擅长:python、mysql、java
<p>我认为您最好的办法是首先使用numpy作为记录数组读取数据。</p>
<pre><code># what you described:
In [15]: import numpy as np
In [16]: import pandas
In [17]: x = pandas.read_csv('weird.csv')
In [19]: x.dtypes
Out[19]:
int_field int64
floatlike_field float64 # what you don't want?
str_field object
In [20]: datatypes = [('int_field','i4'),('floatlike','S10'),('strfield','S10')]
In [21]: y_np = np.loadtxt('weird.csv', dtype=datatypes, delimiter=',', skiprows=1)
In [22]: y_np
Out[22]:
array([(1, '2.31', 'one'), (2, '3.12', 'two'), (3, '1.32', 'three ')],
dtype=[('int_field', '<i4'), ('floatlike', '|S10'), ('strfield', '|S10')])
In [23]: y_pandas = pandas.DataFrame.from_records(y_np)
In [25]: y_pandas.dtypes
Out[25]:
int_field int64
floatlike object # better?
strfield object
</code></pre>