# Why agg() in PySpark is only able to summarize one column of a DataFrame at a time?

## Question:

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?

## Answers:

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|
+---------+---------+---------+---------+
```