[[FrontPage/Python]] アプリのデータ永続化のためにSQLite3を使う ** ndarrayの格納方法 [#ca61ac23] データ解析ではndarrayで多次元配列を操作するのがほとんど.毎回呼び出していると時間がかかるのでDBに突っ込みたい. - 方法1 -- http://stackoverflow.com/questions/18621513/python-insert-numpy-array-into-sqlite3-database You could register a new array data type with sqlite3: import sqlite3 import numpy as np import io def adapt_array(arr): """ http://stackoverflow.com/a/31312102/190597 (SoulNibbler) """ out = io.BytesIO() np.save(out, arr) out.seek(0) return sqlite3.Binary(out.read()) def convert_array(text): out = io.BytesIO(text) out.seek(0) return np.load(out) # Converts np.array to TEXT when inserting sqlite3.register_adapter(np.ndarray, adapt_array) # Converts TEXT to np.array when selecting sqlite3.register_converter("array", convert_array) x = np.arange(12).reshape(2,6) con = sqlite3.connect(":memory:", detect_types=sqlite3.PARSE_DECLTYPES) cur = con.cursor() cur.execute("create table test (arr array)") With this setup, you can simply insert the NumPy array with no change in syntax: cur.execute("insert into test (arr) values (?)", (x, )) And retrieve the array directly from sqlite as a NumPy array: cur.execute("select arr from test") data = cur.fetchone()[0] print(data) # [[ 0 1 2 3 4 5] # [ 6 7 8 9 10 11]] print(type(data)) # <type 'numpy.ndarray'>