# Numpy¶

A popular solution for scientific computing with Python is `numpy`

.

`rpy2`

has features to ease bidirectional communication with `numpy`

.

## High-level interface¶

### From rpy2 to numpy:¶

R vectors or arrays can be converted to `numpy`

arrays using
`numpy.array()`

or `numpy.asarray()`

:

```
import numpy
ltr = robjects.r.letters
ltr_np = numpy.array(ltr)
```

This behavior is inherited from the low-level interface;
vector-like objects inheriting from `rpy2.rinterface.SexpVector`

present an interface recognized by numpy.

```
from rpy2.robjects.packages import importr, data
import numpy
datasets = importr('datasets')
ostatus = data(datasets).fetch('occupationalStatus')['occupationalStatus']
ostatus_np = numpy.array(ostatus)
ostatus_npnc = numpy.asarray(ostatus)
```

The matrix *ostatus* is an 8x8 matrix:

```
>>> print(ostatus)
destination
origin 1 2 3 4 5 6 7 8
1 50 19 26 8 7 11 6 2
2 16 40 34 18 11 20 8 3
3 12 35 65 66 35 88 23 21
4 11 20 58 110 40 183 64 32
5 2 8 12 23 25 46 28 12
6 12 28 102 162 90 554 230 177
7 0 6 19 40 21 158 143 71
8 0 3 14 32 15 126 91 106
```

Its content has been copied to a numpy array:

```
>>> ostatus_np
array([[ 50, 19, 26, 8, 7, 11, 6, 2],
[ 16, 40, 34, 18, 11, 20, 8, 3],
[ 12, 35, 65, 66, 35, 88, 23, 21],
[ 11, 20, 58, 110, 40, 183, 64, 32],
[ 2, 8, 12, 23, 25, 46, 28, 12],
[ 12, 28, 102, 162, 90, 554, 230, 177],
[ 0, 6, 19, 40, 21, 158, 143, 71],
[ 0, 3, 14, 32, 15, 126, 91, 106]])
>>> ostatus_np[0, 0]
50
>>> ostatus_np[0, 0] = 123
>>> ostatus_np[0, 0]
123
>>> ostatus.rx(1, 1)[0]
50
```

On the other hand, *ostatus_npnc* is a view on *ostatus*; no copy was made:

```
>>> ostatus_npnc[0, 0] = 456
>>> ostatus.rx(1, 1)[0]
456
```

Since we did modify an actual R dataset for the session, we should restore it:

```
>>> ostatus_npnc[0, 0] = 50
```

As we see, `numpy.asarray()`

: provides a way to build a *view* on the underlying
R array, without making a copy. This will be of particular appeal to developpers whishing
to mix `rpy2`

and `numpy`

code, with the `rpy2`

objects or the `numpy`

view passed to
functions, or for interactive users much more familiar with the `numpy`

syntax.

Note

The current interface is relying on the *__array_struct__* defined
in numpy.

Python buffers, as defined in **PEP 3118**, is the way to the future,
and rpy2 is already offering them… although as a (poorly documented)
experimental feature.

### From numpy to rpy2:¶

Some of the conversions operations require the copy of data in R structures into Python structures. Whenever this happens, the time it takes and the memory required will depend on object sizes. Because of this reason the use of a local converter is recommended: it makes limiting the use of conversion rules to code blocks of interest easier to achieve.

```
from rpy2.robjects import numpy2ri
from rpy2.robjects import default_converter
# Create a converter that starts with rpy2's default converter
# to which the numpy conversion rules are added.
np_cv_rules = default_converter + numpy2ri.converter
with np_cv_rules:
# Anything here and until the `with` block is exited
# will use our numpy converter whenever objects are
# passed to R or are returned by R while calling
# rpy2.robjects functions.
pass
```

An example of usage is:

```
from rpy2.robjects.packages import importr
stats = importr('base')
with np_cv_rules.context():
v_np = stats.rlogis(100, location=0, scale=1)
# `v_np` is a numpy array
# Outside of the scope of the local converter the
# result will not be automatically converted to a
# numpy object.
v_nonp = stats.rlogis(100, location=0, scale=1)
```

Note

Why make `numpy`

an optional feature for `rpy2`

?
This was a design decision taken in order to:
- ensure that `rpy2`

can function without `numpy`

. An early motivation for
this was compatibility with Python 3 and dropping support for Python 2.
`rpy2`

did that much earlier than `numpy`

did.
- make potentially resource-consuming conversions optional

Note

The module `numpy2ri`

is an example of how custom conversion to
and from `rpy2.robjects`

can be performed.

## Low-level interface¶

The `rpy2.rinterface.SexpVector`

objects are made to
behave like arrays, as defined in the Python package `numpy`

.

The functions `numpy.array()`

and `numpy.asarray()`

can
be used to construct numpy arrays:

```
>>> import numpy
>>> rx = rinterface.SexpVector([1,2,3,4], rinterface.INTSXP)
>>> nx = numpy.array(rx)
>>> nx_nc = numpy.asarray(rx)
```

Note

when using `numpy.asarray()`

, the data are not copied.

```
>>> rx[2]
3
>>> nx_nc[2] = 42
>>> rx[2]
42
>>>
```