Outliers_Removing_Technique.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Outliers Removing Technique.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNh7aKL6J+qAHLFjwNNm0Dt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/abidshafee/dataAnalysis/blob/master/Outliers_Removing_Technique.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "An Outlier of a dataset defines as a value that is more than 3 standard deviations away from the mean. So removing outliers from a df removes any row in the dataset which contains an outlier. Outlier calculation are performed seperately for each column."
      ],
      "metadata": {
        "id": "WWRKnisPaBhV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "RF3uqQTTVV1X"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "from datetime import datetime"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mpl.rcParams['figure.figsize'] = (12, 7)"
      ],
      "metadata": {
        "id": "uC_CVCAJXjIP"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/abidshafee/autoML-tsModel/main/throughput_metrics.csv\", parse_dates=['Time'], index_col='Time')"
      ],
      "metadata": {
        "id": "maaW8s0FWGMp"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "WHzBhdwKWNej",
        "outputId": "b6aad989-7e00-4cff-87ed-1db479270da7"
      },
      "execution_count": 4,
      "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>SiteA</th>\n",
              "      <th>SiteB</th>\n",
              "      <th>SiteC</th>\n",
              "      <th>SiteD</th>\n",
              "      <th>SiteE</th>\n",
              "      <th>SiteF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.760000e+03</td>\n",
              "      <td>8.760000e+03</td>\n",
              "      <td>8.760000e+03</td>\n",
              "      <td>8.760000e+03</td>\n",
              "      <td>8.760000e+03</td>\n",
              "      <td>8.760000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.134300e+10</td>\n",
              "      <td>1.050114e+10</td>\n",
              "      <td>2.422851e+07</td>\n",
              "      <td>7.645930e+07</td>\n",
              "      <td>8.169740e+07</td>\n",
              "      <td>1.194613e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.843792e+09</td>\n",
              "      <td>8.509006e+09</td>\n",
              "      <td>3.285736e+07</td>\n",
              "      <td>6.293577e+07</td>\n",
              "      <td>1.297177e+08</td>\n",
              "      <td>8.569518e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.227624e+08</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.013134e+04</td>\n",
              "      <td>7.408801e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.786450e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.677623e+09</td>\n",
              "      <td>3.942119e+09</td>\n",
              "      <td>4.546207e+04</td>\n",
              "      <td>2.792179e+07</td>\n",
              "      <td>1.817469e+07</td>\n",
              "      <td>8.628346e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.002482e+10</td>\n",
              "      <td>8.836137e+09</td>\n",
              "      <td>8.613927e+04</td>\n",
              "      <td>5.725981e+07</td>\n",
              "      <td>5.025162e+07</td>\n",
              "      <td>9.951363e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.335055e+10</td>\n",
              "      <td>1.452307e+10</td>\n",
              "      <td>4.363564e+07</td>\n",
              "      <td>1.097949e+08</td>\n",
              "      <td>9.435435e+07</td>\n",
              "      <td>1.243718e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.553718e+10</td>\n",
              "      <td>5.229297e+10</td>\n",
              "      <td>2.513275e+08</td>\n",
              "      <td>3.593521e+08</td>\n",
              "      <td>1.578908e+09</td>\n",
              "      <td>2.659389e+07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              SiteA         SiteB  ...         SiteE         SiteF\n",
              "count  8.760000e+03  8.760000e+03  ...  8.760000e+03  8.760000e+03\n",
              "mean   1.134300e+10  1.050114e+10  ...  8.169740e+07  1.194613e+06\n",
              "std    5.843792e+09  8.509006e+09  ...  1.297177e+08  8.569518e+05\n",
              "min    3.227624e+08  0.000000e+00  ...  0.000000e+00  3.786450e+05\n",
              "25%    7.677623e+09  3.942119e+09  ...  1.817469e+07  8.628346e+05\n",
              "50%    1.002482e+10  8.836137e+09  ...  5.025162e+07  9.951363e+05\n",
              "75%    1.335055e+10  1.452307e+10  ...  9.435435e+07  1.243718e+06\n",
              "max    7.553718e+10  5.229297e+10  ...  1.578908e+09  2.659389e+07\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "LmIh_lt4WjZh",
        "outputId": "014a640d-497d-4b7e-b008-711b07164016"
      },
      "execution_count": 5,
      "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>SiteA</th>\n",
              "      <th>SiteB</th>\n",
              "      <th>SiteC</th>\n",
              "      <th>SiteD</th>\n",
              "      <th>SiteE</th>\n",
              "      <th>SiteF</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2018-01-01 08:00:00</th>\n",
              "      <td>1.411093e+10</td>\n",
              "      <td>1.109243e+09</td>\n",
              "      <td>8.289831e+07</td>\n",
              "      <td>56650005.21</td>\n",
              "      <td>1.117868e+07</td>\n",
              "      <td>827420.9772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 09:00:00</th>\n",
              "      <td>1.345362e+10</td>\n",
              "      <td>1.242256e+09</td>\n",
              "      <td>4.375739e+04</td>\n",
              "      <td>49307351.26</td>\n",
              "      <td>7.070847e+06</td>\n",
              "      <td>913017.6007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 10:00:00</th>\n",
              "      <td>1.216888e+10</td>\n",
              "      <td>2.006322e+09</td>\n",
              "      <td>4.315725e+04</td>\n",
              "      <td>56843652.59</td>\n",
              "      <td>5.132826e+07</td>\n",
              "      <td>874471.3644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 11:00:00</th>\n",
              "      <td>1.123120e+10</td>\n",
              "      <td>1.073181e+09</td>\n",
              "      <td>8.277154e+07</td>\n",
              "      <td>46645240.59</td>\n",
              "      <td>5.217827e+06</td>\n",
              "      <td>837246.7783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018-01-01 12:00:00</th>\n",
              "      <td>1.078085e+10</td>\n",
              "      <td>8.645069e+08</td>\n",
              "      <td>3.343548e+04</td>\n",
              "      <td>30966947.37</td>\n",
              "      <td>7.495363e+06</td>\n",
              "      <td>827143.7497</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            SiteA         SiteB  ...         SiteE        SiteF\n",
              "Time                                             ...                           \n",
              "2018-01-01 08:00:00  1.411093e+10  1.109243e+09  ...  1.117868e+07  827420.9772\n",
              "2018-01-01 09:00:00  1.345362e+10  1.242256e+09  ...  7.070847e+06  913017.6007\n",
              "2018-01-01 10:00:00  1.216888e+10  2.006322e+09  ...  5.132826e+07  874471.3644\n",
              "2018-01-01 11:00:00  1.123120e+10  1.073181e+09  ...  5.217827e+06  837246.7783\n",
              "2018-01-01 12:00:00  1.078085e+10  8.645069e+08  ...  7.495363e+06  827143.7497\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['SiteF','SiteE','SiteD','SiteC','SiteB','SiteA']].plot(subplots=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "e9E0ga7gW4Wl",
        "outputId": "86e9c43b-bc04-4626-f0ee-6ae6fd37da45"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f6eddbb02d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ec9e72c10>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ec9262750>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ec91405d0>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ec90f7910>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f6ec90b0c50>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x504 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Detecting Outliers"
      ],
      "metadata": {
        "id": "iYnZ3YI6Xs_A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxenplot(df['SiteA'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "0yKgV2NtXa_v",
        "outputId": "31e3460a-11d6-4a4e-87d1-f6ee38ef0ea7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ec7ababd0>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Removing Outliers"
      ],
      "metadata": {
        "id": "xhC-mVzfZNwo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as sst"
      ],
      "metadata": {
        "id": "eZy53kxRZc6s"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "z_score = sst.zscore(df)"
      ],
      "metadata": {
        "id": "POEr_p25YH0E"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "abs_z_scores = np.abs(z_score)\n",
        "filter_data = (abs_z_scores<3).all(axis=1)\n",
        "ndf = df[filter_data]"
      ],
      "metadata": {
        "id": "IAd9C3dRZenK"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Because Outlier of a dataset defines as a value that is more than 3 (std) standard deviations away from the mean"
      ],
      "metadata": {
        "id": "qC96fH5_Z8RY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "SjTOcNawbeBZ",
        "outputId": "8b9c2806-c4ad-47e7-9b07-b159bb8389ce"
      },
      "execution_count": 11,
      "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>SiteA</th>\n",
              "      <th>SiteB</th>\n",
              "      <th>SiteC</th>\n",
              "      <th>SiteD</th>\n",
              "      <th>SiteE</th>\n",
              "      <th>SiteF</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.020000e+03</td>\n",
              "      <td>8.020000e+03</td>\n",
              "      <td>8.020000e+03</td>\n",
              "      <td>8.020000e+03</td>\n",
              "      <td>8.020000e+03</td>\n",
              "      <td>8.020000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.089490e+10</td>\n",
              "      <td>1.017486e+10</td>\n",
              "      <td>2.221227e+07</td>\n",
              "      <td>7.463415e+07</td>\n",
              "      <td>6.493073e+07</td>\n",
              "      <td>1.138001e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.677106e+09</td>\n",
              "      <td>7.827163e+09</td>\n",
              "      <td>2.948598e+07</td>\n",
              "      <td>5.944100e+07</td>\n",
              "      <td>6.554482e+07</td>\n",
              "      <td>4.968708e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>3.227624e+08</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.013134e+04</td>\n",
              "      <td>7.408801e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.786450e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.585398e+09</td>\n",
              "      <td>3.924188e+09</td>\n",
              "      <td>4.462231e+04</td>\n",
              "      <td>2.795316e+07</td>\n",
              "      <td>1.805817e+07</td>\n",
              "      <td>8.577731e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.947878e+09</td>\n",
              "      <td>8.804392e+09</td>\n",
              "      <td>8.309603e+04</td>\n",
              "      <td>5.714333e+07</td>\n",
              "      <td>4.778185e+07</td>\n",
              "      <td>9.873443e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.309531e+10</td>\n",
              "      <td>1.430653e+10</td>\n",
              "      <td>4.361442e+07</td>\n",
              "      <td>1.074430e+08</td>\n",
              "      <td>8.731451e+07</td>\n",
              "      <td>1.223998e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.886420e+10</td>\n",
              "      <td>3.595057e+10</td>\n",
              "      <td>1.227131e+08</td>\n",
              "      <td>2.628644e+08</td>\n",
              "      <td>4.685181e+08</td>\n",
              "      <td>3.764824e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              SiteA         SiteB  ...         SiteE         SiteF\n",
              "count  8.020000e+03  8.020000e+03  ...  8.020000e+03  8.020000e+03\n",
              "mean   1.089490e+10  1.017486e+10  ...  6.493073e+07  1.138001e+06\n",
              "std    4.677106e+09  7.827163e+09  ...  6.554482e+07  4.968708e+05\n",
              "min    3.227624e+08  0.000000e+00  ...  0.000000e+00  3.786450e+05\n",
              "25%    7.585398e+09  3.924188e+09  ...  1.805817e+07  8.577731e+05\n",
              "50%    9.947878e+09  8.804392e+09  ...  4.778185e+07  9.873443e+05\n",
              "75%    1.309531e+10  1.430653e+10  ...  8.731451e+07  1.223998e+06\n",
              "max    2.886420e+10  3.595057e+10  ...  4.685181e+08  3.764824e+06\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ndf.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-EW9fjEbrOQ",
        "outputId": "0a22e1e8-ee2c-42f1-943d-27762cfaf233"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "DatetimeIndex: 8020 entries, 2018-01-01 08:00:00 to 2019-01-01 07:00:00\n",
            "Data columns (total 6 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   SiteA   8020 non-null   float64\n",
            " 1   SiteB   8020 non-null   float64\n",
            " 2   SiteC   8020 non-null   float64\n",
            " 3   SiteD   8020 non-null   float64\n",
            " 4   SiteE   8020 non-null   float64\n",
            " 5   SiteF   8020 non-null   float64\n",
            "dtypes: float64(6)\n",
            "memory usage: 438.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxenplot(ndf['SiteA'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "QsU27yAzcS0y",
        "outputId": "e4871314-bdcd-4da6-8536-3a45262522e6"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6eddbb0290>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.shape)\n",
        "print(ndf.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nEnkJcU0cZPv",
        "outputId": "a47437c7-5cab-453c-839c-f830a5fcf1d6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8760, 6)\n",
            "(8020, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_bins = 25\n",
        "fig, axs = plt.subplots(1, 2, sharey=True, tight_layout=True)\n",
        "\n",
        "# We can set the number of bins with the *bins* keyword argument.\n",
        "axs[0].hist(ndf['SiteA'], bins=n_bins)\n",
        "axs[1].hist(ndf['SiteB'], bins=n_bins)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "TMczzscYpztw",
        "outputId": "8aa93d31-15c1-4176-b497-20db1dee0a05"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([814., 714., 654., 603., 570., 589., 583., 551., 511., 444., 374.,\n",
              "        266., 221., 194., 149., 134., 114., 107.,  75.,  78.,  62.,  63.,\n",
              "         67.,  50.,  33.]),\n",
              " array([0.00000000e+00, 1.43802260e+09, 2.87604520e+09, 4.31406780e+09,\n",
              "        5.75209040e+09, 7.19011300e+09, 8.62813560e+09, 1.00661582e+10,\n",
              "        1.15041808e+10, 1.29422034e+10, 1.43802260e+10, 1.58182486e+10,\n",
              "        1.72562712e+10, 1.86942938e+10, 2.01323164e+10, 2.15703390e+10,\n",
              "        2.30083616e+10, 2.44463842e+10, 2.58844068e+10, 2.73224294e+10,\n",
              "        2.87604520e+10, 3.01984746e+10, 3.16364972e+10, 3.30745198e+10,\n",
              "        3.45125424e+10, 3.59505650e+10]),\n",
              " <a list of 25 Patch objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 864x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "KySMIkAQrD6M"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}