Credit: code from https://towardsdatascience.com/step-by-step-exploratory-data-analysis-on-stroke-dataset-840aefea8739 and https://www.kaggle.com/lirilkumaramal/heart-stroke

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

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.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 43400 entries, 0 to 43399
Data columns (total 12 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   id                 43400 non-null  int64  
 1   gender             43400 non-null  object 
 2   age                43400 non-null  float64
 3   hypertension       43400 non-null  int64  
 4   heart_disease      43400 non-null  int64  
 5   ever_married       43400 non-null  object 
 6   work_type          43400 non-null  object 
 7   Residence_type     43400 non-null  object 
 8   avg_glucose_level  43400 non-null  float64
 9   bmi                41938 non-null  float64
 10  smoking_status     30108 non-null  object 
 11  stroke             43400 non-null  int64  
dtypes: float64(3), int64(4), object(5)
memory usage: 4.0+ MB

df['stroke'].value_counts()

0    42617
1      783
Name: stroke, dtype: int64
# labeled target is unbalanced

# Drop the id column
df.drop(columns=['id'], inplace=True)

# Showing records where patient suffered from stroke but had missing value in bmi attribute.
df[df['bmi'].isna() & df['stroke'] == 1]
gender age hypertension heart_disease ever_married work_type Residence_type avg_glucose_level bmi smoking_status stroke
81 Female 61.0 0 0 Yes Self-employed Rural 202.21 NaN never smoked 1
407 Female 59.0 0 0 Yes Private Rural 76.15 NaN NaN 1
747 Male 78.0 0 1 Yes Private Urban 219.84 NaN NaN 1
1139 Male 57.0 0 1 No Govt_job Urban 217.08 NaN NaN 1
1613 Male 58.0 0 0 Yes Private Rural 189.84 NaN NaN 1
... ... ... ... ... ... ... ... ... ... ... ...
42530 Male 66.0 0 0 Yes Self-employed Urban 182.89 NaN never smoked 1
42839 Female 67.0 1 0 Yes Govt_job Urban 234.43 NaN never smoked 1
43007 Female 69.0 0 1 Yes Self-employed Rural 89.19 NaN smokes 1
43100 Male 67.0 0 0 Yes Self-employed Urban 136.79 NaN smokes 1
43339 Female 76.0 0 0 No Private Rural 100.55 NaN never smoked 1

140 rows × 11 columns

# Replace the missing values with mean of bmi attribute
df['bmi'].fillna(np.round(df['bmi'].mean(), 1), inplace = True)

# Create a new category named 'not known'
df['smoking_status'].fillna('not known', inplace=True)
print(df['smoking_status'].value_counts())

never smoked       16053
not known          13292
formerly smoked     7493
smokes              6562
Name: smoking_status, dtype: int64

# 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))

<ipython-input-23-b4c4cb89b1bd>:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['age_binned'] = pd.cut(df['age'], np.arange(0, 91, 5))
<ipython-input-23-b4c4cb89b1bd>:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['avg_glucose_level_binned'] = pd.cut(df['avg_glucose_level'], np.arange(0, 301, 10))
<ipython-input-23-b4c4cb89b1bd>:4: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df['bmi_binned'] = pd.cut(df['bmi'], np.arange(0, 101, 5))

# Create the correlation heatmap
heatmap = sns.heatmap(df[['age_norm', 'avg_glucose_level_norm', 'bmi_norm']].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 5438 49.098750
Never_worked 177 17.757062
Private 24827 45.016837
Self-employed 6793 59.307817
children 6154 6.698018 
get_100_percent_stacked_bar_chart('ever_married')
df.groupby(['ever_married'])[['age']].agg(['count', 'mean'])
age
count mean
ever_married
No 15456 21.237487
Yes 27933 53.829735 
g = sns.catplot(x="Residence_type", hue="smoking_status", col="work_type",
                data=df, kind="count",
                height=4, aspect=.7)

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

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-38-4ca028d03d7b> in <module>
----> 1 missingno.matrix(df, figsize = (30,5))

NameError: name 'missingno' is not defined
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")

<seaborn.axisgrid.FacetGrid at 0x7fe05aa233d0>
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)
age hypertension heart_disease avg_glucose_level bmi stroke age_norm avg_glucose_level_norm bmi_norm
age 1.00 0.27 0.25 0.24 0.35 0.16 1.00 0.24 0.35
hypertension 0.27 1.00 0.12 0.16 0.15 0.08 0.27 0.16 0.15
heart_disease 0.25 0.12 1.00 0.15 0.05 0.11 0.25 0.15 0.05
avg_glucose_level 0.24 0.16 0.15 1.00 0.18 0.08 0.24 1.00 0.18
bmi 0.35 0.15 0.05 0.18 1.00 0.02 0.35 0.18 1.00
stroke 0.16 0.08 0.11 0.08 0.02 1.00 0.16 0.08 0.02
age_norm 1.00 0.27 0.25 0.24 0.35 0.16 1.00 0.24 0.35
avg_glucose_level_norm 0.24 0.16 0.15 1.00 0.18 0.08 0.24 1.00 0.18
bmi_norm 0.35 0.15 0.05 0.18 1.00 0.02 0.35 0.18 1.00