Anomaly_Detection_using_Isolation_Forest.ipynb

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      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "from datetime import datetime\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "\n",
        "from sklearn.ensemble import IsolationForest\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (10, 8)\n",
        "mpl.rcParams['axes.grid'] = False\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "PJaoc_EcR1pB"
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      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/numenta/NAB/master/data/realKnownCause/nyc_taxi.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "df['hour']=df.timestamp.dt.hour"
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      "source": [
        "df['weekday']=pd.Categorical(df.timestamp.dt.strftime('%A'), categories=['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday'], ordered=True)"
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            "<class 'pandas.core.frame.DataFrame'>\n",
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            "text/plain": [
              "               timestamp    value  hour   weekday\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday\n",
              "...                  ...      ...   ...       ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday\n",
              "\n",
              "[5160 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iP4nsGOG_B0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "64286b6a-1b4f-4ec5-9ee8-d84745298607"
      },
      "source": [
        "df[['value','weekday']].groupby('weekday').mean().plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2524845208>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4I-m1BsDALW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        },
        "outputId": "77b07d2b-3e25-436c-8c5d-d318b170831c"
      },
      "source": [
        "df[['value','hour']].groupby('hour').mean().plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f2524769c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JXUXpkNsuGIEB/vj+IadL6XUKYyIiIp2w/kAlrR6r/mIOGZowgKuz03jh40JO1Dc7XU6vUhgTERHphDV7yomJCCU3M9HpUoLWXfNHcqrFzbINR5wupVcpjImIiHTAWsu6veWcPzqJMLW0cEzW4FguGjeIpRsOc6o5cLr060+UiIhIB/aXnaS4+hTz1dLCcXfNH0VVfTMvbSp0upReozAmIiLSgTV72lpaLFBLC8edl5lIzrAEnnjvIK1uj9Pl9AqFMRERkQ6s3VtO1uAYhiYMcLqUoGeM4e75oyisOsXr20udLqdXKIyJiIicQ31TKx8dqtJVlD7k4vGDGZkSzR/WHsBa/99MXGFMRETkHDYcqKTZ7VF/MR/ichnumjeSHcdqeX9/hdPl9JjCmIiIyDms3VtOVHiIWlr4mGty0hgUG8Ef1h50upQeUxgTERE5C2sta/aWMWdUEhGhIU6XI+2c3rfy/f0VbC+ucbqcHlEYExEROYuDFfUUVp1ivqYofdJXZg4jJiKU3/v5BuIdhjFjTIYxZrUxZqcxZocx5lve8YHGmLeNMfu8PxO948YY87AxZr8xZqsxZlq797rFe/w+Y8wt7canG2O2eV/zsDHG9MWXFRER6Yq1p1taaPG+T4qLDOOrM4fx+rYSjlb67wbinTkz1gp8x1o7AZgF3GuMmQA8BLxrrR0DvOt9DHApMMZ7uxN4DNrCG/ADYCYwA/jB6QDnPeaOdq9b2POvJiIi0jNr9pYzMiWajIFRTpciZ3HbBSMIcRmeeM9/1451GMastSXW2s3e+3XALiANuBpY6j1sKXCN9/7VwDLbZiOQYIxJBS4B3rbWVllrTwBvAwu9z8VZazfatutTl7V7LxEREUc0trj58GAlC9R136cNjovk2pw0XswrpPJkk9PldEuX1owZYzKBHOBDYLC1tsT7VCkw2Hs/DWi/R0GRd+xc40VnGBcREXHMhoOVNLV6mK+u+z7vznmjaGr1sHT9YadL6ZZOhzFjTAzwMvCAtba2/XPeM1p93nXNGHOnMSbPGJNXXl7e1x8nIiJBbO2eciLDXMwcMdDpUqQDowfFcPGEwSzdcIT6plany+myToUxY0wYbUHsWWvtCu/wce8UI96fZd7xYiCj3cvTvWPnGk8/w/jnWGsft9bmWmtzU1L0LxUREek7a/eWM3tkEpFhamnhD+6eP5KaUy28mOd/G4h35mpKA/wR2GWt/VW7p14FTl8ReQvwSrvxJd6rKmcBNd7pzLeALxljEr0L978EvOV9rtYYM8v7WUvavZeIiEi/O1JZz6GKem2B5EemDx/IeZmJPPneIVr8bAPxzpwZOx+4GbjQGJPvvV0G/BS42BizD/ii9zHA68BBYD/wBHAPgLW2Cvgx8LH39iPvGN5jnvS+5gDwRi98NxERkW5Zc7qlhfqL+ZW75o2iuPoUf9ta0vHBPiS0owOste8DZ+v7ddEZjrfAvWd5r6eAp84wngdM6qgWERGR/rB2bznDk6LITI52uhTpggvHDWLMoBh+v/YAV2cPxV/alqoDv4iISDuNLW7WH6hQo1c/5HIZ7pw3kt2ldazd6z8X+imMiYiItPPRoSoaWzyaovRTV2enMSQu0q+2SFIYExERaWft3nLCQ13MGpnkdCnSDeGhLm6/YAQbD1aRX1jtdDmdojAmIiLSzpo9ZcwcMZAB4Wpp4a9unJFBbGQof/CTs2MKYyIiIl6FVQ0cKK/XFKWfi40M4+ZZw3lzRymHKuqdLqdDCmMiIiJepxd9q7+Y/7v1/EzCQlx+sYG4wpiIiIjXmj3lpCcOYFSKWlr4u0GxkVw3LZ3lm4ooq2t0upxzUhgTEREBmls9rD9QwfysFL/pTyXndsfcEbS4fX8DcYUxERERIO9wFQ3Nbq0XCyAjU2JYOHEIz2w4wkkf3kBcYUxERARYs7ecsBDDnFFqaRFI7pw3ktrGVp7/6KjTpZyVwpiIiAiwdk8552UOJDqiw50CxY/kDEtk5oiBPPneIZpbfXMDcYUxEREJeseqT7HneB0LxuoqykB094JRlNY28mrBMadLOSOFMRERCXqnW1povVhgWpCVwrghsTy+7gAej3W6nM9RGBMRkaC3dk85qfGRjBkU43Qp0geMMdw1fyR7j59k9Z4yp8v5HIUxEREJai1uDx/sr2DBWLW0CGRXTBlKWsIA/rDW95rAKoyJiEhQ23zkBHVNreq6H+DCQto2EP/ocBWbjpxwupxPURgTEZGgtmZvOaEuw/mjk50uRfrYDedlED8gzOc2EFcYExGRoLZmTznThycSGxnmdCnSx6IjQlkyezhv7zrO/rKTTpfzCYUxEREJWsdrG9lVUst8tbQIGrfMySQ8xMUT63xn7ZjCmIiIBK1PWlpkqaVFsEiOiWBxbjortxRzvNY3NhBXGBMRkaC1dk85g2IjGJ8a63Qp0o/umDuSVo+Hpz445HQpgMKYiIgEqVa3h/f2lTM/Sy0tgs3wpGgunZzKXzYepbaxxelyFMZERCQ45RdWU9vYqq77QerueaOoa2rlLx86v4G4wpiIiASltXvLcRm4QC0tgtLk9HjOH53EU+8foqnV7WgtCmMiIhKU1uwpZ9qwROKj1NIiWN01bxRV9c1sPlLtaB0KYyIiEnTK65rYVlzDArW0CGpzxyTz/n9cyOxRSY7WEerop4uISJ+rbWzh92sOEDcgjDvmjiTEpcXq7+1ra2kxXy0tgpoxhiHxkU6XoTAmIhKoPB7Lii3F/PSN3VScbAJg9e4yfnNjjk/8AnLSmj3lJMeEM3FonNOliGiaUkQkEG0vruHLv1/Pv75UQMbAAfz1vgv45eKpbCuu4dLfrOPdXcedLtExbo9l3b5y5mWl4NJZQvEBOjMmIhJATtQ384u/7+EvHx0lKTqcXyyeyqKcNFwuw+T0eLKHJfDNv2zh9qV5fP38TB66dBwRoSFOl92vthZVU93QwvwsrRcT36AwJiISANwey3MfHeUXf99DXWMrX58zggcuHkPcZza/HpUSw8p75/A/r+/mTx8c5qNDVfz2phxGpsQ4VHn/W7OnHGNg3hiFMfENCmMiIn5u05Eqvv/KDnYcq2X2yCR+eNVExg45+/Y+EaEh/PCqiZw/Opl/W17AFb99n59cM4lF09L7sWrnrNlbztT0BBKjw50uRQTQmjEREb9VVtfIgy/mc91jG6iqb+aRr+TwlztmnjOItXfxhMG88a25TEqL58EXC3jwxXzqm1r7uGpnVdU3s7WoWi0txKfozJiIiJ9pcXtYuv4wv35nH02tbu5ZMIp7vzCa6Iiu/y89NX4Az90xi9/+Yx8Pv7uPLUer+e1NOUxKi++Dyp333r5yrEVbIIlPURgTEfEj6/dX8INXd7Cv7CQLxqbw/Ssm9Hi9V4jL8MAXs5g9MolvPZ/Pot+t56FLx/H18zMDbgPtd3aVkRgVxuQADZvinzRNKSLiB45Vn+LeZzfzlSc/pLHVzZNLcvnTref16sL7mSOTeONbc5mXlcyPXtvJHcvyqKpv7rX3d9rJplbe3lnKZZNT1fhWfIrOjImI+LCmVjdPvneIR/6xH4vlwYuzuHPeSCLD+qYdRWJ0OE8syWXp+sP8v9d3c+lv1vGbG3OYNdLZ7WJ6w993lNLY4uHanDSnSxH5FIUxEREftXp3Gf/91x0crmzg0klD+N7l40lPjOrzzzXGcOv5I8jNHMj9z23hK09s5L4Lx3D/haMJDfHfCZWVW4pJTxzA9OGJTpci8ikKYyIi3dDq9nCqxU1YiItQlyHEZXptfdWRynp+/NpO3tlVxqiUaJ65fQZzHeiJNSktnr9+8wK+/8oOHn53HxsPVPLrG7MZmjCg32vpqbLaRj7YX8G9XxgdcOvgxP8pjImIdFF+YTX/8udNlNQ0fmo8LMQQ6nIRGmI+CWlhIa628XaPQ0MMYd7jQkNchLnMJ/ex8Pau44S5DP952ThunTOC8FDnzkZFR4Tyy+uncsGYJP5r5XYue/g9fnbdFL40cYhjNXXHqwXH8Fi4OltTlOJ7FMZERLrg5U1FfHflNgbHRfDdS8fhtpZWt6XV7aHF4/3ptrR6PLS67afuN7s9tLo9tHosLW4Pza0e6pvdbWNuS4v3uCunDOXfF45lcJzvbOZ9bU462RmJfPO5zdz5zCZumT2c7142vs/WrvW2VfnFTEmPZ/Sg4NlpQPyHwpiISCe0uj389I3dPPn+IeaMSuLRr0wLug7uI5Kjeflf5vCzN/fwx/cP8dHhEzzylRxG+fhWSvuO17G9uJbvXzHB6VJEzqjDc9/GmKeMMWXGmO3txl4wxuR7b4eNMfne8UxjzKl2z/2+3WumG2O2GWP2G2MeNt5Je2PMQGPM28aYfd6fWlkpIj6lpqGFrz/9MU++f4hb52Sy9LYZQRfETosIDeH/XDGBp27N5XhtI1c8/D6vbytxuqxzWpVfTIjLcOXUoU6XInJGnVmI8DSwsP2AtfYGa222tTYbeBlY0e7pA6efs9be3W78MeAOYIz3dvo9HwLetdaOAd71PhYR8Qn7y+q45ncfsPFgJT9dNJkfXjWRMD++orC3XDiubSulMYNj+M+V22hscTtd0hl5PJZVW45xwehkUmIjnC5H5Iw6/D+KtXYdUHWm57xnt64HnjvXexhjUoE4a+1Ga60FlgHXeJ++Gljqvb+03biIiKPe3XWcax5dT11jC8/dMYsbZwxzuiSfMjgukocWjqO6oYW/FhxzupwzyjtyguLqU+otJj6tp/+8mwsct9buazc2whizxRiz1hgz1zuWBhS1O6bIOwYw2Fp7+hx3KTC4hzWJiPSItZZHV+/nG8vyyEyO4tX7LiA3c6DTZfmk2aOSGD0ohmc2HnG6lDNauaWYqPAQvjRRv1rEd/U0jN3Ep8+KlQDDrLU5wIPAX4wxcZ19M+9ZM3u2540xdxpj8owxeeXl5d2tWUTkrE41u7n/+Xx+/tYerpgylJfumuOXfbX6izGGm2cNZ2tRDfmF1U6X8ylNrW7+tvUYl0wcQlS4rlcT39XtMGaMCQUWAS+cHrPWNllrK733NwEHgCygGEhv9/J07xjAce805unpzLKzfaa19nFrba61Njclpf8bIIpIYDtWfYrFf1jPa1uP8e8Lx/LwjdkMCPeP1g1OWjQtjejwEJZtOOx0KZ+yenc5tY2tXKMpSvFxPTkz9kVgt7X2k+lHY0yKMSbEe38kbQv1D3qnIWuNMbO868yWAK94X/YqcIv3/i3txkVE+k3e4SqueuR9jlQ08MdbcrlngTq1d1ZsZBiLpqXz2tYSn9pYfNWWYpJjIjh/lP/vqymBrTOtLZ4DNgBjjTFFxpjbvU/dyOcX7s8DtnpbXSwH7rbWnl78fw/wJLCftjNmb3jHfwpcbIzZR1vA+2kPvo+ISJc9/9FRbnpiI7GRYay8dw4XjtP6oq66efZwmls9vPBxodOlAG3tSP6xu4wrp6b69X6aEhw6nES31t50lvFbzzD2Mm2tLs50fB4w6QzjlcBFHdUhItLbWtwefvzaTpZtOMK8rBR+e2MO8VFhTpfll7IGxzJr5ED+vPEId84bSYjL2bOKr28vodnt0VWU4hf0zwURCUpV9c0s+eNHLNtwhDvmjuBPt56nINZDS2ZnUlx9itW7z7r0t9+s3FLMyJRoJqfFO12KSIcUxkQk6OwureXqR99n09ET/HLxVL53+QTHz+QEgosnDGZwXATLHG5zUXSigY8OVXFtdprW/YlfUBgTkaDy5vZSFv1uPU0tHl68azbXTU/v+EXSKWEhLr4yYzjr9pZzqKLesTpeyW9rQHt1tqYoxT8ojIlIUPB4LL9+Zy93/3kTWYNj+es3LyA7I8HpsgLOTTMyCHUZ/uzQ2TFrLSu3FJM7PJFhSVGO1CDSVQpjIhLw6ptauefZzfz6nX1cNy2d5++cxeC4SKfLCkiD4iJZOGkIL+UVcqq5//er3HGslv1lJ9VbTPyKwpiIBLTCqgaue2w9f99Zyn9dPp5fLJ5CZJgaufalJbMzqW1s5ZX84o4P7mWrthQTFmK4fHJqv3+2SHcpjIlIwFq7t5yrHnmfY9WnePrrM/jG3JFa0N0PzstMZNyQWJZtOELbLnf9w+2xvMYjC/8AACAASURBVFJwjAVjB5EYHd5vnyvSUwpjIhJwPB7Lb97Zx61/+ojBcZG8et8FzMvSFmr9xRjDzbOHs7Okls1HT/Tb564/UEF5XZN6i4nfURgTkYBS3dDMbUs/5n/f2cu12WmsvOd8MpOjnS4r6FyTnUZsRCjLNvTfQv6VW4qJjQjlwnGD+u0zRXqDwpiIBIxtRTVc/vD7rN9fyU+umcQvr5+qjb4dEh0RynXT03l9WwnldU19/nkNza28tb2Uyyanak2g+B2FMRHxe9Zanv/oKNf9fj3WWl68ezZfmzVc68McdvPs4bS4LS98fLTPP+vtncepb3brKkrxSwpjIuLXGlvc/PvyrTy0YhszRwzktfvnqn+YjxiVEsMFo5N59sOjtLo9ffpZq7YUkxofycwRA/v0c0T6gsKYiPitI5X1LPrdel7aVMT9F43h6a/PYKCuovMpN88eTklNI+/s6rv9KitONrFuXwVXZ6fh0rZW4odCnS5ARKQ73tl5nG+/mI/LGP5063l8QYu2fdJF4wYxND6SZzYeZuGkIX3yGa8VHMPtsbqKUvyWzoyJiF9xeyw/f2s331iWx/CkKF775gUKYj4sNMTFV2cN54P9lewvq+uTz1iZf4zxqXGMHRLbJ+8v0tcUxkTEb1SebGLJUx/y6OoD3HheBsvvnkPGQO0/6OtuOC+D8BAXz/RBm4uD5ScpKKzm2pyhvf7eIv1FYUxE/MLmoye44rfvk3f4BD+7bgo/vU7bGvmL5JgILps8hJc3F3OyqbVX33tV/jGMgaumaopS/JfCmIj4NGstS9cf5oY/bCA0xPDyv8zh+vMynC5Luujm2ZmcbGpl5Zbe26/SWsuqLcXMGZXEkHht/C7+S2FMRHxWQ3MrD7yQzw9e3cG8MSm8dt9cJqXFO12WdMO0YQlMHBrHMxsO99p+lZuPVnO0qoFrsnVWTPybwpiI+KQD5Se55tEP+GvBMf7tkrE8sSSX+Kgwp8uSbjLGcMvsTPYeP8mHh6p65T1XbSkmItTVZ1dpivQXhTER8TlvbCvh6kc+oOJkM8tum8m9Xxit/lEB4MqpQ4kfENYrC/mbWz28tvUYF08YTGykQrr4N/UZExGf0eL28LM3d/PEe4fIzkjgd1+dxtCEAU6XJb1kQHgI1+em86cPDnO8tpHBcd1f57VubzknGlrUW0wCgs6MiYhPKKtt5KtPfMgT7x1iyezhvHjXbAWxAPS1WcNxW8tfPuzZfpUr84tJjApjXlZKL1Um4hydGRORXtfU6qa6oYXqhhZONDRT3dDsvd9C9almquvbfp5oaKHGe8yJhmZCXS5+fUO2NnsOYMOTopmflcJzHx3lvgtHExbS9XMCtY0tvLPzODecl9Gt14v4GoUxEek0j8fy9q7jHK1s+CRMtQ9aNQ1tY6da3Gd9j/BQF4lRYSQMCCchKozM5ChyohJIjA5nUU4aYwari3qgWzJ7OLc9ncdbO0q5YkrXm7W+ub2UplaPQrsEDIUxEemUfcfr+O6KbeQdOQFAiMuQMCCMhKgwEqLCSUuIZOLQOBIGhJEYHU78gDASo8JJjAojPqrtfkJUGAPCQjBGi/GD2fysQWQMHMCyDUe6FcZWbSlmeFIUORkJfVCdSP9TGBORc2pqdfPo6gM8tmY/0RGh/PzLU7hk0hBiI0IVqqRbQlyGr80czv+8sZvdpbWMGxLX6deW1Jxiw8FK7r9wjP78ScDQZLuInNVHh6q49Dfv8fC7+7h8cirvPDifxbkZxEWG6Reh9Mj1uRlEhHZ9v8pX849hLZqilICiMCYin1NzqoXvrtjG9X/YQHOrh6W3zeDXN+aQHBPhdGkSIBKjw7ly6lBWbimmtrGl069buaWY7IwERiRH92F1Iv1LYUxEPmGt5W9bS/jir9bywsdHuXPeSP7+7XnMV/sA6QNLZg+nodnNik1FnTp+d2ktu0vr1FtMAo7WjIkIAMeqT/H9V7bzzq4yJqXF8adbz9M+kNKnpqQnMDUjgWc2HuGWOZkdTn2v2nKMEJfhiimp/VShSP/QmTGRIOf2WJ7+4BAX/2otH+yv5L8uH8+qe85XEJN+sWTWcA6U17P+QOU5j/N4LK/kFzM/K4UkTZdLgFEYEwliu0pqWfTYen74151MzxzI3789j2/MHUmoGmlKP7l8SioDo8NZtuHwOY/78FAVJTWNWrgvAUnTlCJBqLHFzcPv7uPxdQeJHxDGb27M5qqpQ3WFpPS7yLAQrs/N4PF1BzhWfeqsW2Ct2lJMdHgIF48f3M8VivQ9/fNXJMis31/Bwl+v43drDnBNThrvPDifq7PTFMTEMV+dOQwLZ92vsrHFzevbSlg4KZUB4SH9W5xIP1AYEwkSJ+qb+deXCvjKkx8C8JdvzOQXi6eSGB3ucGUS7DIGRnHRuEE8//FRmlo/v5XWP3aXUdfUqqsoJWApjIkEOGvbFj5/8VdrWbWlmHsWjOLNB+YxZ3Sy06WJfOLm2ZlUnGzmze2ln3tu5ZZiBsVGMHtUkgOVifQ9rRkTCWCFVQ3816rtrN1bztSMBP68aDLjUzu/9YxIf5k7OpnMpCiWbTjC1dn/PAN2or6ZNXvKuHVOJiEuTaVLYFIYEwlQT71/iJ+/tQeXgR9eOYGbZ+uXmfgul8vwtVnD+cnfdrHjWA0Th7a1VvnbthJa3FZXUUpA0zSlSADadOQEP3ptJzNHDuTtB+dz6/kjFMTE5y2enkFk2Kf3q1y1pZiswTFM0BldCWAKYyIBaMXmIiLDXPz2ppyztgoQ8TXxUWFck53GqvxiahpaOFrZQN6RE1yTo6t9JbApjIkEmKZWN69tLeFLE4YQGxnmdDkiXXLz7OE0tnh4aVMhr+QXA3xqDZlIINKaMZEAs3p3GTWnWlg0Tb/AxP9MHBrP9OGJ/HnjEVwuw8wRA0nT2V0JcB2eGTPGPGWMKTPGbG839kNjTLExJt97u6zdc981xuw3xuwxxlzSbnyhd2y/MeahduMjjDEfesdfMMao6ZFID6zYXExyTAQXqHWF+Kkls4dzuLKBg+X1WrgvQaEz05RPAwvPMP6/1tps7+11AGPMBOBGYKL3Nb8zxoQYY0KAR4FLgQnATd5jAf4/73uNBk4At/fkC4kEsxP1zazeU8Y12UO1v6T4rUsnpZIcE0F4iIvLJqU6XY5In+twmtJau84Yk9nJ97saeN5a2wQcMsbsB2Z4n9tvrT0IYIx5HrjaGLMLuBD4iveYpcAPgcc6+wVE5J9e23qMFrflWk1Rih8LD3Xx31dNpOJkE/FRWvcoga8na8buM8YsAfKA71hrTwBpwMZ2xxR5xwAKPzM+E0gCqq21rWc4/nOMMXcCdwIMGzasB6WLBKaXNxczbkis2gCI37t8is6ISfDo7jzGY8AoIBsoAX7ZaxWdg7X2cWttrrU2NyUlpT8+UsRvHCw/SX5hNYumqQ2AiIg/6daZMWvt8dP3jTFPAK95HxYDGe0OTfeOcZbxSiDBGBPqPTvW/ngR6YKVW4pxGbUBEBHxN906M2aMaX/++Frg9JWWrwI3GmMijDEjgDHAR8DHwBjvlZPhtC3yf9Vaa4HVwJe9r78FeKU7NYkEM4/HsmJzMeePTmZwXKTT5YiISBd0eGbMGPMcsABINsYUAT8AFhhjsgELHAbuArDW7jDGvAjsBFqBe621bu/73Ae8BYQAT1lrd3g/4j+A540xPwG2AH/stW8nEiQ+PlxFcfUp/vWSLKdLERGRLurM1ZQ3nWH4rIHJWvt/gf97hvHXgdfPMH6Qf15xKSLdsGJzMVHhIVwycYjTpYiISBepEdFZNLW62V1a63QZIh1qbHHz+rYSFk4aQlS4NtUQEfE3CmNn8f1VO7jx8Y24PdbpUkTO6e2dx6lrauW6aelOlyIiIt2gMHYWF4xJprqhhfzCaqdLETmnlVuKSY2PZNbIJKdLERGRblAYO4u5Y5JxGVi7p8zpUkTOqryuibV7y7k6O40Ql3qLiYj4I4Wxs0iICidnWCJr9pY7XYrIWf214Bhuj2WRtj8SEfFbCmPnsCArha1FNVScbHK6FJEzWrGliElpcWQNjnW6FBER6SaFsXNYMHYQAO/t09kx8T17j9exvbiWRTlauC8i4s8Uxs5h4tA4kmPCWbNHYUx8z4rNxYS4DFdlD3W6FBER6QGFsXNwuQzzxqSwbm+5WlyIT3F7LKu2FDM/K4XkmAinyxERkR5QGOvA/LEpnGhoYWuRWlyI79h4sJLS2kYt3BcRCQAKYx2YNyYFl0FTleJTXt5cRGxEKF8cP9jpUkREpIcUxjqQGB3O1IwEtbgQn9HQ3Mqb20u5fEoqkWEhTpcjIiI9pDDWCQuyBrG1qJpKtbgQH/DWjlIamt1cm6MpShGRQKAw1gkLxqZgLby3r8LpUkRYsbmY9MQBnJc50OlSRESkFyiMdcLktHiSosNZo62RxGHHaxv5YH8F1+ak4dL2RyIiAUFhrBNcLsO8rBTW7avAoxYX4qBX8ovxWDRFKSISQBTGOml+VgpV9c1sK65xuhQJUtZaXt5UTM6wBEamxDhdjoiI9BKFsU6al5WCUYsLcdDOklr2HK9jkc6KiYgEFIWxThoYHc6U9ATW7NW6MXHGys3FhIUYrpii7Y9ERAKJwlgXLMhKIb+wmhP1zU6XIkGm1e1hVf4xvjB2EInR4U6XIyIivUhhrAtOt7hYt09TldK/3t9fQcXJJhZNS3e6FBER6WUKY10wJT2BxKgw1mrdmPSzFZuLiR8QxhfGpThdioiI9DKFsS4I8ba4WLu3XC0upN/UNbbw952lXDk1lYhQbX8kIhJoFMa6aMHYFCrrm9lxrNbpUiRIvLG9lMYWj6YoRUQClMJYF80bc7rFha6qlP6xcnMxI5KjyclIcLoUERHpAwpjXZQUE8HktHjW7NW6Mel7RSca2HCwkmtz0jBG2x+JiAQihbFuWJCVwpajJ6huUIsL6Vuv5B8DtP2RiEggUxjrhvljB+Gx8N6+CqdLkQBmrWXF5iJmZA4kY2CU0+WIiEgfURjrhuyMBBKiwrQ1kvSprUU1HCiv59ppOismIhLIFMa6IcRlmDtGLS6kb63cUkx4qIvLJqc6XYqIiPQhhbFuWpCVQsXJJnaWqMWF9L4Wt4dXC45x8fjBxA8Ic7ocERHpQwpj3TQvq60TulpcSF9Yu6ecqvpmFmmKUkQk4CmMdVNKbFuLi7VqcSF9YMWWIpKiwz8J/SIiErgUxnpgflYKm49WU3OqxelSJIDUNLTwzq4yrpw6lLAQ/RUVEQl0+j99DywYm4LbY3lfLS6kF/1tWwnNrR6u0/ZHIiJBQWGsB7IzEoiLDNW6MelVK7cUMXpQDJPS4pwuRURE+oHCWA+EhriYm9XW4sJatbiQnjtSWc/Hh0+waJq2PxIRCRYKYz20ICuFsjq1uJDesXJLMcbANdm6ilJEJFgojPXQ/LGnW1zoqkrpGWstK7cUM3tkEkMTBjhdjoiI9BOFsR4aFBvJxKFxrFUYkx7afPQERyobWKSF+yIiQUVhrBcsGJvCpqMn1OJCemTF5mIiw1wsnDTE6VJERKQfKYz1ggVjB+H2WNbvV4sL6Z6mVjevbS1h4cQhxESEOl2OiIj0ow7DmDHmKWNMmTFme7uxnxtjdhtjthpjVhpjErzjmcaYU8aYfO/t9+1eM90Ys80Ys98Y87DxXipmjBlojHnbGLPP+zOxL75oX8rJSCA2MlTrxqTbVu8uo+ZUC9dqilJEJOh05szY08DCz4y9DUyy1k4B9gLfbffcAWtttvd2d7vxx4A7gDHe2+n3fAh411o7BnjX+9ivhIa4mDsmWS0upNte3lzMoNgIzh+V5HQpIiLSzzoMY9badUDVZ8b+bq1t9T7cCJzzn/PGmFQgzlq70ballWXANd6nrwaWeu8vbTfuVxZkDaK0tpHdpXVOlyJ+pqq+mTV7yrg6eyih2v5IRCTo9Mb/+W8D3mj3eIQxZosxZq0xZq53LA0oandMkXcMYLC1tsR7vxQYfLYPMsbcaYzJM8bklZf71pSgWlxId7229RgtbqurKEVEglSPwpgx5ntAK/Csd6gEGGatzQEeBP5ijOn0ni7es2Znneez1j5urc211uampKT0oPLeNzgukvGpcdoaSbrs5c3FjE+NY3yqtj8SEQlG3Q5jxphbgSuAr3pDFNbaJmttpff+JuAAkAUU8+mpzHTvGMBx7zTm6elMv00zC8amsOnICeoa1eJCOudoZQMFhdUsylHHfRGRYNWtMGaMWQj8O3CVtbah3XiKMSbEe38kbQv1D3qnIWuNMbO8V1EuAV7xvuxV4Bbv/VvajfudBVkptHosH6jFhXTSpqNtyzHnZfnWmV4REek/nWlt8RywARhrjCkyxtwOPALEAm9/poXFPGCrMSYfWA7cba09vfj/HuBJYD9tZ8xOrzP7KXCxMWYf8EXvY780bXgisRFqcSGdV1BYQ1R4CKMHxThdioiIOKTD7pLW2pvOMPzHsxz7MvDyWZ7LAyadYbwSuKijOvxBWIiL80f/s8WFt5WayFnlF1YzOS2eEJf+rIiIBCtdR9/LFoxNoaSmkb3HTzpdivi45lYPO4/Vkp2R4HQpIiLiIIWxXvbPFhd+ex2C9JPdpbU0uz1MVRgTEQlqCmO9LDV+AOOGxGrdmHSooLAaQGfGRESCnMJYH5g/NoW8I1WcbGrt+GAJWvmFNaTERpAaH+l0KSIi4iCFsT6wIGsQLW61uJBzyy88wdT0BF3oISIS5BTG+kBuZiIxanEh51Db2MKB8nqyM+KdLkVERBymMNYH2lpcJLF2TxnezQlEPmVbUQ2AFu+LiIjCWF+ZnzWIYzWN7C9Tiwv5vHzv4v0paQpjIiLBTmGsjyz4pMWFpirl8woKqxmZHE18VJjTpYiIiMMUxvrI0IQBZA2OYc1e9RuTzysoqtYUpYiIAApjfWrB2EF8fOgE9WpxIe2U1jRyvLaJqelavC8iIgpjfWpBVgrNbg/rD1Q6XYr4kNPrxbKHJTpciYiI+AKFsT6UmzmQ6PAQbY0kn5JfWE1YiGF8aqzTpYiIiA9QGOtD4aEu5oxOZs2ecrW4kE8UFFYzITWOiNAQp0sREREfoDDWxxaMTaG4+hQHytXiQsDtsWwrrtHifRER+YTCWB+bn6UWF/JPB8tPcrKplanpCmMiItJGYayPpSdGMXpQDGv3KozJPxfv68yYiIicpjDWDxZkpfDhwSoamtXiItgVFFUTGxHKyORop0sREREfoTDWDxaMHUSz28MGtbgIegWFNUzJiMflMk6XIiIiPkJhrB+cNyKRqPAQrRsLco0tbnaV1Gq9mIiIfIrCWD+ICA1hzqgk1uwtU4uLILbjWC2tHku21ouJiEg7CmP9ZP7YQRRWneJgRb3TpYhDCk533lcYExGRdhTG+skCtbgIegVF1aTGRzIoLtLpUkRExIcojPWTjIFRjEqJ1tZIQaygsFrrxURE5HMUxvrR/KxBfHioilPNbqdLkX5W3dDM4coG9RcTEZHPURjrRwvGptDc6mHjQbW4CDYFRTUATM2Id7gSERHxNQpj/WjGiIEMCAvRVGUQKiisxhiYnKYwJiIin6Yw1o8iw0KYPSqJ1XvK1eIiyBQUVjM6JYbYyDCnSxERER+jMNbPLp4wmKNVDWz1TltJ4LPWkl9YrfViIiJyRgpj/ezyKalEhLp4aVOh06VIPyk6cYrK+mb1FxMRkTNSGOtncZFhLJw0hFfzj9HYoqsqg0FBkZq9iojI2SmMOWDx9AxqG1t5e+dxp0uRflBQWE14qIuxQ2KdLkVERHyQwpgD5oxKIi1hAC9tKnK6FOkHBYU1TBoaR1iI/rqJiMjn6beDA1wuw3XT0nhvXzklNaecLkf6UKvbw7biGi3eFxGRs1IYc8h109OxFlZsLna6FOlD+8pOcqrFrfViIiJyVgpjDhmeFM2MEQN5Ka9QPccCWEFh2+J97UkpIiJnozDmoMXT0zlc2cCmIyecLkX6SEFRNfEDwhieFOV0KSIi4qMUxhx02eRUosJDeClPC/kD1Zajbc1ejTFOlyIiIj5KYcxB0RGhXD45lde2HqOhudXpcqSXNTS3svd4ndaLiYjIOSmMOWxxbgb1zW7e2FbqdCnSy7YX1+KxkJ2hzcFFROTsFMYcdl5mIsOTorQ9UgA6vXh/ihbvi4jIOSiMOcwYw5enpbPxYBWFVQ1OlyO9KL+omvTEASTHRDhdioiI+LBOhTFjzFPGmDJjzPZ2YwONMW8bY/Z5fyZ6x40x5mFjzH5jzFZjzLR2r7nFe/w+Y8wt7canG2O2eV/zsAmy1c7XTU/HGFiujvwBpaCwWs1eRUSkQ509M/Y0sPAzYw8B71prxwDveh8DXAqM8d7uBB6DtvAG/ACYCcwAfnA6wHmPuaPd6z77WQFtaMIALhidzPJNRXg86jkWCCpONlF04hTZmqIUEZEOdCqMWWvXAVWfGb4aWOq9vxS4pt34MttmI5BgjEkFLgHettZWWWtPAG8DC73PxVlrN9q27qfL2r1X0Pjy9HSKq0+x8WCl06VIL9ha5G32qjNjIiLSgZ6sGRtsrS3x3i8FBnvvpwHtV6MXecfONV50hvGgcsnEIcRGhmrz8ACRf7Qal4FJaXFOlyIiIj6uVxbwe89o9fn8mjHmTmNMnjEmr7y8vK8/rl9FhoVw5dShvLG9hNrGFqfLkR7KL6oha3AsUeGhTpciIiI+ridh7Lh3ihHvzzLveDGQ0e64dO/YucbTzzD+Odbax621udba3JSUlB6U7psWT0+nscXD61tLOj5YfJa1loLCanKGaYpSREQ61pMw9ipw+orIW4BX2o0v8V5VOQuo8U5nvgV8yRiT6F24/yXgLe9ztcaYWd6rKJe0e6+gkp2RwOhBMZqq9HNHKhuoOdWizcFFRKRTOtva4jlgAzDWGFNkjLkd+ClwsTFmH/BF72OA14GDwH7gCeAeAGttFfBj4GPv7UfeMbzHPOl9zQHgjZ5/Nf9jjGHx9HQ2HTnBgfKTTpcj3VSgxfsiItIFnVrQYq296SxPXXSGYy1w71ne5yngqTOM5wGTOlNLoLs2J42fvbWH5ZuK+I+F45wuR7ohv7CaAWEhjBkU43QpIiLiB9SB38cMiotkflYKKzYX4VbPMb9UUFjN5LR4QkP010tERDqm3xY+aPH0dI7XNvHevsC6YjQYtLg9bD9Wy1RtDi4iIp2kMOaDLho/mMSoMC3k90O7S+pobvVovZiIiHSawpgPCg91cXV2Gm/vOE51Q7PT5UgX5J9evK8rKUVEpJMUxnzU4tx0mt0eXi045nQp0gUFhdUkRYeTnjjA6VJERMRPKIz5qIlD4xmfGsdLeZqq9CcFhdVkZyTQ1jJPRESkYwpjPmzx9HS2Fdewu7TW6VKkE+oaW9hfflLrxUREpEsUxnzYNTlphIUYluvsmF/YVlyDtWr2KiIiXaMw5sMGRodz0bjBrMovpsXtcboc6UBBYQ0AU9PV1kJERDpPYczHLc5Np+JkM6t3l3V8sDiqoLCazKQoEqLCnS5FRET8iMKYj5uflUJyTIR6jvmB/MJqTVGKiEiXKYz5uNAQF4umpbF6dxkVJ5ucLkfOorSmkdLaRvUXExGRLlMY8wOLp6fT6rGs2lLsdClyFgWnm73qzJiIiHSRwpgfGDM4lqkZCSzfVIS12jzcFxUUVhPqMkwcGud0KSIi4mcUxvzE4unp7C6tY3uxeo75ooKiasanxhEZFuJ0KSIi4mcUxvzElVOHEh7q4qVNhU6XIp/h8Vi2FtYwNUMtLUREpOsUxvxE/IAwLpk4hFfyj9HY4na6HGnnYEU9dU2tWrwvIiLdojDmRxZPT6fmVAvv7DrudCnSTkFh2+L9bC3eFxGRblAY8yPnj04mNT6S5eo55lPyC6uJiQhlZEqM06WIiIgfUhjzIyEuw3XT0lm3t5zSmkanyxGvgqJqJqfFE+IyTpciIiJ+SGHMz3x5ejoeCyu26OyYL2hscbOrpFb9xUREpNsUxvxMZnI0MzIHsjxPPcd8wa6SWlrclmxdSSkiIt2kMOaHvjw9nYMV9Ww+esLpUoLePxfvJzpciYiI+CuFMT902ZRUBoSFaCG/DygoqmFwXARD4iOdLkVERPyUwpgfiokI5bLJqfy1oIRTzeo55qSCwmr1FxMRkR5RGPNTi3PTOdnUyps7SpwuJWjVNLRwsKJei/dFRKRHFMb81IzMgQwbGMVLeZqqdEpBkZq9iohIzymM+SmXt+fY+gOVFFY1OF1OUDq9eH9yuq6kFBGR7lMY82PXTU/DGHh5s86OOaGgqJpRKdHERYY5XYqIiPgxhTE/lp4YxZxRSby8uQiPRz3H+pO1lvzCGq0XExGRHlMY83OLp2dQWHWKDw9VOV1KUDlW00jFySatFxMRkR5TGPNzl0wcQmxEKC9tKnS6lKDyz2avCmMiItIzCmN+bkB4CFdMHcob20o52dTqdDlBo6CwmvAQF+OGxDldioiI+DmFsQDw5enpnGpx87etx5wuJWjkF1YzYWgc4aH6KyQiIj2j3yQBYNqwBEamRGt7pH7i9li2FddoilJERHqFwlgAMMZwQ24GHx8+wfbiGqfLCXj7yupoaHYzNUP9xUREpOcUxgLETTOHERsZyiP/2O90KQHv9OJ97UkpIiK9QWEsQMRFhvH1OZm8uaOUvcfrnC4noOUX1hAXGUpmUrTTpYiISABQGAsgXz9/BNHhITo71scKCquZmpGAy2WcLkVERAKAwlgASYwO52uzh/Pa1mMcLD/pdDkB6VSzmz3H6zRFKSIivUZhLMDcMXck4aEuHl19wOlSAtKOYzW4PVZXUoqISK9RGAswyTERfGXGcFblF3O0ssHpcgJOvnfx/hRdSSkiIr2k22HMGDPWGJPf7lZrjHnAGPNDY0xxu/HL2r3mu8aY/caYE5wvggAAE6dJREFUPcaYS9qNL/SO7TfGPNTTLxXs7po/khCX4bG1WjvW2wqKakhLGMCg2EinSxERkQDR7TBmrd1jrc221mYD04EGYKX36f89/Zy19nUAY8wE4EZgIrAQ+J0xJsQYEwI8ClwKTABu8h4r3TQ4LpIbcjNYvqmI4upTTpcTUP7/9u4+uKr6zuP455snEsJDIAkBgjwmkIqUx8UnhITWx92VurbWTteybXfVFuoWOzu6O7Oju62d2W2rturQastWu1ala12tdVVKLuAzoEYETSA8g8C9CYSHAAlJfvtHDjYK4Sk393du7vs1w9xzf/fcc7/OmWM+c37nfE/V9n30FwMAxFW8pik/J2mjc27rKdaZI+lJ51yTc26zpFpJ04N/tc65Tc65ZklPBuuiC24tHyNJ+sVyrh2Ll/pDTdq+9wgX7wMA4ipeYexGSU90eD/fzNaY2SIzGxCMFUva3mGdHcFYZ+PoguK8HF0/ZZieXLVd0QNHfZfTI6zZ0f50g4lcvA8AiKMuhzEzy5J0raTfBUMLJY2RNEnSLkk/6epvdPitm81stZmtjsVi8dpsj/Xt8hK1tjn9YsUm36X0CFXbG5Rm0oRipikBAPETjzNjV0t6xzm3R5Kcc3ucc63OuTZJj6h9GlKSdko6r8P3hgVjnY2fwDn3sHNumnNuWmFhYRxK79mG5/fWnIlD9fhbW1V/qMl3OUnvvR0NKh3UV7m9MnyXAgDoQeIRxr6iDlOUZjakw2fXSVobLD8n6UYz62VmoySVSlopaZWkUjMbFZxluzFYF3Hw7YoSNbW06ZevbvZdSlJzzgWd9zkrBgCIry6FMTPLlXS5pN93GP5PM3vfzNZIqpC0QJKcc+skLZb0gaQXJc0LzqC1SJov6SVJH0paHKyLOCgZ1Ed/OWGIHnt9ixoON/suJ2lt23tY+w4f06TzBpx+ZQAAzkKX5lucc42S8j81dtMp1r9H0j0nGX9B0gtdqQWdmz+7RM+v2aVFr23R7ZeP9V1OUnqttl6SNG0kYQwAEF904E8BZYP76crxRfqv1zbrwNFjvstJSpGaqIrzclQ6qI/vUgAAPQxhLEV8Z3apDh5t0WOvb/FdStJpamnVa7V1qigrlJn5LgcA0MMQxlLEBcX9NbtskH716mY1NrX4LieprNq8T4ebW1UxbpDvUgAAPRBhLIV8Z3aJ9h0+pv9+81QPSsCnVVZHlZWRpovH5J9+ZQAAzhJhLIVMHj5Al5UW6JFXNulIc6vvcpLGspqoLh6dr95Z9BcDAMQfYSzFzK8oUd2hZj2xcpvvUpLClrpGbaprVMU4mgwDALoHYSzFXDg6X9NHDdQvVmxUUwtnx04nUhOVJFWUcb0YAKB7EMZS0G2zS7XnQJN+t3qH71JCL1IT0+jCXI3Iz/VdCgCghyKMpaBLS/I1eXieFi7bqGOtbb7LCa3DzS16c1M9d1ECALoVYSwFmZlum12qnQ1H9Mw7J30mOyS9sbFezS1thDEAQLcijKWo8nGFmlDcXw8tq1ULZ8dOqrI6qt5Z6fqLUTwCCQDQfQhjKcrMNH92ibbWH9Yf1nzku5zQcc5pWU1MM0oK1Csj3Xc5AIAejDCWwi7/TJHKBvfVg5W1am1zvssJlQ3RQ9rZcIS7KAEA3Y4wlsLS0trPjm2MNer/1u7yXU6oVFa3t7Qop78YAKCbEcZS3NUXDNHowlw9WFmrNs6OfSxSHdVnhvTTkP45vksBAPRwhLEUl55mml9RourdB7Xkwz2+ywmFA0ePafXWfXTdBwAkBGEMunbiUI3I760HK2vlHGfHXllfp9Y2x/ViAICEIIxBGelp+nb5GL2/c7+WrY/5Lse7SE1U/XMyNfm8PN+lAABSAGEMkqTrJg9TcV6OHli6IaXPjrW1tbe0mDm2UBnpHB4AgO7HXxtIkrIy0nRr+Ri9s61Br2+s912ON+s+OqC6Q01cLwYASBjCGD72panDVNSvl362dIPvUryprI7KTJo1ljAGAEgMwhg+lp2ZrltmjtFbm/dq5ea9vsvxIlIT1cRhecrv08t3KQCAFEEYwyd8ZfpwFfTJ0gOVqXd2rP5Qk97b0cCDwQEACUUYwyfkZKXrHy4brVc21Ondbft8l5NQy9fH5JxUUcYUJQAgcQhjOMFXLxqhvN6ZeqCy1ncpCRWpiamgTy9dMLS/71IAACmEMIYT9OmVoW9eOkqV1VGt3bnfdzkJ0dLaphXrYyofV6i0NPNdDgAghRDGcFJzLx2pvtkZejBFzo5VbW/Q/iPHuF4MAJBwhDGcVL/sTH39kpF6cd1u1ew+6LucbldZHVV6mmlGaYHvUgAAKYYwhk59Y8Yo5Wal68FIzz87FqmJadqIAeqfk+m7FABAiiGMoVN5vbN008Uj9fyaj/ROD76zcvf+o/pw1wEeDA4A8IIwhlOaVzFGQ/vnaMFTVWpsavFdTreI1EQlievFAABeEMZwSn2zM3Xflydp297D+sEfP/BdTreIVEdVnJejsUV9fJcCAEhBhDGc1vRRA3XLzDF6YuV2Lflgj+9y4qqppVWv1dapfFyhzGhpAQBIPMIYzsjtl4/V+UP66c6n1yh2sMl3OXGzavM+NTa3MkUJAPCGMIYzkpWRpp/eOEmHmlp0x9Nr5JzzXVJcRGqiyspI0yUl+b5LAQCkKMIYzlhpUV/deXWZKquj+u3Kbb7LiYtITVQXjc5X76wM36UAAFIUYQxnZe7FI3VZaYF+8PyH2hQ75LucLtla36hNsUZVjOPB4AAAfwhjOCtpaaYff2miemWmacHi93Sstc13SecsUk1LCwCAf4QxnLWiftn64XUT9N72Bj2QxM+ujNTENLogVyMLcn2XAgBIYYQxnJNrJgzR30wp1kOR2qTszn+kuVVvbKpXOWfFAACeEcZwzu6+drwG98tOyu78r2+sU3NLmyrKuF4MAOAXYQznrF8Sd+eP1ETVOytd00cN9F0KACDFdTmMmdkWM3vfzKrMbHUwNtDMlpjZhuB1QDBuZvYzM6s1szVmNqXDduYG628ws7ldrQuJMX3UQN06K7m68zvnFKmO6dKSAvXKSPddDgAgxcXrzFiFc26Sc25a8P5OSUudc6WSlgbvJelqSaXBv5slLZTaw5ukuyRdKGm6pLuOBziE34LPj9X4ocnTnX9D9JB2NhzhLkoAQCh01zTlHEmPBsuPSvpCh/HHXLs3JeWZ2RBJV0pa4pzb65zbJ2mJpKu6qTbEWVZGmu7/cvJ05z/e0qKc/mIAgBCIRxhzkl42s7fN7OZgrMg5tytY3i2pKFgulrS9w3d3BGOdjSNJJFN3/khNVGWD+2poXo7vUgAAiEsYm+Gcm6L2Kch5Zjaz44eu/TRJXE6VmNnNZrbazFbHYrF4bBJxlAzd+Q8cPabVW/apoowpSgBAOHQ5jDnndgavUUnPqP2arz3B9KOC12iw+k5J53X4+rBgrLPxT//Ww865ac65aYWFTDGFzSe68z9VFcru/K9uqFNLm+N6MQBAaHQpjJlZrpn1Pb4s6QpJayU9J+n4HZFzJT0bLD8n6WvBXZUXSdofTGe+JOkKMxsQXLh/RTCGJPNxd/4d+0PZnT9SHVW/7AxNGZ7nuxQAACRJGV38fpGkZ8zs+LZ+65x70cxWSVpsZt+UtFXSDcH6L0i6RlKtpMOSvi5Jzrm9ZvZ9SauC9f7dObe3i7XBk47d+WeNLdTUEeG4MbatzWnZ+phmji1URjot9gAA4dClMOac2yRp4knG6yV97iTjTtK8Tra1SNKirtSD8Pi3a8dr5ea9un1xlV647TLl9upq7u+6dR8dUOxgE1OUAIBQ4fQAukXf7Ezde0N7d/7vPx+O7vyRmqjMpFm0tAAAhAhhDN3meHf+J1eFozt/pCaqzw7LU0GfXr5LAQDgY4QxdKvj3fnveHqNogePeqtjb2OzqrY3qIKzYgCAkCGMoVsd787f2NSiO/7HX3f+5eujck5cLwYACB3CGLpdaVFf/fPVZYrUxPT4W36680eqYyrok6UJxf29/D4AAJ0hjCEhvhZ057/nj4nvzt/a5rR8fUyzxg5SWpol9LcBADgdwhgSwmd3/ne37dP+I8dUUcb1YgCA8CGMIWE+0Z1/6YaE/W6kJqr0NNNlpYQxAED4EMaQUNdMGKLrpwzTg5Favb11X0J+M1Id09QRA9Q/JzMhvwcAwNkgjCHh7r72fA3Ny9GCp6q0pa6xW39r9/6j+mDXAe6iBACEFmEMCdc3O1M/vXGS9jU268r7V+ihSG23XUO2rCYqSVwvBgAILcIYvJg6YqD+9L1Zml02SD96qUZ//cCremdb/KctIzVRDe2frXFFfeO+bQAA4oEwBm+K+mVr4d9O1cM3TVXD4WO6fuHruuvZtTp49Fhctt/c0qZXN9SpvGyQzGhpAQAIJ8IYvLti/GAtuX2m5l48Uo+9uVWX37tCL63b3eXtrtqyV43NrVwvBgAINcIYQqFvdqbuvna8fv+tS5TXO1O3/OZt3fKb1dq9/9yfZxmpjiorPU2XluTHsVIAAOKLMIZQmTx8gP7wnRm646oyLauJ6fP3Ltdv3tiitrazf6ZlpCaqC0cPVO+sjPgXCgBAnBDGEDqZ6Wn6VvkYvbxgpiYPz9O/PrtO1//8dVXvPnDG29hWf1gbY41MUQIAQo8whtAakZ+rx74xXfd9eaK21h/WX/3sVf3opWodPdZ62u9GPm5pQRgDAIQbYQyhZma6bvIw/en2WZozqVgPRTbqqvtX6LXaulN+L1IT1aiCXI0qyE1QpQAAnBvCGJLCwNws/eSGiXr87y+UJH31l2/pe4vf097G5hPWPdLcqjc21qt8HI1eAQDhRxhDUrm0pEAvfnem5lWM0bNVO/X5e5frmXd3yLk/X+D/xqY6NbW0cb0YACApEMaQdLIz0/VPV5bp+dtmaER+by146j19bdFKba1vf85lpDqmnMx0XTh6oOdKAQA4PcIYklbZ4H56+tZL9P054/XutgZdcd8KLVy2UZXVUV1aUqBeGem+SwQA4LQIY0hqaWmmmy4eqT/dPksV4wbpP16s1s6GIzwYHACQNOiGiR5hcP9s/fymqXpp3W49W7VT11wwxHdJAACcEcIYepQrxw/WleMH+y4DAIAzxjQlAACAR4QxAAAAjwhjAAAAHhHGAAAAPCKMAQAAeEQYAwAA8IgwBgAA4BFhDAAAwCPCGAAAgEeEMQAAAI8IYwAAAB4RxgAAADwijAEAAHhEGAMAAPCIMAYAAOARYQwAAMAjwhgAAIBHhDEAAACPzDnnu4ZzYmYxSVu7+WcKJNV1828g/thvyYt9l5zYb8mJ/ZZYI5xzhSf7IGnDWCKY2Wrn3DTfdeDssN+SF/suObHfkhP7LTyYpgQAAPCIMAYAAOARYezUHvZdAM4J+y15se+SE/stObHfQoJrxgAAADzizBgAAIBHhLFOmNlVZlZjZrVmdqfvenBmzGyLmb1vZlVmttp3PeicmS0ys6iZre0wNtDMlpjZhuB1gM8acaJO9tvdZrYzOO6qzOwanzXiRGZ2nplFzOwDM1tnZv8YjHPMhQBh7CTMLF3SQ5KulnS+pK+Y2fl+q8JZqHDOTeKW7dD7taSrPjV2p6SlzrlSSUuD9wiXX+vE/SZJ9wXH3STn3AsJrgmn1yLpe8658yVdJGle8HeNYy4ECGMnN11SrXNuk3OuWdKTkuZ4rgnoUZxzKyTt/dTwHEmPBsuPSvpCQovCaXWy3xByzrldzrl3guWDkj6UVCyOuVAgjJ1csaTtHd7vCMYQfk7Sy2b2tpnd7LsYnLUi59yuYHm3pCKfxeCszDezNcE0JlNdIWZmIyVNlvSWOOZCgTCGnmaGc26K2qeY55nZTN8F4dy49lu9ud07OSyUNEbSJEm7JP3EbznojJn1kfS0pO865w50/Ixjzh/C2MntlHReh/fDgjGEnHNuZ/AalfSM2qeckTz2mNkQSQpeo57rwRlwzu1xzrU659okPSKOu1Ays0y1B7HHnXO/D4Y55kKAMHZyqySVmtkoM8uSdKOk5zzXhNMws1wz63t8WdIVktae+lsImeckzQ2W50p61mMtOEPH/5gHrhPHXeiYmUn6laQPnXP3dviIYy4EaPraieDW7PslpUta5Jy7x3NJOA0zG632s2GSlCHpt+y38DKzJySVSyqQtEfSXZL+V9JiScMlbZV0g3OOi8VDpJP9Vq72KUonaYukWzpch4QQMLMZkl6R9L6ktmD4X9R+3RjHnGeEMQAAAI+YpgQAAPCIMAYAAOARYQwAAMAjwhgAAIBHhDEAAACPCGMAejwzG2lm9L4CEEqEMQA4B2aW4bsGAD0DYQxAqkg3s0fMbJ2ZvWxmOWY2yczeDB5w/czxB1yb2TIzmxYsF5jZlmD578zsOTOrlLTU338KgJ6EMAYgVZRKesg5N15Sg6TrJT0m6Q7n3GfV3pn8rjPYzhRJX3TOzeq2SgGkFMIYgFSx2TlXFSy/LWmMpDzn3PJg7FFJM89gO0t4XAyAeCKMAUgVTR2WWyXlnWLdFv35/4/Zn/qsMZ5FAQBhDECq2i9pn5ldFry/SdLxs2RbJE0Nlr+Y4LoApBjuBgKQyuZK+rmZ9Za0SdLXg/EfS1psZjdL+qOv4gCkBnPO+a4BAAAgZTFNCQAA4BFhDAAAwCPCGAAAgEeEMQAAAI8IYwAAAB4RxgAAADwijAEAAHhEGAMAAPDo/wHvRY+96/VYAgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GURYuQ2tX9mE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "be40caf0-600e-42b3-afb1-1b4d30289809"
      },
      "source": [
        "fig = px.line(df.reset_index(), x='timestamp', y='value', title='NYC Taxi Demand')\n",
        "\n",
        "fig.update_xaxes(\n",
        "    rangeslider_visible=True,\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"NYC Taxi Demand\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"rangeslider\": {\"visible\": true}, \"title\": {\"text\": \"timestamp\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"value\"}}},\n",
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          },
          "metadata": {
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfaGCvsSZdoy"
      },
      "source": [
        "Anomalous Point\n",
        "\n",
        "```\n",
        "NYC Marathon - 2014-11-02\n",
        "Thanksgiving - 2014-11-27\n",
        "Christmas - 2014-12-25\n",
        "New Years - 2015-01-01\n",
        "Snow Blizzard - 2015-01-26 and 2015-01-27```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwvrxTpCHZF5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "0dfc122f-db9d-4745-d61f-6979f643524a"
      },
      "source": [
        "model =  IsolationForest(contamination=0.004)\n",
        "model.fit(df[['value']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsolationForest(behaviour='deprecated', bootstrap=False, contamination=0.004,\n",
              "                max_features=1.0, max_samples='auto', n_estimators=100,\n",
              "                n_jobs=None, random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LsrVVLVjIrmW"
      },
      "source": [
        "df['outliers']=pd.Series(model.predict(df[['value']])).apply(lambda x: 'yes' if (x == -1) else 'no' )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynUws7-BMnf3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 695
        },
        "outputId": "26bb484b-65c1-4514-ee4e-37b141e97ef0"
      },
      "source": [
        "df.query('outliers==\"yes\"')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>2014-09-06 23:00:00</td>\n",
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              "      <th>2971</th>\n",
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              "      <td>2014-11-08 23:00:00</td>\n",
              "      <td>27926.0</td>\n",
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              "      <th>3311</th>\n",
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              "      <th>3479</th>\n",
              "      <td>2014-11-22 23:00:00</td>\n",
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              "      <td>yes</td>\n",
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              "      <th>4655</th>\n",
              "      <td>2015-01-10 23:00:00</td>\n",
              "      <td>28351.0</td>\n",
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              "      <td>Saturday</td>\n",
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              "    <tr>\n",
              "      <th>5039</th>\n",
              "      <td>2015-01-26 23:00:00</td>\n",
              "      <td>243.0</td>\n",
              "      <td>23</td>\n",
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              "      <th>5040</th>\n",
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              "      <th>5041</th>\n",
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              "      <th>5042</th>\n",
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              "      <th>5043</th>\n",
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              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
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              "      <th>5044</th>\n",
              "      <td>2015-01-27 04:00:00</td>\n",
              "      <td>14.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
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              "    <tr>\n",
              "      <th>5045</th>\n",
              "      <td>2015-01-27 05:00:00</td>\n",
              "      <td>29.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
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              "      <th>5046</th>\n",
              "      <td>2015-01-27 06:00:00</td>\n",
              "      <td>88.0</td>\n",
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              "      <td>yes</td>\n",
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              "      <th>5047</th>\n",
              "      <td>2015-01-27 07:00:00</td>\n",
              "      <td>274.0</td>\n",
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              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
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              "    <tr>\n",
              "      <th>5131</th>\n",
              "      <td>2015-01-30 19:00:00</td>\n",
              "      <td>27698.0</td>\n",
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              "      <td>Friday</td>\n",
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              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour   weekday outliers\n",
              "1631 2014-09-06 23:00:00  29418.5    23  Saturday      yes\n",
              "2135 2014-09-27 23:00:00  28024.5    23  Saturday      yes\n",
              "2639 2014-10-18 23:00:00  28253.5    23  Saturday      yes\n",
              "2971 2014-11-01 19:00:00  27912.0    19  Saturday      yes\n",
              "2977 2014-11-02 01:00:00  37204.5     1    Sunday      yes\n",
              "3143 2014-11-08 23:00:00  27926.0    23  Saturday      yes\n",
              "3311 2014-11-15 23:00:00  27713.0    23  Saturday      yes\n",
              "3479 2014-11-22 23:00:00  28299.0    23  Saturday      yes\n",
              "4417 2015-01-01 01:00:00  29292.0     1  Thursday      yes\n",
              "4655 2015-01-10 23:00:00  28351.0    23  Saturday      yes\n",
              "5039 2015-01-26 23:00:00    243.0    23    Monday      yes\n",
              "5040 2015-01-27 00:00:00     94.5     0   Tuesday      yes\n",
              "5041 2015-01-27 01:00:00     39.5     1   Tuesday      yes\n",
              "5042 2015-01-27 02:00:00     29.0     2   Tuesday      yes\n",
              "5043 2015-01-27 03:00:00      9.5     3   Tuesday      yes\n",
              "5044 2015-01-27 04:00:00     14.5     4   Tuesday      yes\n",
              "5045 2015-01-27 05:00:00     29.0     5   Tuesday      yes\n",
              "5046 2015-01-27 06:00:00     88.0     6   Tuesday      yes\n",
              "5047 2015-01-27 07:00:00    274.0     7   Tuesday      yes\n",
              "5131 2015-01-30 19:00:00  27698.0    19    Friday      yes\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday      yes"
            ]
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      "source": [
        "df"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>value</th>\n",
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              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-07-01 01:00:00</td>\n",
              "      <td>5433.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-07-01 02:00:00</td>\n",
              "      <td>3346.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-07-01 03:00:00</td>\n",
              "      <td>2216.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-07-01 04:00:00</td>\n",
              "      <td>2189.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5156</th>\n",
              "      <td>2015-01-31 20:00:00</td>\n",
              "      <td>24138.0</td>\n",
              "      <td>20</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5157</th>\n",
              "      <td>2015-01-31 21:00:00</td>\n",
              "      <td>24194.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5158</th>\n",
              "      <td>2015-01-31 22:00:00</td>\n",
              "      <td>26515.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5159</th>\n",
              "      <td>2015-01-31 23:00:00</td>\n",
              "      <td>26439.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour   weekday outliers\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday       no\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday       no\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday       no\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday       no\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday       no\n",
              "...                  ...      ...   ...       ...      ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday      yes\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday       no\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday       no\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday       no\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday       no\n",
              "\n",
              "[5160 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cBUWAXzoJ7rH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "66dde8c6-5e0e-4c29-f6c1-23479a4bdbd4"
      },
      "source": [
        "fig = px.scatter(df.reset_index(), x='timestamp', y='value', color='outliers', hover_data=['weekday'], title='NYC Taxi Demand')\n",
        "\n",
        "fig.update_xaxes(\n",
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('e22f9a03-80fc-4248-aaf3-3a61ff3b83df');\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AOKhQ5LRbn2T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "ced8aada-7b98-40f2-fda3-f579b7e9224f"
      },
      "source": [
        "model =  IsolationForest()\n",
        "model.fit(df[['value']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "IsolationForest(behaviour='deprecated', bootstrap=False, contamination='auto',\n",
              "                max_features=1.0, max_samples='auto', n_estimators=100,\n",
              "                n_jobs=None, random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqZNyXV2OmkU"
      },
      "source": [
        "score=model.decision_function(df[['value']])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYPDjWa1OmVs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "4e64fce3-bced-4a48-e253-27d981e707be"
      },
      "source": [
        "score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-0.00988052, -0.05792931,  0.00304147, ..., -0.01569826,\n",
              "       -0.11083899, -0.11105714])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LZJ73LqpOl9N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 789
        },
        "outputId": "add2d1ee-0525-4736-b575-b6facad864da"
      },
      "source": [
        "plt.hist(score, bins=50)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([  3.,   7.,   2.,   0.,  13.,   5.,   5.,   0.,   2.,   3.,   5.,\n",
              "          5.,   5.,   5.,   8.,  10.,  15.,   7.,  15.,  25.,  48.,   3.,\n",
              "         18.,  26.,  23.,  49.,  85.,  73., 118., 148., 234., 180., 197.,\n",
              "        174., 282., 232., 282., 346., 277., 152., 106.,  40., 134., 105.,\n",
              "        290., 477., 285., 183., 115., 338.]),\n",
              " array([-0.23950554, -0.23305646, -0.22660739, -0.22015832, -0.21370924,\n",
              "        -0.20726017, -0.2008111 , -0.19436202, -0.18791295, -0.18146387,\n",
              "        -0.1750148 , -0.16856573, -0.16211665, -0.15566758, -0.1492185 ,\n",
              "        -0.14276943, -0.13632036, -0.12987128, -0.12342221, -0.11697313,\n",
              "        -0.11052406, -0.10407499, -0.09762591, -0.09117684, -0.08472777,\n",
              "        -0.07827869, -0.07182962, -0.06538054, -0.05893147, -0.0524824 ,\n",
              "        -0.04603332, -0.03958425, -0.03313517, -0.0266861 , -0.02023703,\n",
              "        -0.01378795, -0.00733888, -0.00088981,  0.00555927,  0.01200834,\n",
              "         0.01845742,  0.02490649,  0.03135556,  0.03780464,  0.04425371,\n",
              "         0.05070279,  0.05715186,  0.06360093,  0.07005001,  0.07649908,\n",
              "         0.08294816]),\n",
              " <a list of 50 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdDP3nreOl2q"
      },
      "source": [
        "df['scores'] = score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8ZjGOKiEOll7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "b1516a7f-6b00-4c0e-a160-4640f5591a59"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "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>timestamp</th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
              "      <th>outliers</th>\n",
              "      <th>scores</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-07-01 00:00:00</td>\n",
              "      <td>9485.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.009881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-07-01 01:00:00</td>\n",
              "      <td>5433.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.057929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-07-01 02:00:00</td>\n",
              "      <td>3346.5</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "      <td>0.003041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-07-01 03:00:00</td>\n",
              "      <td>2216.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.066890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-07-01 04:00:00</td>\n",
              "      <td>2189.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.068618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.233030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5156</th>\n",
              "      <td>2015-01-31 20:00:00</td>\n",
              "      <td>24138.0</td>\n",
              "      <td>20</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.012900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5157</th>\n",
              "      <td>2015-01-31 21:00:00</td>\n",
              "      <td>24194.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.015698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5158</th>\n",
              "      <td>2015-01-31 22:00:00</td>\n",
              "      <td>26515.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.110839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5159</th>\n",
              "      <td>2015-01-31 23:00:00</td>\n",
              "      <td>26439.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.111057</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5160 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour   weekday outliers    scores\n",
              "0    2014-07-01 00:00:00   9485.5     0   Tuesday       no -0.009881\n",
              "1    2014-07-01 01:00:00   5433.0     1   Tuesday       no -0.057929\n",
              "2    2014-07-01 02:00:00   3346.5     2   Tuesday       no  0.003041\n",
              "3    2014-07-01 03:00:00   2216.5     3   Tuesday       no -0.066890\n",
              "4    2014-07-01 04:00:00   2189.5     4   Tuesday       no -0.068618\n",
              "...                  ...      ...   ...       ...      ...       ...\n",
              "5155 2015-01-31 19:00:00  28288.5    19  Saturday      yes -0.233030\n",
              "5156 2015-01-31 20:00:00  24138.0    20  Saturday       no -0.012900\n",
              "5157 2015-01-31 21:00:00  24194.5    21  Saturday       no -0.015698\n",
              "5158 2015-01-31 22:00:00  26515.0    22  Saturday       no -0.110839\n",
              "5159 2015-01-31 23:00:00  26439.5    23  Saturday       no -0.111057\n",
              "\n",
              "[5160 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0ASvjelOlQU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3808d99b-c809-4bf8-9a7b-0f5f22c3c7c7"
      },
      "source": [
        "df.query('scores<-0.20')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>value</th>\n",
              "      <th>hour</th>\n",
              "      <th>weekday</th>\n",
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              "      <th>scores</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1630</th>\n",
              "      <td>2014-09-06 22:00:00</td>\n",
              "      <td>27414.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.204555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1631</th>\n",
              "      <td>2014-09-06 23:00:00</td>\n",
              "      <td>29418.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.238506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1775</th>\n",
              "      <td>2014-09-12 23:00:00</td>\n",
              "      <td>27331.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Friday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.200348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1942</th>\n",
              "      <td>2014-09-19 22:00:00</td>\n",
              "      <td>27385.5</td>\n",
              "      <td>22</td>\n",
              "      <td>Friday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.204555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2135</th>\n",
              "      <td>2014-09-27 23:00:00</td>\n",
              "      <td>28024.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.228477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2635</th>\n",
              "      <td>2014-10-18 19:00:00</td>\n",
              "      <td>27341.0</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.200348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2639</th>\n",
              "      <td>2014-10-18 23:00:00</td>\n",
              "      <td>28253.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.231543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2807</th>\n",
              "      <td>2014-10-25 23:00:00</td>\n",
              "      <td>27554.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.209818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2971</th>\n",
              "      <td>2014-11-01 19:00:00</td>\n",
              "      <td>27912.0</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.227000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2977</th>\n",
              "      <td>2014-11-02 01:00:00</td>\n",
              "      <td>37204.5</td>\n",
              "      <td>1</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.239506</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3118</th>\n",
              "      <td>2014-11-07 22:00:00</td>\n",
              "      <td>27471.0</td>\n",
              "      <td>22</td>\n",
              "      <td>Friday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.207421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3139</th>\n",
              "      <td>2014-11-08 19:00:00</td>\n",
              "      <td>27344.0</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.200348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3143</th>\n",
              "      <td>2014-11-08 23:00:00</td>\n",
              "      <td>27926.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.227985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3311</th>\n",
              "      <td>2014-11-15 23:00:00</td>\n",
              "      <td>27713.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.221122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3455</th>\n",
              "      <td>2014-11-21 23:00:00</td>\n",
              "      <td>27316.5</td>\n",
              "      <td>23</td>\n",
              "      <td>Friday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.200348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3479</th>\n",
              "      <td>2014-11-22 23:00:00</td>\n",
              "      <td>28299.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.233030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3815</th>\n",
              "      <td>2014-12-06 23:00:00</td>\n",
              "      <td>27454.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.206464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4413</th>\n",
              "      <td>2014-12-31 21:00:00</td>\n",
              "      <td>27559.5</td>\n",
              "      <td>21</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.209338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4417</th>\n",
              "      <td>2015-01-01 01:00:00</td>\n",
              "      <td>29292.0</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.235514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4627</th>\n",
              "      <td>2015-01-09 19:00:00</td>\n",
              "      <td>27559.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Friday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.209338</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4651</th>\n",
              "      <td>2015-01-10 19:00:00</td>\n",
              "      <td>27448.0</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.206464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4655</th>\n",
              "      <td>2015-01-10 23:00:00</td>\n",
              "      <td>28351.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.233030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4795</th>\n",
              "      <td>2015-01-16 19:00:00</td>\n",
              "      <td>27571.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Friday</td>\n",
              "      <td>no</td>\n",
              "      <td>-0.210779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5039</th>\n",
              "      <td>2015-01-26 23:00:00</td>\n",
              "      <td>243.0</td>\n",
              "      <td>23</td>\n",
              "      <td>Monday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.211142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5040</th>\n",
              "      <td>2015-01-27 00:00:00</td>\n",
              "      <td>94.5</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.211624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5041</th>\n",
              "      <td>2015-01-27 01:00:00</td>\n",
              "      <td>39.5</td>\n",
              "      <td>1</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.211624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5042</th>\n",
              "      <td>2015-01-27 02:00:00</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.211624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5043</th>\n",
              "      <td>2015-01-27 03:00:00</td>\n",
              "      <td>9.5</td>\n",
              "      <td>3</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.211624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5044</th>\n",
              "      <td>2015-01-27 04:00:00</td>\n",
              "      <td>14.5</td>\n",
              "      <td>4</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.211624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5045</th>\n",
              "      <td>2015-01-27 05:00:00</td>\n",
              "      <td>29.0</td>\n",
              "      <td>5</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.211624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5046</th>\n",
              "      <td>2015-01-27 06:00:00</td>\n",
              "      <td>88.0</td>\n",
              "      <td>6</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.211624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5047</th>\n",
              "      <td>2015-01-27 07:00:00</td>\n",
              "      <td>274.0</td>\n",
              "      <td>7</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.206375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5131</th>\n",
              "      <td>2015-01-30 19:00:00</td>\n",
              "      <td>27698.0</td>\n",
              "      <td>19</td>\n",
              "      <td>Friday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.221122</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5155</th>\n",
              "      <td>2015-01-31 19:00:00</td>\n",
              "      <td>28288.5</td>\n",
              "      <td>19</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>yes</td>\n",
              "      <td>-0.233030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               timestamp    value  hour    weekday outliers    scores\n",
              "1630 2014-09-06 22:00:00  27414.0    22   Saturday       no -0.204555\n",
              "1631 2014-09-06 23:00:00  29418.5    23   Saturday      yes -0.238506\n",
              "1775 2014-09-12 23:00:00  27331.5    23     Friday       no -0.200348\n",
              "1942 2014-09-19 22:00:00  27385.5    22     Friday       no -0.204555\n",
              "2135 2014-09-27 23:00:00  28024.5    23   Saturday      yes -0.228477\n",
              "2635 2014-10-18 19:00:00  27341.0    19   Saturday       no -0.200348\n",
              "2639 2014-10-18 23:00:00  28253.5    23   Saturday      yes -0.231543\n",
              "2807 2014-10-25 23:00:00  27554.5    23   Saturday       no -0.209818\n",
              "2971 2014-11-01 19:00:00  27912.0    19   Saturday      yes -0.227000\n",
              "2977 2014-11-02 01:00:00  37204.5     1     Sunday      yes -0.239506\n",
              "3118 2014-11-07 22:00:00  27471.0    22     Friday       no -0.207421\n",
              "3139 2014-11-08 19:00:00  27344.0    19   Saturday       no -0.200348\n",
              "3143 2014-11-08 23:00:00  27926.0    23   Saturday      yes -0.227985\n",
              "3311 2014-11-15 23:00:00  27713.0    23   Saturday      yes -0.221122\n",
              "3455 2014-11-21 23:00:00  27316.5    23     Friday       no -0.200348\n",
              "3479 2014-11-22 23:00:00  28299.0    23   Saturday      yes -0.233030\n",
              "3815 2014-12-06 23:00:00  27454.0    23   Saturday       no -0.206464\n",
              "4413 2014-12-31 21:00:00  27559.5    21  Wednesday       no -0.209338\n",
              "4417 2015-01-01 01:00:00  29292.0     1   Thursday      yes -0.235514\n",
              "4627 2015-01-09 19:00:00  27559.5    19     Friday       no -0.209338\n",
              "4651 2015-01-10 19:00:00  27448.0    19   Saturday       no -0.206464\n",
              "4655 2015-01-10 23:00:00  28351.0    23   Saturday      yes -0.233030\n",
              "4795 2015-01-16 19:00:00  27571.5    19     Friday       no -0.210779\n",
              "5039 2015-01-26 23:00:00    243.0    23     Monday      yes -0.211142\n",
              "5040 2015-01-27 00:00:00     94.5     0    Tuesday      yes -0.211624\n",
              "5041 2015-01-27 01:00:00     39.5     1    Tuesday      yes -0.211624\n",
              "5042 2015-01-27 02:00:00     29.0     2    Tuesday      yes -0.211624\n",
              "5043 2015-01-27 03:00:00      9.5     3    Tuesday      yes -0.211624\n",
              "5044 2015-01-27 04:00:00     14.5     4    Tuesday      yes -0.211624\n",
              "5045 2015-01-27 05:00:00     29.0     5    Tuesday      yes -0.211624\n",
              "5046 2015-01-27 06:00:00     88.0     6    Tuesday      yes -0.211624\n",
              "5047 2015-01-27 07:00:00    274.0     7    Tuesday      yes -0.206375\n",
              "5131 2015-01-30 19:00:00  27698.0    19     Friday      yes -0.221122\n",
              "5155 2015-01-31 19:00:00  28288.5    19   Saturday      yes -0.233030"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
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