Exploratory data analysis workflow in Pandas - Mini Project: Build & Apply

Exploratory data analysis workflow

📖 Scenario: You are a data analyst working with sales data from a small store. You want to understand the data better by exploring it step-by-step.

🎯 Goal: Build a simple exploratory data analysis workflow using pandas to load data, set a filter, analyze the filtered data, and print the results.

📋 What You'll Learn

Use pandas to create a DataFrame

Create a filter condition variable

Use the filter to select rows

Calculate the average sales from filtered data

Print the average sales

💡 Why This Matters

🌍 Real World

Exploratory data analysis helps businesses understand their sales data to make better decisions.

💼 Career

Data analysts and scientists use these skills daily to clean, filter, and summarize data before deeper analysis.

1

Create the sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with these exact columns and values:
'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
'Sales': [100, 150, 80, 200, 50],
'Region': ['North', 'South', 'East', 'West', 'North'].

Pandas

# Import pandas and create the sales_data DataFrame with the given data
# Your code here

Hint

Use pd.DataFrame with a dictionary of lists for columns.

2

Set a filter for the North region

Create a variable called north_filter that holds a filter condition selecting rows where the 'Region' column equals 'North'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [100, 150, 80, 200, 50],
    'Region': ['North', 'South', 'East', 'West', 'North']
})

# Create north_filter to select rows where Region is 'North'
# Your code here

Hint

Use sales_data['Region'] == 'North' to create the filter.

3

Calculate average sales for North region

Use the north_filter to select rows from sales_data and calculate the average of the 'Sales' column. Store this average in a variable called average_sales_north.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [100, 150, 80, 200, 50],
    'Region': ['North', 'South', 'East', 'West', 'North']
})
north_filter = sales_data['Region'] == 'North'

# Calculate average_sales_north using north_filter
# Your code here

Hint

Use sales_data.loc[north_filter, 'Sales'].mean() to get the average.

4

Print the average sales for North region

Write a print statement to display the text "Average sales in North region:" followed by the value of average_sales_north.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apples', 'Bananas', 'Cherries', 'Dates', 'Elderberries'],
    'Sales': [100, 150, 80, 200, 50],
    'Region': ['North', 'South', 'East', 'West', 'North']
})
north_filter = sales_data['Region'] == 'North'
average_sales_north = sales_data.loc[north_filter, 'Sales'].mean()

# Print the average sales for North region
# Your code here

Hint

Use print("Average sales in North region:", average_sales_north).

Practice

1. Which step is usually done first in an exploratory data analysis workflow?

easy

A. Viewing the first few rows of the dataset

B. Creating complex machine learning models

C. Removing all missing values immediately

D. Generating final reports without checking data

Solution

1. Step 1: Understand the initial data

   Exploratory data analysis starts by looking at the first few rows to get a feel for the data structure and values.

2. Step 2: Avoid premature actions

   Jumping to modeling or removing missing data without understanding can cause mistakes.

3. Final Answer:

   Viewing the first few rows of the dataset -> Option A

4. Quick Check:

   First step = View data sample [OK]

Hint: Always start by looking at data samples [OK]

Common Mistakes:

• Starting with modeling before understanding data
• Removing missing values without checking their impact
• Ignoring data overview and jumping to conclusions

2. Which pandas command correctly shows summary statistics of numeric columns in a DataFrame df?

easy

A. df.head()

B. df.info()

C. df.describe()

D. df.sample()

Solution

1. Step 1: Identify summary statistics function

   df.describe() returns count, mean, std, min, max, and quartiles for numeric columns.

2. Step 2: Differentiate other commands

   df.head() shows first rows, df.info() shows data types, and df.sample() shows random rows.

3. Final Answer:

   df.describe() -> Option C

4. Quick Check:

   Summary stats = df.describe() [OK]

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

Common Mistakes:

• Using df.head() to get statistics
• Confusing df.info() with statistical summary
• Expecting df.sample() to summarize data

3. Given the DataFrame df below, what is the output of df.isnull().sum()?

import pandas as pd
data = {'A': [1, 2, None], 'B': [None, 2, 3], 'C': [1, 2, 3]}
df = pd.DataFrame(data)

medium

A. {'A': 1, 'B': 1, 'C': 0}

B. {'A': 0, 'B': 0, 'C': 0}

C. {'A': 1, 'B': 2, 'C': 1}

D. {'A': 2, 'B': 1, 'C': 0}

Solution

1. Step 1: Check missing values per column

   Column 'A' has one None, 'B' has one None, 'C' has none.

2. Step 2: Use isnull().sum() to count missing

   This method counts missing values per column, so output is {'A': 1, 'B': 1, 'C': 0}.

3. Final Answer:

   {'A': 1, 'B': 1, 'C': 0} -> Option A

4. Quick Check:

   Missing counts = {'A':1,'B':1,'C':0} [OK]

Hint: Count missing with df.isnull().sum() [OK]

Common Mistakes:

• Counting total missing instead of per column
• Confusing None with zero or empty string
• Misreading which columns have missing values

4. You run df.info() but get an error: AttributeError: 'DataFrame' object has no attribute 'infoo'. What is the likely cause?

medium

A. The DataFrame is empty

B. You typed the method name incorrectly

C. The pandas library is not imported

D. The DataFrame has no columns

Solution

1. Step 1: Check the error message

   The error says 'infoo' attribute not found, which suggests a typo in method name.

2. Step 2: Confirm correct method name

   The correct method is df.info() with one 'o'.

3. Final Answer:

   You typed the method name incorrectly -> Option B

4. Quick Check:

   Typo in method name = error [OK]

Hint: Check spelling of method names carefully [OK]

Common Mistakes:

• Assuming empty DataFrame causes this error
• Forgetting to import pandas
• Thinking DataFrame must have columns to use info()

5. You have a DataFrame df with columns ['Age', 'Salary', 'Department']. You want to find the average salary per department but only for employees older than 30. Which pandas workflow is correct?

hard

A. df.groupby('Department')['Salary'].mean()

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

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

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

Solution

1. Step 1: Filter rows where Age > 30

   Use df[df['Age'] > 30] to keep only employees older than 30.

2. Step 2: Group filtered data by Department and calculate mean Salary

   Apply .groupby('Department')['Salary'].mean() on filtered data to get average salary per department.

3. Final Answer:

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

4. Quick Check:

   Filter then group and mean = df[df['Age'] > 30].groupby('Department')['Salary'].mean() [OK]

Hint: Filter first, then group and aggregate [OK]

Common Mistakes:

• Grouping before filtering data
• Trying to filter after aggregation
• Using mean() without grouping