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Merge pull request realpython#441 from svisser/python_into_Python
Changed python into Python where needed
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docs/scenarios/scientific.rst

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@@ -129,7 +129,7 @@ Anaconda
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`Continuum Analytics <http://continuum.io/>`_ offers the `Anaconda
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Python Distribution <https://store.continuum.io/cshop/anaconda>`_ which
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includes all the common scientific python packages and additionally many
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includes all the common scientific Python packages and additionally many
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packages related to data analytics and big data. Anaconda itself is free, and
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Continuum sells a number of proprietary add-ons. Free
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licenses for the add-ons are available for academics and researchers.

docs/starting/install/win.rst

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@@ -53,7 +53,7 @@ download and install any compliant Python software product with a single
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command. It also enables you to add this network installation capability to
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your own Python software with very little work.
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To obtain the latest version of Setuptools for Windows, run the python script
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To obtain the latest version of Setuptools for Windows, run the Python script
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available here: `ez_setup.py <https://bitbucket.org/pypa/setuptools/raw/bootstrap/ez_setup.py>`_
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**pip**. Pip allows for uninstallation of packages, and is actively maintained,
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unlike easy_install.
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To install pip, run the python script available here:
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To install pip, run the Python script available here:
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`get-pip.py <https://raw.github.com/pypa/pip/master/contrib/get-pip.py>`_
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docs/writing/documentation.rst

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@@ -17,7 +17,7 @@ assuming the user knows anything about the project), the url of the
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main source for the software, and some basic credit information. This
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file is the main entry point for readers of the code.
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An :file:`INSTALL` file is less necessary with python. The installation
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An :file:`INSTALL` file is less necessary with Python. The installation
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instructions are often reduced to one command, such as ``pip install
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module`` or ``python setup.py install`` and added to the :file:`README`
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file.
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Sphinx
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~~~~~~
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Sphinx_ is far and away the most popular python documentation
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Sphinx_ is far and away the most popular Python documentation
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tool. **Use it.** It converts :ref:`restructuredtext-ref` markup language
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into a range of output formats including HTML, LaTeX (for printable
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PDF versions), manual pages, and plain text.

docs/writing/license.rst

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@@ -31,7 +31,7 @@ To help you choose one for your project, there's a `license chooser <http://thre
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**More Permissive**
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- PSFL (Python Software Foundation License) -- for contributing to python itself
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- PSFL (Python Software Foundation License) -- for contributing to Python itself
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- MIT / BSD / ISC
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+ MIT (X11)

docs/writing/structure.rst

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@@ -92,12 +92,12 @@ environment, or your project's internal modules.
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To keep in line with the style guide, keep module names short, lowercase, and
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be sure to avoid using special symbols like the dot (.) or question mark (?).
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So a file name like :file:`my.spam.py` is one you should avoid! Naming this way
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will interfere with the way python looks for modules.
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will interfere with the way Python looks for modules.
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In the case of `my.spam.py` python expects to find a :file:`spam.py` file in a folder named :file:`my`
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In the case of `my.spam.py` Python expects to find a :file:`spam.py` file in a folder named :file:`my`
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which is not the case. There is an
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`example <http://docs.python.org/tutorial/modules.html#packages>`_ of how the
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dot notation should be used in the python docs.
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dot notation should be used in the Python docs.
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If you'd like you could name it as :file:`my_spam.py` but even our friend the
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underscore should not be seen often in module names.

docs/writing/style.rst

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@@ -360,7 +360,7 @@ Take the following code for example:
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def lookup_list(l):
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return 's' in l
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Even though both functions look identical, because *lookup_dict* is utilizing the fact that dictionaries in python are hashtables, the lookup performance between the two is very different.
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Even though both functions look identical, because *lookup_dict* is utilizing the fact that dictionaries in Python are hashtables, the lookup performance between the two is very different.
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Python will have to go through each item in the list to find a matching case, which is time consuming. By analysing the hash of the dictionary, finding keys in the dict can be done very quickly.
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For more information see this `StackOverflow <http://stackoverflow.com/questions/513882/python-list-vs-dict-for-look-up-table>`_ page.
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docs/writing/tests.rst

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class MyTest(unittest.TestCase):
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...
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This way if you ever switch to a newer python version and no longer need the
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This way if you ever switch to a newer Python version and no longer need the
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unittest2 module, you can simply change the import in your test module without
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the need to change any other code.
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