Spellcheck (pandas-dev#19017)

doc/source/10min.rst

@@ -48,7 +48,7 @@ a default integer index:
    s = pd.Series([1,3,5,np.nan,6,8])
    s
 
-Creating a :class:`DataFrame` by passing a numpy array, with a datetime index
+Creating a :class:`DataFrame` by passing a NumPy array, with a datetime index
 and labeled columns:
 
 .. ipython:: python
@@ -114,7 +114,7 @@ Here is how to view the top and bottom rows of the frame:
    df.head()
    df.tail(3)
 
-Display the index, columns, and the underlying numpy data:
+Display the index, columns, and the underlying NumPy data:
 
 .. ipython:: python
 
@@ -311,7 +311,7 @@ Setting values by position:
 
    df.iat[0,1] = 0
 
-Setting by assigning with a numpy array:
+Setting by assigning with a NumPy array:
 
 .. ipython:: python
 

doc/source/advanced.rst

@@ -316,7 +316,9 @@ Basic multi-index slicing using slices, lists, and labels.
 
    dfmi.loc[(slice('A1','A3'), slice(None), ['C1', 'C3']), :]
 
-You can use :class:`pandas.IndexSlice` to facilitate a more natural syntax using ``:``, rather than using ``slice(None)``.
+
+You can use :class:`pandas.IndexSlice` to facilitate a more natural syntax 
+using ``:``, rather than using ``slice(None)``.
 
 .. ipython:: python
 
@@ -557,7 +559,7 @@ Take Methods
 
 .. _advanced.take:
 
-Similar to numpy ndarrays, pandas Index, Series, and DataFrame also provides
+Similar to NumPy ndarrays, pandas Index, Series, and DataFrame also provides
 the ``take`` method that retrieves elements along a given axis at the given
 indices. The given indices must be either a list or an ndarray of integer
 index positions. ``take`` will also accept negative integers as relative positions to the end of the object.
@@ -729,7 +731,7 @@ This is an Immutable array implementing an ordered, sliceable set.
 Prior to 0.18.0, the ``Int64Index`` would provide the default index for all ``NDFrame`` objects.
 
 ``RangeIndex`` is a sub-class of ``Int64Index`` added in version 0.18.0, now providing the default index for all ``NDFrame`` objects.
-``RangeIndex`` is an optimized version of ``Int64Index`` that can represent a monotonic ordered set. These are analogous to python `range types <https://docs.python.org/3/library/stdtypes.html#typesseq-range>`__.
+``RangeIndex`` is an optimized version of ``Int64Index`` that can represent a monotonic ordered set. These are analogous to Python `range types <https://docs.python.org/3/library/stdtypes.html#typesseq-range>`__.
 
 .. _indexing.float64index:
 
@@ -763,7 +765,6 @@ The only positional indexing is via ``iloc``.
    sf.iloc[3]
 
 A scalar index that is not found will raise a ``KeyError``.
-
 Slicing is primarily on the values of the index when using ``[],ix,loc``, and 
 **always** positional when using ``iloc``. The exception is when the slice is
 boolean, in which case it will always be positional.

doc/source/api.rst

@@ -730,7 +730,7 @@ The dtype information is available on the ``Categorical``
    Categorical.codes
 
 ``np.asarray(categorical)`` works by implementing the array interface. Be aware, that this converts
-the Categorical back to a numpy array, so categories and order information is not preserved!
+the Categorical back to a NumPy array, so categories and order information is not preserved!
 
 .. autosummary::
    :toctree: generated/

doc/source/basics.rst

@@ -395,7 +395,7 @@ raise a ValueError:
     In [56]: pd.Series(['foo', 'bar', 'baz']) == pd.Series(['foo'])
     ValueError: Series lengths must match to compare
 
-Note that this is different from the numpy behavior where a comparison can
+Note that this is different from the NumPy behavior where a comparison can
 be broadcast:
 
 .. ipython:: python
@@ -1000,7 +1000,7 @@ We create a frame similar to the one used in the above sections.
    tsdf.iloc[3:7] = np.nan
    tsdf
 
-Transform the entire frame. ``.transform()`` allows input functions as: a numpy function, a string
+Transform the entire frame. ``.transform()`` allows input functions as: a NumPy function, a string
 function name or a user defined function.
 
 .. ipython:: python
@@ -1510,7 +1510,7 @@ To iterate over the rows of a DataFrame, you can use the following methods:
   one of the following approaches:
 
   * Look for a *vectorized* solution: many operations can be performed using
-    built-in methods or numpy functions, (boolean) indexing, ...
+    built-in methods or NumPy functions, (boolean) indexing, ...
 
   * When you have a function that cannot work on the full DataFrame/Series
     at once, it is better to use :meth:`~DataFrame.apply` instead of iterating
@@ -1971,7 +1971,7 @@ from the current type (e.g. ``int`` to ``float``).
    df3.dtypes
 
 The ``values`` attribute on a DataFrame return the *lower-common-denominator* of the dtypes, meaning
-the dtype that can accommodate **ALL** of the types in the resulting homogeneous dtyped numpy array. This can
+the dtype that can accommodate **ALL** of the types in the resulting homogeneous dtyped NumPy array. This can
 force some *upcasting*.
 
 .. ipython:: python
@@ -2253,7 +2253,7 @@ can define a function that returns a tree of child dtypes:
            return dtype
        return [dtype, [subdtypes(dt) for dt in subs]]
 
-All numpy dtypes are subclasses of ``numpy.generic``:
+All NumPy dtypes are subclasses of ``numpy.generic``:
 
 .. ipython:: python
 
@@ -2262,4 +2262,4 @@ All numpy dtypes are subclasses of ``numpy.generic``:
 .. note::
 
     Pandas also defines the types ``category``, and ``datetime64[ns, tz]``, which are not integrated into the normal
-    numpy hierarchy and wont show up with the above function.
+    NumPy hierarchy and wont show up with the above function.

doc/source/categorical.rst

@@ -40,7 +40,7 @@ The categorical data type is useful in the following cases:
 * The lexical order of a variable is not the same as the logical order ("one", "two", "three").
   By converting to a categorical and specifying an order on the categories, sorting and
   min/max will use the logical order instead of the lexical order, see :ref:`here <categorical.sort>`.
-* As a signal to other python libraries that this column should be treated as a categorical
+* As a signal to other Python libraries that this column should be treated as a categorical
   variable (e.g. to use suitable statistical methods or plot types).
 
 See also the :ref:`API docs on categoricals<api.categorical>`.
@@ -366,7 +366,7 @@ or simply set the categories to a predefined scale, use :func:`Categorical.set_c
 .. note::
     Be aware that :func:`Categorical.set_categories` cannot know whether some category is omitted
     intentionally or because it is misspelled or (under Python3) due to a type difference (e.g.,
-    numpys S1 dtype and python strings). This can result in surprising behaviour!
+    numpys S1 dtype and Python strings). This can result in surprising behaviour!
 
 Sorting and Order
 -----------------

doc/source/comparison_with_sas.rst

@@ -10,7 +10,7 @@ performed in pandas.
 If you're new to pandas, you might want to first read through :ref:`10 Minutes to pandas<10min>`
 to familiarize yourself with the library.
 
-As is customary, we import pandas and numpy as follows:
+As is customary, we import pandas and NumPy as follows:
 
 .. ipython:: python
 
@@ -100,7 +100,7 @@ specifying the column names.
 
 A pandas ``DataFrame`` can be constructed in many different ways,
 but for a small number of values, it is often convenient to specify it as
-a python dictionary, where the keys are the column names
+a Python dictionary, where the keys are the column names
 and the values are the data.
 
 .. ipython:: python

doc/source/comparison_with_sql.rst

@@ -10,7 +10,7 @@ various SQL operations would be performed using pandas.
 If you're new to pandas, you might want to first read through :ref:`10 Minutes to pandas<10min>`
 to familiarize yourself with the library.
 
-As is customary, we import pandas and numpy as follows:
+As is customary, we import pandas and NumPy as follows:
 
 .. ipython:: python
 

doc/source/computation.rst

@@ -57,9 +57,8 @@ Covariance
    s2 = pd.Series(np.random.randn(1000))
    s1.cov(s2)
 
-Analogously, :meth:`DataFrame.cov` to compute 
-pairwise covariances among the series in the DataFrame, also excluding 
-NA/null values.
+Analogously, :meth:`DataFrame.cov` to compute pairwise covariances among the 
+series in the DataFrame, also excluding NA/null values.
 
 .. _computation.covariance.caveats:
 

doc/source/contributing.rst

@@ -118,7 +118,7 @@ Creating a development environment
 ----------------------------------
 
 To test out code changes, you'll need to build pandas from source, which
-requires a C compiler and python environment. If you're making documentation
+requires a C compiler and Python environment. If you're making documentation
 changes, you can skip to :ref:`contributing.documentation` but you won't be able
 to build the documentation locally before pushing your changes.
 
@@ -187,7 +187,7 @@ At this point you should be able to import pandas from your locally built versio
    0.22.0.dev0+29.g4ad6d4d74
 
 This will create the new environment, and not touch any of your existing environments,
-nor any existing python installation.
+nor any existing Python installation.
 
 To view your environments::
 

doc/source/cookbook.rst

@@ -41,7 +41,7 @@ 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 3.4.  Minor tweaks might be necessary for earlier python
+These examples are written for Python 3.  Minor tweaks might be necessary for earlier python
 versions.
 
 Idioms
@@ -750,7 +750,7 @@ Timeseries
 <http://nipunbatra.github.io/2015/06/timeseries/>`__
 
 Turn a matrix with hours in columns and days in rows into a continuous row sequence in the form of a time series.
-`How to rearrange a python pandas DataFrame?
+`How to rearrange a Python pandas DataFrame?
 <http://stackoverflow.com/questions/15432659/how-to-rearrange-a-python-pandas-dataframe>`__
 
 `Dealing with duplicates when reindexing a timeseries to a specified frequency
@@ -1152,7 +1152,7 @@ Storing Attributes to a group node
    store = pd.HDFStore('test.h5')
    store.put('df',df)
 
-   # you can store an arbitrary python object via pickle
+   # you can store an arbitrary Python object via pickle
    store.get_storer('df').attrs.my_attribute = dict(A = 10)
    store.get_storer('df').attrs.my_attribute
 
@@ -1167,7 +1167,7 @@ Storing Attributes to a group node
 Binary Files
 ************
 
-pandas readily accepts numpy record arrays, if you need to read in a binary
+pandas readily accepts NumPy record arrays, if you need to read in a binary
 file consisting of an array of C structs. For example, given this C program
 in a file called ``main.c`` compiled with ``gcc main.c -std=gnu99`` on a
 64-bit machine,

doc/source/dsintro.rst

@@ -23,7 +23,7 @@ Intro to Data Structures
 We'll start with a quick, non-comprehensive overview of the fundamental data
 structures in pandas to get you started. The fundamental behavior about data
 types, indexing, and axis labeling / alignment apply across all of the
-objects. To get started, import numpy and load pandas into your namespace:
+objects. To get started, import NumPy and load pandas into your namespace:
 
 .. ipython:: python
 
@@ -877,7 +877,7 @@ of DataFrames:
    wp['Item3'] = wp['Item1'] / wp['Item2']
 
 The API for insertion and deletion is the same as for DataFrame. And as with
-DataFrame, if the item is a valid python identifier, you can access it as an
+DataFrame, if the item is a valid Python identifier, you can access it as an
 attribute and tab-complete it in IPython.
 
 Transposing

doc/source/ecosystem.rst

@@ -27,7 +27,7 @@ Statistics and Machine Learning
 `Statsmodels <http://www.statsmodels.org/>`__
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-Statsmodels is the prominent python "statistics and econometrics library" and it has
+Statsmodels is the prominent Python "statistics and econometrics library" and it has
 a long-standing special relationship with pandas. Statsmodels provides powerful statistics,
 econometrics, analysis and modeling functionality that is out of pandas' scope.
 Statsmodels leverages pandas objects as the underlying data container for computation.
@@ -72,7 +72,7 @@ Hadley Wickham's `ggplot2 <http://ggplot2.org/>`__ is a foundational exploratory
 Based on `"The Grammar of Graphics" <http://www.cs.uic.edu/~wilkinson/TheGrammarOfGraphics/GOG.html>`__ it
 provides a powerful, declarative and extremely general way to generate bespoke plots of any kind of data.
 It's really quite incredible. Various implementations to other languages are available,
-but a faithful implementation for python users has long been missing. Although still young
+but a faithful implementation for Python users has long been missing. Although still young
 (as of Jan-2014), the `yhat/ggplot <https://github.com/yhat/ggplot>`__ project has been
 progressing quickly in that direction.
 
@@ -192,7 +192,7 @@ or multi-indexed DataFrames.
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 fredapi is a Python interface to the `Federal Reserve Economic Data (FRED) <http://research.stlouisfed.org/fred2/>`__
 provided by the Federal Reserve Bank of St. Louis. It works with both the FRED database and ALFRED database that
-contains point-in-time data (i.e. historic data revisions). fredapi provides a wrapper in python to the FRED
+contains point-in-time data (i.e. historic data revisions). fredapi provides a wrapper in Python to the FRED
 HTTP API, and also provides several convenient methods for parsing and analyzing point-in-time data from ALFRED.
 fredapi makes use of pandas and returns data in a Series or DataFrame. This module requires a FRED API key that
 you can obtain for free on the FRED website.

doc/source/enhancingperf.rst

@@ -24,13 +24,13 @@ Enhancing Performance
 Cython (Writing C extensions for pandas)
 ----------------------------------------
 
-For many use cases writing pandas in pure python and numpy is sufficient. In some
+For many use cases writing pandas in pure Python and NumPy is sufficient. In some
 computationally heavy applications however, it can be possible to achieve sizeable
 speed-ups by offloading work to `cython <http://cython.org/>`__.
 
 This tutorial assumes you have refactored as much as possible in python, for example
-trying to remove for loops and making use of numpy vectorization, it's always worth
-optimising in python first.
+trying to remove for loops and making use of NumPy vectorization, it's always worth
+optimising in Python first.
 
 This tutorial walks through a "typical" process of cythonizing a slow computation.
 We use an `example from the cython documentation <http://docs.cython.org/src/quickstart/cythonize.html>`__
@@ -86,8 +86,8 @@ hence we'll concentrate our efforts cythonizing these two functions.
 
 .. note::
 
-  In python 2 replacing the ``range`` with its generator counterpart (``xrange``)
-  would mean the ``range`` line would vanish. In python 3 ``range`` is already a generator.
+  In Python 2 replacing the ``range`` with its generator counterpart (``xrange``)
+  would mean the ``range`` line would vanish. In Python 3 ``range`` is already a generator.
 
 .. _enhancingperf.plain:
 
@@ -232,7 +232,7 @@ the rows, applying our ``integrate_f_typed``, and putting this in the zeros arra
 .. note::
 
     Loops like this would be *extremely* slow in python, but in Cython looping
-    over numpy arrays is *fast*.
+    over NumPy arrays is *fast*.
 
 .. code-block:: ipython
 
@@ -315,7 +315,7 @@ Numba works by generating optimized machine code using the LLVM compiler infrast
 Jit
 ~~~
 
-Using ``numba`` to just-in-time compile your code. We simply take the plain python code from above and annotate with the ``@jit`` decorator.
+Using ``numba`` to just-in-time compile your code. We simply take the plain Python code from above and annotate with the ``@jit`` decorator.
 
 .. code-block:: python
 
@@ -391,7 +391,7 @@ Caveats
 
     ``numba`` will execute on any function, but can only accelerate certain classes of functions.
 
-``numba`` is best at accelerating functions that apply numerical functions to numpy arrays. When passed a function that only uses operations it knows how to accelerate, it will execute in ``nopython`` mode.
+``numba`` is best at accelerating functions that apply numerical functions to NumPy arrays. When passed a function that only uses operations it knows how to accelerate, it will execute in ``nopython`` mode.
 
 If ``numba`` is passed a function that includes something it doesn't know how to work with -- a category that currently includes sets, lists, dictionaries, or string functions -- it will revert to ``object mode``. In ``object mode``, numba will execute but your code will not speed up significantly. If you would prefer that ``numba`` throw an error if it cannot compile a function in a way that speeds up your code, pass numba the argument ``nopython=True`` (e.g.  ``@numba.jit(nopython=True)``). For more on troubleshooting ``numba`` modes, see the `numba troubleshooting page <http://numba.pydata.org/numba-doc/0.20.0/user/troubleshoot.html#the-compiled-code-is-too-slow>`__.
 
@@ -779,7 +779,7 @@ Technical Minutia Regarding Expression Evaluation
 
 Expressions that would result in an object dtype or involve datetime operations
 (because of ``NaT``) must be evaluated in Python space. The main reason for
-this behavior is to maintain backwards compatibility with versions of numpy <
+this behavior is to maintain backwards compatibility with versions of NumPy <
 1.7. In those versions of ``numpy`` a call to ``ndarray.astype(str)`` will
 truncate any strings that are more than 60 characters in length. Second, we
 can't pass ``object`` arrays to ``numexpr`` thus string comparisons must be

doc/source/gotchas.rst

@@ -91,7 +91,7 @@ See also :ref:`Categorical Memory Usage <categorical.memory>`.
 Using If/Truth Statements with pandas
 -------------------------------------
 
-pandas follows the numpy convention of raising an error when you try to convert something to a ``bool``.
+pandas follows the NumPy convention of raising an error when you try to convert something to a ``bool``.
 This happens in a ``if`` or when using the boolean operations, ``and``, ``or``, or ``not``.  It is not clear
 what the result of