Interactive online version:

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:

from larray import *

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

from larray import __version__
__version__

'0.34.3'

Warning: The tutorial is generated from Jupyter notebooks which work in the “interactive” mode (like in the LArray Editor console). In the interactive mode, there is no need to use the print() function to display the content of a variable. Simply writing its name is enough. The same remark applies for the returned value of an expression. In a Python script (file with .py extension), you always need to use the print() function to display the content of a variable or the value returned by a function or an expression.

s = 1 + 2

# In the interactive mode, there is no need to use the print() function
# to display the content of the variable 's'.
# Simply typing 's' is enough
s

3

# In the interactive mode, there is no need to use the print() function
# to display the result of an expression
1 + 2

3

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. They are several ways to create an axis:

# create an axis using one string
age = Axis('age=0-9,10-17,18-66,67+')
# labels generated using the special syntax start..end
time = Axis('time=2015..2017')
# labels given as a list
gender = Axis(['female', 'male'], 'gender')

age, gender, time

(Axis(['0-9', '10-17', '18-66', '67+'], 'age'),
 Axis(['female', 'male'], 'gender'),
 Axis([2015, 2016, 2017], 'time'))

Warning: When using the string syntax "axis_name=list,of,labels" or "axis_name=start..end", LArray will automatically infer the type of labels. For example, age = Axis("age=0..100") will create an age axis with labels of type int. Mixing numbers with letters or special characters like + will create an axis with labels of type str instead of int. For example, age = Axis("age=0..98,99+") will create an age axis with labels of type str instead of int!

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:

# 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

  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

You can optionally attach some metadata to an array:

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

# display metadata
population.meta

title: population by age, gender and year
source: Eurostat

To get a short summary of an array, type:

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

title: population by age, gender and year
source: Eurostat
4 x 2 x 3
 age [4]: '0-9' '10-17' '18-66' '67+'
 gender [2]: 'female' 'male'
 time [3]: 2015 2016 2017
dtype: int64
memory used: 192 bytes

To get the axes of an array, type:

population.axes

AxisCollection([
    Axis(['0-9', '10-17', '18-66', '67+'], 'age'),
    Axis(['female', 'male'], 'gender'),
    Axis([2015, 2016, 2017], 'time')
])

It is also possible to extract one axis belonging to an array using its name:

# extract the 'time' axis belonging to the 'population' array
time = population.time
time

Axis([2015, 2016, 2017], 'time')

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

zeros([age, gender])

age\gender  female  male
       0-9     0.0   0.0
     10-17     0.0   0.0
     18-66     0.0   0.0
       67+     0.0   0.0

ones([age, gender])

age\gender  female  male
       0-9     1.0   1.0
     10-17     1.0   1.0
     18-66     1.0   1.0
       67+     1.0   1.0

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

age\gender  female  male
       0-9    10.0  10.0
     10-17    10.0  10.0
     18-66    10.0  10.0
       67+    10.0  10.0

# With initial=1.0 and inc=0.5, we generate the sequence 1.0, 1.5, 2.0, 2.5, 3.0, ...
sequence(age, initial=1.0, inc=0.5)

age  0-9  10-17  18-66  67+
     1.0    1.5    2.0  2.5

ndtest([age, gender])

age\gender  female  male
       0-9       0     1
     10-17       2     3
     18-66       4     5
       67+       6     7

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:

# 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:

population = read_csv('population_belgium.csv')
population

  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

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:

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

1053

Labels can be given in arbitrary order:

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

1053

When selecting a larger subset the result is an array:

population['female']

age\time  2015  2016  2017
     0-9   633   635   634
   10-17   484   486   491
   18-66  3572  3581  3583
     67+  1023  1038  1053

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

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

age\time  2015  2016  2017
     0-9   633   635   634
   10-17   484   486   491

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:

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

age\time  2015  2016  2017
   10-17   484   486   491
   18-66  3572  3581  3583
     67+  1023  1038  1053

# :'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:]

  age  gender\time  2017
  0-9       female   634
  0-9         male   664
10-17       female   491
10-17         male   516
18-66       female  3583
18-66         male  3616

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

Warning: 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: <somelabel> is ambiguous (valid in <axis1>, <axis2>)

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

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

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

immigration

    country  citizenship\time  2015  2016  2017
    Belgium           Belgium     0     1     2
    Belgium       Netherlands     3     4     5
    Belgium           Germany     6     7     8
Netherlands           Belgium     9    10    11
Netherlands       Netherlands    12    13    14
Netherlands           Germany    15    16    17
    Germany           Belgium    18    19    20
    Germany       Netherlands    21    22    23
    Germany           Germany    24    25    26

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:

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

11

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:

population

  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

We can sum along the ‘gender’ axis using:

population.sum(gender)

age\time  2015  2016  2017
     0-9  1296  1300  1298
   10-17   989   997  1007
   18-66  7172  7199  7199
     67+  1779  1813  1846

Or sum along both ‘age’ and ‘gender’:

population.sum(age, gender)

time   2015   2016   2017
      11236  11309  11350

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

population.sum_by(time)

time   2015   2016   2017
      11236  11309  11350

Groups

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

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

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

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

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

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

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

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

Still using the same population array:

population

  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

Groups can be used in selections:

population[working]

gender\time  2015  2016  2017
     female  3572  3581  3583
       male  3600  3618  3616

population[nonworking]

  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
  67+       female  1023  1038  1053
  67+         male   756   775   793

or aggregations:

population.sum(nonworking)

gender\time  2015  2016  2017
     female  2140  2159  2178
       male  1924  1951  1973

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 :

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

       age  gender\time  2015  2016  2017
 0-9,10-17       female  1117  1121  1125
 0-9,10-17         male  1168  1176  1180
   working       female  3572  3581  3583
   working         male  3600  3618  3616
nonworking       female  2140  2159  2178
nonworking         male  1924  1951  1973

Warning: Mixing slices and individual labels inside the [ ] will generate several groups (a tuple of groups) instead of a single group. If you want to create a single group using both slices and individual labels, you need to use the .union() method (see below).

age_100 = Axis('age=0..100')

# mixing slices and individual labels leads to the creation of several groups (a tuple of groups)
age_100[0:10, 20, 30, 40]

(age[0:10], age[20], age[30], age[40])

# the union() method allows to mix slices and individual labels to create a single group
age_100[0:10].union(age_100[20, 30, 40])

age[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40].set()

Grouping arrays in a Session

Variables (arrays) may be grouped in Session objects. A session is an ordered dict-like container with special I/O methods:

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

# create a session containing the arrays of the model
demography_session = Session(population=population, births=births, deaths=deaths)

# get an array (option 1)
demography_session['population']

# get an array (option 2)
demography_session.births

# modify an array
demography_session.deaths['male'] = 1

# add an array
demography_session.foreigners = zeros([age, gender, time])

# displays names of arrays contained in the session
# (in alphabetical order)
demography_session.names

['births', 'deaths', 'foreigners', 'population']

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

# 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')

ValueError: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject

However, development tools like PyCharm do not provide autocomplete for objects in Session objects.

Autocomplete is the feature in which development tools try to predict the variable or function a user intends to enter after only a few characters have been typed (like word completion in cell phones).

Another way to group objects of a model is to use CheckedSession. The CheckedSession provide the same methods than Session but enable the autocomplete feature on objects it contains.

For more details about Session and CheckedSession, see the Working With Sessions section of the tutorial.

To get the list of methods belonging to the Session and CheckedSession ojects, check the corresponding section in the API Reference.

Graphical User Interface (Editor)

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

The view() function displays the content of (an) array(s) in a graphical user interface in read-only mode.

For instance, the statement

view(population)

will open a new window showing the values and axes of the ‘population’ array.

The statement

view(demography_session)

will show all arrays contained in the ‘demography_session’.

A session can be directly loaded from a file

view('demography.h5')

Calling

view()

with no passed argument creates a session with all existing arrays from the current namespace and shows its content.

Notes:

• Calling view() will block the execution of the rest of code until the graphical user interface is closed!

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

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 *.