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withColumn("point", ST.point("X","Y", 26971)) print(np.corrcoef(pred_results.predictions.flatten(), pred_y.flatten())[0][1]) Export data and results Now, we can write the resulting DataFrame to a feature service, which will allow for the exported data to be visualized in different ways in an ArcGIS environment. groupBy("bins").agg(
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