Basic Pie Chart Construction

Pie charts provide an intuitive way to visualize proportional data. The pie() function in Matplotlib simplifies this process.

import matplotlib.pyplot as plt

plt.figure(figsize=(16, 9))

regions = ['China', 'Japan', 'Korea', 'USA', 'Russia']
tax_revenue = [60, 10, 20, 70, 50]
color_palette = ['red', '#FEDD62', 'blue', 'gray', 'green']
offsets = [0.1, 0, 0, 0, 0]  # Emphasize China's segment

plt.pie(tax_revenue,
        labels=regions,
        colors=color_palette,
        explode=offsets,
        startangle=90,
        shadow=True,
        autopct='%1.1f%%')

plt.legend(loc=2)
plt.show()

Real-World Tax Data Visualization

Using national tax data from 2006–2015 (source: National Bureau of Statistics of China), we compute and display the proporsion of each tax category for 2015.

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties

font_style = FontProperties(fname='/Library/Fonts/Songti.ttc', size=18)
year_col = '2015年'

tax_df = pd.read_csv('国家各项税收.csv',
                     index_col=0,
                     skiprows=2,
                     skipfooter=1,
                     engine='python')

# Derive 'Other Taxes' as residual
main_total = tax_df[year_col].iloc[0]
sum_of_categories = tax_df[year_col].iloc[1:].sum()
other_taxes = main_total - sum_of_categories

tax_series = tax_df[year_col].copy()
tax_series['其他税收'] = other_taxes

plt.figure(figsize=(16, 9))

# Highlight personal income tax
highlight = [0.2 if label == '个人所得税(亿元)' else 0 for label in tax_series.index]

pie_artists = plt.pie(tax_series.values,
                      labels=tax_series.index,
                      shadow=True,
                      autopct='%1.1f%%',
                      explode=highlight,
                      startangle=30)

# Apply Chinese font to labels
for label_text in pie_artists[1]:
    label_text.set_fontproperties(font_style)
    label_text.set_fontsize(14)

plt.show()

Multi-Plot Layouts with Subplots

A Figure can contain multiple Axes objects, arranged using subplot().

import matplotlib.pyplot as plt

plt.figure(figsize=(15, 9))

x_vals = [0, 1, 2, 5, 4, 5]
y_vals = [0, 2, 3, 4, 5, 6]

for i in range(1, 7):
    plt.subplot(2, 3, i)
    if i == 1:
        plt.plot(x_vals, y_vals, 'ro')
        plt.legend(['First plot'])
        plt.grid(True)
    else:
        plt.plot(x_vals, y_vals)
    plt.title(f'this is my picture{i}')

plt.show()

Customized Cooridnate System for Line Plots

Axes spines can be repositioned or hidden to create centered coordinate systems.

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(-8, 8, 2)
y = np.random.randint(-4, 4, size=len(x))

plt.plot(x, y)
plt.axis([-10, 10, -10, 10])

ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')

ax.xaxis.set_ticks_position('bottom')
ax.yaxis.set_ticks_position('left')

ax.spines['left'].set_position(('data', 0))
ax.spines['bottom'].set_position(('data', 0))

plt.show()