In this code snippet, we create a DataFramedfwith two columns: “name” of type StringType and “age” of type StringType. Let’s say we want to change the data type of the “age” column from StringType to IntegerType. We can do this using thecast()function: df=df.withColumn("age...
# add a new column data = data.withColumn("newCol",df.oldCol+1) # replace the old column data = data.withColumn("oldCol",newCol) # rename the column data.withColumnRenamed("oldName","newName") # change column data type data.withColumn("oldColumn", data.oldColumn.cast("integer")) (...
fillna(0) #change data type for col in cat_features: df = df.withColumn(col,df[col].cast(StringType())) for col in num_features: df = df.withColumn(col,df[col].cast(DoubleType())) df = df.withColumn('is_true_flag',df['ist_true_flag'].cast(IntegerType())) ?转onehot 代码...
# To convert the type of a column using the .cast() method, you can write code like this: dataframe = dataframe.withColumn("col", dataframe.col.cast("new_type")) # Cast the columns to integers model_data = model_data.withColumn("arr_delay", model_data.arr_delay.cast("integer")) m...
# To convert the type of a column using the .cast() method, you can write code like this:dataframe=dataframe.withColumn("col",dataframe.col.cast("new_type"))# Cast the columns to integersmodel_data=model_data.withColumn("arr_delay",model_data.arr_delay.cast("integer"))model_data=model...
DataFrame column operations withcolumn select when Partitioning and lazy processing cache 计算时间 集群配置 json PYSPARK学习笔记 Defining a schema # Import the pyspark.sql.types library from pyspark.sql.types import * # Define a new schema using the StructType method people_schema = StructType([ # ...
In some cases you may want to change the data type for one or more of the columns in your DataFrame. To do this, use the cast method to convert between column data types. The following example shows how to convert a column from an integer to string type, using the col method to ...
numChange1 = data.filter(data.is_acct==1).count() numChange0 = data.filter(data.is_acct==0).count() # filter(condition:Column):通过给定条件过滤行。 # count():返回DataFrame行数。 numInstances = int(numChange0/10000)*10000 train = data.filter(data.is_acct_aft==1).sample(False,num...
selects.append(column) return df.select(*selects) 函数complex_dtypes_to_json将一个给定的Spark数据帧转换为一个新的数据帧,其中所有具有复杂类型的列都被JSON字符串替换。除了转换后的数据帧外,它还返回一个带有列名及其转换后的原始数据类型的字典。
StructField("PHONE_CHANGE", IntegerType(), nullable=True), StructField("AGE", IntegerType(), nullable=True), StructField("OPEN_DATE", DateType(), nullable=True), StructField("REMOVE_TAG", IntegerType(), nullable=True), ] ) # Load housing data ...