Docs: Capitalisation of title line changed to lower case

doc/source/getting_started/10min.rst

@@ -3,7 +3,7 @@
 {{ header }}
 
 ********************
-10 Minutes to pandas
+10 minutes to pandas
 ********************
 
 This is a short introduction to pandas, geared mainly for new users.
@@ -16,7 +16,7 @@ Customarily, we import as follows:
    import numpy as np
    import pandas as pd
 
-Object Creation
+Object creation
 ---------------
 
 See the :ref:`Data Structure Intro section <dsintro>`.
@@ -83,7 +83,7 @@ As you can see, the columns ``A``, ``B``, ``C``, and ``D`` are automatically
 tab completed. ``E`` is there as well; the rest of the attributes have been
 truncated for brevity.
 
-Viewing Data
+Viewing data
 ------------
 
 See the :ref:`Basics section <basics>`.
@@ -183,7 +183,7 @@ Selecting via ``[]``, which slices the rows.
    df[0:3]
    df['20130102':'20130104']
 
-Selection by Label
+Selection by label
 ~~~~~~~~~~~~~~~~~~
 
 See more in :ref:`Selection by Label <indexing.label>`.
@@ -224,7 +224,7 @@ For getting fast access to a scalar (equivalent to the prior method):
 
    df.at[dates[0], 'A']
 
-Selection by Position
+Selection by position
 ~~~~~~~~~~~~~~~~~~~~~
 
 See more in :ref:`Selection by Position <indexing.integer>`.
@@ -271,7 +271,7 @@ For getting fast access to a scalar (equivalent to the prior method):
 
    df.iat[1, 1]
 
-Boolean Indexing
+Boolean indexing
 ~~~~~~~~~~~~~~~~
 
 Using a single column's values to select data.
@@ -340,7 +340,7 @@ A ``where`` operation with setting.
    df2
 
 
-Missing Data
+Missing data
 ------------
 
 pandas primarily uses the value ``np.nan`` to represent missing data. It is by
@@ -428,7 +428,7 @@ See more at :ref:`Histogramming and Discretization <basics.discretization>`.
    s
    s.value_counts()
 
-String Methods
+String methods
 ~~~~~~~~~~~~~~
 
 Series is equipped with a set of string processing methods in the `str`
@@ -580,7 +580,7 @@ With a "stacked" DataFrame or Series (having a ``MultiIndex`` as the
    stacked.unstack(1)
    stacked.unstack(0)
 
-Pivot Tables
+Pivot tables
 ~~~~~~~~~~~~
 See the section on :ref:`Pivot Tables <reshaping.pivot>`.
 
@@ -600,7 +600,7 @@ We can produce pivot tables from this data very easily:
    pd.pivot_table(df, values='D', index=['A', 'B'], columns=['C'])
 
 
-Time Series
+Time series
 -----------
 
 pandas has simple, powerful, and efficient functionality for performing
@@ -734,7 +734,7 @@ of the columns with labels:
    @savefig frame_plot_basic.png
    plt.legend(loc='best')
 
-Getting Data In/Out
+Getting data in/out
 -------------------
 
 CSV

doc/source/getting_started/basics.rst

@@ -3,7 +3,7 @@
 {{ header }}
 
 ==============================
- Essential Basic Functionality
+ Essential basic functionality
 ==============================
 
 Here we discuss a lot of the essential functionality common to the pandas data
@@ -19,7 +19,7 @@ the previous section:
 
 .. _basics.head_tail:
 
-Head and Tail
+Head and tail
 -------------
 
 To view a small sample of a Series or DataFrame object, use the
@@ -34,7 +34,7 @@ of elements to display is five, but you may pass a custom number.
 
 .. _basics.attrs:
 
-Attributes and Underlying Data
+Attributes and underlying data
 ------------------------------
 
 pandas objects have a number of attributes enabling you to access the metadata
@@ -286,7 +286,7 @@ using ``fillna`` if you wish).
 
 .. _basics.compare:
 
-Flexible Comparisons
+Flexible comparisons
 ~~~~~~~~~~~~~~~~~~~~
 
 Series and DataFrame have the binary comparison methods ``eq``, ``ne``, ``lt``, ``gt``,
@@ -304,7 +304,7 @@ indexing operations, see the section on :ref:`Boolean indexing<indexing.boolean>
 
 .. _basics.reductions:
 
-Boolean Reductions
+Boolean reductions
 ~~~~~~~~~~~~~~~~~~
 
 You can apply the reductions: :attr:`~DataFrame.empty`, :meth:`~DataFrame.any`,

doc/source/getting_started/dsintro.rst

@@ -3,7 +3,7 @@
 {{ header }}
 
 ************************
-Intro to Data Structures
+Intro to data structures
 ************************
 
 We'll start with a quick, non-comprehensive overview of the fundamental data
@@ -407,7 +407,7 @@ represent missing values. Alternatively, you may pass a ``numpy.MaskedArray``
 as the data argument to the DataFrame constructor, and its masked entries will
 be considered missing.
 
-Alternate Constructors
+Alternate constructors
 ~~~~~~~~~~~~~~~~~~~~~~
 
 .. _basics.dataframe.from_dict:
@@ -498,7 +498,7 @@ available to insert at a particular location in the columns:
 
 .. _dsintro.chained_assignment:
 
-Assigning New Columns in Method Chains
+Assigning new columns in method Chains
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Inspired by `dplyr's
@@ -614,7 +614,7 @@ To write code compatible with all versions of Python, split the assignment in tw
 
 
 
-Indexing / Selection
+Indexing / selection
 ~~~~~~~~~~~~~~~~~~~~
 The basics of indexing are as follows:
 

doc/source/getting_started/overview.rst

@@ -81,7 +81,7 @@ Some other notes
 
  - pandas has been used extensively in production in financial applications.
 
-Data Structures
+Data structures
 ---------------
 
 .. csv-table::
@@ -131,7 +131,7 @@ changed, but, for example, columns can be inserted into a DataFrame. However,
 the vast majority of methods produce new objects and leave the input data
 untouched. In general we like to **favor immutability** where sensible.
 
-Getting Support
+Getting support
 ---------------
 
 The first stop for pandas issues and ideas is the `Github Issue Tracker
@@ -152,21 +152,21 @@ pandas is a `NumFOCUS <https://www.numfocus.org/open-source-projects/>`__ sponso
 This will help ensure the success of development of pandas as a world-class open-source
 project, and makes it possible to `donate <https://pandas.pydata.org/donate.html>`__ to the project.
 
-Project Governance
+Project governance
 ------------------
 
 The governance process that pandas project has used informally since its inception in 2008 is formalized in `Project Governance documents <https://github.com/pandas-dev/pandas-governance>`__.
 The documents clarify how decisions are made and how the various elements of our community interact, including the relationship between open source collaborative development and work that may be funded by for-profit or non-profit entities.
 
 Wes McKinney is the Benevolent Dictator for Life (BDFL).
 
-Development Team
+Development team
 -----------------
 
 The list of the Core Team members and more detailed information can be found on the `people’s page <https://github.com/pandas-dev/pandas-governance/blob/master/people.md>`__ of the governance repo.
 
 
-Institutional Partners
+Institutional partners
 ----------------------
 
 The information about current institutional partners can be found on `pandas website page <https://pandas.pydata.org/about.html>`__.

doc/source/getting_started/tutorials.rst

@@ -8,7 +8,7 @@ Tutorials
 
 This is a guide to many pandas tutorials, geared mainly for new users.
 
-Internal Guides
+Internal guides
 ===============
 
 pandas' own :ref:`10 Minutes to pandas<10min>`.
@@ -17,10 +17,10 @@ More complex recipes are in the :ref:`Cookbook<cookbook>`.
 
 A handy pandas `cheat sheet <http://pandas.pydata.org/Pandas_Cheat_Sheet.pdf>`_.
 
-Community Guides
+Community guides
 ================
 
-pandas Cookbook by Julia Evans
+pandas cookbook by Julia Evans
 ------------------------------
 
 The goal of this 2015 cookbook (by `Julia Evans <http://jvns.ca>`_) is to
@@ -30,7 +30,7 @@ entails.
 For the table of contents, see the `pandas-cookbook GitHub
 repository <http://github.com/jvns/pandas-cookbook>`_.
 
-Learn Pandas by Hernan Rojas
+Learn pandas by Hernan Rojas
 ----------------------------
 
 A set of lesson for new pandas users: https://bitbucket.org/hrojas/learn-pandas
@@ -74,7 +74,7 @@ Excel charts with pandas, vincent and xlsxwriter
 
 *  `Using Pandas and XlsxWriter to create Excel charts <https://pandas-xlsxwriter-charts.readthedocs.io/>`_
 
-Video Tutorials
+Video tutorials
 ---------------
 
 * `Pandas From The Ground Up <https://www.youtube.com/watch?v=5JnMutdy6Fw>`_
@@ -96,7 +96,7 @@ Video Tutorials
   `Jupyter Notebook <http://nbviewer.jupyter.org/github/justmarkham/pycon-2018-tutorial/blob/master/tutorial.ipynb>`__
 
 
-Various Tutorials
+Various tutorials
 -----------------
 
 * `Wes McKinney's (pandas BDFL) blog <http://blog.wesmckinney.com/>`_

doc/source/reference/arrays.rst

@@ -3,7 +3,7 @@
 .. _api.arrays:
 
 =============
-Pandas Arrays
+Pandas arrays
 =============
 
 .. currentmodule:: pandas
@@ -15,7 +15,7 @@ objects contained with a :class:`Index`, :class:`Series`, or
 For some data types, pandas extends NumPy's type system.
 
 =================== ========================= ================== =============================
-Kind of Data        Pandas Data Type          Scalar             Array
+Kind of data        Pandas data type          Scalar             Array
 =================== ========================= ================== =============================
 TZ-aware datetime   :class:`DatetimeTZDtype`  :class:`Timestamp` :ref:`api.arrays.datetime`
 Timedeltas          (none)                    :class:`Timedelta` :ref:`api.arrays.timedelta`
@@ -37,7 +37,7 @@ stored in a :class:`Series`, :class:`Index`, or as a column in a :class:`DataFra
 
 .. _api.arrays.datetime:
 
-Datetime Data
+Datetime data
 -------------
 
 NumPy cannot natively represent timezone-aware datetimes. Pandas supports this
@@ -155,7 +155,7 @@ If the data are tz-aware, then every value in the array must have the same timez
 
 .. _api.arrays.timedelta:
 
-Timedelta Data
+Timedelta data
 --------------
 
 NumPy can natively represent timedeltas. Pandas provides :class:`Timedelta`
@@ -209,7 +209,7 @@ A collection of timedeltas may be stored in a :class:`TimedeltaArray`.
 
 .. _api.arrays.period:
 
-Timespan Data
+Timespan data
 -------------
 
 Pandas represents spans of times as :class:`Period` objects.
@@ -274,7 +274,7 @@ Every period in a ``PeriodArray`` must have the same ``freq``.
 
 .. _api.arrays.interval:
 
-Interval Data
+Interval data
 -------------
 
 Arbitrary intervals can be represented as :class:`Interval` objects.
@@ -315,7 +315,7 @@ A collection of intervals may be stored in an :class:`arrays.IntervalArray`.
 
 .. _api.arrays.integer_na:
 
-Nullable Integer
+Nullable integer
 ----------------
 
 :class:`numpy.ndarray` cannot natively represent integer-data with missing values.
@@ -341,7 +341,7 @@ Pandas provides this through :class:`arrays.IntegerArray`.
 
 .. _api.arrays.categorical:
 
-Categorical Data
+Categorical data
 ----------------
 
 Pandas defines a custom data type for representing data that can take only a
@@ -406,7 +406,7 @@ data. See :ref:`api.series.cat` for more.
 
 .. _api.arrays.sparse:
 
-Sparse Data
+Sparse data
 -----------
 
 Data where a single value is repeated many times (e.g. ``0`` or ``NaN``) may
@@ -439,4 +439,4 @@ and methods if the :class:`Series` contains sparse values. See
       DatetimeTZDtype.unit
       DatetimeTZDtype.tz
       PeriodDtype.freq
-      IntervalDtype.subtype
+      IntervalDtype.subtype