Lisp has some very effective way to get jobs done, this article give you a direct way to use Python like Lisp.```
cons = lambda el, lst: (el, lst)
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mklist = lambda *args: reduce(lambda lst, el: cons(el, lst), reversed(args), None)
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car = lambda lst: lst[0] if lst else lst
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cdr = lambda lst: lst[1] if lst else lst
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nth = lambda n, lst: nth(n-1, cdr(lst)) if n ]]> 0 else car(lst)
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length = lambda lst, count=0: length(cdr(lst), count+1) if lst else count
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begin = lambda *args: args[-1]
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display = lambda lst: begin(w("%s " % car(lst)), display(cdr(lst))) if lst else w("niln")
```where `w = sys.stdout.write````
foldr = lambda f, i: lambda s: reduce(f, s, i)
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foldl = reduce
mapcar = map
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Pay attention about the speed when you use Python, here is a benchmarks for 5 languages:
from [The Great Computer Language Shootout](http://www.bagley.org/~doug/shootout/).
Test
Lisp
Java
Python
Perl
C++
hash access
1.06
3.23
4.01
1.85
1.00
exception handling
0.01
0.90
1.54
1.73
1.00
Legend
sum numbers from file
7.54
2.63
8.34
2.49
1.00
\> 100 x C++
reverse lines
1.61
1.22
1.38
1.25
1.00
50-100 x C++
matrix multiplication
3.30
8.90
278.00
226.00
1.00
10-50 x C++
heapsort
1.67
7.00
84.42
75.67
1.00
5-10 x C++
array access
1.75
6.83
141.08
127.25
1.00
2-5 x C++
list processing
0.93
20.47
20.33
11.27
1.00
1-2 x C++
object instantiation
1.32
2.39
49.11
89.21
1.00
< 1 x C++
word count
0.73
4.61
2.57
1.64
1.00
**Median**
1.67
4.61
20.33
11.27
1.00
**25% to 75%**
0.93 to 1.67
2.63 to 7.00
2.57 to 84.42
1.73 to 89.21
1.00 to 1.00
**Range**
0.01 to 7.54
0.90 to 20.47
1.38 to 278
1.25 to 226
1.00 to 1.00
Relative Resource: _[Python for Lisp Programmers](http://norvig.com/python-lisp.html)_ by _[Peter Norvig](http://www.norvig.com/)_
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