Commit 5578e606 authored by iregon's avatar iregon
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parent a956de60
...@@ -6,7 +6,7 @@ Created on Tue Apr 30 09:38:17 2019 ...@@ -6,7 +6,7 @@ Created on Tue Apr 30 09:38:17 2019
Splits string reports in sections using a data model layout. Splits string reports in sections using a data model layout.
Input and output are simple pandas dataframes, with the output dataframe 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. 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... ...@@ -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 provided the section is in a sequential parsing_order group
@author: iregon @author: iregon
Have to documents the threads approach!!!!
""" """
import pandas as pd import pandas as pd
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...@@ -6,7 +6,7 @@ Created on Fri Jan 10 13:17:43 2020 ...@@ -6,7 +6,7 @@ Created on Fri Jan 10 13:17:43 2020
FUNCTION TO PREPARE SOURCE DATA TO WHAT GET_SECTIONS() EXPECTS: FUNCTION TO PREPARE SOURCE DATA TO WHAT GET_SECTIONS() EXPECTS:
AN ITERABLE WITH DATAFRAMES 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: OUTPUT IS AN ITERABLE, DEPENDING ON CHUNKSIZE BEING SET:
- a single dataframe in a list - a single dataframe in a list
...@@ -38,7 +38,6 @@ OPTIONS IN OLD DEVELOPMENT: ...@@ -38,7 +38,6 @@ OPTIONS IN OLD DEVELOPMENT:
import pandas as pd import pandas as pd
import os import os
import io
from .. import properties from .. import properties
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...@@ -13,7 +13,7 @@ where appropriate and ensure its data type consistency. ...@@ -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 Output is a dataframe with columns as follows depending on the data model
structure: 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] 2) Data model with no sections[element0...element1]
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