把.drop_duplicates("column_name")改为.drop_duplicates(subset=["column_name"])
(*columns_to_drop) #增加一列 from pyspark.sql.functions...,接下来将对这个带有缺失值的dataframe进行操作 # 1.删除有缺失值的行 clean_data=final_data.na.drop() clean_data.show() # 2.用均值替换缺失值...(authors, columns=["FirstName","LastName","Dob"]) df.drop_duplicates(subset=['...
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本文简要介绍 pyspark.pandas.Index.drop_duplicates 的用法。用法:Index.drop_duplicates() → pyspark.pandas.indexes.base.Index返回删除重复值的索引。 返回: deduplicated: index 例子: 生成具有重复值的 pandas.Index。 >>> idx = ps.Index(['lama', 'cow', 'lama', 'beetle', 'lama', 'hippo'])...
print("Dropping duplicates strings:\n", result) # Output: # Dropping duplicates strings: # 0 Spark # 1 Pandas # 2 Python # 4 PySpark # dtype: object Frequently Asked Questions on Pandas Series drop duplicates() Function What is the purpose of the drop_duplicates() function in pandas Serie...
pandas框架，那么drop_duplicates将起作用。否则，如果你使用的是简单的pyspark框架，那么dropDuplicates将起...
dropDisDF = df.dropDuplicates(["salary"]).select("salary") dropDisDF.show(truncate=False) print(dropDisDF.collect()) 5. Conclusion In this article, you have learned what is the difference between PySpark distinct and dropDuplicate functions, both these functions are from DataFrame class and ...
pyspark Spark SQL DataFrame - distinct()vs dropDuplicates()主要的区别是考虑了列的子集，这很棒！当...
由于groupby不允许我在sparksql中执行上述查询,因此我删除了groupby,并在生成的Dataframe中使用了dropduplicates。以下是修改后的代码: from pyspark.sql import SparkSession spark = SparkSession \ .builder \ .appName("Python Spark SQL basic example") \ .config("spark.sql.crossJoin.enabled", "true") \...
本文简要介绍 pyspark.pandas.Series.drop_duplicates 的用法。用法:Series.drop_duplicates(keep: str = 'first', inplace: bool = False)→ Optional[pyspark.pandas.series.Series]返回删除重复值的系列。参数: keep:{‘first’, ‘last’, False },默认 ‘first’ 处理删除重复项的方法: - ‘first’ :...