利用Python的matplotlib庫(kù)繪制動(dòng)態(tài)圖形

一、python 繪制動(dòng)畫(huà)圖

python繪制動(dòng)態(tài)圖形是數(shù)據(jù)可視化更直觀、更好看的一種方式 ，matplotlib工具包是常用的繪圖工具，也可以用來(lái)繪制動(dòng)態(tài)圖形 。本文介紹四種繪制動(dòng)態(tài)圖形的方法 ，包括生成圖形的代碼和動(dòng)態(tài)圖形演示示例。

用matplotlib工具包創(chuàng)建動(dòng)畫(huà)圖有兩種方法：

• 使用 pause() 函數(shù)
• 使用 FuncAnimation() 函數(shù)

動(dòng)畫(huà)柱狀圖，使用FuncAnimation() 函數(shù)

代碼如下 ：

from matplotlib import pyplot as pltfrom matplotlib.animation import FuncAnimation, writersimport numpy as np  fig = plt.figure(figsize = (7,5))axes = fig.add_subplot(1,1,1)axes.set_ylim(0, 300)palette = ['blue', 'red', 'green',            'darkorange', 'maroon', 'black']  y1, y2, y3, y4, y5, y6 = [], [], [], [], [], []  def animation_function(i):    y1 = i    y2 = 5 * i    y3 = 3 * i    y4 = 2 * i    y5 = 6 * i    y6 = 3 * i      plt.xlabel("Country")    plt.ylabel("GDP of Country")          plt.bar(["India", "China", "Germany",              "USA", "Canada", "UK"],            [y1, y2, y3, y4, y5, y6],            color = palette)  plt.title("Bar Chart Animation")  animation = FuncAnimation(fig, animation_function,                           interval = 50)plt.show()

如下圖
：

橫向柱狀跑圖 （Horizontal Bar Chart Race） ，使用FuncAnimation() 函數(shù)

以下代碼是繪制世界1500年-2018年主要城市人口變化橫向柱狀跑圖，需要數(shù)據(jù)集文件city_populations.csv評(píng)論區(qū)留言。

程序代碼如下：

import pandas as pdimport matplotlib.pyplot as pltimport matplotlib.ticker as tickerfrom matplotlib.animation import FuncAnimationdf = pd.read_csv('city_populations.csv',usecols=['name', 'group', 'year', 'value'])colors = dict(zip(['India','Europe','Asia','Latin America','Middle East','North America','Africa'],['#adb0ff', '#ffb3ff', '#90d595','#e48381', '#aafbff', '#f7bb5f','#eafb50']))group_lk = df.set_index('name')['group'].to_dict()def draw_barchart(year):dff = df[df['year'].eq(year)].sort_values(by='value',ascending=True).tail(10)ax.clear()ax.barh(dff['name'], dff['value'],color=[colors[group_lk[x]] for x in dff['name']])dx = dff['value'].max() / 200for i, (value, name) in enumerate(zip(dff['value'],dff['name'])):ax.text(value-dx, i, name,size=14, weight=600,ha='right', va='bottom')ax.text(value-dx, i-.25, group_lk[name],size=10, color='#444444',ha='right', va='baseline')ax.text(value+dx, i, f'{ value:,.0f}',size=14, ha='left', va='center')# polished stylesax.text(1, 0.4, year, transform=ax.transAxes,color='#777777', size=46, ha='right',weight=800)ax.text(0, 1.06, 'Population (thousands)',transform=ax.transAxes, size=12,color='#777777')ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{ x:,.0f}'))ax.xaxis.set_ticks_position('top')ax.tick_params(axis='x', colors='#777777', labelsize=12)ax.set_yticks([])ax.margins(0, 0.01)ax.grid(which='major', axis='x', linestyle='-')ax.set_axisbelow(True)ax.text(0, 1.12, 'The most populous cities in the world from 1500 to 2018',transform=ax.transAxes, size=24, weight=600, ha='left')ax.text(1, 0, ' ',transform=ax.transAxes, ha='right', color='#777777',bbox=dict(facecolor='white', alpha=0.8, edgecolor='white'))plt.box(False)plt.show()fig, ax = plt.subplots(figsize=(15, 8))animator = FuncAnimation(fig, draw_barchart,frames = range(1990, 2019))plt.show()

散點(diǎn)圖動(dòng)畫(huà)