pct_change() for percentage change in Pandas - Mini Project: Build & Apply

Calculate Percentage Change Using pct_change()

📖 Scenario: You work in a small shop and track daily sales. You want to see how sales change day to day in percentage to understand growth or decline.

🎯 Goal: Build a small program that creates a sales data table, sets a configuration for percentage change periods, calculates the percentage change using pct_change(), and prints the result.

📋 What You'll Learn

Create a pandas DataFrame with daily sales data

Set a variable for the number of periods to calculate percentage change

Use the pct_change() method with the periods variable

Print the resulting DataFrame with percentage changes

💡 Why This Matters

🌍 Real World

Tracking percentage change in sales helps businesses understand growth trends and make better decisions.

💼 Career

Data analysts and business intelligence professionals often calculate percentage changes to report performance metrics.

1

Create sales data DataFrame

Import pandas as pd and create a DataFrame called sales_data with a column 'Sales' containing these exact values: 100, 120, 90, 150, 130.

Pandas

# Import pandas and create the sales_data DataFrame
# Your code here

Hint

Use pd.DataFrame with a dictionary where the key is 'Sales' and the value is the list of sales numbers.

2

Set periods for percentage change

Create a variable called periods and set it to 1 to calculate the percentage change compared to the previous day.

Pandas

import pandas as pd

sales_data = pd.DataFrame({'Sales': [100, 120, 90, 150, 130]})

# Set periods variable to 1
# Your code here

Hint

Just create a variable named periods and assign it the value 1.

3

Calculate percentage change

Create a new column in sales_data called 'Pct_Change' that stores the percentage change of 'Sales' using the pct_change() method with the periods variable.

Pandas

import pandas as pd

sales_data = pd.DataFrame({'Sales': [100, 120, 90, 150, 130]})

periods = 1

# Calculate percentage change and add as 'Pct_Change' column
# Your code here

Hint

Use sales_data['Sales'].pct_change(periods=periods) and assign it to sales_data['Pct_Change'].

4

Print the sales data with percentage change

Print the sales_data DataFrame to display the sales and their percentage changes.

Pandas

import pandas as pd

sales_data = pd.DataFrame({'Sales': [100, 120, 90, 150, 130]})

periods = 1

sales_data['Pct_Change'] = sales_data['Sales'].pct_change(periods=periods)

# Print the sales_data DataFrame
# Your code here

Hint

Use print(sales_data) to show the DataFrame with the new column.

Practice

1. What does the pct_change() function in pandas do?

easy

A. Calculates the percentage change between consecutive rows.

B. Calculates the sum of all values in a column.

C. Sorts the DataFrame by index.

D. Removes missing values from the DataFrame.

Solution

1. Step 1: Understand the purpose of pct_change()

   The pct_change() function calculates how much values change in percentage from one row to the next.

2. Step 2: Compare with other options

   Options A, B, and C describe different pandas functions unrelated to percentage change.

3. Final Answer:

   Calculates the percentage change between consecutive rows. -> Option A

4. Quick Check:

   pct_change() = percentage change [OK]

Hint: Think: pct_change means percent change between rows [OK]

Common Mistakes:

• Confusing pct_change() with sum()
• Thinking it sorts data
• Assuming it removes missing values

2. Which of the following is the correct way to use pct_change() on a pandas DataFrame named df?

easy

A. df.pct_change()

B. pct_change(df)

C. df.change_pct()

D. df.percent_change()

Solution

1. Step 1: Recall pandas method syntax

   In pandas, pct_change() is a method called on a DataFrame or Series object, so the correct syntax is df.pct_change().

2. Step 2: Check other options

   Options A, B, and D are not valid pandas syntax or method names.

3. Final Answer:

   df.pct_change() -> Option A

4. Quick Check:

   Method called on DataFrame = df.pct_change() [OK]

Hint: Remember: pandas methods use dot notation like df.method() [OK]

Common Mistakes:

• Using pct_change() as a standalone function
• Mixing method names
• Using incorrect method names

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'Sales': [100, 120, 150, 180]})
print(df.pct_change())

What is the output?

medium

A. 0.2, 0.25, 0.2, NaN

B. 0, 20, 30, 30

C. NaN, 20, 30, 30

D. NaN, 0.2, 0.25, 0.2

Solution

1. Step 1: Calculate percentage changes row by row

   From 100 to 120: (120-100)/100 = 0.2
   From 120 to 150: (150-120)/120 = 0.25
   From 150 to 180: (180-150)/150 = 0.2

2. Step 2: Understand first row result

   The first row has no previous row, so result is NaN.

3. Final Answer:

   NaN, 0.2, 0.25, 0.2 -> Option D

4. Quick Check:

   pct_change() = NaN, 0.2, 0.25, 0.2 [OK]

Hint: First row is always NaN; next rows show percent change [OK]

Common Mistakes:

• Confusing absolute change with percent change
• Forgetting first row is NaN
• Mixing up order of results

4. What is wrong with this code snippet?

import pandas as pd
df = pd.DataFrame({'A': [10, 20, 30]})
result = df.pct_change(axis=1)
print(result)

medium

A. The DataFrame has no numeric columns.

B. The code is correct and will run without errors.

C. pct_change() requires a parameter 'periods' to work.

D. pct_change() cannot be used with axis=1 on this DataFrame.

Solution

1. Step 1: Understand axis parameter in pct_change()

   By default, pct_change() calculates percentage change down rows (axis=0). axis=1 calculates change across columns.

2. Step 2: Check DataFrame shape and axis=1 effect

   The DataFrame has only one column. pct_change(axis=1) produces NaN values everywhere since there are no multiple columns to compare, but the code runs without errors or exceptions.

3. Final Answer:

   The code is correct and will run without errors. -> Option B

4. Quick Check:

   axis=1 on single column = NaN output, no error [OK]

Hint: axis=1 on single column gives NaN but runs fine [OK]

Common Mistakes:

• Thinking axis=1 causes error on single-column DataFrame
• Believing periods parameter is required
• Claiming no numeric columns

5. You have a DataFrame df with daily stock prices:

import pandas as pd
df = pd.DataFrame({'Price': [50, 52, 51, 53, 54]})

You want to calculate the percentage change over 2 days instead of 1. Which code will give the correct result?

hard

A. df.pct_change(axis=1)

B. df.pct_change(periods=1)

C. df.pct_change(periods=2)

D. df.pct_change(periods=0)

Solution

1. Step 1: Understand the periods parameter

   The periods parameter in pct_change() sets how many rows to look back for percentage change calculation. For 2-day change, use periods=2.

2. Step 2: Check other options

   df.pct_change(periods=1) calculates 1-day change, df.pct_change(axis=1) uses axis=1 which is incorrect for this data, and df.pct_change(periods=0) computes zero change.

3. Final Answer:

   df.pct_change(periods=2) -> Option C

4. Quick Check:

   periods=2 for 2-day change [OK]

Hint: Use periods=2 to compare with 2 rows before [OK]

Common Mistakes:

• Using default periods=1 for 2-day change
• Using axis=1 incorrectly
• Setting periods=0 which is invalid