Thanks, Enrico.I just found that I need to group the data frame then calculate
the correlation. So I will get a list of dataframe, not columns. So I used
following solution:use following codes to create a mutable data frame df_all. I
used the first datacol to calculate correlation.
df.groupby("groupid").agg(functions.corr("datacol1","corr_col")iterate all
remaining datacol columns, create a temp data frame for this iteration. In this
iteration, use df_all to join the temp data frame on the groupid column, then
drop duplicated groupid column.after the iteration, I will get the dataframe
which contains all correlation data.
I need to verify the data to make sure it is valid.
Liang----- 原始邮件 -----
发件人：Enrico Minack <i...@enrico.minack.dev>
收件人：ckgppl_...@sina.cn, Sean Owen <sro...@gmail.com>
抄送人：user <user@spark.apache.org>
主题：Re: 回复：Re: calculate correlation
between_multiple_columns_and_one_specific_column_after_groupby_the_spark_data_frame
日期：2022年03月16日 19点53分
If you have a list of Columns called
`columns`, you can pass them to the `agg` method as:
agg(columns.head, columns.tail: _*)
Enrico
Am 16.03.22 um 08:02 schrieb
ckgppl_...@sina.cn:
Thanks, Sean. I modified the codes and have generated a list
of columns.
I am working on convert a list of columns to a new data
frame. It seems that there is no direct API to do this.
----- 原始邮件 -----
发件人：Sean Owen <sro...@gmail.com>
收件人：ckgppl_...@sina.cn
抄送人：user <user@spark.apache.org>
主题：Re: calculate correlation between multiple columns and one
specific column after groupby the spark data frame
日期：2022年03月16日 11点55分
Are you just trying to avoid writing the function call 30
times? Just put this in a loop over all the columns instead,
which adds a new corr col every time to a list.
On Tue, Mar 15, 2022, 10:30 PM
<ckgppl_...@sina.cn>
wrote:
Hi all,
I am stuck at
a correlation calculation problem. I have a
dataframe like below:
groupid
datacol1
datacol2
datacol3
datacol*
corr_co
00001
1
2
3
4
5
00001
2
3
4
6
5
00002
4
2
1
7
5
00002
8
9
3
2
5
00003
7
1
2
3
5
00003
3
5
3
1
5
I want to calculate the
correlation between all datacol columns and
corr_col column by each groupid.
So I used the following spark
scala-api codes:
df.groupby("groupid").agg(functions.corr("datacol1","corr_col"),functions.corr("datacol2","corr_col"),functions.corr("datacol3","corr_col"),functions.corr("datacol*","corr_col"))
This is very inefficient. If I
have 30 data_col columns, I need to input 30 times
functions.corr to calculate correlation.
I have searched, it seems
that functions.corr doesn't accept a List/Array
parameter, and df.agg doesn't accept a function to
be parameter.
So any spark scala API codes can do this job
efficiently?
Thanks
Liang
