Housing_Price_DecisionTree.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Housing Price DecisionTree.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HarshitCodex/DL-ML_Projects/blob/master/Housing_Price_DecisionTree.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v4q-lvB9ZDl0",
        "colab_type": "code",
        "outputId": "85d6d5b0-8313-4480-f815-11b515a9fc63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "import os\n",
        "print(os.listdir(\"sample_data\"))\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['anscombe.json', 'README.md', 'test.csv', 'train.csv', 'california_housing_train.csv', 'mnist_test.csv', 'california_housing_test.csv', 'mnist_train_small.csv']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "metadata": {
        "id": "9ddWlDxqZKSj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b4ur2p5hZLy3",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 2090
        }
      },
      "source": [
        "!pip install fastai==0.7.0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzqYBC_fZZ_9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from fastai.imports import *\n",
        "from fastai.structured import *\n",
        "from pandas_summary import DataFrameSummary\n",
        "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
        "from IPython.display import display\n",
        "from sklearn import metrics"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rjmHLWWzZcZK",
        "colab_type": "code",
        "outputId": "6b187c81-583b-46a6-e307-0ff68645318c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "path='sample_data/'\n",
        "!ls {path}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "anscombe.json\t\t      mnist_test.csv\t     test.csv\n",
            "california_housing_test.csv   mnist_train_small.csv  train.csv\n",
            "california_housing_train.csv  README.md\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXwHDpS5ZhcV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_raw=pd.read_csv(f'{path}train.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AxaCKIAYZi2F",
        "colab_type": "code",
        "outputId": "d53b1dd3-1ebb-47fc-8026-fce63de999c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1143
        }
      },
      "source": [
        "df_raw.SalePrice"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "1430    192140\n",
              "1431    143750\n",
              "1432     64500\n",
              "1433    186500\n",
              "1434    160000\n",
              "1435    174000\n",
              "1436    120500\n",
              "1437    394617\n",
              "1438    149700\n",
              "1439    197000\n",
              "1440    191000\n",
              "1441    149300\n",
              "1442    310000\n",
              "1443    121000\n",
              "1444    179600\n",
              "1445    129000\n",
              "1446    157900\n",
              "1447    240000\n",
              "1448    112000\n",
              "1449     92000\n",
              "1450    136000\n",
              "1451    287090\n",
              "1452    145000\n",
              "1453     84500\n",
              "1454    185000\n",
              "1455    175000\n",
              "1456    210000\n",
              "1457    266500\n",
              "1458    142125\n",
              "1459    147500\n",
              "Name: SalePrice, Length: 1460, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zhU4esSfZkNy",
        "colab_type": "code",
        "outputId": "0a41fb3a-c743-4252-d68d-775e7e165862",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1143
        }
      },
      "source": [
        "df_raw.SalePrice=np.log(df_raw.SalePrice)\n",
        "df_raw.SalePrice.head"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of 0       12.247694\n",
              "1       12.109011\n",
              "2       12.317167\n",
              "3       11.849398\n",
              "4       12.429216\n",
              "5       11.870600\n",
              "6       12.634603\n",
              "7       12.206073\n",
              "8       11.774520\n",
              "9       11.678440\n",
              "10      11.771436\n",
              "11      12.751300\n",
              "12      11.877569\n",
              "13      12.540758\n",
              "14      11.964001\n",
              "15      11.790557\n",
              "16      11.911702\n",
              "17      11.407565\n",
              "18      11.976659\n",
              "19      11.842229\n",
              "20      12.692503\n",
              "21      11.845103\n",
              "22      12.345835\n",
              "23      11.774520\n",
              "24      11.944708\n",
              "25      12.454104\n",
              "26      11.811547\n",
              "27      12.631340\n",
              "28      12.242887\n",
              "29      11.134589\n",
              "          ...    \n",
              "1430    12.165980\n",
              "1431    11.875831\n",
              "1432    11.074421\n",
              "1433    12.136187\n",
              "1434    11.982929\n",
              "1435    12.066811\n",
              "1436    11.699405\n",
              "1437    12.885671\n",
              "1438    11.916389\n",
              "1439    12.190959\n",
              "1440    12.160029\n",
              "1441    11.913713\n",
              "1442    12.644328\n",
              "1443    11.703546\n",
              "1444    12.098487\n",
              "1445    11.767568\n",
              "1446    11.969717\n",
              "1447    12.388394\n",
              "1448    11.626254\n",
              "1449    11.429544\n",
              "1450    11.820410\n",
              "1451    12.567551\n",
              "1452    11.884489\n",
              "1453    11.344507\n",
              "1454    12.128111\n",
              "1455    12.072541\n",
              "1456    12.254863\n",
              "1457    12.493130\n",
              "1458    11.864462\n",
              "1459    11.901583\n",
              "Name: SalePrice, Length: 1460, dtype: float64>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SATOOKyZmoY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def rmse(x,y):\n",
        "    return math.sqrt(((x-y)**2).mean())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oz-WlUqvZtwv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_cats(df_raw)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1cdUsWrfZvsT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df,y,nas=proc_df(df_raw,'SalePrice',max_n_cat=5)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TreAa0RMZzuz",
        "colab_type": "code",
        "outputId": "02a3400f-d778-425c-f78a-322fe2a1feab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 188)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "naHEhMdrZ0tH",
        "colab_type": "code",
        "outputId": "f3ad8607-0f2b-4e3e-d561-ea8b926517cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df_raw.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1460, 81)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5j7AQx0fZ2EL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_trn,y_trn,nas=proc_df(df_raw,'SalePrice')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-RM09AG3Z5es",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_vals(a,n):return a[:n].copy(), a[n:].copy()\n",
        "n_valid=200\n",
        "n_trn=len(df_trn)-n_valid\n",
        "X_train,X_valid=split_vals(df_trn,n_trn)\n",
        "y_train,y_valid=split_vals(y_trn,n_trn)\n",
        "raw_train,raw_valid=split_vals(df_raw,n_trn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTUNPvilbHIu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def print_score(m):\n",
        "    res = [rmse(m.predict(X_train), y_train), rmse(m.predict(X_valid), y_valid),\n",
        "                m.score(X_train, y_train), m.score(X_valid, y_valid)]\n",
        "    if hasattr(m, 'oob_score_'): res.append(m.oob_score_)\n",
        "    print(res)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyYurWh-bZxE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cols=df_trn.columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VngtLGdKbbC8",
        "colab_type": "code",
        "outputId": "a87053d8-4891-41f3-a1e8-c395e4c8cfad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "m=RandomForestRegressor(n_estimators=40,min_samples_leaf=3,max_features=0.5,n_jobs=-1,oob_score=True)\n",
        "m.fit(X_train,y_train)\n",
        "print_score(m)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.07728779626310527, 0.1535949559284973, 0.9628298544466133, 0.8443485907040089, 0.8687925892118863]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWTvx974bf4h",
        "colab_type": "code",
        "outputId": "9e25725d-123c-4abf-cf8e-83c14912e4e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "#using parallel processing\n",
        "def get_preds(t): return t.predict(X_valid)\n",
        "%time preds=np.stack(parallel_trees(m,get_preds))\n",
        "np.mean(preds[:,0]),np.std(preds[:,0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 28.6 ms, sys: 41.1 ms, total: 69.7 ms\n",
            "Wall time: 116 ms\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12.127435943607221, 0.07369061579913415)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ip9Y2w5Pb9IL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_OBBjhcbkbl",
        "colab_type": "code",
        "outputId": "2cdbb5b7-dcdf-4b03-a1ef-2f999a08a4ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "x=raw_valid.copy()\n",
        "x['pred_std']=np.std(preds,axis=0)\n",
        "x['pred']=np.mean(preds,axis=0)\n",
        "x.OverallCond.value_counts().plot.barh();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QzJgxI6ZPn97Xo/edogo2btxY3HHHHUVRFMX27duLGTNmVOOwNae9vb34/Oc/XxRFURw4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\nIgsASUQWAJKILAAkEVkASPL/RU95RicBMlkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hcnIUfaibxKh",
        "colab_type": "code",
        "outputId": "7876d48c-24e9-471b-e4ef-598cb08ffcf9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        }
      },
      "source": [
        "x=raw_valid.copy()\n",
        "x['pred_std']=np.std(preds,axis=0)\n",
        "x['pred']=np.mean(preds,axis=0)\n",
        "x.MoSold.value_counts().plot.barh();"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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clytnz6edW1tblZaWppSUFK9vAQCAkzyXbyQS0TXXXOPnLAAAOMFz+ebl5Wnbtm1+zgIA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\nAbwv3SpyijLZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MoLNA0yghMnE",
        "colab_type": "code",
        "outputId": "ba90e409-3add-42bd-c39e-504b552656bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "source": [
        "raw_valid.Fence.value_counts().plot.barh()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c9a9a3b38>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AMIyQAwBgGCEHAMAwQg4AgGGEHAAAwwg5AACGEXIAAAwj5AAAGEbIAQAwLDTQC7gdI6f8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\nAADDCDkAAIYRcgAADCPkAAAY9n/MhuwCKWzNkAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-ngZHR4hxAF",
        "colab_type": "code",
        "outputId": "aeab2866-1ce9-4887-fa7e-aa1f4c5d8892",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "flds=['OverallCond','SalePrice','pred','pred_std']\n",
        "enc_summ=x[flds].groupby('OverallCond',as_index=False).mean()\n",
        "enc_summ"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>OverallCond</th>\n",
              "      <th>SalePrice</th>\n",
              "      <th>pred</th>\n",
              "      <th>pred_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>11.468797</td>\n",
              "      <td>11.587949</td>\n",
              "      <td>0.235833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>11.418422</td>\n",
              "      <td>11.613958</td>\n",
              "      <td>0.218337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>12.149712</td>\n",
              "      <td>12.179882</td>\n",
              "      <td>0.136631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>11.867814</td>\n",
              "      <td>11.859222</td>\n",
              "      <td>0.165445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>11.943916</td>\n",
              "      <td>11.882698</td>\n",
              "      <td>0.172342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>12.035681</td>\n",
              "      <td>11.922780</td>\n",
              "      <td>0.160609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9</td>\n",
              "      <td>12.200382</td>\n",
              "      <td>12.098012</td>\n",
              "      <td>0.169922</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   OverallCond  SalePrice       pred  pred_std\n",
              "0            3  11.468797  11.587949  0.235833\n",
              "1            4  11.418422  11.613958  0.218337\n",
              "2            5  12.149712  12.179882  0.136631\n",
              "3            6  11.867814  11.859222  0.165445\n",
              "4            7  11.943916  11.882698  0.172342\n",
              "5            8  12.035681  11.922780  0.160609\n",
              "6            9  12.200382  12.098012  0.169922"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGqEE_EHiN8G",
        "colab_type": "code",
        "outputId": "f316eab6-2cc8-451f-a26e-1dc7dedd56d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1992
        }
      },
      "source": [
        "fi=rf_feat_importance(m,df_trn)\n",
        "fi"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>cols</th>\n",
              "      <th>imp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>OverallQual</td>\n",
              "      <td>0.376153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>GrLivArea</td>\n",
              "      <td>0.127926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>YearBuilt</td>\n",
              "      <td>0.067656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>GarageCars</td>\n",
              "      <td>0.057303</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>TotalBsmtSF</td>\n",
              "      <td>0.048968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>ExterQual</td>\n",
              "      <td>0.037695</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>GarageArea</td>\n",
              "      <td>0.030022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>1stFlrSF</td>\n",
              "      <td>0.026347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>BsmtFinSF1</td>\n",
              "      <td>0.019216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LotArea</td>\n",
              "      <td>0.017012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>FullBath</td>\n",
              "      <td>0.015319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>YearRemodAdd</td>\n",
              "      <td>0.013055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LotFrontage</td>\n",
              "      <td>0.012929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>2ndFlrSF</td>\n",
              "      <td>0.011784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>CentralAir</td>\n",
              "      <td>0.011406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Fireplaces</td>\n",
              "      <td>0.011295</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>OverallCond</td>\n",
              "      <td>0.008646</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>FireplaceQu</td>\n",
              "      <td>0.007883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MSZoning</td>\n",
              "      <td>0.007225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>GarageYrBlt</td>\n",
              "      <td>0.006288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Neighborhood</td>\n",
              "      <td>0.005742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>BsmtUnfSF</td>\n",
              "      <td>0.005206</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>GarageType</td>\n",
              "      <td>0.005112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>TotRmsAbvGrd</td>\n",
              "      <td>0.004992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>BsmtQual</td>\n",
              "      <td>0.004430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>KitchenQual</td>\n",
              "      <td>0.003818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>OpenPorchSF</td>\n",
              "      <td>0.003691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Id</td>\n",
              "      <td>0.003557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>WoodDeckSF</td>\n",
              "      <td>0.002798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>BedroomAbvGr</td>\n",
              "      <td>0.002778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>RoofStyle</td>\n",
              "      <td>0.000810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LandSlope</td>\n",
              "      <td>0.000801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>HalfBath</td>\n",
              "      <td>0.000608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Fence</td>\n",
              "      <td>0.000586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>MasVnrType</td>\n",
              "      <td>0.000538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Condition1</td>\n",
              "      <td>0.000521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Foundation</td>\n",
              "      <td>0.000506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>LandContour</td>\n",
              "      <td>0.000501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>GarageYrBlt_na</td>\n",
              "      <td>0.000498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>BsmtFinType2</td>\n",
              "      <td>0.000437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Functional</td>\n",
              "      <td>0.000433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LotConfig</td>\n",
              "      <td>0.000418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>BsmtFinSF2</td>\n",
              "      <td>0.000384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>SaleType</td>\n",
              "      <td>0.000364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>ScreenPorch</td>\n",
              "      <td>0.000361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>LowQualFinSF</td>\n",
              "      <td>0.000237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>BsmtHalfBath</td>\n",
              "      <td>0.000131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>LotFrontage_na</td>\n",
              "      <td>0.000119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Heating</td>\n",
              "      <td>0.000078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>MiscFeature</td>\n",
              "      <td>0.000038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>MiscVal</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>MasVnrArea_na</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Alley</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>RoofMatl</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Utilities</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>PoolArea</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>3SsnPorch</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Street</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Condition2</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>PoolQC</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>83 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "              cols       imp\n",
              "17     OverallQual  0.376153\n",
              "46       GrLivArea  0.127926\n",
              "19       YearBuilt  0.067656\n",
              "61      GarageCars  0.057303\n",
              "38     TotalBsmtSF  0.048968\n",
              "27       ExterQual  0.037695\n",
              "62      GarageArea  0.030022\n",
              "43        1stFlrSF  0.026347\n",
              "34      BsmtFinSF1  0.019216\n",
              "4          LotArea  0.017012\n",
              "49        FullBath  0.015319\n",
              "20    YearRemodAdd  0.013055\n",
              "3      LotFrontage  0.012929\n",
              "44        2ndFlrSF  0.011784\n",
              "41      CentralAir  0.011406\n",
              "56      Fireplaces  0.011295\n",
              "18     OverallCond  0.008646\n",
              "57     FireplaceQu  0.007883\n",
              "2         MSZoning  0.007225\n",
              "59     GarageYrBlt  0.006288\n",
              "12    Neighborhood  0.005742\n",
              "37       BsmtUnfSF  0.005206\n",
              "58      GarageType  0.005112\n",
              "54    TotRmsAbvGrd  0.004992\n",
              "30        BsmtQual  0.004430\n",
              "53     KitchenQual  0.003818\n",
              "67     OpenPorchSF  0.003691\n",
              "0               Id  0.003557\n",
              "66      WoodDeckSF  0.002798\n",
              "51    BedroomAbvGr  0.002778\n",
              "..             ...       ...\n",
              "21       RoofStyle  0.000810\n",
              "11       LandSlope  0.000801\n",
              "50        HalfBath  0.000608\n",
              "73           Fence  0.000586\n",
              "25      MasVnrType  0.000538\n",
              "13      Condition1  0.000521\n",
              "29      Foundation  0.000506\n",
              "8      LandContour  0.000501\n",
              "82  GarageYrBlt_na  0.000498\n",
              "35    BsmtFinType2  0.000437\n",
              "55      Functional  0.000433\n",
              "10       LotConfig  0.000418\n",
              "36      BsmtFinSF2  0.000384\n",
              "78        SaleType  0.000364\n",
              "70     ScreenPorch  0.000361\n",
              "45    LowQualFinSF  0.000237\n",
              "48    BsmtHalfBath  0.000131\n",
              "80  LotFrontage_na  0.000119\n",
              "39         Heating  0.000078\n",
              "74     MiscFeature  0.000038\n",
              "75         MiscVal  0.000000\n",
              "81   MasVnrArea_na  0.000000\n",
              "6            Alley  0.000000\n",
              "22        RoofMatl  0.000000\n",
              "9        Utilities  0.000000\n",
              "71        PoolArea  0.000000\n",
              "69       3SsnPorch  0.000000\n",
              "5           Street  0.000000\n",
              "14      Condition2  0.000000\n",
              "72          PoolQC  0.000000\n",
              "\n",
              "[83 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0ZxNS6ujRrC",
        "colab_type": "code",
        "outputId": "73e7b149-bc67-415d-bc68-e89ad0645ff6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "source": [
        "fi.plot('cols','imp',figsize=(10,6),legend=False);"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nY5tcQEE7AACwQ8aZq3A4rKqqqvRxSUmJQqGQfD6fJKUfu7q6dPLkSe3du1dPPPHEtG0m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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qnkAFK5XjdHS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_fi(fi):return fi.plot('cols','imp','barh',figsize=(12,7),legend=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "blp-4rSsjh9G",
        "colab_type": "code",
        "outputId": "ebb5352a-09e5-4781-f4d3-a50a85cefdc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        }
      },
      "source": [
        "plot_fi(fi[:30])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c9a8cec18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8GXt7exo3bmw9AA2gcuXKzJkz57bvGfTlzlten9rC4676LSIiIiJSFMVSHOTn5zNixAjC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\nIiIiIiICqDkQEREREZFe/wM8U4p6TaZnSgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SZCMv9fjj8r",
        "colab_type": "code",
        "outputId": "964a3ae2-4f13-45ed-e940-bebe85a17303",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "to_keep=fi[fi.imp>0.0025].cols;\n",
        "len(to_keep)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Lsbq_iWkFuR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_keep=df_trn[to_keep].copy();\n",
        "X_train,X_valid=split_vals(df_keep,n_trn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptKPXAT7kKOw",
        "colab_type": "code",
        "outputId": "981fb0e4-7b01-4b1d-fda5-b26d13000ded",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "m=RandomForestRegressor(n_estimators=40,min_samples_leaf=3,max_features=0.5,n_jobs=-1,oob_score=True)\n",
        "m.fit(X_train,y_train)\n",
        "print_score(m)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.0800689346938318, 0.15530690528360414, 0.9601066493939407, 0.8408595134331379, 0.8688003284228604]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Bu5bd2dWIWW",
        "colab_type": "code",
        "outputId": "a0e5041c-22b9-4578-d2c0-bffa2fb9a181",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df_trn2,y_trn,nas=proc_df(df_raw,'SalePrice',max_n_cat=3)\n",
        "X_train,X_valid=split_vals(df_trn2,n_trn)\n",
        "m=RandomForestRegressor(n_estimators=40,min_samples_leaf=3,max_features=0.6,n_jobs=-1,oob_score=True)\n",
        "m.fit(X_train,y_train)\n",
        "print_score(m)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.07584323995480043, 0.1501619908935405, 0.9642063350936649, 0.8512286906770566, 0.8715703633906489]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gc5a9GbHXcub",
        "colab_type": "code",
        "outputId": "574e866d-c546-4ce0-d758-ba2ba1a606a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df_trn2.columns.shape,df_trn.columns.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((103,), (83,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRJ9JJaPXgcn",
        "colab_type": "code",
        "outputId": "cc5bed93-f875-4385-d1f3-76507af59fb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        }
      },
      "source": [
        "fi=rf_feat_importance(m,df_trn2)\n",
        "plot_fi(fi[:25])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c9a775630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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avYUiIiIiIvKAFYqZA4C+ffuyZs0aKlWqhK+vL71798bGxgYvL69sj+S0aNGC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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mq4nyobqgMVk",
        "colab_type": "code",
        "outputId": "b7785f2b-5673-4757-cb0e-5b61cab420dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "to_keep=fi[fi.imp>0.003].cols;len(to_keep)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42cl69sTgW-y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_keep=df_trn2[to_keep].copy()\n",
        "X_train,X_valid=split_vals(df_keep,n_trn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akHIo_bYfwOh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from scipy.cluster import hierarchy as hc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ChMvhJVRgEIP",
        "colab_type": "code",
        "outputId": "6a71563d-a273-439f-89bc-12cc298fd6ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        }
      },
      "source": [
        "corr=np.round(scipy.stats.spearmanr(df_keep).correlation,4)\n",
        "corr_condensed=hc.distance.squareform(1-corr)\n",
        "z=hc.linkage(corr_condensed,method='average')\n",
        "fig=plt.figure(figsize=(12,10))\n",
        "dendrogram=hc.dendrogram(z,labels=df_keep.columns,orientation='left',leaf_font_size=16)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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D1apV2bJlC6mpqTRv3pyNGzdatK1fvz6bN29mz5497Nq1i127drFs2TKWLl3K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Uu2RERERMTiOTk5pTuLEB8fn26gAqlLqs6ePcvMmTNp1aqVyTF/DwoyomLFiqSkpHDmzBnKlClD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\nLEiIiIiIiEhjWJAQEREREZHG/B/BGrhRsKoESAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o4hfrvKchYOq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_oob(df):\n",
        "    m = RandomForestRegressor(n_estimators=160, min_samples_leaf=3, max_features=0.5, n_jobs=-1, oob_score=True)\n",
        "    x, _ = split_vals(df, n_trn)\n",
        "    m.fit(x, y_train)\n",
        "    return m.oob_score_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgjIK_D0iENB",
        "colab_type": "code",
        "outputId": "f1a39c70-f3dc-4e6e-9311-909246c0b5b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "get_oob(df_keep)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8785243194325689"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SOS3aUNF3yp",
        "colab_type": "code",
        "outputId": "3744d906-a91e-416d-ee58-4e7e546563ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "for c in ('GarageArea','GarageCars','1stFlrSF','TotalBsmtSF','GarageYrBlt','YearBuilt'):\n",
        "  print(c,get_oob(df_keep.drop(c,axis=1)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "GarageArea 0.8788753796654262\n",
            "GarageCars 0.877711587557339\n",
            "1stFlrSF 0.8819276787824788\n",
            "TotalBsmtSF 0.8763035834542132\n",
            "GarageYrBlt 0.8788243616953328\n",
            "YearBuilt 0.8768644664798868\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMspA6n-GojK",
        "colab_type": "code",
        "outputId": "c2df9518-8872-43cd-9305-ac5ce2cbff6f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "to_drop=['GarageArea','GarageYrBlt','1stFlrSF']\n",
        "get_oob(df_keep.drop(to_drop,axis=1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8800936928921945"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMeCMDHmG-hn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_keep.drop(to_drop,axis=1,inplace=True)\n",
        "X_train,X_valid=split_vals(df_keep,n_trn)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_WgeQNfHlJr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save('sample_data/keep_cols.npy',np.array(df_keep.columns))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpeQn49SIKNS",
        "colab_type": "code",
        "outputId": "4c2018a8-b42c-41dd-8a15-2d9226b285ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        }
      },
      "source": [
        "keep_cols=np.load('sample_data/keep_cols.npy')\n",
        "df_keep=df_trn[keep_cols]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-27fee5f362a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mkeep_cols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sample_data/keep_cols.npy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_keep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_trn\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkeep_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2131\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\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[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\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   2132\u001b[0m             \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2133\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2134\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2135\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2175\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2176\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-> 2177\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\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   2178\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_take\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[1;32m   1267\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\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   1268\u001b[0m                     raise KeyError('{mask} not in index'\n\u001b[0;32m-> 1269\u001b[0;31m                                    .format(mask=objarr[mask]))\n\u001b[0m\u001b[1;32m   1270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1271\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['CentralAir_N' 'CentralAir_Y'] not in index\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2Wq13KLIUL_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        " reset_rf_samples()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVcmG_n1IaU2",
        "colab_type": "code",
        "outputId": "fa89426c-7ef5-4215-d4b5-1dac1b588d5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "get_oob(df_keep)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8797914812068746"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_b64vSrvKH1o",
        "colab_type": "code",
        "outputId": "d90a115d-f78f-46bf-8f9a-dc0922155ce9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 980
        }
      },
      "source": [
        "!pip3 install pdpbox\n",
        "!pip3 install plotnine"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pdpbox\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/87/23/ac7da5ba1c6c03a87c412e7e7b6e91a10d6ecf4474906c3e736f93940d49/PDPbox-0.2.0.tar.gz (57.7MB)\n",
            "\u001b[K    100% |████████████████████████████████| 57.7MB 592kB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from pdpbox) (0.22.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from pdpbox) (1.14.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from pdpbox) (1.1.0)\n",
            "Requirement already satisfied: matplotlib>=2.1.2 in /usr/local/lib/python3.6/dist-packages (from pdpbox) (3.0.3)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from pdpbox) (0.12.5)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.6/dist-packages (from pdpbox) (5.4.8)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from pdpbox) (0.20.3)\n",
            "Requirement already satisfied: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas->pdpbox) (2.5.3)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->pdpbox) (2018.9)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.2->pdpbox) (2.4.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.2->pdpbox) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=2.1.2->pdpbox) (1.0.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2->pandas->pdpbox) (1.11.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.1.2->pdpbox) (40.9.0)\n",
            "Building wheels for collected packages: pdpbox\n",
            "  Building wheel for pdpbox (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/7d/08/51/63fd122b04a2c87d780464eeffb94867c75bd96a64d500a3fe\n",
            "Successfully built pdpbox\n",
            "Installing collected packages: pdpbox\n",
            "Successfully installed pdpbox-0.2.0\n",
            "Requirement already satisfied: plotnine in /usr/local/lib/python3.6/dist-packages (0.5.1)\n",
            "Collecting pandas>=0.23.4 (from plotnine)\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/19/74/e50234bc82c553fecdbd566d8650801e3fe2d6d8c8d940638e3d8a7c5522/pandas-0.24.2-cp36-cp36m-manylinux1_x86_64.whl (10.1MB)\n",
            "\u001b[K    100% |████████████████████████████████| 10.1MB 4.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from plotnine) (1.1.0)\n",
            "Requirement already satisfied: patsy>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from plotnine) (0.5.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from plotnine) (1.14.6)\n",
            "Requirement already satisfied: mizani>=0.5.2 in /usr/local/lib/python3.6/dist-packages (from plotnine) (0.5.4)\n",
            "Requirement already satisfied: descartes>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from plotnine) (1.1.0)\n",
            "Requirement already satisfied: statsmodels>=0.8.0 in /usr/local/lib/python3.6/dist-packages (from plotnine) (0.8.0)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from plotnine) (3.0.3)\n",
            "Requirement already satisfied: python-dateutil>=2.5.0 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.4->plotnine) (2.5.3)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.23.4->plotnine) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from patsy>=0.4.1->plotnine) (1.11.0)\n",
            "Requirement already satisfied: palettable in /usr/local/lib/python3.6/dist-packages (from mizani>=0.5.2->plotnine) (3.1.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->plotnine) (1.0.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->plotnine) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=3.0.0->plotnine) (2.4.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib>=3.0.0->plotnine) (40.9.0)\n",
            "Installing collected packages: pandas\n",
            "  Found existing installation: pandas 0.22.0\n",
            "    Uninstalling pandas-0.22.0:\n",
            "      Successfully uninstalled pandas-0.22.0\n",
            "Successfully installed pandas-0.24.2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "pandas"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fq_rEHoYJynW",
        "colab_type": "code",
        "outputId": "f73a37ea-adca-43a1-89c7-1151edacbf7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8420
        }
      },
      "source": [
        "from pdpbox import pdp\n",
        "from plotnine import *"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[autoreload of pandas failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas._libs failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'iNaT'\n",
            "]\n",
            "[autoreload of pandas._libs.tslibs failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "TypeError: C function pandas._libs.tslibs.timedeltas.cast_from_unit has wrong signature (expected __pyx_t_5numpy_int64_t (PyObject *, PyObject *), got __pyx_t_5numpy_int64_t (PyObject *, PyObject *, int __pyx_skip_dispatch))\n",
            "]\n",
            "[autoreload of pandas.core.config_init failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'config'\n",
            "]\n",
            "[autoreload of pandas.core.dtypes.inference failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas._libs.lib' has no attribute 'is_scalar'\n",
            "]\n",
            "[autoreload of pandas.core.api failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.core.algorithms failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'construct_1d_object_array_from_listlike'\n",
            "]\n",
            "[autoreload of pandas.core.dtypes.cast failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'NaT'\n",
            "]\n",
            "[autoreload of pandas.core.dtypes.common failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'PandasExtensionDtype'\n",
            "]\n",
            "[autoreload of pandas.core.dtypes.dtypes failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'NaT'\n",
            "]\n",
            "[autoreload of pandas.core.dtypes.missing failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'NaT'\n",
            "]\n",
            "[autoreload of pandas.core.common failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'construct_1d_object_array_from_listlike'\n",
            "]\n",
            "[autoreload of pandas.api failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'register_dataframe_accessor'\n",
            "]\n",
            "[autoreload of pandas.core.dtypes.api failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_array_like'\n",
            "]\n",
            "[autoreload of pandas.core.dtypes.concat failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.errors failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'OutOfBoundsDatetime'\n",
            "]\n",
            "[autoreload of pandas.core.categorical failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.core.base failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'AbstractMethodError'\n",
            "]\n",
            "[autoreload of pandas.core.util.hashing failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.core.nanops failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.core.missing failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_float64'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.api failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.base failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'maybe_cast_to_integer_array'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.frozen failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ValueError: searchsorted() requires a code object with 1 free vars, not 5\n",
            "]\n",
            "[autoreload of pandas.core.sorting failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_int64'\n",
            "]\n",
            "[autoreload of pandas.core.ops failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'NullFrequencyError'\n",
            "]\n",
            "[autoreload of pandas.core.strings failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_object'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.category failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_platform_int'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.multi failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_int64'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.interval failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_platform_int'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.datetimes failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_int64'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.numeric failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.tseries.frequencies failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'unique_deltas'\n",
            "]\n",
            "[autoreload of pandas.tseries.offsets failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'NaT'\n",
            "]\n",
            "[autoreload of pandas.core.tools.datetimes failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'Timestamp'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.datetimelike failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'AbstractMethodError'\n",
            "]\n",
            "[autoreload of pandas.core.tools.timedeltas failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'Timedelta'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.timedeltas failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_int64'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.range failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.period failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "TypeError: C function pandas._libs.tslibs.timezones.is_utc has wrong signature (expected int (PyObject *, int __pyx_skip_dispatch), got int (PyObject *))\n",
            "]\n",
            "[autoreload of pandas.core.frame failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.core.generic failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'AbstractMethodError'\n",
            "]\n",
            "[autoreload of pandas.core.indexing failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'AbstractMethodError'\n",
            "]\n",
            "[autoreload of pandas.io.formats.format failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'NaT'\n",
            "]\n",
            "[autoreload of pandas.io.common failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'AbstractMethodError'\n",
            "]\n",
            "[autoreload of pandas.core.series failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_platform_int'\n",
            "]\n",
            "[autoreload of pandas.core.indexes.accessors failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.plotting._compat failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ValueError: _mpl_ge_2_0_1() requires a code object with 0 free vars, not 2\n",
            "]\n",
            "[autoreload of pandas.plotting._core failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'AbstractMethodError'\n",
            "]\n",
            "[autoreload of pandas.plotting._converter failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'FreqGroup'\n",
            "]\n",
            "[autoreload of pandas.core.window failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_float64'\n",
            "]\n",
            "[autoreload of pandas.core.panel failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.core.reshape.reshape failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_platform_int'\n",
            "]\n",
            "[autoreload of pandas.core.sparse.api failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.core.sparse.series failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.core.sparse.frame failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_platform_int'\n",
            "]\n",
            "[autoreload of pandas.core.tools.numeric failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_object'\n",
            "]\n",
            "[autoreload of pandas.tseries.api failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.tseries' has no attribute 'offsets'\n",
            "]\n",
            "[autoreload of pandas.core.computation.scope failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'computation'\n",
            "]\n",
            "[autoreload of pandas.core.computation.engines failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.io' has no attribute 'formats'\n",
            "]\n",
            "[autoreload of pandas.core.computation.align failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.core.computation.ops failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.core.reshape.api failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.core.reshape.concat failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'dtypes'\n",
            "]\n",
            "[autoreload of pandas.core.reshape.merge failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_float64'\n",
            "]\n",
            "[autoreload of pandas.core.reshape.pivot failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.core.reshape.tile failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_int64'\n",
            "]\n",
            "[autoreload of pandas.io.parsers failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'AbstractMethodError'\n",
            "]\n",
            "[autoreload of pandas.io.pytables failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'ensure_int64'\n",
            "]\n",
            "[autoreload of pandas.core.computation.pytables failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.core.computation.expr failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.io' has no attribute 'formats'\n",
            "]\n",
            "[autoreload of pandas.io.json failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "NameError: name 'json' is not defined\n",
            "]\n",
            "[autoreload of pandas.io.json.json failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'iNaT'\n",
            "]\n",
            "[autoreload of pandas.io.json.table_schema failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.io.html failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'AbstractMethodError'\n",
            "]\n",
            "[autoreload of pandas.io.feather_format failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ValueError: read_feather() requires a code object with 0 free vars, not 5\n",
            "]\n",
            "[autoreload of pandas.io.parquet failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'AbstractMethodError'\n",
            "]\n",
            "[autoreload of pandas.io.stata failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'NaT'\n",
            "]\n",
            "[autoreload of pandas.io.packers failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.util.testing failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'is_extension_array_dtype'\n",
            "]\n",
            "[autoreload of pandas.core.computation.expressions failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "AttributeError: module 'pandas.core' has no attribute 'common'\n",
            "]\n",
            "[autoreload of pandas.plotting._timeseries failed: Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.6/dist-packages/IPython/extensions/autoreload.py\", line 247, in check\n",
            "    superreload(m, reload, self.old_objects)\n",
            "ImportError: cannot import name 'FreqGroup'\n",
            "]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-c99046110fa5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpdpbox\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpdp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mplotnine\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/plotnine/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mqplot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mqplot\u001b[0m            \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mggplot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mggplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mggsave\u001b[0m  \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mggplot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msave_as_pdf_pages\u001b[0m  \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mwatermark\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwatermark\u001b[0m    \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0maes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m                  \u001b[0;31m# noqa: F401,F403,E261\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/plotnine/qplot.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpatsy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mEvalEnvironment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mggplot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mggplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0maes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_aesthetics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaled_aesthetics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mlabels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlabs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/plotnine/ggplot.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0maes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmake_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mlayer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLayers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfacets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfacet_null\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfacets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayout\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLayout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/plotnine/layer.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mexceptions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPlotnineError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0marray_kind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mninteraction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcheck_required_aesthetics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefaults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0maes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mget_calculated_aes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmake_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/plotnine/utils.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffsetbox\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDrawingArea\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatches\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRectangle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mmizani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbounds\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mzero_range\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmizani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmultitype_sort\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/mizani/bounds.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtypes\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpdtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommon\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdates\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdate2num\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'pandas.core' has no attribute 'dtypes'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1IL31rMLjXl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}