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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Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow
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Description Kats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Ti
ETL framework based on Yaml configs in Python A light framework for creating data streams. Setting up streams through configuration in the Yaml file.
Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilis
PandaPy "I came across PandaPy last week and have already used it in my current project. It is a fascinating Python library with a lot of potential to
Pandas Profiling Documentation | Slack | Stack Overflow Generates profile reports from a pandas DataFrame. The pandas df.describe() function is great
Sentiment analysis on streaming twitter data using Spark Structured Streaming & Python This project is a good starting point for those who have little
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