Vietnam in World Development Indicators

Investigating Vietnam Development Indicators
Visualization
Python
Author

Nguyen Truong Thinh

Published

June 5, 2023

import wbgapi as wb
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
wb.series.info('NY.GDP.PCAP.CD')
id value
NY.GDP.PCAP.CD GDP per capita (current US$)
1 elements 
wb.region.info()
code name
AFE Africa Eastern and Southern
AFR Africa
AFW Africa Western and Central
ARB Arab World
CAA Sub-Saharan Africa (IFC classification)
CEA East Asia and the Pacific (IFC classification)
CEB Central Europe and the Baltics
CEU Europe and Central Asia (IFC classification)
CLA Latin America and the Caribbean (IFC classification)
CME Middle East and North Africa (IFC classification)
CSA South Asia (IFC classification)
CSS Caribbean small states
EAP East Asia & Pacific (excluding high income)
EAR Early-demographic dividend
EAS East Asia & Pacific
ECA Europe & Central Asia (excluding high income)
ECS Europe & Central Asia
EMU Euro area
EUU European Union
FCS Fragile and conflict affected situations
HPC Heavily indebted poor countries (HIPC)
LAC Latin America & Caribbean (excluding high income)
LCN Latin America & Caribbean
LDC Least developed countries: UN classification
LTE Late-demographic dividend
MDE Middle East (developing only)
MEA Middle East & North Africa
MNA Middle East & North Africa (excluding high income)
NAC North America
NAF North Africa
NRS Non-resource rich Sub-Saharan Africa countries
OED OECD members
OSS Other small states
PRE Pre-demographic dividend
PSS Pacific island small states
PST Post-demographic dividend
RRS Resource rich Sub-Saharan Africa countries
SAS South Asia
SSA Sub-Saharan Africa (excluding high income)
SSF Sub-Saharan Africa
SST Small states
SXZ Sub-Saharan Africa excluding South Africa
WLD World
XZN Sub-Saharan Africa excluding South Africa and Nigeria
44 elements 
df = wb.data.DataFrame(
    ['NY.GDP.PCAP.CD'], 
    wb.region.members('WLD'),
    skipBlanks=True,
    labels=True).reset_index()
    
df = df.melt(id_vars=['economy', 'Country'], var_name='Year', value_name='GDP Per Capita')
df['Year'] = df['Year'].str.replace('YR', '').astype(int)


df.head()
economy Country Year GDP Per Capita
0 CUB Cuba 1960 NaN
1 GNB Guinea-Bissau 1960 NaN
2 NRU Nauru 1960 NaN
3 VCT St. Vincent and the Grenadines 1960 155.294494
4 EGY Egypt, Arab Rep. 1960 NaN

Turn into log scale for more apparent map

hist = px.histogram(np.log10(df[df['Year']==2022]['GDP Per Capita']))
hist.update_traces(marker=dict(color='rgb(111, 198, 107)'))

hist.show()
fig = go.Figure()

fig = go.Figure(data=go.Choropleth(
    locations=df[df['Year']==2022]['economy'],
    z=np.log10(df[df['Year']==2022]['GDP Per Capita']),
    customdata = df[df['Year']==2022]['GDP Per Capita'],
    text=df['Country'],
    hovertemplate="Country: %{text} <br>%{customdata:$,.2f}<extra></extra>",
    colorscale="haline",
    colorbar=dict(title="GDP Per Capita (US$)",
                  tickvals = [2, 3, 4, 5],
                  ticktext = ['100', '1,000', '10,000', '100,000'],
                  xanchor="left"),
    autocolorscale=False,
    marker_line_color='darkgray'
))

fig.update_geos(fitbounds="locations", visible=False)
fig.update_layout(
  title_text="World GDP Per Capita in 2022",
  geo=dict(
    resolution=50,
    scope='world')
)


fig.show(config={
  'scrollZoom': False
})
df_vn = wb.data.DataFrame(
    wb.topic.members(3),
    'VNM',
    mrv=50,
    skipBlanks=True,
    labels=True).reset_index()
    
df_vn = df_vn.rename(columns={"series":"Code", "Series":"Indicators"})
df_vn = df_vn.melt(id_vars=['Code', 'Indicators'], var_name='Year', value_name='Value')
df_vn['Year'] = df_vn['Year'].str.replace('YR', '').astype(int)

df_vn.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11400 entries, 0 to 11399
Data columns (total 4 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   Code        11400 non-null  object 
 1   Indicators  11400 non-null  object 
 2   Year        11400 non-null  int32  
 3   Value       6963 non-null   float64
dtypes: float64(1), int32(1), object(2)
memory usage: 311.8+ KB
default_code = df_vn['Code'].unique()[12]

select_ind=list([dict(label=ind, 
                       method="update",
                       args=[{"y": [df_vn[df_vn['Code'] == code]['Value']],
                              "x": [df_vn[df_vn['Code'] == code]['Year']]}])
                  for code, ind in zip(df_vn['Code'].unique(), df_vn['Indicators'].unique())
                ])

select_chart=[dict(label="Bar",
                   method="restyle",
                   args=['type', 'bar']),
              dict(label="Line",
                   method="restyle",
                   args=[{"type": "scatter", "mode": "lines+markers"}])
              ]
# Create a bar trace
bar_trace = go.Bar(
    x=df_vn[df_vn['Code'] == default_code]['Year'],
    y=df_vn[df_vn['Code'] == default_code]['Value']
)

# Create the layout
layout = go.Layout(
    xaxis=dict(title='Year'),
    yaxis=dict(title='Value')
)

# Create the figure
fig = go.Figure(data=[bar_trace], layout=layout)
fig.update_layout(
                  hovermode="x unified",
                  xaxis=dict(
                      rangeslider=dict(
                          visible=True
                      ),
                      type="linear"
                  ),
                  updatemenus=[
                      dict(
                          buttons=select_ind,
                          direction="down",
                          showactive=False,
                          active=12,
                          x=0.06,
                          xanchor="left",
                          y=1.2,
                          yanchor="top",
                          font = dict(size=11)
                      ),
                      dict(
                          buttons=select_chart,
                          direction="down",
                          showactive=False,
                          active=0,
                          x=0,
                          xanchor="left",
                          y=1.2,
                          yanchor="top",
                          font = dict(size=11)
                      )],
                  template='plotly_white'
)

fig.update_traces(marker=dict(color='rgb(111, 198, 107)'))

fig.show()