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

# plotly
import chart_studio.plotly as py
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
import plotly.graph_objs as go
import seaborn as sns
# word cloud library
from wordcloud import WordCloud

# matplotlib
import matplotlib.pyplot as plt
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

dataframe = pd.read_csv("F:\\kaggleDataSet\\healthcare-data\\test_2v.csv")

import chart_studio.plotly as py
from plotly.graph_objs import *

df_heart_disease = dataframe[dataframe.heart_disease== 1] 
labels = df_heart_disease.gender
pie1_list=df_heart_disease.heart_disease

df_hypertension= dataframe[dataframe.hypertension == 1] 
labels1 = df_hypertension.gender
pie1_list1=df_hypertension.hypertension


labels2 = dataframe.Residence_type
pie1_list2 = dataframe.heart_disease

labels3 = dataframe.work_type
pie1_list3 = dataframe.heart_disease



fig = {
    \'data\': [
        {
            \'labels\': labels,
            \'values\': pie1_list,
            \'type\': \'pie\',
            \'name\': \'Heart Disease\',
            \'marker\': {\'colors\': [\'rgb(56, 75, 126)\',
                                  \'rgb(18, 36, 37)\',
                                  \'rgb(34, 53, 101)\',
                                  \'rgb(36, 55, 57)\',
                                  \'rgb(6, 4, 4)\']},
            \'domain\': {\'x\': [0, .48],
                       \'y\': [0, .49]},
            \'hoverinfo\':\'label+percent+name\',
            \'textinfo\':\'none\'
        },
        {
            \'labels\': labels1,
            \'values\': pie1_list1,
            \'marker\': {\'colors\': [\'rgb(177, 127, 38)\',
                                  \'rgb(205, 152, 36)\',
                                  \'rgb(99, 79, 37)\',
                                  \'rgb(129, 180, 179)\',
                                  \'rgb(124, 103, 37)\']},
            \'type\': \'pie\',
            \'name\': \'Hypertension\',
            \'domain\': {\'x\': [.52, 1],
                       \'y\': [0, .49]},
            \'hoverinfo\':\'label+percent+name\',
            \'textinfo\':\'none\'

        },
        {
            \'labels\': labels2,
            \'values\': pie1_list2,
            \'marker\': {\'colors\': [\'rgb(33, 75, 99)\',
                                  \'rgb(79, 129, 102)\',
                                  \'rgb(151, 179, 100)\',
                                  \'rgb(175, 49, 35)\',
                                  \'rgb(36, 73, 147)\']},
            \'type\': \'pie\',
            \'name\': \'Residence Type\',
            \'domain\': {\'x\': [0, .48],
                       \'y\': [.51, 1]},
            \'hoverinfo\':\'label+percent+name\',
            \'textinfo\':\'none\'
        },
        {
            \'labels\': labels3,
            \'values\': pie1_list3,
            \'marker\': {\'colors\': [\'rgb(146, 123, 21)\',
                                  \'rgb(177, 180, 34)\',
                                  \'rgb(206, 206, 40)\',
                                  \'rgb(175, 51, 21)\',
                                  \'rgb(35, 36, 21)\']},
            \'type\': \'pie\',
            \'name\':\'Work Type\',
            \'domain\': {\'x\': [.52, 1],
                       \'y\': [.51, 1]},
            \'hoverinfo\':\'label+percent+name\',
            \'textinfo\':\'none\'
        }
        
    ],
    \'layout\': {\'title\': \'\',
               \'showlegend\': False}
}

iplot(fig)

import chart_studio.plotly as py
import plotly.graph_objs as go

# Create random data with numpy
import numpy as np

df_250 = dataframe.iloc[:250,:]


random_x = df_250.index
random_y0 =  df_250.avg_glucose_level
random_y1 =  df_250.bmi
random_y2 =  df_250.age

# Create traces
trace0 = go.Scatter(
    x = random_x,
    y = random_y0,
    mode = \'markers\',
    name = \'Avg. Glucose Level\'
)
trace1 = go.Scatter(
    x = random_x,
    y = random_y1,
    mode = \'lines+markers\',
    name = \'BMI\'
)
trace2 = go.Scatter(
    x = random_x,
    y = random_y2,
    mode = \'lines\',
    name = \'Age\'
)

data = [trace0, trace1, trace2]
iplot(data, filename=\'scatter-mode\')

import chart_studio.plotly as py
import plotly.graph_objs as go
df_heart_disease = dataframe[dataframe.heart_disease==1] 
labels = df_heart_disease.gender
x = labels

trace0 = go.Box(
    y=dataframe.age,
    x=x,
    name=\'Age\',
    marker=dict(
        color=\'#3D9970\'
    )
)
trace1 = go.Box(
    y=dataframe.avg_glucose_level,
    x=x,
    name=\'Avg. Glucose Level\',
    marker=dict(
        color=\'#FF4136\'
    )
)
trace2 = go.Box(
    y=dataframe.bmi,
    x=x,
    name=\'BMI\',
    marker=dict(
        color=\'#FF851B\'
    )
)
data = [trace0, trace1, trace2]
layout = go.Layout(
    yaxis=dict(
        title=\'Attendants Who Has Heart Disease\',
        zeroline=False
    ),
    boxmode=\'group\'
)
fig = go.Figure(data=data, layout=layout)
iplot(fig)

import chart_studio.plotly as py
import plotly.graph_objs as go
df_hypertension= dataframe[dataframe.hypertension == 1] 
labels1 = df_hypertension.gender
x = labels1

trace0 = go.Box(
    y=dataframe.age,
    x=x,
    name=\'Age\',
    marker=dict(
        color=\'#3D9970\'
    )
)
trace1 = go.Box(
    y=dataframe.avg_glucose_level,
    x=x,
    name=\'Avg. Glucose Level\',
    marker=dict(
        color=\'#FF4136\'
    )
)
trace2 = go.Box(
    y=dataframe.bmi,
    x=x,
    name=\'BMI\',
    marker=dict(
        color=\'#FF851B\'
    )
)
data = [trace0, trace1, trace2]
layout = go.Layout(
    yaxis=dict(
        title=\'Attendants Who Has Hypertension\',
        zeroline=False
    ),
    boxmode=\'group\'
)
fig = go.Figure(data=data, layout=layout)
iplot(fig)

df_heart_disease_1 = dataframe.smoking_status [dataframe.heart_disease == 1  ]        
df_hypertension_1  = dataframe.smoking_status [dataframe.hypertension  == 1   ]       
trace1 = go.Histogram(
    x=df_heart_disease_1,
    opacity=0.75,
    name = "Heart Disease",
    marker=dict(color=\'rgba(171, 50, 96, 0.6)\'))
trace2 = go.Histogram(
    x=df_hypertension_1,
    opacity=0.75,
    name = "Hypertension",
    marker=dict(color=\'rgba(12, 50, 196, 0.6)\'))



data = [trace1, trace2]
layout = go.Layout(barmode=\'overlay\',
                   title=\' Association Between Smoking, Heart Disease & Hypertension\',
                   xaxis=dict(title=\'Smoking Status\'),
                   yaxis=dict( title=\'Attendants\'),
)
fig = go.Figure(data=data, layout=layout)
iplot(fig)

df_heart_disease_1 = dataframe.work_type [dataframe.heart_disease    == 1  ]        
df_hypertension_1 = dataframe.work_type [dataframe.hypertension    == 1   ]     

trace1 = go.Histogram(
    x=df_heart_disease_1,
    opacity=0.75,
    name = "Heart Disease",
    marker=dict(color=\'rgba(171, 50, 96, 0.6)\'))
trace2 = go.Histogram(
    x=df_hypertension_1,
    opacity=0.75,
    name = "Hypertension",
    marker=dict(color=\'rgba(12, 50, 196, 0.6)\'))

data = [trace1, trace2]
layout = go.Layout(barmode=\'overlay\',
                   title=\' Association Between Work Type, Heart Disease & Hypertension\',
                   xaxis=dict(title=\'\'),
                   yaxis=dict( title=\'Attendants\'),
)
fig = go.Figure(data=data, layout=layout)
iplot(fig)