{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5beea67c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
       "    return false;\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "IPython.OutputArea.prototype._should_scroll = function(lines) {\n",
    "    return false;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99b92926",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       ".container {width:84% !important;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'euro-daily-hist_1999_2020.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/bz/5cj1tzbj5xn319rpqrw6gpmh0000gn/T/ipykernel_33875/3007294150.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;31m# open file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mx_rates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"euro-daily-hist_1999_2020.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;31m# head, tail, and basic info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m                 )\n\u001b[0;32m--> 311\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, error_bad_lines, warn_bad_lines, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[1;32m    584\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 482\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    809\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"has_index_names\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 811\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    812\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1038\u001b[0m             )\n\u001b[1;32m   1039\u001b[0m         \u001b[0;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1042\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m         \u001b[0;31m# open handles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/pandas/io/parsers/base_parser.py\u001b[0m in \u001b[0;36m_open_handles\u001b[0;34m(self, src, kwds)\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \"\"\"\n\u001b[0;32m--> 222\u001b[0;31m         self.handles = get_handle(\n\u001b[0m\u001b[1;32m    223\u001b[0m             \u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m             \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/envs/geo_env/lib/python3.9/site-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    700\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    701\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    703\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'euro-daily-hist_1999_2020.csv'"
     ]
    }
   ],
   "source": [
    "# import libraries and set display options\n",
    "import pandas as pd\n",
    "import pprint\n",
    "\n",
    "# set frame width and center output\n",
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"\"\"\n",
    "<style>\n",
    ".container {width:84% !important;\n",
    "}\n",
    "</style>\n",
    "\"\"\"))\n",
    "\n",
    "# pretty print options\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', 3000)\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",
    "# head, tail, and basic info\n",
    "# display(x_rates.head())\n",
    "# display(x_rates.tail())\n",
    "# display(x_rates.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eed88132",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53c4ed0a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# create a us dollar to euro dataframe and add 30 day rolling mean column\n",
    "us_dollar = x_rates[[\"date\",\"us_dollar\"]].copy()\n",
    "us_dollar[\"rolling_mean_30\"] = us_dollar[\"us_dollar\"].rolling(30).mean()\n",
    "\n",
    "# line graph for dollar to euro exchange rate\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "plt.style.use('ggplot') # set style for graphs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1153ca91",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# create nan group and get dates\n",
    "nan_dollar_group = us_dollar[us_dollar[\"us_dollar\"].isnull()]\n",
    "nan_days = nan_dollar_group[\"date\"]\n",
    "\n",
    "# create a dollar to euro dataframe with NaN dates removed\n",
    "US_dollar_nonull = x_rates[x_rates[\"us_dollar\"].notna()].copy()\n",
    "US_dollar_nonull[\"rolling_mean_30\"] = US_dollar_nonull[\"us_dollar\"].rolling(30).mean()\n",
    "\n",
    "# line graph showing dollar to euro showing NaN values with and without highlight\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27c42da0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# import colormap and set up color iteration\n",
    "from matplotlib.pyplot import cm\n",
    "colors = iter([plt.cm.tab10(i) for i in range(8)])\n",
    "\n",
    "# create a list of data frames for each currency with log rate of the exchange rate, 30 day rolling mean, and year\n",
    "df_dict = {}\n",
    "for currency in x_rates.columns[1:]:\n",
    "    df_name = currency\n",
    "    df = x_rates[[\"date\", currency]].copy()\n",
    "    df = df[df[currency].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[currency].rolling(30).mean()\n",
    "    df[\"year\"] = df[\"date\"].dt.year\n",
    "    df_dict[currency] = df\n",
    "\n",
    "# currencies for comparison\n",
    "high_freq = [df_dict[\"us_dollar\"], df_dict[\"japanese_yen\"], df_dict[\"uk_pound\"], df_dict[\"australian_dollar\"]]\n",
    "low_freq = [df_dict[\"israeli_shekel\"], df_dict[\"philippine_peso\"], df_dict[\"malaysian_ringgit\"], df_dict[\"romanian_leu\"]]\n",
    "\n",
    "# line graph showing Euro to each high frequency trading pairs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c79d5a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# line graph showing Euro to four high frequency trading pairs overlapped\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9c862e9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# function to create df with year and annual volatility for every row\n",
    "volatility_df_list = []\n",
    "def volatizer(currency):\n",
    "    annual_df_list = [currency[currency['year'] == y] for y in currency['year'].unique()] # list of annual dfs\n",
    "    c_name = currency.columns[1]\n",
    "    row_dict = {} # dictionary with year:annual_volatility as key:value \n",
    "    for frame in annual_df_list:\n",
    "        year_name = frame.iat[0,4] # first cell of the \"year\" column, becomes the \"year\" key for row_dict\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",
    "        row_dict[year_name] = annual_volatility # dictionary with year:annual_volatility as key:value \n",
    "    df = pd.DataFrame.from_dict(row_dict, orient=\"index\", columns=[c_name]) # new df from dictionary indexing on year\n",
    "    volatility_df_list.append(df)\n",
    "\n",
    "# apply volatizer to each currency df\n",
    "for key in df_dict:\n",
    "    df_dict[key] = volatizer(df_dict[key])\n",
    "    \n",
    "# create an annual volatility data frame with all 40 currencies\n",
    "annual_volatility = pd.concat(volatility_df_list, axis=1)\n",
    "annual_volatility[\"year\"] = annual_volatility.index\n",
    "# move year to position 0 and set index as int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09a24666",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# bar charts comparing volatility for four high and low frequency pairs\n",
    "high_freq = [annual_volatility[\"us_dollar\"], annual_volatility[\"japanese_yen\"], annual_volatility[\"uk_pound\"], annual_volatility[\"australian_dollar\"]]\n",
    "low_freq = [annual_volatility[\"israeli_shekel\"], annual_volatility[\"philippine_peso\"], annual_volatility[\"malaysian_ringgit\"], annual_volatility[\"romanian_leu\"]]\n",
    "years = annual_volatility.iloc[:,40]\n",
    "colors = iter([plt.cm.tab10(i) for i in range(8)])\n",
    "\n",
    "# high_freq bar charts\n",
    "\n",
    "\n",
    "# low_freq bar charts\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef0e4c27",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# uncommenting this cell and running in a geopandas environemnt\n",
    "# will produce a series of downloaded map png files\n",
    "\n",
    "# this is a very, very kuldge way for me to get the info from\n",
    "# the annual volatility datasat to the map dataset\n",
    "av_transposed = annual_volatility.transpose()\n",
    "av_transposed = av_transposed.drop(\"year\")\n",
    "iso_list = [\"AU\",\"BG\",\"BR\",\"CA\",\"CH\",\"CN\",\"CY\",\"CZ\",\"DK\",\"EE\",\"GB\",\"GR\",\n",
    "            \"CN\",\"HR\",\"HU\",\"ID\",\"IL\",\"IN\",\"IS\",\"JP\",\"KR\",\"LT\",\"LV\",\"MT\",\n",
    "            \"MX\",\"MY\",\"NO\",\"NZ\",\"PH\",\"PL\",\"RO\",\"RU\",\"SE\",\"SG\",\"SI\",\"SK\",\n",
    "            \"TH\",\"TR\",\"US\",\"ZA\"]\n",
    "av_transposed.insert(loc=0, column='iso', value=iso_list)\n",
    "av_transposed.reset_index(inplace=True)\n",
    "av_transposed = av_transposed.rename(columns = {'index':'currency'})\n",
    "\n",
    "# import geopandas and other libraries\n",
    "#  consider re-writing using geopandas map\n",
    "# geo_map = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "import geopandas as gpd\n",
    "import seaborn as sns\n",
    "import os\n",
    "map_data = gpd.read_file(\"maps/World_Countries__Generalized_.shp\")\n",
    "map_data[\"Volatility\"] = None\n",
    "concat_map = map_data.merge(av_transposed, left_on=\"ISO\", right_on=\"iso\", how=\"left\")\n",
    "map_data = concat_map\n",
    "\n",
    "# make maps for the gif\n",
    "# replaced -> map_data.plot(column=column, figsize=(18,12), cmap=\"plasma\", missing_kwds=missing_kwds, legend=True, legend_kwds={'label': \"Annual Volatility\", 'orientation':\"horizontal\"})\n",
    "for column in concat_map.iloc[:,11:34]:\n",
    "    map_name = \"map_\"+str(column)\n",
    "    map_data.plot(column=column, figsize=(18,12), cmap=\"plasma\", legend=True,\n",
    "                         legend_kwds={'label': \"Annual Volatility\", 'orientation':\"horizontal\"})\n",
    "    plt.title(\" \"*5 + str(column) + \" Euro to National Currency Exchange Rate\" + \" \"*5,\n",
    "             color = \"#f0f0f0\", backgroundcolor = \"#4d4d4d\", fontsize=30, weight=\"bold\")\n",
    "    plt.savefig(/vmaps/map_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7706257",
   "metadata": {},
   "source": [
    "<!-- <img src=\"map_gif.gif\" width=\"950\" align=\"center\"> -->"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "geo_env",
   "language": "python",
   "name": "geo_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}