06 - Scatter plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ------------------------------------------------------------\n",
    "# Create the scatter plot of Sales Vs cost from the file data\n",
    "# ------------------------------------------------------------\n",
    "\n",
    "# Import pyplot\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open the file in read mode and read lines\n",
    "f = open('salesdata2.csv','r')\n",
    "salefile = f.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the sales List\n",
    "s_list = []\n",
    "c_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Append all the records from the file to the saleslist\n",
    "for records in salefile:\n",
    "    sale, cost = records.split(sep=',')\n",
    "    s_list.append(int(sale))\n",
    "    c_list.append(int(cost))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cost')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "# Change the chart labels\n",
    "plt.title(\"Sales Vs Cost\")\n",
    "plt.xlabel(\"Sale\")\n",
    "plt.ylabel(\"Cost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qkmOyJbVqzi6UzPwvIGb5+Pr2lrP4OCZbUqu8E7PHHJMtqVUGeI85JltSq5zMqsccky2pVQZ4HxhZO2RgS2qaXSiSVCgDXJIKZYBLUqEMcEkqlAEuSYUywCWpUAa4JBXKAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmFMsAlqVAGuCQVqpGn0n8uIo5HxOPT2i6IiN0Rcbh6Pb+zZUqSZmrkDPzzwI0z2u4A9mTmamBPtSxJ6qI5AzwzvwH8dEbzemBb9X4bMNLmuiRJc2j1kWqXZOYxgMw8FhEXz7ZiRGwENgKsXLmy6Q3t2D/m8yIlqY6OX8TMzK2ZOZyZw8uWLWvquzv2j7F5+wHGJiZJYGxiks3bD7Bj/1hnipWkgrQa4M9GxKUA1evx9pX0oi27DjF54uQpbZMnTrJl16FObE6SitJqgD8EbKjebwB2tqecUx2dmGyqXZIWk0aGEd4L/DewJiKORMStwF3ADRFxGLihWm675YMDTbVL0mIy50XMzHzHLB9d3+ZaTrNp3Ro2bz9wSjfKwNIlbFq3ptOblqS+1+oolK6YGm3iKBRJOl1fBzjUQtzAlqTTOReKJBXKAJekQhngklQoA1ySCmWAS1KhIjO7t7GIceAHXdvg3C4CftzrItrI/elv7k9/6+f9+b3MPG0yqa4GeL+JiNHMHO51He3i/vQ396e/lbg/dqFIUqEMcEkq1GIP8K29LqDN3J/+5v70t+L2Z1H3gUtSyRb7GbgkFcsAl6RCLYoAj4iXR8TDEXEwIp6IiNuq9gsiYndEHK5ez+91rc2IiCURsT8ivlwtXxYRe6v9uT8izu51jY2KiMGIeCAinqyO0+tKPj4R8f7qb+3xiLg3Is4p7fhExOci4nhEPD6tre4xiZq/j4inIuKxiHht7yqvb5b92VL9zT0WEQ9GxOC0zzZX+3MoItb1puozWxQBDrwAfDAzXwVcA7w7Il4N3AHsyczVwJ5quSS3AQenLX8M+GS1P88Bt/akqtZ8GvhKZr4SuJLafhV5fCJiCHgvMJyZlwNLgFso7/h8HrhxRttsx+QmYHX1sxH4TJdqbMbnOX1/dgOXZ+YfAP8LbAao8uEW4DXVd/4xIpZ0r9TGLIoAz8xjmflI9f4X1MJhCFgPbKtW2waM9KbC5kXECuDNwGer5QCuAx6oVilmfyLiZcAbgLsBMvPXmTlBwceH2lz7AxFxFnAucIzCjk9mfgP46Yzm2Y7JeuBfsuabwODUg8/7Rb39ycyvZuYL1eI3gRXV+/XAfZn5fGZ+D3gKuLprxTZoUQT4dBGxClgL7AUuycxjUAt54OLeVda0TwEfAn5TLV8ITEz7YzxC7R+pErwCGAf+ueoS+mxEnEehxyczx4CPA89QC+6fAfso9/hMN9sxGQJ+OG29Evfvr4H/qN4XsT+LKsAj4qXAl4D3ZebPe11PqyLiLcDxzNw3vbnOqqWMET0LeC3wmcxcC/ySQrpL6qn6hdcDlwHLgfOodTHMVMrxaUTJf39ExIepdbXeM9VUZ7W+259FE+ARsZRaeN+Tmdur5men/ptXvR7vVX1NuhZ4a0R8H7iP2n/NP0Xtv61Tj8lbARztTXlNOwIcycy91fID1AK91OPzRuB7mTmemSeA7cDrKff4TDfbMTkCvHzaesXsX0RsAN4CvDNfvDGmiP1ZFAFe9Q/fDRzMzE9M++ghYEP1fgOws9u1tSIzN2fmisxcRe1Cy9cy853Aw8DbqtVK2p8fAT+MiDVV0/XAdyj0+FDrOrkmIs6t/vam9qfI4zPDbMfkIeAvq9Eo1wA/m+pq6WcRcSNwO/DWzPzVtI8eAm6JiJdExGXULs5+qxc1nlFmLvgf4I+o/ffnMeDR6udN1PqN9wCHq9cLel1rC/v2x8CXq/evoPZH9hTwReAlva6vif24ChitjtEO4PySjw/wUeBJ4HHgX4GXlHZ8gHup9eGfoHZGeutsx4Ral8M/AN8FDlAbgdPzfWhgf56i1tc9lQv/NG39D1f7cwi4qdf11/vxVnpJKtSi6EKRpIXIAJekQhngklQoA1ySCmWAS1KhDHBJKpQBLkmF+n+sQs1exHRLiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the scatter plot\n",
    "plt.scatter(s_list,c_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}