Module Diabetes-Detection-Web-Application.testing.src.preprocessing.plots

Classes

class Plot

Expand source code

    # import global libraries
    import pandas as pd
    import matplotlib.pyplot as plt
    import seaborn as sns
    
    class Plot:
    
        def __init__(self, data, path):
            self.data = data
            self.path = path
            self.hist_observation()
            self.heatmap()
    
        def hist_observation(self):
            fig, ax = plt.subplots()
            self.data[['Insulin', 'Glucose', 'BloodPressure',]].hist(bins=30, figsize=(1500,1000), ax=ax)
            fig.savefig("{}/histogram.png".format(self.path))
    
        def heatmap(self):
            fig, ax = plt.subplots()
            d = self.data[['Pregnancies', 'Glucose', 'BloodPressure',
           'SkinThickness', 'Insulin',
           'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome']]
            dataplot = sns.heatmap(d.corr(), annot=True, cmap="YlGnBu")
            fig.savefig("{}/heatmap.png".format(self.path))
    

Functions

def hist_observation(self)

Expand source code

def hist_observation(self):
    fig, ax = plt.subplots()
    self.data[['Insulin', 'Glucose', 'BloodPressure',]].hist(bins=30, figsize=(1500,1000), ax=ax)
    fig.savefig("{}/histogram.png".format(self.path))

def heatmap(self)

Expand source code

def heatmap(self):
        fig, ax = plt.subplots()
        d = self.data[['Pregnancies', 'Glucose', 'BloodPressure',
       'SkinThickness', 'Insulin',
       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome']]
        dataplot = sns.heatmap(d.corr(), annot=True, cmap="YlGnBu")
        fig.savefig("{}/heatmap.png".format(self.path))