How to Plot a Pie Chart in Python ?

import matplotlib.pyplot as plt

# Data
labels = ['Apples', 'Bananas', 'Cherries', 'Dates']
sizes = [30, 20, 35, 15]
colors = ['#ff9999','#66b3ff','#99ff99','#ffcc99']
explode = (0.05, 0, 0, 0)  # Slightly separate the first slice

# Create pie chart
plt.figure(figsize=(6,6))
plt.pie(
    sizes,
    labels=labels,
    colors=colors,
    explode=explode,
    autopct='%1.1f%%',  # display percentages with 1 decimal
    startangle=90,       # rotate so first slice starts at 90°
    shadow=True
)

plt.title("Fruit Distribution")
plt.show()

import matplotlib.pyplot as plt

# Data
sizes = [40, 25, 20, 10, 5]
labels = ['Python', 'JavaScript', 'C++', 'Java', 'Other']
colors = plt.cm.viridis([0.1, 0.3, 0.5, 0.7, 0.9])
explode = [0.1 if s == max(sizes) else 0 for s in sizes]

# Plot
plt.figure(figsize=(7,7))
wedges, texts, autotexts = plt.pie(
    sizes,
    labels=labels,
    autopct=lambda p: f'{p:.1f}%\n({int(p*sum(sizes)/100)})',
    colors=colors,
    explode=explode,
    startangle=140,
    textprops={'color':"w", 'fontsize':12},
    shadow=True
)

plt.setp(autotexts, size=10, weight="bold")
plt.title("Programming Language Popularity", fontsize=14)
plt.show()

import matplotlib.pyplot as plt

sizes = [50, 30, 15, 5]
labels = ['North America', 'Europe', 'Asia', 'Other']

fig, ax = plt.subplots(figsize=(6,6))
wedges, texts, autotexts = ax.pie(
    sizes,
    labels=labels,
    autopct='%1.1f%%',
    startangle=90,
    wedgeprops={'width':0.4, 'edgecolor':'white'}
)

ax.set_title("Market Share by Region")
plt.show()

import matplotlib.pyplot as plt

outer_sizes = [50, 30, 20]
outer_labels = ['Americas', 'Europe', 'Asia']
inner_sizes = [25, 15, 10, 10, 8, 12, 5, 3, 12]
inner_labels = ['USA', 'Canada', 'Brazil', 'UK', 'France', 'Germany', 'China', 'India', 'Japan']

fig, ax = plt.subplots(figsize=(7,7))
ax.pie(outer_sizes, radius=1, labels=outer_labels, 
       wedgeprops=dict(width=0.3, edgecolor='white'))
ax.pie(inner_sizes, radius=0.7, labels=inner_labels, 
       wedgeprops=dict(width=0.3, edgecolor='white'))

ax.set(aspect="equal", title='Global Sales by Region and Country')
plt.show()

import matplotlib.pyplot as plt
import numpy as np

categories = ['A','B','C','D','E','F','G']
values = np.random.randint(10, 50, size=len(categories))

plt.figure(figsize=(7,7))
plt.pie(values, labels=categories, 
        colors=plt.cm.plasma(np.linspace(0, 1, len(categories))),
        autopct='%1.1f%%', startangle=90)
plt.title('Dynamic Colors with Colormap')
plt.show()

plt.cm.<colormap>

import pandas as pd
import matplotlib.pyplot as plt

data = {'Fruit': ['Apple', 'Banana', 'Cherry', 'Date'],
        'Count': [30, 20, 35, 15]}
df = pd.DataFrame(data)

df.set_index('Fruit')['Count'].plot(kind='pie', autopct='%1.1f%%', figsize=(6,6))
plt.ylabel('')  # remove y-label
plt.title('Fruit Distribution')
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Category': ['Energy', 'Transport', 'Agriculture', 'Industry', 'Residential'],
    'Emissions': [35, 25, 20, 15, 5]
})

df.set_index('Category')['Emissions'].plot(
    kind='pie', 
    autopct='%1.1f%%',
    cmap='coolwarm',
    figsize=(6,6),
    title='Global CO₂ Emissions by Sector'
)
plt.ylabel('')
plt.show()

import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style="whitegrid")  # apply seaborn style

sizes = [40, 25, 20, 15]
labels = ['A', 'B', 'C', 'D']

plt.figure(figsize=(6,6))
plt.pie(
    sizes,
    labels=labels,
    autopct='%1.1f%%',
    startangle=90,
    colors=sns.color_palette("pastel")
)
plt.title("Pie Chart with Seaborn Style")
plt.show()

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

data = pd.DataFrame({
    "Category": ["A", "B", "C", "D"],
    "Value": [40, 25, 20, 15]
})

sns.barplot(data=data, x="Category", y="Value", palette="pastel")
plt.title("Proportion Comparison (Better than Pie Chart)")
plt.show()