怎么使用python的可视化工具Pandas_Alive

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数据动画可视化制作在日常工作中是非常实用的一项技能。目前支持动画可视化的库主要以Matplotlib-Animation为主，其特点为：配置复杂，保存动图容易报错。

安装方法

pip install pandas_alive  # 或者conda install pandas_alive -c conda-forge

使用说明

pandas_alive 的设计灵感来自 bar_chart_race，为方便快速进行动画可视化制作，在数据的格式上需要满足如下条件：

• 每行表示单个时间段

• 每列包含特定类别的值

• 索引包含时间组件（可选）

支持示例展示

水平条形图

import pandas_alivecovid_df = pandas_alive.load_dataset()covid_df.plot_animated(filename='examples/perpendicular-example.gif',perpendicular_bar_func='mean')

垂直条形图比赛

import pandas_alivecovid_df = pandas_alive.load_dataset()covid_df.plot_animated(filename='examples/example-barv-chart.gif',orientation='v')

条形图

与时间与 x 轴一起显示的折线图类似

import pandas_alivecovid_df = pandas_alive.load_dataset()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)

饼图

import pandas_alivecovid_df = pandas_alive.load_dataset()covid_df.plot_animated(filename='examples/example-pie-chart.gif',kind="pie",rotatelabels=True,period_label={'x':0,'y':0})

多边形地理空间图

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})

多个图表

pandas_alive 支持单个可视化中的多个动画图表。

示例1

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)

示例2

import pandas_alivecovid_df = pandas_alive.load_dataset()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)

示例3

import pandas_aliveimport pandas as pddata_raw = pd.read_csv(    "https://raw.githubusercontent.com/owid/owid-datasets/master/datasets/Long%20run%20life%20expectancy%20-%20Gapminder%2C%20UN/Long%20run%20life%20expectancy%20-%20Gapminder%2C%20UN.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].valuesdata = 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")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)

示例4

import geopandasimport pandas as pdimport pandas_aliveimport contextilyimport matplotlib.pyplot as pltimport urllib.request, jsonwith urllib.request.urlopen(    "https://data.nsw.gov.au/data/api/3/action/package_show?id=aefcde60-3b0c-4bc0-9af1-6fe652944ec2") as url:    data = json.loads(url.read().decode())# Extract url to csv componentcovid_nsw_data_url = data["result"]["resources"][0]["url"]# Read csv from data API urlnsw_covid = pd.read_csv(covid_nsw_data_url)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"]).size()grouped_df = pd.DataFrame(grouped_df).unstack()grouped_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.indexgdf.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 Mercatormap_chart = gdf.plot_animated(basemap_format={'source':contextily.providers.Stamen.Terrain},cmap='cool')cases_df.to_csv('data/nsw-covid-cases-by-postcode.csv')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])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.sum()    s = 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.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(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)

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