fix CRLF

doc/source/core.rst

@@ -1,15 +1,15 @@
-.. _core:
-
-***************
-Data Structures
-***************
-
-.. currentmodule:: pandas
-
-.. toctree::
-   :maxdepth: 2
-
-   indexobj
-   series
-   dataframe
-   panel
+.. _core:
+
+***************
+Data Structures
+***************
+
+.. currentmodule:: pandas
+
+.. toctree::
+   :maxdepth: 2
+
+   indexobj
+   series
+   dataframe
+   panel

doc/source/index.rst

@@ -1,96 +1,96 @@
-.. Pandas documentation master file, created by
-
-
-pandas: a python data analysis library
-======================================
-
-:mod:`pandas` is a python package providing convenient data structures
-for time series, cross-sectional, or any other form of "labeled" data,
-with tools for building statistical and econometric models.
-
-This library was created with the following design principles:
-
-  - Working with time series and cross-sectional data should be easy
-  - The user should not have to worry (much) about handling missing data
-  - Data alignment should be automatic and transparent
-  - Speed matters
-  - Perhaps most importantly: *things should work just like you want them to*
-
-Many of these principles are here to address the shortcomings
-frequently experienced using other languages / scientific research
-environments. In MATLAB, for example, you spend a lot of time coercing
-data into matrices, cleaning and aligning it, and keeping everything
-homogeneous. You have to use lots of functions like **nanmean, nanstd,
-repmat** (for broadcasting), and other functions which help you to
-maintain reliable data. Using `NumPy <http://www.numpy.org>`__ and a
-Pythonic approach, pandas helps hide the dirty details of working with
-unclean data, allowing you to focus on the problem you're trying to
-solve rather than the implementation.
-
-pandas is implemented primarily using NumPy and is intended to be able
-to integrate very easily with other NumPy-based scientific libraries,
-such as :mod:`scikits.statsmodels`.
-
-.. note::
-
-   This documentation assumes general familiarity with NumPy. If you
-   haven't used NumPy much or at all, please check out the `NumPy
-   documentation <http://docs.scipy.org>`__ first.
-
-See the package overview for more detail about what's in the library.
-
-User manual
------------
-
-`PDF Version <pandas.pdf>`__
-
-.. module:: pandas
-
-**Date**: |today|
-
-**Version**: |version|
-
-**License:** BSD
-
-**Requirements:** python 2.4 to 2.7, NumPy, and dateutil
-
-**Code Repository:** http://github.com/wesm/pandas
-
-Library documentation
-~~~~~~~~~~~~~~~~~~~~~
-
-.. toctree::
-    :maxdepth: 2
-
-    overview
-    core
-    groupby
-    datetools
-    stats
-    r_interface
-    io
-
-Other topics of interest
-~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. toctree::
-    :maxdepth: 2
-
-    examples
-    frame_vs_matrix
-    r_guide
-    missing_data
-    related
-
-Indices and tables
-------------------
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-
-Contact
--------
-
-Please feel free to send comments or questions directly to
-wesmckinn@gmail.com or the pystatsmodels mailing list.
+.. Pandas documentation master file, created by
+
+
+pandas: a python data analysis library
+======================================
+
+:mod:`pandas` is a python package providing convenient data structures
+for time series, cross-sectional, or any other form of "labeled" data,
+with tools for building statistical and econometric models.
+
+This library was created with the following design principles:
+
+  - Working with time series and cross-sectional data should be easy
+  - The user should not have to worry (much) about handling missing data
+  - Data alignment should be automatic and transparent
+  - Speed matters
+  - Perhaps most importantly: *things should work just like you want them to*
+
+Many of these principles are here to address the shortcomings
+frequently experienced using other languages / scientific research
+environments. In MATLAB, for example, you spend a lot of time coercing
+data into matrices, cleaning and aligning it, and keeping everything
+homogeneous. You have to use lots of functions like **nanmean, nanstd,
+repmat** (for broadcasting), and other functions which help you to
+maintain reliable data. Using `NumPy <http://www.numpy.org>`__ and a
+Pythonic approach, pandas helps hide the dirty details of working with
+unclean data, allowing you to focus on the problem you're trying to
+solve rather than the implementation.
+
+pandas is implemented primarily using NumPy and is intended to be able
+to integrate very easily with other NumPy-based scientific libraries,
+such as :mod:`scikits.statsmodels`.
+
+.. note::
+
+   This documentation assumes general familiarity with NumPy. If you
+   haven't used NumPy much or at all, please check out the `NumPy
+   documentation <http://docs.scipy.org>`__ first.
+
+See the package overview for more detail about what's in the library.
+
+User manual
+-----------
+
+`PDF Version <pandas.pdf>`__
+
+.. module:: pandas
+
+**Date**: |today|
+
+**Version**: |version|
+
+**License:** BSD
+
+**Requirements:** python 2.4 to 2.7, NumPy, and dateutil
+
+**Code Repository:** http://github.com/wesm/pandas
+
+Library documentation
+~~~~~~~~~~~~~~~~~~~~~
+
+.. toctree::
+    :maxdepth: 2
+
+    overview
+    core
+    groupby
+    datetools
+    stats
+    r_interface
+    io
+
+Other topics of interest
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. toctree::
+    :maxdepth: 2
+
+    examples
+    frame_vs_matrix
+    r_guide
+    missing_data
+    related
+
+Indices and tables
+------------------
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
+
+Contact
+-------
+
+Please feel free to send comments or questions directly to
+wesmckinn@gmail.com or the pystatsmodels mailing list.

doc/source/stats.rst

@@ -1,15 +1,15 @@
-.. currentmodule:: pandas.stats.api
-
-.. _stats:
-
-**********************************
-Built-in statistical functionality
-**********************************
-
-.. currentmodule:: pandas
-
-.. toctree::
-   :maxdepth: 2
-
-   stats_moments
-   stats_ols
+.. currentmodule:: pandas.stats.api
+
+.. _stats:
+
+**********************************
+Built-in statistical functionality
+**********************************
+
+.. currentmodule:: pandas
+
+.. toctree::
+   :maxdepth: 2
+
+   stats_moments
+   stats_ols