Setting up Excel Python Integration: 2 Easy Methods

• April 6th, 2021

Setting up Excel Python Integration

Microsoft Excel was developed as a tool for creating spreadsheets for your operations to help you organize your data and perform analysis on that data. Along with this it also provides additional functionalities like graphing tools, calculations, and pivot tables to name a few. Python on the other hand is a programming language developed for easy code readability. It can be used for developing web applications, building machine learning models, and carrying out Exploratory Data Analysis on any given data to extract actionable insights. 

In this article, you will learn about the two methods that you can use to set up the Excel Python Integration using Python libraries after looking at a brief introduction of Microsoft Excel and Python.  

Table of Contents

Introduction to Microsoft Excel

Microsoft Excel Logo
Image Source

Microsoft Excel was the bedrock of data analysis before technology brought forth more comprehensive tools that made the task of data analysis a lot easier. Microsoft Excel houses a large number of functionalities and along with its ease of use, it has been an integral tool for all manners of education, business, finance, and research to name a few areas. Microsoft Excel continues to be a popular data analysis tool to organize and present large amounts of data. However, with the entry of Python and similar programming languages data analysis has become an entirely different ball game. 

Before going into how Python has changed the landscape for web development, infrastructure management, and data analysis, here is a look at the challenges Microsoft Excel is working to overcome:

  • Syntax Errors: Excel users usually encounter errors when inputting formulas manually, or while copying and pasting data in some cell ranges. 
  • Security Issues: Companies need to be wary of the information that they place in Excel sheets, which makes this data vulnerable to misuse and cyber attacks.
  • Increasing Data Volume: A few years back, data was stored in Excel and operations could be carried out smoothly on this data. But with a monumental increase in data volume, it has become infeasible to keep on storing this data in Excel spreadsheets because it leads to complex analytical issues. This happens because Microsoft Excel is primarily geared towards smaller sets of data. 

Introduction to Python

Python Logo
Image Source

Python initially developed as a general-purpose programming language to automate tedious tasks that developers might face daily. But it has now grown to spearhead the development of a wider data science community, owing to its sheer potential and capabilities. Python can be used for a wide range of applications as discussed before, from developing apps to carrying out Exploratory Data Analysis on customer data to extract actionable insights from it. The shift from Microsoft Excel to Python took place due to the following use cases:

  • Data Mining: You can say, extract data from Twitter, and perform a sentiment analysis on the tweets to identify the percentage of people that may be happy/unhappy with something. You can do the same thing on an eCommerce site like Amazon to find out the feedback for a particular product and gain insightful inputs that can help you improve upon the customer experience.
  • Blockchain Building: You can build a Blockchain using Python that can be used for any kind of financial transaction.
  • Data Automation: You can automate a lot of simple tasks like formatting data, renaming files, spell checking, generation of Microsoft Excel reports using Python for a start.
  • App Building: You can build interactive apps that help you achieve a specific task using Python by leveraging in-built Python libraries in place that offers you countless possibilities.  

Simplify your Data Analysis with Hevo’s No-code Data Pipeline

A fully managed No-code Data Pipeline platform like Hevo helps you integrate data from Microsoft Excel and Python with 100+ other data sources to a destination of your choice in real-time in an effortless manner. Hevo with its minimal learning curve can be set up in just a few minutes allowing the users to load data without having to compromise performance. Its strong integration with umpteenth sources provides users with the flexibility to bring in data of different kinds, in a smooth fashion without having to code a single line. 

Check out some of the cool features of Hevo:

  • Completely Automated: The Hevo platform can be set up in just a few minutes and requires minimal maintenance.
  • Real-Time Data Transfer: Hevo provides real-time data migration, so you can have analysis-ready data always.
  • 100% Complete & Accurate Data Transfer: Hevo’s robust infrastructure ensures reliable data transfer with zero data loss.
  • Scalable Infrastructure: Hevo has in-built integrations for 100+ sources like Excel and Python, that can help you scale your data infrastructure as required.
  • 24/7 Live Support: The Hevo team is available round the clock to extend exceptional support to you through chat, email, and support calls.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Live Monitoring: Hevo allows you to monitor the data flow so you can check where your data is at a particular point in time.

You can try Hevo for free by signing up for a 14-day free trial.

Understanding the Excel Python Integration Setup

To set up Excel Python Integration, you can use a few handy Python libraries and start this process. A Python Library is an assortment of methods and functions that let you perform actions without having to write the code for it from scratch. This lends efficiency to the work of a Data Analyst as they can simply import the required libraries. For instance, Google Tensorflow is a Python library that is used for developing machine learning projects. SciKit Learn is a Python library that comes in handy when dealing with complex datasets.  

The two libraries you can use for Excel Python Integration are openpyxl and pandas. The next sections discuss how you can use them in detail. 

Method 1: Using the openpyxl Library for Excel Python Integration

Openpyxl for Excel Python Integration
Image Source

openpyxl can be used for Excel Python Integration to either extract data from a database into an Excel spreadsheet or convert an Excel spreadsheet to a programmatic format. These are a few use cases where you might need to use openpyxl for Excel Python Integration:

  • Exporting to a Spreadsheet: Given a database table where you store all customer information. This information will be required by the Marketing team in order to promote new products of the said company. In the absence of access to the database or no-to-minimal SQL knowledge to extract the information, openpyxl can be used to convert this database table into an Excel spreadsheet. 
  • Adding Additional Information to a Spreadsheet: Now for the above example, say you need to add the total amount spent by the customers in your store to your Excel spreadsheet. To perform this, you will have to iterate through the entire spreadsheet, iterate through each row and extract the total amount across the database. This value would then be added to the Excel spreadsheet. 

Here are the steps for using openpyxl for Excel Python Integration:

  • Step 1: First, you need to install the openpyxl package using pip. Write the following command in your terminal:
$ pip install openpyxl
  • Step 2: Once you are done with the installation of openpyxl, you can set up a sample Excel spreadsheet using the following code snippet:
from openpyxl import Workbook
workbook = Workbook()
spreadsheet = workbook.active
spreadsheet["A1"] = "Hello"
spreadsheet["B1"] = "World!"
workbook.save(filename="HelloWorld.xlsx")
  • Step 3: The next step would be to see if you can read the sample spreadsheet you created before, for the Excel Python Integration. You can use the following code snippet to open it:
>>>from openpyxl import load_workbook
>>> workbook = load_workbook(filename="sample.xlsx")

>>> workbook.sheetnames
['Sheet 1']

>>> spreadsheet = workbook.active
>>> spreadsheet
<Worksheet "Sheet 1">

>>> spreadsheet.title
  • Step 4: Retrieve data from the Excel spreadsheet using the following code snippet:
>>> spreadsheet["A1"]
<Cell 'Sheet 1'.A1>

>>> spreadsheet["A1"].value
'marketplace'

>>> spreadsheet["F10"].value
"G-Shock Men's Grey Sport Watch"
  • Step 5: You can now iterate through the data and begin converting it into a format that can be used for data analysis, which is one of the main objectives of Excel Python Integration. The iteration can be carried out by using a combination of rows and columns for slicing:
>>> spreadsheet["A1:C2"]
((<Cell 'Sheet 1'.A1>, <Cell 'Sheet 1'.B1>, <Cell 'Sheet 1'.C1>), 
 (<Cell 'Sheet 1'.A2>, <Cell 'Sheet 1'.B2>, <Cell 'Sheet 1'.C2>))

This indicates all the spreadsheet data from A1 to C2.

  • Step 6: The next use case of Excel Python Integration discussed was appending data to the existing Excel spreadsheet. Here is the code snippet to add some data to the sample spreadsheet created in the second step:
from openpyxl import load_workbook
# Begin by opening the spreadsheet and selecting the main sheet 
workbook = load_workbook(filename="hello_world.xlsx")
spreadsheet = workbook.active
# Enter what you want into a specific cell
spreadsheet["C1"]="Manipulating_Data ;)"
# Save the spreadsheet
workbook.save(filename)="hello_world_append.xlsx"

On opening the document, you will notice that Manipulating_Data has been added to your Excel spreadsheet.   

Method 2: Using the pandas Library for Excel Python Integration

Pandas for Excel Python Integration
Image Source

You can also use the pandas library for Excel Python Integration. Pandas is the most widely used Python library to interact with Excel files. You can use this library to load data from your Excel spreadsheet and perform key operations like inserting, deleting columns and rows, and appending information if necessary. This Excel Python Integration method is mainly used for Exploratory Data Analysis where you take the data present in the Excel spreadsheets, organize and clean it up, thereby making this data ready for analysis.

Here you will see how to import data from an Excel spreadsheet using pandas:

  • Step 1: Install pandas using pip in your terminal as follows:
$ pip install pandas
  • Step 2: Suppose you have a Microsoft Excel sheet as follows: 
Microsoft Excel spreadsheet example
Image Source

You can export data from this Excel file to pandas Dataframe using the following code snippet:

Code snippet to import Excel sheet with pandas
Image Source

You can select columns similar to method 1, by using slicing. You can append a new set of data to a pandas Dataframe by using the dataframe. append() function. Here the dataframe will be replaced by the name of your dataframe. For more information on the same, refer to the pandas documentation.   

Conclusion

This article talks about two simple methods you can use to set up Excel Python Integration for your business operations. It initially talks about Microsoft Excel and Python before delving into Excel Python Integration.

Extracting complex data from a diverse set of data sources can be a challenging task and this is where Hevo saves the day! Hevo offers a faster way to move data from Databases or SaaS applications into your Data Warehouse to be visualized in a BI tool. Hevo is fully automated and hence does not require you to code. You can try Hevo for free by signing up for a 14-day free trial. You can also have a look at the unbeatable pricing that will help you choose the right plan for your business needs.

No-code Data Pipeline For Your Data Warehouse