I am stuck in an issue while trying to read a csv file with multiline=true option in spark that has characters like Ř and Á. The csv is read in utf-8 format ; But when we try to read the data by using multiline=true we get characters that are not equivalent to the ones that we had read. We get something like ŘÃ�. So essentially a word read as ZŘÁKO gets transformed to ZŘÃ�KO.I went through several other questions asked on stack overflow around the same issue but none of solution actually works !
I tried the following encodings while read/write operations : ‘US-ASCII’
‘ISO-8859-1’,‘UTF-8’,‘UTF-16BE’,‘UTF-16LE’,‘UTF-16’,SJIS and couple more but none of them could give me the expected result. But multiline=false generates the correct output somehow.
I cannot read/write the file as text as the current framework policy of project is around an ingestion framework where we read the file only once and then everything is expected to be done in-memory and I must use multiline as true.
I would really appreciate any thoughts on this matter. Thank You !
sample data:
id|name
1|ZŘÁKO
df=spark.read.format('csv').option('header',true).
option('delimter','|').option('multiline',true).option('encoding','utf-8').load()
df.show()
ouptut :
1|Z�KO
#trying to force utf-8 encoding as below :
df.withColumn("name", sql.functions.encode("name", 'utf-8'))
gives me this :
1|[22 5A c3..]
I tried the above steps with all the supported encodings in spark
Related
My input parquet file has a column defined as optional binary title (UTF8);, which may include special characters such as the German umlat (i.e. Schrödinger).
When using Spark to load the contents of the parquet to a DataFrame, the contents of the row are loading the value Schrödinger as Schrödinger. I believe the best explanation of why this could be happening is answered here, though I was under the impression that Spark will read the parquet file as UTF-8 by default anyway.
I have attempted to force UTF-8 encoding by using the option argument as described here, but still no luck. Any suggestions?
Can you try with encoding CP1252. It worked for us for most of the special characters which are not supported in UTF8.
I have a .csv file that contains multiple columns with texts in it. These texts contain commas, which makes things messy when I try to read the file into Python.
When I tried:
import pandas as pd
directory = 'some directory'
dataset = pd.read_csv(directory)
I got the following error:
ParserError: Error tokenizing data. C error: Expected 3 fields in line 42, saw 5
After doing some research, I found the clevercsv package.
So, I ran:
import clevercsv as csv
dataset = csv.read_csv(directory)
Running this, I got the error:
UnicodeDecodeError: 'charmap' codec can't decode byte 0x8f in position 4359705: character maps to <undefined>
To overcome this, I tried:
dataset = csv.read_csv(directory, encoding="utf8")
However, 10 hours later my computer was still working on reading it. So I expect that something went wrong there.
Furthermore, when I open the file in Excel, it does split cells well. Therefore, What I tried was to save the .csv file as a .xlsx and then save it as .csv in Python with an uncommon delimiter ('~'). However, when I save my .csv file as a .xlsx file, the size of the file gets smaller, which indicates that only a part of the file is saved and that is not what I want.
Lastly, I have tried the solutions given here and here. But neither seem to work for my problem.
Given that Excel reads in the file without problems, I do expect that it should be possible to read it into Python as well. Who can help me with this?
UPDATE:
When using dataset = pd.read_csv(directory, sep = ',', error_bad_lines=False)I manage to read in the .csv. But many lines are skipped. Is there a better way for this?
panda should be work https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html
Dou you tried somthing like dataset = pd.read_csv(directory, sep = ',', header = None)
Regards
I'm using Jupyter NoteBook to run pySpark code to import CSV file to Cassandra v3.11.3. Getting below error.
... 1 more[![enter image description here][1]][1]
---------------------------------------------------------------------------
pySpark Code i have attached as picture:
[![pyspark_code][1]][1]
Any inputs...
Without the full trace it's hard to know exactly where this is failing. The method you pasted is just the p4yj wrapper method and we really would need to see the underlying Java Exception.
From what I can tell it looks like you are attempting to also use some options on the C* write that are unsupported. For example "MODE" - "DROPMALFORMED" is not a valid C* connector option. DataFrame Writer and Reader options are source specific so you are unfortunately unable to mix and match.
This makes me think that the data being written actually has a malformed date string or two and this code is dying when attempting to write the broken record. One way around this would be to attempt to do the date casting on CSV read which I believe does support DROPMALFORMED style parsing options.
In connection with my earlier question, when I give the command,
filePath = sc.textFile("/user/cloudera/input/Hin*/datafile.txt")
filePath.collect()
some part of the data has '\xa0' prefixed to every word, and other part of the data doesn't have that special character. I am attaching 2 pictures, one with '\xa0', and another without '\xa0'. The content shown in 2 pictures belong to same file. Only some part of the data from same file is read that way by Spark. I have checked the original data file present in HDFS, and it was problem free.
I feel that it has something to do with encoding. I tried all methods like using replaceoption in flatMap like flatMap(lambda line: line.replace(u'\xa0', ' ').split(" ")), flatMap(lambda line: line.replace(u'\xa0', u' ').split(" ")), but none worked for me. This question might sound dump, but I am newbie in using Apache Spark, and I require some assistance to overcome this problem.
Can anyone please help me? Thanks in advance.
Check the encoding of your file. When you use sc.textFile, spark expects an UTF-8 encoded file.
One of the solution is to acquire your file with sc.binaryFiles and then apply the expected encoding.
sc.binaryFile create a key/value rdd where key is the path to file and value is the content as a byte.
If you need to keep only the text and apply an decoding function, :
filePath = sc.binaryFile("/user/cloudera/input/Hin*/datafile.txt")
filePath.map(lambda x :x[1].decode('utf-8')) #or another encoding depending on your file
Read CSV with linebreaks in pyspark
I want to read with pyspark a "legal" (it follows RFC4180) CSV that has breaklines (CRLF) in some of the rows. The next code sample shows how it does seem when opened it with Notepad++:
I try to read it with sqlCtx.read.load using format ='com.databricks.spark.csv. and the resulting dataset shows two rows instead of one in these specific cases. I am using Spark 2.1.0.2 version.
Is there any command or alternative way of reading the csv that allows me to read these two lines only as one?
You can use "csv" instead of Databricks CSV - the last one redirects now to default Spark reader. But, it's only a hint :)
In Spark 2.2 there was added new option - wholeFile. If you write this:
spark.read.option("wholeFile", "true").csv("file.csv")
it will read all file and handle multiline CSV.
There is no such option in Spark 2.1. You can read file using sparkContext.wholeTextFile or just use newer verison
wholeFile does not exist (anymore?) in the spark api documentation:
https://spark.apache.org/docs/2.2.0/api/python/pyspark.sql.html
This solution will work:
spark.read.option("multiLine", "true").csv("file.csv")
From the api documentation:
multiLine – parse records, which may span multiple lines. If None is set, it uses the default value, false