lareynolds/univariate_analysis.ipynb Secret

## univariate_analysis.ipynb
{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/lareynolds/3d39c96f78f12c38f99e5d5c7cff4170/univariate_analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Univariate analysis is a crucial step in data analysis that focuses on examining and summarizing the characteristics of a single variable or attribute from the dataset. Univariate analysis provides a foundation for understanding the characteristics of individual variables, which is essential for more advanced multivariate analyses and modeling. It helps identify patterns, outliers, and potential data quality issues, making it a crucial step in the data analysis pipeline."
      ],
      "metadata": {
        "id": "lkAUT7eoAkB_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6q8Q_fbsY3t4"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "zPSz1j0A0bDb"
      },
      "outputs": [],
      "source": [
        "# import and store the dataset\n",
        "qbs = pd.read_excel(\"https://myordinaryjourney.com/wp-content/uploads/2023/09/cleaned_qbs.xlsx\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcWZU1bW0bDb",
        "outputId": "96c0d27c-6d7e-4ce6-a667-3577e2047ec6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Player  Total  2009  2010  2011  2012  2013  2014  2015  2016  ...  \\\n",
            "0   A.Dalton     25     0     0     4     1     1     5     0     1  ...   \n",
            "1     A.Luck     17     0     0     0     5     3     4     1     1  ...   \n",
            "2  A.Rodgers     41     3     3     3     5     2     2     5     4  ...   \n",
            "3    A.Smith     23     1     1     1     1     1     6     4     1  ...   \n",
            "4  B.Bortles     12     0     0     0     0     0     1     2     1  ...   \n",
            "\n",
            "   2023  Games  Per Game  Attempts  Per 100 Att  Sacked  Per Sack  \\\n",
            "0     0    170      0.15      5557         0.45     361     0.069   \n",
            "1     0     94      0.18      3620         0.47     186     0.091   \n",
            "2     0    228      0.18      7840         0.52     542     0.076   \n",
            "3     0    149      0.15      4648         0.49     367     0.063   \n",
            "4     0     79      0.15      2719         0.44     201     0.060   \n",
            "\n",
            "   Sack Per Att  Third Down %  qboc  \n",
            "0         0.065         40.00     0  \n",
            "1         0.051         29.41     0  \n",
            "2         0.069         39.02     0  \n",
            "3         0.079         26.09     0  \n",
            "4         0.074         33.33     0  \n",
            "\n",
            "[5 rows x 26 columns]\n"
          ]
        }
      ],
      "source": [
        "print(qbs.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we should look to see what the variable datatypes are and if there are any null or missing values."
      ],
      "metadata": {
        "id": "Iu8j7p0JBDm9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qbs.info(verbose=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GmRnEOKW5g5J",
        "outputId": "aa677e32-a8a9-47e9-ec25-3d16694978c7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 66 entries, 0 to 65\n",
            "Data columns (total 26 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Player        66 non-null     object \n",
            " 1   Total         66 non-null     int64  \n",
            " 2   2009          66 non-null     int64  \n",
            " 3   2010          66 non-null     int64  \n",
            " 4   2011          66 non-null     int64  \n",
            " 5   2012          66 non-null     int64  \n",
            " 6   2013          66 non-null     int64  \n",
            " 7   2014          66 non-null     int64  \n",
            " 8   2015          66 non-null     int64  \n",
            " 9   2016          66 non-null     int64  \n",
            " 10  2017          66 non-null     int64  \n",
            " 11  2018          66 non-null     int64  \n",
            " 12  2019          66 non-null     int64  \n",
            " 13  2020          66 non-null     int64  \n",
            " 14  2021          66 non-null     int64  \n",
            " 15  2022          66 non-null     int64  \n",
            " 16  2023          66 non-null     int64  \n",
            " 17  Games         66 non-null     int64  \n",
            " 18  Per Game      66 non-null     float64\n",
            " 19  Attempts      66 non-null     int64  \n",
            " 20  Per 100 Att   66 non-null     float64\n",
            " 21  Sacked        66 non-null     int64  \n",
            " 22  Per Sack      66 non-null     float64\n",
            " 23  Sack Per Att  66 non-null     float64\n",
            " 24  Third Down %  66 non-null     float64\n",
            " 25  qboc          66 non-null     int64  \n",
            "dtypes: float64(5), int64(20), object(1)\n",
            "memory usage: 13.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7WRzEgbA0bDc"
      },
      "source": [
        "The variables I will examine more closely are the total number of Roughing the Passer (RTP) calls per quarterback, the number of RTP calls per year for each quarterback, and the number of RTP calls per games played for their distribution, central tendency, and variance. I will be using Python and Jupyter Notebooks."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "2VhmnLVc0bDc"
      },
      "outputs": [],
      "source": [
        "table_stats = qbs.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6V1UsslH0bDd",
        "outputId": "13c723b0-38e9-4768-db7e-bd9b9cd95aea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "           Total       2009       2010       2011       2012       2013  \\\n",
            "count  66.000000  66.000000  66.000000  66.000000  66.000000  66.000000   \n",
            "mean   18.090909   0.696970   0.833333   1.121212   1.257576   1.075758   \n",
            "std    12.505663   1.176301   1.452672   1.767355   1.774266   1.825550   \n",
            "min     2.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
            "25%     8.250000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
            "50%    15.500000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
            "75%    24.750000   1.000000   1.000000   2.000000   2.000000   1.000000   \n",
            "max    57.000000   5.000000   6.000000   8.000000   6.000000   8.000000   \n",
            "\n",
            "            2014       2015       2016      2017  ...       2023       Games  \\\n",
            "count  66.000000  66.000000  66.000000  66.00000  ...  66.000000   66.000000   \n",
            "mean    1.333333   1.454545   1.287879   1.30303  ...   0.030303   99.530303   \n",
            "std     1.842518   1.832878   1.698553   1.75385  ...   0.172733   53.915952   \n",
            "min     0.000000   0.000000   0.000000   0.00000  ...   0.000000   42.000000   \n",
            "25%     0.000000   0.000000   0.000000   0.00000  ...   0.000000   61.250000   \n",
            "50%     0.500000   1.000000   1.000000   0.00000  ...   0.000000   77.500000   \n",
            "75%     2.000000   2.000000   2.000000   2.00000  ...   0.000000  138.000000   \n",
            "max     7.000000   7.000000   6.000000   7.00000  ...   1.000000  253.000000   \n",
            "\n",
            "        Per Game     Attempts  Per 100 Att      Sacked   Per Sack  \\\n",
            "count  66.000000    66.000000    66.000000   66.000000  66.000000   \n",
            "mean    0.176818  3250.303030     0.573636  211.212121   0.083424   \n",
            "std     0.073801  2085.250348     0.241951  117.910594   0.034212   \n",
            "min     0.050000   289.000000     0.130000   26.000000   0.024000   \n",
            "25%     0.112500  1794.500000     0.390000  131.500000   0.058500   \n",
            "50%     0.165000  2315.500000     0.550000  170.000000   0.078000   \n",
            "75%     0.220000  4476.000000     0.740000  264.500000   0.107250   \n",
            "max     0.350000  9725.000000     1.240000  542.000000   0.202000   \n",
            "\n",
            "       Sack Per Att  Third Down %       qboc  \n",
            "count     66.000000     66.000000  66.000000  \n",
            "mean       0.070091     31.143788   0.272727  \n",
            "std        0.016449     13.872813   0.448775  \n",
            "min        0.030000      0.000000   0.000000  \n",
            "25%        0.058000     25.000000   0.000000  \n",
            "50%        0.069000     30.770000   0.000000  \n",
            "75%        0.083500     39.755000   1.000000  \n",
            "max        0.103000     77.780000   1.000000  \n",
            "\n",
            "[8 rows x 25 columns]\n"
          ]
        }
      ],
      "source": [
        "print(table_stats)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Well, that was easy. Using the [.describe()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.describe.html) function allows for the examination of the data set's values for central tendency (mean in this case) and the spread (standard deviation) and dispersion (technically). Although all the information is present to observe the dispersion of the information, it may hard to conceptualize the shape without using a visualization help. [Histograms](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html) can aid in our observations."
      ],
      "metadata": {
        "id": "Frw-BVGn1gvp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qbs.hist(figsize=(10, 15))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0J7OCQUu4jG8",
        "outputId": "15aea38c-d962-4bf2-c498-c9b01c14a47b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<Axes: title={'center': 'Total'}>,\n",
              "        <Axes: title={'center': '2009'}>,\n",
              "        <Axes: title={'center': '2010'}>,\n",
              "        <Axes: title={'center': '2011'}>,\n",
              "        <Axes: title={'center': '2012'}>],\n",
              "       [<Axes: title={'center': '2013'}>,\n",
              "        <Axes: title={'center': '2014'}>,\n",
              "        <Axes: title={'center': '2015'}>,\n",
              "        <Axes: title={'center': '2016'}>,\n",
              "        <Axes: title={'center': '2017'}>],\n",
              "       [<Axes: title={'center': '2018'}>,\n",
              "        <Axes: title={'center': '2019'}>,\n",
              "        <Axes: title={'center': '2020'}>,\n",
              "        <Axes: title={'center': '2021'}>,\n",
              "        <Axes: title={'center': '2022'}>],\n",
              "       [<Axes: title={'center': '2023'}>,\n",
              "        <Axes: title={'center': 'Games'}>,\n",
              "        <Axes: title={'center': 'Per Game'}>,\n",
              "        <Axes: title={'center': 'Attempts'}>,\n",
              "        <Axes: title={'center': 'Per 100 Att'}>],\n",
              "       [<Axes: title={'center': 'Sacked'}>,\n",
              "        <Axes: title={'center': 'Per Sack'}>,\n",
              "        <Axes: title={'center': 'Sack Per Att'}>,\n",
              "        <Axes: title={'center': 'Third Down %'}>,\n",
              "        <Axes: title={'center': 'qboc'}>]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1500 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adjust the figure size to be able to view all the histogram outputs more clearly. The distributions for Per Game and Per 100 Attempts look nearly normal, so that will allow us to use some parametric tests for analysis.\n",
        "\n",
        "Another visual that is helpful to see the distribution is a [boxplot](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.boxplot.html) (box-and whisker plot). This time let us just look at just the Per Game and Per 100 Attempts."
      ],
      "metadata": {
        "id": "fL8a5SGl9-Hv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qbs.boxplot(column=['Per Game', 'Per 100 Att'], figsize=(10,10))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "3QDP95n29uzQ",
        "outputId": "acb84eb9-2ac1-4534-c2aa-b3280388428f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The wonderful thing about boxplots is that they make it easy to identify outliers - observations that are outside the whiskers (either top or bottom). You can also easily see the centrality and spread of the data.  \n",
        "\n",
        "One additional variable that we should look at is the 'qboc'. Did you notice that there was an interesting distribution when the histograms plotted above? Let's take a closer look.  "
      ],
      "metadata": {
        "id": "HIMAGI4C3bpW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "qbs['qboc'].hist(figsize=(10, 15))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Xb90Abc9CtY7",
        "outputId": "dc5a1448-f13c-48d3-89bc-966adbe582ef"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As you can see there are only two values for this variable. And this makes sense since we are categorizing the quarterbacks based on if they are (1) or are not (0) a quarterback of color. I am going to do a bit of foreshadowing, but this means that if we wanted to do any sort of predictive analysis, we need to think about some additional models beyond regular linear regression, logistic regression to be specific... but that is a post for another day. For now, I have identified the variables we can use for some multivariate analysis:\n",
        "\n",
        "*   Per Game\n",
        "*   Per 100 Attempts\n",
        "*   qboc"
      ],
      "metadata": {
        "id": "JOA1F1y15tM5"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}