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)
