import csv
with open('thefile.csv', 'rb') as f:
data = list(csv.reader(f))
import collections
counter = collections.defaultdict(int)
for row in data:
counter[row[10]] += 1
with open('/pythonwork/thefile_subset11.csv', 'w') as outfile:
writer = csv.writer(outfile)
for row in data:
if counter[row[10]] >= 504:
writer.writerow(row)
This code reads thefile.csv, makes changes, and writes results to thefile_subset1.
However, when I open the resulting csv in Microsoft Excel, there is an extra blank line after each record!
Is there a way to make it not put an extra blank line?
The csv.writer module directly controls line endings and writes \r\n into the file directly. In Python 3 the file must be opened in untranslated text mode with the parameters 'w', newline='' (empty string) or it will write \r\r\n on Windows, where the default text mode will translate each \n into \r\n.
#!python3
with open('/pythonwork/thefile_subset11.csv', 'w', newline='') as outfile:
writer = csv.writer(outfile)
In Python 2, use binary mode to open outfile with mode 'wb' instead of 'w' to prevent Windows newline translation. Python 2 also has problems with Unicode and requires other workarounds to write non-ASCII text. See the Python 2 link below and the UnicodeReader and UnicodeWriter examples at the end of the page if you have to deal with writing Unicode strings to CSVs on Python 2, or look into the 3rd party unicodecsv module:
#!python2
with open('/pythonwork/thefile_subset11.csv', 'wb') as outfile:
writer = csv.writer(outfile)
Documentation Links
https://docs.python.org/3/library/csv.html#csv.writer
https://docs.python.org/2/library/csv.html#csv.writer
Opening the file in binary mode "wb" will not work in Python 3+. Or rather, you'd have to convert your data to binary before writing it. That's just a hassle.
Instead, you should keep it in text mode, but override the newline as empty. Like so:
with open('/pythonwork/thefile_subset11.csv', 'w', newline='') as outfile:
Note: It seems this is not the preferred solution because of how the extra line was being added on a Windows system. As stated in the python document:
If csvfile is a file object, it must be opened with the ‘b’ flag on platforms where that makes a difference.
Windows is one such platform where that makes a difference. While changing the line terminator as I described below may have fixed the problem, the problem could be avoided altogether by opening the file in binary mode. One might say this solution is more "elegent". "Fiddling" with the line terminator would have likely resulted in unportable code between systems in this case, where opening a file in binary mode on a unix system results in no effect. ie. it results in cross system compatible code.
From Python Docs:
On Windows, 'b' appended to the mode
opens the file in binary mode, so
there are also modes like 'rb', 'wb',
and 'r+b'. Python on Windows makes a
distinction between text and binary
files; the end-of-line characters in
text files are automatically altered
slightly when data is read or written.
This behind-the-scenes modification to
file data is fine for ASCII text
files, but it’ll corrupt binary data
like that in JPEG or EXE files. Be
very careful to use binary mode when
reading and writing such files. On
Unix, it doesn’t hurt to append a 'b'
to the mode, so you can use it
platform-independently for all binary
files.
Original:
As part of optional paramaters for the csv.writer if you are getting extra blank lines you may have to change the lineterminator (info here). Example below adapated from the python page csv docs. Change it from '\n' to whatever it should be. As this is just a stab in the dark at the problem this may or may not work, but it's my best guess.
>>> import csv
>>> spamWriter = csv.writer(open('eggs.csv', 'w'), lineterminator='\n')
>>> spamWriter.writerow(['Spam'] * 5 + ['Baked Beans'])
>>> spamWriter.writerow(['Spam', 'Lovely Spam', 'Wonderful Spam'])
The simple answer is that csv files should always be opened in binary mode whether for input or output, as otherwise on Windows there are problems with the line ending. Specifically on output the csv module will write \r\n (the standard CSV row terminator) and then (in text mode) the runtime will replace the \n by \r\n (the Windows standard line terminator) giving a result of \r\r\n.
Fiddling with the lineterminator is NOT the solution.
A lot of the other answers have become out of date in the ten years since the original question. For Python3, the answer is right in the documentation:
If csvfile is a file object, it should be opened with newline=''
The footnote explains in more detail:
If newline='' is not specified, newlines embedded inside quoted fields will not be interpreted correctly, and on platforms that use \r\n linendings on write an extra \r will be added. It should always be safe to specify newline='', since the csv module does its own (universal) newline handling.
Use the method defined below to write data to the CSV file.
open('outputFile.csv', 'a',newline='')
Just add an additional newline='' parameter inside the open method :
def writePhoneSpecsToCSV():
rowData=["field1", "field2"]
with open('outputFile.csv', 'a',newline='') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(rowData)
This will write CSV rows without creating additional rows!
I'm writing this answer w.r.t. to python 3, as I've initially got the same problem.
I was supposed to get data from arduino using PySerial, and write them in a .csv file. Each reading in my case ended with '\r\n', so newline was always separating each line.
In my case, newline='' option didn't work. Because it showed some error like :
with open('op.csv', 'a',newline=' ') as csv_file:
ValueError: illegal newline value: ''
So it seemed that they don't accept omission of newline here.
Seeing one of the answers here only, I mentioned line terminator in the writer object, like,
writer = csv.writer(csv_file, delimiter=' ',lineterminator='\r')
and that worked for me for skipping the extra newlines.
with open(destPath+'\\'+csvXML, 'a+') as csvFile:
writer = csv.writer(csvFile, delimiter=';', lineterminator='\r')
writer.writerows(xmlList)
The "lineterminator='\r'" permit to pass to next row, without empty row between two.
Borrowing from this answer, it seems like the cleanest solution is to use io.TextIOWrapper. I managed to solve this problem for myself as follows:
from io import TextIOWrapper
...
with open(filename, 'wb') as csvfile, TextIOWrapper(csvfile, encoding='utf-8', newline='') as wrapper:
csvwriter = csv.writer(wrapper)
for data_row in data:
csvwriter.writerow(data_row)
The above answer is not compatible with Python 2. To have compatibility, I suppose one would simply need to wrap all the writing logic in an if block:
if sys.version_info < (3,):
# Python 2 way of handling CSVs
else:
# The above logic
I used writerow
def write_csv(writer, var1, var2, var3, var4):
"""
write four variables into a csv file
"""
writer.writerow([var1, var2, var3, var4])
numbers=set([1,2,3,4,5,6,7,2,4,6,8,10,12,14,16])
rules = list(permutations(numbers, 4))
#print(rules)
selection=[]
with open("count.csv", 'w',newline='') as csvfile:
writer = csv.writer(csvfile)
for rule in rules:
number1,number2,number3,number4=rule
if ((number1+number2+number3+number4)%5==0):
#print(rule)
selection.append(rule)
write_csv(writer,number1,number2,number3,number4)
When using Python 3 the empty lines can be avoid by using the codecs module. As stated in the documentation, files are opened in binary mode so no change of the newline kwarg is necessary. I was running into the same issue recently and that worked for me:
with codecs.open( csv_file, mode='w', encoding='utf-8') as out_csv:
csv_out_file = csv.DictWriter(out_csv)
I have a .dat file that I had been reading with pd.read_csv and always needed to use encoding="latin" for it to read properly / without error. When I use pyarrow.csv.read_csv I dont see a parameter to select the encoding of the file but it still works without issue(which is great! but i dont understand why / if it only auto handles certain encodings). The only parameters im using are setting the delimiter="|" (with ParseOptions) and auto_dict_encode=True with (ConvertOptions).
How is pyarrow handling different encoding types?
pyarrow currently has no functionality to deal with different encodings, and assumes UTF8 for string/text data.
But the reason it doesn't raise an error is that pyarrow will read any non-UTF8 strings as a "binary" type column, instead of "string" type.
A small example:
# writing a small file with latin encoding
with open("test.csv", "w", encoding="latin") as f:
f.writelines(["col1,col2\n", "u,ù"])
Reading with pyarrow gives string for the first column (which only contains ASCII characters, thus also valid UTF8), but reads the second column as binary:
>>> from pyarrow import csv
>>> csv.read_csv("test.csv")
pyarrow.Table
col1: string
col2: binary
With pandas you indeed get an error by default (because pandas has no binary data type, and will try to read all text columns as python strings, thus UTF8):
>>> pd.read_csv("test.csv")
...
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xf9 in position 0: invalid start byte
>>> pd.read_csv("test.csv", encoding="latin")
col1 col2
0 u ù
It's now possible to specify encodings with pyarrow.read_csv.
According to the pyarrow docs for read_csv:
The encoding can be changed using the ReadOptions class.
A minimal example follows:
from pyarrow import csv
options = csv.ReadOptions(encoding='latin1')
table = csv.read_csv('path/to/file', options)
From what I can tell, the functionality was added in this PR, so it should work starting with pyarrow 1.0.
I am trying to read a csv into pyspark but the problem is that it has a text column due to which there are some bad line in the data
This text column also contains the new line characters due to which the data in further columns is getting corrupted
I have tried using pandas and use some extra parameters to load my csv
a = pd.read_csv("Mycsvname.csv",sep = '~',quoting=csv.QUOTE_NONE, dtype = str,error_bad_lines=False, quotechar='~', lineterminator='\n' )
It is working fine in pandas but I want to load the csv in pyspark
So, is there any similar way to load a csv in pyspark with all the above parameters?
In the current version of spark (I think it is even there from spark 2.2 onwards), you can also read multi-line from csv.
If the newline is your only problem with the text column you can use a read command like this:
spark.read.csv("YOUR_FILE_NAME", header="true", escape="\"", quote="\"", multiLine=True)
Note: in our case the escape and quotation characters where both " so you might want to edit those options with your ~ and include sep = '~'.
You can also look at the documentation (http://spark.apache.org/docs/2.1.0/api/python/pyspark.sql.html?highlight=csv#pyspark.sql.DataFrameReader.csv) for more details
I've exported a client database to a csv file, and tried to import it to Spark using:
spark.sqlContext.read
.format("csv")
.option("header", "true")
.option("inferSchema", "true")
.load("table.csv")
After doing some validations, I find out that some ids were null because a column sometimes has a carriage return. And that dislocated all next columns, with a domino effect, corrupting all the data.
What is strange is that when calling printSchema the resulting table structure is good.
How to fix the issue?
You seemed to have had a lot of luck with inferSchema that it worked fine (since it only reads few records to infer the schema) and so printSchema gives you a correct result.
Since the CSV export file is broken and assuming you want to process the file using Spark (given its size for example) read it using textFile and fix the ids. Save it as CSV format and load it back.
I'm not sure what version of spark you are using, but beginning in 2.2 (I believe), there is a 'multiLine' option that can be used to keep fields together that have line breaks in them. From some other things I've read, you may need to apply some quoting and/or escape character options to get it working just how you want it.
spark.read
.csv("table.csv")
.option("header", "true")
.option("inferSchema", "true")
**.option("multiLine", "true")**
I have a csv data file containing commas within a column value. For example,
value_1,value_2,value_3
AAA_A,BBB,B,CCC_C
Here, the values are "AAA_A","BBB,B","CCC_C". But, when trying to split the line by comma, it is giving me 4 values, i.e. "AAA_A","BBB","B","CCC_C".
How to get the right values after splitting the line by commas in PySpark?
Use spark-csv class from databriks.
Delimiters between quotes, by default ("), are ignored.
Example:
val df = sqlContext.read
.format("com.databricks.spark.csv")
.option("header", "true") // Use first line of all files as header
.option("inferSchema", "true") // Automatically infer data types
.load("cars.csv")
For more info, review https://github.com/databricks/spark-csv
If your quote is (') instance of ("), you could configure with this class.
EDIT:
For python API:
df = sqlContext.read.format('com.databricks.spark.csv').options(header='true', inferschema='true').load('cars.csv')
Best regards.
If you do not mind the extra package dependency, you could use Pandas to parse the CSV file. It handles internal commas just fine.
Dependencies:
from pyspark import SparkContext
from pyspark.sql import SQLContext
import pandas as pd
Read the whole file at once into a Spark DataFrame:
sc = SparkContext('local','example') # if using locally
sql_sc = SQLContext(sc)
pandas_df = pd.read_csv('file.csv') # assuming the file contains a header
# If no header:
# pandas_df = pd.read_csv('file.csv', names = ['column 1','column 2'])
s_df = sql_sc.createDataFrame(pandas_df)
Or, even more data-consciously, you can chunk the data into a Spark RDD then DF:
chunk_100k = pd.read_csv('file.csv', chunksize=100000)
for chunky in chunk_100k:
Spark_temp_rdd = sc.parallelize(chunky.values.tolist())
try:
Spark_full_rdd += Spark_temp_rdd
except NameError:
Spark_full_rdd = Spark_temp_rdd
del Spark_temp_rdd
Spark_DF = Spark_full_rdd.toDF(['column 1','column 2'])
I'm (really) new to Pyspark, but have been using Pandas for the past years. What I'm going to put here might not be ultimately the best solution, but it works for me so I think it's worth posting here.
I'm encountering the same issue loading in a CSV file with extra comma embedded in one special field, which triggered an error if using Pyspark, but had no problem if using Pandas. So I looked around for a solution to deal with this extra delimiter, and the following piece of code solved my issue:
df = sqlContext.read.format('csv').option('header','true').option('maxColumns','3').option('escape','"').load('cars.csv')
I personally like to force the 'maxColumns' parameter to allow only a specific number of columns. So if the "BBB,B" somehow got parsed into two strings, spark is going to give an error message and print the whole line for you. And the 'escape' option is the one that really fixed my issue. I don't know if this helps, but hopefully that's something to run experiments with.