Lecture 21 In-Class Exercise Solution - Computing in Context (SIPA)

Fix rendering:

import plotly.io as pio

pio.renderers.default = "notebook_connected+plotly_mimetype"

Download data¶

More about the URL below:
- https://datahelpdesk.worldbank.org/knowledgebase/articles/898581-api-basic-call-structures
- https://datahelpdesk.worldbank.org/knowledgebase/articles/898599-indicator-api-queries
This code does the download and unzipping; not expecting your code to do that.

import urllib.request
from pathlib import Path

data_dir = Path("data")
# Create data/ directory if it doesn't exist
data_dir.mkdir(exist_ok=True)

# Download the file
url = "https://api.worldbank.org/v2/country/all/indicator/EG.ELC.ACCS.ZS?source=2&date=2002:2022&downloadformat=csv"
zip_path = data_dir / "electricity.zip"
urllib.request.urlretrieve(url, zip_path)
print(f"Downloaded {zip_path}.")

Downloaded data/electricity.zip.

from zipfile import ZipFile

# Extract the zip file
electricity_dir = data_dir / "electricity"
with ZipFile(zip_path, "r") as zip_ref:
    zip_ref.extractall(electricity_dir)

print(f"Extracted to {electricity_dir}.")

Extracted to data/electricity.

Load data¶

import pandas as pd

electricity_file = electricity_dir / "API_EG.ELC.ACCS.ZS_DS2_EN_csv_v2_225978.csv"
electricity = pd.read_csv(electricity_file, header=2)
electricity

<frozen importlib._bootstrap>:491: RuntimeWarning:

The global interpreter lock (GIL) has been enabled to load module 'pandas._libs.pandas_parser', which has not declared that it can run safely without the GIL. To override this behavior and keep the GIL disabled (at your own risk), run with PYTHON_GIL=0 or -Xgil=0.

Reshape¶

electricity_by_country_by_year = (
    electricity.drop(columns=["Indicator Name", "Indicator Code"])
    .melt(
        id_vars=["Country Name", "Country Code"],
        var_name="Year",
        value_name="Access to electricity (% of population)",
    )
    .dropna()
)

electricity_by_country_by_year

Plot¶

import plotly.express as px

fig = px.line(
    electricity_by_country_by_year,
    x="Year",
    y="Access to electricity (% of population)",
    color="Country Name",
    title="Access to electricity over time",
)
fig.show()

Plotly showing that many values on an axis (the Year in this case) indicates that it’s a string.

Fix year dtype¶

electricity_by_country_by_year.dtypes

Country Name                                object
Country Code                                object
Year                                        object
Access to electricity (% of population)    float64
dtype: object

electricity_by_country_by_year["Year"] = electricity_by_country_by_year["Year"].astype(int)

Filter¶

avg_electricity = electricity_by_country_by_year.groupby("Country Name")["Access to electricity (% of population)"].mean()
avg_electricity

Country Name
Afghanistan                    61.876190
Africa Eastern and Southern    32.881725
Africa Western and Central     45.452160
Albania                        99.757143
Algeria                        99.142857
                                 ...    
West Bank and Gaza             99.742857
World                          85.250561
Yemen, Rep.                    63.404762
Zambia                         30.523810
Zimbabwe                       40.633333
Name: Access to electricity (% of population), Length: 263, dtype: float64

bottom_cutoff = avg_electricity.quantile(0.05)
bottom_cutoff

np.float64(21.837142857142858)

bottom_countries = avg_electricity[avg_electricity < bottom_cutoff]
bottom_countries

Country Name
Burkina Faso                14.676190
Burundi                      6.547619
Central African Republic    10.990476
Chad                         7.566667
Congo, Dem. Rep.            14.438095
Guinea-Bissau               16.771429
Liberia                     14.768750
Madagascar                  21.823810
Malawi                       9.547619
Mozambique                  20.528571
Niger                       14.133333
Papua New Guinea            16.957143
Sierra Leone                17.604762
South Sudan                  4.475000
Name: Access to electricity (% of population), dtype: float64

bottom_countries_electricity = electricity_by_country_by_year[electricity_by_country_by_year["Country Name"].isin(bottom_countries.index)]

fig = px.line(
    bottom_countries_electricity,
    x="Year",
    y="Access to electricity (% of population)",
    color="Country Name",
    title="Access to electricity over time by country, 5th percentile",
)
fig.show()

World Happiness Report data¶

Requires openpyxl package. How to install.

happiness = pd.read_excel("https://files.worldhappiness.report/WHR25_Data_Figure_2.1v3.xlsx")
happiness

fig = px.line(happiness, x="Year", y="Life evaluation (3-year average)", color="Country name", title="World Happiness")
fig = fig.show()

happiness.dtypes

Year                                            int64
Rank                                            int64
Country name                                   object
Life evaluation (3-year average)              float64
Lower whisker                                 float64
Upper whisker                                 float64
Explained by: Log GDP per capita              float64
Explained by: Social support                  float64
Explained by: Healthy life expectancy         float64
Explained by: Freedom to make life choices    float64
Explained by: Generosity                      float64
Explained by: Perceptions of corruption       float64
Dystopia + residual                           float64
dtype: object

Single country¶

country_name = "Madagascar"

madagascar_electricity = electricity_by_country_by_year[electricity_by_country_by_year["Country Name"] == country_name]
madagascar_electricity

madagascar_happiness = happiness[happiness["Country name"] == country_name]
madagascar_happiness

Two Y Axes¶

Based on Plotly example.

import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Create figure with secondary y-axis
fig = make_subplots(specs=[[{"secondary_y": True}]])

# Add traces
fig.add_trace(
    go.Scatter(
        x=madagascar_electricity["Year"],
        y=madagascar_electricity["Access to electricity (% of population)"],
        name="Access to electricity",
    ),
    secondary_y=False,
)

fig.add_trace(
    go.Scatter(
        x=madagascar_happiness["Year"],
        y=madagascar_happiness["Life evaluation (3-year average)"],
        name="Life evaluation (3-year average)",
    ),
    secondary_y=True,
)

# Add figure title
fig.update_layout(title_text=f"Access to electricity vs. Happiness, {country_name}")

# Set x-axis title
fig.update_xaxes(title_text="Year")

# Set y-axes titles
fig.update_yaxes(title_text="% of population", secondary_y=False)
fig.update_yaxes(title_text="Happiness score", secondary_y=True)

fig.show()