Concatenate several CSV files in a single dataframe

Question

I have currently 600 CSV files (and this number will grow) of 50K lines each i would like to put in one single dataframe. I did this, it works well and it takes 3 minutes :

colNames = ['COLUMN_A', 'COLUMN_B',...,'COLUMN_Z']
folder = 'PATH_TO_FOLDER'

# Dictionnary of type for each column of the csv which is not string    
dictTypes = {'COLUMN_B' : bool,'COLUMN_D' :int, ... ,'COLUMN_Y':float}

try:
   # Get all the column names, if it's not in the dict of type, it's a string and we add it to the dict
   dictTypes.update({col: str for col in colNames if col not in dictTypes})  
except:
    print('Problem with the column names.')
    
# Function allowing to parse the dates from string to date, we put in the read_csv method
cache = {}
def cached_date_parser(s):
    if s in cache:
        return cache[s]
    dt = pd.to_datetime(s, format='%Y-%m-%d', errors="coerce")
    cache[s] = dt
    return dt

# Concatenate each df in finalData
allFiles = glob.glob(os.path.join(folder, "*.csv")) 
finalData = pd.DataFrame()
finalData = pd.concat([pd.read_csv(file, index_col=False, dtype=dictTypes, parse_dates=[6,14],
                    date_parser=cached_date_parser) for file in allFiles ], ignore_index=True)

It takes one minute less without the parsing date thing. So i was wondering if i could improve the speed or it was a standard amount of time regarding the number of files. Thanks !

Answer 1

Here is my untested feedback on your code. Some remarks:

• Encapsulate the functionality as a named function. I assumed folder_path as the main "variant" your calling code might want to vary, but your use case might "call" for a different first argument.
• Use PEP8 recommandations for variable names.
• Comb/separate the different concerns within the function:
  1. gather input files
  2. handle column types
  3. read CSVs and parse dates
• Depending on how much each of those concerns grows in size over time, multiple separate functions could organically grow out of these separate paragraphs, ultimately leading to a whole utility package or class (depending on how much "instance" configuration you would need to preserve, moving the column_names and dtypes parameters to object attributes of a class XyzCsvReader's __init__ method.)
• Concerning the date parsing: probably the bottleneck is not caused by caching or not, but how often you invoke the heavy machinery behind pd.to_datetime. My guess is that only calling it once in the end, but with infer_datetime_format enabled will be much faster than calling it once per row (even with your manual cache).

import glob
import os
import pandas as pd

def read_xyz_csv_folder(
        folder_path,
        column_names=None,
        dtypes=None):
    all_files = glob.glob(os.path.join(folder_path, "*.csv"))

    if column_names is None:
        column_names = [
            'COLUMN_A',
            'COLUMN_B',  # ...
            'COLUMN_Z']
    if dtypes is None:
        dtypes = {
            'COLUMN_B': bool,
            'COLUMN_D': int,
            'COLUMN_Y': float}
    dtypes.update({col: str for col in column_names 
                   if col not in dtypes})

    result =  pd.concat((
            pd.read_csv(file, index_col=False, dtype=dtypes)
            for file in all_files),
        ignore_index=True)
    
    # untested pseudo-code, but idea: call to_datetime only once
    result['date'] = pd.to_datetime(
        result[[6, 14]],
        infer_datetime_format=True,
        errors='coerce')
    
    return result
        
# use as
read_xyz_csv_folder('PATH_TO_FOLDER')

Edit: as suggested by user FMc in their comment, switch from a list comprehension to a generator expression within pd.concat to not create an unneeded list.