{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "867bfa18", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from IPython.core.display import display, HTML\n", "display(HTML(\"\"))" ] }, { "cell_type": "code", "execution_count": 2, "id": "419ed573", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/bz/5cj1tzbj5xn319rpqrw6gpmh0000gn/T/ipykernel_9008/758582820.py:17: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n", " x_rates.columns = x_rates.columns.str.replace(\"[\",\"\").str.replace(\"]\",\"\")\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " date australian_dollar bulgarian_lev brazilian_real canadian_dollar swiss_franc chinese_yuan cypriot_pound czech_koruna danish_krone estonian_kroon uk_pound 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 \n", "0 1999-01-04 1.9100 NaN NaN 1.8004 1.6168 NaN 0.5823 35.1070 7.4501 15.6466 0.7111 327.1500 9.1332 NaN 251.4800 9,433.6100 NaN NaN 81.4800 133.7300 1,398.5900 4.7170 0.6668 0.4432 11.6446 4.4798 8.8550 2.2229 45.5100 4.0712 1.3111 25.2875 9.4696 1.9554 189.0450 42.9910 42.6799 0.3723 1.1789 6.9358\n", "1 1999-01-05 1.8944 NaN NaN 1.7965 1.6123 NaN 0.5823 34.9170 7.4495 15.6466 0.7122 324.7000 9.1341 NaN 250.8000 9,314.5100 NaN NaN 81.5300 130.9600 1,373.0100 4.7174 0.6657 0.4432 11.5960 4.4805 8.7745 2.2011 44.7450 4.0245 1.3168 26.5876 9.4025 1.9655 188.7750 42.8480 42.5048 0.3728 1.1790 6.7975" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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" ], "text/plain": [ " date australian_dollar bulgarian_lev brazilian_real canadian_dollar swiss_franc chinese_yuan cypriot_pound czech_koruna danish_krone estonian_kroon uk_pound 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 \n", "4998 2018-04-13 1.5801 1.9558 4.1979 1.5482 1.1854 7.7363 NaN 25.3070 7.4467 NaN 0.8640 NaN 9.6687 7.4165 311.1300 16,939.5700 4.3316 80.3160 121.6000 132.6400 1,316.2600 NaN NaN NaN 22.3162 4.7714 9.5643 1.6703 63.9690 4.1763 4.6603 76.2186 10.3798 1.6158 NaN NaN 38.3670 5.0411 1.2317 14.8457\n", "4999 2018-04-16 1.5928 1.9558 4.2300 1.5596 1.1878 7.7726 NaN 25.2650 7.4473 NaN 0.8647 NaN 9.7104 7.4128 310.3000 17,039.9800 4.3327 81.0175 122.0000 132.7700 1,328.3300 NaN NaN NaN 22.3399 4.8057 9.5950 1.6853 64.3870 4.1627 4.6508 76.9420 10.4045 1.6221 NaN NaN 38.6440 5.0816 1.2370 14.9467" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import pprint\n", "\n", "pd.options.display.float_format = '{:20,.4f}'.format\n", "pd.set_option('display.max_rows', None)\n", "pd.set_option('display.max_columns', None)\n", "pd.set_option('display.width', 1000)\n", "pd.set_option('display.colheader_justify', 'center')\n", "pd.set_option('display.precision', 3)\n", "\n", "# open file\n", "x_rates = pd.read_csv(\"euro-daily-hist_1999_2020.csv\")\n", "\n", "# strip brackets and trailing space from country names\n", "# replace remaining space with underscore\n", "# lower case all column names\n", "x_rates.columns = x_rates.columns.str.replace(\"[\",\"\").str.replace(\"]\",\"\")\n", "x_rates.columns = x_rates.columns.str.rstrip()\n", "x_rates.columns = x_rates.columns.str.replace(\" \",\"_\")\n", "x_rates.columns = x_rates.columns.str.lower()\n", "\n", "# rename columns\n", "x_rates.rename(columns={\"period\\\\unit:\":\"date\",\n", " \"chinese_yuan_renminbi\":\"chinese_yuan\",\n", " \"uk_pound_sterling\":\"uk_pound\"}, inplace=True)\n", "\n", "# convert datetime\n", "x_rates[\"date\"] = pd.to_datetime(x_rates[\"date\"])\n", "\n", "# resort and reindex\n", "x_rates.sort_values(\"date\", inplace=True)\n", "x_rates.reset_index(drop=True, inplace=True)\n", "\n", "# convert hyphens in currency columns to NaN\n", "import numpy as np\n", "x_rates = x_rates.replace(\"-\", np.nan)\n", "\n", "# convert exchange rate values to float\n", "x_rates.iloc[:,1:] = x_rates.iloc[:,1:].astype(float)\n", "\n", "mini = x_rates.iloc[:5000]\n", "display(mini.head(2))\n", "display(mini.tail(2))" ] }, { "cell_type": "code", "execution_count": 3, "id": "200faa27", "metadata": {}, "outputs": [], "source": [ "# create a list of data frame names\n", "# not sure this is neccessary but trying df = df_list.index(currency) in the framer function doesn't seem to work\n", "# create a list of data frames for each currency with log rate of the exchange rate, 30 day rolling mean, and year\n", "col_names = []\n", "df_list = []\n", "df_dict = {}\n", "for column in mini.columns[1:]:\n", " df_name = column\n", " col_names.append(df_name)\n", " df = mini[[\"date\", column]].copy()\n", " df = df[df[column].notna()]\n", " df[\"log_rate\"] = np.log(df.iloc[:,1]/df.iloc[:,1].shift()) # getting the log of the exchange rate # double check this is the correct way to get log\n", " df[\"rolling_mean_30\"] = df[column].rolling(30).mean()\n", " df[\"year\"] = df[\"date\"].dt.year\n", "# df_dict[column] = df\n", " df_list.append(df)" ] }, { "cell_type": "code", "execution_count": 4, "id": "56ba3657", "metadata": {}, "outputs": [], "source": [ "# functions to build annual volatility given string of currency name\n", "# function to assign dataframe to variable name\n", "# i could include this in volatizer, but for now keep separate because these are frames of all the original data\n", "def framer(currency):\n", " index = col_names.index(currency)\n", " df = df_list[index] # this is a dataframe containing a single currency and the columns built in cell 3\n", " return df\n", "\n", "# function to create df with year and annual volatility for every row # i think i could use aggregation here but don't know how\n", "def volatizer(currency):\n", " all_the_years = [currency[currency['year'] == y] for y in currency['year'].unique()] # list of dataframes for each year\n", " c_name = currency.columns[1]\n", " df_dict = {}\n", " for frame in all_the_years:\n", " year_name = frame.iat[0,4] # the year for each df, becomes the \"year\" cell for annual volatility df\n", " annual_volatility = frame[\"log_rate\"].std()*253**.5 # volatility measured by standard deviation * 253 trading days per year raised to the 0.5 power\n", " df_dict[year_name] = annual_volatility\n", " df = pd.DataFrame.from_dict(df_dict, orient=\"index\", columns=[c_name+\"_annual_vol\"]) # indexing on year, not sure if this is cool\n", " return df\n", " \n", "brazilian_real = framer(\"brazilian_real\")\n", "br_vol = volatizer(brazilian_real)\n", "# now i need to run this on all 40 df's and get a new df with all of them joined\n", "# then build an hbar chart with the eight from above\n", "# maybe two hbar charts for cell phones?" ] }, { "cell_type": "code", "execution_count": 5, "id": "63114fb2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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brazilian_real_annual_vol
20000.2621
20010.2217
20020.3007
20030.2003
20040.1244
20050.1670
20060.1669
20070.1549
20080.3049
20090.1553
20100.1202
20110.1200
20120.1074
20130.1360
20140.1450
20150.2342
20160.1724
20170.1217
20180.1082
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" ], "text/plain": [ " brazilian_real_annual_vol\n", "2000 0.2621 \n", "2001 0.2217 \n", "2002 0.3007 \n", "2003 0.2003 \n", "2004 0.1244 \n", "2005 0.1670 \n", "2006 0.1669 \n", "2007 0.1549 \n", "2008 0.3049 \n", "2009 0.1553 \n", "2010 0.1202 \n", "2011 0.1200 \n", "2012 0.1074 \n", "2013 0.1360 \n", "2014 0.1450 \n", "2015 0.2342 \n", "2016 0.1724 \n", "2017 0.1217 \n", "2018 0.1082 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(br_vol)" ] }, { "cell_type": "code", "execution_count": 10, "id": "93f27d7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "# use string of currency to get index position and assign it to variable name\n", "index = col_names.index(\"chinese_yuan\")\n", "print(index)\n", "chinese_yuan = df_list[5]\n", "\n", "# function to create df for each year in currency df\n", "def split_years(df):\n", " return [df[df['year'] == y] for y in df['year'].unique()]\n", "\n", "all_the_years = split_years(chinese_yuan)\n", "# see if I can make split_years split and heads\n", "\n", "\n", "# # determine annual volatility for euro to currency\n", "# for frame in all_the_years:\n", "# year_name = frame.iat[0,4]\n", "# annual_volatility = frame[\"log_rate\"].std()*253**.5 # volatility measured by standard deviation * 253 trading days per year\n", "# print(\"The Euro to Chinese yuan volatility for\", year_name, \"is\" ,annual_volatility)" ] }, { "cell_type": "code", "execution_count": 11, "id": "02f68967", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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3632000-05-257.4336-0.01297.59122000
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3652000-05-297.66620.01297.57012000
3662000-05-307.74640.01047.56522000
3672000-05-317.7001-0.00607.55862000
3682000-06-017.72240.00297.55192000
3692000-06-027.74310.00277.54802000
3702000-06-057.80770.00837.54722000
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3752000-06-127.88550.00397.59152000
3762000-06-137.93180.00597.60442000
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3782000-06-157.8830-0.01177.64032000
3792000-06-167.91700.00437.65632000
3802000-06-197.97900.00787.67532000
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3882000-06-297.86650.00947.76372000
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3962000-07-117.88550.00047.85532000
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3992000-07-147.7340-0.00057.86092000
4002000-07-177.74070.00097.85872000
4012000-07-187.74150.00017.85502000
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4032000-07-207.63140.00047.83332000
4042000-07-217.74980.01547.82982000
4052000-07-247.7241-0.00337.82442000
4062000-07-257.78870.00837.81972000
4072000-07-267.7721-0.00217.81292000
4082000-07-277.7713-0.00017.80922000
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4102000-07-317.6504-0.00367.79022000
4112000-08-017.66780.00237.78272000
4122000-08-027.5668-0.01337.77362000
4132000-08-037.4725-0.01257.76322000
4142000-08-047.47500.00037.75272000
4152000-08-077.51880.00587.74602000
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4172000-08-097.4270-0.00827.72402000
4182000-08-107.47500.00647.71102000
4192000-08-117.55860.01117.69932000
4202000-08-147.4584-0.01337.68602000
4212000-08-157.54700.01187.67582000
4222000-08-167.5089-0.00517.66302000
4232000-08-177.56520.00757.65192000
4242000-08-187.5544-0.00147.64212000
4252000-08-217.4683-0.01157.62832000
4262000-08-227.4261-0.00577.61292000
4272000-08-237.3814-0.00607.59692000
4282000-08-247.45920.01057.58762000
4292000-08-257.46670.00107.57872000
4302000-08-287.4551-0.00167.56922000
4312000-08-297.4203-0.00477.55852000
4322000-08-307.3740-0.00637.55002000
4332000-08-317.3715-0.00037.54132000
4342000-09-017.3682-0.00047.52862000
4352000-09-047.44930.01097.51952000
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4382000-09-077.1811-0.01627.46952000
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4412000-09-127.12980.00067.41922000
4422000-09-137.16290.00467.40572000
4432000-09-147.19520.00457.39652000
4442000-09-157.1306-0.00907.38502000
4452000-09-187.0570-0.01047.36962000
4462000-09-197.06940.00187.35572000
4472000-09-207.0156-0.00767.34192000
4482000-09-217.05530.00567.32802000
4492000-09-227.35830.04207.32132000
4502000-09-257.2589-0.01367.31462000
4512000-09-267.2482-0.00157.30472000
4522000-09-277.33420.01187.29892000
4532000-09-287.3102-0.00337.29042000
4542000-09-297.2548-0.00767.28042000
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4562000-10-037.2531-0.00447.26852000
4572000-10-047.2258-0.00387.26332000
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4602000-10-097.1969-0.00097.23972000
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4652000-10-167.0446-0.01357.19962000
4662000-10-177.0321-0.00187.18882000
4672000-10-187.05610.00347.18072000
4682000-10-196.9618-0.01357.17342000
4692000-10-206.99570.00497.16562000
4702000-10-236.9336-0.00897.15922000
4712000-10-246.94110.00117.15292000
4722000-10-256.8757-0.00957.14332000
4732000-10-266.8302-0.00667.13122000
4742000-10-276.88980.00877.12312000
4752000-10-307.02060.01887.12192000
4762000-10-316.9668-0.00777.11852000
4772000-11-017.08010.01617.12062000
4782000-11-027.15630.01077.12402000
4792000-11-037.22580.00977.11962000
4802000-11-067.1737-0.00727.11682000
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4822000-11-087.0843-0.00337.10372000
4832000-11-097.0611-0.00337.09542000
4842000-11-107.17860.01657.09292000
4852000-11-137.1348-0.00617.08792000
4862000-11-147.1041-0.00437.08292000
4872000-11-157.11490.00157.07922000
4882000-11-167.0926-0.00317.07322000
4892000-11-177.0644-0.00407.06862000
4902000-11-207.0255-0.00557.06292000
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4922000-11-226.9767-0.00537.04802000
4932000-11-236.9750-0.00027.04192000
4942000-11-246.98250.00117.03672000
4952000-11-276.9576-0.00367.03382000
4962000-11-287.08590.01837.03562000
4972000-11-297.15960.01037.03902000
4982000-11-307.18770.00397.04652000
4992000-12-017.23000.00597.05432000
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5092000-12-157.43610.01637.16652000
5102000-12-187.4212-0.00207.17482000
5112000-12-197.3690-0.00717.18352000
5122000-12-207.49810.01747.19732000
5132000-12-217.57010.00967.21432000
5142000-12-227.64790.01027.22992000
5172000-12-277.70590.00767.24892000
5182000-12-287.6852-0.00277.26832000
5192000-12-297.70170.00217.28792000
\n", "
" ], "text/plain": [ " date chinese_yuan log_rate rolling_mean_30 year\n", "268 2000-01-13 8.5054 NaN NaN 2000\n", "269 2000-01-14 8.4632 -0.0050 NaN 2000\n", "270 2000-01-17 8.3548 -0.0129 NaN 2000\n", "271 2000-01-18 8.3540 -0.0001 NaN 2000\n", "272 2000-01-19 8.3639 0.0012 NaN 2000\n", "273 2000-01-20 8.3507 -0.0016 NaN 2000\n", "274 2000-01-21 8.3573 0.0008 NaN 2000\n", "275 2000-01-24 8.2993 -0.0070 NaN 2000\n", "276 2000-01-25 8.2836 -0.0019 NaN 2000\n", "277 2000-01-26 8.2927 0.0011 NaN 2000\n", "278 2000-01-27 8.2571 -0.0043 NaN 2000\n", "279 2000-01-28 8.1512 -0.0129 NaN 2000\n", "280 2000-01-31 8.1040 -0.0058 NaN 2000\n", "281 2000-02-01 8.0370 -0.0083 NaN 2000\n", "282 2000-02-02 8.0428 0.0007 NaN 2000\n", "283 2000-02-03 8.0684 0.0032 NaN 2000\n", "284 2000-02-04 8.1404 0.0089 NaN 2000\n", "285 2000-02-07 8.0841 -0.0069 NaN 2000\n", "286 2000-02-08 8.1967 0.0138 NaN 2000\n", "287 2000-02-09 8.2224 0.0031 NaN 2000\n", "288 2000-02-10 8.1677 -0.0067 NaN 2000\n", "289 2000-02-11 8.1123 -0.0068 NaN 2000\n", "290 2000-02-14 8.1669 0.0067 NaN 2000\n", "291 2000-02-15 8.0933 -0.0091 NaN 2000\n", "292 2000-02-16 8.1148 0.0027 NaN 2000\n", "293 2000-02-17 8.2100 0.0117 NaN 2000\n", "294 2000-02-18 8.1586 -0.0063 NaN 2000\n", "295 2000-02-21 8.1586 0.0000 NaN 2000\n", "296 2000-02-22 8.2952 0.0166 NaN 2000\n", "297 2000-02-23 8.3333 0.0046 8.2247 2000\n", "298 2000-02-24 8.1926 -0.0170 8.2142 2000\n", "299 2000-02-25 8.1222 -0.0086 8.2029 2000\n", "300 2000-02-28 7.9782 -0.0179 8.1903 2000\n", "301 2000-02-29 8.0403 0.0078 8.1799 2000\n", "302 2000-03-01 8.0014 -0.0048 8.1678 2000\n", "303 2000-03-02 8.0494 0.0060 8.1577 2000\n", "304 2000-03-03 7.9948 -0.0068 8.1456 2000\n", "305 2000-03-06 7.9790 -0.0020 8.1350 2000\n", "306 2000-03-07 7.9401 -0.0049 8.1235 2000\n", "307 2000-03-08 7.8987 -0.0052 8.1104 2000\n", "308 2000-03-09 7.9592 0.0076 8.1005 2000\n", "309 2000-03-10 7.9567 -0.0003 8.0940 2000\n", "310 2000-03-13 8.0419 0.0107 8.0919 2000\n", "311 2000-03-14 7.9625 -0.0099 8.0894 2000\n", "312 2000-03-15 7.9848 0.0028 8.0875 2000\n", "313 2000-03-16 7.9981 0.0017 8.0851 2000\n", "314 2000-03-17 8.0055 0.0009 8.0806 2000\n", "315 2000-03-20 8.0411 0.0044 8.0792 2000\n", "316 2000-03-21 8.0295 -0.0014 8.0736 2000\n", "317 2000-03-22 7.9484 -0.0102 8.0645 2000\n", "318 2000-03-23 7.9923 0.0055 8.0587 2000\n", "319 2000-03-24 8.0411 0.0061 8.0563 2000\n", "320 2000-03-27 8.0386 -0.0003 8.0520 2000\n", "321 2000-03-28 7.9848 -0.0067 8.0484 2000\n", "322 2000-03-29 7.9194 -0.0082 8.0419 2000\n", "323 2000-03-30 7.9087 -0.0014 8.0318 2000\n", "324 2000-03-31 7.9070 -0.0002 8.0234 2000\n", "325 2000-04-03 7.9161 0.0012 8.0154 2000\n", "326 2000-04-04 7.8946 -0.0027 8.0020 2000\n", "327 2000-04-05 8.0063 0.0140 7.9911 2000\n", "328 2000-04-06 7.9666 -0.0050 7.9836 2000\n", "329 2000-04-07 7.9161 -0.0064 7.9767 2000\n", "330 2000-04-10 7.9376 0.0027 7.9754 2000\n", "331 2000-04-11 7.9410 0.0004 7.9720 2000\n", "332 2000-04-12 7.9261 -0.0019 7.9695 2000\n", "333 2000-04-13 7.8938 -0.0041 7.9643 2000\n", "334 2000-04-14 7.8963 0.0003 7.9611 2000\n", "335 2000-04-17 7.9236 0.0035 7.9592 2000\n", "336 2000-04-18 7.8607 -0.0080 7.9566 2000\n", "337 2000-04-19 7.8309 -0.0038 7.9543 2000\n", "338 2000-04-20 7.7605 -0.0090 7.9477 2000\n", "341 2000-04-25 7.6993 -0.0079 7.9391 2000\n", "342 2000-04-26 7.6090 -0.0118 7.9247 2000\n", "343 2000-04-27 7.5842 -0.0033 7.9121 2000\n", "344 2000-04-28 7.5197 -0.0085 7.8966 2000\n", "346 2000-05-02 7.5453 0.0034 7.8815 2000\n", "347 2000-05-03 7.3773 -0.0225 7.8605 2000\n", "348 2000-05-04 7.4054 0.0038 7.8393 2000\n", "349 2000-05-05 7.4361 0.0041 7.8196 2000\n", "350 2000-05-08 7.4096 -0.0036 7.8016 2000\n", "351 2000-05-09 7.4311 0.0029 7.7829 2000\n", "352 2000-05-10 7.5379 0.0143 7.7661 2000\n", "353 2000-05-11 7.5147 -0.0031 7.7487 2000\n", "354 2000-05-12 7.4733 -0.0055 7.7316 2000\n", "355 2000-05-15 7.5710 0.0130 7.7200 2000\n", "356 2000-05-16 7.5031 -0.0090 7.7065 2000\n", "357 2000-05-17 7.3847 -0.0159 7.6891 2000\n", "358 2000-05-18 7.4013 0.0022 7.6719 2000\n", "359 2000-05-19 7.3458 -0.0075 7.6536 2000\n", "360 2000-05-22 7.4493 0.0140 7.6350 2000\n", "361 2000-05-23 7.5412 0.0123 7.6209 2000\n", "362 2000-05-24 7.5304 -0.0014 7.6080 2000\n", "363 2000-05-25 7.4336 -0.0129 7.5912 2000\n", "364 2000-05-26 7.5677 0.0179 7.5788 2000\n", "365 2000-05-29 7.6662 0.0129 7.5701 2000\n", "366 2000-05-30 7.7464 0.0104 7.5652 2000\n", "367 2000-05-31 7.7001 -0.0060 7.5586 2000\n", "368 2000-06-01 7.7224 0.0029 7.5519 2000\n", "369 2000-06-02 7.7431 0.0027 7.5480 2000\n", "370 2000-06-05 7.8077 0.0083 7.5472 2000\n", "371 2000-06-06 7.8507 0.0055 7.5503 2000\n", "372 2000-06-07 7.9078 0.0072 7.5572 2000\n", "373 2000-06-08 8.0022 0.0119 7.5703 2000\n", "374 2000-06-09 7.8549 -0.0186 7.5793 2000\n", "375 2000-06-12 7.8855 0.0039 7.5915 2000\n", "376 2000-06-13 7.9318 0.0059 7.6044 2000\n", "377 2000-06-14 7.9757 0.0055 7.6244 2000\n", "378 2000-06-15 7.8830 -0.0117 7.6403 2000\n", "379 2000-06-16 7.9170 0.0043 7.6563 2000\n", "380 2000-06-19 7.9790 0.0078 7.6753 2000\n", "381 2000-06-20 7.8946 -0.0106 7.6907 2000\n", "382 2000-06-21 7.8383 -0.0072 7.7008 2000\n", "383 2000-06-22 7.7845 -0.0069 7.7097 2000\n", "384 2000-06-23 7.7911 0.0008 7.7203 2000\n", "385 2000-06-26 7.7200 -0.0092 7.7253 2000\n", "386 2000-06-27 7.7812 0.0079 7.7346 2000\n", "387 2000-06-28 7.7928 0.0015 7.7482 2000\n", "388 2000-06-29 7.8665 0.0094 7.7637 2000\n", "389 2000-06-30 7.9095 0.0055 7.7825 2000\n", "390 2000-07-03 7.8574 -0.0066 7.7961 2000\n", "391 2000-07-04 7.8524 -0.0006 7.8065 2000\n", "392 2000-07-05 7.8938 0.0053 7.8186 2000\n", "393 2000-07-06 7.8979 0.0005 7.8340 2000\n", "394 2000-07-07 7.8491 -0.0062 7.8434 2000\n", "395 2000-07-10 7.8822 0.0042 7.8506 2000\n", "396 2000-07-11 7.8855 0.0004 7.8553 2000\n", "397 2000-07-12 7.8623 -0.0029 7.8607 2000\n", "398 2000-07-13 7.7382 -0.0159 7.8612 2000\n", "399 2000-07-14 7.7340 -0.0005 7.8609 2000\n", "400 2000-07-17 7.7407 0.0009 7.8587 2000\n", "401 2000-07-18 7.7415 0.0001 7.8550 2000\n", "402 2000-07-19 7.6281 -0.0148 7.8457 2000\n", "403 2000-07-20 7.6314 0.0004 7.8333 2000\n", "404 2000-07-21 7.7498 0.0154 7.8298 2000\n", "405 2000-07-24 7.7241 -0.0033 7.8244 2000\n", "406 2000-07-25 7.7887 0.0083 7.8197 2000\n", "407 2000-07-26 7.7721 -0.0021 7.8129 2000\n", "408 2000-07-27 7.7713 -0.0001 7.8092 2000\n", "409 2000-07-28 7.6777 -0.0121 7.8012 2000\n", "410 2000-07-31 7.6504 -0.0036 7.7902 2000\n", "411 2000-08-01 7.6678 0.0023 7.7827 2000\n", "412 2000-08-02 7.5668 -0.0133 7.7736 2000\n", "413 2000-08-03 7.4725 -0.0125 7.7632 2000\n", "414 2000-08-04 7.4750 0.0003 7.7527 2000\n", "415 2000-08-07 7.5188 0.0058 7.7460 2000\n", "416 2000-08-08 7.4882 -0.0041 7.7362 2000\n", "417 2000-08-09 7.4270 -0.0082 7.7240 2000\n", "418 2000-08-10 7.4750 0.0064 7.7110 2000\n", "419 2000-08-11 7.5586 0.0111 7.6993 2000\n", "420 2000-08-14 7.4584 -0.0133 7.6860 2000\n", "421 2000-08-15 7.5470 0.0118 7.6758 2000\n", "422 2000-08-16 7.5089 -0.0051 7.6630 2000\n", "423 2000-08-17 7.5652 0.0075 7.6519 2000\n", "424 2000-08-18 7.5544 -0.0014 7.6421 2000\n", "425 2000-08-21 7.4683 -0.0115 7.6283 2000\n", "426 2000-08-22 7.4261 -0.0057 7.6129 2000\n", "427 2000-08-23 7.3814 -0.0060 7.5969 2000\n", "428 2000-08-24 7.4592 0.0105 7.5876 2000\n", "429 2000-08-25 7.4667 0.0010 7.5787 2000\n", "430 2000-08-28 7.4551 -0.0016 7.5692 2000\n", "431 2000-08-29 7.4203 -0.0047 7.5585 2000\n", "432 2000-08-30 7.3740 -0.0063 7.5500 2000\n", "433 2000-08-31 7.3715 -0.0003 7.5413 2000\n", "434 2000-09-01 7.3682 -0.0004 7.5286 2000\n", "435 2000-09-04 7.4493 0.0109 7.5195 2000\n", "436 2000-09-05 7.3549 -0.0128 7.5050 2000\n", "437 2000-09-06 7.2987 -0.0077 7.4892 2000\n", "438 2000-09-07 7.1811 -0.0162 7.4695 2000\n", "439 2000-09-08 7.2300 0.0068 7.4546 2000\n", "440 2000-09-11 7.1257 -0.0145 7.4371 2000\n", "441 2000-09-12 7.1298 0.0006 7.4192 2000\n", "442 2000-09-13 7.1629 0.0046 7.4057 2000\n", "443 2000-09-14 7.1952 0.0045 7.3965 2000\n", "444 2000-09-15 7.1306 -0.0090 7.3850 2000\n", "445 2000-09-18 7.0570 -0.0104 7.3696 2000\n", "446 2000-09-19 7.0694 0.0018 7.3557 2000\n", "447 2000-09-20 7.0156 -0.0076 7.3419 2000\n", "448 2000-09-21 7.0553 0.0056 7.3280 2000\n", "449 2000-09-22 7.3583 0.0420 7.3213 2000\n", "450 2000-09-25 7.2589 -0.0136 7.3146 2000\n", "451 2000-09-26 7.2482 -0.0015 7.3047 2000\n", "452 2000-09-27 7.3342 0.0118 7.2989 2000\n", "453 2000-09-28 7.3102 -0.0033 7.2904 2000\n", "454 2000-09-29 7.2548 -0.0076 7.2804 2000\n", "455 2000-10-02 7.2854 0.0042 7.2743 2000\n", "456 2000-10-03 7.2531 -0.0044 7.2685 2000\n", "457 2000-10-04 7.2258 -0.0038 7.2633 2000\n", "458 2000-10-05 7.2722 0.0064 7.2571 2000\n", "459 2000-10-06 7.2035 -0.0095 7.2483 2000\n", "460 2000-10-09 7.1969 -0.0009 7.2397 2000\n", "461 2000-10-10 7.2184 0.0030 7.2330 2000\n", "462 2000-10-11 7.2175 -0.0001 7.2278 2000\n", "463 2000-10-12 7.1579 -0.0083 7.2206 2000\n", "464 2000-10-13 7.1406 -0.0024 7.2130 2000\n", "465 2000-10-16 7.0446 -0.0135 7.1996 2000\n", "466 2000-10-17 7.0321 -0.0018 7.1888 2000\n", "467 2000-10-18 7.0561 0.0034 7.1807 2000\n", "468 2000-10-19 6.9618 -0.0135 7.1734 2000\n", "469 2000-10-20 6.9957 0.0049 7.1656 2000\n", "470 2000-10-23 6.9336 -0.0089 7.1592 2000\n", "471 2000-10-24 6.9411 0.0011 7.1529 2000\n", "472 2000-10-25 6.8757 -0.0095 7.1433 2000\n", "473 2000-10-26 6.8302 -0.0066 7.1312 2000\n", "474 2000-10-27 6.8898 0.0087 7.1231 2000\n", "475 2000-10-30 7.0206 0.0188 7.1219 2000\n", "476 2000-10-31 6.9668 -0.0077 7.1185 2000\n", "477 2000-11-01 7.0801 0.0161 7.1206 2000\n", "478 2000-11-02 7.1563 0.0107 7.1240 2000\n", "479 2000-11-03 7.2258 0.0097 7.1196 2000\n", "480 2000-11-06 7.1737 -0.0072 7.1168 2000\n", "481 2000-11-07 7.1075 -0.0093 7.1121 2000\n", "482 2000-11-08 7.0843 -0.0033 7.1037 2000\n", "483 2000-11-09 7.0611 -0.0033 7.0954 2000\n", "484 2000-11-10 7.1786 0.0165 7.0929 2000\n", "485 2000-11-13 7.1348 -0.0061 7.0879 2000\n", "486 2000-11-14 7.1041 -0.0043 7.0829 2000\n", "487 2000-11-15 7.1149 0.0015 7.0792 2000\n", "488 2000-11-16 7.0926 -0.0031 7.0732 2000\n", "489 2000-11-17 7.0644 -0.0040 7.0686 2000\n", "490 2000-11-20 7.0255 -0.0055 7.0629 2000\n", "491 2000-11-21 7.0139 -0.0017 7.0561 2000\n", "492 2000-11-22 6.9767 -0.0053 7.0480 2000\n", "493 2000-11-23 6.9750 -0.0002 7.0419 2000\n", "494 2000-11-24 6.9825 0.0011 7.0367 2000\n", "495 2000-11-27 6.9576 -0.0036 7.0338 2000\n", "496 2000-11-28 7.0859 0.0183 7.0356 2000\n", "497 2000-11-29 7.1596 0.0103 7.0390 2000\n", "498 2000-11-30 7.1877 0.0039 7.0465 2000\n", "499 2000-12-01 7.2300 0.0059 7.0543 2000\n", "500 2000-12-04 7.3732 0.0196 7.0690 2000\n", "501 2000-12-05 7.2920 -0.0111 7.0807 2000\n", "502 2000-12-06 7.3069 0.0020 7.0951 2000\n", "503 2000-12-07 7.4021 0.0129 7.1141 2000\n", "504 2000-12-08 7.3624 -0.0054 7.1299 2000\n", "505 2000-12-11 7.2871 -0.0103 7.1388 2000\n", "506 2000-12-12 7.2689 -0.0025 7.1488 2000\n", "507 2000-12-13 7.2407 -0.0039 7.1542 2000\n", "508 2000-12-14 7.3160 0.0103 7.1595 2000\n", "509 2000-12-15 7.4361 0.0163 7.1665 2000\n", "510 2000-12-18 7.4212 -0.0020 7.1748 2000\n", "511 2000-12-19 7.3690 -0.0071 7.1835 2000\n", "512 2000-12-20 7.4981 0.0174 7.1973 2000\n", "513 2000-12-21 7.5701 0.0096 7.2143 2000\n", "514 2000-12-22 7.6479 0.0102 7.2299 2000\n", "517 2000-12-27 7.7059 0.0076 7.2489 2000\n", "518 2000-12-28 7.6852 -0.0027 7.2683 2000\n", "519 2000-12-29 7.7017 0.0021 7.2879 2000" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(all_the_years[0])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, 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