Use Python to draw cool gif animations that amaze everyone

In a previous article, the editor shared how to use ​​gif​## in Python​​​ #​​Module to make​​gif​​format charts, is awesome, use Python to draw dynamic visualization charts, and save them in gif format. Today I will introduce it to you. A new method of making ​

​gif​

​​ format charts, calling the relevant modules of ​​matplotlib​​, the steps and methods are also quite simple and easy to understand. .

Download and import database

The data set we used this time is the data set that comes with the ​

​bokeh​

​module, through The following line of code can directly download <div class="code" style="position:relative; padding:0px; margin:0px;"><pre class="brush:python;toolbar:false;">import bokeh bokeh.sampledata.download() </pre><div class="contentsignin">Copy after login</div></div> and then import the data set to be used later. We selected the data on the proportion of the population of different age groups in the specified country from 1950 to the present

from bokeh.sampledata.population import data
import numpy as np

data = filter_loc('United States of America')
data.head()

output

##First draw several static charts

We can draw several static charts first, and then draw these Just combine several charts into one animation in the format of It's very simple. We want to filter the data based on the year, and then draw a chart based on the filtered data. The charts are different for each year

import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patheffects as fx

# 绘制图表的函数
def make_plot(year):
    
    # 根据年份来筛选出数据
    df = data[data.Year == year]
        
    # 制作图表
    fig, (ax1, ax2) = plt.subplots(1, 2, sharey = True)
    ax1.invert_xaxis()
    fig.subplots_adjust(wspace = 0) 
    
    ax1.barh(df[df.Sex == 'Male'].AgeGrp, df[df.Sex == 'Male'].percent, label = 'Male')
    ax2.barh(df[df.Sex == 'Female'].AgeGrp, df[df.Sex == 'Female'].percent, label = 'Female', color = 'C1')
    
    country = df.Location.iloc[0]
    if country == 'United States of America': country == 'US'
        
    fig.suptitle(f'......')
    fig.supxlabel('......')
    fig.legend(bbox_to_anchor = (0.9, 0.88), loc = 'upper right')
    ax1.set_ylabel('Age Groups')
    
    return fig

output

In this way, we generated several static charts, and then assembled them into several charts in ​

​gif​

​format. The code is as follows

years = [i for i in set(data.Year) if i < 2022]
years.sort()

for year in years:
    fig = make_plot(year)
    fig.savefig(f'{year}.jpeg',bbox_inches = 'tight')

output

There is another way of thinking

Maybe after seeing this, some people will find the method mentioned above a little troublesome. After all, we Dozens of static charts need to be generated first. If the computer disk space is a little tight, or there is no such place to store these dozens of charts. So you will wonder if it can be done in one step. Of course it is possible. For example, if we plan to draw the distribution of population proportions at different ages from 1950 to 2020, the first step is to draw the distribution of population proportions at different ages in 1950, which is the starting year. Chart, the code is as follows

import matplotlib.animation as animation
fig, ax = plt.subplots()
ims = []

for year in years:
    im = ax.imshow(plt.imread(f'{year}.jpeg'), animated = True)
    ims.append([im])

ani = animation.ArtistAnimation(fig, ims, interval=600)
ani.save('us_population.gif')

output

Then we customize a function to draw the chart, where the parameter is the year, the purpose is to filter out the phase by year Corresponding data and draw corresponding charts

def run(year):
    # 通过年份来筛选出数据
    df = data[data.Year == year]
    # 针对不同地性别来绘制
    total_pop = df.Value.sum()
    df['percent'] = df.Value / total_pop * 100
    male.remove()
    y_pos = [i for i in range(len(df[df.Sex == 'Male']))]
    male.patches = ax1.barh(y_pos, df[df.Sex == 'Male'].percent, label = 'Male', 
                     color = 'C0', tick_label = df[df.Sex == 'Male'].AgeGrp)
    female.remove()
    female.patches = ax2.barh(y_pos, df[df.Sex == 'Female'].percent, label = 'Female',
                 
                 color = 'C1', tick_label = df[df.Sex == 'Female'].AgeGrp)

    text.set_text(year)
    return male#, female

然后我们调用​​animation.FuncAnimation()​​方法，

ani = animation.FuncAnimation(fig, run, years, blit = True, repeat = True, 
                              interval = 600)
ani.save('文件名.gif')

output

这样就可以一步到位生成​​gif​​格式的图表，避免生成数十张繁多地静态图片了。

将若干张​​<span style="color: #2b2b2b;">gif</span>​​动图放置在一张大图当中

最后我们可以将若干张​​gif​​动图放置在一张大的图表当中，代码如下

import matplotlib.animation as animation

# 创建一个新的画布
fig, (ax, ax2, ax3) = plt.subplots(1, 3, figsize = (10, 3))

ims = []
for year in years:
    im = ax.imshow(plt.imread(f'文件1{year}.jpeg'), animated = True)
    im2 = ax2.imshow(plt.imread(f'文件2{year}.jpeg'), animated = True)
    im3 = ax3.imshow(plt.imread(f'文件3{year}.jpeg'), animated = True)
    ims.append([im, im2, im3])

ani = animation.ArtistAnimation(fig, ims, interval=600)
ani.save('comparison.gif')

output

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