eval - 'Call' nodes are not implemented

[glaucouri]

df = P.DataFrame({'a':[1,1,2,2,3,3]})
df.eval("a.mean()")

raise:
NotImplementedError: 'Call' nodes are not implemented

i hope this will be possible
thank's
Gla

[cpcloud]

What you've got there doesn't make a whole lot of sense (maybe you're doing something that does and this is just an example):

df = DataFrame({'a': [1, 1, 2, 2, 3, 3]})
m = df.a.mean()
df.eval('m') == m
pd.eval('m') == m

so just evaluate it in Python.

No new features will be added to eval, query for the next release (0.13) and possibly even 0.14

[cpcloud]

Slightly OT: @glaucouri Just in case that error message is a bit cryptic: A "Call" node is a node in the abstract syntax tree created by parsing Python source. Here is the language reference on callable objects.

[glaucouri]

What i'm trying to explain (with my poor english) is that in an eval environment if [a] is a P.Series i expect that i can use a this syntax : ( this is my working program, that users can use for do personalized equations)

safe_env = dict( [(k,df[k]) for k in df.keys() ])
safe_env # some other injections from numpy
val = eval(equation,safe_env)
#now val is the result of the equation, so with a little of broadcasting i can insert it in the df

Actually, with this solution users can do filtering, grouping and equation in a DataFrame environment.

Thank's
Gla

[cpcloud]

@glaucouri What you've just described is essentially what happens under the hood in DataFrame.query()/DataFrame.eval(). Currently, you cannot call functions with either of those methods. Does that make sense?

[cpcloud]

Closing as stale ... not really a demand for this right now

[xenoscopic]

Out of curiosity, would a (well-written) pull request adding support for the numexpr intrinsic functions be accepted? I've noticed that there is a variable defined in the expression parser called _numexpr_supported_calls, which seems to enumerate these functions, but this variable doesn't seem to be used anywhere. Stuff like abs or some of the transcendental functions might be quite useful.

[jreback]

we always accept a well-written PR (even some not so well-written!) :)

[cpcloud]

@havoc-io Yes, a PR would be great! To get started (if you haven't already) take a look in pandas/computation/expr.py. This is where the parsing logic is implemented. You'd need to allow Call nodes by removing them from the set exclusion happening in the decorator.

Here's an example of what a Call node looks like for the code f(a, b, c=1):

import ast
from ast import *  # forgive the star import here
node = ast.parse('f(a, b, c=1)', mode='eval').body
node.func == Name(id='f', ctx=Load())
node.args == [Name(id='a', ctx=Load()), Name(id='b', ctx=Load())]
node.keywords == [keyword(arg='c', value=Num(n=1))]

You could probably skip implementing keyword arguments for now and just implement unary or binary functions to get a feel for how difficult (or not) it would be to do this.