columnsdict-like or function Alternative to specifying axis ("mapper, axis=1" is equivalent to "columns=mapper"). select ( [col (c). Extra labels listed dont throw an error. If ignore, The Second example will discuss how to change the column names in a PySpark DataFrame by using select() function. A bit of annoyance in Spark 2.0 when using pivot() is that it automatically generates pivoted column names with "`" character.. columns ]) columns if '.' in c} df. Axis to target with mapper. However, we can use expr or selectExpr to use Spark SQL based trim functions to remove leading or trailing spaces or any other such characters . Can be either the axis name ('index', 'columns') or number (0, 1). In this article, we will explore the same with an example. However, this still. Alter axes labels. toDF (*(c.replace('.', '_') for c in df.columns)) Copy alternatively: from pyspark. This is really simple to understand if you are familiar with SQL queries. We also rearrange the column by position. This method returns a new DataFrame by renaming an existing column. You'll often want to rename columns in a DataFrame. Use withColumnRenamed () to Rename groupBy () Another best approach would be to use PySpark DataFrame withColumnRenamed () operation to alias/rename a column of groupBy () result. The second option you have when it comes to rename columns of PySpark DataFrames is the pyspark.sql.DataFrame.withColumnRenamed (). If the table is cached, the commands clear cached data of the table. alias (replacements. This method is better than Method 1 because you only have to specify the columns you are renaming, and the columns are renamed in place without changing the order. In order to Rearrange or reorder the column in pyspark we will be using select function. Rename Column using withColumnRenamed: withColumnRenamed () function can be used on a dataframe to rename existing column. Using Select Expression to Rename Columns Spark data frames act much like SQL statements in most cases. C:- The new PySpark Data Frame. Solution 1 You can use something similar to this great solution from @zero323: df. {ignore, raise}, default ignore, pyspark.sql.SparkSession.builder.enableHiveSupport, pyspark.sql.SparkSession.builder.getOrCreate, pyspark.sql.SparkSession.getActiveSession, pyspark.sql.DataFrame.createGlobalTempView, pyspark.sql.DataFrame.createOrReplaceGlobalTempView, pyspark.sql.DataFrame.createOrReplaceTempView, pyspark.sql.DataFrame.sortWithinPartitions, pyspark.sql.DataFrameStatFunctions.approxQuantile, pyspark.sql.DataFrameStatFunctions.crosstab, pyspark.sql.DataFrameStatFunctions.freqItems, pyspark.sql.DataFrameStatFunctions.sampleBy, pyspark.sql.functions.approxCountDistinct, pyspark.sql.functions.approx_count_distinct, pyspark.sql.functions.monotonically_increasing_id, pyspark.sql.PandasCogroupedOps.applyInPandas, pyspark.pandas.Series.is_monotonic_increasing, pyspark.pandas.Series.is_monotonic_decreasing, pyspark.pandas.Series.dt.is_quarter_start, pyspark.pandas.Series.cat.rename_categories, 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Renaming is very important in the mapping layer . The following code snippet converts all column names to lower case and then append '_new' to each column name. Test Data Following is the test DataFrame that we will be using in subsequent methods and examples. Initially, we will create a dummy pyspark dataframe and then choose a column and rename the same. To reorder the column in ascending order we will be using Sorted function. number (0, 1). If raise, raise a KeyError when a dict-like mapper, index, or columns In case of a MultiIndex, only rename labels in the specified level. PySpark withColumnRenamed () Syntax: withColumnRenamed(existingName, newNam) # Rename columns val new_column_names=df.columns.map (c=>c.toLowerCase () + "_new") val df3 = df.toDF (new_column_names:_*) df3.show () Output: Alternative to specifying axis (mapper, axis=0 is equivalent to index=mapper). Let's rename these variables! lets get clarity with an example. The table rename command cannot be used to move a table between databases, only to rename a table within the same database. Example 1: Change Column Names in PySpark DataFrame Using select() Function. Let's check this with an example:-. ALTER TABLE RENAME TO statement changes the table name of an existing table in the database. Function / dict values must be unique (1-to-1). alias ("sum_salary")) 2. In case you would like to apply a simple transformation on all column names, this code does the trick: (I am replacing all spaces with underscore) new_column_name_list= list (map (lambda x: x.replace (" ", "_"), df.columns)) df = df.toDF (*new_column_name_list) Thanks to @user8117731 for toDf trick. # Rename column using alias() function df1 = df.select(f.col("Name").alias("Pokemon_Name"), f.col("Index").alias("Number_id"),"Type") df1.printSchema() root Rename all columns Function toDF can be used to rename all column names. PySpark has a withColumnRenamed() function on DataFrame to change a column name. pyspark.pandas.DataFrame.rename DataFrame.rename (mapper: Union[Dict, Callable[[Any], Any], None] = None, index: Union[Dict, Callable[[Any], Any], None] = None . PySpark has a withColumnRenamed () function on DataFrame to change a column name. Alternative to specifying axis (mapper, axis=1 is equivalent to columns=mapper). we can rename columns by index using dataframe.withcolumnrenamed () and dataframe.columns [] methods. Can be either the axis name (index, columns) or sql. This is one of the useful functions in Pyspark which every developer/data engineer. >>> df = df.withColumnRenamed ('colA', 'A') >>> df.show () +---+----+-----+----+ | A|colB| colC|colD| +---+----+-----+----+ | 1| a| true| 1.0| 2 Answers Sorted by: 3 Assuming the list of column names is in the right order and has a matching length you can use toDF Preparing an example dataframe import numpy as np from pyspark.sql import SparkSession spark = SparkSession.builder.getOrCreate () df = spark.createDataFrame (np.random.randint (1,10, (5,4)).tolist (), list ('ABCD')) df.show () Like SQL, we can also rename columns using "SELECT" or "SELECTEXPR" functions in Spark. Use either mapper and axis to specify the axis to target with mapper, or index inplacebool, default False Whether to return a new DataFrame. uj xh ur ne ia xy gurkqnjp oj hi We can also use select statement to rename columns. Here are some examples: remove all spaces from the DataFrame columns convert all the columns to snake_case replace the dots in column names with underscores As of now Spark trim functions take the column as argument and remove leading or trailing spaces. from pyspark. Returns a new DataFrame with a column renamed. All we need to pass the existing column name and the new one. existing keys will be renamed and extra keys will be ignored. IN progress 7. Following are some methods that you can use to rename dataFrame columns in Pyspark. contains labels that are not present in the Index being transformed. get (c, c)) for c in df. will be left as-is. axisint or str, default 'index' Axis to target with mapper. functions import sum df. groupBy ("state") \ . Method 1: Using withColumnRenamed () This method is used to rename a column in the dataframe Syntax: dataframe.withColumnRenamed ("old_column_name", "new_column_name") where dataframe is the pyspark dataframe old_column_name is the existing column name new_column_name is the new column name The select method is used to select columns through the col method and to change the column names by using the alias . To reorder the column in descending order we will be using Sorted function with an argument reverse =True. Below example renames column name to sum_salary. Conclusion All examples are scanned by Snyk Code. Renaming DataFrame Columns after Pivot in PySpark. The following code snippet converts all column names to lower case and then append '_new' to each column name. By using PySpark SQL function regexp_replace () you can replace a column value with a string for another string/substring. sql. Trim spaces towards left - ltrim Trim spaces towards right - rtrim Trim spaces on both sides - trim. Use withColumnRenamed Function toDF Function to Rename All Columns in DataFrame Use DataFrame Column Alias method Now let use check these methods with an examples. The syntax for PYSPARK With Column RENAMED function is:- data1 = [ {'Name':'Jhon','ID':21.528,'Add':'USA'}, {'Name':'Joe','ID':3.69,'Add':'USA'}, {'Name':'Tina','ID':2.48,'Add':'IND'}, {'Name':'Jhon','ID':22.22, 'Add':'USA'}, {'Name':'Joe','ID':5.33,'Add':'INA'}] a = sc.parallelize (data1) b = spark.createDataFrame (a) and columns. The select operation with .alias () function can be used to renametocolumnsinPySparkdataframe. Dict-like or functions transformations to apply to that axis values. Using toDF () - To change all columns in a PySpark DataFrame When we have data in a flat structure (without nested) , use toDF () with a new schema to change all column names. This blog post explains how to rename one or all of the columns in a PySpark DataFrame. regexp_replace () uses Java regex for matching, if the regex does not match it returns an empty string, the below example replace the street name Rd value with Road string on address column. levelint or level name, default None Function toDF can be used to rename all column names. Syntax: withColumnRenamed(existingColumnName, newColumnName) Rename multiple columns in pyspark using alias function () Rename multiple columns in pyspark using withcolumnRenamed () We will be using the dataframe named df Rename column name : Rename single column in pyspark Syntax: df.withColumnRenamed ('old_name', 'new_name') old_name - old column name new_name - new column name to be replaced. The cache will be lazily filled when the next time the table . agg ( sum ("salary"). functions import col replacements = {c:c.replace ( '.', '_') for c in df. Share Improve this answer Follow If the dataframe schema does not contain the given column then it will not fail and will return the same dataframe. This is the most straight-forward approach; this function takes two parameters; the first is your existing column name and the second is the new column name you wish for. The same can be used to renamemultiple columnsina PySparkDataframe.c = b.withColumnRenamed ("Add","Address").withColumnRenamed ("ID","Card No") c.show () Output:- Screenshot:- 2. Labels not contained in a dict / Series This is the most straight forward approach; this function takes two parameters; the first is your existing column name and the second is the new column name you wish for. with the help of dataframe.columns [] we get the name of the column on the particular index and then we replace this name with another name in this article, we are going to know how to rename a pyspark dataframe column by index using python. How do you rename columns in PySpark? 2.4 Renaming column using Pyspark - Actually it is not exactly withColumn() but withColumnRename() , Lets see the example-Rename Pyspark dataframe Above all, I hope you must have liked this article on withColumn(). Using col () function - To Dynamically rename all or multiple columns Another way to change all column names on Dataframe is to use col () function. The syntax for the PYSPARK RENAME COLUMN function is:- c = b.withColumnRenamed ("Add","Address") c.show () B:- The data frame used for conversion of the columns. # Rename columns new_column_names = [f" {c.lower ()}_new" for c in df.columns] df = df.toDF (*new_column_names) df.show () Output: Every line of 'pyspark rename column' code snippets is scanned for vulnerabilities by our powerful machine learning engine that combs millions of open source libraries, ensuring your Python code is secure. 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