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   "source": [
    "[![Open in Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/justmarkham/scikit-learn-tips/master?filepath=notebooks%2F07_handle_unknown_categories.ipynb)\n",
    "\n",
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/justmarkham/scikit-learn-tips/blob/master/notebooks/07_handle_unknown_categories.ipynb)\n",
    "\n",
    "# 🤖⚡ scikit-learn tip #7 ([video](https://www.youtube.com/watch?v=bA6mYC1a_Eg&list=PL5-da3qGB5ID7YYAqireYEew2mWVvgmj6&index=7))\n",
    "\n",
    "Q: For a one-hot encoded feature, what can you do if new data contains categories that weren't seen during training?\n",
    "\n",
    "A: Set handle_unknown='ignore' to encode new categories as all zeros.\n",
    "\n",
    "See example 👇\n",
    "\n",
    "P.S. If you know all possible categories that might ever appear, you can instead specify the categories manually. handle_unknown='ignore' is useful specifically when you don't know all possible categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "X = pd.DataFrame({'col':['A', 'B', 'C', 'B']})\n",
    "X_new = pd.DataFrame({'col':['A', 'C', 'D']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ohe = OneHotEncoder(sparse=False, handle_unknown='ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>B</td>\n",
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      "text/plain": [
       "  col\n",
       "0   A\n",
       "1   B\n",
       "2   C\n",
       "3   B"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 1., 0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# three columns represent categories A, B, and C\n",
    "ohe.fit_transform(X[['col']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  col\n",
       "0   A\n",
       "1   C\n",
       "2   D"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# category D was not learned by OneHotEncoder during the \"fit\" step\n",
    "X_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 0., 1.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# category D is encoded as all zeros\n",
    "ohe.transform(X_new[['col']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Want more tips? [View all tips on GitHub](https://github.com/justmarkham/scikit-learn-tips) or [Sign up to receive 2 tips by email every week](https://scikit-learn.tips) 💌\n",
    "\n",
    "© 2020 [Data School](https://www.dataschool.io). All rights reserved."
   ]
  }
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