How to change dataframe column names in PySpark?

df = df.withColumnRenamed("colName", "newColName")
       .withColumnRenamed("colName2", "newColName2")

Advantage of using this way: With long list of columns you would like to change only few column names. This can be very convenient in these scenarios. Very useful when joining tables with duplicate column names.

If you want to rename a single column and keep the rest as it is:

from pyspark.sql.functions import col
new_df = old_df.select(*[col(s).alias(new_name) if s == column_to_change else s for s in old_df.columns])

If you want to change all columns names, try df.toDF(*cols)

For a single column rename, you can still use toDF(). For example,

df1.selectExpr("SALARY*2").toDF("REVISED_SALARY").show()

we can use col.alias for renaming the column:

from pyspark.sql.functions import col
df.select(['vin',col('timeStamp').alias('Date')]).show()

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.

Another way to rename just one column (using import pyspark.sql.functions as F):

df = df.select( '*', F.col('count').alias('new_count') ).drop('count')

df.withColumnRenamed('age', 'age2')

this is the approach that I used:

create pyspark session:

import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('changeColNames').getOrCreate()

create dataframe:

df = spark.createDataFrame(data = [('Bob', 5.62,'juice'),  ('Sue',0.85,'milk')], schema = ["Name", "Amount","Item"])

view df with column names:

df.show()
+----+------+-----+
|Name|Amount| Item|
+----+------+-----+
| Bob|  5.62|juice|
| Sue|  0.85| milk|
+----+------+-----+

create a list with new column names:

newcolnames = ['NameNew','AmountNew','ItemNew']

change the column names of the df:

for c,n in zip(df.columns,newcolnames):
    df=df.withColumnRenamed(c,n)

view df with new column names:

df.show()
+-------+---------+-------+
|NameNew|AmountNew|ItemNew|
+-------+---------+-------+
|    Bob|     5.62|  juice|
|    Sue|     0.85|   milk|
+-------+---------+-------+

I made an easy to use function to rename multiple columns for a pyspark dataframe,
in case anyone wants to use it:

def renameCols(df, old_columns, new_columns):
    for old_col,new_col in zip(old_columns,new_columns):
        df = df.withColumnRenamed(old_col,new_col)
    return df

old_columns = ['old_name1','old_name2']
new_columns = ['new_name1', 'new_name2']
df_renamed = renameCols(df, old_columns, new_columns)

Be careful, both lists must be the same length.

You can use the following function to rename all the columns of your dataframe.

def df_col_rename(X, to_rename, replace_with):
    """
    :param X: spark dataframe
    :param to_rename: list of original names
    :param replace_with: list of new names
    :return: dataframe with updated names
    """
    import pyspark.sql.functions as F
    mapping = dict(zip(to_rename, replace_with))
    X = X.select([F.col(c).alias(mapping.get(c, c)) for c in to_rename])
    return X

In case you need to update only a few columns’ names, you can use the same column name in the replace_with list

To rename all columns

df_col_rename(X,['a', 'b', 'c'], ['x', 'y', 'z'])

To rename a some columns

df_col_rename(X,['a', 'b', 'c'], ['a', 'y', 'z'])

We can use various approaches to rename the column name.

First, let create a simple DataFrame.

df = spark.createDataFrame([("x", 1), ("y", 2)], 
                                  ["col_1", "col_2"])

Now let’s try to rename col_1 to col_3. PFB a few approaches to do the same.

# Approach - 1 : using withColumnRenamed function.
df.withColumnRenamed("col_1", "col_3").show()

# Approach - 2 : using alias function.
df.select(df["col_1"].alias("col3"), "col_2").show()

# Approach - 3 : using selectExpr function.
df.selectExpr("col_1 as col_3", "col_2").show()

# Rename all columns
# Approach - 4 : using toDF function. Here you need to pass the list of all columns present in DataFrame.
df.toDF("col_3", "col_2").show()

Here is the output.

+-----+-----+
|col_3|col_2|
+-----+-----+
|    x|    1|
|    y|    2|
+-----+-----+

I hope this helps.

There are multiple approaches you can use:

1. df1=df.withColumn("new_column","old_column").drop(col("old_column"))

2. df1=df.withColumn("new_column","old_column")

3. df1=df.select("old_column".alias("new_column"))

You can put into for loop, and use zip to pairs each column name in two array.

new_name = ["id", "sepal_length_cm", "sepal_width_cm", "petal_length_cm", "petal_width_cm", "species"]

new_df = df
for old, new in zip(df.columns, new_name):
    new_df = new_df.withColumnRenamed(old, new)

I like to use a dict to rename the df.

rename = {'old1': 'new1', 'old2': 'new2'}
for col in df.schema.names:
    df = df.withColumnRenamed(col, rename[col])

Method 1:

df = df.withColumnRenamed("old_column_name", "new_column_name")

Method 2:
If you want to do some computation and rename the new values

df = df.withColumn("old_column_name", F.when(F.col("old_column_name") > 1, F.lit(1)).otherwise(F.col("old_column_name"))
df = df.drop("new_column_name", "old_column_name")

from pyspark.sql.types import StructType,StructField, StringType, IntegerType

CreatingDataFrame = [("James","Sales","NY",90000,34,10000),
    ("Michael","Sales","NY",86000,56,20000),
    ("Robert","Sales","CA",81000,30,23000),
    ("Maria","Finance","CA",90000,24,23000),
    ("Raman","Finance","CA",99000,40,24000),
    ("Scott","Finance","NY",83000,36,19000),
    ("Jen","Finance","NY",79000,53,15000),
    ("Jeff","Marketing","CA",80000,25,18000),
    ("Kumar","Marketing","NY",91000,50,21000)
  ]

schema = StructType([ 
    StructField("employee_name",StringType(),True), 
    StructField("department",StringType(),True), 
    StructField("state",StringType(),True), 
    StructField("salary", IntegerType(), True), 
    StructField("age", StringType(), True), 
    StructField("bonus", IntegerType(), True) 
  ])

 
OurData = spark.createDataFrame(data=CreatingDataFrame,schema=schema)

OurData.show()

# COMMAND ----------

GrouppedBonusData=OurData.groupBy("department").sum("bonus")


# COMMAND ----------

GrouppedBonusData.show()


# COMMAND ----------

GrouppedBonusData.printSchema()

# COMMAND ----------

from pyspark.sql.functions import col

BonusColumnRenamed = GrouppedBonusData.select(col("department").alias("department"), col("sum(bonus)").alias("Total_Bonus"))
BonusColumnRenamed.show()

# COMMAND ----------

GrouppedBonusData.groupBy("department").count().show()

# COMMAND ----------

GrouppedSalaryData=OurData.groupBy("department").sum("salary")

# COMMAND ----------

GrouppedSalaryData.show()

# COMMAND ----------

from pyspark.sql.functions import col

SalaryColumnRenamed = GrouppedSalaryData.select(col("department").alias("Department"), col("sum(salary)").alias("Total_Salary"))
SalaryColumnRenamed.show()

A way that you can use ‘alias’ to change the column name:

col('my_column').alias('new_name')

Another way that you can use ‘alias’ (possibly not mentioned):

df.my_column.alias('new_name')

Try the following method. The following method can allow you rename columns of multiple files

Reference: https://www.linkedin.com/pulse/pyspark-methods-rename-columns-kyle-gibson/

df_initial = spark.read.load('com.databricks.spark.csv')
    
    rename_dict = {
      'Alberto':'Name',
      'Dakota':'askdaosdka'
    }
    
    df_renamed = df_initial 
    .select([col(c).alias(rename_dict.get(c, c)) for c in df_initial.columns])

    
     rename_dict = {
       'FName':'FirstName',
       'LName':'LastName',
       'DOB':'BirthDate'
        }

     return df.select([col(c).alias(rename_dict.get(c, c)) for c in df.columns])


df_renamed = spark.read.load('/mnt/datalake/bronze/testData') 
.transform(renameColumns)

The simplest solution is using withColumnRenamed:

renamed_df = df.withColumnRenamed(‘name_1’, ‘New_name_1’).withColumnRenamed(‘name_2’, ‘New_name_2’)
renamed_df.show()

And if you would like to do this like we do with Pandas, you can use toDF:

Create an order of list of new columns and pass it to toDF

df_list = ["newName_1", “newName_2", “newName_3", “newName_4"]
renamed_df = df.toDF(*df_list)
renamed_df.show()

This is an easy way to rename multiple columns with a loop:

cols_to_rename = ["col1","col2","col3"]

for col in cols_to_rename:
  df = df.withColumnRenamed(col,"new_{}".format(col))

List comprehension + f-string:

df = df.toDF(*[f'n_{c}' for c in df.columns])

Simple list comprehension:

df = df.toDF(*[c.lower() for c in df.columns])

The closest statement to df.columns = new_column_name_list is:

import pyspark.sql.functions as F
df = df.select(*[F.col(name_old).alias(name_new) 
                 for (name_old, name_new) 
                 in zip(df.columns, new_column_name_list)]

This doesn’t require any rarely-used functions, and emphasizes some patterns that are very helpful in Spark. You could also break up the steps if you find this one-liner to be doing too many things:

import pyspark.sql.functions as F
column_mapping = [F.col(name_old).alias(name_new) 
                  for (name_old, name_new) 
                  in zip(df.columns, new_column_name_list)]
df = df.select(*column_mapping)

f.withColumnRenamed(‘name’, ‘rename’)