Hello everyone
About dynamic charts , I believe everyone has been in contact with some more or less , If the code level is relatively good , You can choose Matplotlib, You can also use it pyecharts Related functions , However, these tools focus on the production of charts , That is, the data of the chart , You need to switch yourself . And the visual Gallery introduced today , The perfect combination Pandas data format , Supplemented by Matplotlib The power of , It makes it much easier for us to make moving pictures .
Awesome visual Library , Namely pandas_alive, Although currently GitHub Upper star The number is not very high , But I believe that with its powerful function , It's only a matter of time before you emerge
Project installation ：
With general Python Like the library , Use it directly pip Can be installed , Here's one thing to note , Because it's through Matplotlib To make a motion map , So you need to install it manually Matplotlib We rely on tools imagemagick, This is a picture processing tool , Interested students can check it by themselves
Project functions ：
This visual Gallery , There are many chart types that can be supported , Include dynamic bar chart 、 Dynamic graph 、 Bubble chart 、 Pie charts and maps, etc , These charts are almost enough for our daily use
Here, let's take a brief look at how to make dynamic charts , The first is the dynamic bar chart , basic 4 Line code , Two lines or 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})What about? , Is it super convenient
Let's take a look at other chart making methods ！
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')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')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})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)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')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})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)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})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})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)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)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/VallÃe D'Aoste\r\nValle D'Aosta/VallÃe 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)
