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from pyspark.ml.feature import PolynomialExpansion data = [ (0, Vectors.dense([1.0, 2.0])), (1, Vectors.dense([2.0, 3.0])), (2, Vectors.dense([3.0, 4.
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posted @ 2025-11-23 22:13
ZhangZhihuiAAA
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from pyspark.ml.feature import PCA pca = PCA(k=2, inputCol='features', outputCol='pca_features') pca_model = pca.fit(df) pca_df = pca_model.transform(
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posted @ 2025-11-23 21:47
ZhangZhihuiAAA
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from pyspark.ml.feature import Normalizer normalizer = Normalizer(inputCol='features', outputCol='normalized_features', p=2.0) normalized_df = normali
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posted @ 2025-11-23 21:19
ZhangZhihuiAAA
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from pyspark.ml.feature import MinMaxScaler scaler = MinMaxScaler(inputCol='features', outputCol='scaled_features') scaler_model = scaler.fit(df) scal
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posted @ 2025-11-23 21:05
ZhangZhihuiAAA
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from pyspark.ml.feature import OneHotEncoder data = [(0.0, 1.0), (1.0, 0.0), (2.0, 1.0)] columns = ["input1", "input2"] df = spark.createDataFrame(dat
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posted @ 2025-11-23 17:37
ZhangZhihuiAAA
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In Spark, I ran below code snippet: # The vocabSize parameter was set to 7 (0 to 6 - a total of 7 words), # meaaning that the vocabulary size (unique
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posted @ 2025-11-23 10:47
ZhangZhihuiAAA
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To sort a Pandas DataFrame by a column, use sort_values(). Here are the common usages: 1. Sort by one column df_sorted = df.sort_values(by="column_nam
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posted @ 2025-11-23 08:01
ZhangZhihuiAAA
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