How to make better plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# based on https://www.youtube.com/watch?v=_56pLwmYJBs&feature=youtu.be&utm_medium=email&utm_source=getresponse&utm_content=Lesson+3%3A+Plot+like+a+designer&utm_campaign="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=[\"milk\", \"dark\", \"hazelnut\", \"white\", \"orange\"]\n",
    "sales= np.array([80,83,63,66,49])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Not a good plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax= plt.subplots(figsize=(6,6))\n",
    "ax.set_axisbelow(True)\n",
    "ax.yaxis.grid(color='k', linestyle='dashed')\n",
    "ax.bar(labels, sales, label=\"Sales\")\n",
    "ax.legend(fancybox=True, shadow=True)\n",
    "ax.set_ylim(20,100) #bad practice when comparing levels\n",
    "ax.set_title(\"Units sold\")\n",
    "ax.set_xlabel(\"Type\", fontsize=14)\n",
    "ax.set_ylabel(\"Sales\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting a better plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "cmap= cm.get_cmap('magma')\n",
    "loc =(sales-20)/(100-30)\n",
    "colours=cmap(loc)\n",
    "\n",
    "fig, ax= plt.subplots(figsize=(6,6))\n",
    "ax.set_axisbelow(True)\n",
    "ax.bar(labels, sales, label=\"Sales\",color=colours)\n",
    "for i,v in enumerate (sales):\n",
    "    ax.text(i,v , v, ha=\"center\", va=\"bottom\",\\\n",
    "            fontsize=12, color=\"gray\")\n",
    "ax.set_ylabel(\"Sales\", fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(2, -12, 'Units sold of chocolate per week')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "cmap= cm.get_cmap('magma')\n",
    "loc =(sales-30)/(100-30)\n",
    "colours=cmap(loc)\n",
    "\n",
    "fig, ax= plt.subplots(figsize=(6,6))\n",
    "ax.set_axisbelow(True)\n",
    "ax.bar(labels, sales, label=\"Sales\",color=colours)\n",
    "for i,v in enumerate (sales):\n",
    "    ax.text(i,v , v, ha=\"center\", va=\"bottom\",\\\n",
    "            fontsize=12, color=colours[i])\n",
    "ax.yaxis.set_ticks([])\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "ax.text(2,-12, \"Units sold of chocolate per week\", fontsize=14, ha=\"center\", alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(2, -12, 'Units sold of chocolate per week')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlJGhK0kZGbqSlFGklJqfGXEvsGG+cpbLhsDkShdRIWtz22Htbr9tr26TU0pHNDWjxdBdHUTEcymlgZWuoxLW5rbD2t1+2776tt3hBUnKyNCVpIzWhNC9stIFVNDa3HZYu9tv21dTq/2YriStTtaEnq4krTZW69CNiKMi4oflzz+LiLPKnx+OiNX27mZjDdu2KpbPISI2i4hXVtG2D46IISuw/skRscnKrGlliohZzUw/IyJOKn+u6jZoidpKF7AiUkp3AndWuo5qEhGr9XNaIScDrwDjKlzHMkkpXd7g4cmshm1oLCKCYtizvtK1rCpV29Mte0YjIuLqiHglIm6MiMMi4omIeCsiPlG+u/9PC9toFxE3RMTPc9a+MkTE2RHxRkQ8AGxbTjstIp6NiOER8deI6FJOvz4iLomIh4CLG23ntIi4JyI652/Fx9RExFUR8WpE/CMiOrfQphcb/PsoIg6KiK4RcW25/LCIOLrxDspe/rXl1c6oiPiPcvpSPe2IOKtc9jhgIHBjua/sxykivt+gzv+KiH+WPw+KiD+XP/+iPEZPRUSvBm09q6k2RMSAiHgkIp6PiPsionfudjUnIr5TvqZfiYgzy+fm9Yj4A/AC0C8iLouI58pz5bwG674TEedFxAsR8XJEbFdO3ygi7i+nXxERYyJiw3LeiRHxTHlsroiImsq0vFC1oVvaCvgdsAuwHXACsD9wFvDjVtatBW4E3kwpnbMqi1zZImIA8EVgd+BzwJ7lrNtSSnumlHYFXgdObbDaNsBhKaXvNtjOvwNHAoNTSh9lKb5lWwOXppR2BKYDx9JMm1JKu6WUdgN+AjwHDAXOBv6ZUtoTOAT4dUR0bWI/2wGHA58Azo2I9s0VlFL6v3L7Xy73WYnj9ChwQPnzQGCdsub9gceArsBT5TF6FDit4cqN2wAsBP4bOC6lNAC4FvhFjoa0pjy3TwH2AvamaMsGFB2LP6aUdk8pjQHOLn8BYhfgoIjYpcFmJqeU9gAuo8gCgHMpzo09gNuB/uX+tge+AOxXHps64MuruJktqvZL0dEppZcBIuJV4MGUUoqIl4HNWln3CuCWlFJVnGzL6ADg9pTSHICIWDSEslPZa18fWAe4r8E6t6aU6ho8/gowliJwF2SouS1Gp5ReLH9+nuI5bLZNEbE18Gvg0JTSgoj4FHBULBmv7kT54mrk7ymlecC8iJgI9FolrVl5ngcGREQ3YB5Fb28gxXnwH8B8YEiDZT/Zyva2BXYC7o8IgBpg/Move7nsT3FuzwaIiNso2jkmpfRUg+U+HxH/SpFRvYEdgJfKebeV/z9P0SlZtN1jAFJK90bEtHL6IGAA8Gx5LDoDE1dBu9qs2kN3XoOf6xs8rqf12ocCh0TEb1JKc1dFcatYU5/lu54iRIdHxMnAwQ3mzW607CvAbkBfYPQqqG95NHw+6yheANfTRJvKHuwtwGkppUXjlAEcm1J6o+FGF11ut7CfWoreX8Mru04r0pCVqXxDeYeiBziUIlwOAbak6P0vSEs+27moPS0J4NWU0j6rpuIVEs1MX3z+RsTmFD3YPVNK0yLiepZ+vhY9vw2PRXPbDeCGlNKPlrvilazahxdWxDXA3cCtsfrdXHoUOKYcm+tGMUQA0A0YX156tnaJNAw4HbgzqvuudnNtug64LqX0WINp9wHfirLLEhG7L8N+PgB6RkSPiOgI/EuDeR+WdVTSoxRB8yjFkMIZwIsNwrY1DdvwBrBRROwDEBHtI2LHlVzv8noUGBwRXco31mMo2tvQuhQhPKN8Q/10G7b7OPB5gPKKaINy+oPAcRHRs5zXPSI2XfFmLL81OXRJKV1Ccan2p4hYbdqaUnoB+AvwIvBXlpyUPwGeBu4HRrRhO49TvJD/vuimQhX6WJvKF8VxwNca3EwbCFwAtAdeKm+KXdDWnZRDLOeX+xrC0sfveuDySt1IKz1GcRn9ZErpA2AuHw+jllxP2QaK4YTjgIsjYjjFebTvyi13+ZTn9vXAMxTPxdXAtEbLDKfoNLxKMR79RBs2fR7wqYh4gSKkxwMfppReA84B/hERL1GcZxW9qehvpEla7ZVXL3UppYVlD/+y8sZZ1VndLrslqSn9gVvKK9r5NPqERzWxpytJGa0245yStCYwdCUpI0NXkjIydCUpI0NXkjIydCUpI0NXkjIydCUpI0NXkjIydCUpI0NXkjIydCUpI0NXkjIydCUpI0NXkjIydCUpI0N3BUXEmRHx00rX0VBEDIqIW1tZ5piIuGYl7OuEiPhzRNwVEYOWc/1LV7SOFRUR10TEMZWuY00UETuX58e6la6lGqyVoRsRF0XEGU1MbzWsmnAV8JvWtr0mKr9A8kvApcBJLNsXKa7WlvNckfyOtBWVUppd6RoqaNG3qj61DF8VrkwiojaltLDSdWhphm4LIuJMYF2Kr7A+FugIPEXxTaPzGi6TUjq//HknYKeI+Gy5mVOBqcDXgP3K7U0HHk4p3dDMfrsCpwN7AF3K9e9KKd1Rzt8I+Fdg0bedDgOuTClNbqEtxwKDgU7Ak8CENrR/M+DrwPYUX/b3NHBVSml2RJxA0csFuDMiSCkd2cx2upft34PiGL4PXJ1SeqnBMgcCXwHWB4YDv08pzSznBfAF4PBy/vvAn1JKT7d1HxFxBPA5YCNgEvDXlNJ9LbR9MDCI4o1lNvA8cE3Z9p2BM8vl7ipX+d+U0k0RUQucCBwMrAO8C/y5/Orx5vZ1ETAWWAAcWk7+B3Ddojez1rZb1nQhxVeRnwBsXj5+ttG+TgY2Syn9rHx8OPDvwK9SSo+V034FPJdSuqV8/Ilym/0pvi794bK9C9tSWxPtbQ98H+gJ/DSlNKO5Y7MmMnRbtyPFiXYOsCHwA4oXdFOXllcBfSheQH8sp80Ajgb2AX4NfFBup08L+zwR2Aw4nyKgewHrweIAOociBH9cLn86cHZEfKepHmdE7F9u8wrgZYrwPw74sLkCyq+0Pg94C/guxYvpW8B/ABcBtwGTy2kntbCdTsAvy3ZcCEyhCISGegIHAL+geFP4PkUALxrrPYoiMC8F3qZ4cZ8dEWemlEa1to/yK7nPAK6meIPaA/hGRExLKT3TTOmJ4vmcUNZ3evnvEuD1ct5JLPnW2Y/K/88ENgb+szw+A4GflM/N6OaOU9mmB4CzKJ77b1G82f5tGbd7MnANML5BTQ29DHw2ImpSSnXAzsBMYBfgsfJ53xq4DiAi9ihruhJ4leJN69+A9sC1y9rmiOhCcf4G8KOU0pwWjskaydBt3RzgD+UJ+l5EPAHsShOhW/aCFgLzUkrTFk0ve6bjgFfLUJxE8cJtTk9gVErpzfLxxAbzdqUIlK+nlCaW2/9PihfFrhS98saOBv6ZUrq3fHxLROzCkuGBphxMEYC/SSl9VO7nf4ALI6J3Sml8RMwu2z2t+c1wELABcNainitFIDRUA/x20VBNRNwLHNZg/ueA21JKj5SPb4yInYBjKMbTW9vHMcBDKaUh5eP3I2JLijeeJkN30VVFaWJEXA+cExH/lVJaWLY9NXqeewMHAqemlCaVk4dExG7AEcBlTe2rNJXiaiUBYyOiD8WVyd+Wcbs3pZSGtbCfVykCc2tgBMWV2e0sOd7bA3UUb7YAn6c49g+Uj8eXx+K7EXEdRdi2tbb1KN7ApwAXp5Tmt1DnGsvQbd17ZeAuMhXYZhm38SBwAXBFRAwDnqO4fGtuHPRu4EdlMAwDnkkpvVLO6wdMWRS4ACmlCRExtZzXVOj2o7hcbWgELYduP+CdRYFbep2iB9ifjwdnc7YARjcIw6ZMbDQ2PpViGGFRz6g7H3+Teo2iR9WWffQD7m9i/b2aK6h8Uzq+XLcrxU3nWopwn9rMaltS9OD+UFyQLNYeeKnJNZZ4o9H5MAI4sWz/smz37ZZ2klKaGxEjgZ0jYibF8NUQ4EvlEM3OwOsNxoK3ArYph6cWaQd0oDgWy1Lb+cBI4KJGr6m1ytoaunMoTrbG1innNdT4RkRiGT/1kVIaGRGnAgMoLuO+DYyOiHOaCt6U0vMR8bVy+V2BcyPiiZTSbylO8Fyae1NYlptmbam38QswNbFeU/tcNG15j0mT7YiInsC5wH3AjRTDMFsC36Pl10yU2/wOHz9vVqRXtyzbnduG7b1McR7OpLj6mhsRb1IE7s4UnYKG+/5f4PEmtjNjGWt7Ftgf2BQY1YY610hr5UfGKMZkt4pGb80UL6yxK7jthTRxXFNKH6WUHk8p/YFirHQXYJPmNpJSmplSeqgM2t8Dh5Y3IN4FepTBAEBEbEzRG3y3mc29B2zbaFrjx02ts3lEdG4wbXuKF9l7razb0MhyO8v1Gc1yzG8qsEOjWTuwpL2t7eO9ZtZvrh1bUfTUrk4pjUgpvU9xfBtq6nkeRXF8NkgpjW/0b0oz+1pkm0bn47bA1LL9K7LdprxM8VzuVv4M8AqwJ8Www8sNlh0J9G1iv+PL3uqy1PZn4B7g5xGxxXLUvUZYW0P3boqbU6dHxOYR0ScijqYYm7p9Bbf9AcULqGdErBuFwRFxYET0K8fnDqLoUTf5aYOI+HJE7B0Rm0REP2BfYEJKaQHFnf3RwFkRsVVEbE1xo2MkzV/C3gkMiojDy20eT+uh+zBFr+k7EbFZOYb6TWBoSqmtQwsAj1Dc4DonInaMiF4RsVd5+d5WtwGfK49hn4j4MsUNzkU3mVrbx23AIRHx2bL9R1KMWf+1mf2NowiSo8ttHUgxLt7QRKBDROxWPs8dy3B+GDgzIvaLiI0jYusofhFl31ba2AM4rWzffhTj2H8DWMHtNuVVih77viwJ2JcpbmY2HM8FuBk4qDwnN42IvmUNpyxPbSmlPwH3UgRv4xuqa4W1cnihHAP9IcUd8gsoejVjgV+mlJ5rceXW3U4xfHAZxbjXqRR3kY+l6NkmioD82aKPnTVhAcWd8V4Ul2hvlHWSUkoR8XOKO+kXlcu/CFzR3BhxSumxsjf8FYqPUz1D8YJu9jfIUkrzIuJcirvzvylreorirn2blZeuP6I4Dj9hybG+ehk2cyfQGTiFJR8ZuzClNKot+0gpPRURV1DcUDuNIjAva+6TCymldyLiSoobbSdSjK9eQ/HJlUXLvB4R91B80qIbxSX4TcDvKG4+nUIRpLOAN1m699iUhyk6QYt+0eZ+oOHNvOXdblPtmxsRb1OMV48sJ4+gCNyG47mklF6IiPOAL1K8EdRRHP8Hl7e2lNIfy179LyLi7FY+1RwEOy4AAABHSURBVLHGiebv5UjKIYrP6Y5JKV1e6Vq06q2twwuSVBGGriRl5PCCJGVkT1eSMjJ0JSkjQ1eSMjJ0JSkjQ1eSMjJ0JSmj/w8sKZociEc/IwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cm\n",
    "cmap= cm.get_cmap('magma')\n",
    "loc =(sales-30)/(100-30)\n",
    "colours=cmap(loc)\n",
    "\n",
    "fig, ax= plt.subplots(figsize=(6,6))\n",
    "ax.set_axisbelow(True)\n",
    "ax.bar(labels, sales, label=\"Sales\",color=colours)\n",
    "for i,v in enumerate (sales):\n",
    "    ax.text(i,v-1 , v, ha=\"center\", va=\"top\",\\\n",
    "            fontsize=14, color=\"w\")\n",
    "ax.yaxis.set_ticks([])\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"left\"].set_visible(False)\n",
    "ax.text(2,-12, \"Units sold of chocolate per week\", fontsize=14, ha=\"center\", alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ML",
   "language": "python",
   "name": "ml"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}