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Almost all of the answers here are either unnecessarily complex (glob pattern matching) or rely on additional 3rd party libraries. You can do this in 2 lines using everything Pandas and python (all versions) already have built in.

For a few files - 1 liner

df = pd.concat(map(pd.read_csv, ['d1.csv', 'd2.csv','d3.csv']))

For many files

import os

filepaths = [f for f in os.listdir(".") if f.endswith('.csv')]
df = pd.concat(map(pd.read_csv, filepaths))

For No Headers

If you have specific things you want to change with pd.read_csv (i.e. no headers) you can make a separate function and call that with your map:

def f(i):
    return pd.read_csv(i, header=None)

df = pd.concat(map(f, filepaths))

This pandas line which sets the df utilizes 3 things:

1. Python's map (function, iterable) sends to the function (the pd.read_csv()) the iterable (our list) which is every csv element in filepaths).
2. Panda's read_csv() function reads in each CSV file as normal.
3. Panda's concat() brings all these under one df variable.

Almost all of the answers here are either unnecessarily complex (glob pattern matching) or rely on additional third-party libraries. You can do this in two lines using everything Pandas and Python (all versions) already have built in.

For a few files - one-liner

df = pd.concat(map(pd.read_csv, ['d1.csv', 'd2.csv','d3.csv']))

For many files

import os

filepaths = [f for f in os.listdir(".") if f.endswith('.csv')]
df = pd.concat(map(pd.read_csv, filepaths))

For No Headers

If you have specific things you want to change with pd.read_csv (i.e., no headers) you can make a separate function and call that with your map:

def f(i):
    return pd.read_csv(i, header=None)

df = pd.concat(map(f, filepaths))

This pandas line, which sets the df, utilizes three things:

1. Python's map (function, iterable) sends to the function (the pd.read_csv()) the iterable (our list) which is every CSV element in filepaths).
2. Panda's read_csv() function reads in each CSV file as normal.
3. Panda's concat() brings all these under one df variable.

Almost all of the answers here are either unnecessarily complex (glob pattern matching) or rely on additional 3rd party libraries. You can do this in 2 lines using everything Pandas and python (all versions) already have built in.

For a few files - 1 liner:

df = pd.concat(map(pd.read_csv, ['data/d1.csv', 'data/d2.csv','data/d3.csv']))

For many files:

from os import listdir

filepaths = [f for f in listdir("./data") if f.endswith('.csv')]
df = pd.concat(map(pd.read_csv, filepaths))

This pandas line which sets the df utilizes 3 things:

1. Python's map (function, iterable) sends to the function (the pd.read_csv()) the iterable (our list) which is every csv element in filepaths).
2. Panda's read_csv() function reads in each CSV file as normal.
3. Panda's concat() brings all these under one df variable.

Almost all of the answers here are either unnecessarily complex (glob pattern matching) or rely on additional 3rd party libraries. You can do this in 2 lines using everything Pandas and python (all versions) already have built in.

For a few files - 1 liner

df = pd.concat(map(pd.read_csv, ['d1.csv', 'd2.csv','d3.csv']))

For many files

import os

filepaths = [f for f in os.listdir(".") if f.endswith('.csv')]
df = pd.concat(map(pd.read_csv, filepaths))

For No Headers

If you have specific things you want to change with pd.read_csv (i.e. no headers) you can make a separate function and call that with your map:

def f(i):
    return pd.read_csv(i, header=None)

df = pd.concat(map(f, filepaths))

This pandas line which sets the df utilizes 3 things:

1. Python's map (function, iterable) sends to the function (the pd.read_csv()) the iterable (our list) which is every csv element in filepaths).
2. Panda's read_csv() function reads in each CSV file as normal.
3. Panda's concat() brings all these under one df variable.

Almost all of the answers here are either unnecessarily complex (glob pattern matching) or rely on additional 3rd party libraries. You can do this in 2 lines using everything Pandas and python (all versions) already have built in.

For a few files:

filepaths = ['data/d1.csv', 'data/d2.csv','data/d3.csv','data/d4.csv']
df = pd.concat(map(pd.read_csv, filepaths))

For many files:

from os import listdir

filepaths = [f for f in listdir("./data") if f.endswith('.csv')]
df = pd.concat(map(pd.read_csv, filepaths))

This pandas line which sets the df utilizes 3 things:

1. Python's map (function, iterable) sends to the function (the pd.read_csv()) the iterable (our list) which is every csv element in filepaths).
2. Panda's read_csv() function reads in each CSV file as normal.
3. Panda's concat() brings all these under one df variable.

Almost all of the answers here are either unnecessarily complex (glob pattern matching) or rely on additional 3rd party libraries. You can do this in 2 lines using everything Pandas and python (all versions) already have built in.

For a few files - 1 liner:

df = pd.concat(map(pd.read_csv, ['data/d1.csv', 'data/d2.csv','data/d3.csv']))

For many files:

from os import listdir

filepaths = [f for f in listdir("./data") if f.endswith('.csv')]
df = pd.concat(map(pd.read_csv, filepaths))

This pandas line which sets the df utilizes 3 things:

1. Python's map (function, iterable) sends to the function (the pd.read_csv()) the iterable (our list) which is every csv element in filepaths).
2. Panda's read_csv() function reads in each CSV file as normal.
3. Panda's concat() brings all these under one df variable.