Guided Project: Storytelling Data Visualization on Exchange Rate¶
This Guided Project would revolve around explanatory data visualization by adopting Gestalt principles utilizing Matplotlib built-in styles, in this case 'FiveThirtyEight' style. We will be using Euro daily exchange rates between 1999 and 2021. The dataset was gathered by Daria Chemkaeva and was taken from Kaggle The data source is the European Central Bank and it gets regular updates. For this case the data was downloaded in January 2021.
In [65]:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
exchange_rates = pd.read_csv('euro-daily-hist_1999_2020.csv')
exchange_rates.info()
exchange_rates.head()
Out[65]:
Data Cleaning¶
Main focus is on the exchange rate between the euro and the American dollar.
In [66]:
exchange_rates.rename(columns={'[US dollar ]':'US_dollar','[Indonesian rupiah ]':'ID_rupiah',
'Period\\Unit:': 'Time'},
inplace=True)
exchange_rates['Time'] = pd.to_datetime(exchange_rates[
'Time'])
exchange_rates.sort_values('Time',inplace=True)
exchange_rates.reset_index(drop=True, inplace=True)
In [67]:
euro_to_dollar = exchange_rates[['Time','US_dollar']]
euro_to_dollar['US_dollar'].value_counts()
Out[67]:
In [68]:
# Drop all the rows where the - character appears in the US_dollar column
euro_to_dollar=euro_to_dollar[euro_to_dollar['US_dollar'] != '-']
euro_to_dollar['US_dollar']= euro_to_dollar['US_dollar'].astype(float)
euro_to_dollar.info()
In [69]:
euro_to_dollar.head()
Out[69]:
In [70]:
# Visualizing the evolution of the euto-dollar exchange rate
In [71]:
plt.plot(euro_to_dollar['Time'], euro_to_dollar['US_dollar'])
plt.show()
Rolling Mean¶
- As we can see, the chart above shows a lot of wiggle (coming from daily average being condensed).
- To smoothen the line chart, we are going to plot a moving average (rolling mean) of Euro against US_dollar for 30 days rolling windows (or moving window).This is to smoothen the curve.
In [72]:
euro_to_dollar['rolling_mean'] = euro_to_dollar['US_dollar']. rolling(30). mean()
euro_to_dollar
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In [73]:
# Plotting different values of moving windows
In [74]:
plt.style.use('default')
plt.figure(figsize= (9,6))
plt.subplot(3,2,1)
plt.plot(euro_to_dollar['Time'], euro_to_dollar['US_dollar'])
plt.title('Original values', weight='bold')
for i, rolling_mean in zip([2,3,4,5,6], [7,30, 50, 100, 365]):
plt.subplot(3,2,i)
plt.plot(euro_to_dollar['Time'], euro_to_dollar['US_dollar']. rolling(rolling_mean).mean())
plt.title('Rolling Window:' + str(rolling_mean), weight='bold')
plt.tight_layout() # Auto-adjust the padding between subplots
plt.show()
Storytelling Data Visualization¶
Let's see how certain currency like Indonesian Rupiah which closely tied to USD affected during the sub-prime crisis leading to Globaal Financial Crisis (GFC) back in 2007-2009. So, I am going to use the dataset to find out how Indonesian Rupiah fare against EUR and USD particularly during the 2007-2009. We may refer to the background story here and from this source.
In [75]:
# We need to rename the Indonesian Rupiah column
In [76]:
euro_to_idr= exchange_rates[['Time', 'ID_rupiah']]
euro_to_idr.info()
In [77]:
euro_to_idr['ID_rupiah'].value_counts()
Out[77]:
In [78]:
# Removing the 'IDR_rupiah'with value '-'
euro_to_idr=euro_to_idr. loc[euro_to_idr['ID_rupiah']!='-']
euro_to_idr['ID_rupiah'].value_counts()
Out[78]:
In [79]:
#converting to float
euro_to_idr['ID_rupiah']=euro_to_idr['ID_rupiah'].astype(float)
euro_to_idr.info()
In [80]:
# Plotting idr rolling mean
euro_to_idr['rolling_mean']= euro_to_idr['ID_rupiah'].rolling(30).mean()
euro_to_idr.head()
Out[80]:
In [81]:
idr_before_GFC=euro_to_idr.copy()[(euro_to_idr['Time'].dt.year>=2005) & (euro_to_idr['Time'].dt.year<=2008)]
idr_during_GFC = euro_to_idr.copy()[(euro_to_idr['Time'].dt.year>=2008)&(euro_to_idr['Time'].dt.year<=2010)]
idr_after_GFC=euro_to_idr.copy()[(euro_to_idr['Time'].dt.year>=2010)]
In [82]:
euro_to_idr['rolling_mean'] = euro_to_idr['ID_rupiah'].rolling(30).mean()
plt.plot(euro_to_idr['Time'], euro_to_idr['rolling_mean'])
plt.show()
In [83]:
import matplotlib.style as style
from matplotlib.patches import Circle
from datetime import datetime
style.use('fivethirtyeight')
fig, ax = plt.subplots(figsize=(8,5))
ax.plot(idr_before_GFC['Time'], idr_before_GFC['rolling_mean'], lw=2, color='b')
ax.plot(idr_during_GFC['Time'], idr_during_GFC['rolling_mean'], lw=2, color='red', label= 'Indonesia GFC Period')
ax.plot(idr_after_GFC['Time'], idr_after_GFC['rolling_mean'], lw=2, color='b')
# print('ax:', ax.get_xticks()); to determine coordinate for text on the span
# print('ax:', ax.get_yticks()); to determine coordinatte for title
fig.set_dpi(80)
ax.grid(alpha=0.2)
ax.text((datetime(2005,1,1)), 18000,' Indonesian Rupiah Against Euro', size=18, weight='bold')
ax.text((datetime(2005,1,1)),17500, 'During Global Financial Crisis (GFC) 2007-2009', size= 16)
ax.text(733030,15800, 'Indonesia\nGFC-Period', size=10)
ax.set_xlabel('Years', fontsize= 12)
ax.set_ylabel('Indonesian IDR against Euro', fontsize = 12)
ax.axvspan(xmin=733042, xmax= 733773, ymax = 0.7, alpha=0.3, color='grey')
plt.show()
- IDR was depreciated above IDR 13,000 per Euro in the year before 2008 to become IDR 15,500 during the peak of GFC
- In the year of 2011 to 2012 IDR strengthened again at the same level as in the years prior to GFC
- However, in the year of 2013 to 2016, IDR depreciated again. What happened?
In [84]:
fig, ax = plt.subplots(figsize=(8,5), dpi=80)
ax.plot(idr_before_GFC['Time'], idr_before_GFC['rolling_mean'], lw=2, color='b')
ax.plot(idr_during_GFC['Time'], idr_during_GFC['rolling_mean'], lw=2, color='red', label= 'Indonesia GFC Period')
ax.text(733030, 17500, 'Impact of GFC to IDR against Euro', fontsize=15, weight='bold')
ax.plot(idr_after_GFC['Time'], idr_after_GFC['rolling_mean'], lw=2, color='b')
ax.grid(alpha=0.2)
ax.set_xlabel('Years', fontsize= 12)
ax.set_ylabel('Indonesian IDR against Euro', fontsize = 12)
ax.text(733030,15800, 'Indonesia\nGFC-Period', size=10)
ax.axvspan(xmin=733042, xmax= 733773, ymax = 0.7, alpha=0.2, color='grey')
ax.text(735100, 16770, 'What happen\nin this period?', size = 10)
ax.axvspan(xmin=735150, xmax=735950, ymax=0.84, alpha=0.3, color='red')
plt.show()
- Indonesian Rupiah currency is pegged to US Dollar. The meaning of pegging can be learned from here. Therefore,as we try to learn the effect of Euro fluctuation against Indonesian Rupiah, we should also take into consideration the fluctuation of Euro against US Dollar.
- In the following chart, we will see how the fluctuation of Euro affect IDR and USD especially during 2014 to 2016 the period when Euro was weakening against USD because the FED started to tighten the Quantitative Easing (QE), with the potential of increasing US Interest Rate.
In [85]:
idr_2014to2016=euro_to_idr.copy()[(euro_to_idr['Time'].dt.year>=2014)&(euro_to_idr['Time'].dt.year<=2016)]
dollar_2014to2016=euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year>=2014)&(euro_to_dollar['Time'].dt.year<=2016)]
plt.figure(figsize=(16,10),dpi=70)
ax1=plt.subplot(2,2,1)
ax2=plt.subplot(2,2,2)
axes=[ax1,ax2]
for ax in axes:
ax.grid(alpha=0.2)
ax.set_xlabel('Years', fontsize=12)
ax1.plot(idr_2014to2016['Time'],idr_2014to2016['rolling_mean'], label="IDR", color='blue', lw=2)
ax1.set_xticklabels(['','2014','', '','', '2015','','', '', '2016'])
ax1.text(735599, 16800,'IDR Response against Euro',fontsize=14, weight='bold', alpha=0.6 )
ax1.text(735599, 16600, 'Mirror USD responses', weight= 'bold', alpha=0.6)
# print('ax1:', ax1.get_xticks())
ax2.plot(dollar_2014to2016['Time'],dollar_2014to2016['rolling_mean'], label='USD', color='red', lw=2)
ax2.set_xticklabels(['','2014', '','','', '2015','', '','', '2016'])
ax2.text(735719, 1.42, 'USD Strengthening', fontsize=14, weight='bold', alpha=0.6)
ax2.text(735719, 1.40, 'With Tightening of QE', fontsize=12, weight='bold', alpha=0.6)
ax1.text(735150, 13200, '©DATAQUEST',color='k',size = 14, weight='bold', alpha=0.7)
ax2.text(735750,0.96, 'Source:European Central Bank',color= 'k', size=14, weight='bold', alpha=0.7)
# print('ax2:', ax2.get_xticks)
plt.show()
Conclusion¶
- The fluctuation of IDR looks similar with the fluctuation of USD against Euro. Althoungh during 2015, Indonesian Rupiah showed a weakening value.
- Upon further investigation, this weakening was due to the increase of foreign debt In Indonesia with mainly US Dollar denomination
- From the chart above, with the pegging of IDR to USD the two currency would response in similar patters against the fluctuation of the Euro.