def guesstypes(filename, has_header, headers, dialect, missing_values):
print 'guessing types..'
if filename.endswith('.gz'):
csvfile = gzip.open(filename, 'rb')
else:
csvfile = open(filename, 'rb')
reader = DictReader(csvfile, fieldnames=headers, dialect=dialect)
if has_header:
reader.next()
types = defaultdict(int) # 0 = numeric, 1 = categoric
# values=defaultdict(set)
line = 0
for row in reader:
for k, v in row.iteritems():
if (v is not None) and (v.lower() not in missing_values):
if not is_number(v):
types[k] = 1
line += 1
if line > 1000: break
# values[k].add(v)
csvfile.close()
return types
def skipHeaders(reader):
""" skip over the header lines in the csv documents so we can access
the data """
# skip 'comment' section at header of DATA_FILE
currentLine = reader.next()
while (currentLine[0][0] == HEADER_TAG):
currentLine = reader.next()
def localization_table(reader):
reader.next() # pop the header
table = {}
for row in reader:
key = L8nKey(*row[0:2])
row = L8nRow(*row[4:7])
table[key] = row
return table
def csv2json(filename, has_header, headers, dialect, missing_values,types):
if filename.endswith('.gz'):
csvfile=gzip.open(filename,'rb')
else:
csvfile=open(filename,'rb')
reader = DictReader(csvfile,fieldnames=headers, dialect=dialect)
if has_header:
reader.next()
for row in reader:
# try to convert all numbers
# filter out missing cells
event = dict([(k,convert(k,v,types)) for k,v in row.iteritems() if (v is not None) and (v.lower() not in missing_values)])
yield event
csvfile.close()
def csv2json(filename, has_header, headers, dialect, missing_values, types):
if filename.endswith('.gz'):
csvfile = gzip.open(filename, 'rb')
else:
csvfile = open(filename, 'rb')
reader = DictReader(csvfile, fieldnames=headers, dialect=dialect)
if has_header:
reader.next()
for row in reader:
# try to convert all numbers
# filter out missing cells
event = dict([(k, convert(k, v, types)) for k, v in row.iteritems()
if (v is not None) and (v.lower() not in missing_values)
])
yield event
csvfile.close()
def guesstypes(filename, has_header, headers, dialect, missing_values):
print 'guessing types..'
if filename.endswith('.gz'):
csvfile=gzip.open(filename,'rb')
else:
csvfile=open(filename,'rb')
reader = DictReader(csvfile,fieldnames=headers, dialect=dialect)
if has_header:
reader.next()
types=defaultdict(int) # 0 = numeric, 1 = categoric
# values=defaultdict(set)
line=0
for row in reader:
for k,v in row.iteritems():
if (v is not None) and (v.lower() not in missing_values):
if not is_number(v):
types[k]=1
line+=1
if line>1000: break
# values[k].add(v)
csvfile.close()
return types
def schema(self, vid):
"""Render documentation for a single bundle."""
from csv import reader
from StringIO import StringIO
import json
template = self.env.get_template('bundle/schema.html')
b_data = self.doc_cache.bundle(vid)
b = self.library.bundle(vid)
reader = reader(StringIO(b.schema.as_csv()))
del b_data['partitions']
del b_data['tables']
schema = dict(header=reader.next(), rows=[x for x in reader])
return self.render(template, b=b_data, schema=schema, **self.cc())
from csv import reader
import numpy as np
# vocab size
vocabulary_size = 8000
vocabulary_size = 8000
unknown_token = "UNKNOWN_TOKEN"
sentence_start_token = "SENTENCE_START"
sentence_end_token = "SENTENCE_END"
# Read the data and append SENTENCE_START and SENTENCE_END tokens
print "Reading CSV file..."
with open('data/reddit-comments-2015-08.csv', 'rb') as f:
reader = csv.reader(f, skipinitialspace=True)
reader.next()
# Split full comments into sentences
sentences = itertools.chain(
*[nltk.sent_tokenize(x[0].decode('utf-8').lower()) for x in reader])
# Append SENTENCE_START and SENTENCE_END
sentences = [
"%s %s %s" % (sentence_start_token, x, sentence_end_token)
for x in sentences
]
print "Parsed %d sentences." % (len(sentences))
# Tokenize the sentences into words
tokenized_sentences = [nltk.word_tokenize(sent) for sent in sentences]
# Count the word frequencies
word_freq = nltk.FreqDist(itertools.chain(*tokenized_sentences))
from csv import reader
from pymongo import MongoClient
filename = "C:\\Users\\Greg\\Desktop\\Flask Tutorial\\twitterApp\\twitterSampleData.csv"
reader = reader(open(filename, 'r'))
data = []
titleRow = reader.next()
for row in reader:
dataDict = {}
for key, value in zip(titleRow, row):
dataDict[key] = value
data.append(dataDict)
client = MongoClient()
db = client['twitterData']
posts = db.posts
posts.insert(data)
client.close()
#imports
from csv import reader
import os
# user params
relative_path = '../Data/text'
limit = 10
# initialize path
dir = os.path.dirname(__file__)
abs_path = os.path.join(dir, relative_path)
# open file
print '\n------------------'
print 'Open File...'
file = open(abs_path)
reader = reader(file, dialect="excel-tab")
reader.next()
# loop over lines
i = 0
for line in reader:
# show line
print line
# early exit
if i>limit:
break
def header(filename,delimiter=","): datafile = open(filename,"rU") reader = csv.reader(datafile,delimiter=delimiter) header = reader.next() datafile.close() return header
# file is in unicode and must be decoded when read back into Python for further processing
with open('comments.csv','ab') as f: #NEEDS TO BE DECODED FOR UTF-8 WHEN READ BACK IN
writer=csv.writer(f)
for yarn_index, permalink, num_comments in izip(yarn_stats.index, yarn_stats["permalink"], yarn_stats["num_comments"]):
if num_comments < 100:
writeFewYarnComments(yarn_index, permalink, br)
else:
writeManyYarnComments(yarn_index, permalink, num_comments, br)
#to get comments and yarn IDs in long format
from csv import reader
new_comments_index = []
new_comments = []
with open('comments.csv', 'rb') as f_in: #read back in the file we just created
reader = reader(f_in)
reader.next() #skip header row in csv
for row in reader:
this_index = row.pop(0) #get yarn index
row_mod = [s.decode('utf-8') for s in row if s != '']
this_index = [this_index for s in row_mod]
new_comments.extend(row_mod)
new_comments_index.extend(this_index)
#print new_comments
comms_with_index = pd.DataFrame({'comment' : new_comments}, index=new_comments_index)
comms_with_index.to_csv('comments_with_yarn_ids.csv', encoding='utf-8', index_label='yarn_id')
#to get comments and yarn IDs with each set of comments collapsed (to one document per yarn)
from csv import reader
new_comments_index = []
new_comments_collapsed = []
