MLlib VS Pandas
Compare MLlib VS Pandas and see what are their differences
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives.
featured
MLlib
Scikit-learn
NumPy
OpenCV
Dataiku
Exploratory
htm.java
WEKA
Pandas
NumPy
Scikit-learn
OpenCV
Dataiku
Exploratory
htm.java
WEKA
MLlib PandasBased on our record, Pandas seems to be a lot more popular than MLlib. While we know about 198 links to Pandas, we've tracked only 2 mentions of MLlib. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
The MLlib library gives us a very wide range of available Machine Learning algorithms and additional tools for standardisation, tokenisation and many others (for more information visit the official website Apache Spark MLlib). (Apache Spark Machine Learning predicting diabetes in patients). Source: about 2 years ago
Totally agree with the current responses, especially for the purposes of understanding exactly what's going on under the hood, but did want to just call out the fact that you can simply use a machine learning library that's implemented in a distributed way. Examples would be MLlib From Spark and h2o. H2O in particular will take care of pretty much everything for you in terms of initializing a cluster, and has a... Source: about 2 years ago
Python is a natural fit for serverless development. It boasts a vast array of libraries, including Powertools for AWS and robust libraries for data engineers. Its versatility and excellent developer experience make it a top choice for serverless projects, offering a seamless and enjoyable development experience. - Source: dev.to / 14 days ago
In data analysis, managing the structure and layout of data before analyzing them is crucial. Python offers versatile tools to manipulate data, including the often-used Pandas reset_index() method. - Source: dev.to / 7 days ago
Dash is a Python framework that enables you to build interactive frontend applications without writing a single line of Javascript. Internally and in projects we like to use it in order to build a quick proof of concept for data driven applications because of the nice integration with Plotly and pandas. For this post, I'm going to assume that you're already familiar with Dash and won't explain that part in detail.... - Source: dev.to / 2 months ago
Last year I worked through the challenges using VisiData, Datasette, and Pandas. I walked through my thought process and solutions in a series of posts. - Source: dev.to / 5 months ago
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
NumPy - NumPy is the fundamental package for scientific computing with Python
OpenCV - OpenCV is the world's biggest computer vision library
Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.
Exploratory - Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.
htm.java - htm.java is a Hierarchical Temporal Memory implementation in Java, it provide a Java version of NuPIC that has a 1-to-1 correspondence to all systems, functionality and tests provided by Numenta's open source implementation.