2022-08-20 14:44:25 +00:00
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```{python}
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#| echo: false
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import matplotlib.pyplot as plt
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def prepare_plot_colors():
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# "Tableau 20" colors as RGB.
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colors = [(31, 119, 180), (174, 199, 232), (255, 127, 14), (255, 187, 120),
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(44, 160, 44), (152, 223, 138), (214, 39, 40), (255, 152, 150),
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(148, 103, 189), (197, 176, 213), (140, 86, 75), (196, 156, 148),
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(227, 119, 194), (247, 182, 210), (127, 127, 127), (199, 199, 199),
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(188, 189, 34), (219, 219, 141), (23, 190, 207), (158, 218, 229)]
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# Scale RGB values to the [0, 1] range for matplotlib
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for i in range(len(colors)):
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r, g, b = colors[i]
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colors[i] = (r / 255., g / 255., b / 255.)
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return colors
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colors=prepare_plot_colors()
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```
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```{python}
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#| echo: false
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import openpyxl
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import pandas as pd
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df = pd.read_csv('data/cleaned/UNU-WIDER-WIID/WIID-30JUN2022_cty-select.csv', index_col="id", parse_dates=True)
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```
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```{python}
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#| echo: false
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df = df.loc[df['year'] > 2000]
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ben = df.loc[df['c3'] == "BEN"]
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dji = df.loc[df['c3'] == "DJI"]
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uga = df.loc[df['c3'] == "UGA"]
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vnm = df.loc[df['c3'] == "VNM"]
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```
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```{python}
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# Set up the data extraction and figure drawing functions
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import plotly.express as px
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import plotly.io as pio
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def gini_plot(country_df):
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if svg_render:
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pio.renderers.default = "png"
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2022-09-02 08:02:45 +00:00
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fig = px.line(country_df, x="year", y="gini", markers=True, labels={"year": "Year", "gini": "Gini coefficient"}, template="seaborn", range_y=[0,100])
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2022-08-20 14:44:25 +00:00
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fig.update_traces(marker_size=10)
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fig.show()
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def plot_consumption_gini_percapita(country_df):
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gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
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gni_cnsmpt_percapita = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
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gini_plot(gni_cnsmpt_percapita)
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def plot_consumption_gini_percapita_ruralurban(country_df):
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gni_cnsmpt = country_df[country_df['resource'].str.contains("Consumption")]
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gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['scale'].str.contains("Per capita")]
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gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['source'].str.contains("World Bank")]
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gni_cnsmpt = gni_cnsmpt[gni_cnsmpt['areacovr'].str.contains("All")]
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gini_plot(gni_cnsmpt)
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```
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