Statistical analysis on 2019 Global GDP using Choropleth maps.

Imports

import pandas as pd
import chart_studio.plotly as py
import plotly.graph_objs as go 
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

init_notebook_mode(connected=True) 

Raw data

raw_df = pd.read_csv('dataset-gdp/data.csv')

# info()
raw_df.size

17160

raw_df.head(3)

	Country Name	Country Code	Indicator Name	Indicator Code	1960	1961	1962	1963	1964	1965	...	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
0	Aruba	ABW	GDP (current US$)	NY.GDP.MKTP.CD	NaN	NaN	NaN	NaN	NaN	NaN	...	2.549721e+09	2.534637e+09	2.701676e+09	2.765363e+09	2.919553e+09	2.965922e+09	3.056425e+09	NaN	NaN	NaN
1	Afghanistan	AFG	GDP (current US$)	NY.GDP.MKTP.CD	5.377778e+08	5.488889e+08	5.466667e+08	7.511112e+08	8.000000e+08	1.006667e+09	...	1.780429e+10	2.000160e+10	2.056107e+10	2.048489e+10	1.990711e+10	1.801775e+10	1.886995e+10	1.835388e+10	1.929110e+10	NaN
2	Angola	AGO	GDP (current US$)	NY.GDP.MKTP.CD	NaN	NaN	NaN	NaN	NaN	NaN	...	1.117897e+11	1.280529e+11	1.367099e+11	1.457122e+11	1.161936e+11	1.011239e+11	1.221238e+11	1.013532e+11	8.881570e+10	NaN

3 rows × 65 columns

Only 2019

df = [raw_df[raw_df.columns[0]], 
          raw_df[raw_df.columns[1]],
          raw_df[raw_df.columns[63]]]

headers = ["country", "code", "2019"]

df = pd.concat(df, 
                     axis = 1, 
                     keys = headers)

df.head(3)

	country	code	2019
0	Aruba	ABW	NaN
1	Afghanistan	AFG	1.929110e+10
2	Angola	AGO	8.881570e+10

Choropleth maps

data = dict(
    type = 'choropleth',
    locations = df['code'],
    z = df['2019'],
    colorscale= 'Reds',
    colorbar = {
        'title': 'USD'
    }
)

choromap = go.Figure(
    data = [data],
    layout = {
        'title':'2019 Global GDP in USD',
    }
)

iplot(choromap)

Ref:

https://data.worldbank.org/