# Loading and saving data (io)¶

Orange.data.Table supports loading from several file formats:

• Comma-separated values (*.csv) file,
• Tab-separated values (*.tab, *.tsv) file,
• Python pickle.

In addition, the text-based files (CSV, TSV) can be compressed with gzip, bzip2 or xz (e.g. *.csv.gz).

The data in CSV, TSV, and Excel files can be described in an extended three-line header format, or a condensed single-line header format.

1. Feature names on the first line. Feature names can include any combination of characters.
2. Feature types on the second line. The type is determined automatically, or, if set, can be any of the following:
3. Flags (optional) on the third header line. Feature’s flag can be empty, or it can contain, space-separated, a consistent combination of:
• class (or c) — feature will be imported as a class variable. Most algorithms expect a single class variable.
• meta (or m) — feature will be imported as a meta-attribute, just describing the data instance but not actually used for learning,
• weight (or w) — the feature marks the weight of examples (in algorithms that support weighted examples),
• ignore (or i) — feature will not be imported,
• <key>=<value> custom attributes.

Example of iris dataset in Orange’s three-line format (iris.tab).

sepal length	sepal width	petal length	petal width	iris
c	c	c	c	d
class
5.1	3.5	1.4	0.2	Iris-setosa
4.9	3.0	1.4	0.2	Iris-setosa
4.7	3.2	1.3	0.2	Iris-setosa
4.6	3.1	1.5	0.2	Iris-setosa


Single-line header consists of feature names prefixed by an optional “<flags>#” string, i.e. flags followed by a hash (‘#’) sign. The flags can be a consistent combination of:

• c for class feature,
• i for feature to be ignored,
• m for meta attributes (not used in learning),
• C for features that are continuous,
• D for features that are discrete,
• T for features that represent date and/or time in one of the ISO 8601 formats,
• S for string features.

If some (all) names or flags are omitted, the names, types, and flags are discerned automatically, and correctly (most of the time).

Baskets can be used for storing sparse data in tab delimited files. They were specifically designed for text mining needs. If text mining and sparse data is not your business, you can skip this section.

Baskets are given as a list of space-separated <name>=<value> atoms. A continuous meta attribute named <name> will be created and added to the domain as optional if it is not already there. A meta value for that variable will be added to the example. If the value is 1, you can omit the =<value> part.

It is not possible to put meta attributes of other types than continuous in the basket.

A tab delimited file with a basket can look like this:

K       Ca      b_foo     Ba  y
meta              i   class
0.06    8.75    a b a c   0   1
0.48            b=2 d     0   1
0.39    7.78              0   1
0.57    8.22    c=13      0   1


These are the examples read from such a file:

[0.06, 1], {"Ca":8.75, "a":2.000, "b":1.000, "c":1.000}
[0.48, 1], {"Ca":?, "b":2.000, "d":1.000}
[0.39, 1], {"Ca":7.78}
[0.57, 1], {"Ca":8.22, "c":13.000}


It is recommended to have the basket as the last column, especially if it contains a lot of data.

Note a few things. The basket column’s name, b_foo, is not used. In the first example, the value of a is 2 since it appears twice. The ordinary meta attribute, Ca, appears in all examples, even in those where its value is undefined. Meta attributes from the basket appear only where they are defined. This is due to the different nature of these meta attributes: Ca is required while the others are optional.

>>> d.domain.metas()
{-6: FloatVariable 'd', -22: FloatVariable 'Ca', -5: FloatVariable 'c', -4: FloatVariable 'b', -3: FloatVariable 'a'}


To fully understand all this, you should read the documentation on meta attributes in Domain and on the basket file format (a simple format that is limited to baskets only).

Basket files (.basket) are suitable for representing sparse data. Each example is represented by a line in the file. The line is written as a comma-separated list of name-value pairs. Here’s an example of such file.

nobody, expects, the, Spanish, Inquisition=5
our, chief, weapon, is, surprise=3, surprise=2, and, fear,fear, and, surprise
our, two, weapons, are, fear, and, surprise, and, ruthless, efficiency
to, the, Pope, and, nice, red, uniforms, oh damn


The file contains four examples. The first examples has five attributes defined, “nobody”, “expects”, “the”, “Spanish” and “Inquisition”; the first four have (the default) value of 1.0 and the last has a value of 5.0.

The attributes that appear in the domain aren’t defined in any headers or even separate files, as with other formats supported by Orange.

If attribute appears more than once, its values are added. For instance, the value of attribute “surprise” in the second examples is 6.0 and the value of “fear” is 2.0; the former appears three times with values of 3.0, 2.0 and 1.0, and the latter appears twice with value of 1.0.

All attributes are loaded as optional meta-attributes, so zero values don’t take any memory (unless they are given, but initialized to zero). See also section on meta attributes in the reference for domain descriptors.

Notice that at the time of writing this reference only association rules can directly use examples presented in the basket format.