教你如何用 Python 三行代碼做動圖

作者 | 小F 責編 | 張文

頭圖 | CSDN 下載自東方 IC

來源 | 法納斯特（ID：walker398）

關於動態條形圖，小F以前推薦過 Bar、 Chart、 Race 這個庫。三行代碼就能實現動態條形圖的繪製。但有些同學在使用的時候，會出現一些錯誤。一個是加載文件報錯，另一個是生成 GIF 的時候報錯。這是因為作者的示例是網絡加載數據，會讀取不到。通過讀取本地文件，就不會出錯。GIF 生成失敗一般是需要安裝 imagemagick (圖片處理工具)。

最近小 F 又發現一個可視化圖庫 Pandas_Alive，不僅包含動態條形圖，還可以繪製動態曲線圖、氣泡圖、餅狀圖、地圖等。

同樣也是幾行代碼就能完成動態圖表的繪製。

GitHub地址：

https://github.com/JackMcKew/pandas_alive

使用文檔：https://jackmckew.github.io/pandas_alive/

安裝版本建議是0.2.3，matplotlib版本是3.2.1。

同時需自行安裝 tqdm (顯示進度條)和 descartes (繪製地圖相關庫)。

要不然會出現報錯，估計是作者的 requestment.txt 沒包含這兩個庫。

好了，成功安裝後就可以引入這個第三方庫，直接選擇加載本地文件。

import pandas_aliveimport pandas as pd
covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0])covid_df.plot_animated(filename='examples/example-barh-chart.gif', n_visible=15)

生成了一個 GIF 圖，具體如下：

剛開始學習這個庫的時候，大家可以減少數據，這樣生成 GIF 的時間就會快一些。

比如小 F 在接下來的實踐中，基本都只選取了 20 天左右的數據。

對於其他圖表，我們可以查看官方文檔的 API 說明，得以瞭解。

下面我們就來看看其他動態圖表的繪製方法吧！

動態條形圖

elec_df = pd.read_csv("data/Aus_Elec_Gen_1980_2018.csv", index_col=0, parse_dates=[0], thousands=',')elec_df = elec_df.iloc[:20, :]elec_df.fillna(0).plot_animated('examples/example-electricity-generated-australia.gif', period_fmt="%Y", title='Australian Electricity Generation Sources 1980-2018')

動態柱狀圖

covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0])covid_df.plot_animated(filename='examples/example-barv-chart.gif', orientation='v', n_visible=15)

動態曲線圖

covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0])covid_df.diff.fillna(0).plot_animated(filename='examples/example-line-chart.gif', kind='line', period_label={'x': 0.25, 'y': 0.9})

動態面積圖

covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0])covid_df.sum(axis=1).fillna(0).plot_animated(filename='examples/example-bar-chart.gif', kind='bar', period_label={'x': 0.1, 'y': 0.9}, enable_progress_bar=True, steps_per_period=2, interpolate_period=True, period_length=200)

動態散點圖

max_temp_df = pd.read_csv( "data/Newcastle_Australia_Max_Temps.csv", parse_dates={"Timestamp": ["Year", "Month", "Day"]},)min_temp_df = pd.read_csv( "data/Newcastle_Australia_Min_Temps.csv", parse_dates={"Timestamp": ["Year", "Month", "Day"]},)
max_temp_df = max_temp_df.iloc[:5000, :]min_temp_df = min_temp_df.iloc[:5000, :]
merged_temp_df = pd.merge_asof(max_temp_df, min_temp_df, on="Timestamp")merged_temp_df.index = pd.to_datetime(merged_temp_df["Timestamp"].dt.strftime('%Y/%m/%d'))
keep_columns = ["Minimum temperature (Degree C)", "Maximum temperature (Degree C)"]merged_temp_df[keep_columns].resample("Y").mean.plot_animated(filename='examples/example-scatter-chart.gif', kind="scatter", title='Max & Min Temperature Newcastle, Australia')

動態餅狀圖

covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0])covid_df.plot_animated(filename='examples/example-pie-chart.gif', kind="pie", rotatelabels=True, period_label={'x': 0, 'y': 0})

動態氣泡圖

multi_index_df = pd.read_csv("data/multi.csv", header=[0, 1], index_col=0)multi_index_df.index = pd.to_datetime(multi_index_df.index, dayfirst=True)
map_chart = multi_index_df.plot_animated( kind="bubble", filename="examples/example-bubble-chart.gif", x_data_label="Longitude", y_data_label="Latitude", size_data_label="Cases", color_data_label="Cases", vmax=5, steps_per_period=3, interpolate_period=True, period_length=500, dpi=100)

地理空間點圖表

import geopandasimport pandas_aliveimport contextily
gdf = geopandas.read_file('data/nsw-covid19-cases-by-postcode.gpkg')gdf.index = gdf.postcodegdf = gdf.drop('postcode',axis=1)
result = gdf.iloc[:, :20]result['geometry'] = gdf.iloc[:, -1:]['geometry']
map_chart = result.plot_animated(filename='examples/example-geo-point-chart.gif', basemap_format={'source':contextily.providers.Stamen.Terrain})

多邊形地理圖表

import geopandasimport pandas_aliveimport contextily
gdf = geopandas.read_file('data/italy-covid-region.gpkg')gdf.index = gdf.regiongdf = gdf.drop('region',axis=1)
result = gdf.iloc[:, :20]result['geometry'] = gdf.iloc[:, -1:]['geometry']
map_chart = result.plot_animated(filename='examples/example-geo-polygon-chart.gif', basemap_format={'source': contextily.providers.Stamen.Terrain})

多個動態圖表

covid_df = pd.read_csv('data/covid19.csv', index_col=0, parse_dates=[0])
animated_line_chart = covid_df.diff.fillna(0).plot_animated(kind='line', period_label=False,add_legend=False)animated_bar_chart = covid_df.plot_animated(n_visible=10)
pandas_alive.animate_multiple_plots('examples/example-bar-and-line-chart.gif', [animated_bar_chart, animated_line_chart], enable_progress_bar=True)

城市人口

def population: urban_df = pd.read_csv("data/urban_pop.csv", index_col=0, parse_dates=[0])
 animated_line_chart = ( urban_df.sum(axis=1) .pct_change .fillna(method='bfill') .mul(100) .plot_animated(kind="line", title="Total % Change in Population", period_label=False, add_legend=False) )
 animated_bar_chart = urban_df.plot_animated(n_visible=10, title='Top 10 Populous Countries', period_fmt="%Y")
 pandas_alive.animate_multiple_plots('examples/example-bar-and-line-urban-chart.gif', [animated_bar_chart, animated_line_chart], title='Urban Population 1977 - 2018', adjust_subplot_top=0.85, enable_progress_bar=True)

G7國家平均壽命

def life: data_raw = pd.read_csv("data/long.csv")
 list_G7 = [ "Canada", "France", "Germany", "Italy", "Japan", "United Kingdom", "United States", ]
 data_raw = data_raw.pivot( index="Year", columns="Entity", values="Life expectancy (Gapminder, UN)" )
 data = pd.DataFrame data["Year"] = data_raw.reset_index["Year"] for country in list_G7: data[country] = data_raw[country].values
 data = data.fillna(method="pad") data = data.fillna(0) data = data.set_index("Year").loc[1900:].reset_index
 data["Year"] = pd.to_datetime(data.reset_index["Year"].astype(str))
 data = data.set_index("Year") data = data.iloc[:25, :]
 animated_bar_chart = data.plot_animated( period_fmt="%Y", perpendicular_bar_func="mean", period_length=200, fixed_max=True )
 animated_line_chart = data.plot_animated( kind="line", period_fmt="%Y", period_length=200, fixed_max=True )
 pandas_alive.animate_multiple_plots( "examples/life-expectancy.gif", plots=[animated_bar_chart, animated_line_chart], title="Life expectancy in G7 countries up to 2015", adjust_subplot_left=0.2, adjust_subplot_top=0.9, enable_progress_bar=True )

新南威爾斯州COVID可視化

def nsw: import geopandas import pandas as pd import pandas_alive import contextily import matplotlib.pyplot as plt import json
 with open('data/package_show.json', 'r', encoding='utf8')as fp: data = json.load(fp)
 # Extract url to csv component covid_nsw_data_url = data["result"]["resources"][0]["url"] print(covid_nsw_data_url)
 # Read csv from data API url nsw_covid = pd.read_csv('data/confirmed_cases_table1_location.csv') postcode_dataset = pd.read_csv("data/postcode-data.csv")
 # Prepare data from NSW health dataset
 nsw_covid = nsw_covid.fillna(9999) nsw_covid["postcode"] = nsw_covid["postcode"].astype(int)
 grouped_df = nsw_covid.groupby(["notification_date", "postcode"]).size grouped_df = pd.DataFrame(grouped_df).unstack grouped_df.columns = grouped_df.columns.droplevel.astype(str)
 grouped_df = grouped_df.fillna(0) grouped_df.index = pd.to_datetime(grouped_df.index)
 cases_df = grouped_df
 # Clean data in postcode dataset prior to matching
 grouped_df = grouped_df.T postcode_dataset = postcode_dataset[postcode_dataset['Longitude'].notna] postcode_dataset = postcode_dataset[postcode_dataset['Longitude'] != 0] postcode_dataset = postcode_dataset[postcode_dataset['Latitude'].notna] postcode_dataset = postcode_dataset[postcode_dataset['Latitude'] != 0] postcode_dataset['Postcode'] = postcode_dataset['Postcode'].astype(str)
 # Build GeoDataFrame from Lat Long dataset and make map chart grouped_df['Longitude'] = grouped_df.index.map(postcode_dataset.set_index('Postcode')['Longitude'].to_dict) grouped_df['Latitude'] = grouped_df.index.map(postcode_dataset.set_index('Postcode')['Latitude'].to_dict) gdf = geopandas.GeoDataFrame( grouped_df, geometry=geopandas.points_from_xy(grouped_df.Longitude, grouped_df.Latitude), crs="EPSG:4326") gdf = gdf.dropna
 # Prepare GeoDataFrame for writing to geopackage gdf = gdf.drop(['Longitude', 'Latitude'], axis=1) gdf.columns = gdf.columns.astype(str) gdf['postcode'] = gdf.index # gdf.to_file("data/nsw-covid19-cases-by-postcode.gpkg", layer='nsw-postcode-covid', driver="GPKG")
 # Prepare GeoDataFrame for plotting gdf.index = gdf.postcode gdf = gdf.drop('postcode', axis=1) gdf = gdf.to_crs("EPSG:3857") # Web Mercator
 result = gdf.iloc[:, :22] result['geometry'] = gdf.iloc[:, -1:]['geometry'] gdf = result
 map_chart = gdf.plot_animated(basemap_format={'source': contextily.providers.Stamen.Terrain}, cmap='cool')
 # cases_df.to_csv('data/nsw-covid-cases-by-postcode.csv') cases_df = cases_df.iloc[:22, :]
 from datetime import datetime
 bar_chart = cases_df.sum(axis=1).plot_animated( kind='line', label_events={ 'Ruby Princess Disembark': datetime.strptime("19/03/2020", "%d/%m/%Y"), # 'Lockdown': datetime.strptime("31/03/2020", "%d/%m/%Y") }, fill_under_line_color="blue", add_legend=False )
 map_chart.ax.set_title('Cases by Location')
 grouped_df = pd.read_csv('data/nsw-covid-cases-by-postcode.csv', index_col=0, parse_dates=[0]) grouped_df = grouped_df.iloc[:22, :]
 line_chart = ( grouped_df.sum(axis=1) .cumsum .fillna(0) .plot_animated(kind="line", period_label=False, title="Cumulative Total Cases", add_legend=False) )
 def current_total(values): total = values.sum s = f'Total : {int(total)}' return {'x': .85, 'y': .2, 's': s, 'ha': 'right', 'size': 11}
 race_chart = grouped_df.cumsum.plot_animated( n_visible=5, title="Cases by Postcode", period_label=False, period_summary_func=current_total )
 import time
 timestr = time.strftime("%d/%m/%Y")
 plots = [bar_chart, line_chart, map_chart, race_chart]
 from matplotlib import rcParams
 rcParams.update({"figure.autolayout": False}) # make sure figures are `Figure` instances figs = plt.Figure gs = figs.add_gridspec(2, 3, hspace=0.5) f3_ax1 = figs.add_subplot(gs[0, :]) f3_ax1.set_title(bar_chart.title) bar_chart.ax = f3_ax1
 f3_ax2 = figs.add_subplot(gs[1, 0]) f3_ax2.set_title(line_chart.title) line_chart.ax = f3_ax2
 f3_ax3 = figs.add_subplot(gs[1, 1]) f3_ax3.set_title(map_chart.title) map_chart.ax = f3_ax3
 f3_ax4 = figs.add_subplot(gs[1, 2]) f3_ax4.set_title(race_chart.title) race_chart.ax = f3_ax4
 timestr = cases_df.index.max.strftime("%d/%m/%Y") figs.suptitle(f"NSW COVID-19 Confirmed Cases up to {timestr}")
 pandas_alive.animate_multiple_plots( 'examples/nsw-covid.gif', plots, figs, enable_progress_bar=True )

義大利COVID可視化

def italy: import geopandas import pandas as pd import pandas_alive import contextily import matplotlib.pyplot as plt
 region_gdf = geopandas.read_file('data/geo-data/italy-with-regions') region_gdf.NOME_REG = region_gdf.NOME_REG.str.lower.str.title region_gdf = region_gdf.replace('Trentino-Alto Adige/Sudtirol', 'Trentino-Alto Adige') region_gdf = region_gdf.replace("Valle D'Aosta/Vallée D'Aoste\r\nValle D'Aosta/Vallée D'Aoste", "Valle d'Aosta")
 italy_df = pd.read_csv('data/Regional Data - Sheet1.csv', index_col=0, header=1, parse_dates=[0])
 italy_df = italy_df[italy_df['Region'] != 'NA']
 cases_df = italy_df.iloc[:, :3] cases_df['Date'] = cases_df.index pivoted = cases_df.pivot(values='New positives', index='Date', columns='Region') pivoted.columns = pivoted.columns.astype(str) pivoted = pivoted.rename(columns={'nan': 'Unknown Region'})
 cases_gdf = pivoted.T cases_gdf['geometry'] = cases_gdf.index.map(region_gdf.set_index('NOME_REG')['geometry'].to_dict)
 cases_gdf = cases_gdf[cases_gdf['geometry'].notna]
 cases_gdf = geopandas.GeoDataFrame(cases_gdf, crs=region_gdf.crs, geometry=cases_gdf.geometry)
 gdf = cases_gdf
 result = gdf.iloc[:, :22] result['geometry'] = gdf.iloc[:, -1:]['geometry'] gdf = result
 map_chart = gdf.plot_animated(basemap_format={'source': contextily.providers.Stamen.Terrain}, cmap='viridis')
 cases_df = pivoted cases_df = cases_df.iloc[:22, :]
 from datetime import datetime
 bar_chart = cases_df.sum(axis=1).plot_animated( kind='line', label_events={ 'Schools Close': datetime.strptime("4/03/2020", "%d/%m/%Y"), 'Phase I Lockdown': datetime.strptime("11/03/2020", "%d/%m/%Y"), # '1M Global Cases': datetime.strptime("02/04/2020", "%d/%m/%Y"), # '100k Global Deaths': datetime.strptime("10/04/2020", "%d/%m/%Y"), # 'Manufacturing Reopens': datetime.strptime("26/04/2020", "%d/%m/%Y"), # 'Phase II Lockdown': datetime.strptime("4/05/2020", "%d/%m/%Y"), }, fill_under_line_color="blue", add_legend=False )
 map_chart.ax.set_title('Cases by Location')
 line_chart = ( cases_df.sum(axis=1) .cumsum .fillna(0) .plot_animated(kind="line", period_label=False, title="Cumulative Total Cases", add_legend=False) )
 def current_total(values): total = values.sum s = f'Total : {int(total)}' return {'x': .85, 'y': .1, 's': s, 'ha': 'right', 'size': 11}
 race_chart = cases_df.cumsum.plot_animated( n_visible=5, title="Cases by Region", period_label=False, period_summary_func=current_total )
 import time
 timestr = time.strftime("%d/%m/%Y")
 plots = [bar_chart, race_chart, map_chart, line_chart]
 # Otherwise titles overlap and adjust_subplot does nothing from matplotlib import rcParams from matplotlib.animation import FuncAnimation
 rcParams.update({"figure.autolayout": False}) # make sure figures are `Figure` instances figs = plt.Figure gs = figs.add_gridspec(2, 3, hspace=0.5) f3_ax1 = figs.add_subplot(gs[0, :]) f3_ax1.set_title(bar_chart.title) bar_chart.ax = f3_ax1
 f3_ax2 = figs.add_subplot(gs[1, 0]) f3_ax2.set_title(race_chart.title) race_chart.ax = f3_ax2
 f3_ax3 = figs.add_subplot(gs[1, 1]) f3_ax3.set_title(map_chart.title) map_chart.ax = f3_ax3
 f3_ax4 = figs.add_subplot(gs[1, 2]) f3_ax4.set_title(line_chart.title) line_chart.ax = f3_ax4
 axes = [f3_ax1, f3_ax2, f3_ax3, f3_ax4] timestr = cases_df.index.max.strftime("%d/%m/%Y") figs.suptitle(f"Italy COVID-19 Confirmed Cases up to {timestr}")
 pandas_alive.animate_multiple_plots( 'examples/italy-covid.gif', plots, figs, enable_progress_bar=True )

單擺運動

def simple: import pandas as pd import matplotlib.pyplot as plt import pandas_alive import numpy as np
 # Physical constants g = 9.81 L = .4 mu = 0.2
 THETA_0 = np.pi * 70 / 180 # init angle = 70degs THETA_DOT_0 = 0 # no init angVel DELTA_T = 0.01 # time stepping T = 1.5 # time period
 # Definition of ODE (ordinary differential equation) def get_theta_double_dot(theta, theta_dot): return -mu * theta_dot - (g / L) * np.sin(theta)
 # Solution to the differential equation def pendulum(t): # initialise changing values theta = THETA_0 theta_dot = THETA_DOT_0 delta_t = DELTA_T ang =  ang_vel =  ang_acc =  times =  for time in np.arange(0, t, delta_t): theta_double_dot = get_theta_double_dot( theta, theta_dot ) theta += theta_dot * delta_t theta_dot += theta_double_dot * delta_t times.append(time) ang.append(theta) ang_vel.append(theta_dot) ang_acc.append(theta_double_dot) data = np.array([ang, ang_vel, ang_acc]) return pd.DataFrame(data=data.T, index=np.array(times), columns=["angle", "ang_vel", "ang_acc"])
 # units used for ref: ["angle [rad]", "ang_vel [rad/s]", "ang_acc [rad/s^2]"] df = pendulum(T) df.index.names = ["Time (s)"] print(df)
 # generate dataFrame for animated bubble plot df2 = pd.DataFrame(index=df.index) df2["dx (m)"] = L * np.sin(df["angle"]) df2["dy (m)"] = -L * np.cos(df["angle"]) df2["ang_vel"] = abs(df["ang_vel"]) df2["size"] = df2["ang_vel"] * 100 # scale angular vels to get nice size on bubble plot print(df2)
 # static pandas plots # # print(plt.style.available) # NOTE: 2 lines below required in Jupyter to switch styles correctly plt.rcParams.update(plt.rcParamsDefault) plt.style.use("ggplot") # set plot style
 fig, (ax1a, ax2b) = plt.subplots(1, 2, figsize=(8, 4), dpi=100) # 1 row, 2 subplots # fig.subplots_adjust(wspace=0.1) # space subplots in row fig.set_tight_layout(True) fontsize = "small"
 df.plot(ax=ax1a).legend(fontsize=fontsize) ax1a.set_title("Outputs vs Time", fontsize="medium") ax1a.set_xlabel('Time [s]', fontsize=fontsize) ax1a.set_ylabel('Amplitudes', fontsize=fontsize);
 df.plot(ax=ax2b, x="angle", y=["ang_vel", "ang_acc"]).legend(fontsize=fontsize) ax2b.set_title("Outputs vs Angle | Phase-Space", fontsize="medium") ax2b.set_xlabel('Angle [rad]', fontsize=fontsize) ax2b.set_ylabel('Angular Velocity / Acc', fontsize=fontsize)
 # sample scatter plot with colorbar fig, ax = plt.subplots sc = ax.scatter(df2["dx (m)"], df2["dy (m)"], s=df2["size"] * .1, c=df2["ang_vel"], cmap="jet") cbar = fig.colorbar(sc) cbar.set_label(label="ang_vel [rad/s]", fontsize="small") # sc.set_clim(350, 400) ax.tick_params(labelrotation=0, labelsize="medium") ax_scale = 1. ax.set_xlim(-L * ax_scale, L * ax_scale) ax.set_ylim(-L * ax_scale - 0.1, L * ax_scale - 0.1) # make axes square: a circle shows as a circle ax.set_aspect(1 / ax.get_data_ratio) ax.arrow(0, 0, df2["dx (m)"].iloc[-1], df2["dy (m)"].iloc[-1], color="dimgray", ls=":", lw=2.5, width=.0, head_width=0, zorder=-1 ) ax.text(0, 0.15, s="size and colour of pendulum bob\nbased on pd column\nfor angular velocity", ha='center', va='center')
 # plt.show
 dpi = 100 ax_scale = 1.1 figsize = (3, 3) fontsize = "small"
 # set up figure to pass onto `pandas_alive` # NOTE: by using Figure (capital F) instead of figure `FuncAnimation` seems to run twice as fast! # fig1, ax1 = plt.subplots fig1 = plt.Figure ax1 = fig1.add_subplot fig1.set_size_inches(figsize) ax1.set_title("Simple pendulum animation, L=" + str(L) + "m", fontsize="medium") ax1.set_xlabel("Time (s)", color='dimgray', fontsize=fontsize) ax1.set_ylabel("Amplitudes", color='dimgray', fontsize=fontsize) ax1.tick_params(labelsize=fontsize)
 # pandas_alive line_chart = df.plot_animated(filename="pend-line.gif", kind='line', period_label={'x': 0.05, 'y': 0.9}, steps_per_period=1, interpolate_period=False, period_length=50, period_fmt='Time:{x:10.2f}', enable_progress_bar=True, fixed_max=True, dpi=100, fig=fig1 ) plt.close
 # Video('examples/pend-line.mp4', html_attributes="controls muted autoplay")
 # set up and generate animated scatter plot #
 # set up figure to pass onto `pandas_alive` # NOTE: by using Figure (capital F) instead of figure `FuncAnimation` seems to run twice as fast! fig1sc = plt.Figure ax1sc = fig1sc.add_subplot fig1sc.set_size_inches(figsize) ax1sc.set_title("Simple pendulum animation, L=" + str(L) + "m", fontsize="medium") ax1sc.set_xlabel("Time (s)", color='dimgray', fontsize=fontsize) ax1sc.set_ylabel("Amplitudes", color='dimgray', fontsize=fontsize) ax1sc.tick_params(labelsize=fontsize)
 # pandas_alive scatter_chart = df.plot_animated(filename="pend-scatter.gif", kind='scatter', period_label={'x': 0.05, 'y': 0.9}, steps_per_period=1, interpolate_period=False, period_length=50, period_fmt='Time:{x:10.2f}', enable_progress_bar=True, fixed_max=True, dpi=100, fig=fig1sc, size="ang_vel" ) plt.close
 print("Points size follows one of the pd columns: ang_vel") # Video('./pend-scatter.gif', html_attributes="controls muted autoplay")
 # set up and generate animated bar race chart # # set up figure to pass onto `pandas_alive` # NOTE: by using Figure (capital F) instead of figure `FuncAnimation` seems to run twice as fast! fig2 = plt.Figure ax2 = fig2.add_subplot fig2.set_size_inches(figsize) ax2.set_title("Simple pendulum animation, L=" + str(L) + "m", fontsize="medium") ax2.set_xlabel("Amplitudes", color='dimgray', fontsize=fontsize) ax2.set_ylabel("", color='dimgray', fontsize="x-small") ax2.tick_params(labelsize=fontsize)
 # pandas_alive race_chart = df.plot_animated(filename="pend-race.gif", kind='race', period_label={'x': 0.05, 'y': 0.9}, steps_per_period=1, interpolate_period=False, period_length=50, period_fmt='Time:{x:10.2f}', enable_progress_bar=True, fixed_max=False, dpi=100, fig=fig2 ) plt.close
 # set up and generate bubble animated plot #
 # set up figure to pass onto `pandas_alive` # NOTE: by using Figure (capital F) instead of figure `FuncAnimation` seems to run twice as fast! fig3 = plt.Figure ax3 = fig3.add_subplot fig3.set_size_inches(figsize) ax3.set_title("Simple pendulum animation, L=" + str(L) + "m", fontsize="medium") ax3.set_xlabel("Hor Displacement (m)", color='dimgray', fontsize=fontsize) ax3.set_ylabel("Ver Displacement (m)", color='dimgray', fontsize=fontsize) # limits & ratio below get the graph square ax3.set_xlim(-L * ax_scale, L * ax_scale) ax3.set_ylim(-L * ax_scale - 0.1, L * ax_scale - 0.1) ratio = 1. # this is visual ratio of axes ax3.set_aspect(ratio / ax3.get_data_ratio)
 ax3.arrow(0, 0, df2["dx (m)"].iloc[-1], df2["dy (m)"].iloc[-1], color="dimgray", ls=":", lw=1, width=.0, head_width=0, zorder=-1)
 # pandas_alive bubble_chart = df2.plot_animated( kind="bubble", filename="pend-bubble.gif", x_data_label="dx (m)", y_data_label="dy (m)", size_data_label="size", color_data_label="ang_vel", cmap="jet", period_label={'x': 0.05, 'y': 0.9}, vmin=None, vmax=None, steps_per_period=1, interpolate_period=False, period_length=50, period_fmt='Time:{x:10.2f}s', enable_progress_bar=True, fixed_max=False, dpi=dpi, fig=fig3 ) plt.close
 print("Bubble size & colour animates with pd data column for ang_vel.")
 # Combined plots # fontsize = "x-small" # Otherwise titles overlap and subplots_adjust does nothing from matplotlib import rcParams rcParams.update({"figure.autolayout": False})
 figs = plt.Figure(figsize=(9, 4), dpi=100) figs.subplots_adjust(wspace=0.1) gs = figs.add_gridspec(2, 2)
 ax1 = figs.add_subplot(gs[0, 0]) ax1.set_xlabel("Time(s)", color='dimgray', fontsize=fontsize) ax1.set_ylabel("Amplitudes", color='dimgray', fontsize=fontsize) ax1.tick_params(labelsize=fontsize)
 ax2 = figs.add_subplot(gs[1, 0]) ax2.set_xlabel("Amplitudes", color='dimgray', fontsize=fontsize) ax2.set_ylabel("", color='dimgray', fontsize=fontsize) ax2.tick_params(labelsize=fontsize)
 ax3 = figs.add_subplot(gs[:, 1]) ax3.set_xlabel("Hor Displacement (m)", color='dimgray', fontsize=fontsize) ax3.set_ylabel("Ver Displacement (m)", color='dimgray', fontsize=fontsize) ax3.tick_params(labelsize=fontsize) # limits & ratio below get the graph square ax3.set_xlim(-L * ax_scale, L * ax_scale) ax3.set_ylim(-L * ax_scale - 0.1, L * ax_scale - 0.1) ratio = 1. # this is visual ratio of axes ax3.set_aspect(ratio / ax3.get_data_ratio)
 line_chart.ax = ax1 race_chart.ax = ax2 bubble_chart.ax = ax3
 plots = [line_chart, race_chart, bubble_chart] # pandas_alive combined using custom figure pandas_alive.animate_multiple_plots( filename='pend-combined.gif', plots=plots, custom_fig=figs, dpi=100, enable_progress_bar=True, adjust_subplot_left=0.2, adjust_subplot_right=None, title="Simple pendulum animations, L=" + str(L) + "m", title_fontsize="medium" ) plt.close

最後如果你想完成中文動態圖表的製作，加入中文顯示代碼即可。

# 中文顯示plt.rcParams['font.sans-serif'] = ['SimHei'] # Windowsplt.rcParams['font.sans-serif'] = ['Hiragino Sans GB'] # Macplt.rcParams['axes.unicode_minus'] = False
# 讀取數據df_result = pd.read_csv('data/yuhuanshui.csv', index_col=0, parse_dates=[0])# 生成圖表animated_line_chart = df_result.diff.fillna(0).plot_animated(kind='line', period_label=False, add_legend=False)animated_bar_chart = df_result.plot_animated(n_visible=10)pandas_alive.animate_multiple_plots('examples/yuhuanshui.gif', [animated_bar_chart, animated_line_chart], enable_progress_bar=True, title='我是餘歡水演職人員熱度排行')

還是使用演員的百度指數數據。