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大家好
对于动静图表，置信大家都或多或少的接触过一些，如果是代码程度比拟不错的，能够抉择 Matplotlib，当然也能够应用 pyecharts 的相干性能，不过这些工具都专一于图表的制作，也就是对于图表的数据，你是须要自行转换的。而明天介绍的这个可视化图库，完满的联合了 Pandas 数据格式，又辅以 Matplotlib 的弱小性能，使得咱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，尽管以后在 GitHub 上的 star 数量不是很高，然而置信凭借其弱小的性能，锋芒毕露也是迟早的事件
我的项目装置：
与个别的 Python 库一样，间接应用 pip 装置即可，这里有一点须要留神，就是因为是通过 Matplotlib 来制作动图，所以须要手动装置下 Matplotlib 的依赖工具 imagemagick，这是一个图片解决工具，感兴趣的同学能够自行查看下
我的项目性能：
这款可视化图库，能够反对的图表类型是十分多的，包含动静条形图、动静曲线图、气泡图、饼状图以及地图等等，这些图表差不多能够满足咱们日常的应用了
制图简介
这里咱们就来简略看一下该如何制作动静图表吧，首先是动静条形图，根本 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，是不是超级不便呢
上面咱们就来看看其余图表的制作方法吧！
01 动静条形图
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 动静柱状图
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 动静曲线图
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 动静面积图
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)
05 动静散点图
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 动静饼图
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 动静气泡图
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 动静天文图表
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})
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})
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 城市人口变动
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/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.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)
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