I am loading a CSV into Apache Beam, but the CSV I am loading has commas in the fields. It looks like this:
ID, Name
1, Barack Obama
2, Barry, Bonds
How can I go about fixing this issue?
This is not specific to Beam, but a general problem with CSV. It's unclear if the second line should have ID="2, Barry" Name="Bonds" or the other way around.
If you can use some context (e.g. ID is always an integer, only one field could possibly contain commas) you could solve this by reading it as a text file line by line and parsing it into separate fields with a custom DoFn (assuming rows also contain newlines).
Generally, non-separating commas should be inside quotes in well-formed CSV, which makes this much more tractable (e.g. it would just work with the Beam Dataframes read_csv.)
I have a csv file with delimiter as , (comma) and few of the data column of same file has comma in it .
Hence while linking / importing the file, data is getting jumbled in next column.
I have tried all possible means like skip column etc , but not getting any fruitful results.
Please let me know if this can be handled through VBA function in ms-access.
If the CSV file contains text fields that contain commas and are not surrounded by a text qualifier (usually ") then the file is malformed and cannot be parsed in a bulletproof way. That is,
1,Hello world!,1.414
2,"Goodbye, cruel world!",3.142
can be reliably parsed, but
1,Hello world!,1.414
2,Goodbye, cruel world!,3.142
cannot. However, if you have additional information about the file, e.g., that it should contain three columns
a Long Integer column,
a Short Text column, and
a Double column
then your VBA code could read the file line-by-line and split the string on commas into an array. The first array element would be the Long Integer, the last array element would be the Double value, and the remaining "columns" in between could be concatenated together to reconstruct the string.
As you can imagine, that approach could easily be confounded (e.g., if there was more than one text field that might contain commas). Therefore it is not particularly appealing.
(Also worth noting is that the CSV parser in Access has never been able to properly handle text fields that contain line breaks, but at least we can import those CSV files into Excel and then import into Access from the Excel file.)
TL;DR - If the CSV file contains unqualified text containing commas then the system that produced it is broken and should be fixed.
I am trying to import reports in csv format to MySQL for further analysis process. But, I have find several negative numbers enclosed by bracket e.g ($184,919.02),
($182,246.50). If I use double format, it will become 0, but using varchar or text it appears.
I need it to be recorded in double format to automate some calculations in further analysis process. Is there any way to solve this problem? And also how to remove the $ (dollar) sign as well?
Thanks in advance.
Load into a VARCHAR column. Then update the column with REPLACE(col, '$', '') to get rid of the $.
Repeat to get rid of ,, -, (, ')` and any other garbage that is in the way.
Better yet, us a real programming language (not SQL) to cleanse the data. Many languages let you remove -$,() in a single pass.
I use these files constantly in my application, but aren't CSV, TSV or TXT files all flat files?
The content is:
"sample","sample"
They are all text files, following the same "guidelines". The difference between the files are - as long as the creator followed some "rules", that:
A csv file will have comma separated values and a tsv file will have tab seperated values.
For .txt files, there is no formatting specified.
.csv stands for comma separated values, .tsv stands for tab separated values.
As the names suggest, different elements in the file are separated by ',' and '\t' respectively.
The type is chosen depending on the data. If we have say numbers larger than 3 digits, we might need commas as part of the content ans it would be better to use a csv in that case.
Both are types of text files and are increasingly used for classification and data mining purposes.
They do not have any other technical distinguishing factor.
A text file (which might have a txt file extension) will have lines separated by a platform specific line separator (CRLF on Windows, LF on Linux, and so on), and it will tend to contain characters human readable as text in some encoding. Apart from that human readability expectation this allows pretty much any file content on some platforms, so this is more of a content classification than a specific file format.
The other two formats are usually considered special cases of a text file intended to allow easy automated processing; tsv, a "tab separated values" file is simpler than csv, a "comma separated values" file.
csv will have commas as field separators, and it may use quoting and escaping especially to handle commas and quotes occurring in those fields. It may also include a header line as the first line in the file. The last line in the file may or may not end with a line separator.
(Details.)
tsv simply disallows tabs in the values, the header line is mandatory, the final line separator is mandatory.
(Details.)
A "flat file", in connection with databases, is a text file as opposed to a machine optimized storage method (such as a fixed size record file or a compressed backup file or a file using more elaborate markup language supporting data validation); a flat file tends to be csv or tsv or similar.
This answer benefited from a comment by Alex Shpilkin.
I have a program that outputs a table, and I was wondering if there are any advantages/disadvantages between the csv and tsv formats.
TSV is a very efficient for Javascript/Perl/Python to process, without losing
any typing information, and also easy for humans to read.
The format has been supported in 4store since its public release, and
it's reasonably widely used.
The way I look at it is: CSV is for loading into spreadsheets, TSV is
for processing by bespoke software.
You can see here the technical specification of each here.
The choice depends on the application. In a nutshell, if your fields don't contain commas, use CSV; otherwise TSV is the way to go.
TL;DR
In both formats, the problem arises when the delimiter can appear within the fields, so it is necessary to indicate that the delimiter is not working as a field separator but as a value within the field, which can be somewhat painful.
For example, using CSV: Kalman, Rudolf, von Neumann, John, Gabor, Dennis
Some basic approaches are:
Delete all the delimiters that appear within the field.
E.g. Kalman Rudolf, von Neumann John, Gabor Dennis
Escape the character (usually pre-appending a backslash \).
E.g. Kalman\, Rudolf, von Neumann\, John, Gabor\, Dennis
Enclose each field with other character (usually double quotes ").
E.g. "Kalman, Rudolf", "von Neumann, John", "Gabor, Dennis"
CSV
The fields are separated by a comma ,.
For example:
Name,Score,Country
Peter,156,GB
Piero,89,IT
Pedro,31415,ES
Advantages:
It is more generic and useful when sharing with non-technical people,
as most of software packages can read it without playing with the
settings.
Disadvantages:
Escaping the comma within the fields can be frustrating because not
everybody follows the standards.
All the extra escaping characters and quotes add weight to the final file size.
TSV
The fields are separated by a tabulation <TAB> or \t
For example:
Name<TAB>Score<TAB>Country
Peter<TAB>156<TAB>GB
Piero<TAB>89<TAB>IT
Pedro<TAB>31415<TAB>ES
Advantages:
It is not necessary to escape the delimiter as it is not usual to have the tab-character within a field. Otherwise, it should be removed.
Disadvantages:
It is less widespread.
TSV-utils makes an interesting comparison, copied here after. In a nutshell, use TSV.
Comparing TSV and CSV formats
The differences between TSV and CSV formats can be confusing. The obvious distinction is the default field delimiter: TSV uses TAB, CSV uses comma. Both use newline as the record delimiter.
By itself, using different field delimiters is not especially significant. Far more important is the approach to delimiters occurring in the data. CSV uses an escape syntax to represent comma and newlines in the data. TSV takes a different approach, disallowing TABs and newlines in the data.
The escape syntax enables CSV to fully represent common written text. This is a good fit for human edited documents, notably spreadsheets. This generality has a cost: reading it requires programs to parse the escape syntax. While not overly difficult, it is still easy to do incorrectly, especially when writing one-off programs. It is good practice is to use a CSV parser when processing CSV files. Traditional Unix tools like cut, sort, awk, and diff do not process CSV escapes, alternate tools are needed.
By contrast, parsing TSV data is simple. Records can be read using the typical readline routines found in most programming languages. The fields in each record can be found using split routines. Unix utilities can be called by providing the correct field delimiter, e.g. awk -F "\t", sort -t $'\t'. No special parser is needed. This is much more reliable. It is also faster, no CPU time is used parsing the escape syntax.
The speed advantages are especially pronounced for record oriented operations. Record counts (wc -l), deduplication (uniq, tsv-uniq), file splitting (head, tail, split), shuffling (GNU shuf, tsv-sample), etc. TSV is faster because record boundaries can be found using highly optimized newline search routines (e.g. memchr). Identifying CSV record boundaries requires fully parsing each record.
These characteristics makes TSV format well suited for the large tabular data sets common in data mining and machine learning environments. These data sets rarely need TAB and newline characters in the fields.
The most common CSV escape format uses quotes to delimit fields containing delimiters. Quotes must also be escaped, this is done by using a pair of quotes to represent a single quote. Consider the data in this table:
Field-1
Field-2
Field-3
abc
hello, world!
def
ghi
Say "hello, world!"
jkl
In Field-2, the first value contains a comma, the second value contain both quotes and a comma. Here is the CSV representation, using escapes to represent commas and quotes in the data.
Field-1,Field-2,Field-3
abc,"hello, world!",def
ghi,"Say ""hello, world!""",jkl
In the above example, only fields with delimiters are quoted. It is also common to quote all fields whether or not they contain delimiters. The following CSV file is equivalent:
"Field-1","Field-2","Field-3"
"abc","hello, world!","def"
"ghi","Say ""hello, world!""","jkl"
Here's the same data in TSV. It is much simpler as no escapes are involved:
Field-1 Field-2 Field-3
abc hello, world! def
ghi Say "hello, world!" jkl
The similarity between TSV and CSV can lead to confusion about which tools are appropriate. Furthering this confusion, it is somewhat common to have data files using comma as the field delimiter, but without comma, quote, or newlines in the data. No CSV escapes are needed in these files, with the implication that traditional Unix tools like cut and awk can be used to process these files. Such files are sometimes referred to as "simple CSV". They are equivalent to TSV files with comma as a field delimiter. Traditional Unix tools and tsv-utils tools can process these files correctly by specifying the field delimiter. However, "simple csv" is a very ad hoc and ill defined notion. A simple precaution when working with these files is to run a CSV-to-TSV converter like csv2tsv prior to other processing steps.
Note that many CSV-to-TSV conversion tools don't actually remove the CSV escapes. Instead, many tools replace comma with TAB as the record delimiter, but still use CSV escapes to represent TAB, newline, and quote characters in the data. Such data cannot be reliably processed by Unix tools like sort, awk, and cut. The csv2tsv tool in tsv-utils avoids escapes by replacing TAB and newline with a space (customizable). This works well in the vast majority of data mining scenarios.
To see what a specific CSV-to-TSV conversion tool does, convert CSV data containing quotes, commas, TABs, newlines, and double-quoted fields. For example:
$ echo $'Line,Field1,Field2\n1,"Comma: |,|","Quote: |""|"\n"2","TAB: |\t|","Newline: |\n|"' | <csv-to-tsv-converter>
Approaches that generate CSV escapes will enclose a number of the output fields in double quotes.
References:
Wikipedia: Tab-separated values - Useful description of TSV format.
IANA TSV specification - Formal definition of the tab-separated-values mime type.
Wikipedia: Comma-separated-values - Describes CSV and related formats.
RFC 4180 - IETF CSV format description, the closest thing to an actual standard for CSV.
brendano/tsvutils: The philosophy of tsvutils - Brendan O'Connor's discussion of the rationale for using TSV format in his open source toolkit.
So You Want To Write Your Own CSV code? - Thomas Burette's humorous, and accurate, blog post describing the troubles with ad-hoc CSV parsing. Of course, you could use TSV and avoid these problems!
You can use any delimiter you want, but tabs and commas are supported by many applications, including Excel, MySQL, PostgreSQL. Commas are common in text fields, so if you escape them, more of them need to be escaped. If you don't escape them and your fields might contain commas, then you can't confidently run "sort -k2,4" on your file. You might need to escape some characters in fields anyway (null bytes, newlines, etc.). For these reasons and more, my preference is to use TSVs, and escape tabs, null bytes, and newlines within fields. Additionally, it is usually easier to work with TSVs. Just split each line by the tab delimiter. With CSVs there are quoted fields, possibly fields with newlines, etc. I only use CSVs when I'm forced to.
I think that generally csv, are supported more often than the tsv format.