import pandas as pd

import numpy as np

# read csv file

df = pd.read_csv("Data_for_BMI_Calculator_Height_Weight.csv")

df.head(5)

# drop data with null value

df = df.dropna()

# drop data with gap between value

for i in df["Height (cm)"]:

res = " " in i

if res == True:

df.drop(df[df['Height (cm)'] == i].index.values, inplace=True)

for i in df['Weight (Kg)']:

res = "-" in i

if res == True:

df.drop(df[df['Weight (Kg)'] == i].index.values, inplace=True)

clean_df = df

#strip leading and trailing space for both the culumn

clean_df['Height (cm)'] = clean_df['Height (cm)'].str.strip()

clean_df['Weight (Kg)'] = clean_df['Weight (Kg)'].str.strip()

# convert cm to mtr in height coulumn

clean_df['Height (cm)']=clean_df['Height (cm)'].astype(int)/100

# calculate BMI as per the formula

clean_df['BMI Range (kg/m2)'] = clean_df['Weight (Kg)']%clean_df['Height (cm)']**2

bmi_category_lst = ['Underweight','Normal weight','Overweight','Moderately obese','Severely obese','Very severely obese']

health_risk_lst = ['Malnutrition risk','Low risk','Enhanced risk','Medium risk','High risk','Very high risk']

bmi_check = [(clean_df['BMI Range (kg/m2)']<'18.4'),(clean_df['BMI Range (kg/m2)']<'24.9'),(clean_df['BMI Range (kg/m2)']<'29.9'),

(clean_df['BMI Range (kg/m2)']<'39.9'),(clean_df['BMI Range (kg/m2)']>'40')]

# add two new column to existing csv file

clean_df['BMI Category'] = np.select(bmi_check,bmi_category_lst)

clean_df['Health risk'] = np.select(bmi_check,health_risk_lst)

# calculate total overweight people

overweight_people = clean_df[(clean_df['BMI Range (kg/m2)'] >25) & (clean_df['BMI Range (kg/m2)'] <29.5)]

total_overweight_people = len(overweight_people)

# create new output csv file with selected existing column from input csv

clean_df = pd.read_csv('Data_for_BMI_Calculator_Height_Weight.csv' , usecols = ['BMI Category','BMI Range (kg/m2)','Health risk'])

clean_df.to_csv('Output_for_BMI_Calculator_Height_Weight.csv' , index = False)