Python 为什么 PySpark 中的 agg() 一次只能汇总一列？
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Why agg() in PySpark is only able to summarize one column at a time?
提问by GeorgeOfTheRF
For the below dataframe
对于下面的数据框
df=spark.createDataFrame(data=[('Alice',4.300),('Bob',7.677)],schema=['name','High'])
When I try to find min & max I am only getting min value in output.
当我尝试找到 min 和 max 时，我只得到输出中的最小值。
df.agg({'High':'max','High':'min'}).show()
+-----------+
|min(High) |
+-----------+
| 2094900|
+-----------+
Why can't agg() give both max & min like in Pandas?
为什么 agg() 不能像 Pandas 那样同时给出最大值和最小值？
回答by titiro89
As you can see here:
正如你在这里看到的：
agg(*exprs)
Compute aggregates and returns the result as a DataFrame.
The available aggregate functions are avg, max, min, sum, count.
If exprs is a single dict mapping from string to string, then the key is the column to perform aggregation on, and the value is the aggregate function.
Alternatively, exprs can also be a list of aggregate Column expressions.
Parameters: exprs– a dict mapping from column name (string) to aggregate functions (string), or a list of Column.
agg(*exprs)
Compute 聚合并将结果作为 DataFrame 返回。
可用的聚合函数有 avg、max、min、sum、count。
如果 exprs 是从字符串到字符串的单个 dict 映射，则键是要对其执行聚合的列，值是聚合函数。
或者， exprs 也可以是聚合列表达式的列表。
参数： exprs– 从列名（字符串）到聚合函数（字符串）的字典映射，或列的列表。
You can use a list of column and apply the function that you need on every column, like this:
您可以使用列列表并在每一列上应用您需要的功能，如下所示：
>>> from pyspark.sql import functions as F
>>> df.agg(F.min(df.High),F.max(df.High),F.avg(df.High),F.sum(df.High)).show()
+---------+---------+---------+---------+
|min(High)|max(High)|avg(High)|sum(High)|
+---------+---------+---------+---------+
| 4.3| 7.677| 5.9885| 11.977|
+---------+---------+---------+---------+
