Data Analysis helps you understand data before making decisions. For example, a store manager can see which days have high sales.

💼 Career

Data analysts and scientists use EDA to clean and understand data before building models or reports.

1

DATA SETUP: Create the sales data dictionary

Create a dictionary called daily_sales with these exact entries: 'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100

Data Analysis Python

# Create the daily_sales dictionary with exact day and sales values
# Your code here

Hint

Use curly braces to create a dictionary with day names as keys and sales numbers as values.

2

CONFIGURATION: Set the sales threshold

Create a variable called sales_threshold and set it to 100

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
# Set the sales_threshold variable to 100
# Your code here

Hint

Just assign the number 100 to the variable sales_threshold.

3

CORE LOGIC: Filter days with sales above the threshold

Create a new dictionary called filtered_sales using a for loop with variables day and sales to iterate over daily_sales.items(). Add entries to filtered_sales only if sales is greater than sales_threshold

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
sales_threshold = 100
# Create filtered_sales dictionary with days having sales above sales_threshold
# Your code here

Hint

Start with an empty dictionary. Use a for loop to check each day's sales. Add to filtered_sales only if sales are above the threshold.

4

OUTPUT: Print the filtered sales dictionary

Write a print statement to display the filtered_sales dictionary

Data Analysis Python

daily_sales = {'Monday': 150, 'Tuesday': 200, 'Wednesday': 50, 'Thursday': 300, 'Friday': 100}
sales_threshold = 100
filtered_sales = {}
for day, sales in daily_sales.items():
    if sales > sales_threshold:
        filtered_sales[day] = sales
# Print the filtered_sales dictionary
# Your code here

Hint

Use print(filtered_sales) to show the filtered dictionary.

Practice

1. What is the main purpose of Exploratory Data Analysis (EDA) in data science?

easy

A. To build machine learning models directly

B. To clean data without checking it

C. To understand the data quickly and find patterns

D. To create final reports without analysis

Solution

1. Step 1: Understand EDA's role

   EDA is used to explore and understand data before any modeling or cleaning.

2. Step 2: Compare options

   Only To understand the data quickly and find patterns correctly states EDA's purpose as understanding data quickly and finding patterns.

3. Final Answer:

   To understand the data quickly and find patterns -> Option C

4. Quick Check:

   EDA = Understand data fast [OK]

Hint: EDA means exploring data first, not modeling [OK]

Common Mistakes:

• Confusing EDA with model building
• Thinking EDA skips data checking
• Assuming EDA creates final reports

2. Which Python command is commonly used to get a quick summary of a DataFrame's structure during EDA?

easy

A. df.describe()

B. df.info()

C. df.plot()

D. df.head()

Solution

1. Step 1: Recall DataFrame methods

   df.info() shows data types, non-null counts, and memory usage.

2. Step 2: Compare with other methods

   df.describe() gives stats, df.plot() makes graphs, df.head() shows rows. Only df.info() summarizes structure.

3. Final Answer:

   df.info() -> Option B

4. Quick Check:

   DataFrame structure = df.info() [OK]

Hint: Use df.info() for quick data structure summary [OK]

Common Mistakes:

• Using df.describe() for structure instead of stats
• Confusing df.head() with info summary
• Trying to plot before understanding data

3. Given the code:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 2, 3], 'B': [4, 5, 6, 7]})
print(df['A'].value_counts())

What is the output?

medium

A. 2 2
1 1
3 1
Name: A, dtype: int64

B. 1 2
2 1
3 1
Name: A, dtype: int64

C. 4 1
5 1
6 1
7 1
Name: B, dtype: int64

D. Error: value_counts() not found

Solution

1. Step 1: Understand value_counts()

   It counts how many times each unique value appears in the column.

2. Step 2: Count values in column 'A'

   Values are 1 (once), 2 (twice), 3 (once). value_counts() sorts by frequency descending, so: 2 2 1 1 3 1 Name: A, dtype: int64

3. Final Answer:

   2 2 1 1 3 1 Name: A, dtype: int64 -> Option A

4. Quick Check:

   Count unique values = 2 2 1 1 3 1 Name: A, dtype: int64 [OK]

Hint: value_counts() counts unique values in a column [OK]

Common Mistakes:

• Mixing counts of column B instead of A
• Misreading counts order
• Expecting error from value_counts()

4. You run this code:

import pandas as pd
df = pd.DataFrame({'X': [1, 2, None, 4], 'Y': [5, None, 7, 8]})
print(df.describe())

But the output misses some rows. What is the likely problem?

medium

A. describe() excludes rows with missing values by default

B. describe() ignores non-numeric columns

C. DataFrame has no numeric columns

D. Syntax error in code

Solution

1. Step 1: Understand describe() behavior

   describe() calculates stats on numeric columns but skips missing values in calculations.

2. Step 2: Check missing data effect

   Rows with None are ignored in count and stats, so output shows fewer rows counted.

3. Final Answer:

   describe() excludes rows with missing values by default -> Option A

4. Quick Check:

   Missing data skipped in describe() = describe() excludes rows with missing values by default [OK]

Hint: describe() skips missing values in stats by default [OK]

Common Mistakes:

• Thinking describe() drops entire rows
• Assuming syntax error causes missing rows
• Believing describe() works only on non-numeric data

5. You have a DataFrame df with columns age, income, and gender. You want to quickly check if income differs by gender. Which EDA step is best?

hard

A. Plot df['age'] histogram

B. Use df.describe() only

C. Drop missing values without checking

D. Use df.groupby('gender')['income'].mean() to compare averages

Solution

1. Step 1: Identify goal

   You want to compare income by gender groups.

2. Step 2: Choose correct EDA method

   Grouping by gender and calculating mean income shows differences clearly.

3. Final Answer:

   Use df.groupby('gender')['income'].mean() to compare averages -> Option D

4. Quick Check:

   Group and average to compare = Use df.groupby('gender')['income'].mean() to compare averages [OK]

Hint: Group by category and calculate mean to compare groups [OK]

Common Mistakes:

• Using describe() without grouping
• Plotting unrelated columns
• Dropping data before checking