To aggregate data in a DataFrame, similar to a GROUP BY in SQL, use the groupBy method to specify columns to group by and the agg method to specify aggregations. Import common aggregations including avg, sum, max, and min from pyspark.sql.functions. The following example shows the average ...
# columns of dataframe 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) 1. 2. 打印字段...
Grouping on Multiple Columns in PySpark can be performed by passing two or more columns to the groupBy() method, this returns apyspark.sql.GroupedDataobject which contains agg(), sum(), count(), min(), max(), avg() e.t.c to perform aggregations. When you execute a groupby operation o...
Aggregate function: returns the level of grouping, equals to (grouping(c1) << (n-1)) + (grouping(c2) << (n-2)) + ... + grouping(cn) note:: The list of columns should match with grouping columns exactly, or empty (means all the grouping columns). 1. 2. 3. 4. 5. df.cube...
[In]: df.groupBy('mobile').max().show(5,False) [Out]: [In]:df.groupBy('mobile').min().show(5,False) [Out]: 聚集 我们也可以使用agg函数来获得与上面类似的结果。让我们使用 PySpark 中的agg函数来简单地计算每个手机品牌的总体验。
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PySpark between() range of values PySpark foreach() Example PySpark Pandas UDF Example PySpark transform() Function PySpark max() – Different Methods Explained PySpark sum() Columns Example PySpark union two DataFrames PySpark Broadcast Variable ...
#展示数字或string列的统计信息,可以指定列,默认是所有列,包括count, mean, stddev, min, and max df.describe(['age', 'weight', 'height']).show() #展示数字或string列的统计信息,处理describe的信息,还包括25%,50%,75% df.select("age", "weight", "height").summary().show() ...
We will delve into PySpark’s StringIndexer, an essential feature that converts categorical string columns into numerical indices. This guide will provide a deep understanding of PySpark’s StringIndexer, complete with examples that highlight its relevance in machine learning tasks. What is ...
我的做法稍有不同，改用row_number：