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Python store value into list and run a grep with if/else statement?

Im creating a code in which I need to check the list ip addresses from npat variable which i need to create a loop, the code will run two things 1 is grep and 2 is lookup using whois both of this task has 2 possible output and its either match or unmatch and result should be in the list.
Q's:
store the if/else statement result into a list that is the result from grep/whois?
What pattern should I use to match route: (spaces) from whois? so far my regex pattern for this work especially matching the address but I'm having issue matching the word "route:(spaces).
Some output:
npat list = ['6.120.0.0/18', '6.120.0.0/17', '13.44.61.0/24', '13.44.62.0/24']
Whois possible output:
1.
RADB: % No entries found for the selected source(s).
RADB: route: 6.120.0.0/18
descr: name.com
origin: AS1111
notify: network#email.com
source: RADB
Here's the code:
import re, base64, os, sys
#SAMPLE STRING
teststr = """router#sh ip bgp
Status codes: s suppressed, d damped, h history, * valid, > best, i - internal,
r RIB-failure, S Stale, m multipath, b backup-path, x best-external
Origin codes: i - IGP, e - EGP, ? - incomplete
Network Next Hop Metric LocPrf Weight Path
*> 6.120.0.0/18 2.2.2.11 0 3111 2000 2485 43754 i
*> 6.120.0.0/17 2.2.2.11 0 3111 2000 2485 43754 i
*> 13.44.61.0/24 2.2.2.11 0 3111 4559 i
*> 13.44.62.0/24 2.2.2.11 0 3111 4559 i"""
##print (teststr,"\n")
#SEARCH NETWORK ENTRY*Working)
npat = re.findall(r'(?:[\d]{1,3})\.(?:[\d]{1,3})\.(?:[\d]{1,3})\.(?:[\d]{1,3})/\d+',teststr)
print ("List: \n",npat)
for ips in npat:
ipnet = ips.strip()
print ("Processing ..... ", ipnet)
fgen = "grep " +ipnet+ " /mnt/hgfs/IRR/fgen.txt"
f2pat = re.findall(ipnet,fgen)
print ("\nCommand: ",fgen)
os.system(fgen)
print ("\n NEW NPATH: ",f2pat)
if ipnet in f2pat:
flist = "Grep Found"
print ("Result ", flist)
else:
flist = "Grep Not found"
print ("Result: ",flist)
f = os.popen('whois -h whois.radb.net ' + ipnet)
who = f.read()
radbpat = re.findall(ipnet,who)
print ("\nRADB: ", who)
radbpat = re.findall(r'(?<=route: )(?:[\d]{1,3})\.(?:[\d]{1,3})\.(?:[\d]{1,3})\.(?:[\d]{1,3})/\d+',who)
print ("Radb :",radbpat)
if ipnet in radbpat:
rlist = "Found in RADB"
print ("Result ", rlist)
else:
rlist = "Not found in RADB"
print ("Result: ",rlist)
## OUTPUT
titles = ['RS-SET', 'GREP', 'RADB']
data = [titles] + list(zip(npat, flist, rlist))
for i, d in enumerate(data):
line = '|'.join(str(x).ljust(15) for x in d)
print(line)
if i == 0:
print('-' * len(line))
My target is to create a loop so I could check all the list of ip address from npat then the output shows the result from task 1 and 2??
I have created a table so my target output should be like this.
RS-SET |Grep |RADB
--------------------------------------------
xx.xx.xx.0/yy |not found |Found
My Current output is like this:
RS-SET |GREP |RADB
-----------------------------------------------
27.54.41.0/24 |G |N
223.253.0.0/20 |r |o
27.54.41.0/24 |e |t
27.54.42.0/24 |p |
27.54.43.0/24 | |f
Grep and radb output has been vertically added... my flist and rlist has only 1 data.

Fastest way to count non spacing chars in Unicode text in Python

Given the Unicode non spacing marks list - https://www.fileformat.info/info/unicode/category/Mn/list.htm
UNICODE_NSM = ['\u0300', '\u0301', '\u0302', '\u0303', '\u0304', '\u0305', '\u0306', '\u0307', '\u0308', '\u0309', '\u030A', '\u030B', '\u030C', '\u030D', '\u030E', '\u030F', '\u0310', '\u0311', '\u0312', '\u0313', '\u0314', '\u0315', '\u0316', '\u0317', '\u0318', '\u0319', '\u031A', '\u031B', '\u031C', '\u031D', '\u031E', '\u031F', '\u0320', '\u0321', '\u0322', '\u0323', '\u0324', '\u0325', '\u0326', '\u0327', '\u0328', '\u0329', '\u032A', '\u032B', '\u032C', '\u032D', '\u032E', '\u032F', '\u0330', '\u0331', '\u0332', '\u0333', '\u0334', '\u0335', '\u0336', '\u0337', '\u0338', '\u0339', '\u033A', '\u033B', '\u033C', '\u033D', '\u033E', '\u033F', '\u0340', '\u0341', '\u0342', '\u0343', '\u0344', '\u0345', '\u0346', '\u0347', '\u0348', '\u0349', '\u034A', '\u034B', '\u034C', '\u034D', '\u034E', '\u034F', '\u0350', '\u0351', '\u0352', '\u0353', '\u0354', '\u0355', '\u0356', '\u0357', '\u0358', '\u0359', '\u035A', '\u035B', '\u035C', '\u035D', '\u035E', '\u035F', '\u0360', '\u0361', '\u0362', '\u0363', '\u0364', '\u0365', '\u0366', '\u0367', '\u0368', '\u0369', '\u036A', '\u036B', '\u036C', '\u036D', '\u036E', '\u036F', '\u0483', '\u0484', '\u0485', '\u0486', '\u0487', '\u0591', '\u0592', '\u0593', '\u0594', '\u0595', '\u0596', '\u0597', '\u0598', '\u0599', '\u059A', '\u059B', '\u059C', '\u059D', '\u059E', '\u059F', '\u05A0', '\u05A1', '\u05A2', '\u05A3', '\u05A4', '\u05A5', '\u05A6', '\u05A7', '\u05A8', '\u05A9', '\u05AA', '\u05AB', '\u05AC', '\u05AD', '\u05AE', '\u05AF', '\u05B0', '\u05B1', '\u05B2', '\u05B3', '\u05B4', '\u05B5', '\u05B6', '\u05B7', '\u05B8', '\u05B9', '\u05BA', '\u05BB', '\u05BC', '\u05BD', '\u05BF', '\u05C1', '\u05C2', '\u05C4', '\u05C5', '\u05C7', '\u0610', '\u0611', '\u0612', '\u0613', '\u0614', '\u0615', '\u0616', '\u0617', '\u0618', '\u0619', '\u061A', '\u064B', '\u064C', '\u064D', '\u064E', '\u064F', '\u0650', '\u0651', '\u0652', '\u0653', '\u0654', '\u0655', '\u0656', '\u0657', '\u0658', '\u0659', '\u065A', '\u065B', '\u065C', '\u065D', '\u065E', '\u065F', '\u0670', '\u06D6', '\u06D7', '\u06D8', '\u06D9', '\u06DA', '\u06DB', '\u06DC', '\u06DF', '\u06E0', '\u06E1', '\u06E2', '\u06E3', '\u06E4', '\u06E7', '\u06E8', '\u06EA', '\u06EB', '\u06EC', '\u06ED', '\u0711', '\u0730', '\u0731', '\u0732', '\u0733', '\u0734', '\u0735', '\u0736', '\u0737', '\u0738', '\u0739', '\u073A', '\u073B', '\u073C', '\u073D', '\u073E', '\u073F', '\u0740', '\u0741', '\u0742', '\u0743', '\u0744', '\u0745', '\u0746', '\u0747', '\u0748', '\u0749', '\u074A', '\u07A6', '\u07A7', '\u07A8', '\u07A9', '\u07AA', '\u07AB', '\u07AC', '\u07AD', '\u07AE', '\u07AF', '\u07B0', '\u07EB', '\u07EC', '\u07ED', '\u07EE', '\u07EF', '\u07F0', '\u07F1', '\u07F2', '\u07F3', '\u0816', '\u0817', '\u0818', '\u0819', '\u081B', '\u081C', '\u081D', '\u081E', '\u081F', '\u0820', '\u0821', '\u0822', '\u0823', '\u0825', '\u0826', '\u0827', '\u0829', '\u082A', '\u082B', '\u082C', '\u082D', '\u0859', '\u085A', '\u085B', '\u08E4', '\u08E5', '\u08E6', '\u08E7', '\u08E8', '\u08E9', '\u08EA', '\u08EB', '\u08EC', '\u08ED', '\u08EE', '\u08EF', '\u08F0', '\u08F1', '\u08F2', '\u08F3', '\u08F4', '\u08F5', '\u08F6', '\u08F7', '\u08F8', '\u08F9', '\u08FA', '\u08FB', '\u08FC', '\u08FD', '\u08FE', '\u0900', '\u0901', '\u0902', '\u093A', '\u093C', '\u093E', '\u0941', '\u0942', '\u0943', '\u0944', '\u0945', '\u0946', '\u0947', '\u0948', '\u094D', '\u0951', '\u0952', '\u0953', '\u0954', '\u0955', '\u0956', '\u0957', '\u0962', '\u0963', '\u0981', '\u09BC', '\u09C1', '\u09C2', '\u09C3', '\u09C4', '\u09CD', '\u09E2', '\u09E3', '\u0A01', '\u0A02', '\u0A3C', '\u0A41', '\u0A42', '\u0A47', '\u0A48', '\u0A4B', '\u0A4C', '\u0A4D', '\u0A51', '\u0A70', '\u0A71', '\u0A75', '\u0A81', '\u0A82', '\u0ABC', '\u0AC1', '\u0AC2', '\u0AC3', '\u0AC4', '\u0AC5', '\u0AC7', '\u0AC8', '\u0ACD', '\u0AE2', '\u0AE3', '\u0B01', '\u0B3C', '\u0B3F', '\u0B41', '\u0B42', '\u0B43', '\u0B44', '\u0B4D', '\u0B56', '\u0B62', '\u0B63', '\u0B82', '\u0BC0', '\u0BCD', '\u0C3E', '\u0C3F', '\u0C40', '\u0C46', '\u0C47', '\u0C48', '\u0C4A', '\u0C4B', '\u0C4C', '\u0C4D', '\u0C55', '\u0C56', '\u0C62', '\u0C63', '\u0CBC', '\u0CBF', '\u0CC6', '\u0CCC', '\u0CCD', '\u0CE2', '\u0CE3', '\u0D41', '\u0D42', '\u0D43', '\u0D44', '\u0D4D', '\u0D62', '\u0D63', '\u0DCA', '\u0DD2', '\u0DD3', '\u0DD4', '\u0DD6', '\u0E31', '\u0E34', '\u0E35', '\u0E36', '\u0E37', '\u0E38', '\u0E39', '\u0E3A', '\u0E47', '\u0E48', '\u0E49', '\u0E4A', '\u0E4B', '\u0E4C', '\u0E4D', '\u0E4E', '\u0EB1', '\u0EB4', '\u0EB5', '\u0EB6', '\u0EB7', '\u0EB8', '\u0EB9', '\u0EBB', '\u0EBC', '\u0EC8', '\u0EC9', '\u0ECA', '\u0ECB', '\u0ECC', '\u0ECD', '\u0F18', '\u0F19', '\u0F35', '\u0F37', '\u0F39', '\u0F71', '\u0F72', '\u0F73', '\u0F74', '\u0F75', '\u0F76', '\u0F77', '\u0F78', '\u0F79', '\u0F7A', '\u0F7B', '\u0F7C', '\u0F7D', '\u0F7E', '\u0F80', '\u0F81', '\u0F82', '\u0F83', '\u0F84', '\u0F86', '\u0F87', '\u0F8D', '\u0F8E', '\u0F8F', '\u0F90', '\u0F91', '\u0F92', '\u0F93', '\u0F94', '\u0F95', '\u0F96', '\u0F97', '\u0F99', '\u0F9A', '\u0F9B', '\u0F9C', '\u0F9D', '\u0F9E', '\u0F9F', '\u0FA0', '\u0FA1', '\u0FA2', '\u0FA3', '\u0FA4', '\u0FA5', '\u0FA6', '\u0FA7', '\u0FA8', '\u0FA9', '\u0FAA', '\u0FAB', '\u0FAC', '\u0FAD', '\u0FAE', '\u0FAF', '\u0FB0', '\u0FB1', '\u0FB2', '\u0FB3', '\u0FB4', '\u0FB5', '\u0FB6', '\u0FB7', '\u0FB8', '\u0FB9', '\u0FBA', '\u0FBB', '\u0FBC', '\u0FC6', '\u102D', '\u102E', '\u102F', '\u1030', '\u1032', '\u1033', '\u1034', '\u1035', '\u1036', '\u1037', '\u1039', '\u103A', '\u103D', '\u103E', '\u1058', '\u1059', '\u105E', '\u105F', '\u1060', '\u1071', '\u1072', '\u1073', '\u1074', '\u1082', '\u1085', '\u1086', '\u108D', '\u109D', '\u135D', '\u135E', '\u135F', '\u1712', '\u1713', '\u1714', '\u1732', '\u1733', '\u1734', '\u1752', '\u1753', '\u1772', '\u1773', '\u17B4', '\u17B5', '\u17B7', '\u17B8', '\u17B9', '\u17BA', '\u17BB', '\u17BC', '\u17BD', '\u17C6', '\u17C9', '\u17CA', '\u17CB', '\u17CC', '\u17CD', '\u17CE', '\u17CF', '\u17D0', '\u17D1', '\u17D2', '\u17D3', '\u17DD', '\u180B', '\u180C', '\u180D', '\u18A9', '\u1920', '\u1921', '\u1922', '\u1927', '\u1928', '\u1932', '\u1939', '\u193A', '\u193B', '\u1A17', '\u1A18', '\u1A56', '\u1A58', '\u1A59', '\u1A5A', '\u1A5B', '\u1A5C', '\u1A5D', '\u1A5E', '\u1A60', '\u1A62', '\u1A65', '\u1A66', '\u1A67', '\u1A68', '\u1A69', '\u1A6A', '\u1A6B', '\u1A6C', '\u1A73', '\u1A74', '\u1A75', '\u1A76', '\u1A77', '\u1A78', '\u1A79', '\u1A7A', '\u1A7B', '\u1A7C', '\u1A7F', '\u1B00', '\u1B01', '\u1B02', '\u1B03', '\u1B34', '\u1B36', '\u1B37', '\u1B38', '\u1B39', '\u1B3A', '\u1B3C', '\u1B42', '\u1B6B', '\u1B6C', '\u1B6D', '\u1B6E', '\u1B6F', '\u1B70', '\u1B71', '\u1B72', '\u1B73', '\u1B80', '\u1B81', '\u1BA2', '\u1BA3', '\u1BA4', '\u1BA5', '\u1BA8', '\u1BA9', '\u1BAB', '\u1BE6', '\u1BE8', '\u1BE9', '\u1BED', '\u1BEF', '\u1BF0', '\u1BF1', '\u1C2C', '\u1C2D', '\u1C2E', '\u1C2F', '\u1C30', '\u1C31', '\u1C32', '\u1C33', '\u1C36', '\u1C37', '\u1CD0', '\u1CD1', '\u1CD2', '\u1CD4', '\u1CD5', '\u1CD6', '\u1CD7', '\u1CD8', '\u1CD9', '\u1CDA', '\u1CDB', '\u1CDC', '\u1CDD', '\u1CDE', '\u1CDF', '\u1CE0', '\u1CE2', '\u1CE3', '\u1CE4', '\u1CE5', '\u1CE6', '\u1CE7', '\u1CE8', '\u1CED', '\u1CF4', '\u1DC0', '\u1DC1', '\u1DC2', '\u1DC3', '\u1DC4', '\u1DC5', '\u1DC6', '\u1DC7', '\u1DC8', '\u1DC9', '\u1DCA', '\u1DCB', '\u1DCC', '\u1DCD', '\u1DCE', '\u1DCF', '\u1DD0', '\u1DD1', '\u1DD2', '\u1DD3', '\u1DD4', '\u1DD5', '\u1DD6', '\u1DD7', '\u1DD8', '\u1DD9', '\u1DDA', '\u1DDB', '\u1DDC', '\u1DDD', '\u1DDE', '\u1DDF', '\u1DE0', '\u1DE1', '\u1DE2', '\u1DE3', '\u1DE4', '\u1DE5', '\u1DE6', '\u1DFC', '\u1DFD', '\u1DFE', '\u1DFF', '\u20D0', '\u20D1', '\u20D2', '\u20D3', '\u20D4', '\u20D5', '\u20D6', '\u20D7', '\u20D8', '\u20D9', '\u20DA', '\u20DB', '\u20DC', '\u20E1', '\u20E5', '\u20E6', '\u20E7', '\u20E8', '\u20E9', '\u20EA', '\u20EB', '\u20EC', '\u20ED', '\u20EE', '\u20EF', '\u20F0', '\u2CEF', '\u2CF0', '\u2CF1', '\u2D7F', '\u2DE0', '\u2DE1', '\u2DE2', '\u2DE3', '\u2DE4', '\u2DE5', '\u2DE6', '\u2DE7', '\u2DE8', '\u2DE9', '\u2DEA', '\u2DEB', '\u2DEC', '\u2DED', '\u2DEE', '\u2DEF', '\u2DF0', '\u2DF1', '\u2DF2', '\u2DF3', '\u2DF4', '\u2DF5', '\u2DF6', '\u2DF7', '\u2DF8', '\u2DF9', '\u2DFA', '\u2DFB', '\u2DFC', '\u2DFD', '\u2DFE', '\u2DFF', '\u302A', '\u302B', '\u302C', '\u302D', '\u3099', '\u309A', '\uA66F', '\uA674', '\uA675', '\uA676', '\uA677', '\uA678', '\uA679', '\uA67A', '\uA67B', '\uA67C', '\uA67D', '\uA69F', '\uA6F0', '\uA6F1', '\uA802', '\uA806', '\uA80B', '\uA825', '\uA826', '\uA8C4', '\uA8E0', '\uA8E1', '\uA8E2', '\uA8E3', '\uA8E4', '\uA8E5', '\uA8E6', '\uA8E7', '\uA8E8', '\uA8E9', '\uA8EA', '\uA8EB', '\uA8EC', '\uA8ED', '\uA8EE', '\uA8EF', '\uA8F0', '\uA8F1', '\uA926', '\uA927', '\uA928', '\uA929', '\uA92A', '\uA92B', '\uA92C', '\uA92D', '\uA947', '\uA948', '\uA949', '\uA94A', '\uA94B', '\uA94C', '\uA94D', '\uA94E', '\uA94F', '\uA950', '\uA951', '\uA980', '\uA981', '\uA982', '\uA9B3', '\uA9B6', '\uA9B7', '\uA9B8', '\uA9B9', '\uA9BC', '\uAA29', '\uAA2A', '\uAA2B', '\uAA2C', '\uAA2D', '\uAA2E', '\uAA31', '\uAA32', '\uAA35', '\uAA36', '\uAA43', '\uAA4C', '\uAAB0', '\uAAB2', '\uAAB3', '\uAAB4', '\uAAB7', '\uAAB8', '\uAABE', '\uAABF', '\uAAC1', '\uAAEC', '\uAAED', '\uAAF6', '\uABE5', '\uABE8', '\uABED', '\uFB1E', '\uFE00', '\uFE01', '\uFE02', '\uFE03', '\uFE04', '\uFE05', '\uFE06', '\uFE07', '\uFE08', '\uFE09', '\uFE0A', '\uFE0B', '\uFE0C', '\uFE0D', '\uFE0E', '\uFE0F', '\uFE20', '\uFE21', '\uFE22', '\uFE23', '\uFE24', '\uFE25', '\uFE26', '\U000101FD', '\U00010A01', '\U00010A02', '\U00010A03', '\U00010A05', '\U00010A06', '\U00010A0C', '\U00010A0D', '\U00010A0E', '\U00010A0F', '\U00010A38', '\U00010A39', '\U00010A3A', '\U00010A3F', '\U00011001', '\U00011038', '\U00011039', '\U0001103A', '\U0001103B', '\U0001103C', '\U0001103D', '\U0001103E', '\U0001103F', '\U00011040', '\U00011041', '\U00011042', '\U00011043', '\U00011044', '\U00011045', '\U00011046', '\U00011080', '\U00011081', '\U000110B3', '\U000110B4', '\U000110B5', '\U000110B6', '\U000110B9', '\U000110BA', '\U00011100', '\U00011101', '\U00011102', '\U00011127', '\U00011128', '\U00011129', '\U0001112A', '\U0001112B', '\U0001112D', '\U0001112E', '\U0001112F', '\U00011130', '\U00011131', '\U00011132', '\U00011133', '\U00011134', '\U00011180', '\U00011181', '\U000111B6', '\U000111B7', '\U000111B8', '\U000111B9', '\U000111BA', '\U000111BB', '\U000111BC', '\U000111BD', '\U000111BE', '\U000116AB', '\U000116AD', '\U000116B0', '\U000116B1', '\U000116B2', '\U000116B3', '\U000116B4', '\U000116B5', '\U000116B7', '\U00016F8F', '\U00016F90', '\U00016F91', '\U00016F92', '\U0001D167', '\U0001D168', '\U0001D169', '\U0001D17B', '\U0001D17C', '\U0001D17D', '\U0001D17E', '\U0001D17F', '\U0001D180', '\U0001D181', '\U0001D182', '\U0001D185', '\U0001D186', '\U0001D187', '\U0001D188', '\U0001D189', '\U0001D18A', '\U0001D18B', '\U0001D1AA', '\U0001D1AB', '\U0001D1AC', '\U0001D1AD', '\U0001D242', '\U0001D243', '\U0001D244', '\U000E0100', '\U000E0101', '\U000E0102', '\U000E0103', '\U000E0104', '\U000E0105', '\U000E0106', '\U000E0107', '\U000E0108', '\U000E0109', '\U000E010A', '\U000E010B', '\U000E010C', '\U000E010D', '\U000E010E', '\U000E010F', '\U000E0110', '\U000E0111', '\U000E0112', '\U000E0113', '\U000E0114', '\U000E0115', '\U000E0116', '\U000E0117', '\U000E0118', '\U000E0119', '\U000E011A', '\U000E011B', '\U000E011C', '\U000E011D', '\U000E011E', '\U000E011F', '\U000E0120', '\U000E0121', '\U000E0122', '\U000E0123', '\U000E0124', '\U000E0125', '\U000E0126', '\U000E0127', '\U000E0128', '\U000E0129', '\U000E012A', '\U000E012B', '\uE012C', '\U000E012D', '\U000E012E', '\U000E012F', '\U000E0130', '\U000E0131', '\U000E0132', '\U000E0133', '\U000E0134', '\U000E0135', '\U000E0136', '\U000E0137', '\U000E0138', '\U000E0139', '\U000E013A', '\U000E013B', '\U000E013C', '\U000E013D', '\U000E013E', '\U000E013F', '\U000E0140', '\U000E0141', '\U000E0142', '\U000E0143', '\U000E0144', '\U000E0145', '\U000E0146', '\U000E0147', '\U000E0148', '\U000E0149', '\U000E014A', '\U000E014B', '\U000E014C', '\U000E014D', '\U000E014E', '\U000E014F', '\U000E0150', '\U000E0151', '\U000E0152', '\U000E0153', '\U000E0154', '\U000E0155', '\U000E0156', '\U000E0157', '\U000E0158', '\U000E0159', '\U000E015A', '\U000E015B', '\U000E015C', '\U000E015D', '\U000E015E', '\U000E015F', '\U000E0160', '\U000E0161', '\U000E0162', '\U000E0163', '\U000E0164', '\U000E0165', '\U000E0166', '\U000E0167', '\U000E0168', '\U000E0169', '\U000E016A', '\U000E016B', '\U000E016C', '\U000E016D', '\U000E016E', '\U000E016F', '\U000E0170', '\U000E0171', '\U000E0172', '\U000E0173', '\U000E0174', '\U000E0175', '\U000E0176', '\U000E0177', '\U000E0178', '\U000E0179', '\U000E017A', '\U000E017B', '\U000E017C', '\U000E017D', '\U000E017E', '\U000E017F', '\U000E0180', '\U000E0181', '\U000E0182', '\U000E0183', '\U000E0184', '\U000E0185', '\uE0186', '\U000E0187', '\U000E0188', '\U000E0189', '\U000E018A', '\U000E018B', '\U000E018C', '\U000E018D', '\U000E018E', '\U000E018F', '\U000E0190', '\U000E0191', '\U000E0192', '\U000E0193', '\U000E0194', '\U000E0195', '\U000E0196', '\U000E0197', '\U000E0198', '\U000E0199', '\U000E019A', '\U000E019B', '\U000E019C', '\U000E019D', '\U000E019E', '\U000E019F', '\U000E01A0', '\U000E01A1', '\U000E01A2', '\U000E01A3', '\U000E01A4', '\U000E01A5', '\U000E01A6', '\U000E01A7', '\U000E01A8', '\U000E01A9', '\U000E01AA', '\U000E01AB', '\U000E01AC', '\U000E01AD', '\U000E01AE', '\U000E01AF', '\U000E01B0', '\U000E01B1', '\U000E01B2', '\U000E01B3', '\U000E01B4', '\U000E01B5', '\U000E01B6', '\U000E01B7', '\U000E01B8', '\U000E01B9', '\U000E01BA', '\U000E01BB', '\U000E01BC', '\U000E01BD', '\U000E01BE', '\U000E01BF', '\U000E01C0', '\U000E01C1', '\U000E01C2', '\U000E01C3', '\U000E01C4', '\U000E01C5', '\U000E01C6', '\U000E01C7', '\U000E01C8', '\U000E01C9', '\U000E01CA', '\U000E01CB', '\U000E01CC', '\U000E01CD', '\U000E01CE', '\U000E01CF', '\U000E01D0', '\U000E01D1', '\U000E01D2', '\U000E01D3', '\U000E01D4', '\U000E01D5', '\U000E01D6', '\U000E01D7', '\U000E01D8', '\U000E01D9', '\U000E01DA', '\U000E01DB', '\U000E01DC', '\U000E01DD', '\U000E01DE', '\U000E01DF', '\U000E01E0', '\U000E01E1', '\U000E01E2', '\U000E01E3', '\U000E01E4', '\U000E01E5', '\U000E01E6', '\U000E01E7', '\U000E01E8', '\U000E01E9', '\U000E01EA', '\U000E01EB', '\U000E01EC', '\U000E01ED', '\U000E01EE', '\U000E01EF'];
NOTE.
Please note that we have both \U000XXXXX and \uXXXX representations here.
I want to count the Unicode input text like this Hindi string "अब यहां से कहा जाएँ हम" or just a token word like "समझा", excluding the non spacing characters.
My implementation looks like
def countNonSpacingCharString(str):
count = 0;
for char in str:
if char not in UNICODE_NSM:
count = count + 1
return count
Thanks to the help provided in the answers below I have put all together in this github. There is also a mark codepoints list ready to be used in JavaScript / Node.js - https://github.com/loretoparisi/unicode_marks

Fastest way I came up with. len was slightly faster than sum. I built a set of all combining mark types in the setup.
test.py:
import sys
from unicodedata import category
MARK_SET = set(chr(c) for c in range(sys.maxunicode + 1) if category(chr(c))[0] == 'M')
s = "अब यहां से कहा जाएँ हम"
def count_len(s):
return len([c for c in s if c not in MARK_SET])
def count_sum(s):
return sum([c not in MARK_SET for c in s])
if __name__ == '__main__':
print(len(s))
print(count_len(s))
print(count_sum(s))
Output:
22
16
16
Timings:
C:\>py -m timeit -s "from test import count_sum,s" "count_sum(s)"
50000 loops, best of 5: 4.62 usec per loop
C:\>py -m timeit -s "from test import count_len,s" "count_len(s)"
50000 loops, best of 5: 3.97 usec per loop
It's worth noting that there is a grapheme 3rd party library. grapheme.length(s) == 16, but it was much slower (118us). The full grapheme-detecting algorithm is more complicated than skipping the modifier category. Consider the combining emojis for families and skin colors.
See also Unicode Text Segmentation.

This might be a better alternative:
def countNonSpacingCharString(str):
return len([char for char in str if not(char in UNICODE_NSM)])

How about using a dictionary to look up the values and if not present, increment the count? It should be faster than the former approach because the time complexity to check the presence of the character reduces to O(1).
The implementation should look somewhat like this:
Create a dict and populate it:
lookup_dict = {}
for alpha in UNICODE_NSM:
lookup_dict[alpha] = 1
Look it up while looping through the string:
def countNonSpacingCharString(str):
count = 0;
for char in str:
start_time = time.time()
if not lookup_dict.get(char):
count = count + 1
print("--- %s seconds ---" % (time.time() - start_time))
return count

I must note that using str, as variable name in Python is bad idea, as it is name of built-in function. Anyway I would implement your function following way:
def countNonSpacingCharString(s):
return len(filter(lambda x:not x in UNICODE_NSM,s))
in Python 2
def countNonSpacingCharString(s):
return sum(1 for _ in filter(lambda x:not x in UNICODE_NSM,s))
in Python 3
Inspecting my function using dis.dis showed that it produced less bytecode than your version with count, thus suggesting it might be faster, though this need further investigation.
EDIT: I tested my code in Python 2, but not Python 3 - version for Python 3 added, using Mohammad Banisaeid answer from this topic.
EDIT 2: If you uses UNICODE_NSM only for that, you might try to use set instead of list, which should boost in operator, though again this need further investigation. For discussion about list vs set performance see this thread.

Perhaps the easiest way to do this is to use the unicodedata module. In part, because it will be more rigorously tested. Indeed, I found your list appeared to be including categories other than Mn. That is, it includes Unicode points from Mc (Mark, spacing combining) as well, but you said you only wanted to exclude Unicode points from Mn (Mark, Nonspacing).
eg.
import unicodedata
def countNonSpacingCharString(string):
category = unicodedata.category
return sum(category(char) != 'Mn' for char in string)
This appears to be about 60 times faster according to timeit.
You might get a TypeError, if your version of Python and therefore unicodedata is not up-to-date, and so not aware of recent additions to Unicode. You can get around this by installing unicodedata2 and using that instead.

From your comments it looks like you're really after counting "user perceived characters". This is a complicated process with a number of edge cases. If you can then you should to install regex on your environment (that would be micropython?). You can then do:
>>> parts = regex.findall(r'\X', 'अब यहां से कहा जाएँ हम')
>>> parts
['अ', 'ब', ' ', 'य', 'हां', ' ', 'से', ' ', 'क', 'हा', ' ', 'जा', 'एँ', ' ', 'ह', 'म']
>>> len(parts)
16
Which splits your string into "user perceived characters", and then you can work on this list of strings to get what you need.
Failing that, your current solution of just ignoring Mark code points is an 80/20 solution (gets you most of the way their for the least amount of effort). You will have to revise what your list of Unicode marks though. My tests showed that your list was missing 113 code points across all the Indo-European and Dravidian scripts in Unicode (Devanagari, Bengali, Gurmukhi, Gujarati, Oriya, Tamil, Telugu, Kannada, Malayalam, and Sinhala).
I extracted these characters by downloading and parsing: https://www.unicode.org/Public/11.0.0/ucd/UnicodeData.txt with the following code:
indian_script_range = range(0x0900, 0x0E00) # doesn't include all indic scripts (eg. Thai)
basic_multilingual_plane = range(0x0000, 0x10000)
# use the latter if you want to be more thorough and include all indic scripts and non-indic scripts
codepoint_range = indian_script_range
codepoints = []
with open('UnicodeData.txt') as f:
for line in f:
hex_string, name, category, *rest = line.strip().split(';')
codepoint_number = int(hex_string, base=16)
if (
category in ('Mn', 'Mc', 'Me')
and (
codepoint_number in codepoint_range
or name.startswith('VARIATION SELECTOR') # you seemed to want to include these
)
):
codepoints.append(chr(codepoint_number))
missing = set(codepoints) - set(UNICODE_NSM)

Mark Tolonens answer is the fastest, because it uses a set for comparison. If you have a text of length n and m whitespace-characters to compare with, then your worst-case runtime using two lists is O(nm). Using a set for the whitespace characters reduces that to O(n).
Using unicodedata.category is just nicer because it is shorter and less prone to human error.
Performance comparison
You can clearly see that the markset_count and the category_count are way faster than the generator_count and the loop_count. Also the speed of the latter two varies way more. Interestingly, the generator_count is slower than the loop_count.
The markset_count is a bit faster than the category_count. I think that is the case because looking up the category and doing the string comparison also takes a bit of time. The difference is way more clear when you only plot the two and increase the text length:
import timeit
import sys
import unicodedata
import numpy as np
UNICODE_NSM = ['\u0300', '\u0301', '\u0302', '\u0303', '\u0304', '\u0305', '\u0306', '\u0307', '\u0308', '\u0309', '\u030A', '\u030B', '\u030C', '\u030D', '\u030E', '\u030F', '\u0310', '\u0311', '\u0312', '\u0313', '\u0314', '\u0315', '\u0316', '\u0317', '\u0318', '\u0319', '\u031A', '\u031B', '\u031C', '\u031D', '\u031E', '\u031F', '\u0320', '\u0321', '\u0322', '\u0323', '\u0324', '\u0325', '\u0326', '\u0327', '\u0328', '\u0329', '\u032A', '\u032B', '\u032C', '\u032D', '\u032E', '\u032F', '\u0330', '\u0331', '\u0332', '\u0333', '\u0334', '\u0335', '\u0336', '\u0337', '\u0338', '\u0339', '\u033A', '\u033B', '\u033C', '\u033D', '\u033E', '\u033F', '\u0340', '\u0341', '\u0342', '\u0343', '\u0344', '\u0345', '\u0346', '\u0347', '\u0348', '\u0349', '\u034A', '\u034B', '\u034C', '\u034D', '\u034E', '\u034F', '\u0350', '\u0351', '\u0352', '\u0353', '\u0354', '\u0355', '\u0356', '\u0357', '\u0358', '\u0359', '\u035A', '\u035B', '\u035C', '\u035D', '\u035E', '\u035F', '\u0360', '\u0361', '\u0362', '\u0363', '\u0364', '\u0365', '\u0366', '\u0367', '\u0368', '\u0369', '\u036A', '\u036B', '\u036C', '\u036D', '\u036E', '\u036F', '\u0483', '\u0484', '\u0485', '\u0486', '\u0487', '\u0591', '\u0592', '\u0593', '\u0594', '\u0595', '\u0596', '\u0597', '\u0598', '\u0599', '\u059A', '\u059B', '\u059C', '\u059D', '\u059E', '\u059F', '\u05A0', '\u05A1', '\u05A2', '\u05A3', '\u05A4', '\u05A5', '\u05A6', '\u05A7', '\u05A8', '\u05A9', '\u05AA', '\u05AB', '\u05AC', '\u05AD', '\u05AE', '\u05AF', '\u05B0', '\u05B1', '\u05B2', '\u05B3', '\u05B4', '\u05B5', '\u05B6', '\u05B7', '\u05B8', '\u05B9', '\u05BA', '\u05BB', '\u05BC', '\u05BD', '\u05BF', '\u05C1', '\u05C2', '\u05C4', '\u05C5', '\u05C7', '\u0610', '\u0611', '\u0612', '\u0613', '\u0614', '\u0615', '\u0616', '\u0617', '\u0618', '\u0619', '\u061A', '\u064B', '\u064C', '\u064D', '\u064E', '\u064F', '\u0650', '\u0651', '\u0652', '\u0653', '\u0654', '\u0655', '\u0656', '\u0657', '\u0658', '\u0659', '\u065A', '\u065B', '\u065C', '\u065D', '\u065E', '\u065F', '\u0670', '\u06D6', '\u06D7', '\u06D8', '\u06D9', '\u06DA', '\u06DB', '\u06DC', '\u06DF', '\u06E0', '\u06E1', '\u06E2', '\u06E3', '\u06E4', '\u06E7', '\u06E8', '\u06EA', '\u06EB', '\u06EC', '\u06ED', '\u0711', '\u0730', '\u0731', '\u0732', '\u0733', '\u0734', '\u0735', '\u0736', '\u0737', '\u0738', '\u0739', '\u073A', '\u073B', '\u073C', '\u073D', '\u073E', '\u073F', '\u0740', '\u0741', '\u0742', '\u0743', '\u0744', '\u0745', '\u0746', '\u0747', '\u0748', '\u0749', '\u074A', '\u07A6', '\u07A7', '\u07A8', '\u07A9', '\u07AA', '\u07AB', '\u07AC', '\u07AD', '\u07AE', '\u07AF', '\u07B0', '\u07EB', '\u07EC', '\u07ED', '\u07EE', '\u07EF', '\u07F0', '\u07F1', '\u07F2', '\u07F3', '\u0816', '\u0817', '\u0818', '\u0819', '\u081B', '\u081C', '\u081D', '\u081E', '\u081F', '\u0820', '\u0821', '\u0822', '\u0823', '\u0825', '\u0826', '\u0827', '\u0829', '\u082A', '\u082B', '\u082C', '\u082D', '\u0859', '\u085A', '\u085B', '\u08E4', '\u08E5', '\u08E6', '\u08E7', '\u08E8', '\u08E9', '\u08EA', '\u08EB', '\u08EC', '\u08ED', '\u08EE', '\u08EF', '\u08F0', '\u08F1', '\u08F2', '\u08F3', '\u08F4', '\u08F5', '\u08F6', '\u08F7', '\u08F8', '\u08F9', '\u08FA', '\u08FB', '\u08FC', '\u08FD', '\u08FE', '\u0900', '\u0901', '\u0902', '\u093A', '\u093C', '\u093E', '\u0941', '\u0942', '\u0943', '\u0944', '\u0945', '\u0946', '\u0947', '\u0948', '\u094D', '\u0951', '\u0952', '\u0953', '\u0954', '\u0955', '\u0956', '\u0957', '\u0962', '\u0963', '\u0981', '\u09BC', '\u09C1', '\u09C2', '\u09C3', '\u09C4', '\u09CD', '\u09E2', '\u09E3', '\u0A01', '\u0A02', '\u0A3C', '\u0A41', '\u0A42', '\u0A47', '\u0A48', '\u0A4B', '\u0A4C', '\u0A4D', '\u0A51', '\u0A70', '\u0A71', '\u0A75', '\u0A81', '\u0A82', '\u0ABC', '\u0AC1', '\u0AC2', '\u0AC3', '\u0AC4', '\u0AC5', '\u0AC7', '\u0AC8', '\u0ACD', '\u0AE2', '\u0AE3', '\u0B01', '\u0B3C', '\u0B3F', '\u0B41', '\u0B42', '\u0B43', '\u0B44', '\u0B4D', '\u0B56', '\u0B62', '\u0B63', '\u0B82', '\u0BC0', '\u0BCD', '\u0C3E', '\u0C3F', '\u0C40', '\u0C46', '\u0C47', '\u0C48', '\u0C4A', '\u0C4B', '\u0C4C', '\u0C4D', '\u0C55', '\u0C56', '\u0C62', '\u0C63', '\u0CBC', '\u0CBF', '\u0CC6', '\u0CCC', '\u0CCD', '\u0CE2', '\u0CE3', '\u0D41', '\u0D42', '\u0D43', '\u0D44', '\u0D4D', '\u0D62', '\u0D63', '\u0DCA', '\u0DD2', '\u0DD3', '\u0DD4', '\u0DD6', '\u0E31', '\u0E34', '\u0E35', '\u0E36', '\u0E37', '\u0E38', '\u0E39', '\u0E3A', '\u0E47', '\u0E48', '\u0E49', '\u0E4A', '\u0E4B', '\u0E4C', '\u0E4D', '\u0E4E', '\u0EB1', '\u0EB4', '\u0EB5', '\u0EB6', '\u0EB7', '\u0EB8', '\u0EB9', '\u0EBB', '\u0EBC', '\u0EC8', '\u0EC9', '\u0ECA', '\u0ECB', '\u0ECC', '\u0ECD', '\u0F18', '\u0F19', '\u0F35', '\u0F37', '\u0F39', '\u0F71', '\u0F72', '\u0F73', '\u0F74', '\u0F75', '\u0F76', '\u0F77', '\u0F78', '\u0F79', '\u0F7A', '\u0F7B', '\u0F7C', '\u0F7D', '\u0F7E', '\u0F80', '\u0F81', '\u0F82', '\u0F83', '\u0F84', '\u0F86', '\u0F87', '\u0F8D', '\u0F8E', '\u0F8F', '\u0F90', '\u0F91', '\u0F92', '\u0F93', '\u0F94', '\u0F95', '\u0F96', '\u0F97', '\u0F99', '\u0F9A', '\u0F9B', '\u0F9C', '\u0F9D', '\u0F9E', '\u0F9F', '\u0FA0', '\u0FA1', '\u0FA2', '\u0FA3', '\u0FA4', '\u0FA5', '\u0FA6', '\u0FA7', '\u0FA8', '\u0FA9', '\u0FAA', '\u0FAB', '\u0FAC', '\u0FAD', '\u0FAE', '\u0FAF', '\u0FB0', '\u0FB1', '\u0FB2', '\u0FB3', '\u0FB4', '\u0FB5', '\u0FB6', '\u0FB7', '\u0FB8', '\u0FB9', '\u0FBA', '\u0FBB', '\u0FBC', '\u0FC6', '\u102D', '\u102E', '\u102F', '\u1030', '\u1032', '\u1033', '\u1034', '\u1035', '\u1036', '\u1037', '\u1039', '\u103A', '\u103D', '\u103E', '\u1058', '\u1059', '\u105E', '\u105F', '\u1060', '\u1071', '\u1072', '\u1073', '\u1074', '\u1082', '\u1085', '\u1086', '\u108D', '\u109D', '\u135D', '\u135E', '\u135F', '\u1712', '\u1713', '\u1714', '\u1732', '\u1733', '\u1734', '\u1752', '\u1753', '\u1772', '\u1773', '\u17B4', '\u17B5', '\u17B7', '\u17B8', '\u17B9', '\u17BA', '\u17BB', '\u17BC', '\u17BD', '\u17C6', '\u17C9', '\u17CA', '\u17CB', '\u17CC', '\u17CD', '\u17CE', '\u17CF', '\u17D0', '\u17D1', '\u17D2', '\u17D3', '\u17DD', '\u180B', '\u180C', '\u180D', '\u18A9', '\u1920', '\u1921', '\u1922', '\u1927', '\u1928', '\u1932', '\u1939', '\u193A', '\u193B', '\u1A17', '\u1A18', '\u1A56', '\u1A58', '\u1A59', '\u1A5A', '\u1A5B', '\u1A5C', '\u1A5D', '\u1A5E', '\u1A60', '\u1A62', '\u1A65', '\u1A66', '\u1A67', '\u1A68', '\u1A69', '\u1A6A', '\u1A6B', '\u1A6C', '\u1A73', '\u1A74', '\u1A75', '\u1A76', '\u1A77', '\u1A78', '\u1A79', '\u1A7A', '\u1A7B', '\u1A7C', '\u1A7F', '\u1B00', '\u1B01', '\u1B02', '\u1B03', '\u1B34', '\u1B36', '\u1B37', '\u1B38', '\u1B39', '\u1B3A', '\u1B3C', '\u1B42', '\u1B6B', '\u1B6C', '\u1B6D', '\u1B6E', '\u1B6F', '\u1B70', '\u1B71', '\u1B72', '\u1B73', '\u1B80', '\u1B81', '\u1BA2', '\u1BA3', '\u1BA4', '\u1BA5', '\u1BA8', '\u1BA9', '\u1BAB', '\u1BE6', '\u1BE8', '\u1BE9', '\u1BED', '\u1BEF', '\u1BF0', '\u1BF1', '\u1C2C', '\u1C2D', '\u1C2E', '\u1C2F', '\u1C30', '\u1C31', '\u1C32', '\u1C33', '\u1C36', '\u1C37', '\u1CD0', '\u1CD1', '\u1CD2', '\u1CD4', '\u1CD5', '\u1CD6', '\u1CD7', '\u1CD8', '\u1CD9', '\u1CDA', '\u1CDB', '\u1CDC', '\u1CDD', '\u1CDE', '\u1CDF', '\u1CE0', '\u1CE2', '\u1CE3', '\u1CE4', '\u1CE5', '\u1CE6', '\u1CE7', '\u1CE8', '\u1CED', '\u1CF4', '\u1DC0', '\u1DC1', '\u1DC2', '\u1DC3', '\u1DC4', '\u1DC5', '\u1DC6', '\u1DC7', '\u1DC8', '\u1DC9', '\u1DCA', '\u1DCB', '\u1DCC', '\u1DCD', '\u1DCE', '\u1DCF', '\u1DD0', '\u1DD1', '\u1DD2', '\u1DD3', '\u1DD4', '\u1DD5', '\u1DD6', '\u1DD7', '\u1DD8', '\u1DD9', '\u1DDA', '\u1DDB', '\u1DDC', '\u1DDD', '\u1DDE', '\u1DDF', '\u1DE0', '\u1DE1', '\u1DE2', '\u1DE3', '\u1DE4', '\u1DE5', '\u1DE6', '\u1DFC', '\u1DFD', '\u1DFE', '\u1DFF', '\u20D0', '\u20D1', '\u20D2', '\u20D3', '\u20D4', '\u20D5', '\u20D6', '\u20D7', '\u20D8', '\u20D9', '\u20DA', '\u20DB', '\u20DC', '\u20E1', '\u20E5', '\u20E6', '\u20E7', '\u20E8', '\u20E9', '\u20EA', '\u20EB', '\u20EC', '\u20ED', '\u20EE', '\u20EF', '\u20F0', '\u2CEF', '\u2CF0', '\u2CF1', '\u2D7F', '\u2DE0', '\u2DE1', '\u2DE2', '\u2DE3', '\u2DE4', '\u2DE5', '\u2DE6', '\u2DE7', '\u2DE8', '\u2DE9', '\u2DEA', '\u2DEB', '\u2DEC', '\u2DED', '\u2DEE', '\u2DEF', '\u2DF0', '\u2DF1', '\u2DF2', '\u2DF3', '\u2DF4', '\u2DF5', '\u2DF6', '\u2DF7', '\u2DF8', '\u2DF9', '\u2DFA', '\u2DFB', '\u2DFC', '\u2DFD', '\u2DFE', '\u2DFF', '\u302A', '\u302B', '\u302C', '\u302D', '\u3099', '\u309A', '\uA66F', '\uA674', '\uA675', '\uA676', '\uA677', '\uA678', '\uA679', '\uA67A', '\uA67B', '\uA67C', '\uA67D', '\uA69F', '\uA6F0', '\uA6F1', '\uA802', '\uA806', '\uA80B', '\uA825', '\uA826', '\uA8C4', '\uA8E0', '\uA8E1', '\uA8E2', '\uA8E3', '\uA8E4', '\uA8E5', '\uA8E6', '\uA8E7', '\uA8E8', '\uA8E9', '\uA8EA', '\uA8EB', '\uA8EC', '\uA8ED', '\uA8EE', '\uA8EF', '\uA8F0', '\uA8F1', '\uA926', '\uA927', '\uA928', '\uA929', '\uA92A', '\uA92B', '\uA92C', '\uA92D', '\uA947', '\uA948', '\uA949', '\uA94A', '\uA94B', '\uA94C', '\uA94D', '\uA94E', '\uA94F', '\uA950', '\uA951', '\uA980', '\uA981', '\uA982', '\uA9B3', '\uA9B6', '\uA9B7', '\uA9B8', '\uA9B9', '\uA9BC', '\uAA29', '\uAA2A', '\uAA2B', '\uAA2C', '\uAA2D', '\uAA2E', '\uAA31', '\uAA32', '\uAA35', '\uAA36', '\uAA43', '\uAA4C', '\uAAB0', '\uAAB2', '\uAAB3', '\uAAB4', '\uAAB7', '\uAAB8', '\uAABE', '\uAABF', '\uAAC1', '\uAAEC', '\uAAED', '\uAAF6', '\uABE5', '\uABE8', '\uABED', '\uFB1E', '\uFE00', '\uFE01', '\uFE02', '\uFE03', '\uFE04', '\uFE05', '\uFE06', '\uFE07', '\uFE08', '\uFE09', '\uFE0A', '\uFE0B', '\uFE0C', '\uFE0D', '\uFE0E', '\uFE0F', '\uFE20', '\uFE21', '\uFE22', '\uFE23', '\uFE24', '\uFE25', '\uFE26', '\U000101FD', '\U00010A01', '\U00010A02', '\U00010A03', '\U00010A05', '\U00010A06', '\U00010A0C', '\U00010A0D', '\U00010A0E', '\U00010A0F', '\U00010A38', '\U00010A39', '\U00010A3A', '\U00010A3F', '\U00011001', '\U00011038', '\U00011039', '\U0001103A', '\U0001103B', '\U0001103C', '\U0001103D', '\U0001103E', '\U0001103F', '\U00011040', '\U00011041', '\U00011042', '\U00011043', '\U00011044', '\U00011045', '\U00011046', '\U00011080', '\U00011081', '\U000110B3', '\U000110B4', '\U000110B5', '\U000110B6', '\U000110B9', '\U000110BA', '\U00011100', '\U00011101', '\U00011102', '\U00011127', '\U00011128', '\U00011129', '\U0001112A', '\U0001112B', '\U0001112D', '\U0001112E', '\U0001112F', '\U00011130', '\U00011131', '\U00011132', '\U00011133', '\U00011134', '\U00011180', '\U00011181', '\U000111B6', '\U000111B7', '\U000111B8', '\U000111B9', '\U000111BA', '\U000111BB', '\U000111BC', '\U000111BD', '\U000111BE', '\U000116AB', '\U000116AD', '\U000116B0', '\U000116B1', '\U000116B2', '\U000116B3', '\U000116B4', '\U000116B5', '\U000116B7', '\U00016F8F', '\U00016F90', '\U00016F91', '\U00016F92', '\U0001D167', '\U0001D168', '\U0001D169', '\U0001D17B', '\U0001D17C', '\U0001D17D', '\U0001D17E', '\U0001D17F', '\U0001D180', '\U0001D181', '\U0001D182', '\U0001D185', '\U0001D186', '\U0001D187', '\U0001D188', '\U0001D189', '\U0001D18A', '\U0001D18B', '\U0001D1AA', '\U0001D1AB', '\U0001D1AC', '\U0001D1AD', '\U0001D242', '\U0001D243', '\U0001D244', '\U000E0100', '\U000E0101', '\U000E0102', '\U000E0103', '\U000E0104', '\U000E0105', '\U000E0106', '\U000E0107', '\U000E0108', '\U000E0109', '\U000E010A', '\U000E010B', '\U000E010C', '\U000E010D', '\U000E010E', '\U000E010F', '\U000E0110', '\U000E0111', '\U000E0112', '\U000E0113', '\U000E0114', '\U000E0115', '\U000E0116', '\U000E0117', '\U000E0118', '\U000E0119', '\U000E011A', '\U000E011B', '\U000E011C', '\U000E011D', '\U000E011E', '\U000E011F', '\U000E0120', '\U000E0121', '\U000E0122', '\U000E0123', '\U000E0124', '\U000E0125', '\U000E0126', '\U000E0127', '\U000E0128', '\U000E0129', '\U000E012A', '\U000E012B', '\uE012C', '\U000E012D', '\U000E012E', '\U000E012F', '\U000E0130', '\U000E0131', '\U000E0132', '\U000E0133', '\U000E0134', '\U000E0135', '\U000E0136', '\U000E0137', '\U000E0138', '\U000E0139', '\U000E013A', '\U000E013B', '\U000E013C', '\U000E013D', '\U000E013E', '\U000E013F', '\U000E0140', '\U000E0141', '\U000E0142', '\U000E0143', '\U000E0144', '\U000E0145', '\U000E0146', '\U000E0147', '\U000E0148', '\U000E0149', '\U000E014A', '\U000E014B', '\U000E014C', '\U000E014D', '\U000E014E', '\U000E014F', '\U000E0150', '\U000E0151', '\U000E0152', '\U000E0153', '\U000E0154', '\U000E0155', '\U000E0156', '\U000E0157', '\U000E0158', '\U000E0159', '\U000E015A', '\U000E015B', '\U000E015C', '\U000E015D', '\U000E015E', '\U000E015F', '\U000E0160', '\U000E0161', '\U000E0162', '\U000E0163', '\U000E0164', '\U000E0165', '\U000E0166', '\U000E0167', '\U000E0168', '\U000E0169', '\U000E016A', '\U000E016B', '\U000E016C', '\U000E016D', '\U000E016E', '\U000E016F', '\U000E0170', '\U000E0171', '\U000E0172', '\U000E0173', '\U000E0174', '\U000E0175', '\U000E0176', '\U000E0177', '\U000E0178', '\U000E0179', '\U000E017A', '\U000E017B', '\U000E017C', '\U000E017D', '\U000E017E', '\U000E017F', '\U000E0180', '\U000E0181', '\U000E0182', '\U000E0183', '\U000E0184', '\U000E0185', '\uE0186', '\U000E0187', '\U000E0188', '\U000E0189', '\U000E018A', '\U000E018B', '\U000E018C', '\U000E018D', '\U000E018E', '\U000E018F', '\U000E0190', '\U000E0191', '\U000E0192', '\U000E0193', '\U000E0194', '\U000E0195', '\U000E0196', '\U000E0197', '\U000E0198', '\U000E0199', '\U000E019A', '\U000E019B', '\U000E019C', '\U000E019D', '\U000E019E', '\U000E019F', '\U000E01A0', '\U000E01A1', '\U000E01A2', '\U000E01A3', '\U000E01A4', '\U000E01A5', '\U000E01A6', '\U000E01A7', '\U000E01A8', '\U000E01A9', '\U000E01AA', '\U000E01AB', '\U000E01AC', '\U000E01AD', '\U000E01AE', '\U000E01AF', '\U000E01B0', '\U000E01B1', '\U000E01B2', '\U000E01B3', '\U000E01B4', '\U000E01B5', '\U000E01B6', '\U000E01B7', '\U000E01B8', '\U000E01B9', '\U000E01BA', '\U000E01BB', '\U000E01BC', '\U000E01BD', '\U000E01BE', '\U000E01BF', '\U000E01C0', '\U000E01C1', '\U000E01C2', '\U000E01C3', '\U000E01C4', '\U000E01C5', '\U000E01C6', '\U000E01C7', '\U000E01C8', '\U000E01C9', '\U000E01CA', '\U000E01CB', '\U000E01CC', '\U000E01CD', '\U000E01CE', '\U000E01CF', '\U000E01D0', '\U000E01D1', '\U000E01D2', '\U000E01D3', '\U000E01D4', '\U000E01D5', '\U000E01D6', '\U000E01D7', '\U000E01D8', '\U000E01D9', '\U000E01DA', '\U000E01DB', '\U000E01DC', '\U000E01DD', '\U000E01DE', '\U000E01DF', '\U000E01E0', '\U000E01E1', '\U000E01E2', '\U000E01E3', '\U000E01E4', '\U000E01E5', '\U000E01E6', '\U000E01E7', '\U000E01E8', '\U000E01E9', '\U000E01EA', '\U000E01EB', '\U000E01EC', '\U000E01ED', '\U000E01EE', '\U000E01EF']
MARK_SET = set(chr(c) for c in range(sys.maxunicode + 1) if unicodedata.category(chr(c))[0] == 'M')
print('len(UNICODE_NSM) = {}'.format(len(UNICODE_NSM)))
print('len(MARK_SET) = {}'.format(len(MARK_SET)))
filepath = "UnicodeData.txt"
with open(filepath) as f:
text = f.read()
text = text[:1000]
def main():
ground_truth = loop_count(text)
functions = [(loop_count, 'loop_count'),
(generator_count, 'generator_count'),
(category_count, 'category_count'),
(markset_count, 'markset_count'),
]
functions = functions[::-1]
duration_list = {}
for func, name in functions:
is_correct = func(text) == ground_truth
durations = timeit.repeat(lambda: func(text), repeat=500, number=3)
if is_correct:
correctness = 'correct'
else:
correctness = 'NOT correct'
duration_list[name] = durations
print('{func:<20}: {correctness}, '
'min: {min:0.3f}s, mean: {mean:0.3f}s, max: {max:0.3f}s'
.format(func=name,
correctness=correctness,
min=min(durations),
mean=np.mean(durations),
max=max(durations),
))
create_boxplot(duration_list)
def create_boxplot(duration_list):
import seaborn as sns
import matplotlib.pyplot as plt
import operator
plt.figure(num=None, figsize=(8, 4), dpi=300,
facecolor='w', edgecolor='k')
sns.set(style="whitegrid")
sorted_keys, sorted_vals = zip(*sorted(duration_list.items(), key=operator.itemgetter(1)))
flierprops = dict(markerfacecolor='0.75', markersize=1,
linestyle='none')
ax = sns.boxplot(data=sorted_vals, width=.3, orient='h',
flierprops=flierprops,)
ax.set(xlabel="Time in s", ylabel="")
plt.yticks(plt.yticks()[0], sorted_keys)
plt.tight_layout()
plt.savefig("output.png")
def generator_count(text):
return sum(1 for char in text if char not in UNICODE_NSM)
def loop_count(text):
# 1769137
count = 0
for char in text:
if char not in UNICODE_NSM:
count += 1
return count
def markset_count(text):
return sum(char not in MARK_SET for char in text)
def category_count(text):
return sum(unicodedata.category(char) != 'Mn' for char in text)
if __name__ == '__main__':
main()

How to replace some special characters from user input for different Python platforms

I need to replace some special characters from user input for different platform (i.e. Linux and Windows) using Python. Here is my code:
if request.method == 'POST':
rname1 = request.POST.get('react')
Here I am getting the user input by post method. I need to the following characters to remove from the user input (if there is any).
1- Escape or filter special characters for windows, ( ) < > * ‘ = ? ; [ ] ^ ~ ! . ” % # / \ : + , `
2- Escape or filter special characters for Linux, { } ( ) < > * ‘ = ? ; [ ] $ – # ~ ! . ” % / \ : + , `
The special characters are given above. Here I need to remove for both Linux and Windows.

Python strings have a built in method translate for substitution/deletion of characters. You need to build a translation table and then call the function.
import sys
if "win" in sys.platform:
special = """( ) < > * ‘ = ? ; [ ] ^ ~ ! . ” % # / \ : + , `""".split()
else:
special = """{ } ( ) < > * ‘ = ? ; [ ] $ – # ~ ! . ” % / \ : + , `""".split()
trans_dict = {character: None for character in special}
trans_table = str.maketrans(trans_dict)
print("Lo+=r?e~~m ipsum dol;or sit!! amet, consectet..ur ad%".translate(trans_table))
Will print Lorem ipsum dolor sit amet consectetur ad.
If you want to use a replacement character instead of deleting, then replace None above with the character. You can build a translation table with specific substitutions, `{"a": "m", "b": "n", ...}
Edit: The above snippet is indeed in Python3. In Python2 (TiO) it's easier to delete characters:
>>> import sys
>>> import string
>>> if "win" in sys.platform:
... special = """()<>*'=?;[]^~!%#/\:=,`"""
... else:
... special = """{}()<>*'=?;[]$-#~!."%/\:+"""
...
>>> s = "Lo+r?e~~/\#<>m ips()u;m"
>>> string.translate(s, None, special)
'Lorem ipsum'
Note that I've substituted ‘ with ' and similarly replaced ” with " because I think you're only dealing with ascii strings.

Extracting numbers in text file

I have a text file which came from excel. I dont know how to take five digits after a specific character.
I want to take only five digits after #ACA in a text file.
my text is like:
ERROR_MESSAGE
(((#ACA16018)|(#ACA16019))&(#AQV71767='')&(#AQV71765='2'))?1:((#AQV71765='4')?1:((#AQV71767$'')?(((#AQV71765='1')|(#AQV71765='3'))?1:'Hasar veya Lehe Hukuk seçebilirsiniz'):'Rücu sıra numarasını yazıp Hasar veya Lehe Hukuk seçebilirsiniz'))
Rücu Oranı Girilmesi Zorunludur...'
#ACA17660
#ACA16560
#ACA15623
#ACA17804
BU ALANI BOŞ GEÇEMEZSİNİZ.EKSPER RAPORU GELMEDEN DY YE GERİ GÖNDEREMEZSİNİZ. PERT İHBARI VARSA PERT ÇALINMA OPERASYONU AKTİVİTESİ OLUŞTURULMALIDIR.
(#TSC[T008UNSMAS;FIRM_CODE=2 AND UNIT_TYPE='SG' AND UNIT_NO=#AQV71830]>0)?1:'Girdiğiniz değer fihristte yoktur'
#ACA17602
#ACA17604
#ACA56169
BU ALANI BOŞ GEÇEMEZSİNİZ
#ACA17606
#ACA17608
(#AQV71835='')?'Boş geçilemez':1
Lütfen Gönderilecek Kişinin Mail Adresini Giriniz ! '
LÜTFEN RED NEDENİNİ GİRİNİZ.
EKSİK BİLGİ / BELGE ALANINA GİRMİŞ OLDUĞUNUZ DEĞER YANLIŞ VEYA GEÇERŞİZDİR!!! LÜTFEN KONTROL EDİP TEKRAR DENEYİNİZ.'
BU ALAN BOŞ GEÇİLEMEZ. ÖDEME YAPILMADAN EK ÖDEME SÜRECİNİ BAŞLATAMAZSINIZ.
ONAYLANDI VE REDDEDİLDİ SEÇENEKLERİNİ KULLANAMAZSINIZ
BU ALAN BOŞ GEÇİLEMEZ.EVRAKLARINIZI , VARSA EKSPER RAPORUNU VE MUALLAĞI KONTROL EDİNİZ.
Muallak Tutarını kontrol ediniz.
'OTO BRANŞINDA REDDEDİLDİ NEDENİ SEÇMELİSİNİZ'
'OTODIŞI BRANŞINDA REDDEDİLDİ NEDENİ SEÇMELİSİNİZ'
(#AQV70003$'')?((#TSC[T001HASIHB;FIRM_CODE=#FP10100 AND COMPANY_CODE=2 AND CLAIM_NO=#AQV70003]$0)?1:'Bu dosya sistemde bulunmamaktadır'):'Bu alan boş geçilemez'
(#AQV70503='')?'Bu alan boş geçilemez.':((#ACA18635=1)?1:'Mağdura ait uygun kriterli ödeme kaydı mevcut değildir.')
(#AQV71809=0)?'Boş geçilemez':1
(#FD101AQV71904_AFDS<0)?'Tarih bugünün tarihinden büyük olamaz
I want to take every 5 digits which comes after #ACA, so:
16018, 16019, 17660, etc...

grep -oP '#ACA\K[0-9]{5}' file.txt
#ACA\K will match #ACA but not printed as part of output
[0-9]{5} five digits following #ACA
If variable number of digits are needed, use
grep -oP '#ACA\K[0-9]+' file.txt

If you don't know or don't like regular expressions, you can do this, although the code is a bit longer :
if __name__ == '__main__':
pattern = '#ACA'
filename = 'yourfile.txt'
res = list()
with open(filename, 'rb') as f: # open 'yourfile.txt' in byte-reading mode
for line in f: # for each line in the file
for s in line.split(pattern)[1:]: # split the line on '#ACA'
try:
nb = int(s[:5]) # take the first 5 characters after as an int
res.append(nb) # add it to the list of numbers we found
except (NameError, ValueError): # if conversion fails, that wasn't an int
pass
print res # if you want them in the same order as in the file
print sorted(res) # if you want them in ascending order

This should do it
import re
print(re.findall("#ACA(\d+)",str_var))
If you have the whole text in the variable str_var
Output:
['16018', '16019', '17660', '16560', '15623', '17804', '17602', '17604', '56169', '17606', '17608', '18635']

re.findall(r'#ACA(\d{5})', str_var)

[x[:5] for x in content.split("#ACA")[1:]]

PowerShell solution:
$contet = Get-Content -Raw 'your_file'
$match = [regex]::Matches($contet, '#ACA(\d{5})')
$match | ForEach-Object {
$_.Groups[1].Value
}
Output:
16018
16019
17660
16560
15623
17804
17602
17604
56169
17606
17608
18635

Pyparsing error when evaluating WFF logic expressions?

I'm new to Python and pyparsing, and I'm making a logic expression evaluator.
The formula must be a WFF. The BNF of WFF is:
<alpha set> ::= p | q | r | s | t | u | ...
(the arbitrary finite set of propositional variables)
<form> ::= <alpha set> | ¬<form> | (<form>V<form>) | (<form>^<form>)
| (<form> -> <form>) | (<form> <-> <form>)
My code is:
'''
Created on 17/02/2012
#author: Juanjo
'''
from pyparsing import *
from string import lowercase
def fbf():
atom = Word(lowercase, max=1) #aphabet
op = oneOf('^ V => <=>') #Operators
identOp = oneOf('( [ {')
identCl = oneOf(') ] }')
form = Forward() #Iniciar de manera recursiva
#Grammar:
form << ( (Group(Literal('~') + form)) | ( Group(identOp + form + op + form + identCl) ) | ( Group(identOp + form + identCl) ) | (atom) )
return form
entrada = raw_input("Input please: ") #userinput
print fbf().parseString(entrada)
The problem is when I use these expressions: a^b and aVb.
The parser should return an error, but there's no error; instead it returns a. Actually, any symbol after a will be ignored.
The WFF version of those forms are: (a^b) and (aVb)
Both work correctly. I think the problem is in the atom definition.
What am I doing wrong?

By default parseString will just parse the beginning of the string.
You can force it to parse the entire string by changing the code to:
print fbf().parseString(entrada, parseAll=True)
Alternatively, you can end the grammar with the StringEnd() token - see the documentation under parseString in http://packages.python.org/pyparsing/ for more details.