unix_timestamp of pyspark.sql.functions get null only for the majority of values
I try to convert a columns from string to timestamp with this code
from pyspark.sql import SparkSession
from pyspark.sql.functions import sum, avg, count, first, col
from pyspark.sql.functions import unix_timestamp, col, datediff
import csv
if __name__ == "__main__":
spark = SparkSession
.builder
.config("spark.some.config.option", "some-value")
.getOrCreate()
df=spark.read.csv("inSETP11.csv")
df = df.selectExpr('_c0 as USER_ID', '_c1 as timestamp', '_c2 as location')
df.show()
df = df.withColumn('timestamp', unix_timestamp('timestamp', 'yyyy-mm-dd hh:mm:ss'))
df.show()
but i get null (the function return a value only for some)
+-------+---------+--------+
|USER_ID|timestamp|location|
+-------+---------+--------+
| 1| null| 321|
| 1|1359262.2| 386|
| 1|1359271.2| 386|
| 1|1359273.6| 386|
| 1|1359273.6| 386|
| 1|1359274.8| 386|
| 1|1359281.4| 386|
| 1|1359282.0| 386|
| 1| null| 386|
| 1| null| 386|
| 1| null| 386|
| 1| null| 386|
+-------+---------+--------+
how to fix this ?, where is the problem ?
keyword : python apache-spark pyspark unix-timestamp
apache-spark pyspark unix-timestamp pyspark-sql
add a comment |
I try to convert a columns from string to timestamp with this code
from pyspark.sql import SparkSession
from pyspark.sql.functions import sum, avg, count, first, col
from pyspark.sql.functions import unix_timestamp, col, datediff
import csv
if __name__ == "__main__":
spark = SparkSession
.builder
.config("spark.some.config.option", "some-value")
.getOrCreate()
df=spark.read.csv("inSETP11.csv")
df = df.selectExpr('_c0 as USER_ID', '_c1 as timestamp', '_c2 as location')
df.show()
df = df.withColumn('timestamp', unix_timestamp('timestamp', 'yyyy-mm-dd hh:mm:ss'))
df.show()
but i get null (the function return a value only for some)
+-------+---------+--------+
|USER_ID|timestamp|location|
+-------+---------+--------+
| 1| null| 321|
| 1|1359262.2| 386|
| 1|1359271.2| 386|
| 1|1359273.6| 386|
| 1|1359273.6| 386|
| 1|1359274.8| 386|
| 1|1359281.4| 386|
| 1|1359282.0| 386|
| 1| null| 386|
| 1| null| 386|
| 1| null| 386|
| 1| null| 386|
+-------+---------+--------+
how to fix this ?, where is the problem ?
keyword : python apache-spark pyspark unix-timestamp
apache-spark pyspark unix-timestamp pyspark-sql
3
Can you show the raw datainSETP11.csv
?null
is when the format does not match.
– Jacek Laskowski
Nov 21 '18 at 10:37
I feel you are selecting wrong columns. Try,df = df.withColumn('timestamp', col('timestamp).cast(DoubleType())
– karma4917
Nov 21 '18 at 16:30
add a comment |
I try to convert a columns from string to timestamp with this code
from pyspark.sql import SparkSession
from pyspark.sql.functions import sum, avg, count, first, col
from pyspark.sql.functions import unix_timestamp, col, datediff
import csv
if __name__ == "__main__":
spark = SparkSession
.builder
.config("spark.some.config.option", "some-value")
.getOrCreate()
df=spark.read.csv("inSETP11.csv")
df = df.selectExpr('_c0 as USER_ID', '_c1 as timestamp', '_c2 as location')
df.show()
df = df.withColumn('timestamp', unix_timestamp('timestamp', 'yyyy-mm-dd hh:mm:ss'))
df.show()
but i get null (the function return a value only for some)
+-------+---------+--------+
|USER_ID|timestamp|location|
+-------+---------+--------+
| 1| null| 321|
| 1|1359262.2| 386|
| 1|1359271.2| 386|
| 1|1359273.6| 386|
| 1|1359273.6| 386|
| 1|1359274.8| 386|
| 1|1359281.4| 386|
| 1|1359282.0| 386|
| 1| null| 386|
| 1| null| 386|
| 1| null| 386|
| 1| null| 386|
+-------+---------+--------+
how to fix this ?, where is the problem ?
keyword : python apache-spark pyspark unix-timestamp
apache-spark pyspark unix-timestamp pyspark-sql
I try to convert a columns from string to timestamp with this code
from pyspark.sql import SparkSession
from pyspark.sql.functions import sum, avg, count, first, col
from pyspark.sql.functions import unix_timestamp, col, datediff
import csv
if __name__ == "__main__":
spark = SparkSession
.builder
.config("spark.some.config.option", "some-value")
.getOrCreate()
df=spark.read.csv("inSETP11.csv")
df = df.selectExpr('_c0 as USER_ID', '_c1 as timestamp', '_c2 as location')
df.show()
df = df.withColumn('timestamp', unix_timestamp('timestamp', 'yyyy-mm-dd hh:mm:ss'))
df.show()
but i get null (the function return a value only for some)
+-------+---------+--------+
|USER_ID|timestamp|location|
+-------+---------+--------+
| 1| null| 321|
| 1|1359262.2| 386|
| 1|1359271.2| 386|
| 1|1359273.6| 386|
| 1|1359273.6| 386|
| 1|1359274.8| 386|
| 1|1359281.4| 386|
| 1|1359282.0| 386|
| 1| null| 386|
| 1| null| 386|
| 1| null| 386|
| 1| null| 386|
+-------+---------+--------+
how to fix this ?, where is the problem ?
keyword : python apache-spark pyspark unix-timestamp
apache-spark pyspark unix-timestamp pyspark-sql
apache-spark pyspark unix-timestamp pyspark-sql
asked Nov 21 '18 at 8:57
SpecialOneDzSpecialOneDz
11
11
3
Can you show the raw datainSETP11.csv
?null
is when the format does not match.
– Jacek Laskowski
Nov 21 '18 at 10:37
I feel you are selecting wrong columns. Try,df = df.withColumn('timestamp', col('timestamp).cast(DoubleType())
– karma4917
Nov 21 '18 at 16:30
add a comment |
3
Can you show the raw datainSETP11.csv
?null
is when the format does not match.
– Jacek Laskowski
Nov 21 '18 at 10:37
I feel you are selecting wrong columns. Try,df = df.withColumn('timestamp', col('timestamp).cast(DoubleType())
– karma4917
Nov 21 '18 at 16:30
3
3
Can you show the raw data
inSETP11.csv
? null
is when the format does not match.– Jacek Laskowski
Nov 21 '18 at 10:37
Can you show the raw data
inSETP11.csv
? null
is when the format does not match.– Jacek Laskowski
Nov 21 '18 at 10:37
I feel you are selecting wrong columns. Try,
df = df.withColumn('timestamp', col('timestamp).cast(DoubleType())
– karma4917
Nov 21 '18 at 16:30
I feel you are selecting wrong columns. Try,
df = df.withColumn('timestamp', col('timestamp).cast(DoubleType())
– karma4917
Nov 21 '18 at 16:30
add a comment |
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3
Can you show the raw data
inSETP11.csv
?null
is when the format does not match.– Jacek Laskowski
Nov 21 '18 at 10:37
I feel you are selecting wrong columns. Try,
df = df.withColumn('timestamp', col('timestamp).cast(DoubleType())
– karma4917
Nov 21 '18 at 16:30