Related to a previous question, I'm trying to do replacements over a number of large CSV files.
The column order (and contents) change between files, but for each file there are about 10 columns that I want and can identify by the column header names. I also have 1-2 dictionaries for each column I want. So for the columns I want, I want to use only the correct dictionaries and want to implement them sequentially.
An example of how I've tried to solve this:
# -*- coding: utf-8 -*-
import re
# imaginary csv file. pretend that we do not know the column order.
Header = [u'col1', u'col2']
Line1 = [u'A',u'X']
Line2 = [u'B',u'Y']
fileLines = [Line1,Line2]
# dicts to translate lines
D1a = {u'A':u'a'}
D1b = {u'B':u'b'}
D2 = {u'X':u'x',u'Y':u'y'}
# dict to correspond header names with the correct dictionary.
# i would like the dictionaries to be read sequentially in col1.
refD = {u'col1':[D1a,D1b],u'col2':[D2]}
# clunky replace function
def freplace(str, dict):
rc = re.compile('|'.join(re.escape(k) for k in dict))
def trans(m):
return dict[m.group(0)]
return rc.sub(trans, str)
# get correspondence between dictionary and column
C = []
for i in range(len(Header)):
if Header[i] in refD:
C.append([refD[Header[i]],i])
# loop through lines and make replacements
for line in fileLines:
for i in range(len(line)):
for j in range(len(C)):
if C[j][1] == i:
for dict in C[j][0]:
line[i] = freplace(line[i], dict)
My problem is that this code is quite slow, and I can't figure out how to speed it up. I'm a beginner, and my guess was that my freplace function is largely what is slowing things down, because it has to compile for each column in each row. I would like to take the line rc = re.compile('|'.join(re.escape(k) for k in dict)) out of that function, but don't know how to do that and still preserve what the rest of my code is doing.
There's a ton of things that you can do to speed this up:
First, use the csv module. It provides efficient and bug-free methods for reading and writing CSV files. The DictReader object in particular is what you're interested in: it will present every row it reads from the file as a dictionary keyed by its column name.
Second, compile your regexes once, not every time you use them. Save the compiled regexes in a dictionary keyed by the column that you're going to apply them to.
Third, consider that if you apply a hundred regexes to a long string, you're going to be scanning the string from start to finish a hundred times. That may not be the best approach to your problem; you might be better off investing some time in an approach that lets you read the string from start to end once.
You don't need re:
# -*- coding: utf-8 -*-
# imaginary csv file. pretend that we do not know the column order.
Header = [u'col1', u'col2']
Line1 = [u'A',u'X']
Line2 = [u'B',u'Y']
fileLines = [Line1,Line2]
# dicts to translate lines
D1a = {u'A':u'a'}
D1b = {u'B':u'b'}
D2 = {u'X':u'x',u'Y':u'y'}
# dict to correspond header names with the correct dictionary
refD = {u'col1':[D1a,D1b],u'col2':[D2]}
# now let's have some fun...
for line in fileLines:
for i, (param, word) in enumerate(zip(Header, line)):
for minitranslator in refD[param]:
if word in minitranslator:
line[i] = minitranslator[word]
returns:
[[u'a', u'x'], [u'b', u'y']]
So if that's the case, and all 10 columns have the same names each time, but out of order, (I'm not sure if this is what you're doing up there, but here goes) keep one array for the heading names, and one for each column split into elements (should be 10 items each line), now just offset which regex by doing a case/select combo, compare the element number of your header array, then inside the case, reference the data array at the same offset, since the name is what will get to the right case you should be able to use the same 10 regex's repeatedly, and not have to recompile a new "command" each time.
I hope that makes sense. I'm sorry i don't know the syntax to help you out, but I hope my idea is what you're looking for
EDIT:
I.E.
initialize all regexes before starting your loops.
then after you read a line (and after the header line)
select array[n]
case "column1"
regex(data[0]);
case "column2"
regex(data[1]);
.
.
.
.
end select
This should call the right regex for the right columns
Related
I am a biologist that is just trying to use python to automate a ton of calculations, so I have very little experience.
I have a very large array that contains values that are formatted into two columns of observations. Sometimes the observations will be the same between the columns:
v1,v2
x,y
a,b
a,a
x,x
In order to save time and effort I wanted to make an if statement that just prints 0 if the two columns are the same and then moves on. If the values are the same there is no need to run those instances through the downstream analyses.
This is what I have so far just to test out the if statement. It has yet to recognize any instances where the columns are equivalen.
Script:
mylines=[]
with open('xxxx','r') as myfile:
for myline in myfile:
mylines.append(myline) ##reads the data into the two column format mentioned above
rang=len(open ('xxxxx,'r').readlines( )) ##returns the number or lines in the file
for x in range(1, rang):
li = mylines[x] ##selected row as defined by x and the number of lines in the file
spit = li.split(',',2) ##splits the selected values so they can be accessed seperately
print(spit[0]) ##first value
print(spit[1]) ##second value
if spit[0] == spit[1]:
print(0)
else:
print('Issue')
Output:
192Alhe52
192Alhe52
Issue ##should be 0
188Alhe48
192Alhe52
Issue
191Alhe51
192Alhe52
Issue
How do I get python to recgonize that certain observations are actually equal?
When you read the values and store them in the array, you can be storing '\n' as well, which is a break line character, so your array actually looks like this
print(mylist)
['x,y\n', 'a,b\n', 'a,a\n', 'x,x\n']
To work around this issue, you have to use strip(), which will remove this character and occasional blank spaces in the end of the string that would also affect the comparison
mylines.append(myline.strip())
You shouldn't use rang=len(open ('xxxxx,'r').readlines( )), because you are reading the file again
rang=len(mylines)
There is a more readable, pythonic way to replicate your for
for li in mylines[1:]:
spit = li.split(',')
if spit[0] == spit[1]:
print(0)
else:
print('Issue')
Or even
for spit.split(',') in mylines[1:]:
if spit[0] == spit[1]:
print(0)
else:
print('Issue')
will iterate on the array mylines, starting from the first element.
Also, if you're interested in python packages, you should have a look at pandas. Assuming you have a csv file:
import pandas as pd
df = pd.read_csv('xxxx')
for i, elements in df.iterrows():
if elements['v1'] == elements['v2']:
print('Equal')
else:
print('Different')
will do the trick. If you need to modify values and write another file
df.to_csv('nameYouWant')
For one, your issue with the equals test might be because iterating over lines like this also yields the newline character. There is a string function that can get rid of that, .strip(). Also, your argument to split is 2, which splits your row into three groups - but that probably doesn't show here. You can avoid having to parse it yourself when using the csv module, as your file presumably is that:
import csv
with open("yourfile.txt") as file:
reader = csv.reader(file)
next(reader) # skip header
for first, second in reader:
print(first)
print(second)
if first == second:
print(0)
else:
print("Issue")
and thanks in advance for any advice. First-time poster here, so I'll do my best to put in all required info. I am also quite beginner with Python, have been doing some online tutorials, and some copy/paste coding from StackOverflow, it's FrankenCoding... So I'm probably approaching this wrong...
I need to compare two CSV files, that will have a changing number of columns, there will only ever be 2 columns that match (for example, email_address in one file, and EMAIL in the other). Both files will have headers, however the names of these headers may change. The file sizes may be anywhere from a few thousand lines up to +2,000,000, with potentially 100+ columns (but more likely to have a handful).
Output is to a third 'results.csv' file, containing all the info. It may be a merge (all unique entries), a substract (remove entries present in one or the other) or an intersect (all entries present in both).
I have searched here, and found a lot of good information, but all of the ones I saw had a fixed number of columns in the files. I've tried dict and dictreader, and I know the answer is in there somewhere, but right now, I'm a bit confused. But since I haven't made any progress in several days, and I can only devote so much time on this, I'm hoping that I can get a nudge in the right direction.
Ideally, I want to learn how to do it myself, which means understanding how the data is 'moving around'.
Extract of CSV files below, I didn't add more columns then (I think) necessary, the dataset I have now will match on Originalid/UID or emailaddress/email, but this may not always be the case.
Original.csv
"originalid","emailaddress",""
"12345678","Bob#mail.com",""
"23456789","NORMA#EMAIL.COM",""
"34567890","HENRY#some-mail.com",""
"45678901","Analisa#sports.com",""
"56789012","greta#mail.org",""
"67890123","STEVEN#EMAIL.ORG",""
Compare.CSV
"email","","DATEOFINVALIDATION_WITH_TIME","OPTOUTDATE_WITH_TIME","EMAIL_USERS"
"Bob#mail.com",,,"true"
"NORMA#EMAIL.COM",,,"true"
"HENRY#some-mail.com",,,"true"
"Henrietta#AWESOME.CA",,,"true"
"NORMAN#sports.CA",,,"true"
"albertina#justemail.CA",,,"true"
Data in results.csv should be all columns from Original.CSV + all columns in Compare.csv, but not the matching one (email) :
"originalid","emailaddress","","DATEOFINVALIDATION_WITH_TIME","OPTOUTDATE_WITH_TIME","EMAIL_USERS"
"12345678","Bob#mail.com","",,,"true"
"23456789","NORMA#EMAIL.COM","",,,"true"
"34567890","HENRY#some-mail.com","",,,"true"
Here are my results as they are now:
email,,DATEOFINVALIDATION_WITH_TIME,OPTOUTDATE_WITH_TIME,EMAIL_USERS
Bob#mail.com,,,true,"['12345678', 'Bob#mail.com', '']"
NORMA#EMAIL.COM,,,true,"['23456789', 'NORMA#EMAIL.COM', '']"
HENRY#some-mail.com,,,true,"['34567890', 'HENRY#some-mail.com', '']"
And here's where I'm at with the code, the print statement returns matching data from the files to screen but not to file, so I'm missing something in there.
***** And I'm not getting the headers from the original.csv file, data is coming in.
import csv
def get_column_from_file(filename, column_name):
f = open(filename, 'r')
reader = csv.reader(f)
headers = next(reader, None)
i = 0
max = (len(headers))
while i < max:
if headers[i] == column_name:
column_header = i
# print(headers[i])
i = i + 1
return(column_header)
file_to_check = "Original.csv"
file_console = "Compare.csv"
column_to_read = get_column_from_file(file_console, 'email')
column_to_compare = get_column_from_file(file_to_check, 'emailaddress')
with open(file_console, 'r') as master:
master_indices = dict((r[1], r) for i, r in enumerate(csv.reader(master)))
with open('Compare.csv', 'r') as hosts:
with open('results.csv', 'w', newline='') as results:
reader = csv.reader(hosts)
writer = csv.writer(results)
writer.writerow(next(reader, []))
for row in reader:
index = master_indices.get(row[0])
if index is not None:
print (row +[master_indices.get(row[0])])
writer.writerow(row +[master_indices.get(row[0])])
Thanks for your time!
Pat
I like that you want to do this yourself, and recognize a need to "understand how the data is moving around." This is exactly how you should be thinking of the problem: focusing on the movement of data rather than the result. Some people may disagree with me, but I think this is a good philosophy to follow as it will make future reuse easier.
You're not trying to build a tool that combines two CSVs, you're trying to organize data (that happens to come from a CSV) according to a common reference (email address) and output the result as a CSV. Because you are talking about potentially large data sets (+2,000,000 [rows] with potentially 100+ columns) recognize that it is important to pay attention to the asymptotic runtime. If you do not know what this is, I recommend you read up on Big-O notation and asymptotic algorithm analysis. You might be okay without this.
First you decide what, from each CSV, is your key. You've already done this, 'email' for 'Compare.csv' and 'emailaddress' from 'Original.csv'.
Now, build yourself a function to produce dictionaries from the CSV based off the key.
def get_dict_from_csv(path_to_csv, key):
with open(path_to_csv, 'r') as f:
reader = csv.reader(f)
headers, *rest = reader # requires python3
key_index = headers.index(key) # find index of key
# dictionary comprehensions are your friend, just think about what you want the dict to look like
d = {row[key_index]: row[:key_index] + row[key_index+1:] # +1 to skip the email entry
for row in rest}
headers.remove(key)
d['HEADERS'] = headers # add headers so you know what the information in the dict is
return d
Now you can call this function on both of your CSVs.
file_console_dict = get_dict_from_csv('Compare.csv', 'email')
file_to_check_dict = get_dict_from_csv('Original.csv', 'emailaddress')
Now you have two dicts which are keyed off the same information. Now we need a function to combine these into one dict.
def combine_dicts(*dicts):
d, *rest = dicts # requires python3
# iteratively pull other dicts into the first one, d
for r in rest:
original_headers = d['HEADERS'][:]
new_headers = r['HEADERS'][:]
# copy headers
d['HEADERS'].extend(new_headers)
# find missing keys
s = set(d.keys()) - set(r.keys()) # keys present in d but not in r
for k in s:
d[k].extend(['', ] * len(new_headers))
del r['HEADERS'] # we don't want to copy this a second time in the loop below
for k, v in r.items():
# use setdefault in case the key didn't exist in the first dict
d.setdefault(k, ['', ] * len(original_headers)).extend(v)
return d
Now you have one dict which has all the information you want, all you need to do is write it back as a CSV.
def write_dict_to_csv(output_file, d, include_key=False):
with open(output_file, 'w', newline='') as results:
writer = csv.writer(results)
# email isn't in your HEADERS, so you'll need to add it
if include_key:
headers = ['email',] + d['HEADERS']
else:
headers = d['HEADERS']
writer.writerow(headers)
# now remove it from the dict so we can iterate over it without including it twice
del d['HEADERS']
for k, v in d.items():
if include_key:
row = [k,] + v
else:
row = v
writer.writerow(row)
And that should be it. To call all of this is just
file_console_dict = get_dict_from_csv('Compare.csv', 'email')
file_to_check_dict = get_dict_from_csv('Original.csv', 'emailaddress')
results_dict = combine_dicts(file_to_check_dict, file_console_dict)
write_dict_to_csv('results.csv', results_dict)
And you can easily see how this can be extended to arbitrarily many dictionaries.
You said you didn't want the email to be in the final CSV. This is counter-intuitive to me, so I made it an option in write_dict_to_csv() in case you change your mind.
When I run all the above I get
email,originalid,,,DATEOFINVALIDATION_WITH_TIME,OPTOUTDATE_WITH_TIME,EMAIL_USERS
Bob#mail.com,12345678,,,,true
NORMA#EMAIL.COM,23456789,,,,true
HENRY#some-mail.com,34567890,,,,true
Analisa#sports.com,45678901,,,,,
greta#mail.org,56789012,,,,,
STEVEN#EMAIL.ORG,67890123,,,,,
Henrietta#AWESOME.CA,,,,,true
NORMAN#sports.CA,,,,,true
albertina#justemail.CA,,,,,true
Right now it looks like you only use writerow once for the header:
writer.writerow(next(reader, []))
As francisco pointed out, uncommenting that last line may fix your problem. You can do this by removing the "#" at the beginning of the line.
I'm pretty new to Python, and put together a script to parse a csv and ultimately output its data into a repeated html table.
I got most of it working, but there's one weird problem I haven't been able to fix. My script will find the index of the last column, but won't print out the data in that column. If I add another column to the end, even an empty one, it'll print out the data in the formerly-last column - so it's not a problem with the contents of that column.
Abridged (but still grumpy) version of the code:
import os
os.chdir('C:\\Python34\\andrea')
import csv
csvOpen = open('my.csv')
exampleReader = csv.reader(csvOpen)
tableHeader = next(exampleReader)
if 'phone' in tableHeader:
phoneIndex = tableHeader.index('phone')
else:
phoneIndex = -1
for row in exampleReader:
row[-1] =''
print(phoneIndex)
print(row[phoneIndex])
csvOpen.close()
my.csv
stuff,phone
1,3235556177
1,3235556170
Output
1
1
Same script, small change to the CSV file:
my.csv
stuff,phone,more
1,3235556177,
1,3235556170,
Output
1
3235556177
1
3235556170
I'm using Python 3.4.3 via Idle 3.4.3
I've had the same problem with CSVs generated directly by mysql, ones that I've opened in Excel first then re-saved as CSVs, and ones I've edited in Notepad++ and re-saved as CSVs.
I tried adding several different modes to the open function (r, rU, b, etc.) and either it made no difference or gave me an error (for example, it didn't like 'b').
My workaround is just to add an extra column to the end, but since this is a frequently used script, it'd be much better if it just worked right.
Thank you in advance for your help.
row[-1] =''
The CSV reader returns to you a list representing the row from the file. On this line you set the last value in the list to an empty string. Then you print it afterwards. Delete this line if you don't want the last column to be set to an empty string.
If you know it is the last column, you can count them and then use that value minus 1. Likewise you can use your string comparison method if you know it will always be "phone". I recommend if you are using the string compare, convert the value from the csv to lower case so that you don't have to worry about capitalization.
In my code below I created functions that show how to use either method.
import os
import csv
os.chdir('C:\\temp')
csvOpen = open('my.csv')
exampleReader = csv.reader(csvOpen)
tableHeader = next(exampleReader)
phoneColIndex = None;#init to a value that can imply state
lastColIndex = None;#init to a value that can imply state
def getPhoneIndex(header):
for i, col in enumerate(header): #use this syntax to get index of item
if col.lower() == 'phone':
return i;
return -1; #send back invalid index
def findLastColIndex(header):
return len(tableHeader) - 1;
## methods to check for phone col. 1. by string comparison
#and 2. by assuming it's the last col.
if len(tableHeader) > 1:# if only one row or less, why go any further?
phoneColIndex = getPhoneIndex(tableHeader);
lastColIndex = findLastColIndex(tableHeader)
for row in exampleReader:
print(row[phoneColIndex])
print('----------')
print(row[lastColIndex])
print('----------')
csvOpen.close()
I have a 5GB file of businesses and I'm trying to extract all the businesses that whose business type codes (SNACODE) start with the SNACODE corresponding to grocery stores. For example, SNACODEs for some businesses could be 42443013, 44511003, 44419041, 44512001, 44522004 and I want all businesses whose codes start with my list of grocery SNACODES codes = [4451,4452,447,772,45299,45291,45212]. In this case, I'd get the rows for 44511003, 44512001, and 44522004
Based on what I googled, the most efficient way to read in the file seemed to be one row at a time (if not the SQL route). I then used a for loop and checked if my SNACODE column started with any of my codes (which probably was a bad idea but the only way I could get to work).
I have no idea how many rows are in the file, but there are 84 columns. My computer was running for so long that I asked a friend who said it should only take 10-20 min to complete this task. My friend edited the code but I think he misunderstood what I was trying to do because his result returns nothing.
I am now trying to find a more efficient method than re-doing my 9.5 hours and having my laptop run for an unknown amount of time. The closest thing I've been able to find is most efficient way to find partial string matches in large file of strings (python), but it doesn't seem like what I was looking for.
Questions:
What's the best way to do this? How long should this take?
Is there any way that I can start where I stopped? (I have no idea how many rows of my 5gb file I read, but I have the last saved line of data--is there a fast/easy way to find the line corresponding to a unique ID in the file without having to read each line?)
This is what I tried -- in 9.5 hours it outputted a 72MB file (200k+ rows) of grocery stores
codes = [4451,4452,447,772,45299,45291,45212] #codes for grocery stores
for df in pd.read_csv('infogroup_bus_2010.csv',sep=',', chunksize=1):
data = np.asarray(df)
data = pd.DataFrame(data, columns = headers)
for code in codes:
if np.char.startswith(str(data["SNACODE"][0]), str(code)):
with open("grocery.csv", "a") as myfile:
data.to_csv(myfile, header = False)
print code
break #break code for loop if match
grocery.to_csv("grocery.csv", sep = '\t')
This is what my friend edited it to. I'm pretty sure the x = df[df.SNACODE.isin(codes)] is only matching perfect matches, and thus returning nothing.
codes = [4451,4452,447,772,45299,45291,45212]
matched = []
for df in pd.read_csv('infogroup_bus_2010.csv',sep=',', chunksize=1024*1024, dtype = str, low_memory=False):
x = df[df.SNACODE.isin(codes)]
if len(x):
matched.append(x)
print "Processed chunk and found {} matches".format(len(x))
output = pd.concat(matched, axis=0)
output.to_csv("grocery.csv", index = False)
Thanks!
To increase speed you could pre-build a single regexp matching the lines you need and the read the raw file lines (no csv parsing) and check them with the regexp...
codes = [4451,4452,447,772,45299,45291,45212]
col_number = 4 # Column number of SNACODE
expr = re.compile("[^,]*," * col_num +
"|".join(map(str, codes)) +
".*")
for L in open('infogroup_bus_2010.csv'):
if expr.match(L):
print L
Note that this is just a simple sketch as no escaping is considered... if the SNACODE column is not the first one and preceding fields may contain a comma you need a more sophisticated regexp like:
...
'([^"][^,]*,|"([^"]|"")*",)' * col_num +
...
that ignores commas inside double-quotes
You can probably make your pandas solution much faster:
codes = [4451, 4452, 447, 772, 45299, 45291, 45212]
codes = [str(code) for code in codes]
sna = pd.read_csv('infogroup_bus_2010.csv', usecols=['SNACODE'],
chunksize=int(1e6), dtype={'SNACODE': str})
with open('grocery.csv', 'w') as fout:
for chunk in sna:
for code in chunk['SNACODE']:
for target_code in codes:
if code.startswith(target_code):
fout.write('{}\n'.format(code))
Read only the needed column with usecols=['SNACODE']. You can adjust the chunk size with chunksize=int(1e6). Depending on your RAM you can likely make it much bigger.
I have this task that I've been working on, but am having extreme misgivings about my methodology.
So the problem is that I have a ton of excel files that are formatted strangely (and not consistently) and I need to extract certain fields for each entry. An example data set is
My original approach was this:
Export to csv
Separate into counties
Separate into districts
Analyze each district individually, pull out values
write to output.csv
The problem I've run into is that the format (seemingly well organized) is almost random across files. Each line contains the same fields, but in a different order, spacing, and wording. I wrote a script to correctly process one file, but it doesn't work on any other files.
So my question is, is there a more robust method of approaching this problem rather than simple string processing? What I had in mind was more of a fuzzy logic approach for trying to pin which field an item was, which could handle the inputs being a little arbitrary. How would you approach this problem?
If it helps clear up the problem, here is the script I wrote:
# This file takes a tax CSV file as input
# and separates it into counties
# then appends each county's entries onto
# the end of the master out.csv
# which will contain everything including
# taxes, bonds, etc from all years
#import the data csv
import sys
import re
import csv
def cleancommas(x):
toggle=False
for i,j in enumerate(x):
if j=="\"":
toggle=not toggle
if toggle==True:
if j==",":
x=x[:i]+" "+x[i+1:]
return x
def districtatize(x):
#list indexes of entries starting with "for" or "to" of length >5
indices=[1]
for i,j in enumerate(x):
if len(j)>2:
if j[:2]=="to":
indices.append(i)
if len(j)>3:
if j[:3]==" to" or j[:3]=="for":
indices.append(i)
if len(j)>5:
if j[:5]==" \"for" or j[:5]==" \'for":
indices.append(i)
if len(j)>4:
if j[:4]==" \"to" or j[:4]==" \'to" or j[:4]==" for":
indices.append(i)
if len(indices)==1:
return [x[0],x[1:len(x)-1]]
new=[x[0],x[1:indices[1]+1]]
z=1
while z<len(indices)-1:
new.append(x[indices[z]+1:indices[z+1]+1])
z+=1
return new
#should return a list of lists. First entry will be county
#each successive element in list will be list by district
def splitforstos(string):
for itemind,item in enumerate(string): # take all exception cases that didn't get processed
splitfor=re.split('(?<=\d)\s\s(?=for)',item) # correctly and split them up so that the for begins
splitto=re.split('(?<=\d)\s\s(?=to)',item) # a cell
if len(splitfor)>1:
print "\n\n\nfor detected\n\n"
string.remove(item)
string.insert(itemind,splitfor[0])
string.insert(itemind+1,splitfor[1])
elif len(splitto)>1:
print "\n\n\nto detected\n\n"
string.remove(item)
string.insert(itemind,splitto[0])
string.insert(itemind+1,splitto[1])
def analyze(x):
#input should be a string of content
#target values are nomills,levytype,term,yearcom,yeardue
clean=cleancommas(x)
countylist=clean.split(',')
emptystrip=filter(lambda a: a != '',countylist)
empt2strip=filter(lambda a: a != ' ', emptystrip)
singstrip=filter(lambda a: a != '\' \'',empt2strip)
quotestrip=filter(lambda a: a !='\" \"',singstrip)
splitforstos(quotestrip)
distd=districtatize(quotestrip)
print '\n\ndistrictized\n\n',distd
county = distd[0]
for x in distd[1:]:
if len(x)>8:
district=x[0]
vote1=x[1]
votemil=x[2]
spaceindex=[m.start() for m in re.finditer(' ', votemil)][-1]
vote2=votemil[:spaceindex]
mills=votemil[spaceindex+1:]
votetype=x[4]
numyears=x[6]
yearcom=x[8]
yeardue=x[10]
reason=x[11]
data = [filename,county,district, vote1, vote2, mills, votetype, numyears, yearcom, yeardue, reason]
print "data",data
else:
print "x\n\n",x
district=x[0]
vote1=x[1]
votemil=x[2]
spaceindex=[m.start() for m in re.finditer(' ', votemil)][-1]
vote2=votemil[:spaceindex]
mills=votemil[spaceindex+1:]
votetype=x[4]
special=x[5]
splitspec=special.split(' ')
try:
forind=[i for i,j in enumerate(splitspec) if j=='for'][0]
numyears=splitspec[forind+1]
yearcom=splitspec[forind+6]
except:
forind=[i for i,j in enumerate(splitspec) if j=='commencing'][0]
numyears=None
yearcom=splitspec[forind+2]
yeardue=str(x[6])[-4:]
reason=x[7]
data = [filename,county,district,vote1,vote2,mills,votetype,numyears,yearcom,yeardue,reason]
print "data other", data
openfile=csv.writer(open('out.csv','a'),delimiter=',', quotechar='|',quoting=csv.QUOTE_MINIMAL)
openfile.writerow(data)
# call the file like so: python tax.py 2007May8Tax.csv
filename = sys.argv[1] #the file is the first argument
f=open(filename,'r')
contents=f.read() #entire csv as string
#find index of every instance of the word county
separators=[m.start() for m in re.finditer('\w+\sCOUNTY',contents)] #alternative implementation in regex
# split contents into sections by county
# analyze each section and append to out.csv
for x,y in enumerate(separators):
try:
data = contents[y:separators[x+1]]
except:
data = contents[y:]
analyze(data)
is there a more robust method of approaching this problem rather than simple string processing?
Not really.
What I had in mind was more of a fuzzy logic approach for trying to pin which field an item was, which could handle the inputs being a little arbitrary. How would you approach this problem?
After a ton of analysis and programming, it won't be significantly better than what you've got.
Reading stuff prepared by people requires -- sadly -- people-like brains.
You can mess with NLTK to try and do a better job, but it doesn't work out terribly well either.
You don't need a radically new approach. You need to streamline the approach you have.
For example.
district=x[0]
vote1=x[1]
votemil=x[2]
spaceindex=[m.start() for m in re.finditer(' ', votemil)][-1]
vote2=votemil[:spaceindex]
mills=votemil[spaceindex+1:]
votetype=x[4]
numyears=x[6]
yearcom=x[8]
yeardue=x[10]
reason=x[11]
data = [filename,county,district, vote1, vote2, mills, votetype, numyears, yearcom, yeardue, reason]
print "data",data
Might be improved by using a named tuple.
Then build something like this.
data = SomeSensibleName(
district= x[0],
vote1=x[1], ... etc.
)
So that you're not creating a lot of intermediate (and largely uninformative) loose variables.
Also, keep looking at your analyze function (and any other function) to pull out the various "pattern matching" rules. The idea is that you'll examine a county's data, step through a bunch of functions until one matches the pattern; this will also create the named tuple. You want something like this.
for p in ( some, list, of, functions ):
match= p(data)
if match:
return match
Each function either returns a named tuple (because it liked the row) or None (because it didn't like the row).