Embeddable Excel Tables in Python Scripts using Pandas

Introduction

As a developer, you often find yourself working with data from various sources, including Excel files. However, when it comes to reading and manipulating this data in your Python scripts, there are several challenges you may face. One common issue is dealing with large or complex datasets that don’t fit neatly into the native data structures of your programming language.

In this article, we will explore how to embeddable read Excel tables from pandas-exported json files using the popular Python library Pandas.

Understanding the Problem

The question provided in the Stack Overflow post highlights a common issue developers face when working with large or complex datasets. The original code attempts to read an Excel file into a Pandas DataFrame and then convert it to JSON format, which is then stored as a string variable in the script. However, this approach has limitations.

Native Data Structure for 2D Matrices

The native data structure able to store 2D matrices is a list of lists. This can be obtained from your Excel file by using the pd.read_excel function with the values attribute.

import pandas as pd
df = pd.read_excel("/your/excel/here/TEST.xlsx")
print("mat =", df.values.tolist())

This code reads the Excel file into a Pandas DataFrame and then prints out the 2D matrix stored in the df variable. The values attribute of the DataFrame is used to access the underlying data as a NumPy array.

Embedding the Data

To embed the data directly into your script, you can simply copy the printed lines with your mouse and paste them at the beginning of your code, creating a matrix mat that stores your data.

import pandas as pd
df = pd.read_excel("/your/excel/here/TEST.xlsx")
print("mat =", df.values.tolist())
# Paste the output into your script here...

Creating a Pandas DataFrame

If you need a Pandas DataFrame, you can modify the print statement to include the pd.DataFrame constructor:

import pandas as pd
df = pd.read_excel("/your/excel/here/TEST.xlsx")
print("df =", df)
# or
print("df =", pd.DataFrame(df.values.tolist()))

The first option will output a regular DataFrame, while the second option will create an equivalent DataFrame using the pd.DataFrame constructor.

Converting to JSON

If you need to convert your data to JSON format, you can use the to_json method provided by Pandas DataFrames:

import pandas as pd
df = pd.read_excel("/your/excel/here/TEST.xlsx")
print("json =", df.to_json(orient='values'))

This code will output a JSON string representing your 2D matrix.

Conclusion

Embedding Excel tables into Python scripts using Pandas is a common requirement in data science and scientific computing. By understanding the native data structures used by Pandas, developers can easily read and manipulate large or complex datasets directly within their scripts.

In this article, we explored how to embeddable read Excel tables from pandas-exported json files using Pandas. We discussed the importance of using a list of lists as the native data structure for 2D matrices, and provided examples on how to create matrices, convert them to JSON format, and work with Pandas DataFrames.

By mastering these techniques, developers can efficiently process large datasets and integrate them into their Python scripts, making it easier to analyze, visualize, and model complex data.

Last modified on 2023-10-09