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Dask Bags

Dask Bag implements operations like map, filter, groupby and aggregations on collections of Python objects. It does this in parallel and in small memory using Python iterators. It is similar to a parallel version of itertools or a Pythonic version of the PySpark RDD.

Dask Bags are often used to do simple preprocessing on log files, JSON records, or other user defined Python objects.

Full API documentation is available here:

Start Dask Client for Dashboard

Starting the Dask Client is optional. It will provide a dashboard which is useful to gain insight on the computation.

The link to the dashboard will become visible when you create the client below. We recommend having it open on one side of your screen while using your notebook on the other side. This can take some effort to arrange your windows, but seeing them both at the same is very useful when learning.

from dask.distributed import Client, progress
client = Client(n_workers=4, threads_per_worker=1)



  • Workers: 4
  • Cores: 4
  • Memory: 7.84 GB

Create Random Data

We create a random set of record data and store it to disk as many JSON files. This will serve as our data for this notebook.

import dask
import json
import os

os.makedirs('data', exist_ok=True)              # Create data/ directory

b = dask.datasets.make_people()                 # Make records of people'data/*.json')   # Encode as JSON, write to disk

Read JSON data

Now that we have some JSON data in a file lets take a look at it with Dask Bag and Python JSON module.

!head -n 2 data/0.json
{"age": 28, "name": ["Karleen", "Medina"], "occupation": "Bus Valeter", "telephone": "(379) 007-5536", "address": {"address": "398 Danton Highway", "city": "Petersburg"}, "credit-card": {"number": "2536 9121 5935 1068", "expiration-date": "10/22"}}
{"age": 37, "name": ["Bebe", "Stephenson"], "occupation": "Sales Administrator", "telephone": "748-209-3589", "address": {"address": "146 Dashiell Hammett Bypass", "city": "Kennesaw"}, "credit-card": {"number": "3449 951065 47736", "expiration-date": "05/17"}}
import dask.bag as db
import json

b = db.read_text('data/*.json').map(json.loads)
dask.bag<loads-d..., npartitions=10>
({'age': 28,
  'name': ['Karleen', 'Medina'],
  'occupation': 'Bus Valeter',
  'telephone': '(379) 007-5536',
  'address': {'address': '398 Danton Highway', 'city': 'Petersburg'},
  'credit-card': {'number': '2536 9121 5935 1068',
   'expiration-date': '10/22'}},
 {'age': 37,
  'name': ['Bebe', 'Stephenson'],
  'occupation': 'Sales Administrator',
  'telephone': '748-209-3589',
  'address': {'address': '146 Dashiell Hammett Bypass', 'city': 'Kennesaw'},
  'credit-card': {'number': '3449 951065 47736', 'expiration-date': '05/17'}})

Map, Filter, Aggregate

We can process this data by filtering out only certain records of interest, mapping functions over it to process our data, and aggregating those results to a total value.

b.filter(lambda record: record['age'] > 30).take(2)  # Select only people over 30
({'age': 37,
  'name': ['Bebe', 'Stephenson'],
  'occupation': 'Sales Administrator',
  'telephone': '748-209-3589',
  'address': {'address': '146 Dashiell Hammett Bypass', 'city': 'Kennesaw'},
  'credit-card': {'number': '3449 951065 47736', 'expiration-date': '05/17'}},
 {'age': 61,
  'name': ['Estell', 'Mckee'],
  'occupation': 'Parts Manager',
  'telephone': '636-224-6535',
  'address': {'address': '69 Liberty Path', 'city': 'Buckeye'},
  'credit-card': {'number': '4865 7710 2170 7940',
   'expiration-date': '01/17'}})
[7]: record: record['occupation']).take(2)  # Select the occupation field
('Bus Valeter', 'Sales Administrator')
b.count().compute()  # Count total number of records

Chain computations

It is common to do many of these steps in one pipeline, only calling compute or take at the end.

result = (b.filter(lambda record: record['age'] > 30)
           .map(lambda record: record['occupation'])
           .topk(10, key=1))
dask.bag<topk-ag..., npartitions=1>

As with all lazy Dask collections, we need to call compute to actually evaluate our result. The take method used in earlier examples is also like compute and will also trigger computation.

[('Horse Trainer', 15),
 ('Receptionist', 14),
 ('Payroll Clerk', 13),
 ('Probation Worker', 13),
 ('Fork Lift Truck Driver', 13),
 ('Recorder', 13),
 ('Arts', 13),
 ('Panel Beater', 13),
 ('Training Advisor', 13),
 ('Hop Merchant', 13)]

Transform and Store

Sometimes we want to compute aggregations as above, but sometimes we want to store results to disk for future analyses. For that we can use methods like to_textfiles and json.dumps, or we can convert to Dask Dataframes and use their storage systems, which we’ll see more of in the next section.

(b.filter(lambda record: record['age'] > 30)  # Select records of interest
  .map(json.dumps)                            # Convert Python objects to text
  .to_textfiles('data/processed.*.json'))     # Write to local disk

Convert to Dask Dataframes

Dask Bags are good for reading in initial data, doing a bit of pre-processing, and then handing off to some other more efficient form like Dask Dataframes. Dask Dataframes use Pandas internally, and so can be much faster on numeric data and also have more complex algorithms.

However, Dask Dataframes also expect data that is organized as flat columns. It does not support nested JSON data very well (Bag is better for this).

Here we make a function to flatten down our nested data structure, map that across our records, and then convert that to a Dask Dataframe.

({'age': 28,
  'name': ['Karleen', 'Medina'],
  'occupation': 'Bus Valeter',
  'telephone': '(379) 007-5536',
  'address': {'address': '398 Danton Highway', 'city': 'Petersburg'},
  'credit-card': {'number': '2536 9121 5935 1068',
   'expiration-date': '10/22'}},)
def flatten(record):
    return {
        'age': record['age'],
        'occupation': record['occupation'],
        'telephone': record['telephone'],
        'credit-card-number': record['credit-card']['number'],
        'credit-card-expiration': record['credit-card']['expiration-date'],
        'name': ' '.join(record['name']),
        'street-address': record['address']['address'],
        'city': record['address']['city']
({'age': 28,
  'occupation': 'Bus Valeter',
  'telephone': '(379) 007-5536',
  'credit-card-number': '2536 9121 5935 1068',
  'credit-card-expiration': '10/22',
  'name': 'Karleen Medina',
  'street-address': '398 Danton Highway',
  'city': 'Petersburg'},)
df =
age occupation telephone credit-card-number credit-card-expiration name street-address city
0 28 Bus Valeter (379) 007-5536 2536 9121 5935 1068 10/22 Karleen Medina 398 Danton Highway Petersburg
1 37 Sales Administrator 748-209-3589 3449 951065 47736 05/17 Bebe Stephenson 146 Dashiell Hammett Bypass Kennesaw
2 28 Gamekeeper 855-942-3923 5288 1230 0324 4185 09/22 Betsey Duke 1229 Gorham Plantation Buena Park
3 27 Mining Engineer 1-241-708-4421 3763 066352 23487 12/22 Justa Shannon 69 Quincy Sideline O'Fallon
4 61 Parts Manager 636-224-6535 4865 7710 2170 7940 01/17 Estell Mckee 69 Liberty Path Buckeye

We can now perform the same computation as before, but now using Pandas and Dask dataframe.

df[df.age > 30].occupation.value_counts().nlargest(10).compute()
Horse Trainer             15
Receptionist              14
Recorder                  13
Arts                      13
Panel Beater              13
Probation Worker          13
Fork Lift Truck Driver    13
Payroll Clerk             13
Training Advisor          13
Hop Merchant              13
Name: occupation, dtype: int64

Learn More

You may be interested in the following links: