World Bank Indicators - GDP per capita from 1960 to 2018¶

GDP per capita is gross domestic product divided by midyear total population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. GDP per capita is a good measurement of a country's standard of living, by describing how much citizens benefit from their country's economy. Small, rich countries and more developed industrial countries tend to have the highest per capita GDP.

Data are in current U.S. dollars.

Import required libraries¶

In [1]:

import pandas as pd
from datetime import datetime
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import matplotlib.animation as animation
import matplotlib
import randomcolor
from IPython.display import HTML

rand_color = randomcolor.RandomColor()

In [2]:

plt.style.use('dark_background')

def draw_barchart_race(fig, ax, df, timedelta='D', timeunit='D', interval=250, title='Title', xlabel='Label'):
    items = df['item'].unique().tolist()
    colors = dict(zip(items, rand_color.generate(luminosity='luminosity', count=len(items))))

    df.dropna(inplace=True)
    df = df.set_index('time')
    df = df.groupby('item').resample(timedelta).mean()
    df['value'] = df['value'].interpolate()

    def draw(time):
        ax.clear()
        
        time_plot_df = df[df.index.isin([time], level=1)].sort_values(by='value', ascending=True).tail(10)
        time_plot_df = time_plot_df.reset_index()
        label = np.datetime_as_string(time, unit=timeunit)

        draw_barchart_date(ax, time_plot_df, title=title, label=label, xlabel=xlabel, colors=colors)

    frames = df.index.to_frame()['time'].unique()
    animator = animation.FuncAnimation(fig, draw, frames=frames, interval=interval)
    return animator


def draw_barchart_date(ax, df, title='Title', label='Label', xlabel='Label', colors={}):
    labelsize = 12
    textcolor = 'white'
    
    ax.text(0, 1.06, xlabel, transform=ax.transAxes,
            size=labelsize, color=textcolor)
    ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:,.0f}'))
    ax.xaxis.set_ticks_position('top')
    ax.tick_params(axis='x', colors=textcolor, labelsize=labelsize)
    ax.set_yticks([])
    ax.set_axisbelow(True)
    ax.text(0, 1.15, title, transform=ax.transAxes,
            size=24, weight=600, ha='left', va='top')
    ax.text(1, 0, 'Data Show', transform=ax.transAxes, color=textcolor, ha='right', size=16)
    plt.box(False)
    ax.barh(df['item'], df['value'], color=[
           colors[item] if item in colors.keys() else '#8383fc' for item in df['item']], alpha=0.8)
    
    dx = df['value'].max() / 100
    for i, (value, item) in enumerate(zip(df['value'], df['item'])):
        ax.text(
            value - dx, i,
            item,
            size=16,
            weight=600,
            ha='right',
            va='center'
        )
        ax.text(
            value + dx,
            i,
            f'{value: ,.0f}',
            size=14,
            ha='left',
            va='center'
        )

    ax.text(1, 0.06, label, transform=ax.transAxes,
            color='white', size=52, ha='right', weight=800)
    
    return ax

Data pre-processing¶

In [3]:

df = pd.read_csv('Data/World_Development_Indicators.csv',
                 header=0,
                 names=['year', 'timecode', 'country', 'countrycode', 'gdp'],
                 usecols=['year', 'country', 'gdp'],
                 dtype={
                     'gdp': np.float
                 },
                 na_values='..')

df.head()

<ipython-input-3-e238b6a8b3f7>:6: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  'gdp': np.float

Out[3]:

	year	country	gdp
0	1960	Afghanistan	59.773194
1	1960	Albania	NaN
2	1960	Algeria	246.308763
3	1960	American Samoa	NaN
4	1960	Andorra	NaN

Cleaning¶

In [4]:

df.drop(df.tail(5).index, inplace=True)

df.head()

Out[4]:

	year	country	gdp
0	1960	Afghanistan	59.773194
1	1960	Albania	NaN
2	1960	Algeria	246.308763
3	1960	American Samoa	NaN
4	1960	Andorra	NaN

Barchart¶

In [5]:

fig, ax = plt.subplots(figsize=(20, 10))
ax.clear()
year = '2018'
date = np.datetime64(year)

chart_df = df.copy()
chart_df['year'] = pd.to_datetime(chart_df['year'])
chart_df.rename(columns={'year': 'time', 'country': 'item', 'gdp': 'value'}, inplace=True)

fig, ax = plt.subplots(figsize=(20, 10))

interval = 500
animator = draw_barchart_race(fig, ax,
                              chart_df,
                              timedelta='Y',
                              timeunit='Y',
                              interval=interval,
                              title='GDP per capita from 1960 to 2018',
                              xlabel='GDP/capita (current U.S. dollars)')

HTML(animator.to_jshtml())

Out[5]:

Sources¶

World Bank national accounts data, and OECD National Accounts data files under CC BY-4.0 License

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