DataFrame?A DataFrame is a commonly used 2-dimensional data structure.
It is a table that consists of columns and rows and is used primarily as an object in pandas.
This requires the pandas library, as shown below:
import pandas as pd
A DataFrame can be formed as shown below. This contains countries that have been put in different groups and are given a different a_score and b_score.
Both the scores are imaginary values for this example:
import pandas as pda_score = [4, 5, 7, 8, 2, 3, 1, 6, 9, 10]b_score = [1, 2, 3, 4, 5, 6, 7, 10, 8, 9]country = ['Pakistan', 'USA', 'Canada', 'Brazil', 'India', 'Beligium', 'Malaysia', 'Peru', 'England', 'Scotland']groups = ['A','A','B','A','B','B','C','A','C','C']df = pd.DataFrame({'group':groups, 'country':country, 'a_score':a_score, 'b_score':b_score})print(df)
pct_change() functionThe pct_change function calculates the percentage change in the values through a series. For example, if the values are [2,3,6], the returned values would be [NaN, 0.5, 1.0]. This is because there is a 50% increase from the first element to the second and a 100% increase from the second element to the third.
The function prototype is as follows:
mychange = df.a_score.pct_change()
The function takes no parameter.
The return value of this method is the percentage change values of the elements in the series.
The following example prints the percentage change values in a_score:
import pandas as pda_score = [4, 5, 7, 8, 2, 3, 1, 6, 9, 10]b_score = [1, 2, 3, 4, 5, 6, 7, 10, 8, 9]country = ['Pakistan', 'USA', 'Canada', 'Brazil', 'India', 'Beligium', 'Malaysia', 'Peru', 'England', 'Scotland']groups = ['A','A','B','A','B','B','C','A','C','C']df = pd.DataFrame({'group':groups, 'country':country, 'a_score':a_score, 'b_score':b_score}) #creating dataframeprint("the main dataframe")print(df) #printing initial dataframeprint("")print("change ")print(df.a_score.pct_change()) #applying pct_change based on a_score