import plotly.graph_objects as go
import plotly.offline as py
autosize =False


# Use `hole` to create a donut-like pie chart
values=[4100000, 1400000, 282000]
labels=['Confirmed',"Recovered","Deaths"]
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.update_traces(hoverinfo='label+percent', textinfo='value',textfont_size=15,
                  marker=dict(colors=['#00008b','#fffdd0'], line=dict(color='#FFFFFF', width=2.5)))
fig.update_layout(
    title='COVID-19 ACTIVE CASES VS CURED WORLDWIDE')
py.iplot(fig)

# Use `hole` to create a donut-like pie chart
values=[67152, 20917, 2206]
labels=['Confirmed',"Recovered","Deaths"]
fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.update_traces(hoverinfo='label+percent', textinfo='value',textfont_size=15,
                  marker=dict(colors=['#DAA520','#800000'], line=dict(color='#FFFFFF', width=2.5)))
fig.update_layout(
    title='COVID-19 ACTIVE CASES VS CURED INDIA')
austosize=False
py.iplot(fig)

# 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 in 

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 "../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))

# Any results you write to the current directory are saved as output.

import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
%matplotlib inline
import geopandas
import seaborn as sns

data=pd.read_csv("../input/covid19-corona-virus-india-dataset/complete.csv")

data.tail()

import json
import folium

statecases=data.groupby('Name of State / UT')['Total Confirmed cases','Death','Cured/Discharged/Migrated'].max().reset_index()

with open('/kaggle/input/indian-state-geojson-data/india_state_geo.json') as file:
    geojsonData = json.load(file)

for i in geojsonData['features']:
    if(i['properties']['NAME_1']=='Orissa'):
        i['properties']['NAME_1']='Odisha'
    elif(i['properties']['NAME_1']=='Uttaranchal'):
        i['properties']['NAME_1']='Uttarakhand'
        
for i in geojsonData['features']:
    i['id'] = i['properties']['NAME_1']
    

map_choropleth = folium.Map(location = [20.5937,78.9629], zoom_start = 4)

folium.Choropleth(geo_data=geojsonData,
                 data=statecases,
                 name='CHOROPLETH',
                 key_on='feature.id',
                 columns = ['Name of State / UT','Total Confirmed cases'],
                 fill_color='YlOrRd',
                 fill_opacity=0.7,
                 line_opacity=0.8,
                 legend_name='Confirmed Cases',
                 highlight=True).add_to(map_choropleth)

folium.LayerControl().add_to(map_choropleth)
display(map_choropleth)

zones=pd.read_csv('/kaggle/input/covid19indiazones/India-District-Zones.csv')

import plotly.express as px
fig = px.treemap(zones, path=['State','District'],
                  color='Zone', hover_data=['Zone'], color_discrete_map={'Red Zone':'red', 'Green Zone':'green', 'Orange Zone':'orange'})
autosize=False
py.iplot(fig)

data.shape

# Create a plot
plt.figure(figsize=(8,12))

# Add title
plt.title("Total cases by state")

grouped_data=data.groupby("Name of State / UT").sum()

sns.barplot(x=grouped_data['Total Confirmed cases'], y=grouped_data.index)

data['Total Confirmed cases'].sum()

grouped_by_date_data=data.groupby("Date").sum()

plt.figure(figsize=(17,16))

plt.xticks(rotation=90)
sns.lineplot(data=grouped_by_date_data["Total Confirmed cases"],label="Total Confirmed cases")

symptoms={'symptom':['Fever',
        'Dry cough',
        'Fatigue',
        'Sputum production',
        'Shortness of breath',
        'Muscle pain',
        'Sore throat',
        'Headache',
        'Chills',
        'Nausea or vomiting',
        'Nasal congestion',
        'Diarrhoea',
        'Haemoptysis',
        'Conjunctival congestion'],
        'percentage':[87.9,67.7,38.1,33.4,18.6,14.8,13.9,13.6,11.4,5.0,4.8,3.7,0.9,0.8],
          'parent':['high','high','high','high','medium','medium','medium','medium','medium','medium','low','low','low',"low"]}

symptoms=pd.DataFrame(symptoms)

fig =px.sunburst(
    symptoms,
    path=['symptom','parent'],
    values='percentage',
    color='percentage')
autosize=False
py.iplot(fig)

plt.figure(figsize=(20,20))

# Add title
plt.title("Active cases")

#Total Active cases
data["Active cases"]=data["Total Confirmed cases"]-data["Cured/Discharged/Migrated"]-data["Death"]

grouped_active_data=data.groupby("Name of State / UT").sum()
grouped_active_data=(grouped_active_data.sort_values(by="Active cases"))
plt.xticks(rotation=90)
sns.barplot(x=grouped_active_data['Active cases'], y=grouped_active_data.index,)

plt.figure(figsize=(12,6))
plt.plot(grouped_by_date_data["Total Confirmed cases"].diff().fillna(0),linewidth=3,label="Confirmed Cases")
plt.plot(grouped_by_date_data["Cured/Discharged/Migrated"].diff().fillna(0),linewidth=3,label="Recovered Cases")
plt.plot(grouped_by_date_data["Death"].diff().fillna(0),linewidth=3,label="Death Cases")
plt.ylabel("Increase in Number of Cases")
plt.xlabel("Date")
plt.title("Daily increase in different types of cases in India")
plt.xticks(rotation=90)
plt.legend()

def color_negative_red(val):
    """
    Takes a scalar and returns a string with
    the css property `'color: red'` for negative
    strings, black otherwise.
    """
    color = 'red' if val > 30 else 'green'
    return 'color: %s' % color

total_cases=grouped_data["Total Confirmed cases"].sum()
rec_cases=grouped_data["Cured/Discharged/Migrated"].sum()
death=grouped_data["Death"].sum()
y_axis=[total_cases,rec_cases,death]
x_axis=["Total Confirmed cases","Recovered","Deaths"]

plt.figure(figsize=(8,5))
sns.barplot(x=x_axis, y=y_axis)
plt.title('Status of affected people.')
plt.xlabel('Status', fontsize=15)
plt.ylabel('Count of people affected', fontsize=15)
plt.show()

testing=pd.read_csv("/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv")
testing.tail()

sttest=testing.groupby("State").sum()

plt.figure(figsize=(20,20))
plt.barh(sttest.index,sttest['TotalSamples'],label="Total Samples",color='black')
plt.barh(sttest.index, sttest['Positive'],label="Positive Cases",color='coral')
plt.xlabel('Cases',size=30)
plt.ylabel("States",size=30)
plt.legend(frameon=True, fontsize=12)
plt.title('Recoveries and Total Number of Cases Statewise',fontsize = 20)
plt.show()

age=pd.read_csv("/kaggle/input/covid19-in-india/AgeGroupDetails.csv")

age

plt.figure(figsize=(10,10))
#plt.title("Current age group scenario in india",fontsize=50)
labels=age['AgeGroup']
len(labels)
sizes=['3.18','3.9','24.86','21.10','16.18','11.13','12.86','4.05','1.45','1.3']
plt.pie(sizes,labels=labels,autopct='%1.1f%%')
plt.show() 

hospbeds=pd.read_csv("/kaggle/input/covid19-in-india/HospitalBedsIndia.csv")
hospbeds.head()

hospbeds= hospbeds.fillna(0)
hospbeds.head()

centers=['NumPrimaryHealthCenters_HMIS','NumCommunityHealthCenters_HMIS','NumSubDistrictHospitals_HMIS','NumDistrictHospitals_HMIS','TotalPublicHealthFacilities_HMIS','NumPublicBeds_HMIS','NumRuralHospitals_NHP18','NumRuralBeds_NHP18','NumUrbanHospitals_NHP18','NumUrbanHospitals_NHP18']

hospbeds['NumPrimaryHealthCenters_HMIS'] = hospbeds['NumPrimaryHealthCenters_HMIS'].str.replace(',', '')
hospbeds['NumPrimaryHealthCenters_HMIS']=hospbeds['NumPrimaryHealthCenters_HMIS'].astype(str).astype(int)

hospbeds.dtypes

plt.figure(figsize=(20,60))
for i,col in enumerate(centers):
    plt.subplot(8,2,i+1)
    sns.barplot(data=hospbeds,y='State/UT',x=col)
    plt.xlabel('Number of Cases')
    plt.ylabel('')
    plt.title(col)
plt.tight_layout()
plt.show()

icmrtestlabs= pd.read_csv("/kaggle/input/covid19-in-india/ICMRTestingLabs.csv")
icmrtestlabs

import plotly.express as px
fig = px.treemap(icmrtestlabs, path=['state','city'],
                  color='city', hover_data=['lab','address'],color_continuous_scale='Purples')
autosize=False
py.iplot(fig)