A data structure that extends pyspark.sql.DataFrame with metadata information.
from metaframe import MetaFrame
mf = MetaFrame(df=df, metadata={"columns": ["a", "b"]})
mf = mf.withColumn("new_col", F.lit(1))
mf = mf.set_metadata(columns=["a", "b", "new_col"])
mf.show()
assert mf.metadata["columns"] == ["a", "b", "new_col"]
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Disclaimer This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to chang
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WeRateDogs Twitter Data from 2015 to 2017 Udacity - Data Analyst Nanodegree - Project 4 - Wrangle and Analyze Data Table of Contents Introduction Proj
A feature store is a data storage system for data science and machine-learning. It can store raw data and also transformed features, which can be fed straight into an ML model or training script.
Using Streaming Twitter Data with Kafka and Spark Reading streams of Twitter data, publishing them to Kafka topic, process message using Kafka Stream
Linked Data are becoming more popular for user-created metadata in HDF5 files.
Airflow ETL With EKS EFS & Sagemaker (en desarrollo) Diagrama de la solución Imp
StatisticalDistributions statDistros statDistros is a Python library for dealing with various statistical distributions. Now it provides various stati
hmmlearn hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models. For supervised learning learning of HMMs and
Red Bull Racing Analytics Hands-On Labs Introduction Are you interested in learning machine learning (ML)? How about doing this in the context of the
This is an example of how to automate Ridit Analysis for a dataset with large amount of questions and many item attributes
Used for data processing in machine learning, and help us to construct ML model more easily from scratch. Can be used in linear model, logistic regression model, and decision tree.
Toolchest Python Client Toolchest provides APIs for scientific and bioinformatic data analysis. It allows you to abstract away the costliness of runni
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