Why pct_change() for percentage change in Pandas? - Purpose & Use Cases

The Big Idea

What if you could instantly see how your data changes day by day without any math mistakes?

The Scenario

Imagine you have a list of daily sales numbers in a spreadsheet. You want to know how much sales changed each day compared to the day before. Doing this by hand means subtracting and dividing numbers for every day, which is slow and boring.

The Problem

Manually calculating percentage changes is slow and easy to mess up. You might forget to divide by the right number or mix up the order. If you have hundreds of days, it becomes a huge headache and wastes time.

The Solution

The pct_change() function in pandas quickly calculates the percentage change between rows in a column. It does all the math for you, so you get accurate results instantly without any manual errors.

Before vs After

✗ Before

percentage_change = [(sales[i] - sales[i-1]) / sales[i-1] for i in range(1, len(sales))]

✓ After

df['sales'].pct_change()

What It Enables

With pct_change(), you can easily track trends and growth rates in your data, helping you make smarter decisions faster.

Real Life Example

A store manager uses pct_change() to see how daily sales grow or shrink, spotting busy days or slow periods without crunching numbers manually.

Key Takeaways

Manual percentage change is slow and error-prone.

pct_change() automates and simplifies this calculation.

It helps quickly understand data trends and changes over time.

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