Data-Science-For-Beginners/1-Introduction/04-stats-and-probability/notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Introduction to Probability and Statistics\r\n",
    "|\r\n",
    "In this notebook, we will play around with some of the concepts we have previously discussed. Many concepts from probability and statistics are well-represented in major libraries for data processing in Python, such as `numpy` and `pandas`."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "import numpy as np\r\n",
    "import pandas as pd\r\n",
    "import random\r\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "\r\n",
    "## Random Variables and Distributions\r\n",
    "\r\n",
    "Let's start with drawing a sample of 30 variables from a uniform disribution from 0 to 9. We will also compute mean and variance."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "sample = [ random.randint(0,10) for _ in range(30) ]\r\n",
    "print(f\"Sample: {sample}\")\r\n",
    "print(f\"Mean = {np.mean(sample)}\")\r\n",
    "print(f\"Variance = {np.var(sample)}\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Sample: [4, 6, 3, 0, 3, 4, 7, 7, 9, 6, 8, 2, 0, 3, 10, 7, 2, 0, 2, 1, 1, 6, 5, 0, 9, 0, 1, 8, 2, 9]\n",
      "Mean = 4.166666666666667\n",
      "Variance = 10.272222222222222\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "To visually estimate how many different values are there in the sample, we can plot the **histogram**:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "plt.hist(sample)\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
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     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Analyzing Real Data\r\n",
    "\r\n",
    "Mean and variance are very important when analyzing real-world data. Let's load the data about baseball players from [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "source": [
    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t',header=None,names=['Name','Team','Rome','Height','Weight','Age'])\r\n",
    "df"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 Name Team            Rome  Height  Weight    Age\n",
       "0       Adam_Donachie  BAL         Catcher      74   180.0  22.99\n",
       "1           Paul_Bako  BAL         Catcher      74   215.0  34.69\n",
       "2     Ramon_Hernandez  BAL         Catcher      72   210.0  30.78\n",
       "3        Kevin_Millar  BAL   First_Baseman      72   210.0  35.43\n",
       "4         Chris_Gomez  BAL   First_Baseman      73   188.0  35.71\n",
       "...               ...  ...             ...     ...     ...    ...\n",
       "1029    Brad_Thompson  STL  Relief_Pitcher      73   190.0  25.08\n",
       "1030    Tyler_Johnson  STL  Relief_Pitcher      74   180.0  25.73\n",
       "1031   Chris_Narveson  STL  Relief_Pitcher      75   205.0  25.19\n",
       "1032    Randy_Keisler  STL  Relief_Pitcher      75   190.0  31.01\n",
       "1033      Josh_Kinney  STL  Relief_Pitcher      73   195.0  27.92\n",
       "\n",
       "[1034 rows x 6 columns]"
      ],
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       "      <th>Name</th>\n",
       "      <th>Team</th>\n",
       "      <th>Rome</th>\n",
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       "      <th>0</th>\n",
       "      <td>Adam_Donachie</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Catcher</td>\n",
       "      <td>74</td>\n",
       "      <td>180.0</td>\n",
       "      <td>22.99</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Paul_Bako</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Catcher</td>\n",
       "      <td>74</td>\n",
       "      <td>215.0</td>\n",
       "      <td>34.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ramon_Hernandez</td>\n",
       "      <td>BAL</td>\n",
       "      <td>Catcher</td>\n",
       "      <td>72</td>\n",
       "      <td>210.0</td>\n",
       "      <td>30.78</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Kevin_Millar</td>\n",
       "      <td>BAL</td>\n",
       "      <td>First_Baseman</td>\n",
       "      <td>72</td>\n",
       "      <td>210.0</td>\n",
       "      <td>35.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chris_Gomez</td>\n",
       "      <td>BAL</td>\n",
       "      <td>First_Baseman</td>\n",
       "      <td>73</td>\n",
       "      <td>188.0</td>\n",
       "      <td>35.71</td>\n",
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       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>Brad_Thompson</td>\n",
       "      <td>STL</td>\n",
       "      <td>Relief_Pitcher</td>\n",
       "      <td>73</td>\n",
       "      <td>190.0</td>\n",
       "      <td>25.08</td>\n",
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       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>Tyler_Johnson</td>\n",
       "      <td>STL</td>\n",
       "      <td>Relief_Pitcher</td>\n",
       "      <td>74</td>\n",
       "      <td>180.0</td>\n",
       "      <td>25.73</td>\n",
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       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>Chris_Narveson</td>\n",
       "      <td>STL</td>\n",
       "      <td>Relief_Pitcher</td>\n",
       "      <td>75</td>\n",
       "      <td>205.0</td>\n",
       "      <td>25.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>Randy_Keisler</td>\n",
       "      <td>STL</td>\n",
       "      <td>Relief_Pitcher</td>\n",
       "      <td>75</td>\n",
       "      <td>190.0</td>\n",
       "      <td>31.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>Josh_Kinney</td>\n",
       "      <td>STL</td>\n",
       "      <td>Relief_Pitcher</td>\n",
       "      <td>73</td>\n",
       "      <td>195.0</td>\n",
       "      <td>27.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1034 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "> We are using a package called **Pandas** here for data analysis. We will talk more about Pandas and working with data in Python later in this course.\r\n",
    "\r\n",
    "Let's compute average values for age, height and weight:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "source": [
    "df[['Age','Height','Weight']].mean()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Age        28.736712\n",
       "Height     73.697292\n",
       "Weight    201.689255\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 30
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Age, height and weight are all continuous random variables. What do you think their distribution is? A good way to find out is to plot the histogram of values: "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "source": [
    "df['Weight'].hist(bins=15)\r\n",
    "plt.suptitle('Weight distribution of MLB Players')\r\n",
    "plt.xlabel('Weight')\r\n",
    "plt.ylabel('Count')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
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     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "print(list(df['Weight'])[:20])"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[180.0, 215.0, 210.0, 210.0, 188.0, 176.0, 209.0, 200.0, 231.0, 180.0, 188.0, 180.0, 185.0, 160.0, 180.0, 185.0, 197.0, 189.0, 185.0, 219.0]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
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