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Pandas vs angular-gantt: What are the differences?
1. Pandas vs Angular-gantt
1. Data Handling: Pandas is a powerful data manipulation tool in Python that provides data structures and functions to quickly manipulate and analyze data, while Angular-gantt is a flexible Gantt chart component for AngularJS that allows users to visualize schedules and timelines.
2. Purpose: Pandas is mainly focused on data analysis and manipulation tasks on structured data, whereas Angular-gantt is specifically designed for creating interactive and dynamic Gantt charts for project management and scheduling purposes.
3. Language: Pandas is written in Python and is compatible with various Python libraries, while Angular-gantt is developed using AngularJS, a JavaScript framework, making it ideal for web-based applications.
4. Dependencies: Pandas has minimal dependencies and can be easily integrated into existing Python workflows, while Angular-gantt requires AngularJS as a prerequisite for incorporating Gantt functionality into Angular applications.
5. Community Support: Pandas has a large community of users and contributors, providing extensive documentation, tutorials, and resources for users, whereas Angular-gantt has a smaller but dedicated community that focuses on enhancing the Gantt chart capabilities within Angular applications.
6. Customization Options: Pandas offers a wide range of built-in functions and methods for data manipulation and analysis, allowing users to customize and automate tasks efficiently, while Angular-gantt provides customizable templates and themes to tailor the appearance and functionality of Gantt charts according to specific project requirements.
In Summary, Pandas and Angular-gantt serve different purposes as data manipulation tools and Gantt chart components, respectively, catering to distinct needs in data analysis and project management.
ML Model Training and Benchmarking
We choose python for ML and data analysis. Because:
- Simple syntax and easy to use
- ML Library and framework support
The python libraries and frameworks we choose for ML are:
TensorFlow
- High performance (GPU support/ highly parallel)
- Easy to debug
- visualization support
Numpy
- Easy matrix manipulation
- datatype with high compatibility
Pandas
- High efficiency when handling large data
- Dataset manipulation and customization
Matplotlib
- Simple and easy to use
A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.
We decided to use scikit-learn as our machine-learning library as provides a large set of ML algorihms that are easy to use. scikit-learn is also scalable which makes it great when shifting from using test data to handling real-world data. scikit-learn also works very well with Flask. Numpy and Pandas are used with scikit-learn for data processing and manipulation.
Pros of angular-gantt
Pros of Pandas
- Easy data frame management21
- Extensive file format compatibility2
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What is angular-gantt?
What is Pandas?
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