def getAllValueCounts(filename, keys):
print 'getAllValueCounts', filename, keys
f = open(filename, 'rt')
header = csv.readCsvLine(f)
data = csv.readCsvGen(f)
print header
column_index = dict(zip(keys, [header.index(k) for k in keys]))
counts = dict(zip(keys, [{} for k in keys]))
print 'indexes', column_index
print ' counts', counts
num_lines = 0
for row in data:
num_lines += 1
for k in keys:
val = row[column_index[k]]
counts[k][val] = counts[k].get(val,0) + 1
print filename, num_lines, 'lines'
for k in keys:
val = counts[k]
print k, len(val), val
total = sum(val.values())
cumulative = 0.0
if True:
for v in sorted(val.keys(), key = lambda x: -val[x]):
percent = val[v]*100.0/total
cumulative += percent
print '%5s %8d %3d%% %3d%%' % (v, val[v], round(percent), round(cumulative))
print '%5s %8d %3d%% %3d%%' % ('total', total, round(sum([v*100.0/total for v in val.values()])), round(cumulative))
f.close()
return counts
def getAllStats(filename, keys, max_rows = sys.maxint):
print 'getAllStats', filename, keys
f = open(filename, 'rt')
header = csv.readCsvLine(f)
data = csv.readCsvGen(f)
print header
column_index = dict(zip(keys, [header.index(k) for k in keys]))
stats = dict(zip(keys, [{'lo':sys.maxint, 'hi':-sys.maxint, 'mean': 0} for k in keys]))
num_rows = 0
for row in data:
for k in keys:
val = float(row[column_index[k]])
s = stats[k]
if stats[k]['lo'] > val:
stats[k]['lo'] = val
if stats[k]['hi'] < val:
stats[k]['hi'] = val
stats[k]['mean'] += val
num_rows += 1
if num_rows > max_rows:
break
for k in keys:
stats[k]['mean'] = stats[k]['mean']/num_rows
print filename, num_rows, 'rows'
for k in keys:
print k, stats[k]
f.close()
return stats
def filterBadValues(in_filename, out_filename, keys):
print 'filterBadValues', in_filename, out_filename, keys
fin = open(in_filename, 'rt')
fout = open(out_filename, 'wt')
header = csv.readCsvLine(fin)
csv.writeCsvRow(fout, header)
print header
data = csv.readCsvGen(fin)
column_index = dict(zip(keys, [header.index(k) for k in keys]))
num_rows = 0
num_bad = 0
for row in data:
bad_row = False
for k in keys:
val = float(row[column_index[k]])
if val < 0:
bad_row = True
num_bad += 1
if not bad_row:
csv.writeCsvRow(fout, row)
num_rows += 1
fin.close()
fout.close()
print in_filename, num_rows, 'rows'
print out_filename, num_rows - num_bad, 'rows'
def getStats(filename):
f = open(filename, 'rt')
header = csv.readCsvLine(f)
lines = csv.readCsvGen(f)
num_rows = 0
for l in lines:
num_rows += 1
print filename, num_rows, 'lines'
f.close()
def sampleCsv(in_filename, out_filename, ratio):
""" Sample a csv file. """
print 'sampleCsv', in_filename, out_filename, ratio
fin = open(in_filename, 'rt')
fout = open(out_filename, 'wt')
header = csv.readCsvLine(fin)
print 'header:', header
csv.writeCsvRow(fout, header)
data = csv.readCsvGen(fin)
num_sampled = 0
for irow,row in enumerate(data):
if irow % 100000 == 0:
print (irow,num_sampled),
if num_sampled < ratio * irow:
csv.writeCsvRow(fout, row)
num_sampled += 1
print
fin.close()
fout.close()
print in_filename, irow, 'rows'
print out_filename, num_sampled, 'rows'
if True:
fin = open(out_filename, 'rt')
header = csv.readCsvLine(fin)
data = csv.readCsvGen(fin)
for irow,row in enumerate(data):
if len(row) != len(header):
print irow, len(row), row, len(header), header
assert(len(row) == len(header))
fin.close()
def populateHistogram(filename, histo, max_rows):
print 'populateHistogram', histo.keys()
f = open(filename, 'rU')
header = csv.readCsvLine(f)
data = csv.readCsvGen(f)
column_index = dict(zip(header, [header.index(k) for k in header]))
for i,row in enumerate(data):
for k in histo.keys():
x = float(row[column_index[k]])
bin = binarySearch(histo[k]['levels'], x)
histo[k]['counts'][bin] += 1
if i >= max_rows:
break
f.close()
sales_histo = histo['sales']
for i,count in enumerate(sales_histo['counts']):
print '%4d: %7d %8.2f %8.2f' % (i, count, sales_histo['levels'][i], sales_histo['levels'][i+1]-sales_histo['levels'][i])
assert(sales_histo['levels'][i+1] >= sales_histo['levels'][i])
def getHistogram(filename, keys, stats, max_rows = sys.maxint):
""" Return a histogram of the form
[(upper<i>, count<i>) for i=1..N]
"""
print 'getHistogram', filename, keys, stats
# Max equal width bins
num_bins = 10
histo = dict(zip(keys,
[{'counts':[0 for i in range(num_bins)],
'levels':[stats[k]['lo'] + i *(stats[k]['hi']-stats[k]['lo']) for i in range(num_bins+1)]}
for k in keys]))
populateHistogram(filename, histo, max_rows)
for num_bins in [20,40]:
histo = makeHistogram(histo, num_bins)
populateHistogram(filename, histo, max_rows)
return histo
for i,row in enumerate(data):
for k in keys:
val = float(row[column_index[k]])
bin = int((num_bins-1)*(val-stats[k]['lo'])/(stats[k]['hi']-stats[k]['lo']))
histo[k][bin] += 1
if i >= max_rows:
break
f.close()
num_rows = i
print 'read', num_rows, 'to make', num_bins, 'equal depth bins'
for k in keys:
print k, stats[k]['lo'], stats[k]['hi'], histo[k]
num_equal = 10
equal_depth = dict(zip(keys, [[None for i in range(num_equal)] for k in keys]))
# Make equal depth bins
for k in keys:
bin_num = 0
cumulative = 0
for i in range(num_equal):
while cumulative/num_rows < i/num_equal:
cumulative += histo[k][bin_num]
bin_num += 1
equal_depth[k][i] = [cumulative, 0]
# print ' ', i, bin_num, cumulative
print 'bin_num', bin_num, ' len(histo)', len(histo[k])
assert(bin_num <= len(histo[k])-1)
if False:
f = open(filename, 'rU')
header = csv.readCsvLine(f)
data = csv.readCsvGen(f)
for k in keys:
print k, stats[k]['lo'], stats[k]['hi'], equal_depth[k]
for i,row in enumerate(data):
for k in ['sales']: # keys:
val = float(row[column_index[k]])
bin_num = binarySearch(equal_depth[k], val)
equal_depth[k][bin_num][1] += 1
print bin_num, val
if i >= max_rows:
break
f.close()
for k in keys:
print '&&', k, len(equal_depth[k])
return equal_depth
