How To Convert CSV To Excel In Python Using Pandas

In this tutorial, we will learn how to convert a CSV (Comma Separated Values) file to an Excel file using Python’s powerful library, Pandas.

The conversion of a CSV file to an Excel file is a common and essential task for data analysts and programmers as it allows them to manipulate and analyze data efficiently.

By following the simple steps outlined below, you will be able to easily convert CSV files to Excel using Pandas in no time!

Step 1: Create a file

Create a CSV file called “data.csv” and insert this text into it:

Column 1,Column 2,Column 3
Value 10,Value 11,Value 12
Value 13,Value 14,Value 15
Value 16,Value 17,Value 18

Step 2: Install Pandas

Before we can start converting CSV files to Excel using Pandas, we need to install Pandas in our Python environment. You can install the library using the following pip command:

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pip install pandas

Once Pandas is installed, we can start working with CSV files and convert them to Excel.

Step 3: Read the CSV file using Pandas

We can use the read_csv() function in Pandas to read the contents of a CSV file. First, let’s import the Pandas library and then use the function to read the contents of a CSV file and store them in a DataFrame:

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import pandas as pd
 
csv_file = 'data.csv' # Replace with the path to your CSV file
df = pd.read_csv(csv_file)

The DataFrame, df, will now hold the contents of the CSV file, which we can then convert to an Excel file.

Step 4: Convert the DataFrame to an Excel file

To convert the DataFrame, df, to an Excel file, we can use the to_excel() function. This function accepts the name of the output Excel file as its argument. By default, it will create a new Excel file with the specified name and write the DataFrame to the first sheet of the file:

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output_excel = 'data.xlsx' # Replace with the desired path for your Excel file
df.to_excel(output_excel, index=False)

By passing index=False to the to_excel() function, we exclude the DataFrame’s index column from the output Excel file. This is optional and can be adjusted according to your needs.

Step 5: Verify the conversion

You can now check the generated Excel file to make sure that it contains the data from the CSV file. If you want to verify the conversion programmatically, you can use the read_excel() function, which is really similar to the read_csv() function:

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read_excel_df = pd.read_excel(output_excel)
print(read_excel_df.head()) # Prints the first 5 rows of the Excel file

This should display the first 5 rows of the data in the generated Excel file, and you can compare this with the contents of the original CSV file to verify the conversion.

Full Code

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import pandas as pd
 
# Step 2: Read the CSV file
csv_file = 'data.csv' # Replace with the path to your CSV file
df = pd.read_csv(csv_file)
 
# Step 3: Convert the DataFrame to an Excel file
output_excel = 'data.xlsx' # Replace with the desired path for your Excel file
df.to_excel(output_excel, index=False)
 
# Step 4: Verify the conversion
read_excel_df = pd.read_excel(output_excel)
print(read_excel_df.head()) # Prints the first 5 rows of the Excel file

Conclusion

In this tutorial, we learned how to convert a CSV file to an Excel file using the Pandas library in Python. This powerful library makes it quick and easy to accomplish this important data-processing task.

Furthermore, Pandas offers many other functions for working with CSV and Excel files that can further aid you in your data analysis journey!