how do you drop columns in pyspark

Discussing different ways for dropping columns from DataFrames in PySpark

Full Answer

How to drop a column from a pyspark Dataframe?

You can use the Pyspark drop () function to drop one or more columns from a Pyspark dataframe. Pass the column (or columns) you want to drop as arguments to the function. The following is the syntax –

How to drop multiple columns which contains Nan/Na values in pyspark?

Dropping multiple columns which contains NAN/NA values in pyspark accomplished in a roundabout way by creating a user defined function. column names which contains NA/NAN values are extracted using isnan () function and then it is passed to drop () function as shown below.

How to use second signature in pyspark?

To use second signature you need to import pyspark.sql.functions import col The above 3 examples drops column “firstname” from DataFrame. You can use either one of these according to your need.

What is pyspark in Python?

In the Python programming language, the PySpark is considered an API that is developed by Apache SparkThe purpose of this API is to provide an interface for Apache Spark in Python and let you write spark applications, analyze data, and process data in a large scale. Like Pandas, you can also create a DataFrame and perform many operations with it.

How do I drop multiple columns in spark?

The Spark DataFrame provides the drop() method to drop the column or the field from the DataFrame or the Dataset. The drop() method is also used to remove the multiple columns from the Spark DataFrame or the Database.

How do you drop multiple columns after join in Pyspark?

Show activity on this post.df. dropDuplicates() works only for rows.You can df1.drop(df2.column("value"))You can specify columns you want to select, for example, with df.select(Seq of columns)

How do I drop a Pyspark DataFrame?

In pyspark the drop() function can be used to remove values/columns from the dataframe. thresh – This takes an integer value and drops rows that have less than that thresh hold non-null values.

How do I drop the last column in Pyspark DataFrame?

2.2 Using drop() You can also use DataFrame. drop() method to delete the last n columns. Use axis=1 to specify the columns and inplace=True to apply the change on the existing DataFrame. On below example df.

How do I drop a column in Databricks?

Break down of the steps :Read the table in the dataframe.Drop the columns that you don't want in your final table.Drop the actual table from which you have read the data.now save the newly created dataframe after dropping the columns as the same table name.More items...•

How do you drop duplicates in PySpark?

PySpark distinct() function is used to drop/remove the duplicate rows (all columns) from DataFrame and dropDuplicates() is used to drop rows based on selected (one or multiple) columns.

How do you drop all columns with null values in a PySpark DataFrame?

In order to remove Rows with NULL values on selected columns of PySpark DataFrame, use drop(columns:Seq[String]) or drop(columns:Array[String]). To these functions pass the names of the columns you wanted to check for NULL values to delete rows.

How do I drop a column in Pandas?

During the data analysis operation on a dataframe, you may need to drop a column in Pandas. You can drop column in pandas dataframe using the df. drop(“column_name”, axis=1, inplace=True) statement.

What is withColumn in PySpark?

DataFrame. withColumn (colName, col)[source] Returns a new DataFrame by adding a column or replacing the existing column that has the same name. The column expression must be an expression over this DataFrame ; attempting to add a column from some other DataFrame will raise an error.

How do you remove the last 3 columns in Python?

Use del keyword to remove last N columns of pandas dataframe Iterate over last N column names of dataframe and for each of them select the column by passing column name in subscript operator i.e. df[df. columns[-1]]. Then call del keyword on that selected column. It deleted the last 3 columns of dataframe in place.

How do I remove the last column?

In this article, we will discuss different ways to delete the last column of a pandas dataframe in python. Use iloc to drop last column of pandas dataframe. Use drop() to remove last column of pandas dataframe. Use del keyword to drop last column of pandas dataframe.

How do I drop the first column in a Dataframe?

Use drop() to remove first column of pandas dataframe. Use del keyword to remove first column of pandas dataframe. Use pop() to remove first column of pandas dataframe.

How do I drop a column after join?

Method 1: Using drop() function dataframe1 is the second dataframe. inner specifies inner join. drop() will delete the common column and delete first dataframe column.

How do I join Pyspark DataFrames on multiple columns?

PySpark Join Two DataFrames Following is the syntax of join. The first join syntax takes, right dataset, joinExprs and joinType as arguments and we use joinExprs to provide a join condition. The second join syntax takes just the right dataset and joinExprs and it considers default join as inner join .

How do I join multiple conditions in Pyspark?

join(other, on=None, how=None) Joins with another DataFrame, using the given join expression. The following performs a full outer join between df1 and df2. Parameters: other – Right side of the join on – a string for join column name, a list of column names, , a join expression (Column) or a list of Columns.

How do you do a cross join in Pyspark?

Cross join This join simply combines each row of the first table with each row of the second table. For example, we have m rows in one table and n rows in another, this gives us m*n rows in the resulting table.

pyspark drop column : ( Example)-

It will make more sense if you see the use of drop () pyspark function practically. For the same, the prerequisites are to create a dummy pyspark dataframe. Then we will drop column from that pyspark dataframe. Now here we go –

Use drop () function in pyspark –

Let’s say if we want to drop “specialization_id” from the above dataframe. You may use the below code part.

Dropping multiple columns-

Hey! it’s so simple, In the place of a single column, we can pass multiple entries. Here is an example of the same. Let’s understand with the above example. Suppose if we want to drop the “salary” column along with the “specialization_id” column. Check out the code with output-