LAB: Fake report

Motivation

'Labore modi labore voluptatem non ipsum dolor. Ut neque tempora voluptatem porro numquam dolor adipisci. Consectetur velit eius est. Modi ipsum ut aliquam tempora quaerat consectetur eius. Neque labore porro porro labore. Sed consectetur velit velit sed sed porro. Etincidunt quiquia dolorem est dolor magnam. Magnam tempora modi etincidunt consectetur etincidunt.'

Reproducible data gathering

Data and metadata should be retrieved programmatically.

Here is an example from the OECD API documentation. Other providers have their own API for example Eurostat.

url = "https://sdmx.oecd.org/public/rest/data/OECD.SDD.STES,DSD_STES@DF_CLI/.M.LI...AA...H?startPeriod=2023-02&dimensionAtObservation=AllDimensions&format=csvfilewithlabels"

# df =  pd.read_csv(url)

# df.info

Note that in your report, data gathering chunks should go to the appendix.

200 countries, 50 years, 20 lines of code

You should build your report and your presentation around plots and tables.

/home/boucheron/Documents/IFEBY310/.nlp-venv/lib/python3.12/site-packages/gapminder/data.py:1: UserWarning:

pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.

Display tables with style (no kitsch please).

	Continent	level_1	Country	Year	Life Expectancy	Population (Millions Inhab.)	GDP per Capita
0	Africa	1262	Reunion	1962	57.7	0.4	$3,174
1	Africa	1348	Sierra Leone	1972	35.4	2.9	$1,354
2	Americas	1115	Nicaragua	2007	72.9	5.7	$2,749
3	Americas	1610	United States	1962	70.2	186.5	$16,173
4	Asia	702	India	1982	56.6	708.0	$856
5	Asia	1213	Philippines	1957	51.3	26.1	$1,548
6	Europe	1148	Norway	1992	77.3	4.3	$33,966
7	Europe	150	Bosnia and Herzegovina	1982	70.7	4.2	$4,127
8	Oceania	68	Australia	1992	77.6	17.5	$23,425
9	Oceania	1096	New Zealand	1972	71.9	2.9	$16,046

Visualization matters

Wizardry

Appendix

The appendix should contain the code that helped you create the document

# Load the data
#
import pandas as pd
import plotly
import plotly.express as px

pd.options.plotting.backend = "plotly"
from gapminder import gapminder




#
# Sample 2 rows per continent and format for display
#
df = (
  gapminder
    .groupby("continent", group_keys=True)
    .apply(lambda g: 
           g.sample(n=2, random_state=42), include_groups=False)
    .reset_index()
    .assign(pop=lambda x: x["pop"] / 1e6)
)

df = df.rename(columns={
    "country": "Country",
    "continent": "Continent",
    "year": "Year",
    "lifeExp": "Life Expectancy",
    "pop": "Population (Millions Inhab.)",
    "gdpPercap": "GDP per Capita",
})

df.style.format(
  {
    "Population (Millions Inhab.)": "{:.1f}", 
    "Life Expectancy": "{:.1f}", 
    "GDP per Capita": "${:,.0f}"
    }
)



#
#
# A neater color scale 
#
neat_color_scale = {
      "Africa" : "#01d4e5",
      "Americas" : "#7dea01" ,
      "Asia" : "#fc5173",
      "Europe" : "#fde803",
      "Oceania" : "#536227"
}




pd.options.plotting.backend = 'plotly'



#
# Use plotly backend for dataframes
#


a_year = gapminder["year"].sample(1, random_state=42).iloc[0]
df = gapminder[gapminder["year"] == a_year].copy()

p = px.scatter(
    data_frame=df,
    x="gdpPercap",
    y="lifeExp",
    size="pop",
    color="continent",
    hover_data={
      "country": True, 
      "pop": ":,", 
      "gdpPercap": ":,.0f", 
      "lifeExp": ":.1f"},
    log_x=True,
    size_max=15,
    color_discrete_map=neat_color_scale,
    opacity=0.5,
    title=f"Gapminder {a_year}",
    labels={
        "gdpPercap": "Yearly Income per Capita",
        "lifeExp": "Life Expectancy",
    },
)

p.update_layout(
    xaxis_title="Yearly Income per Capita",
    yaxis_title="Life Expectancy",
    annotations=[
        dict(
            text="From sick and poor (bottom left) to healthy and rich (top right)",
            xref="paper",
            yref="paper",
            x=0.5,
            y=-0.12,
            showarrow=False,
            font=dict(size=10),
        )
    ],
)
p



#
# Animated scatter on the whole gapminder dataframe, year as frame
#
p_animated = px.scatter(
    data_frame=gapminder,
    x="gdpPercap",
    y="lifeExp",
    size="pop",
    color="continent",
    animation_frame="year",
    hover_data={"country": True, "pop": ":,", "gdpPercap": ":,.0f", "lifeExp": ":.1f"},
    log_x=True,
    size_max=15,
    color_discrete_map=neat_color_scale,
    opacity=0.5,
    title="Gapminder",
    labels={
        "gdpPercap": "Yearly Income per Capita",
        "lifeExp": "Life Expectancy",
    },
)
p_animated.update_layout(
    height=500,
    width=750,
    xaxis_title="Yearly Income per Capita",
    yaxis_title="Life Expectancy",
    showlegend=True,
)
p_animated