From 692c968771bb19d4f12fac3d1eb76f8e611db165 Mon Sep 17 00:00:00 2001
From: Derek Snow <d.snow@jbs.cam.ac.uk>
Date: Sat, 25 Jan 2020 17:58:15 +1300
Subject: [PATCH] Update README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 20ea728..50c389e 100644
--- a/README.md
+++ b/README.md
@@ -14,6 +14,7 @@ import pandapy as pp
 #### Why PandaPy? 
 
 1. Maintains the full functionality and speed of structured NumPy datatype (eg., ```array[col1] + array[col2], or np.log(array[col1]```)
+1. The performance claims only hold for small datasets, 1,000-100,000 numpy rows.  
 2. Provides wrapper functions over NumPy to give you the usability of Pandas (eg., ```pp.group(array, [col1, col2, col2], ['mean', 'std'], ['Adj_Close','Close'])```
 3. If you need Pandas for speciality functions, you can easily ```df = pp.pandas(array)``` and back ```array = pp.structured(df)```
 4. For simple calculations (i.e, plus, mult, log) PandaPy is 25x - 80x faster than Pandas.