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 example uses array type. Note:In order to use join columns as an array, you ...
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(...
>>> df.join(df2,'name','inner').drop('age','height').collect()[Row(name=u'Bob')] New in version 1.4. dropDuplicates(subset=None)[source] Return a new DataFrame with duplicate rows removed, optionally only considering certain columns. ...
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)...