5.jupyter-labs-eda-dataviz.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment: Exploring and Preparing Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **70** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this assignment, we will predict if the Falcon 9 first stage will land successfully. SpaceX advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is due to the fact that SpaceX can reuse the first stage.\n",
    "\n",
    "In this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falcon 9 first stage will land successfully\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n",
    "\n",
    "Perform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n",
    "\n",
    "*   Exploratory Data Analysis\n",
    "*   Preparing Data  Feature Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# andas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "#NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df=pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# df = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
    "df=pd.read_csv('dataset_part_2.csv')\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n",
    "\n",
    "We can plot out the <code>FlightNumber</code> vs. <code>PayloadMass</code>and overlay the outcome of the launch. We see that as the flight number increases, the first stage is more likely to land successfully. The payload mass is also important; it seems the more massive the payload, the less likely the first stage will return.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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cxzE9evSwllH99/WDDz4wkmp8/W688UaTm5trNauyWF7h8Pvvvzc5OTlm+PDhvmW9+eabpkWLFsZxHJOZmWk+/vhjX7IeffRRc8EFF1T86zE/r3D4t7/9zeTn55tVq1aZWbNmmV69epns7Gyzf/9+aznbtm0zkkxKSoqZNGmSWbFihZkwYYJxHMcsXLiw1jnBsqp7/PHHTevWrSt62HbWgQMHzDXXXGMkmcaNG5umTZuav/71r1ZzDh48aLp06WKuuOIK85///MccOHDATJgwwUiK+vc32N9cP/rC7W+7za7wsh9hqy/CZdnsi1A5fnRFqCw/uiJYll994eXnwkZfhMrxoyuCZdnui1D7zLZ7wsu+ua2e8Po6wEZPuGXZ7IlwWTa7IlyO7Z4IleVHT3j9uYi2J8Ll2O6JUFm2eyLca17bXeH19bWNvojktXy0feEly0ZfuOXY7Aq3LJt9ES7LZl9E8jMRbVe4Zdnsi3BZNvsi3HEr213h9RhZtF0RybG4aHvCS5at/Qq3LFtd4ZZjsyfCZdner4jk5yLarnDLstUV4XJs71e4HXe22ReRHOOOpi8iPZYeTV94zbLRF16ybPSFlxxbfeGWZbMvIv25iKYvvGTZ6Au3HL+Ob1Y/b+TneZBw56hsHtt0Oxdm8zxIqCw/zoMEy/LrPEiwLD+ObVbP8fM8iNvPhc3zIMGy/Di2WT3Hdk+4nfe12ReRnGOOpi8iPZcdTV94zbLRF16ybPWFlywbfeGWY7MvIv25iKYvvGTZ6Au3HNt9EW7thp/7FogcCw7rsVgtOBw5cqTp0qWL2bx5s28ZBw4cMMXFxWbZsmXm3nvvNW3btjWFhYVWM7766iuTnp5uPv3004ptfi04rG7Pnj0mIyPDTJw40fpzN2nSxPTt27fKtttuu82cccYZ1rMqy83NNYMHD/bt+V999VXTqVMn8+qrr5rPPvvM/PWvfzVt2rQxL7zwgvWsdevWmbPPPttIMklJSaZPnz5m+PDhpmfPntYyQi043Lp1a5XH/fKXvzSDBg2ymlVZrBYcHjx40AwdOtSceuqpJhAI+Ja1Z88eU1xcbJYsWWKuv/56c+yxx5rt27dbzVq+fLnJyMiosuPi54LD6rZu3WqaNGli/vWvf1nL+frrr40k84tf/KLK4y699FJz1VVX1TonWFZ1J5xwghk9enRUGeGynnjiCdOjRw8za9Yss3LlSvP000+bli1bmnnz5lnNWb58uenVq1dFbwwaNMhcdNFF5qKLLqp1jjHB/+b60Rduf9ttdoVbls2+CJdlsy+C5fjVFV73w2x0RbAsv/rCy7xs9EWoHD+6IlSWzb4Itc9suye87Jvb6gkvWbZ6wi3LZk+EyrLdFZG8joq2J0Jl+dETXucVbU+Ey7HdE+GybPWE22tem10RyevraPsikqxo+8JrVrR94ZZjsytqcyyktn3hlmWrLyKdUzRd4SXLVl94yfLrdUjl41Z+Hq+onlWZ7VsfhsqxfbwiVJYfxyuqZ/l5vMLtWKaN1x/Bsvw8XlE9qzqbxyuCZfnxGiRYjs2ecDvubLMvIjnGHU1fRJITbV94zbLRF25ZtvqiNuciatsXblk2+yLSeUXTF16ybPSFlxw/9iuqnzfyc78i3Dkqm/sV4XJs71eEyvJjv6J6lp/7FV7OJ9rYt6ie4+d+hducbO5XBMvyY78iWI7NnnA772uzLyI5xxxNX0SSE21feM2y0RduWTb7ojbrAWrTF245Nvsi0jlF0xdesmz0hZccm30Rbu2G38csEBkWHNZjsVhwOHr0aNOpUyezYcMGX3OqO++888xNN91k9Tlff/31ilIqf5NkHMcxSUlJ5ocffrCaV935559vbrnlFuvPe8wxx5gbbrihyra8vDyTmZlpPavcpk2bTKNGjcwbb7zhW0anTp3M5MmTq2x75JFHzAknnOBb5p49eyr+OF155ZXm4osvtvbc1X9f169fbySZTz75pMrjhgwZYq655hqrWZXFYsHhwYMHzWWXXWZOPvlka1f19Np33bp1i/pKmNWznnzyyYqeqNwdjRo1Ml26dLGaFUq3bt3MY489Zi3nwIEDpnHjxuaRRx6p8rjf/OY3pl+/frXOCZZV2fvvv28kVTmBZjNr3759pkmTJiY/P7/K42644YaodjLDzem7774zO3bsMMYYc9ppp5mRI0fWOifU31zbfeHlb7utrnDLstkXke6z1LYvQuX40RW1mVNtuyJUlh994WVeNvoiVI4fXeFlTjb7olz5PrOf+xWVcyqzvSggVJYf+xWhsqqzsV9RPcvP/YrKOaFEu08RLMvP/YrqWZXZ3q+onOPXPkWwrMqi7Qm317zr1q2z1hWRvL6Oti+8Ztnoi9oeN4i0L9xy/vCHP1jrimjmFGlfuGXt37/fSl9EMqdou8Ita8+ePdb6IpJ5+bFfUX7cyu/9ispZlfmxb1E9x8/9Crfjfjb3K8qz/N6v8DInW/sV5Vmx2K8INi8/9isqZ/m9bxFsTjZ6wu24s82+iOQYdzR94TXHRl/U9rh9bfrCLctWX0Qzp0j7wi3LZl9EMq9o+8Ity1ZfRDInW/sVwc4b+bVf4XaOytZ+Rbgc2/sVkZx3i3a/IliWX/sVkc6rtvsWwXL82q9wm5PN/YpgWX7sV7jNyUZPuJ33tdkXkZxjjqYvvObY6IvanjevTV+4Zdnsi2jmFUlfuOXY7ItI5hRtX7hl2eqLSOZk83hFsLUbsThmAe8aCQjCGKPRo0frtdde0/z589W1a9eY55ff692W8847T6tWrdKnn35a8da7d28NHz5cn376qZKSkqzmVXbgwAEVFRWpQ4cO1p/7zDPP1Jo1a6psW7t2rbp06WI9q9y0adOUnp6uSy65xLeMffv2qVGjqhWVlJSkI0eO+JbZokULdejQQbt27dLcuXM1dOhQ37K6du2q9u3ba968eRXbDh48qPfee0/9+vXzLddvhw4d0pVXXqni4mK98847Ovroo2Oa70d3XH311frss8+qdEdmZqbuvvtuzZ0712pWMN9++602b95stT+aNm2qPn36xLw7nn/+eeXk5KhXr16+PP+hQ4d06NChmHZHWlqa2rVrp+LiYi1fvrxWveH2N9dWX8Tyb7uXLFt9Udt5RdoXbjk2u6I2c6ptV7hl2eyLSOYVTV+45djsikjmZKMvguUfOHDA9/0KP/6+esnye7/CbV42513+XH7vV4Qbs+19ivKsWOxXBJuXH/sV5Tmx2KcINqdoe8LtNe9xxx1nrSti+fraS5atvqjtvCLtC7ec6667zlpX1GZOte0Lt6zk5GQrfRHJnKLtCresw4cPW+uLSOZle7+i8nErv/cr/DxGFi7Hz/0KL3OytV9ROcvP/Qq3Odncr6ic5fd+Rah5+bFfUTnLz32LUHOy0RNux51t9kWsjnF7ybHVF7WdU236wi3LVl/UZk617Qu3LJt9Ecm8ou0LtyxbfRHJnGztVwQ7b+TXfkUszlGFy/FjvyKSOUW7XxEsy6/9Cq/zinbfIliOX/sVbnOyuV8RLMuP/Qq3OdnoCbfzvjb7IlbnmL3k2OqL2s6pNn3hlmWzL2ozr9r0hVuOzb6IZE7R9oVblq2+iGRONo9XBFu7kahrLOqtWKxqhD27d+82K1asMCtWrDCSKu4h/+WXX1rNufXWW01aWppZuHCh2bZtW8Xbvn37rOYYY8x9991n3n//fbNx40bz2Wefmd/+9remUaNG5t///rf1rOr8uqXymDFjzMKFC82GDRvM0qVLzeDBg02rVq3Mpk2brGd9/PHHpnHjxmb8+PGmuLjYvPzyyyYlJcVMnz7depYxxhw+fNgcc8wx5p577vHl+ctde+21pmPHjiY/P99s3LjRvPbaa6Zt27bmN7/5jfWst99+28yZM8ds2LDB/Pvf/za9evUyp512mjl48GBUz+v2+/rYY4+ZtLQ089prr5lVq1aZX/ziF6ZDhw6mtLTUeta3335rVqxYYWbPnm0kmb/97W9mxYoVZtu2bdZyDh06ZIYMGWI6depkPv300yrdceDAAatz2rNnj7nvvvvMkiVLzKZNm0xBQYG54YYbTHJyslm9erXVrGCiuZVAuKzdu3ebMWPGmA8//NBs3LjRLFiwwPTt29d07Ngx4p8Ltzm99tprpkmTJmbq1KmmuLjYPP300yYpKcksWrTI6pzKBQIBk5KSYp555pmInz+SrHPOOcdkZ2ebBQsWmA0bNphp06aZZs2amby8PKs5f//7382CBQvM+vXrzRtvvGG6dOliLr/88lrNycvfXBt94SXHVle4ZdnsC7csW31Rm32j2naFW5bNrvAyL1t94fVrGG1feMmx1RVesmz1hds+s639CrccWz3hlmV7vyJclu39ikhf39S2K8Ll2OwJL3OyuV/h5etnY7/CLcdWT3jJsrlfUV3117w2X4O4Zdnsi3BZtvsiXJbtvgiVE4yt25lVz7LdF+GyjLHbF+FyjLH3GsQty2ZfuGXZ6gu341Y2u8Ity1ZXhMux3RPhsmz3RKTHGGvbFeFybPeE25xs9oSXr5+trnDLstUVbjk29yu8HHe21Rdesmz0hVuOzb5wy7LZF7U5R1CbvnDLsdkXXuZkqy+8fv1s9IWXLBt94SXHZl+EO29k+zVIuCybr0FC5fjx+iNUlh+vPyI5xxfta5BQWbb3LcLNyfbrD7evn83XIOGybL4GCZdjsye8nPe11Rdesmz0hVuOzb5wy7LZF7U5R1/bvnDLstUXXuZkqy+8fv1s9IWXLBt94SXHZl+4rd3w8/gmIsOCw3pmwYIFRlKNt2uvvdZqTrAMSWbatGlWc4wx5vrrrzddunQxTZs2Ne3atTPnnXdeTBYbGuPfgsNhw4aZDh06mCZNmpjMzExz+eWXm8LCQus55d58801z4oknmuTkZJOVlWWmTp3qW9bcuXONJLNmzRrfMowxprS01PzqV78yxxxzjGnWrJk57rjjzNixY62cMKpuxowZ5rj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      "text/plain": [
       "<Figure size 2558.88x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n",
    "plt.xlabel(\"Flight Number\",fontsize=20)\n",
    "plt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that different launch sites have different success rates.  <code>CCAFS LC-40</code>, has a success rate of 60 %, while  <code>KSC LC-39A</code> and <code>VAFB SLC 4E</code> has a success rate of 77%.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's drill down to each site visualize its detailed launch records.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>catplot</code> to plot <code>FlightNumber</code> vs <code>LaunchSite</code>, set the  parameter <code>x</code>  parameter to <code>FlightNumber</code>,set the  <code>y</code> to <code>Launch Site</code> and set the parameter <code>hue</code> to <code>'class'</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648.25x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Flight Number and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(x='FlightNumber',y = 'LaunchSite',data=df, hue = 'LaunchSite')\n",
    "plt.legend(fontsize=7,loc = (1.1,0.8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK 2: Visualize the relationship between Payload and Launch Site\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also want to observe if there is any relationship between launch sites and their payload mass.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Pay Load Mass (kg) and y axis to be the launch site, and hue to be the class value\n",
    "sns.catplot(x='PayloadMass',y = 'LaunchSite',data=df, hue = 'Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now try to explain any patterns you found in the Payload Vs. Launch Site scatter point chart.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  3: Visualize the relationship between success rate of each orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a `bar chart` for the sucess rate of each orbit\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# HINT use groupby method on Orbit column and get the mean of Class column\n",
    "bar_data = df.groupby('Orbit').mean().sort_values(by='Class',ascending=False)\n",
    "plt.bar(bar_data.index,bar_data.Class)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be FlightNumber and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(x='FlightNumber',y='Orbit',data=df,hue='Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should see that in the LEO orbit the Success appears related to the number of flights; on the other hand, there seems to be no relationship between flight number when in GTO orbit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  5: Visualize the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 558.875x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a scatter point chart with x axis to be Payload and y axis to be the Orbit, and hue to be the class value\n",
    "sns.catplot(x='PayloadMass',y='Orbit',data=df,hue='Class')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should observe that Heavy payloads have a negative influence on GTO orbits and positive on GTO and Polar LEO (ISS) orbits.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  6: Visualize the launch success yearly trend\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can plot a line chart with x axis to be <code>Year</code> and y axis to be average success rate, to get the average launch success trend.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function will help you get the year from the date:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function to Extract years from the date \n",
    "year=[]\n",
    "def Extract_year(date):\n",
    "    for i in df[\"Date\"]:\n",
    "        year.append(i.split(\"-\")[0])\n",
    "    return year\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\n",
    "sns.lineplot(x=Extract_year(df.Date),y='Class',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "#personal test\n",
    "year=[]\n",
    "success_data = pd.DataFrame({'year':Extract_year(df.Date)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012</td>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013</td>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  FlightNumber        Date BoosterVersion  PayloadMass Orbit  \\\n",
       "0  2010             1  2010-06-04       Falcon 9  6104.959412   LEO   \n",
       "1  2012             2  2012-05-22       Falcon 9   525.000000   LEO   \n",
       "2  2013             3  2013-03-01       Falcon 9   677.000000   ISS   \n",
       "3  2013             4  2013-09-29       Falcon 9   500.000000    PO   \n",
       "4  2013             5  2013-12-03       Falcon 9  3170.000000   GTO   \n",
       "\n",
       "     LaunchSite      Outcome  Flights  GridFins  Reused   Legs LandingPad  \\\n",
       "0  CCAFS SLC 40    None None        1     False   False  False        NaN   \n",
       "1  CCAFS SLC 40    None None        1     False   False  False        NaN   \n",
       "2  CCAFS SLC 40    None None        1     False   False  False        NaN   \n",
       "3   VAFB SLC 4E  False Ocean        1     False   False  False        NaN   \n",
       "4  CCAFS SLC 40    None None        1     False   False  False        NaN   \n",
       "\n",
       "   Block  ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0    1.0            0  B0003  -80.577366  28.561857      0  \n",
       "1    1.0            0  B0005  -80.577366  28.561857      0  \n",
       "2    1.0            0  B0007  -80.577366  28.561857      0  \n",
       "3    1.0            0  B1003 -120.610829  34.632093      0  \n",
       "4    1.0            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sucess_data = pd.concat([success_data,df],axis=1)\n",
    "sucess_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>0.611111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>0.842105</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Class\n",
       "year          \n",
       "2010  0.000000\n",
       "2012  0.000000\n",
       "2013  0.000000\n",
       "2014  0.333333\n",
       "2015  0.333333\n",
       "2016  0.625000\n",
       "2017  0.833333\n",
       "2018  0.611111\n",
       "2019  0.900000\n",
       "2020  0.842105"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probability = sucess_data[['year','Class']].groupby('year').sum()/sucess_data[['year','Class']].groupby('year').count()\n",
    "probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='year',y='Class',data=probability)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can observe that the sucess rate since 2013 kept increasing till 2020\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Features Engineering\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By now, you should obtain some preliminary insights about how each important variable would affect the success rate, we will select the features that will be used in success prediction in the future module.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
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       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
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       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>GTO</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n",
       "0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n",
       "1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n",
       "2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n",
       "3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n",
       "4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n",
       "\n",
       "    Legs LandingPad  Block  ReusedCount Serial  \n",
       "0  False        NaN    1.0            0  B0003  \n",
       "1  False        NaN    1.0            0  B0005  \n",
       "2  False        NaN    1.0            0  B0007  \n",
       "3  False        NaN    1.0            0  B1003  \n",
       "4  False        NaN    1.0            0  B1004  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  7: Create dummy variables to categorical columns\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function <code>get_dummies</code> and <code>features</code> dataframe to apply OneHotEncoder to the column <code>Orbits</code>, <code>LaunchSite</code>, <code>LandingPad</code>, and <code>Serial</code>. Assign the value to the variable <code>features_one_hot</code>, display the results using the method head. Your result dataframe must include all features including the encoded ones.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HINT: Use get_dummies() function on the categorical columns\n",
    "features_one_hot = pd.get_dummies(features,columns=['Orbit','LaunchSite','LandingPad','Serial'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
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       "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0             1  6104.959412        1     False   False  False    1.0   \n",
       "1             2   525.000000        1     False   False  False    1.0   \n",
       "2             3   677.000000        1     False   False  False    1.0   \n",
       "3             4   500.000000        1     False   False  False    1.0   \n",
       "4             5  3170.000000        1     False   False  False    1.0   \n",
       "\n",
       "   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0            0            0          0  ...             0             0   \n",
       "1            0            0          0  ...             0             0   \n",
       "2            0            0          0  ...             0             0   \n",
       "3            0            0          0  ...             0             0   \n",
       "4            0            0          0  ...             0             0   \n",
       "\n",
       "   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0             0             0             0  \n",
       "1             0             0             0  \n",
       "2             0             0             0  \n",
       "3             0             0             0  \n",
       "4             0             0             0  \n",
       "\n",
       "[5 rows x 80 columns]"
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     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_one_hot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TASK  8: Cast all numeric columns to `float64`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90 entries, 0 to 89\n",
      "Columns: 80 entries, FlightNumber to Serial_B1062\n",
      "dtypes: bool(3), float64(2), int64(3), uint8(72)\n",
      "memory usage: 10.2 KB\n"
     ]
    }
   ],
   "source": [
    "# HINT: use astype function\n",
    "features_one_hot.info(verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n",
       "0             1  6104.959412        1    1.0            0            0   \n",
       "1             2   525.000000        1    1.0            0            0   \n",
       "2             3   677.000000        1    1.0            0            0   \n",
       "3             4   500.000000        1    1.0            0            0   \n",
       "4             5  3170.000000        1    1.0            0            0   \n",
       "\n",
       "   Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  Serial_B1048  \\\n",
       "0          0          0          0          0  ...             0   \n",
       "1          0          0          0          0  ...             0   \n",
       "2          0          0          0          1  ...             0   \n",
       "3          0          0          0          0  ...             0   \n",
       "4          0          1          0          0  ...             0   \n",
       "\n",
       "   Serial_B1049  Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Serial_B1058  Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0             0             0             0             0  \n",
       "1             0             0             0             0  \n",
       "2             0             0             0             0  \n",
       "3             0             0             0             0  \n",
       "4             0             0             0             0  \n",
       "\n",
       "[5 rows x 77 columns]"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#select all number column\n",
    "a = features_one_hot.select_dtypes(include='number')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 80 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FlightNumber   PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n",
       "0              1   6104.959412        1     False   False  False    1.0   \n",
       "1              2    525.000000        1     False   False  False    1.0   \n",
       "2              3    677.000000        1     False   False  False    1.0   \n",
       "3              4    500.000000        1     False   False  False    1.0   \n",
       "4              5   3170.000000        1     False   False  False    1.0   \n",
       "..           ...           ...      ...       ...     ...    ...    ...   \n",
       "85            86  15400.000000        2      True    True   True    5.0   \n",
       "86            87  15400.000000        3      True    True   True    5.0   \n",
       "87            88  15400.000000        6      True    True   True    5.0   \n",
       "88            89  15400.000000        3      True    True   True    5.0   \n",
       "89            90   3681.000000        1      True   False   True    5.0   \n",
       "\n",
       "    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n",
       "0             0            0          0  ...             0             0   \n",
       "1             0            0          0  ...             0             0   \n",
       "2             0            0          0  ...             0             0   \n",
       "3             0            0          0  ...             0             0   \n",
       "4             0            0          0  ...             0             0   \n",
       "..          ...          ...        ...  ...           ...           ...   \n",
       "85            2            0          0  ...             0             0   \n",
       "86            2            0          0  ...             0             0   \n",
       "87            5            0          0  ...             0             0   \n",
       "88            2            0          0  ...             0             0   \n",
       "89            0            0          0  ...             0             0   \n",
       "\n",
       "    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n",
       "0              0             0             0             0             0   \n",
       "1              0             0             0             0             0   \n",
       "2              0             0             0             0             0   \n",
       "3              0             0             0             0             0   \n",
       "4              0             0             0             0             0   \n",
       "..           ...           ...           ...           ...           ...   \n",
       "85             0             0             0             0             0   \n",
       "86             0             0             0             0             1   \n",
       "87             0             1             0             0             0   \n",
       "88             0             0             0             0             0   \n",
       "89             0             0             0             0             0   \n",
       "\n",
       "    Serial_B1059  Serial_B1060  Serial_B1062  \n",
       "0              0             0             0  \n",
       "1              0             0             0  \n",
       "2              0             0             0  \n",
       "3              0             0             0  \n",
       "4              0             0             0  \n",
       "..           ...           ...           ...  \n",
       "85             0             1             0  \n",
       "86             0             0             0  \n",
       "87             0             0             0  \n",
       "88             0             1             0  \n",
       "89             0             0             1  \n",
       "\n",
       "[90 rows x 80 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features_one_hot.apply(lambda x:pd.to_numeric(x,errors='ignore'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_one_hot = features_one_hot.astype('float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_one_hot.to_csv('dataset_part_3.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now export it to a <b>CSV</b> for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a> is a Data Scientist at IBM and pursuing a Master of Management in Artificial intelligence degree at Queen's University.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n",
    "| ----------------- | ------- | ---------- | ----------------------- |\n",
    "| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas |\n",
    "| 2020-11-10       | 1.1    | Nayef      | updating the input data |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
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