pandas stylers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow2_col0\" class=\"data row2 col0\" >3</td> \n",
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       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow2_col4\" class=\"data row2 col4\" >1.88927</td> \n",
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       "        <th id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cblevel0_row3\" class=\"row_heading level0 row3\" >3</th> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow3_col1\" class=\"data row3 col1\" >0.961538</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow3_col2\" class=\"data row3 col2\" >0.104011</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow3_col3\" class=\"data row3 col3\" >-0.481165</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow3_col4\" class=\"data row3 col4\" >0.850229</td> \n",
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       "        <th id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cblevel0_row4\" class=\"row_heading level0 row4\" >4</th> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow4_col1\" class=\"data row4 col1\" >1.45342</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow4_col2\" class=\"data row4 col2\" >1.05774</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow4_col3\" class=\"data row4 col3\" >0.165562</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow4_col4\" class=\"data row4 col4\" >0.515018</td> \n",
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       "        <th id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cblevel0_row5\" class=\"row_heading level0 row5\" >5</th> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow5_col1\" class=\"data row5 col1\" >-1.33694</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow5_col2\" class=\"data row5 col2\" >0.562861</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow5_col3\" class=\"data row5 col3\" >1.39285</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow5_col4\" class=\"data row5 col4\" >-0.063328</td> \n",
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       "        <th id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cblevel0_row6\" class=\"row_heading level0 row6\" >6</th> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow6_col1\" class=\"data row6 col1\" >0.121668</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow6_col2\" class=\"data row6 col2\" >1.2076</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow6_col3\" class=\"data row6 col3\" >-0.00204021</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow6_col4\" class=\"data row6 col4\" >1.6278</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cblevel0_row7\" class=\"row_heading level0 row7\" >7</th> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow7_col1\" class=\"data row7 col1\" >0.354493</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow7_col2\" class=\"data row7 col2\" >1.03753</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow7_col3\" class=\"data row7 col3\" >-0.385684</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow7_col4\" class=\"data row7 col4\" >0.519818</td> \n",
       "    </tr>    <tr> \n",
       "        <th id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cblevel0_row8\" class=\"row_heading level0 row8\" >8</th> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow8_col1\" class=\"data row8 col1\" >1.68658</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow8_col2\" class=\"data row8 col2\" >-1.32596</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow8_col3\" class=\"data row8 col3\" >1.42898</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow8_col4\" class=\"data row8 col4\" >-2.08935</td> \n",
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       "        <th id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cblevel0_row9\" class=\"row_heading level0 row9\" >9</th> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow9_col1\" class=\"data row9 col1\" >-0.12982</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow9_col2\" class=\"data row9 col2\" >0.631523</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow9_col3\" class=\"data row9 col3\" >-0.586538</td> \n",
       "        <td id=\"T_02bb0c3e_22a9_11e9_8ef6_76b2f96296cbrow9_col4\" class=\"data row9 col4\" >0.29072</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f117165d5c0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(24)\n",
    "df = pd.DataFrame({'A': np.linspace(1, 10, 10)})\n",
    "\n",
    "df = pd.concat([df, pd.DataFrame(np.random.randn(10, 4), columns=list('BCDE'))],\n",
    "               axis=1)\n",
    "df.iloc[0, 2] = np.nan\n",
    "\n",
    "def highlight_greaterthan(s,threshold,column):\n",
    "    is_max = pd.Series(data=False, index=s.index)\n",
    "    is_max[column] = s.loc[column] >= threshold\n",
    "    return ['background-color: yellow' if is_max.any() else '' for v in is_max]\n",
    "\n",
    "\n",
    "df.style.apply(highlight_greaterthan,threshold=1.0,column=['C','B'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['background-color: yellow',\n",
       " 'background-color: yellow',\n",
       " 'background-color: yellow',\n",
       " 'background-color: yellow',\n",
       " 'background-color: yellow']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highlight_greaterthan(df.loc[0], 1.0, ['C','B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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