# create a new col based on another col's value data = data.withColumn('newCol', F.when(condition, value)) # multiple conditions data = data.withColumn("newCol", F.when(condition1, value1) .when(condition2, valu
spark = SparkSession.builder.appName("Multiple WHEN Conditions").getOrCreate() # 创建示例数据 data = [("John", 25), ("Alice", 30), ("Mike", 35)] df = spark.createDataFrame(data, ["Name", "Age"]) # 添加一个新的列根据不同的条件逻辑进行处理 df = df.withColumn("Category", when...
df.withColumn("age_after_10_yrs",(df["age"]+10)).show(10,False) 1. 2. 3. 4. 修改某一列的类型 df.withColumn('age_double',df['age'].cast(DoubleType())).show(10,False) 1. # with column df.withColumn("age_after_10_yrs",(df["age"]+10)).show(10,False) 1. 2. filter过...
To filter on multiple conditions, use logical operators. For example, & and | enable you to AND and OR conditions, respectively. The following example filters rows where the c_nationkey is equal to 20 and c_acctbal is greater than 1000.Python Копирај ...
PySpark provides us with the .withColumnRenamed() method that helps us rename columns. Conclusion In this tutorial, we’ve learned how to drop single and multiple columns using the .drop() and .select() methods. We also described alternative methods to leverage SQL expressions if we require ...
Multiple when conditions can be chained together. from pyspark.sql.functions import col, when df = auto_df.withColumn( "mpg_class", when(col("mpg") <= 20, "low") .when(col("mpg") <= 30, "mid") .when(col("mpg") <= 40, "high") .otherwise("very high"), ) # Code snippet...
person_id, 'left') # Match on multiple columns df = df.join(other_table, ['first_name', 'last_name'], 'left') Column Operations # Add a new static column df = df.withColumn('status', F.lit('PASS')) # Construct a new dynamic column df = df.withColumn('full_name', F.when(...
I can create new columns in Spark using .withColumn(). I have yet found a convenient way to create multiple columns at once without chaining multiple .withColumn() methods. df2.withColumn('AgeTimesFare', df2.Age*df2.Fare).show() +---+---+---+---+---+ |PassengerId|Age|Fare|...
data = data.withColumn("Churn", churn_func(data.page)) 利用udf方法来创建一个适用于添加对应逻辑列的对象 udf方法类似于pandas的map和apply方法 新建一个Churn列,当用户确认取消订阅和降级的时候,我们将该批用户的Churn标记为1,否则当作正常用户,标记为0. ...
PySpark Where Filter Function | Multiple Conditions PySpark String Functions with Examples PySpark Column Class | Operators & Functions References In conclusion, PySpark Window functions are analytical functions that operate on a subset of rows, known as a window, within a larger result set. They are...