Geopandas and Pandas Alive

import geopandas
import pandas as pd
import pandas_alive
import contextily
import matplotlib.pyplot as plt

import urllib.request, json

with 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 component
covid_nsw_data_url = data["result"]["resources"][0]["url"]

# Read csv from data API url
nsw_covid = pd.read_csv(covid_nsw_data_url)

# Source for postcode dataset https://www.matthewproctor.com/australian_postcodes
postcode_dataset = pd.read_csv("data/postcode-data.csv")

display(nsw_covid.head())
display(postcode_dataset.head())

# Prepare data from NSW health dataset
nsw_covid = nsw_covid.fillna(9999)
nsw_covid["postcode"] = nsw_covid["postcode"].astype(int)

# Convert the date time string to a datetime object
nsw_covid['notification_date'] = pd.to_datetime(nsw_covid['notification_date'],dayfirst=True)

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

cases_df.to_csv('data/nsw-covid-cases-by-postcode.csv')

from datetime import datetime

bar_chart = cases_df.sum(axis=1).plot_animated(
    filename='area-chart.gif',
    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",
    enable_progress_bar=True
)

# Clean data in postcode dataset prior to matching

grouped_df = grouped_df.T
postcode_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 chart
grouped_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 geopackage
gdf = gdf.drop(['Longitude','Latitude'],axis=1)
gdf.columns = gdf.columns.astype(str)
gdf['postcode'] = gdf.index
gdf.to_file("data/nsw-covid19-cases-by-postcode.gpkg", layer='nsw-postcode-covid', driver="GPKG")

# Prepare GeoDataFrame for plotting
gdf.index = gdf.postcode
gdf = gdf.drop('postcode',axis=1)
gdf = gdf.to_crs("EPSG:3857") #Web Mercator

map_chart = gdf.plot_animated(filename='map-chart.gif',title="Cases by Location",basemap_format={'source':contextily.providers.Stamen.Terrain},cmap='cool')

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")
)


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 time

timestr = time.strftime("%d/%m/%Y")

plots = [bar_chart, line_chart, map_chart, race_chart]

from matplotlib import rcParams

rcParams.update({"figure.autolayout": False})

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(line_chart.title)
line_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(race_chart.title)
race_chart.ax = f3_ax4

timestr = cases_df.index.max().strftime("%d/%m/%Y")
figs.suptitle(f"NSW COVID-19 Confirmed Cases up to {timestr}")

pandas_alive.animate_multiple_plots(
    'nsw-covid.gif',
    plots,
    figs
)

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})