Apache Superset is a Data Visualization and Data Exploration Platform

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal. Easy handling of missing data (represented as NaN, NA, or NaT) 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 Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects Intelligent label-based slicing, fancy indexing, and subsetting of large data sets Intuitive merging and joining data sets Flexible reshaping and pivoting of data sets Hierarchical labeling of axes (possible to have multiple labels per tick) Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging 

The fundamental package for scientific computing with Python.

Create HTML profiling reports from pandas DataFrame objects

Documentation | Slack | Stack Overflow | Latest changelog. Generates profile reports from a pandas DataFrame. The pandas df.describe() function is great but a little basic for serious exploratory data analysis. pandas_profiling extends the pandas DataFrame with df.profile_report() for quick data analysis. Type inference: detect the types of columns in a dataframe. Essentials: type, unique values, missing values Quantile statistics like minimum value, Q1, median, Q3, maximum, range, interquartile range Descriptive statistics like mean, mode, standard deviation, sum, median absolute deviation, coefficient of variation, kurtosis, skewness Most frequent values Histogram Correlations highlighting of highly correlated variables, Spearman, Pearson and Kendall matrices Missing values matrix, count, heatmap and dendrogram of missing values Text analysis learn about categories (Uppercase, Space), scripts (Latin, Cyrillic) and blocks (ASCII) of text data. File and Image analysis extract file sizes, creation dates and dimensions and scan for truncated images or those containing EXIF information. 

Plotly Express - Simple syntax for complex charts. Now integrated into plotly.py!

Plotly Express is now part of Plotly.py version 4 and so the plotly_express module now just re-exports the contents of plotly.express. 

Machine learning with Sci-kit Learn and Tensorflow (V)

Machine Learning is one of the most transformative and impactful technologies of our time. From advertising to healthcare, to self-driving cars, it is hard to find an industry that has not been or is not being revolutionized by machine learning. Using the two most popular frameworks, Tensor Flow and Scikit-Learn, this course will show you insightful tools and techniques for building intelligent systems. Using Scikit-learn you will create a Machine Learning project from scratch, and, use the Tensor Flow library to build and train professional neural networks. We will use these frameworks to build a variety of applications for problems such as ad ranking and sentiment classification. The course will then take you through the methods for unsupervised learning and what to do when you have limited or no labels for your data. We use the techniques we have learned, along with some new ones, to build a sentiment classifier, an autocomplete keyboard and a topic discoverer. The course will also cover applications for Natural Language Processing, explaining the types of language processing. We will cover TensorFlow, the most popular deep learning framework, and use it to build convolutional neural networks for object recognition and segmentation. We will then discuss recurrent neural networks and build applications for sentiment classification and stock prediction. We will then show you how to process sequences of data with recurrent neural networks with applications in sentiment classification and stock price prediction. Finally, you will learn applications with deep unsupervised learning and generative models. By the end of the course, you will have mastered Machine Learning in your everyday tasks. 

BuildwithAI Hackathon Food Monitoring Platform

Group Name 1

supersetby apache

supersetby apache

scikit-learnby scikit-learn

scikit-learnby scikit-learn

pandasby pandas-dev

pandasby pandas-dev

Group Name 2

numpyby numpy

numpyby numpy

pandas-profilingby pandas-profiling

pandas-profilingby pandas-profiling

plotly_expressby plotly

plotly_expressby plotly

Group Name 3

Machine-learning-with-Sci-kit-Learn-and-Tensorflow-V-by PacktPublishing

Machine-learning-with-Sci-kit-Learn-and-Tensorflow-V-by PacktPublishing