EUR USD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    " \n",
    "plt.style.use('default')\n",
    "sns.set()\n",
    "sns.set_style('darkgrid')\n",
    "sns.set_palette('bright')\n",
    "\n",
    "x = np.array([7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17])\n",
    "gene_1 = np.array([1.3450, 1.3450, 1.3450, 1.3450, 1.3450, 1.3450, 1.3450, 1.3450, 1.3450, 1.3450, 1.3450])\n",
    "gene_2 = np.array([1.1696, 1.1696, 1.1696, 1.1696, 1.1696, 1.1696, 1.1696, 1.1696, 1.1696, 1.1696, 1.1696])\n",
    "gene_3 = np.array([0.9942, 0.9942, 0.9942, 0.9942, 0.9942, 0.9942, 0.9942, 0.9942, 0.9942, 0.9942, 0.9942])\n",
    "gene_4 = np.array([1.1061, 1.0908, 1.0689, 1.0560, 1.0801, 1.0913, 1.0907, 1.0789, 1.0867, 1.0730, 1.0806])\n",
    "\n",
    "plt.plot(x, gene_1, label='top')\n",
    "plt.plot(x, gene_2, label='avg')\n",
    "plt.plot(x, gene_3, label='bottom')\n",
    "plt.plot(x, gene_4, label='EUR USD')\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel(\"2024\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.ylim(0.8, 1.45)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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,
 "nbformat_minor": 2
}