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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-0aW8_526nAN"
+      },
+      "source": [
+        "# Inferential statistics\n",
+        "## Part I - Data Cleaning\n",
+        "\n",
+        "Your family is very passionate about basketball. You always have discussions over players, games, statistics and whatnot. As you can imagine those discussions never reach a conclusion since everyone is simply sharing their opinion with no statistics to back them up!\n",
+        "\n",
+        "![](../images/basket.jpg)\n",
+        "\n",
+        "Since you are attending a data analysis bootcamp you'd like to take advantage of your newfound knowledge to finally put an end to your family's discussions.\n",
+        "\n",
+        "Luckily we have found a dataset containing data related to the players of the WNBA for the 2016-2017 season that we can use.\n",
+        "\n",
+        "Let's start with cleaning the data and then we'll continue with a general exploratory analysis and some inferential statistics.\n",
+        "\n",
+        "### Dataset\n",
+        "\n",
+        "The dataset we will be using contains the statistics from the WNBA players for the 2016-2017 season. You will be able to find more information on the dataset in the [codebook](../data/codebook.md) uploaded to the repository.\n",
+        "\n",
+        "### Libraries\n",
+        "\n",
+        "First we'll import the necessary libraries first and increase the maximum number of displayed columns so you will be able to see all the dataset in the same window."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 29,
+      "metadata": {
+        "id": "r9T_fOPa6nAQ"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "pd.set_option('display.max_columns', 100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iIxcvuNz6nAS"
+      },
+      "source": [
+        "### Load the dataset\n",
+        "\n",
+        "Load the dataset into a df called `wnba` and take an initial look at it using the `head()` method."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yPrvj9F46nAS"
+      },
+      "outputs": [],
+      "source": [
+        "wnba = pd.read_csv(\"/content/wnba.csv\")\n",
+        "wnba.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(f\"The current shape of our dataframe is {wnba.shape}\")"
+      ],
+      "metadata": {
+        "id": "zu8XCg9g8ct9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "77bfcac1-2c91-4fbc-ea7e-4a1ca984e7e9"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The current shape of our dataframe is (143, 32)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8kNFHCIJ6nAT"
+      },
+      "source": [
+        "### Check NaN values\n",
+        "As you know, one of our first steps is to check if there are any NaN values in the dataset to find any issues. Look for the columns that cointain NaN values and count how many rows there are with that value."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dTYYJtiu6nAT"
+      },
+      "outputs": [],
+      "source": [
+        "wnba.isnull().sum()\n",
+        "# only 2 null values, probably same row because if we miss the weight, off course you won't have BMI. Still, I wouldn´t drop the row,\n",
+        "# because you still can have access to all gaming statitics/features of the athelet."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GunQuifz6nAU"
+      },
+      "source": [
+        "We can see that there are only two NaNs in the whole dataset, one in the Weight column and one in the BMI one. Let's look at the actual rows that contain the NaN values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "T-NicFw16nAU"
+      },
+      "outputs": [],
+      "source": [
+        "nan_row = wnba[wnba[\"Weight\"].isnull() & wnba[\"BMI\"].isnull()]\n",
+        "nan_row\n",
+        "# exactly what I had in mind"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SQWsKMUg6nAV"
+      },
+      "source": [
+        "It looks like there is only a single row that has NaN values in it, which is good! Just in case, let's check how much removing a single row may influence our dataset by calculating the percentage of values we will be removing."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "B1FE8WJU6nAV"
+      },
+      "outputs": [],
+      "source": [
+        "shape_original_values = wnba.shape\n",
+        "print(\"Number of rows and columns is:\", wnba.shape)\n",
+        "original_values = wnba.size\n",
+        "print(\"Size - total entries - is:\", original_values)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# so, if I remove 1 row from 143:\n",
+        "one_row_data_percentage = round((1/143) * 100, 3)\n",
+        "print(\" I will be removing\",one_row_data_percentage,\"%\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KfQKPxkK_X_G",
+        "outputId": "01463a5e-a346-4558-ff7f-2264797f0234"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " I will be removing 0.699 %\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# so, if I remove 2 entries from 4576:\n",
+        "two_entries_data_percentage = round((2/4576) * 100, 3)\n",
+        "print(\" I will be removing\",two_entries_data_percentage,\"%\")"
+      ],
+      "metadata": {
+        "id": "CtKdXtBc_071",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b0c95ecc-ec5b-4170-ade4-5185a4d5ee62"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            " I will be removing 0.044 %\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fHGaOpkU6nAV"
+      },
+      "source": [
+        "It is very important to be as careful as possible when dealing with NaN values and only drop data when it is strictly necessary. This decision can also be influenced by the nature of our analysis. If, for example, our analysis will not require the Weight and BMI of the players at all we can simply keep the row, given that the NaN values are only present in the Weight and BMI column.\n",
+        "\n",
+        "In this specific example, let's say our decision is to drop it. Write some code to drop the NaN values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "U6EFcpvg6nAW",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 634
+        },
+        "outputId": "a26494d7-b08c-4305-fb1c-82d92be15bad"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                 Name Team  Pos  Height  Weight        BMI Birth_Place  \\\n",
+              "0       Aerial Powers  DAL    F     183    71.0  21.200991          US   \n",
+              "1         Alana Beard   LA  G/F     185    73.0  21.329438          US   \n",
+              "2        Alex Bentley  CON    G     170    69.0  23.875433          US   \n",
+              "3     Alex Montgomery  SAN  G/F     185    84.0  24.543462          US   \n",
+              "4        Alexis Jones  MIN    G     175    78.0  25.469388          US   \n",
+              "..                ...  ...  ...     ...     ...        ...         ...   \n",
+              "138     Tiffany Hayes  ATL    G     178    70.0  22.093170          US   \n",
+              "139   Tiffany Jackson   LA    F     191    84.0  23.025685          US   \n",
+              "140  Tiffany Mitchell  IND    G     175    69.0  22.530612          US   \n",
+              "141      Tina Charles   NY  F/C     193    84.0  22.550941          US   \n",
+              "142     Yvonne Turner  PHO    G     175    59.0  19.265306          US   \n",
+              "\n",
+              "              Birthdate  Age         College Experience  Games Played  MIN  \\\n",
+              "0      January 17, 1994   23  Michigan State          2             8  173   \n",
+              "1          May 14, 1982   35            Duke         12            30  947   \n",
+              "2      October 27, 1990   26      Penn State          4            26  617   \n",
+              "3     December 11, 1988   28    Georgia Tech          6            31  721   \n",
+              "4        August 5, 1994   23          Baylor          R            24  137   \n",
+              "..                  ...  ...             ...        ...           ...  ...   \n",
+              "138  September 20, 1989   27     Connecticut          6            29  861   \n",
+              "139      April 26, 1985   32           Texas          9            22  127   \n",
+              "140  September 23, 1984   32  South Carolina          2            27  671   \n",
+              "141        May 12, 1988   29     Connecticut          8            29  952   \n",
+              "142    October 13, 1987   29        Nebraska          2            30  356   \n",
+              "\n",
+              "     FGM  FGA   FG%  3PM  3PA   3P%  FTM  FTA   FT%  OREB  DREB  REB  AST  \\\n",
+              "0     30   85  35.3   12   32  37.5   21   26  80.8     6    22   28   12   \n",
+              "1     90  177  50.8    5   18  27.8   32   41  78.0    19    82  101   72   \n",
+              "2     82  218  37.6   19   64  29.7   35   42  83.3     4    36   40   78   \n",
+              "3     75  195  38.5   21   68  30.9   17   21  81.0    35   134  169   65   \n",
+              "4     16   50  32.0    7   20  35.0   11   12  91.7     3     9   12   12   \n",
+              "..   ...  ...   ...  ...  ...   ...  ...  ...   ...   ...   ...  ...  ...   \n",
+              "138  144  331  43.5   43  112  38.4  136  161  84.5    28    89  117   69   \n",
+              "139   12   25  48.0    0    1   0.0    4    6  66.7     5    18   23    3   \n",
+              "140   83  238  34.9   17   69  24.6   94  102  92.2    16    70   86   39   \n",
+              "141  227  509  44.6   18   56  32.1  110  135  81.5    56   212  268   75   \n",
+              "142   59  140  42.1   11   47  23.4   22   28  78.6    11    13   24   30   \n",
+              "\n",
+              "     STL  BLK  TO  PTS  DD2  TD3  \n",
+              "0      3    6  12   93    0    0  \n",
+              "1     63   13  40  217    0    0  \n",
+              "2     22    3  24  218    0    0  \n",
+              "3     20   10  38  188    2    0  \n",
+              "4      7    0  14   50    0    0  \n",
+              "..   ...  ...  ..  ...  ...  ...  \n",
+              "138   37    8  50  467    0    0  \n",
+              "139    1    3   8   28    0    0  \n",
+              "140   31    5  40  277    0    0  \n",
+              "141   21   22  71  582   11    0  \n",
+              "142   18    1  32  151    0    0  \n",
+              "\n",
+              "[142 rows x 32 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6335dd9d-da18-4c9d-93ae-1a408bf834ac\" 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",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Team</th>\n",
+              "      <th>Pos</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>Birth_Place</th>\n",
+              "      <th>Birthdate</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>College</th>\n",
+              "      <th>Experience</th>\n",
+              "      <th>Games Played</th>\n",
+              "      <th>MIN</th>\n",
+              "      <th>FGM</th>\n",
+              "      <th>FGA</th>\n",
+              "      <th>FG%</th>\n",
+              "      <th>3PM</th>\n",
+              "      <th>3PA</th>\n",
+              "      <th>3P%</th>\n",
+              "      <th>FTM</th>\n",
+              "      <th>FTA</th>\n",
+              "      <th>FT%</th>\n",
+              "      <th>OREB</th>\n",
+              "      <th>DREB</th>\n",
+              "      <th>REB</th>\n",
+              "      <th>AST</th>\n",
+              "      <th>STL</th>\n",
+              "      <th>BLK</th>\n",
+              "      <th>TO</th>\n",
+              "      <th>PTS</th>\n",
+              "      <th>DD2</th>\n",
+              "      <th>TD3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Aerial Powers</td>\n",
+              "      <td>DAL</td>\n",
+              "      <td>F</td>\n",
+              "      <td>183</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>21.200991</td>\n",
+              "      <td>US</td>\n",
+              "      <td>January 17, 1994</td>\n",
+              "      <td>23</td>\n",
+              "      <td>Michigan State</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>173</td>\n",
+              "      <td>30</td>\n",
+              "      <td>85</td>\n",
+              "      <td>35.3</td>\n",
+              "      <td>12</td>\n",
+              "      <td>32</td>\n",
+              "      <td>37.5</td>\n",
+              "      <td>21</td>\n",
+              "      <td>26</td>\n",
+              "      <td>80.8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>22</td>\n",
+              "      <td>28</td>\n",
+              "      <td>12</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>12</td>\n",
+              "      <td>93</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Alana Beard</td>\n",
+              "      <td>LA</td>\n",
+              "      <td>G/F</td>\n",
+              "      <td>185</td>\n",
+              "      <td>73.0</td>\n",
+              "      <td>21.329438</td>\n",
+              "      <td>US</td>\n",
+              "      <td>May 14, 1982</td>\n",
+              "      <td>35</td>\n",
+              "      <td>Duke</td>\n",
+              "      <td>12</td>\n",
+              "      <td>30</td>\n",
+              "      <td>947</td>\n",
+              "      <td>90</td>\n",
+              "      <td>177</td>\n",
+              "      <td>50.8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>18</td>\n",
+              "      <td>27.8</td>\n",
+              "      <td>32</td>\n",
+              "      <td>41</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>19</td>\n",
+              "      <td>82</td>\n",
+              "      <td>101</td>\n",
+              "      <td>72</td>\n",
+              "      <td>63</td>\n",
+              "      <td>13</td>\n",
+              "      <td>40</td>\n",
+              "      <td>217</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Alex Bentley</td>\n",
+              "      <td>CON</td>\n",
+              "      <td>G</td>\n",
+              "      <td>170</td>\n",
+              "      <td>69.0</td>\n",
+              "      <td>23.875433</td>\n",
+              "      <td>US</td>\n",
+              "      <td>October 27, 1990</td>\n",
+              "      <td>26</td>\n",
+              "      <td>Penn State</td>\n",
+              "      <td>4</td>\n",
+              "      <td>26</td>\n",
+              "      <td>617</td>\n",
+              "      <td>82</td>\n",
+              "      <td>218</td>\n",
+              "      <td>37.6</td>\n",
+              "      <td>19</td>\n",
+              "      <td>64</td>\n",
+              "      <td>29.7</td>\n",
+              "      <td>35</td>\n",
+              "      <td>42</td>\n",
+              "      <td>83.3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>36</td>\n",
+              "      <td>40</td>\n",
+              "      <td>78</td>\n",
+              "      <td>22</td>\n",
+              "      <td>3</td>\n",
+              "      <td>24</td>\n",
+              "      <td>218</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Alex Montgomery</td>\n",
+              "      <td>SAN</td>\n",
+              "      <td>G/F</td>\n",
+              "      <td>185</td>\n",
+              "      <td>84.0</td>\n",
+              "      <td>24.543462</td>\n",
+              "      <td>US</td>\n",
+              "      <td>December 11, 1988</td>\n",
+              "      <td>28</td>\n",
+              "      <td>Georgia Tech</td>\n",
+              "      <td>6</td>\n",
+              "      <td>31</td>\n",
+              "      <td>721</td>\n",
+              "      <td>75</td>\n",
+              "      <td>195</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>21</td>\n",
+              "      <td>68</td>\n",
+              "      <td>30.9</td>\n",
+              "      <td>17</td>\n",
+              "      <td>21</td>\n",
+              "      <td>81.0</td>\n",
+              "      <td>35</td>\n",
+              "      <td>134</td>\n",
+              "      <td>169</td>\n",
+              "      <td>65</td>\n",
+              "      <td>20</td>\n",
+              "      <td>10</td>\n",
+              "      <td>38</td>\n",
+              "      <td>188</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Alexis Jones</td>\n",
+              "      <td>MIN</td>\n",
+              "      <td>G</td>\n",
+              "      <td>175</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>25.469388</td>\n",
+              "      <td>US</td>\n",
+              "      <td>August 5, 1994</td>\n",
+              "      <td>23</td>\n",
+              "      <td>Baylor</td>\n",
+              "      <td>R</td>\n",
+              "      <td>24</td>\n",
+              "      <td>137</td>\n",
+              "      <td>16</td>\n",
+              "      <td>50</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>20</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>11</td>\n",
+              "      <td>12</td>\n",
+              "      <td>91.7</td>\n",
+              "      <td>3</td>\n",
+              "      <td>9</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>14</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>138</th>\n",
+              "      <td>Tiffany Hayes</td>\n",
+              "      <td>ATL</td>\n",
+              "      <td>G</td>\n",
+              "      <td>178</td>\n",
+              "      <td>70.0</td>\n",
+              "      <td>22.093170</td>\n",
+              "      <td>US</td>\n",
+              "      <td>September 20, 1989</td>\n",
+              "      <td>27</td>\n",
+              "      <td>Connecticut</td>\n",
+              "      <td>6</td>\n",
+              "      <td>29</td>\n",
+              "      <td>861</td>\n",
+              "      <td>144</td>\n",
+              "      <td>331</td>\n",
+              "      <td>43.5</td>\n",
+              "      <td>43</td>\n",
+              "      <td>112</td>\n",
+              "      <td>38.4</td>\n",
+              "      <td>136</td>\n",
+              "      <td>161</td>\n",
+              "      <td>84.5</td>\n",
+              "      <td>28</td>\n",
+              "      <td>89</td>\n",
+              "      <td>117</td>\n",
+              "      <td>69</td>\n",
+              "      <td>37</td>\n",
+              "      <td>8</td>\n",
+              "      <td>50</td>\n",
+              "      <td>467</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>139</th>\n",
+              "      <td>Tiffany Jackson</td>\n",
+              "      <td>LA</td>\n",
+              "      <td>F</td>\n",
+              "      <td>191</td>\n",
+              "      <td>84.0</td>\n",
+              "      <td>23.025685</td>\n",
+              "      <td>US</td>\n",
+              "      <td>April 26, 1985</td>\n",
+              "      <td>32</td>\n",
+              "      <td>Texas</td>\n",
+              "      <td>9</td>\n",
+              "      <td>22</td>\n",
+              "      <td>127</td>\n",
+              "      <td>12</td>\n",
+              "      <td>25</td>\n",
+              "      <td>48.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>4</td>\n",
+              "      <td>6</td>\n",
+              "      <td>66.7</td>\n",
+              "      <td>5</td>\n",
+              "      <td>18</td>\n",
+              "      <td>23</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>8</td>\n",
+              "      <td>28</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>140</th>\n",
+              "      <td>Tiffany Mitchell</td>\n",
+              "      <td>IND</td>\n",
+              "      <td>G</td>\n",
+              "      <td>175</td>\n",
+              "      <td>69.0</td>\n",
+              "      <td>22.530612</td>\n",
+              "      <td>US</td>\n",
+              "      <td>September 23, 1984</td>\n",
+              "      <td>32</td>\n",
+              "      <td>South Carolina</td>\n",
+              "      <td>2</td>\n",
+              "      <td>27</td>\n",
+              "      <td>671</td>\n",
+              "      <td>83</td>\n",
+              "      <td>238</td>\n",
+              "      <td>34.9</td>\n",
+              "      <td>17</td>\n",
+              "      <td>69</td>\n",
+              "      <td>24.6</td>\n",
+              "      <td>94</td>\n",
+              "      <td>102</td>\n",
+              "      <td>92.2</td>\n",
+              "      <td>16</td>\n",
+              "      <td>70</td>\n",
+              "      <td>86</td>\n",
+              "      <td>39</td>\n",
+              "      <td>31</td>\n",
+              "      <td>5</td>\n",
+              "      <td>40</td>\n",
+              "      <td>277</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>141</th>\n",
+              "      <td>Tina Charles</td>\n",
+              "      <td>NY</td>\n",
+              "      <td>F/C</td>\n",
+              "      <td>193</td>\n",
+              "      <td>84.0</td>\n",
+              "      <td>22.550941</td>\n",
+              "      <td>US</td>\n",
+              "      <td>May 12, 1988</td>\n",
+              "      <td>29</td>\n",
+              "      <td>Connecticut</td>\n",
+              "      <td>8</td>\n",
+              "      <td>29</td>\n",
+              "      <td>952</td>\n",
+              "      <td>227</td>\n",
+              "      <td>509</td>\n",
+              "      <td>44.6</td>\n",
+              "      <td>18</td>\n",
+              "      <td>56</td>\n",
+              "      <td>32.1</td>\n",
+              "      <td>110</td>\n",
+              "      <td>135</td>\n",
+              "      <td>81.5</td>\n",
+              "      <td>56</td>\n",
+              "      <td>212</td>\n",
+              "      <td>268</td>\n",
+              "      <td>75</td>\n",
+              "      <td>21</td>\n",
+              "      <td>22</td>\n",
+              "      <td>71</td>\n",
+              "      <td>582</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>142</th>\n",
+              "      <td>Yvonne Turner</td>\n",
+              "      <td>PHO</td>\n",
+              "      <td>G</td>\n",
+              "      <td>175</td>\n",
+              "      <td>59.0</td>\n",
+              "      <td>19.265306</td>\n",
+              "      <td>US</td>\n",
+              "      <td>October 13, 1987</td>\n",
+              "      <td>29</td>\n",
+              "      <td>Nebraska</td>\n",
+              "      <td>2</td>\n",
+              "      <td>30</td>\n",
+              "      <td>356</td>\n",
+              "      <td>59</td>\n",
+              "      <td>140</td>\n",
+              "      <td>42.1</td>\n",
+              "      <td>11</td>\n",
+              "      <td>47</td>\n",
+              "      <td>23.4</td>\n",
+              "      <td>22</td>\n",
+              "      <td>28</td>\n",
+              "      <td>78.6</td>\n",
+              "      <td>11</td>\n",
+              "      <td>13</td>\n",
+              "      <td>24</td>\n",
+              "      <td>30</td>\n",
+              "      <td>18</td>\n",
+              "      <td>1</td>\n",
+              "      <td>32</td>\n",
+              "      <td>151</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>142 rows × 32 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6335dd9d-da18-4c9d-93ae-1a408bf834ac')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-6335dd9d-da18-4c9d-93ae-1a408bf834ac 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-6335dd9d-da18-4c9d-93ae-1a408bf834ac');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-b3e6ccd9-b833-4ab7-9b4d-059f6494dd96\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b3e6ccd9-b833-4ab7-9b4d-059f6494dd96')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-b3e6ccd9-b833-4ab7-9b4d-059f6494dd96 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ],
+      "source": [
+        "#I wouldn't drop the row! but still, I do what I am told:\n",
+        "\n",
+        "wnba_cleaned = wnba.dropna(subset=[\"Weight\", \"BMI\"])\n",
+        "wnba_cleaned"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7vDOJFcr6nAW"
+      },
+      "source": [
+        "**Do you think it is a good decision? Think about a case in which you wouldn't want to drop the value.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "OG3lzSul6nAW",
+        "outputId": "2af29df9-45a2-4767-90f1-26aaceeeb9c2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "We just took out an entire row, which means 32 entries\n"
+          ]
+        }
+      ],
+      "source": [
+        "cleaned_values = original_values - wnba_cleaned.size\n",
+        "print(\"We just took out an entire row, which means\", cleaned_values,\"entries\")\n",
+        "# I wouldn't have drop the entire row. could have filled it with the mean weight /BMI\n",
+        "# since they are all athletes, that would not be a huge diferrence."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rMJBC3aj6nAX"
+      },
+      "source": [
+        "### Let's make an overview of the dataset\n",
+        "First, check the data types of our data:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "R9WInXQd6nAX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "57c31bb6-b5b5-4262-d4de-e9a725cc04cf"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Name             object\n",
+              "Team             object\n",
+              "Pos              object\n",
+              "Height            int64\n",
+              "Weight          float64\n",
+              "BMI             float64\n",
+              "Birth_Place      object\n",
+              "Birthdate        object\n",
+              "Age               int64\n",
+              "College          object\n",
+              "Experience       object\n",
+              "Games Played      int64\n",
+              "MIN               int64\n",
+              "FGM               int64\n",
+              "FGA               int64\n",
+              "FG%             float64\n",
+              "3PM               int64\n",
+              "3PA               int64\n",
+              "3P%             float64\n",
+              "FTM               int64\n",
+              "FTA               int64\n",
+              "FT%             float64\n",
+              "OREB              int64\n",
+              "DREB              int64\n",
+              "REB               int64\n",
+              "AST               int64\n",
+              "STL               int64\n",
+              "BLK               int64\n",
+              "TO                int64\n",
+              "PTS               int64\n",
+              "DD2               int64\n",
+              "TD3               int64\n",
+              "dtype: object"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ],
+      "source": [
+        "wnba_cleaned.dtypes\n",
+        "# Well i agree that weight can be a float (or should)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K5l-DRE16nAX"
+      },
+      "source": [
+        "It looks like most of the data types are correct. Birthdate column could be casted to a `datetime` type, however, we won't use it in our analysis so for simplicity, let's leave it as an `object`. Weight column could also be casted to an `int64` type as all numbers are integers."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SZdbCE156nAX"
+      },
+      "source": [
+        "**Let's change the type of Weight column for practice.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "id": "Mszr0bNk6nAY",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "663fa3b2-1280-418a-f5c3-6a845a3f285f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-42-5faef37f58fa>:1: SettingWithCopyWarning: \n",
+            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+            "Try using .loc[row_indexer,col_indexer] = value instead\n",
+            "\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  wnba_cleaned[\"Weight\"] = pd.to_numeric(wnba_cleaned[\"Weight\"], errors= \"coerce\") # took this from labs of week 2. Melting.\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Name             object\n",
+              "Team             object\n",
+              "Pos              object\n",
+              "Height            int64\n",
+              "Weight          float64\n",
+              "BMI             float64\n",
+              "Birth_Place      object\n",
+              "Birthdate        object\n",
+              "Age               int64\n",
+              "College          object\n",
+              "Experience       object\n",
+              "Games Played      int64\n",
+              "MIN               int64\n",
+              "FGM               int64\n",
+              "FGA               int64\n",
+              "FG%             float64\n",
+              "3PM               int64\n",
+              "3PA               int64\n",
+              "3P%             float64\n",
+              "FTM               int64\n",
+              "FTA               int64\n",
+              "FT%             float64\n",
+              "OREB              int64\n",
+              "DREB              int64\n",
+              "REB               int64\n",
+              "AST               int64\n",
+              "STL               int64\n",
+              "BLK               int64\n",
+              "TO                int64\n",
+              "PTS               int64\n",
+              "DD2               int64\n",
+              "TD3               int64\n",
+              "dtype: object"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 42
+        }
+      ],
+      "source": [
+        "wnba_cleaned[\"Weight\"] = pd.to_numeric(wnba_cleaned[\"Weight\"], errors= \"coerce\") # took this from labs of week 2. Melting.\n",
+        "wnba_cleaned.dtypes"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_scCBq6w6nAY"
+      },
+      "source": [
+        "**After checking the data types, let's check for outliers using the describe() method.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kluJBvyW6nAY"
+      },
+      "outputs": [],
+      "source": [
+        "wnba_cleaned.describe() # method deprecated."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sIFvmUXq6nAY"
+      },
+      "source": [
+        "**Comment on your result. What do you see?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "wnba_cleaned.describe() # method deprecated, TA permission."
+      ],
+      "metadata": {
+        "id": "7ypyCLOOtRJ8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ctDdR3Lt6nAZ"
+      },
+      "source": [
+        "**Now we can save the cleaned data to a new .csv file called `wnba_clean.csv` in the data folder.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "id": "dunfkarl6nAZ"
+      },
+      "outputs": [],
+      "source": [
+        "wnba_cleaned.to_csv(\"wnba_cleaned.csv\", index = False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "CxISmzULvbZd"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/1_Data_Cleaning_wnba_cleaned.csv b/1_Data_Cleaning_wnba_cleaned.csv
new file mode 100644
index 0000000..ef97f1f
--- /dev/null
+++ b/1_Data_Cleaning_wnba_cleaned.csv
@@ -0,0 +1,143 @@
+Name,Team,Pos,Height,Weight,BMI,Birth_Place,Birthdate,Age,College,Experience,Games Played,MIN,FGM,FGA,FG%,3PM,3PA,3P%,FTM,FTA,FT%,OREB,DREB,REB,AST,STL,BLK,TO,PTS,DD2,TD3
+Aerial Powers,DAL,F,183,71.0,21.20099137,US,"January 17, 1994",23,Michigan State,2,8,173,30,85,35.3,12,32,37.5,21,26,80.8,6,22,28,12,3,6,12,93,0,0
+Alana Beard,LA,G/F,185,73.0,21.32943755,US,"May 14, 1982",35,Duke,12,30,947,90,177,50.8,5,18,27.8,32,41,78.0,19,82,101,72,63,13,40,217,0,0
+Alex Bentley,CON,G,170,69.0,23.87543253,US,"October 27, 1990",26,Penn State,4,26,617,82,218,37.6,19,64,29.7,35,42,83.3,4,36,40,78,22,3,24,218,0,0
+Alex Montgomery,SAN,G/F,185,84.0,24.54346238,US,"December 11, 1988",28,Georgia Tech,6,31,721,75,195,38.5,21,68,30.9,17,21,81.0,35,134,169,65,20,10,38,188,2,0
+Alexis Jones,MIN,G,175,78.0,25.46938776,US,"August 5, 1994",23,Baylor,R,24,137,16,50,32.0,7,20,35.0,11,12,91.7,3,9,12,12,7,0,14,50,0,0
+Alexis Peterson,SEA,G,170,63.0,21.79930796,US,"June 20, 1995",22,Syracuse,R,14,90,9,34,26.5,2,9,22.2,6,6,100.0,3,13,16,11,5,0,11,26,0,0
+Alexis Prince,PHO,G,188,81.0,22.91760978,US,"February 5, 1994",23,Baylor,R,16,112,9,34,26.5,4,15,26.7,2,2,100.0,1,14,15,5,4,3,3,24,0,0
+Allie Quigley,CHI,G,178,64.0,20.19946976,US,"June 20, 1986",31,DePaul,8,26,847,166,319,52.0,70,150,46.7,40,46,87.0,9,83,92,95,20,13,59,442,0,0
+Allisha Gray,DAL,G,185,76.0,22.20598977,US,"October 20, 1992",24,South Carolina,2,30,834,131,346,37.9,29,103,28.2,104,129,80.6,52,75,127,40,47,19,37,395,0,0
+Allison Hightower,WAS,G,178,77.0,24.30248706,US,"June 4, 1988",29,LSU,5,7,103,14,38,36.8,2,11,18.2,6,6,100.0,3,7,10,10,5,0,2,36,0,0
+Alysha Clark,SEA,F,180,76.0,23.45679012,US,"July 7, 1987",30,Middle Tennessee,6,30,843,93,183,50.8,20,62,32.3,38,51,74.5,29,97,126,50,22,4,32,244,0,0
+Alyssa Thomas,CON,F,188,84.0,23.76641014,US,"December 4, 1992",24,Maryland,3,28,833,154,303,50.8,0,3,0.0,91,158,57.6,34,158,192,136,48,11,87,399,4,0
+Amanda Zahui B.,NY,C,196,113.0,29.41482716,SE,"August 9, 1993",24,Minnesota,3,25,133,20,53,37.7,2,8,25.0,9,12,75.0,5,18,23,7,4,5,12,51,0,0
+Amber Harris,CHI,F,196,88.0,22.90712203,US,"January 16, 1988",29,Xavier,3,22,146,18,44,40.9,0,10,0.0,5,8,62.5,12,28,40,5,3,9,6,41,0,0
+Aneika Henry,ATL,F/C,193,87.0,23.35633171,JM,"February 13, 1986",31,Florida,6,4,22,4,4,100.0,0,0,0.0,0,0,0.0,0,4,4,1,2,0,3,8,0,0
+Angel Robinson,PHO,F/C,198,88.0,22.44668911,US,"August 30, 1995",21,Arizona State,1,15,237,25,44,56.8,1,1,100.0,7,7,100.0,16,42,58,8,1,11,16,58,0,0
+Asia Taylor,WAS,F,185,76.0,22.20598977,US,"August 22, 1991",26,Louisville,3,20,128,10,31,32.3,0,0,0.0,11,18,61.1,16,21,37,9,5,2,10,31,0,0
+Bashaara Graves,CHI,F,188,91.0,25.74694432,US,"March 17, 1994",23,Tennessee,1,5,59,8,14,57.1,0,0,0.0,3,4,75.0,4,13,17,3,0,1,3,19,0,0
+Breanna Lewis,DAL,C,196,93.0,24.20866306,US,"June 22, 1994",23,Kansas State,R,12,50,2,12,16.7,0,0,0.0,3,4,75.0,2,7,9,2,0,0,7,7,0,0
+Breanna Stewart,SEA,F/C,193,77.0,20.67169588,US,"August 27, 1994",22,Connecticut,2,29,952,201,417,48.2,46,123,37.4,136,171,79.5,43,206,249,78,29,47,68,584,8,0
+Bria Hartley,NY,G,173,66.0,22.05219018,US,"September 30, 1992",24,Connecticut,4,29,598,80,192,41.7,32,93,34.4,25,33,75.8,7,50,57,58,15,5,44,217,0,0
+Bria Holmes,ATL,G,185,77.0,22.49817385,US,"April 19, 1994",23,West Virginia,R,28,655,85,231,36.8,9,50,18.0,56,84,66.7,29,56,85,52,23,7,31,235,0,0
+Briann January,IND,G,173,65.0,21.71806609,US,"November 1, 1987",29,Arizona State,9,25,657,81,205,39.5,18,57,31.6,58,71,81.7,12,25,37,98,23,4,53,238,0,0
+Brionna Jones,CON,F,191,104.0,28.50799046,US,"December 18, 1995",21,Maryland,R,19,112,14,26,53.8,0,0,0.0,16,19,84.2,11,14,25,2,7,1,7,44,0,0
+Brittany Boyd,NY,G,175,71.0,23.18367347,US,"November 6, 1993",23,UC Berkeley,3,2,32,9,15,60.0,0,1,0.0,8,11,72.7,3,5,8,5,3,0,2,26,0,0
+Brittney Griner,PHO,C,206,93.0,21.91535489,US,"October 18, 1990",26,Baylor,5,22,682,167,293,57.0,0,0,0.0,127,154,82.5,43,129,172,39,13,54,52,461,6,0
+Brittney Sykes,ATL,G,175,66.0,21.55102041,US,"July 2, 1994",23,Rutgers,10,30,734,146,362,40.3,29,87,33.3,76,102,74.5,25,94,119,59,18,17,49,397,1,0
+Camille Little,PHO,F,188,82.0,23.20054323,US,"January 18, 1985",32,North Carolina,11,30,759,93,219,42.5,9,52,17.3,33,52,63.5,42,71,113,42,28,13,50,228,0,0
+Candace Parker,LA,F/C,193,79.0,21.20862305,US,"April 19, 1986",31,Tennessee,10,29,889,183,383,47.8,40,114,35.1,88,115,76.5,37,205,242,127,43,53,80,494,10,1
+Candice Dupree,IND,F,188,81.0,22.91760978,US,"February 25, 1984",33,Temple,12,29,911,189,370,51.1,0,2,0.0,57,65,87.7,31,124,155,47,28,12,42,435,2,0
+Cappie Pondexter,CHI,G,175,73.0,23.83673469,US,"July 1, 1983",34,Rutgers,11,24,676,94,258,36.4,8,32,25.0,54,67,80.6,10,59,69,104,17,5,56,250,2,0
+Carolyn Swords,SEA,C,198,95.0,24.2322212,US,"July 19, 1989",28,Boston College,6,26,218,19,39,48.7,0,0,0.0,16,20,80.0,10,29,39,9,5,4,22,54,0,0
+Cayla George,PHO,C,193,87.0,23.35633171,AU,"April 20, 1987",30,Georgia,1,28,365,40,105,38.1,13,45,28.9,7,12,58.3,10,71,81,15,9,11,13,100,1,0
+Chelsea Gray,LA,G,180,77.0,23.7654321,US,"August 10, 1992",25,Duke,3,30,996,165,326,50.6,48,100,48.0,78,94,83.0,19,80,99,132,29,7,61,456,1,0
+Cheyenne Parker,CHI,F,193,86.0,23.08786813,US,"August 22, 1992",25,Middle Tennessee,2,23,286,32,69,46.4,0,3,0.0,23,36,63.9,31,47,78,13,8,15,21,87,0,0
+Clarissa dos Santos,SAN,C,185,89.0,26.00438276,BR,"October 3, 1988",28,Brazil,4,7,52,8,14,57.1,1,1,100.0,0,0,0.0,3,7,10,7,1,1,5,17,0,0
+Courtney Paris,DAL,C,193,113.0,30.33638487,US,"September 21, 1987",29,Oklahoma,7,16,217,32,57,56.1,0,0,0.0,6,12,50.0,28,34,62,5,6,8,18,70,0,0
+Courtney Vandersloot,CHI,G,173,66.0,22.05219018,US,"August 2, 1989",28,Gonzaga,6,22,673,104,199,52.3,23,60,38.3,24,29,82.8,13,75,88,175,22,5,64,255,10,0
+Courtney Williams,CON,G,173,62.0,20.71569381,US,"November 5, 1994",22,South Florida,1,29,755,168,338,49.7,8,30,26.7,31,36,86.1,38,84,122,60,15,6,39,375,1,0
+Crystal Langhorne,SEA,F/C,188,84.0,23.76641014,US,"October 27, 1986",30,Maryland,10,30,848,160,240,66.7,1,2,50.0,49,68,72.1,35,140,175,46,16,11,50,370,2,0
+Damiris Dantas,ATL,C,191,89.0,24.39626107,BR,"November 17, 1992",24,Brazil,4,30,569,98,243,40.3,25,91,27.5,33,43,76.7,29,84,113,19,17,18,26,254,0,0
+Danielle Adams,CON,F/C,185,108.0,31.5558802,US,"February 19, 1989",28,Texas A&M,5,18,81,16,43,37.2,12,30,40.0,5,5,100.0,6,4,10,4,4,4,7,49,0,0
+Danielle Robinson,PHO,G,175,57.0,18.6122449,US,"October 5, 1989",27,Oklahoma,7,28,680,79,178,44.4,0,5,0.0,51,61,83.6,13,73,86,106,33,4,58,209,0,0
+Dearica Hamby,SAN,F,191,86.0,23.57391519,US,"June 11, 1993",24,Wake Forest,2,31,650,96,207,46.4,3,8,37.5,58,95,61.1,48,91,139,32,29,8,43,253,1,0
+Devereaux Peters,IND,F,188,79.0,22.35174287,US,"August 10, 1989",28,Notre Dame,6,28,796,154,380,40.5,88,225,39.1,118,130,90.8,8,69,77,76,16,9,56,514,0,0
+Diana Taurasi,PHO,G,183,74.0,22.09680791,US,"November 6, 1982",34,Connecticut,13,20,591,121,255,47.5,22,66,33.3,112,118,94.9,31,98,129,32,20,31,28,376,3,0
+Elena Delle Donne,WAS,G/F,196,85.0,22.12619742,US,"May 9, 1989",28,Delaware,5,30,939,133,272,48.9,0,1,0.0,51,78,65.4,99,116,215,43,32,64,36,317,4,0
+Elizabeth Williams,ATL,F/C,191,87.0,23.84803048,US,"June 23, 1993",24,Duke,3,30,377,48,96,50.0,0,1,0.0,32,55,58.2,35,61,96,5,5,4,21,128,0,0
+Emma Cannon,PHO,F,188,86.0,24.33227705,US,"January 6, 1989",28,Central Florida,R,18,508,105,220,47.7,11,33,33.3,31,34,91.2,33,72,105,52,21,27,30,252,1,0
+Emma Meesseman,WAS,C,193,83.0,22.28247738,BE,"May 13, 1993",24,Belgium,5,23,617,89,233,38.2,25,79,31.6,56,65,86.2,23,58,81,70,34,5,30,259,0,0
+Epiphanny Prince,NY,G,175,81.0,26.44897959,US,"November 1, 1988",28,Rutgers,8,26,282,36,86,41.9,1,3,33.3,15,22,68.2,17,44,61,5,4,8,17,88,0,0
+Erica Wheeler,IND,G,170,65.0,22.49134948,US,"February 5, 1991",26,Rutgers,3,30,767,130,321,40.5,42,129,32.6,34,40,85.0,11,57,68,117,38,1,68,336,0,0
+Érika de Souza,SAN,C,196,86.0,22.38650562,BR,"September 3, 1982",34,Brazil,13,30,579,65,112,58.0,0,0,0.0,29,32,90.6,58,74,132,35,18,7,37,159,0,0
+Erlana Larkins,IND,F,185,93.0,27.17311907,US,"February 4, 1986",31,North Carolina,9,20,386,36,92,39.1,9,35,25.7,21,24,87.5,9,26,35,24,11,8,13,102,0,0
+Essence Carson,LA,G/F,183,74.0,22.09680791,US,"July 28, 1986",31,Rutgers,10,15,61,4,16,25.0,0,0,0.0,5,6,83.3,7,2,9,0,1,3,5,13,0,0
+Evelyn Akhator,DAL,F,191,82.0,22.47745402,NG,"March 2, 1995",22,Kentucky,R,30,926,165,365,45.2,20,60,33.3,92,117,78.6,73,199,272,50,37,13,67,442,13,0
+Glory Johnson,DAL,F,191,77.0,21.10687755,US,"July 27, 1990",27,Tennessee,4,4,42,3,9,33.3,3,6,50.0,0,0,0.0,0,3,3,1,0,0,4,9,0,0
+Imani Boyette,ATL,C,201,88.0,21.78163907,US,"November 10, 1992",24,Texas,1,29,410,56,119,47.1,1,3,33.3,14,20,70.0,43,75,118,14,9,23,22,127,1,0
+Isabelle Harrison,SAN,C,191,83.0,22.75156931,US,"September 27, 1993",23,Kentucky,3,31,832,154,300,51.3,1,2,50.0,55,85,64.7,66,134,200,46,26,24,63,364,5,0
+Ivory Latta,WAS,G,168,63.0,22.32142857,US,"September 25, 1984",32,North Carolina,12,29,499,79,218,36.2,40,114,35.1,47,55,85.5,7,20,27,49,12,1,22,245,0,0
+Jantel Lavender,LA,C,193,84.0,22.55094096,US,"December 11, 1988",28,Ohio State,7,28,481,89,184,48.4,4,13,30.8,18,22,81.8,31,56,87,28,8,5,35,200,0,0
+Jasmine Thomas,CON,G,175,66.0,21.55102041,US,"September 30, 1989",27,Duke,6,27,762,151,341,44.3,50,116,43.1,39,55,70.9,9,55,64,118,45,4,58,391,1,0
+Jazmon Gwathmey,IND,G,188,65.0,18.39067451,PR,"January 24, 1993",24,James Madison,2,24,371,50,140,35.7,12,49,24.5,30,39,76.9,15,34,49,17,13,19,32,142,0,0
+Jeanette Pohlen,IND,G,183,78.0,23.29122996,US,"February 5, 1989",28,Stanford,6,25,278,20,52,38.5,13,29,44.8,17,20,85.0,3,19,22,13,5,0,15,70,0,0
+Jennifer Hamson,IND,C,201,95.0,23.51426945,US,"January 23, 1992",25,Brigham Young,1,10,50,2,12,16.7,0,3,0.0,8,10,80.0,5,6,11,6,2,2,3,12,0,0
+Jessica Breland,CHI,F,191,77.0,21.10687755,US,"February 23, 1988",29,North Carolina,5,10,78,9,16,56.3,0,0,0.0,4,5,80.0,5,13,18,2,1,9,3,22,0,0
+Jewell Loyd,SEA,G,178,67.0,21.14631991,US,"May 10, 1993",24,Notre Dame,3,29,715,116,245,47.3,8,21,38.1,28,37,75.7,50,139,189,46,18,50,57,268,4,0
+Jia Perkins,MIN,G,173,75.0,25.05930703,US,"February 23, 1982",35,Texas Tech,14,30,932,178,420,42.4,47,123,38.2,114,134,85.1,24,72,96,103,41,11,83,517,0,0
+Jonquel Jones,CON,F/C,198,86.0,21.93653709,BS,"May 1, 1994",23,George Washington,1,29,463,47,124,37.9,11,32,34.4,11,15,73.3,11,46,57,39,30,1,24,116,0,0
+Jordan Hooper,CHI,F,188,84.0,23.76641014,US,"February 20, 1992",25,Nebraska,3,29,833,164,299,54.8,22,49,44.9,117,142,82.4,108,226,334,40,29,46,46,467,17,0
+Kaela Davis,DAL,G,188,77.0,21.78587596,US,"March 15, 1995",22,South Carolina,R,23,208,27,75,36.0,20,55,36.4,3,4,75.0,2,20,22,5,7,1,6,77,0,0
+Kahleah Copper,CHI,G/F,185,70.0,20.45288532,US,"August 28, 1994",22,Rutgers,1,29,475,62,163,38.0,12,32,37.5,49,65,75.4,10,33,43,32,13,3,48,185,0,0
+Kaleena Mosqueda-Lewis,SEA,F,180,82.0,25.30864198,US,"March 11, 1993",24,Connecticut,3,29,369,60,140,42.9,5,23,21.7,36,45,80.0,11,43,54,11,9,2,22,161,0,0
+Karima Christmas-Kelly,DAL,G/F,183,82.0,24.48565201,US,"November 9, 1989",27,Duke,6,14,142,23,43,53.5,9,21,42.9,10,10,100.0,4,10,14,6,1,1,13,65,0,0
+Kayla Alexander,SAN,C,193,88.0,23.6247953,CA,"May 1, 1991",26,Arizona State,4,30,889,91,239,38.1,25,83,30.1,111,129,86.0,45,75,120,65,39,5,50,318,0,0
+Kayla McBride,SAN,G/F,180,79.0,24.38271605,US,"June 25, 1992",25,Notre Dame,3,31,433,78,141,55.3,0,0,0.0,15,16,93.8,40,47,87,17,13,15,30,171,0,0
+Kayla Pedersen,CON,F,193,86.0,23.08786813,US,"April 14, 1989",28,Stanford,5,27,882,128,337,38.0,47,147,32.0,108,118,91.5,12,93,105,59,32,5,54,411,0,0
+Kayla Thornton,DAL,F,185,86.0,25.12783053,US,"October 20, 1992",24,Texas–El Paso,2,21,224,11,30,36.7,0,1,0.0,10,14,71.4,19,26,45,13,6,2,9,32,0,0
+Keisha Hampton,CHI,F,185,78.0,22.79035793,US,"February 22, 1990",27,DePaul,1,30,504,64,157,40.8,14,52,26.9,65,81,80.2,36,59,95,24,20,7,21,207,0,0
+Kelsey Plum,SAN,G,173,66.0,22.05219018,US,"August 24, 1994",23,Washington,R,28,610,73,210,34.8,29,78,37.2,50,58,86.2,11,42,53,91,13,4,72,225,0,0
+Kia Vaughn,NY,C,193,90.0,24.16172246,US,"January 24, 1987",30,Rutgers,9,23,455,62,116,53.4,0,0,0.0,10,19,52.6,39,71,110,16,8,9,21,134,1,0
+Kiah Stokes,NY,C,191,87.0,23.84803048,US,"March 30, 1993",24,Connecticut,3,29,576,50,98,51.0,0,1,0.0,41,52,78.8,63,122,185,21,8,32,33,141,3,0
+Kristi Toliver,WAS,G,170,59.0,20.41522491,US,"January 27, 1987",30,Maryland,9,29,845,119,284,41.9,67,194,34.5,44,49,89.8,9,50,59,91,20,8,48,349,0,0
+Krystal Thomas,WAS,C,196,88.0,22.90712203,US,"October 6, 1989",27,Duke,6,29,737,81,149,54.4,0,0,0.0,37,61,60.7,97,172,269,30,15,31,45,199,2,0
+Lanay Montgomery,SEA,C,196,96.0,24.98958767,US,"September 17, 1993",23,West Virginia,R,7,28,3,7,42.9,0,0,0.0,0,0,0.0,0,5,5,0,1,4,2,6,0,0
+Layshia Clarendon,ATL,G,175,64.0,20.89795918,US,"February 5, 1991",26,UC Berkeley,5,30,900,124,320,38.8,8,53,15.1,73,81,90.1,27,88,115,206,29,1,82,329,3,0
+Leilani Mitchell,PHO,G,165,58.0,21.30394858,US,"June 15, 1985",32,Utah,9,30,623,70,182,38.5,31,92,33.7,62,75,82.7,12,57,69,108,26,9,50,233,0,0
+Lindsay Allen,NY,G,173,65.0,21.71806609,US,"March 20, 1995",22,Notre Dame,R,23,314,21,50,42.0,0,11,0.0,6,9,66.7,8,28,36,47,13,1,18,48,0,0
+Lindsay Whalen,MIN,G,175,78.0,25.46938776,US,"September 5, 1982",34,Minnesota,14,22,520,69,153,45.1,12,34,35.3,27,36,75.0,8,46,54,90,11,2,44,177,0,0
+Lynetta Kizer,CON,C,193,104.0,27.92021262,US,"April 4, 1990",27,Maryland,5,20,238,48,100,48.0,0,1,0.0,23,30,76.7,22,35,57,6,11,7,10,119,0,0
+Maimouna Diarra,LA,C,198,90.0,22.95684114,SN,"January 30, 1991",26,Sengal,R,9,16,1,3,33.3,0,0,0.0,1,2,50.0,3,4,7,1,1,0,3,3,0,0
+Marissa Coleman,IND,G/F,185,73.0,21.32943755,US,"April 1, 1987",30,Maryland,9,30,539,50,152,32.9,27,79,34.2,27,33,81.8,7,53,60,25,8,4,34,154,0,0
+Matee Ajavon,ATL,G,173,73.0,24.39105884,US,"July 5, 1986",31,Syracruse,R,27,218,22,69,31.9,0,3,0.0,29,35,82.9,8,26,34,27,10,0,26,73,0,0
+Maya Moore,MIN,F,183,80.0,23.88844098,US,"November 6, 1989",27,Connecticut,7,29,904,170,398,42.7,52,132,39.4,98,114,86.0,50,106,156,99,53,13,56,490,3,0
+Monique Currie,PHO,G/F,183,80.0,23.88844098,US,"February 25, 1983",34,Duke,11,32,717,121,284,42.6,37,93,39.8,85,103,82.5,19,103,122,67,22,11,48,364,0,0
+Morgan Tuck,CON,F,188,91.0,25.74694432,US,"April 30, 1994",23,Connecticut,1,17,294,35,101,34.7,8,28,28.6,13,16,81.3,9,34,43,19,7,0,15,91,1,0
+Moriah Jefferson,SAN,G,168,55.0,19.48696145,US,"August 3, 1994",23,Connecticut,1,21,514,81,155,52.3,9,20,45.0,20,27,74.1,6,31,37,92,33,2,43,191,0,0
+Natalie Achonwa,IND,C,193,83.0,22.28247738,CA,"November 22, 1992",24,Notre Dame,3,30,529,82,151,54.3,0,0,0.0,43,55,78.2,31,70,101,21,11,16,25,207,0,0
+Natasha Cloud,WAS,G,183,73.0,21.79820239,US,"February 22, 1992",25,Saint Joseph's,3,24,448,37,118,31.4,12,51,23.5,20,27,74.1,7,52,59,69,17,3,23,106,0,0
+Natasha Howard,MIN,F,188,75.0,21.22000905,US,"February 9, 1991",26,Florida State,4,29,315,48,104,46.2,3,13,23.1,17,23,73.9,25,38,63,16,11,19,20,116,0,0
+Nayo Raincock-Ekunwe,NY,F/C,188,79.0,22.35174287,CA,"August 29, 1991",25,Simon Fraser,R,27,243,33,63,52.4,0,4,0.0,30,49,61.2,24,22,46,8,2,1,13,96,0,0
+Nia Coffey,SAN,F,185,77.0,22.49817385,US,"May 21, 1995",22,Northwestern,R,25,203,16,59,27.1,0,4,0.0,16,22,72.7,16,30,46,6,5,6,14,48,0,0
+Nneka Ogwumike,LA,F,188,79.0,22.35174287,US,"February 7, 1990",27,Stanford,6,30,948,215,386,55.7,18,49,36.7,129,148,87.2,57,179,236,63,53,14,47,577,9,0
+Noelle Quinn,SEA,G,183,81.0,24.18704649,US,"March 1, 1985",32,UCLA,11,29,459,24,58,41.4,14,35,40.0,17,18,94.4,1,48,49,78,12,5,27,79,0,0
+Odyssey Sims,LA,G,173,73.0,24.39105884,US,"July 13, 1992",25,Baylor,4,27,626,86,198,43.4,11,49,22.4,47,55,85.5,10,34,44,87,38,5,39,230,1,0
+Plenette Pierson,MIN,F/C,188,88.0,24.89814396,US,"August 31, 1981",35,Texas Tech,15,29,402,54,142,38.0,17,51,33.3,15,20,75.0,13,49,62,48,12,4,33,140,0,0
+Rachel Banham,CON,G,175,76.0,24.81632653,US,"July 15, 1993",24,Minnesota,2,26,238,32,87,36.8,16,48,33.3,16,20,80.0,2,27,29,20,4,0,12,96,0,0
+Ramu Tokashiki,SEA,F,193,80.0,21.47708663,JP,"November 6, 1991",25,Japan,1,29,378,42,92,45.7,0,3,0.0,22,27,81.5,19,29,48,16,8,8,25,106,0,0
+Rebecca Allen,NY,G/F,188,74.0,20.9370756,AU,"June 11, 1992",25,Australia,3,28,254,31,86,36.0,14,40,35.0,2,6,33.3,13,51,64,15,9,12,17,78,0,0
+Rebekkah Brunson,MIN,F,188,84.0,23.76641014,US,"November 12, 1981",35,Georgetown,14,26,719,97,218,44.5,22,60,36.7,62,83,74.7,46,135,181,40,31,9,42,278,2,0
+Renee Montgomery,MIN,G,170,63.0,21.79930796,US,"February 12, 1986",31,Connecticut,9,29,614,71,181,39.2,30,89,33.7,44,51,86.3,12,34,46,96,24,1,43,216,0,0
+Riquna Williams,LA,G,170,75.0,25.95155709,US,"May 28, 1990",27,Miami (FL),5,23,408,45,140,32.1,20,74,27.0,38,44,86.4,6,26,32,16,19,3,26,148,0,0
+Sami Whitcomb,SEA,G,178,66.0,20.83070319,US,"July 20, 1988",29,Washington,R,29,354,46,120,38.3,33,94,35.1,14,17,82.4,12,40,52,24,22,0,24,139,0,0
+Sancho Lyttle,ATL,F,193,79.0,21.20862305,ES,"September 20, 1983",33,Houston,13,25,703,71,163,43.6,1,7,14.3,13,19,68.4,42,138,180,41,40,17,34,156,0,0
+Sandrine Gruda,LA,F/C,193,84.0,22.55094096,FR,"June 25, 1987",30,France,5,4,12,1,3,33.3,0,0,0.0,0,0,0.0,0,2,2,0,0,0,2,2,0,0
+Saniya Chong,DAL,G,173,64.0,21.383942,US,"June 27, 1994",23,Connecticut,R,29,348,27,74,36.5,8,35,22.9,25,29,86.2,9,19,28,33,21,3,23,87,0,0
+Seimone Augustus,MIN,G/F,183,77.0,22.99262444,US,"April 30, 1984",33,LSU,12,27,756,125,251,49.8,18,41,43.9,30,35,85.7,12,70,82,108,17,1,39,298,1,0
+Sequoia Holmes,SAN,G,185,70.0,20.45288532,US,"June 13, 1986",31,UNLV,2,24,280,31,89,34.8,13,46,28.3,6,11,54.5,12,12,24,23,13,5,11,81,0,0
+Shatori Walker-Kimbrough,WAS,G,180,64.0,19.75308642,US,"May 18, 1995",22,Maryland,R,22,260,29,78,37.2,9,26,34.6,29,32,90.6,4,13,17,10,11,1,12,96,0,0
+Shavonte Zellous,NY,G,178,85.0,26.82742078,US,"August 28, 1986",30,Pittsburgh,9,29,865,107,249,43.0,14,41,34.1,118,144,81.9,30,92,122,87,23,8,62,346,1,0
+Shay Murphy,SAN,G,180,74.0,22.83950617,US,"April 15, 1985",32,Southern California,9,23,242,23,62,37.1,12,35,34.3,8,12,66.7,12,26,38,17,10,1,12,66,0,0
+Shekinna Stricklen,CON,G/F,188,81.0,22.91760978,US,"July 30, 1990",27,Tennessee,5,29,795,80,202,39.6,59,149,39.6,26,31,83.9,15,71,86,30,36,2,23,245,0,0
+Shenise Johnson,IND,G,180,78.0,24.07407407,US,"September 12, 1990",26,Miami (FL),6,14,348,55,127,43.3,10,30,33.3,38,40,95.0,13,35,48,35,21,4,18,158,0,0
+Skylar Diggins-Smith,DAL,G,175,66.0,21.55102041,US,"February 8, 1990",27,Notre Dame,4,30,1018,167,394,42.4,43,119,36.1,168,186,90.3,21,86,107,173,38,24,83,545,1,0
+Stefanie Dolson,CHI,C,196,97.0,25.24989588,US,"August 1, 1992",25,Connecticut,3,28,823,162,293,55.3,24,60,40.0,50,58,86.2,35,121,156,65,14,37,65,398,3,0
+Stephanie Talbot,PHO,G,185,87.0,25.42001461,AU,"December 20, 1990",26,Australia,R,30,555,47,114,41.2,15,38,39.5,29,44,65.9,28,58,86,50,22,8,28,138,0,0
+Sue Bird,SEA,G,175,68.0,22.20408163,US,"October 16, 1980",36,Connecticut,15,27,806,103,244,42.2,50,134,37.3,17,24,70.8,7,46,53,177,31,3,57,273,1,0
+Sugar Rodgers,NY,G,175,75.0,24.48979592,US,"August 12, 1989",28,Georgetown,6,28,745,108,310,34.8,59,163,36.2,42,52,80.8,21,85,106,68,28,17,43,317,0,0
+Sydney Colson,SAN,G,173,64.0,21.383942,US,"June 8, 1989",28,Texas A&M,3,25,296,25,78,32.1,2,10,20.0,20,30,66.7,3,11,14,51,13,2,25,72,0,0
+Sydney Wiese,LA,G,183,68.0,20.30517483,US,"July 13, 1992",25,Oregon State,R,25,189,19,50,38.0,13,32,40.6,4,8,50.0,3,18,21,6,4,3,2,55,0,0
+Sylvia Fowles,MIN,C,198,96.0,24.48729721,US,"June 10, 1985",32,LSU,10,29,895,222,336,66.1,0,0,0.0,128,162,79.0,113,184,297,39,39,61,71,572,16,0
+Tamera Young,ATL,G/F,188,77.0,21.78587596,US,"October 30, 1986",30,Tennessee,9,31,820,105,297,35.4,23,70,32.9,44,65,67.7,23,87,110,66,36,14,61,277,0,0
+Tayler Hill,WAS,G,175,66.0,21.55102041,US,"October 23, 1990",26,Ohio State,5,18,462,69,191,36.1,27,89,30.3,75,80,93.8,5,29,34,47,16,1,26,240,0,0
+Temi Fagbenle,MIN,C,193,89.0,23.89325888,UK,"August 9, 1992",25,Southern California,R,17,74,6,14,42.9,0,0,0.0,5,6,83.3,3,13,16,1,3,3,8,17,0,0
+Theresa Plaisance,DAL,F,196,91.0,23.68804665,US,"May 18, 1992",25,LSU,4,30,604,80,213,37.6,35,101,34.7,22,24,91.7,38,89,127,24,23,22,24,217,1,0
+Tianna Hawkins,WAS,F,191,87.0,23.84803048,US,"February 3, 1991",26,Maryland,4,29,483,79,165,47.9,11,41,26.8,41,43,95.3,42,82,124,9,15,7,23,210,0,0
+Tierra Ruffin-Pratt,WAS,G,178,83.0,26.19618735,US,"November 4, 1991",25,North Carolina,5,29,703,77,217,35.5,0,4,0.0,71,96,74.0,45,120,165,68,30,16,47,225,2,0
+Tiffany Hayes,ATL,G,178,70.0,22.09317005,US,"September 20, 1989",27,Connecticut,6,29,861,144,331,43.5,43,112,38.4,136,161,84.5,28,89,117,69,37,8,50,467,0,0
+Tiffany Jackson,LA,F,191,84.0,23.0256846,US,"April 26, 1985",32,Texas,9,22,127,12,25,48.0,0,1,0.0,4,6,66.7,5,18,23,3,1,3,8,28,0,0
+Tiffany Mitchell,IND,G,175,69.0,22.53061224,US,"September 23, 1984",32,South Carolina,2,27,671,83,238,34.9,17,69,24.6,94,102,92.2,16,70,86,39,31,5,40,277,0,0
+Tina Charles,NY,F/C,193,84.0,22.55094096,US,"May 12, 1988",29,Connecticut,8,29,952,227,509,44.6,18,56,32.1,110,135,81.5,56,212,268,75,21,22,71,582,11,0
+Yvonne Turner,PHO,G,175,59.0,19.26530612,US,"October 13, 1987",29,Nebraska,2,30,356,59,140,42.1,11,47,23.4,22,28,78.6,11,13,24,30,18,1,32,151,0,0
diff --git a/2_Exploratory_Data_Analysis.ipynb b/2_Exploratory_Data_Analysis.ipynb
new file mode 100644
index 0000000..89ed85b
--- /dev/null
+++ b/2_Exploratory_Data_Analysis.ipynb
@@ -0,0 +1,1989 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GNu34KqYYATH"
+      },
+      "source": [
+        "# Inferential statistics\n",
+        "## Part II - Exploratory Data Analysis\n",
+        "\n",
+        "Before starting the actual analysis it's a good idea to explore the data that we will be using, to give yourself a first idea of the questions you will be able to answer with your data, the bias you could have, other data you could need, etc.\n",
+        "\n",
+        "### Libraries\n",
+        "In addition to pandas we will also import matplolib and seaborn so that we will able to plot our data to better understand it."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "RHoJzMeGYATL"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "pd.set_option('display.max_columns', 100)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4PehPpNZYATM"
+      },
+      "source": [
+        "### Explore the dataset\n",
+        "\n",
+        "Let's load the cleaned dataset first. Import it with the name `wnba` and show the head."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "ydeRtY6CYATN",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 330
+        },
+        "outputId": "4682049f-fd70-44d4-ed64-00ae5b11e593"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              Name Team  Pos  Height  Weight        BMI Birth_Place  \\\n",
+              "0    Aerial Powers  DAL    F     183    71.0  21.200991          US   \n",
+              "1      Alana Beard   LA  G/F     185    73.0  21.329438          US   \n",
+              "2     Alex Bentley  CON    G     170    69.0  23.875433          US   \n",
+              "3  Alex Montgomery  SAN  G/F     185    84.0  24.543462          US   \n",
+              "4     Alexis Jones  MIN    G     175    78.0  25.469388          US   \n",
+              "\n",
+              "           Birthdate  Age         College Experience  Games Played  MIN  FGM  \\\n",
+              "0   January 17, 1994   23  Michigan State          2             8  173   30   \n",
+              "1       May 14, 1982   35            Duke         12            30  947   90   \n",
+              "2   October 27, 1990   26      Penn State          4            26  617   82   \n",
+              "3  December 11, 1988   28    Georgia Tech          6            31  721   75   \n",
+              "4     August 5, 1994   23          Baylor          R            24  137   16   \n",
+              "\n",
+              "   FGA   FG%  3PM  3PA   3P%  FTM  FTA   FT%  OREB  DREB  REB  AST  STL  BLK  \\\n",
+              "0   85  35.3   12   32  37.5   21   26  80.8     6    22   28   12    3    6   \n",
+              "1  177  50.8    5   18  27.8   32   41  78.0    19    82  101   72   63   13   \n",
+              "2  218  37.6   19   64  29.7   35   42  83.3     4    36   40   78   22    3   \n",
+              "3  195  38.5   21   68  30.9   17   21  81.0    35   134  169   65   20   10   \n",
+              "4   50  32.0    7   20  35.0   11   12  91.7     3     9   12   12    7    0   \n",
+              "\n",
+              "   TO  PTS  DD2  TD3  \n",
+              "0  12   93    0    0  \n",
+              "1  40  217    0    0  \n",
+              "2  24  218    0    0  \n",
+              "3  38  188    2    0  \n",
+              "4  14   50    0    0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8ec9e87a-cbd9-437e-bf90-5a6547bfbd05\" class=\"colab-df-container\">\n",
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+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Team</th>\n",
+              "      <th>Pos</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>Birth_Place</th>\n",
+              "      <th>Birthdate</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>College</th>\n",
+              "      <th>Experience</th>\n",
+              "      <th>Games Played</th>\n",
+              "      <th>MIN</th>\n",
+              "      <th>FGM</th>\n",
+              "      <th>FGA</th>\n",
+              "      <th>FG%</th>\n",
+              "      <th>3PM</th>\n",
+              "      <th>3PA</th>\n",
+              "      <th>3P%</th>\n",
+              "      <th>FTM</th>\n",
+              "      <th>FTA</th>\n",
+              "      <th>FT%</th>\n",
+              "      <th>OREB</th>\n",
+              "      <th>DREB</th>\n",
+              "      <th>REB</th>\n",
+              "      <th>AST</th>\n",
+              "      <th>STL</th>\n",
+              "      <th>BLK</th>\n",
+              "      <th>TO</th>\n",
+              "      <th>PTS</th>\n",
+              "      <th>DD2</th>\n",
+              "      <th>TD3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Aerial Powers</td>\n",
+              "      <td>DAL</td>\n",
+              "      <td>F</td>\n",
+              "      <td>183</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>21.200991</td>\n",
+              "      <td>US</td>\n",
+              "      <td>January 17, 1994</td>\n",
+              "      <td>23</td>\n",
+              "      <td>Michigan State</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>173</td>\n",
+              "      <td>30</td>\n",
+              "      <td>85</td>\n",
+              "      <td>35.3</td>\n",
+              "      <td>12</td>\n",
+              "      <td>32</td>\n",
+              "      <td>37.5</td>\n",
+              "      <td>21</td>\n",
+              "      <td>26</td>\n",
+              "      <td>80.8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>22</td>\n",
+              "      <td>28</td>\n",
+              "      <td>12</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>12</td>\n",
+              "      <td>93</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Alana Beard</td>\n",
+              "      <td>LA</td>\n",
+              "      <td>G/F</td>\n",
+              "      <td>185</td>\n",
+              "      <td>73.0</td>\n",
+              "      <td>21.329438</td>\n",
+              "      <td>US</td>\n",
+              "      <td>May 14, 1982</td>\n",
+              "      <td>35</td>\n",
+              "      <td>Duke</td>\n",
+              "      <td>12</td>\n",
+              "      <td>30</td>\n",
+              "      <td>947</td>\n",
+              "      <td>90</td>\n",
+              "      <td>177</td>\n",
+              "      <td>50.8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>18</td>\n",
+              "      <td>27.8</td>\n",
+              "      <td>32</td>\n",
+              "      <td>41</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>19</td>\n",
+              "      <td>82</td>\n",
+              "      <td>101</td>\n",
+              "      <td>72</td>\n",
+              "      <td>63</td>\n",
+              "      <td>13</td>\n",
+              "      <td>40</td>\n",
+              "      <td>217</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Alex Bentley</td>\n",
+              "      <td>CON</td>\n",
+              "      <td>G</td>\n",
+              "      <td>170</td>\n",
+              "      <td>69.0</td>\n",
+              "      <td>23.875433</td>\n",
+              "      <td>US</td>\n",
+              "      <td>October 27, 1990</td>\n",
+              "      <td>26</td>\n",
+              "      <td>Penn State</td>\n",
+              "      <td>4</td>\n",
+              "      <td>26</td>\n",
+              "      <td>617</td>\n",
+              "      <td>82</td>\n",
+              "      <td>218</td>\n",
+              "      <td>37.6</td>\n",
+              "      <td>19</td>\n",
+              "      <td>64</td>\n",
+              "      <td>29.7</td>\n",
+              "      <td>35</td>\n",
+              "      <td>42</td>\n",
+              "      <td>83.3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>36</td>\n",
+              "      <td>40</td>\n",
+              "      <td>78</td>\n",
+              "      <td>22</td>\n",
+              "      <td>3</td>\n",
+              "      <td>24</td>\n",
+              "      <td>218</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Alex Montgomery</td>\n",
+              "      <td>SAN</td>\n",
+              "      <td>G/F</td>\n",
+              "      <td>185</td>\n",
+              "      <td>84.0</td>\n",
+              "      <td>24.543462</td>\n",
+              "      <td>US</td>\n",
+              "      <td>December 11, 1988</td>\n",
+              "      <td>28</td>\n",
+              "      <td>Georgia Tech</td>\n",
+              "      <td>6</td>\n",
+              "      <td>31</td>\n",
+              "      <td>721</td>\n",
+              "      <td>75</td>\n",
+              "      <td>195</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>21</td>\n",
+              "      <td>68</td>\n",
+              "      <td>30.9</td>\n",
+              "      <td>17</td>\n",
+              "      <td>21</td>\n",
+              "      <td>81.0</td>\n",
+              "      <td>35</td>\n",
+              "      <td>134</td>\n",
+              "      <td>169</td>\n",
+              "      <td>65</td>\n",
+              "      <td>20</td>\n",
+              "      <td>10</td>\n",
+              "      <td>38</td>\n",
+              "      <td>188</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Alexis Jones</td>\n",
+              "      <td>MIN</td>\n",
+              "      <td>G</td>\n",
+              "      <td>175</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>25.469388</td>\n",
+              "      <td>US</td>\n",
+              "      <td>August 5, 1994</td>\n",
+              "      <td>23</td>\n",
+              "      <td>Baylor</td>\n",
+              "      <td>R</td>\n",
+              "      <td>24</td>\n",
+              "      <td>137</td>\n",
+              "      <td>16</td>\n",
+              "      <td>50</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>20</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>11</td>\n",
+              "      <td>12</td>\n",
+              "      <td>91.7</td>\n",
+              "      <td>3</td>\n",
+              "      <td>9</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>14</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ec9e87a-cbd9-437e-bf90-5a6547bfbd05')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-8ec9e87a-cbd9-437e-bf90-5a6547bfbd05 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-8ec9e87a-cbd9-437e-bf90-5a6547bfbd05');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-f0635ce0-b9c4-4e8d-b960-dd613cfe0036\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f0635ce0-b9c4-4e8d-b960-dd613cfe0036')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-f0635ce0-b9c4-4e8d-b960-dd613cfe0036 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "#imported \"my cleaned dataset\" from previous lab\n",
+        "\n",
+        "wnba = pd.read_csv(\"/content/1_Data_Cleaning_wnba_cleaned.csv\")\n",
+        "wnba.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ARZq7EF3YATN"
+      },
+      "source": [
+        "**Use describe() to take an initial look at the data.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "e1n_R-0tYATO"
+      },
+      "outputs": [],
+      "source": [
+        "wnba.describe()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FZqJdMv4YATO"
+      },
+      "source": [
+        "Most of the game-related stats have a very high range of values which can be explained by the fact that the dataset contains data on both players that play the majority of games and also players that may spend almost the entirety of the season on the bench.\n",
+        "\n",
+        "There are also some extremes in the weight and age columns. Feel free, if you'd like, to check which are the players with a very high (or low) age/weight and do some research on them. This is useful to confirm that they are simply outliers and not errors in the data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YcutKGPQYATP"
+      },
+      "outputs": [],
+      "source": [
+        "max_weight = wnba[\"Weight\"].max()\n",
+        "max_weight_players = wnba.loc[wnba[\"Weight\"] == max_weight]\n",
+        "max_weight_players\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "min_weight = wnba[\"Weight\"].min()\n",
+        "min_weight_players = wnba.loc[wnba[\"Weight\"] == min_weight]\n",
+        "min_weight_players\n",
+        "\n",
+        "# Analysis: Here, maybe some years ago the weight was the same, but actually today they have 30 kg apart, so there must be a typo on Amanda, which is 83kg today"
+      ],
+      "metadata": {
+        "id": "pL0A30aOhkiF"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "max_height = wnba[\"Height\"].max()\n",
+        "max_height_players = wnba.loc[wnba[\"Height\"] == max_height]\n",
+        "max_height_players\n",
+        "\n",
+        "# Right"
+      ],
+      "metadata": {
+        "id": "2VVRgtwUj0nq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "min_height = wnba[\"Height\"].min()\n",
+        "min_height_players = wnba.loc[wnba[\"Height\"] == min_height]\n",
+        "min_height_players\n",
+        "\n",
+        "# Right"
+      ],
+      "metadata": {
+        "id": "f_xSFD8GjoOS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "min_age = wnba[\"Age\"].min()\n",
+        "min_age_players = wnba.loc[wnba[\"Age\"] == min_age]\n",
+        "min_age_players\n"
+      ],
+      "metadata": {
+        "id": "qe9ZpWxIhvyS"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "max_age = wnba[\"Age\"].max()\n",
+        "max_age_players = wnba.loc[wnba[\"Age\"] == max_age]\n",
+        "max_age_players\n",
+        "\n",
+        "# Analysis: There must be a typo here because Angel Robinson was born in 1987 and Brionna Jones in 1995, so age cannot be the same."
+      ],
+      "metadata": {
+        "id": "bd8EwxBykRgN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GcY8BXzAYATQ"
+      },
+      "source": [
+        "### Looking at the distributions of the data\n",
+        "Let's take a look at the distribution of the 4 stats that describe the physical characteristics of the players.\n",
+        "\n",
+        "**Plot the four distributions about `height`, `weight`, `age` and `BMI`.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "cIl7BlooYATR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "fab470b1-037e-459a-b8bc-9d05c3a9aee5"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean weight is \n",
+            " 78.97887323943662\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "print(\"Mean weight is \\n\", wnba[\"Weight\"].mean())\n",
+        "plt.hist(wnba[\"Weight\"], bins = 10)\n",
+        "plt.xlabel(\"Weight\")\n",
+        "plt.ylabel(\"Players\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean height is \\n\", wnba[\"Height\"].mean())\n",
+        "plt.hist(wnba[\"Height\"], bins = 10)\n",
+        "plt.xlabel(\"Height\")\n",
+        "plt.ylabel(\"Players\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "buCQuWewnUyY",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "4521918b-df99-4331-b851-697192ef6a87"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean height is \n",
+            " 184.61267605633802\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean age is \\n\", wnba[\"Age\"].mean())\n",
+        "plt.hist(wnba[\"Age\"], bins = 10)\n",
+        "plt.xlabel(\"Age\")\n",
+        "plt.ylabel(\"Players\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "06g5rCPPniiA",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "8c97f11e-7793-4160-8703-6c7239af0ae0"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean age is \n",
+            " 27.112676056338028\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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FTP/+/TVkyBCVlZVp4cKFKi8v16xZs9SnTx//MTfffLM+//zzkA0KAADwVUF9jswNN9ygO+64Q+eee26jx3Tt2lX19fVBDwYAANCUoELmF7/4RajnAAAAOGNBf7Lv3r17tWrVKu3Zs0dHjx4N2Dd79uyzHgwAAKApQYXMunXr9L3vfU/nnXeePv30U11yySX67LPPZIzR5ZdfHuoZAQAAGhTUzb55eXn6yU9+om3btik2NlavvvqqysvLdfXVV+sHP/hBs9/nvffe07XXXqvk5GS5XC6tXLkyYP/tt98ul8sV8Bg1alQwIwMAgAgUVMh88sknuu222yRJ0dHR+vLLL9WhQwc9+uij+vWvf93s96mtrdWll16qefPmNXrMqFGjtG/fPv/jpZdeCmZkAAAQgYL60lJcXJz/vpju3btr9+7duvjiiyVJ//nPf5r9PqNHj9bo0aNPe4zb7VZSUlKz39Pn8wX87Cev19vs1wIAALsEFTJDhgzRhg0bdOGFF2rMmDGaPn26tm3bptdee01DhgwJ6YDr169XYmKiOnfurOHDh+uxxx5Tly5dGj0+Pz9fM2fODOkMkaZX7mqnRwAAICSCCpnZs2erpqZGkjRz5kzV1NRo2bJluuCCC0L6HUujRo3S+PHj1bt3b+3evVs/+9nPNHr0aBUWFioqKqrB1+Tl5SknJ8f/3Ov1KiUlJWQzAQCA8BFUyJx33nn+X8fFxWnBggUhG+hkN910k//X/fv3V1pams4//3ytX79eI0aMaPA1brdbbre7ReYBAADh5ax/aGRrOu+889S1a1ft2rXL6VEAAEAYaPYVmc6dO8vlcjXr2IMHDwY90Ons3btX//3vf9W9e/cWeX8AAGCXZofM3LlzQ/6b19TUBFxdKSsrU3FxsRISEpSQkKCZM2cqKytLSUlJ2r17tx588EH16dNHI0eODPksAADAPs0OmYkTJ+r48eN64okntGrVKh09elQjRozQww8/rHbt2gX1m2/dulXXXHON//mJm3QnTpyo+fPnq6SkRM8//7yqqqqUnJyszMxM/fKXv+QeGAAAIOkMb/b91a9+pUceeUQZGRlq166dnnzySR04cEDPPfdcUL/5sGHDZIxpdP/atWuDel8AAPD1cEY3+77wwgt6+umntXbtWq1cuVJvvPGGXnzxRdXX17fUfAAAAI06o5DZs2ePxowZ43+ekZEhl8ulioqKkA8GAADQlDMKmWPHjik2NjZgW9u2bVVXVxfSoQAAAJrjjO6RMcbo9ttvD7jZ9siRI5o8ebLi4uL821577bXQTQgAANCIMwqZiRMnnrLt1ltvDdkwAAAAZ+KMQmbRokUtNQcAAMAZs+pHFAAAAJyMkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgrWinBwAiVa/c1U6PAAARjysyAADAWoQMAACwFiEDAACsRcgAAABrETIAAMBahAwAALAWIQMAAKxFyAAAAGsRMgAAwFqEDAAAsBYhAwAArMXPWjoL/CwdAACcxRUZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANZyNGTee+89XXvttUpOTpbL5dLKlSsD9htj9NBDD6l79+5q166dMjIytHPnTmeGBQAAYcfRkKmtrdWll16qefPmNbj/N7/5jX7/+99rwYIF2rRpk+Li4jRy5EgdOXKklScFAADhyNFP9h09erRGjx7d4D5jjObOnasZM2bouuuukyS98MIL6tatm1auXKmbbrqpwdf5fD75fD7/c6/XG/rBAQBAWAjbe2TKyspUWVmpjIwM/zaPx6PBgwersLCw0dfl5+fL4/H4HykpKa0xLgAAcEDYhkxlZaUkqVu3bgHbu3Xr5t/XkLy8PFVXV/sf5eXlLTonAABwTsT90Ei32y232+30GAAAoBWE7RWZpKQkSdL+/fsDtu/fv9+/DwAAfL2Fbcj07t1bSUlJWrdunX+b1+vVpk2blJ6e7uBkAAAgXDj6paWamhrt2rXL/7ysrEzFxcVKSEhQamqqHnjgAT322GO64IIL1Lt3b/3iF79QcnKyxo0b59zQAAAgbDgaMlu3btU111zjf56TkyNJmjhxohYvXqwHH3xQtbW1uuuuu1RVVaVvf/vbWrNmjWJjY50aGQAAhBFHQ2bYsGEyxjS63+Vy6dFHH9Wjjz7ailMBAABbhO09MgAAAE0hZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYK1opwcAgLPRK3e10yN8LfDPuXV8Nmus0yNYhysyAADAWoQMAACwFiEDAACsRcgAAABrETIAAMBahAwAALAWIQMAAKxFyAAAAGsRMgAAwFqEDAAAsBYhAwAArEXIAAAAaxEyAADAWoQMAACwFiEDAACsRcgAAABrETIAAMBahAwAALAWIQMAAKxFyAAAAGsRMgAAwFqEDAAAsBYhAwAArEXIAAAAaxEyAADAWoQMAACwFiEDAACsRcgAAABrETIAAMBaYR0yjzzyiFwuV8CjX79+To8FAADCRLTTAzTl4osv1jvvvON/Hh0d9iMDAIBWEvZVEB0draSkJKfHAAAAYSisv7QkSTt37lRycrLOO+883XLLLdqzZ89pj/f5fPJ6vQEPAAAQmVzGGOP0EI156623VFNTo759+2rfvn2aOXOm/v3vf2v79u3q2LFjg6955JFHNHPmzFO2V1dXKz4+PqTz9cpdHdL3AwDANp/NGtsi7+v1euXxeJr8+zusQ+arqqqq1LNnT82ePVuTJk1q8Bifzyefz+d/7vV6lZKSQsgAANACnA6ZsL9H5mSdOnXSN77xDe3atavRY9xut9xudytOBQAAnBL298icrKamRrt371b37t2dHgUAAISBsA6Zn/zkJyooKNBnn32mv//97/r+97+vqKgo3XzzzU6PBgAAwkBYf2lp7969uvnmm/Xf//5X55xzjr797W9r48aNOuecc5weDQAAhIGwDpmlS5c6PQIAAAhjYf2lJQAAgNMhZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgLUIGAABYi5ABAADWImQAAIC1rAiZefPmqVevXoqNjdXgwYO1efNmp0cCAABhIOxDZtmyZcrJydHDDz+sDz74QJdeeqlGjhypAwcOOD0aAABwWNiHzOzZs/XjH/9YP/rRj3TRRRdpwYIFat++vZ577jmnRwMAAA6LdnqA0zl69KiKioqUl5fn39amTRtlZGSosLCwwdf4fD75fD7/8+rqakmS1+sN+Xz1vsMhf08AAGzSEn+/nvy+xpjTHhfWIfOf//xHx48fV7du3QK2d+vWTZ9++mmDr8nPz9fMmTNP2Z6SktIiMwIA8HXmmduy73/o0CF5PJ5G94d1yAQjLy9POTk5/uf19fU6ePCgunTpIpfL5eBkoeP1epWSkqLy8nLFx8c7PU6LY72RjfVGNtYb2VpyvcYYHTp0SMnJyac9LqxDpmvXroqKitL+/fsDtu/fv19JSUkNvsbtdsvtdgds69SpU0uN6Kj4+PivxX8oJ7DeyMZ6IxvrjWwttd7TXYk5Iaxv9o2JidGAAQO0bt06/7b6+nqtW7dO6enpDk4GAADCQVhfkZGknJwcTZw4UVdccYUGDRqkuXPnqra2Vj/60Y+cHg0AADgs7EPmxhtv1Oeff66HHnpIlZWV+uY3v6k1a9accgPw14nb7dbDDz98ypfQIhXrjWysN7Kx3sgWDut1maa+rwkAACBMhfU9MgAAAKdDyAAAAGsRMgAAwFqEDAAAsBYhE6by8/M1cOBAdezYUYmJiRo3bpxKS0v9+w8ePKj77rtPffv2Vbt27ZSamqqpU6f6f7aUbZpa78mMMRo9erRcLpdWrlzZuoOGSHPXW1hYqOHDhysuLk7x8fG66qqr9OWXXzow8dlpznorKyv1wx/+UElJSYqLi9Pll1+uV1991aGJz878+fOVlpbm/5Cw9PR0vfXWW/79R44cUXZ2trp06aIOHTooKyvrlA/+tMnp1htp56oTmvozPiESzldS89br1PmKkAlTBQUFys7O1saNG/X222+rrq5OmZmZqq2tlSRVVFSooqJCTzzxhLZv367FixdrzZo1mjRpksOTB6ep9Z5s7ty51v+4ieast7CwUKNGjVJmZqY2b96sLVu2aMqUKWrTxr7/bJuz3ttuu02lpaVatWqVtm3bpvHjx+uGG27Qhx9+6ODkwenRo4dmzZqloqIibd26VcOHD9d1112njz76SJI0bdo0vfHGG1q+fLkKCgpUUVGh8ePHOzx18E633kg7V53Q1J/xCZFwvpKaXq+j5ysDKxw4cMBIMgUFBY0e8/LLL5uYmBhTV1fXipO1jMbW++GHH5pzzz3X7Nu3z0gyK1ascGbAEGtovYMHDzYzZsxwcKqW09B64+LizAsvvBBwXEJCgnnmmWdae7wW0blzZ/Pss8+aqqoq07ZtW7N8+XL/vk8++cRIMoWFhQ5OGFon1tuQSDpXneyra47U89UJJ6/XyfOVff9r9zV14jJsQkLCaY+Jj49XdHTYf85hkxpa7+HDhzVhwgTNmzev0Z+1ZauvrvfAgQPatGmTEhMT9a1vfUvdunXT1VdfrQ0bNjg5Zsg09Of7rW99S8uWLdPBgwdVX1+vpUuX6siRIxo2bJhDU4bG8ePHtXTpUtXW1io9PV1FRUWqq6tTRkaG/5h+/fopNTVVhYWFDk4aGl9db0Mi6VwlNbzmSD5ffXW9jp+vHMknnJHjx4+bsWPHmqFDhzZ6zOeff25SU1PNz372s1acrGU0tt677rrLTJo0yf9cEfJ/OA2tt7Cw0EgyCQkJ5rnnnjMffPCBeeCBB0xMTIzZsWOHg9Oevcb+fL/44guTmZlpJJno6GgTHx9v1q5d69CUZ6+kpMTExcWZqKgo4/F4zOrVq40xxrz44osmJibmlOMHDhxoHnzwwdYeM2QaW+9XRdK56nRrjsTzVWPrdfp8RchYYPLkyaZnz56mvLy8wf3V1dVm0KBBZtSoUebo0aOtPF3oNbTe119/3fTp08ccOnTIvy0STgzGNLze999/30gyeXl5Acf279/f5ObmtvaIIdXYv89TpkwxgwYNMu+8844pLi42jzzyiPF4PKakpMShSc+Oz+czO3fuNFu3bjW5ubmma9eu5qOPPorYkGlsvSeLtHNVY2uO1PNVY+t1+nxFyIS57Oxs06NHD/PPf/6zwf1er9ekp6ebESNGmC+//LKVpwu9xtZ7//33G5fLZaKiovwPSaZNmzbm6quvdmbYEGhsvf/85z+NJPOnP/0pYPsNN9xgJkyY0JojhlRj6921a5eRZLZv3x6wfcSIEebuu+9uzRFbzIgRI8xdd91l1q1bZySZL774ImB/amqqmT17tjPDtYAT6z0h0s5VDTmx5kg9X33VifU6fb7iHpkwZYzRlClTtGLFCr377rvq3bv3Kcd4vV5lZmYqJiZGq1atUmxsrAOThkZT683NzVVJSYmKi4v9D0maM2eOFi1a5MDEZ6ep9fbq1UvJycmnfIvyjh071LNnz9YcNSSaWu/hw4cl6ZTvcIiKilJ9fX2rzdmS6uvr5fP5NGDAALVt21br1q3z7ystLdWePXsavafERifWK0XWuep0Tqw50s5XjTmxXsfPVy2eSgjKPffcYzwej1m/fr3Zt2+f/3H48GFjzP8u0Q4ePNj079/f7Nq1K+CYY8eOOTz9mWtqvQ2RxZdqm7PeOXPmmPj4eLN8+XKzc+dOM2PGDBMbG2t27drl4OTBaWq9R48eNX369DFXXnml2bRpk9m1a5d54oknjMvlavRei3CWm5trCgoKTFlZmSkpKTG5ubnG5XKZv/zlL8aY/315LTU11bz77rtm69atJj093aSnpzs8dfBOt95IO1ed0NSf8VfZfL4ypun1Onm+ImTClKQGH4sWLTLGGPPXv/610WPKysocnT0YTa23sdfYemJo7nrz8/NNjx49TPv27U16err529/+5szAZ6k5692xY4cZP368SUxMNO3btzdpaWmnfDu2Le644w7Ts2dPExMTY8455xwzYsSIgL/gvvzyS3Pvvfeazp07m/bt25vvf//7Zt++fQ5OfHZOt95IO1ed0NSf8VfZfL4ypnnrdep85TLGmJa73gMAANByuEcGAABYi5ABAADWImQAAIC1CBkAAGAtQgYAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQARCWCgsLFRUVpbFjxzo9CoAwxo8oABCW7rzzTnXo0EELFy5UaWmpkpOTnR4JQBjiigyAsFNTU6Nly5bpnnvu0dixY7V48eKA/atWrdIFF1yg2NhYXXPNNXr++eflcrlUVVXlP2bDhg268sor1a5dO6WkpGjq1Kmqra1t3YUAaHGEDICw8/LLL6tfv37q27evbr31Vj333HM6cfG4rKxM119/vcaNG6d//OMfuvvuu/Xzn/884PW7d+/WqFGjlJWVpZKSEi1btkwbNmzQlClTnFgOgBbEl5YAhJ2hQ4fqhhtu0P33369jx46pe/fuWr58uYYNG6bc3FytXr1a27Zt8x8/Y8YMPf744/riiy/UqVMn3XnnnYqKitIf//hH/zEbNmzQ1VdfrdraWsXGxjqxLAAtgCsyAMJKaWmpNm/erJtvvlmSFB0drRtvvFELFy707x84cGDAawYNGhTw/B//+IcWL16sDh06+B8jR45UfX29ysrKWmchAFpFtNMDAMDJFi5cqGPHjgXc3GuMkdvt1lNPPdWs96ipqdHdd9+tqVOnnrIvNTU1ZLMCcB4hAyBsHDt2TC+88IJ+97vfKTMzM2DfuHHj9NJLL6lv377685//HLBvy5YtAc8vv/xyffzxx+rTp0+LzwzAWdwjAyBsrFy5UjfeeKMOHDggj8cTsO+nP/2p3n33Xb388svq27evpk2bpkmTJqm4uFjTp0/X3r17VVVVJY/Ho5KSEg0ZMkR33HGH7rzzTsXFxenjjz/W22+/3eyrOgDswD0yAMLGwoULlZGRcUrESFJWVpa2bt2qQ4cO6ZVXXtFrr72mtLQ0zZ8/3/9dS263W5KUlpamgoIC7dixQ1deeaUuu+wyPfTQQ3wWDRCBuCIDwHqPP/64FixYoPLycqdHAdDKuEcGgHWefvppDRw4UF26dNH777+v3/72t3xGDPA1RcgAsM7OnTv12GOP6eDBg0pNTdX06dOVl5fn9FgAHMCXlgAAgLW42RcAAFiLkAEAANYiZAAAgLUIGQAAYC1CBgAAWIuQAQAA1iJkAACAtQgZAABgrf8HevLTgzhua1kAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean BMI is \\n\", wnba[\"BMI\"].mean())\n",
+        "plt.hist(wnba[\"BMI\"], bins = 10)\n",
+        "plt.xlabel(\"BMI\")\n",
+        "plt.ylabel(\"Players\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "wMksscetnwsQ",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "cf6025f8-f82d-4cb6-cf46-95912420043a"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean BMI is \n",
+            " 23.09121422774648\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nj3tlow-YATS"
+      },
+      "source": [
+        "**What conclusions do you think we can take from this plots?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8V2rTZmyYATS"
+      },
+      "outputs": [],
+      "source": [
+        "# I think BMI is the most truethfull to the definition of normal distribution - in my opinion - and can even see that for the number of\n",
+        "# values around the mean\n",
+        "\n",
+        "# Age of course, skewed to the right, due to the fact that a lot of players will play most of time at \"younger ages\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tmuhGOoLYATS"
+      },
+      "source": [
+        "In addition to what the describe() method already told us we can see that the physical characteristics of the players (apart from the age) more or less follow a standard distribution, which is expected when looking at the distribution of values that arise from a lot of factors that are independent from each other as is the case for many physical quantities.\n",
+        "\n",
+        "The height distribution looks like a bimodal. This may be due to the fact that players in basketball fall under two main categories (please note that this is a very gross generalization): shorter and more agile, and taller and less agile. Therefore there are less \"average\" height players since they will neither be as agile as the shorter players nor have the same impact in the paint (that is, under the basket) as a taller player.\n",
+        "\n",
+        "The age distribution is a bit skewed to the right which is expected since most professional players stop playing after their prime physical years come to an end."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p87UGrR5YATT"
+      },
+      "source": [
+        "We could do the same for the main game stats. They are points, assists, blocks, rebounds and steals.\n",
+        "\n",
+        "**Now plot the distribution of the columns `REB`, `AST`, `STL`, `PTS` and `BLK` the same way you did in the last cell.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "wnba.head(1)"
+      ],
+      "metadata": {
+        "id": "0HOMKdEnwTst",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 135
+        },
+        "outputId": "fdae2bd7-cf5a-4516-fd52-d779d8a99bea"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Name Team Pos  Height  Weight        BMI Birth_Place  \\\n",
+              "0  Aerial Powers  DAL   F     183    71.0  21.200991          US   \n",
+              "\n",
+              "          Birthdate  Age         College Experience  Games Played  MIN  FGM  \\\n",
+              "0  January 17, 1994   23  Michigan State          2             8  173   30   \n",
+              "\n",
+              "   FGA   FG%  3PM  3PA   3P%  FTM  FTA   FT%  OREB  DREB  REB  AST  STL  BLK  \\\n",
+              "0   85  35.3   12   32  37.5   21   26  80.8     6    22   28   12    3    6   \n",
+              "\n",
+              "   TO  PTS  DD2  TD3  \n",
+              "0  12   93    0    0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e88c7a93-20fa-4f46-b087-f0b0e3229ef6\" 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",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Team</th>\n",
+              "      <th>Pos</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>Birth_Place</th>\n",
+              "      <th>Birthdate</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>College</th>\n",
+              "      <th>Experience</th>\n",
+              "      <th>Games Played</th>\n",
+              "      <th>MIN</th>\n",
+              "      <th>FGM</th>\n",
+              "      <th>FGA</th>\n",
+              "      <th>FG%</th>\n",
+              "      <th>3PM</th>\n",
+              "      <th>3PA</th>\n",
+              "      <th>3P%</th>\n",
+              "      <th>FTM</th>\n",
+              "      <th>FTA</th>\n",
+              "      <th>FT%</th>\n",
+              "      <th>OREB</th>\n",
+              "      <th>DREB</th>\n",
+              "      <th>REB</th>\n",
+              "      <th>AST</th>\n",
+              "      <th>STL</th>\n",
+              "      <th>BLK</th>\n",
+              "      <th>TO</th>\n",
+              "      <th>PTS</th>\n",
+              "      <th>DD2</th>\n",
+              "      <th>TD3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Aerial Powers</td>\n",
+              "      <td>DAL</td>\n",
+              "      <td>F</td>\n",
+              "      <td>183</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>21.200991</td>\n",
+              "      <td>US</td>\n",
+              "      <td>January 17, 1994</td>\n",
+              "      <td>23</td>\n",
+              "      <td>Michigan State</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>173</td>\n",
+              "      <td>30</td>\n",
+              "      <td>85</td>\n",
+              "      <td>35.3</td>\n",
+              "      <td>12</td>\n",
+              "      <td>32</td>\n",
+              "      <td>37.5</td>\n",
+              "      <td>21</td>\n",
+              "      <td>26</td>\n",
+              "      <td>80.8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>22</td>\n",
+              "      <td>28</td>\n",
+              "      <td>12</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>12</td>\n",
+              "      <td>93</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e88c7a93-20fa-4f46-b087-f0b0e3229ef6')\"\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 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-e88c7a93-20fa-4f46-b087-f0b0e3229ef6 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-e88c7a93-20fa-4f46-b087-f0b0e3229ef6');\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",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "J8F-uAL1YATT"
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Mean Rebound is \\n\", wnba[\"REB\"].mean())\n",
+        "plt.hist(wnba[\"REB\"], bins = 10)\n",
+        "plt.xlabel(\"Rebounds\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"MeanAssists is \\n\", wnba[\"AST\"].mean())\n",
+        "plt.hist(wnba[\"AST\"], bins = 10)\n",
+        "plt.xlabel(\"Assists\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "PBEOF9hnw5A8"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean Steals is \\n\", wnba[\"STL\"].mean())\n",
+        "plt.hist(wnba[\"STL\"], bins = 20)\n",
+        "plt.xlabel(\"Steals\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "DxBwMTpJxZMl"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean Points is \\n\", wnba[\"PTS\"].mean())\n",
+        "plt.hist(wnba[\"PTS\"], bins = 25)\n",
+        "plt.xlabel(\"Points\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "UGZfgXerxjmT",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "7129d700-9970-45f8-9c65-7f90bec36b7b"
+      },
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Points is \n",
+            " 203.16901408450704\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean Blocks is \\n\", wnba[\"BLK\"].mean())\n",
+        "plt.hist(wnba[\"BLK\"], bins = 10)\n",
+        "plt.xlabel(\"Blocks\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "o1CWVMZNx8cn",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 486
+        },
+        "outputId": "e40fe069-7fe8-49bf-d062-b0c31004fdf9"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Blocks is \n",
+            " 9.78169014084507\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lF-mBUnzYATT"
+      },
+      "source": [
+        "**What conclusions do you think we can take from this plots?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wZTIfF7QYATU"
+      },
+      "outputs": [],
+      "source": [
+        "# They all assume an exponencial distributions, long tail to the right (skewed to the right)\n",
+        "# Maybe if all players played a full game, this would fall on a normal distribution.\n",
+        "# Or am I wrong?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RVfzcnqUYATU"
+      },
+      "source": [
+        "As expected all of the above distribution are heavily skewed to the right, since most players will have very low to average stats while there will be a handful of star players whose stats peak above everyone else. It is also important to think about the fact that we are simply taking the stats as they are without considering the minutes played by each player. Even though skill plays a very important factor in determining this kind of stats we also have to consider that players that play more minutes will, on average, score more points (or blocks, assists, etc.)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yUWZsJrAYATU"
+      },
+      "source": [
+        "**For the sake of it let's look at the same distributions by dividing those stats by the minutes played for each player in the dataset.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "wnba.head(2)"
+      ],
+      "metadata": {
+        "id": "VzUdNq6hy8Bi",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 184
+        },
+        "outputId": "5f635a69-8477-48cf-c51f-7ed40236a9e4"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Name Team  Pos  Height  Weight        BMI Birth_Place  \\\n",
+              "0  Aerial Powers  DAL    F     183    71.0  21.200991          US   \n",
+              "1    Alana Beard   LA  G/F     185    73.0  21.329438          US   \n",
+              "\n",
+              "          Birthdate  Age         College Experience  Games Played  MIN  FGM  \\\n",
+              "0  January 17, 1994   23  Michigan State          2             8  173   30   \n",
+              "1      May 14, 1982   35            Duke         12            30  947   90   \n",
+              "\n",
+              "   FGA   FG%  3PM  3PA   3P%  FTM  FTA   FT%  OREB  DREB  REB  AST  STL  BLK  \\\n",
+              "0   85  35.3   12   32  37.5   21   26  80.8     6    22   28   12    3    6   \n",
+              "1  177  50.8    5   18  27.8   32   41  78.0    19    82  101   72   63   13   \n",
+              "\n",
+              "   TO  PTS  DD2  TD3  \n",
+              "0  12   93    0    0  \n",
+              "1  40  217    0    0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6e6244ee-0ed5-4751-bbb1-fd3568c44260\" 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",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Team</th>\n",
+              "      <th>Pos</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>Birth_Place</th>\n",
+              "      <th>Birthdate</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>College</th>\n",
+              "      <th>Experience</th>\n",
+              "      <th>Games Played</th>\n",
+              "      <th>MIN</th>\n",
+              "      <th>FGM</th>\n",
+              "      <th>FGA</th>\n",
+              "      <th>FG%</th>\n",
+              "      <th>3PM</th>\n",
+              "      <th>3PA</th>\n",
+              "      <th>3P%</th>\n",
+              "      <th>FTM</th>\n",
+              "      <th>FTA</th>\n",
+              "      <th>FT%</th>\n",
+              "      <th>OREB</th>\n",
+              "      <th>DREB</th>\n",
+              "      <th>REB</th>\n",
+              "      <th>AST</th>\n",
+              "      <th>STL</th>\n",
+              "      <th>BLK</th>\n",
+              "      <th>TO</th>\n",
+              "      <th>PTS</th>\n",
+              "      <th>DD2</th>\n",
+              "      <th>TD3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Aerial Powers</td>\n",
+              "      <td>DAL</td>\n",
+              "      <td>F</td>\n",
+              "      <td>183</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>21.200991</td>\n",
+              "      <td>US</td>\n",
+              "      <td>January 17, 1994</td>\n",
+              "      <td>23</td>\n",
+              "      <td>Michigan State</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>173</td>\n",
+              "      <td>30</td>\n",
+              "      <td>85</td>\n",
+              "      <td>35.3</td>\n",
+              "      <td>12</td>\n",
+              "      <td>32</td>\n",
+              "      <td>37.5</td>\n",
+              "      <td>21</td>\n",
+              "      <td>26</td>\n",
+              "      <td>80.8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>22</td>\n",
+              "      <td>28</td>\n",
+              "      <td>12</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>12</td>\n",
+              "      <td>93</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Alana Beard</td>\n",
+              "      <td>LA</td>\n",
+              "      <td>G/F</td>\n",
+              "      <td>185</td>\n",
+              "      <td>73.0</td>\n",
+              "      <td>21.329438</td>\n",
+              "      <td>US</td>\n",
+              "      <td>May 14, 1982</td>\n",
+              "      <td>35</td>\n",
+              "      <td>Duke</td>\n",
+              "      <td>12</td>\n",
+              "      <td>30</td>\n",
+              "      <td>947</td>\n",
+              "      <td>90</td>\n",
+              "      <td>177</td>\n",
+              "      <td>50.8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>18</td>\n",
+              "      <td>27.8</td>\n",
+              "      <td>32</td>\n",
+              "      <td>41</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>19</td>\n",
+              "      <td>82</td>\n",
+              "      <td>101</td>\n",
+              "      <td>72</td>\n",
+              "      <td>63</td>\n",
+              "      <td>13</td>\n",
+              "      <td>40</td>\n",
+              "      <td>217</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e6244ee-0ed5-4751-bbb1-fd3568c44260')\"\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 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-6e6244ee-0ed5-4751-bbb1-fd3568c44260 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-6e6244ee-0ed5-4751-bbb1-fd3568c44260');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-51a91ccf-8391-4d21-93be-cbfb1242d79f\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-51a91ccf-8391-4d21-93be-cbfb1242d79f')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-51a91ccf-8391-4d21-93be-cbfb1242d79f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "tpJwMysqYATU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "fce82f86-b61e-41be-88c7-757456e75d4b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Rebound is \n",
+            " 0.16902863707427523\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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DB6tHjx4yxjhVFwAAgN98CjqnT5/WhAkTlJKSoilTpqiyslKSlJWVxUfLAQBA0PAp6CxevFhhYWGqqKhQ9+7dPdvnzJmjbdu2XfV5Xn/9dU2bNk0JCQlyuVzasmWL135jjB599FHFx8erW7duSk9PV2lpqS8lAwCAa5BPQedPf/qTVqxYob59+3ptHzx4sP7+979f9XnOnj2rkSNHat26da3uX7lypX76059q/fr12rt3r3r06KFJkybp/PnzvpQNAACuMT4tRj579qzXnZyLPvzwQ7nd7qs+z+TJkzV58uRW9xljtHbtWj3yyCOaPn26JGnTpk2KjY3Vli1bNHfuXF9KBwAA1xCf7uiMGzdOmzZt8jx3uVxqbm7WypUrNX78eEcKKy8vV1VVldLT0z3boqKilJqaquLi4sse19DQoNraWq8HAAC4Nvl0R2flypWaMGGC9u3bp08++UQPPfSQDh8+rA8//FC7d+92pLCqqipJUmxsrNf22NhYz77W5Obm6vHHH3ekBgAA0Ln5dEdn+PDheu+99zR27FhNnz5dZ8+e1axZs3TgwAENHDjQ6RrbZOnSpaqpqfE83n///YDWAwAAAqfNd3QaGxuVkZGh9evX6+GHH26PmiRJcXFxkqTq6mrFx8d7tldXV2vUqFGXPc7tdrdpnRAAALBXm+/ohIWF6dChQ+1Ri5fk5GTFxcWpqKjIs622tlZ79+5VWlpau18fAAB0fj69dXX33Xdrw4YNfl+8vr5eJSUlKikpkfTpAuSSkhJVVFTI5XLpu9/9rp544gn99re/1dtvv6177rlHCQkJmjFjht/XBgAA9vNpMfKFCxe0ceNGbd++XaNHj27xHVdr1qy5qvPs27fP61Na2dnZkqTMzEwVFBTooYce0tmzZ/X1r39dZ86c0dixY7Vt2zZ17drVl7IBAMA1xmXa8AVVf/vb35SUlKQJEyZc/oQul1577TVHinNCbW2toqKiVFNTo8jISEfPnbRkq6PnA9CxjudNDXQJAC7DqX+/23RHZ/DgwaqsrNSOHTskffqVDz/96U9bfAQcAAAgGLRpjc6lN39eeeUVnT171tGCAAAAnOLTYuSL2vCuFwAAQIdrU9BxuVxyuVwttgEAAASjNq3RMcZowYIFnl/Id/78eX3zm99s8amrzZs3O1chAACAj9oUdDIzM72e33333Y4WAwAA4KQ2BZ38/Pz2qgMAAMBxfi1GBgAACGYEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgrdBAFwAAgZK0ZGtArns8b2pArgtci7ijAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALBWaKALAIBrTdKSrQG57vG8qQG5LhBI3NEBAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLWCOug89thjcrlcXo+hQ4cGuiwAANBJBP2Xeg4bNkzbt2/3PA8NDfqSAQBAkAj61BAaGqq4uLhAlwEAADqhoH7rSpJKS0uVkJCgAQMG6K677lJFRcUVxzc0NKi2ttbrAQAArk1BHXRSU1NVUFCgbdu26ZlnnlF5ebnGjRunurq6yx6Tm5urqKgozyMxMbEDKwYAAMHEZYwxgS7iap05c0b9+/fXmjVrlJWV1eqYhoYGNTQ0eJ7X1tYqMTFRNTU1ioyMdLSepCVbHT0fALSn43lTA10CcNVqa2sVFRXl97/fQb9G519FR0crJSVFx44du+wYt9stt9vdgVUBAIBgFdRvXV2qvr5eZWVlio+PD3QpAACgEwjqoJOTk6Ndu3bp+PHjeuONNzRz5kyFhIRo3rx5gS4NAAB0AkH91tWJEyc0b948nT59Wr1799bYsWO1Z88e9e7dO9ClAQCATiCog05hYWGgSwAAAJ1YUL91BQAA4A+CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtQg6AADAWgQdAABgLYIOAACwFkEHAABYi6ADAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGCt0EAXAADoGElLtgbkusfzpgbkuoDEHR0AAGAxgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYC2CDgAAsBZBBwAAWIugAwAArEXQAQAA1iLoAAAAaxF0AACAtUIDXQAAAO0hacnWQJdwTTieNzXQJVwRd3QAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQAAYK1OEXTWrVunpKQkde3aVampqXrzzTcDXRIAAOgEgj7ovPDCC8rOztayZcv0l7/8RSNHjtSkSZP0wQcfBLo0AAAQ5II+6KxZs0b33nuvFi5cqJtuuknr169X9+7dtXHjxkCXBgAAglxooAu4kk8++UT79+/X0qVLPdu6dOmi9PR0FRcXt3pMQ0ODGhoaPM9ramokSbW1tY7X19xwzvFzAoBt2uO/v1eD/0Z3jPaa34vnNcb4dZ6gDjr//Oc/1dTUpNjYWK/tsbGx+utf/9rqMbm5uXr88cdbbE9MTGyXGgEAVxa1NtAVoD219/zW1dUpKirK5+ODOuj4YunSpcrOzvY8b25u1ocffqjrr79eLper3a5bW1urxMREvf/++4qMjGy36wTStdCjRJ+2uRb6vBZ6lOjTNp/VpzFGdXV1SkhI8Os6QR10YmJiFBISourqaq/t1dXViouLa/UYt9stt9vttS06Orq9SmwhMjLS6r+Y0rXRo0SftrkW+rwWepTo0zZX6tOfOzkXBfVi5PDwcI0ePVpFRUWebc3NzSoqKlJaWloAKwMAAJ1BUN/RkaTs7GxlZmbqlltu0a233qq1a9fq7NmzWrhwYaBLAwAAQS7og86cOXN06tQpPfroo6qqqtKoUaO0bdu2FguUA83tdmvZsmUt3jazybXQo0SftrkW+rwWepTo0zYd1afL+Pu5LQAAgCAV1Gt0AAAA/EHQAQAA1iLoAAAAaxF0AACAtQg6l7Fu3TolJSWpa9euSk1N1ZtvvnnF8S+99JKGDh2qrl276uabb9Yf/vAHr/0LFiyQy+XyemRkZLRnC1elLX0ePnxYs2fPVlJSklwul9auXev3OTuK030+9thjLeZz6NCh7djBZ2tLj88++6zGjRunnj17qmfPnkpPT28x3hijRx99VPHx8erWrZvS09NVWlra3m18Jqf7tOG1uXnzZt1yyy2Kjo5Wjx49NGrUKP33f/+31xgb5vNq+rRhPv9VYWGhXC6XZsyY4bU9GOfT6R4dm0uDFgoLC014eLjZuHGjOXz4sLn33ntNdHS0qa6ubnX87t27TUhIiFm5cqV59913zSOPPGLCwsLM22+/7RmTmZlpMjIyTGVlpefx4YcfdlRLrWprn2+++abJyckxzz//vImLizM//vGP/T5nR2iPPpctW2aGDRvmNZ+nTp1q504ur609zp8/36xbt84cOHDAHDlyxCxYsMBERUWZEydOeMbk5eWZqKgos2XLFnPw4EHzpS99ySQnJ5uPP/64o9pqoT36tOG1uWPHDrN582bz7rvvmmPHjpm1a9eakJAQs23bNs8YG+bzavq0YT4vKi8vNzfccIMZN26cmT59ute+YJvP9ujRqbkk6LTi1ltvNYsWLfI8b2pqMgkJCSY3N7fV8V/96lfN1KlTvbalpqaab3zjG57nmZmZLSYx0Nra57/q379/qwHAn3O2l/boc9myZWbkyJEOVukff3/uFy5cMBEREea5554zxhjT3Nxs4uLizJNPPukZc+bMGeN2u83zzz/vbPFt4HSfxtj32rzo3/7t38wjjzxijLF3Po3x7tMYe+bzwoUL5t///d/NL37xixY9BeN8Ot2jMc7NJW9dXeKTTz7R/v37lZ6e7tnWpUsXpaenq7i4uNVjiouLvcZL0qRJk1qM37lzp/r06aMhQ4boW9/6lk6fPu18A1fJlz4DcU5/tWdNpaWlSkhI0IABA3TXXXepoqLC33J94kSP586dU2Njo3r16iVJKi8vV1VVldc5o6KilJqa2qnn8tI+L7LptWmMUVFRkY4ePaovfvGLkuycz9b6vMiG+fzhD3+oPn36KCsrq8W+YJvP9ujxIifmMuh/M3JH++c//6mmpqYWv3k5NjZWf/3rX1s9pqqqqtXxVVVVnucZGRmaNWuWkpOTVVZWpu9///uaPHmyiouLFRIS4nwjn8GXPgNxTn+1V02pqakqKCjQkCFDVFlZqccff1zjxo3TO++8o4iICH/LbhMnevze976nhIQEz3+oLv7d/ay/1x2pPfqU7Hlt1tTU6IYbblBDQ4NCQkL09NNP684775Rk13xeqU/Jjvn885//rA0bNqikpKTV/cE2n+3Ro+TcXBJ0OsjcuXM9f7755ps1YsQIDRw4UDt37tSECRMCWBl8MXnyZM+fR4wYodTUVPXv318vvvjiFf/vJBjl5eWpsLBQO3fuVNeuXQNdTru5XJ+2vDYjIiJUUlKi+vp6FRUVKTs7WwMGDNDtt98e6NIc9Vl9dvb5rKur09e+9jU9++yziomJCXQ57eJqe3RqLgk6l4iJiVFISIiqq6u9tldXVysuLq7VY+Li4to0XpIGDBigmJgYHTt2LCAvPl/6DMQ5/dVRNUVHRyslJUXHjh1z7JxXy58eV61apby8PG3fvl0jRozwbL94XHV1teLj473OOWrUKOeKb4P26LM1nfW12aVLFw0aNEiSNGrUKB05ckS5ubm6/fbbrZrPK/XZms42n2VlZTp+/LimTZvm2dbc3CxJCg0N1dGjR4NuPtujx4EDB7Y4zte5ZI3OJcLDwzV69GgVFRV5tjU3N6uoqEhpaWmtHpOWluY1XpJeffXVy46XpBMnTuj06dNef0k7ki99BuKc/uqomurr61VWVhaQ+fS1x5UrV2r58uXatm2bbrnlFq99ycnJiouL8zpnbW2t9u7d2+nm8kp9tsaW12Zzc7MaGhok2TWfl/rXPlvT2eZz6NChevvtt1VSUuJ5fOlLX9L48eNVUlKixMTEoJvP9uixNT7Ppd/LmS1UWFho3G63KSgoMO+++675+te/bqKjo01VVZUxxpivfe1rZsmSJZ7xu3fvNqGhoWbVqlXmyJEjZtmyZV4fL6+rqzM5OTmmuLjYlJeXm+3bt5vPfe5zZvDgweb8+fMB6dGYtvfZ0NBgDhw4YA4cOGDi4+NNTk6OOXDggCktLb3qcwZCe/T5wAMPmJ07d5ry8nKze/duk56ebmJiYswHH3zQ4f0Z0/Ye8/LyTHh4uHn55Ze9PrpZV1fnNSY6Otr85je/MYcOHTLTp08Pio8jO9mnLa/NH/3oR+ZPf/qTKSsrM++++65ZtWqVCQ0NNc8++6xnjA3z+Vl92jKfl2rt00fBNp9O9+jkXBJ0LuNnP/uZ6devnwkPDze33nqr2bNnj2ffbbfdZjIzM73Gv/jiiyYlJcWEh4ebYcOGma1bt3r2nTt3zkycONH07t3bhIWFmf79+5t77703oP/4X9SWPsvLy42kFo/bbrvtqs8ZKE73OWfOHBMfH2/Cw8PNDTfcYObMmWOOHTvWgR211JYe+/fv32qPy5Yt84xpbm42P/jBD0xsbKxxu91mwoQJ5ujRox3YUeuc7NOW1+bDDz9sBg0aZLp27Wp69uxp0tLSTGFhodf5bJjPz+rTlvm8VGtBJxjn08kenZxLlzHGtO0eEAAAQOfAGh0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGsRdAAAgLUIOgAAwFoEHQABs3PnTrlcLp05cybQpbSqoKBA0dHRgS4DgB8IOgB8tmDBArlcLrlcLoWFhSk5OVkPPfSQzp8/H+jSAECSFBroAgB0bhkZGcrPz1djY6P279+vzMxMuVwurVixItClAQB3dAD4x+12Ky4uTomJiZoxY4bS09P16quvSpKam5uVm5ur5ORkdevWTSNHjtTLL7/c4hy7d+/WiBEj1LVrV33hC1/QO++847X/V7/6lYYNGya3262kpCStXr3aa7/L5dKWLVu8tkVHR6ugoECSdPz4cblcLm3evFnjx49X9+7dNXLkSBUXF3sdU1BQoH79+ql79+6aOXOmTp8+7bX/4MGDGj9+vCIiIhQZGanRo0dr3759vvzYAHQQgg4Ax7zzzjt64403FB4eLknKzc3Vpk2btH79eh0+fFiLFy/W3XffrV27dnkd9+CDD2r16tV666231Lt3b02bNk2NjY2SpP379+urX/2q5s6dq7fffluPPfaYfvCDH3hCTFs8/PDDysnJUUlJiVJSUjRv3jxduHBBkrR3715lZWXpvvvuU0lJicaPH68nnnjC6/i77rpLffv21VtvvaX9+/dryZIlCgsL8+EnBaDDtPn7zgHg/8vMzDQhISGmR48exu12G0mmS5cu5uWXXzbnz5833bt3N2+88YbXMVlZWWbevHnGGGN27NhhJJnCwkLP/tOnT5tu3bqZF154wRhjzPz5882dd97pdY4HH3zQ3HTTTZ7nksyvf/1rrzFRUVEmPz/fGGNMeXm5kWR+8YtfePYfPnzYSDJHjhwxxhgzb948M2XKFK9zzJkzx0RFRXmeR0REmIKCgjb8hAAEGnd0APhl/PjxKikp0d69e5WZmamFCxdq9uzZOnbsmM6dO6c777xT1113neexadMmlZWVeZ0jLS3N8+devXppyJAhOnLkiCTpyJEjGjNmjNf4MWPGqLS0VE1NTW2qdcSIEZ4/x8fHS5I++OADz3VSU1MvW5ckZWdn6z//8z+Vnp6uvLy8Fn0ACD4sRgbglx49emjQoEGSpI0bN2rkyJHasGGDhg8fLknaunWrbrjhBq9j3G63ozW4XC4ZY7y2XXzr61/969tMLpdL0qfriK7WY489pvnz52vr1q165ZVXtGzZMhUWFmrmzJk+Vg6gvRF0ADimS5cu+v73v6/s7Gy99957crvdqqio0G233XbF4/bs2aN+/fpJkj766CO99957uvHGGyVJN954o3bv3u01fvfu3UpJSVFISIgkqXfv3qqsrPTsLy0t1blz59pU+4033qi9e/e2qOtSKSkpSklJ0eLFizVv3jzl5+cTdIAgRtAB4KivfOUrevDBB/Xzn/9cOTk5Wrx4sZqbmzV27FjV1NRo9+7dioyMVGZmpueYH/7wh7r++usVGxurhx9+WDExMZoxY4Yk6YEHHtDnP/95LV++XHPmzFFxcbGeeuopPf30057j77jjDj311FNKS0tTU1OTvve977V5kfB3vvMdjRkzRqtWrdL06dP1xz/+Udu2bfPs//jjj/Xggw/qy1/+spKTk3XixAm99dZbmj17tn8/MADtK9CLhAB0XpmZmWb69Okttufm5prevXub+vp6s3btWjNkyBATFhZmevfubSZNmmR27dpljPm/xci/+93vzLBhw0x4eLi59dZbzcGDB73O9/LLL5ubbrrJhIWFmX79+pknn3zSa/8//vEPM3HiRNOjRw8zePBg84c//KHVxcgHDhzwHPPRRx8ZSWbHjh2ebRs2bDB9+/Y13bp1M9OmTTOrVq3yLEZuaGgwc+fONYmJiSY8PNwkJCSY++67z3z88cd+/xwBtB+XMZe8sQ0AAGAJPnUFAACsRdABAADWIugAAABrEXQAAIC1CDoAAMBaBB0AAGAtgg4AALAWQQcAAFiLoAMAAKxF0AEAANYi6AAAAGv9PxoSAeirjYUaAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "print(\"Mean Rebound is \\n\", (wnba[\"REB\"]/wnba[\"MIN\"]).mean())\n",
+        "plt.hist(wnba[\"REB\"]/wnba[\"MIN\"], bins = 15)\n",
+        "plt.xlabel(\"Rebounds\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean Assists is \\n\", (wnba[\"AST\"]/wnba[\"MIN\"]).mean())\n",
+        "plt.hist(wnba[\"AST\"]/wnba[\"MIN\"], bins = 15)\n",
+        "plt.xlabel(\"Assists\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "a_tlPX8xy3tb",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "0af94935-9f5e-4db6-cc42-d4d50f3a3e0a"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Assists is \n",
+            " 0.07915086084279187\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean Steals is \\n\", (wnba[\"STL\"]/wnba[\"MIN\"]).mean())\n",
+        "plt.hist(wnba[\"STL\"]/wnba[\"MIN\"], bins = 10)\n",
+        "plt.xlabel(\"Steals\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "Pw-0po6Ey3qs",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "578b2d96-17c9-498b-9ec1-96b8be351212"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Steals is \n",
+            " 0.03438895685748749\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean Points is \\n\", (wnba[\"PTS\"]/wnba[\"MIN\"]).mean())\n",
+        "plt.hist(wnba[\"PTS\"]/wnba[\"MIN\"], bins = 10)\n",
+        "plt.xlabel(\"Points\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "qQd2lWf1y3oU",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "31dd1992-d353-4ecf-a3ca-a086e7466aba"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Points is \n",
+            " 0.3742523329115144\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Mean Blocks is \\n\", (wnba[\"BLK\"]/wnba[\"MIN\"]).mean())\n",
+        "plt.hist(wnba[\"BLK\"]/wnba[\"MIN\"], bins = 10)\n",
+        "plt.xlabel(\"Blocks\")\n",
+        "plt.ylabel(\"Frequency\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "HRUFkXESy3mV",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 484
+        },
+        "outputId": "359dc2a7-dee9-4c06-d8bd-90889aabc1e4"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mean Blocks is \n",
+            " 0.020352463793082275\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aV2I_kfOYATU"
+      },
+      "source": [
+        "**What conclusions do you think we can take from this plots?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "hHmaGYdKYATU"
+      },
+      "outputs": [],
+      "source": [
+        "# Well, now that there all in the same time frame, we actually see shapes more as a normal distribution, having the usual relationship\n",
+        "# with the mean (in the centre of the distribution). Just having doubts on the Blocks chart, still looking kinda of exponential,\n",
+        "# or just due to the fact that the mean is actually really close to zero (?!)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OBGLawvXYATV"
+      },
+      "source": [
+        "### Summary\n",
+        "\n",
+        "The main insights we obtained from this exploratory analysis are:\n",
+        "- Game-related stats have a very high range of values.\n",
+        "- There are some extremes in the weight and age columns.\n",
+        "- The physical characteristics of the players more or less follow a standard distribution.\n",
+        "- We need to take into account that our dataset contains data on both players that play the majority of games and also players that may spend almost the entirety of the season on the bench.\n",
+        "\n",
+        "Now, it's time to try to put an end to your family's discussions. As seen on the README, the main discussions are:\n",
+        "- Your grandmother says that your sister couldn't play in a professional basketball league (not only the WNBA, but ANY professional basketball league) because she's too skinny and lacks muscle.\n",
+        "- Your sister says that most female professional players fail their free throws.\n",
+        "- Your brother-in-law heard on the TV that the average assists among NBA (male) and WNBA (female) players is 52 for the 2016-2017 season. He is convinced this average would be higher if we only considered the players from the WNBA.\n",
+        "\n",
+        "**Do you think you have all the necessary data to answer these questions?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "A1NuPQD9YATV"
+      },
+      "outputs": [],
+      "source": [
+        "# Your grandmother says that your sister couldn't play in a professional basketball league (not only the WNBA, but ANY professional basketball league)\n",
+        "# because she's too skinny and lacks muscleyour comments here\n",
+        "\n",
+        "# well, even from BMI we can´t tell how much muscle mass a person has: it's just a relation between weight and height and by the way, you could work on your body composition.\n",
+        "# Height actually could be more of an exclusion factor, if she was \"as tall\" as I am, 152 cm would cut the dream right away.\n",
+        "\n",
+        "# but answering the question, we no not have the necessary data to confirm that granny is right or wrong!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your sister says that most female professional players fail their free throws.\n",
+        "FT_percentage = (wnba[\"FTM\"] / wnba[\"FTA\"]) * 100\n",
+        "print(\"The mean percentage of success in free throws is:\",FT_percentage.mean())\n",
+        "# don't agree... almost 80% is a good rate\n",
+        "\n",
+        "# \tKobe Bryant*\tSG\tLos Angeles Lakers (1996–2016) ---> .837\n",
+        "#   Michael Jordan*\tSG\tChicago Bulls (1984–1993, 1995–1998) --->\t.835"
+      ],
+      "metadata": {
+        "id": "D9pPiAWc1MNk",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e827fe7d-75e8-4d04-d3d1-b1d3d2ec5633"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The mean percentage of success in free throws is: 78.59271907346114\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Your brother-in-law heard on the TV that the average assists among NBA (male) and WNBA (female) players is 52 for the 2016-2017 season.\n",
+        "# He is convinced this average would be higher if we only considered the players from the WNBA."
+      ],
+      "metadata": {
+        "id": "SZhAJiGK1RK0"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "av_assists_wnba = wnba[\"AST\"].mean()\n",
+        "av_assists_wnba\n",
+        "\n",
+        "# brother in law is wrong. considering just WNBA, the average is lower than 52."
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EXU9xOsxTNxs",
+        "outputId": "2dc242a2-6370-4e33-bd26-c0b37655d086"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "44.514084507042256"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "TqzEIaiQTNvI"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/3_Inferential_Analysis.ipynb b/3_Inferential_Analysis.ipynb
new file mode 100644
index 0000000..4603a7a
--- /dev/null
+++ b/3_Inferential_Analysis.ipynb
@@ -0,0 +1,1210 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Uf-hnJxTg2AJ"
+      },
+      "source": [
+        "# Inferential statistics\n",
+        "## Part III - Inferential Analysis\n",
+        "\n",
+        "We're now going to look for answers to the ongoing basketball discussions between you and your family. The main ones we want to reasearch are the following:\n",
+        "\n",
+        "- Your grandmother says that your sister couldn't play in a professional basketball league (not only the WNBA, but ANY professional basketball league) because she's too skinny and lacks muscle.\n",
+        "- Your sister says that most female professional players fail their free throws.\n",
+        "- Your brother-in-law heard on the TV that the average assists among NBA (male) and WNBA (female) players is 52 for the 2016-2017 season. He is convinced this average would be higher if we only considered the players from the WNBA.\n",
+        "\n",
+        "Let's investigate these claims and see if we can find proof to refute or support them.\n",
+        "\n",
+        "### Libraries\n",
+        "Import the necessary libraries first."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "NrQylzJAg2AK"
+      },
+      "outputs": [],
+      "source": [
+        "# Libraries\n",
+        "import math\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import scipy.stats as st\n",
+        "import matplotlib.pyplot as plt\n",
+        "from scipy.stats import ttest_1samp\n",
+        "pd.set_option('display.max_columns', 50)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "I8_ZkJp5g2AL"
+      },
+      "source": [
+        "### Load the dataset\n",
+        "\n",
+        "Load the cleaned dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "wmGIbApIg2AL",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 330
+        },
+        "outputId": "11553b2b-67d7-4d66-815a-0e18ba517238"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              Name Team  Pos  Height  Weight        BMI Birth_Place  \\\n",
+              "0    Aerial Powers  DAL    F     183    71.0  21.200991          US   \n",
+              "1      Alana Beard   LA  G/F     185    73.0  21.329438          US   \n",
+              "2     Alex Bentley  CON    G     170    69.0  23.875433          US   \n",
+              "3  Alex Montgomery  SAN  G/F     185    84.0  24.543462          US   \n",
+              "4     Alexis Jones  MIN    G     175    78.0  25.469388          US   \n",
+              "\n",
+              "           Birthdate  Age         College Experience  Games Played  MIN  FGM  \\\n",
+              "0   January 17, 1994   23  Michigan State          2             8  173   30   \n",
+              "1       May 14, 1982   35            Duke         12            30  947   90   \n",
+              "2   October 27, 1990   26      Penn State          4            26  617   82   \n",
+              "3  December 11, 1988   28    Georgia Tech          6            31  721   75   \n",
+              "4     August 5, 1994   23          Baylor          R            24  137   16   \n",
+              "\n",
+              "   FGA   FG%  3PM  3PA   3P%  FTM  FTA   FT%  OREB  DREB  REB  AST  STL  BLK  \\\n",
+              "0   85  35.3   12   32  37.5   21   26  80.8     6    22   28   12    3    6   \n",
+              "1  177  50.8    5   18  27.8   32   41  78.0    19    82  101   72   63   13   \n",
+              "2  218  37.6   19   64  29.7   35   42  83.3     4    36   40   78   22    3   \n",
+              "3  195  38.5   21   68  30.9   17   21  81.0    35   134  169   65   20   10   \n",
+              "4   50  32.0    7   20  35.0   11   12  91.7     3     9   12   12    7    0   \n",
+              "\n",
+              "   TO  PTS  DD2  TD3  \n",
+              "0  12   93    0    0  \n",
+              "1  40  217    0    0  \n",
+              "2  24  218    0    0  \n",
+              "3  38  188    2    0  \n",
+              "4  14   50    0    0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-2eaa4cb2-1fd8-49b3-8417-303f7305e6e7\" 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",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Team</th>\n",
+              "      <th>Pos</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>BMI</th>\n",
+              "      <th>Birth_Place</th>\n",
+              "      <th>Birthdate</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>College</th>\n",
+              "      <th>Experience</th>\n",
+              "      <th>Games Played</th>\n",
+              "      <th>MIN</th>\n",
+              "      <th>FGM</th>\n",
+              "      <th>FGA</th>\n",
+              "      <th>FG%</th>\n",
+              "      <th>3PM</th>\n",
+              "      <th>3PA</th>\n",
+              "      <th>3P%</th>\n",
+              "      <th>FTM</th>\n",
+              "      <th>FTA</th>\n",
+              "      <th>FT%</th>\n",
+              "      <th>OREB</th>\n",
+              "      <th>DREB</th>\n",
+              "      <th>REB</th>\n",
+              "      <th>AST</th>\n",
+              "      <th>STL</th>\n",
+              "      <th>BLK</th>\n",
+              "      <th>TO</th>\n",
+              "      <th>PTS</th>\n",
+              "      <th>DD2</th>\n",
+              "      <th>TD3</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Aerial Powers</td>\n",
+              "      <td>DAL</td>\n",
+              "      <td>F</td>\n",
+              "      <td>183</td>\n",
+              "      <td>71.0</td>\n",
+              "      <td>21.200991</td>\n",
+              "      <td>US</td>\n",
+              "      <td>January 17, 1994</td>\n",
+              "      <td>23</td>\n",
+              "      <td>Michigan State</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>173</td>\n",
+              "      <td>30</td>\n",
+              "      <td>85</td>\n",
+              "      <td>35.3</td>\n",
+              "      <td>12</td>\n",
+              "      <td>32</td>\n",
+              "      <td>37.5</td>\n",
+              "      <td>21</td>\n",
+              "      <td>26</td>\n",
+              "      <td>80.8</td>\n",
+              "      <td>6</td>\n",
+              "      <td>22</td>\n",
+              "      <td>28</td>\n",
+              "      <td>12</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>12</td>\n",
+              "      <td>93</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Alana Beard</td>\n",
+              "      <td>LA</td>\n",
+              "      <td>G/F</td>\n",
+              "      <td>185</td>\n",
+              "      <td>73.0</td>\n",
+              "      <td>21.329438</td>\n",
+              "      <td>US</td>\n",
+              "      <td>May 14, 1982</td>\n",
+              "      <td>35</td>\n",
+              "      <td>Duke</td>\n",
+              "      <td>12</td>\n",
+              "      <td>30</td>\n",
+              "      <td>947</td>\n",
+              "      <td>90</td>\n",
+              "      <td>177</td>\n",
+              "      <td>50.8</td>\n",
+              "      <td>5</td>\n",
+              "      <td>18</td>\n",
+              "      <td>27.8</td>\n",
+              "      <td>32</td>\n",
+              "      <td>41</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>19</td>\n",
+              "      <td>82</td>\n",
+              "      <td>101</td>\n",
+              "      <td>72</td>\n",
+              "      <td>63</td>\n",
+              "      <td>13</td>\n",
+              "      <td>40</td>\n",
+              "      <td>217</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Alex Bentley</td>\n",
+              "      <td>CON</td>\n",
+              "      <td>G</td>\n",
+              "      <td>170</td>\n",
+              "      <td>69.0</td>\n",
+              "      <td>23.875433</td>\n",
+              "      <td>US</td>\n",
+              "      <td>October 27, 1990</td>\n",
+              "      <td>26</td>\n",
+              "      <td>Penn State</td>\n",
+              "      <td>4</td>\n",
+              "      <td>26</td>\n",
+              "      <td>617</td>\n",
+              "      <td>82</td>\n",
+              "      <td>218</td>\n",
+              "      <td>37.6</td>\n",
+              "      <td>19</td>\n",
+              "      <td>64</td>\n",
+              "      <td>29.7</td>\n",
+              "      <td>35</td>\n",
+              "      <td>42</td>\n",
+              "      <td>83.3</td>\n",
+              "      <td>4</td>\n",
+              "      <td>36</td>\n",
+              "      <td>40</td>\n",
+              "      <td>78</td>\n",
+              "      <td>22</td>\n",
+              "      <td>3</td>\n",
+              "      <td>24</td>\n",
+              "      <td>218</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Alex Montgomery</td>\n",
+              "      <td>SAN</td>\n",
+              "      <td>G/F</td>\n",
+              "      <td>185</td>\n",
+              "      <td>84.0</td>\n",
+              "      <td>24.543462</td>\n",
+              "      <td>US</td>\n",
+              "      <td>December 11, 1988</td>\n",
+              "      <td>28</td>\n",
+              "      <td>Georgia Tech</td>\n",
+              "      <td>6</td>\n",
+              "      <td>31</td>\n",
+              "      <td>721</td>\n",
+              "      <td>75</td>\n",
+              "      <td>195</td>\n",
+              "      <td>38.5</td>\n",
+              "      <td>21</td>\n",
+              "      <td>68</td>\n",
+              "      <td>30.9</td>\n",
+              "      <td>17</td>\n",
+              "      <td>21</td>\n",
+              "      <td>81.0</td>\n",
+              "      <td>35</td>\n",
+              "      <td>134</td>\n",
+              "      <td>169</td>\n",
+              "      <td>65</td>\n",
+              "      <td>20</td>\n",
+              "      <td>10</td>\n",
+              "      <td>38</td>\n",
+              "      <td>188</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Alexis Jones</td>\n",
+              "      <td>MIN</td>\n",
+              "      <td>G</td>\n",
+              "      <td>175</td>\n",
+              "      <td>78.0</td>\n",
+              "      <td>25.469388</td>\n",
+              "      <td>US</td>\n",
+              "      <td>August 5, 1994</td>\n",
+              "      <td>23</td>\n",
+              "      <td>Baylor</td>\n",
+              "      <td>R</td>\n",
+              "      <td>24</td>\n",
+              "      <td>137</td>\n",
+              "      <td>16</td>\n",
+              "      <td>50</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>20</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>11</td>\n",
+              "      <td>12</td>\n",
+              "      <td>91.7</td>\n",
+              "      <td>3</td>\n",
+              "      <td>9</td>\n",
+              "      <td>12</td>\n",
+              "      <td>12</td>\n",
+              "      <td>7</td>\n",
+              "      <td>0</td>\n",
+              "      <td>14</td>\n",
+              "      <td>50</td>\n",
+              "      <td>0</td>\n",
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+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-2eaa4cb2-1fd8-49b3-8417-303f7305e6e7');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-25909997-69e8-487b-90a0-852f2f01c81f\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-25909997-69e8-487b-90a0-852f2f01c81f')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-25909997-69e8-487b-90a0-852f2f01c81f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ],
+      "source": [
+        "#my cleaned dataset\n",
+        "\n",
+        "wnba = pd.read_csv(\"/content/1_Data_Cleaning_wnba_cleaned.csv\")\n",
+        "wnba.head()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pKw0Hx9bg2AL"
+      },
+      "source": [
+        "# Question 1: Can my sister play in a professional female basketball league?\n",
+        "\n",
+        "As we said, you grandmother is convinced that your sister couldn't play in a professional league because of her physique and weight (her weight is 67kg).\n",
+        "\n",
+        "To find an actual answer to the question we first need to know what's the average weight of a professional female basketball player. The data we have only refers to the WNBA league and not to every female professional basketball league in the world, therefore we have no way of actually calculating it.\n",
+        "\n",
+        "Still, given that we do have *some* data we can **infer** it using a sample of players like the one we have.\n",
+        "\n",
+        "**How would you do it? Try and think about the requirements that your sample must satisfy in order to be used to infer the average weight. Do you feel it actually fulfills those requirements? Do you need to make any assumptions? We could calculate a confidence interval to do the inference, but do you know any other ways?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#1. My Hypothesis are bad written (?)\n",
+        "\n",
+        "# H0: The weight mu for a professional female basketball player is ----> greater than or equal to 67.\n",
+        "# H1: The sister's weight is an obstacle and she will never fulfill her dream because she's too skinny -----> less than 67.\n",
+        "\n",
+        "# sample\n",
+        "sample_weight = wnba[\"Weight\"]\n",
+        "\n",
+        "st.ttest_1samp(sample_weight, 67, alternative = \"less\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tC6kCXQKi2Zl",
+        "outputId": "d68004f5-55e6-4c63-f28b-606c8d0ae180"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TtestResult(statistic=12.981385575989545, pvalue=1.0, df=141)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vzoi3HPxg2AL",
+        "outputId": "7b7a6b2e-7101-4298-9d85-a2b0e624e8fe"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "I can not reject the null hypothesis\n"
+          ]
+        }
+      ],
+      "source": [
+        "# to answer the question:\n",
+        "\n",
+        "stats = st.ttest_1samp(sample_weight, 67, alternative = \"less\")[0]\n",
+        "p_value = st.ttest_1samp(sample_weight, 67, alternative = \"less\")[1]\n",
+        "\n",
+        "if p_value > 0.05:\n",
+        "  print(\"I can not reject the null hypothesis\")\n",
+        "else:\n",
+        "  print(\"We can reject the null hypothesis\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XFSiILW6g2AL"
+      },
+      "source": [
+        "**Now that all the requirements have been taken into account, compute the confidence interval of the average weight with a confidence level of 95%.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# from houses near/far\n",
+        "\n",
+        "# 1. We need a sample\n",
+        "# 2. the mean of the sample\n",
+        "# 3. number observations\n",
+        "# 4. The std of the population\n",
+        "\n",
+        "# 1.\n",
+        "sample_weight = wnba[\"Weight\"]\n",
+        "\n",
+        "# 2. sample mean\n",
+        "mean = wnba[\"Weight\"].mean()\n",
+        "\n",
+        "# 3. print(sample_weight.count())\n",
+        "n = 142\n",
+        "\n",
+        "# 4.\n",
+        "std = wnba[\"Weight\"].std()\n",
+        "\n",
+        "# now we can build our 95% interval\n",
+        "print(\"left end: \", mean - 2*(std/np.sqrt(n-1)))\n",
+        "print(\"right end: \", mean + 2*(std/np.sqrt(n-1)))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "q3JpoNkynw16",
+        "outputId": "6c07ccdc-fe68-4677-f533-c6224f6940b8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "left end:  77.1267941385151\n",
+            "right end:  80.83095234035815\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RIpOBFcmg2AM"
+      },
+      "source": [
+        "**What can you say about these results?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\"\"\" with left end:  77.1267941385151 and right end:  80.83095234035815 and with 95% confidence, we can estimate - with wbna sample - that the true average\n",
+        "weight for female players is between these values.\n",
+        "\n",
+        "From this interval, maybe granny is a little right because her weight is actually significantly different from the average weight.\n",
+        "STILL, it's a sample and weight is not decisive, in my opinion"
+      ],
+      "metadata": {
+        "id": "GIm5Ld4Tt4be"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nP_gVDQ0g2AM"
+      },
+      "source": [
+        "**If your sister weighs 67kg what would you tell your grandmother in regards to her assumption?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "x2F-NjPWg2AM"
+      },
+      "outputs": [],
+      "source": [
+        "# we know the drill: mothers(grannies) are always right <3 :)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Qpfw5lZwg2AM"
+      },
+      "source": [
+        "## Bonus: Can you plot the probability distribution of the average weight, indicating where the critical region is?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JumRYxuxg2AM"
+      },
+      "source": [
+        "# Question 2: Do female professional basketball players fail the majority of their free throws?\n",
+        "\n",
+        "You do not agree with your sister when she says that most female players fail their free throws. You decide to try and estimate the percentage of players that fail more than 40% of their free throws using, you guessed it, the WNBA sample.\n",
+        "\n",
+        "**How would you do it? Try and think about the requirements that your sample must satisfy in order to be used to infer the proportion of players that miss more than 40% of their free throws. Do you feel it actually fulfills those requirements? Do you need to make any assumptions?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "wnba.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CoquXO7cF13N",
+        "outputId": "aac40fbd-0e88-407a-c9db-1c24dc17a4ef"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(142, 32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "wnba"
+      ],
+      "metadata": {
+        "id": "GdfS9yjzozB6"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bJvZlrUcg2AM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1ab9d3b4-7e74-43d6-b523-aabee599e68d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The mean percentage of success in free throws is: 78.59271907346114\n"
+          ]
+        }
+      ],
+      "source": [
+        "# H0: Players in wnba fail less than 40% of their free throws -------- > fails <= 40% p = 0.6\n",
+        "# H1: Players in wnba that fail more than 40% of their free throws --- > fails > 40%\n",
+        "\n",
+        "# Your sister says that most female professional players fail their free throws.\n",
+        "FT_percentage = (wnba[\"FTM\"] / wnba[\"FTA\"]) * 100 # this is in total of course.\n",
+        "print(\"The mean percentage of success in free throws is:\",FT_percentage.mean())  # so let's check it out:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "## The sample proportion is then: P = M/N\n",
+        "successes = wnba[\"FTM\"].sum()\n",
+        "sample = wnba[\"FTA\"].sum()\n",
+        "\n",
+        "## Sample proportion :\n",
+        "sample_p = successes/ sample  #### djius.. this is points / attempts\n",
+        "sample_p\n",
+        "\n",
+        "## N = sample\n",
+        "n = len(wnba[\"FTA\"]) # dunno... this is all players\n",
+        "\n",
+        "# to get the standard error\n",
+        "std_error = np.sqrt(sample_p * (1 - sample_p) / n) ##### ddof = 1 ???\n",
+        "std_error\n",
+        "\n",
+        "# the mean --- >  μˆP = p # so the proportion of successes will actually work as my mean ---- i think so"
+      ],
+      "metadata": {
+        "id": "MwDeXXvZIRNQ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CpuSzepbg2AN"
+      },
+      "source": [
+        "**Now that all the requirements have been taken into account, compute the confidence interval of the proportion with a confidence level of 95%:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "LIIZlILXg2AN"
+      },
+      "outputs": [],
+      "source": [
+        "conf_at_95 = st.norm.interval(0.95, loc = sample_p, scale = std_error)\n",
+        "left_end = conf_at_95[0]\n",
+        "right_end = conf_at_95[1]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "chYKYLtgg2AN"
+      },
+      "source": [
+        "**What can you comment about our result? What would you tell your sister?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "rsz9Rw50g2AN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3fc6ab77-2b42-458f-9a2d-2e557b34a19e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "wnba players fail betwenn 13.425935687396995 % and 26.585463571651157 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "# actually players in wnba fail less than 40% of their free throws\n",
+        "print(\"wnba players fail betwenn\", (1 - right_end)*100,\"% and\",(1 - left_end)*100,\"%\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_OMEWZAmg2AN"
+      },
+      "source": [
+        "# Bonus: Can you plot the probability distribution of the proportion of missed free throws, indicating where the critical region is?"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4BpqrHt-g2AN"
+      },
+      "outputs": [],
+      "source": [
+        "#your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lqepolv6g2AN"
+      },
+      "source": [
+        "# Question 3: Is the average number of assists for WNBA players only higher than the average for WNBA and NBA players together?"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "D73hSwcYg2AN"
+      },
+      "source": [
+        "Your brother-in-law is convinced that the average assists for female professional players is higher than the average of both female and male players combined (which is 52 for the 2016-2017 season). You would like to actually prove if this is true or not but you remember your stats teacher saying \"you can't *prove* anything, you just can say that *you are not* saying foolishness\".\n",
+        "\n",
+        "**How would you do it? Try and think about the requirements that your sample must satisfy in order to do that. Do you feel it actually fulfills those requirements? Do you need to make any assumptions?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "ast_mean = wnba[\"AST\"].mean()\n",
+        "print(\"The mean of assists in wnba is:\",ast_mean.mean())  # so let's check it out:"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XGqhm2J4bmmE",
+        "outputId": "504f386e-35cd-4bda-ac2c-2be07778d068"
+      },
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "The mean of assists in wnba is: 44.514084507042256\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z8A3cwGBg2AN"
+      },
+      "source": [
+        "**Use a two-tailed one-sample t-test to see if we can reject (or not) the null hypothesis with a 95% confidence level.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "DvwEv6Smg2AN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "211e0ab8-55e0-40ac-916e-49a10eee7b10"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TtestResult(statistic=-2.1499947192482898, pvalue=0.033261541354107166, df=141)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ],
+      "source": [
+        "# 1.\n",
+        "# H0: mu female assists = 52\n",
+        "# H1: mu female assists != 52\n",
+        "\n",
+        "# 2. significance level\n",
+        "alpha = 0.05\n",
+        "\n",
+        "# sample\n",
+        "ast_sample = wnba[\"AST\"]#.sample(30) should I sample?\n",
+        "\n",
+        "# for stats and p\n",
+        "st.ttest_1samp(ast_sample, 52, alternative = \"two-sided\") # equal is actually default, but like this, looks like I'm inteligent :D"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "stats, p_value = st.ttest_1samp(ast_sample, 52, alternative = \"two-sided\")\n",
+        "print(p_value)\n",
+        "print(stats)\n",
+        "\n",
+        "if p_value > 0.05:\n",
+        "  print(\"I can not reject the null hypothesis\")\n",
+        "else:\n",
+        "  print(\"We can reject the null hypothesis\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "WxnNQtF0_JRf",
+        "outputId": "74c150f5-aa70-4b4f-89b0-fb06f4fa3d5c"
+      },
+      "execution_count": 39,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.033261541354107166\n",
+            "-2.1499947192482898\n",
+            "We can reject the null hypothesis\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Because p_value is lower than significance level ---> sure we know that the null has to go"
+      ],
+      "metadata": {
+        "id": "XWfw8SB8_Ihv"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-z97C0qkg2AO"
+      },
+      "source": [
+        "**Now use a one-tailed one-sample t-test to see if we can reject (or not) the null hypothesis with a 95% confidence level.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "xq9QdYbbg2AO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a859f1f8-1f11-4577-97c6-85adb441c39e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "0.9833692293229463\n",
+            "-2.1499947192482898\n",
+            "I can not reject the null hypothesis\n"
+          ]
+        }
+      ],
+      "source": [
+        "# H0: mu female assists <= 52\n",
+        "# H1: mu female assists > 52\n",
+        "\n",
+        "stats, p_value = st.ttest_1samp(ast_sample, 52, alternative = \"greater\")\n",
+        "print(p_value)\n",
+        "print(stats)\n",
+        "\n",
+        "if p_value > 0.05:\n",
+        "  print(\"I can not reject the null hypothesis\")\n",
+        "else:\n",
+        "  print(\"We can reject the null hypothesis\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "fLvXQsAtDxF2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5Nh_-ZTmg2AO"
+      },
+      "source": [
+        "# Bonus: Can you plot the resulting t-distribution of both tests? Indicate where the is the critical region and where does your statistic fall.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TV0q8038g2AO"
+      },
+      "outputs": [],
+      "source": [
+        "#your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DiIIPGPxg2AO"
+      },
+      "source": [
+        "# Bonus: Satisfying your curiosity\n",
+        "\n",
+        "You finally managed to solve your family's debates over basketball! While you were doing that you started to take an interest in the normal distribution.\n",
+        "\n",
+        "You read that the normal distribution is present in a lot of natural phenomenons, like blood pressure, IQ, weight and height. If, for example, we could plot the distribution of the weights of every human on the planet right now it would have the shape of a normal distribution.\n",
+        "\n",
+        "In light of this you would like to see if it's possible to check if the distribution of the weights of the WNBA players is a sample distribution that comes from a population that has a normal distribution, because theoretically this should be the case.\n",
+        "\n",
+        "**How would you try to demonstrate that our sample fits a normal distribution? What kind of test would you use? Would you have to make any assumptions?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "fSlo2glrg2AO"
+      },
+      "outputs": [],
+      "source": [
+        "#your-answer-here"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KGrLR4x5g2AO"
+      },
+      "outputs": [],
+      "source": [
+        "# your code here"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jpErGRWIg2AO"
+      },
+      "source": [
+        "**What are your comments in regards to the results of the test?**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Lq8XlXmyg2AO"
+      },
+      "outputs": [],
+      "source": [
+        "#your-answer-here"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.8"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
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