Getting Started

The purpose of the present Getting Started section is to give a quick overview of the main objects and features of the LArray library. To get a more detailed presentation of all capabilities of LArray, read the next sections of the tutorial.

The API Reference section of the documentation give you the list of all objects, methods and functions with their individual documentation and examples.

To use the LArray library, the first thing to do is to import it:



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from larray import *

To know the version of the LArray library installed on your machine, type:



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from larray import __version__
__version__

Create an array

Working with the LArray library mainly consists of manipulating Array data structures. They represent N-dimensional labelled arrays and are composed of raw data (NumPy ndarray), axes and optionally some metadata.

An Axis object represents a dimension of an array. It contains a list of labels and has a name:



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# define some axes to be used later
age = Axis(['0-9', '10-17', '18-66', '67+'], 'age')
gender = Axis(['female', 'male'], 'gender')
time = Axis([2015, 2016, 2017], 'time')

The labels allow to select subsets and to manipulate the data without working with the positions of array elements directly.

To create an array from scratch, you need to supply data and axes:



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# define some data. This is the belgian population (in thousands). Source: eurostat.
data = [[[633, 635, 634],
         [663, 665, 664]],
        [[484, 486, 491],
         [505, 511, 516]],
        [[3572, 3581, 3583],
         [3600, 3618, 3616]],
        [[1023, 1038, 1053],
         [756, 775, 793]]]

# create an Array object
population = Array(data, axes=[age, gender, time])
population

You can optionally attach some metadata to an array:



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# attach some metadata to the population array
population.meta.title = 'population by age, gender and year'
population.meta.source = 'Eurostat'

# display metadata
population.meta

To get a short summary of an array, type:



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# Array summary: metadata + dimensions + description of axes
population.info

Create an array filled with predefined values

Arrays filled with predefined values can be generated through dedicated functions:

zeros : creates an array filled with 0
ones : creates an array filled with 1
full : creates an array filled with a given value
sequence : creates an array by sequentially applying modifications to the array along axis.
ndtest : creates a test array with increasing numbers as data



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zeros([age, gender])



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ones([age, gender])



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full([age, gender], fill_value=10.0)



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sequence(age)



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ndtest([age, gender])

Save/Load an array

The LArray library offers many I/O functions to read and write arrays in various formats (CSV, Excel, HDF5). For example, to save an array in a CSV file, call the method to_csv:



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# save our population array to a CSV file
population.to_csv('population_belgium.csv')

The content of the CSV file is then:

age,gender\time,2015,2016,2017
0-9,female,633,635,634
0-9,male,663,665,664
10-17,female,484,486,491
10-17,male,505,511,516
18-66,female,3572,3581,3583
18-66,male,3600,3618,3616
67+,female,1023,1038,1053
67+,male,756,775,793

Note: In CSV or Excel files, the last dimension is horizontal and the names of the last two dimensions are separated by a backslash \.

To load a saved array, call the function read_csv:



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population = read_csv('population_belgium.csv')
population

Other input/output functions are described in the Input/Output section of the API documentation.

Selecting a subset

To select an element or a subset of an array, use brackets [ ]. In Python we usually use the term indexing for this operation.

Let us start by selecting a single element:



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population['67+', 'female', 2017]

Labels can be given in arbitrary order:



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population[2017, 'female', '67+']

When selecting a larger subset the result is an array:



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population['female']

When selecting several labels for the same axis, they must be given as a list (enclosed by [ ])



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population['female', ['0-9', '10-17']]

You can also select slices, which are all labels between two bounds (we usually call them the start and stop bounds). Specifying the start and stop bounds of a slice is optional: when not given, start is the first label of the corresponding axis, stop the last one:



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# in this case '10-17':'67+' is equivalent to ['10-17', '18-66', '67+']
population['female', '10-17':'67+']



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# :'18-66' selects all labels between the first one and '18-66'
# 2017: selects all labels between 2017 and the last one
population[:'18-66', 2017:]

Note: Contrary to slices on normal Python lists, the stop bound is included in the selection.

Selecting by labels as above only works as long as there is no ambiguity. When several axes have some labels in common and you do not specify explicitly on which axis to work, it fails with an error ending with something like ValueError: is ambiguous (valid in , ).

For example, imagine you need to work with an 'immigration' array containing two axes sharing some common labels:



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country = Axis(['Belgium', 'Netherlands', 'Germany'], 'country')
citizenship = Axis(['Belgium', 'Netherlands', 'Germany'], 'citizenship')

immigration = ndtest((country, citizenship, time))

immigration

If we try to get the number of Belgians living in the Netherlands for the year 2017, we might try something like:

immigration['Netherlands', 'Belgium', 2017]

... but we receive back a volley of insults:

    [some long error message ending with the line below]
    [...]
    ValueError: Netherlands is ambiguous (valid in country, citizenship)

In that case, we have to specify explicitly which axes the 'Netherlands' and 'Belgium' labels we want to select belong to:



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immigration[country['Netherlands'], citizenship['Belgium'], 2017]

Iterating over an axis

To iterate over an axis, use the following syntax:



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for year in time:
    print(year)

Aggregation

The LArray library includes many aggregations methods: sum, mean, min, max, std, var, ...

For example, assuming we still have an array in the population variable:



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population

We can sum along the 'gender' axis using:



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population.sum(gender)

Or sum along both 'age' and 'gender':



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population.sum(age, gender)

It is sometimes more convenient to aggregate along all axes except some. In that case, use the aggregation methods ending with _by. For example:



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population.sum_by(time)

Groups

A Group object represents a subset of labels or positions of an axis:



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children = age['0-9', '10-17']
children

It is often useful to attach them an explicit name using the >> operator:



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working = age['18-66'] >> 'working'
working



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nonworking = age['0-9', '10-17', '67+'] >> 'nonworking'
nonworking

Still using the same population array:



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population

Groups can be used in selections:



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population[working]



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population[nonworking]

or aggregations:



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population.sum(nonworking)

When aggregating several groups, the names we set above using >> determines the label on the aggregated axis. Since we did not give a name for the children group, the resulting label is generated automatically :



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population.sum((children, working, nonworking))

Grouping arrays in a Session

Arrays may be grouped in Session objects. A session is an ordered dict-like container of Array objects with special I/O methods. To create a session, you need to pass a list of pairs (array_name, array):



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population = zeros([age, gender, time])
births = zeros([age, gender, time])
deaths = zeros([age, gender, time])

# create a session containing the three arrays 'population', 'births' and 'deaths'
demography_session = Session(population=population, births=births, deaths=deaths)

# displays names of arrays contained in the session
demography_session.names
# get an array (option 1)
demography_session['population']
# get an array (option 2)
demography_session.births
# add/modify an array
demography_session['foreigners'] = zeros([age, gender, time])

If you are using a Python version prior to 3.6, you will have to pass a list of pairs to the Session constructor otherwise the arrays will be stored in an arbitrary order in the new session. For example, the session above must be created using the syntax: `demography_session=Session([('population', population), ('births', births), ('deaths', deaths)])`.

One of the main interests of using sessions is to save and load many arrays at once:



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# dump all arrays contained in demography_session in one HDF5 file
demography_session.save('demography.h5')
# load all arrays saved in the HDF5 file 'demography.h5' and store them in the 'demography_session' variable
demography_session = Session('demography.h5')

Graphical User Interface (viewer)

The LArray project provides an optional package called larray-editor allowing users to explore and edit arrays through a graphical interface.

The larray-editor tool is automatically available when installing the larrayenv metapackage from conda.

To explore the content of arrays in read-only mode, call the view function:

# shows the arrays of a given session in a graphical user interface
    view(demography_session)

    # the session may be directly loaded from a file
    view('demography.h5')

    # creates a session with all existing arrays from the current namespace
    # and shows its content
    view()

To open the user interface in edit mode, call the edit function instead.

Finally, you can also visually compare two arrays or sessions using the compare function:

arr0 = ndtest((3, 3))
    arr1 = ndtest((3, 3))
    arr1[['a1', 'a2']] = -arr1[['a1', 'a2']]
    compare(arr0, arr1)

For Windows Users

Installing the larray-editor package on Windows will create a LArray menu in the Windows Start Menu. This menu contains:

a shortcut to open the documentation of the last stable version of the library
a shortcut to open the graphical interface in edit mode.
a shortcut to update larrayenv.

Once the graphical interface is open, all LArray objects and functions are directly accessible. No need to start by from larray import *.