In R, I can use with(obj, a + b + c + d) instead of obj$a + obj$b + obj$c + obj$d, where obj can be a list or data.frame.
Is there any similar function for dict, pandas.Series, pandas.DataFrame in python?
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Seems there is not similar function in python and most of other programming language. (stackoverflow.com/a/6061156/3054161)su79eu7k– su79eu7k2016-05-11 08:04:53 +00:00Commented May 11, 2016 at 8:04
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2 Answers
In a way, no. But there are lots of somewhat similar alternatives. The with function of R seems quite versatile, so in Python one has to replace it case by case.
You could use itemgetter() for simple collections:
In [1]: d = dict(a=1, b=2, c=3, d=4)
In [2]: from operator import itemgetter
In [3]: sum(itemgetter('a', 'b', 'c', 'd')(d))
Out[3]: 10
Or attrgetter() for, again simple, objects:
In [4]: from collections import namedtuple
In [5]: from operator import attrgetter
In [8]: sum(attrgetter('a', 'b', 'c', 'd')(
namedtuple('sdf', 'a b c d')(1, 2, 3, 4)))
Out[8]: 10
Pandas' DataFrames support directly accessing specific columns and applying operations on them. Summing is an easy example, as it has a function as is:
In [10]: df = pd.DataFrame({'A': range(10), 'B': range(10), 'C': range(10)})
In [21]: df[['A', 'B']].sum(axis=1) # row sums
Out[21]:
0 0
1 2
2 4
3 6
4 8
5 10
6 12
7 14
8 16
9 18
dtype: int64
There's also DataFrame.eval, which is closest to what you're after, I think:
Evaluate an expression in the context of the calling DataFrame instance.
In [9]: df.eval('(A + B) ** C')
Out[9]:
0 1
1 2
2 16
3 216
4 4096
5 100000
6 2985984
7 105413504
8 4294967296
9 198359290368
dtype: int64
Comments
Not really. R and Python have pretty different philosophies when it comes to this kind of thing--in R it's possible to write a function which parses the entire syntax of its arguments before they are evaluated, whereas in Python it's not. So in Python, this is impossible:
df = pd.DataFrame({'a':[1,2],'b':[3,4],'c':[5,6],'d':[7,8]})
with(df, a + b + c)
However, this works:
sum(map(df.get, ('a','b','c'))) # gives Series([9,12])
If you wanted to apply other chained operations, you could implement support for something like this:
def chain(op, df, name, *names):
res = df[name]
while names:
res = op(res, df[names[0]])
names = names[1:]
return res
Then you can do this:
from operator import div
chain(div, df, 'a', 'b', 'c')
