Script: newline caused append behavior (undesirable).

README.md

@@ -23,6 +23,9 @@ containing the user's entire cryptocurrency transaction history, the software wi
 
 *The tracking isn't pooled by `ticker`.  Rather, it's tracked at the account/wallet level.
 
+There is a helper Python script at the root of the repo that will assist you in sanitizing your CSV file
+so it can be successfully imported into `cryptools`.
+
 ---
 
 ### Features
@@ -52,7 +55,7 @@ when appreciated cryptocurrency was used to make a tax-deductible charitable con
 * Precision is limited to eight decimal places.  Additional digits will be stripped during
 import and may cause unintended rounding issues.
 
-* Microsoft Excel.  Don't let this cause you to bang your head against a wall.
+* Microsoft Excel.  Don't let Excel cause you to bang your head against a wall.
 Picture this scenario.  You keep your transactions for your input file in a Google Sheet,
 and you're meticulous about making sure it's perfect.
 You then download it as a CSV file and import it into `cryptools`.
@@ -60,10 +63,12 @@ It works perfectly, and you have all your reports.
 Then you realize you'd like to quickly change a memo and re-run the reports, so you open the CSV file in Excel and edit it.
 Then you import it into `cryptools` again and the program panics!
 What happened is most likely that Excel changed the rounding of your precise decimals underneath you!
-As a result, it appears your input file has been clearly incorrectly prepared 
-because it appears that you're spending more coins than you actually owned at that time.
+Depending on the rounding, `cryptools` may think your input file has been incorrectly prepared
+because you've supposedly spent more coins than you actually owned at that time.
 `Cryptools` does not let you spend coins you don't own, and it will exit upon finding such a condition.
-The program is right, and your data is right, but Excel modified your data, and it can be infuriating when the program crashes for "no reason."
+The program is right, and your data is right, but Excel modified your data, so the program crashed for "no reason."
+The solution is to have Excel already open, then in the ribbon's Data tab, you'll import your CSV file "From Text."
+You'll choose Delimited, and Comma, and then highlight every column and choose Text as the data type.
 
 ## Installation
 

clean_input_csv.py

@@ -0,0 +1,160 @@
+#!/usr/bin/env python3
+
+## Purpose:	Allows user to keep additional data in their CSV Input File to increase its usefulness and
+##			enhance readability, yet be able to properly format it prior to importing into `cryptools`.
+##			e.g.:
+##				-Keep an additional first column for flagging/noting important transactions
+##				-Keep additional columns for tracking a running balance
+##				-Rows beneath transactions for life-to-date totals and other calculations and notes
+##				-Ability to use number formatting with parenthesis for negative numbers and commas
+##					-This script will change (1,000.00) to 1000.00
+##
+##			If a column doesn't have a header, this script will exclude it from the sanitized output.
+##			Similarly, this script will exclude transaction rows missing data in either of the first
+##			two fields of the row.
+
+## Usage:
+
+# 1.  Export/Save crypto activity as csv
+# 2.  Move the csv file to your desired directory
+# 3.  Rename file to <unedited>.csv (see variable below)
+# 4.  Build/run this file in an editor or on command line (from same directory), creating the input file
+# 5.  Import the input file into cryptools
+
+import csv
+import re
+import os
+
+unedited = "DigiTrnx.csv" 	# To be replaced with a launch arg, presumably	
+
+stage1 = "stage1.csv"
+
+##		First, writes all header rows.  Then attempts to write all transaction rows.
+##      In the transaction rows, if it finds blank/empty transaction date or proceeds fields,
+##		it discards the row.
+
+##		This allows notes/sums/calculations/etc under the transaction rows to be discarded
+
+with open(unedited) as fin, open(stage1, 'a') as fout:
+	rdr = csv.reader(fin) 	
+	wtr = csv.writer(fout)
+	header = next(rdr)
+	header2 = next(rdr)
+	header3 = next(rdr)
+	header4 = next(rdr)		
+	wtr.writerow(header)
+	wtr.writerow(header2)
+	wtr.writerow(header3)
+	wtr.writerow(header4)
+
+	for row in rdr:
+		if row[0] == "" or row[1] == "":
+			pass
+		else:
+			wtr.writerow(row)
+
+stage2 = "stage2.csv"
+
+##		Iterates over the fields in the first header row to search for empty/blank cells.
+##      Keeps a list of every column index that does contain data, and disregards all the
+##		indices for columns with a blank.
+
+##		Using the indicies of valid columns, writes a new CSV file using only valid columns.
+
+##		This is useful when the input file is also used to manually keep a running tally or
+##		columns with additional notes, but which must be discarded to prepare a proper
+##		CSV input file.
+
+with open(stage1) as fin, open(stage2, 'a') as fout:
+	rdr = csv.reader(fin) 	
+	wtr = csv.writer(fout)
+	header = next(rdr)
+	header2 = next(rdr)
+	header3 = next(rdr)
+	header4 = next(rdr)		
+
+	colListKept = []
+
+	for col in header:
+		if col == "":
+			pass
+		else:
+			colListKept.append(header.index(col))
+
+	output = [v for (i,v) in enumerate(header) if i in colListKept]
+	wtr.writerow(output)
+
+	output = [v for (i,v) in enumerate(header2) if i in colListKept]
+	wtr.writerow(output)
+
+	output = [v for (i,v) in enumerate(header3) if i in colListKept]
+	wtr.writerow(output)
+
+	output = [v for (i,v) in enumerate(header4) if i in colListKept]
+	wtr.writerow(output)
+
+	for row in rdr:
+		output = [v for (i,v) in enumerate(row) if i in colListKept]
+		wtr.writerow(output)
+
+
+stage3 = "InputFile-pycleaned.csv"
+
+##		Performs final formatting changes to ensure values can be successfully parsed.
+##	 	Numbers must have commas removed.  Negative numbers must have parentheses replaced
+##		with a minus sign.  Could also be used to substitute the date separation character.
+
+##	 	i.e., (1.01) -> -1.01  (1,000.00) -> -1000.00
+
+with open(stage2) as fin, open(stage3, 'w') as fout:
+	rdr = csv.reader(fin, quoting=csv.QUOTE_ALL) 	
+	wtr = csv.writer(fout)
+	
+	header = next(rdr)
+	header2 = next(rdr)
+	header3 = next(rdr)
+	header4 = next(rdr)
+	wtr.writerow(header)
+	wtr.writerow(header2)
+	wtr.writerow(header3)
+	wtr.writerow(header4)
+
+	for row in rdr:
+		listRow = []
+		for field in row:
+			fieldStr = str(field) # cast as string, just so there's no funny business
+			try:
+				# Handles negative numbers
+				if fieldStr[0] == "(":
+					fieldStr = fieldStr.replace('(','-').replace(')', '').replace(',', '')
+					listRow.append(fieldStr)
+					continue
+
+			# Uncomment the below and modify as necessary if you want to change date formatting
+				# elif re.search(r'\d\d-\d\d-\d\d',fieldStr):#	Find dates and change formatting
+				# 	fieldStr = fieldStr.replace('-', '/')
+				# 	listRow.append(fieldStr)
+				# 	continue
+
+				# Handle commas in remaining fields
+				else: 
+					try:
+						# if you remove commas from a string and are able to convert to float...
+						fieldStr_test = fieldStr.replace(',', '')
+						fieldStr_float = float(fieldStr_test)
+						# then it is definitely a positive number, so remove the comma.
+						fieldStr = fieldStr.replace(',', '')
+						listRow.append(fieldStr)
+						continue
+					except: # If the 'try' block fails, it's a memo, not a number, so leave any commas
+						listRow.append(fieldStr)
+						continue
+			except: # If the `try` block fails, it's a blank/empty string
+				listRow.append(fieldStr)
+				continue
+		wtr.writerow(listRow)
+
+os.remove(stage1)
+os.remove(stage2)
+
+print("Input file ready")