DOC: capitalize Python as proper noun

doc/source/development/contributing.rst

@@ -442,7 +442,7 @@ Some other important things to know about the docs:
 
      contributing_docstring.rst
 
-* The tutorials make heavy use of the `ipython directive
+* The tutorials make heavy use of the `IPython directive
   <https://matplotlib.org/sampledoc/ipython_directive.html>`_ sphinx extension.
   This directive lets you put code in the documentation which will be run
   during the doc build. For example::

doc/source/ecosystem.rst

@@ -174,7 +174,7 @@ invoked with the following command
 
     dtale.show(df)
 
-D-Tale integrates seamlessly with jupyter notebooks, python terminals, kaggle
+D-Tale integrates seamlessly with Jupyter notebooks, Python terminals, Kaggle
 & Google Colab. Here are some demos of the `grid <http://alphatechadmin.pythonanywhere.com/>`__
 and `chart-builder <http://alphatechadmin.pythonanywhere.com/charts/4?chart_type=surface&query=&x=date&z=Col0&agg=raw&cpg=false&y=%5B%22security_id%22%5D>`__.
 
@@ -421,7 +421,7 @@ If also displays progress bars.
 `Vaex <https://docs.vaex.io/>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. Vaex is a python library for Out-of-Core DataFrames (similar to pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (10\ :sup:`9`) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).
+Increasingly, packages are being built on top of pandas to address specific needs in data preparation, analysis and visualization. Vaex is a Python library for Out-of-Core DataFrames (similar to pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (10\ :sup:`9`) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).
 
  * vaex.from_pandas
  * vaex.to_pandas_df

doc/source/getting_started/intro_tutorials/04_plotting.rst

@@ -131,8 +131,8 @@ standard Python to get an overview of the available plot methods:
     ]
 
 .. note::
-    In many development environments as well as ipython and
-    jupyter notebook, use the TAB button to get an overview of the available
+    In many development environments as well as IPython and
+    Jupyter notebook, use the TAB button to get an overview of the available
     methods, for example ``air_quality.plot.`` + TAB.
 
 One of the options is :meth:`DataFrame.plot.box`, which refers to a

doc/source/user_guide/10min.rst

@@ -239,13 +239,13 @@ Select via the position of the passed integers:
 
    df.iloc[3]
 
-By integer slices, acting similar to numpy/python:
+By integer slices, acting similar to numpy/Python:
 
 .. ipython:: python
 
    df.iloc[3:5, 0:2]
 
-By lists of integer position locations, similar to the numpy/python style:
+By lists of integer position locations, similar to the numpy/Python style:
 
 .. ipython:: python
 

doc/source/user_guide/basics.rst

@@ -845,7 +845,7 @@ For example, we can fit a regression using statsmodels. Their API expects a form
 
 The pipe method is inspired by unix pipes and more recently dplyr_ and magrittr_, which
 have introduced the popular ``(%>%)`` (read pipe) operator for R_.
-The implementation of ``pipe`` here is quite clean and feels right at home in python.
+The implementation of ``pipe`` here is quite clean and feels right at home in Python.
 We encourage you to view the source code of :meth:`~DataFrame.pipe`.
 
 .. _dplyr: https://github.com/hadley/dplyr

doc/source/user_guide/cookbook.rst

@@ -18,9 +18,6 @@ above what the in-line examples offer.
 pandas (pd) and Numpy (np) are the only two abbreviated imported modules. The rest are kept
 explicitly imported for newer users.
 
-These examples are written for Python 3.  Minor tweaks might be necessary for earlier python
-versions.
-
 Idioms
 ------
 
@@ -1013,7 +1010,7 @@ The :ref:`Plotting <visualization>` docs.
 `Setting x-axis major and minor labels
 <https://stackoverflow.com/questions/12945971/pandas-timeseries-plot-setting-x-axis-major-and-minor-ticks-and-labels>`__
 
-`Plotting multiple charts in an ipython notebook
+`Plotting multiple charts in an IPython Jupyter notebook
 <https://stackoverflow.com/questions/16392921/make-more-than-one-chart-in-same-ipython-notebook-cell>`__
 
 `Creating a multi-line plot

doc/source/user_guide/enhancingperf.rst

@@ -96,7 +96,7 @@ hence we'll concentrate our efforts cythonizing these two functions.
 Plain Cython
 ~~~~~~~~~~~~
 
-First we're going to need to import the Cython magic function to ipython:
+First we're going to need to import the Cython magic function to IPython:
 
 .. ipython:: python
    :okwarning:
@@ -123,7 +123,7 @@ is here to distinguish between function versions):
 .. note::
 
   If you're having trouble pasting the above into your ipython, you may need
-  to be using bleeding edge ipython for paste to play well with cell magics.
+  to be using bleeding edge IPython for paste to play well with cell magics.
 
 
 .. code-block:: ipython
@@ -160,7 +160,7 @@ We get another huge improvement simply by providing type information:
    In [4]: %timeit df.apply(lambda x: integrate_f_typed(x["a"], x["b"], x["N"]), axis=1)
    10 loops, best of 3: 20.3 ms per loop
 
-Now, we're talking! It's now over ten times faster than the original python
+Now, we're talking! It's now over ten times faster than the original Python
 implementation, and we haven't *really* modified the code. Let's have another
 look at what's eating up time:
 

doc/source/user_guide/groupby.rst

@@ -672,7 +672,7 @@ accepts the special syntax in :meth:`GroupBy.agg`, known as "named aggregation",
    )
 
 
-If your desired output column names are not valid python keywords, construct a dictionary
+If your desired output column names are not valid Python keywords, construct a dictionary
 and unpack the keyword arguments
 
 .. ipython:: python

doc/source/user_guide/indexing.rst

@@ -55,7 +55,7 @@ of multi-axis indexing.
       *label* of the index. This use is **not** an integer position along the
       index.).
     * A list or array of labels ``['a', 'b', 'c']``.
-    * A slice object with labels ``'a':'f'`` (Note that contrary to usual python
+    * A slice object with labels ``'a':'f'`` (Note that contrary to usual Python
       slices, **both** the start and the stop are included, when present in the
       index! See :ref:`Slicing with labels <indexing.slicing_with_labels>`
       and :ref:`Endpoints are inclusive <advanced.endpoints_are_inclusive>`.)
@@ -327,7 +327,7 @@ The ``.loc`` attribute is the primary access method. The following are valid inp
 
 * A single label, e.g. ``5`` or ``'a'`` (Note that ``5`` is interpreted as a *label* of the index. This use is **not** an integer position along the index.).
 * A list or array of labels ``['a', 'b', 'c']``.
-* A slice object with labels ``'a':'f'`` (Note that contrary to usual python
+* A slice object with labels ``'a':'f'`` (Note that contrary to usual Python
   slices, **both** the start and the stop are included, when present in the
   index! See :ref:`Slicing with labels <indexing.slicing_with_labels>`.
 * A boolean array.
@@ -513,7 +513,7 @@ Out of range slice indexes are handled gracefully just as in Python/Numpy.
 
 .. ipython:: python
 
-    # these are allowed in python/numpy.
+    # these are allowed in Python/numpy.
     x = list('abcdef')
     x
     x[4:10]

doc/source/user_guide/options.rst

@@ -124,13 +124,13 @@ are restored automatically when you exit the ``with`` block:
 Setting startup options in Python/IPython environment
 -----------------------------------------------------
 
-Using startup scripts for the Python/IPython environment to import pandas and set options makes working with pandas more efficient.  To do this, create a .py or .ipy script in the startup directory of the desired profile.  An example where the startup folder is in a default ipython profile can be found at:
+Using startup scripts for the Python/IPython environment to import pandas and set options makes working with pandas more efficient.  To do this, create a .py or .ipy script in the startup directory of the desired profile.  An example where the startup folder is in a default IPython profile can be found at:
 
 .. code-block:: none
 
   $IPYTHONDIR/profile_default/startup
 
-More information can be found in the `ipython documentation
+More information can be found in the `IPython documentation
 <https://ipython.org/ipython-doc/stable/interactive/tutorial.html#startup-files>`__.  An example startup script for pandas is displayed below:
 
 .. code-block:: python
@@ -332,7 +332,7 @@ display.large_repr                      truncate     For DataFrames exceeding ma
                                                      (the behaviour in earlier versions of pandas).
                                                      allowable settings, ['truncate', 'info']
 display.latex.repr                      False        Whether to produce a latex DataFrame
-                                                     representation for jupyter frontends
+                                                     representation for Jupyter frontends
                                                      that support it.
 display.latex.escape                    True         Escapes special characters in DataFrames, when
                                                      using the to_latex method.
@@ -413,7 +413,7 @@ display.show_dimensions                 truncate     Whether to print out dimens
                                                      frame is truncated (e.g. not display
                                                      all rows and/or columns)
 display.width                           80           Width of the display in characters.
-                                                     In case python/IPython is running in
+                                                     In case Python/IPython is running in
                                                      a terminal this can be set to None
                                                      and pandas will correctly auto-detect
                                                      the width. Note that the IPython notebook,

doc/source/user_guide/sparse.rst

@@ -179,7 +179,7 @@ sparse values instead.
   rather than a SparseSeries or SparseDataFrame.
 
 This section provides some guidance on migrating your code to the new style. As a reminder,
-you can use the python warnings module to control warnings. But we recommend modifying
+you can use the Python warnings module to control warnings. But we recommend modifying
 your code, rather than ignoring the warning.
 
 **Construction**