SKlearning大部分的输入数据都是M * N数组.
然而我们从数据库或文件读取得来的通常是Python内定的类型tuple或list
它们的优势就不说了,但是直接把list或tuple构成的二维数组传入scikit是会出问题的.
如:
DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
DeprecationWarning)
下面贴上如何把list/tuple转为scikit使用的array
首先, 准备数据如下:
读取一行数据变为一维数组
conn = sql.connect('result_sale.db')
conn.text_factory = str
dataSet = conn.execute('select * from sampleData')
tpRows = dataSet.fetchone()
conn.close()
print type(tpRows)
print tpRows
lstRows = list(tpRows)
aryRows1 = np.array(lstRows) # 转成数组
#aryRows2 = np.array(lstRows).reshape(1, -1) # 转成1行N列 (二维数组)
#aryRows3 = np.array(lstRows).reshape(-1, 1) # 转成N行1列 (二维数组)
print lstRows
print aryRows1
输入如下: 请留意输入的不同点 :)
( '00' , '01' , '02' , '03' , '04' , '05' , '06' , '07' , '08' ) (tuple)
[ '00' , '01' , '02' , '03' , '04' , '05' , '06' , '07' , '08' ] (list)
[ '00' '01' '02' '03' '04' '05' '06' '07' '08' ] (array)
Process finished with exit code 0
一次性转换整个数据集
conn = sql.connect('result_sale.db')
conn.text_factory = str
dataSet = conn.execute('select * from sampleData')
tpRows = dataSet.fetchall()
conn.close()
aryRows1 = np.array(tpRows) # 转成数组
#aryRows2 = np.array(tpRows).reshape(1, -1) # 转成1行N列 (二维数组)
#aryRows3 = np.array(tpRows).reshape(-1, 1) # 转成N行1列 (二维数组)
print aryRows1
#print aryRows2
#print aryRows3
输入如下:
[[ '00' '01' '02' '03' '04' '05' '06' '07' '08' ]
[ '10' '11' '12' '13' '14' '15' '16' '17' '18' ]
[ '20' '21' '22' '23' '24' '25' '26' '27' '28' ]
[ '30' '31' '32' '33' '34' '35' '36' '37' '38' ]
[ '40' '41' '42' '43' '44' '45' '46' '47' '48' ]
[ '50' '51' '52' '53' '54' '55' '56' '57' '58' ]
[ '60' '61' '62' '63' '64' '65' '66' '67' '68' ]
[ '70' '71' '72' '73' '74' '75' '76' '77' '78' ]
[ '80' '81' '82' '83' '84' '85' '86' '87' '88' ]]
Process finished with exit code 0
逐条纪录转换, 可以用下标来引用数组
conn = sql.connect('result_sale.db')
conn.text_factory = str
dataSet = conn.execute('select * from sampleData')
tpRows = dataSet.fetchall()
conn.close()
#aryRows = np.zeros([len(tpRows), len(tpRows[0])])
aryRows = np.ones_like(tpRows) #亦可使用 empty, empty_like, zeros, zeros_like 等方法
j=0
for row in tpRows:
aryRows[j][:] = row
j += 1
print aryRows
输入如下:
[[ '00' '01' '02' '03' '04' '05' '06' '07' '08' ]
[ '10' '11' '12' '13' '14' '15' '16' '17' '18' ]
[ '20' '21' '22' '23' '24' '25' '26' '27' '28' ]
[ '30' '31' '32' '33' '34' '35' '36' '37' '38' ]
[ '40' '41' '42' '43' '44' '45' '46' '47' '48' ]
[ '50' '51' '52' '53' '54' '55' '56' '57' '58' ]
[ '60' '61' '62' '63' '64' '65' '66' '67' '68' ]
[ '70' '71' '72' '73' '74' '75' '76' '77' '78' ]
[ '80' '81' '82' '83' '84' '85' '86' '87' '88' ]]
Process finished with exit code 0
