Reading multiple csv files in a single dataframe

Question

I have a lot of compressed csv files in a directory. I want to read all those files in a single dataframe. This is what I have done till now:

df = pd.DataFrame(columns=col_names)
for filename in os.listdir(path):
 with gzip.open(path+"/"+filename, 'rb') as f:
 temp = pd.read_csv(f, names=col_names)
 df = df.append(temp)

I have noticed that the above code runs quite fast initially, but it keeps on getting slower and slower as it reads more and more files. How can I improve this?

• RomanPerekhrest: what's your files extension .tar.gz or .gz ?
• Ankur: @RomanPerekhrest file extension is .gz

Answer 1

Ultimate optimization

• avoid calling pd.DataFrame.append function within a loop as it'll create a copy of accumulated dataframe on each loop iteration. Apply pandas.concat to concatenate pandas objects at once.

• no need to gzip.open as pandas.read_csv already allows on-the-fly decompression of on-disk data.

  compression : {‘infer’, ‘gzip’, ‘bz2’, ‘zip’, ‘xz’, None}, default ‘infer’

• avoid hardcoding filepathes with path+"/"+filename. Instead use suitable os.path.join feature: os.path.join(dirpath, fname)

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The final optimized approach:

import os
import pandas as pd
dirpath = 'path_to_gz_files' # your directory path
df = pd.concat([pd.read_csv(os.path.join(dirpath, fname))
 for fname in os.listdir(dirpath)], ignore_index=True)