Creating Common Charts with Matplotlib in Python: Line, Histogram, Bar, and Pie

In daily work, developers often need to analyze project data and produce visual reports; this article introduces how to use the Matplotlib library to create common 2D charts in Python, including line, histogram, bar, and pie charts.

Matplotlib is a widely used Python visualization tool; its core module pyplot allows developers to generate charts with just a few lines of code.

The basic plotting workflow consists of creating a canvas, optionally creating subplots, adding titles and axis labels, setting axis limits and ticks, drawing the graphics, displaying the figure, and finally saving it.

Line chart example :

import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']

x = np.arange(0,10,1)   # horizontal data
y = x**2                 # vertical data

plt.figure(figsize=(6,4), dpi=90)
plt.title('折线图')
plt.xlabel('x轴')
plt.ylabel('y轴')
plt.xlim(0,10)
plt.ylim(0,105)
plt.xticks([0,2,4,6,8,10])
plt.yticks([0,25,50,75,100])
plt.plot(x, y, label='平方')
plt.legend()
plt.grid(True)
plt.show()

The matplotlib.pyplot.plot function signature is:

matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)

Other common chart types are demonstrated with their function prototypes:

Histogram :

matplotlib.pyplot.hist(x, bins=None, range=None, density=False, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, *, data=None, **kwargs)

Bar chart :

matplotlib.pyplot.bar(x, height, width=0.8, bottom=None, *, align='center', data=None, **kwargs)

Pie chart :

matplotlib.pyplot.pie(x, explode=None, labels=None, colors=None, autopct=None, pctdistance=0.6, shadow=False, labeldistance=1.1, startangle=0, radius=1, counterclock=True, wedgeprops=None, textprops=None, center=(0,0), frame=False, rotatelabels=False, *, normalize=True, data=None)

By following these examples, developers can quickly produce attractive visualizations, allowing them to focus on data acquisition, processing, analysis, and storage rather than the intricacies of chart rendering.