Pandas vs Data Miner: What are the differences?
Developers describe Pandas as "High-performance, easy-to-use data structures and data analysis tools for the Python programming language". Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. On the other hand, Data Miner is detailed as "Extract Data From any Website in Seconds". It is a Google Chrome extension that helps you scrape data from web pages and into a CSV file or Excel spreadsheet.
Pandas and Data Miner belong to "Data Science Tools" category of the tech stack.
Some of the features offered by Pandas are:
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
- Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
- Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
On the other hand, Data Miner provides the following key features:
- Scrape with one click
- No coding
- No bots
Pandas is an open source tool with 21.4K GitHub stars and 8.46K GitHub forks. Here's a link to Pandas's open source repository on GitHub.
What is Data Miner?
What is Pandas?
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What are the cons of using Pandas?
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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