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。
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。
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())
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
更详细的用法大家可以查阅官方文档
地址:https://www.dexplo.org/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:
https://bar-chart-race-app.herokuapp.com/
三连在看,年入百万。下期开讲白嫖服务器+部署,敬请期待。
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