Fastest Way To Run Through 50k Lines of Excel File

Question

I'm using openpyxl in python, and I'm trying to run through 50k lines and grab data from each row and place it into a file. However.. what I'm finding is it runs incredibely slow the farther I get into it. The first 1k lines goes super fast, less than a minute, but after that it takes longer and longer and longer to do the next 1k lines.

I was opening a .xlsx file. I wonder if it is faster to open a .txt file as a csv or something or to read a json file or something? Or to convert somehow to something that will read faster?

I have 20 unique values in a given column, and then values are random for each value. I'm trying to grab a string of the entire unique value column for each value.

Value1: 1243,345,34,124, Value2: 1243,345,34,124, etc, etc

I'm running through the Value list, seeing if the name exists in a file, if it does, then it will access that file and append to it the new value, if the file doesn't exist, it will create the file and then set it to append. I have a dictionary that has all the "append write file" things connected to it, so anytime I want to write something, it will grab the file name, and the append thing will be available in the dict, it will look it up and write to that file, so it doesn't keep opening new files everytime it runs.

The first 1k took less than a minute.. now I'm on 4k to 5k records, and it's running all ready 5 minutes.. it seems to take longer as it goes up in records, I wonder how to speed it up. It's not printing to the console at all.

writeFile = 1
theDict = {}

for row in ws.iter_rows(rowRange):
    for cell in row:
        #grabbing the value
        theStringValueLocation = "B" + str(counter)
        theValue = ws[theStringValueLocation].value
        theName = cell.value
        textfilename = theName + ".txt"

        if os.path.isfile(textfilename):
            listToAddTo = theDict[theName]
            listToAddTo.write("," + theValue)
            if counter == 1000:
                print "1000"
                st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')

        else:
            writeFileName = open(textfilename, 'w')
            writeFileName.write(theValue)
            writeFileName = open(textfilename, 'a')
            theDict[theName] = writeFileName
        counter = counter + 1

I added some time stamps to the above code, it is not there, but you can see the output below. The problem I'm seeing is that it is going up higher and higher each 1k run. 2 minutes the firs ttime, thne 3 minutes, then 5 minutes, then 7 minutes. By the time it hits 50k, I'm worried it's going to be taking an hour or something and it will be taking too long.

1000
2016-02-25 15:15:08
20002016-02-25 15:17:07
30002016-02-25 15:20:52
2016-02-25 15:25:28
4000
2016-02-25 15:32:00
5000
2016-02-25 15:40:02
6000
2016-02-25 15:51:34
7000
2016-02-25 16:03:29
8000
2016-02-25 16:18:52
9000
2016-02-25 16:35:30
10000

Somethings I should make clear.. I don't know the names of the values ahead of time, maybe I should run through and grab those in a seperate python script to make this go faster?

Second, I need a string of all values seperated by comma, that's why I put it into a text file to grab later. I was thinking of doing it by a list as was suggested to me, but I'm wondering if that will have the same problem. I'm thinking the problem has to do with reading off excel. Anyway I can get a string out of it seperated by comma, I can do it another way.

Or maybe I could do try/catch instead of searching for the file everytime, and if there is an error, I can assume to create a new file? Maybe the lookup everytime is making it go really slow? the If the file exists?

this question is a continuation from my original here and I took some suggestions from there.... What is the fastest performance tuple for large data sets in python?

Answer 1

It looks like you only want cells from the B-column. In this case you can use ws.get_squared_range() to restrict the number of cells to look at.

for row in ws.get_squared_range(min_col=2, max_col=2, min_row=1, max_row=ws.max_row):
    for cell in row: # each row is always a sequence
         filename = cell.value
         if os.path.isfilename(filename):
              …

It's not clear what's happening with the else branch of your code but you should probably be closing any files you open as soon as you have finished with them.

Answer 2

Based on the other question you linked to, and the code above, it appears you have a spreadsheet of name - value pairs. The name in in column A and the value is in column B. A name can appear multiple times in column A, and there can be a different value in column B each time. The goal is to create a list of all the values that show up for each name.

First, a few observations on the code above:

1. counter is never initialized. Presumably it is initialized to 1.

2. open(textfilename,...) is called twice without closing the file in between. Calling open allocates some memory to hold data related to operating on the file. The memory allocated for the first open call may not get freed until much later, maybe not until the program ends. It is better practice to close files when you are done with them (see using open as a context manager).

3. The looping logic isn't correct. Consider:

First iteration of inner loop:

for cell in row:                        # cell refers to A1
    valueLocation = "B" + str(counter)  # valueLocation is "B1"
    value = ws[valueLocation].value     # value gets contents of cell B1
    name = cell.value                   # name gets contents of cell A1
    textfilename = name + ".txt"
    ...
    opens file with name based on contents of cell A1, and
    writes value from cell B1 to the file
    ...
    counter = counter + 1                        # counter = 2

But each row has at least two cells, so on the second iteration of the inner loop:

for cell in row:                          # cell now refers to cell B1
    valueLocation = "B" + str(counter)    # valueLocation is "B2"
    value = ws[valueLocation].value       # value gets contents of cell B2
    name = cell.value                     # name gets contents of cell B1
    textfilename = name + ".txt"
    ...
    opens file with name based on contents of cell "B1"  <<<< wrong file
    writes the value of cell "B2" to the file            <<<< wrong value
    ...
    counter = counter + 1        # counter = 3 when cell B1 is processed

Repeat for each of 50K rows. Depending on how many unique values are in column B, the program could be trying to have hundreds or thousands of open files (based on contents of cells A1, B1, A2, B2, ...) ==>> very slow or program crashes.

4. iter_rows() returns a tuple of the cells in the row.

5. As people suggested in the other question, use a dictionary and lists to store the values and write them all out at the end. Like so (Im using python 3.5, so you may have to adjust this if you are using 2.7)

Here is a straight forward solution:

from collections import defaultdict

data = defaultdict(list)

# gather the values into lists associated with each name
# data will look like { 'name1':['value1', 'value42', ...],
#                       'name2':['value7', 'value23', ...],
#                       ...}
for row in ws.iter_rows():
    name = row[0].value
    value = row[1].value
    data[name].append(value)

for key,valuelist in data.items():
    # turn list of strings in to a long comma-separated string
    # e.g., ['value1', 'value42', ...] => 'value1,value42, ...'
    value = ",".join(valuelist)

    with open(key + ".txt", "w") as f:
        f.write(value)

Answer 3

I think what you're trying to do is get a key out of column B of the row, and use that for the filename to append to. Let's speed it up a lot:

from collections import defaultdict
Value_entries = defaultdict(list) # dict of lists of row data

for row in ws.iter_rows(rowRange):
    key = row[1].value

    Value_entries[key].extend([cell.value for cell in row])

# All done. Now write files:
for key in Value_entries.keys():
    with open(key + '.txt', 'w') as f:
        f.write(','.join(Value_entries[key]))