{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['head', 'tail', 'head', 'head', 'head', 'head', 'head', 'tail', 'tail', 'head']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.random.seed(123)\n",
    "coins = []\n",
    "coins_random_walk = [0]\n",
    "for i in range(10):\n",
    "    if np.random.randint(0,2) == 0:\n",
    "        coins_random_walk.append(0)\n",
    "        coins.append(\"head\")\n",
    "    else:\n",
    "        coins.append(\"tail\")\n",
    "print(coins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = [1,2,3]\n",
    "B = A + [4,5]\n",
    "B.append(6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "is_executing": false,
     "name": "#%% \n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": [
       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\n"
      ],
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.scatter([1,2,3], [7,8,9], s=[3,60,90], c=['red','blue','green'], alpha=0.5)\n",
    "\n",
    "plt.text(10, 8.5, \"Here is some\")\n",
    "\n",
    "plt.grid(True)\n",
    "\n",
    "plt.xscale('log') \n",
    "plt.xlabel(\"X Label\")\n",
    "plt.ylabel(\"Y Label\")\n",
    "plt.title(\"Title\")\n",
    "plt.xticks([10,20,30], [\"10x\", \"20x\",\"30x\"])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "Morteza Ansarinia"
   }
  ],
  "description": "Some random codes to learn/train/remember pandas :-)\n",
  "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": "3.7.3"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": [
     "Simulate \n"
    ]
   }
  },
  "tags": [
   "pandas",
   "playground"
  ],
  "title": "Pandas Playground"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}