今天用numpy 的linalg.det()求矩陣的逆的過程中出現了一個錯誤：

TypeError: No loop matching the specified signature and casting was found for ufunc det 

查了半天發現是數據類型的問題，numpy在算逆的時候會先檢查一下數據類型是否一致，若不一致就會報錯（話說這個錯誤提示信息也太難理解了，還得看源碼o(╯□╰)o）。

由于我的數據是用pandas.DataFrame讀取的，所以每一列的數據類型有可能不同。

回頭檢查一下數據，果然有的是int，有的是float。所以全部改為float64類型。

找到了如下的方法，以及DataFrame數據類型：

DataFrame 類型轉換方法—astype()

import pandas as pddf = pd.DataFrame([{'col1':'a', 'col2':'1'}, {'col1':'b', 'col2':'2'}])print df.dtypesdf['col2'] = df['col2'].astype('int')print '-----------'print df.dtypesdf['col2'] = df['col2'].astype('float64')print '-----------'print df.dtypes

輸出：

col1 objectcol2 objectdtype: object-----------col1 objectcol2  int32dtype: object-----------col1  objectcol2 float64dtype: object

astype()也能一次改變所有數據的類型：

In[30]:aOut[31]:    a   b   c   d0 0.891380 0.442167 -0.539450 1.0234581 -0.488131 -1.847104 -0.209799 -0.7687132 1.290434 0.327096 0.358406 0.422209In[32]:a.astype('int32')Out[32]:  a b c d0 0 0 0 11 0 -1 0 02 1 0 0 0

附：data type list

Data type Descriptionbool_ Boolean (True or False) stored as a byteint_ Default integer type (same as C long; normally either int64 or int32)intc Identical to C int (normally int32 or int64)intp Integer used for indexing (same as C ssize_t; normally either int32 or int64)int8 Byte (-128 to 127)int16 Integer (-32768 to 32767)int32 Integer (-2147483648 to 2147483647)int64 Integer (-9223372036854775808 to 9223372036854775807)uint8 Unsigned integer (0 to 255)uint16 Unsigned integer (0 to 65535)uint32 Unsigned integer (0 to 4294967295)uint64 Unsigned integer (0 to 18446744073709551615)float_ Shorthand for float64.float16 Half precision float: sign bit, 5 bits exponent, 10 bits mantissafloat32 Single precision float: sign bit, 8 bits exponent, 23 bits mantissafloat64 Double precision float: sign bit, 11 bits exponent, 52 bits mantissacomplex_ Shorthand for complex128.complex64 Complex number, represented by two 32-bit floats (real and imaginary components)complex128 Complex number, represented by two 64-bit floats (real and imaginary components)

以上這篇基于DataFrame改變列類型的方法就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持武林站長站。