#!/usr/bin/env python
# coding: utf-8

# <div>
#     <img align="left" src="https://github.com/lux-org/lux-resources/blob/master/icon/table.png?raw=True" width="30">
#     <h1 style="padding-left: 40px;">Visualizations of dataframes beyond simple tables</h1>
# </div>

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import lux
import pandas as pd


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df = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/hpi_cleaned.csv?raw=True")


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df


# <div>
#     <img align="left" src="https://github.com/lux-org/lux-resources/blob/master/icon/steering-wheel.png?raw=True" width="30">
#     <h1 style="padding-left: 40px;">Steering analysis with intent</h1>
# </div>

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df.intent = ["Inequality","AvrgLifeExpectancy"]


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df


# <div>
#     <img align="left" src="https://github.com/lux-org/lux-resources/blob/master/icon/fix.png?raw=True" width="30">
#     <h1 style="padding-left: 40px;">Data Manipulation + Vis without changing a line of your Pandas code</h1>
# </div>

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covid = pd.read_csv("https://github.com/lux-org/lux-datasets/blob/master/data/covid_cleaned.csv?raw=True")


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df = covid.merge(df,left_on=["Entity","Code"],right_on=["Country","cca3"])


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df


# # 🤔 Some interesting findings

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df.intent = ["Inequality","AvrgLifeExpectancy"]
df


# <img src="https://github.com/lux-org/lux-resources/blob/master/doc_img/hpi-covid-outlier.png?raw=True" width="250"></img>

# <div>
#     <img align="left" style="margin-top: -10px;" src="https://github.com/lux-org/lux-resources/blob/master/icon/present.png?raw=True" width="50">
#     <h1 style="padding-left: 55px;">Exporting visualization insight to edit and share</h1>
# </div>

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df


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df.exported


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print(df.exported[0].to_code("altair"))


# We can copy-and-paste the output Altair code into a separate cell. Then let's tweak the plotting code a bit before sharing this insight with our colleagues.

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import altair as alt

c = "#e7298a"
chart = alt.Chart(df,title="Check out this cool insight!").mark_circle().encode(
    x=alt.X('Inequality',scale=alt.Scale(domain=(0.04, 0.51)),type='quantitative', axis=alt.Axis(title='Inequality')),
    y=alt.Y('AvrgLifeExpectancy',scale=alt.Scale(domain=(48.9, 83.6)),type='quantitative', axis=alt.Axis(title='AvrgLifeExpectancy'))
)
highlight = df[(df["Inequality"]>0.35)&(df["stringency_level"]=="High")]

hchart = alt.Chart(highlight).mark_point(color=c,size=50,shape="cross").encode(
    x=alt.X('Inequality',scale=alt.Scale(domain=(0.04, 0.51)),type='quantitative', axis=alt.Axis(title='Inequality')),
    y=alt.Y('AvrgLifeExpectancy',scale=alt.Scale(domain=(48.9, 83.6)),type='quantitative', axis=alt.Axis(title='AvrgLifeExpectancy')),
)

text = alt.Chart(highlight).mark_text(color=c,dx=-35,dy=0,fontWeight=800).encode(
    x=alt.X('Inequality',scale=alt.Scale(domain=(0.04, 0.51)),type='quantitative', axis=alt.Axis(title='Inequality')),
    y=alt.Y('AvrgLifeExpectancy',scale=alt.Scale(domain=(48.9, 83.6)),type='quantitative', axis=alt.Axis(title='AvrgLifeExpectancy')),
    text=alt.Text('Country')
)

chart = chart.encode(color=alt.Color('stringency_level',type='nominal'))
chart = chart.properties(width=160,height=150)

(chart + hchart + text).configure_title(color=c)