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)
