test2.ipynb

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    "Problem 2.\tFind information on the exchange rates for the last year and load this information into the program in accordance with your variant. Display the result as a graph using any available graphical output program\n",
    "\n",
    "Problem 3.\tFind the maximum, minimum and average rate for the considered period. Find the median. \n",
    "\n",
    "Problem 4.\tCalculate the average rates of the currencies for each month and display the results in the form of graphs. Place daily rate charts and monthly averages on the same picture.\n",
    "\n",
    "Problem 5.\tCompute parameters a and b and draw a graph of the linear function Y (k) = ak + b, which best approximates the graphs of the exchange rates \n",
    "\n",
    "Problem 6. Interpolate the given values. Use interpolation methods described below. To solve the problem you need to find all the interpolated values for all indices with some small step, for example 0.01. Set the random values not bigger than 100 by absolute value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5f7bba7d-cfda-4b4d-a7f9-ae1513478e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import requests\n",
    "\n",
    "# url = 'https://api.exchangerate.host/timeseries'\n",
    "# response = requests.get(url)\n",
    "# data = response.json()\n",
    "\n",
    "# print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28936591",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import statistics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5e2e771e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def year_rates(amount, currency, converted_currency, amount_of_days):\n",
    "    today_date = (datetime.datetime.now() - datetime.timedelta(days=1 * (304 + 19)))\n",
    "    print(today_date)\n",
    "    date_1year = (today_date - datetime.timedelta(days=1 * amount_of_days))\n",
    "    print(date_1year)\n",
    "    url = f'https://api.exchangerate.host/timeseries'\n",
    "    payload = {'base': currency, 'amount': amount, 'start_date': date_1year.date(), \n",
    "               'end_date': today_date.date()}\n",
    "    response = requests.get(url, params=payload)\n",
    "    data = response.json()\n",
    "    \n",
    "    currency_history = {}\n",
    "    rate_history_array = []\n",
    "#     rate_history_array_month = []\n",
    "    b = []\n",
    "    \n",
    "    for item in data['rates']:\n",
    "        current_date = item\n",
    "        currency_rate = data['rates'][item][converted_currency]\n",
    "#         print(item)\n",
    "        currency_history[current_date] = [currency_rate]\n",
    "        rate_history_array.append(currency_rate)\n",
    "        print(currency_rate)\n",
    "#         print(current_data)\n",
    "#         b.append(current_date)\n",
    "#         print(currency_rate)\n",
    "    \n",
    "#     print(type(currency_history[0]))\n",
    "    pd_data = pd.DataFrame(currency_history).transpose()\n",
    "    pd_data.columns = ['Rate']\n",
    "    pd.set_option('display.max_rows', None)\n",
    "    print(pd_data)\n",
    "#     print(b)\n",
    "#     print('\\n')\n",
    "#     print(rate_history_array)\n",
    "    \n",
    "    largest_so_far = rate_history_array[0]\n",
    "    smallest_so_far = rate_history_array[0]\n",
    "    sum_of_els = 0\n",
    "    count = 0\n",
    "    for i in rate_history_array:\n",
    "        sum_of_els = sum_of_els + i\n",
    "        count = count + 1\n",
    "        if largest_so_far < i:\n",
    "            largest_so_far = i\n",
    "        if smallest_so_far > i:\n",
    "            smallest_so_far = i\n",
    "    average = sum_of_els / count\n",
    "\n",
    "#     print(f'Maximum value is {largest_so_far}')\n",
    "    \n",
    "#     print(f'Minimum value is {smallest_so_far}')\n",
    "    \n",
    "#     print(f'Average is {average}')\n",
    "\n",
    "#     if (count % 2 == 0):\n",
    "#         print(f'Median for an even number of sequence {(rate_history_array[count/2] + rate_history_array[(count/2)+1])/2}')\n",
    "#     else:\n",
    "#         print(f'Median for an odd number of sequence {rate_history_array[(count/2)+1]}')\n",
    "#     print(f'Median is {statistics.median(rate_history_array)}')\n",
    "    \n",
    "#     integers_list = []\n",
    "#     for i in range(1, 366):\n",
    "#         integers_list.append(i)\n",
    "    \n",
    "#     print(integers_list)\n",
    "    \n",
    "#     regressor = LinearRegression()\n",
    "#     regressor.fit(rate_history_array)\n",
    "    \n",
    "    plt.plot(rate_history_array)\n",
    "#     plt.plot(rate_history_array, regressor.predict(rate_history_array), color='blue')\n",
    "    plt.ylabel(f'{amount} {currency} to {converted_currency}')\n",
    "    plt.xlabel('Days')\n",
    "#     plt.title(f'Current rate for {amount} {currency} to {converted_currency} is {rate_history_array[-1]}')\n",
    "#     plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9392c7b",
   "metadata": {
    "scrolled": false
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      "2020-01-02  68.986800\n",
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      "2020-01-04  68.928800\n",
      "2020-01-05  68.928800\n",
      "2020-01-06  69.279200\n",
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      "2020-01-08  69.165900\n",
      "2020-01-09  68.102500\n",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_rates(1, 'EUR', 'RUB', 366)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef568bc4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7af10756",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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