2. Drop Duplicate Columns After Join If you notice above Join DataFrameemp_idis duplicated on the result, In order to remove this duplicate column, specify the join column as an array type or string. The below
resulting in null values in the “dept” columns. Similarly, the “dept_id” 30 does not have a record in the “emp” dataset, hence you observe null values in the “emp” columns. Below is the output of the provided join example. ...
# 1. df.dropDuplicate() :数据去重,无参数按整理去重;也可指定列去重 pd_data = pd.DataFrame({'name':['张三','李四','王五','张三','李四','王五'] ,'score':[65,35,89,65,67,97]}) df = spark.createDataFrame(pd_data) df.show() df.dropDuplicates().show() df.dropDuplicates(['na...
dropDuplicates() # or df = df.distinct() # Drop duplicate rows, but consider only specific columns df = df.dropDuplicates(['name', 'height']) # Replace empty strings with null (leave out subset keyword arg to replace in all columns) df = df.replace({"": None}, subset=["name"])...
drop(probe_prob_col) .join(probe_cv_df.rdd .map(lambda row: (row['id'], float(row['probability'][1]))) .toDF(['id', probe_prob_col]), 'id') .cache()) print(res_cv_df.count()) print(time() - t0) 25133 6.502754211425781 # Getting probabilities for Test data t0 = time(...
PySpark distinct() transformation is used to drop/remove the duplicate rows (all columns) from DataFrame and dropDuplicates() is used to drop rows based
('N/A')))# Drop duplicate rows in a dataset (distinct)df=df.dropDuplicates()# ordf=df.distinct()# Drop duplicate rows, but consider only specific columnsdf=df.dropDuplicates(['name','height'])# Replace empty strings with null (leave out subset keyword arg to replace in all columns)...