PySpark Count Distinct Multiple Columns To count the number of distinct values in multiple columns, we will use the following steps. We will first select the specified columns using theselect()method. Next, we will use thedistinct()method to find thedistinct()pairs of values in the given colu...
DataFramedistinct()returns a new DataFrame after eliminating duplicate rows (distinct on all columns). if you want to get count distinct on selected multiple columns, use the PySpark SQL functioncountDistinct(). This function returns the number of distinct elements in a group. In order to use t...
To select distinct rows based on multiple columns, we can pass the column names by which we want to decide the uniqueness of the rows in a list to thedropDuplicates()method. After execution, thedropDuplicates()method will return a dataframe containing a unique set of values in the specified...
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 columns to eliminate duplicates. Note that calling dropDuplicates() on DataFr...
df.columns 1. 2. 查看列(字段)个数 # check number of columns len(df.columns) # 5 1. 2. 查看记录数 # number of records in dataframe df.count() # 33 1. 2. 查看维度 # shape of dataset print((df.count(),len(df.columns))) # (33, 5) ...
Remove columnsTo remove columns, you can omit columns during a select or select(*) except or you can use the drop method:Python Копирај df_customer_flag_renamed.drop("balance_flag_renamed") You can also drop multiple columns at once:Python Копирај ...
pd.DataFrame(rdd3_ls.sort(asc('time')).take(5), columns=rdd3_ls.columns)``` 1. 2. 组合统计 分组df.groupBy("key").count().orderBy("key").show() 唯一值、去重:distinct()、dropDuplicates() df.distinct() df.dropDuplicates(['staff_id']).orderBy('staff_id').limit(10).show() ...
Get distinct values of a column Remove duplicates Grouping count(*) on a particular column Group and sort Filter groups based on an aggregate value, equivalent to SQL HAVING clause Group by multiple columns Aggregate multiple columns Aggregate multiple columns with custom orderings Get the maximum...
distinct() spark_df_filter = spark_df.drop_duplicates(["col_name"]) pandas_df.drop_duplicates(["col_name"], keep='first', inplace=True) # 缺失数据处理 spark_df.na.fill() spark_df.na.drop(subset=['A', "B"]) #同dropna pandas_df.fillna() pandas_df.dropna(subset=['A', "B"...
agg(countDistinct('CustomerID').alias('country_count')).orderBy(desc('country_count')).show() Run code Powered By The output displayed is now sorted in descending order: When was the most recent purchase made by a customer on the e-commerce platform? To find when the latest purchase ...