Spellcheck (#19017)

Author: tommyod

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,