What is Panda and what are its top alternatives?
Panda is a popular data manipulation library in Python that offers data structures and functions for data analysis tasks. It provides easy-to-use data structures like DataFrame which allows users to manipulate and analyze data effectively. Panda's key features include data cleaning, reshaping, merging, slicing, and groupby operations. However, some limitations of Panda include slower performance with larger datasets and a steeper learning curve for beginners.
- NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
- Dask: Dask is a flexible parallel computing library in Python that enables efficient parallel computing and handling of larger than memory datasets. It provides dynamic task scheduling and parallel computing capabilities.
- Modin: Modin is a scalable and fast distributed dataframe library in Python that aims to optimize data processing tasks using parallel computing techniques. It provides seamless integration with Pandas syntax.
- Vaex: Vaex is a high-performance Python library for lazy and out-of-core data processing. It is designed to handle large datasets efficiently through memory mapping and provides various advanced data manipulation functions.
- Datarah: Datarah is a data manipulation library in Python that focuses on simplifying data cleaning, manipulation, and analysis tasks in a user-friendly manner. It offers an intuitive interface for handling complex data operations.
- Koalas: Koalas is an open-source Python library that provides a familiar Pandas API on top of Apache Spark for scalable data processing. It allows users to leverage Spark's distributed computing capabilities with Pandas syntax.
- Pyspark: PySpark is the Python API for Apache Spark, a popular distributed computing framework. It enables faster data processing and analysis on large datasets using Spark's parallel computing architecture.
- Cudf: Cudf is a Python GPU DataFrame library built on top of the RAPIDS ecosystem. It leverages GPU acceleration for data processing tasks, providing significant speedups compared to CPU-based processing.
- DolphinDB: DolphinDB is a distributed analytical processing database system that offers efficient and scalable data processing capabilities for big data analytics. It provides high-performance data manipulation functions for in-memory and distributed computing.
- Arrow: Apache Arrow is a cross-language development platform for in-memory data processing. It provides a standardized columnar memory format for efficient data interchange between different systems and languages.
Top Alternatives to Panda
- Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...
- NumPy
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. ...
- Grizzly
Writing scalable server applications in the Java™ programming language has always been difficult. Before its advent, thread management issues made it impossible for a server to scale to thousands of users. This framework has been designed to help developers to take advantage of the Java™ NIO API. ...
- Amazon Elastic Transcoder
Convert or transcode media files from their source format into versions that will playback on devices like smartphones, tablets and PCs. Create a transcoding “job” specifying the location of your source media file and how you want it transcoded. Amazon Elastic Transcoder also provides transcoding presets for popular output formats. All these features are available via service API, AWS SDKs and the AWS Management Console. ...
- GStreamer
It is a library for constructing graphs of media-handling components. The applications it supports range from simple Ogg/Vorbis playback, audio/video streaming to complex audio (mixing) and video (non-linear editing) processing. ...
- Cloudflare Stream
Cloudflare Stream makes integrating high-quality streaming video into a web or mobile application easy. Using a single, integrated workflow through a robust API or drag and drop UI, application owners can focus on creating the best video experience. ...
- AWS Elemental MediaConvert
AWS Elemental MediaConvert is a file-based video transcoding service with broadcast-grade features. It allows you to easily create video-on-demand (VOD) content for broadcast and multiscreen delivery at scale. ...
- Kurento
It is a WebRTC media server and a set of client APIs making simple the development of advanced video applications for WWW and smartphone platforms. Media Server features include group communications, transcoding and more. ...
Panda alternatives & related posts
Pandas
- Easy data frame management21
- Extensive file format compatibility2
related Pandas posts
Server side
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.
Client side
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.
Cache
- 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
- 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.
Infrastructure
- 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.
Other Tools
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?
- Great for data analysis10
- Faster than list4
related NumPy posts
Server side
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.
Client side
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.
Cache
- 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
- 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.
Infrastructure
- 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.
Other Tools
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?
related Grizzly posts
related Amazon Elastic Transcoder posts
We were looking for a versatile #MediaTranscoding service for #video to convert TV shows and movies from large content providers into web #VideoStreaming formats. These content providers gave us files ranging from Apple ProRes to h.264, with file sizes from 1 GB to 100 GB, and we needed a tool that could cope with all of it. We looked at Amazon Elastic Transcoder and Zencoder, and eventually chose @Zencoder because it had support for every format we needed, good handling of sound channel remapping, and a clear UI with fast processing times. We automated our usage with it by writing a simple Python script to interact with it's API, and hosted the input and output AV files on Amazon S3, which it could easily talk to. So far we've converted 15 TB representing several thousand files using the service and are quite happy!
- Ease of use2
- Cross Platform1
- Open Source1
related GStreamer posts
I have a situation to convert the H264 streams into MP4 format using FFMPEG/GStreamer.
However Im stuck with the gst-ugly plugin, now trying my luck with ffmeg. How big are the ffmeg libs and licensing complications?
- Love this tool3