Skip to main content

Python: passing a mix of keyword arguments and dictionary arguments to a function

So Python is cool because of keyword arguments:

def foo(a=1,b=2,c=3):
  print a,b,c

foo(a=1) # -> 1 2 3

Python is cool because you can pass a dictionary whose keys match the argument names:

def foo(a=1,b=2,c=3):
  print a,b,c

args = {'a': 1, 'b':2}
foo(**args) # -> 1 2 3

But, can you mix the two? Yes, yes you can!

def foo(a=1,b=2,c=3):
  print a,b,c

args = {'a': 1, 'b':2}
foo(c=3, **args) # -> 1 2 3

Hmm, can we screw up the interpreter? What happens if we send the same argument as a keyword AND a dictionary?

def foo(a=1,b=2,c=3):
  print a,b,c

args = {'a': 1, 'b':2}
foo(a=4, **args) # -> TypeError: foo() got multiple values for keyword argument 'a'

Nothing gets past Python, eh?


Popular posts from this blog

Flowing text in inkscape (Poster making)

You can flow text into arbitrary shapes in inkscape. (From a hint here).

You simply create a text box, type your text into it, create a frame with some drawing tool, select both the text box and the frame (click and shift) and then go to text->flow into frame.


The omnipresent anonymous asked:
Trying to enter sentence so that text forms the number three...any ideas?
The solution:
Type '3' using the text toolConvert to path using object->pathSize as necessaryRemove fillUngroupType in actual text in new text boxSelect the text and the '3' pathFlow the text

Python: Multiprocessing: passing multiple arguments to a function

Write a wrapper function to unpack the arguments before calling the real function. Lambda won't work, for some strange un-Pythonic reason.

import multiprocessing as mp def myfun(a,b): print a + b def mf_wrap(args): return myfun(*args) p = mp.Pool(4) fl = [(a,b) for a in range(3) for b in range(2)] #mf_wrap = lambda args: myfun(*args) -> this sucker, though more pythonic and compact, won't work, fl)

Pandas panel = collection of tables/data frames aligned by index and column

Pandas panel provides a nice way to collect related data frames together while maintaining correspondence between the index and column values:

import pandas as pd, pylab #Full dimensions of a slice of our panel index = ['1','2','3','4'] #major_index columns = ['a','b','c'] #minor_index df = pd.DataFrame(pylab.randn(4,3),columns=columns,index=index) #A full slice of the panel df2 = pd.DataFrame(pylab.randn(3,2),columns=['a','c'],index=['1','3','4']) #A partial slice df3 = pd.DataFrame(pylab.randn(2,2),columns=['a','b'],index=['2','4']) #Another partial slice df4 = pd.DataFrame(pylab.randn(2,2),columns=['d','e'],index=['5','6']) #Partial slice with a new column and index pn = pd.Panel({'A': df}) pn['B'] = df2 pn['C'] = df3 pn['D'] = df4 for key in pn.items: print pn[key] -> output …