13.1 Introducing Matplotlib Library.ipynb

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   "source": [
    "![title](img/cover4.png)"
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   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 13.1 Introducing Matplotlib Library"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import matplot library like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Usually we import **pyplot** module with the matplotlib library like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To display plots nicely within jupyter notebooks we use this magic command with matplotlib library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
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   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a numpy array using the function **np.arange()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "array = np.arange(10,20)\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot this array using the function **plt.plot()**:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2243b3105e0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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      "image/png": 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Xw6fE66jAxafkFJYyZ1kyKxL3MbBLG16/cTiR3YKcjiVSJypw8QnWWuI27mbu8hRKKqq45/z+TD+zl4ZPiVdTgUujl3WomPuWJPHFllxierRj/rRo+oS0cjqWyClTgUuj5XJZ/v7dTh5fmQrAQ1MGcd2oHjTR8ClpJFTg0ihtyz3MzMUJrN95iDP7deTRqZF0b6fhU9K4qMClUamocrHgiwye+2QrLf2a8tTlg5k2tJuWwUujdMICN8YsBCYDOdbayKOe+xPwJNDRWru/fiKK1E7S7nxmxiWQvKeAC6I6M2fKIEJaa/iUNF61OQN/A3geeOvIjcaYUOBcINP9sURqr7Siir98spW/fZFB+0B/Xr52KBMjuzgdS6TenbDArbVfGGPCj/HUM8AMYKm7Q4nU1rodB5kZl0BGbhGXD+vO7AsHEhTg53QskQZRp2vgxpgpwG5r7aYTXVs0xkwHpgOEhYXVZXci/+NwWSVPrEzlrW930q1tS/5+ywjO6KvhU+JbTrrAjTEBwCzgvNq83lq7AFgAEBMTY092fyJH+3xLLvfFJ7Inv4Qbx4Rzz/n9CdTwKfFBdfmt7w30BP599t0d2GiMGWGt3efOcCJHyisu5+HlKcRv3E3vjoEsvn00w3po+JT4rpMucGttIhDy78fGmB1AjO5Ckfq0InEvDyxNIq+4gt+d04ffnt1Hw6fE59XmNsK3gXFAsDEmC3jQWvtafQcTAcgpKOWBpcmsTN5HVLcg3rp5JAO7tnE6lohHqM1dKFed4Plwt6URqWGt5b0NWcxbnkJZpYvYSRHcenpPmmn4lMh/6JMf8Ti7DhZz35JEvty6nxHh7Zk/LYpeHTV8SuRoKnDxGFUuy1vf7uCJlWk0MTD3kkiuGRGm4VMix6ECF4+QnlPIjMUJbMzMY1z/jjwyNYpubVs6HUvEo6nAxVEVVS7+9vk2/vJJOoHNm/LMFYO5ZIiGT4nUhgpcHJOYlc89izeRuq+QydFdmDNlEMGtmjsdS8RrqMClwZVWVPHsmq288mUGHQL9WXDdMM4b1NnpWCJeRwUuDer7jAPExieyfX8RVw4P5d4LBhDUUsOnROpCBS4NorC0gidWpvH373YS2r4li24dydg+wU7HEvFqKnCpd2tTc5i1JJG9BaXccnpP/nhePwL89asncqr0LpJ6c7ConLnLU1jy4276hrQi7o4xDA1r53QskUZDBS5uZ63lw8S9PLg0mfySCu4a35ffnN2b5s00fErEnVTg4lbZBaXMfj+J1SnZRHcPYtFtI4norOFTIvVBBS5uYa3l3XW7eGTFZsorXcy6YAA3jQ3X8CmReqQCl1OWeaCY2PgEvtl2gJE92/P4tGjCgwOdjiXS6KnApc6qXJbXv97OU6vSaNakCY9OjeLK4aEaPiXSQFTgUidbsquHT/20K4/xESHMmxpJlyANnxJpSCpwOSnllS5e+mwbz6/dSusWfjx35RCmDO6q4VMiDlCBS61t2pXHjMUJpGUXcvGQrjwweSAdNHxKxDEqcDmhkvIq/rw6jde+2k5I6xa8en0MEwZ2cjqWiM9Tgcsv+nbbAWLjE9h5oJirR4YROymCNi00fErEE6jA5ZgKSit4bEUqb/+QSY8OAfzztpGM6a3hUyKeRAUu/2NNSjaz3k8kt7CM6Wf24u4J/Wjpr2XwIp5GBS7/ceBwGQ99kMKyTXuI6NyaBdfFMDi0rdOxROQ4VOCCtZZlm/YwZ1kyh8squXtCP+4Y1xv/ZloGL+LJVOA+bm9+CbOXJPFJag5DQtvyxGXR9OvU2ulYIlILKnAf5XJZ3l6XyWMrUql0uZh94QBuGtuTploGL+I1VOA+aMf+ImLjE/gu4yBjendg/qXRhHUIcDqWiJwkFbgPqaxysfDr7Ty9agv+TZsw/9IorhgeqmXwIl5KBe4jNu8tYGZcAglZ+UwY0Il5l0TSOaiF07FE5BSowBu5ssoqXli7jRfXphPU0o/nrz6NC6O66KxbpBFQgTdiGzMPMXNxAltzDjP1tG7cP3kg7QP9nY4lIm6iAm+EissreXrVFhZ+vZ3ObVrw+o3DOTsixOlYIuJmKvBG5uv0/cTGJ7DrYAnXjgpj5sQIWmv4lEijpAJvJPJLKnhsxWbeWbeLnsGBvDt9FCN7dXA6lojUIxV4I7AqeR+z30/iQFE5t5/Vmz9M6EsLPw2fEmnsTljgxpiFwGQgx1obWbPtSeAioBzYBtxkrc2rx5xyDLmFZcz5IJkPE/YyoEsbXrthOFHdg5yOJSINpDbTit4AJh61bTUQaa2NBrYA97o5l/wCay1Lfszi3Gc+Z3VyNn86rx/L7hyr8hbxMSc8A7fWfmGMCT9q26ojHn4HXObmXHIcu/NKmLUkkc/SchkaVj18qk+Ihk+J+CJ3XAO/GXj3eE8aY6YD0wHCwsLcsDvf5HJZFn2/k/kfpWKBORcN5LrR4Ro+JeLDTqnAjTGzgEpg0fFeY61dACwAiImJsaeyP1+VkXuY2LhEfthxkDP6BvPo1ChC22v4lIivq3OBG2NuoPrDzfHWWhVzPaiscvHKl9t5Zs0WWjRrwpOXRXPZsO5aBi8iQB0L3BgzEZgJnGWtLXZvJAFI2VPAjLhNJO0u4PxBnZh7cSQhbTR8SkT+qza3Eb4NjAOCjTFZwINU33XSHFhdczb4nbX29nrM6TNKK6p4/tN0Xv58G20D/HnpmqFMiuridCwR8UC1uQvlqmNsfq0esvi8DTsPMmNxAttyi5g2tDv3Tx5A2wANnxKRY9NKTA9QVFbJkx+n8ea3O+ga1JI3bx7BWf06Oh1LRDycCtxhX2zJ5d74RPbkl3D9qB7cMzGCVs311yIiJ6amcEh+cQVzP0xh8YYsenUM5L1fjyYmvL3TsUTEi6jAHbAyaS/3L03mYFE5vz27N787R8OnROTkqcAbUE5hKQ8uTeajpH0M6tqGN24azqCuml8iInWjAm8A1lriNu5m7vIUSiqqmDGxP7ed0Qu/prWZJSYicmwq8Hq262Ax9y1J5Mut+xke3o7506Lp3bGV07FEpBFQgdcTl8vy1rc7eOLjNAzw8MWDuHZkD5po+JSIuIkKvB6k5xwmNi6B9TsPcWa/jjw6NZLu7TR8SkTcSwXuRhVVLhZ8kcFza7bS0r8pT18+mEuHdtPwKRGpFypwN0nanc+MxQmk7C3gwqguzJkyiI6tmzsdS0QaMRX4KSqtqOK5T7ay4IsM2gf68/K1w5gY2dnpWCLiA1Tgp2DdjoPMXJxAxv4ifhXTnVkXDCQowM/pWCLiI1TgdXC4rJInVqby1rc76d6uJf+4ZSSn9w12OpaI+BgV+En6LC2HWUuS2JNfwk1jw/nTef0J1PApEXGAmqeWDhWVM/fDFOI37qZPSCsW3z6GYT3aOR1LRHyYCvwErLWsSNzHg8uSyCuu4M6z+/C78X1o3kzDp0TEWSrwX5BTUMrs95NYlZJNVLcg3rp5JAO7tnE6logIoAI/Jmst763PYu6HKZRXurh3UgS3nN6TZho+JSIeRAV+lF0Hi7k3PpGv0vczomd75l8aRS8NnxIRD6QCr1Hlsrz5zQ6e/DiNpk0M8y6J5OoRYRo+JSIeSwUObM0uZEZcAj9m5jGuf0cenRpF17YtnY4lIvKLfLrAyytdvPz5Np7/NJ3A5k159oohXDykq4ZPiYhX8NkCT8jKY8biBFL3FTI5unr4VHArDZ8SEe/hcwVeWlHFM6u38MqXGXRs3ZwF1w3jvEEaPiUi3senCvy7jAPExiWw40AxV40IJXbSAIJaaviUiHgnnyjwwtIK5n+UyqLvMwlrH8A/bx3JmD4aPiUi3q3RF/inqdnMWpJEdkEpt57ek/87rx8B/o3+f1tEfECjbbKDReU8/EEy7/+0h74hrXjxjjGcFqbhUyLSeDS6ArfW8kHCXuYsS6agpIK7xvflN2f31vApEWl0GlWB78uvHj61ZnM2g7sH8fhtI4norOFTItI4NYoCt9byzrpdPPrhZipcLmZdMICbT+9JUy2DF5FGzOsLfOeBImLjEvk24wCjerVn/qXRhAcHOh1LRKTeeW2BV7ksr3+9nadWpeHXpAmPTo3iyuGhGj4lIj7DKws8bV/18KlNu/IYHxHCvKmRdAnS8CkR8S0nLHBjzEJgMpBjrY2s2dYeeBcIB3YAv7LWHqq/mNXKK128+Fk6L6xNp3ULP567cghTBmv4lIj4ptp8xcwbwMSjtsUCn1hr+wKf1DyuVz/tyuOiv37Fs2u2ckFUF1bffSYXD+mm8hYRn3XCM3Br7RfGmPCjNl8MjKv5+U3gM2CmO4Md6a+fbOWZNVsIad2C126IYfyATvW1KxERr1HXa+CdrLV7Aay1e40xIcd7oTFmOjAdICwsrE47C+sQwJUjwoidFEGbFho+JSICYKy1J35R9Rn48iOugedZa9se8fwha+0J16nHxMTY9evX1z2tiIgPMsZssNbGHL29rl+znm2M6VLzB3cBck4lnIiInLy6Fvgy4Iaan28AlronjoiI1NYJC9wY8zbwLdDfGJNljLkFmA+ca4zZCpxb81hERBpQbe5Cueo4T413cxYRETkJdb2EIiIiDlOBi4h4KRW4iIiXUoGLiHipWi3kcdvOjMkFdtbxPw8G9rsxjrfT8fgvHYuf0/H4ucZwPHpYazsevbFBC/xUGGPWH2slkq/S8fgvHYuf0/H4ucZ8PHQJRUTES6nARUS8lDcV+AKnA3gYHY//0rH4OR2Pn2u0x8NrroGLiMjPedMZuIiIHEEFLiLipbyiwI0xE40xacaYdGNMvX//pqcyxoQaY9YaYzYbY5KNMXc5nckTGGOaGmN+NMYsdzqL04wxbY0xi40xqTW/J6OdzuQUY8zdNe+TJGPM28aYFk5ncjePL3BjTFPgBWASMBC4yhgz0NlUjqkE/mitHQCMAn7rw8fiSHcBm50O4SGeA1ZaayOAwfjocTHGdAN+D8TUfJNYU+BKZ1O5n8cXODACSLfWZlhry4F3qP5SZZ9jrd1rrd1Y83Mh1W/Obs6mcpYxpjtwIfCq01mcZoxpA5wJvAZgrS231uY5GspZzYCWxphmQACwx+E8bucNBd4N2HXE4yx8vLTgP99TehrwvcNRnPYsMANwOZzDE/QCcoHXay4pvWqMCXQ6lBOstbuBp4BMYC+Qb61d5Wwq9/OGAjfH2ObT9z4aY1oBccAfrLUFTudxijFmMpBjrd3gdBYP0QwYCrxkrT0NKAJ88jMjY0w7qv+l3hPoCgQaY651NpX7eUOBZwGhRzzuTiP8p1BtGWP8qC7vRdbaeKfzOGwsMMUYs4PqS2vnGGP+4WwkR2UBWdbaf/+rbDHVhe6LJgDbrbW51toKIB4Y43Amt/OGAl8H9DXG9DTG+FP9QcQyhzM5whhjqL6+udla+2en8zjNWnuvtba7tTac6t+LT621je4sq7astfuAXcaY/jWbxgMpDkZyUiYwyhgTUPO+GU8j/ED3hN+J6TRrbaUx5k7gY6o/SV5orU12OJZTxgLXAYnGmJ9qtt1nrV3hXCTxML8DFtWc7GQANzmcxxHW2u+NMYuBjVTfvfUjjXBJvZbSi4h4KW+4hCIiIsegAhcR8VIqcBERL6UCFxHxUipwEREvpQIXEfFSKnARES/1/w4jih3hCowIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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