data-collection.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SpaceX  Falcon 9 first stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 1: Collecting the data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/lab_v2/images/landing_1.gif)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Import Libraries**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getBoosterVersion(data): \n",
    "    for x in data[\"rocket\"]: \n",
    "        if x: \n",
    "            response = requests.get(\"https://api.spacexdata.com/v4/rockets/\"+str(x)).json()\n",
    "            BoosterVersion.append(response[\"name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getLaunchSite(data):\n",
    "    for x in data[\"launchpad\"]:\n",
    "        if x:\n",
    "            response = requests.get(\"https://api.spacexdata.com/v4/launchpads/\"+str(x)).json()\n",
    "            Longitude.append(response[\"longitude\"])\n",
    "            Latitude.append(response[\"latitude\"])\n",
    "            LaunchSite.append(response[\"name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getPayloadData(data):\n",
    "    for load in data['payloads']:\n",
    "       if load:\n",
    "        response = requests.get(\"https://api.spacexdata.com/v4/payloads/\"+load).json()\n",
    "        PayloadMass.append(response['mass_kg'])\n",
    "        Orbit.append(response['orbit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCoreData(data): \n",
    "    for core in data[\"cores\"]: \n",
    "        if core[\"core\"] != None: \n",
    "            response = requests.get(\"https://api.spacexdata.com/v4/cores/\"+core['core']).json()\n",
    "            Block.append(response[\"block\"])\n",
    "            ReusedCount.append(response[\"reuse_count\"])\n",
    "            Serial.append(response[\"serial\"])\n",
    "        \n",
    "        else: \n",
    "            Block.append(None)\n",
    "            ReusedCount.append(None)\n",
    "            Serial.append(None)\n",
    "        \n",
    "        Outcome.append(str(core['landing_success'])+' '+str(core['landing_type']))\n",
    "        Flights.append(core['flight'])\n",
    "        GridFins.append(core['gridfins'])\n",
    "        Reused.append(core['reused'])\n",
    "        Legs.append(core['legs'])\n",
    "        LandingPad.append(core['landpad'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://api.spacexdata.com/v4/launches/past\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response = requests.get(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# response.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "static_json_url='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/API_call_spacex_api.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "response.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.json_normalize(response.json())\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets take a subset of our dataframe keeping only the features we want and the flight number, and date_utc.\n",
    "data = data[['rocket', 'payloads', 'launchpad', 'cores', 'flight_number', 'date_utc']]\n",
    "\n",
    "# We will remove rows with multiple cores because those are falcon rockets with 2 extra rocket boosters and rows that have multiple payloads in a single rocket.\n",
    "data = data[data['cores'].map(len)==1]\n",
    "data = data[data['payloads'].map(len)==1]\n",
    "\n",
    "# Since payloads and cores are lists of size 1 we will also extract the single value in the list and replace the feature.\n",
    "data['cores'] = data['cores'].map(lambda x : x[0])\n",
    "data['payloads'] = data['payloads'].map(lambda x : x[0])\n",
    "\n",
    "# We also want to convert the date_utc to a datetime datatype and then extracting the date leaving the time\n",
    "data['date'] = pd.to_datetime(data['date_utc']).dt.date\n",
    "\n",
    "# Using the date we will restrict the dates of the launches\n",
    "data = data[data['date'] <= datetime.date(2020, 11, 13)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Global variables \n",
    "BoosterVersion = []\n",
    "PayloadMass = []\n",
    "Orbit = []\n",
    "LaunchSite = []\n",
    "Outcome = []\n",
    "Flights = []\n",
    "GridFins = []\n",
    "Reused = []\n",
    "Legs = []\n",
    "LandingPad = []\n",
    "Block = []\n",
    "ReusedCount = []\n",
    "Serial = []\n",
    "Longitude = []\n",
    "Latitude = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "BoosterVersion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "getBoosterVersion(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "BoosterVersion[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "getLaunchSite(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "getPayloadData(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "getCoreData(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "launch_dict = {'FlightNumber': list(data['flight_number']),\n",
    "'Date': list(data['date']),\n",
    "'BoosterVersion':BoosterVersion,\n",
    "'PayloadMass':PayloadMass,\n",
    "'Orbit':Orbit,\n",
    "'LaunchSite':LaunchSite,\n",
    "'Outcome':Outcome,\n",
    "'Flights':Flights,\n",
    "'GridFins':GridFins,\n",
    "'Reused':Reused,\n",
    "'Legs':Legs,\n",
    "'LandingPad':LandingPad,\n",
    "'Block':Block,\n",
    "'ReusedCount':ReusedCount,\n",
    "'Serial':Serial,\n",
    "'Longitude': Longitude,\n",
    "'Latitude': Latitude}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.DataFrame({ key:pd.Series(value) for key, value in launch_dict.items() })\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_falcon9 = df[df[\"BoosterVersion\"] != \"Falcon 1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_falcon9.loc[:,'FlightNumber'] = list(range(1, data_falcon9.shape[0]+1))\n",
    "data_falcon9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_falcon9.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = data_falcon9[\"PayloadMass\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_falcon9[\"PayloadMass\"].replace(np.nan, mean, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_falcon9.to_csv(\"dataset_part_1.csv\", index = False)"
   ]
  }
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