Report_SpotifyAPI.md

Spotify API

Part 1: Retrieving the data using the API

Step 1: Importing libraries

First, we import the necessary library needed to start retrieving data which in this case is the requests library which is used for making HTTP requests. It allows the code to interact with the Spotify API to retrieve information

import requests

Step 2: Getting access token from Spotify API

Next, to use the Spotify API, we need to authenticate the requests by providing an access token. This access token is a credential that will be used to access the Spotify API. To get this access token, we need to insert client_id and client_secret which we can get from Spotify web developer into the coding below.

url = 'https://accounts.spotify.com/api/token'
data = {
    'grant_type': 'client_credentials',
    'client_id': '39d34046ceb846029127453746e14629',
    'client_secret': 'c263ba986458411fbd24c41fd3b504e3'
}
headers = {
    'Content-Type': 'application/x-www-form-urlencoded'
}

response = requests.post(url, data=data, headers=headers)

print(response.json())

Step 3: Getting top track's data from Spotify

Here, we use Taylor Swift as our artist to get her top track. First, enter 'Taylor Swift' as the artist, then enter access token and token type as its authorization.

url = 'https://api.spotify.com/v1/search?type=track&q=artist:Taylor Swift'
headers = {
    'Authorization': 'Bearer BQCg1QL1zKvt741T7kDn93tECz4IxaIzKqbbFZJg7CByhk9bc8an4mlwDYCdyOKxtUPCLCZFWTluCO3OYSOLVsAB-FanoEFPgf5qsgrHs6PTW4SKUQd5'
}

response = requests.get(url, headers=headers)

data=response.json()

print(data)

Step 4: Saving as csv file

Lastly, with all the data that we got, we need to save it as csv file so that we can upload it into MongoDB. We have narrowed down the data that we want to keep which are name, popularity, album_name and track_url.

with open('/content/drive/MyDrive/Taylor_Swift.csv', 'w', newline='', encoding='utf-8') as csvfile:
    writer = csv.writer(csvfile)
    # Write the header row
    writer.writerow(['name', 'popularity', 'album', 'track_url'])
    # Write each track to a new row
    for track in data['tracks']['items']:
        name = track['name']
        popularity = track['popularity']
        album_name = track['album']['name']
        track_url = track['external_urls']['spotify']
        writer.writerow([name, popularity, album_name, track_url])

print('Taylor_Swift.csv')

Part 2: Uploading .csv file to MongoDB

Step 1: Install database library

This is the most significant stage since it will provide us with a list of libraries that we will need to link our database.

!pip install pymongo
from pymongo import MongoClient

Step 2: Create database

In this stage, we set a new database in MongoDB Compass and name it, followed by the creation of a collection within the database.

Step 3: import library and declare Variable

In this step, we import the library we just loaded and define three key variables: client (connection string), database, and collection.

import pymongo
client = pymongo.MongoClient('<connection string>')
db = client['<database name>']
collection = db['<collection name>']

Step 4: Insert the record into MongoDB

declare variable names based on csv table columns

 # Insert the record into MongoDB
            collection.insert_one({
                'name': row['name'],
                'popularity': int(row['popularity']),
                'album': row['album'],
                'track_url': row['track_url']
            })

Step 5: Check the data

Open MongoDB to see if the data has been added. The snapshot below displays a MongoDB Compass screenshot of documents successfully added into a database collection.