The most common SQLite interface I've seen in Python is sqlite3, but is there anything that works well with NumPy arrays or recarrays? By that I mean one that recognizes data types and does not require inserting row by row, and extracts into a NumPy (rec)array...? Kind of like R's SQL functions in the RDB or sqldf libraries, if anyone is familiar with those (they import/export/append whole tables or subsets of tables to or from R data tables).

why not give redis a try?

Drivers for your two platforms of interest are available--python (redis, via package index]2), and R (rredis, CRAN).

The genius of redis is not that it will magically recognize the NumPy data type and allow you to insert and extract multi-dimensional NumPy arrays as if they were native redis datatypes, rather its genius is in the remarkable ease with which you can create such an interface with just a few lines of code.

There are (at least) several tutorials on redis in python; the one on the DeGizmo blog is particularly good.

import numpy as NP# create some data
A = NP.random.randint(0, 10, 40).reshape(8, 5)# a couple of utility functions to (i) manipulate NumPy arrays prior to insertion 
# into redis db for more compact storage & 
# (ii) to restore the original NumPy data types upon retrieval from redis db
fnx2 = lambda v : map(int, list(v))
fnx = lambda v : ''.join(map(str, v))# start the redis server (e.g. from a bash prompt)
$> cd /usr/local/bin      # default install directory for 'nix
$> redis-server           # starts the redis server# start the redis client:
from redis import Redis
r0 = Redis(db=0, port=6379, host='localhost')       # same as: r0 = Redis()# to insert items using redis 'string' datatype, call 'set' on the database, r0, and
# just pass in a key, and the item to insert
r0.set('k1', A[0,:])# row-wise insertion the 2D array into redis, iterate over the array:
for c in range(A.shape[0]):r0.set( "k{0}".format(c), fnx(A[c,:]) )# or to insert all rows at once
# use 'mset' ('multi set') and pass in a key-value mapping: 
x = dict([sublist for sublist in enumerate(A.tolist())])
r0.mset(x1)# to retrieve a row, pass its key to 'get'
>>> r0.get('k0')'63295'# retrieve the entire array from redis:
kx = r0.keys('*')           # returns all keys in redis database, r0for key in kx :r0.get(key)# to retrieve it in original form:
A = []
for key in kx:A.append(fnx2(r0.get("{0}".format(key))))>>> A = NP.array(A)
>>> Aarray([[ 6.,  2.,  3.,  3.,  9.],[ 4.,  9.,  6.,  2.,  3.],[ 3.,  7.,  9.,  5.,  0.],[ 5.,  2.,  6.,  3.,  4.],[ 7.,  1.,  5.,  0.,  2.],[ 8.,  6.,  1.,  5.,  8.],[ 1.,  7.,  6.,  4.,  9.],[ 6.,  4.,  1.,  3.,  6.]])