Difference between revisions of "Data Files"
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* For numbers the decimal separator is a dot, not a comma. There is no thousands separator. | * For numbers the decimal separator is a dot, not a comma. There is no thousands separator. | ||
− | == File | + | == Example == |
+ | |||
+ | This is what the file format looks like: | ||
+ | <pre> | ||
+ | User ID	Hair color	Response time	 | ||
+ | 1	brown	1.4	 | ||
+ | 2	blond	1230.434	 | ||
+ | 3	brown	0.399	 | ||
+ | |||
+ | </pre> | ||
+ | An example file can be downloaded here [[File:Example.zip|thumb]] (sorry, it is zipped). | ||
+ | |||
+ | == Parsing == | ||
+ | Importing such files can be done in many languages: | ||
+ | === Python Standard Library=== | ||
+ | <nowiki> | ||
+ | import csv | ||
+ | with open('example.tsv', 'rb') as csvfile: | ||
+ | reader = csv.reader(csvfile, delimiter='\t', quoting=csv.QUOTE_NONE) | ||
+ | for row in reader: | ||
+ | print(', '.join(row)) | ||
+ | </nowiki> | ||
+ | or with header extraction | ||
+ | <nowiki> | ||
+ | import csv | ||
+ | with open('example.tsv', 'rb') as csvfile: | ||
+ | reader = csv.DictReader(csvfile, delimiter='\t', quoting=csv.QUOTE_NONE) | ||
+ | print(', '.join(reader.fieldnames)) # print header | ||
+ | for row in reader: | ||
+ | print(', '.join([row[key] for key in reader.fieldnames])) | ||
+ | </nowiki> | ||
+ | Note that when using Python 2 the field content will remain UTF-8 encoded (type=str). In Python3 strings will unicode (type=string). | ||
+ | |||
+ | === Python Pandas === | ||
+ | Pandas can interpret column type. You will have to store it separately or hardcode it. | ||
+ | <nowiki> | ||
+ | import pandas as pd | ||
+ | |||
+ | d = pd.read_csv('example.tsv', delimiter='\t', skip_blank_lines=False, quoting=csv.QUOTE_NONE) | ||
+ | </nowiki> | ||
+ | === GNU R === | ||
+ | <nowiki> | ||
+ | d <- read.csv("example.tsv", head=TRUE, sep = "\t") | ||
+ | </nowiki> | ||
+ | |||
+ | == Alternatives == | ||
Data can be saved in a lot of file formats. If there is no reason to do otherwise, we prefer delimited files with the options shown in bold. Alternative options are also shown. | Data can be saved in a lot of file formats. If there is no reason to do otherwise, we prefer delimited files with the options shown in bold. Alternative options are also shown. | ||
{| class="wikitable" | {| class="wikitable" | ||
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|} | |} | ||
Note that tab characters and newlines cannot be present in field content. | Note that tab characters and newlines cannot be present in field content. | ||
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Revision as of 11:59, 20 February 2017
TSG suggested file format for experiment data
The TSG suggests a common file format for storing experimental data. Adhering to this format whenever practical makes it easier to re-use files and tools. The file is plain text for easy inspection and manipulation. The file format is a tab-separated values (tsv) file with the following specifications:
File
- File encoding is ASCII or UTF-8.
- The file contains no byte order mark (BOM) or other magic number.
Lines
- Lines are separated by the \r\n line delimiter for better compatibility between operating systems.
- The line delimiter should also be added after the last line, because...
- The first line contains a header with column/field names.
Fields
- Field are separated by the tab field delimiter, because they rarely occur in texts and therefore require no escaping.
- The field delimiter should also be added after each line's last field, because...
- The last field in a line must not be empty, because... if there is no value, wat do...
- Fields are not surrounded by a quoting character.
- White space between field delimiters are considered part of the field.
- There is no defined escape character. If your data can contain tabs, use a different field delimiter or file format.
Data
- For numbers the decimal separator is a dot, not a comma. There is no thousands separator.
Example
This is what the file format looks like:
User ID Hair color Response time 1 brown 1.4 2 blond 1230.434 3 brown 0.399
An example file can be downloaded here File:Example.zip (sorry, it is zipped).
Parsing
Importing such files can be done in many languages:
Python Standard Library
import csv with open('example.tsv', 'rb') as csvfile: reader = csv.reader(csvfile, delimiter='\t', quoting=csv.QUOTE_NONE) for row in reader: print(', '.join(row))
or with header extraction
import csv with open('example.tsv', 'rb') as csvfile: reader = csv.DictReader(csvfile, delimiter='\t', quoting=csv.QUOTE_NONE) print(', '.join(reader.fieldnames)) # print header for row in reader: print(', '.join([row[key] for key in reader.fieldnames]))
Note that when using Python 2 the field content will remain UTF-8 encoded (type=str). In Python3 strings will unicode (type=string).
Python Pandas
Pandas can interpret column type. You will have to store it separately or hardcode it.
import pandas as pd d = pd.read_csv('example.tsv', delimiter='\t', skip_blank_lines=False, quoting=csv.QUOTE_NONE)
GNU R
d <- read.csv("example.tsv", head=TRUE, sep = "\t")
Alternatives
Data can be saved in a lot of file formats. If there is no reason to do otherwise, we prefer delimited files with the options shown in bold. Alternative options are also shown.
file extension | tsv | csv | dat | txt | |
file extension | ascii | UTF-8 | UTF-16BE | UTF-16LE | UCS-4/UTF-32 |
magic number | None | <BOM> | |||
line delimiter | \n | \r | \r\n | ||
line delimiter after last line | no | yes | |||
field delimiter | <tab> | , | ; | ||
field delimiter after last field | no | yes | |||
quoting character | None | " | ' | ||
escape qc by doubling | no | yes | |||
escape character | none | \ | |||
first line | contains header | contains data | |||
last field in line | must not be empty | may be empty | |||
whitespace following delimiter | part of field | not part of field | |||
decimal separator | . | , | |||
thousands separator | none | . | ␣ | U+2009 |
Note that tab characters and newlines cannot be present in field content.