Лаб 2 / lb2

import numpy as np
import math
import copy as copy
import pandas as pd
import matplotlib.pyplot as plt

# Check if value is nan
def isNa(value):
    if isinstance(value, int) or isinstance(value, float):
        if math.isnan(value) and value != 0:
            return True
    return False

# Drop maxNaPercents percents of nan values in DataFrame
def dropNaRowByPercent (df, maxNaPercents):
    for y, colObject in df.iteritems():
        countOfNa = 0;
        countOfRows = len(colObject);
        for key in colObject:
            if isNa(key):
                countOfNa += 1
        percentNaInRow = (countOfNa / countOfRows) * 100;
        #print('index =', rowObject.name, 'count_na =' ,countOfNa, '|',percentNaInRow, '%')
        if percentNaInRow > maxNaPercents:
            print('deleting', colObject.name)
            df = df.drop(colObject.name, axis=1)
    return df 

# Generate value instead nan
def generateValue(naPosX, naPosY, rowCount):
    distances = []
    for x, naFreeRow in df_dropNa.iterrows():
        res = 0
        for y, value in df_res.iloc[naPosX].iteritems():
            if (isNa(value) != True) and (isinstance(value, int) or isinstance(value, float)):
                res += abs(naFreeRow[y] - value)
        distances.append(res/rowCount)
    #print("distances: " + str(distances))
    
    inverseDistancesSum = 0
    for distance in distances:
        inverseDistancesSum += 1/distance
    #print("inverse distance sum: " + str(inverseDistancesSum))
    
    affiliationLevels = []
    for distance in distances:
        affiliationLevels.append((1/distance)/inverseDistancesSum)
    #print("affiliation levels: " + str(affiliationLevels))
    
    naValue = 0
    iterator = 0
    for x, value in df_dropNa[naPosY].iteritems():
        naValue += value * affiliationLevels[iterator]
        iterator += 1
    return naValue
                    

file = pd.ExcelFile('Dataset.xlsx')
df = pd.read_excel(file, sheet_name='Лист1', header=1);
df_res = dropNaRowByPercent(df, 20);     
df_dropNa = df_res.dropna()
resultData = df_res.copy()  

print('DATA FRAME DESCRIBE -----------------')
df_desc = df.describe()

print('DATA FRAME FILLNA(0) -----------------')
df_1 = df.fillna(0)


print('DATA FRAME FILLNA(backfill) -----------------')
df_2 = df.fillna('backfill')


print('DATA FRAME FILLNA(bfill) -----------------')
df_3 = df.fillna('bfill')

print('DATA FRAME FILLNA(pad) -----------------')
df_4 = df.fillna('pad')


print('DATA FRAME FILLNA(ffill) -----------------')
df_5 = df.fillna('ffill')

print('DATA FRAME dropna() -----------------')
df_6 = df.dropna()

for x, row in df_res.iterrows():
   for y, value in row.iteritems():
        if isNa(value):
            resultData.loc[x, y] = generateValue(x, y, len(df_dropNa.index) + 1)

df_describe_resultData = resultData.describe()