import pandas as pd
import numpy as np
import missingno as msno
import datetime as dt
from stringcase import snakecase
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
#with pd.option_context('display.max_rows', None, 'display.max_columns', None):
#with np.printoptions(threshold=np.inf):
# create a new class which makes it possible to format text in colour or bold etc.
class color:
PURPLE = '\033[95m'
CYAN = '\033[96m'
DARKCYAN = '\033[36m'
BLUE = '\033[94m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
END = '\033[0m'
exchange_rates = pd.read_csv('euro-daily-hist_1999_2020.csv')
with pd.option_context('display.max_columns', 41):
display(exchange_rates.head(3))
display(exchange_rates.tail(3))
Period\Unit: | [Australian dollar ] | [Bulgarian lev ] | [Brazilian real ] | [Canadian dollar ] | [Swiss franc ] | [Chinese yuan renminbi ] | [Cypriot pound ] | [Czech koruna ] | [Danish krone ] | [Estonian kroon ] | [UK pound sterling ] | [Greek drachma ] | [Hong Kong dollar ] | [Croatian kuna ] | [Hungarian forint ] | [Indonesian rupiah ] | [Israeli shekel ] | [Indian rupee ] | [Iceland krona ] | [Japanese yen ] | [Korean won ] | [Lithuanian litas ] | [Latvian lats ] | [Maltese lira ] | [Mexican peso ] | [Malaysian ringgit ] | [Norwegian krone ] | [New Zealand dollar ] | [Philippine peso ] | [Polish zloty ] | [Romanian leu ] | [Russian rouble ] | [Swedish krona ] | [Singapore dollar ] | [Slovenian tolar ] | [Slovak koruna ] | [Thai baht ] | [Turkish lira ] | [US dollar ] | [South African rand ] | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2021-01-08 | 1.5758 | 1.9558 | 6.5748 | 1.5543 | 1.0827 | 7.9184 | NaN | 26.163 | 7.4369 | NaN | 0.90128 | NaN | 9.4982 | 7.5690 | 359.62 | 17247.33 | 3.8981 | 89.7975 | 155.5 | 127.26 | 1337.90 | NaN | NaN | NaN | 24.4718 | 4.9359 | 10.2863 | 1.6883 | 58.947 | 4.5113 | 4.8708 | 90.8000 | 10.0510 | 1.6228 | NaN | NaN | 36.8480 | 9.0146 | 1.2250 | 18.7212 |
1 | 2021-01-07 | 1.5836 | 1.9558 | 6.5172 | 1.5601 | 1.0833 | 7.9392 | NaN | 26.147 | 7.4392 | NaN | 0.90190 | NaN | 9.5176 | 7.5660 | 357.79 | 17259.99 | 3.9027 | 90.0455 | 155.3 | 127.13 | 1342.29 | NaN | NaN | NaN | 24.2552 | 4.9570 | 10.3435 | 1.6907 | 59.043 | 4.4998 | 4.8712 | 91.2000 | 10.0575 | 1.6253 | NaN | NaN | 36.8590 | 8.9987 | 1.2276 | 18.7919 |
2 | 2021-01-06 | 1.5824 | 1.9558 | 6.5119 | 1.5640 | 1.0821 | 7.9653 | NaN | 26.145 | 7.4393 | NaN | 0.90635 | NaN | 9.5659 | 7.5595 | 357.86 | 17168.20 | 3.9289 | 90.2040 | 156.3 | 127.03 | 1339.30 | NaN | NaN | NaN | 24.3543 | 4.9482 | 10.3810 | 1.6916 | 59.296 | 4.5160 | 4.8720 | 90.8175 | 10.0653 | 1.6246 | NaN | NaN | 36.9210 | 9.0554 | 1.2338 | 18.5123 |
Period\Unit: | [Australian dollar ] | [Bulgarian lev ] | [Brazilian real ] | [Canadian dollar ] | [Swiss franc ] | [Chinese yuan renminbi ] | [Cypriot pound ] | [Czech koruna ] | [Danish krone ] | [Estonian kroon ] | [UK pound sterling ] | [Greek drachma ] | [Hong Kong dollar ] | [Croatian kuna ] | [Hungarian forint ] | [Indonesian rupiah ] | [Israeli shekel ] | [Indian rupee ] | [Iceland krona ] | [Japanese yen ] | [Korean won ] | [Lithuanian litas ] | [Latvian lats ] | [Maltese lira ] | [Mexican peso ] | [Malaysian ringgit ] | [Norwegian krone ] | [New Zealand dollar ] | [Philippine peso ] | [Polish zloty ] | [Romanian leu ] | [Russian rouble ] | [Swedish krona ] | [Singapore dollar ] | [Slovenian tolar ] | [Slovak koruna ] | [Thai baht ] | [Turkish lira ] | [US dollar ] | [South African rand ] | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5696 | 1999-01-06 | 1.8820 | NaN | NaN | 1.7711 | 1.6116 | NaN | 0.58200 | 34.850 | 7.4452 | 15.6466 | 0.70760 | 324.72 | 9.1010 | NaN | 250.67 | 9337.68 | NaN | NaN | 81.54 | 131.42 | 1359.54 | 4.69940 | 0.6649 | 0.4420 | 11.4705 | 4.4637 | 8.7335 | 2.1890 | 44.872 | 4.0065 | 1.3168 | 27.4315 | 9.3050 | 1.9699 | 188.7000 | 42.778 | 42.6949 | 0.3722 | 1.1743 | 6.7307 |
5697 | 1999-01-05 | 1.8944 | NaN | NaN | 1.7965 | 1.6123 | NaN | 0.58230 | 34.917 | 7.4495 | 15.6466 | 0.71220 | 324.70 | 9.1341 | NaN | 250.80 | 9314.51 | NaN | NaN | 81.53 | 130.96 | 1373.01 | 4.71740 | 0.6657 | 0.4432 | 11.5960 | 4.4805 | 8.7745 | 2.2011 | 44.745 | 4.0245 | 1.3168 | 26.5876 | 9.4025 | 1.9655 | 188.7750 | 42.848 | 42.5048 | 0.3728 | 1.1790 | 6.7975 |
5698 | 1999-01-04 | 1.9100 | NaN | NaN | 1.8004 | 1.6168 | NaN | 0.58231 | 35.107 | 7.4501 | 15.6466 | 0.71110 | 327.15 | 9.1332 | NaN | 251.48 | 9433.61 | NaN | NaN | 81.48 | 133.73 | 1398.59 | 4.71700 | 0.6668 | 0.4432 | 11.6446 | 4.4798 | 8.8550 | 2.2229 | 45.510 | 4.0712 | 1.3111 | 25.2875 | 9.4696 | 1.9554 | 189.0450 | 42.991 | 42.6799 | 0.3723 | 1.1789 | 6.9358 |
exchange_rates.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5699 entries, 0 to 5698 Data columns (total 41 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Period\Unit: 5699 non-null object 1 [Australian dollar ] 5699 non-null object 2 [Bulgarian lev ] 5297 non-null object 3 [Brazilian real ] 5431 non-null object 4 [Canadian dollar ] 5699 non-null object 5 [Swiss franc ] 5699 non-null object 6 [Chinese yuan renminbi ] 5431 non-null object 7 [Cypriot pound ] 2346 non-null object 8 [Czech koruna ] 5699 non-null object 9 [Danish krone ] 5699 non-null object 10 [Estonian kroon ] 3130 non-null object 11 [UK pound sterling ] 5699 non-null object 12 [Greek drachma ] 520 non-null object 13 [Hong Kong dollar ] 5699 non-null object 14 [Croatian kuna ] 5431 non-null object 15 [Hungarian forint ] 5699 non-null object 16 [Indonesian rupiah ] 5699 non-null object 17 [Israeli shekel ] 5431 non-null object 18 [Indian rupee ] 5431 non-null object 19 [Iceland krona ] 3292 non-null float64 20 [Japanese yen ] 5699 non-null object 21 [Korean won ] 5699 non-null object 22 [Lithuanian litas ] 4159 non-null object 23 [Latvian lats ] 3904 non-null object 24 [Maltese lira ] 2346 non-null object 25 [Mexican peso ] 5699 non-null object 26 [Malaysian ringgit ] 5699 non-null object 27 [Norwegian krone ] 5699 non-null object 28 [New Zealand dollar ] 5699 non-null object 29 [Philippine peso ] 5699 non-null object 30 [Polish zloty ] 5699 non-null object 31 [Romanian leu ] 5637 non-null float64 32 [Russian rouble ] 5699 non-null object 33 [Swedish krona ] 5699 non-null object 34 [Singapore dollar ] 5699 non-null object 35 [Slovenian tolar ] 2085 non-null object 36 [Slovak koruna ] 2608 non-null object 37 [Thai baht ] 5699 non-null object 38 [Turkish lira ] 5637 non-null float64 39 [US dollar ] 5699 non-null object 40 [South African rand ] 5699 non-null object dtypes: float64(3), object(38) memory usage: 1.8+ MB
#Changing column directly
exchange_rates = exchange_rates.rename(columns={'Period\\Unit:':'Time'})
#Removing certain characters with a for loop
badchar = ['[',' ]',':']
colnames = []
for col in exchange_rates.columns:
for char in badchar:
col = col.replace(char,"")
col = col.replace(" ", "_") #replace space with underscore to create snakecase names
colnames.append(col)
exchange_rates.columns = colnames
print(exchange_rates.columns)
Index(['Time', 'Australian_dollar', 'Bulgarian_lev', 'Brazilian_real', 'Canadian_dollar', 'Swiss_franc', 'Chinese_yuan_renminbi', 'Cypriot_pound', 'Czech_koruna', 'Danish_krone', 'Estonian_kroon', 'UK_pound_sterling', 'Greek_drachma', 'Hong_Kong_dollar', 'Croatian_kuna', 'Hungarian_forint', 'Indonesian_rupiah', 'Israeli_shekel', 'Indian_rupee', 'Iceland_krona', 'Japanese_yen', 'Korean_won', 'Lithuanian_litas', 'Latvian_lats', 'Maltese_lira', 'Mexican_peso', 'Malaysian_ringgit', 'Norwegian_krone', 'New_Zealand_dollar', 'Philippine_peso', 'Polish_zloty', 'Romanian_leu', 'Russian_rouble', 'Swedish_krona', 'Singapore_dollar', 'Slovenian_tolar', 'Slovak_koruna', 'Thai_baht', 'Turkish_lira', 'US_dollar', 'South_African_rand'], dtype='object')
msno.bar(exchange_rates)
<AxesSubplot:>
Lot of columns with missing values. 9 columns missing have more then 20% NaN.
complete_exchange_rates = exchange_rates.dropna()
complete_exchange_rates.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 116 entries, 5179 to 5296 Data columns (total 41 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Time 116 non-null object 1 Australian_dollar 116 non-null object 2 Bulgarian_lev 116 non-null object 3 Brazilian_real 116 non-null object 4 Canadian_dollar 116 non-null object 5 Swiss_franc 116 non-null object 6 Chinese_yuan_renminbi 116 non-null object 7 Cypriot_pound 116 non-null object 8 Czech_koruna 116 non-null object 9 Danish_krone 116 non-null object 10 Estonian_kroon 116 non-null object 11 UK_pound_sterling 116 non-null object 12 Greek_drachma 116 non-null object 13 Hong_Kong_dollar 116 non-null object 14 Croatian_kuna 116 non-null object 15 Hungarian_forint 116 non-null object 16 Indonesian_rupiah 116 non-null object 17 Israeli_shekel 116 non-null object 18 Indian_rupee 116 non-null object 19 Iceland_krona 116 non-null float64 20 Japanese_yen 116 non-null object 21 Korean_won 116 non-null object 22 Lithuanian_litas 116 non-null object 23 Latvian_lats 116 non-null object 24 Maltese_lira 116 non-null object 25 Mexican_peso 116 non-null object 26 Malaysian_ringgit 116 non-null object 27 Norwegian_krone 116 non-null object 28 New_Zealand_dollar 116 non-null object 29 Philippine_peso 116 non-null object 30 Polish_zloty 116 non-null object 31 Romanian_leu 116 non-null float64 32 Russian_rouble 116 non-null object 33 Swedish_krona 116 non-null object 34 Singapore_dollar 116 non-null object 35 Slovenian_tolar 116 non-null object 36 Slovak_koruna 116 non-null object 37 Thai_baht 116 non-null object 38 Turkish_lira 116 non-null float64 39 US_dollar 116 non-null object 40 South_African_rand 116 non-null object dtypes: float64(3), object(38) memory usage: 38.1+ KB
Not possible to work delete all rows that are containing 'NAN', as this will reduce the dataset to only 116 rows.
exchange_rates.loc[:,'Time'] = pd.to_datetime(exchange_rates.loc[:,'Time'])
exchange_rates.sort_values(by='Time',inplace=True)
exchange_rates.reset_index(inplace=True,drop=True)
exchange_rates
Time | Australian_dollar | Bulgarian_lev | Brazilian_real | Canadian_dollar | Swiss_franc | Chinese_yuan_renminbi | Cypriot_pound | Czech_koruna | Danish_krone | ... | Romanian_leu | Russian_rouble | Swedish_krona | Singapore_dollar | Slovenian_tolar | Slovak_koruna | Thai_baht | Turkish_lira | US_dollar | South_African_rand | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1999-01-04 | 1.9100 | NaN | NaN | 1.8004 | 1.6168 | NaN | 0.58231 | 35.107 | 7.4501 | ... | 1.3111 | 25.2875 | 9.4696 | 1.9554 | 189.0450 | 42.991 | 42.6799 | 0.3723 | 1.1789 | 6.9358 |
1 | 1999-01-05 | 1.8944 | NaN | NaN | 1.7965 | 1.6123 | NaN | 0.58230 | 34.917 | 7.4495 | ... | 1.3168 | 26.5876 | 9.4025 | 1.9655 | 188.7750 | 42.848 | 42.5048 | 0.3728 | 1.1790 | 6.7975 |
2 | 1999-01-06 | 1.8820 | NaN | NaN | 1.7711 | 1.6116 | NaN | 0.58200 | 34.850 | 7.4452 | ... | 1.3168 | 27.4315 | 9.3050 | 1.9699 | 188.7000 | 42.778 | 42.6949 | 0.3722 | 1.1743 | 6.7307 |
3 | 1999-01-07 | 1.8474 | NaN | NaN | 1.7602 | 1.6165 | NaN | 0.58187 | 34.886 | 7.4431 | ... | 1.3092 | 26.9876 | 9.1800 | 1.9436 | 188.8000 | 42.765 | 42.1678 | 0.3701 | 1.1632 | 6.8283 |
4 | 1999-01-08 | 1.8406 | NaN | NaN | 1.7643 | 1.6138 | NaN | 0.58187 | 34.938 | 7.4433 | ... | 1.3143 | 27.2075 | 9.1650 | 1.9537 | 188.8400 | 42.560 | 42.5590 | 0.3718 | 1.1659 | 6.7855 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
5694 | 2021-01-04 | 1.5928 | 1.9558 | 6.3241 | 1.5621 | 1.0811 | 7.9484 | NaN | 26.141 | 7.4379 | ... | 4.8713 | 90.3420 | 10.0895 | 1.6198 | NaN | NaN | 36.7280 | 9.0579 | 1.2296 | 17.9214 |
5695 | 2021-01-05 | 1.5927 | 1.9558 | 6.5517 | 1.5651 | 1.0803 | 7.9315 | NaN | 26.227 | 7.4387 | ... | 4.8721 | 91.6715 | 10.0570 | 1.6180 | NaN | NaN | 36.7760 | 9.0694 | 1.2271 | 18.4194 |
5696 | 2021-01-06 | 1.5824 | 1.9558 | 6.5119 | 1.5640 | 1.0821 | 7.9653 | NaN | 26.145 | 7.4393 | ... | 4.8720 | 90.8175 | 10.0653 | 1.6246 | NaN | NaN | 36.9210 | 9.0554 | 1.2338 | 18.5123 |
5697 | 2021-01-07 | 1.5836 | 1.9558 | 6.5172 | 1.5601 | 1.0833 | 7.9392 | NaN | 26.147 | 7.4392 | ... | 4.8712 | 91.2000 | 10.0575 | 1.6253 | NaN | NaN | 36.8590 | 8.9987 | 1.2276 | 18.7919 |
5698 | 2021-01-08 | 1.5758 | 1.9558 | 6.5748 | 1.5543 | 1.0827 | 7.9184 | NaN | 26.163 | 7.4369 | ... | 4.8708 | 90.8000 | 10.0510 | 1.6228 | NaN | NaN | 36.8480 | 9.0146 | 1.2250 | 18.7212 |
5699 rows × 41 columns
eur_to_dol = exchange_rates.loc[:,['Time','US_dollar']].copy()
eur_to_dol = eur_to_dol.rename(columns={'Time':'time','US_dollar':'US'})
eur_to_dol['US']
0 1.1789 1 1.1790 2 1.1743 3 1.1632 4 1.1659 ... 5694 1.2296 5695 1.2271 5696 1.2338 5697 1.2276 5698 1.2250 Name: US, Length: 5699, dtype: object
with pd.option_context('display.max_rows', None):
print(eur_to_dol['US'].value_counts())
- 62 1.2276 9 1.1215 8 1.1305 7 1.0867 6 1.3373 6 1.1218 6 1.3086 6 1.1193 6 1.1268 6 1.3532 6 1.1346 6 1.2713 6 1.0888 6 1.1797 6 1.1185 5 1.1284 5 1.1154 5 1.1389 5 1.1221 5 1.1765 5 1.3615 5 1.1146 5 1.1236 5 1.0592 5 1.1813 5 1.1115 5 1.2309 5 1.3246 5 1.1025 5 0.8803 5 1.1168 5 1.2737 5 1.2219 5 1.3200 5 1.0737 5 1.1187 5 1.3588 5 1.3574 5 1.2850 5 1.3260 5 1.3596 5 1.1328 5 1.1279 5 1.3035 5 1.1354 5 1.1318 4 1.2792 4 1.1244 4 1.0572 4 0.9857 4 1.2762 4 1.0982 4 1.0868 4 1.2346 4 1.0388 4 1.3667 4 1.1117 4 1.3850 4 1.3658 4 1.1781 4 1.3384 4 1.3192 4 1.0973 4 1.0772 4 1.1933 4 1.0860 4 1.2239 4 1.3641 4 1.0818 4 1.1198 4 1.1226 4 1.3612 4 1.1692 4 1.0891 4 1.3145 4 1.3627 4 1.1375 4 1.3547 4 1.1427 4 1.2258 4 1.3352 4 1.3266 4 1.0927 4 0.8919 4 1.1188 4 1.2944 4 1.3349 4 1.2301 4 1.3394 4 1.1172 4 1.2063 4 1.1202 4 1.2953 4 1.1342 4 1.2930 4 1.1031 4 1.1200 4 1.2168 4 1.0631 4 1.1785 4 1.1194 4 1.2271 4 1.1588 4 1.1217 4 1.2993 4 1.1806 4 1.1387 4 1.2818 4 1.1173 4 1.0668 4 1.3773 4 1.3057 4 1.1787 4 1.2948 4 1.0630 4 1.3608 4 1.2736 4 1.1403 4 1.0963 4 1.4488 4 1.1150 4 1.1697 4 1.3327 4 1.1290 4 1.1348 4 1.0808 4 1.1579 4 1.1174 4 1.1156 4 1.0667 4 1.1109 4 1.2817 4 1.2596 4 1.2418 4 1.2756 4 1.3280 4 1.1138 4 1.1090 4 1.3607 4 1.1312 4 1.1139 4 1.0816 4 1.3115 4 1.3040 4 1.2312 4 1.1795 4 1.4089 4 1.2958 4 1.3803 4 1.0898 3 1.1788 3 1.0024 3 1.3388 3 1.3517 3 1.1329 3 1.1298 3 0.8952 3 1.2659 3 1.4229 3 1.1301 3 1.4207 3 1.1409 3 0.9118 3 0.9064 3 1.1932 3 1.2339 3 1.1058 3 1.1232 3 1.3270 3 1.1254 3 1.1169 3 1.3383 3 0.8922 3 1.1783 3 1.3006 3 1.0711 3 1.3238 3 1.1740 3 1.3033 3 1.0864 3 1.1694 3 1.2939 3 1.1823 3 1.0586 3 1.1789 3 1.1161 3 1.2735 3 1.1009 3 1.2285 3 1.2910 3 1.3668 3 1.0951 3 0.8973 3 1.3295 3 1.3074 3 1.1644 3 1.1220 3 1.3510 3 1.1424 3 1.3118 3 1.1355 3 1.2331 3 1.2017 3 1.0665 3 1.3356 3 1.2911 3 1.2524 3 1.1790 3 1.3340 3 1.2262 3 1.0901 3 1.2246 3 1.1771 3 1.3768 3 1.3518 3 1.1077 3 1.2065 3 1.0758 3 1.3631 3 0.8817 3 1.2073 3 1.3160 3 1.1883 3 1.2187 3 1.2697 3 1.1171 3 1.2492 3 1.3173 3 1.1744 3 1.1253 3 1.2828 3 1.1008 3 1.3705 3 1.3178 3 1.3731 3 1.2045 3 1.1569 3 1.0800 3 1.3244 3 1.2679 3 1.3039 3 1.1326 3 1.1096 3 1.3519 3 1.2039 3 1.2961 3 1.1212 3 1.1652 3 1.1091 3 1.1410 3 1.2042 3 1.3106 3 1.3505 3 1.3726 3 1.1769 3 1.3975 3 1.0893 3 1.3834 3 1.1992 3 1.1219 3 1.2061 3 1.3323 3 1.0919 3 1.3191 3 1.2790 3 1.3611 3 1.0634 3 1.2548 3 1.4166 3 1.4814 3 1.1852 3 1.2066 3 1.2776 3 0.8768 3 1.2800 3 1.2277 3 1.1349 3 1.2392 3 1.1136 3 1.4874 3 1.2384 3 1.1003 3 1.0146 3 1.3020 3 1.1759 3 1.1153 3 1.0783 3 1.2023 3 1.2634 3 1.3315 3 1.2260 3 1.1104 3 1.2296 3 1.1250 3 1.1634 3 1.1133 3 1.2022 3 1.3005 3 1.0627 3 1.2337 3 1.4705 3 1.2294 3 1.3707 3 1.3507 3 1.2428 3 1.0801 3 1.1616 3 0.9873 3 1.2377 3 1.2967 3 1.2254 3 1.1768 3 1.0786 3 0.8786 3 1.2315 3 1.1034 3 1.1741 3 1.3901 3 1.1699 3 1.1710 3 1.2971 3 1.0836 3 1.3582 3 0.8778 3 1.1362 3 1.0742 3 1.3203 3 1.2935 3 1.1736 3 1.3745 3 1.0564 3 1.1333 3 1.1285 3 1.3024 3 1.1275 3 1.3113 3 1.3348 3 1.3527 3 1.2942 3 1.2424 3 1.1543 3 1.1983 3 1.2680 3 1.3360 3 1.2372 3 1.3225 3 1.3119 3 1.2259 3 1.1075 3 0.9910 3 1.3276 3 1.2087 3 1.4385 3 1.3592 3 1.1030 3 1.2660 3 1.3155 3 1.2338 3 1.3788 3 1.1854 3 1.3684 3 1.1222 3 1.2541 3 1.3817 3 1.3274 3 1.0723 3 1.3136 3 1.2810 3 1.3132 3 1.1922 3 1.2947 3 1.3604 3 0.9193 3 1.2988 3 1.1948 3 1.1612 3 1.0748 3 1.1155 3 1.1321 3 1.2069 3 1.3795 3 1.1336 3 1.3472 3 1.1230 3 1.1203 3 1.0702 3 1.3649 3 1.3771 3 1.3606 3 1.1066 3 1.1345 3 1.0981 3 1.0950 3 1.2824 3 1.1370 3 1.0089 3 0.9835 3 1.3566 3 1.2198 3 1.1875 3 1.1162 3 1.2436 3 1.3549 3 1.2493 3 1.4270 3 1.0613 3 0.8947 3 1.2272 3 1.3131 3 1.3454 3 1.3535 3 1.1180 3 1.2984 3 1.2307 3 1.1885 3 1.3183 3 1.1002 3 1.3676 3 0.8840 3 1.3794 3 1.3129 3 1.3525 3 1.2954 3 1.0666 3 1.2088 3 1.2531 3 1.2118 3 1.3300 3 1.1413 3 1.1766 3 1.1296 3 0.9213 3 1.3262 3 1.0889 3 1.2770 3 1.2185 3 1.1325 3 0.9146 3 1.2213 3 1.3494 3 1.3659 3 1.2905 3 1.2768 3 1.1151 3 1.2607 3 1.1726 3 1.0843 3 1.2692 3 1.0822 3 1.3120 3 1.4220 3 1.1306 3 1.3656 3 1.0629 3 0.8818 3 1.2279 3 0.8763 3 1.0875 3 1.0726 3 1.1343 3 0.8909 3 1.3647 3 1.3460 3 1.1213 3 1.2874 3 1.1707 3 1.3077 3 1.3856 3 1.2092 3 1.1295 3 1.1708 3 1.1466 3 1.1337 3 1.3337 3 1.2820 3 0.9725 3 1.2767 3 1.3092 3 1.1080 3 1.3114 3 1.3290 3 1.1925 3 0.8889 3 1.3594 3 1.3193 2 1.2923 2 1.1578 2 1.3639 2 0.8611 2 1.4829 2 1.2252 2 1.3268 2 1.2718 2 0.8676 2 1.3177 2 0.9305 2 1.1980 2 1.3135 2 1.1810 2 1.3480 2 1.0824 2 1.1698 2 1.2990 2 1.3037 2 1.1234 2 1.4918 2 0.8886 2 1.1263 2 1.3215 2 1.0915 2 1.0385 2 1.1968 2 0.9647 2 1.3080 2 1.1157 2 1.2281 2 0.9497 2 0.8810 2 1.1049 2 0.8664 2 1.4160 2 1.2693 2 1.1064 2 1.3618 2 1.1084 2 1.1100 2 1.0068 2 1.2879 2 1.0008 2 1.2587 2 1.1658 2 1.3346 2 1.1371 2 1.2144 2 1.2100 2 1.5577 2 1.3230 2 1.0622 2 1.0887 2 1.4780 2 1.2203 2 1.1247 2 1.3425 2 1.0956 2 0.8961 2 1.0969 2 1.3610 2 1.2263 2 1.2638 2 1.2096 2 1.4260 2 1.2603 2 1.0784 2 1.5417 2 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1.0421 1 1.1067 1 1.0504 1 1.3377 1 1.3198 1 0.9808 1 1.1794 1 1.0763 1 1.0105 1 1.0942 1 1.1060 1 1.3256 1 1.0636 1 1.2438 1 1.3397 1 1.1942 1 0.8942 1 0.9783 1 1.2633 1 1.0581 1 0.9302 1 1.3538 1 1.4126 1 1.4337 1 1.0861 1 0.8834 1 1.2917 1 0.8559 1 1.0094 1 1.3073 1 1.3222 1 0.9228 1 1.1872 1 0.9804 1 1.1422 1 1.4240 1 1.1617 1 0.8607 1 1.0972 1 1.2154 1 1.2526 1 1.1016 1 1.1163 1 1.0479 1 1.1803 1 1.3858 1 1.2709 1 1.0734 1 1.2985 1 0.8494 1 1.0499 1 1.2754 1 1.2068 1 1.2081 1 1.4757 1 1.3370 1 1.1784 1 1.3022 1 1.3157 1 1.0339 1 1.5919 1 1.0719 1 1.1657 1 1.2797 1 1.2750 1 1.3362 1 1.3211 1 1.0548 1 1.0284 1 1.3468 1 1.0946 1 1.3503 1 1.1572 1 1.2542 1 0.8584 1 1.5473 1 1.1725 1 1.1746 1 1.3344 1 0.8705 1 1.5722 1 1.0430 1 1.3391 1 1.3318 1 1.1364 1 1.5319 1 1.3777 1 1.1654 1 0.9915 1 1.3330 1 1.3758 1 1.1860 1 1.0527 1 1.2469 1 1.4304 1 1.3742 1 1.1182 1 1.2576 1 1.1938 1 0.9359 1 1.1686 1 1.2350 1 1.2062 1 1.1291 1 0.8806 1 0.8476 1 1.0466 1 1.3319 1 1.0452 1 1.2918 1 1.2328 1 0.9568 1 1.4453 1 1.4389 1 0.8813 1 1.4212 1 0.9791 1 1.5742 1 1.0684 1 1.1489 1 1.0828 1 1.3872 1 1.1724 1 1.1907 1 0.9136 1 1.5708 1 1.0380 1 0.9376 1 1.2853 1 1.1302 1 1.4144 1 1.1944 1 1.1718 1 0.9147 1 1.5775 1 0.8872 1 1.1323 1 1.3855 1 1.0983 1 1.5872 1 0.8545 1 0.8622 1 1.1900 1 1.3479 1 1.2706 1 1.0465 1 1.2179 1 1.2545 1 1.4261 1 1.4720 1 1.2151 1 1.1048 1 1.4153 1 1.2847 1 1.4563 1 1.0840 1 0.8673 1 1.0520 1 1.0892 1 1.1054 1 1.2122 1 1.2160 1 1.3353 1 1.2336 1 1.3585 1 1.5731 1 1.3546 1 0.9860 1 1.3484 1 1.5858 1 0.9493 1 0.9890 1 1.5771 1 1.2229 1 1.2622 1 0.9108 1 0.8561 1 1.5990 1 1.1767 1 0.9005 1 1.2744 1 1.2499 1 1.3182 1 1.4104 1 0.9781 1 1.4500 1 1.2926 1 1.4224 1 1.0443 1 1.0602 1 1.3932 1 1.0316 1 0.9792 1 1.2074 1 0.8651 1 1.0424 1 1.1252 1 1.3223 1 1.3591 1 1.3166 1 1.5806 1 1.4268 1 1.4617 1 1.4132 1 1.3251 1 1.1013 1 1.4200 1 1.2041 1 1.0410 1 1.3236 1 1.4750 1 1.1039 1 1.3144 1 0.8966 1 1.4225 1 1.4168 1 0.8921 1 1.3754 1 1.0937 1 1.4135 1 1.5710 1 1.1014 1 1.0882 1 1.0715 1 1.1620 1 1.4037 1 0.8614 1 1.0501 1 0.8939 1 1.3333 1 0.9470 1 1.2602 1 1.3715 1 1.2552 1 1.0124 1 1.2779 1 1.4547 1 1.1330 1 0.9083 1 1.3434 1 1.3536 1 1.3322 1 0.9913 1 1.3499 1 1.1289 1 1.1443 1 1.3220 1 1.4922 1 1.3587 1 1.1623 1 1.5044 1 1.3877 1 0.9976 1 1.2181 1 1.1626 1 1.2010 1 0.9826 1 1.1347 1 1.0433 1 1.2914 1 1.4373 1 1.3430 1 0.9620 1 1.0841 1 1.3421 1 1.3443 1 0.9735 1 1.2892 1 1.5705 1 1.3918 1 0.9762 1 0.8743 1 0.9415 1 0.8427 1 1.1583 1 1.2670 1 1.2335 1 1.3116 1 0.9209 1 0.9800 1 0.9917 1 1.3784 1 1.5196 1 1.5835 1 1.3545 1 1.2019 1 1.3663 1 1.1665 1 1.4862 1 0.9433 1 1.3789 1 0.9459 1 1.2778 1 1.3703 1 1.4044 1 0.9504 1 1.4324 1 1.2095 1 1.3163 1 0.8964 1 1.2717 1 0.9182 1 1.3570 1 1.0961 1 1.4232 1 1.0091 1 0.8478 1 1.3019 1 0.9138 1 Name: US, dtype: int64
eur_to_dol.drop(eur_to_dol[eur_to_dol['US']=="-"].index, inplace=True)
eur_to_dol
time | US | |
---|---|---|
0 | 1999-01-04 | 1.1789 |
1 | 1999-01-05 | 1.1790 |
2 | 1999-01-06 | 1.1743 |
3 | 1999-01-07 | 1.1632 |
4 | 1999-01-08 | 1.1659 |
... | ... | ... |
5694 | 2021-01-04 | 1.2296 |
5695 | 2021-01-05 | 1.2271 |
5696 | 2021-01-06 | 1.2338 |
5697 | 2021-01-07 | 1.2276 |
5698 | 2021-01-08 | 1.2250 |
5637 rows × 2 columns
eur_to_dol['US'] = eur_to_dol['US'].astype(float)
eur_to_dol
time | US | |
---|---|---|
0 | 1999-01-04 | 1.1789 |
1 | 1999-01-05 | 1.1790 |
2 | 1999-01-06 | 1.1743 |
3 | 1999-01-07 | 1.1632 |
4 | 1999-01-08 | 1.1659 |
... | ... | ... |
5694 | 2021-01-04 | 1.2296 |
5695 | 2021-01-05 | 1.2271 |
5696 | 2021-01-06 | 1.2338 |
5697 | 2021-01-07 | 1.2276 |
5698 | 2021-01-08 | 1.2250 |
5637 rows × 2 columns
fig, ax = plt.subplots()
ax.plot(eur_to_dol['time'],
eur_to_dol['US'])
plt.show()
eur_to_dol['rolling_30'] = eur_to_dol['US'].rolling(30).mean()
fig, ax = plt.subplots()
ax.plot(eur_to_dol['time'],
eur_to_dol['rolling_30'])
plt.show()
eur_to_dol_total = eur_to_dol.loc[(eur_to_dol['time']>='2001-01-01')&(eur_to_dol['time']<'2021-01-01')].copy()
eur_to_dol_bush = eur_to_dol.loc[(eur_to_dol['time']>='2001-01-01')&(eur_to_dol['time']<'2009-01-01')].copy()
eur_to_dol_obama = eur_to_dol.loc[(eur_to_dol['time']>='2009-01-01')&(eur_to_dol['time']<'2017-01-01')].copy()
eur_to_dol_trump = eur_to_dol.loc[(eur_to_dol['time']>='2017-01-01')&(eur_to_dol['time']<'2021-01-01')].copy()
import matplotlib.style as style
style.use('fivethirtyeight')
plt.figure(figsize=(12,8))
grid = plt.GridSpec(1,1, wspace = .25, hspace = .25)
ax1 = plt.subplot(grid[0,0])
for ax in [ax1]:
ax.set_yticks([.8,1,1.2,1.4,1.6])
ax.set_ylim(0.75,1.6)
ax.set_xlim(dt.datetime(2000,10,1),dt.datetime(2021,2,28))
ax.set_xticks([dt.datetime(2001,1,1),dt.datetime(2009,1,1), dt.datetime(2017,1,1), dt.datetime(2021,1,1)])
ax.set_xticklabels(['2001','2009','2017','2021'])
ax1.plot(eur_to_dol_bush['time'], eur_to_dol_bush['rolling_30'], color='lightgreen')
ax1.plot(eur_to_dol_obama['time'], eur_to_dol_obama['rolling_30'], color='lightsalmon')
ax1.plot(eur_to_dol_trump['time'], eur_to_dol_trump['rolling_30'], color='skyblue')
plt.axvline(dt.datetime(2009,1,1), alpha=0.2, linewidth=4, color='grey',linestyle='dotted')
plt.axvline(dt.datetime(2017,1,1), alpha=0.2, linewidth=4, color='grey',linestyle='dotted')
plt.axvline(dt.datetime(2021,1,1), alpha=0.2, linewidth=4, color='grey',linestyle='dotted')
plt.axvline(dt.datetime(2001,1,1), alpha=0.2, linewidth=4, color='grey',linestyle='dotted')
#plt.axhline(1.22, linewidth=2, color='black',linestyle='dashed', xmin=0.01, xmax=0.99)
ax.text(dt.datetime(2004,3,1),1.55,s="BUSH",size=15, color= 'lightgreen', weight='bold')
ax.text(dt.datetime(2012,1,1),1.55,s="OBAMA",size=15, color= 'lightsalmon', weight='bold')
ax.text(dt.datetime(2018,5,1),1.55,s="TRUMP",size=15, color= 'skyblue', weight='bold')
ax.text(dt.datetime(2000,2,1),1.7,s="EUR-USD rate averaged 1.22 under the last 3 US presidents", size=20, weight='bold')
ax.text(dt.datetime(2000,2,1),1.65,s="Some subtitle", size=20)
ax.text(dt.datetime(2000,1,1),0.65,s="DATAQUEST" + ' '*115+ 'Léon Hekkert',backgroundcolor='#4d4d4d', color='#f0f0f0', size=16)
ax.grid(axis='x')
plt.show()
above_avg = eur_to_dol_total.loc[eur_to_dol_total['rolling_30'] > 1.22]
below_avg = eur_to_dol_total.loc[eur_to_dol_total['rolling_30'] <= 1.22]
import matplotlib.style as style
style.use('fivethirtyeight')
plt.figure(figsize=(12,8))
grid = plt.GridSpec(1,1, wspace = .25, hspace = .25)
ax1 = plt.subplot(grid[0,0])
for ax in [ax1]:
ax.set_yticks([.8,1,1.2,1.4,1.6])
ax.set_ylim(0.75,1.6)
ax.set_xlim(dt.datetime(2000,10,1),dt.datetime(2021,2,28))
ax.set_xticks([dt.datetime(2001,1,1),dt.datetime(2009,1,1), dt.datetime(2017,1,1), dt.datetime(2021,1,1)])
ax.set_xticklabels(['2001','2009','2017','2021'])
ax1.plot(above_avg['time'], above_avg['rolling_30'], color='green')
ax1.plot(below_avg['time'], below_avg['rolling_30'], color='red')
plt.axvline(dt.datetime(2009,1,1), alpha=0.2, linewidth=4, color='grey',linestyle='dotted')
plt.axvline(dt.datetime(2017,1,1), alpha=0.2, linewidth=4, color='grey',linestyle='dotted')
plt.axvline(dt.datetime(2021,1,1), alpha=0.2, linewidth=4, color='grey',linestyle='dotted')
plt.axvline(dt.datetime(2001,1,1), alpha=0.2, linewidth=4, color='grey',linestyle='dotted')
#plt.axhline(1.22, linewidth=2, color='black',linestyle='dashed', xmin=0.01, xmax=0.99)
ax.text(dt.datetime(2004,3,1),1.55,s="BUSH",size=15, color= 'lightgreen', weight='bold')
ax.text(dt.datetime(2012,1,1),1.55,s="OBAMA",size=15, color= 'lightsalmon', weight='bold')
ax.text(dt.datetime(2018,5,1),1.55,s="TRUMP",size=15, color= 'skyblue', weight='bold')
ax.text(dt.datetime(2000,2,1),1.7,s="EUR-USD rate averaged 1.22 under the last 3 US presidents", size=20, weight='bold')
ax.text(dt.datetime(2000,2,1),1.65,s="Some subtitle", size=20)
ax.text(dt.datetime(2000,1,1),0.65,s="DATAQUEST" + ' '*115+ 'Léon Hekkert',backgroundcolor='#4d4d4d', color='#f0f0f0', size=16)
ax.grid(axis='x')
plt.show()