技巧｜Python 制作动态图表的正确方式 python数据可视化

大家好
【技巧｜Python 制作动态图表的正确方式】关于动态图表，相信大家都或多或少的接触过一些，如果是代码水平比较不错的，可以选择 Matplotlib，当然也可以使用 pyecharts 的相关功能，不过这些工具都专注于图表的制作，也就是对于图表的数据，你是需要自行转换的。而今天介绍的这个可视化图库，完美的结合了 Pandas 数据格式，又辅以 Matplotlib 的强大功能，使得我们制作动图变得容易的多了。
图库简介
这款给力的可视化图库，就是 pandas_alive，虽然当前在 GitHub 上的 star 数量不是很高，但是相信凭借其强大的功能，崭露头角也是迟早的事情
项目安装：
与一般的 Python 库一样，直接使用 pip 安装即可，这里有一点需要注意，就是由于是通过 Matplotlib 来制作动图，所以需要手动安装下 Matplotlib 的依赖工具 imagemagick，这是一个图片处理工具，感兴趣的同学可以自行查看下
项目功能：
这款可视化图库，可以支持的图表类型是非常多的，包括动态条形图、动态曲线图、气泡图、饼状图以及地图等等，这些图表差不多可以满足我们日常的使用了
制图简介
这里我们就来简单看一下该如何制作动态图表吧，首先是动态条形图，基本4行代码搞定，有两行还是 import

import pandas_aliveimport pandas as pdcovid_df = pd.read_csv('covid19.csv', index_col=0, parse_dates=[0])covid_df.diff().fillna(0).plot_animated(filename='line_chart.gif',kind='line',period_label={'x':0.25,'y':0.9})

怎么样，是不是超级方便呢
下面我们就来看看其他图表的制作方法吧！
01 动态条形图

import pandas_aliveimport pandas as pdcovid_df = pd.read_csv('covid19.csv', index_col=0, parse_dates=[0])covid_df.plot_animated(filename='examples/perpendicular-example.gif',perpendicular_bar_func='mean')

02 动态柱状图

import pandas_aliveimport pandas as pdcovid_df = pd.read_csv('covid19.csv', index_col=0, parse_dates=[0])covid_df.plot_animated(filename='examples/example-barv-chart.gif',orientation='v')

03 动态曲线图

import pandas_aliveimport pandas as pdcovid_df = pd.read_csv('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

import pandas_aliveimport pandas as pdcovid_df = pd.read_csv('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)

文章图片

05 动态散点图

import pandas as pdimport pandas_alivemax_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"]},)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 动态饼图

import pandas_aliveimport pandas as pdcovid_df = pd.read_csv('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

import pandas_alivemulti_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 动态地理图表

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

文章图片

09 行政区域动图

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

文章图片

10 多动图组合

import pandas_aliveimport pandas as pdcovid_df = pd.read_csv('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 城市人口变化

import pandas_aliveurban_df = pandas_alive.load_dataset("urban_pop")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 意大利疫情

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/Vall??e D'Aoste\r\nValle D'Aosta/Vall??e 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.indexpivoted = 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.Tcases_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_gdfmap_chart = gdf.plot_animated(basemap_format={'source':contextily.providers.Stamen.Terrain},cmap='viridis')cases_df = pivotedfrom 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()` instancesfigs = 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)