Koalas: pandas API on Apache Spark

A high-level plotting API for pandas, dask, xarray, and networkx built on HoloViews

ASP.NET Core Dynamic Restful WebApi. Generating WebApi from Classes. Such as: Direct Generation of WebApi Based on Business Logic Layer.

Panda api is a simple and powerful api docs design tool.

pandas wrapper for Bloomberg Open API

Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

A ROS/Python API for controlling and managing the Franka Emika Panda robot (real and simulated).

A Pythonic introduction to methods for scaling your data science and machine learning work to larger datasets and larger models, using the tools and APIs you know and love from the PyData stack (such as numpy, pandas, and scikit-learn).

Bloomberg Open API with pandas

A small timeseries transformation API built on Flask and Pandas

This is the cheat sheet Jupyter Notebook I made for my Pandas Learn in One Video Tutorial. I basically condensed the Pandas API down into this one cheat sheet with hundreds of examples. I hope you find it useful.

API to fetch elements of the periodic table in JSON format. Uses Pandas for dumping .csv data to .json and Flask for API Integration. Deployed on "pythonanywhere.com".

FHIR-PYrate is a package that provides a high-level API to query FHIR Servers for bundles of resources and return the structured information as pandas DataFrames. It can also be used to filter resources using RegEx and SpaCy and download DICOM studies and series.

Data exploration library with a pandas-like API

Model definitions and Python API for controlling a Franka Emika Panda robot in the mujoco physics simulator.

High level pandas-based API for batch analysis of Calcium Imaging data using CaImAn

Gone are the days of black-box dataframes in otherwise type-safe code! Pandantic builds off the Pydantic API to enable validation and filtering of the usual dataframe types (i.e., pandas, etc.)

Python Streamlit web app allowing users to interact with their data from a CSV or XLSX file, utilizing OpenAI API and LangChain. It imports necessary libraries, handles API key loading, displays a user-friendly interface for file upload and data preview, creates a Pandas DF agent with OpenAI, and executes user queries.

Alfred Workflow for TensorFlow, PyTorch, Scikit-learn, NumPy, Pandas, Matplotlib, Statsmodels, Jax, RLLib API Docs

Python Bloomberg API and Pandas Wrapper

A lightweight pandas-compatible DataFrame API built on top of Polars for speed and simplicity

This course is designed to get a good grip on python programming, logic building, solving algorithm-based questions, data structures, understanding of data analytics, working with pandas, professional practices, and API building.

Interactive F1 strategy dashboard tutorial using the OpenF1 API, Streamlit, Plotly, and pandas.

Convenient pyarrow operations following the Pandas API

Movie Recommendation Chatbot provides information about a movie like plot, genre, revenue, budget, imdb rating, imdb links, etc. The model was trained with Kaggle’s movies metadata dataset. To give a recommendation of similar movies, Cosine Similarity and TFID vectorizer were used. Slack API was used to provide a Front End for the chatbot. IBM Watson was used to link the Python code for Natural Language Processing with the front end hosted on Slack API. Libraries like nltk, sklearn, pandas and nlp were used to perform Natural Language Processing and cater to user queries and responses.

GAPandas4 is a Python package for querying the Google Analytics Data API for GA4 and displaying the results in a Pandas dataframe.

Modular full-stack ML project leveraging Groq API, Streamlit, Supabase, JSON, SciPy, SciKit-Learn, Plotly & EmailJS, alongside libraries - NumPy, Pandas, Utils, OS, Base64, Re, Pillow & DateTime.

Tutorial para acessar a API do Sistema de Orçamento e Finanças _SOF da cidade de São Paulo, utilizando Python e a biblioteca Pandas, realizar análises e salvar arquivo CSV/Excel

Tasks and projects solved while passing a Data Analysis course: Python, Pandas, Numpy, Seaborn, Google APIs, Git, Airflow, SQL, ClickHouse, PostgreSQL, Redash, Supersеt, Statistics, A/B-tests, Bootstrapping, Amplitude, Tableau, DAU, MAU, ARPU, LTV, Retention and other metrics.

Streaming API for pandas applied to big datasets