numpy的阵列中使用SQLite [英] NumPy arrays with SQLite

问题描述

我在Python中最常见的SQLite的接口是的sqlite3 ，但有什么与numpy的数组或recarrays效果很好？我的意思是一个可识别的数据类型，并不需要按行插入一行，并提取到一个numpy的（REC）阵列...？有点像在 RDB 或 sqldf 库，如果有人熟悉这些（他们进口/出口研究的SQL函数/整个表或表的子集追加到或者从R数据表）。

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).

推荐答案

为什么不给的 Redis的 的一试？

为您所关注两个平台的驱动程序可以 - 蟒蛇（ Redis的的，通过包索引] 2 ）和R（ rredis 的， CRAN ）。

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

Redis的的天才之处的不的，它会神奇地认识到numpy的数据类型和允许你插入和好像他们是本地Redis的数据类型，而它的天才在于提取多维numpy的阵列卓越的易用性，使用它可以创建这样一个接口与code的只有几行。

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.

有（至少）对蟒蛇Redis的几个教程;在 DeGizmo博客中的一个是特别好。

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, r0

for 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)
>>> A
  array([[ 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.]])

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