Pie Chart Customization

This document consolidates the key customization parameters for pie charts: startangle, shadow, counterclock, explode, radius, and autopct.

Parameter Overview

Parameter	Type	Default	Description
startangle	float	0	Rotation angle in degrees (counterclockwise from 3 o'clock)
shadow	bool	False	Draw shadow beneath pie
counterclock	bool	True	Wedge direction (True=CCW, False=CW)
explode	array-like	None	Offset distance for each wedge
radius	float	1	Pie chart radius
autopct	str or callable	None	Format string for percentage labels

Setup

import matplotlib.pyplot as plt
import numpy as np

vals = [1400, 600, 300, 410, 250]
labels = ["Home Rent", "Food", "Phone/Internet", "Car", "Utilities"]
colors = ['#e74c3c', '#3498db', '#9b59b6', '#f39c12', '#2ecc71']

---

startangle

Controls the starting position of the first wedge. The angle is measured counterclockwise from the positive x-axis (3 o'clock position).

Basic Usage

fig, axes = plt.subplots(1, 4, figsize=(16, 4))

angles = [0, 45, 90, 180]

for ax, angle in zip(axes, angles):
    ax.pie(vals, labels=labels, startangle=angle)
    ax.set_title(f'startangle={angle}°')

plt.tight_layout()
plt.show()

Common Patterns

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Default: starts at 3 o'clock
axes[0].pie(vals, labels=labels, startangle=0)
axes[0].set_title('startangle=0° (3 o\'clock)')

# Top start: common for presentations
axes[1].pie(vals, labels=labels, startangle=90)
axes[1].set_title('startangle=90° (12 o\'clock)')

# Custom positioning
axes[2].pie(vals, labels=labels, startangle=140)
axes[2].set_title('startangle=140°')

plt.tight_layout()
plt.show()

---

shadow

Adds a shadow effect beneath the pie chart for visual depth.

Basic Usage

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

axes[0].pie(vals, labels=labels, shadow=False)
axes[0].set_title('shadow=False')

axes[1].pie(vals, labels=labels, shadow=True)
axes[1].set_title('shadow=True')

plt.tight_layout()
plt.show()

Shadow with Other Parameters

fig, ax = plt.subplots(figsize=(8, 8))
ax.pie(
    vals,
    labels=labels,
    colors=colors,
    shadow=True,
    startangle=90,
    autopct='%1.1f%%'
)
ax.set_title('Shadow with Styling')
plt.show()

---

counterclock

Controls the direction in which wedges are drawn.

Basic Usage

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

axes[0].pie(vals, labels=labels, counterclock=True, startangle=90)
axes[0].set_title('counterclock=True (default)')

axes[1].pie(vals, labels=labels, counterclock=False, startangle=90)
axes[1].set_title('counterclock=False')

plt.tight_layout()
plt.show()

Direction Comparison

fig, axes = plt.subplots(2, 2, figsize=(12, 10))

configs = [
    {'startangle': 90, 'counterclock': True},
    {'startangle': 90, 'counterclock': False},
    {'startangle': 0, 'counterclock': True},
    {'startangle': 0, 'counterclock': False},
]

for ax, config in zip(axes.flat, configs):
    ax.pie(vals, labels=labels, **config)
    ax.set_title(f"startangle={config['startangle']}°, counterclock={config['counterclock']}")

plt.tight_layout()
plt.show()

---

explode

Offsets wedges from the center to emphasize specific slices.

Explode Syntax

# Array of offset distances, one per wedge
# 0 = no offset, 0.1 = slight offset, 0.3 = large offset
explode = [0.1, 0, 0, 0, 0]  # Explode first slice only

Basic Usage

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Single slice
axes[0].pie(vals, labels=labels, explode=[0.1, 0, 0, 0, 0])
axes[0].set_title('Explode First Slice')

# Multiple slices
axes[1].pie(vals, labels=labels, explode=[0.1, 0, 0.15, 0, 0.1])
axes[1].set_title('Explode Multiple')

# All slices
axes[2].pie(vals, labels=labels, explode=[0.05, 0.05, 0.05, 0.05, 0.05])
axes[2].set_title('Explode All')

plt.tight_layout()
plt.show()

Dynamic Explode

# Explode the largest slice
max_idx = vals.index(max(vals))
explode = [0.1 if i == max_idx else 0 for i in range(len(vals))]

fig, ax = plt.subplots()
ax.pie(vals, labels=labels, explode=explode, autopct='%1.1f%%')
ax.set_title('Highlight Maximum Value')
plt.show()

Explode Values Comparison

fig, axes = plt.subplots(1, 4, figsize=(16, 4))

offsets = [0.05, 0.1, 0.2, 0.3]

for ax, offset in zip(axes, offsets):
    ax.pie(vals, labels=labels, explode=[offset, 0, 0, 0, 0])
    ax.set_title(f'explode={offset}')

plt.tight_layout()
plt.show()

---

radius

Controls the size of the pie chart.

Basic Usage

fig, axes = plt.subplots(1, 4, figsize=(16, 4))

radii = [0.5, 0.8, 1.0, 1.3]

for ax, r in zip(axes, radii):
    ax.pie(vals, labels=labels, radius=r)
    ax.set_title(f'radius={r}')

plt.tight_layout()
plt.show()

Donut Charts

Use radius with wedgeprops to create donut charts.

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Basic donut
axes[0].pie(vals, radius=1.2, wedgeprops=dict(width=0.4))
axes[0].set_title('Basic Donut')

# Thin donut
axes[1].pie(vals, radius=1.2, wedgeprops=dict(width=0.2))
axes[1].set_title('Thin Donut')

# Thick donut
axes[2].pie(vals, radius=1.2, wedgeprops=dict(width=0.6))
axes[2].set_title('Thick Donut')

plt.tight_layout()
plt.show()

Nested Donut Chart

outer_vals = [1400, 600, 300, 410, 250]
inner_vals = [800, 600, 500, 400, 660]

fig, ax = plt.subplots(figsize=(9, 9))

ax.pie(outer_vals, radius=1.3, wedgeprops=dict(width=0.3),
       autopct='%1.0f%%', pctdistance=0.85)
ax.pie(inner_vals, radius=1.0, wedgeprops=dict(width=0.3),
       autopct='%1.0f%%', pctdistance=0.75)

ax.set_title('Nested Donut Chart')
plt.show()

---

autopct

Automatically calculates and displays percentage values on each wedge.

Format String Syntax

autopct = '%[width].[precision]f%%'

Component	Description	Example
%	Format initiator	Required
width	Minimum total characters	4
.precision	Decimal places	.1
f	Float type	Required
%%	Literal % symbol	%%

Basic Usage

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

formats = ['%.0f%%', '%.1f%%', '%.2f%%']
titles = ['No Decimal', 'One Decimal', 'Two Decimals']

for ax, fmt, title in zip(axes, formats, titles):
    ax.pie(vals, labels=labels, autopct=fmt)
    ax.set_title(f"{title}: autopct='{fmt}'")

plt.tight_layout()
plt.show()

Custom Format Strings

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# Parentheses
axes[0].pie(vals, labels=labels, autopct='(%.1f%%)')
axes[0].set_title('Parentheses')

# Brackets
axes[1].pie(vals, labels=labels, autopct='[%.0f%%]')
axes[1].set_title('Brackets')

# Custom text
axes[2].pie(vals, labels=labels, autopct='%.1f pct')
axes[2].set_title('Custom Text')

plt.tight_layout()
plt.show()

Callable Function

Use a function for complete control over formatting.

def make_autopct(values):
    def autopct(pct):
        total = sum(values)
        val = int(round(pct * total / 100.0))
        return f'{pct:.1f}%\n(${val:,})'
    return autopct

fig, ax = plt.subplots(figsize=(10, 8))
ax.pie(vals, labels=labels, autopct=make_autopct(vals))
ax.set_title('Percentage with Absolute Values')
plt.show()

Conditional Display

def threshold_autopct(pct, threshold=10):
    return f'{pct:.1f}%' if pct >= threshold else ''

fig, ax = plt.subplots()
ax.pie(vals, labels=labels, autopct=lambda p: threshold_autopct(p, 15))
ax.set_title('Show Only Values ≥ 15%')
plt.show()

---

Styling Percentage Labels

Access returned text objects for custom styling.

fig, ax = plt.subplots(figsize=(10, 8))
wedges, texts, autotexts = ax.pie(
    vals,
    labels=labels,
    colors=colors,
    autopct='%1.1f%%'
)

for autotext in autotexts:
    autotext.set_fontweight('bold')
    autotext.set_color('white')
    autotext.set_fontsize(11)

plt.show()

---

Combined Customization

Full Example

fig, ax = plt.subplots(figsize=(10, 8))

wedges, texts, autotexts = ax.pie(
    vals,
    labels=labels,
    colors=colors,
    startangle=90,
    counterclock=False,
    shadow=True,
    explode=[0.05, 0, 0, 0.1, 0.15],
    radius=1.2,
    autopct='%1.1f%%',
    pctdistance=0.6,
    labeldistance=1.15
)

for autotext in autotexts:
    autotext.set_fontweight('bold')
    autotext.set_color('white')

ax.set_title('Monthly Budget Distribution', fontsize=14, fontweight='bold')
plt.show()

Professional Dashboard

fig = plt.figure(figsize=(14, 6))

# Main pie chart
ax1 = fig.add_subplot(121)
wedges, texts, autotexts = ax1.pie(
    vals,
    labels=labels,
    colors=colors,
    startangle=90,
    counterclock=False,
    shadow=True,
    explode=[0.03, 0, 0, 0, 0],
    autopct='%1.1f%%',
    pctdistance=0.6
)
for autotext in autotexts:
    autotext.set_fontweight('bold')
    autotext.set_color('white')
ax1.set_title('Budget Breakdown', fontsize=13)

# Donut chart
ax2 = fig.add_subplot(122)
ax2.pie(
    vals,
    colors=colors,
    startangle=90,
    counterclock=False,
    radius=1.2,
    wedgeprops=dict(width=0.4),
    autopct='%1.0f%%',
    pctdistance=0.8
)
ax2.set_title('Donut Visualization', fontsize=13)

plt.suptitle('Monthly Expenses Analysis', fontsize=15, fontweight='bold')
plt.tight_layout()
plt.show()

---

Parameter Quick Reference

ax.pie(
    data,                    # Values
    labels=labels,           # Wedge labels
    colors=colors,           # Wedge colors
    startangle=90,           # Start at 12 o'clock
    counterclock=False,      # Clockwise direction
    shadow=True,             # Add shadow
    explode=[0.1, 0, 0, 0],  # Offset slices
    radius=1.2,              # Chart size
    autopct='%1.1f%%',       # Percentage format
    pctdistance=0.6,         # % label position
    labeldistance=1.1,       # Label position
    wedgeprops=dict(         # Wedge styling
        width=0.4,           # For donut charts
        edgecolor='white'
    ),
    textprops=dict(          # Text styling
        fontsize=11,
        fontweight='bold'
    )
)