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

# create a simple pie chart
prod= [500, 300]
prod_name= ['iphone', 'ipad']
color= ['#ccdbfd', '#f4845f']

#visualize the pie chart
plt.figure(figsize=(4,4))
plt.pie(prod, labels=prod_name, colors=color)
plt.show()

df= pd.read_csv('student.csv')
df.head()

	gender	ethnicity	parental_education	lunch	test_preparation_course	math_score	reading_score	writing_score
0	female	group B	bachelor's degree	standard	none	72	72	74
1	female	group C	some college	standard	completed	69	90	88
2	female	group B	master's degree	standard	none	90	95	93
3	male	group A	associate's degree	free/reduced	none	47	57	44
4	male	group C	some college	standard	none	76	78	75

#visualize gender distribution via pie chart
gender_count= df['gender'].value_counts()
gender_count

	count
gender
female	518
male	482

dtype: int64

#extract labels
names= df['gender'].value_counts().index.tolist()
names

['female', 'male']

#extract numbers
values= df['gender'].value_counts().tolist()
values

[518, 482]

colors=['#fc809f','#1976d2']

#visualize the pie for gender distribution
plt.figure(figsize=(4,4))
plt.pie(values, labels=names, colors=colors)
plt.show()

#pie chart for ethnicity
ethnicity_count= df['ethnicity'].value_counts()
ethnicity_count

	count
ethnicity
group C	319
group D	262
group B	190
group E	140
group A	89

dtype: int64

#pie for ethnicity
ethnicity= df['ethnicity'].value_counts().index.tolist()
ethnicity

['group C', 'group D', 'group B', 'group E', 'group A']

colors=['blue','red', 'green', 'orange', 'brown']

plt.figure(figsize=(6,6))
plt.pie(ethnicity_count, labels=ethnicity, colors=colors, autopct= '%1.2f%%')
plt.show()