问题
For the below dataframe
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.
df.agg({'High':'max','High':'min'}).show()
+-----------+
|min(High) |
+-----------+
| 2094900|
+-----------+
Why can't agg() give both max & min like in Pandas?
回答1:
As you can see here:
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.
You can use a list of column and apply the function that you need on every column, like this:
>>> 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|
+---------+---------+---------+---------+
来源：https://stackoverflow.com/questions/44384102/why-agg-in-pyspark-is-only-able-to-summarize-one-column-at-a-time