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import pandas as pd

boc_forex_names = pd.read_csv("/Volumes/GoogleDrive/My Drive/Forecasting/bootcamp_datasets/boc_exchange/column_names.csv")
boc_forex_names
id label description
0 IEXE1601.CL AUD_CLOSE Australian dollar (close)
1 IEXE0301.CL DKK_CLOSE Danish krone (close)
2 EUROCAE01.CL EUR_CLOSE European Euro (close)
3 IEXE1401.CL HKD_CLOSE Hong Kong dollar (close)
4 IEXE0701.CL JPY_CLOSE Japanese yen (close)
5 IEXE2001.CL MXN_CLOSE Mexican peso (close)
6 IEXE1901.CL NZD_CLOSE New Zealand dollar (close)
7 IEXE0901.CL NOK_CLOSE Norwegian krone (close)
8 IEXE1001.CL SEK_CLOSE Swedish krona (close)
9 IEXE1101.CL CHF_CLOSE Swiss franc (close)
10 IEXE1201.CL GBP_CLOSE U.K. pound sterling (close)
11 IEXE0102 USD_CLOSE U.S. dollar (close)
12 IEXE0103 USD_HIGH U.S. dollar (high)
13 IEXE0104 USD_LOW U.S. dollar (low)
14 IEXE0106 USD_CLOSE_90_DAY U.S. dollar closing,90-day 
column_name_map = boc_forex_names[['id', 'label']].set_index('id').to_dict()['label']

boc_forex_df = pd.read_csv("/Volumes/GoogleDrive/My Drive/Forecasting/bootcamp_datasets/boc_exchange/LEGACY_CLOSING_RATES.csv")
boc_forex_df
date IEXE1601.CL IEXE0301.CL EUROCAE01.CL IEXE1401.CL IEXE0701.CL IEXE2001.CL IEXE1901.CL IEXE0901.CL IEXE1001.CL IEXE1101.CL IEXE1201.CL IEXE0102 IEXE0103 IEXE0104 IEXE0106
0 2007-05-01 0.9200 0.2000 1.5100 0.141970 0.009271 0.10000 0.8200 0.1860 0.1700 0.9100 2.2199 1.1105 1.1116 1.1048 1.1075
1 2007-05-02 0.9100 0.2000 1.5100 0.141740 0.009232 0.10000 0.8200 0.1854 0.1700 0.9100 2.2055 1.1087 1.1115 1.1066 1.1057
2 2007-05-03 0.9100 0.2000 1.5000 0.141496 0.009190 0.10000 0.8100 0.1847 0.1600 0.9100 2.1999 1.1066 1.1086 1.1054 1.1036
3 2007-05-04 0.9100 0.2000 1.5100 0.141616 0.009218 0.10000 0.8100 0.1854 0.1600 0.9100 2.2075 1.1075 1.1077 1.1032 1.1046
4 2007-05-07 0.9100 0.2000 1.5000 0.140908 0.009177 0.10000 0.8100 0.1843 0.1600 0.9100 2.1957 1.1018 1.1042 1.1007 1.0988
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2499 2017-04-24 1.0218 0.1974 1.4684 0.173684 0.012310 0.07215 0.9477 0.1579 0.1525 1.3568 1.7280 1.3511 1.3521 1.3425 1.3493
2500 2017-04-25 1.0224 0.1994 1.4837 0.174374 0.012210 0.07183 0.9426 0.1586 0.1548 1.3661 1.7407 1.3565 1.3626 1.3565 1.3547
2501 2017-04-26 1.0176 0.1995 1.4847 0.174965 0.012260 0.07098 0.9382 0.1585 0.1554 1.3707 1.7493 1.3612 1.3623 1.3543 1.3594
2502 2017-04-27 1.0176 0.1992 1.4815 0.175103 0.012250 0.07151 0.9369 0.1590 0.1543 1.3704 1.7584 1.3624 1.3670 1.3574 1.3605
2503 2017-04-28 1.0222 0.1999 1.4870 0.175485 0.012250 0.07253 0.9373 0.1590 0.1541 1.3719 1.7679 1.3650 1.3697 1.3635 1.3631

2504 rows × 16 columns

boc_forex_df = boc_forex_df.rename(column_name_map, axis=1)
boc_forex_df = boc_forex_df[['date'] + [col for col in boc_forex_df if col.endswith('_CLOSE')]]
boc_forex_df
date AUD_CLOSE DKK_CLOSE EUR_CLOSE HKD_CLOSE JPY_CLOSE MXN_CLOSE NZD_CLOSE NOK_CLOSE SEK_CLOSE CHF_CLOSE GBP_CLOSE USD_CLOSE
0 2007-05-01 0.9200 0.2000 1.5100 0.141970 0.009271 0.10000 0.8200 0.1860 0.1700 0.9100 2.2199 1.1105
1 2007-05-02 0.9100 0.2000 1.5100 0.141740 0.009232 0.10000 0.8200 0.1854 0.1700 0.9100 2.2055 1.1087
2 2007-05-03 0.9100 0.2000 1.5000 0.141496 0.009190 0.10000 0.8100 0.1847 0.1600 0.9100 2.1999 1.1066
3 2007-05-04 0.9100 0.2000 1.5100 0.141616 0.009218 0.10000 0.8100 0.1854 0.1600 0.9100 2.2075 1.1075
4 2007-05-07 0.9100 0.2000 1.5000 0.140908 0.009177 0.10000 0.8100 0.1843 0.1600 0.9100 2.1957 1.1018
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2499 2017-04-24 1.0218 0.1974 1.4684 0.173684 0.012310 0.07215 0.9477 0.1579 0.1525 1.3568 1.7280 1.3511
2500 2017-04-25 1.0224 0.1994 1.4837 0.174374 0.012210 0.07183 0.9426 0.1586 0.1548 1.3661 1.7407 1.3565
2501 2017-04-26 1.0176 0.1995 1.4847 0.174965 0.012260 0.07098 0.9382 0.1585 0.1554 1.3707 1.7493 1.3612
2502 2017-04-27 1.0176 0.1992 1.4815 0.175103 0.012250 0.07151 0.9369 0.1590 0.1543 1.3704 1.7584 1.3624
2503 2017-04-28 1.0222 0.1999 1.4870 0.175485 0.012250 0.07253 0.9373 0.1590 0.1541 1.3719 1.7679 1.3650

2504 rows × 13 columns

boc_forex_df.plot()

<AxesSubplot:>
../../_images/4c9c530b8a4cee58a1d68cd8c4dbb957d9ac8be0f633f2ac0963e1aa2022a5ad.png 
boc_forex_df.to_csv("/Volumes/GoogleDrive/My Drive/Forecasting/bootcamp_datasets/boc_exchange/dataset.csv", index=False)

pd.read_csv("/Volumes/GoogleDrive/My Drive/Forecasting/bootcamp_datasets/boc_exchange/dataset.csv")
date AUD_CLOSE DKK_CLOSE EUR_CLOSE HKD_CLOSE JPY_CLOSE MXN_CLOSE NZD_CLOSE NOK_CLOSE SEK_CLOSE CHF_CLOSE GBP_CLOSE USD_CLOSE
0 2007-05-01 0.9200 0.2000 1.5100 0.141970 0.009271 0.10000 0.8200 0.1860 0.1700 0.9100 2.2199 1.1105
1 2007-05-02 0.9100 0.2000 1.5100 0.141740 0.009232 0.10000 0.8200 0.1854 0.1700 0.9100 2.2055 1.1087
2 2007-05-03 0.9100 0.2000 1.5000 0.141496 0.009190 0.10000 0.8100 0.1847 0.1600 0.9100 2.1999 1.1066
3 2007-05-04 0.9100 0.2000 1.5100 0.141616 0.009218 0.10000 0.8100 0.1854 0.1600 0.9100 2.2075 1.1075
4 2007-05-07 0.9100 0.2000 1.5000 0.140908 0.009177 0.10000 0.8100 0.1843 0.1600 0.9100 2.1957 1.1018
... ... ... ... ... ... ... ... ... ... ... ... ... ...
2499 2017-04-24 1.0218 0.1974 1.4684 0.173684 0.012310 0.07215 0.9477 0.1579 0.1525 1.3568 1.7280 1.3511
2500 2017-04-25 1.0224 0.1994 1.4837 0.174374 0.012210 0.07183 0.9426 0.1586 0.1548 1.3661 1.7407 1.3565
2501 2017-04-26 1.0176 0.1995 1.4847 0.174965 0.012260 0.07098 0.9382 0.1585 0.1554 1.3707 1.7493 1.3612
2502 2017-04-27 1.0176 0.1992 1.4815 0.175103 0.012250 0.07151 0.9369 0.1590 0.1543 1.3704 1.7584 1.3624
2503 2017-04-28 1.0222 0.1999 1.4870 0.175485 0.012250 0.07253 0.9373 0.1590 0.1541 1.3719 1.7679 1.3650

2504 rows × 13 columns

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