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I tried a simple example like:
data = sqlContext.read.format("csv").option("header", "true").option("inferSchema", "true").load("/databricks-datasets/samples/population-vs-price/data_geo.csv")
data.cache() # Cache data for faster reuse
data = data.dropna() # drop rows with missing values
data = data.select("2014 Population estimate", "2015 median sales price").map(lambda r: LabeledPoint(r[1], [r[0]])).toDF()
It works well, But when i try something very similar like:
data = sqlContext.read.format("csv").option("header", "true").option("inferSchema", "true").load('/mnt/%s/OnlineNewsTrainingAndValidation.csv' % MOUNT_NAME)
data.cache() # Cache data for faster reuse
data = data.dropna() # drop rows with missing values
data = data.select("timedelta", "shares").map(lambda r: LabeledPoint(r[1], [r[0]])).toDF()
display(data)
It raise error:
AnalysisException: u"cannot resolve 'timedelta' given input columns: [ data_channel_is_tech,...
off-course I imported LabeledPoint and LinearRegression
What could be wrong?
Even the simpler case
df_cleaned = df_cleaned.select("shares")
raises same AnalysisException (error).
*please note: df_cleaned.printSchema() works well.
I found the issue: some of the column names contain white spaces before the name itself.
data = data.select(" timedelta", " shares").map(lambda r: LabeledPoint(r[1], [r[0]])).toDF()
worked.
I could catch the white spaces using
assert " " not in ''.join(df.columns)
Now I am thinking of a way to remove the white spaces. Any idea is much appreciated!
.builder \
.appName("Python Spark SQL basic example") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
df=spark.read.csv(r'test.csv',header=True,sep='^')
print("#################################################################")
print df.printSchema()
df.createOrReplaceTempView("test")
re=spark.sql("select max_seq from test")
print(re.show())
print("################################################################")
2) Input file,here 'max_seq ' contains space so we are getting bellow exception
Trx_ID^max_seq ^Trx_Type^Trx_Record_Type^Trx_Date
Traceback (most recent call last):
File "D:/spark-2.1.0-bin-hadoop2.7/bin/test.py", line 14, in <module>
re=spark.sql("select max_seq from test")
File "D:\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\sql\session.py", line 541, in sql
File "D:\spark-2.1.0-bin-hadoop2.7\python\lib\py4j-0.10.4-src.zip\py4j\java_gateway.py", line 1133, in __call__
File "D:\spark-2.1.0-bin-hadoop2.7\python\lib\pyspark.zip\pyspark\sql\utils.py", line 69, in deco
pyspark.sql.utils.AnalysisException: u"cannot resolve '`max_seq`' given input columns: [Venue_City_Name, Trx_Type, Trx_Booking_Status_Committed, Payment_Reference1, Trx_Date, max_seq , Event_ItemVariable_Name, Amount_CurrentPrice, cinema_screen_count, Payment_IsMyPayment, r
2) Remove space after 'max_seq' column then it will work fine
Trx_ID^max_seq^Trx_Type^Trx_Record_Type^Trx_Date
17/03/20 12:16:25 INFO DAGScheduler: Job 3 finished: showString at <unknown>:0, took 0.047602 s
17/03/20 12:16:25 INFO CodeGenerator: Code generated in 8.494073 ms
max_seq
only showing top 20 rows
##############################################################
–
As there were tabs in my input file, removing the tabs or spaces in the header helped display the answer.
My example:
saledf = spark.read.csv("SalesLTProduct.txt", header=True, inferSchema= True, sep='\t')
saledf.printSchema()
|-- ProductID: string (nullable = true)
|-- Name: string (nullable = true)
|-- ProductNumber: string (nullable = true)
saledf.describe('ProductNumber').show()
+-------+-------------+
|summary|ProductNumber|
+-------+-------------+
| count| 295|
| mean| null|
| stddev| null|
| min| BB-7421|
| max| WB-H098|
+-------+-------------+
If you don't have whitespaces in headers, this error also raised when you not specify headers for csv at all like this:
df = sqlContext.read.csv('data.csv')
So you need to change it to this:
df = sqlContext.read.csv('data.csv', header=True)
Recently, I came across this issue while working on Azure synapse analytics; my error was the same.
analysisexception: cannot resolve '`xxxxxx`' given input columns: [];; 'filter ('passenger_count > 0) +- relation[] csv traceback (most recent call last):
file "/opt/spark/python/lib/pyspark.zip/pyspark/sql/dataframe.py", line 1364, in filter jdf = self._jdf.filter(condition._jc) file "/opt/spark/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", line 1257, in __call__ answer, self.gateway_client, self.target_id, self.name)
file "/opt/spark/python/lib/pyspark.zip/pyspark/sql/utils.py", line 75, in deco raise analysisexception(s.split(': ', 1)[1], stacktrace)""
This error came due to improper wording in our code or in CSV file
use this code to read csv file:
-df = spark.read.load("examples/src/main/resources/people.csv",
format="csv", sep=";", inferSchema="true", header="true")
If you are again stuck somewhere in synapse or pyspark visit this site FOR Error info: https://docs.actian.com/avalanche/index.html#page/User/Common_Data_Loading_Error_Messages.htm
and for more info visit documentation: https://spark.apache.org/docs/latest/api/python/
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