Product Data with Python and JSON

For this guide, we're going to assume the following you're interested in using Datafiniti's product data to analyze trends in the women's luxury shoe market. Let's say you're a data scientist that's been tasked with the following:

1. Collect pricing data on women's luxury shoes from multiple online retailers.
2. Sort the data by brand.
3. Determine the average price of each brand.

Your environment and data needs:

1. You're working with Python.
2. You want to work with JSON data.

Here are the steps we'll take:

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Note that we are using Python 3 for the examples below.

1. Install the requests module for Python

In your terminal, run the following to install the requests module for Python:

pip3 install requests

2. Get your API token

The next thing you'll need is your API token. The API token lets you authenticate with Datafiniti API and tells it who you are, what you have access to, and so on. Without it, you can't use the API.

To get your API token, go the Datafiniti Web Portal (https://portal.datafiniti.co), login, and click your settings in the left navigation bar. From there, you'll see a page showing your token. Your API token will be a long string of letters and numbers. Copy the API token or store it somewhere you can easily reference.

For security reasons, your API token will be automatically changed whenever you change your password.

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For the rest of this document, we'll use AAAXXXXXXXXXXXX as a substitute example for your actual API token when showing example API calls.

.

3. Run your first search

The first thing we'll do is do write some code that will run a test search. This test search will give us a sense for what sort of data might be available. Eventually we'll refine our search so that we get back the most relevant data.

Since we want hotels, let's try a simple search that will just give us online listings for hotels.

Write the following code in your code editor (replace the dummy API token with your real API token):

# Illustrates an API call to Datafiniti's Product Database.
import requests
import urllib.parse
import json

# Set your API parameters here.
API_token = 'AAAXXXXXXXXXXXX'
format = 'JSON'
query = 'categories:shoes'
num_records = 1
download = False

request_headers = {
	'Authorization': 'Bearer ' + API_token,
	'Content-Type': 'application/json',
}
request_data = {
    'query': query,
    'format': format,
    'num_records': num_records,
    'download': download
}

# Make the API call.
r = requests.post('https://api.datafiniti.co/v4/products/search',json=request_data,headers=request_headers);

# Do something with the response.
if r.status_code == 200:
	print(r.content)
else:
	print('Request failed')

You should get a response similar to this:

{
  "num_found": 884885,
  "total_cost": 1,
  "records": [
    {
      "asins": [
        "B010XYTFM6"
      ],
      "brand": "inktastic",
      "categories": [
        "Novelty",
        "Tops & Tees",
        "Novelty & More",
        "Women",
        "Clothing, Shoes & Jewelry",
        "T-Shirts",
        "Clothing"
      ],
      "dateAdded": "2015-11-09T01:55:09Z",
      "dateUpdated": "2016-04-12T16:19:26Z",
      "imageURLs": [
        "http://ecx.images-amazon.com/images/I/41W6xSgsBbL._SX342_QL70_.jpg",
        "http://ecx.images-amazon.com/images/I/41StiamgorL._SR38,50_.jpg",
        "http://ecx.images-amazon.com/images/I/41StiamgorL._SX342_QL70_.jpg",
        "http://ecx.images-amazon.com/images/I/41W6xSgsBbL._SR38,50_.jpg"
      ],
      "keys": [
        "inktasticwomensbutterflybanjochickjuniorvnecktshirts/b010xytfm6"
      ],
      "name": "Inktastic Women's Butterfly Banjo Chick Junior V-neck T-shirts",
      "sourceURLs": [
        "http://www.amazon.com/Inktastic-Womens-Butterfly-Junior-T-Shirts/dp/B010XYTK1W",
        "http://www.amazon.com/Inktastic-Womens-Butterfly-Junior-T-Shirts/dp/B016HKYYN0",
        "http://www.amazon.com/Inktastic-Butterfly-T-Shirts-Athletic-Heather/dp/B016HKYPYS"
      ],
      "id": "AVkzMzokUmTPEltRlaJ_"
    }
  ]
}

Let's break down each of the parameters we sent in our request:

Now let's dive through the response the API returned:

Within the records field, you'll see a single product returned with multiple fields and the values associated with that product. The JSON response will show all fields that have a value. It won't show any fields that don't have a value.

Each product record will have multiple fields associated with it. You can see a full list of available fields in our Product Data Schema.

4. Refine your search

If you take a look at the sample record shown above, you'll notice that it's not actually a pair of shoes. It's actually a shirt. It was returned as a match because its category keywords included Clothing, Shoes & Jewelry. If we downloaded all matching records, we would find several products that really are shoes, but we'd also find other products like this one, which aren't.

We'll need to refine our search to make sure we're only getting shoes. Modify your code to look like this:

# Illustrates an API call to Datafiniti's Product Database.
import requests
import urllib.parse
import json

# Set your API parameters here.
API_token = 'AAAXXXXXXXXXXXX'
format = 'JSON'
query = 'categories:shoes AND -categories:shirts AND categories:women AND (brand:* OR manufacturer:*) AND prices:*'
num_records = 10
download = False

request_headers = {
	'Authorization': 'Bearer ' + API_token,
	'Content-Type': 'application/json',
}
request_data = {
    'query': query,
    'format': format,
    'num_records': num_records,
    'download': download
}

# Make the API call.
r = requests.post('https://api.datafiniti.co/v4/products/search',json=request_data,headers=request_headers);

# Do something with the response.
if r.status_code == 200:
	print(r.content)
else:
	print('Request failed')

This code is different in a couple ways:

1. It uses AND -categories:shirts to filter out any products that might be shirts. Note the - in front of categories.
2. It adds AND categories:women to narrow down results to just products for women. (We were interested in just women's shoes.)
3. It adds AND (brand:* OR manufacturer:*). This ensures the brand or manufacturer field is filled out in all the records I request. We call the * a "wildcard" value. Matching against a wildcard is a useful way to ensure the fields you're searching aren't empty.
4. It adds AND prices:*. Again, matching against a wildcard here means we're sure to only get products that have pricing information.
5. It changes records=1 to records=10 so we can look at more sample matches.

Notice how Datafiniti lets you construct very refined boolean queries. In the API call above, we're using a mix of AND and OR to get exactly what we want.

If you would like to narrow your search to just exact matches you can place the search term in quotation marks.

# Illustrates an API call to Datafiniti's Product Database.
import requests
import urllib.parse
import json

# Set your API parameters here.
API_token = 'AAAXXXXXXXXXXXX'
format = 'JSON'
query = 'name:"Apple Iphone 10"'
num_records = 10
download = False

request_headers = {
	'Authorization': 'Bearer ' + API_token,
	'Content-Type': 'application/json',
}
request_data = {
    'query': query,
    'format': format,
    'num_records': num_records,
    'download': download
}

# Make the API call.
r = requests.post('https://api.datafiniti.co/v4/products/search',json=request_data,headers=request_headers);

# Do something with the response.
if r.status_code == 200:
	print(r.content)
else:
	print('Request failed')

The above query will only return products with the exact name of Apple Iphone 10.

5. Initiate a download of the data

Once we like what we see from the sample matches, it's time to download a larger data set! To do this, we're going to update our code a fair bit (an explanation follows):

# Illustrates an API call to Datafiniti's Product Database.
import requests
import urllib.parse
import json
import time

# Set your API parameters here.
API_token = 'AAAXXXXXXXX'
format = 'JSON'
query = 'categories:shoes AND -categories:shirts AND categories:women AND (brand:* OR manufacturer:*) AND prices:*'
num_records = 50
download = True

request_headers = {
	'Authorization': 'Bearer ' + API_token,
	'Content-Type': 'application/json',
}
request_data = {
    'query': query,
    'format': format,
    'num_records': num_records,
    'download': download
}

# Make the API call.
r = requests.post('https://api.datafiniti.co/v4/products/search',json=request_data,headers=request_headers);

# Do something with the response.
if r.status_code == 200:
	request_response = r.json()
	print(request_response)

	# Keep checking the request status until the download has completed
	download_id = request_response['id']
	download_status = request_response['status']

	while (download_status != 'completed'):
		time.sleep(5)
		download_r = requests.get('https://api.datafiniti.co/v4/downloads/' + str(download_id),headers=request_headers);
		download_response = download_r.json()
		download_status = download_response['status']
		print('Records downloaded: ' + str(download_response['num_downloaded']))

	# Once the download has completed, get the list of links to the result files and download each file
	if download_status == 'completed':
		result_list = download_response['results']
		i = 1;
		for result in result_list:
			filename = str(download_id) + '_' + str(i) + '.' + format
			urllib.request.urlretrieve(result,filename)
			print('File: ' + str(i) + ' out of ' + str(len(result_list)) + ' saved: ' + filename)
			i += 1

else:
	print('Request failed')
	print(r)

A few things to pay attention to in the above code:

1. We changed num_records from 10 to 50. This will download the first 50 matching records. If we wanted to download all matching records, we would remove num_records. num_records will tell the API to default to all available records.
2. We changed download from false to true.

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If num_records is not specified, ALL of the records matching the query will be downloaded.

Since we've handled multiple steps of the download process in this code, we won't go into the details here, but we do recommend you familiarize yourself with those steps. Checking them out in our Product Data with Postman and JSON guide.

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When using the API, you will not receive any warning if you are going past your monthly record limit. Keep a track on how many records you have left by checking your account. You are responsible for any overage fees if you go past your monthly limit.

6. Parse the JSON data

The download code will save one or more result files to your project folder.

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The JSON data will actually be a text file, instead of a single JSON object. Each line in the text file is a JSON object. We format the data this way because most programming languages won't handle parsing the entire data set as a JSON object with their standard system calls very well.

We'll need to parse the file into an array of JSON objects. We can use code similar to this to handle the parsing:

import json

# Set the location of your file here
filename = 'xxxx_x.txt'

records = []

with open(filename) as myFile:
	for line in myFile:
		records.append(json.loads(line))

for record in records:
	# Edit these lines to do more with the data
	print json.dumps(record, indent=4, sort_keys=True)

You can edit the code in the for loop above to do whatever you'd like with the data, such as store the data in a database, write it out to your console, etc.