Example-style_sheet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will create a style sheet that you can use to help you create visualizations with consistent style and quality. First, we'll load the packages and a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pickup</th>\n",
       "      <th>dropoff</th>\n",
       "      <th>passengers</th>\n",
       "      <th>distance</th>\n",
       "      <th>fare</th>\n",
       "      <th>tip</th>\n",
       "      <th>tolls</th>\n",
       "      <th>total</th>\n",
       "      <th>color</th>\n",
       "      <th>payment</th>\n",
       "      <th>pickup_zone</th>\n",
       "      <th>dropoff_zone</th>\n",
       "      <th>pickup_borough</th>\n",
       "      <th>dropoff_borough</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-03-23 20:21:09</td>\n",
       "      <td>2019-03-23 20:27:24</td>\n",
       "      <td>1</td>\n",
       "      <td>1.60</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.95</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Lenox Hill West</td>\n",
       "      <td>UN/Turtle Bay South</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-03-04 16:11:55</td>\n",
       "      <td>2019-03-04 16:19:00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.79</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.30</td>\n",
       "      <td>yellow</td>\n",
       "      <td>cash</td>\n",
       "      <td>Upper West Side South</td>\n",
       "      <td>Upper West Side South</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-03-27 17:53:01</td>\n",
       "      <td>2019-03-27 18:00:25</td>\n",
       "      <td>1</td>\n",
       "      <td>1.37</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.16</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Alphabet City</td>\n",
       "      <td>West Village</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-03-10 01:23:59</td>\n",
       "      <td>2019-03-10 01:49:51</td>\n",
       "      <td>1</td>\n",
       "      <td>7.70</td>\n",
       "      <td>27.0</td>\n",
       "      <td>6.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.95</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Hudson Sq</td>\n",
       "      <td>Yorkville West</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-03-30 13:27:42</td>\n",
       "      <td>2019-03-30 13:37:14</td>\n",
       "      <td>3</td>\n",
       "      <td>2.16</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>yellow</td>\n",
       "      <td>credit card</td>\n",
       "      <td>Midtown East</td>\n",
       "      <td>Yorkville West</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               pickup             dropoff  passengers  distance  fare   tip  \\\n",
       "0 2019-03-23 20:21:09 2019-03-23 20:27:24           1      1.60   7.0  2.15   \n",
       "1 2019-03-04 16:11:55 2019-03-04 16:19:00           1      0.79   5.0  0.00   \n",
       "2 2019-03-27 17:53:01 2019-03-27 18:00:25           1      1.37   7.5  2.36   \n",
       "3 2019-03-10 01:23:59 2019-03-10 01:49:51           1      7.70  27.0  6.15   \n",
       "4 2019-03-30 13:27:42 2019-03-30 13:37:14           3      2.16   9.0  1.10   \n",
       "\n",
       "   tolls  total   color      payment            pickup_zone  \\\n",
       "0    0.0  12.95  yellow  credit card        Lenox Hill West   \n",
       "1    0.0   9.30  yellow         cash  Upper West Side South   \n",
       "2    0.0  14.16  yellow  credit card          Alphabet City   \n",
       "3    0.0  36.95  yellow  credit card              Hudson Sq   \n",
       "4    0.0  13.40  yellow  credit card           Midtown East   \n",
       "\n",
       "            dropoff_zone pickup_borough dropoff_borough  \n",
       "0    UN/Turtle Bay South      Manhattan       Manhattan  \n",
       "1  Upper West Side South      Manhattan       Manhattan  \n",
       "2           West Village      Manhattan       Manhattan  \n",
       "3         Yorkville West      Manhattan       Manhattan  \n",
       "4         Yorkville West      Manhattan       Manhattan  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import warnings                         #We do this to keep from seeing lots of info junk printing out\n",
    "warnings.filterwarnings('ignore')\n",
    "import plotly\n",
    "from matplotlib import pyplot as plt\n",
    "df = sns.load_dataset('taxis');\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will mostly use the Seaborn library for visualization in Python. Seaborn is built on Matplotlib, so a lot of your code may use a combo of Seaborn (sns) and Matplotlib Pyplot (plt) functions. Matplotlib has a built-in style library (plt.style.use()), as does Seaborn (sns.set_style()).   \n",
    "\n",
    "Here, we'll start out manually defining a style. Once we have one we like, we can drop the code into a mplstyle file and save it where the other style files are, so we can call it in the same way. The first thing you can do is to look at all the different style options that you can play with. This first list is for seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'axes.facecolor': 'white',\n",
       " 'axes.edgecolor': 'black',\n",
       " 'axes.grid': False,\n",
       " 'axes.axisbelow': 'line',\n",
       " 'axes.labelcolor': 'black',\n",
       " 'figure.facecolor': 'white',\n",
       " 'grid.color': '#b0b0b0',\n",
       " 'grid.linestyle': '-',\n",
       " 'text.color': 'black',\n",
       " 'xtick.color': 'black',\n",
       " 'ytick.color': 'black',\n",
       " 'xtick.direction': 'out',\n",
       " 'ytick.direction': 'out',\n",
       " 'lines.solid_capstyle': <CapStyle.projecting: 'projecting'>,\n",
       " 'patch.edgecolor': 'black',\n",
       " 'patch.force_edgecolor': False,\n",
       " 'image.cmap': 'viridis',\n",
       " 'font.family': ['sans-serif'],\n",
       " 'font.sans-serif': ['DejaVu Sans',\n",
       "  'Bitstream Vera Sans',\n",
       "  'Computer Modern Sans Serif',\n",
       "  'Lucida Grande',\n",
       "  'Verdana',\n",
       "  'Geneva',\n",
       "  'Lucid',\n",
       "  'Arial',\n",
       "  'Helvetica',\n",
       "  'Avant Garde',\n",
       "  'sans-serif'],\n",
       " 'xtick.bottom': True,\n",
       " 'xtick.top': False,\n",
       " 'ytick.left': True,\n",
       " 'ytick.right': False,\n",
       " 'axes.spines.left': True,\n",
       " 'axes.spines.bottom': True,\n",
       " 'axes.spines.right': True,\n",
       " 'axes.spines.top': True}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sns.axes_style()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This next one is for matplotlib. You can see visual examples of matplotlib styles at https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " '_mpl-gallery',\n",
       " '_mpl-gallery-nogrid',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-v0_8',\n",
       " 'seaborn-v0_8-bright',\n",
       " 'seaborn-v0_8-colorblind',\n",
       " 'seaborn-v0_8-dark',\n",
       " 'seaborn-v0_8-dark-palette',\n",
       " 'seaborn-v0_8-darkgrid',\n",
       " 'seaborn-v0_8-deep',\n",
       " 'seaborn-v0_8-muted',\n",
       " 'seaborn-v0_8-notebook',\n",
       " 'seaborn-v0_8-paper',\n",
       " 'seaborn-v0_8-pastel',\n",
       " 'seaborn-v0_8-poster',\n",
       " 'seaborn-v0_8-talk',\n",
       " 'seaborn-v0_8-ticks',\n",
       " 'seaborn-v0_8-white',\n",
       " 'seaborn-v0_8-whitegrid',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try changing some of the options above to see how the aesthetics change. First, let's look at the default plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='total', ylabel='Count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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VXnNtk52dLTc3t1LzEz/22GMKDg7Wr371Kz355JMO3+Oh4rZv367Q0FC1adNGEydOdLhtPNd41cnKytIHH3ygcePGlWpzmWvc6gQHFVeSip4+fdr4+PiYEydOmBMnTph69eqZM2fOOIxAcXbmzBkjyRw8eNAY83+/WH7ppZfsfdLS0owkc/jwYWOMsY8UuXTkSlJSkgkLC7tsjUVFRcbPz8+sW7fOvk5lJI5X+uXOpdauXWuCg4Pty7+kJtQ8qampV0yqn376aSPJZGVlXXUEyj//+U/Tpk0b+y+UjPn51/f169c3GzduNMYYc/3115vVq1c77OOhhx4ycXFxxpjy/Xvp2LGjmTdv3rUcNmq5hIQE4+HhYXx9fY2vr6+RZJo0aWIftVdynS1evNhhu4iICPPII484rOvWrZuZNGmSw3ZXuj6NMWbnzp3Gy8vLzJkzx3h6epp///vfVXWoqEGq6rp84YUXTGBgoMnNzbW3f/DBB8bd3d1kZmban/uWW24x77zzjmnYsKFZs2aNw/6c3ys8/vjjpl27dvblt99+2zRs2ND+HLxHAFzTt99+ayQ5jIw0xpj77rvPdO/e3aKqaqeioiIzZMgQ06tXL4f1//jHP8yGDRvMgQMHzGuvvWauu+46c+utt1pUZc324YcfmjfffNPs37/fbNiwwcTFxZmoqCiTk5NjVq1aZby9vUtt061bN3P//fdbUG3t88YbbxgPDw+HX31zfVce58/WO3bsMJJKjea97bbbzO23326MMeaRRx4xrVu3LrWvkJAQs3Tp0iqtt6a70ncexhjz008/mV//+tfmD3/4g8P6p556ymzbts3s37/fLFu2zDRq1MhMnz69iqutHco656+//rp5//33zYEDB8y7775r2rVrZ7p162YuXrxojOEavxZXu8Yff/xxExgYaH766SeH9a50jXtWX1SDyhYSEqIhQ4Zo5cqVMsZoyJAhaty4sUOfI0eOaO7cudq1a5e+++47+8iT9PR0dejQwd4vNjbW/neTJk0kSadPn7ZPhNegQQNdf/31Dn0uvVdxVlaWZs+ere3bt+v06dMqKirSjz/+aP8VTkVt3rxZCxcu1JdffqmcnBxdvHhR+fn5+vHHH9WgQYNy1YTaw1xlhIm3t/dV97F//34dPXpUfn5+Duvz8/N17Ngx5eXl6dixYxo3bpzGjx9vb7948aICAgIctrnSv5c///nPmjhxoj7++GMNGDBAI0aMcOgPSFLfvn21bNkySdIPP/ygpUuXavDgwQ4T6V76y5acnBydOnVKvXr1cthPr169tH//fod1V3s9j4uL07333quHHnpIM2fO1A033FC5B4caqyquy8OHD6tTp07y9fV1aC8uLpbNZlNYWJgkadeuXVq/fr3eeustDRs27Ip1jhkzRrNnz1Zqaqp69uyplStX6vbbb3d4Dt4jAKjLEhMTdejQIYc5OSQ53Ke9Y8eOatKkifr3769jx445vGbi6gYPHmz/OzY2Vj169FCzZs305ptvlho5j8q3fPlyDR48WBEREfZ1XN+ojQoLC3X77bfLGGN/n15ixowZ9r9jY2Pl7e2te+65RwsXLpSPj091l1rj3Xnnnfa/O3bsqNjYWF1//fXavn27+vfvb2Fltd/LL7+sUaNGqV69eg7rXeka5xZeNdzdd9+tlStX6pVXXnG4dUWJoUOH6uzZs3rxxRe1a9cu+7CyklsWlfDy8rL/XTIE9tLbfF3aXtLn0i+1ExIStG/fPi1ZskQ7d+7Uvn37FBwcXOp5yuPEiRP6/e9/r9jYWL399tvau3evfdKhS/d3tZpQ87Vs2VJubm46fPhwme2HDx9WSEiIGjVqVOZ//0snOM7NzVWXLl20b98+h8dXX32lP/zhD8rNzZUkvfjiiw7thw4dUmpqqsN+r/Tv5Y9//KP++9//6q677tLBgwfVtWtXPfvss9d+MlCr+Pr6qmXLlmrZsqW6deuml156SXl5eXrxxRcd+vwSV3s9Ly4u1o4dO+Th4aGjR4/+wiNAbVSV1+XVXH/99Wrbtq1efvnlq05OHxoaqqFDh2rFihXKysrSRx99VOo9EO8RANfTuHFjeXh4KCsry2F9VlaWwsPDLaqq9pk8ebLWr1+vbdu2qWnTplfs26NHD0ni/UAlaNSokVq3bq2jR48qPDxcFy5c0Llz5xz6cK1Xjq+//lqbN2/WH//4xyv24/quPCXX7ZVev8PDw0v9WOXixYs6e/Ys1/0vVBKefP3119q0aZP8/f2v2L9Hjx66ePGiTpw4UT0F1nItWrRQ48aN7a8hXONV49///rdsNttVX9Mla69xApQarmT+hpL5HS71/fffy2azafbs2erfv7/atWunH374oUrq2LFjh/785z/rpptuUkxMjHx8fPTdd9859PHy8lJRUZHDOm9v71Lr9u7dq+LiYj311FPq2bOnWrdurVOnTlVJ3XBtwcHBuvHGG7V06VL99NNPDm2ZmZlatWqVxowZI+nnEVkZGRn29iNHjjjc8/bXv/61jhw5otDQUPsXhCWPgIAAhYWFKSIiQv/9739LtUdHR1eo7sjISP3pT3/SO++8o7/85S8OXz4CZXFzc5O7u3up67yEv7+/IiIitGPHDof1O3bsUPv27Sv0XE8++aS+/PJLJScna8OGDQ5zaQGXqozrsl27dtq/f7/y8vIc2t3d3dWmTRv7usaNG2vr1q06evSobr/99quGKH/84x/1xhtv6IUXXtD1119fahQMANfj7e2tLl26aMuWLfZ1xcXF2rJli+Li4iysrHYwxmjy5Ml69913tXXr1nK9f923b5+k/xuxil8uNzdXx44dU5MmTdSlSxd5eXk5XOs2m03p6elc65VgxYoVCg0N1ZAhQ67Yj+u78kRHRys8PNzhms7JydGuXbvs13RcXJzOnTunvXv32vts3bpVxcXF9jAL5VcSnhw5ckSbN292mJPwcvbt2yd3d3eFhoZWQ4W13zfffKPvv//e/hrCNV41li9fri5duqhTp05X7WvlNc4tvGo4Dw8P+6/zPTw8HNoCAwMVHBysF154QU2aNFF6erp94uDK1qpVK/3zn/9U165dlZOTo/vuu6/U0OXmzZtry5Yt6tWrl3x8fBQYGKjmzZvr+PHj2rdvn5o2bSo/Pz+1bNlShYWFevbZZzV06FDt2LFDzz//fJXUDdf33HPP6Te/+Y3i4+P18MMPKzo6WmlpabrvvvvUunVrzZ07V5LUr18/Pffcc4qLi1NRUZFmzpzp8AvkUaNG6cknn9Qtt9yiBQsWqGnTpvr666/1zjvv6P7771fTpk01f/58/fnPf1ZAQIAGDRqkgoICffrpp/rhhx8chg5eybRp0zR48GC1bt1aP/zwg7Zt26Z27dpVyblBzVVQUKDMzExJP98q6bnnnlNubq6GDh162W3uu+8+Pfjgg7r++uvVuXNnrVixQvv27dOqVavK/byff/655s6dq7feeku9evXS008/ralTp+p3v/udWrRocc3HhZqtKq7LUaNG6cEHH1RCQoLmzZunM2fOaMqUKbrrrrvst+8qERoaqq1bt6pv374aOXKk1qxZI0/Pst+qxsfHy9/fXw8//LAWLFhQSWcAQFWbMWOGEhIS1LVrV3Xv3l2LFy9WXl6exo4da3VpNV5iYqJWr16t999/X35+fvbX84CAANWvX1/Hjh3T6tWrddNNNyk4OFgHDhzQ9OnT1bt3b243+wvce++9Gjp0qJo1a6ZTp07pwQcflIeHh0aOHKmAgACNGzdOM2bMUFBQkPz9/TVlyhTFxcWpZ8+eVpdeoxUXF2vFihVKSEhweI/A9X3tcnNzHUbrlHxPExQUpKioKE2bNk0PP/ywWrVqpejoaM2ZM0cRERH2W6+2a9dOgwYN0vjx4/X888+rsLBQkydP1p133ulwqzX87Ernu0mTJvqf//kfffbZZ1q/fr2Kiorsr+lBQUHy9vZWSkqKdu3apb59+8rPz08pKSmaPn26/vd//1eBgYFWHZZLu9I5DwoK0vz58zVixAiFh4fr2LFjuv/++9WyZUv7j9W5xivmaq8p0s9B7Nq1a/XUU0+V2t7lrnFLZl7BNSmZbPVyLp1EftOmTaZdu3bGx8fHxMbGmu3btztM3lMyKeznn39u3/6HH34wksy2bduMMT9PxhoQEODwHO+++6659PL57LPPTNeuXU29evVMq1atzNq1a02zZs3MokWL7H3+9a9/mZYtWxpPT0/TrFkzY4wx+fn5ZsSIEaZRo0ZGkn3C76effto0adLE1K9f38THx5tXX33VSDI//PBDuWtC7XH8+HGTkJBgwsLCjJubm5Fkhg8fbvLy8ux9vv32WzNw4EDj6+trWrVqZT788EOHSeSNMSYjI8OMHj3aNG7c2Pj4+JgWLVqY8ePHm+zsbHufVatWmc6dOxtvb28TGBhoevfubd555x17HVf79zJ58mRz/fXXGx8fHxMSEmLuuusu891331Xp+UHNkpCQYCTZH35+fqZbt27mrbfeMsaUfZ0Z8/OEsPPmzTPXXXed8fLyMp06dTIfffSRvf1q1+dPP/1k2rdvbyZMmOCw35tvvtn85je/sU+Oh7qpqq5LY4w5cOCA6du3r6lXr54JCgoy48ePN+fPn3d47kvf15w6dcq0bt3a3H777ebixYulJpEvMWfOHOPh4VFqQlPeIwCu7dlnnzVRUVHG29vbdO/e3aSmplpdUq1w6Wv4pY+S98Lp6emmd+/eJigoyPj4+JiWLVua++67z+F9MMrvjjvuME2aNDHe3t7muuuuM3fccYc5evSovf2nn34ykyZNMoGBgaZBgwbm1ltvNRkZGRZWXDts3LjRSDI2m81hPdf3tdu2bVuZryEl3y0VFxebOXPmmLCwMOPj42P69+9f6r/D999/b0aOHGkaNmxo/P39zdixYx3e8+H/XOl8l7zvLutR8r3D3r17TY8ePUxAQICpV6+eadeunXn00UdNfn6+tQf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      "text/plain": [
       "<Figure size 2000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize=(20,5))\n",
    "#sns.barplot(df['pickup_borough'],df['total'], ax = ax[0])\n",
    "sns.barplot(data = df, x = 'pickup_borough', y = 'total', ax = ax[0])\n",
    "sns.histplot(df['total'], ax = ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's what I need to define my own personal styles. I can save this as a style to call from the matplotlib folder. That way, instead of all the stuff that is needed for my_rcParams() being in this file, I would just call plt.style.use(my_rcParams) in the my_styleXY function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "axes = {'spines.right' : False,\n",
    "        'spines.left' : False,\n",
    "        'grid' : True,\n",
    "        'grid.axis' : 'y',\n",
    "        'grid.which' : 'major',\n",
    "        'titlesize' : 16,\n",
    "        'titleweight' : 'bold'}\n",
    "lines = {'markerfacecolor' : 'gray'}\n",
    "grid = {'color' : 'black',\n",
    "        'alpha' : 0.1}\n",
    "figure = {'figsize' : (10,5),\n",
    "          'titlesize' : 25}\n",
    "font = {'font.family':'serif',\n",
    "        'font.style':'italic',\n",
    "        'font.weight':800}\n",
    "xtick = {'labelsize' : 5}\n",
    "ytick = {'labelsize' : 10}\n",
    "\n",
    "def my_rcParams():\n",
    "        plt.style.use('default')\n",
    "        plt.rc('axes', **axes),\n",
    "        plt.rc('grid', **grid),\n",
    "        plt.rc('ytick', **ytick),\n",
    "        plt.rc('figure', **figure),\n",
    "        plt.rc('lines', **lines),\n",
    "\n",
    "#You could, in theory, turn this function into a library package, just like any other that you'd install / import.        \n",
    "def my_styleXY(kind, x, y, nrows, ncols, index, xtext, ytext, suptitle, title, source, highlight):\n",
    "        my_rcParams()\n",
    "        ax = plt.subplot(nrows, ncols, index)\n",
    "        if kind == 'bar': sns.barplot(x = x, y = y, color = 'gray', ax = ax)\n",
    "        if kind == 'scatter': sns.scatterplot(x = x, y = y, color = 'gray', ax = ax)\n",
    "        if kind == 'violin': sns.violinplot(x = x, y = y, color = 'gray', ax = ax)\n",
    "        ax.xaxis.set_label_text(xtext)\n",
    "        ax.yaxis.set_label_text(ytext)\n",
    "        heights = [p.get_height() for p in ax.patches]\n",
    "        highest = sorted(heights)[len(heights)-1]\n",
    "        for p in ax.patches:\n",
    "                if p.get_height() == highest:\n",
    "                        if highlight == \"TRUE\":\n",
    "                                p.set_color('darkred')\n",
    "        print(heights)\n",
    "        fig.suptitle(suptitle, x = .15, fontsize = 16, \n",
    "                     fontstyle = 'italic', font = 'times new roman', \n",
    "                     transform = fig.transFigure, clip_on = False)\n",
    "        ax.set_title(title, loc = 'left', fontsize = 12, fontstyle = 'italic', font = 'arial')\n",
    "        ax.text(.04, -.01, source, transform=fig.transFigure, fontsize = 10, alpha = .7)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once my style is developed, this is all I need to do to create consistently styled graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16.670506833712984, 31.66010654490107, 22.76525252525253, 19.236240208877287]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "# We define my_styleXY with the following arguments: (kind (can be bar, scatter, violin), x, y, nrows (in figure), ncols (in figure), index (of subplot in figure), \n",
    "# xtext (x axis label), ytext (y axis label), suptitle (overall figure title), title (subplot title), source (where you got the data), and\n",
    "# highlight (whether or not to highlight the largest value))\n",
    "\n",
    "my_styleXY('bar', df['pickup_borough'], df['total'], 1, 1, 1, \"Pickup borough\",\n",
    "               \"Total bill\",\"Queens has the best tips\",\"Pickup boroughs vary greatly on tips\",\n",
    "               \"Source: This dataset is on taxis from Seaborn\", 'FALSE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph above uses several principles we discussed as fundamental:\n",
    "\n",
    "- start with gray\n",
    "- readable axes labels\n",
    "- main takeaway title / subtitle\n",
    "- source is cited\n",
    "- no lie factor\n",
    "- minimal chart junk\n",
    "- aligned objects (if we had stacked bars instead, we would have misaligned 'y' values)\n",
    "\n",
    "Still, we could do better with this graph. It's a bit boring. For instance, how would you draw attention to the most notable element? How would you lead the viewer through the graph? How would you tell the story? What question does this answer?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16.670506833712984, 31.66010654490107, 22.76525252525253, 19.236240208877287]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "# We define my_styleXY with the following arguments: (kind (can be bar, scatter, violin), x, y, nrows (in figure), ncols (in figure), index (of subplot in figure), \n",
    "# xtext (x axis label), ytext (y axis label), suptitle (overall figure title), title (subplot title), source (where you got the data), and\n",
    "# highlight (whether or not to highlight the largest value))\n",
    "\n",
    "my_styleXY('bar', df['pickup_borough'], df['total'], 1, 1, 1, \"Pickup borough\",\n",
    "               \"Total bill\",\"Queens has the best tips\",\"Pickup boroughs vary greatly on tips\",\n",
    "               \"Source: This dataset is on taxis from Seaborn\", \"TRUE\")     #By specifying True in the last argument, we will highlight a bar.\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 64-bit (microsoft store)",
   "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": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "7edd4e905d9b7ca730f7b0e9c082715bdac78ae21b6c7d0ae05faf51bf553083"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}