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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Libraries\n",
+    "\n",
+    "First, you need to install the following librairies:\n",
+    "- [matplotlib](https://python-graph-gallery.com/matplotlib/) is used for plot creating the charts\n",
+    "- [pandas](https://python-graph-gallery.com/pandas/) is used to put the data into a dataframe and custom the table\n",
+    "- `numpy` is used to generate some data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib as mpl\n",
+    "\n",
+    "# data generation\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Dataset\n",
+    "\n",
+    "When creating **nice output tables**, we first need to have the dataframe with the values we want. \n",
+    "\n",
+    "In this post, we'll use *fake weather data* from different cities. We'll take a look at different simple features of [pandas](https://python-graph-gallery.com/pandas/)  to make this table more **aesthetically appealing**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "sample_size = 6\n",
+    "\n",
+    "new_york = np.random.uniform(20,60,sample_size)\n",
+    "paris = np.random.uniform(20,40,sample_size)\n",
+    "london = np.random.uniform(5,30,sample_size)\n",
+    "\n",
+    "df = pd.DataFrame({'new_york': new_york,\n",
+    "                   'paris': paris,\n",
+    "                   'london': london},\n",
+    "                 \n",
+    "                 # generate date values in the index of the dataframe\n",
+    "                 index=pd.date_range(start=\"2020-01-01\", periods=sample_size).strftime(\"%d-%m-%Y\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Default output\n",
+    "\n",
+    "The default result **isn't very pretty**, but it's from this base that we'll build something more pleasing to the eye."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "tags": []
+   },
+   "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>new_york</th>\n",
+       "      <th>paris</th>\n",
+       "      <th>london</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>01-01-2020</th>\n",
+       "      <td>56.128507</td>\n",
+       "      <td>26.748610</td>\n",
+       "      <td>5.122318</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>02-01-2020</th>\n",
+       "      <td>48.734402</td>\n",
+       "      <td>28.092822</td>\n",
+       "      <td>13.278814</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>03-01-2020</th>\n",
+       "      <td>30.813020</td>\n",
+       "      <td>24.750032</td>\n",
+       "      <td>24.622958</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>04-01-2020</th>\n",
+       "      <td>44.404172</td>\n",
+       "      <td>39.844309</td>\n",
+       "      <td>8.658434</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>05-01-2020</th>\n",
+       "      <td>25.292521</td>\n",
+       "      <td>25.400085</td>\n",
+       "      <td>22.398493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>06-01-2020</th>\n",
+       "      <td>35.903078</td>\n",
+       "      <td>26.666148</td>\n",
+       "      <td>13.565218</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             new_york      paris     london\n",
+       "01-01-2020  56.128507  26.748610   5.122318\n",
+       "02-01-2020  48.734402  28.092822  13.278814\n",
+       "03-01-2020  30.813020  24.750032  24.622958\n",
+       "04-01-2020  44.404172  39.844309   8.658434\n",
+       "05-01-2020  25.292521  25.400085  22.398493\n",
+       "06-01-2020  35.903078  26.666148  13.565218"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Change colors\n",
+    "\n",
+    "A clean way to apply modifications to [Pandas](https://python-graph-gallery.com) tables is to create a function that performs the **modifications**, then apply this function to our data frame.\n",
+    "\n",
+    "- We use a colormap named *\"Reds\"*, that will put the **background color** of each cell depending on the value in the cell.\n",
+    "\n",
+    "Once the function is defined, we use the `style()` and `pipe()` from [Pandas](https://python-graph-gallery.com) to **apply this function** to the dataframe. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'ColormapRegistry' object has no attribute 'get_cmap'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    406\u001b[0m         \"\"\"\n\u001b[1;32m    407\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"styler.render.repr\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"html\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    409\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36mto_html\u001b[0;34m(self, buf, table_uuid, table_attributes, sparse_index, sparse_columns, bold_headers, caption, max_rows, max_columns, encoding, doctype_html, exclude_styles, **kwargs)\u001b[0m\n\u001b[1;32m   1346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m         \u001b[0;31m# Build HTML string..\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1348\u001b[0;31m         html = obj._render_html(\n\u001b[0m\u001b[1;32m   1349\u001b[0m             \u001b[0msparse_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msparse_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m             \u001b[0msparse_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msparse_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style_render.py\u001b[0m in \u001b[0;36m_render_html\u001b[0;34m(self, sparse_index, sparse_columns, max_rows, max_cols, **kwargs)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0mGenerates\u001b[0m \u001b[0ma\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnecessary\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mjinja2\u001b[0m \u001b[0mtemplate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \"\"\"\n\u001b[0;32m--> 204\u001b[0;31m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msparse_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse_columns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_cols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"&nbsp;\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m         \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m         return self.template_html.render(\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style_render.py\u001b[0m in \u001b[0;36m_render\u001b[0;34m(self, sparse_index, sparse_columns, max_rows, max_cols, blank)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0mstylers\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0muse\u001b[0m \u001b[0mwithin\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0m_translate_latex\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m         \"\"\"\n\u001b[0;32m--> 161\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m         \u001b[0mdxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0mctx_len\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style_render.py\u001b[0m in \u001b[0;36m_compute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_todo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, func, axis, subset, **kwargs)\u001b[0m\n\u001b[1;32m   1710\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1711\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1712\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1713\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1714\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_background_gradient\u001b[0;34m(data, cmap, low, high, text_color_threshold, vmin, vmax, gmap, text_only)\u001b[0m\n\u001b[1;32m   3828\u001b[0m             )\n\u001b[1;32m   3829\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3830\u001b[0;31m             \u001b[0mrgbas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolormaps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3831\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3832\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mrelative_luminance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrgba\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'ColormapRegistry' object has no attribute 'get_cmap'"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fbb3058c880>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def custom_table(styler):\n",
+    "    styler.background_gradient(cmap=\"Reds\", axis=None)\n",
+    "    return styler\n",
+    "\n",
+    "df.style.pipe(custom_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here, for example, it's much easier to see that temperatures in New York are **higher** compared to London."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add aggregate metrics\n",
+    "\n",
+    "To make our table more meaningful, we can then add **aggregation measures**. To do this, we use pandas' `agg()` and `concat()` functions.\n",
+    "\n",
+    "We also add a line to our function to **round off the values** in our table, using the `format()` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'ColormapRegistry' object has no attribute 'get_cmap'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_repr_html_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    406\u001b[0m         \"\"\"\n\u001b[1;32m    407\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mget_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"styler.render.repr\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"html\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    409\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36mto_html\u001b[0;34m(self, buf, table_uuid, table_attributes, sparse_index, sparse_columns, bold_headers, caption, max_rows, max_columns, encoding, doctype_html, exclude_styles, **kwargs)\u001b[0m\n\u001b[1;32m   1346\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1347\u001b[0m         \u001b[0;31m# Build HTML string..\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1348\u001b[0;31m         html = obj._render_html(\n\u001b[0m\u001b[1;32m   1349\u001b[0m             \u001b[0msparse_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msparse_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1350\u001b[0m             \u001b[0msparse_columns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msparse_columns\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style_render.py\u001b[0m in \u001b[0;36m_render_html\u001b[0;34m(self, sparse_index, sparse_columns, max_rows, max_cols, **kwargs)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0mGenerates\u001b[0m \u001b[0ma\u001b[0m \u001b[0mdict\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnecessary\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0mpassed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mjinja2\u001b[0m \u001b[0mtemplate\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \"\"\"\n\u001b[0;32m--> 204\u001b[0;31m         \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msparse_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msparse_columns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_rows\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_cols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"&nbsp;\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m         \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m         return self.template_html.render(\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style_render.py\u001b[0m in \u001b[0;36m_render\u001b[0;34m(self, sparse_index, sparse_columns, max_rows, max_cols, blank)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[0mstylers\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0muse\u001b[0m \u001b[0mwithin\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0m_translate_latex\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m         \"\"\"\n\u001b[0;32m--> 161\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m         \u001b[0mdxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m         \u001b[0mctx_len\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style_render.py\u001b[0m in \u001b[0;36m_compute\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_todo\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, func, axis, subset, **kwargs)\u001b[0m\n\u001b[1;32m   1710\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1711\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1712\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1713\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1714\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/io/formats/style.py\u001b[0m in \u001b[0;36m_background_gradient\u001b[0;34m(data, cmap, low, high, text_color_threshold, vmin, vmax, gmap, text_only)\u001b[0m\n\u001b[1;32m   3828\u001b[0m             )\n\u001b[1;32m   3829\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3830\u001b[0;31m             \u001b[0mrgbas\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_matplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolormaps\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3831\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3832\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mrelative_luminance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrgba\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'ColormapRegistry' object has no attribute 'get_cmap'"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fc0b0aaf5e0>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def custom_table(styler):\n",
+    "    styler.background_gradient(cmap=\"Blues\", axis=None)\n",
+    "    styler.format(precision=2)\n",
+    "    return styler\n",
+    "\n",
+    "agg_metrics = df.agg([\"sum\", \"mean\", \"max\"])\n",
+    "pd.concat([df, agg_metrics]).style.pipe(custom_table)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Going further\n",
+    "\n",
+    "This post explains how to create a simple custom table with [pandas](https://python-graph-gallery.com/pandas/).\n",
+    "\n",
+    "For more examples of **how to create or customize** your tables, see the [table section](https://python-graph-gallery.com/table/). You may also be interested in how to [add HTML and CCS to your table](https://python-graph-gallery.com/5xx-pandas-table-with-html-and-css/)."
+   ]
+  }
+ ],
+ "metadata": {
+  "chartType": "colors",
+  "description": "<b>Creating elegant tables</b> with the <a href='https://python-graph-gallery.com/pandas/'>Pandas</a> library in Python is a useful way to organize and display <b>structured data</b>. <a href='https://python-graph-gallery.com/pandas/'>Pandas</a> tables allow you to present information in a neat and organized format, making it easy to analyze and understand various datasets.<br>  You can use Pandas to create tables that display data such as <b>numerical values, text, and categorical</b> information. These tables can be <b>customized and styled</b> to enhance their visual appeal, making them a versatile tool for <i>data presentation and analysis</i>.",
+  "family": "general",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "keywords": "table, pandas, customization, styler",
+  "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"
+  },
+  "seoDescription": "How to custom a table with pandas",
+  "slug": "550-intro-table-with-pandas",
+  "title": "Introduction to table with Pandas"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}