How does mapping on an rdd work in pyspark?
I was learning pyspark when I encounterd this.
from pyspark.sql import Row
df = spark.createDataFrame([Row([0,45,63,0,0,0,0]),
Row([0,0,0,85,0,69,0]),
Row([0,89,56,0,0,0,0])],
['features'])
+--------------------+
| features|
+--------------------+
|[0, 45, 63, 0, 0,...|
|[0, 0, 0, 85, 0, ...|
|[0, 89, 56, 0, 0,...|
+--------------------+
sample = df.rdd.map(lambda row: row[0]*2)
sample.collect()
[[0, 45, 63, 0, 0, 0, 0, 0, 45, 63, 0, 0, 0, 0],
[0, 0, 0, 85, 0, 69, 0, 0, 0, 0, 85, 0, 69, 0],
[0, 89, 56, 0, 0, 0, 0, 0, 89, 56, 0, 0, 0, 0]]
My question is why is row[0] is taken as a complete list rather than one value?
What is the property that gives the above output
pyspark apache-spark-sql rdd
asked 19 hours ago
ShilpaShilpa
62
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Shilpa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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I was learning pyspark when I encounterd this.
from pyspark.sql import Row
df = spark.createDataFrame([Row([0,45,63,0,0,0,0]),
Row([0,0,0,85,0,69,0]),
Row([0,89,56,0,0,0,0])],
['features'])
+--------------------+
| features|
+--------------------+
|[0, 45, 63, 0, 0,...|
|[0, 0, 0, 85, 0, ...|
|[0, 89, 56, 0, 0,...|
+--------------------+
sample = df.rdd.map(lambda row: row[0]*2)
sample.collect()
[[0, 45, 63, 0, 0, 0, 0, 0, 45, 63, 0, 0, 0, 0],
[0, 0, 0, 85, 0, 69, 0, 0, 0, 0, 85, 0, 69, 0],
[0, 89, 56, 0, 0, 0, 0, 0, 89, 56, 0, 0, 0, 0]]
My question is why is row[0] is taken as a complete list rather than one value?
What is the property that gives the above output
pyspark apache-spark-sql rdd
asked 19 hours ago
ShilpaShilpa
62
New contributor
Shilpa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
I was learning pyspark when I encounterd this.
from pyspark.sql import Row
df = spark.createDataFrame([Row([0,45,63,0,0,0,0]),
Row([0,0,0,85,0,69,0]),
Row([0,89,56,0,0,0,0])],
['features'])
+--------------------+
| features|
+--------------------+
|[0, 45, 63, 0, 0,...|
|[0, 0, 0, 85, 0, ...|
|[0, 89, 56, 0, 0,...|
+--------------------+
sample = df.rdd.map(lambda row: row[0]*2)
sample.collect()
[[0, 45, 63, 0, 0, 0, 0, 0, 45, 63, 0, 0, 0, 0],
[0, 0, 0, 85, 0, 69, 0, 0, 0, 0, 85, 0, 69, 0],
[0, 89, 56, 0, 0, 0, 0, 0, 89, 56, 0, 0, 0, 0]]
My question is why is row[0] is taken as a complete list rather than one value?
What is the property that gives the above output
pyspark apache-spark-sql rdd
asked 19 hours ago
ShilpaShilpa
62
New contributor
Shilpa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
I was learning pyspark when I encounterd this.
from pyspark.sql import Row
df = spark.createDataFrame([Row([0,45,63,0,0,0,0]),
Row([0,0,0,85,0,69,0]),
Row([0,89,56,0,0,0,0])],
['features'])
+--------------------+
| features|
+--------------------+
|[0, 45, 63, 0, 0,...|
|[0, 0, 0, 85, 0, ...|
|[0, 89, 56, 0, 0,...|
+--------------------+
sample = df.rdd.map(lambda row: row[0]*2)
sample.collect()
[[0, 45, 63, 0, 0, 0, 0, 0, 45, 63, 0, 0, 0, 0],
[0, 0, 0, 85, 0, 69, 0, 0, 0, 0, 85, 0, 69, 0],
[0, 89, 56, 0, 0, 0, 0, 0, 89, 56, 0, 0, 0, 0]]
My question is why is row[0] is taken as a complete list rather than one value?
What is the property that gives the above output
pyspark apache-spark-sql rdd
pyspark apache-spark-sql rdd
asked 19 hours ago
ShilpaShilpa
62
New contributor
Shilpa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 19 hours ago
ShilpaShilpa
62
New contributor
Shilpa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 19 hours ago
ShilpaShilpa
62
New contributor
Shilpa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 19 hours ago
ShilpaShilpa
62
asked 19 hours ago
ShilpaShilpa
62
62
New contributor
Shilpa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Shilpa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Shilpa is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
1 Answer
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It is Taken as Complete list as you have given it as one, and you have also defined it under one column "features"
when You are saying
df.rdd.map(lambda row: row[0]*2)
You are just asking spark that "I want all values in this list to occur twice". Hence you get the output that you are getting.
Now How to get Individual values in list.
df = spark.createDataFrame([Row(0,45,63,0,0,0,0),
Row(0,0,0,85,0,69,0),
Row(0,89,56,0,0,0,0)],
['feature1' , 'feature2' , 'feature3' , 'feature4', 'feature5' , 'feature6' , 'feature7'])
This should give you access to individual values in a dedicated column.
Note : syntax for schema is just representation. please refer spark docs for exact syntax.
Hope This helps :)
answered 16 hours ago
Harjeet KumarHarjeet Kumar
3115
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
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active
oldest
votes
It is Taken as Complete list as you have given it as one, and you have also defined it under one column "features"
when You are saying
df.rdd.map(lambda row: row[0]*2)
You are just asking spark that "I want all values in this list to occur twice". Hence you get the output that you are getting.
Now How to get Individual values in list.
df = spark.createDataFrame([Row(0,45,63,0,0,0,0),
Row(0,0,0,85,0,69,0),
Row(0,89,56,0,0,0,0)],
['feature1' , 'feature2' , 'feature3' , 'feature4', 'feature5' , 'feature6' , 'feature7'])
This should give you access to individual values in a dedicated column.
Note : syntax for schema is just representation. please refer spark docs for exact syntax.
Hope This helps :)
answered 16 hours ago
Harjeet KumarHarjeet Kumar
3115
add a comment |
It is Taken as Complete list as you have given it as one, and you have also defined it under one column "features"
when You are saying
df.rdd.map(lambda row: row[0]*2)
You are just asking spark that "I want all values in this list to occur twice". Hence you get the output that you are getting.
Now How to get Individual values in list.
df = spark.createDataFrame([Row(0,45,63,0,0,0,0),
Row(0,0,0,85,0,69,0),
Row(0,89,56,0,0,0,0)],
['feature1' , 'feature2' , 'feature3' , 'feature4', 'feature5' , 'feature6' , 'feature7'])
This should give you access to individual values in a dedicated column.
Note : syntax for schema is just representation. please refer spark docs for exact syntax.
Hope This helps :)
answered 16 hours ago
Harjeet KumarHarjeet Kumar
3115
add a comment |
It is Taken as Complete list as you have given it as one, and you have also defined it under one column "features"
when You are saying
df.rdd.map(lambda row: row[0]*2)
You are just asking spark that "I want all values in this list to occur twice". Hence you get the output that you are getting.
Now How to get Individual values in list.
df = spark.createDataFrame([Row(0,45,63,0,0,0,0),
Row(0,0,0,85,0,69,0),
Row(0,89,56,0,0,0,0)],
['feature1' , 'feature2' , 'feature3' , 'feature4', 'feature5' , 'feature6' , 'feature7'])
This should give you access to individual values in a dedicated column.
Note : syntax for schema is just representation. please refer spark docs for exact syntax.
Hope This helps :)
answered 16 hours ago
Harjeet KumarHarjeet Kumar
3115
It is Taken as Complete list as you have given it as one, and you have also defined it under one column "features"
when You are saying
df.rdd.map(lambda row: row[0]*2)
You are just asking spark that "I want all values in this list to occur twice". Hence you get the output that you are getting.
Now How to get Individual values in list.
df = spark.createDataFrame([Row(0,45,63,0,0,0,0),
Row(0,0,0,85,0,69,0),
Row(0,89,56,0,0,0,0)],
['feature1' , 'feature2' , 'feature3' , 'feature4', 'feature5' , 'feature6' , 'feature7'])
This should give you access to individual values in a dedicated column.
Note : syntax for schema is just representation. please refer spark docs for exact syntax.
Hope This helps :)
answered 16 hours ago
Harjeet KumarHarjeet Kumar
3115
answered 16 hours ago
Harjeet KumarHarjeet Kumar
3115
answered 16 hours ago
Harjeet KumarHarjeet Kumar
3115
answered 16 hours ago
Harjeet KumarHarjeet Kumar
3115
3115
add a comment |
add a comment |
Shilpa is a new contributor. Be nice, and check out our Code of Conduct.
Shilpa is a new contributor. Be nice, and check out our Code of Conduct.
Shilpa is a new contributor. Be nice, and check out our Code of Conduct.
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