Update README.md

README.md

@@ -24,7 +24,7 @@ import pandapy as pp
 9. For finance applications the speed of simple calculations takes preference over table function speed.
 10. PandaPy is not created to allow you to scale up to clusters for multiple computer processing like Dask, Modin, and Spark, instead it is focused on speed and usability within a single computer's Memory.
 11. Machines are getting large, EC2 X1 has 2TB of RAM and is remarkably affordable. If it can be done on a single machine then it should be done on a single machine. Quoting Dask - "For data that fits into RAM, Pandas can often be faster and easier to use than Dask DataFrame"
-12. If your dataset is very small you can load your data using PandaPy's ```read()``` function, for medium sized data, it is best to load it with datatable or pyspark and convert it to structured Numpy, if it is large pyspark, Dask, or Modin, if it is very large use pyspark. 
+12. If your dataset is very small you can load your data using PandaPy's ```read()``` function, for medium sized data, it is best to load it with datatable or pyspark and convert it to structured Numpy, if it is large, pyspark, Dask, or Modin, if it is very large use pyspark. 
 13. Lastly PandaPy can have as input any multidimensional object and does not have to conform to the basic NumPy datatypes. It can include nested datatypes, subarrays, functions as long as each column conforms to the array lenght, this allows for a great amount of flexibility. You can for example, ```add(array, "panda function",[[pd for i in range(len(multiple_stocks))]])``` to create a list of the panda (pd) module and access it along any index value ```array["panda function"][0].read_csv(url)```.
 
 PandaPy software, similar to the original Pandas project, is developed to improve the usability of python for finance. Structured datatypes are designed to be able to mimic ‘structs’ in the C language, and share a similar memory layout. PandaPy currently houses more than 30 functions. Structured NumPy are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure.