International-Debt-Statistics¶
GitHub Repository: https://github.com/skhiearth/International-Debt-Statistics
Analysing international debt of various countries using data from International Monetary Fund using Visualisations.
The data used is provided by International Monetary Fund. It contains debt statistics for several countries across the globe as recorded from 1950 to 2018.
In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="ticks", color_codes=True)
import warnings
warnings.filterwarnings('ignore')
# Read the XLS
df = pd.read_csv('Dataset/imf-debt.csv', na_values = 'no data')
df.head(3)
Out[1]:
In [2]:
df.drop(df.index[0], inplace = True)
df.head(2)
Out[2]:
In [3]:
# Converting dataframe from wide to tall
df2 = df.reset_index()
cols = list(df2.columns[2:])
time_series = pd.melt(df2, id_vars='Central Government Debt (Percent of GDP)', value_vars=cols)
time_series.tail()
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In [4]:
# Rename columns
time_series.rename(columns = {'Central Government Debt (Percent of GDP)':'Debt'}, inplace = True)
# Dropping redunant rows
filtered_df = time_series[time_series['Debt'].notnull()]
filtered_df = filtered_df[:-1]
filter = ['1950', '1955', '1960', '1965', '1970', '1975', '1980', '1985', '1990',
'1995', '2000', '2005', '2010', '2015', '2018']
filtered_df = filtered_df[filtered_df['variable'].isin(filter)]
print(filtered_df.shape)
filtered_df.tail()
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In [5]:
# Selected Countries
countries = ["India", "United States", "Switzerland", "Germany", "United Kingdom", "France"]
select_countries = filtered_df[filtered_df['Debt'].isin(countries)]
# Rename columns
select_countries.rename(columns = {'Debt': 'Country'}, inplace = True)
# Convert to numeric
select_countries['variable'] = select_countries['variable'].astype(int)
select_countries['value'] = select_countries['value'].astype(float)
In [6]:
rel = sns.relplot(x = "variable", y = "value", data = select_countries,
hue = "Country", kind = "line", height = 6, aspect = 2.0)
rel.set(xlabel='Year', ylabel='Central Government Debt (Percent of GDP)')
rel.set(title = "Change in Central Government Debt of Selected Countries")
Out[6]:
In [7]:
sns.set_style("white")
facet = sns.FacetGrid(select_countries, col = "Country", col_wrap=3, height = 4)
facet.map(sns.regplot, "variable", "value").set(xlim=(1950,2018), ylim=(0,100))
# Adding supertitle
plt.subplots_adjust(top=0.87)
supertitle = "Regression models fitted to the change in Central Government Debt of Selected Countries"
facet.fig.suptitle(supertitle, size=16)
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In [8]:
# Choropleth Map
import geopandas as gpd
shapefile = 'dataset/countries_110m/ne_110m_admin_0_countries.shp'
# Read shapefile using Geopandas
gdf = gpd.read_file(shapefile)[['ADMIN', 'ADM0_A3', 'geometry']]
# Rename columns
gdf.columns = ['Country', 'country_code', 'geometry']
gdf = gdf.drop(gdf.index[159]) # Row corresponding to Antarctica
gdf.at[4,'Country']='United States'
gdf.at[18,'Country']='Russian Federation'
gdf.head(5)
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In [9]:
import ipywidgets as widgets
from IPython.display import display
import json
years = ['1950', '1955', '1960', '1965', '1970', '1975', '1980', '1985', '1990',
'1995', '2000', '2005', '2010', '2015', '2018']
year_to_show = ''
def custom_df_define(year):
year_to_show = year
custom_df = filtered_df[filtered_df['variable'] == year]
merged = gdf.merge(custom_df, left_on = 'Country', right_on = 'Debt', how = 'left')
merged.value.fillna('No data', inplace = True)
merged_json = json.loads(merged.to_json())
json_data = json.dumps(merged_json)
plot_geo()
dropdown_year = widgets.Dropdown(options = years)
def dropdown_year_eventhandler(change):
year_to_show = change.new
custom_df_define(change.new)
dropdown_year.observe(dropdown_year_eventhandler, names='value')
display(dropdown_year)
In [10]:
from bokeh.io import output_notebook, show, output_file
from bokeh.plotting import figure
from bokeh.models import GeoJSONDataSource, LinearColorMapper, ColorBar
from bokeh.palettes import brewer
def plot_geo():
# Input GeoJSON source that contains features for plotting.
geosource = GeoJSONDataSource(geojson = json_data)
# Dark blue is highest debt percentage.
palette = brewer['YlGnBu'][8]
palette = palette[::-1]
# Instantiate LinearColorMapper that linearly maps numbers in a range, into a sequence of colors
color_mapper = LinearColorMapper(palette = palette, low = 0, high = 85, nan_color = '#d9d9d9')
#Define custom tick labels for color bar.
tick_labels = {'0': '0%', '5': '5%', '10':'10%', '15':'15%', '20':'20%',
'25':'25%', '30':'30%','35':'35%', '40': '40%',
'50':'50%', '55':'55%','60':'60%', '65': '65%',
'70':'70%', '75':'75%','80':'80%', '85': '>85%',}
#Create color bar.
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8, width = 500,
height = 20, border_line_color=None,location = (0,0),
orientation = 'horizontal', major_label_overrides = tick_labels)
title_ = 'Central Government Debt (Percent of GDP), ' + year
p = figure(title = title_, plot_height = 530 ,
plot_width = 950, toolbar_location = None)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.axis.visible = False
# Add patch renderer to figure.
p.patches('xs','ys', source = geosource,fill_color = {'field' :'value', 'transform' : color_mapper},
line_color = 'black', line_width = 0.25, fill_alpha = 1)
#Specify figure layout.
p.add_layout(color_bar, 'below')
output_notebook()
show(p)
output_file("map.html", title="Choropleth GDP", mode='inline')