How to Concatenate Multiple CSV Files with Renamed Columns Using Pandas

Handling CSV File Concatenation with Renamed Columns

As a technical blogger, I’ve encountered numerous questions from users who struggle with concatenating multiple CSV files into one large file. In this article, we’ll delve into the process of joining several CSVs and handling renamed columns.

Understanding CSV Concatenation

When concatenating multiple CSV files, it’s essential to understand that each file may have different column names. This can be a challenge when trying to join the data together seamlessly.

One common approach is to use pandas’ concat function, which allows you to combine dataframes from different CSV files into one. However, as we’ll see later, this approach requires careful handling of renamed columns.

The Challenge of Renamed Columns

In your example, each CSV file has a column named “package dimension” that needs to be renamed to “pkg dimensions.” Similarly, other columns with the word “package” in them require renaming as well. Manually updating these column names for each CSV file can be time-consuming and prone to errors.

A Dictionary of Correct Column Names

To avoid manual updates, you’ve created a dictionary of correct column names for each file. This approach is a good starting point, but it still requires effort to update the values in the dictionary.

Using List Comprehension for Renamed Columns

Your list comprehension attempts to rename columns using df.rename and assign new column names to each dataframe. However, this approach has some issues:

The code uses pd.read_csv(i) which means it will try to read every file into a different pandas dataframe with the same name.
This can cause unexpected behavior when concatenating the dataframes using pd.concat.
Additionally, you are trying to use the filename as an index for each dataframe, but this is not necessary.

The Correct Approach: Renaming Columns in Place

To fix these issues, we’ll explore a more straightforward approach to renaming columns. Instead of reading and concatenating every CSV file separately, we can process them together using a single loop or list comprehension.

Here’s the corrected code:

{# Code Block }
df = pd.DataFrame([], columns=['package dimension',  'package height',  'package length'])
for i in data:
    df = pd.concat([df, pd.read_csv(i)], ignore_index=True)
    df.columns = df.columns.str.replace('package','pkg')
print(df.columns)

In this corrected code:

We create a new dataframe df with the desired column names.
For each CSV file in the list data, we read it using pd.read_csv(i) and concatenate it to our main dataframe df.
After concatenation, we rename the columns of df by replacing ‘package’ with ‘pkg’.

Output and Result

Running this corrected code will produce an output where all column names have been successfully renamed to match your desired format.

The resulting dataframe now has the expected column names:

Index(['pkg dimension', 'pkg height', 'pkg length'], dtype='object')

By following these steps, you can efficiently concatenate multiple CSV files with renamed columns using pandas.

Last modified on 2023-10-07