diff --git a/reader/get_sections.py b/reader/get_sections.py
index ef43f7f63b270d246391411c920a77ee0d8070d1..acb0f2b0285601c7e8b838078ba18c7a4f1caa1c 100644
--- a/reader/get_sections.py
+++ b/reader/get_sections.py
@@ -6,7 +6,7 @@ Created on Tue Apr 30 09:38:17 2019
 Splits string reports in sections using a data model layout.
 
 Input and output are simple pandas dataframes, with the output dataframe
-column names being section names
+column names being the section names
 
 To work with a pandas TextParser, loop through this module.
 
@@ -29,6 +29,9 @@ use, also support to chunking would make converting to series a bit dirty...
         provided the section is in a sequential parsing_order group
 
 @author: iregon
+
+Have to documents the threads approach!!!!
+
 """
 
 import pandas as pd
diff --git a/reader/import_data.py b/reader/import_data.py
index 7ecf0c8cc899e48e472101f10f08042ebf9abc01..889c5f56a75a370c35cb4d8784088337c1eb6c16 100644
--- a/reader/import_data.py
+++ b/reader/import_data.py
@@ -6,7 +6,7 @@ Created on Fri Jan 10 13:17:43 2020
 FUNCTION TO PREPARE SOURCE DATA TO WHAT GET_SECTIONS() EXPECTS:
     AN ITERABLE WITH DATAFRAMES
 
-INPUT IS EITHER NOW ONLY A FILE PATH
+INPUT IS NOW ONLY A FILE PATH. COULD OPTIONALLY GET OTHER TYPE OBJECTS...
 
 OUTPUT IS AN ITERABLE, DEPENDING ON CHUNKSIZE BEING SET:
     - a single dataframe in a list
@@ -38,7 +38,6 @@ OPTIONS IN OLD DEVELOPMENT:
 
 import pandas as pd
 import os
-import io
 
 from .. import properties
 
diff --git a/reader/read_sections.py b/reader/read_sections.py
index bfaf4aea262ef9804ae3a7603a296f3552c698d1..5beab895c4d466f3760237470bd6d98bcc87d1f1 100644
--- a/reader/read_sections.py
+++ b/reader/read_sections.py
@@ -13,7 +13,7 @@ where appropriate and ensure its data type consistency.
 
 Output is a dataframe with columns as follows depending on the data model
 structure:
-    1) Data model with sections (1 or more): [(section0,element0),.......(sectionN,elementN)]
+    1) Data model with sections (1 or more): [(section0,element0),.......(sectionN,elementM)]
     2) Data model with no sections[element0...element1]