CCE_GraphMatplotlib_class6.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CREATING SCATTER GRAPH\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "height = [120, 125, 128,135,150,135]\n",
    "weight = [70, 68, 75, 90, 98, 100]\n",
    "\n",
    "# plotting the points  \n",
    "#alpha = 0 (plots fully transparent and unable to view on a white background)\n",
    "# alpha = 1 (default value with non-transparent points)\n",
    "\n",
    "plt.scatter(height, weight, color = 'red', s=75, alpha = 0.8) \n",
    "  \n",
    "# naming the x axis \n",
    "plt.xlabel('height') \n",
    "# naming the y axis \n",
    "plt.ylabel('weight') \n",
    "  \n",
    "# giving a title to my graph \n",
    "plt.title('My first graph!') \n",
    "  \n",
    "# function to show the plot \n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Unknown property line",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-83-b04079b943bf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# plotting the points\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mline\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"---\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# naming the x axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   3356\u001b[0m                       mplDeprecation)\n\u001b[0;32m   3357\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3358\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3359\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3360\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1853\u001b[0m                         \u001b[1;34m\"the Matplotlib list!)\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1854\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1855\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1856\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1857\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1525\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_alias_map\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1527\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1528\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1529\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    404\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m                 \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 406\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    407\u001b[0m                 \u001b[1;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    394\u001b[0m                                   \"with non-matching shapes is deprecated.\")\n\u001b[0;32m    395\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mncx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mncy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 396\u001b[1;33m             \u001b[0mseg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mncx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mncy\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    397\u001b[0m             \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_makeline\u001b[1;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[0;32m    298\u001b[0m         \u001b[0mdefault_dict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getdefaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    299\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setdefaults\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdefault_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 300\u001b[1;33m         \u001b[0mseg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mLine2D\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    301\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\lines.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[0;32m    419\u001b[0m         \u001b[1;31m# update kwargs before updating data to give the caller a\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    420\u001b[0m         \u001b[1;31m# chance to init axes (and hence unit support)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 421\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    422\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpickradius\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpickradius\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mind_offset\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n\u001b[0;32m    886\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    887\u001b[0m             ret = [_update_property(self, k, v)\n\u001b[1;32m--> 888\u001b[1;33m                    for k, v in props.items()]\n\u001b[0m\u001b[0;32m    889\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    890\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meventson\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstore\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    886\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    887\u001b[0m             ret = [_update_property(self, k, v)\n\u001b[1;32m--> 888\u001b[1;33m                    for k, v in props.items()]\n\u001b[0m\u001b[0;32m    889\u001b[0m         \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    890\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meventson\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mstore\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[1;34m(self, k, v)\u001b[0m\n\u001b[0;32m    879\u001b[0m                 \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'set_'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    880\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 881\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unknown property %s'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    882\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: Unknown property line"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "height = [120, 125, 128,135,150,135]\n",
    "weight = [70, 68, 75, 90, 98, 100]\n",
    "\n",
    "# plotting the points  \n",
    "plt.plot(height, weight, line = \"---\", linewidth = 5) \n",
    "  \n",
    "# naming the x axis \n",
    "plt.xlabel('height') \n",
    "# naming the y axis \n",
    "plt.ylabel('weight') \n",
    "\n",
    " \n",
    "#giving a title to the graph\n",
    "plt.title('My first graph!') \n",
    "  \n",
    "# function to show the plot \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "  \n",
    "# x-coordinates of left sides of bars  \n",
    "left = [1, 2, 3, 4, 5] \n",
    "  \n",
    "# heights of bars \n",
    "height = [10, 24, 36, 40, 5] \n",
    "  \n",
    "# labels for bars \n",
    "tick_label = ['one', 'two', 'three', 'four', 'five'] \n",
    "  \n",
    "# plotting a bar chart \n",
    "plt.bar(left, height, tick_label = tick_label, width = 0.8, color = ['red', 'green']) \n",
    "  \n",
    "# naming the x-axis \n",
    "plt.xlabel('x - axis') \n",
    "# naming the y-axis \n",
    "plt.ylabel('y - axis') \n",
    "# plot title \n",
    "plt.title('My bar chart!') \n",
    "  \n",
    "# function to show the plot ---- SOMETIMES \";\" IS NEEDED AFTER PLT.SHOW() TO DISPLAY THE GRAPH\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CREATING HISTOGRAM\n",
    "  \n",
    "# frequencies \n",
    "ages = [2,5,70,40,30,45,50,45,43,40,44, \n",
    "        60,7,13,57,18,90,77,32,21,20,40] \n",
    "  \n",
    "# setting the ranges and no. of intervals \n",
    "range = (0, 100) \n",
    "bins = 10  \n",
    "  \n",
    "# plotting a histogram \n",
    "plt.hist(ages, bins, range, color = 'green', \n",
    "        histtype = 'bar', rwidth = 0.8) \n",
    "  \n",
    "# x-axis label \n",
    "plt.xlabel('age') \n",
    "# frequency label \n",
    "plt.ylabel('No. of people') \n",
    "# plot title \n",
    "plt.title('My histogram') \n",
    "  \n",
    "# function to show the plot \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CREATING PIE CHART\n",
    "#More details about the parameters of plt.pie can be found at: \n",
    "#\"https://matplotlib.org/3.2.1/api/_as_gen/matplotlib.pyplot.pie.html\"\n",
    "\n",
    "labels = 'Python', 'C++', 'Ruby', 'Java'\n",
    "sizes = [215, 130, 245, 210]\n",
    "colors = ['pink', 'lightgreen', 'gold', 'skyblue']\n",
    "explode = (0.1, 0, 0, 0)  # explode 1st slice\n",
    "\n",
    "# Plot\n",
    "#\n",
    "plt.pie(sizes, explode=explode, labels=labels, colors=colors, shadow=True, startangle=90)\n",
    "\n",
    "plt.axis(\"equal\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#CREATING HEATMAPS\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "vegetables = [\"cucumber\", \"tomato\", \"lettuce\", \"asparagus\",\n",
    "              \"potato\", \"wheat\", \"barley\"]\n",
    "farmers = [\"Farmer Joe\", \"Upland Bros.\", \"Smith Gardening\",\n",
    "           \"Agrifun\", \"Organiculture\", \"BioGoods Ltd.\", \"Cornylee Corp.\"]\n",
    "\n",
    "harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],\n",
    "                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],\n",
    "                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],\n",
    "                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],\n",
    "                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],\n",
    "                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],\n",
    "                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "im = ax.imshow(harvest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}