SpaceX-5-EDA-Data_Visualization.ipynb

{"cells":[{"cell_type":"markdown","id":"bc9e6346-4fc9-4eea-9a07-244dd9c1c060","metadata":{},"source":["<p style=\"text-align:center\">\n","    <a href=\"https://skills.network\" target=\"_blank\">\n","    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n","    </a>\n","</p>\n"]},{"cell_type":"markdown","id":"c47a6135-3406-401e-b0b9-4fa70b7e10bb","metadata":{},"source":["# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"]},{"cell_type":"markdown","id":"411a9eb2-317d-44f5-a135-3e1e8101c03c","metadata":{},"source":[" ## Assignment: Exploring and Preparing Data\n"]},{"cell_type":"markdown","id":"823800d9-57ff-42fe-8dae-3467add58b3c","metadata":{},"source":["Estimated time needed: **70** minutes\n"]},{"cell_type":"markdown","id":"9c9b46a0-3944-496f-b278-209b1ad85ab4","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","id":"cab6c513-db96-487f-9c36-a58464288b8b","metadata":{},"source":["Falcon 9 first stage will land successfully\n"]},{"cell_type":"markdown","id":"c97b5347-7e42-4486-934d-f5c2156319a2","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","id":"f429fac7-34bc-4781-b157-b24616461c3f","metadata":{},"source":["Several examples of an unsuccessful landing are shown here:\n"]},{"cell_type":"markdown","id":"5289a4a7-15d2-4fe1-a66e-7abc9aa67982","metadata":{},"source":["![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"]},{"cell_type":"markdown","id":"9ca89292-87c6-4eec-96ec-6143a775b839","metadata":{},"source":["Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans. \n"]},{"cell_type":"markdown","id":"5e91b273-b1fc-4813-8650-fa7371ad5923","metadata":{},"source":["  ## Objectives\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","id":"2603b3e4-a534-4b66-aa7c-58f38689a0c2","metadata":{},"source":["----\n"]},{"cell_type":"markdown","id":"ff7adcf4-d685-4f72-af58-f62186954a82","metadata":{},"source":["### Import Libraries and Define Auxiliary Functions\n"]},{"cell_type":"markdown","id":"588b1826-7dc8-45b5-b75f-89ba7dc8871b","metadata":{},"source":["We will import the following libraries the lab \n","\n"]},{"cell_type":"code","execution_count":1,"id":"01033290-467f-4ed2-88cf-0ad38a9f2cf3","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","id":"16730ffb-0471-47d0-956b-3039de0cb273","metadata":{},"source":["## Exploratory Data Analysis \n"]},{"cell_type":"markdown","id":"835b40f6-4d88-4341-9a38-fed67d3afa48","metadata":{},"source":["First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"]},{"cell_type":"code","execution_count":2,"id":"ef3c3323-eaaa-4ff5-a7e4-9fbe37a35c84","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","\n","df.head(5)"]},{"cell_type":"markdown","id":"9e961948-d560-407d-a886-56477dfad328","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,"id":"1bc04f16-52d5-4d0e-807e-aa09e5116e44","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = 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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 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."]},{"cell_type":"markdown","id":"ff4a7d71-64ce-4c20-a70e-4ecf4eeabea2","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","id":"208f37ad-fbe1-4eef-b4a8-0073baa0339c","metadata":{},"source":["Next, let's drill down to each site visualize its detailed launch records.\n"]},{"cell_type":"markdown","id":"c04cb0f2-beb4-4568-ad88-91067c23fb17","metadata":{},"source":["### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"]},{"cell_type":"markdown","id":"22ae7592-35af-47e2-85b1-9a5b32c91f2e","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,"id":"6288e806-65f5-476c-95c2-396dafdfe109","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n"]},{"data":{"image/png":"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size 2558.88x500 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(y=\"LaunchSite\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n","plt.xlabel(\"Flight Number\",fontsize=20)\n","plt.ylabel(\"Launch Site\",fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","id":"ef01e1d7-1662-4496-bd89-0b0459525a1d","metadata":{},"source":["We see that the more flights the higher the probabilty of a success flight. 'CCAFS' launch site has the most succesfull flights."]},{"cell_type":"markdown","id":"2b969ab8-ec0b-4c48-9cbb-074f272167ca","metadata":{},"source":["### TASK 2: Visualize the relationship between Payload and Launch Site\n"]},{"cell_type":"markdown","id":"705c1531-039c-437e-898b-623b01801827","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":5,"id":"10f0a9da-e0f2-4e64-9ad1-05e78cda985a","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = grouped_data.get_group(pd_key)\n"]},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAACgcAAAH3CAYAAACVGB36AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAACc0ElEQVR4nOzdd5iU1cE+4GeWKmUXFQREBSyI2GPvvcceNRprTDEx8YuamPKLsSSmmBhNNJrYNTGxd429d0FRIlYEsYAKyC7Sd5nfHxuJ6y4IbhkY7vu65vqYc973Pc+QfAR2nz2nUCwWiwEAAAAAAAAAAADKRkWpAwAAAAAAAAAAAAAtSzkQAAAAAAAAAAAAyoxyIAAAAAAAAAAAAJQZ5UAAAAAAAAAAAAAoM8qBAAAAAAAAAAAAUGaUAwEAAAAAAAAAAKDMKAcCAAAAAAAAAABAmVEOpNUUi8XU1NSkWCyWOgoAAAAAAAAAAMASRTmQVjNlypRUVVVlypQppY4CAAAAAAAAAACwRFEOBAAAAAAAAAAAgDKjHAgAAAAAAAAAAABlRjkQAAAAAAAAAAAAyoxyIAAAAAAAAAAAAJQZ5UAAAAAAAAAAAAAoM8qBAAAAAAAAAAAAUGaUAwEAAAAAAAAAAKDMKAcCAAAAAAAAAABAmVEOBAAAAAAAAAAAgDKjHAgAAAAAAAAAAABlRjkQAAAAAAAAAAAAyoxyIAAAAAAAAAAAAJQZ5UAAAAAAAAAAAAAoM8qBAAAAAAAAAAAAUGaUAwEAAAAAAAAAAKDMKAcCAAAAAAAAAABAmVEOBAAAAAAAAAAAgDKjHAgAAAAAAAAAAABlRjkQAAAAAAAAAAAAyoxyIAAAAAAAAAAAAJQZ5UAAAAAAAAAAAAAoM8qBAAAAAAAAAAAAUGaUAwEAAAAAAAAAAKDMKAcCAAAAAAAAAABAmVEOBAAAAAAAAAAAgDKjHAgAAAAAAAAAAABlRjkQAAAAAAAAAAAAyoxyIAAAAAAAAAAAAJQZ5UAAAAAAAAAAAAAoM8qBAAAAAAAAAAAAUGaUAwEAAAAAAAAAAKDMKAcCAAAAAAAAAABAmVEOBAAAAAAAAAAAgDKjHAgAAAAAAAAAAABlRjkQAAAAAAAAAAAAyoxyIAAAAAAAAAAAAJQZ5UAAAAAAAAAAAAAoM8qBAAAAAAAAAAAAUGaUAwEAAAAAAAAAAKDMKAcCAAAAAAAAAABAmVEOBAAAAAAAAAAAgDKjHAgAAAAAAAAAAABlRjkQAAAAAAAAAAAAyoxyIAAAAAAAAAAAAJQZ5UAAAAAAAAAAAAAoM8qBAAAAAAAAAAAAUGaUAwEAAAAAAAAAAKDMKAcCAAAAAAAAAABAmVEOBAAAAAAAAAAAgDKjHAgAAAAAAAAAAABlRjkQAAAAAAAAAAAAyoxyIAAAAAAAAAAAAJQZ5UAAAAAAAAAAAAAoM+1LHQAAAACAJVD1O8nLt9f/eo0vJ1UrlDYPAAAAAECZKRSLxWKpQ1CeampqUlVVlerq6lRWVpY6DgAAZerxNybkwkfezFsTp2bI8pX57rarZq1+VaWOtXiYOSV55sLkjfuTTt2T9Q9N1tiz1KkoI2MnTsvYSdOyep/u6dW9U6njsCgZdnly+wlJsa7+faFdssdZyYZHlTQWAAAAAEA5UQ6k1SgHAi2qWExGPZC8dlfSYalknYOS3muWOhWtZcr7ycfvJ71WT9orErAEq6tN2tnse37ueWl8jvnHsMz51L9qOrWvyLXf3izrrtijZLkWC7NnJJfukowb3nB8m58k2/20JJEoH9Nm1eb4a4bnnpHvp1hMOrQr5LBNB+TkL6+RQqFQ6niUWs17yTlrJ3NqG44X2iU/GJFU9StNLgAAAACAMlNR6gDAEmjmlOSVO5I37kvqZpc6DYuLm7+b/GO/+t2NHv9TcsEWydDLSp2qdX38YfLMRclj5yQfvFLqNG2iOKMmH1z61cw5a3Dyt61SPGtwMvTSUseC/5kzp/XXKBaTx/+cnDU4+eWyyd+2Tl6/t/XXXUz98d7XGhQDk2Rm7Zz85cE3ShNocTLi2sbFwCR57Oxk6oQ2j0N5+fWdL+ful+qLgUkyu66YSx8fnaueHlvaYCwaXrqlcTEwqd9F8JU72j4PAAAAAECZsg0J0LZevDa5/fhk1sf177v1SQ76e7LixqXNxaJt1IPJC//8zGAxuftnyZr7Jkv1KEWq1vXKncn1RyW1M+rf33dKsuUJyY6ntOgy1w19O5c8NjrvfDQ9a/eryg92XC2brLxsi66xoKbPqsvQPx2eraY/OHesMH1S/Z8ZSw9MVtmuJLk+8fakafntv1/JfS+/n07tK7Lv+v3yo10Hp1snf51aIrz9bHL/acmYx5Iuy9YfebjNT1pnV79H/5A88Kv/vR/3QvKvryZH3pGstGnLr7cYm103J6+Mn9Lk3Ih3q9s4zWJo7FNNj9fNTN57Plltp7bNQ9mom1PMjc+92+TctUPfzqGb9m/jRCxS/nND8tCv5z1vZ0kAAACA8jVlfPLRmGTZ1ZKupfmeJCxpfDcbaDuT3kxuOqZ+N4hPfDw+ufpryfH/cXQo8zavHbNmT0tGP5IM2att87S2WdOSm4/5XzHwE4/9MVl992TFjVpkmb8/OSYn3/JSOmZ2jmp3V/Z456lUXJG8t+EBWX7VdZKXbq4viAz+crL2AUlFuxZZd17+8eDwHDntkaSp7wcPvbSk5cCpM2vz1QufyruTpyep35XsiiffypsTpubvR29Ssly0kYmjkiv3TmZPrX8/bULyyO+TaZOSL/+xZdeqm508eX7j8Tm1yZPnKQd+Rod2Felb1Tnjqmc0mltxmS4lSNTYsLcm5fph72TKjNpsu/py2Xu95dOh3SKygXv3vvOZ69N2OSg7s+vmZNqsuibnqqfbOXyJNu6F5IZvNvw34adVtK//uycAAAAA5aV2Vv2GIC/8q/5rQ+06JRt/M9n5V35YFFqZciAsqcaPqD+utPrtpN+G9f/D22251l1zxPVNfxNo6gfJqAeS1Xdr3fX5fNMnJy9cnUx8Pem9ZrL2gUmnbqVOlXScT8GjY9e2y9FWRj+czJjHjlcjb26RcuCcOcX85cFRSZILOpyTHdo9/7/JYWcmwz518cu31b8O+scX/sv57Lo5mVk7Z7677D350qh8s9D0N4rrpnyQ1q0mzt/Nw9+dWwz8tEdfn5AR71Rn7RWqSpCKNvPMRf8rBn7a839Ptv950mWZlltr+uRk+qSm5ya+2XLrlJFvbLVyfnn7yMbjWw4sQZqGrnhiTE659aW5729/cVxuGf5uLjtyo7RfFAqCXzosefIvSe1n/nxbafOkz9qlyfRf/3m3Opc+PjqjJ0zNGn0r840tB2blXovA30lYIJ07tMvGA5fJM6Mb/3m2zaBeJUjEIuO5K+ddDCy0T/b8U1I5n+IyAAAAAIunh36dDP/H/97XzazfFKHHSskm3269dWvGJTMmJz0HtfpGKLCoWgS+IwW0udfvSy7cLnnuivpS3iNnJhdum1Q3ffRXi5nVRLFiQeZoGxNHJX/ZJLnrx8mzF9f/5MZft6z/C1OprXNQUmjif7Iq+yUDt2n7PK2tqc+6IHMLoWbG7IyvmZENCq82LAbOyyu3J28+tNDrzJhdl1NvfSnrnXZP1jrl7ux93mNNFgWSZEL7Pnmn2LPJuTn9t1zotVvSmx/O+8+oNyd83IZJKImJbzQ9XjcrmTy2Zdfqskz9n21NKXFZa1F19JYD8/92XyO9utfvQNx/2S7544HrZuc1S7vzXc2M2fntv19pNP7o6xPy7/+ML0GiJiw9IDnk6voviiT1/xuz+u7JgVeWNNaToyZmv/OfyI3PvZvnx07OP58em73Oezwvj6spaS4Wzs/3WCPdOzf8oYAVl1kq39tu1RIlYpEw9cN5zx10ZbL+oW2XBQAAAIC28+zFTY8//dfWWW/qhOSqA5M/rpGcv2lyzjrJyFtbZy1YxCkHwpLonp8ncz5znFfNu8kTf27ddQft2vR4u07JyqU7LpT/uvcX9cc8f9pHo5OHflOaPJ/Wc7Vk778kHT+1Y1DlCslXr0raleEmuAO3Sbos2/TcWvu1yBKVnTukd2WnrFcxasFvGv3wQq/z0xtH5PInxmTqf48WfOGd6hx+6dN588PGhbo91u2XM2Z/LbXFhn89eb99v3TY/LsLvXZLWr1P93nODeo97znKRJ+1mh7v0CVZpoV3p6tol2xzUhNrdU22+L+WXauMfHPrlfP0T3fIf07bJQ/9cNvs96UVSh0pw976KNNnN7071mOvT2jjNPOx8rbJ955N/u+F5IevJwf/K+lW2p3d/nDPq5lVN6fB2Mcza/Pn+18vUSK+iHVW6JF7jt86x+2wWvZZb/n8fI81cvv3t8pylZ1LHW3JNOGN5Pl/JG/cn8yZ8/nXt5YBWzU93rnKvwkBAAAAytWcOcnMKU3P1bTSBkbXfz15/e4kxf+u805y/VHJ+y/N9zYoR2XYqADma+rE5MOXm54b/Wjrrt1/s2SDo5Jhl31qsJDsckbSdR5FKNrOa3cv3HhbW++QZI09kzGPJR2Wqv/GYrlu/dyhc7L/xck1hyez/vsX5UJFst3/S5Zfv0WWqKgo5DvbrJIn71iIAkiXpnf1m5f3a2bk1hfeazQ+Y/acXPX02Jz85SENxo/aYmC+O2av7PtKrxza7r70LnyUUUutlT2O/nnJ/4zYa93l89eHRuXNCQ13ENx5SO+s0beyRKloMxt9s/4YxGkTG45v+t36MkNL2+DI+uc+dUFS/U7S70vJ1j9Keg/53FuXZBUVhfkeXd7WeizVYd5zXec9VzJLDyh1giRJsVjMsLc+anJu6DzGWXT1rVoqJ+w0qNQxlmzFYv2O4MMuz9wvhC67WnLoDcnS/ds+z3qHJM//PRn3QsPxHU5JOnZp+zwAAAAAlFih5R854Y2mNz2ZU5sMuyLZ/cyWXxMWYYvOd8+AttGxa/1OR7OnNZ5ri11i9jynfuezV+6sL0CtfUDSe83WX5fP17FLMqO66fFFRafuyeq7lTpF21hl++SEkfXH+c6amgzaJemxUosuceQWA9Ol/WF59+6r02/O5xwf3aFrss6BC/X8dydPT92cYpNzYyc1/jOoY/uKXHzEhnlu7CoZ8c6eqVt6qRy5+nJpV9EK/yhYSJ07tMvV3940Z9/7eu57+f107lCRfdfrl2O3dzTiEqGqX/L1e5KHf5eMfiTp2ivZ8Khko6Nbb801961/sdhaf6WlM7hP97wyvuFPQ7avKOSADUq/s+GiqlAopHdlp7xfM7PRXB87zsHCe/Gaz/xwVpKJrye3HJsceXvb5+nYNTnyzvrS/ZsPJkstk3zp8GTAFm2fBQAAAIC2UVFRvwnJtCZO1Vlm5ZZfb+oH8577+P2WXw8WccqBsKTp0Ll+t4ZnL248t2Erlhw+beDW9S8WLesenDz91ybGD2n7LNTrXFn//6+t6MBNVklWvye544fJG/fW7y6z0qbJtEnJhFfrL+rRP9nr3OTjD5J7T0k+fCVZbo1ks2PnW+5ddbluWapDuyaP1Vyn37x3W/vSSkvnSyst3ezP1tKW6945v9lv7fwma5c6CqXQc9Vk/4tKnYJWMuytj/LhlJn5Uv8eWa57yxXQLjxswxz7z+cy4t368v2yXTvm1L3WzKrLOY58fo7YfEDOvOvVJseBhTTiuqbHxzyaTBmfdO/TtnmSpFO3ZLPv1r8AAAAAWDIM2CoZeVPj8VV3bPm1+qyTdKpMZtY0kWPLll8PFnHKgbAk2vmMZPb05MVrkzmz648u3PqkZMhepU5GKe3wi2TSm8nr9/xvbK39ky1/ULJItJEeKyVfuzaZ+XGSYv0OjUny4atJ7Yyk99rJu0OTi3eof58k7z2X/OfG+h1nVtiwycdWdu6Qb229cv50/+sNxvtWdc4hm7TsLoi0jNl1c3L7i+/lkdcmpLJz+xyw4YpZaz5FTljcjauenqMvH5qR4+q/QNChXSHf3nqV/HCX1Vvk+Sst2yW3fX/LvDp+SqbMmJ11VuiRju0rWuTZ5eyYrVfJtJl1ufyJMfl4Zm2W6dox39lmlXzFjouw8Gob78I5V92stssBAAAAwJLt3aFNj7/1RMuv1albsv3Pk3+f1HC8zzqtvjELLIoKxWKx6fP+oJlqampSVVWV6urqVFZWljoOTZk6oX63iGVXSTosVeo0LCrefymZ+Eay3JCk52qlTsOi4oq9ktEPNx5fZYfksBvne+v1w97J1c+MzaSps7LFqj3znW1XyfI9/JmzqJldNydHXfZsHnvjf1u6VxSS3+6/Tg7ccMUSJoPWc8hFT+WJURMbjV942AbZec0S7KZFA9Nn1WXCxzOzXGWndGrfrtRxYPH01F+Tu37ceLzPOskxj7Z9HgAAAFgQdbOT1+5KJr+d9NsgWWmTUicCmmPOnOT0eZwa1r5z8vNWOur3zYeS565Mpn+UrLxdsuFR/9skBZYgi+22FePHj8/3v//9rLzyyunUqVNWXHHF7Lnnnrn//vsbXPf888/ngAMOSO/evdO5c+cMGjQo3/zmN/Paa681eubOO++cdu3a5amnnmo0d+SRR6ZQKDR6vfHGG3PX+fKXv5zlllsunTt3zoABA3LQQQdlwoQmzkz/rzfffDMHH3xwll9++XTu3DkrrLBC9t577wbZCoVCbr755nk+o1gs5sILL8wmm2ySbt26pUePHtlwww1zzjnnZNq0aZ/325iJEydmhRVWSKFQyOTJkxvMjRgxIttss02WWmqp9OvXL6effnp0SctM155Jn7UUA2mo95rJkL0VA2no7WeaHn/n2c+99SsbrJDrv7N5HvjhtvnlPmspBi6i7nhxXINiYJLMKSa/un1kps9qfDQ0LJAZNf/dlXTRM656epPFwCS54bl32jgNTVmqY7usuEwXxcDFXN2cYv728Kh87cx/5dDT/pLjr3o6oydMLXWsJceGR9V/4fPTOvdI9jynFGkAAADg8330VnLeRsk1hyZ3/zS5dOfknwcltXbAh7JUnNN6z15522SL/6t/bfh1xUCWWIvlscJjxozJFltskR49euTMM8/MOuusk9mzZ+fuu+/Osccem1deeSVJcvvtt2f//ffPLrvskquuuiqrrLJKPvjgg1x33XU5+eSTc80118x95tixY/Pkk0/me9/7Xi655JJsuummjdbdddddc9lllzUY69WrVz744IPsuOOO2XPPPXP33XenR48eGT16dG699dZ5FvRmzZqVnXbaKYMHD86NN96Yvn375p133smdd96Z6urqBf69OOyww3LjjTfm5z//ec4777z06tUrL7zwQs4555wMGDAg++yzz3zvP/roo7POOuvk3XffbTBeU1OTnXbaKdttt12effbZvPbaaznyyCPTtWvXnHjiiQucD4AyUbl8MmlU0+Msvj54uf6I+dqZefe91ZIs1+iSmhm1eX7sR9l81Z5tn4/F14Q3kjt/WP9TeYWKZPXdkt3/kFT2LXWyuabNp/Q6vzlg4fzxxoey1Ys/zbcrXk6STHitMn8adWSOO/7n6dW9U4nTLQHad0oOvTF5475k7JNJ977JOgckS83jJ7UBAACg1O44MflodMOx1+5Knvlbsvn3S5MJaJ6KiqRjt2RWE5sJtNb3Gj96K7n2sGTcC/XvO3ZPdj69viQIS5jF8ljh3XffPS+++GJeffXVdO3atcHc5MmT06NHj0ybNi39+/fPlltumZtuuqnRMz657hOnnXZaXnnllZxyyinZeOONM27cuAbPPvLIIzN58uQmd/G7+eabc8ABB2T69Olp337B+pbDhw/P+uuvnzFjxqR///7zvK5QKOSmm25qsuR37bXX5qCDDsrNN9+cvffeu8FcsVice6zvvFxwwQW55ppr8otf/CI77LBDPvroo7m/JxdccEF++tOf5v3330+nTvXfsPntb3+bc889N++8804KhUKj582cOTMzZ86c+76mpiYrrriiY4UBysHTFyb//lHj8T3+mGx09AI94sMpM3Pjc+9kfM2MbDRgmew8pHfat1tsNzFe/D17cXLHD5P876+Cf6/dMSfXNv5H0e3f3zJr9Zv33ymggVlTk3M3TKa813B8uSHJMY/XfxFgEVAsFrP9WQ83uYPZKXsOyVFbDCxBKigvH06ZmTG/3zobVbzSYLyuWMi1X/p7Dt57zxIlAwAAABZJM2qS366UT3/deq5+GyTffKDNIwEt5P5fJo/+ofH47n9INv5my6930fbJu8M+M1hIvnFfssKGLb8eLMIWje/MLYRJkyblrrvuyrHHHtuoGJhkbrnt7rvvzoQJE3LSSSc1+ZxPFwOLxWIuu+yyHHrooRk8eHAGDRqUa6+9doEz9enTJ7W1tbnpppsW+NjdXr16paKiItdff33q6r7YziRXXXVVVl999UbFwKS+VDi/YuDIkSNz+umn58orr0xFE9+gffLJJ7PNNtvMLQYmyS677JL33nsvY8aMafKZv/nNb1JVVTX3teKKKy78hwJg0bTJt5LtT/7fLjNLLZPscMoCFwOHvfVRtv/DQ/nNv1/JZY+PyXevei4HX/SU42pLZerE5K6f5bNfYDms/X3ZoPBqg7G1+lUqBrJwXrqpcTEwST4Ymby56HzxrlAo5Ix91spSHRoeWbvxwGVy8MYrlSgVlJf3Xh/eqBiYJO0KxSz/5vUlSNQ6Rn34cV5/f0qpYwAAAEAZmM/32lvz6FGg9W37k2SDo5KKDvXv2y+VbHl8stE3Wn6t90c2UQxMkmLy/D9afj1YxC125cA33ngjxWIxgwcPnu91r7/+epJ87nVJct9992XatGnZZZddkiSHHnpoLrnkkkbX3X777enWrdvc1wEHHJAk2XTTTfOzn/0shxxySHr27Jnddtstv//97/P+++/Pc81+/frlz3/+c37xi19k6aWXzvbbb59f/vKXefPNNz8376c/4+qrr77A139i5syZOfjgg/P73/8+K63U9Dc+x48fn969ezcY++T9+PHjm7znpz/9aaqrq+e+3n777YXORvmYM6eYB1/5IGfd82quevqtTJkxu9SRgOba+ofJia8mPxiRnPhKstUJC3zr/7tpRKbMrG0w9uyYj/KPp95q6ZQsiDcfTOpmNjm1e6fhc3+9Vr/KXPC1DdooFGVj8tgvNlcCm6/aM/efuE1O3GlQDtu0f849eP1c9Y1N0vkzhcHU1Sav/jsZemn9cdzAAlmx8/R5zvVu33jXzsXNa+9PyR5/fjQ7nPVwdjr7kWx/1kMZ9tZHpY4FAAAAi6/OVckq2zU9N2SfNo0CtLB2HZI9z6n/HuM3H6j/vzuemjRxamWzzayZ99yM6pZfDxZxC3YG7iLkk535mjrWtqnrFsQll1ySgw46aO6RwAcffHB+9KMf5dVXX21Qvttuu+1ywQUXzH3/6Z0LzzjjjJxwwgl54IEH8tRTT+Wvf/1rfv3rX+eRRx7J2muv3eS6xx57bA4//PA8+OCDefrpp3Pdddfl17/+dW699dbstNNOn5u7WCx+7u9DU376059mjTXWyKGHHjrf6z777M/7ve/UqVODnQZZcs2YXZevX/5snhg1ce7YH+95Lf/4xiZZo68jpilv/3jqrfzz6bGZNHVWNl9l2Ry3w2oZ0LPxTreLrfadkh4Lt6PWOx9Nyyvjm95N596X3883t165JZKxMDp0mefU4VsPyRr9N0nVUh2y5vJ2DOQL6DefQun85kpk+R5L5fs7rDbvCya9mfx93+SjMf8bW//QZM9zF5kjkmFRtcyqG2daRbd0mfNxo7m+6+9SgkQtZ1btnBx56TN5r3rG3LE3P5yaIy97Jo+dtH2qunQoYToAAABYjO1xVnLF3kn1p37QeJXtk02/U7pMQMvp2rP+1Zr6rld/Ctr0SY3nVt2xddeGRdBi992s1VZbLYVCIS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size 2558.88x500 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(y=\"LaunchSite\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n","plt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\n","plt.ylabel(\"Launch Site\",fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","id":"bc666ed4-3b9a-40f7-bac5-d4d4891e38a1","metadata":{},"source":["Now if you observe Payload Vs. Launch Site scatter point chart you will find for the VAFB-SLC  launchsite there are no  rockets  launched for  heavypayload mass(greater than 10000).\n"]},{"cell_type":"markdown","id":"18814e49-9c99-4665-b9f7-f3a1095468b7","metadata":{},"source":["### TASK  3: Visualize the relationship between success rate of each orbit type\n"]},{"cell_type":"markdown","id":"14e1da57-4f48-4f2b-933d-c11ca0ef08f3","metadata":{},"source":["Next, we want to visually check if there are any relationship between success rate and orbit type.\n"]},{"cell_type":"markdown","id":"93c0fd98-aecc-482d-a853-0b2a4de4e22c","metadata":{},"source":["Let's create a `bar chart` for the sucess rate of each orbit\n"]},{"cell_type":"code","execution_count":11,"id":"51d6d50b-086a-4ef1-af95-1b9090fc994e","metadata":{},"outputs":[{"data":{"image/png":"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","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","df_grouped = df.groupby(['Orbit'])['Class'].mean()\n","sns.barplot(x=df_grouped.index, y=df_grouped)\n","plt.xlabel(\"Orbit\",fontsize=20)\n","plt.ylabel(\"Success Rate\",fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","id":"f3481fb5-42a7-47bb-b781-2997405aa5c1","metadata":{},"source":["The orbits with the highest success rates are 'ES-L1', 'GEO', 'HEO' and 'SSO'"]},{"cell_type":"markdown","id":"b2d8d9b5-81ef-4ccc-b990-9deda277c545","metadata":{},"source":["### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"]},{"cell_type":"markdown","id":"a470b22a-fcdb-423c-86ac-85918111a562","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":12,"id":"32fd80f5-82a2-4657-abb9-0cda44816281","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = 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ain":["<Figure size 2558.88x500 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(y=\"Orbit\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\n","plt.xlabel(\"Flight Number\",fontsize=20)\n","plt.ylabel(\"Orbit\",fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","id":"7fb57630-96f5-4ea5-9780-57e5d8bcfb18","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","id":"c7d4677e-36d2-47fa-a922-efaa1c281b86","metadata":{},"source":["### TASK  5: Visualize the relationship between Payload and Orbit type\n"]},{"cell_type":"markdown","id":"b79fbf82-024a-47e4-b0d2-d6ff83a13d9b","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":13,"id":"9f9381f6-4b93-4e8a-8b81-aadd9d2551f4","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1075: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n","  data_subset = 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size 2558.88x500 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(y=\"Orbit\", x=\"PayloadMass\", hue=\"Class\", data=df, aspect = 5)\n","plt.xlabel(\"Pay load Mass (kg)\",fontsize=20)\n","plt.ylabel(\"Orbit\",fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","id":"d981252e-57c3-46e6-ae12-663f87a30159","metadata":{},"source":["With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.   \n","\n","However for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n","\n","\n"]},{"cell_type":"markdown","id":"7e7f6c96-37d8-41da-b6c8-c99067bd154b","metadata":{},"source":["### TASK  6: Visualize the launch success yearly trend\n"]},{"cell_type":"markdown","id":"1f1b949c-7fe6-4f7d-b815-b225b8822b15","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","id":"24791309-af8f-409d-94c2-4715d7648a9c","metadata":{},"source":["The function will help you get the year from the date:\n"]},{"cell_type":"code","execution_count":14,"id":"a264d7fa-1ee1-47d7-bcd6-044d610f640f","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":15,"id":"af7c0739-fd5b-4f57-bab2-9da096a0c8bb","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n","/home/anzoar/IBMDataSceince/envs/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n","  with pd.option_context('mode.use_inf_as_na', True):\n"]},{"data":{"image/png":"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","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","df[\"Year\"]=Extract_year(df[\"Date\"])\n","df_grouped = df.groupby(['Year'])['Class'].mean()\n","sns.lineplot(x=df_grouped.index, y=df_grouped)\n","plt.xlabel(\"Year\",fontsize=20)\n","plt.ylabel(\"Success Rate\",fontsize=20)\n","plt.show()"]},{"cell_type":"markdown","id":"5eddacf7-09f2-4072-8cd9-44d7b5465854","metadata":{},"source":["You can observe that the success rate since 2013 kept increasing till 2017 (stable in 2014) and after 2015 it started increasing.\n"]},{"cell_type":"markdown","id":"01db1ac5-464e-47fe-931e-bca307de23a7","metadata":{},"source":["## Features Engineering \n"]},{"cell_type":"markdown","id":"4ee063ab-a077-4b39-ad6f-0b6e634be97d","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":21,"id":"8b196033-1113-4317-af8c-eee9287530b0","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>PayloadMass</th>\n","      <th>Orbit</th>\n","      <th>LaunchSite</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","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>6104.959412</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</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","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>525.000000</td>\n","      <td>LEO</td>\n","      <td>CCAFS SLC 40</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","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>677.000000</td>\n","      <td>ISS</td>\n","      <td>CCAFS SLC 40</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","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>500.000000</td>\n","      <td>PO</td>\n","      <td>VAFB SLC 4E</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","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>3170.000000</td>\n","      <td>GTO</td>\n","      <td>CCAFS SLC 40</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","    </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":21,"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","id":"87f481ed-662d-4a08-8aad-bb8c886e8b9e","metadata":{},"source":[" ### TASK  7: Create dummy variables to categorical columns\n"]},{"cell_type":"markdown","id":"4d00b53f-d439-4eb4-a22f-ecece9d41ca1","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":22,"id":"b5ed0919-ce8d-4819-85b4-9d7028dd6619","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>PayloadMass</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Orbit_ES-L1</th>\n","      <th>Orbit_GEO</th>\n","      <th>...</th>\n","      <th>Serial_B1048</th>\n","      <th>Serial_B1049</th>\n","      <th>Serial_B1050</th>\n","      <th>Serial_B1051</th>\n","      <th>Serial_B1054</th>\n","      <th>Serial_B1056</th>\n","      <th>Serial_B1058</th>\n","      <th>Serial_B1059</th>\n","      <th>Serial_B1060</th>\n","      <th>Serial_B1062</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>6104.959412</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>525.000000</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>677.000000</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>500.000000</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>3170.000000</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>...</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 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","   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n","0            0        False      False  ...         False         False   \n","1            0        False      False  ...         False         False   \n","2            0        False      False  ...         False         False   \n","3            0        False      False  ...         False         False   \n","4            0        False      False  ...         False         False   \n","\n","   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n","0         False         False         False         False         False   \n","1         False         False         False         False         False   \n","2         False         False         False         False         False   \n","3         False         False         False         False         False   \n","4         False         False         False         False         False   \n","\n","   Serial_B1059  Serial_B1060  Serial_B1062  \n","0         False         False         False  \n","1         False         False         False  \n","2         False         False         False  \n","3         False         False         False  \n","4         False         False         False  \n","\n","[5 rows x 80 columns]"]},"execution_count":22,"metadata":{},"output_type":"execute_result"}],"source":["# HINT: Use get_dummies() function on the categorical columns\n","features_one_hot = pd.get_dummies(features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\n","features_one_hot.head()"]},{"cell_type":"markdown","id":"a6ae2bb7-0668-47a5-85e3-7fc1fd02ea71","metadata":{},"source":["### TASK  8: Cast all numeric columns to `float64`\n"]},{"cell_type":"markdown","id":"9f11c2f8-4187-46bb-931c-7c5697420a0e","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":23,"id":"697994fa-2869-4727-9209-d342a66a6e2d","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>PayloadMass</th>\n","      <th>Flights</th>\n","      <th>GridFins</th>\n","      <th>Reused</th>\n","      <th>Legs</th>\n","      <th>Block</th>\n","      <th>ReusedCount</th>\n","      <th>Orbit_ES-L1</th>\n","      <th>Orbit_GEO</th>\n","      <th>...</th>\n","      <th>Serial_B1048</th>\n","      <th>Serial_B1049</th>\n","      <th>Serial_B1050</th>\n","      <th>Serial_B1051</th>\n","      <th>Serial_B1054</th>\n","      <th>Serial_B1056</th>\n","      <th>Serial_B1058</th>\n","      <th>Serial_B1059</th>\n","      <th>Serial_B1060</th>\n","      <th>Serial_B1062</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1.0</td>\n","      <td>6104.959412</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2.0</td>\n","      <td>525.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3.0</td>\n","      <td>677.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.0</td>\n","      <td>500.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5.0</td>\n","      <td>3170.000000</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</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","    </tr>\n","    <tr>\n","      <th>85</th>\n","      <td>86.0</td>\n","      <td>15400.000000</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>86</th>\n","      <td>87.0</td>\n","      <td>15400.000000</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>87</th>\n","      <td>88.0</td>\n","      <td>15400.000000</td>\n","      <td>6.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>88</th>\n","      <td>89.0</td>\n","      <td>15400.000000</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>89</th>\n","      <td>90.0</td>\n","      <td>3681.000000</td>\n","      <td>1.0</td>\n","      <td>1.0</td>\n","      <td>0.0</td>\n","      <td>1.0</td>\n","      <td>5.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>...</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>1.0</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            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n","1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n","2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n","3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n","4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n","..           ...           ...      ...       ...     ...   ...    ...   \n","85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n","86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n","87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n","88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n","89          90.0   3681.000000      1.0       1.0     0.0   1.0    5.0   \n","\n","    ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n","0           0.0          0.0        0.0  ...           0.0           0.0   \n","1           0.0          0.0        0.0  ...           0.0           0.0   \n","2           0.0          0.0        0.0  ...           0.0           0.0   \n","3           0.0          0.0        0.0  ...           0.0           0.0   \n","4           0.0          0.0        0.0  ...           0.0           0.0   \n","..          ...          ...        ...  ...           ...           ...   \n","85          2.0          0.0        0.0  ...           0.0           0.0   \n","86          2.0          0.0        0.0  ...           0.0           0.0   \n","87          5.0          0.0        0.0  ...           0.0           0.0   \n","88          2.0          0.0        0.0  ...           0.0           0.0   \n","89          0.0          0.0        0.0  ...           0.0           0.0   \n","\n","    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n","0            0.0           0.0           0.0           0.0           0.0   \n","1            0.0           0.0           0.0           0.0           0.0   \n","2            0.0           0.0           0.0           0.0           0.0   \n","3            0.0           0.0           0.0           0.0           0.0   \n","4            0.0           0.0           0.0           0.0           0.0   \n","..           ...           ...           ...           ...           ...   \n","85           0.0           0.0           0.0           0.0           0.0   \n","86           0.0           0.0           0.0           0.0           1.0   \n","87           0.0           1.0           0.0           0.0           0.0   \n","88           0.0           0.0           0.0           0.0           0.0   \n","89           0.0           0.0           0.0           0.0           0.0   \n","\n","    Serial_B1059  Serial_B1060  Serial_B1062  \n","0            0.0           0.0           0.0  \n","1            0.0           0.0           0.0  \n","2            0.0           0.0           0.0  \n","3            0.0           0.0           0.0  \n","4            0.0           0.0           0.0  \n","..           ...           ...           ...  \n","85           0.0           1.0           0.0  \n","86           0.0           0.0           0.0  \n","87           0.0           0.0           0.0  \n","88           0.0           1.0           0.0  \n","89           0.0           0.0           1.0  \n","\n","[90 rows x 80 columns]"]},"execution_count":23,"metadata":{},"output_type":"execute_result"}],"source":["# HINT: use astype function\n","features_one_hot.astype('float64')"]},{"cell_type":"markdown","id":"17e92568-7467-4414-ae0a-9056ee9cdb29","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","id":"42514972-e81e-42f0-8164-1b0cbe3fd3b9","metadata":{},"source":["<code>features_one_hot.to_csv('dataset_part_3.csv', index=False)</code>\n"]},{"cell_type":"markdown","id":"b5add060-2b34-4373-b45a-adf2a4ba18d6","metadata":{},"source":["## Authors\n"]},{"cell_type":"markdown","id":"727088d9-e8dc-4ad5-95be-710c01265079","metadata":{},"source":["<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">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","id":"3cb1512c-d610-4540-9441-b38ba9bcf4eb","metadata":{},"source":["<a href=\"https://www.linkedin.com/in/nayefaboutayoun/\">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","id":"2ae0ef00-f0b8-4cf4-bff5-09f68484f212","metadata":{},"source":["## Change Log\n"]},{"cell_type":"markdown","id":"92c0d50d-d980-4356-b25c-9852622c128e","metadata":{},"source":["| Date (YYYY-MM-DD) | Version | Changed By | Change Description      |\n","| ----------------- | ------- | ---------- | ----------------------- |\n","| 2021-10-12        | 1.1     | Lakshmi Holla     | Modified markdown |\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","id":"4807b791-f33f-4f1c-b9f0-feb509398130","metadata":{},"source":["Copyright © 2020 IBM Corporation. All rights reserved.\n"]}],"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":"3.10.13"}},"nbformat":4,"nbformat_minor":4}