Pandas DataFrames are fantastic. However, concatenating them using standard approaches, such as pandas.concat(), can be very slow with large dataframes. This is a work around for that problem.

Note: this approach assumes that: (a) the goal is a row-wise concatenation (i.e., axis=0) and (b) all dataframes share the same column names.

If these assumptions are not met, this approach could still work…​but it will likely need to be modified.

Start by importing os, Pandas and chain from itertools.

import os
import pandas as pd
from itertools import chain

Set the path to the data files:

PATH_TO_FILES = '/your/path/here/'

Read in the Data as Pandas DataFrames (csv files, in this example):

frames = list()
for csv in [os.path.join(PATH_TO_FILES, f) for f in os.listdir(PATH_TO_FILES) if f.endswith('.csv')]:
    frames.append(pd.read_csv(csv))

Define a function to flatten large 2D lists quickly:

def fast_flatten(input_list):
    return list(chain.from_iterable(input_list))

Next, construct a dictionary using the column names from one of the dataframes (located at index 0):

COLUMN_NAMES = frames[0].columns

Now, construct a dictionary from the column names:

df_dict = dict.fromkeys(COLUMN_NAMES, [])

Iterate though the columns:

for col in COLUMN_NAMES:
    extracted = (frame[col] for frame in frames)

    # Flatten and save to df_dict
    df_dict[col] = fast_flatten(extracted)

Lastly use the from_dict method to produce the combined DataFrame:

df = pd.DataFrame.from_dict(df_dict)[COLUMN_NAMES]

While this method is not very pretty, it typically is much faster than pd.concat() and yields the exact same result.