這是三個關(guān)鍵輸入，當(dāng)然還有更多可選輸入，感興趣的讀者可查看原文檔，這里不再贅述。下一步要做的就是將數(shù)據(jù)圖表參數(shù)化，從而轉(zhuǎn)換為一個函數(shù)，然后將該函數(shù)時間序列中的點作為輸入，設(shè)置完成后就可以正式開始了。在開始之前依舊需要確認你是否對基本的數(shù)據(jù)可視化有所了解。也就是說，我們先要將數(shù)據(jù)進行可視化處理，再進行動態(tài)處理。按照以下代碼進行基本調(diào)用。另外，這里將采用大型流行病的傳播數(shù)據(jù)作為案例數(shù)據(jù)(包括每天的死亡人數(shù))。import matplotlib.animation as aniimport matplotlib.pyplot as pltimport numpy as npimport pandas as pdurl = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv'df = pd.read_csv(url, delimiter=',', header='infer')df_interest = df.loc[df['Country/Region'].isin(['United Kingdom', 'US', 'Italy', 'Germany'])& df['Province/State'].isna]df_interest.rename(index=lambda x: df_interest.at[x, 'Country/Region'], inplace=True)df1 = df_interest.transposedf1 = df1.drop(['Province/State', 'Country/Region', 'Lat', 'Long'])df1 = df1.loc[(df1 != 0).any(1)]df1.index = pd.to_datetime(df1.index)繪制三種常見動態(tài)圖表1、繪制動態(tài)線型圖如下所示，首先需要做的第一件事是定義圖的各項，這些基礎(chǔ)項設(shè)定之后就會保持不變。它們包括：創(chuàng)建 figure 對象，x 標(biāo)和 y 標(biāo)，設(shè)置線條顏色和 figure 邊距等：import numpy as npimport matplotlib.pyplot as pltcolor = ['red', 'green', 'blue', 'orange']fig = plt.figureplt.xticks(rotation=45, ha="right", rotation_mode="anchor") #rotate the x-axis valuesplt.subplots_adjust(bottom = 0.2, top = 0.9) #ensuring the dates (on the x-axis) fit in the screenplt.ylabel('No of Deaths')plt.xlabel('Dates')接下來設(shè)置 curve 函數(shù)，進而使用 .FuncAnimation 讓它動起來：defbuildmebarchart(i=int):plt.legend(df1.columns)p = plt.plot(df1[:i].index, df1[:i].values) #note it only returns the dataset, up to the point ifor i in range(0,4):p[i].set_color(color[i]) #set the colour of each curveimport matplotlib.animation as anianimator = ani.FuncAnimation(fig, buildmebarchart, interval = 100)plt.show2、動態(tài)餅狀圖可以觀察到，其代碼結(jié)構(gòu)看起來與線型圖并無太大差異，但依舊有細小的差別。import numpy as npimport matplotlib.pyplot as pltfig,ax = plt.subplotsexplode=[0.01,0.01,0.01,0.01] #pop out each slice from the piedef getmepie(i):defabsolute_value(val): #turn % back to a numbera = np.round(val/100.*df1.head(i).max.sum, 0)return int(a)ax.clearplot = df1.head(i).max.plot.pie(y=df1.columns,autopct=absolute_value, label='',explode = explode, shadow = True)plot.set_title('Total Number of Deaths\n' + str(df1.index[min( i, len(df1.index)-1 )].strftime('%y-%m-%d')), fontsize=12)import matplotlib.animation as anianimator = ani.FuncAnimation(fig, getmepie, interval = 200)plt.show主要區(qū)別在于，動態(tài)餅狀圖的代碼每次循環(huán)都會返回一組數(shù)值，但在線型圖中返回的是我們所在點之前的整個時間序列。返回時間序列通過 df1.head(i) 來實現(xiàn)，而. max則保證了我們僅獲得最新的數(shù)據(jù)，因為流行病導(dǎo)致死亡的總數(shù)只有兩種變化：維持現(xiàn)有數(shù)量或持續(xù)上升。df1.head(i).max3、動態(tài)條形圖創(chuàng)建動態(tài)條形圖的難度與上述兩個案例并無太大差別。在這個案例中，作者定義了水平和垂直兩種條形圖，讀者可以根據(jù)自己的實際需求來選擇圖表類型并定義變量欄。fig = plt.figurebar = ''def buildmebarchart(i=int):iv = min(i, len(df1.index)-1) #the loop iterates an extra one time, which causes the dataframes to go out of bounds. This was the easiest (most lazy) way to solve this :)objects = df1.max.indexy_pos = np.arange(len(objects))performance = df1.iloc[[iv]].values.tolist[0]if bar == 'vertical':plt.bar(y_pos, performance, align='center', color=['red', 'green', 'blue', 'orange'])plt.xticks(y_pos, objects)plt.ylabel('Deaths')plt.xlabel('Countries')plt.title('Deaths per Country \n' + str(df1.index[iv].strftime('%y-%m-%d')))else:plt.barh(y_pos, performance, align='center', color=['red', 'green', 'blue', 'orange'])plt.yticks(y_pos, objects)plt.xlabel('Deaths')plt.ylabel('Countries')animator = ani.FuncAnimation(fig, buildmebarchart, interval=100)plt.show在制作完成后，存儲這些動態(tài)圖就非常簡單了，可直接使用以下代碼：animator.save(r'C:\temp\myfirstAnimation.gif')感興趣的讀者如想獲得詳細信息可參考：https://matplotlib.org/3.1.1/api/animation_api.html。
