Assume, you have dataframe and the result for percentage change between Id and Age columns top 2 and bottom 2 value
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
Solution
To solve this, we will follow the steps given below −
Define a dataframe
Apply df[[‘Id’,’Age’]].pct_change() inside slicing [0:2]
df[['Id','Age']].pct_change()[0:2]
Apply df[[‘Id’,’Age’]].pct_change() inside slicing [-2:]
df[['Id','Age']].pct_change()[0:2]
Example
Let’s check the following code to get a better understanding −
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("Dataframe is:\n",df)
print("Id and Age-top 2 values")
print(df[['Id','Age']].pct_change()[0:2])
print("Id and Age-bottom 2 values")
print(df[['Id','Age']].pct_change()[-2:])Output
Dataframe is: 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