/BIES-figure67

Primary LanguageJupyter Notebook

# Data source
source='https://data.imf.org/?sk=F8032E80-B36C-43B1-AC26-493C5B1CD33B'
print('Accessed 07/07/2021 from IMF website -> '+source)
Accessed 07/07/2021 from IMF website -> https://data.imf.org/?sk=F8032E80-B36C-43B1-AC26-493C5B1CD33B

# Reading the data from my github. Downloaded from
import pandas as pd

url='https://github.com/imedkrisna/BIES-figure67/raw/main/FD%20Index%20Database%20(Excel).xlsx'
df=pd.read_excel(url)
df.head()
ifs code country imf_region imf_income year FD FI FM FID FIA FIE FMD FMA FME
0 314 ABW Aruba Western Hemisphere EM 2018 0.295090 0.571631 0.007356 0.272385 0.695166 0.641013 0.019856 0.0 0.0
1 314 ABW Aruba Western Hemisphere EM 2017 0.297746 0.577792 0.006406 0.272385 0.709758 0.641990 0.017292 0.0 0.0
2 314 ABW Aruba Western Hemisphere EM 2016 0.302239 0.586555 0.006459 0.280556 0.724822 0.640035 0.017434 0.0 0.0
3 314 ABW Aruba Western Hemisphere EM 2015 0.297722 0.577775 0.006375 0.270970 0.713883 0.638080 0.017210 0.0 0.0
4 314 ABW Aruba Western Hemisphere EM 2014 0.298137 0.578489 0.006477 0.265957 0.716189 0.644303 0.017483 0.0 0.0 
# Data for figure 6
FD=df.query('code=="IDN" or code=="USA" or code=="CHN" or code=="SGP" or code=="MYS" or code=="THA" or code=="VNM"')
# Plotting figure 6
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(style="white")

# Plot the responses for different events and regions
sns.lineplot(x="year", y="FD",
             hue="country", style="country",
             data=FD)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.title('Figure 6. Financial Development Index for Seven Select Economies, 1980–2018')
plt.savefig('fig6.png')

png

# Creating dataset for figure 7
FDID=df.query('code=="IDN"')
a=FDID.melt(id_vars=['code','year'], value_vars=['FID'])
b=FDID.melt(id_vars=['code','year'], value_vars=['FIA'])
c=FDID.melt(id_vars=['code','year'], value_vars=['FIE'])
d=FDID.melt(id_vars=['code','year'], value_vars=['FMD'])
e=FDID.melt(id_vars=['code','year'], value_vars=['FMA'])
f=FDID.melt(id_vars=['code','year'], value_vars=['FME'])
FDII=pd.concat([a,b,c])
FDMM=pd.concat([d,e,f])
# creating figure 7 in a subplot
fig, axes = plt.subplots(1, 2, figsize=(10,5))
fig.suptitle('Figure 7. Indonesian Financial Institution Index (LHS) and Financial Market Index (RHS), 1980–2018')
sns.lineplot(ax=axes[0],x="year", y="value",
             hue="variable", style="variable",
             data=FDII,legend=False)
axes[0].set_title('Financial Institution')

# Charmander
sns.lineplot(ax=axes[1],x="year", y="value",
             hue="variable", style="variable",
             data=FDMM)
axes[1].set_title('Financial Market')
plt.legend(labels=['Depth','Access','Efficiency'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.savefig('fig7.png')

png

# figure 7 left
sns.lineplot(x="year", y="value",
             hue="variable", style="variable",
             data=FDII)
plt.legend(labels=['Depth','Access','Efficiency'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
<matplotlib.legend.Legend at 0x191349c6f10>

png

#figure 7 right
sns.lineplot(x="year", y="value",
             hue="variable", style="variable",
             data=FDMM)
plt.legend(labels=['Depth','Access','Efficiency'],bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
<matplotlib.legend.Legend at 0x19134a0a460>

png