from IPython.display import HTML

HTML('''<script>
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function code_toggle() {
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 $('div.input').show();
 } else {
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 }
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<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')

import pandas as pd
import holoviews as hv

hv.extension('bokeh')

# 100% width
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))

import plotly.express as px

Hans Rosling Gapminder

TED talk

• https://www.ted.com/talks/hans_rosling_the_best_stats_you_ve_ever_seen
• we can recreate that with their data

1. let's get the data

df = px.data.gapminder()

2. let's look at a snippet(top 10 records) of data

df.head(10)

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4
5	Afghanistan	Asia	1977	38.438	14880372	786.113360	AFG	4
6	Afghanistan	Asia	1982	39.854	12881816	978.011439	AFG	4
7	Afghanistan	Asia	1987	40.822	13867957	852.395945	AFG	4
8	Afghanistan	Asia	1992	41.674	16317921	649.341395	AFG	4
9	Afghanistan	Asia	1997	41.763	22227415	635.341351	AFG	4

3. let's visualize this data

display('We have a total of # countries : {0}'.format(len(df.country.unique())))

'We have a total of # countries : 142'

countries = df.country.unique()

# we can narrow down to these countries
#countries = ['United States', 'United Kingdom', 'Japan', 'Rwanda','Guatemala', 'China', 'India', ]

df_filtered = df[df['country'].isin(countries)]

fig = px.scatter(df_filtered, x = "gdpPercap", y = "lifeExp", animation_frame = "year", animation_group = "country",
          size = "pop", color = "country", log_x = True, size_max = 45,
          range_x = [100, 100000], range_y = [25, 90], height=800
          #text = 'country'
            
)
fig.show()