# The data is as follows:

# 1.    age (age in years)
# 2.    sex (1:Male, 0:Female)
# 3.    chest pain type (4 values) aka cp
# 4.    resting blood pressure (in mm Hg on admission to the hospital) aka trestbps
# 5.    serum cholesterol (in mg/dl) aka chol
# 6.    fasting blood sugar > 120 mg/dl aka fbs
# 7.    resting electrocardiographic results (values 0,1,2)
# 8.    maximum heart rate achieved
# 9.    exercise induced angina
# 10.   old peak = ST depression induced by exercise relative to rest
# 11.   the slope of the peak exercise ST segment
# 12.   number of major vessels (0-3) colored by fluoroscopy
# 13.   thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
# 14.   target

#_______________________________________________________________________________

heart['cp'].value_counts()

# The result of the above line:

# 0    143
# 2     87
# 1     50
# 3     23
# Name: cp, dtype: int64

labels = ["Type 0","Type 1","Type 2","Type 3"]
cp_types = heart['cp'].value_counts().values
plt.pie(cp_types,autopct="%.1f%%", labels = labels)
plt.title("Chest Pain Types")
plt.show()

#As we can notice from the data chest pain type zero is the most common in our data
#_______________________________________________________________________________

sns.countplot(x='sex',hue='target',data=heart)
# As we can see in the plot the number of males who had heart disease is much greater than the number of females

#_______________________________________________________________________________