What is xlwings and what are its top alternatives?
Top Alternatives to xlwings
Build live streaming dashboards and complex mathematical models, all in Excel. You can use DataNitro to turn a spreadsheet into a database GUI - or a web server backend. ...
Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA! ...
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...
Working with Airtable is as fast and easy as editing a spreadsheet. But only Airtable is backed by the power of a full database, giving you rich features far beyond what a spreadsheet can offer. ...
It is an intuitive online project management tool enabling teams to increase productivity using cloud, collaboration, & mobile technologies. It provides your organization with a powerful work platform that offers exceptional speed to business value ...
Pick a sheet, customize your app, share it with a link. Add your data to the sheet and share your custom app! Only pay for apps that need whitelabeling or store distribution. ...
It helps teams easily track & automate tasks, enabling them to save time and work smarter. ...
Use spreadsheet as your database. Give data to your users the nice way, directly from the tool you know. Without bothering webdeveloper. ...
xlwings alternatives & related posts
- DataNitro is no longer for sale or being developed3
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- Fully replace VBA with Python5
- Excellent support2
- Very good performance1
- Cannot be deloyed to mac users1
related PyXLL posts
- Easy data frame management21
- Extensive file format compatibility1
related Pandas posts
We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.
Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.
Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.
Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.
UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.
State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.
Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.
- Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.
- Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.
- Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.
Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.
Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.
Should I continue learning Django or take this Spring opportunity? I have been coding in python for about 2 years. I am currently learning Django and I am enjoying it. I also have some knowledge of data science libraries (Pandas, NumPy, scikit-learn, PyTorch). I am currently enhancing my web development and software engineering skills and may shift later into data science since I came from a medical background. The issue is that I am offered now a very trustworthy 9 months program teaching Java/Spring. The graduates of this program work directly in well know tech companies. Although I have been planning to continue with my Python, the other opportunity makes me hesitant since it will put me to work in a specific roadmap with deadlines and mentors. I also found on glassdoor that Spring jobs are way more than Django. Should I apply for this program or continue my journey?
- Powerful and easy to use19
- Robust and dynamic8
- Quick UI Layer6
- Practical built in views4
- Robust API documentation3
- Great flexibility0
related Airtable posts
If you're a developer using Google Docs or Google Sheets... just stop. There are much better alternatives these days that provide a better user and developer experience.
At FeaturePeek, we use slite for our internal documents and knowledge tracking. Slite's look and feel is similar to Slack's, so if you use Slack, you'll feel right at home. Slite is great for keeping tabs on meeting notes, internal documentation, drafting marketing content, writing pitches... any long-form text writing that we do as a company happens in Slite. I'm able to be up-to-date with everyone on my team by viewing our team activity. I feel more organized using Slite as opposed to GDocs or GDrive.
Airtable is also absolutely killer – you'll never want to use Google Sheets again. Have you noticed that with most spreadsheet apps, if you have a tall or wide cell, your screen jumps all over the place when you scroll? With Airtable, you can scroll by screen pixels instead of by spreadsheet cells – this makes a huge difference! It's one of those things that you don't really notice at first, but once you do, you can't go back. This is just one example of the UX improvements that Airtable has to the previous generation of spreadsheet apps – there are plenty more.
Also, their API is a breeze to use. If you're logged in, the docs fill in values from your tables and account, so it feels personalized to you.
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- Easy setup8