<p>我认为可以将参数<code>thousands</code>添加到<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html" rel="nofollow">^{<cd2>}</a>,然后<code>Total Apples</code>列和<code>Good Apples</code>列中的值转换为<code>integers</code>:</p>
<p>也许你的<code>separator</code>与众不同,别忘了改变它。如果分隔符是空白,则将其更改为<code>sep='\s+'</code>。</p>
<pre><code>import pandas as pd
import io
temp=u"""Farm_Name;Total Apples;Good Apples
EM;18,327;14,176
EE;18,785;14,146
IW;635;486
L;33,929;24,586
NE;12,497;9,609
NW;30,756;23,765
SC;8,515;6,438
SE;22,896;17,914
SW;11,972;9,114
WM;27,251;20,931
Y;21,495;16,662"""
#after testing replace io.StringIO(temp) to filename
df = pd.read_csv(io.StringIO(temp), sep=";",thousands=',')
print df
Farm_Name Total Apples Good Apples
0 EM 18327 14176
1 EE 18785 14146
2 IW 635 486
3 L 33929 24586
4 NE 12497 9609
5 NW 30756 23765
6 SC 8515 6438
7 SE 22896 17914
8 SW 11972 9114
9 WM 27251 20931
10 Y 21495 16662
</code></pre>
<pre><code>print df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11 entries, 0 to 10
Data columns (total 3 columns):
Farm_Name 11 non-null object
Total Apples 11 non-null int64
Good Apples 11 non-null int64
dtypes: int64(2), object(1)
memory usage: 336.0+ bytes
None
</code></pre>