# Notebook from talibkateeb/IBM_Applied_DS_Capstone
Path: Data_Collection.ipynb

_____no_output_____# **SpaceX  Falcon 9 first stage Landing Prediction**
_____no_output_____# Lab 1: Collecting the data
_____no_output_____Estimated time needed: **45** minutes
_____no_output_____In this capstone, 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 because SpaceX can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against SpaceX for a rocket launch. In this lab, you will collect and make sure the data is in the correct format from an API. The following is an example of a successful and launch.
_____no_output_____![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/lab_v2/images/landing\_1.gif)
_____no_output_____Several examples of an unsuccessful landing are shown here:
_____no_output_____![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/lab_v2/images/crash.gif)
_____no_output_____Most unsuccessful landings are planned. Space X performs a controlled landing in the oceans.
_____no_output_____## Objectives
_____no_output_____In this lab, you will make a get request to the SpaceX API. You will also do some basic data wrangling and formating.

*   Request to the SpaceX API
*   Clean the requested data
_____no_output_____***
_____no_output_____## Import Libraries and Define Auxiliary Functions
_____no_output_____We will import the following libraries into the lab
_____no_output_____

# Requests allows us to make HTTP requests which we will use to get data from an API
import requests
# Pandas is a software library written for the Python programming language for data manipulation and analysis.
import pandas as pd
# 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
import numpy as np
# Datetime is a library that allows us to represent dates
import datetime

# Setting this option will print all collumns of a dataframe
pd.set_option('display.max_columns', None)
# Setting this option will print all of the data in a feature
pd.set_option('display.max_colwidth', None)_____no_output_____

Below we will define a series of helper functions that will help us use the API to extract information using identification numbers in the launch data.

From the rocket column we would like to learn the booster name.
_____no_output_____

# Takes the dataset and uses the rocket column to call the API and append the data to the list
def getBoosterVersion(data):
    for x in data['rocket']:
        response = requests.get("https://api.spacexdata.com/v4/rockets/"+str(x)).json()
        BoosterVersion.append(response['name'])_____no_output_____

From the launchpad we would like to know the name of the launch site being used, the logitude, and the latitude.
_____no_output_____

# Takes the dataset and uses the launchpad column to call the API and append the data to the list
def getLaunchSite(data):
    for x in data['launchpad']:
        response = requests.get("https://api.spacexdata.com/v4/launchpads/"+str(x)).json()
        Longitude.append(response['longitude'])
        Latitude.append(response['latitude'])
        LaunchSite.append(response['name'])_____no_output_____

From the payload we would like to learn the mass of the payload and the orbit that it is going to.
_____no_output_____

# Takes the dataset and uses the payloads column to call the API and append the data to the lists
def getPayloadData(data):
    for load in data['payloads']:
        response = requests.get("https://api.spacexdata.com/v4/payloads/"+load).json()
        PayloadMass.append(response['mass_kg'])
        Orbit.append(response['orbit'])_____no_output_____

From cores we would like to learn the outcome of the landing, the type of the landing, number of flights with that core, whether gridfins were used, wheter the core is reused, wheter legs were used, the landing pad used, the block of the core which is a number used to seperate version of cores, the number of times this specific core has been reused, and the serial of the core.
_____no_output_____

# Takes the dataset and uses the cores column to call the API and append the data to the lists
def getCoreData(data):
    for core in data['cores']:
            if core['core'] != None:
                response = requests.get("https://api.spacexdata.com/v4/cores/"+core['core']).json()
                Block.append(response['block'])
                ReusedCount.append(response['reuse_count'])
                Serial.append(response['serial'])
            else:
                Block.append(None)
                ReusedCount.append(None)
                Serial.append(None)
            Outcome.append(str(core['landing_success'])+' '+str(core['landing_type']))
            Flights.append(core['flight'])
            GridFins.append(core['gridfins'])
            Reused.append(core['reused'])
            Legs.append(core['legs'])
            LandingPad.append(core['landpad'])_____no_output_____

Now let's start requesting rocket launch data from SpaceX API with the following URL:
_____no_output_____

spacex_url="https://api.spacexdata.com/v4/launches/past"_____no_output_____response = requests.get(spacex_url)_____no_output_____

Check the content of the response
_____no_output_____

print(response.content)b'[{"fairings":{"reused":false,"recovery_attempt":false,"recovered":false,"ships":[]},"links":{"patch":{"small":"https://images2.imgbox.com/3c/0e/T8iJcSN3_o.png","large":"https://images2.imgbox.com/40/e3/GypSkayF_o.png"},"reddit":{"campaign":null,"launch":null,"media":null,"recovery":null},"flickr":{"small":[],"original":[]},"presskit":null,"webcast":"https://www.youtube.com/watch?v=0a_00nJ_Y88","youtube_id":"0a_00nJ_Y88","article":"https://www.space.com/2196-spacex-inaugural-falcon-1-rocket-lost-launch.html","wikipedia":"https://en.wikipedia.org/wiki/DemoSat"},"static_fire_date_utc":"2006-03-17T00:00:00.000Z","static_fire_date_unix":1142553600,"net":false,"window":0,"rocket":"5e9d0d95eda69955f709d1eb","success":false,"failures":[{"time":33,"altitude":null,"reason":"merlin engine failure"}],"details":"Engine failure at 33 seconds and loss of vehicle","crew":[],"ships":[],"capsules":[],"payloads":["5eb0e4b5b6c3bb0006eeb1e1"],"launchpad":"5e9e4502f5090995de566f86","flight_number":1,"name":"FalconSat","date_utc":"2006-03-24T22:30:00.000Z","date_unix":1143239400,"date_local":"2006-03-25T10:30:00+12:00","date_precision":"hour","upcoming":false,"cores":[{"core":"5e9e289df35918033d3b2623","flight":1,"gridfins":false,"legs":false,"reused":false,"landing_attempt":false,"landing_success":null,"landing_type":null,"landpad":null}],"auto_update":true,"tbd":false,"launch_library_id":null,"id":"5eb87cd9ffd86e000604b32a"},{"fairings":{"reused":false,"recovery_attempt":false,"recovered":false,"ships":[]},"links":{"patch":{"small":"https://images2.imgbox.com/4f/e3/I0lkuJ2e_o.png","large":"https://images2.imgbox.com/be/e7/iNqsqVYM_o.png"},"reddit":{"campaign":null,"launch":null,"media":null,"recovery":null},"flickr":{"small":[],"original":[]},"presskit":null,"webcast":"https://www.youtube.com/watch?v=Lk4zQ2wP-Nc","youtube_id":"Lk4zQ2wP-Nc","article":"https://www.space.com/3590-spacex-falcon-1-rocket-fails-reach-orbit.html","wikipedia":"https://en.wikipedia.org/wiki/DemoSat"},"static_fire_date_utc":null,"static_fire_date_unix":null,"net":false,"window":0,"rocket":"5e9d0d95eda69955f709d1eb","success":false,"failures":[{"time":301,"altitude":289,"reason":"harmonic 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You should see the response contains massive information about SpaceX launches. Next, let's try to discover some more relevant information for this project.
_____no_output_____### Task 1: Request and parse the SpaceX launch data using the GET request
_____no_output_____To make the requested JSON results more consistent, we will use the following static response object for this project:
_____no_output_____

static_json_url='https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/API_call_spacex_api.json'_____no_output_____

We should see that the request was successfull with the 200 status response code
_____no_output_____

response.status_code_____no_output_____

Now we decode the response content as a Json using .json() and turn it into a Pandas dataframe using .json_normalize()
_____no_output_____

# Use json_normalize meethod to convert the json result into a dataframe
data = pd.json_normalize(response.json())
_____no_output_____

Using the dataframe data print the first 5 rows
_____no_output_____

# Get the head of the dataframe
data.head(5)
_____no_output_____

You will notice that a lot of the data are IDs. For example the rocket column has no information about the rocket just an identification number.

We will now use the API again to get information about the launches using the IDs given for each launch. Specifically we will be using columns rocket, payloads, launchpad, and cores.
_____no_output_____

# Lets take a subset of our dataframe keeping only the features we want and the flight number, and date_utc.
data = data[['rocket', 'payloads', 'launchpad', 'cores', 'flight_number', 'date_utc']]

# 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.
data = data[data['cores'].map(len)==1]
data = data[data['payloads'].map(len)==1]

# Since payloads and cores are lists of size 1 we will also extract the single value in the list and replace the feature.
data['cores'] = data['cores'].map(lambda x : x[0])
data['payloads'] = data['payloads'].map(lambda x : x[0])

# We also want to convert the date_utc to a datetime datatype and then extracting the date leaving the time
data['date'] = pd.to_datetime(data['date_utc']).dt.date

# Using the date we will restrict the dates of the launches
data = data[data['date'] <= datetime.date(2020, 11, 13)]_____no_output_____

*   From the rocket we would like to learn the booster name

*   From the payload we would like to learn the mass of the payload and the orbit that it is going to

*   From the launchpad we would like to know the name of the launch site being used, the longitude, and the latitude.

*   From cores we would like to learn the outcome of the landing, the type of the landing, number of flights with that core, whether gridfins were used, whether the core is reused, whether legs were used, the landing pad used, the block of the core which is a number used to seperate version of cores, the number of times this specific core has been reused, and the serial of the core.

The data from these requests will be stored in lists and will be used to create a new dataframe.
_____no_output_____

#Global variables
BoosterVersion = []
PayloadMass = []
Orbit = []
LaunchSite = []
Outcome = []
Flights = []
GridFins = []
Reused = []
Legs = []
LandingPad = []
Block = []
ReusedCount = []
Serial = []
Longitude = []
Latitude = []_____no_output_____

These functions will apply the outputs globally to the above variables. Let's take a looks at BoosterVersion variable. Before we apply  getBoosterVersion the list is empty:
_____no_output_____

BoosterVersion_____no_output_____

Now, let's apply  getBoosterVersion function method to get the booster version
_____no_output_____

# Call getBoosterVersion
getBoosterVersion(data)_____no_output_____

the list has now been update
_____no_output_____

BoosterVersion[0:5]_____no_output_____

we can apply the rest of the  functions here:
_____no_output_____

# Call getLaunchSite
getLaunchSite(data)_____no_output_____# Call getPayloadData
getPayloadData(data)_____no_output_____# Call getCoreData
getCoreData(data)_____no_output_____

Finally lets construct our dataset using the data we have obtained. We we combine the columns into a dictionary.
_____no_output_____

launch_dict = {'FlightNumber': list(data['flight_number']),
'Date': list(data['date']),
'BoosterVersion':BoosterVersion,
'PayloadMass':PayloadMass,
'Orbit':Orbit,
'LaunchSite':LaunchSite,
'Outcome':Outcome,
'Flights':Flights,
'GridFins':GridFins,
'Reused':Reused,
'Legs':Legs,
'LandingPad':LandingPad,
'Block':Block,
'ReusedCount':ReusedCount,
'Serial':Serial,
'Longitude': Longitude,
'Latitude': Latitude}
_____no_output_____

Then, we need to create a Pandas data frame from the dictionary launch_dict.
_____no_output_____

# Create a data from launch_dict
data2 = pd.DataFrame(launch_dict)
_____no_output_____

Show the summary of the dataframe
_____no_output_____

# Show the head of the dataframe
data2.head(5)
_____no_output_____

### Task 2: Filter the dataframe to only include `Falcon 9` launches
_____no_output_____Finally we will remove the Falcon 1 launches keeping only the Falcon 9 launches. Filter the data dataframe using the BoosterVersion column to only keep the Falcon 9 launches. Save the filtered data to a new dataframe called data_falcon9.
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# Hint data['BoosterVersion']!='Falcon 1'
data_falcon9 = data2[data2['BoosterVersion']=='Falcon 9']
data_falcon9_____no_output_____

Now that we have removed some values we should reset the FlgihtNumber column
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data_falcon9.loc[:,'FlightNumber'] = list(range(1, data_falcon9.shape[0]+1))
data_falcon9/opt/conda/envs/Python-3.8-main/lib/python3.8/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_column(ilocs[0], value, pi)

## Data Wrangling
_____no_output_____We can see below that some of the rows are missing values in our dataset.
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data_falcon9.isnull().sum()_____no_output_____

Before we can continue we must deal with these missing values. The LandingPad column will retain None values to represent when landing pads were not used.
_____no_output_____### Task 3: Dealing with Missing Values
_____no_output_____Calculate below the mean for the PayloadMass using the .mean(). Then use the mean and the .replace() function to replace `np.nan` values in the data with the mean you calculated.
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# Calculate the mean value of PayloadMass column
pm_mean = data_falcon9['PayloadMass'].mean()

# Replace the np.nan values with its mean value
temp = data_falcon9['PayloadMass'].replace(np.nan, pm_mean)
data_falcon9['PayloadMass'] = temp
data_falcon9<ipython-input-27-915aac30ba1b>:6: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  data_falcon9['PayloadMass'] = temp

You should see the number of missing values of the PayLoadMass change to zero.
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data_falcon9.isnull().sum()
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Now we should have no missing values in our dataset except for in LandingPad.
_____no_output_____We can now export it to a CSV for the next section,but to make the answers consistent, in the next lab we will provide data in a pre-selected date range.
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data_falcon9.to_csv('dataset_part\_1.csv', index=False)_____no_output_____

## Authors
_____no_output_____Joseph Santarcangelo 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.
_____no_output_____## Change Log
_____no_output_____| Date (YYYY-MM-DD) | Version | Changed By | Change Description                  |
| ----------------- | ------- | ---------- | ----------------------------------- |
| 2020-09-20        | 1.1     | Joseph     | get result each time you run        |
| 2020-09-20        | 1.1     | Azim       | Created Part 1 Lab using SpaceX API |
| 2020-09-20        | 1.0     | Joseph     | Modified Multiple Areas             |
_____no_output_____Copyright © 2021 IBM Corporation. All rights reserved.
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