How to Read and Write Excel Files with Python: A Step-by-Step Guide

Reading and Writing Excel Files with Python: A Step-by-Step Guide

Reading and writing Excel files is a common task in data analysis and science. In this article, we will explore how to read a portion of an existing Excel sheet, filter the data, and write a single value from the filtered dataframe to a specific cell in the same sheet using Python.

Prerequisites

Before we begin, make sure you have the necessary libraries installed:

pandas for data manipulation and analysis
openpyxl for reading and writing Excel files

You can install these libraries using pip:

pip install pandas openpyxl

Reading an Existing Excel Sheet

To read an existing Excel sheet, we will use the openpyxl library. First, we need to load the workbook:

import openpyxl

wb = openpyxl.load_workbook('test.xlsx')

This code loads the workbook from a file named test.xlsx.

Creating a DataFrame

Next, we create a pandas DataFrame from the data in the Excel sheet:

df = pd.DataFrame(data=[1,2,3], columns=['col'])

In this example, we are creating a simple DataFrame with two rows and one column.

Filtering the DataFrame

To filter the DataFrame, we use the df[df.col == 1].values[0][0] expression. This expression creates a new Series from the filtered dataframe:

filtered_dataframe = df[df.col == 1].values[0][0]

This code filters the DataFrame to only include rows where the value in the ‘col’ column is equal to 1.

Writing to an Excel Cell

To write to an Excel cell, we use the wb['Sheet1'].cell(column=1, row=2, value=filtered_dataframe) expression. This expression creates a new cell at position (1,2) and writes the value of filtered_dataframe to it:

wb['Sheet1'].cell(column=1, row=2, value=filtered_dataframe)

Saving the Workbook

Finally, we need to save the updated workbook:

wb.save(filename)

This code saves the workbook with the new changes.

Understanding the Issues with the Original Code

The original code had a few issues:

The writer object was not called, which prevented the data from being written to the Excel file.
The to_excel method was used incorrectly. This method is typically used for writing large datasets, but it can be slow and inefficient for small datasets.

Best Practices for Reading and Writing Excel Files

Here are some best practices to keep in mind when reading and writing Excel files:

Always use the openpyxl library to read and write Excel files.
Use the pandas library to create and manipulate DataFrames.
When filtering DataFrames, use the .values[0][0] expression to extract a single value.
When writing to Excel cells, use the .cell() method to specify the position of the cell.

Common Use Cases for Reading and Writing Excel Files

Here are some common use cases for reading and writing Excel files:

Data analysis: Use pandas to read in data from an Excel file and perform calculations.
Data visualization: Use matplotlib or seaborn to visualize data from an Excel file.
Automation: Use openpyxl to automate tasks such as formatting or editing Excel files.

Conclusion

Reading and writing Excel files is a common task in data analysis and science. By following the best practices outlined in this article, you can efficiently read and write Excel files using Python. Remember to use the openpyxl library to read and write Excel files, and pandas for data manipulation and analysis.

Last modified on 2024-01-07