PySpark apply same StringIndexer on multiple columns
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I have the following Dataframe
+--------------+---------------+
| SrcAddr| DstAddr|
+--------------+---------------+
| 192.168.100.5| 192.168.220.16|
| 192.168.100.5| 192.168.220.15|
|192.168.220.15| 192.168.100.5|
|192.168.220.16| 192.168.100.5|
| 192.168.100.5| 192.168.220.15|
|192.168.220.16| 192.168.100.5|
| 192.168.220.9| 192.168.100.5|
| 192.168.100.5| 192.168.220.9|
| 192.168.220.9| 192.168.100.5|
+--------------+---------------+
containing source and destination address IPs.
I want to transform them in numerical index by means of StringIndexer, but I want to learn a common mapping between the columns.
Unfortunately StringIndexer does not provide such a rich interface in PySpark. Thus I found a workaround, but I wanted to know if there is a better way to do it.
What I have done is the following:
First, I compute the union between the two columns
src_addr_df = df.select(["SrcAddr"]).withColumnRenamed("SrcAddr", "Addr")
dst_addr_df = df.select(["DstAddr"]).withColumnRenamed("DstAddr", "Addr")
all_addr_df = src_addr_df.union(dst_addr_df)
Then, I learned a common StringIndexer over the newly created DataFrame:
addrIndexer = StringIndexer(inputCol="Addr", outputCol="AddrIdx")
addrModel = addrIndexer.fit(all_addr_df)
Finally, I used the learned model to transform the original dataframe. This, is the tricky part because I need to rename the columns quite often to obtain the desired results:
df = addrModel.transform(df.withColumnRenamed("SrcAddr", "Addr")).withColumnRenamed("Addr", "SrcAddr").withColumnRenamed("AddrIdx", "SrcAddrIdx")
df = addrModel.transform(df.withColumnRenamed("DstAddr", "Addr")).withColumnRenamed("Addr", "DstAddr").withColumnRenamed("AddrIdx", "DstAddrIdx")
Thus, I'm wandering if there is the possibility to rather change the InputCol value of the StringIndexer, which would create a much readable code
Best regards,
Sandro
python dataframe pyspark
edited Nov 22 '18 at 8:43
JoSSte
1,07921833
asked Nov 22 '18 at 7:52
Sandro CavallariSandro Cavallari
164
add a comment |
I have the following Dataframe
+--------------+---------------+
| SrcAddr| DstAddr|
+--------------+---------------+
| 192.168.100.5| 192.168.220.16|
| 192.168.100.5| 192.168.220.15|
|192.168.220.15| 192.168.100.5|
|192.168.220.16| 192.168.100.5|
| 192.168.100.5| 192.168.220.15|
|192.168.220.16| 192.168.100.5|
| 192.168.220.9| 192.168.100.5|
| 192.168.100.5| 192.168.220.9|
| 192.168.220.9| 192.168.100.5|
+--------------+---------------+
containing source and destination address IPs.
I want to transform them in numerical index by means of StringIndexer, but I want to learn a common mapping between the columns.
Unfortunately StringIndexer does not provide such a rich interface in PySpark. Thus I found a workaround, but I wanted to know if there is a better way to do it.
What I have done is the following:
First, I compute the union between the two columns
src_addr_df = df.select(["SrcAddr"]).withColumnRenamed("SrcAddr", "Addr")
dst_addr_df = df.select(["DstAddr"]).withColumnRenamed("DstAddr", "Addr")
all_addr_df = src_addr_df.union(dst_addr_df)
Then, I learned a common StringIndexer over the newly created DataFrame:
addrIndexer = StringIndexer(inputCol="Addr", outputCol="AddrIdx")
addrModel = addrIndexer.fit(all_addr_df)
Finally, I used the learned model to transform the original dataframe. This, is the tricky part because I need to rename the columns quite often to obtain the desired results:
df = addrModel.transform(df.withColumnRenamed("SrcAddr", "Addr")).withColumnRenamed("Addr", "SrcAddr").withColumnRenamed("AddrIdx", "SrcAddrIdx")
df = addrModel.transform(df.withColumnRenamed("DstAddr", "Addr")).withColumnRenamed("Addr", "DstAddr").withColumnRenamed("AddrIdx", "DstAddrIdx")
Thus, I'm wandering if there is the possibility to rather change the InputCol value of the StringIndexer, which would create a much readable code
Best regards,
Sandro
python dataframe pyspark
edited Nov 22 '18 at 8:43
JoSSte
1,07921833
asked Nov 22 '18 at 7:52
Sandro CavallariSandro Cavallari
164
add a comment |
I have the following Dataframe
+--------------+---------------+
| SrcAddr| DstAddr|
+--------------+---------------+
| 192.168.100.5| 192.168.220.16|
| 192.168.100.5| 192.168.220.15|
|192.168.220.15| 192.168.100.5|
|192.168.220.16| 192.168.100.5|
| 192.168.100.5| 192.168.220.15|
|192.168.220.16| 192.168.100.5|
| 192.168.220.9| 192.168.100.5|
| 192.168.100.5| 192.168.220.9|
| 192.168.220.9| 192.168.100.5|
+--------------+---------------+
containing source and destination address IPs.
I want to transform them in numerical index by means of StringIndexer, but I want to learn a common mapping between the columns.
Unfortunately StringIndexer does not provide such a rich interface in PySpark. Thus I found a workaround, but I wanted to know if there is a better way to do it.
What I have done is the following:
First, I compute the union between the two columns
src_addr_df = df.select(["SrcAddr"]).withColumnRenamed("SrcAddr", "Addr")
dst_addr_df = df.select(["DstAddr"]).withColumnRenamed("DstAddr", "Addr")
all_addr_df = src_addr_df.union(dst_addr_df)
Then, I learned a common StringIndexer over the newly created DataFrame:
addrIndexer = StringIndexer(inputCol="Addr", outputCol="AddrIdx")
addrModel = addrIndexer.fit(all_addr_df)
Finally, I used the learned model to transform the original dataframe. This, is the tricky part because I need to rename the columns quite often to obtain the desired results:
df = addrModel.transform(df.withColumnRenamed("SrcAddr", "Addr")).withColumnRenamed("Addr", "SrcAddr").withColumnRenamed("AddrIdx", "SrcAddrIdx")
df = addrModel.transform(df.withColumnRenamed("DstAddr", "Addr")).withColumnRenamed("Addr", "DstAddr").withColumnRenamed("AddrIdx", "DstAddrIdx")
Thus, I'm wandering if there is the possibility to rather change the InputCol value of the StringIndexer, which would create a much readable code
Best regards,
Sandro
python dataframe pyspark
edited Nov 22 '18 at 8:43
JoSSte
1,07921833
asked Nov 22 '18 at 7:52
Sandro CavallariSandro Cavallari
164
I have the following Dataframe
+--------------+---------------+
| SrcAddr| DstAddr|
+--------------+---------------+
| 192.168.100.5| 192.168.220.16|
| 192.168.100.5| 192.168.220.15|
|192.168.220.15| 192.168.100.5|
|192.168.220.16| 192.168.100.5|
| 192.168.100.5| 192.168.220.15|
|192.168.220.16| 192.168.100.5|
| 192.168.220.9| 192.168.100.5|
| 192.168.100.5| 192.168.220.9|
| 192.168.220.9| 192.168.100.5|
+--------------+---------------+
containing source and destination address IPs.
I want to transform them in numerical index by means of StringIndexer, but I want to learn a common mapping between the columns.
Unfortunately StringIndexer does not provide such a rich interface in PySpark. Thus I found a workaround, but I wanted to know if there is a better way to do it.
What I have done is the following:
First, I compute the union between the two columns
src_addr_df = df.select(["SrcAddr"]).withColumnRenamed("SrcAddr", "Addr")
dst_addr_df = df.select(["DstAddr"]).withColumnRenamed("DstAddr", "Addr")
all_addr_df = src_addr_df.union(dst_addr_df)
Then, I learned a common StringIndexer over the newly created DataFrame:
addrIndexer = StringIndexer(inputCol="Addr", outputCol="AddrIdx")
addrModel = addrIndexer.fit(all_addr_df)
Finally, I used the learned model to transform the original dataframe. This, is the tricky part because I need to rename the columns quite often to obtain the desired results:
df = addrModel.transform(df.withColumnRenamed("SrcAddr", "Addr")).withColumnRenamed("Addr", "SrcAddr").withColumnRenamed("AddrIdx", "SrcAddrIdx")
df = addrModel.transform(df.withColumnRenamed("DstAddr", "Addr")).withColumnRenamed("Addr", "DstAddr").withColumnRenamed("AddrIdx", "DstAddrIdx")
Thus, I'm wandering if there is the possibility to rather change the InputCol value of the StringIndexer, which would create a much readable code
Best regards,
Sandro
python dataframe pyspark
python dataframe pyspark
edited Nov 22 '18 at 8:43
JoSSte
1,07921833
asked Nov 22 '18 at 7:52
Sandro CavallariSandro Cavallari
164
edited Nov 22 '18 at 8:43
JoSSte
1,07921833
asked Nov 22 '18 at 7:52
Sandro CavallariSandro Cavallari
164
edited Nov 22 '18 at 8:43
JoSSte
1,07921833
edited Nov 22 '18 at 8:43
JoSSte
1,07921833
edited Nov 22 '18 at 8:43
JoSSte
1,07921833
1,07921833
asked Nov 22 '18 at 7:52
Sandro CavallariSandro Cavallari
164
asked Nov 22 '18 at 7:52
Sandro CavallariSandro Cavallari
164
asked Nov 22 '18 at 7:52
Sandro CavallariSandro Cavallari
164
164
add a comment |
add a comment |
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