我正在尝试将字符串列转换为数字，但是在PySpark中却遇到异常。我在下面提供了代码和错误消息。
是否可以将csv文件中的特定列作为数字导入？ （默认设置是作为字符串导入）。
我还有什么选择？
我的代码和错误消息如下：
import pandas as pd
import seaborn as sns
import findspark
findspark.init()
import pyspark
from pyspark.sql import SparkSession
# Loads data. Be careful of indentations and whitespace
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.master('local') \
.appName('Data cleaning') \
.getOrCreate()
# These lines enable the run of spark commands
from pyspark.context import SparkContext
#from pyspark.sql.session import SparkSession
sc = SparkContext.getOrCreate()
#spark = SparkSession(sc)
import os
os.chdir('D:\\DIGITAL_LIBRARY\\DataCamp')
df = spark.read.format('csv').option('header','true').option('mode','DROPMALFORMED').\
load('D:\DIGITAL_LIBRARY\DataCamp\\df.csv')
from pyspark.sql.functions import *
df.columns
['sku_id',
'promo_start_week',
'hierarchy2_name',
'brand',
'region',
'store_norm_group',
'holiday_names',
'holiday_types',
'list_price_net_q0.7',
'promoted_price_net_q0.7',
'list_price_net_q0.3_relative',
'discount_rate',
'promoted_price_net_q0.9',
'list_price_net_q0.3',
'list_price_net_q0.7_relative',
'promoted_price_net_q0.5_relative',
'promoted_price_net_q0.7_relative',
'promoted_price_net',
'promoted_price_net_q0.1_relative',
'list_price_net_q0.1',
'list_price_net_q0.5_relative',
'promoted_price_net_q0.3_relative',
'promoted_price_net_q0.5',
'list_price_net_q0.5',
'revenue',
'promoted_price_net_q0.3',
'list_price_net_q0.9',
'list_price_net_q0.1_relative',
'promoted_price_net_q0.9_relative',
'First_week_of_promo',
'list_price_net_q0.9_relative',
'promoted_price_net_q0.1']
cols_to_numeric = ['list_price_net_q0.7',
'promoted_price_net_q0.7',
'list_price_net_q0.3_relative',
'discount_rate',
'promoted_price_net_q0.9',
'list_price_net_q0.3',
'list_price_net_q0.7_relative',
'promoted_price_net_q0.5_relative',
'promoted_price_net_q0.7_relative',
'promoted_price_net',
'promoted_price_net_q0.1_relative',
'list_price_net_q0.1',
'list_price_net_q0.5_relative',
'promoted_price_net_q0.3_relative',
'promoted_price_net_q0.5',
'list_price_net_q0.5',
'revenue',
'promoted_price_net_q0.3',
'list_price_net_q0.9',
'list_price_net_q0.1_relative',
'promoted_price_net_q0.9_relative',
'First_week_of_promo',
'list_price_net_q0.9_relative',
'promoted_price_net_q0.1']
df1 = df.select(*(col(c).cast("float").alias(c) for c in cols_to_numeric))
---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
D:\Spark\python\pyspark\sql\utils.py in deco(*a, **kw)
62 try:
---> 63 return f(*a, **kw)
64 except py4j.protocol.Py4JJavaError as e:
D:\Spark\python\lib\py4j-0.10.6-src.zip\py4j\protocol.py in get_return_value(answer, gateway_client, target_id, name)
319 "An error occurred while calling {0}{1}{2}.\n".
--> 320 format(target_id, ".", name), value)
321 else:
Py4JJavaError: An error occurred while calling o36.select.
: org.apache.spark.sql.AnalysisException: cannot resolve '`list_price_net_q0.7`' given input columns: [promoted_price_net_q0.1, promoted_price_net_q0.1_relative, promo_start_week, promoted_price_net_q0.9, discount_rate, promoted_price_net, brand, holiday_names, list_price_net_q0.1, list_price_net_q0.7_relative, revenue, promoted_price_net_q0.7, First_week_of_promo, promoted_price_net_q0.5_relative, promoted_price_net_q0.3_relative, promoted_price_net_q0.5, list_price_net_q0.5, promoted_price_net_q0.9_relative, sku_id, promoted_price_net_q0.3, list_price_net_q0.3, list_price_net_q0.1_relative, hierarchy2_name, store_norm_group, list_price_net_q0.5_relative, list_price_net_q0.9_relative, region, promoted_price_net_q0.7_relative, list_price_net_q0.9, holiday_types, list_price_net_q0.7, list_price_net_q0.3_relative];;
'Project [cast('list_price_net_q0.7 as float) AS list_price_net_q0.7#109, cast('promoted_price_net_q0.7 as float) AS promoted_price_net_q0.7#110, cast('list_price_net_q0.3_relative as float) AS list_price_net_q0.3_relative#111, cast(discount_rate#22 as float) AS discount_rate#112, cast('promoted_price_net_q0.9 as float) AS promoted_price_net_q0.9#113, cast('list_price_net_q0.3 as float) AS list_price_net_q0.3#114, cast('list_price_net_q0.7_relative as float) AS list_price_net_q0.7_relative#115, cast('promoted_price_net_q0.5_relative as float) AS promoted_price_net_q0.5_relative#116, cast('promoted_price_net_q0.7_relative as float) AS promoted_price_net_q0.7_relative#117, cast(promoted_price_net#28 as float) AS promoted_price_net#118, cast('promoted_price_net_q0.1_relative as float) AS promoted_price_net_q0.1_relative#119, cast('list_price_net_q0.1 as float) AS list_price_net_q0.1#120, cast('list_price_net_q0.5_relative as float) AS list_price_net_q0.5_relative#121, cast('promoted_price_net_q0.3_relative as float) AS promoted_price_net_q0.3_relative#122, cast('promoted_price_net_q0.5 as float) AS promoted_price_net_q0.5#123, cast('list_price_net_q0.5 as float) AS list_price_net_q0.5#124, cast(revenue#35 as float) AS revenue#125, cast('promoted_price_net_q0.3 as float) AS promoted_price_net_q0.3#126, cast('list_price_net_q0.9 as float) AS list_price_net_q0.9#127, cast('list_price_net_q0.1_relative as float) AS list_price_net_q0.1_relative#128, cast('promoted_price_net_q0.9_relative as float) AS promoted_price_net_q0.9_relative#129, cast(First_week_of_promo#40 as float) AS First_week_of_promo#130, cast('list_price_net_q0.9_relative as float) AS list_price_net_q0.9_relative#131, cast('promoted_price_net_q0.1 as float) AS promoted_price_net_q0.1#132]
+- AnalysisBarrier
+- Project [sku_id#11, promo_start_week#12, hierarchy2_name#13, brand#14, region#15, store_norm_group#16, holiday_names#17, holiday_types#18, list_price_net_q0.7#19, promoted_price_net_q0.7#20, list_price_net_q0.3_relative#21, discount_rate#22, promoted_price_net_q0.9#23, list_price_net_q0.3#24, list_price_net_q0.7_relative#25, promoted_price_net_q0.5_relative#26, promoted_price_net_q0.7_relative#27, promoted_price_net#28, promoted_price_net_q0.1_relative#29, list_price_net_q0.1#30, list_price_net_q0.5_relative#31, promoted_price_net_q0.3_relative#32, promoted_price_net_q0.5#33, list_price_net_q0.5#34, ... 8 more fields]
+- Relation[_c0#10,sku_id#11,promo_start_week#12,hierarchy2_name#13,brand#14,region#15,store_norm_group#16,holiday_names#17,holiday_types#18,list_price_net_q0.7#19,promoted_price_net_q0.7#20,list_price_net_q0.3_relative#21,discount_rate#22,promoted_price_net_q0.9#23,list_price_net_q0.3#24,list_price_net_q0.7_relative#25,promoted_price_net_q0.5_relative#26,promoted_price_net_q0.7_relative#27,promoted_price_net#28,promoted_price_net_q0.1_relative#29,list_price_net_q0.1#30,list_price_net_q0.5_relative#31,promoted_price_net_q0.3_relative#32,promoted_price_net_q0.5#33,... 9 more fields] csv
at org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:88)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:85)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)
at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$3.apply(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)
at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:286)
at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsUp$1.apply(QueryPlan.scala:95)
at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsUp$1.apply(QueryPlan.scala:95)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:106)
at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:116)
at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1$1.apply(QueryPlan.scala:120)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:120)
at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$1.apply(QueryPlan.scala:125)
at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)
at org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:125)
at org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:95)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:85)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:80)
at org.apache.spark.sql.catalyst.trees.TreeNode.foreachUp(TreeNode.scala:127)
at org.apache.spark.sql.catalyst.analysis.CheckAnalysis$class.checkAnalysis(CheckAnalysis.scala:80)
at org.apache.spark.sql.catalyst.analysis.Analyzer.checkAnalysis(Analyzer.scala:91)
at org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:104)
at org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)
at org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)
at org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)
at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74)
at org.apache.spark.sql.Dataset.org$apache$spark$sql$Dataset$$withPlan(Dataset.scala:3295)
at org.apache.spark.sql.Dataset.select(Dataset.scala:1307)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
at java.lang.reflect.Method.invoke(Unknown Source)
at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
at py4j.Gateway.invoke(Gateway.java:282)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Unknown Source)
During handling of the above exception, another exception occurred:
AnalysisException Traceback (most recent call last)
<ipython-input-7-f7e0007723d8> in <module>()
----> 1 df1 = df.select(*(col(c).cast("float").alias(c) for c in cols_to_numeric))
D:\Spark\python\pyspark\sql\dataframe.py in select(self, *cols)
1200 [Row(name=u'Alice', age=12), Row(name=u'Bob', age=15)]
1201 """
-> 1202 jdf = self._jdf.select(self._jcols(*cols))
1203 return DataFrame(jdf, self.sql_ctx)
1204
D:\Spark\python\lib\py4j-0.10.6-src.zip\py4j\java_gateway.py in __call__(self, *args)
1158 answer = self.gateway_client.send_command(command)
1159 return_value = get_return_value(
-> 1160 answer, self.gateway_client, self.target_id, self.name)
1161
1162 for temp_arg in temp_args:
D:\Spark\python\pyspark\sql\utils.py in deco(*a, **kw)
67 e.java_exception.getStackTrace()))
68 if s.startswith('org.apache.spark.sql.AnalysisException: '):
---> 69 raise AnalysisException(s.split(': ', 1)[1], stackTrace)
70 if s.startswith('org.apache.spark.sql.catalyst.analysis'):
71 raise AnalysisException(s.split(': ', 1)[1], stackTrace)
AnalysisException: "cannot resolve '`list_price_net_q0.7`' given input columns: [promoted_price_net_q0.1, promoted_price_net_q0.1_relative, promo_start_week, promoted_price_net_q0.9, discount_rate, promoted_price_net, brand, holiday_names, list_price_net_q0.1, list_price_net_q0.7_relative, revenue, promoted_price_net_q0.7, First_week_of_promo, promoted_price_net_q0.5_relative, promoted_price_net_q0.3_relative, promoted_price_net_q0.5, list_price_net_q0.5, promoted_price_net_q0.9_relative, sku_id, promoted_price_net_q0.3, list_price_net_q0.3, list_price_net_q0.1_relative, hierarchy2_name, store_norm_group, list_price_net_q0.5_relative, list_price_net_q0.9_relative, region, promoted_price_net_q0.7_relative, list_price_net_q0.9, holiday_types, list_price_net_q0.7, list_price_net_q0.3_relative];;\n'Project [cast('list_price_net_q0.7 as float) AS list_price_net_q0.7#109, cast('promoted_price_net_q0.7 as float) AS promoted_price_net_q0.7#110, cast('list_price_net_q0.3_relative as float) AS list_price_net_q0.3_relative#111, cast(discount_rate#22 as float) AS discount_rate#112, cast('promoted_price_net_q0.9 as float) AS promoted_price_net_q0.9#113, cast('list_price_net_q0.3 as float) AS list_price_net_q0.3#114, cast('list_price_net_q0.7_relative as float) AS list_price_net_q0.7_relative#115, cast('promoted_price_net_q0.5_relative as float) AS promoted_price_net_q0.5_relative#116, cast('promoted_price_net_q0.7_relative as float) AS promoted_price_net_q0.7_relative#117, cast(promoted_price_net#28 as float) AS promoted_price_net#118, cast('promoted_price_net_q0.1_relative as float) AS promoted_price_net_q0.1_relative#119, cast('list_price_net_q0.1 as float) AS list_price_net_q0.1#120, cast('list_price_net_q0.5_relative as float) AS list_price_net_q0.5_relative#121, cast('promoted_price_net_q0.3_relative as float) AS promoted_price_net_q0.3_relative#122, cast('promoted_price_net_q0.5 as float) AS promoted_price_net_q0.5#123, cast('list_price_net_q0.5 as float) AS list_price_net_q0.5#124, cast(revenue#35 as float) AS revenue#125, cast('promoted_price_net_q0.3 as float) AS promoted_price_net_q0.3#126, cast('list_price_net_q0.9 as float) AS list_price_net_q0.9#127, cast('list_price_net_q0.1_relative as float) AS list_price_net_q0.1_relative#128, cast('promoted_price_net_q0.9_relative as float) AS promoted_price_net_q0.9_relative#129, cast(First_week_of_promo#40 as float) AS First_week_of_promo#130, cast('list_price_net_q0.9_relative as float) AS list_price_net_q0.9_relative#131, cast('promoted_price_net_q0.1 as float) AS promoted_price_net_q0.1#132]\n+- AnalysisBarrier\n +- Project [sku_id#11, promo_start_week#12, hierarchy2_name#13, brand#14, region#15, store_norm_group#16, holiday_names#17, holiday_types#18, list_price_net_q0.7#19, promoted_price_net_q0.7#20, list_price_net_q0.3_relative#21, discount_rate#22, promoted_price_net_q0.9#23, list_price_net_q0.3#24, list_price_net_q0.7_relative#25, promoted_price_net_q0.5_relative#26, promoted_price_net_q0.7_relative#27, promoted_price_net#28, promoted_price_net_q0.1_relative#29, list_price_net_q0.1#30, list_price_net_q0.5_relative#31, promoted_price_net_q0.3_relative#32, promoted_price_net_q0.5#33, list_price_net_q0.5#34, ... 8 more fields]\n +- Relation[_c0#10,sku_id#11,promo_start_week#12,hierarchy2_name#13,brand#14,region#15,store_norm_group#16,holiday_names#17,holiday_types#18,list_price_net_q0.7#19,promoted_price_net_q0.7#20,list_price_net_q0.3_relative#21,discount_rate#22,promoted_price_net_q0.9#23,list_price_net_q0.3#24,list_price_net_q0.7_relative#25,promoted_price_net_q0.5_relative#26,promoted_price_net_q0.7_relative#27,promoted_price_net#28,promoted_price_net_q0.1_relative#29,list_price_net_q0.1#30,list_price_net_q0.5_relative#31,promoted_price_net_q0.3_relative#32,promoted_price_net_q0.5#33,... 9 more fields] csv\n"
当我尝试时会产生类似的错误：
df = df.withColumn('list_price_net_q0.7', col('list_price_net_q0.7').cast('float'))
答案 0 :(得分：0)
由于错误，Spark无法解析带点的列名（有关更多信息，请参见此bug report）。只需使用下面的代码来清理您的列名：
columns = ['id', 'list_price_net_q0.7', 'bla']
vals = [(1.0, '2.0', 0),(2.0, '3.0', 1)]
df = spark.createDataFrame(vals, columns)
df.printSchema()
#actual clean up
x = [s.replace('.', 'DOT') for s in df.columns]
df = df.toDF(*x)
#prove that you can cast now
df.withColumn("float", col("list_price_net_q0DOT7").cast("float")).show()
另一个选择是将inferSchema参数设置为True。这可能会创建一个数据框，其中“ list_price_net_q0.7”是一列浮点数，但是一旦您应用另一个函数，您就会遇到相同的错误。