Despite the odd usage, it does the trick, thanks Reynold!
On Fri, May 22, 2015 at 2:47 PM Reynold Xin wrote:
> In 1.4 it actually shows col1 by default.
>
> In 1.3, you can add "col1" to the output, i.e.
>
> df.groupBy($"col1").agg($"col1", count($"col1").as("c")).show()
>
>
> On Thu, May 21, 20
In 1.4 it actually shows col1 by default.
In 1.3, you can add "col1" to the output, i.e.
df.groupBy($"col1").agg($"col1", count($"col1").as("c")).show()
On Thu, May 21, 2015 at 11:22 PM, SLiZn Liu wrote:
> However this returns a single column of c, without showing the original
> col1.
>
>
>
However this returns a single column of c, without showing the original col1
.
On Thu, May 21, 2015 at 11:25 PM Ram Sriharsha
wrote:
> df.groupBy($"col1").agg(count($"col1").as("c")).show
>
> On Thu, May 21, 2015 at 3:09 AM, SLiZn Liu wrote:
>
>> Hi Spark Users Group,
>>
>> I’m doing groupby
df.groupBy($"col1").agg(count($"col1").as("c")).show
On Thu, May 21, 2015 at 3:09 AM, SLiZn Liu wrote:
> Hi Spark Users Group,
>
> I’m doing groupby operations on my DataFrame *df* as following, to get
> count for each value of col1:
>
> > df.groupBy("col1").agg("col1" -> "count").show // I don'
Hi Spark Users Group,
I’m doing groupby operations on my DataFrame *df* as following, to get
count for each value of col1:
> df.groupBy("col1").agg("col1" -> "count").show // I don't know if I should
> write like this.
col1 COUNT(col1#347)
aaa2
bbb4
ccc4
...
and more...
As I ‘d li
