API Import Tutorial

PANDA’s API is designed to allow you to programmatically import (and, to a lesser extent, export) data from PANDA. In this tutorial we will show you how you can use the PANDA API to pull data froma web scraper into PANDA.. Our example will be written in Python using the Requests module, but should be easily portable to any language.


If you just want to skip to the code, check out all our API examples on Github.

The problem of synchronization

Writing a PANDA scraper usually means that you are trying to synchronize PANDA with some data source. True synchornization, where all new, changed and deleted rows get replicated to PANDA is often not possible. Because of this it is important to think in advance about how you will design your import process.

The first question to to ask yourself is if each row of data has a unique id that will allow you to identify it. (A primary key in SQL parlance.) In PANDA we call this value the external_id, because it is generated external to PANDA. An external_id could be anything from a row number to a social security number.

If you can provide an external_id for your data you will be able to read and update your individual rows of data at a unique URL:

GET http://localhost:8000/api/1.0/dataset/[slug]/data/[external_id]/

If your data doesn’t have an external_id then you won’t be able to read or update individual rows of data. (You can still find them via search or delete them in bulk.) In this case the only way to synchronize changes between PANDA and your source dataset will be to delete and reimport all rows.

Even if you do have an external_id you may still need to delete all rows if your source doesn’t provide a changelog. A changelog is a stream of metadata that describes when rows of data are modified. Without a changelog it will be impossible to tell if a row of data has been deleted. (Because, by definition, it won’t be there anymore to find out about.) CouchDB is a rare example of a database that provides a changelog. (See our CouchDB example.)

In most cases you will have an external_id, but not a changelog, so you will need to decide if it is important that rows deleted in the source dataset are also deleted in your PANDA. If so, you will need to wipe out all the data before importing the new data.


There is no technical difference between creating and updating a row in PANDA. In either case data will be overwritten if there is a row with a matching external_id already in the dataset, otherwise a new row will be created.

Our source data

In this tutorial we are going to be scraping the results of a very simple web scraper, hosted on Scraperwiki, that is aggregating all tweets about the PANDA Project. Because Scraperwiki has an API, we can write a simple script that will allow us to import the results and then run that script as often as we like.

The data we are importing has three simple columns:

  • text, the full-text of the tweet.
  • id, Twitter’s unique id for the tweet.
  • from_user, the username of the person who sent the tweet.

Although Twitter doesn’t provide a changelog, they do provide a unique id, which we can use as our external_id. We won’t be able to track deleted tweets, but in this case that may actually be to our advantage, since deleted tweets can themselves be interesting. So for this data we will take the approach of reimporting all data, but not deleting anything. This will ensure we pick up any new tweets and any changes (though tweets should never change).

Step 1: Getting setup

Before we get started we are going to need to import the Python standard JSON module, as well as Requests. If you don’t have requests you can install it with pip install requests (or easy_install requests).

import json
import requests

Now we will define some global variables for values we are going to reuse throughout the script.

# Replace "localhost:8000" with your PANDA's DNS name or IP address
PANDA_API = 'http://localhost:8000/api/1.0'

# Replace these parameters with your administrator's email and api key
    'email': 'panda@pandaproject.net',
    'api_key': 'edfe6c5ffd1be4d3bf22f69188ac6bc0fc04c84b'

# This will be the slug of the dataset we are creating/updating
PANDA_DATASET_SLUG = 'twitter-pandaproject'

# This is the url where the dataset will live, we send our GET/PUT requests here

# This is the url under which all data for this dataset lives
PANDA_DATA_URL = '%s/dataset/%s/data/' % (PANDA_API, PANDA_DATASET_SLUG)

# Change this value to configure how many rows should be sent to PANDA in each batch

# This is the url of the Scraperwiki endpoint for our Twitter scraper
# This was generated from: https://scraperwiki.com/docs/api#sqlite
SCRAPERWIKI_URL = 'https://api.scraperwiki.com/api/1.0/datastore/sqlite?format=jsonlist&name=basic_twitter_scraper_437&query=select%20*%20from%20%60swdata%60'

# These are the three columns in our dataset
COLUMNS = ['text', 'id', 'from_user']

Next we will define two convenience methods that handle reading from and writing to PANDA. These will save us repeating ourselves each time we need to send an authenticated request to PANDA.

# Wrapper around a GET request
def panda_get(url, params={}):
    return requests.get(url, params=params)

# Wrapper around a PUT request
def panda_put(url, data, params={}):
    return requests.put(url, data, params=params, headers={ 'Content-Type': 'application/json' })

Step 2: Creating the dataset

Before we start importing our data we will first check to see if the dataset exists. If it has not yet been created we will create it.

# Attempt to fetch the dataset at its url
response = panda_get(PANDA_DATASET_URL)

# If it doesn't exist the response will be a 404 (not found)
if response.status_code == 404:
    # This is will be serialized as JSON and sent to PANDA to create the dataset
    dataset = {
        'name': 'PANDA Project Twitter Search',
        'description': 'Results of the scraper at <a href="https://scraperwiki.com/scrapers/basic_twitter_scraper_437/">https://scraperwiki.com/scrapers/basic_twitter_scraper_437/</a>.'

    # In addition to the name and description, we also use the "column" querystring parameter
    # to define the dataset's columns. You will always need to do this when creating datasets
    # via the API.
    response = panda_put(PANDA_DATASET_URL, json.dumps(dataset), params={
        'columns': ','.join(COLUMNS),


See also: complete documentation for the Datasets API.

Step 3: Fetching data from Scraperwiki

To get data from Scraperwiki we simply request the url we defined above. Because we specified the jsonlist parameter the response is a JSON document containing an array of keys and an array of rows.

# Request the latest data
response = requests.get(SCRAPERWIKI_URL)

# The response is json, so deserialize it
data = json.loads(response.content)

Step 4: Load the data into PANDA!

Now that we have our data from Scraperwiki we simply iterate over the rows and convert them into batches of data to be sent to PANDA.

# This is the data structure that will be sent to PANDA
put_data = {
    'objects': []

# The row data from Scraperwiki is inside the "data" key
# We enumerate the rows as we go so we can load the data in batches
for i, row in enumerate(data['data']):
    # Each row we send to PANDA consists of "data", an array of column values
    # and the "external_id", which *must* be unicode
        'data': row,
        'external_id': unicode(row[1])

    # Everytime we've processed 1000 records, we send them to PANDA
    if i and i % PANDA_BULK_UPDATE_SIZE == 0:
        panda_put(PANDA_DATA_URL, json.dumps(put_data))
        put_data['objects'] = []

# At the end we will probably have records left over, so we send the rest
if put_data['objects']:
    print 'Updating %i rows' % len(put_data['objects'])
    response = panda_put(PANDA_DATA_URL, json.dumps(put_data))


See also: complete documentation for the Data API.

Step 5: PANDAmonium!

And that’s it, we’re done! PANDA now has the data. We can run this script as frequently as we like. Existing rows will be overwritten with any changes and new rows will be added. For very large datasets we suggest running these scripts during off hours.

You can see the complete script on Github.

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