爱了！Python 动态图表太太太秀了
本文转自：法纳斯特，作者：小F
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。
GitHub地址：
https://github.com/JackMcKew/pandas_alive
使用文档：https://jackmckew.github.io/pandas_alive/
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_alive
import 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！
01 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')
02 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)
03 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})
04 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
)
05 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')
06 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})
07 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)
08 地理空间点图表
import geopandas
import pandas_alive
import contextily
gdf = geopandas.read_file('data/nsw-covid19-cases-by-postcode.gpkg')
gdf.index = gdf.postcode
gdf = 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})
09 多边形地理图表
import geopandasimport pandas_aliveimport contextilygdf = 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})
10 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)
11 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)
12 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
)
13 新南威尔斯州COVID可视化
def nsw:import geopandasimport pandas as pdimport pandas_aliveimport contextilyimport matplotlib.pyplot as pltimport jsonwith open('data/package_show.json', 'r', encoding='utf8')as fp:data = json.load(fp)# Extract url to csv componentcovid_nsw_data_url = data['result']['resources'][0]['url']print(covid_nsw_data_url)# Read csv from data API urlnsw_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']).sizegrouped_df = pd.DataFrame(grouped_df).unstackgrouped_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.Tpostcode_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 chartgrouped_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 geopackagegdf = 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 plottinggdf.index = gdf.postcodegdf = 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.sums = 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` instancesfigs = plt.Figuregs = 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)
14 意大利COVID可视化
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
)
15 单摆运动
def simple:import pandas as pdimport matplotlib.pyplot as pltimport pandas_aliveimport numpy as np# Physical constantsg = 9.81L = .4mu = 0.2THETA_0 = np.pi * 70 / 180 # init angle = 70degsTHETA_DOT_0 = 0 # no init angVelDELTA_T = 0.01 # time steppingT = 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 valuestheta = THETA_0theta_dot = THETA_DOT_0delta_t = DELTA_Tang = 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_ttheta_dot += theta_double_dot * delta_ttimes.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 plotdf2 = 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 plotprint(df2)# static pandas plots## print(plt.style.available)# NOTE: 2 lines below required in Jupyter to switch styles correctlyplt.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 rowfig.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 colorbarfig, ax = plt.subplotssc = 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 circleax.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.showdpi = 100ax_scale = 1.1figsize = (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.subplotsfig1 = plt.Figureax1 = fig1.add_subplotfig1.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_aliveline_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.Figureax1sc = fig1sc.add_subplotfig1sc.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_alivescatter_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.closeprint('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.Figureax2 = fig2.add_subplotfig2.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_aliverace_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.Figureax3 = fig3.add_subplotfig3.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 squareax3.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 axesax3.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_alivebubble_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.closeprint('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 rcParamsrcParams.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 squareax3.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 axesax3.set_aspect(ratio / ax3.get_data_ratio)line_chart.ax = ax1race_chart.ax = ax2bubble_chart.ax = ax3plots = [line_chart, race_chart, bubble_chart]# pandas_alive combined using custom figurepandas_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'] # Windows
plt.rcParams['font.sans-serif'] = ['Hiragino Sans GB'] # Mac
plt.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。