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pip install pandas_alive # 或者

conda install pandas_alive -c conda-forge

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import pandas_alive

covid_df = pandas_alive.load_dataset()

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

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import pandas_alive

covid_df = pandas_alive.load_dataset()

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

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import pandas_alive

covid_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

)

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import pandas_alive

covid_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})

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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})

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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)

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import pandas_alive

covid_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)

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import pandas_alive

import pandas as pd

data_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].values

data = 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

)

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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)

postcode_dataset = pd.read_csv("data/postcode-data.csv")

# Prepare data from NSW health dataset

nsw_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 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(basemap_format={'source':contextily.providers.Stamen.Terrain},cmap='cool')

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

from datetime import datetime

bar_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 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})

# 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(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(

'examples/nsw-covid.gif',

plots,

figs,

enable_progress_bar=True

)