Write a Python code to calculate percentage change between Id and Age columns of the top 2 and bottom 2 values

Sometimes we need to calculate percentage changes between consecutive rows in specific columns of a DataFrame. The pct_change() method calculates the percentage change from the previous row, which is useful for analyzing trends in data.

Understanding Percentage Change

The pct_change() method computes the percentage change between the current and previous element. The formula is: (current - previous) / previous.

Example Dataset

Let's start by creating a sample DataFrame with Id and Age columns ?

import pandas as pd

df = pd.DataFrame({
    "Id": [1, 2, 3, None, 5],
    "Age": [12, 12, 14, 13, None],
    "Mark": [80, 90, None, 95, 85]
})
print("Original DataFrame:")
print(df)

Original DataFrame:
    Id   Age  Mark
0  1.0  12.0  80.0
1  2.0  12.0  90.0
2  3.0  14.0   NaN
3  NaN  13.0  95.0
4  5.0   NaN  85.0

Calculating Percentage Change

Now let's calculate the percentage change for Id and Age columns ?

import pandas as pd

df = pd.DataFrame({
    "Id": [1, 2, 3, None, 5],
    "Age": [12, 12, 14, 13, None],
    "Mark": [80, 90, None, 95, 85]
})

# Calculate percentage change for Id and Age columns
pct_change = df[['Id', 'Age']].pct_change()
print("Percentage change for Id and Age columns:")
print(pct_change)

Percentage change for Id and Age columns:
         Id       Age
0       NaN       NaN
1  1.000000  0.000000
2  0.500000  0.166667
3       NaN -0.071429
4       NaN       NaN

Getting Top 2 Values

To get the top 2 percentage changes (first 2 rows), use slicing with [0:2] ?

import pandas as pd

df = pd.DataFrame({
    "Id": [1, 2, 3, None, 5],
    "Age": [12, 12, 14, 13, None],
    "Mark": [80, 90, None, 95, 85]
})

print("Id and Age - top 2 values:")
top_2 = df[['Id', 'Age']].pct_change()[0:2]
print(top_2)

Id and Age - top 2 values:
   Id  Age
0 NaN  NaN
1 1.0  0.0

Getting Bottom 2 Values

To get the bottom 2 percentage changes (last 2 rows), use slicing with [-2:] ?

import pandas as pd

df = pd.DataFrame({
    "Id": [1, 2, 3, None, 5],
    "Age": [12, 12, 14, 13, None],
    "Mark": [80, 90, None, 95, 85]
})

print("Id and Age - bottom 2 values:")
bottom_2 = df[['Id', 'Age']].pct_change()[-2:]
print(bottom_2)

Id and Age - bottom 2 values:
         Id       Age
3  0.000000 -0.071429
4  0.666667  0.000000

Complete Solution

Here's the complete code that displays both top 2 and bottom 2 percentage changes ?

import pandas as pd

# Create DataFrame
df = pd.DataFrame({
    "Id": [1, 2, 3, None, 5],
    "Age": [12, 12, 14, 13, None],
    "Mark": [80, 90, None, 95, 85]
})

print("Original DataFrame:")
print(df)
print("\nId and Age - top 2 values:")
print(df[['Id', 'Age']].pct_change()[0:2])
print("\nId and Age - bottom 2 values:")
print(df[['Id', 'Age']].pct_change()[-2:])

Original DataFrame:
    Id   Age  Mark
0  1.0  12.0  80.0
1  2.0  12.0  90.0
2  3.0  14.0   NaN
3  NaN  13.0  95.0
4  5.0   NaN  85.0

Id and Age - top 2 values:
   Id  Age
0 NaN  NaN
1 1.0  0.0

Id and Age - bottom 2 values:
         Id       Age
3  0.000000 -0.071429
4  0.666667  0.000000

Key Points

• The first row always shows NaN because there's no previous value to compare

• Missing values (None or NaN) in calculations result in NaN

• Positive values indicate an increase, negative values indicate a decrease

• Use [0:2] for top 2 rows and [-2:] for bottom 2 rows

Conclusion

Use pct_change() to calculate percentage changes between consecutive rows. Combine with slicing to extract specific ranges like top 2 or bottom 2 values for focused analysis.