This tutorial is built as a Jupyter notebook which allows you to run and modify the code inline and can be used as a starting point for new Dataflows projects.
To get started quickly without any installation, click here:
If you want, you can just skip the installation section and follow the tutorial as-is, copy-pasting the relevant code example to your Python interpreter.
Easiest way to get started on any OS is to Download and install the latest Python 3.7 Miniconda distribution
Open a terminal with Miniconda (or Anaconda) and run the following to create a environment:
conda create -n dataflows 'python>=3.7' jupyter jupyterlab ipython leveldbActivate the environment and install dataflows:
. activate dataflows
pip install -U dataflows[speedup]The above command installs Dataflows optimized for speed, if you encounter problems installing it, install without the [speedup] suffix.
Save the tutorial notebook in current working directory (right-click and save on following link): https://raw.githubusercontent.com/datahq/dataflows/master/TUTORIAL.ipynb
Start Jupyter Lab:
jupyter labDouble-click the tutorial notebook you downloaded from the sidebar of Jupyter Lab
Let's start with the traditional 'hello, world' example:
from dataflows import Flow
data = [
{'data': 'Hello'},
{'data': 'World'}
]
def lowerData(row):
row['data'] = row['data'].lower()
Flow(
data,
lowerData
).results()[0][[{'data': 'hello'}, {'data': 'world'}]]
This very simple flow takes a list of dicts and applies a row processing function on each one of them.
We can load data from a file instead:
%%writefile beatles.csv
name,instrument
john,guitar
paul,bass
george,guitar
ringo,drumsWriting beatles.csv
from dataflows import Flow, load
def titleName(row):
row['name'] = row['name'].title()
Flow(
load('beatles.csv'),
titleName
).results()[0][[{'name': 'John', 'instrument': 'guitar'},
{'name': 'Paul', 'instrument': 'bass'},
{'name': 'George', 'instrument': 'guitar'},
{'name': 'Ringo', 'instrument': 'drums'}]]
The source file can be a CSV file, an Excel file or a Json file. You can use a local file name or a URL for a file hosted somewhere on the web.
Data sources can be generators and not just lists or files. Let's take as an example a very simple scraper:
from dataflows import Flow, printer
from xml.etree import ElementTree
from urllib.request import urlopen
# Get from Wikipedia the population count for each country
def country_population():
# Read the Wikipedia page and parse it using etree
page = urlopen('https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population').read()
tree = ElementTree.fromstring(page)
# Iterate on all tables, rows and cells
for table in tree.findall('.//table'):
if 'wikitable' in table.attrib.get('class', ''):
for row in table.find('tbody').findall('tr'):
cells = row.findall('td')
if len(cells) > 3:
# If a matching row is found...
name = cells[1].find('.//a').attrib.get('title')
population = cells[2].text
# ... yield a row with the information
yield dict(
name=name,
population=population
)
Flow(
country_population(),
printer(num_rows=1, tablefmt='html')
).process()[1]| # | name (string) | population (string) |
|---|---|---|
| 1 | China | 1,394,720,000 |
| 2 | India | 1,338,480,000 |
| ... | ||
| 240 | Pitcairn Islands | 50 |
{}
This is nice, but we do prefer the numbers to be actual numbers and not strings.
In order to do that, let's simply define their type to be numeric and truncate to millions:
from dataflows import Flow, set_type
Flow(
country_population(),
set_type('population', type='number', groupChar=','),
lambda row: dict(row, population=row['population']/1000000),
printer(num_rows=1, tablefmt='html')
).process()[1]| # | name (string) | population (number) |
|---|---|---|
| 1 | China | 1394.72 |
| 2 | India | 1338.48 |
| ... | ||
| 240 | Pitcairn Islands | 5e-05 |
{}
Data is automatically converted to the correct native Python type.
Apart from data-types, it's also possible to set other constraints to the data. If the data fails validation (or does not fit the assigned data-type) an exception will be thrown - making this method highly effective for validating data and ensuring data quality.
What about large data files? In the above examples, the results are loaded into memory, which is not always preferrable or acceptable. In many cases, we'd like to store the results directly onto a hard drive - without having the machine's RAM limit in any way the amount of data we can process.
We do it by using dump processors:
from dataflows import Flow, set_type, dump_to_path
Flow(
country_population(),
set_type('population', type='number', groupChar=','),
dump_to_path('country_population')
).process()[1]{'count_of_rows': 240,
'bytes': 5277,
'hash': 'b293685b58a33bd7b02cc275d19d3a95',
'dataset_name': None}
Running this code will create a local directory called county_population, containing two files:
import glob
print("\n".join(glob.glob('country_population/*')))country_population/res_1.csv
country_population/datapackage.json
The CSV file - res_1.csv - is where the data is stored. The datapackage.json file is a metadata file, holding information about the data, including its schema.
We can now open the CSV file with any spreadsheet program or code library supporting the CSV format - or using one of the data package libraries out there, like so:
from datapackage import Package
pkg = Package('country_population/datapackage.json')
it = pkg.resources[0].iter(keyed=True)
print(next(it)){'name': 'China', 'population': Decimal('1394720000')}
Note how using the data package meta-data, data-types are restored and there's no need to 're-parse' the data. This also works with other types too, such as dates, booleans and even lists and dicts.
So far we've seen how to load data, process it row by row, and then inspect the results or store them in a data package.
Let's see how we can do more complex processing by manipulating the entire data stream:
from dataflows import Flow, set_type, dump_to_path, printer
# Generate all triplets (a,b,c) so that 1 <= a <= b < c <= 20
def all_triplets():
for a in range(1, 20):
for b in range(a, 20):
for c in range(b+1, 21):
yield dict(a=a, b=b, c=c)
# Yield row only if a^2 + b^2 == c^1
def filter_pythagorean_triplets(rows):
for row in rows:
if row['a']**2 + row['b']**2 == row['c']**2:
yield row
Flow(
all_triplets(),
set_type('a', type='integer'),
set_type('b', type='integer'),
set_type('c', type='integer'),
filter_pythagorean_triplets,
dump_to_path('pythagorean_triplets'),
printer(num_rows=1, tablefmt='html')
).process()[1]| # | a (integer) | b (integer) | c (integer) |
|---|---|---|---|
| 1 | 3 | 4 | 5 |
| 2 | 5 | 12 | 13 |
| ... | |||
| 6 | 12 | 16 | 20 |
{'count_of_rows': 6,
'bytes': 744,
'hash': '1f0df7ed401ccff9f6c1674e98c62467',
'dataset_name': None}
The filter_pythagorean_triplets function takes an iterator of rows, and yields only the ones that pass its condition.
The flow framework knows whether a function is meant to hande a single row or a row iterator based on its parameters:
- if it accepts a single
rowparameter, then it's a row processor. - if it accepts a single
rowsparameter, then it's a rows processor. - if it accepts a single
packageparameter, then it's a package processor.
Let's see a few examples of what we can do with a package processors.
First, let's add a field to the data:
from dataflows import Flow, load, dump_to_path, printer
def add_is_guitarist_column_to_schema(package):
# Add a new field to the first resource
package.pkg.descriptor['resources'][0]['schema']['fields'].append(dict(
name='is_guitarist',
type='boolean'
))
# Must yield the modified datapackage
yield package.pkg
# And its resources
yield from package
def add_is_guitarist_column(row):
row['is_guitarist'] = row['instrument'] == 'guitar'
Flow(
# Same one as above
load('beatles.csv'),
add_is_guitarist_column_to_schema,
add_is_guitarist_column,
dump_to_path('beatles_guitarists'),
printer(num_rows=1, tablefmt='html')
).process()[1]| # | name (string) | instrument (string) | is_guitarist (boolean) |
|---|---|---|---|
| 1 | john | guitar | True |
| 2 | paul | bass | False |
| ... | |||
| 4 | ringo | drums | False |
{'count_of_rows': 4,
'bytes': 896,
'hash': 'ae319bad0ad1e345a2a86d8dc9de8375',
'dataset_name': None}
In this example we create two steps - one for adding the new field (is_guitarist) to the schema and another step to modify the actual data.
We can combine the two into one step:
from dataflows import Flow, load, dump_to_path
def add_is_guitarist_column(package):
# Add a new field to the first resource
package.pkg.descriptor['resources'][0]['schema']['fields'].append(dict(
name='is_guitarist',
type='boolean'
))
# Must yield the modified datapackage
yield package.pkg
# Now iterate on all resources
resources = iter(package)
# Take the first resource
beatles = next(resources)
# And yield it with with the modification
def f(row):
row['is_guitarist'] = row['instrument'] == 'guitar'
return row
yield map(f, beatles)
Flow(
# Same one as above
load('beatles.csv'),
add_is_guitarist_column,
dump_to_path('beatles_guitarists'),
printer(num_rows=1, tablefmt='html')
).process()[1]| # | name (string) | instrument (string) | is_guitarist (boolean) |
|---|---|---|---|
| 1 | john | guitar | True |
| 2 | paul | bass | False |
| ... | |||
| 4 | ringo | drums | False |
{'count_of_rows': 4,
'bytes': 896,
'hash': 'ae319bad0ad1e345a2a86d8dc9de8375',
'dataset_name': None}
The contract for the package processing function is simple:
First modify package.pkg (which is a Package instance) and yield it.
Then, yield any resources that should exist on the output, with or without modifications.
In the next example we're removing an entire resource in a package processor - this next one filters the list of Academy Award nominees to those who won both the Oscar and an Emmy award:
from dataflows import Flow, load, dump_to_path, checkpoint, printer
def find_double_winners(package):
# Remove the emmies resource -
# we're going to consume it now
package.pkg.remove_resource('emmies')
# Must yield the modified datapackage
yield package.pkg
# Now iterate on all resources
resources = iter(package)
# Emmies is the first -
# read all its data and create a set of winner names
emmy = next(resources)
emmy_winners = set(
map(lambda x: x['nominee'],
filter(lambda x: x['winner'],
emmy))
)
# Oscars are next -
# filter rows based on the emmy winner set
academy = next(resources)
yield filter(lambda row: (row['Winner'] and
row['Name'] in emmy_winners),
academy)
# important to deque generators to ensure finalization steps of previous processors are executed
yield from resources
Flow(
# Emmy award nominees and winners
load('https://raw.githubusercontent.com/datahq/dataflows/master/data/emmy.csv', name='emmies'),
# Academy award nominees and winners
load('https://raw.githubusercontent.com/datahq/dataflows/master/data/academy.csv', encoding='utf8', name='oscars'),
# save a checkpoint so we won't have to re-download the source data each time
checkpoint('emmy-academy-nominees-winners'),
find_double_winners,
dump_to_path('double_winners'),
printer(num_rows=1, tablefmt='html')
).process()[1]saving checkpoint to: .checkpoints/emmy-academy-nominees-winners
| # | Year (string) | Ceremony (integer) | Award (string) | Winner (string) | Name (string) | Film (string) |
|---|---|---|---|---|---|---|
| 1 | 1931/1932 | 5 | Actress | 1 | Helen Hayes | The Sin of Madelon Claudet |
| 2 | 1932/1933 | 6 | Actress | 1 | Katharine Hepburn | Morning Glory |
| ... | ||||||
| 98 | 2015 | 88 | Honorary Award | 1 | Gena Rowlands |
checkpoint saved: emmy-academy-nominees-winners
{'count_of_rows': 98,
'bytes': 6921,
'hash': '902088336aa4aa4fbab33446a241b5de',
'dataset_name': None}
Previous flow was a bit complicated, but luckily we have the join, concatenate and filter_rows processors which make such combinations a snap
from dataflows import Flow, load, dump_to_path, join, concatenate, filter_rows, printer, checkpoint
Flow(
# load from the checkpoint we saved in the previous flow
checkpoint('emmy-academy-nominees-winners'),
filter_rows(equals=[dict(winner=1)], resources=['emmies']),
concatenate(
dict(emmy_nominee=['nominee'],),
dict(name='emmies_filtered'),
resources='emmies'
),
join(
'emmies_filtered', ['emmy_nominee'], # Source resource
'oscars', ['Name'], # Target resource
full=False # Don't add new fields, remove unmatched rows
),
filter_rows(equals=[dict(Winner='1')]),
dump_to_path('double_winners'),
printer(num_rows=1, tablefmt='html')
).process()[1]using checkpoint data from .checkpoints/emmy-academy-nominees-winners
| # | Year (string) | Ceremony (integer) | Award (string) | Winner (string) | Name (string) | Film (string) |
|---|---|---|---|---|---|---|
| 1 | 1931/1932 | 5 | Actress | 1 | Helen Hayes | The Sin of Madelon Claudet |
| 2 | 1932/1933 | 6 | Actress | 1 | Katharine Hepburn | Morning Glory |
| ... | ||||||
| 98 | 2015 | 88 | Honorary Award | 1 | Gena Rowlands |
{'count_of_rows': 98,
'bytes': 6921,
'hash': '902088336aa4aa4fbab33446a241b5de',
'dataset_name': None}
DataFlows comes with a few built-in processors which do most of the heavy lifting in many common scenarios, leaving you to implement only the minimum code that is specific to your specific problem.
A complete list, which also includes an API reference for each one of them, can be found in the Built-in Processors page.
The flow object itself can be used as a step in another flow, this allows for useful design patterns which promote code reusability and readability:
from dataflows import Flow, printer
# generate a customizable, predefined flow
def text_processing_flow(star_letter_idx):
# run upper on all cell values
def upper(row):
for k in row:
row[k] = row[k].upper()
# star the letter at the index from star_letter_idx argument
def star_letter(row):
for k in row:
s = list(row[k])
s[star_letter_idx] = '*'
row[k] = ''.join(s)
def print_foo(row):
print(' '.join(list(row['foo'])))
return Flow(upper, star_letter, print_foo)
Flow(
[{'foo': 'bar'},
{'foo': 'bax'}],
text_processing_flow(0),
text_processing_flow(1),
text_processing_flow(2),
).process()[1]* A R
* * R
* * *
* A X
* * X
* * *
{}
- DataFlows Processors Reference
- Datapackage Pipelines Tutorial - Use the flows as building blocks for more complex pipelines processing systems.