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PYSPARK: quantile discretizer for all columns
2019 Community Moderator Electionrenaming columns for pyspark dataframes aggregatesHow to change dataframe column names in pyspark?Pyspark - Sum over multiple sparse vectors (CountVectorizer Output)How to use the PySpark CountVectorizer on columns that maybe nullpyspark: org.apache.thrift.transport.TTransportException at ERRORDeciles or other quantile rank for Pyspark columnMedian / quantiles within PySpark groupByEncode a column with integer in pysparkAdditional columns in pyspark GBT Classifier outputConvert PySpark Dense Column Vectors into Rows
Assume I have 1000 columns. How to apply QuantileDiscretizer for all the columns?
It is like the following for discretizing one column:
result_discretizer1 = QuantileDiscretizer(numBuckets=2,
inputCol="I1",outputCol="result1").fit(df).transform(df)
result_discretizer1.show()
+---+----+---+---+---+-------+
| id| I1| I2| I3| I4|result1|
+---+----+---+---+---+-------+
|1.0|1.23|2.5|3.9|5.0| 1.0|
|2.0|1.23|2.5|3.9|6.0| 1.0|
|3.0|1.23|5.8|9.0|6.0| 1.0|
|4.0|1.23|2.5|3.9|6.0| 1.0|
+---+----+---+---+---+-------+
what if i want to apply QuantileDiscretizer for all the columns and to obtain all the discretized columns as output?
pyspark apache-spark-mllib
asked 19 hours ago
priyapriya
5612
add a comment |
Assume I have 1000 columns. How to apply QuantileDiscretizer for all the columns?
It is like the following for discretizing one column:
result_discretizer1 = QuantileDiscretizer(numBuckets=2,
inputCol="I1",outputCol="result1").fit(df).transform(df)
result_discretizer1.show()
+---+----+---+---+---+-------+
| id| I1| I2| I3| I4|result1|
+---+----+---+---+---+-------+
|1.0|1.23|2.5|3.9|5.0| 1.0|
|2.0|1.23|2.5|3.9|6.0| 1.0|
|3.0|1.23|5.8|9.0|6.0| 1.0|
|4.0|1.23|2.5|3.9|6.0| 1.0|
+---+----+---+---+---+-------+
what if i want to apply QuantileDiscretizer for all the columns and to obtain all the discretized columns as output?
pyspark apache-spark-mllib
asked 19 hours ago
priyapriya
5612
add a comment |
Assume I have 1000 columns. How to apply QuantileDiscretizer for all the columns?
It is like the following for discretizing one column:
result_discretizer1 = QuantileDiscretizer(numBuckets=2,
inputCol="I1",outputCol="result1").fit(df).transform(df)
result_discretizer1.show()
+---+----+---+---+---+-------+
| id| I1| I2| I3| I4|result1|
+---+----+---+---+---+-------+
|1.0|1.23|2.5|3.9|5.0| 1.0|
|2.0|1.23|2.5|3.9|6.0| 1.0|
|3.0|1.23|5.8|9.0|6.0| 1.0|
|4.0|1.23|2.5|3.9|6.0| 1.0|
+---+----+---+---+---+-------+
what if i want to apply QuantileDiscretizer for all the columns and to obtain all the discretized columns as output?
pyspark apache-spark-mllib
asked 19 hours ago
priyapriya
5612
Assume I have 1000 columns. How to apply QuantileDiscretizer for all the columns?
It is like the following for discretizing one column:
result_discretizer1 = QuantileDiscretizer(numBuckets=2,
inputCol="I1",outputCol="result1").fit(df).transform(df)
result_discretizer1.show()
+---+----+---+---+---+-------+
| id| I1| I2| I3| I4|result1|
+---+----+---+---+---+-------+
|1.0|1.23|2.5|3.9|5.0| 1.0|
|2.0|1.23|2.5|3.9|6.0| 1.0|
|3.0|1.23|5.8|9.0|6.0| 1.0|
|4.0|1.23|2.5|3.9|6.0| 1.0|
+---+----+---+---+---+-------+
what if i want to apply QuantileDiscretizer for all the columns and to obtain all the discretized columns as output?
pyspark apache-spark-mllib
pyspark apache-spark-mllib
asked 19 hours ago
priyapriya
5612
asked 19 hours ago
priyapriya
5612
edited 9 hours ago
priya
asked 19 hours ago
priyapriya
5612
asked 19 hours ago
priyapriya
5612
asked 19 hours ago
priyapriya
5612
5612
add a comment |
add a comment |
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