Writing Excel Spreadsheets

This may sound contradictory to modern-day data science practices, but Excel has its place in the world of analysis and data storytelling. Exporting data from R and Python to Excel can be beneficial for several reasons, offering users the opportunity to leverage the strengths of both platforms. Excel is a widely used spreadsheet program known for its user-friendly interface, while R and Python is a powerful statistical programming language. By exporting data from R and Python to Excel, users can take advantage of Excel’s familiar and versatile features to further analyze, visualize, and share data.

One significant advantage of exporting data from R and Python to Excel is the ability to harness Excel’s extensive range of data manipulation and visualization capabilities. Excel provides a variety of tools, such as pivot tables, charts, and conditional formatting, which enable users to explore and present data more interactively and intuitively...

Technical requirements

We are going to use the built-in Iris dataset in this chapter. This is a good dataset for demonstration purposes.

This chapter’s code can be found in this book’s GitHub repository: https://github.com/PacktPublishing/Extending-Excel-with-Python-and-R/tree/main/Chapter2.

Packages to write into Excel files

In this section, we are going to go over a few different libraries that we can use to write data.frames/tibbles to Excel files. We are going to use the writexl, openxlsx, and xlsx libraries.

In the following section, we are going to list each package, specify where you can find the function documentation that writes the data to Excel, and go over the functions’ parameters.

writexl

The writexl package is part of the rOpenSci consortium and can be found here: https://docs.ropensci.org/writexl/reference/write_xlsx.html.

The library does not require Java or Excel to work.

The function that writes the data to Excel is write_xlsx(). Let’s go over the different parameters of the function and look at a full pseudo function call.

First, let’s look at the function call itself – that is, write_xlsx():

write_xlsx(
  x,
  path = tempfile(fileext = ".xlsx"),
  col_names = TRUE...

Creating and manipulating Excel sheets using Python

In this section, we will explore how to create and manipulate Excel sheets using Python.

Exporting data to Excel is a common requirement in various data analysis and reporting scenarios. Excel provides a familiar and widely used interface for data visualization, sharing, and further analysis.

We’ll cover various tasks in the sections ahead, including creating new workbooks, adding sheets to existing workbooks, deleting sheets, and manipulating data within an Excel workbook. Python provides several libraries that make these tasks straightforward and efficient. But first, let’s understand why we need to export data to Excel.

Why export data to Excel?

Exporting data to Excel offers several benefits. Firstly, Excel provides a user-friendly environment for data exploration and visualization, allowing users to easily sort, filter, and analyze data. Additionally, Excel’s rich formatting capabilities make...

Keeping it simple – exporting data to Excel with pandas

pandas is a popular data manipulation library in Python that provides powerful tools for data analysis. It also offers excellent functionality for exporting data to Excel. Using pandas, you can effortlessly transform your data into Excel sheets or workbooks.

pandas provides the DataFrame.to_excel() method, allowing you to export data to an Excel file with just a few lines of code. Here’s an example:

import pandas as pd
# Create a DataFrame with sample data
data = {
    'Name': ['John', 'Jane', 'Mike'],
    'Age': [25, 30, 35],
    'City': ['New York', 'London', 'Sydney']
}
df = pd.DataFrame(data)
# Export the DataFrame to an Excel file
df.to_excel('data.xlsx', index=False)

The code doesn’t return anything, but it does have a side effect –...

Advanced mode – openpyxl for Excel manipulation

In this section, we will look at the openpyxl package, which allows for a more nuanced interaction with Excel when writing data.

Creating a new workbook

To start working with Excel sheets in Python, we need to create a new workbook. openpyxl provides an intuitive API to create, modify, and save Excel workbooks. Here’s an example code snippet that demonstrates creating a new workbook:

import openpyxl
# Create a new workbook
workbook = openpyxl.Workbook()

Once again, the preceding code snippet doesn’t return anything but it does have a side effect – it creates the workbook:

Figure 2.4 – Creating a workbook with openpyxl

Adding sheets to the workbook

Once we have a workbook, we can add sheets to it. Adding sheets allows us to organize data into separate sections or categories. openpyxl provides a simple method, create_sheet(), to add sheets to a workbook. Let...

Choosing between openpyxl and pandas

When it comes to exporting data to Excel, both openpyxl and pandas are excellent choices. openpyxl is a dedicated library for working with Excel files as it provides extensive functionality for creating, modifying, and saving Excel workbooks. On the other hand, pandas offers a high-level data manipulation interface with convenient methods for exporting data to Excel, which is ideal when a simple data dump is all you need.

If you require fine-grained control over the Excel file’s structure, such as adding formatting, formulas, or charts, openpyxl is a suitable option. It allows you to work directly with the underlying Excel objects, providing more flexibility. On the other hand, if you primarily focus on data manipulation and want a simpler way to export DataFrames to Excel without worrying about Excel-specific features, pandas is a convenient choice. It abstracts away some of the lower-level details and provides a more straightforward...

Other alternatives

Apart from pandas and openpyxl, there are other libraries available for exporting data to Excel from Python. Some popular alternatives include XlsxWriter, xlrd, and xlwt. These libraries offer different features and capabilities, and the choice depends on your specific requirements. XlsxWriter, for example, emphasizes performance and supports advanced Excel features, while xlrd and xlwt provide functionality for reading and writing older Excel file formats (.xls).

In this section, we explored the benefits of exporting data to Excel, demonstrated how to do so using pandas, went through the process of creating and manipulating Excel sheets using openpyxl, discussed the reasons for choosing openpyxl or pandas based on your needs, and mentioned other alternatives available. By leveraging the power of these libraries, you can seamlessly export your data from Python to Excel, enabling efficient analysis, reporting, and collaboration.

Summary

In Chapter 2, we explored the process of writing data to Excel using different R and Python libraries and benchmarking their performance. We also discussed creating and manipulating Excel sheets from Python using pandas and openpyxl. By comparing their features and exploring alternatives, you have gained insights into the capabilities of both R and Python for Excel tasks.

In the next chapter, we will learn how to execute VBA code with R and Python.