I recently had to write a challenge for a company that was to merge 3 CSV files into one based on the first attribute of each (the attributes were repeating in all files).
I wrote the code and sent it to them, but they said it took 2 minutes to run. That was funny because it ran for 10 seconds on my machine. My machine had the same processor, 16GB of RAM, and had an SSD as well. Very similar environments.
I tried optimising it and resubmitted it. This time they said they ran it on an Ubuntu machine and got 11 seconds, while the code ran for 100 seconds on the Windows 10 still.
Another peculiar thing was that when I tried profiling it with the Profile module, it went on forever, had to terminate after 450 seconds. I moved to cProfiler and it recorded it for 7 seconds.
EDIT: The exact formulation of the problem is
Write a console program to merge the files provided in a timely and
efficient manner. File paths should be supplied as arguments so that
the program can be evaluated on different data sets. The merged file
should be saved as CSV; use the id column as the unique key for
merging; the program should do any necessary data cleaning and error
checking.
Feel free to use any language you’re comfortable with – only
restriction is no external libraries as this defeats the purpose of
the test. If the language provides CSV parsing libraries (like
Python), please avoid using them as well as this is a part of the
test.
Without further ado here's the code:
#!/usr/bin/python3
import sys
from multiprocessing import Pool
HEADERS = ['id']
def csv_tuple_quotes_valid(a_tuple):
"""
checks if a quotes in each attribute of a entry (i.e. a tuple) agree with the csv format
returns True or False
"""
for attribute in a_tuple:
in_quotes = False
attr_len = len(attribute)
skip_next = False
for i in range(0, attr_len):
if not skip_next and attribute[i] == '\"':
if i < attr_len - 1 and attribute[i + 1] == '\"':
skip_next = True
continue
elif i == 0 or i == attr_len - 1:
in_quotes = not in_quotes
else:
return False
else:
skip_next = False
if in_quotes:
return False
return True
def check_and_parse_potential_tuple(to_parse):
"""
receives a string and returns an array of the attributes of the csv line
if the string was not a valid csv line, then returns False
"""
a_tuple = []
attribute_start_index = 0
to_parse_len = len(to_parse)
in_quotes = False
i = 0
#iterate through the string (line from the csv)
while i < to_parse_len:
current_char = to_parse[i]
#this works the following way: if we meet a quote ("), it must be in one
#of five cases: "" | ", | ," | "\0 | (start_of_string)"
#in case we are inside a quoted attribute (i.e. "123"), then commas are ignored
#the following code also extracts the tuples' attributes
if current_char == '\"':
if i == 0 or (to_parse[i - 1] == ',' and not in_quotes): # (start_of_string)" and ," case
#not including the quote in the next attr
attribute_start_index = i + 1
#starting a quoted attr
in_quotes = True
elif i + 1 < to_parse_len:
if to_parse[i + 1] == '\"': # "" case
i += 1 #skip the next " because it is part of a ""
elif to_parse[i + 1] == ',' and in_quotes: # ", case
a_tuple.append(to_parse[attribute_start_index:i].strip())
#not including the quote and comma in the next attr
attribute_start_index = i + 2
in_quotes = False #the quoted attr has ended
#skip the next comma - we know what it is for
i += 1
else:
#since we cannot have a random " in the middle of an attr
return False
elif i == to_parse_len - 1: # "\0 case
a_tuple.append(to_parse[attribute_start_index:i].strip())
#reached end of line, so no more attr's to extract
attribute_start_index = to_parse_len
in_quotes = False
else:
return False
elif current_char == ',':
if not in_quotes:
a_tuple.append(to_parse[attribute_start_index:i].strip())
attribute_start_index = i + 1
i += 1
#in case the last attr was left empty or unquoted
if attribute_start_index < to_parse_len or (not in_quotes and to_parse[-1] == ','):
a_tuple.append(to_parse[attribute_start_index:])
#line ended while parsing; i.e. a quote was openned but not closed
if in_quotes:
return False
return a_tuple
def parse_tuple(to_parse, no_of_headers):
"""
parses a string and returns an array with no_of_headers number of headers
raises an error if the string was not a valid CSV line
"""
#get rid of the newline at the end of every line
to_parse = to_parse.strip()
# return to_parse.split(',') #if we assume the data is in a valid format
#the following checking of the format of the data increases the execution
#time by a factor of 2; if the data is know to be valid, uncomment 3 lines above here
#if there are more commas than fields, then we must take into consideration
#how the quotes parse and then extract the attributes
if to_parse.count(',') + 1 > no_of_headers:
result = check_and_parse_potential_tuple(to_parse)
if result:
a_tuple = result
else:
raise TypeError('Error while parsing CSV line %s. The quotes do not parse' % to_parse)
else:
a_tuple = to_parse.split(',')
if not csv_tuple_quotes_valid(a_tuple):
raise TypeError('Error while parsing CSV line %s. The quotes do not parse' % to_parse)
#if the format is correct but more data fields were provided
#the following works faster than an if statement that checks the length of a_tuple
try:
a_tuple[no_of_headers - 1]
except IndexError:
raise TypeError('Error while parsing CSV line %s. Unknown reason' % to_parse)
#this replaces the use my own hashtables to store the duplicated values for the attributes
for i in range(1, no_of_headers):
a_tuple[i] = sys.intern(a_tuple[i])
return a_tuple
def read_file(path, file_number):
"""
reads the csv file and returns (dict, int)
the dict is the mapping of id's to attributes
the integer is the number of attributes (headers) for the csv file
"""
global HEADERS
try:
file = open(path, 'r');
except FileNotFoundError as e:
print("error in %s:\n%s\nexiting...")
exit(1)
main_table = {}
headers = file.readline().strip().split(',')
no_of_headers = len(headers)
HEADERS.extend(headers[1:]) #keep the headers from the file
lines = file.readlines()
file.close()
args = []
for line in lines:
args.append((line, no_of_headers))
#pool is a pool of worker processes parsing the lines in parallel
with Pool() as workers:
try:
all_tuples = workers.starmap(parse_tuple, args, 1000)
except TypeError as e:
print('Error in file %s:\n%s\nexiting thread...' % (path, e.args))
exit(1)
for a_tuple in all_tuples:
#add quotes to key if needed
key = a_tuple[0] if a_tuple[0][0] == '\"' else ('\"%s\"' % a_tuple[0])
main_table[key] = a_tuple[1:]
return (main_table, no_of_headers)
def merge_files():
"""
produces a file called merged.csv
"""
global HEADERS
no_of_files = len(sys.argv) - 1
processed_files = [None] * no_of_files
for i in range(0, no_of_files):
processed_files[i] = read_file(sys.argv[i + 1], i)
out_file = open('merged.csv', 'w+')
merged_str = ','.join(HEADERS)
all_keys = {}
#this is to ensure that we include all keys in the final file.
#even those that are missing from some files and present in others
for processed_file in processed_files:
all_keys.update(processed_file[0])
for key in all_keys:
merged_str += '\n%s' % key
for i in range(0, no_of_files):
(main_table, no_of_headers) = processed_files[i]
try:
for attr in main_table[key]:
merged_str += ',%s' % attr
except KeyError:
print('NOTE: no values found for id %s in file \"%s\"' % (key, sys.argv[i + 1]))
merged_str += ',' * (no_of_headers - 1)
out_file.write(merged_str)
out_file.close()
if __name__ == '__main__':
# merge_files()
import cProfile
cProfile.run('merge_files()')
# import time
# start = time.time()
# print(time.time() - start);
Here is the profiler report I got on my Windows.
EDIT: The rest of the csv data provided is here. Pastebin was taking too long to process the files, so...
It might not be the best code and I know that, but my question is what slows down Windows so much that doesn't slow down an Ubuntu? The merge_files() function takes the longest, with 94 seconds just for itself, not including the calls to other functions. And there doesn't seem to be anything too obvious to me for why it is so slow.
Thanks
EDIT: Note: We both used the same dataset to run the code with.
It turns out that Windows and Linux handle very long strings differently. When I moved the out_file.write(merged_str) inside the outer for loop (for key in all_keys:) and stopped appending to merged_str, it ran for 11 seconds as expected. I don't have enough knowledge on either of the OS's memory management systems to be able to give a prediction on why it is so different.
But I would say that the way that the second one (the Windows one) is the more fail-safe method because it is unreasonable to keep a 30 MB string in memory. It just turns out that Linux sees that and doesn't always try to keep the string in cache, or to rebuild it every time.
Funny enough, initially I did run it a few times on my Linux machine with these same writing strategies, and the one with the large string seemed to go faster, so I stuck with it. I guess you never know.
Here's the modified code
for key in all_keys:
merged_str = '%s' % key
for i in range(0, no_of_files):
(main_table, no_of_headers) = processed_files[i]
try:
for attr in main_table[key]:
merged_str += ',%s' % attr
except KeyError:
print('NOTE: no values found for id %s in file \"%s\"' % (key, sys.argv[i + 1]))
merged_str += ',' * (no_of_headers - 1)
out_file.write(merged_str + '\n')
out_file.close()
When I run your solution on Ubuntu 16.04 with the three given files, it seems to take ~8 seconds to complete. The only modification I made was to uncomment the timing code at the bottom and use it.
$ python3 dimitar_merge.py file1.csv file2.csv file3.csv
NOTE: no values found for id "aaa5d09b-684b-47d6-8829-3dbefd608b5e" in file "file2.csv"
NOTE: no values found for id "38f79a49-4357-4d5a-90a5-18052ef03882" in file "file2.csv"
NOTE: no values found for id "766590d9-4f5b-4745-885b-83894553394b" in file "file2.csv"
8.039648056030273
$ python3 dimitar_merge.py file1.csv file2.csv file3.csv
NOTE: no values found for id "38f79a49-4357-4d5a-90a5-18052ef03882" in file "file2.csv"
NOTE: no values found for id "766590d9-4f5b-4745-885b-83894553394b" in file "file2.csv"
NOTE: no values found for id "aaa5d09b-684b-47d6-8829-3dbefd608b5e" in file "file2.csv"
7.78482985496521
I rewrote my first attempt without using csv from the standard library and am now getting times of ~4.3 seconds.
$ python3 lettuce_merge.py file1.csv file2.csv file3.csv
4.332579612731934
$ python3 lettuce_merge.py file1.csv file2.csv file3.csv
4.305467367172241
$ python3 lettuce_merge.py file1.csv file2.csv file3.csv
4.27345871925354
This is my solution code (lettuce_merge.py):
from collections import defaultdict
def split_row(csv_row):
return [col.strip('"') for col in csv_row.rstrip().split(',')]
def merge_csv_files(files):
file_headers = []
merged_headers = []
for i, file in enumerate(files):
current_header = split_row(next(file))
unique_key, *current_header = current_header
if i == 0:
merged_headers.append(unique_key)
merged_headers.extend(current_header)
file_headers.append(current_header)
result = defaultdict(lambda: [''] * (len(merged_headers) - 1))
for file_header, file in zip(file_headers, files):
for line in file:
key, *values = split_row(line)
for col_name, col_value in zip(file_header, values):
result[key][merged_headers.index(col_name) - 1] = col_value
file.close()
quotes = '"{}"'.format
with open('lettuce_merged.csv', 'w') as f:
f.write(','.join(quotes(a) for a in merged_headers) + '\n')
for key, values in result.items():
f.write(','.join(quotes(b) for b in [key] + values) + '\n')
if __name__ == '__main__':
from argparse import ArgumentParser, FileType
from time import time
parser = ArgumentParser()
parser.add_argument('files', nargs='*', type=FileType('r'))
args = parser.parse_args()
start_time = time()
merge_csv_files(args.files)
print(time() - start_time)
I'm sure this code could be optimized even further but sometimes just seeing another way to solve a problem can help spark new ideas.
I'm using python to analyze some records bib and ris files. I made two functions for each type. The first function is the one you see below:
def limpiarlineasris(self, data):
cont = data
dic = cont.splitlines()
cont = ""
con = []
i = 0
for a in dic:
if len(a) != 0:
con.append(a)
for a in con:
cont = cont + a + "\n"
return cont
That works well and I can compile without problem. The problem arises when I write the second function see below:
def limpiarlineasbib(self, data):
cont = data
dic = cont.splitlines()
cont = ""
con = []
separador = "°-°-°"
for a in dic:
if len(a)!= 0:
if a.startswith('#'):
con.append(separador)
else:
con.append(a)
for a in con:
cont = cont + a + "\n"
return cont
When building the first function no problem. But when I compile the second compiler shows me an error but does not tell me exactly what or where it is because I am using plyjy a jar to create Jython objects, and the console only shows me an exception Plyjy without the line where it occurs. I'm using Netbeans to compile
I have the following data sets:
Data set #1 that provides shows and the number of viewers of that show:
TVShow1,25
TVShow2,30
TVShow3,7
TVShow1,15
Data set #2 that provides channels that broadcast each show:
TVShow4,BBC
TVShow2,COM
TVShow1,TNT
TVShow3,TNT
I want to calculated the total number of viewers of each show on the channel TNT, e.g.
TVShow1 40
TVShow3 7
I have the following mapper:
#!/usr/bin/env python
import sys
for line in sys.stdin:
line = line.strip()
key_value = line.split(",")
key_in = key_value[0]
value_in = key_value[1]
if (value_in == 'TNT' or value_in.isdigit()):
print( '%s\t%s' % (key_in, value_in) )
And the following reducer:
#!/usr/bin/env python
import sys
prev_TV_show = " "
line_cnt = 0
tnt_found = False
curr_TV_show_total_cnt = 0
for line in sys.stdin:
line = line.strip()
key_value = line.split('\t')
line_cnt = line_cnt+1
curr_TV_show = key_value[0]
value_in = key_value[1]
if curr_TV_show != prev_TV_show:
prev_TV_show = curr_TV_show
if (line_cnt>1 and tnt_found == True):
print('{0} {1}'.format(curr_TV_show,curr_TV_show_total_cnt))
tnt_found = False
curr_TV_show_total_cnt = 0
if (value_in == 'TNT'):
tnt_found = True
else:
curr_TV_show_total_cnt += int(value_in)
Then I tested the code as follows:
cat data_file*.txt | ./my_mapper.py | sort | ./my_reducer.py
However, it seams that total number of viewers of the first line is incorrect. It looks like it is merged between two TV shows. Is there any error in the code related to managing the first line?
I think that there are 2 problems in your code -
Updating prev_TV_show causes you to print the wrong value. You
actually want to print the prev_TV_show with its' count, not the
curr_TV_show
Printing the last iteration value - you need to add an additional print (+condition) outside the loop
Recently I had to make a script for my internship to check if a subnet occurs in a bunch of router/switch configs.
I've made a script that generates the output. Now I need a second script (I couldn't get it to work into one), that reads the output, if the subnet occurs write it to aanwezig.txt, if not write to nietAanwezig.txt.
A lot of other answers helped me to make this script and it works but it only executes for the first 48 IPs and there are over 2000...
The code of checkOutput.py:
def main():
file = open('../iprangesclean.txt', 'rb')
aanwezig = open('../aanwezig.txt', 'w')
nietAanwezig = open('../nietAanwezig.txt', 'w')
output = open('output.txt', 'rb')
for line in file:
originalLine = line
line.rstrip()
line = line.replace(' ', '')
line = line.replace('\n', '')
line = line.replace('\r', '')
one,two,three,four = line.split('.')
# 3Byte IP:
ipaddr = str(one) + "." + str(two) + "." + str(three)
counter = 1
found = 0
for lijn in output:
if re.search("\b{0}\b".format(ipaddr),lijn) and found == 0:
found = 1
else:
found = 2
print counter
counter= counter + 1
if found == 1:
aanwezig.write(originalLine)
print "Written to aanwezig"
elif found == 2:
nietAanwezig.write(originalLine)
print "Written to nietAanwezig"
found = 0
file.close()
aanwezig.close()
nietAanwezig.close()
main()
The format of iprangesclean.txt is like following:
10.35.6.0/24
10.132.42.0/24
10.143.26.0/24
10.143.30.0/24
10.143.31.0/24
10.143.32.0/24
10.35.7.0/24
10.143.35.0/24
10.143.44.0/24
10.143.96.0/24
10.142.224.0/24
10.142.185.0/24
10.142.32.0/24
10.142.208.0/24
10.142.70.0/24
and so on...
Part of output.txt (I can't give you everything because it has user information):
*name of device*.txt:logging 10.138.200.100
*name of device*.txt:access-list 37 permit 10.138.200.96 0.0.0.31
*name of device*.txt:access-list 38 permit 10.138.200.100
*name of device*.txt:snmp-server host 10.138.200.100 *someword*
*name of device*.txt:logging 10.138.200.100
Try this change:
for lijn in output:
found = 0 # put this here
if re.search("\b{0}\b".format(ipaddr),lijn) and found == 0:
found = 1
else:
found = 2
print counter
counter= counter + 1
"""Indent one level so it us in the for statement"""
if found == 1:
aanwezig.write(originalLine)
print "Written to aanwezig"
elif found == 2:
nietAanwezig.write(originalLine)
print "Written to nietAanwezig"
If I understand the problem correctly, this should guide you to the right direction. The if statement is currently not executed in the for statement. If this does solve your problem, then you don't need the found variable either. You can just have something like:
for counter, lijn in enumerate(output, 1):
if re.search("\b{0}\b".format(ipaddr),lijn):
aanwezig.write(originalLine)
print "Written to aanwezig"
else:
nietAanwezig.write(originalLine)
print "Written to nietAanwezig"
print counter
Please let me know if I have misunderstood the question.
Note I haven't tested the code above, try them out as a starting point.
Does anyone have a good method for importing rrd data into python? The only libraries I have found so far are just wrappers for the command line or provide importing data into rrd and graphing it.
I am aware of the export and dump options of rrd, but I am wondering if someone has already done the heavy lifting here.
Here's an excerpt from a script that I wrote to get cacti rrd data out. It's not likely to be exactly what you want, but it might give you a good start. The intent of my script is to turn Cacti into a data warehouse, so I tend to pull out a lot of averages, max, or min data. I also have some flags for tossing upper or lower range spikes, multiplying in case I want to turn "bytes/sec" into something more usable, like "mb/hour"...
If you want exact one-to-one data copy, you might need to tweak this a bit.
value_dict = {}
for file in files:
if file[0] == '':
continue
file = file[0]
value_dict[file] = {}
starttime = 0
endtime = 0
cmd = '%s fetch %s %s -s %s -e %s 2>&1' % (options.rrdtool, file, options.cf, options.start, options.end)
if options.verbose: print cmd
output = os.popen(cmd).readlines()
dsources = output[0].split()
if dsources[0].startswith('ERROR'):
if options.verbose:
print output[0]
continue
if not options.source:
source = 0
else:
try:
source = dsources.index(options.source)
except:
print "Invalid data source, options are: %s" % (dsources)
sys.exit(0)
data = output[3:]
for val in data:
val = val.split()
time = int(val[0][:-1])
val = float(val[source+1])
# make sure it's not invalid numerical data, and also an actual number
ok = 1
if options.lowerrange:
if val < options.lowerrange: ok = 0
if options.upperrange:
if val > options.upperrange: ok = 0
if ((options.toss and val != options.toss and val == val) or val == val) and ok:
if starttime == 0:
# this should be accurate for up to six months in the past
if options.start < -87000:
starttime = time - 1800
else:
starttime = time - 300
else:
starttime = endtime
endtime = time
filehash[file] = 1
val = val * options.multiply
values.append(val)
value_dict[file][time] = val
seconds = seconds + (endtime - starttime)