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a datebase contained 80 tables ,every table is a 18000x4 matrix ,how can I translate these to a 80x18000x4 numpy array?

the db : https://drive.google.com/open?id=0B3bNSNFik5wGdm1DYnJwNHBJMVU

I wrote a function. Do you have any better idea?

import sqlite3 as sql
import os
def db_to_array(db,r,c):
  db = sql.connect(db)
  cursor = db.cursor()
  cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
  tables = cursor.fetchall()
  wd=os.getcwd()
  if not os.path .exists(wd +'/temp/'):
    os.makedirs(wd +'/temp/')
  for table_name in tables:
    table_name = table_name[0]
    table = pd.read_sql_query("SELECT * from %s" % table_name, db)
    table.to_csv(wd +'/temp/'+ table_name + '.csv', index_label='index')
  ss=os.listdir(wd +'/temp/')
  print(len(ss))
  dd=np.empty([len(ss),r,c])
  for i in range(len(ss)):
    ddd=np.array(pd.read_csv(wd +'/temp/'+ss[i]))
    print(i)
    print(ddd.shape)
    dd[i,:,:]=ddd[:,0:r]
  return dd
Improve this question
3
  • By table do you mean 4 fields and 1000 records? Show a sample table definition, and how you would select some records. For example, stackoverflow.com/questions/42914215/… Commented Apr 24, 2017 at 3:35
  • what have you done so far? Commented Apr 26, 2017 at 2:26
  • I wrote a function. Do you have any better idea? Commented Apr 26, 2017 at 7:06
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