What is Pandasql and what are its top alternatives?
Top Alternatives to Pandasql
SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. ...
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...
It is a modern database query and access library for Scala. It allows you to work with stored data almost as if you were using Scala collections while at the same time giving you full control over when a database access happens and which data is transferred. ...
It makes it easy to use data access technologies, relational and non-relational databases, map-reduce frameworks, and cloud-based data services. This is an umbrella project which contains many subprojects that are specific to a given database. ...
A cross-platform IDE that is aimed at DBAs and developers working with SQL databases. ...
Microsoft SQL Server Management Studio
It is an integrated environment for managing any SQL infrastructure, from SQL Server to Azure SQL Database. It provides tools to configure, monitor, and administer instances of SQL Server and databases. Use it to deploy, monitor, and upgrade the data-tier components used by your applications, as well as build queries and scripts. ...
PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL. ...
It is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports all popular databases: MySQL, PostgreSQL, SQLite, Oracle, DB2, SQL Server, Sybase, Teradata, MongoDB, Cassandra, Redis, etc. ...
Pandasql alternatives & related posts
- Open Source5
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Merging Django ORM with SQLAlchemy for Easier Data Analysis (Video Inside)
Development of products with Django framework is usually easy and straightforward; great documentation, many tools out of the box, plenty of open source libraries and big community. Django ORM takes full control about SQL layer protecting you from mistakes, and underlying details of queries so you can spend more time on designing and building your application structure in Python code. However, sometimes such behavior may hurt - for example, when you’re building a project related to data analysis. Building advanced queries with Django is not very easy; it’s hard to read (in Python) and hard to understand what’s going on in SQL-level without logging or printing generated SQL queries somewhere. Moreover, such queries could not be efficient enough, so this will hit you back when you load more data into DB to play with. In one moment, you can find yourself doing too much raw SQL through Django cursor, and this is the moment when you should do a break and take a look on another interesting tool, which is placed right between ORM layer and the layer of raw SQL queries.
- Easy data frame management19
- 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.
Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead
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I need some advice to choose an engine for generation web pages from the Spring Boot app. Which technology is the best solution today? 1) JSP + JSTL 2) Apache FreeMarker 3) Thymeleaf Or you can suggest even other perspective tools. I am using Spring Boot, Spring Web, Spring Data, Spring Security, PostgreSQL, Apache Tomcat in my project. I have already tried to generate pages using jsp, jstl, and it went well. However, I had huge problems via carrying already created static pages, to jsp format, because of syntax. Thanks.
- Works on Linux, Windows and MacOS4
- Wide range of DBMS support2
- Code completion1
- Generate ERD1
- Quick-fixes using keyboard shortcuts1
- Code analysis1
- Database introspection on 21 different dbms1
- Export data using a variety of formats using open api1
- Import data1
- Diff viewer1
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We have a 138 row, 1700 column database likely to grow at least a row and a column every week. We are mostly concerned with how user-friendly the graphical management tools are. I understand MySQL has MySQL WorkBench, and Microsoft SQL Server has Microsoft SQL Server Management Studio. We have about 6 months to migrate our Excel database to one of these DBMS, and continue (hopefully manually) importing excel files from then on. Any tips appreciated!
- De facto GIS in SQL24
- Good Documentation5
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- Platform independent11
- Automatic driver download8
- Import-Export Data6
- Simple to use4
- Wide range of DBMS support4
- Move data between databases4
- SAP Hana DB support1
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Which tools are preferred if I choose to work on more data side? Which one is good if I decide to work on web development? I'm using DBeaver and am now considering a move to AzureDataStudio to break the monotony while working. I would like to hear your opinion. Which one are you using, and what are the things you are missing in dbeaver or data studio.