# keep rows with certain length data.filter("length(col) > 20") # get distinct value of the column data.select("col").distinct() # remove row which has certain character data.filter(~F.col('col').contains('abc')) 列值处理 (1)列值分割 # split column based on space data = data...
This kind of condition if statement is fairly easy to do in Pandas. We would usepd.np.whereordf.apply. In the worst case scenario, we could even iterate through the rows. We can’t do any of that in Pyspark. 这种条件if语句在Pandas中相当容易做到。 我们将使用pd.np.where或df.appl。 ...
Returns a new DataFrame containing union of rows in this and another DataFrame. unpersist([blocking]) Marks the DataFrame as non-persistent, and remove all blocks for it from memory and disk. 清理缓存 where(condition) where() is an alias for filter(). 过滤和filter一样 withColumn(colName, c...
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...
PySpark does not support specifying multiple columns with distinct() in order to remove the duplicates. We can use the dropDuplicates() transformation on specific columns to achieve the uniqueness of the columns. Doesdistinct()maintain the original order of rows?
Yes, we can join on multiple columns. Joining on multiple columns involves more join conditions with multiple keys for matching the rows between the datasets.It can be achieved by passing a list of column names as the join condition when using the.join()method. ...
('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)...
Take the first N rows of a DataFrame 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...
Filters rows using the given condition. where() is an alias for filter(). Parameters:condition –a Column of types.BooleanType or a string of SQL expression. >>> df.filter(df.age>3).collect()[Row(age=5, name=u'Bob')]>>> df.where(df.age==2).collect()[Row(age=2, name=u'Ali...
Remove duplicate rowsTo de-duplicate rows, use distinct, which returns only the unique rows.Python Kopiraj 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 want to ...