这个叫条形竞赛图，非常适合制作随时间变动的数据可视化动图。
我已经用streamlit+bar_chart_race实现了，然后白嫖了heroku的服务器，大家通过下面的网址上传csv格式的表格就可以轻松制作条形竞赛图，生成的视频可以保存本地。
https://bar-chart-race-app.herokuapp.com/
本文我将实现过程介绍一下，白嫖服务器+部署留在下期再讲。
纯matplotlib实现
注：以下所有实现方式都需要提前安装ffmpeg,安装方式我之前在决策树可视化一文中有介绍
matplotlib实现bar-chart-race很简单，直接上代码
import pandas as pd import matplotlib.pyplot as plt import matplotlib.ticker as ticker import matplotlib.animation as animation from IPython.display import HTML url = 'https://gist.githubusercontent.com/johnburnmurdoch/4199dbe55095c3e13de8d5b2e5e5307a/raw/fa018b25c24b7b5f47fd0568937ff6c04e384786/city_populations' df = pd.read_csv(url, 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() fig, ax = plt.subplots(figsize=(15, 8)) def draw_barchart(current_year): dff = df[df['year'].eq(current_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() / 200 for 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') ax.text(1, 0.4, current_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, 1.15, 'The most populous cities in the world from 1500 to 2018', transform=ax.transAxes, size=24, weight=600, ha='left', va='top') ax.text(1, 0, 'by @pratapvardhan; credit @jburnmurdoch', transform=ax.transAxes, color='#777777', ha='right', bbox=dict(facecolor='white', alpha=0.8, edgecolor='white')) plt.box(False) fig, ax = plt.subplots(figsize=(15, 8)) animator = animation.FuncAnimation(fig, draw_barchart, frames=range(1900, 2019)) HTML(animator.to_jshtml())
核心是定义draw_barchart函数绘制当前图表的样式，然后用
animation.FuncAnimation重复调用draw_barchart来制作动画,最后用animator.to_html5_video() 或 animator.save()保存GIF/视频。
xkcd手绘风格
我们也可以用matplotlib.pyplot.xkcd函数绘制XKCD风格的图表，方法也很简单，只需把上面的代码最后一段加上一行
with plt.xkcd(): fig, ax = plt.subplots(figsize=(15, 8)) animator = animation.FuncAnimation(fig, draw_barchart, frames=range(1900, 2019)) HTML(animator.to_jshtml())
bar_chart_race库极简实现
如果嫌麻烦，还可以使用一个库「Bar Chart Race」，堪称Python界最强的动态可视化包。
GitHub地址：https://github.com/dexplo/bar_chart_race
目前主要有0.1和0.2两个版本，0.2版本添加动态曲线图以及Plotly实现的动态条形图。
通过pip install bar_chart_race也只能到0.1版本，因此需要从GitHub上下载下来，再进行安装。
git clone https://github.com/dexplo/bar_chart_race
使用起来就是极简了，三行代码即可实现
import bar_chart_race as bcr # 获取数据 df = bcr.load_dataset('covid19_tutorial') # 生成GIF图像 bcr.bar_chart_race(df, 'covid19_horiz.gif')
实际上bar_chart_race还有很多参数可以输出不同形态的gif
bcr.bar_chart_race( df=df, filename='covid19_horiz.mp4', orientation='h', sort='desc', n_bars=6, fixed_order=False, fixed_max=True, steps_per_period=10, interpolate_period=False, label_bars=True, bar_size=.95, period_label={'x': .99, 'y': .25, 'ha': 'right', 'va': 'center'}, period_fmt='%B %d, %Y', period_summary_func=lambda v, r: {'x': .99, 'y': .18, 's': f'Total deaths: {v.nlargest(6).sum():,.0f}', 'ha': 'right', 'size': 8, 'family': 'Courier New'}, perpendicular_bar_func='median', period_length=500, figsize=(5, 3), dpi=144, cmap='dark12', title='COVID-19 Deaths by Country', title_size='', bar_label_size=7, tick_label_size=7, shared_fontdict={'family' : 'Helvetica', 'color' : '.1'}, scale='linear', writer=None, fig=None, bar_kwargs={'alpha': .7}, filter_column_colors=False)
比如以下几种
streamlit+bar_chart_race
streamlit是我最近特别喜欢玩的一个机器学习应用开发框架，它能帮你不用懂得复杂的HTML,CSS等前端技术就能快速做出来一个炫酷的Web APP。
我之前开发的决策树挑西瓜就是使用了streamlit
下面是streamlit+bar_chart_race整体结构
核心是app.py，代码如下：
from bar_chart_race import bar_chart_race as bcr import pandas as pd import streamlit as st import streamlit.components.v1 as components st.title('Bar Chart Race', anchor=None) uploaded_file = st.file_uploader("", type="csv") if uploaded_file is not None: df = pd.read_csv(uploaded_file,sep=',', encoding='gbk') df = df.set_index("date") st.write(df.head(6)) bcr_html = bcr.bar_chart_race(df=df, n_bars=10) components.html(bcr_html.data, width=800, height=600)
最终效果大家亲自体验吧
