```{python}
#| echo: false
import matplotlib.pyplot as plt
def prepare_plot_colors():
    # "Tableau 20" colors as RGB.
    colors = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
                 (44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
                 (148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
                 (227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
                 (188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]

    # Scale RGB values to the [0, 1] range for matplotlib
    for i in range(len(colors)):
        r, g, b = colors[i]
        colors[i] = (r / 255., g / 255., b / 255.)
    return colors

colors=prepare_plot_colors()
```

```{python}
#| echo: false
import openpyxl
import pandas as pd

df = pd.read_csv('data/cleaned/UNU-WIDER-WIID/WIID-30JUN2022_cty-select.csv', index_col="id", parse_dates=True)
```

```{python}
#| echo: false
df = df.loc[df['year'] > 2000]
ben = df.loc[df['c3'] == "BEN"]
dji = df.loc[df['c3'] == "DJI"]
uga = df.loc[df['c3'] == "UGA"]
vnm = df.loc[df['c3'] == "VNM"]
```

```{python}
# Set up the data extraction and figure drawing functions
import plotly.express as px
import plotly.io as pio

def gini_plot(country_df):
    if svg_render:
        pio.renderers.default = "png"

    fig = px.line(country_df, x="year", y="gini", markers=True, labels={"year": "Year", "gini": "Gini coefficient"}, template="seaborn", range_y=[30,60])
    fig.update_traces(marker_size=10)
    fig.show()

def plot_consumption_gini_percapita(country_df):
    gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
    gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
    gini_plot(gni_cnsmpt_percapita)

def plot_consumption_gini_percapita_ruralurban(country_df):
    gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
    gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
    gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['source'].str.contains("World Bank")]
    gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['areacovr'].str.contains("All")]
    gini_plot(gni_cnsmpt)
```

