df=Spark_Session.createDataFrame(rows,columns) # Printing the DataFrane df.show() # Creating a new DataFrame with a # expression using functions new_df=df.withColumn( "Y",expr("explode(array_repeat(Y,int(Y)))")) # Printing the new DataFrame new_df.show() 输出: 方法2:使用collect()并...
pyspark 来源:https://stackoverflow.com/questions/75082265/pyspark-drop-rows-with-duplicate-values-with-no-column-order 关注 举报1条答案按热度按时间 pdkcd3nj1# (df1.withColumn('x',array_sort(array(col('left'), col('right')))#create sorted array column of columns left and right .dropDuplic...
If the value does exist, those rows will be kept in the result, even if there are duplicate keys in the left DataFrame. Think of left semi joins as filters on a DataFrame, as opposed to the function of a conventional join: joinType = "left_semi" graduateProgram.join(person, joinExpress...
PySpark DataFrame provides adrop()method to drop a single column/field or multiple columns from a DataFrame/Dataset. In this article, I will explain ways to drop columns using PySpark (Spark with Python) example. Advertisements Related:Drop duplicate rows from DataFrame First, let’s create a P...
Remove duplicate rowsTo de-duplicate rows, use distinct, which returns only the unique rows.Python Копирај df_unique = df_customer.distinct() Handle null valuesTo handle null values, drop rows that contain null values using the na.drop method. This method lets you specify if you...
df2.show(truncate=False) 2. PySpark Distinct of Selected Multiple Columns PySpark doesn’t have a distinct method that takes columns that should run distinct (drop duplicate rows on selected multiple columns) however, it provides another signature ofdropDuplicates()transformation which takes multiple ...
# 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...
Now that we have created all the necessary variables to build the model, run the following lines of code to select only the required columns and drop duplicate rows from the dataframe: finaldf = finaldf.select(['recency','frequency','monetary_value','CustomerID']).distinct() Run code Powe...
The dataframe that we create using the csv file has duplicate rows. Hence, when we invoke thedistinct()method on the pyspark dataframe, the duplicate rows are dropped. After this, when we invoke thecount()method on the output of thedistinct()method, we get the number of distinct rows in...
# duplicate values df.count() # 33 # drop duplicate values df=df.dropDuplicates() # validate new count df.count() # 26 1. 2. 3. 4. 5. 6. 7. 8. 删除某列 # drop column of dataframe df_new=df.drop('mobile') df_new.show(10) ...