几行代码完成动态图表绘制 | Python实战

作者 | 小F

来源 | 法纳斯特

头图 | CSDN下载自视觉中国

关于动态条形图，小F以前推荐过「Bar Chart Race」这个库。三行代码就能实现动态条形图的绘制。

有些同学在使用的时候，会出现一些错误。一个是加载文件报错，另一个是生成GIF的时候报错。

这是因为作者的示例是网络加载数据，会读取不到。通过读取本地文件，就不会出错。

GIF生成失败一般是需要安装imagemagick(图片处理工具)。

最近小F又发现一个可视化图库「Pandas_Alive」，不仅包含动态条形图，还可以绘制动态曲线图、气泡图、饼状图、地图等。

同样也是几行代码就能完成动态图表的绘制。

安装版本建议是0.2.3，matplotlib版本是3.2.1。

同时需自行安装tqdm(显示进度条)和descartes(绘制地图相关库)。

要不然会出现报错，估计是作者的requestment.txt没包含这两个库。

好了，成功安装后就可以引入这个第三方库，直接选择加载本地文件。

import pandas_aliveimport pandas as pdcovid_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)

生成了一个GIF图，具体如下。

刚开始学习这个库的时候，大家可以减少数据，这样生成GIF的时间就会快一些。

比如小F在接下来的实践中，基本都只选取了20天左右的数据。

对于其他图表，我们可以查看官方文档的API说明，得以了解。

下面我们就来看看其他动态图表的绘制方法吧！

动态条形图

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')

 动态柱状图

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)

动态曲线图

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})

 动态面积图

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)

动态散点图

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')

动态饼状图

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})

动态气泡图

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)

地理空间点图表

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})

多边形地理图表

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})

多个动态图表

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国家平均寿命

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    )

新南威尔斯州COVID可视化

def nsw():import geopandasimport pandas as pdimport pandas_aliveimport contextilyimport matplotlib.pyplot as pltimport 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 datasetnsw_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 matchinggrouped_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 Mercatorresult = gdf.iloc[:, :22]    result['geometry'] = gdf.iloc[:, -1:]['geometry']    gdf = resultmap_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 datetimebar_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 timetimestr = time.strftime("%d/%m/%Y")plots = [bar_chart, line_chart, map_chart, race_chart]
from matplotlib import rcParamsrcParams.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_ax1f3_ax2 = figs.add_subplot(gs[1, 0])    f3_ax2.set_title(line_chart.title)    line_chart.ax = f3_ax2f3_ax3 = figs.add_subplot(gs[1, 1])    f3_ax3.set_title(map_chart.title)    map_chart.ax = f3_ax3f3_ax4 = figs.add_subplot(gs[1, 2])    f3_ax4.set_title(race_chart.title)    race_chart.ax = f3_ax4timestr = 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    )

意大利COVID可视化

def italy():import geopandasimport pandas as pdimport pandas_aliveimport contextilyimport matplotlib.pyplot as pltregion_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_gdfresult = gdf.iloc[:, :22]    result['geometry'] = gdf.iloc[:, -1:]['geometry']    gdf = resultmap_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 datetimebar_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 timetimestr = time.strftime("%d/%m/%Y")plots = [bar_chart, race_chart, map_chart, line_chart]
# Otherwise titles overlap and adjust_subplot does nothingfrom matplotlib import rcParamsfrom matplotlib.animation import FuncAnimationrcParams.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_ax1f3_ax2 = figs.add_subplot(gs[1, 0])    f3_ax2.set_title(race_chart.title)    race_chart.ax = f3_ax2f3_ax3 = figs.add_subplot(gs[1, 1])    f3_ax3.set_title(map_chart.title)    map_chart.ax = f3_ax3f3_ax4 = figs.add_subplot(gs[1, 2])    f3_ax4.set_title(line_chart.title)    line_chart.ax = f3_ax4axes = [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    )

单摆运动

def simple():import pandas as pdimport matplotlib.pyplot as pltimport pandas_aliveimport numpy as np
# Physical constants    g = 9.81    L = .4    mu = 0.2THETA_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 equationdef 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 stylefig, (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 nothingfrom 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 = ax3plots = [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()

最后如果你想完成中文动态图表的制作，加入中文显示代码即可。

# 中文显示plt.rcParams['font.sans-serif'] = ['SimHei']  # Windowsplt.rcParams['font.sans-serif'] = ['Hiragino Sans GB'] # Macplt.rcParams['axes.unicode_minus'] = False
# 读取数据df_result = pd.read_csv('data/yuhuanshui.csv', index_col=0, parse_dates=[0])# 生成图表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='我是余欢水演职人员热度排行')

还是使用演员的百度指数数据。

GitHub地址：

https://github.com/JackMcKew/pandas_alive

使用文档：

https://jackmckew.github.io/pandas_alive/

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