| commit | 9531bb12acb714fc01a0bbd0bf3b0c8c3601d746 | [log] [tgz] |
|---|---|---|
| author | skrawczyk <skrawczyk@stitchfix.com> | Sun Feb 06 22:39:59 2022 -0800 |
| committer | skrawczyk <skrawczyk@stitchfix.com> | Sun Feb 06 22:39:59 2022 -0800 |
| tree | fdf9f510b0fe6e5049f140ad3a50f7535c155e43 | |
| parent | 8a08a5e3dd69bbf7ddd83b8053c1ba9ed96ab675 [diff] |
Adds example showing how to chunk over data This example shows how one might use Hamilton in a for loop to chunk over data. It is very contrived, but I think illustrates the point.
The micro-framework to create dataframes from functions.
Specifically, Hamilton is a framework that allows for delayed executions of functions in a Directed Acyclic Graph (DAG). This is meant to solve the problem of creating complex data pipelines. Core to the design of Hamilton is a clear mapping of function name to implementation. That is, Hamilton forces a certain paradigm with writing functions, and aims for DAG clarity, easy modifications, unit testing, and documentation.
For the backstory on how Hamilton came about, see our blog post!.
Here's a quick getting started guide to get you up and running in less than 15 minutes.
Requirements:
To get started, first you need to install hamilton. It is published to pypi under sf-hamilton:
pip install sf-hamilton
Note: to use the DAG visualization functionality, you should instead do:
pip install sf-hamilton[visualization]
While it is installing we encourage you to start on the next section.
Note: the content (i.e. names, function bodies) of our example code snippets are for illustrative purposes only, and don't reflect what we actually do internally.
Hamilton is a new paradigm when it comes to creating dataframes. Rather than thinking about manipulating a central dataframe, you instead think about the column(s) you want to create, and what inputs are required. There is no need for you to think about maintaining this dataframe, meaning you do not need to think about any “glue” code; this is all taken care of by the Hamilton framework.
For example rather than writing the following to manipulate a central dataframe object df:
df['col_c'] = df['col_a'] + df['col_b']
you write
def col_c(col_a: pd.Series, col_b: pd.Series) -> pd.Series: """Creating column c from summing column a and column b.""" return col_a + col_b
In diagram form: 
The Hamilton framework will then be able to build a DAG from this function definition.
So let's create a “Hello World” and start using Hamilton!
By now, you should have installed Hamilton, so let's write some code.
my_functions.py and add the following functions:import pandas as pd def avg_3wk_spend(spend: pd.Series) -> pd.Series: """Rolling 3 week average spend.""" return spend.rolling(3).mean() def spend_per_signup(spend: pd.Series, signups: pd.Series) -> pd.Series: """The cost per signup in relation to spend.""" return spend / signups
The astute observer will notice we have not defined spend or signups as functions. That is okay, this just means these need to be provided as input when we come to actually wanting to create a dataframe.
Note: functions can take or create scalar values too.
my_script.py which is where code will live to tell Hamilton what to do:import importlib import logging import sys import pandas as pd from hamilton import driver logger = logging.getLogger(__name__) logging.basicConfig(stream=sys.stdout) initial_columns = { # load from actuals or wherever -- this is our initial data we use as input. # Note: these do not have to be all series, they could be scalar inputs. 'signups': pd.Series([1, 10, 50, 100, 200, 400]), 'spend': pd.Series([10, 10, 20, 40, 40, 50]), } # we need to tell hamilton where to load function definitions from module_name = 'my_functions' module = importlib.import_module(module_name) dr = driver.Driver(initial_columns, module) # can pass in multiple modules # we need to specify what we want in the final dataframe. output_columns = [ 'spend', 'signups', 'avg_3wk_spend', 'spend_per_signup', ] # let's create the dataframe! # if you only did `pip install sf-hamilton` earlier: df = dr.execute(output_columns) # else if you did `pip install sf-hamilton[visualization]` earlier: # df = dr.execute(output_columns, display_graph=True) print(df)
python my_script.py
You should see the following output:
spend signups avg_3wk_spend spend_per_signup 0 10 1 NaN 10.000 1 10 10 NaN 1.000 2 20 50 13.333333 0.400 3 40 100 23.333333 0.400 4 40 200 33.333333 0.200 5 50 400 43.333333 0.125
You should see the following image if you set display_graph=True:
Congratulations - you just created your first dataframe with Hamilton!
Hamilton is released under the BSD 3-Clause Clear License. If you need to get in touch about something, contact us at algorithms-opensource (at) stitchfix.com.
We take contributions, large and small. We operate via a Code of Conduct and expect anyone contributing to do the same.
To see how you can contribute, please read our contributing guidelines and then our developer setup guide.
In general we prescribe the following:
For the backstory on Hamilton we invite you to watch ~9 minute lightning talk on it that we gave at the apply conference: video, slides.
If you‘re using Hamilton, it’s likely that you'll need to migrate some code. Here are some useful tricks we found to speed up that process.
Live templates are a cool feature and allow you to type in a name which expands into some code.
E.g. For example, we wrote one to make it quick to stub out Hamilton functions: typing graphfunc would turn into ->
def _(_: pd.Series) -> pd.Series: """"""" return _
Where the blanks are where you can tab with the cursor and fill things in. See your pycharm preferences for setting this up.
If you are doing a lot of repetitive work, one might consider multiple cursors. Multiple cursors allow you to do things on multiple lines at once.
To use it hit option + mouse click to create multiple cursors. Esc to revert back to a normal mode.