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What are the relative merits / downsides of various Python bundles (EPD / Anaconda) vs. a manual install?
I have installed EPD academic, and I have no issues with it. It provides more packages that I think I will ever need, and it is very easy to update using enpkg enstaller. The EPD academic licence requires yearly renewal however and the free version does not do updates as easily.
At the moment I really only use a handful of packages such as Pandas, NumPy, SciPy, matplotlib, IPython, Statsmodels and their respective dependencies.
For such limited use am I better off with manual install and pip install --upgrade 'package' or do the bundles offer anything over and above this?
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Since ActiveState provides commercial support, ActivePython is the best choice for those focused on building commercial applications. Anaconda is a good choice for those focused on creating non-commercial data science applications since you can take advantage of Anaconda's proven Python ecosystem for free.
Anaconda is a free and open-source Python distribution and collection of hundreds of packages related to data science, scientific programming, development and more. Python is included in the Anaconda distribution. It is not an IDE (like PyCharm that mentioned in the comments) though it can be configured with most IDEs.
Anaconda is popular because it brings many of the tools used in data science and machine learning with just one install, so it's great for having short and simple setup. Like Virtualenv, Anaconda also uses the concept of creating environments so as to isolate different libraries and versions.
Update 2015: Nowadays I always recommend Anaconda. It includes lots of Python packages for scientific computing, data science, web development, etc. It also provides a superior environment tool, conda, which allows to easily switch between environments, even between Python 2 and 3. It is also updated very quickly as soon as a new version of a package is released, and you can just do conda update packagename to update it.
Original answer below:
On Windows, what is complicated is to compile the math packages, so I think a manual install is a viable option only if you are interested only in Python, without other packages.
Therefore better chose either EPD (now Canopy) or Anaconda.
Anaconda has around 270 packages, including the most important for most scientific applications and data analysis, that is, NumPy, SciPy, Pandas, IPython, matplotlib, Scikit-learn. So if this is enough for you, I would choose Anaconda.
Instead, if you are interested in other packages, and even more if you use any of the Enthought packages (Chaco for example is very useful for realtime data visualization), then EPD/Canopy is probably a better choice. The Academic version has a larger number of packages in the base install, and many more in the repository. Anaconda also includes Chaco.
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