run inline python permalink

Executes the context value py as a dynamically interpreted python code block.

This is useful for adding inline Python code right in your pipeline.

You can use all the usual Python built-ins like len, abs and so forth. You can import standard libraries or your own custom modules & objects using the standard Python import syntax (e.g import x as y, from x import y).

For example, this will invoke python print, do some arbitrary addition and print 2:

  - name:
    comment: Example of arb python code. Will print 2.
      py: print(1+1)

multi-line python code block permalink

You can run multiple python statements in the same code block:

- name:
  comment: multi-line statement starts with |, per yaml spec
    py: |
      print(f"py step: {0+1}")
      arbvalue = 420

      # save arbvalue to context

- name:
  comment: here splitting multi-line statements with ; 
           arbvalue survives between steps.
    py: print("py step 2"); arbvalue += 4

You can use whichever of the standard yaml block scalar style & chomping indicators work best for you. For example, |- will strip newlines at the end.

Do remember that if your inline code block gets unwieldy, you can very easily run your own Python .py file from pypyr by using it as a custom step in itself.

You don’t even have to package your python code - pypyr will resolve the path relative to the working directory for you.

working with context permalink

When you are in a py step, context keys exist as variables of the same name.

- name: pypyr.steps.contextsetf
  comment: set some arbitrary values in context
      existing_value: 123
      existing_list: ['one', 'two', 'three']
        a: a value
        b: b value

- name:
  comment: context keys are available as variables of the same name
    py: |
      if existing_value > 100:
        print('no no no!')

      assert existing_list[1] == 'two'


save values to context permalink

A py step scope behaves like a Python function. Which is to say any variables you assign inside the py-step will not persist after the step completes, unless you are altering a mutable type in-place.

If you want to persist variables or key/values to context inside a py code block so that these are available to subsequent steps, use the save function explicitly to specify variable names that you want to save.

- name:
    py: |
      a = 1
      b = 2

      # save a & and create c in context.
      save('a', c=b)      

- name: pypyr.steps.echo
  comment: can use the new context values as normal in subsequent steps.
    echoMe: We set {a} and {c} in the previous step.
            There is no b in context because it wasn't saved.

The save function signature is:

save(*args, **kwargs)


  • args
    • list of variable names to save to context.
  • kwargs
    • list of key=value pairs to save to context.

You can use save as a list of string arguments, or keywords if you want to persist the input with a new name, or both. Here are some examples:

save('a') # save contents of a to context as key 'a'
save('b', 'c') # save contents of b and c to context as b and c respectively
save(d=d) # save contents of d to context as key 'd'
save(e='arb value here') # save new key to context from a literal or expression
save(ff=f, gg=g+1) # save variables to a different name

# combine all the above & create a new variable 'l' on the fly
save('h', 'i', jj=j, kk=k, l=len('arb expression'))

Important, when you just specify object names to save, you have to specify the name of the variable, not the object itself. Simply put, surround it with quotes!

- name:
  comment: this is WRONG
    py: |
      my_var = 123
      another_var = 456
      save('myvar', 'another_var') # NOT save(myvar, another_var)      

mutable objects & scope permalink

Your code inside the py step is an enclosed scope. It behaves like a Python function usually does - context variables pass by assignment. This means that:

  • local assignments to immutable objects do not persist outside of the enclosed scope.
  • in-place changes to mutable objects will persist to context after the py step completes.

This means that strictly speaking you do not need to call save to persist in-place changes to mutable objects - but you won’t break anything if you do, so don’t worry about it.

- name: pypyr.steps.contextsetf
      existing_value: 123
      existing_list: ['one', 'two', 'three']
        a: a value
        b: b value

- name:
  comment: context vars behave like args to a python function
    py: |
      # immutable type re-assignment is local to the current step
      existing_value = max(420, 69)

      # in-place mutable edits endure outside of the current scope
      existing_list[0] = 'mutable types will update'

      existing_dict['a'] = 'mutable types will update'      

This will result in context like this:

{'existing_dict': {'a': 'mutable types will update',
                   'b': 'b value'},
 'existing_list': ['mutable types will update', 'two', 'three'],
 'existing_value': 123}

Notice that the immutable value change did not persist to existing_value, but the in-place changes to the dict and the list did persist.

reserved keywords permalink

If you have your own key in context named save, it will not be accessible in the py style step, because the save() function hides the name. pypyr won’t touch your original save key + value, it’s just hidden for the duration of the py step. Either use a different name for your key, or use the pycode style input for the py step.

using built-ins and imports permalink

All of Python’s built-ins and standard library modules are available to you. You import like you usually do in Python.

- name:
  comment: using imports
    arb_url: http://arbhost/blah
    py: |
      # different styles of python import syntax
      from math import sqrt
      import urllib.parse
      # use imported code like sqrt from math
      arbvalue = sqrt(1764)
      # use builtin functions like int()

      host = urllib.parse.urlparse(arb_url).netloc

      # use builtin functions like len()

import your own modules permalink

You can also import your own custom modules and objects, as long as they resolve in the current Python environment. To help with this, pypyr also looks in the current working directory for your own custom modules, so you can import & re-use modules in your current path without having to package & publish the code to the current environment first.

Assume you have a python file in your working directory, saved in a mydir subdirectory like this:

# ./mydir/

def arb_function(arb_arg):
    return f'arb_function says: {arb_arg}'

Because pypyr will also look in your working directory for modules, mydir.mymodule will resolve on import without you have to do anything special.

So you can use this from your pipeline like this:

- name:
  comment: import custom modules relative to your working dir
    py: |
      from mydir.mymodule import arb_function

      out = arb_function('my input')      

create reusable functions & classes permalink

You can declare your own functions and classes in a py step. You can use these in the py step itself, and you can also use these in subsequent py steps or in !py strings if you save the function or class object to context:

- name:
  comment: create re-usable functions & classes
    py: |
      class MyClass():
        attribute = 'my class!'

        def do_thing(self, my_arg):
          return my_arg + 1

      def my_function(arg1, arg2):
        return arg1 + arg2

      # call your function like you normally do
      result = my_function(420, 69)
      # instantiate & use your class like usual

      # save to context to use class & function in subsequent steps
      save('my_function', 'MyClass')      

- name:
  comment: re-use class & function previously saved to context
    py: |
      my_instance = MyClass()

      for i in range(3):
        my_function(i, 3)      

- name: pypyr.steps.contextsetf
  comment: re-use class & function anywhere you can use a !py string.
  run: !py my_function(1, 2) == 3
      new_key: !py MyClass().do_thing(456)

the context object itself permalink

If you want access to the entire context dictionary object itself, rather than just its contents, you can use the alternative pycode input to the step.

When you use the pycode input, context exists as a dictionary-like object named context. All the usual python dict methods are available.

In general, you probably should prefer using the py style input - although both styles do the same thing, it’s much less annoying and error-prone not to have to type out the dictionary accessors all the time. The pycode style input is not particularly more “advanced”, or “better”, but it will be somewhat faster and also use less memory than the py style. By “somewhat”, meaning that the performance differential is likely to be so small that you won’t notice unless your context is exceptionally big.

In the following example, you can see how to access the context object, add or update values & how to import other modules:

- name: pypyr.steps.contextsetf
  comment: set arb context to manipulate in the next step
      existing_key: existing value
        a: a value
        b: 123

- name:
  comment: runs arb python using context from previous step
            also sets some new values in context.
    # multi-line statement starts with |, per yaml spec
    pycode: |
      import math

      print(f"py step: {0+1}")

      context['arbvalue'] = math.sqrt(36)

      context['existing_key'] = 'updated value'
      context['existing_dict']['a'] = 'a value set in py step'
      context['existing_dict']['b'] = 456
      context['existing_dict']['new_key'] = ['zero', 'one', 2, 'three']


- name:
  description: context['arbvalue'] survives between steps.
    # here splitting multi-line statements with ;
    pycode: print("py step 2"); context['arbvalue'] += 4

- name: pypyr.steps.echo
    echoMe: |
      arbvalue is {arbvalue} and existing_key is now {existing_key}
      you can worked with nested values too: {existing_dict[a]} - {existing_dict[b]}      

These steps will output:

py step: 1
context['arbvalue'] survives between steps.
py step 2
arbvalue is 10.0 and existing_key is now updated value
you can worked with nested values too: a value set in py step - 456

When you use the pycode style you don’t need to use save() to persist values back to context, since you are working directly with the mutable context object itself. If you want to update, edit or remove a value, it’s up to you to persist the change with context['mykey'] = 'added/updated value'.

save() is only available when you use the py style input.

example permalink

See a worked example of inline python using py style and example of inline python using py-code style.

see also

last updated on .