dietrecommendation.py

# -*- coding: utf-8 -*-
"""DietRecommendation.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/14NgZYKZB6K9PXXJgU2k5RGppQIlsuBTv
"""

# Commented out IPython magic to ensure Python compatibility.
#Importing the standard libraries for data manipulation and analysis
import numpy as np    
import pandas as pd
#Importing the plotting and visualization library for the mathematical and numerical analysis
import seaborn as sns
import matplotlib.pyplot as plt
# %matplotlib inline      
#%matplotlib inline sets the backend of matplotlib to the inline backend that displays outputs of plotting commands.
import io
import warnings
warnings.filterwarnings("ignore")   #To ignore any warning

from google.colab import files

uploaded = files.upload()

for fn in uploaded.keys():
  print('User uploaded file "{name}" with length {length} bytes'.format(
      name=fn, length=len(uploaded[fn])))

import io
data = pd.read_csv(io.BytesIO(uploaded['abbrev.csv'])) #reading data

data.head()

data.columns

data.shape

data.isna().sum

data=data.filter(['shrt_desc','potassium_mg'])

data

data.isnull().sum()

data = data.dropna(axis=0)

data

data.isnull().sum()

data.describe

# create a list of our conditions
conditions = [
    (data['potassium_mg'] < 2000/7.16846),
    (data['potassium_mg'] > 2000/7.16846) & (data['potassium_mg'] <= 2600/7.16846),
    (data['potassium_mg'] > 2600/7.16846) & (data['potassium_mg'] < 3500/7.16846),
    ]

# create a list of the values we want to assign for each condition
values = ['0','1','2']

# create a new column and use np.select to assign values to it using our lists as arguments
data['zone_attribute'] = np.select(conditions, values)

# display updated DataFrame
data.head(20)
#data['zone_attribute'] = data.apply(lambda data:zone_attribute(data).axis=1)

data['zone_attribute'].value_counts()

data['zone_attribute']=0
data.head()