deep-learning/missing_values_analysis.py at main · debojit11070/deep-learning · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
from abc import ABC, abstractmethod
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

class MissingValuesAnalysisTemplate(ABC):
    def analyze(self, df:pd.DataFrame):


        self.identify_missing_values(df)
        self.visualize_missing_values(df)

    @abstractmethod
    def identify_missing_values(self, df:pd.DataFrame):
        pass

    @abstractmethod
    def visualize_missing_values(self, df:pd.DataFrame):
        pass


class SimpleMissingValuesAnalysis(MissingValuesAnalysisTemplate):
    def identify_missing_values(self, df:pd.DataFrame):
        print("\n missing values count by columns: ")
        missing_values = df.isnull().sum()
        print(missing_values[missing_values>0])


    def visualize_missing_values(self, df: pd.DataFrame):
        print("\nVisualize Missing Values: ")
        plt.figure(figsize=(12,8))
        sns.heatmap(df.isnull(), cbar = False, cmap= "viridis")
        plt.title("Miissing Values Heatmap")
        plt.show()