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"]
Randomisation-based inference in Python based on data resampling and permutation.
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Predicting 10-Year Treasury Yields Intitially, I wanted to see if the volatility in the stock market, represented by the VIX index (data source), had
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Candlestick-Pattern-Recognition-with-Python-and-TA-Lib Goal Look at the S&P500 to try and get a better understanding of these candlestick patterns and
DaDRA (day-druh) is a Python library for Data-Driven Reachability Analysis. The main goal of the package is to accelerate the process of computing estimates of forward reachable sets for nonlinear dy
weightedcalcs weightedcalcs is a pandas-based Python library for calculating weighted means, medians, standard deviations, and more. Features Plays we
Stock Statistics/Indicators Calculation Helper VERSION: 0.3.2 Introduction Supply a wrapper StockDataFrame based on the pandas.DataFrame with inline s
Hydrosphere examples This repo contains demo scenarios and pre-trained models to show Hydrosphere capabilities. Data and artifacts management Some mod
PyUpBit CS490 Large Scale Data Analytics — Implementation of Updatable Compressed Bitmap Indexing Paper Table of Contents About The Project Usage Cont
Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilis
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