From fc5fb04c6ea55afe967e214da03a12d9ccc7da53 Mon Sep 17 00:00:00 2001
From: Michael Tiemann <72577720+MichaelTiemannOSC@users.noreply.github.com>
Date: Wed, 6 Sep 2023 14:59:22 -0400
Subject: [PATCH] Fix typo (and test readthedocs.io build)

Fix a simple typo, and see whether my account permissions allow the docs to build correctly on readthedocs.io

Signed-off-by: Michael Tiemann <72577720+MichaelTiemannOSC@users.noreply.github.com>
---
 docs/getting/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/getting/index.rst b/docs/getting/index.rst
index da8a44a..6e000fc 100644
--- a/docs/getting/index.rst
+++ b/docs/getting/index.rst
@@ -7,7 +7,7 @@ The getting started guide aims to get you using pint-pandas productively as quic
 What is Pint-pandas?
 --------------------
 
-The Pandas package provides powerful DataFrame and Series abstractions for dealing with numerical, temporal, categorical, string-based, and even user-defined data (using its ExtensionArray feature). The Pint package provides a rich and extensible vocabulary of units for constructing Quantities and an equally rich and extensible range of unit conversions to make it easy to perform unit-safe calculations using Quantities. Pint-pandas provides PintArray, aPandas ExtensionArray that efficiently implements Pandas DataFrame and Series functionality as unit-aware operations where appropriate.
+The Pandas package provides powerful DataFrame and Series abstractions for dealing with numerical, temporal, categorical, string-based, and even user-defined data (using its ExtensionArray feature). The Pint package provides a rich and extensible vocabulary of units for constructing Quantities and an equally rich and extensible range of unit conversions to make it easy to perform unit-safe calculations using Quantities. Pint-pandas provides PintArray, a Pandas ExtensionArray that efficiently implements Pandas DataFrame and Series functionality as unit-aware operations where appropriate.
 
 Those who have used Pint know well that good units discipline often catches not only simple mistakes, but sometimes more fundamental errors as well. Pint-pandas can reveal similar errors when it comes to slicing and dicing Pandas data.