9/28/2023 0 Comments Python csv writerMake sure you have whitespace between operators and commas: # from this I'll keep them as-is (except for the snake_case change) for the sake of your example. ![]() This depends on your use case, and your columns have those names as well, so maybe it makes sense to you. ![]() Also, names like var, var1, var666 should usually be swapped out for names a bit more meaningful to make it easier to deduce what values mean. Objects such as classes should have names like SomeClass. Variables and functions should be lowercased and snake-case, some_variable or var. Someone else can show the light of numpy/pandas as it pertains to this example. I will avoid using pandas for now, since it can be really easy to say "pandas will make X faster" and miss out on making what is currently written performant, even though it may be true. Is using the CSV reader/writer really the fastest way to do this? And if so, is there any way I can make my code run faster? to_csv(), but I never managed to improve the runtime more than a second or so. to_csv() function took as long to run as the entire script #1, so it didn't end up being faster. Knowing that line by line iteration is much slower than performing vectorized operations on a pandas dataframe, I thought I could do better, so I wrote a separate script (which I'll call script #2) where all the math was performed in a vectorized fashion, and then I used pandas. For a 2 million row CSV CAN file, it takes about 40 secs to fully run on my work desktop. It basically uses the CSV reader and writer to generate a processed CSV file line by line for each CSV. Writer.writerow(, var, var, var, var4, var5, var6, var7, var8, var9, var10, var11, var12]) Var13 = float((int(row,dataType)<<8)|(int(row,dataType)))ĭate = start_date+datetime.timedelta(seconds=time_s) Writer = csv.writer(csvOutput) #creates the writer object New_filename= trunc_filepath + folders_to_append + '/processed_' + cur_fileĬsvInput = open(filename, 'r') # opens the csv fileĬsvOutput = open(new_filename, 'w', newline='') New_filepath = trunc_filepath + folders_to_append + '/' ![]() Trunc_filepath = '/'.join(filename.split('/')) #create new filename and filepathĬur_file = ''.join(filename.split('/')) #pulls filename of current fileįolders_to_append = '/Log Files-Processed/' + '/'.join(flist.split('/')) #folders to append to new filepath I wrote a script to parse these files that looks like this: import osįilenames = filedialog.askopenfiles(title="Select. I need to decode the hex, and then extract a bunch of different variables from it depending on the CAN ID. ![]() id Flags DLC Data0 Data1 Data2 Data3 Data4 Data5 Data6 Data7 The files have 9 columns of interest (1 ID and 7 data fields), have about 1-2 million rows, and are encoded in hex. I'm currently working on a project that requires me to parse a a few hundred CSV CAN files at the time. I'm fairly new to python and pandas but trying to get better with it for parsing and processing large data files.
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