今天用numpy 的linalg.det()求矩阵的逆的过程中出现了一个错误：
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TypeError: No loop matching the specified signature and casting was found for ufunc det
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查了半天发现是数据类型的问题，numpy在算逆的时候会先检查一下数据类型是否一致，若不一致就会报错（话说这个错误提示信息也太难理解了，还得看源码o(╯□╰)o）。
由于我的数据是用pandas.DataFrame读取的，所以每一列的数据类型有可能不同。
回头检查一下数据，果然有的是int，有的是float。所以全部改为float64类型。
找到了如下的方法，以及DataFrame数据类型：
DataFrame 类型转换方法—astype()
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import pandas as pd
df = pd.DataFrame([{'col1':'a', 'col2':'1'}, {'col1':'b', 'col2':'2'}])
print df.dtypes
df['col2'] = df['col2'].astype('int')
print '-----------'
print df.dtypes
df['col2'] = df['col2'].astype('float64')
print '-----------'
print df.dtypes
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输出：
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col1 object
col2 object
dtype: object
-----------
col1 object
col2 int32
dtype: object
-----------
col1 object
col2 float64
dtype: object
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astype()也能一次改变所有数据的类型：
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In[30]:a
Out[31]:
a b c d
0 0.891380 0.442167 -0.539450 1.023458
1 -0.488131 -1.847104 -0.209799 -0.768713
2 1.290434 0.327096 0.358406 0.422209
In[32]:a.astype('int32')
Out[32]:
a b c d
0 0 0 0 1
1 0 -1 0 0
2 1 0 0 0
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附：data type list
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Data type Description
bool_ Boolean (True or False) stored as a byte
int_ 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 mantissa
float32 Single precision float: sign bit, 8 bits exponent, 23 bits mantissa
float64 Double precision float: sign bit, 11 bits exponent, 52 bits mantissa
complex_ 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)
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以上这篇基于DataFrame改变列类型的方法就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持服务器之家。
原文链接：https://blog.csdn.net/wenshen1927/article/details/76889546