import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

import pandas as pd
url = 'https://raw.githubusercontent.com/davidrkearney/colab-notebooks/main/datasets/strokes_training.csv'
df = pd.read_csv(url, error_bad_lines=False)
df
id gender age hypertension heart_disease ever_married work_type Residence_type avg_glucose_level bmi smoking_status stroke
0 30669 Male 3.0 0 0 No children Rural 95.12 18.0 NaN 0
1 30468 Male 58.0 1 0 Yes Private Urban 87.96 39.2 never smoked 0
2 16523 Female 8.0 0 0 No Private Urban 110.89 17.6 NaN 0
3 56543 Female 70.0 0 0 Yes Private Rural 69.04 35.9 formerly smoked 0
4 46136 Male 14.0 0 0 No Never_worked Rural 161.28 19.1 NaN 0
... ... ... ... ... ... ... ... ... ... ... ... ...
43395 56196 Female 10.0 0 0 No children Urban 58.64 20.4 never smoked 0
43396 5450 Female 56.0 0 0 Yes Govt_job Urban 213.61 55.4 formerly smoked 0
43397 28375 Female 82.0 1 0 Yes Private Urban 91.94 28.9 formerly smoked 0
43398 27973 Male 40.0 0 0 Yes Private Urban 99.16 33.2 never smoked 0
43399 36271 Female 82.0 0 0 Yes Private Urban 79.48 20.6 never smoked 0

43400 rows × 12 columns

#df = df.drop(columns = ['id'])

import matplotlib.pyplot as plt

from pandas_profiling import ProfileReport

profile = ProfileReport(df, title='Pandas Profiling Report')

profile.to_widgets()
id gender age hypertension heart_disease ever_married work_type Residence_type avg_glucose_level bmi smoking_status stroke
0 30669 Male 3.0 0 0 No children Rural 95.12 18.0 NaN 0
1 30468 Male 58.0 1 0 Yes Private Urban 87.96 39.2 never smoked 0
2 16523 Female 8.0 0 0 No Private Urban 110.89 17.6 NaN 0
3 56543 Female 70.0 0 0 Yes Private Rural 69.04 35.9 formerly smoked 0
4 46136 Male 14.0 0 0 No Never_worked Rural 161.28 19.1 NaN 0
5 32257 Female 47.0 0 0 Yes Private Urban 210.95 50.1 NaN 0
6 52800 Female 52.0 0 0 Yes Private Urban 77.59 17.7 formerly smoked 0
7 41413 Female 75.0 0 1 Yes Self-employed Rural 243.53 27.0 never smoked 0
8 15266 Female 32.0 0 0 Yes Private Rural 77.67 32.3 smokes 0
9 28674 Female 74.0 1 0 Yes Self-employed Urban 205.84 54.6 never smoked 0
id gender age hypertension heart_disease ever_married work_type Residence_type avg_glucose_level bmi smoking_status stroke
43390 10096 Female 69.0 0 0 Yes Self-employed Urban 229.85 31.2 never smoked 0
43391 30077 Male 6.0 0 0 No children Urban 77.48 19.1 NaN 0
43392 45266 Female 18.0 0 0 No Private Urban 131.96 22.8 NaN 0
43393 69344 Male 39.0 0 0 Yes Private Rural 132.22 31.6 never smoked 0
43394 52380 Male 47.0 0 0 No Govt_job Urban 68.52 25.2 formerly smoked 0
43395 56196 Female 10.0 0 0 No children Urban 58.64 20.4 never smoked 0
43396 5450 Female 56.0 0 0 Yes Govt_job Urban 213.61 55.4 formerly smoked 0
43397 28375 Female 82.0 1 0 Yes Private Urban 91.94 28.9 formerly smoked 0
43398 27973 Male 40.0 0 0 Yes Private Urban 99.16 33.2 never smoked 0
43399 36271 Female 82.0 0 0 Yes Private Urban 79.48 20.6 never smoked 0 
profile.to_notebook_iframe()



























    

    
    







    


profile.to_file(output_file="pandas_profiling.html")



    






























    






Find and plot missingness in the data 







    
    







    


pct_list = []
for col in df.columns:
    pct_missing = np.mean(df[col].isnull())
    if round(pct_missing*100) >0:
        pct_list.append([col, round(pct_missing*100)])
    print('{} - {}%'.format(col, round(pct_missing*100)))



    

















id - 0%
gender - 0%
age - 0%
hypertension - 0%
heart_disease - 0%
ever_married - 0%
work_type - 0%
Residence_type - 0%
avg_glucose_level - 0%
bmi - 3%
smoking_status - 31%
stroke - 0%














    

    
    







    


cols = df.columns 
colours = ['darkblue', 'red'] 
sns.heatmap(df[cols].isnull(), cmap=sns.color_palette(colours))



    



















<AxesSubplot:>




























    

    
    







    


df.groupby(["age", 'heart_disease'])['stroke'].agg(['sum']).round(0)



    






















  
    

      
      
      sum
    

    

      age
      heart_disease
      
    

  
  
    

      0.08
      0
      0
    

    

      0.16
      0
      0
    

    

      0.24
      0
      0
    

    

      0.32
      0
      0
    

    

      0.40
      0
      0
    

    

      ...
      ...
      ...
    

    

      80.00
      1
      13
    

    

      81.00
      0
      32
    

    

      1
      11
    

    

      82.00
      0
      24
    

    

      1
      12
    

  



163 rows × 1 columns
















    

    
    







    


df.dtypes



    



















id                     int64
gender                object
age                  float64
hypertension           int64
heart_disease          int64
ever_married          object
work_type             object
Residence_type        object
avg_glucose_level    float64
bmi                  float64
smoking_status        object
stroke                 int64
dtype: object














    

    
    







    


# Discretize with respective equal-width bin
df['age_binned'] = pd.cut(df['age'], np.arange(0, 91, 5))
df['avg_glucose_level_binned'] = pd.cut(df['avg_glucose_level'], np.arange(0, 301, 10))
df['bmi_binned'] = pd.cut(df['bmi'], np.arange(0, 101, 5))



    








    

    
    







    


import seaborn as sns



    








    

    
    







    


# Create the correlation heatmap
heatmap = sns.heatmap(df[['age', 'avg_glucose_level', 'bmi']].corr(), vmin=-1, vmax=1, annot=True)
# Create the title
heatmap.set_title('Correlation Heatmap');



    
































    

    
    







    


def get_stacked_bar_chart(column):
    # Get the count of records by column and stroke    
    df_pct = df.groupby([column, 'stroke'])['age'].count()
    # Create proper DataFrame's format
    df_pct = df_pct.unstack()    
    return df_pct.plot.bar(stacked=True, figsize=(6,6), width=1);



    








    

    
    







    


def get_100_percent_stacked_bar_chart(column, width = 0.5):
    # Get the count of records by column and stroke
    df_breakdown = df.groupby([column, 'stroke'])['age'].count()
    # Get the count of records by gender
    df_total = df.groupby([column])['age'].count()
    # Get the percentage for 100% stacked bar chart
    df_pct = df_breakdown / df_total * 100
    # Create proper DataFrame's format
    df_pct = df_pct.unstack()
    return df_pct.plot.bar(stacked=True, figsize=(6,6), width=width);



    








    

    
    







    


# Age related to risk
get_stacked_bar_chart('age_binned')



    



















<AxesSubplot:xlabel='age_binned'>




























    

    
    







    


get_100_percent_stacked_bar_chart('age_binned', width = 0.9)



    



















<AxesSubplot:xlabel='age_binned'>




























    

    
    







    


get_stacked_bar_chart('bmi_binned')
get_100_percent_stacked_bar_chart('bmi_binned', width = 0.9)



    



















<AxesSubplot:xlabel='bmi_binned'>










































    

    
    







    


get_stacked_bar_chart('avg_glucose_level_binned')
get_100_percent_stacked_bar_chart('avg_glucose_level_binned', width = 0.9)



    



















<AxesSubplot:xlabel='avg_glucose_level_binned'>










































    

    
    







    


get_100_percent_stacked_bar_chart('hypertension')
get_100_percent_stacked_bar_chart('heart_disease')



    



















<AxesSubplot:xlabel='heart_disease'>










































    

    
    







    


get_100_percent_stacked_bar_chart('gender')
get_100_percent_stacked_bar_chart('Residence_type')



    



















<AxesSubplot:xlabel='Residence_type'>










































    

    
    







    


get_100_percent_stacked_bar_chart('work_type')
df.groupby(['work_type'])[['age']].agg(['count', 'mean'])



    






















  
    

      
      age
    

    

      
      count
      mean
    

    

      work_type
      
      
    

  
  
    

      Govt_job
      5440
      49.097610
    

    

      Never_worked
      177
      17.757062
    

    

      Private
      24834
      45.015060
    

    

      Self-employed
      6793
      59.307817
    

    

      children
      6156
      6.699253
    

  































    

    
    







    


get_100_percent_stacked_bar_chart('ever_married')
df.groupby(['ever_married'])[['age']].agg(['count', 'mean'])



    






















  
    

      
      age
    

    

      
      count
      mean
    

    

      ever_married
      
      
    

  
  
    

      No
      15462
      21.238236
    

    

      Yes
      27938
      53.828871
    

  































    

    
    







    


g = sns.catplot(x="Residence_type", hue="smoking_status", col="work_type",
                data=df, kind="count",
                height=4, aspect=.7)



    
































    

    
    







    


import missingno
missingno.matrix(df, figsize = (30,5))



    



















<AxesSubplot:>




























    

    
    







    


fig, (ax1, ax2, ax3, ax4) = plt.subplots(1,4,figsize=(25,7))

fig.suptitle("Countplot for the dataset", fontsize=35)

sns.countplot(x="gender", data=df,ax=ax1)
sns.countplot(x="stroke", data=df,ax=ax2)
sns.countplot(x="ever_married", data=df,ax=ax3)
sns.countplot(x="hypertension", data=df,ax=ax4)



    



















<AxesSubplot:xlabel='hypertension', ylabel='count'>




























    

    
    







    


sns.displot(x="age", data=df, kind="kde", hue="gender", col="smoking_status", row="Residence_type")



    

















/home/david/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
/home/david/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
/home/david/anaconda3/lib/python3.8/site-packages/seaborn/distributions.py:305: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)















<seaborn.axisgrid.FacetGrid at 0x7fb1c3d08f70>




























    

    
    







    


fig, (ax1, ax2, ax3) = plt.subplots(1,3,figsize=(20,7))
fig.suptitle("Boxplot for Dataset", fontsize=35)

sns.boxplot(x="stroke", y="avg_glucose_level", data=df,ax=ax1)
sns.boxplot(x="stroke", y="bmi", data=df,ax=ax2)
sns.boxplot(x="stroke", y="age", data=df,ax=ax3)



    



















<AxesSubplot:xlabel='stroke', ylabel='age'>




























    

    
    







    


# Compute a correlation matrix and convert to long-form
corr_mat = df.corr("kendall").stack().reset_index(name="correlation")

# Draw each cell as a scatter point with varying size and color
g = sns.relplot(
    data=corr_mat,
    x="level_0", y="level_1", hue="correlation", size="correlation",
    palette="vlag", hue_norm=(-1, 1), edgecolor=".7",
    height=5, sizes=(50, 250), size_norm=(-.2, .8),
)

# Tweak the figure to finalize
g.set(xlabel="", ylabel="", aspect="equal")
g.despine(left=True, bottom=True)
g.ax.margins(0.25)
for label in g.ax.get_xticklabels():
    label.set_rotation(90)
for artist in g.legend.legendHandles:
    artist.set_edgecolor(".1")



    
































    

    
    







    


strokes_temp_df=df
strokes_temp_df[['stroke','hypertension']] = df[['stroke','hypertension']].astype('int')
corr = strokes_temp_df.corr()
corr.style.background_gradient()
corr.style.background_gradient().set_precision(2)



    


















    
                id        age        hypertension        heart_disease        avg_glucose_level        bmi        stroke    

                

                        id
                        1.00
                        0.01
                        0.01
                        0.01
                        0.02
                        0.02
                        0.00
            

            

                        age
                        0.01
                        1.00
                        0.27
                        0.25
                        0.24
                        0.36
                        0.16
            

            

                        hypertension
                        0.01
                        0.27
                        1.00
                        0.12
                        0.16
                        0.16
                        0.08
            

            

                        heart_disease
                        0.01
                        0.25
                        0.12
                        1.00
                        0.15
                        0.06
                        0.11
            

            

                        avg_glucose_level
                        0.02
                        0.24
                        0.16
                        0.15
                        1.00
                        0.19
                        0.08
            

            

                        bmi
                        0.02
                        0.36
                        0.16
                        0.06
                        0.19
                        1.00
                        0.02
            

            

                        stroke
                        0.00
                        0.16
                        0.08
                        0.11
                        0.08
                        0.02
                        1.00