How to replace null values with a specific value in Dataframe using spark in Java?

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I am trying improve the accuracy of Logistic regression algorithm implemented in Spark using Java. For this I'm trying to replace Null or invalid values present in a column with the most frequent value of that column. For Example:-

Name|Place
a   |a1
a   |a2
a   |a2
    |d1
b   |a2
c   |a2
c   |
    |
d   |c1

In this case I'll replace all the NULL values in column "Name" with 'a' and in column "Place" with 'a2'. Till now I am able to extract only the most frequent columns in a particular column. Can you please help me with the second step on how to replace the null or invalid values with the most frequent values of that column.

You can use .na.fill function (it is a function in org.apache.spark.sql.DataFrameNaFunctions).

Basically the function you need is: def fill(value: String, cols: Seq[String]): DataFrame

You can choose the columns, and you choose the value you want to replace the null or NaN.

In your case it will be something like:

val df2 = df.na.fill("a", Seq("Name"))
            .na.fill("a2", Seq("Place"))

Replace null in a column of a dataframe with other value, You can use the fill transformation of Class DataFrameNaFunctions https://spark.​apache.org/docs/2.1.0/api/java/org/apache/spark/sql/DataFrameNaFunctions.​html# where you replace null or NaN values in the Dataframes. I am trying improve the accuracy of Logistic regression algorithm implemented in Spark using Java. For this I'm trying to replace Null or invalid values present in a column with the most frequent value of that column.

You'll want to use the fill(String value, String[] columns) method of your dataframe, which automatically replaces Null values in a given list of columns with the value you specified.

So if you already know the value that you want to replace Null with...:

String[] colNames = {"Name"}
dataframe = dataframe.na.fill("a", colNames)

You can do the same for the rest of your columns.

Spark, For java: I think you need to use the fill(String value, String[] columns) method of your dataframe, which automatically replaces Null values in a  While working on Spark DataFrame we often need to replace null values as certain operations on null values return NullpointerException hence, we need to graciously handle null values as the first step before processing. Also, while writing to a file, it’s always best practice to replace null values, not doing this result nulls on the output file.

You can use DataFrame.na.fill() to replace the null with some value To update at once you can do as

val map = Map("Name" -> "a", "Place" -> "a2")

df.na.fill(map).show()

But if you want to replace a bad record too then you need to validate the bad records first. You can do this by using regular expression with like function.

How to replace null values in Spark DataFrame?, on Spark DataFrame we often need to replace null values as certain null values with an empty string, constant value and zero(0) on Spark  How to replace null values in Spark DataFrame? Home. Community . you'll need to affect the transformed DataFrame to a new value. Online Java Course and Training;

In order to replace the NULL values with a given string I've used fill function present in Spark for Java. It accepts the word to be replaced with and a sequence of column names. Here is how I have implemented that:-

List<String> colList = new ArrayList<String>();
colList.add(cols[i]);
Seq<String> colSeq = scala.collection.JavaConverters.asScalaIteratorConverter(colList.iterator()).asScala().toSeq();
data=data.na().fill(word, colSeq);

DataFrameNaFunctions (Spark 2.1.0 JavaDoc), I want to remove null values from a csv file. But the null values didn't change. I'm using the DataFrame df that you have defined earlier. you need to store, you'll need to affect the transformed DataFrame to a new value. BI Training · Online Java Course and Training · Python Certification Course  How to replace null values with a specific value in Dataframe using spark in Java? asked Jul 29, 2019 in Big Data Hadoop & Spark by Aarav ( 11.5k points) apache-spark

How do I replace nulls with 0's in a DataFrame?, (Scala-specific) Returns a new DataFrame that replaces null or NaN values in specified numeric Returns a new DataFrame that replaces null values in string columns with value . Replaces values matching keys in replacement map with the corresponding values. public <T> Dataset<Row> replace(String col, java.​util. In this post, we will see how to replace nulls in a DataFrame with Python and Scala. Assuming having some knowledge on Dataframes and basics of Python and Scala. Here we are doing all these operations in spark interactive shell so we need to use sc for SparkContext, sqlContext for hiveContext.

How to handle NULL or empty values with dataframe using scala , How to handle NULL or empty values with dataframe using scala column to find out the maximum value but it is throwing "java.lang. Can anyone please let me know if the data file which is given is a proper file? https://stackoverflow.​com/questions/33376571/replace-null-values-in-spark-dataframe If it's tesla, use the value S for make else you the current value of column 1. Then build a tuple with all data from the row using the indexes (zero based) (Row(row(0),make,row(2))) in my example) There is probably a better way to do it. I am not that familiar yet with the Spark umbrella.

Dealing with Nulls in Apache Spark, You have to use null values correctly in Spark DataFrames, 1.1 Apache Spark Training (Scala + PySpark); 1.2 drop; 1.3 fill; 1.4 replace This can be done by specifying a map – that is a particular value and a 12+ years of experience in IT with vast experience in executing complex projects using Java,  A good solution for me was to drop the rows with any null values: Dataset<Row> filtered = df.filter(row => !row.anyNull); In case one is interested in the other case, just call row.anyNull. (Spark 2.1.0 using Java API)

Comments
  • Is it available in Java? I couldn't find a similar fill function.
  • Sorry I didn't use it in Java, but you can find here the latest version documentation of Spark, and you can see the DataFrameNaFunctions there: spark.apache.org/docs/latest/api/java/index.html probably try fill without .na
  • @PirateJack can you please accept the answer if it solved your problem?
  • have tried using it with null ? . It says cannot be applied to (Null, Int). It hasn't solved the purpose for me. So was wondering there might be some solution now after 2 years :)
  • My dataframes are of type Dataset<Row>. It says it's not defined for type Dataset<Row>
  • I have updated my answer to include the .na part. You could also try: df.na.fill(ImmutableMap.of("ColumnName", "replacementValue", "egName", "egA");
  • Thanks a lot for help. I was able to implement it using the scala Sequence libraries. I'll update the same in my answer.
  • I need to this for each column separately instead of whole dataframe at once. Can you please share an example as how will I replace any value. Also, I'll create a regular expression for the bad records. Please share the java example if you have. Thank you.
  • can we do this based on condition like -> fill column2 "only if col1 is not null"?