{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "# Markdown Loop 0"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <td>0.645894</td>\n",
       "      <td>0.437587</td>\n",
       "      <td>0.891773</td>\n",
       "      <td>0.963663</td>\n",
       "      <td>0.383442</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.791725</td>\n",
       "      <td>0.528895</td>\n",
       "      <td>0.568045</td>\n",
       "      <td>0.925597</td>\n",
       "      <td>0.071036</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.087129</td>\n",
       "      <td>0.020218</td>\n",
       "      <td>0.832620</td>\n",
       "      <td>0.778157</td>\n",
       "      <td>0.870012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.978618</td>\n",
       "      <td>0.799159</td>\n",
       "      <td>0.461479</td>\n",
       "      <td>0.780529</td>\n",
       "      <td>0.118274</td>\n",
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       "          0         1         2         3         4\n",
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       "1  0.645894  0.437587  0.891773  0.963663  0.383442\n",
       "2  0.791725  0.528895  0.568045  0.925597  0.071036\n",
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       "4  0.978618  0.799159  0.461479  0.780529  0.118274"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/markdown": [
       "# Markdown Loop 1"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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       "      <td>0.774234</td>\n",
       "      <td>0.456150</td>\n",
       "      <td>0.568434</td>\n",
       "      <td>0.018790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.617635</td>\n",
       "      <td>0.612096</td>\n",
       "      <td>0.616934</td>\n",
       "      <td>0.943748</td>\n",
       "      <td>0.681820</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.359508</td>\n",
       "      <td>0.437032</td>\n",
       "      <td>0.697631</td>\n",
       "      <td>0.060225</td>\n",
       "      <td>0.666767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.670638</td>\n",
       "      <td>0.210383</td>\n",
       "      <td>0.128926</td>\n",
       "      <td>0.315428</td>\n",
       "      <td>0.363711</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.639921  0.143353  0.944669  0.521848  0.414662\n",
       "1  0.264556  0.774234  0.456150  0.568434  0.018790\n",
       "2  0.617635  0.612096  0.616934  0.943748  0.681820\n",
       "3  0.359508  0.437032  0.697631  0.060225  0.666767\n",
       "4  0.670638  0.210383  0.128926  0.315428  0.363711"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "# Markdown Loop 2"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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       "      <td>0.208877</td>\n",
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       "      <td>0.653108</td>\n",
       "      <td>0.253292</td>\n",
       "      <td>0.466311</td>\n",
       "      <td>0.244426</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.158970</td>\n",
       "      <td>0.110375</td>\n",
       "      <td>0.656330</td>\n",
       "      <td>0.138183</td>\n",
       "      <td>0.196582</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.368725</td>\n",
       "      <td>0.820993</td>\n",
       "      <td>0.097101</td>\n",
       "      <td>0.837945</td>\n",
       "      <td>0.096098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.976459</td>\n",
       "      <td>0.468651</td>\n",
       "      <td>0.976761</td>\n",
       "      <td>0.604846</td>\n",
       "      <td>0.739264</td>\n",
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      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.570197  0.438602  0.988374  0.102045  0.208877\n",
       "1  0.161310  0.653108  0.253292  0.466311  0.244426\n",
       "2  0.158970  0.110375  0.656330  0.138183  0.196582\n",
       "3  0.368725  0.820993  0.097101  0.837945  0.096098\n",
       "4  0.976459  0.468651  0.976761  0.604846  0.739264"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "# Markdown Loop 3"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <td>0.064147</td>\n",
       "      <td>0.692472</td>\n",
       "      <td>0.566601</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.265389</td>\n",
       "      <td>0.523248</td>\n",
       "      <td>0.093941</td>\n",
       "      <td>0.575946</td>\n",
       "      <td>0.929296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.318569</td>\n",
       "      <td>0.667410</td>\n",
       "      <td>0.131798</td>\n",
       "      <td>0.716327</td>\n",
       "      <td>0.289406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.183191</td>\n",
       "      <td>0.586513</td>\n",
       "      <td>0.020108</td>\n",
       "      <td>0.828940</td>\n",
       "      <td>0.004695</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.039188  0.282807  0.120197  0.296140  0.118728\n",
       "1  0.317983  0.414263  0.064147  0.692472  0.566601\n",
       "2  0.265389  0.523248  0.093941  0.575946  0.929296\n",
       "3  0.318569  0.667410  0.131798  0.716327  0.289406\n",
       "4  0.183191  0.586513  0.020108  0.828940  0.004695"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "# Markdown Loop 4"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "      <td>0.270008</td>\n",
       "      <td>0.735194</td>\n",
       "      <td>0.962189</td>\n",
       "      <td>0.248753</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.576157</td>\n",
       "      <td>0.592042</td>\n",
       "      <td>0.572252</td>\n",
       "      <td>0.223082</td>\n",
       "      <td>0.952749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.447125</td>\n",
       "      <td>0.846409</td>\n",
       "      <td>0.699479</td>\n",
       "      <td>0.297437</td>\n",
       "      <td>0.813798</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.396506</td>\n",
       "      <td>0.881103</td>\n",
       "      <td>0.581273</td>\n",
       "      <td>0.881735</td>\n",
       "      <td>0.692532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.725254</td>\n",
       "      <td>0.501324</td>\n",
       "      <td>0.956084</td>\n",
       "      <td>0.643990</td>\n",
       "      <td>0.423855</td>\n",
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      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.677817  0.270008  0.735194  0.962189  0.248753\n",
       "1  0.576157  0.592042  0.572252  0.223082  0.952749\n",
       "2  0.447125  0.846409  0.699479  0.297437  0.813798\n",
       "3  0.396506  0.881103  0.581273  0.881735  0.692532\n",
       "4  0.725254  0.501324  0.956084  0.643990  0.423855"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "from IPython.display import *\n",
    "\n",
    "for i in range(5):\n",
    "    display(Markdown(f'# Markdown Loop {i}'))\n",
    "    display(pd.DataFrame(pd.np.random.random((5,5))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "version": 3
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   "pygments_lexer": "ipython3",
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