notebook_EDA_with_Visualization_nSvs-IOAv.ipynb

{"cells": [{"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "markdown", "source": "# **SpaceX  Falcon 9 First Stage Landing Prediction**\n"}, {"metadata": {}, "cell_type": "markdown", "source": "## Assignment: Exploring and Preparing\u00a0Data\n"}, {"metadata": {}, "cell_type": "markdown", "source": "Estimated time needed: **70** minutes\n"}, {"metadata": {}, "cell_type": "markdown", "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\nIn this lab, you will perform Exploratory Data Analysis and Feature Engineering.\n"}, {"metadata": {}, "cell_type": "markdown", "source": "Falcon 9 first stage will land successfully\n"}, {"metadata": {}, "cell_type": "markdown", "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"}, {"metadata": {}, "cell_type": "markdown", "source": "Several examples of an unsuccessful landing are shown here:\n"}, {"metadata": {}, "cell_type": "markdown", "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"}, {"metadata": {}, "cell_type": "markdown", "source": "Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.\n"}, {"metadata": {}, "cell_type": "markdown", "source": "## Objectives\n\nPerform exploratory Data Analysis and Feature Engineering using `Pandas` and `Matplotlib`\n\n*   Exploratory Data Analysis\n*   Preparing\u00a0Data  Feature Engineering\n"}, {"metadata": {}, "cell_type": "markdown", "source": "***\n"}, {"metadata": {}, "cell_type": "markdown", "source": "### Import Libraries and Define Auxiliary Functions\n"}, {"metadata": {}, "cell_type": "markdown", "source": "We will import the following libraries the lab\n"}, {"metadata": {}, "cell_type": "code", "source": "# andas is a software library written for the Python programming language for data manipulation and analysis.\nimport 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\nimport 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.\nimport 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\nimport seaborn as sns", "execution_count": 2, "outputs": []}, {"metadata": {}, "cell_type": "markdown", "source": "## Exploratory Data Analysis\n"}, {"metadata": {}, "cell_type": "markdown", "source": "First, let's read the SpaceX dataset into a Pandas dataframe and print its summary\n"}, {"metadata": {}, "cell_type": "code", "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\ndf.head(5)", "execution_count": 3, "outputs": [{"output_type": "execute_result", "execution_count": 3, "data": {"text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ", "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>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "markdown", "source": "First, let's try to see how the `FlightNumber` (indicating the continuous launch attempts.) and `Payload` variables would affect the launch outcome.\n\nWe 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"}, {"metadata": {}, "cell_type": "code", "source": "sns.catplot(y=\"PayloadMass\", x=\"FlightNumber\", hue=\"Class\", data=df, aspect = 5)\nplt.xlabel(\"Flight Number\",fontsize=20)\nplt.ylabel(\"Pay load Mass (kg)\",fontsize=20)\nplt.show()", "execution_count": 4, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 1842.38x360 with 1 Axes>", "image/png": 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\n"}, "metadata": {"needs_background": "light"}}]}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "markdown", "source": "Next, let's drill down to each site visualize its detailed launch records.\n"}, {"metadata": {}, "cell_type": "markdown", "source": "### TASK 1: Visualize the relationship between Flight Number and Launch Site\n"}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "code", "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\nsns.catplot(y='LaunchSite', x='FlightNumber', hue='Class', data=df)\nplt.xlabel('Flight Number')\nplt.ylabel('Launch Site')\nplt.show()", "execution_count": 5, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 402.375x360 with 1 Axes>", "image/png": 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\n"}, "metadata": {"needs_background": "light"}}]}, {"metadata": {}, "cell_type": "markdown", "source": "Now try to explain the patterns you found in the Flight Number vs. Launch Site scatter point plots.\n"}, {"metadata": {}, "cell_type": "markdown", "source": "### TASK 2: Visualize the relationship between Payload and Launch Site\n"}, {"metadata": {}, "cell_type": "markdown", "source": "We also want to observe if there is any relationship between launch sites and their payload mass.\n"}, {"metadata": {}, "cell_type": "code", "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\nsns.catplot(y='LaunchSite', x='PayloadMass', hue='Class', data=df)\nplt.xlabel('Pay Load Mass (kg)')\nplt.ylabel('Launch Site')\nplt.show()", "execution_count": 6, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 402.375x360 with 1 Axes>", "image/png": 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\n"}, "metadata": {"needs_background": "light"}}]}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "markdown", "source": "### TASK  3: Visualize the relationship between success rate of each orbit type\n"}, {"metadata": {}, "cell_type": "markdown", "source": "Next, we want to visually check if there are any relationship between success rate and orbit type.\n"}, {"metadata": {}, "cell_type": "markdown", "source": "Let's create a `bar chart` for the sucess rate of each orbit\n"}, {"metadata": {}, "cell_type": "code", "source": "# HINT use groupby method on Orbit column and get the mean of Class column\ndf_sorted = df.groupby('Orbit').mean()['Class'].reset_index().sort_values(['Class'], ascending=True)\n\nfig, ax = plt.subplots()\n\nax.barh(df_sorted.Orbit, df_sorted.Class * 100)\n\nplt.xlabel('Success Rate (%)')\nplt.ylabel('Orbit Type')\nplt.show()", "execution_count": 7, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 432x288 with 1 Axes>", "image/png": "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\n"}, "metadata": {"needs_background": "light"}}]}, {"metadata": {}, "cell_type": "markdown", "source": "Analyze the ploted bar chart try to find which orbits have high sucess rate.\n"}, {"metadata": {}, "cell_type": "markdown", "source": "### TASK  4: Visualize the relationship between FlightNumber and Orbit type\n"}, {"metadata": {}, "cell_type": "markdown", "source": "For each orbit, we want to see if there is any relationship between FlightNumber and Orbit type.\n"}, {"metadata": {}, "cell_type": "code", "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\nsns.catplot(x='FlightNumber', y='Orbit', hue='Class', data=df)\nplt.xlabel('Fligh Number')\nplt.ylabel('Orbit Type')\nplt.show()", "execution_count": 8, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 402.375x360 with 1 Axes>", "image/png": 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\n"}, "metadata": {"needs_background": "light"}}]}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "markdown", "source": "### TASK  5: Visualize the relationship between Payload and Orbit type\n"}, {"metadata": {}, "cell_type": "markdown", "source": "Similarly, we can plot the Payload vs. Orbit scatter point charts to reveal the relationship between Payload and Orbit type\n"}, {"metadata": {}, "cell_type": "code", "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\nsns.catplot(x='PayloadMass', y='Orbit', hue='Class', data=df)\nplt.xlabel('Pay load Mass (kg)')\nplt.ylabel('Orbit Type')\nplt.show()", "execution_count": 9, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 402.375x360 with 1 Axes>", "image/png": 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\n"}, "metadata": {"needs_background": "light"}}]}, {"metadata": {}, "cell_type": "markdown", "source": "With heavy payloads the successful landing or positive landing rate are more for Polar,LEO and ISS.\n\nHowever for GTO we cannot distinguish this well as both positive landing rate and negative landing(unsuccessful mission) are both there here.\n"}, {"metadata": {}, "cell_type": "markdown", "source": "### TASK  6: Visualize the launch success yearly trend\n"}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "markdown", "source": "The function will help you get the year from the date:\n"}, {"metadata": {}, "cell_type": "code", "source": "# A function to Extract years from the date \nyear=[]\ndef Extract_year(date):\n    for i in df[\"Date\"]:\n        year.append(i.split(\"-\")[0])\n    return year\n    ", "execution_count": 10, "outputs": []}, {"metadata": {}, "cell_type": "code", "source": "# Plot a line chart with x axis to be the extracted year and y axis to be the success rate\ndf.groupby(Extract_year(df['Date'])).mean()['Class'].plot(kind='line')\nplt.xlabel('Year')\nplt.ylabel('Success Rate')\nplt.show()", "execution_count": 11, "outputs": [{"output_type": "display_data", "data": {"text/plain": "<Figure size 432x288 with 1 Axes>", "image/png": 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\n"}, "metadata": {"needs_background": "light"}}]}, {"metadata": {}, "cell_type": "markdown", "source": "you can observe that the sucess rate since 2013 kept increasing till 2020\n"}, {"metadata": {}, "cell_type": "markdown", "source": "## Features Engineering\n"}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "code", "source": "features = df[['FlightNumber', 'PayloadMass', 'Orbit', 'LaunchSite', 'Flights', 'GridFins', 'Reused', 'Legs', 'LandingPad', 'Block', 'ReusedCount', 'Serial']]\nfeatures.head()", "execution_count": 12, "outputs": [{"output_type": "execute_result", "execution_count": 12, "data": {"text/plain": "   FlightNumber  PayloadMass Orbit    LaunchSite  Flights  GridFins  Reused  \\\n0             1  6104.959412   LEO  CCAFS SLC 40        1     False   False   \n1             2   525.000000   LEO  CCAFS SLC 40        1     False   False   \n2             3   677.000000   ISS  CCAFS SLC 40        1     False   False   \n3             4   500.000000    PO   VAFB SLC 4E        1     False   False   \n4             5  3170.000000   GTO  CCAFS SLC 40        1     False   False   \n\n    Legs LandingPad  Block  ReusedCount Serial  \n0  False        NaN    1.0            0  B0003  \n1  False        NaN    1.0            0  B0005  \n2  False        NaN    1.0            0  B0007  \n3  False        NaN    1.0            0  B1003  \n4  False        NaN    1.0            0  B1004  ", "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>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "markdown", "source": "### TASK  7: Create dummy variables to categorical columns\n"}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "code", "source": "# HINT: Use get_dummies() function on the categorical columns\nfeatures_one_hot = pd.get_dummies(features, columns=['Orbit', 'LaunchSite', 'LandingPad', 'Serial'])\nfeatures_one_hot.head()", "execution_count": 13, "outputs": [{"output_type": "execute_result", "execution_count": 13, "data": {"text/plain": "   FlightNumber  PayloadMass  Flights  GridFins  Reused   Legs  Block  \\\n0             1  6104.959412        1     False   False  False    1.0   \n1             2   525.000000        1     False   False  False    1.0   \n2             3   677.000000        1     False   False  False    1.0   \n3             4   500.000000        1     False   False  False    1.0   \n4             5  3170.000000        1     False   False  False    1.0   \n\n   ReusedCount  Orbit_ES-L1  Orbit_GEO  ...  Serial_B1048  Serial_B1049  \\\n0            0            0          0  ...             0             0   \n1            0            0          0  ...             0             0   \n2            0            0          0  ...             0             0   \n3            0            0          0  ...             0             0   \n4            0            0          0  ...             0             0   \n\n   Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0             0             0             0             0             0   \n1             0             0             0             0             0   \n2             0             0             0             0             0   \n3             0             0             0             0             0   \n4             0             0             0             0             0   \n\n   Serial_B1059  Serial_B1060  Serial_B1062  \n0             0             0             0  \n1             0             0             0  \n2             0             0             0  \n3             0             0             0  \n4             0             0             0  \n\n[5 rows x 80 columns]", "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>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</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>0</td>\n      <td>0</td>\n      <td>...</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows \u00d7 80 columns</p>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "markdown", "source": "### TASK  8: Cast all numeric columns to `float64`\n"}, {"metadata": {}, "cell_type": "markdown", "source": "Now that our <code>features_one_hot</code> dataframe only contains numbers cast the entire dataframe to variable type <code>float64</code>\n"}, {"metadata": {}, "cell_type": "code", "source": "# HINT: use astype function\nfeatures_one_hot.astype('float64')", "execution_count": 14, "outputs": [{"output_type": "execute_result", "execution_count": 14, "data": {"text/plain": "    FlightNumber   PayloadMass  Flights  GridFins  Reused  Legs  Block  \\\n0            1.0   6104.959412      1.0       0.0     0.0   0.0    1.0   \n1            2.0    525.000000      1.0       0.0     0.0   0.0    1.0   \n2            3.0    677.000000      1.0       0.0     0.0   0.0    1.0   \n3            4.0    500.000000      1.0       0.0     0.0   0.0    1.0   \n4            5.0   3170.000000      1.0       0.0     0.0   0.0    1.0   \n..           ...           ...      ...       ...     ...   ...    ...   \n85          86.0  15400.000000      2.0       1.0     1.0   1.0    5.0   \n86          87.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n87          88.0  15400.000000      6.0       1.0     1.0   1.0    5.0   \n88          89.0  15400.000000      3.0       1.0     1.0   1.0    5.0   \n89          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  \\\n0           0.0          0.0        0.0  ...           0.0           0.0   \n1           0.0          0.0        0.0  ...           0.0           0.0   \n2           0.0          0.0        0.0  ...           0.0           0.0   \n3           0.0          0.0        0.0  ...           0.0           0.0   \n4           0.0          0.0        0.0  ...           0.0           0.0   \n..          ...          ...        ...  ...           ...           ...   \n85          2.0          0.0        0.0  ...           0.0           0.0   \n86          2.0          0.0        0.0  ...           0.0           0.0   \n87          5.0          0.0        0.0  ...           0.0           0.0   \n88          2.0          0.0        0.0  ...           0.0           0.0   \n89          0.0          0.0        0.0  ...           0.0           0.0   \n\n    Serial_B1050  Serial_B1051  Serial_B1054  Serial_B1056  Serial_B1058  \\\n0            0.0           0.0           0.0           0.0           0.0   \n1            0.0           0.0           0.0           0.0           0.0   \n2            0.0           0.0           0.0           0.0           0.0   \n3            0.0           0.0           0.0           0.0           0.0   \n4            0.0           0.0           0.0           0.0           0.0   \n..           ...           ...           ...           ...           ...   \n85           0.0           0.0           0.0           0.0           0.0   \n86           0.0           0.0           0.0           0.0           1.0   \n87           0.0           1.0           0.0           0.0           0.0   \n88           0.0           0.0           0.0           0.0           0.0   \n89           0.0           0.0           0.0           0.0           0.0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  \n0            0.0           0.0           0.0  \n1            0.0           0.0           0.0  \n2            0.0           0.0           0.0  \n3            0.0           0.0           0.0  \n4            0.0           0.0           0.0  \n..           ...           ...           ...  \n85           0.0           1.0           0.0  \n86           0.0           0.0           0.0  \n87           0.0           0.0           0.0  \n88           0.0           1.0           0.0  \n89           0.0           0.0           1.0  \n\n[90 rows x 80 columns]", "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 \u00d7 80 columns</p>\n</div>"}, "metadata": {}}]}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "markdown", "source": "<code>features_one_hot.to_csv('dataset_part\\_3.csv', index=False)</code>\n"}, {"metadata": {}, "cell_type": "markdown", "source": "## Authors\n"}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "markdown", "source": "## Change Log\n"}, {"metadata": {}, "cell_type": "markdown", "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"}, {"metadata": {}, "cell_type": "markdown", "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"}], "metadata": {"kernelspec": {"name": "python3", "display_name": "Python 3.9", "language": "python"}, "language_info": {"name": "python", "version": "3.9.7", "mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py"}}, "nbformat": 4, "nbformat_minor": 4}