The table below defines Ranking and Analytic functions; for aggregate functions, we can use any existingaggregate functionsas a window function. To operate on a group, first, we need to partition the data using
from pyspark.sql import Window df.withColumn("row_number", F.row_number().over(Window.partitionBy("a","b","c","d").orderBy("time"))).show() # row_number()函数 数据写出 写入集群分区表 1 all_bike.rdd.map(lambda line: u','.join(map(lambda x:unicode(x),line))).saveAsTextFil...
df.select(F.count(df.age)) # 直接统计,经试验,这个函数会去掉缺失值会再统计 from pyspark.sql import Window df.withColumn("row_number", F.row_number().over(Window.partitionBy("a","b","c","d").orderBy("time"))).show() # row_number()函数 1. 2. 3. 4. 5. 6. 7. 8. 9. ...
一样的效果df.select(F.countDistinct(df.age)) # 去重后统计df.select(F.count(df.age)) # 直接统计,经试验,这个函数会去掉缺失值会再统计from pyspark.sql import Windowdf.withColumn("row_number", F.row_number().over(Window.partitionBy("a","b","c","d").orderBy("time")...
Another method to calculate the median is to use the percent_rank() window function, which assigns a percentile rank to each row within a partition. This method is more accurate than the approxQuantile() but can be slower for large datasets. from pyspark.sql.window import Window from pyspark...
'desc': 'Returns a sort expression based on the descending order of the given column name.', 'upper': 'Converts a string expression to upper case.', 'lower': 'Converts a string expression to upper case.', 'sqrt': 'Computes the square root of the specified float value.', ...
orderBy(*cols, **kwargs) Returns a new DataFrame sorted by the specified column(s). Parameters:cols –list of Column or column names to sort by. ascending –boolean or list of boolean (default True). Sort ascending vs. descending. Specify list for multiple sort orders. If a list is sp...