csv - 使用正确的数据类型在 Pyspark 中读取 CSV

Question

当我尝试使用 spark 导入本地 CSV 时，默认情况下每列都作为字符串读入。但是，我的列仅包含整数和时间戳类型。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

我找到了应该在 this question 中工作的代码，但是当我执行它时，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)))

然后在 CSV 中读取:

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|
+--------+---------+--------+----+--------+

我错过了关键的一步吗？我怀疑日期列是问题的根源。注意:我在 GoogleCollab 中运行它。

Answer 1

干得好!

"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，但是当您将其更改为日期类型时，它将将此日期格式视为 NULL。

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

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

所以我们需要将日期格式从 dd.mm.yy 更改为 yy-mm-dd。

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'))

现在为您的数据框列调用此函数:

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|
+--------+----------+--------+-----------+----+---------+--------+

下面是架构:

 |-- 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)

请让我知道这对你有没有用。