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Income Distribution in Malaysia

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Visualization of Income Distribution in Malaysia

Inspired by a tweet from Thevesh, a data scientist in Central Bank of Malaysia. I tried to replicate the ridge plot and other data visualization using Seaborn. The first part of this visualization is a collection of kernel density estimate (KDE) plot for each state and federal regions in Malaysia. KDE represents the data using a continuous probability density curve, hence the y-axis represents the density instead of population.

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This dataset was imported from OpenDOSM, an open-sourced website by Department of Statistics Malaysia. It is based on Household Income Surveys (HIS) and Household Income & Expenditure Surveys (HIES) carried out from 1970 to 2022, the latest of which is HIES 2022.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

import warnings 
warnings.filterwarnings("ignore")

df = pd.read_csv('hiesba_percentiles.csv')
df.head()

	year	state	percentile	mean	median	minimum	maximum
0	2019	Malaysia	1st	957	1001	NaN	1141.0
1	2019	Malaysia	2nd	1284	1244	1141.0	1459.0
2	2019	Malaysia	3rd	1556	1559	1460.0	1641.0
3	2019	Malaysia	4th	1718	1718	1642.0	1789.0
4	2019	Malaysia	5th	1858	1856	1790.0	1928.0

df.describe()

	year	mean	median	minimum	maximum
count	3400.000000	3400.000000	3400.000000	3366.000000	3366.000000
mean	2020.500000	7765.838824	7701.645294	7553.986631	7546.509804
std	1.500221	7189.765820	6752.670857	6118.880789	6092.639737
min	2019.000000	740.000000	750.000000	866.000000	866.000000
25%	2019.000000	3607.000000	3608.750000	3627.750000	3623.500000
50%	2020.500000	5819.000000	5818.500000	5830.000000	5824.000000
75%	2022.000000	9515.250000	9520.250000	9467.500000	9465.000000
max	2022.000000	120000.000000	85605.000000	68147.000000	67299.000000

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3400 entries, 0 to 3399
Data columns (total 7 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   year        3400 non-null   int64  
 1   state       3400 non-null   object 
 2   percentile  3400 non-null   object 
 3   mean        3400 non-null   int64  
 4   median      3400 non-null   int64  
 5   minimum     3366 non-null   float64
 6   maximum     3366 non-null   float64
dtypes: float64(2), int64(3), object(2)
memory usage: 186.1+ KB

# generate a new df for states only without Malaysia
statesdf = df.copy()
statesdf.drop(statesdf[statesdf['state'] == 'Malaysia'].index, inplace=True)

# list of unique states without Malaysia
states = statesdf['state'].unique()
print(states)

['Johor' 'Kedah' 'Kelantan' 'Melaka' 'Negeri Sembilan' 'Pahang'
 'Pulau Pinang' 'Perak' 'Perlis' 'Selangor' 'Terengganu' 'Sabah' 'Sarawak'
 'W.P. Kuala Lumpur' 'W.P. Labuan' 'W.P. Putrajaya']

# generate income mean for each state
state_mean_serie = statesdf.groupby('state')['mean'].mean()
statesdf['state_mean'] = statesdf['state'].map(state_mean_serie)

# sort the states by the income mean
statesdf = statesdf.sort_values(by="state_mean", ascending=False)
print(statesdf['state'].unique())

['W.P. Kuala Lumpur' 'W.P. Putrajaya' 'Selangor' 'W.P. Labuan' 'Johor'
 'Pulau Pinang' 'Melaka' 'Terengganu' 'Negeri Sembilan' 'Sarawak' 'Sabah'
 'Pahang' 'Perak' 'Perlis' 'Kedah' 'Kelantan']

# generate a color palette
pal = sns.color_palette(palette='coolwarm', n_colors=len(states))

# generate a FacetGrid
grid = sns.FacetGrid(statesdf, row='state', hue='state', aspect=8, height=1, palette=pal, xlim=(-1000, 22000), ylim=(0, 0.00025))

# add kdeplots for each state
grid.map(sns.kdeplot, 'minimum', bw_adjust=0.4, clip_on=True, fill=True, alpha=1, linewidth=1, cut=0)

# add blackline as contour for each kdeplot
grid.map(sns.kdeplot, 'minimum', bw_adjust=0.4, clip_on=True, color='black', lw=1, cut=0)

# add horizontal lines for each kdeplot
grid.refline(y=0, linewidth=1, linestyle='-', color=None, clip_on=False)

# add vertical reference lines
for x_value in [0, 5000, 10000, 15000, 20000]:
    grid.refline(x=x_value, linewidth=1, linestyle='--', color='gray', clip_on=False)

def label(state, color, label):
    ax = plt.gca()
    ax.text(-0.2, 0.2, label, fontweight=8, color='black',
            ha="left", va="center", transform=ax.transAxes)

grid.map(label, 'state')

# get the subplots to overlap
grid.fig.subplots_adjust(hspace=-0.4)

# remove axes titles, yticks and spines
grid.set_titles('')
grid.set(yticks=[], ylabel="")
grid.despine(bottom=True, left=True)

plt.xlabel('Monthly Household Income (RM)', fontweight=12, fontsize=10)
grid.fig.suptitle('Income Distribution by State',
               ha='center',
               fontsize=12,
               fontweight=12)

plt.tight_layout()
plt.show()

# create a df with only KL and Kelantan
klkeldf = statesdf[statesdf['state'].str.contains("Kuala Lumpur|Kelantan") == True]

# generate a color palette
pal2 = sns.color_palette(palette='coolwarm', n_colors=2)

# generate a FacetGrid
g = sns.FacetGrid(klkeldf, row='state', hue='state', aspect=8, height=1.5, palette=pal2, xlim=(-100, 21000), ylim=(0, 0.00025))

# add kdeplots for each state
g.map(sns.kdeplot, 'mean', bw_adjust=0.4, clip_on=True, fill=True, alpha=1, linewidth=1, cut=0)

# add blackline as contour for each kdeplot
g.map(sns.kdeplot, 'mean', bw_adjust=0.4, clip_on=True, color='black', lw=1, cut=0)

# add horizontal lines for each kdeplot
g.refline(y=0, linewidth=1, linestyle='-', color=None, clip_on=False)

# add vertical reference lines
for x_value in [0, 5000, 10000, 15000, 20000]:
    g.refline(x=x_value, linewidth=1, linestyle='--', color='gray', clip_on=False)

def label2(state, color, label):
    ax2 = plt.gca()
    ax2.text(-0.13, 0.2, label, fontweight=8, color='black',
            ha="left", va="center", transform=ax2.transAxes)

g.map(label2, 'state')

# get the subplots to overlap
g.fig.subplots_adjust(hspace=0.5)

# remove axes titles, yticks and spines
g.set_titles('')
g.set(yticks=[], ylabel="")
g.despine(bottom=True, left=True)

plt.xlabel('Monthly Household Income (RM)', fontweight=12, fontsize=12)
g.fig.suptitle('Income Distribution: Kuala Lumpur vs Kelantan',
               ha='center',
               fontsize=12,
               fontweight=12)

plt.tight_layout()
plt.show()

# generate income median for each state
state_median_serie = statesdf.groupby('state')['median'].median()
statesdf['state_median'] = statesdf['state'].map(state_median_serie)
statesdf = statesdf.sort_values(by="state_median", ascending=False)

# generate barplot for state medians
plt.figure(figsize=(8, 12))
g2 = sns.barplot(data=statesdf, x="state_median", y="state", hue="state", orient='h', dodge=False, palette='coolwarm', width=0.8)

for bars in g2.containers:
    g2.bar_label(bars, padding= 3, fontsize= 10)
    
g2.figure.suptitle('Median Monthly Household Income',
               ha='center',
               fontsize=10,
               fontweight=10)

g2.set(xticks=[], xlabel="")
sns.despine()

plt.tight_layout()
plt.show()

Copyright © Firdaus Che Kob