You can use the following basic syntax to merge multiple CSV files located in the same folder into a pandas DataFrame:

import pandas as pd
import glob
import os

#define path to CSV files
path = r'C:UsersbobDocumentsmy_data_files'

#identify all CSV files
all_files = glob.glob(os.path.join("*.csv"))

#merge all CSV files into one DataFrame
df = pd.concat((pd.read_csv(f) for f in all_files), ignore_index=True)

This particular example will merge all of the CSV files located in the folder called my_data_files into one pandas DataFrame.

The following example shows how to use this syntax in practice.

Example: Merge Multiple CSV Files in Pandas

Suppose I have a folder on my computer called my_data_files that contains three CSV files:

Each CSV file contains two columns called points and assists, which represents the points and assists of various basketball players.

Here is what the first CSV called df1 looks like:

We can use the following syntax to merge all three CSV files from the folder into one pandas DataFrame:

import pandas as pd
import glob
import os

#define path to CSV files
path = r'C:UsersbobDocumentsmy_data_files'

#identify all CSV files
all_files = glob.glob(os.path.join("*.csv"))

#merge all CSV files into one DataFrame
df = pd.concat((pd.read_csv(f) for f in all_files), ignore_index=True)

#view resulting DataFrameprint(df)

    points  assists
0        4        3
1        5        2
2        5        4
3        6        4
4        8        6
5        9        3
6        2        3
7       10        2
8       14        9
9       15        3
10       6       10
11       8        6
12       9        4

Notice that all three CSV files have been successfully imported and merged into one DataFrame.

The final DataFrame contains 13 rows and 2 columns.

Note: You can find the complete documentation for the pandas read_csv() function .

The following tutorials explain how to perform other common tasks in Python: