Scaling Value

1. Scaling values that have the same range

When all variables have the same range we can provide that range which will later be used for scaling. For example, when we know that school grades have a range of 1-10, we can specify that range of values when creating a radar plot

Here's an example of a dummy dataframe of Jake and Amy's school grades

import pandas as pd

df = pd.DataFrame({
    'student': ['jake', 'amy'],
    'Math': [6, 10],
    'Science': [7, 9],
    'Art': [10, 8],
})

Student	Math	Scienc	Art
jake	6	7	10
amy	10	9	8

From the dummy dataframe we can create a radar plot in this way

import reverievis as rv

rv.radar(data=df, 
            category='student', 
            values=df.columns[1:].to_list(),
            scaled=[0,10],
            colors=['#FF6D60', '#6DA9E4'],
            title='Jake vs Amy School Grades')

student school grades

By giving the argument value range of Jake and Amy's school grades which is 0-10 on the parameter scaled. We have scaled the values so that the resulting radar plot is easy to read

2. Scaling values that have different ranges

When one variable with another variable has a different scale value, it is necessary to scale the value so that the resulting graph becomes readable.

For example, using the Penguins dataset from Seaborn, each physical characteristic of the penguins has a different value scale so it is necessary to do value scaling. here's how to do value scaling using Radarplot

import reverievis as rv
import seaborn as sns

df = sns.load_dataset('penguins')

data_minmax = rd.get_minmax(df, columns=['bill_length_mm', 
                   'bill_depth_mm', 'body_mass_g',
                   'flipper_length_mm'])

rd.get_minmax is used to get the min and max values of each desired column. Where the min and max value is used to scale the column when visualization is carried out

Here's an average of the physical traits of each type of penguin in the dataframe

species_df = df[['species', 'bill_length_mm', 
                   'bill_depth_mm', 'body_mass_g',
                   'flipper_length_mm']].groupby('species').mean().reset_index()

species	bill_length_mm	bill_depth_mm	body_mass_g	flipper_length_mm
Adelie	38.791391	18.346358	3700.662252	189.953642
Chinstrap	48.833824	18.420588	3733.088235	195.823529
Gentoo	47.504878	14.982114	5076.016260	217.186992

species_df is used to create radar plots and data_minmax used to do value scaling behind the scenes and here are the code and graphic results

rv.radar(data=species_df, 
            category='species', 
            data_minmax=data_minmax,
            values=species_mass.columns[1:].to_list())

penguins species charachteristics