What Are the Python Equivalents to Ruby's Bundler/Perl's Carton

What is the perl's equivalent of repr in python/ruby's inspect method

I think you are looking for Data::Dumper; see the documentation for some examples.

Best way to control library versions and sys.path for a Python command-line application

This largely depends upon the audience.

For developers, a good idea is indeed
- Include requirements.txt
- pip install -r requirements.txt

For end users, I'd recommend one of the follow:

py2exe
py2app
cx_Freeze

From this other StackOverflow answer, pbundler may also be helpful.

What are R's equivalents to Perl's map and grep?

R has "grep", but it works entirely different than what you're used to. R has something much better built in: it has the ability to create array slices with a boolean expression:

a1 <- c(1:8)
a2 <- a1 [a1 %% 2 == 0]
a2
[1] 2 4 6 8

For map, you can apply a function as you did above, but it's much simpler to just write:

a2 * 2
[1]  4  8 12 16

Or in one step:

a1[a1 %% 2 == 0] * 2
[1]  4  8 12 16

I have never heard of a Perl to R phrase book, if you ever find one let me know! In general, R has less documentation than either perl or python, because it's such a niche language.

Ruby's times method and for loop does the same thing, but what are the respective processes?

Using the IRB you can create an array that represents all the numbers from to to:

irb(main):043:0> from=10
=> 10
irb(main):044:0> to=20
=> 20
irb(main):047:0> a=(from..to).to_a
=> [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]

You can get a sum of that array:

irb(main):048:0> a.sum
=> 165

And, actually, you don't need to make an array at all:

irb(main):049:0> (from..to).sum
=> 165

Side note. If you have three dots the last value of to is not included and that is a different sum obviously:

irb(main):053:0> (from...to).to_a
=> [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
irb(main):054:0> (from...to).sum
=> 145

So upto should really be called uptoandincluding:

irb(main):055:0> from.upto(to).to_a
=> [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]

You can also use reduce or the alias inject either with a block or symbol to an operation to reduct a list the the pairwise application of the operator.

Example (reduce and inject are the same, but I usually use inject with a block or reduce with the parenthesis):

# block form where n is repeatedly added to an accumulator
irb(main):058:0> (from..to).inject{|sum, n| sum+n}
=> 165

# the operator + is repeated applied from the left to right 
irb(main):059:0> (from..to).reduce(:+)
=> 165

Does Django have an equivalent of Rails's bundle install?

You can freeze requirements. This generates a list of all the Python modules that your project needs. I believe bundle is similar in concept.

For example:

virtualenv --no-site-packages myproject_env # create a blank Python virtual environment
source myproject_env/bin/activate # activate it
(myproject_env)$ pip install django # install django into the virtual environment
(myproject_env)$ pip install other_package # etc.
...
(myproject_env)$ pip freeze > requirements.txt

The last line generates a text file will all the packages that were installed in your custom environment. You can use that file to install the same requirements on other servers:

pip install -r requirements.txt

Of course you don't need to use pip, you can create the requirements file by hand; it doesn't have any special syntax requirements. Just a package and (possibly) version identifier on each line. Here is a sample of a typical django project with some extra packages:

Django==1.4
South==0.7.4
Werkzeug==0.8.3
amqplib==1.0.2
anyjson==0.3.1
celery==2.5.1
django-celery==2.5.1
django-debug-toolbar==0.9.4
django-extensions==0.8
django-guardian==1.0.4
django-picklefield==0.2.0
kombu==2.1.4
psycopg2==2.4.5
python-dateutil==2.1
six==1.1.0
wsgiref==0.1.2
xlwt==0.7.3

Do pip packages differ for different Operating System?

If the package contains C or other compiled language code - yes. This is why wheels have long complicated names detailing the OS and other variables.

Make setting up a venv part of your deployment. That should solve the problem.

Configure virtualenv content path instead of .venv/local/bin/activate

It's a bug in setuptools, source: https://github.com/pypa/setuptools/issues/3278