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   "cell_type": "markdown",
   "id": "0aac5da7-745c-4eda-847a-3d0d07a1bb9b",
   "metadata": {
    "tags": []
   },
   "source": [
    "# This is a markdown cell\n",
    "\n",
    "This paragraph has\n",
    "With\n",
    "Many\n",
    "Lines. How we will he handle MR notes?\n",
    "\n",
    "But I can add another paragraph\n",
    "\n",
    "Another paragraph added"
   ]
  },
  {
   "cell_type": "raw",
   "id": "faecea5b-de0a-49fa-9a3a-61c2add652da",
   "metadata": {},
   "source": [
    "This is a raw cell\n",
    "With\n",
    "Multiple lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "893ca2c0-ab75-4276-9dad-be1c40e16e8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d707fb5-226f-46d6-80bd-489ebfb8905c",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "35467fcf-28b1-4c7b-bb09-4cb192c35293",
   "metadata": {
    "tags": [
     "senoid"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12a4e43d0>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "another_invalid_base64_image_here\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 4*np.pi,50)\n",
    "y = 2 * np.sin(x)\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dc1178cd-c46d-4da3-9ab5-08f000699884",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({\"x\": x, \"y\": y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6e749b4f-b409-4700-870f-f68c39462490",
   "metadata": {
    "tags": [
     "some-table"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.256457</td>\n",
       "      <td>0.507309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0  0.000000  0.000000\n",
       "1  0.256457  0.507309"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ddef5ef-94a3-4afd-9c70-ddee9694f512",
   "metadata": {},
   "outputs": [],
   "source": []
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   "language": "python",
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    "name": "ipython",
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