Looking to make the following code parallel- it reads in data in one large 9gb proprietary format and produces 30 individual csv files based on the 30 columns of data. It currently takes 9 minutes per csv written on a 30 minute data set. The solution space of parallel libraries in Python is a bit overwhelming. Can you direct me to any good tutorials/sample code? I couldn't find anything very informative.
for i in range(0, NumColumns):
aa = datetime.datetime.now()
allData = [TimeStamp]
ColumnData = allColumns[i].data # Get the data within this one Column
Samples = ColumnData.size # Find the number of elements in Column data
print('Formatting Column {0}'.format(i+1))
truncColumnData = [] # Initialize truncColumnData array each time for loop runs
if ColumnScale[i+1] == 'Scale: '+ tempScaleName: # If it's temperature, format every value to 5 characters
for j in range(Samples):
truncValue = '{:.1f}'.format((ColumnData[j]))
truncColumnData.append(truncValue) # Appends formatted value to truncColumnData array
allData.append(truncColumnData) #append the formatted Column data to the all data array
zipObject = zip(*allData)
zipList = list(zipObject)
csvFileColumn = 'Column_' + str('{0:02d}'.format(i+1)) + '.csv'
# Write the information to .csv file
with open(csvFileColumn, 'wb') as csvFile:
print('Writing to .csv file')
writer = csv.writer(csvFile)
counter = 0
for z in zipList:
counter = counter + 1
timeString = '{:.26},'.format(z[0])
zList = list(z)
columnVals = zList[1:]
columnValStrs = list(map(str, columnVals))
formattedStr = ','.join(columnValStrs)
csvFile.write(timeString + formattedStr + '\n') # Writes the time stamps and channel data by columns
one possible solution may be to use Dask http://dask.pydata.org/en/latest/ A coworker recently recommended it to me which is why I thought of it.