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To install the whole set of Python modules needed and iPython in a virtualenv (trick: there is no "pylab" module to install): % virtualenv --distribute --no-

by teoruiz 14y ago

To install the whole set of Python modules needed and iPython in a virtualenv (trick: there is no "pylab" module to install): % virtualenv --distribute --no-site-packages pandas_venv [blahblah] % . pandas_venv/bin/activate (pandas_venv) % easy_install readline # Probably only needed in Mac OS X for iPython to behave [blahblah] (pandas_venv) % pip install ipython [blah blah] (pandas_venv) % pip install numpy [lots of blahblah] (pandas_venv) % pip install matplotlib [quite a bit of blahblah] (pandas_venv) % pip install pandas [some more blah blah] (pandas_venv) % pandas_venv/bin/ipython --no-banner In [1]: import numpy as np In [2]: import pandas as pd In [3]: import pylab as pl In [4]:

jofer 14y ago
The normal convention is to avoid using pylab, and instead use matplotlib directly. Pylab is handy if you're just transitioning from Matlab, but otherwise, there's no reason to use it. It's a gigantic namespace, and all but a couple of functions are from numpy and matplotlib.pyplot. Just do: import matplotlib.pyplot as plt Instead of: import pylab as pl Of course, in the end it's personal preference. As long as you don't need to know where things come from, then using pylab is fine.
takluyver 14y ago
Installing numpy with pip isn't recommended: it might not work (if you don't have the necessary development headers to compile it), the resulting numpy might be slower (if it hasn't managed to compile against properly optimised libraries) and compiling from source isn't a very quick way to install it. For most users, the easiest way to get set up is a complete Python distribution, like Anaconda, EPD or Python(x,y). See the Scipy Stack installation page: http://scipy.github.com/install.html http://scipy.github.com/install.html
- jmmcd 14y ago
  I have wasted so many hours failing to get everything I need to work on OSX 10.6. I tried pip, easy_install, install from source, brew, Enthought. Now Anaconda has just saved my life, thank you! It was the best moment of my day when I was able to type all this without error: >>> import numpy as np >>> import scipy as sp >>> import statsmodels as sm >>> import matplotlib as mpl >>> import pandas as pd >>> import networkx as nx >>> import sklearn as sk >>> import nltk

by teoruiz 14y ago

jofer 14y ago

The normal convention is to avoid using pylab, and instead use matplotlib directly. Pylab is handy if you're just transitioning from Matlab, but otherwise, there's no reason to use it. It's a gigantic namespace, and all but a couple of functions are from numpy and matplotlib.pyplot. Just do: import matplotlib.pyplot as plt Instead of: import pylab as pl Of course, in the end it's personal preference. As long as you don't need to know where things come from, then using pylab is fine.

takluyver 14y ago

Installing numpy with pip isn't recommended: it might not work (if you don't have the necessary development headers to compile it), the resulting numpy might be slower (if it hasn't managed to compile against properly optimised libraries) and compiling from source isn't a very quick way to install it. For most users, the easiest way to get set up is a complete Python distribution, like Anaconda, EPD or Python(x,y). See the Scipy Stack installation page: http://scipy.github.com/install.html http://scipy.github.com/install.html

jmmcd 14y ago
I have wasted so many hours failing to get everything I need to work on OSX 10.6. I tried pip, easy_install, install from source, brew, Enthought. Now Anaconda has just saved my life, thank you! It was the best moment of my day when I was able to type all this without error: >>> import numpy as np >>> import scipy as sp >>> import statsmodels as sm >>> import matplotlib as mpl >>> import pandas as pd >>> import networkx as nx >>> import sklearn as sk >>> import nltk