From 802d868e629ae51964798ce499f5646b96a61d3e Mon Sep 17 00:00:00 2001
From: Mayank Chaudhary <62796466+mayank1897@users.noreply.github.com>
Date: Thu, 1 Oct 2020 18:18:58 +0530
Subject: [PATCH 1/2] Update README.md

---
 README.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index a2f2f1c04442a..99a1def2c8977 100644
--- a/README.md
+++ b/README.md
@@ -32,32 +32,32 @@ its way towards this goal.
 Here are just a few of the things that pandas does well:
 
   - Easy handling of [**missing data**][missing-data] (represented as
-    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
+    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data.
   - Size mutability: columns can be [**inserted and
     deleted**][insertion-deletion] from DataFrame and higher dimensional
-    objects
+    objects.
   - Automatic and explicit [**data alignment**][alignment]: objects can
     be explicitly aligned to a set of labels, or the user can simply
     ignore the labels and let `Series`, `DataFrame`, etc. automatically
-    align the data for you in computations
+    align the data for you in computations.
   - Powerful, flexible [**group by**][groupby] functionality to perform
     split-apply-combine operations on data sets, for both aggregating
-    and transforming data
+    and transforming data.
   - Make it [**easy to convert**][conversion] ragged,
     differently-indexed data in other Python and NumPy data structures
-    into DataFrame objects
+    into DataFrame objects.
   - Intelligent label-based [**slicing**][slicing], [**fancy
     indexing**][fancy-indexing], and [**subsetting**][subsetting] of
-    large data sets
+    large data sets.
   - Intuitive [**merging**][merging] and [**joining**][joining] data
-    sets
+    sets.
   - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
-    data sets
+    data sets.
   - [**Hierarchical**][mi] labeling of axes (possible to have multiple
-    labels per tick)
+    labels per tick).
   - Robust IO tools for loading data from [**flat files**][flat-files]
     (CSV and delimited), [**Excel files**][excel], [**databases**][db],
-    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
+    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore].
   - [**Time series**][timeseries]-specific functionality: date range
     generation and frequency conversion, moving window statistics,
     date shifting and lagging.

From c9e4ba146053f0b59160fd7fad70d9e0f6dab463 Mon Sep 17 00:00:00 2001
From: Mayank Chaudhary <62796466+mayank1897@users.noreply.github.com>
Date: Thu, 1 Oct 2020 19:18:43 +0530
Subject: [PATCH 2/2] Update README.md

---
 README.md | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 99a1def2c8977..da8487d76f4a1 100644
--- a/README.md
+++ b/README.md
@@ -32,32 +32,32 @@ its way towards this goal.
 Here are just a few of the things that pandas does well:
 
   - Easy handling of [**missing data**][missing-data] (represented as
-    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data.
+    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data;
   - Size mutability: columns can be [**inserted and
     deleted**][insertion-deletion] from DataFrame and higher dimensional
-    objects.
+    objects;
   - Automatic and explicit [**data alignment**][alignment]: objects can
     be explicitly aligned to a set of labels, or the user can simply
     ignore the labels and let `Series`, `DataFrame`, etc. automatically
-    align the data for you in computations.
+    align the data for you in computations;
   - Powerful, flexible [**group by**][groupby] functionality to perform
     split-apply-combine operations on data sets, for both aggregating
-    and transforming data.
+    and transforming data;
   - Make it [**easy to convert**][conversion] ragged,
     differently-indexed data in other Python and NumPy data structures
-    into DataFrame objects.
+    into DataFrame objects;
   - Intelligent label-based [**slicing**][slicing], [**fancy
     indexing**][fancy-indexing], and [**subsetting**][subsetting] of
-    large data sets.
+    large data sets;
   - Intuitive [**merging**][merging] and [**joining**][joining] data
-    sets.
+    sets;
   - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
-    data sets.
+    data sets;
   - [**Hierarchical**][mi] labeling of axes (possible to have multiple
-    labels per tick).
+    labels per tick);
   - Robust IO tools for loading data from [**flat files**][flat-files]
     (CSV and delimited), [**Excel files**][excel], [**databases**][db],
-    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore].
+    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore];
   - [**Time series**][timeseries]-specific functionality: date range
     generation and frequency conversion, moving window statistics,
     date shifting and lagging.