使用正确的数据类型在Pyspark中读取CSV

Question

当我尝试导入带有火花的本地CSV时，默认情况下每个列都作为字符串读取。但是，我的列只包括整数和时间戳类型。更具体地说，CSV如下所示：

"Customer","TransDate","Quantity","PurchAmount","Cost","TransID","TransKey"
149332,"15.11.2005",1,199.95,107,127998739,100000

我已经找到了这个问题中应该有效的代码，但当我执行它时，所有条目都返回为NULL。

我使用以下内容来创建自定义架构：

from pyspark.sql.types import LongType, StringType, StructField, StructType, BooleanType, ArrayType, IntegerType, TimestampType

customSchema = StructType(Array(
        StructField("Customer", IntegerType, true),
        StructField("TransDate", TimestampType, true),
        StructField("Quantity", IntegerType, true),
        StructField("Cost", IntegerType, true),
        StructField("TransKey", IntegerType, true)))

然后使用以下命令读取CSV：

myData = spark.read.load('myData.csv', format="csv", header="true", sep=',', schema=customSchema)

返回：

+--------+---------+--------+----+--------+
|Customer|TransDate|Quantity|Cost|Transkey|
+--------+---------+--------+----+--------+
|    null|     null|    null|null|    null|
+--------+---------+--------+----+--------+

我是否错过了关键的一步？我怀疑Date列是问题的根源。注意：我在GoogleCollab中运行这个。

Answer 1

您可以向DataFrameReader指定一个选项（'dateFormat'，'d.M.y'），以解析特定格式的日期。

df = spark.read.format("csv").option("header","true").option("dateFormat","M.d.y").schema(my_schema).load("path_to_csv")

参考

https://spark.apache.org/docs/latest/api/python/pyspark.sql.html#pyspark.sql.DataFrameReaderhttps://spark.apache.org/docs/latest/sql-ref-datetime-pattern.html

Answer 2

在这里你去！

"Customer","TransDate","Quantity","PurchAmount","Cost","TransID","TransKey"
149332,"15.11.2005",1,199.95,107,127998739,100000
PATH_TO_FILE="file:///u/vikrant/LocalTestDateFile"
Loading above file to dataframe:
df = spark.read.format("com.databricks.spark.csv") \
  .option("mode", "DROPMALFORMED") \
  .option("header", "true") \
  .option("inferschema", "true") \
  .option("delimiter", ",").load(PATH_TO_FILE)

您的日期将作为字符串列类型加载，但当您将其更改为日期类型时，它会将此日期格式视为空。

df = (df.withColumn('TransDate',col('TransDate').cast('date'))

+--------+---------+--------+-----------+----+---------+--------+
|Customer|TransDate|Quantity|PurchAmount|Cost|  TransID|TransKey|
+--------+---------+--------+-----------+----+---------+--------+
|  149332|     null|       1|     199.95| 107|127998739|  100000|
+--------+---------+--------+-----------+----+---------+--------+

因此，我们需要更改dd.mm的日期格式。年月日至年月日。

from datetime import datetime
from pyspark.sql.functions import col, udf
from pyspark.sql.types import DateType
from pyspark.sql.functions import col

更改日期格式的Python函数：

  change_dateformat_func =  udf (lambda x: datetime.strptime(x, '%d.%m.%Y').strftime('%Y-%m-%d'))

现在为您的dataframe列调用此函数：

newdf = df.withColumn('TransDate', change_dateformat_func(col('TransDate')).cast(DateType()))

+--------+----------+--------+-----------+----+---------+--------+
|Customer| TransDate|Quantity|PurchAmount|Cost|  TransID|TransKey|
+--------+----------+--------+-----------+----+---------+--------+
|  149332|2005-11-15|       1|     199.95| 107|127998739|  100000|
+--------+----------+--------+-----------+----+---------+--------+

下面是Schema：

 |-- Customer: integer (nullable = true)
 |-- TransDate: date (nullable = true)
 |-- Quantity: integer (nullable = true)
 |-- PurchAmount: double (nullable = true)
 |-- Cost: integer (nullable = true)
 |-- TransID: integer (nullable = true)
 |-- TransKey: integer (nullable = true)

如果对你有用，请告诉我。