Product Data with Postman and CSV

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 Postman.
2. You want to work with CSV data.

Here are the steps we'll take:

1. Download Postman

We recommend using Postman when accessing the Datafiniti API. It's a great client for using any sort of RESTful API and works particularly well with Datafiniti. You can download Postman here.

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.

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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 a test search that 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 women's luxury shoes, let's try a simple search that will just give us listings for shoes sold online.

With Postman open, click on the Datafiniti API V4 collection. Then navigate to Secured Endpoints > Search > Search Product Data. Click on "Body" in the main screen.

Enter the following in the text area:

{
  "query": "categories:shoes",
  "num_records": 1
}

Click the blue "Send" button. 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 what the API call is all about:

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 request body to look like this:

{
  "query": "categories:shoes AND -categories:shirts AND categories:women AND (brand:* OR manufacturer:*) AND prices:*",
  "num_records": 10
}

This API call is different in a few 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 "num_records": 1 to "num_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.

Update your API call in Postman and hit "Send" to see the updated results.

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 further modify our request body to look like this:

{
  "query": "categories:shoes AND -categories:shirts AND categories:women AND (brand:* OR manufacturer:*) AND prices:*",
  "num_records": 50,
  "format": "csv",
  "view": "product_flat_prices",
	"download": true
}

Here's what we changed:

1. We added "format": "csv. If you don't specify format, it will default to json. Using the CSV format will make analyzing our data easier for this example.
2. We change "num_records": 10 to "num_records": 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.
3. We added "view": "product_flat_prices". If you don't specify view, it will use the default view. Using product_flat_prices will nest fields like categories and features into a single cell while splitting each price for the product into its own row.
4. We added "download": true. This tells the API to issue a download request instead of a search request.

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

When you make this API call, you'll see a response similar to:

{
    "id": 7,
    "results": [],
    "user_id": 15,
    "status": "running",
    "date_started": "2017-11-16 17:46:06.0",
    "num_downloaded": 0,
    "data_type": "product",
    "query": "categories:shoes AND -categories:shirts AND categories:women AND (brand:* OR manufacturer:*) AND prices:*",
    "format": "csv",
    "num_records": 50,
  	"total_cost": 50
}

We'll explain each of these fields in the next section.

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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. Monitor the status of the download

As the download request runs, you can check on its status by using the "Get Download" call in your Postman collection. Just replace :id with the id value you saw in the previous step.

If you keep running this call, you'll see some of the values update. Once the download completes, it will look something like this:

{
    "id": 7,
    "results": [
        "https://datafiniti-downloads.s3.amazonaws.com/15/7_1.txt?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20171116T174607Z&X-Amz-SignedHeaders=host&X-Amz-Expires=604800&X-Amz-Credential=AKIAJYCTIF46QVBTXWYA%2F2017xxxx%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=ecf13f1bb4b7adfdde1a99143541afd1d12347292eb9ec3f6ed1316c64d4eekf"
    ],
    "user_id": 15,
    "status": "completed",
    "date_started": "2017-11-16 17:46:06.0",
    "date_updated": "2017-11-16 17:46:07.0",
    "num_downloaded": 50,
    "data_type": "product",
    "query": "categories:shoes AND -categories:shirts AND categories:women AND (brand:* OR manufacturer:*) AND prices:*",
    "format": "csv",
    "num_records": 50,
  	"total_cost": 50
}

Here's what these fields mean:

7. Download the result file(s)

Once the download response shows "status": "completed", you can download the data using the URLs in the results field.

If you've requested a lot of records (i.e., over 10,000), you may see more than 1 result object shown.

To download the result files, copy each url value and paste it into your browser. Your browser will initiate a download to your computer.

8. Open the result file(s) in Excel

Navigate to the file you downloaded and open it. Since it's a CSV file, it should open in Excel automatically. It will look something like:

9. Analyze the results

Using Excel, we can find the average price of all these shoes, by averaging the columns for prices.amountMin or prices.amoundMax.