# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session
"""
**Vikas kr. Mahaur
18SCSE1050038**
"""
"""
https://www.kaggle.com/vikashmahour/notebook45b7ef2407
"""
import pandas as pd

df = pd.read_csv("../input/indian-food-101/indian_food.csv")
df.head()

import matplotlib.pyplot as plt

diettype = df.diet.value_counts().reset_index()
plt.figure(figsize=(7,7))
plt.pie(diettype.diet, labels = diettype['index'],autopct='%0.2f%%',shadow=True)
plt.title("Vegetarian vs Non-Vegetarian")
plt.show()
df['total_time'] = df['prep_time']+df['cook_time']
x = df.loc[df.groupby(['course'])['total_time'].idxmax()][['total_time','course']]
x
import numpy as np
df1=df.pivot_table(values=['prep_time','cook_time'],index=['region','diet'],aggfunc=np.mean)
df1=df1[1:]
df1.plot(kind='bar',stacked=True)