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I have 18 csv files, each is approximately 1.6Gb and each contain approximately 12 million rows. Each file represents one years' worth of data. I need to combine all of these files, extract data for certain geographies, and then analyse the time series. What is the best way to do this?
I have tired using pd.read_csv but i hit a memory limit. I have tried including a chunk size argument but this gives me a TextFileReader object and I don't know how to combine these to make a dataframe. I have also tried pd.concat but this does not work either.
The memory limit is hit because you are trying to load the whole csv in memory. An easy solution would be to read the files line by line (assuming your files all have the same structure), control it, then write it to the target file:
filenames = ["file1.csv", "file2.csv", "file3.csv"] sep = ";" def check_data(data): # ... your tests return True # << True if data should be written into target file, else False with open("/path/to/dir/result.csv", "a+") as targetfile: for filename in filenames : with open("/path/to/dir/"+filename, "r") as f: next(f) # << only if the first line contains headers for line in f: data = line.split(sep) if check_data(data): targetfile.write(line)
Update: An example of the
check_data method, following your comments:
def check_data(data): return data[n] == 'USA' # < where n is the column holding the country
How do I combine large csv files in python?, The memory limit is hit because you are trying to load the whole csv in memory. An easy solution would be to read the files line by line� If you want to do so then this entire post is for you. In this tutorial, you will Know to Join or Merge Two CSV files using the Popular Python Pandas Library. Steps By Step to Merge Two CSV Files Step 1: Import the Necessary Libraries import pandas as pd. Here all things are done using pandas python library. So I am importing pandas only.
Here is the elegant way of using pandas to combine a very large csv files. The technique is to load number of rows (defined as CHUNK_SIZE) to memory per iteration until completed. These rows will be appended to output file in "append" mode.
import pandas as pd CHUNK_SIZE = 50000 csv_file_list = ["file1.csv", "file2.csv", "file3.csv"] output_file = "./result_merge/output.csv" for csv_file_name in csv_file_list: chunk_container = pd.read_csv(csv_file_name, chunksize=CHUNK_SIZE) for chunk in chunk_container: chunk.to_csv(output_file, mode="a", index=False)
But If your files contain headers than it makes sense to skip the header in the upcoming files except the first one. As repeating header is unexpected. In this case the solution is as the following:
import pandas as pd CHUNK_SIZE = 50000 csv_file_list = ["file1.csv", "file2.csv", "file3.csv"] output_file = "./result_merge/output.csv" first_one = True for csv_file_name in csv_file_list: if not first_one: # if it is not the first csv file then skip the header row (row 0) of that file skip_row =  else: skip_row =  chunk_container = pd.read_csv(csv_file_name, chunksize=CHUNK_SIZE, skiprows = skip_row) for chunk in chunk_container: chunk.to_csv(output_file, mode="a", index=False) first_one = False
Merging very large csv files in Python, Good question, sir! Python supports the concept of ‚generators' to execute tasks in a particular iterator like fashion. This is often used in the� It doesn’t use any special Python package to combine the CSV files and can save you a lot of time from going through multiple CSV individually. If you’ve learned something from this example, then care to share it with your colleagues. Also, connect to our social media (Facebook / Twitter) accounts to receive timely updates.
You can convert the
TextFileReader object using pd.DataFrame like so:
df = pd.DataFrame(chunk), where
chunk is of type
TextFileReader. You can then use pd.concat to concatenate the individual dataframes.
Merging large CSV files in Pandas, Merging large CSV files in Pandas � python data pandas. I have two CSV files( each of the file size is in GBs). I am trying to� Use pandas to concatenate all files in the list and export as CSV. The output file is named “combined_csv.csv” located in your working directory. #combine all files in the list combined_csv = pd.concat([pd.read_csv(f) for f in all_filenames ]) #export to csv combined_csv.to_csv("combined_csv.csv", index=False, encoding='utf-8-sig')
Combine CSV files on Low Spec Machine, CSV files on low spec computer or combine very large CSV files that Python has High-level file operations library to do this for us called� """ Python Script: Combine/Merge multiple CSV files using the Pandas library """ from os import chdir from glob import glob import pandas as pdlib # Produce a single CSV after combining all files def produceOneCSV(list_of_files, file_out): # Consolidate all CSV files into one object result_obj = pdlib.concat([pdlib.read_csv(file) for file in list_of_files]) # Convert the above object into a csv file and export result_obj.to_csv(file_out, index=False, encoding="utf-8") # Move to the path that
[Pandas] Merging large csv file with another file in pieces : learnpython, I'm having some trouble merging a large csv file with a smaller one using Pandas . I was re-reading an article about two python pip modules actually being� print pd.read_csv (file, nrows=5) This command uses pandas’ “read_csv” command to read in only 5 rows (nrows=5) and then print those rows to the screen. This lets you understand the structure of the csv file and make sure the data is formatted in a way that makes sense for your work.
Working with large CSV files in Python, When working wth large CSV files in Python, you can sometimes run into memory issue. Using pandas and sqllite can help you work around� import pandas as pd csv1 = pd.read_csv("file1.csv") csv2 = pd.read_csv("file2.csv") csv_out = csv1.merge(csv2, on=['row number','time']) csv_out.to_csv("file_out.csv", index=False) Hope it helps.
- Does it need to be with pandas? Is the csv data format the same across all the files? If they are, you could just look into reading / writing the source / destination files line-by-line, avoiding the memory issue.
- You can try using dask, as it is better suited to manage such large files in memory.
- Possible duplicate of Reading a huge .csv file
- there are several discussions about this topic: stackoverflow.com/questions/17444679/reading-a-huge-csv-file
- @martyn It doesn't need to be with pandas but as a beginner i don't know what else i can use.
- Note that this will fail/behave weirdly if your separator character also appears inside the fields. You might need more sophisticated parsing for the line data in that case.
- So does this create a csv file of the data that i want, which I then re-import this and do my analysis from that?
- No, this will read all of your csv files line by line, and write each line it to the target file only if it pass the
check_datamethod. (No memory was harmed while using this solution)
- So if in the check_data function I want to only take rows with 'USA' in the 'Country' column for each of the 18 files, how would this be written? Sorry for the simple question.
- You should add header=False to to_csv(), otherwise every time you write a chunk a header will be written. In my case, my input data did not have a header, so read_csv() interpreted the first line as header and to_csv() inserted the first line when writing every chunk. If you need the first lines from the input files, then add header=None to read_csv().