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
项目地址：
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_alive import pandas as pd covid_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_alive import pandas as pd covid_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_alive import pandas as pd covid_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_alive import pandas as pd covid_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_alive import pandas as pd covid_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 动态散点图
import pandas as pd import pandas_alive 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"]}, ) 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_alive import pandas as pd covid_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_alive 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 动态地理图表
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) 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 geopandas import pandas_alive import contextily gdf = geopandas.read_file('data/italy-covid-region.gpkg') gdf.index = gdf.region gdf = 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_alive import pandas as pd covid_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_alive urban_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 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 map_chart = gdf.plot_animated(basemap_format={'source':contextily.providers.Stamen.Terrain},cmap='viridis') cases_df = pivoted 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 )
怎么样，是不是心动了，那就快行动吧！
