Why data exploration matters in Pandas - See It in Action

Why Data Exploration Matters

📖 Scenario: You have just received a small dataset about sales in a store. Before making any decisions or building models, you want to understand what the data looks like. This helps you find mistakes, see patterns, and know what questions to ask next.

🎯 Goal: Learn how to load data into a pandas DataFrame, check its basic structure, and get simple statistics to understand the data better.

📋 What You'll Learn

Use pandas to create and explore data

Create a DataFrame with exact sales data

Add a variable to select columns for exploration

Use pandas methods to get summary statistics

Print the summary statistics as output

💡 Why This Matters

🌍 Real World

Data exploration helps you understand new data before making decisions or building models. It reveals patterns, errors, and important features.

💼 Career

Data scientists and analysts always start with data exploration to ensure data quality and to guide further analysis or machine learning.

1

Create the sales data DataFrame

Create a pandas DataFrame called sales_data with these exact columns and values:
'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
'Price': [0.5, 0.3, 0.2, 1.0, 1.5],
'Quantity': [30, 45, 25, 10, 5]

Pandas

import pandas as pd
# Create the sales_data DataFrame with the exact columns and values
# Your code here

Hint

Use pd.DataFrame with a dictionary where keys are column names and values are lists of data.

2

Select columns to explore

Create a variable called columns_to_explore and set it to a list containing the exact strings 'Price' and 'Quantity'.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
    'Price': [0.5, 0.3, 0.2, 1.0, 1.5],
    'Quantity': [30, 45, 25, 10, 5]
})
# Create columns_to_explore list with 'Price' and 'Quantity'
# Your code here

Hint

Just create a list with the two column names as strings.

3

Get summary statistics for selected columns

Use the describe() method on sales_data[columns_to_explore] and save the result in a variable called summary_stats.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
    'Price': [0.5, 0.3, 0.2, 1.0, 1.5],
    'Quantity': [30, 45, 25, 10, 5]
})
columns_to_explore = ['Price', 'Quantity']
# Use describe() on sales_data[columns_to_explore] and save to summary_stats
# Your code here

Hint

Use sales_data[columns_to_explore].describe() to get statistics like mean, min, max.

4

Print the summary statistics

Print the variable summary_stats to see the summary statistics of the selected columns.

Pandas

import pandas as pd
sales_data = pd.DataFrame({
    'Product': ['Apple', 'Banana', 'Carrot', 'Date', 'Eggplant'],
    'Price': [0.5, 0.3, 0.2, 1.0, 1.5],
    'Quantity': [30, 45, 25, 10, 5]
})
columns_to_explore = ['Price', 'Quantity']
summary_stats = sales_data[columns_to_explore].describe()
# Print summary_stats
# Your code here

Hint

Use print(summary_stats) to display the statistics.

Practice

1. Why is data exploration important before starting analysis with pandas?

easy

A. It automatically cleans the data for you.

B. It helps understand the data and find issues early.

C. It creates visualizations without any code.

D. It speeds up the computer's processing power.

Solution

1. Step 1: Understand the purpose of data exploration

   Data exploration is done to get to know the data, its structure, and any problems it may have.

2. Step 2: Identify what data exploration does not do

   It does not clean data automatically, create visualizations by itself, or affect computer speed.

3. Final Answer:

   It helps understand the data and find issues early. -> Option B

4. Quick Check:

   Data exploration = Understand data [OK]

Hint: Remember: Explore first to know your data well [OK]

Common Mistakes:

• Thinking exploration cleans data automatically
• Confusing exploration with visualization
• Believing it improves computer speed

2. Which pandas command shows the first 5 rows of a DataFrame named df?

easy

A. df.head()

B. df.info()

C. df.describe()

D. df.tail()

Solution

1. Step 1: Recall pandas commands for viewing data

   head() shows the first rows, tail() shows last rows, describe() summarizes statistics, info() shows data info.

2. Step 2: Match command to requirement

   To see first 5 rows, df.head() is correct.

3. Final Answer:

   df.head() -> Option A

4. Quick Check:

   First rows = head() [OK]

Hint: head() = first rows, tail() = last rows [OK]

Common Mistakes:

• Using tail() to see first rows
• Confusing describe() with head()
• Using info() to view data rows

3. Given the code:

import pandas as pd
data = {'age': [25, 30, 22, 40], 'score': [88, 92, 85, 95]}
df = pd.DataFrame(data)
print(df.describe())

What will be the output?

medium

A. Summary statistics including count, mean, std, min, max for each column

B. The first 5 rows of the DataFrame

C. Information about data types and memory usage

D. An error because describe() needs numeric data only

Solution

1. Step 1: Understand what df.describe() does

   It calculates summary statistics like count, mean, std, min, max for numeric columns.

2. Step 2: Check the data types in df

   Both 'age' and 'score' are numeric lists, so describe() works and outputs stats.

3. Final Answer:

   Summary statistics including count, mean, std, min, max for each column -> Option A

4. Quick Check:

   describe() = summary stats [OK]

Hint: describe() summarizes numeric columns quickly [OK]

Common Mistakes:

• Thinking describe() shows rows
• Confusing describe() with info()
• Assuming describe() errors on numeric data

4. What is wrong with this code snippet for exploring a DataFrame df?

print(df.head(5))
print(df.describe)
print(df.info())

medium

A. df.info() cannot be printed directly.

B. df.head(5) should be df.tail(5) to see first rows.

C. df.describe is missing parentheses to call the method.

D. There is no error; the code runs fine.

Solution

1. Step 1: Check each line for syntax correctness

   df.head(5) is correct to show first 5 rows. df.describe without parentheses is a method reference, not a call. df.info() can be printed.

2. Step 2: Identify the error

   Missing parentheses on df.describe means it won't execute and print stats.

3. Final Answer:

   df.describe is missing parentheses to call the method. -> Option C

4. Quick Check:

   Method calls need () [OK]

Hint: Always add () to call pandas methods [OK]

Common Mistakes:

• Forgetting parentheses on methods
• Mixing head() and tail() usage
• Thinking info() can't be printed

5. You have a DataFrame df with missing values and mixed data types. Which sequence of pandas commands helps you explore and prepare for cleaning?

hard

A. df.describe() to fix missing values, then df.info() to visualize data.

B. df.head() to see last rows, then df.tail() for summary stats.

C. df.tail() to check data types, then df.head() to drop missing values.

D. df.info() to check data types and missing values, then df.describe() for stats.

Solution

1. Step 1: Use df.info() to check data types and missing values

   This command shows column types and counts of non-null values, helping spot missing data.

2. Step 2: Use df.describe() to get summary statistics

   This helps understand numeric data distribution and spot anomalies before cleaning.

3. Final Answer:

   df.info() to check data types and missing values, then df.describe() for stats. -> Option D

4. Quick Check:

   info() then describe() = explore before cleaning [OK]

Hint: Start with info(), then describe() to explore data [OK]

Common Mistakes:

• Using tail() to see first rows
• Thinking describe() fixes missing values
• Confusing order of commands