project_5- euro daily exchange rates

#First: import the libraries we will need for this project: import pandas as pd import datetime as dt import matplotlib.pyplot as plt import matplotlib.style as style # To enable Jupyter to display graphs %matplotlib inline

#Second: import the csv file into a pandas DataFrame: exchange_rates = pd.read_csv("euro-daily-hist_1999_2020.csv")

#Third: visualize some rows: exchange_rates #as alternative we could print rows using df.head() and df.tail()

#Fourth: check some data details (type, null values, ...): exchange_rates.info()

exchange_rates.rename({"[US dollar ]":"US_dollar", r"Period\Unit:":"Time"}, axis=1, inplace=True) #In order to avoid `unicode error` in the 2nd column name, we need to use "r" before the string name #as we have "\U..." it is interpreted as eight-character Unicode escape, if we don't put "r" or use double "\\"

exchange_rates.columns # to visualize the changes

#format of information in this column is: 2021-01-08 exchange_rates["Time"]=pd.to_datetime(exchange_rates["Time"], format="%Y-%m-%d")

exchange_rates["Time"].dtype #checking the changes

exchange_rates.sort_values("Time", ascending=True,inplace=True) # sort values by time exchange_rates.reset_index(drop=True, inplace=True) # reset the index (and drop the initial index)

exchange_rates # visualize the changes

euro_to_dollar=exchange_rates.copy()[["Time", "US_dollar"]]

euro_to_dollar # checking the new Dataframe

euro_to_dollar.info()

euro_to_dollar["US_dollar"].value_counts()

euro_to_dollar=euro_to_dollar[euro_to_dollar["US_dollar"]!="-"]

euro_to_dollar #checking our new dataset:

euro_to_dollar.loc[:,"US_dollar"]=euro_to_dollar.loc[:,"US_dollar"].astype(float) euro_to_dollar.info()

plt.plot(euro_to_dollar["Time"],euro_to_dollar["US_dollar"]) plt.show()

euro_to_dollar['rolling_mean'] = euro_to_dollar['US_dollar'].rolling(30).mean()

plt.plot(euro_to_dollar['Time'], euro_to_dollar['rolling_mean']) plt.show()

style.use("fivethirtyeight")

decrease_period=euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2007) & (euro_to_dollar['Time'].dt.year <= 2019)]

fig, ax = plt.subplots(figsize=(10,3)) ax.plot(decrease_period['Time'], decrease_period['rolling_mean'], color='#af0b1e',linewidth=2) ax.axvspan(xmin=13950, xmax=14140, ymin=0.09,color="grey", alpha=0.3) ax.axvspan(xmin=15950, xmax=16850, ymin=0.09, color='grey',alpha=0.3) ax.axvspan(xmin=17550, xmax=18250, ymin=0.09, color='grey',alpha=0.3) plt.show()

financial_crisis=euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2006) & (euro_to_dollar['Time'].dt.year <= 2010)]

#Create the plot area and structure fig, (ax1, ax2) = plt.subplots(figsize=(8,3), nrows=2, ncols=1) axes=[ax1, ax2] #First version of plots - like a shadow of every elements for ax in axes: ax.plot(financial_crisis['Time'], financial_crisis['rolling_mean'], color='#af0b1e', alpha=0.1) ax.set_ylim(1.12, 1.6) #use same y axis for both graph #ax.set_yticks([1.0, 1.2,1.4, 1.6]) ax.set_yticklabels([]) #ax.set_facecolor="#dbd7d7" ax.tick_params(colors='#948484', which='both', labelsize=10) ax.grid(b=True, color="grey", alpha=0.1,linestyle=":") ### Highlihting the rate growing before crises ax1.plot(financial_crisis['Time'][:660], financial_crisis['rolling_mean'][:660], color='green', linewidth=2.55, alpha=0.8) #ax1.xaxis.tick_top() ax1.set_xticklabels([]) ax1.text(x=13000,y=1.12,s="r*=1.1826",size=10) #check appendix ax1.text(x=14000,y=1.6,s="r*=1.599",size=10) #check appendix ax1.text(x=14250,y=1.65,s="Positive rate evolution",size=11,color="green", weight="bold") ax1.text(x=14250,y=1.55,s="until middle 2008",size=11, color="green", weight="bold") ### Highlihting the floated rate after crisis ax2.plot((financial_crisis['Time'][660:1300]), (financial_crisis['rolling_mean'][660:1300]),color='#af0b1e', linewidth=2.5, alpha=0.8) ax2.text(x=13030,y=1.45,s="The rate drops and",size=11, color="#af0b1e", weight="bold") ax2.text(x=13030,y=1.35,s="grows after 2008",size=11, color="#af0b1e", weight="bold") ### Highlihting the peak of the crisis ax1.axvspan(xmin=13950, xmax=14140, ymin=0.09, alpha=0.3, color='grey') ax2.axvspan(xmin=13950, xmax=14140, ymin=0.09, alpha=0.3, color='grey') ax2.text(x=14900,y=1.26,s="r*=1.1.2939",size=10) #check appendix ### Highlihting the moment of the dratic rate drop #ax1.axvline(x=14090, ymin=-5, ymax=1.3, c="red",linestyle=":", linewidth=2) #ax2.axvline(x=14090, ymin=-3, ymax=2, c="red", linewidth=2,linestyle=":") #Title and subtitle: ax1.text(x=13000,y=2.05,s="EUR/USD rate drastic drop after the financial crisis",size=18, weight="bold") ax1.text(x=13200,y=1.90,s="The global financial crisis has a big impact on the exchange rate. ",size=13) ax1.text(x=13000,y=1.80,s="The rate started to float after 2008 and never reach again its highest value 1.599",size=13) #r description ax.text(x=13000,y=0.9, s="*r means exchange rate EUR/USD", size=11) #Foot signature/source of graph: ax2.text(13000,0.8,'©Mariana Lourenço' +' '*59 +"Source: European Central Bank", color="#f0f0f0",backgroundcolor="#4d4d4d", size=12) plt.show()

#2016-2019: before_pandemic = euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2016) & (euro_to_dollar['Time'].dt.year <= 2019)] #2020: after_pandemic =euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2020) & (euro_to_dollar['Time'].dt.year <= 2021)]

#import matplotlib.dates as mdates fig, ax= plt.subplots(figsize=(9,1.5)) ax.plot(before_pandemic['Time'], before_pandemic['rolling_mean'],color='lightblue') ax.plot(after_pandemic['Time'], after_pandemic['rolling_mean'],color='green' ) #ax.set_ylim(1.1, 1.3) #use same y axis for both graph #ax.set_yticks([1.1, 1.2,1.3]) #ax.set_yticklabels([1.1, 1.2,1.3]) ax.tick_params(colors='#948484', which='both', labelsize=12) ax.yaxis.grid(False) ax.set_yticklabels([]) ax.axhline(1.22,c="black", linewidth=2.5, alpha=0.8) ax.text(x=16530,y=1.21, s="r*=1.225", size=11) ax.annotate('Mar-2020', xy=(18400, 1.08), xytext=(18500, 0.95), arrowprops=dict(facecolor='black', shrink=0.05), ) #Title ax.text(x=16600,y=1.45,s="COVID-19 Pandemic rises hopes that the euro will valorize ",size=18, weight="bold") ax.text(x=16750,y=1.40, s="Checking evolution of EUR/USD rate, since first cases in Europe (Mar/2020)",size=14) ax.text(x=16800,y=1.35, s=" the rate grew, and in Jan-2021 reached the highest values since 2018",size=14) #r description ax.text(x=16530,y=0.92, s="*r means exchange rate EUR/USD", size=11) #Foot signature/source of graph: ax.text(16530,0.88,'©Mariana Lourenço' +' '*78 +"Source: European Central Bank", backgroundcolor="#4d4d4d", size=12,color="#f0f0f0") plt.show()

before=euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2006) & (euro_to_dollar['Time'].dt.year <= 2008)] print(before.shape)

before.max()

before[before["rolling_mean"]== 1.5743]

before.min()

before[before["Time"]== "2006-01-02"]

euro_to_dollar[euro_to_dollar["Time"]=="2011-12-30"]

pandemic=euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2020) & (euro_to_dollar['Time'].dt.year <= 2021)] print(before.shape)

pandemic.min()

pandemic[pandemic["rolling_mean"]< 1.085]

pandemic[pandemic["US_dollar"]== 1.0707]

pandemic[pandemic["Time"]== "2021-01-08"]