LisaGirard_Capstone_Radio_Data_in_a_Digital_Age_DSFlex.ipynb

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      "source": [
        "Lisa Girard <br>\n",
        "Data Science Flex - Thinkful <br>\n",
        "Capstone #1.2 - 8.16.22<br>\n",
        "\n",
        "**Tracking Charting History on the Billboard Hot 100 Data Set**"
      ],
      "metadata": {
        "id": "yQuNaewykPVT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Billboard Hot 100 is one of Billboard Magazine’s definitive music charts and is powered by Luminate, the entertainment industry data powerhouse. The week’s most popular songs are ranked by audio and video streaming activity on leading digital music services, radio airplay audience impressions based on monitored airplay, and sales data. A publicly-available dataset of all \"Hot 100\" charts released since its inception in 1958 through November 6, 2021 can be found online at https://www.kaggle.com/datasets/dhruvildave/billboard-the-hot-100-songs"
      ],
      "metadata": {
        "id": "WdyMsMMnkSaM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RESEARCH PROPOSAL:**<hr>\n",
        "\n",
        "When looking at past and current trends in the data set, I noted that power gold hits released prior to 1999 did not chart as long as hits in the “digital age” - post 1999. I propose to do an A/B test to compare chart time - known as “weeks-on-board” -  of songs released from 1976-1998 (A) to chart time of songs released after 1999 (B),  the year digital music proliferation began, so that stakeholders can make data-driven decisions regarding content distribution, marketing, and audience development. \n"
      ],
      "metadata": {
        "id": "g1tUndGPETvP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**METHODS:**<hr>\n",
        "I will segment the data to include two time frames:\n",
        "* 1976-1998 - 22 years of chart history prior to the 1999 milestone of the advent of digital file sharing (A)\n",
        "* 1999-2021 - 22 years of chart history (B) <br><Br>\n",
        "I will test each time-frame dataset (A/B) for sample size, normal distribution, skew and kurtosis, histogram plotting, two-tailed t-test to determine hypothesis rejection, and confidence interval determination.\n"
      ],
      "metadata": {
        "id": "lD5MGVmvET60"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**HYPOTHESES:** <hr>\n",
        "H0 - Songs charted from 1976-1998 did not have significantly different chart times than songs released after 1999. <br>\n",
        "H1 -  Songs charted from 1999-2021 did have significantly different chart times (longer) than songs released between 1976-1998. \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "lTE4EESEEUDM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**WHY:** <hr>\n",
        "The biggest stakeholders, or audience, in the music industry that consumes and consults the data output from Billboard Magazine’s charts are: \n",
        "* Record labels\n",
        "* Artists\n",
        "* Streaming services (subscription monetization / ad sales)\n",
        "* Traditional broadcast radio stations (ad sales model)\n",
        "* Satellite radio stations (subscription monetization)\n",
        "\n",
        "Ultimately, these stakeholders are seeking **return on investment** and **potential for additional monetization**. Examining the past charting history helps to understand why current charting trends and future charting potential matter in context of what and how artists and distribution channels can be better monetized and managed. \n",
        "<br><Br>\n",
        "Knowing current charting trends can drive decisions regarding radio clock programming, tour scheduling, album/song releases, artistic creative output, merchandise support, advertising sales (for both online streaming and traditional broadcast radio), and for audience development in both traditional broadcast radio and subscription-driven distribution (online streaming platforms like Spotify and Sirius XM Satellite Radio.) \n",
        "\n",
        "Research Proposal Google Doc at: https://docs.google.com/document/d/1dbOgAfoE25N4wGUV8RfK_WG-iEnprlDQvaUjSueINP8/edit?usp=sharing\n"
      ],
      "metadata": {
        "id": "J0FACs_7EUK5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DATA ANALYSIS"
      ],
      "metadata": {
        "id": "cP0NWxOYuyNh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kqJGgBxnH06q"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import statistics\n",
        "from collections import Counter\n",
        "from numpy.random import seed\n",
        "#pd.options.display.max_rows = 999\n",
        "pd.options.display.max_rows = 500\n",
        "import seaborn as sns \n",
        "sns.set()\n",
        "from scipy import stats\n",
        "from scipy.stats import pearsonr\n",
        "from numpy import mean\n",
        "from numpy import var\n",
        "from math import sqrt\n",
        "import math\n",
        "import statsmodels.api as sm\n",
        "\n",
        "\n",
        "#collect the entire dataframe\n",
        "billboard_df = pd.read_csv(\"/content/drive/MyDrive/Colab Notebooks/billboard_charts.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#get an idea of the DF datatypes and counts\n",
        "billboard_df.info()"
      ],
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        },
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        "outputId": "846a0257-90da-4e76-ec6d-bd78147fdc31"
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      "execution_count": null,
      "outputs": [
        {
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 330087 entries, 0 to 330086\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   date            330087 non-null  object \n",
            " 1   rank            330087 non-null  int64  \n",
            " 2   song            330087 non-null  object \n",
            " 3   artist          330087 non-null  object \n",
            " 4   last-week       297775 non-null  float64\n",
            " 5   peak-rank       330087 non-null  int64  \n",
            " 6   weeks-on-board  330087 non-null  int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 17.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for nulls\n",
        "billboard_df.isnull().sum()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "TW6kXt_eFW3-",
        "outputId": "fba196e0-6a07-4b0e-d56c-1c5b1d6b1ffa"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date                  0\n",
              "rank                  0\n",
              "song                  0\n",
              "artist                0\n",
              "last-week         32312\n",
              "peak-rank             0\n",
              "weeks-on-board        0\n",
              "dtype: int64"
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          "execution_count": 354
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      "cell_type": "code",
      "source": [
        "billboard_df.head()"
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            "text/plain": [
              "         date  rank           song                         artist  last-week  \\\n",
              "0  2021-11-06     1     Easy On Me                          Adele        1.0   \n",
              "1  2021-11-06     2           Stay  The Kid LAROI & Justin Bieber        2.0   \n",
              "2  2021-11-06     3  Industry Baby        Lil Nas X & Jack Harlow        3.0   \n",
              "3  2021-11-06     4     Fancy Like                   Walker Hayes        4.0   \n",
              "4  2021-11-06     5     Bad Habits                     Ed Sheeran        5.0   \n",
              "\n",
              "   peak-rank  weeks-on-board  \n",
              "0          1               3  \n",
              "1          1              16  \n",
              "2          1              14  \n",
              "3          3              19  \n",
              "4          2              18  "
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    {
      "cell_type": "code",
      "source": [
        "#create a dataframe with date,song for 1976 to 1998\n",
        "bb_1976to1998 = billboard_df[(billboard_df['date'] <= '1998-12-31') & (billboard_df['date'] >= '1976-01-01')  & (billboard_df['song'])]"
      ],
      "metadata": {
        "id": "KCRuTWiLFvnb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bb_1976to1998.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BplphF5UGJIA",
        "outputId": "8031932b-edd7-491f-f13f-d0b275e3e815"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 119987 entries, 119300 to 239286\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   date            119987 non-null  object \n",
            " 1   rank            119987 non-null  int64  \n",
            " 2   song            119987 non-null  object \n",
            " 3   artist          119987 non-null  object \n",
            " 4   last-week       110718 non-null  float64\n",
            " 5   peak-rank       119987 non-null  int64  \n",
            " 6   weeks-on-board  119987 non-null  int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 7.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for nulls\n",
        "bb_1976to1998.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BeCb4_TZk-5U",
        "outputId": "8fd09383-987e-4c66-b390-b6e2ab497d88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date                 0\n",
              "rank                 0\n",
              "song                 0\n",
              "artist               0\n",
              "last-week         9269\n",
              "peak-rank            0\n",
              "weeks-on-board       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 358
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a dataframe with date, song for 1999 to 2021\n",
        "bb_after99 = billboard_df[(billboard_df['date'] >= '1999-01-01') & (billboard_df['date'] <= '2022-01-01') & (billboard_df['song'])]\n"
      ],
      "metadata": {
        "id": "McVKoJagGM79"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bb_after99.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k09D8XANGUmx",
        "outputId": "7928e7b5-ead6-44cb-e42b-0637a40cf983"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 119300 entries, 0 to 119299\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count   Dtype  \n",
            "---  ------          --------------   -----  \n",
            " 0   date            119300 non-null  object \n",
            " 1   rank            119300 non-null  int64  \n",
            " 2   song            119300 non-null  object \n",
            " 3   artist          119300 non-null  object \n",
            " 4   last-week       107958 non-null  float64\n",
            " 5   peak-rank       119300 non-null  int64  \n",
            " 6   weeks-on-board  119300 non-null  int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 7.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ou-A25rDkHMn",
        "outputId": "dd4b94a9-25c4-41bb-a44a-fe34d1fc29c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date                  0\n",
              "rank                  0\n",
              "song                  0\n",
              "artist                0\n",
              "last-week         11342\n",
              "peak-rank             0\n",
              "weeks-on-board        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 361
        }
      ],
      "source": [
        "#check for nulls\n",
        "bb_after99.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "BOTH DATAFRAMES HAVE SIMILAR NUMBER OF ROWS - COMPARABLE DATA SETS, LARGE DATASET TO WORK WITH"
      ],
      "metadata": {
        "id": "YTjao2P6GX5R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3000-ROW RANDOM SAMPLE \n",
        "\n",
        "To account for bias - I will take a 3000 random sample from pre-1999 and post-1999 dataframes. I will make this a smaller sample size so that I can affectively apply Shapiro Wilk testing (most affective for sample sizes less than 5000) to help determine distribution normality.\n"
      ],
      "metadata": {
        "id": "nm51nz6fM3wY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#generate 3000 row dataframe for pre 1999\n",
        "pre99_3kr = bb_1976to1998.sample(n=3000)"
      ],
      "metadata": {
        "id": "ve8DCBnTFKZT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#check years/date\n",
        "pre99_3kr.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ceu7WznJFbJt",
        "outputId": "01fbbe1a-07a0-4521-e00a-b66001d608d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              date  rank                           song  \\\n",
              "155208  1992-02-08     9                        Finally   \n",
              "185302  1986-05-03     3                           Kiss   \n",
              "154897  1992-03-07    98  You're All That Matters To Me   \n",
              "184806  1986-06-07     7            Something About You   \n",
              "230220  1977-09-24    21           You Light Up My Life   \n",
              "\n",
              "                           artist  last-week  peak-rank  weeks-on-board  \n",
              "155208              CeCe Peniston        7.0          5              20  \n",
              "185302  Prince And The Revolution        1.0          1              11  \n",
              "154897             Curtis Stigers        NaN         98               1  \n",
              "184806                   Level 42        7.0          7              17  \n",
              "230220                Debby Boone       35.0         21               4  "
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              "      <td>7</td>\n",
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              "      <td>Level 42</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230220</th>\n",
              "      <td>1977-09-24</td>\n",
              "      <td>21</td>\n",
              "      <td>You Light Up My Life</td>\n",
              "      <td>Debby Boone</td>\n",
              "      <td>35.0</td>\n",
              "      <td>21</td>\n",
              "      <td>4</td>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "      fill: #1967D2;\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e7f69957-c9bc-416b-9508-b0e4b32b443a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 363
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#look at info\n",
        "pre99_3kr.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q9_FpA_ImDTv",
        "outputId": "e09d13f4-a767-470a-cd59-b9346096dfab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3000 entries, 155208 to 181645\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   date            3000 non-null   object \n",
            " 1   rank            3000 non-null   int64  \n",
            " 2   song            3000 non-null   object \n",
            " 3   artist          3000 non-null   object \n",
            " 4   last-week       2761 non-null   float64\n",
            " 5   peak-rank       3000 non-null   int64  \n",
            " 6   weeks-on-board  3000 non-null   int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 187.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#generate 3000 row dataframe for post 1999\n",
        "post99_3kr = bb_after99.sample(n=3000)"
      ],
      "metadata": {
        "id": "tafbzAdqmKwK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "post99_3kr.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TD27UE7TmOUz",
        "outputId": "b5febbbb-429c-4634-bc25-b1199cd4a427"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3000 entries, 112890 to 68909\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   date            3000 non-null   object \n",
            " 1   rank            3000 non-null   int64  \n",
            " 2   song            3000 non-null   object \n",
            " 3   artist          3000 non-null   object \n",
            " 4   last-week       2699 non-null   float64\n",
            " 5   peak-rank       3000 non-null   int64  \n",
            " 6   weeks-on-board  3000 non-null   int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 187.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check years/date\n",
        "post99_3kr.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XgPWYPR9mOY_",
        "outputId": "1de038af-e5ac-432e-90b6-1e07ad15b4b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              date  rank              song                            artist  \\\n",
              "112890  2000-03-25    91  The Great Beyond                            R.E.M.   \n",
              "15914   2018-10-20    15     Never Recover  Lil Baby & Gunna Featuring Drake   \n",
              "76875   2007-02-17    76     White & Nerdy               \"Weird Al\" Yankovic   \n",
              "22724   2017-07-01    25            Malibu                       Miley Cyrus   \n",
              "68148   2008-10-18    49          Shake It                     Metro Station   \n",
              "\n",
              "        last-week  peak-rank  weeks-on-board  \n",
              "112890       79.0         57              14  \n",
              "15914         NaN         15               1  \n",
              "76875        65.0          9              19  \n",
              "22724        26.0         10               6  \n",
              "68148        38.0         10              27  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <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",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>rank</th>\n",
              "      <th>song</th>\n",
              "      <th>artist</th>\n",
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              "      <th>112890</th>\n",
              "      <td>2000-03-25</td>\n",
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              "      <td>79.0</td>\n",
              "      <td>57</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15914</th>\n",
              "      <td>2018-10-20</td>\n",
              "      <td>15</td>\n",
              "      <td>Never Recover</td>\n",
              "      <td>Lil Baby &amp; Gunna Featuring Drake</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76875</th>\n",
              "      <td>2007-02-17</td>\n",
              "      <td>76</td>\n",
              "      <td>White &amp; Nerdy</td>\n",
              "      <td>\"Weird Al\" Yankovic</td>\n",
              "      <td>65.0</td>\n",
              "      <td>9</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22724</th>\n",
              "      <td>2017-07-01</td>\n",
              "      <td>25</td>\n",
              "      <td>Malibu</td>\n",
              "      <td>Miley Cyrus</td>\n",
              "      <td>26.0</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>68148</th>\n",
              "      <td>2008-10-18</td>\n",
              "      <td>49</td>\n",
              "      <td>Shake It</td>\n",
              "      <td>Metro Station</td>\n",
              "      <td>38.0</td>\n",
              "      <td>10</td>\n",
              "      <td>27</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0de30b39-d564-4010-a743-42758a46adb6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0de30b39-d564-4010-a743-42758a46adb6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 367
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ACCOUNTING FOR NULLS"
      ],
      "metadata": {
        "id": "eSpb9Yzxm_Vt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for nulls\n",
        "pre99_3kr.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEJm3lE1nDGK",
        "outputId": "0e5a53e0-b0a2-4091-b80a-ac0a49f07b78"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date                0\n",
              "rank                0\n",
              "song                0\n",
              "artist              0\n",
              "last-week         239\n",
              "peak-rank           0\n",
              "weeks-on-board      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 368
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for nulls\n",
        "post99_3kr.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aSv8S-yjnIcY",
        "outputId": "2d669bd4-7d32-4a5e-d706-c41f5ea21568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date                0\n",
              "rank                0\n",
              "song                0\n",
              "artist              0\n",
              "last-week         301\n",
              "peak-rank           0\n",
              "weeks-on-board      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 369
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#droping nulls\n",
        "pre99_3kr_dropped = pre99_3kr.dropna()"
      ],
      "metadata": {
        "id": "E4OUJMzXnOGd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping nulls\n",
        "post99_3kr_dropped = post99_3kr.dropna()"
      ],
      "metadata": {
        "id": "lAwT4c8nnOMd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pre99_3kr_dropped.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQNCYAHEihBr",
        "outputId": "4c9f2654-dfc1-40dd-f6ed-9c17b19d9617"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2761 entries, 155208 to 181645\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   date            2761 non-null   object \n",
            " 1   rank            2761 non-null   int64  \n",
            " 2   song            2761 non-null   object \n",
            " 3   artist          2761 non-null   object \n",
            " 4   last-week       2761 non-null   float64\n",
            " 5   peak-rank       2761 non-null   int64  \n",
            " 6   weeks-on-board  2761 non-null   int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 172.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post99_3kr_dropped.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_KvWbbwVnmrp",
        "outputId": "ea18ee01-b3e4-4712-d3eb-1b46873c61dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2699 entries, 112890 to 68909\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   date            2699 non-null   object \n",
            " 1   rank            2699 non-null   int64  \n",
            " 2   song            2699 non-null   object \n",
            " 3   artist          2699 non-null   object \n",
            " 4   last-week       2699 non-null   float64\n",
            " 5   peak-rank       2699 non-null   int64  \n",
            " 6   weeks-on-board  2699 non-null   int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 168.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pre99_3kr_dropped.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z4l66adCiqPC",
        "outputId": "22fd2305-f9ac-4bbe-d5f2-eb69b4a33151"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date              0\n",
              "rank              0\n",
              "song              0\n",
              "artist            0\n",
              "last-week         0\n",
              "peak-rank         0\n",
              "weeks-on-board    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 374
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post99_3kr_dropped.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qESORSPVnmu7",
        "outputId": "27b4038a-a8b8-4f98-d959-47d53d5e785c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date              0\n",
              "rank              0\n",
              "song              0\n",
              "artist            0\n",
              "last-week         0\n",
              "peak-rank         0\n",
              "weeks-on-board    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 375
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check years/date\n",
        "pre99_3kr_dropped.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Usf_prZbeZ-0",
        "outputId": "a8169492-2908-4a88-dcb3-681993f61ab9"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "              date  rank                  song                     artist  \\\n",
              "155208  1992-02-08     9               Finally              CeCe Peniston   \n",
              "185302  1986-05-03     3                  Kiss  Prince And The Revolution   \n",
              "184806  1986-06-07     7   Something About You                   Level 42   \n",
              "230220  1977-09-24    21  You Light Up My Life                Debby Boone   \n",
              "133056  1996-05-11    57     Tres Delinquentes          Delinquent Habits   \n",
              "\n",
              "        last-week  peak-rank  weeks-on-board  \n",
              "155208        7.0          5              20  \n",
              "185302        1.0          1              11  \n",
              "184806        7.0          7              17  \n",
              "230220       35.0         21               4  \n",
              "133056       61.0         57               3  "
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#check years/date\n",
        "post99_3kr_dropped.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
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              "              date  rank              song               artist  last-week  \\\n",
              "112890  2000-03-25    91  The Great Beyond               R.E.M.       79.0   \n",
              "76875   2007-02-17    76     White & Nerdy  \"Weird Al\" Yankovic       65.0   \n",
              "22724   2017-07-01    25            Malibu          Miley Cyrus       26.0   \n",
              "68148   2008-10-18    49          Shake It        Metro Station       38.0   \n",
              "35542   2015-01-17    43     Shotgun Rider           Tim McGraw       41.0   \n",
              "\n",
              "        peak-rank  weeks-on-board  \n",
              "112890         57              14  \n",
              "76875           9              19  \n",
              "22724          10               6  \n",
              "68148          10              27  \n",
              "35542          38              14  "
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3194b035-76c7-43a2-98aa-77cdb4d2d34e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 377
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.describe(pre99_3kr_dropped['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dac8vOHLueD5",
        "outputId": "00568672-7441-433d-e843-188ee88750b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DescribeResult(nobs=2761, minmax=(2, 60), mean=9.720028975009054, variance=40.68354434698623, skewness=1.3417622193832102, kurtosis=3.7999434950368425)"
            ]
          },
          "metadata": {},
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.describe(post99_3kr_dropped['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3lD_9n-lumMl",
        "outputId": "2af18795-0619-4d67-a53b-d583fbf3d599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DescribeResult(nobs=2699, minmax=(2, 60), mean=12.525009262689885, variance=77.40143138427297, skewness=1.3395426900751615, kurtosis=2.3510099219885845)"
            ]
          },
          "metadata": {},
          "execution_count": 379
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "STATS DESCRIBE SHOW BOTH \"PRE99 3KR DROPPED\" AND \"POST99 3KR DROPPED\" AS SKEWED (>1)  AND HIGH KURTOSIS (>3)"
      ],
      "metadata": {
        "id": "JoyeFIuOvNTN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(pre99_3kr_dropped['weeks-on-board'])\n",
        "plt.title('Pre 1999 \"Weeks on Board\" ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "PTtERzEgH3y8",
        "outputId": "534b6500-95d6-4ff8-ba91-d53f83bb9709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**pre99 3kr dropped is definitley right skewed**"
      ],
      "metadata": {
        "id": "cgbjlLuWIPbX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(post99_3kr_dropped['weeks-on-board'])\n",
        "plt.title('Post 1999 \"Weeks on Board\" ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "9lP3yvroIVlL",
        "outputId": "d287ec15-dad4-47ad-a482-1d7fec250488"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**post99_3kr_dropped is definitely right skewed as well**"
      ],
      "metadata": {
        "id": "oBkaI0I-IY8H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr>\n"
      ],
      "metadata": {
        "id": "JnAqA0-U0Yuj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CHECKING FOR OUTLIERS"
      ],
      "metadata": {
        "id": "gEsBxWboEnrg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#chekcing for outliers with boxplot for PRE99\n",
        "fig = plt.figure() \n",
        "fig.set_size_inches(20,4)\n",
        "sns.boxplot(x = pre99_3kr_dropped['weeks-on-board'], notch=True)\n",
        "plt.title(\"Outliers without nulls for Pre 1999\")\n",
        "plt.xlim(0, 70)\n",
        "plt.locator_params(axis='x', nbins=35)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "AWxTqyocnm5E",
        "outputId": "44b69802-1da7-448c-9e5b-938dbc4cf54b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for outliers with boxplot on POST1999\n",
        "fig = plt.figure() \n",
        "fig.set_size_inches(20,4)\n",
        "sns.boxplot(x = post99_3kr_dropped['weeks-on-board'],notch = True)\n",
        "plt.title(\"Outliers without nulls for Post 1999\")\n",
        "plt.xlim(0, 70)\n",
        "plt.locator_params(axis='x', nbins=35)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "jcUI2cEpzeII",
        "outputId": "10133c21-542b-4f7e-dac7-3c737c25fe19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "EVEN LOOKING AT BOXPLOTS FOR RANDOM 3K DROPPED SAMPLES SHOWS THAT POST1999 CHARTED LONGER THAN PRE99.  \n",
        "\n",
        "AVG WEEKS-ON-BOARD FOR PRE99 IS CONSISTENTLY AT 8 WEEKS (through many runs).\n",
        "\n",
        "AVG WEEKS-ON-BOARD FOR POST99 IS CONSISTENTLY 11 WEEKS (through many runs)."
      ],
      "metadata": {
        "id": "2nVLrG0O0g3F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DROPPING OUTLIERS WITH CALC'D IQR"
      ],
      "metadata": {
        "id": "JKYebI1tJqbD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "going to calculate the upper and lower limits instead of hard qty limit\n"
      ],
      "metadata": {
        "id": "i62dp9qvJzwe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pre99_3kr_dropped[['weeks-on-board']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "jpVCTqXKKERN",
        "outputId": "bab7a243-3217-4821-e02b-9aaa95478d06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       weeks-on-board\n",
              "count      2755.00000\n",
              "mean          9.72196\n",
              "std           6.35574\n",
              "min           2.00000\n",
              "25%           5.00000\n",
              "50%           8.00000\n",
              "75%          14.00000\n",
              "max          46.00000"
            ],
            "text/html": [
              "\n",
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              "      <th>weeks-on-board</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2755.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9.72196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>6.35574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>8.00000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>14.00000</td>\n",
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              "    <tr>\n",
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              "      <td>46.00000</td>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-585d225e-32d0-4bf4-ae3e-132aba32012f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-585d225e-32d0-4bf4-ae3e-132aba32012f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pre99_3kr_dropped['weeks-on-board'].quantile(0.25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70BpyxYdKxnZ",
        "outputId": "a100caca-ac2d-4939-c06e-07235cf90a79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.0"
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pre99_3kr_dropped['weeks-on-board'].quantile(0.75)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXuHX5qLK8a9",
        "outputId": "97024f75-f42d-4c0b-9741-73ae79137194"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.0"
            ]
          },
          "metadata": {},
          "execution_count": 269
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = pre99_3kr_dropped['weeks-on-board'].quantile(0.25)\n",
        "Q3 = pre99_3kr_dropped['weeks-on-board'].quantile(0.75)\n",
        "IQR = Q3 - Q1"
      ],
      "metadata": {
        "id": "FbSbY6GSLCsP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IQR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0r5jnCOpLRVD",
        "outputId": "239dafaa-e632-4824-8c81-d64f3eecc4b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.0"
            ]
          },
          "metadata": {},
          "execution_count": 271
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "upper_lim = Q1 - 1.5 * IQR\n",
        "lower_lim = Q3 + 1.5 * IQR"
      ],
      "metadata": {
        "id": "5s1Iu6jILU07"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lower_lim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vrGteNQYLeOR",
        "outputId": "7f5ef040-a9de-4adc-e4c9-de307f48b52b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27.5"
            ]
          },
          "metadata": {},
          "execution_count": 273
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "upper_lim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cK6Y1zFbLkRK",
        "outputId": "31ff1da9-ae7d-4ebf-af00-388899fa6486"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-8.5"
            ]
          },
          "metadata": {},
          "execution_count": 274
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pre99_dropouts_low = (pre99_3kr_dropped['weeks-on-board'] < lower_lim ) #inclusive (gather data less than lower lim wich is 27.5)"
      ],
      "metadata": {
        "id": "XBi2WEGgOw89"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(pre99_dropouts_low)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tLAIcgQXRc5q",
        "outputId": "28861bc5-04ca-4dd7-c672-0bffdc64c202"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2755"
            ]
          },
          "metadata": {},
          "execution_count": 276
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pre99_dropouts_up = (pre99_3kr_dropped['weeks-on-board'] > upper_lim ) #inclusive (gather data greater than upper lim which is -8.5 - which will be everything because everything is greater than -7 )"
      ],
      "metadata": {
        "id": "eqw8wIZYOxAI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(pre99_dropouts_up)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YsuwiSksSU4z",
        "outputId": "48291564-085f-43fd-de09-1ccfa0a84bf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2755"
            ]
          },
          "metadata": {},
          "execution_count": 278
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#this DF has outliers removed\n",
        "pre99_dropouts = pre99_3kr_dropped[(pre99_3kr_dropped['weeks-on-board'] < lower_lim)  & (pre99_3kr_dropped['weeks-on-board'] > upper_lim)]"
      ],
      "metadata": {
        "id": "evULkX-mUeO_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(pre99_dropouts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "owEB3PhrUvxd",
        "outputId": "58ba89da-34ec-4422-ea9f-dc116b806107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2717"
            ]
          },
          "metadata": {},
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#all of the outliers\n",
        "pre99_dropouts_exclusive = pre99_3kr_dropped[(pre99_3kr_dropped['weeks-on-board'] > lower_lim)]"
      ],
      "metadata": {
        "id": "VyWMqp-NVHQy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(pre99_dropouts_exclusive)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5cAA_thVHV_",
        "outputId": "d39d0471-9511-4fb3-ebd7-23fec5d439a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "metadata": {},
          "execution_count": 282
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure() \n",
        "fig.set_size_inches(20,4)\n",
        "sns.boxplot(x = pre99_dropouts['weeks-on-board'], notch =  True)\n",
        "plt.title(\"Pre 1999 with nulls and outliers removed after IQR\")\n",
        "plt.xlim(0, 30)\n",
        "plt.locator_params(axis='x', nbins=35)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "RWsZklFDYUGh",
        "outputId": "2417a06f-bc17-4bd0-d0ec-566a3e350e11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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AAAAAACyGQgcAAAAAAMBiKHQAAAAAAAAshkIHAAAAAADAYih0AAAAAAAALIZCBwAAAAAAwGIodAAAAAAAACzm/wN664YhFHikpgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#histogram to see if this 'normaled' out any with nulls and outliers removed - IT DID NOT (so it's still a non-normal distro)\n",
        "sns.distplot(pre99_dropouts['weeks-on-board'])\n",
        "plt.title('Pre 1999 \"Weeks on Board\" without nulls or outliers ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "NQXZ4AlRF0cH",
        "outputId": "a1917467-3095-4a80-e7c9-de70a4bf8bee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#calculate for POST99 now\n",
        "post99_3kr_dropped[['weeks-on-board']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "51071eca-ebf1-4971-f148-a97793b2c4e5",
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              "       weeks-on-board\n",
              "count     2701.000000\n",
              "mean        13.038504\n",
              "std          9.216893\n",
              "min          2.000000\n",
              "25%          6.000000\n",
              "50%         11.000000\n",
              "75%         18.000000\n",
              "max         67.000000"
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post99_3kr_dropped['weeks-on-board'].quantile(0.25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        },
        "outputId": "14098139-dafa-4366-d6ca-ea685be9ff9b",
        "id": "VsdmKqEwGlBH"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-b00d2c0ff77c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpost99_3kr_dropped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weeks-on-board'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'post99_3kr_dropped' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post99_3kr_dropped['weeks-on-board'].quantile(0.75)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9792b58-15d0-4d49-c440-6dc506feece6",
        "id": "GJ9mQMT9GlBH"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18.0"
            ]
          },
          "metadata": {},
          "execution_count": 287
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = post99_3kr_dropped['weeks-on-board'].quantile(0.25)\n",
        "Q3 = post99_3kr_dropped['weeks-on-board'].quantile(0.75)\n",
        "IQR = Q3 - Q1"
      ],
      "metadata": {
        "id": "A1yFOOyRGlBH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IQR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "id": "CQ_fKLy0GlBI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12.0"
            ]
          },
          "metadata": {},
          "execution_count": 289
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "upper_lim = Q1 - 1.5 * IQR\n",
        "lower_lim = Q3 + 1.3* IQR #i addjusted the limit here to include the 34s and 35s I kept catching"
      ],
      "metadata": {
        "id": "vV5h8AMdGlBI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lower_lim"
      ],
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        "outputId": "87dc8b85-2f22-4ea4-aff1-71e006aedd52",
        "id": "V6iZNRajGlBI"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33.6"
            ]
          },
          "metadata": {},
          "execution_count": 291
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "upper_lim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ccf23a3-9171-464d-9b14-e92acf6dfb40",
        "id": "Uc1Z6T1yGlBI"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-12.0"
            ]
          },
          "metadata": {},
          "execution_count": 292
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post99_dropouts_low = (post99_3kr_dropped['weeks-on-board'] < lower_lim ) #inclusive (gather data less than lower lim wich is 27.5)"
      ],
      "metadata": {
        "id": "cFeAMUxEGlBI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(post99_dropouts_low)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "436b5fe4-c426-44ec-b5d0-0beb6e4d28ac",
        "id": "-WMAq82XGlBI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2701"
            ]
          },
          "metadata": {},
          "execution_count": 294
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post99_dropouts_up = (post99_3kr_dropped['weeks-on-board'] > upper_lim ) #inclusive (gather data greater than upper lim which is -10.5 - which will be everything because everything is greater than -7 )"
      ],
      "metadata": {
        "id": "YVs5-DHlGlBI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(post99_dropouts_up)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad60da5b-44b8-44a6-f53e-46b7befb0edc",
        "id": "mKH-nk6qGlBI"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2701"
            ]
          },
          "metadata": {},
          "execution_count": 296
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#this DF has outliers removed\n",
        "post99_dropouts= post99_3kr_dropped[(post99_3kr_dropped['weeks-on-board'] < lower_lim)  & (post99_3kr_dropped['weeks-on-board'] > upper_lim)]"
      ],
      "metadata": {
        "id": "Qji7It5IGlBJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(post99_dropouts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "542f1408-27d5-4982-9815-97d84643a936",
        "id": "Vr4ShLDmGlBJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2593"
            ]
          },
          "metadata": {},
          "execution_count": 298
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#all of the outliers\n",
        "post99_dropouts_exclusive = post99_3kr_dropped[(post99_3kr_dropped['weeks-on-board'] > lower_lim)]"
      ],
      "metadata": {
        "id": "jhfKCku5GlBJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(post99_dropouts_exclusive)"
      ],
      "metadata": {
        "colab": {
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        "outputId": "42b78255-7d65-4d21-83e6-0636e854ebee",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "108"
            ]
          },
          "metadata": {},
          "execution_count": 300
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure() \n",
        "fig.set_size_inches(20,4)\n",
        "sns.boxplot(x = post99_dropouts['weeks-on-board'], notch = True)\n",
        "plt.title(\"Post 1999 with nulls and outliers removed after IQR\")\n",
        "plt.xlim(0, 40)\n",
        "plt.locator_params(axis='x', nbins=35)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
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        "id": "1ktopCjYGlBJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#histogram to see if this 'normaled' out any with nulls and outliers removed - IT DID NOT (so it's still a non-normal distro)\n",
        "sns.distplot(post99_dropouts['weeks-on-board'])\n",
        "plt.title('Post 1999 \"Weeks on Board\" without nulls or outliers ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "94cca06e-6670-494d-d482-e443a4b3b682",
        "id": "Pfe24spyGlBJ"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(post99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "inqqzk4yLqPV",
        "outputId": "8e4ebecb-aa1e-4808-fb21-c07642bf5fdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9447929859161377, pvalue=4.0655877074036916e-30)"
            ]
          },
          "metadata": {},
          "execution_count": 303
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(pre99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v19qEvuFLqUL",
        "outputId": "53ff04f5-7280-40b8-cb7e-18f39b3510cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9367316961288452, pvalue=1.5425027889322065e-32)"
            ]
          },
          "metadata": {},
          "execution_count": 304
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SHAPIRO INDICATES NOT NORMAL for pre99_dropouts and post99_dropouts - LOW P-VALUE (\"This is a statistical test used to infer whether a variable's distribution is significantly different than normal.\")"
      ],
      "metadata": {
        "id": "zDmRCnM9L-ce"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.describe(post99_dropouts['weeks-on-board'])\n",
        "#negative kurtosis here probably indicates it's really platykurtic and not normal"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-aiRqaYTMH_4",
        "outputId": "715921ed-6988-4507-88e1-145f5485b3f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-31a603a39fed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpost99_dropouts\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'weeks-on-board'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#negative kurtosis here probably indicates it's really platykurtic and not normal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'stats' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.describe(pre99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sw7eOIzJNGug",
        "outputId": "6d4f8475-4030-43f7-bb0a-aab974c1b4a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DescribeResult(nobs=2717, minmax=(2, 27), mean=9.398601398601398, variance=33.07043986940894, skewness=0.6803864467314997, kurtosis=-0.3117797132489315)"
            ]
          },
          "metadata": {},
          "execution_count": 306
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DROPPING OUTLIERS (manual calc with hard qty limit (26 and 32)\n"
      ],
      "metadata": {
        "id": "kNnHYHQK5-f8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## COLLECTING JUST THE PRE's OF 26 WEEKS OR LESS\n",
        "pre_dropouts_manual= pre99_3kr_dropped[(pre99_3kr_dropped['weeks-on-board']<26) & (pre99_3kr_dropped['date'] <= '1998-12-31') & (pre99_3kr_dropped['date'] >= '1976-01-01')]\n"
      ],
      "metadata": {
        "id": "_z8uMPf3BXxy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pre_dropouts_manual.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GDJNXVwqFCs1",
        "outputId": "e209a1f4-79a2-4bf9-ebc4-a0dc33e1da8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              date  rank                            song  \\\n",
              "219600  1979-10-06     1                        Sad Eyes   \n",
              "143842  1994-04-16    43  Hey D.J. (From \"Mi Vida Loca\")   \n",
              "163159  1990-08-04    60                    Ready Or Not   \n",
              "166754  1989-11-25    55                   It's No Crime   \n",
              "150195  1993-01-30    96           This Could Be The One   \n",
              "\n",
              "                        artist  last-week  peak-rank  weeks-on-board  \n",
              "219600             Robert John        2.0          1              21  \n",
              "143842  Lighter Shade Of Brown       51.0         43               9  \n",
              "163159                 After 7       45.0          7              19  \n",
              "166754                Babyface       42.0          7              16  \n",
              "150195             Bad Company       91.0         87               7  "
            ],
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              "      <th>artist</th>\n",
              "      <th>last-week</th>\n",
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              "      <th>weeks-on-board</th>\n",
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              "      <th>219600</th>\n",
              "      <td>1979-10-06</td>\n",
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              "      <td>Sad Eyes</td>\n",
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              "      <td>21</td>\n",
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              "    <tr>\n",
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              "      <td>1994-04-16</td>\n",
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              "      <td>Hey D.J. (From \"Mi Vida Loca\")</td>\n",
              "      <td>Lighter Shade Of Brown</td>\n",
              "      <td>51.0</td>\n",
              "      <td>43</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163159</th>\n",
              "      <td>1990-08-04</td>\n",
              "      <td>60</td>\n",
              "      <td>Ready Or Not</td>\n",
              "      <td>After 7</td>\n",
              "      <td>45.0</td>\n",
              "      <td>7</td>\n",
              "      <td>19</td>\n",
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              "    <tr>\n",
              "      <th>166754</th>\n",
              "      <td>1989-11-25</td>\n",
              "      <td>55</td>\n",
              "      <td>It's No Crime</td>\n",
              "      <td>Babyface</td>\n",
              "      <td>42.0</td>\n",
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              "    <tr>\n",
              "      <th>150195</th>\n",
              "      <td>1993-01-30</td>\n",
              "      <td>96</td>\n",
              "      <td>This Could Be The One</td>\n",
              "      <td>Bad Company</td>\n",
              "      <td>91.0</td>\n",
              "      <td>87</td>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for outliers with boxplot PRE 1999 with no outliers\n",
        "fig = plt.figure() \n",
        "fig.set_size_inches(20,4)\n",
        "sns.boxplot(x = pre_dropouts_manual['weeks-on-board'],notch = True)\n",
        "plt.xlim(0, 40)\n",
        "plt.locator_params(axis='x', nbins=35)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "uNI9OVSqEoCu",
        "outputId": "0128a7ef-3096-4aa6-96c6-68ecaa2e7c79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "post_dropouts_manual = post99_3kr_dropped[(post99_3kr_dropped['weeks-on-board'] < 32) & (post99_3kr_dropped['date'] <= '2022-01-01') & (post99_3kr_dropped['date'] >= '1999-01-01')]"
      ],
      "metadata": {
        "id": "ol_5DldxGpJc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "post_dropouts_manual.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "q6LyJYdrGxgT",
        "outputId": "ffd993ce-84f0-43e1-ed48-f08ed9379f0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             date  rank                        song                artist  \\\n",
              "97953  2003-02-01    54                     Disease       matchbox twenty   \n",
              "18715  2018-04-07    16               All The Stars  Kendrick Lamar & SZA   \n",
              "67479  2008-12-06    80          Waitin' On A Woman          Brad Paisley   \n",
              "43350  2013-07-20    51               See You Again      Carrie Underwood   \n",
              "58285  2010-09-11    86  The Man Who Can't Be Moved            The Script   \n",
              "\n",
              "       last-week  peak-rank  weeks-on-board  \n",
              "97953       51.0         29              16  \n",
              "18715       14.0          7              12  \n",
              "67479       66.0         44              20  \n",
              "43350       52.0         51              10  \n",
              "58285       90.0         86               4  "
            ],
            "text/html": [
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              "      <th>date</th>\n",
              "      <th>rank</th>\n",
              "      <th>song</th>\n",
              "      <th>artist</th>\n",
              "      <th>last-week</th>\n",
              "      <th>peak-rank</th>\n",
              "      <th>weeks-on-board</th>\n",
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              "  </thead>\n",
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              "      <th>97953</th>\n",
              "      <td>2003-02-01</td>\n",
              "      <td>54</td>\n",
              "      <td>Disease</td>\n",
              "      <td>matchbox twenty</td>\n",
              "      <td>51.0</td>\n",
              "      <td>29</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18715</th>\n",
              "      <td>2018-04-07</td>\n",
              "      <td>16</td>\n",
              "      <td>All The Stars</td>\n",
              "      <td>Kendrick Lamar &amp; SZA</td>\n",
              "      <td>14.0</td>\n",
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              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>67479</th>\n",
              "      <td>2008-12-06</td>\n",
              "      <td>80</td>\n",
              "      <td>Waitin' On A Woman</td>\n",
              "      <td>Brad Paisley</td>\n",
              "      <td>66.0</td>\n",
              "      <td>44</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43350</th>\n",
              "      <td>2013-07-20</td>\n",
              "      <td>51</td>\n",
              "      <td>See You Again</td>\n",
              "      <td>Carrie Underwood</td>\n",
              "      <td>52.0</td>\n",
              "      <td>51</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58285</th>\n",
              "      <td>2010-09-11</td>\n",
              "      <td>86</td>\n",
              "      <td>The Man Who Can't Be Moved</td>\n",
              "      <td>The Script</td>\n",
              "      <td>90.0</td>\n",
              "      <td>86</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4a6ccb58-1201-40cd-8bd9-beb1fb4b0497')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4a6ccb58-1201-40cd-8bd9-beb1fb4b0497 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4a6ccb58-1201-40cd-8bd9-beb1fb4b0497');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 311
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking for outliers with boxplot POST 1999 with no outliers\n",
        "fig = plt.figure() \n",
        "fig.set_size_inches(20,4)\n",
        "sns.boxplot(x = post_dropouts_manual['weeks-on-board'],notch = True)\n",
        "plt.xlim(0, 40)\n",
        "plt.locator_params(axis='x', nbins=35)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "S0CLmnMRHF6D",
        "outputId": "f551898a-da28-4568-934f-26607e0363b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.describe(pre_dropouts_manual['weeks-on-board'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oWbJt0JCIvpv",
        "outputId": "4a312b2e-fd6d-4f4e-d8bf-941ef8249a84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DescribeResult(nobs=2699, minmax=(2, 25), mean=9.286031863653205, variance=31.37627257274267, skewness=0.6184785376986092, kurtosis=-0.4990663458788722)"
            ]
          },
          "metadata": {},
          "execution_count": 313
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.describe(post_dropouts_manual['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tan9uJr5JFzA",
        "outputId": "2c38d77a-dea1-45cf-b0a4-39b3dc78771e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DescribeResult(nobs=2571, minmax=(2, 31), mean=11.690392843251653, variance=49.14379482615888, skewness=0.6151662384992841, kurtosis=-0.32185489873620154)"
            ]
          },
          "metadata": {},
          "execution_count": 314
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "STATS DESCRIBE FOR \"PRE_DROPOUTS\" AND \"POST_dROPOUTS\" ARE MUCH MORE 'NORMAL' - NOT SKEWED, NO KURTOSIS"
      ],
      "metadata": {
        "id": "xTRC8w2CvpRP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#histogram to see if this 'normaled' out any with nulls and outliers removed - IT DID NOT (so it's still a non-normal distro)\n",
        "sns.distplot(pre_dropouts_manual['weeks-on-board'])\n",
        "plt.title('Pre 1999 \"Weeks on Board\" without nulls or outliers-manual ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "ya5UGxQ1KeHR",
        "outputId": "97da9a69-d935-4a85-873b-34351fbc4f48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#histogram to see if this 'normaled' out any with nulls and outliers removed - IT DID NOT (so it's still a non-normal distro)\n",
        "sns.distplot(post_dropouts_manual['weeks-on-board'])\n",
        "plt.title('Post 1999 \"Weeks on Board\" without nulls or outliers-manual ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "lPT_UUw7KpDf",
        "outputId": "30824cb4-4d01-4d67-f122-2c17b1db4d2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(pre_dropouts_manual['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6NldTjFJK1x",
        "outputId": "b30c4fc4-d0ec-4bfd-9829-baf1abc9861d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9382724761962891, pvalue=4.0607611040893283e-32)"
            ]
          },
          "metadata": {},
          "execution_count": 317
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.shapiro(post_dropouts_manual['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGV42EwUJUha",
        "outputId": "a1219b61-ee05-4867-dd8f-d024bd80afb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9481145739555359, pvalue=3.218689354698401e-29)"
            ]
          },
          "metadata": {},
          "execution_count": 318
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SHAPIRO INDICATES NOT NORMAL - LOW PVALUE (\"This is a statistical test used to infer whether a variable's distribution is significantly different than normal.\")"
      ],
      "metadata": {
        "id": "KFXLTzp8v6P0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr>"
      ],
      "metadata": {
        "id": "AJW343j4KNHK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CHECKING FOR RELATION WITH PEARSONS\n",
        "\n",
        "need same sample size for Pearson. Will create 1500 line sample"
      ],
      "metadata": {
        "id": "MbiJ2sOJFbhI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#pre99_pearson = pre99_3kr_dropped.iloc[0:1500] #these still have outliers"
      ],
      "metadata": {
        "id": "Y6t6-ryXFwI-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#post99_pearson = post99_3kr_dropped.iloc[0:1500] #these still have outliers"
      ],
      "metadata": {
        "id": "JW909UMiF6Mz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pre_dropouts_pearson = pre99_dropouts.iloc[0:1500]"
      ],
      "metadata": {
        "id": "4w1M5CbpsZrK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "post_dropouts_pearson = post99_dropouts.iloc[0:1500]"
      ],
      "metadata": {
        "id": "z_YJa5VbsZu2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pre_dropouts_pearson.info(), post_dropouts_pearson.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKRWarjJGGZv",
        "outputId": "e1c9dd3e-4b29-467d-8efa-7bb06e6f12f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1500 entries, 219600 to 197845\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   date            1500 non-null   object \n",
            " 1   rank            1500 non-null   int64  \n",
            " 2   song            1500 non-null   object \n",
            " 3   artist          1500 non-null   object \n",
            " 4   last-week       1500 non-null   float64\n",
            " 5   peak-rank       1500 non-null   int64  \n",
            " 6   weeks-on-board  1500 non-null   int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 93.8+ KB\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1500 entries, 97953 to 110588\n",
            "Data columns (total 7 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   date            1500 non-null   object \n",
            " 1   rank            1500 non-null   int64  \n",
            " 2   song            1500 non-null   object \n",
            " 3   artist          1500 non-null   object \n",
            " 4   last-week       1500 non-null   float64\n",
            " 5   peak-rank       1500 non-null   int64  \n",
            " 6   weeks-on-board  1500 non-null   int64  \n",
            "dtypes: float64(1), int64(3), object(3)\n",
            "memory usage: 93.8+ KB\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, None)"
            ]
          },
          "metadata": {},
          "execution_count": 323
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#pearson correlation\n",
        "corr, _ = pearsonr(pre_dropouts_pearson['weeks-on-board'], post_dropouts_pearson['weeks-on-board'])\n",
        "print('Pearsons correlation: %.3f' % corr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nZh-AK2yFfUw",
        "outputId": "66ed00dc-be4d-4b9b-d194-841544eb4687"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pearsons correlation: -0.018\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pearson's correlation is close to 0.0 therefore indicating NO RELATIONSHIP between pre and post \"dropouts\" version which has no nulls and no outliers"
      ],
      "metadata": {
        "id": "kEmpMnd4Gf6M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "CALCULATING COHEN'S D FOR MORE RELATIONSHIP TESTING"
      ],
      "metadata": {
        "id": "oEUwQU2MHeiG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n1, n2 = len(pre99_dropouts['weeks-on-board']), len(post99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "id": "VB9domFydOLK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(n1,n2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LUJYK9TgdVT5",
        "outputId": "9cdcee6e-cbf4-4dda-cd7d-3e301bb6ce38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2717 2593\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\t# calculate the variance of the samples\n",
        "\ts1, s2 = var(pre99_dropouts['weeks-on-board'], ddof=1), var(post99_dropouts['weeks-on-board'], ddof=1)"
      ],
      "metadata": {
        "id": "QzDOFBohdXZe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(s1, s2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vWGgj9mRdrNa",
        "outputId": "6cd3d0a0-491f-41d2-ad33-bea32b4e3829"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33.07043986940894 52.37310863054853\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def variance(data):\n",
        "#nummber of observations\n",
        "  n = len(data)\n",
        "     # Mean of the data\n",
        "  mean = sum(data) / n\n",
        "     # Square deviations\n",
        "  deviations = [(x - mean) ** 2 for x in data]\n",
        "    # Variance\n",
        "  variance = sum(deviations) / n\n",
        "  return variance\n",
        "\n",
        "\n",
        "pre99_var = variance(pre99_dropouts['weeks-on-board'])\n",
        "print(\"pre99_dropouts variance: \", pre99_var)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxFe5W8VdtPe",
        "outputId": "4ebaa722-ba2f-48c2-e354-f7ad6ffb8656"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pre99_dropouts variance:  33.05826819481545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def variance(data):\n",
        "#nummber of observations\n",
        "  n = len(data)\n",
        "     # Mean of the data\n",
        "  mean = sum(data) / n\n",
        "     # Square deviations\n",
        "  deviations = [(x - mean) ** 2 for x in data]\n",
        "    # Variance\n",
        "  variance = sum(deviations) / n\n",
        "  return variance\n",
        "\n",
        "\n",
        "post99_var = variance(post99_dropouts['weeks-on-board'])\n",
        "print(\"post99_dropouts variance: \", post99_var)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1c4pQFzeDBz",
        "outputId": "e940dab9-53f8-4554-e11a-a2c4e876b1b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "post99_dropouts variance:  52.35291074831543\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calc Standard Deviation\n",
        "sd = sqrt(((n1-1) * pre99_var + (n2 - 1) * post99_var) / (n1 + n2 - 2))"
      ],
      "metadata": {
        "id": "rDryJfJQeNEA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(sd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lvrw_czpeUYb",
        "outputId": "9fe8debc-007c-41c7-953e-1e53758f5e6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6.517685075309362\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate the means of the samples\n",
        "u1 = mean(pre99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "id": "2YLZEEHFeVxj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "u2 = mean(post99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "id": "HEND-fqNebRc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(u1, u2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYYSZoAZekEr",
        "outputId": "eb00ed04-b635-4635-fdda-5b2b687523e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9.398601398601398 11.866949479367529\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate the effect size (aka Cohen's d)\n",
        "effect_size =  (u1 - u2) / sd"
      ],
      "metadata": {
        "id": "AjQkJfdZemHY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(effect_size)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUryzVEhev4Z",
        "outputId": "e42c22d8-165e-462c-ca94-472b06275183"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-0.3787154568294281\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Small Effect Size: d=0.20 <br>\n",
        "Medium Effect Size: d=0.50 <br>\n",
        "Large Effect Size: d=0.80 <br>\n",
        "\n",
        "Effect Size is small. <br>\n",
        "Considering \"post99\" DID stay on charts longer, the mean would be larger, and the Effect Size is negative because a smaller number (pre99) is subtracting a larger number (post99).  In this case, we could reverse the math (post99 minus pre99 for a positive number) or we could just take the absolute value of 0.3 and make our assumption based on 0.3 being relatively small for effect size. <br><br>\n",
        "In addition to Pearson's correlation - this small effect size indicates also that there is no relationship between our two variables \"pre_dropouts 'weeks-on-board' \" and \"post_dropouts 'weeks-on-board' \""
      ],
      "metadata": {
        "id": "qVq4EztFe0I1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DISTRIBUTION TESTING FOR NORMALITY\n",
        "\n",
        "I am going to stick with my random pre99_dropouts and post99_dropouts varibales to account for bias, sample size, and no nulls, and no outliers."
      ],
      "metadata": {
        "id": "xl5QjMtfJh5T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Skew and Kurtosis interpretations**"
      ],
      "metadata": {
        "id": "hI0YPAl2SLhb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# WRITING A FUNCTION TO PERFORM REPETITIVE stats.describe()\n",
        "def describe(pre, post):\n",
        "  pre_stats = stats.describe(pre)\n",
        "  post_stats = stats.describe(post)\n",
        "  return (f\"preStats: {pre_stats}.\",f\"PostStats: {post_stats}\")\n",
        "\n",
        "describe (pre99_dropouts['weeks-on-board'], post99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__zUKzWkmb85",
        "outputId": "0785d76f-75a8-413a-c630-5b3cc9faeb46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('preStats: DescribeResult(nobs=2717, minmax=(2, 27), mean=9.398601398601398, variance=33.07043986940894, skewness=0.6803864467314997, kurtosis=-0.3117797132489315).',\n",
              " 'PostStats: DescribeResult(nobs=2593, minmax=(2, 33), mean=11.866949479367529, variance=52.37310863054853, skewness=0.6833770976978091, kurtosis=-0.14973670319957044)')"
            ]
          },
          "metadata": {},
          "execution_count": 338
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**with outliers stripped ==> presents more normal**\n",
        "\n",
        "Pre99 SKEW and KURTOSIS: <br>\n",
        "skew = ~ 0.6x ==> less than 1  ==> looks normal (moderate skew at btw 0.5 and 1) <br>\n",
        "kurtosis = ~ -0.3x ==> less than 3 ==>  looks normal <br>"
      ],
      "metadata": {
        "id": "pNOUIfjhLcbz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Post99 SKEW and KURTOSIS: <br>\n",
        "skew = ~ 0.6x ==> less than 1  ==> looks normal (moderate skew at btw 0.5 and 1) <br>\n",
        "kurtosis = ~ -0.3x ==> less than 3 ==>  looks normal"
      ],
      "metadata": {
        "id": "lbwNQPhiQFpo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<hr>\n",
        "\n",
        "**with outliers included (they are still in there) = skew and kurtosis are \"not normal\"**"
      ],
      "metadata": {
        "id": "e9HFgp-Pfh3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.describe(pre99_3kr_dropped['weeks-on-board']) #nulls dropped, outliers still in"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82a8519d-3a04-422d-8bfe-91754232c014",
        "id": "1PhPDsyAOifB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DescribeResult(nobs=2755, minmax=(2, 46), mean=9.721960072595282, variance=40.39543261278431, skewness=1.1250042449397293, kurtosis=1.7899456873082835)"
            ]
          },
          "metadata": {},
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.describe(post99_3kr_dropped['weeks-on-board'])  #nulls dropped, outliers still in"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "861ce4c7-61aa-463e-8c5e-c967c647ef85",
        "id": "ntaOZgoYOifB"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DescribeResult(nobs=2701, minmax=(2, 67), mean=13.03850425768234, variance=84.95110946563001, skewness=1.4328862270834106, kurtosis=2.860606925916726)"
            ]
          },
          "metadata": {},
          "execution_count": 340
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#PRE 99 WITH NULLS AND OUTLIERS DROPPED HISTOGRAM\n",
        "#histogram to see if this 'normaled' out any with nulls and outliers removed - IT DID NOT (so it's still a non-normal distro)\n",
        "sns.distplot(pre99_dropouts['weeks-on-board'])\n",
        "plt.title('Pre 1999 \"Weeks on Board\" without nulls or outliers ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "sLBrriOpvvYT",
        "outputId": "b33b9494-43b0-4693-8033-ca4aa7bc5e1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#POST 99 WITH NULLS AND OUTLIERS DROPPED HISTOGRAM\n",
        "#histogram to see if this 'normaled' out any with nulls and outliers removed - IT DID NOT (so it's still a non-normal distro)\n",
        "sns.distplot(post99_dropouts['weeks-on-board'])\n",
        "plt.title('Post 1999 \"Weeks on Board\" without nulls or outliers ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "Tdd8hGcxwHC5",
        "outputId": "506e01fa-25a4-4e3f-f1cb-80db8657b2a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Shapiro interpretations**\n"
      ],
      "metadata": {
        "id": "xXJBEFj2Qc_f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# WRITING A FUNCTION TO PERFORM REPETITIVE stats.shapiro()\n",
        "def shapwilk(pre, post):\n",
        "  pre_shap = stats.shapiro(pre)\n",
        "  post_shap = stats.shapiro(post)\n",
        "  return (f\"pre_Shapiro: {pre_shap}.\",f\"post_Shapiro: {post_shap}\")\n",
        "\n",
        "shapwilk (pre99_dropouts['weeks-on-board'], post99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IrU3NwuKmvNO",
        "outputId": "0632a3be-7cc3-4560-8952-833184ab0def"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('pre_Shapiro: ShapiroResult(statistic=0.9367316961288452, pvalue=1.5425027889322065e-32).',\n",
              " 'post_Shapiro: ShapiroResult(statistic=0.9447929859161377, pvalue=4.0655877074036916e-30)')"
            ]
          },
          "metadata": {},
          "execution_count": 343
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "rule: p-value > 0.05, W stat close to 1  = normal distribution<Br>\n",
        "\"This is a statistical test used to infer whether a variable's distribution is significantly different than normal.\"\n",
        "\n",
        "**pre99: 0.93 close to 1, p-value = way less than 0.05 ==> NOT NORMAL<BR>\n",
        "post99: 0.94 close to 1, p-value = way less than 0.05 ==> NOT NORMAL**"
      ],
      "metadata": {
        "id": "1B5oWZoERZeA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Histrogram Plots**"
      ],
      "metadata": {
        "id": "RGoUfprKSarH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use('dark_background')\n",
        "fig = plt.figure() \n",
        "fig.set_size_inches(15,10)\n",
        "plt.hist(post99_dropouts['weeks-on-board'], alpha = .75, color = \"magenta\")\n",
        "plt.hist(pre99_dropouts['weeks-on-board'], alpha = .85, color = \"cyan\")\n",
        "plt.legend(['After 1999','1976-1998'])\n",
        "plt.title(\"SONGS PER WEEKS ON CHART - RANDOM SAMPLE WITH NO NULLS OR OUTLIERS\")\n",
        "plt.xticks(range(0,50,5))\n",
        "plt.yticks(range(0,800,100))\n",
        "plt.xlabel(\"Weeks On Board\")\n",
        "plt.ylabel(\"Quantity of Songs\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "id": "k9kocdB-R-SX",
        "outputId": "31029cf7-a9e7-4dce-df8e-2b128717a54e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "NOT A NORMAL BELL CURVE  - THIS IS A NON-NORMAL DISTRIBUTION FROM THIS PLOT (in addition to Shapiro-Wilk results)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ucJT4BtxTH4e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#individual histogram plots\n",
        "pre99_dropouts.hist(['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "LJQnMFbiTSFf",
        "outputId": "633956f0-ae39-4393-aa4a-cdeff31b4935"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5fe4abccd0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 345
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#individual histogram plots\n",
        "post99_dropouts.hist(['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "DU4Ya9L_TeTx",
        "outputId": "b4742025-3f18-411c-a909-9a8e11f000b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f5fe3fb7710>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 346
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "AGAIN, NOT NORMAL BELL CURVE DISTRIBUTIONS"
      ],
      "metadata": {
        "id": "LauRr_KTTtq3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**QQ interpretations**"
      ],
      "metadata": {
        "id": "uatyaEArTy9o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting QQ on pre99_dropouts\n",
        "fig = sm.qqplot(pre99_dropouts['weeks-on-board'], line='45')\n",
        "plt.title(\"QQ PLOT FOR PRE-1999 - RANDOM SAMPL, NO NULLS OR OUTLIERS\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "zCBz337CTwL7",
        "outputId": "6980fe0f-daf6-4101-9e3b-0f6c1389c5d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotting QQ on post99_dropouts\n",
        "fig = sm.qqplot(post99_dropouts['weeks-on-board'], line='45')\n",
        "plt.title(\"QQ PLOT FOR POST-1999 - RANDOM SAMPLE, NO NULLS OR OUTLIERS\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "ETUO0VtmVfsR",
        "outputId": "d31051e1-125e-48ec-b005-9eaadce77af5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "THEY DO  NOT LOOK NORMAL AS THEY DO NOT FOLLOW THE 45 LINE\n"
      ],
      "metadata": {
        "id": "UEoAJjjbYGPB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# I DETERMINE THESE 2 DATAFRAMES ARE NOT NORMALLY DISTRIBUTED\n",
        "\n",
        "based on stats.describe (for included outliers), Shapiro- Wilk results, histogram plots, and QQ plots - I will implement Kruskal-Wallis to test with"
      ],
      "metadata": {
        "id": "ufaZC2CBYb-m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats.kruskal(pre99_dropouts['weeks-on-board'], post99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "coLn9ZMvynys",
        "outputId": "41c5fdf9-f774-4d16-91d3-dc3d54adb503"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KruskalResult(statistic=141.73247728230132, pvalue=1.1126973388846684e-32)"
            ]
          },
          "metadata": {},
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "rule: \"p-value < 0.05 = you can reject the null that all variables have the same distribution/ \"are the same\".\n",
        "\n",
        "**CONCLUSION: WE CAN REJECT THE NULL THAT THERE WAS \"NO CHANGE IN CHARTING TIME (WEEKS-ON-BOARD) IN THE DIGITAL AGE FROM 1999 FORWARD\".** <BR><BR>\n",
        "\n",
        "Reject this null hypothesis: Songs charted from 1976-1998 did not have significantly different chart times than songs released after 1999.\n",
        "\n",
        "The low p-value indicates signficant difference btw \"pre99_dropouts for weeks on board\" and \"post99_dropouts for weeks on board\"."
      ],
      "metadata": {
        "id": "nqIQ663zjPro"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CONFIDENCE INTERVAL CALCULATION"
      ],
      "metadata": {
        "id": "mLIa2HsIlyzl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#confidence interval calculation\n",
        "def get_95_ci(array_1, array_2):\n",
        "    sample_1_n = array_1.shape[0] # 1976to1998\n",
        "    sample_2_n = array_2.shape[0] # after99\n",
        "    sample_1_mean = array_1.mean()\n",
        "    sample_2_mean = array_2.mean()\n",
        "    sample_1_var = array_1.var() #squared std deviation\n",
        "    sample_2_var = array_2.var()\n",
        "    mean_difference = sample_2_mean - sample_1_mean\n",
        "    std_err_difference = math.sqrt((sample_1_var/sample_1_n)+(sample_2_var/sample_2_n))\n",
        "    margin_of_error = 1.96 * std_err_difference\n",
        "    ci_lower = mean_difference - margin_of_error\n",
        "    ci_upper = mean_difference + margin_of_error\n",
        "    print(\"ci_upper = \", ci_upper)\n",
        "    print(\"ci_lower = \", ci_lower)\n",
        "    return(\"The difference in means at the 95% confidence interval (two-tail) is between \"+str(ci_lower)+\" and \"+str(ci_upper)+\".\")\n",
        "\n",
        "get_95_ci(pre99_dropouts['weeks-on-board'],  post99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "xvKOBRewjU2a",
        "outputId": "d6b58a59-613d-4e64-a943-da025b156ed6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ci_upper =  2.8209822956455803\n",
            "ci_lower =  2.1157138658866805\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'The difference in means at the 95% confidence interval (two-tail) is between 2.1157138658866805 and 2.8209822956455803.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 350
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "WITH 95% CONFIDENCE - CAN SAY THAT \"POST-\n",
        "1999\" SONGS STAYED ON CHARTS ON AVERAGE BETWEEN 1.99 AND 2.67 WEEKS LONGER THAN \"PRE-1999\" SONGS\n",
        "\n",
        "1.7 vs 2.38<br>\n",
        "1.9 and 3.67<br>\n",
        "1.79 and 2.47<br>\n",
        "2.01 and 2.7<br>\n",
        "1.69 and 2.38 <br>\n",
        "1.58 and 2.25<br>\n",
        "1.68 and 2.34 <br>\n",
        "1.71 and 2.41 <br>\n",
        "1.77 and 2.43<br>\n",
        "1.99 and 2.67<br>\n",
        "2.11 and 2.78<br>"
      ],
      "metadata": {
        "id": "aIH6scUumPaN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "to check:<BR>\n",
        "after1999 mean: 12.7 weeks <br>\n",
        "1976to1998 mean: 9.6 weeks\n",
        "\n",
        "12.7 - 9.6 = 3.1\n",
        "\n",
        "CI: 2.69 + 3.5 / 2 = 3.095"
      ],
      "metadata": {
        "id": "HqZpD0-Rmbij"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#execute CI function and return an average, not just a range. \n",
        "def ci_averages(array_1, array_2):\n",
        "      sample_1_n = array_1.shape[0] # 1976to1998\n",
        "      sample_2_n = array_2.shape[0] # after99\n",
        "      sample_1_mean = array_1.mean()\n",
        "      sample_2_mean = array_2.mean()\n",
        "      sample_1_var = array_1.var() #squared std deviation\n",
        "      sample_2_var = array_2.var()\n",
        "      mean_difference = sample_2_mean - sample_1_mean\n",
        "      std_err_difference = math.sqrt((sample_1_var/sample_1_n)+(sample_2_var/sample_2_n))\n",
        "      margin_of_error = 1.96 * std_err_difference\n",
        "      ci_lower = mean_difference - margin_of_error\n",
        "      ci_upper = mean_difference + margin_of_error\n",
        "      #print(ci_upper)\n",
        "      #print(ci_lower)\n",
        "      avg = (ci_upper+ci_lower)/2\n",
        "      return (\"The average weeks that Post99 charts longer than Pre99 is: \", avg)\n",
        "\n",
        "ci_averages(pre99_dropouts['weeks-on-board'],  post99_dropouts['weeks-on-board'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MhYTrGrp8OPF",
        "outputId": "909774a5-a92f-4a68-f5e7-8435904b061b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('The average weeks that Post99 charts longer than Pre99 is: ',\n",
              " 2.4683480807661304)"
            ]
          },
          "metadata": {},
          "execution_count": 351
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# RECOMMENDATIONS\n"
      ],
      "metadata": {
        "id": "p3JHDICdr7hw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It's clear that the onset of digital file sharing and streaming services in 1999 affected how long songs chart. With this knowledge, stakeholders such as artists, record labels, streaming services, broadcast radio, and satellite radio should consider how they can **most take advantage of digital music content delivery.**\n",
        "\n",
        "**Why Digital?**<br>Digital music content delivery in the forms of streaming services (Pandora, Spotify, Amazon music, iTunes, YouTube, etc.) and file sharing/purchases all offer more equitable access and quantifiable exposure and engagement measurements.<br><br>Equitable access means music on-demand, mobile device in hand (mp3 player, to iPod, to cell phone), wifi proliferation, digital connectivy expansion (increase broadband and high capacity infrastructure), and digital payment expansion (CC on file, Apple Pay, etc). Listeners are no longer tied to a radio station broadcast or a record store sale."
      ],
      "metadata": {
        "id": "SDo2zn3F9h4g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Quantifiable exposure and engagement measurement:<br>\n",
        "Every digital track could return measurable data such as:\n",
        "\n",
        "* user ID (subscriber ID, email, IP, CC info)\n",
        "* user system ID (windows, mac, etc.)\n",
        "* track name\n",
        "* track artist\n",
        "* time listened\n",
        "* user engagement (repeats, FFWD, RW, skip, pause, like, dislike)"
      ],
      "metadata": {
        "id": "RCRBWO6A-UP1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we know now that songs released after 1999 do tend to chart longer than songs released before 1999, stakeholders such as:\n",
        "* record labels\n",
        "* artists\n",
        "* streaming service providers (Spotify, Pandora, Amazon Music, TikTok, etc.)\n",
        "* traditional broadcast radio\n",
        "* satellite radio stations\n",
        "\n",
        "can use this information to make data-informed decisions regarding content distribution, revenue generation, marketing, and audience development. "
      ],
      "metadata": {
        "id": "gFlGjSsusKUG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I recommend that stakeholders consider song chart time (\"weeks-on-board\") in order to help determine things like:\n",
        "\n",
        "*   **concert tour dates** - lengthy-charted songs (especially multiple lengthy charted songs from the same artist) will more likely generate more ticket sales due to more exposure and engagement (REV)<br>\n",
        "* **radio clock programming**- long-charted popular hits play an important role in the radio clock (where it is determined when to play what kind of songs during certain hours of the day). It is this programming that builds and maintains audience, which leads to advertising sales based on audience exposure for traditional broadcast radio.(AD)<br>\n",
        "*   **artists** - artists should consider lengthy-charted hits as to help determine audience/fan preference and whether the artist/band should continue releasing hits in the same style and genre, or, when it's time to perhaps release a new, more contemporary 'sound' for this artist/band. Bands that have been successful in the past of releasing a \"new sound\" after massive hits in one style are U2, Smashing Pumpkins, Sugar Ray, and The Red Hot Chili Peppers. (CD)<br>\n",
        "* **album/song releases** - similar to artists style as mentioned above, monitoring song charting length can help record labels and artists determine when to release new tracks(CD)<BR>\n",
        "* **advertising sales/subscribers** - lengthy-charted songs will play an important role in keeping audiences engaged and active, in both traditional broadcast radio and streaming services. Audiences tune in to hear and download their favorite songs as well has hear new music. But it's the familiarity with the old that keeps a listener engaged and coming back. And it's this \"audience stickiness\" that advertisers want to reach and pay money for.(M, AD, REV)\n",
        "* **audience development** - lengthy-charted songs contribute to audience development in terms of radio clock programming, advertising sales, and even niche programming when tailoring a station's format or streaming services channels to long-charted popular hits (Sirius XM Satellite radio has niche channels like all hits from Pearl Jam, The Grateful Dead, and Dave Matthews Band). The goal of audience dvelopment is to build a relationship with listeners, gain new listeners, and develop loyal listeners. Lengthy-charted songs can help ensure that listeners find joy, comfort, and familiarity in the music and keep coming back for more. (AD)\n",
        "* **sales:** - Continual declining sales of physical music media and digital music media since 2003 --> Don't invest in production or sales of physical media, or digital file purchases. Instead, consider investing in marketing/advertising on streaming media.<br>\n",
        "\n",
        "sales data sources: <br>\n",
        " https://www.statista.com/statistics/239276/growth-of-the-global-music-revenue-by-type/#:~:text=Digital%20music%20streaming%20has%20rapidly,over%20half%20of%20all%20revenue\n",
        "\n",
        " https://www.toptal.com/finance/market-research-analysts/state-of-music-industry\n",
        "\n",
        " https://soundcharts.com/blog/us-music-market-overview#streaming-is-king\n",
        "\n",
        " RIAA data: https://www.riaa.com/u-s-sales-database/\n",
        "\n",
        " https://www.engadget.com/riaa-music-revenue-report-2020-204907874.html\n",
        "\n",
        "RIAA end of year 2020 report:\n",
        " https://www.riaa.com/wp-content/uploads/2021/02/2020-Year-End-Music-Industry-Revenue-Report.pdf\n",
        "\n",
        " RIAA end of year 2021: https://www.riaa.com/wp-content/uploads/2022/03/2021-Year-End-Music-Industry-Revenue-Report.pdf\n",
        "\n",
        "\n",
        "digital media changes how songs break: https://www.wired.com/2017/05/secret-hit-making-power-spotify-playlist/\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Px7xXNqNt-9W"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**FURTHER RESEARCH SUGGESTIONS:**"
      ],
      "metadata": {
        "id": "REfkJhPAWywy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "My next suggestions would be to examine the data with predictive modelling to determine if there are patterns or correlations between previous lengths of time on the charts and future potential hit songs. Can current long-charting songs predict an estimated time-on-chart of future hits? "
      ],
      "metadata": {
        "id": "_oo6NQtezQLd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I would also like to dig deeper into what happens around a breakpoint of weeks 20-22 for both pre-1999 and post-1999. This period seems to be a turning point for both data sets and I'd like to see if there's something in the data that can help define this, or if this an external influence (outside the data)."
      ],
      "metadata": {
        "id": "adRUlFB_5sgO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I would also explore the music styles of pre-1999 and post-1999 to see if genres (especially new genres) influenced time-on-charts. Did songs/artists with more hip-hop sound, or more electronic sound, produce longer-charting hits? Was there a pattern or correlation in the style of music (based on artist) that play a role in time-on-charts?\n"
      ],
      "metadata": {
        "id": "yqTHqeycAf4X"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8RASJ3SQW6Dg"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}