pct_change() for percentage change in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of pct_change() with default parameters

What is the output of the following code snippet using pandas pct_change() with default settings?

Pandas

import pandas as pd

s = pd.Series([100, 120, 144, 172.8])
result = s.pct_change()
print(result)

A

0    NaN
1    1.20
2    1.20
3    1.20
dtype: float64

B

0    NaN
1    0.20
2    0.20
3    0.20
dtype: float64

C

0    0.00
1    0.20
2    0.20
3    0.20
dtype: float64

D

0    NaN
1    20.0
2    20.0
3    20.0
dtype: float64

💡 Hint

Remember pct_change() calculates the relative change between current and prior element.

The pct_change() function returns the percentage change between the current and previous element. The first element has no previous element, so it is NaN. The changes are calculated as (new - old) / old, so 120 to 100 is (120-100)/100 = 0.20 or 20%.

❓ data_output

intermediate

Percentage change with periods parameter

Given the DataFrame below, what is the output of df['value'].pct_change(periods=2)?

Pandas

import pandas as pd

df = pd.DataFrame({'value': [10, 15, 20, 30, 45]})
result = df['value'].pct_change(periods=2)
print(result)

A

0         NaN
1         NaN
2    2.000000
3    2.000000
4    2.250000
Name: value, dtype: float64

B

0         NaN
1         NaN
2    0.500000
3    0.500000
4    0.500000
Name: value, dtype: float64

C

0         NaN
1    0.500000
2    0.333333
3    0.500000
4    0.500000
Name: value, dtype: float64

D

0         NaN
1         NaN
2    1.000000
3    1.000000
4    1.250000
Name: value, dtype: float64

💡 Hint

The periods=2 means compare with the value two rows before.

With periods=2, pct_change() computes the percentage change relative to the value two periods prior: (current - value_two_periods_ago) / value_two_periods_ago.

Calculations:

• Index 0: NaN
• Index 1: NaN
• Index 2: (20 - 10) / 10 = 1.0
• Index 3: (30 - 15) / 15 = 1.0
• Index 4: (45 - 20) / 20 = 1.25

This matches option A.

🔧 Debug

advanced

Identify the error in pct_change usage

What error does the following code raise?

Pandas

import pandas as pd

s = pd.Series([1, 2, 3, 4])
result = s.pct_change(periods='two')
print(result)

A ValueError: periods must be an integer

B AttributeError: 'str' object has no attribute 'pct_change'

C TypeError: unsupported operand type(s) for -: 'str' and 'int'

D No error, outputs percentage change

💡 Hint

Check the type of the periods parameter in pct_change.

The periods parameter must be an integer. Passing a string like 'two' causes a ValueError.

🚀 Application

advanced

Calculate daily returns from stock prices

You have a DataFrame with daily closing prices of a stock. Which code correctly calculates the daily returns as percentage changes?

Pandas

import pandas as pd

df = pd.DataFrame({'close': [100, 105, 110, 121, 115]})

A df['returns'] = df['close'].pct_change()

B df['returns'] = df['close'].diff()

C df['returns'] = df['close'].pct_change().fillna(0)

D df['returns'] = (df['close'] / df['close'].shift(1)) * 100

💡 Hint

Daily returns should be percentage changes, and missing values can be filled with zero.

pct_change() calculates the percentage change. The first value is NaN because there is no previous day. Filling NaN with 0 is common to indicate no return on first day.

Option C leaves NaN as is. Option C calculates absolute difference, not percentage. Option C calculates ratio times 100, which is not the percentage change.

🧠 Conceptual

expert

Understanding pct_change with fill_method parameter

What is the effect of using fill_method='ffill' in pct_change() on a Series with missing values?

A It fills missing values forward before calculating percentage change, reducing NaNs in output.

B It fills missing values backward after calculating percentage change, increasing NaNs in output.

C It drops missing values before calculating percentage change, causing shorter output.

D It replaces missing values with zero before calculating percentage change, causing division errors.

💡 Hint

Think about how forward fill affects data before pct_change calculation.

fill_method='ffill' fills missing values forward before computing percentage change. This reduces NaNs in the result because missing data is replaced by the last valid value.

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