<p>另一个可能的解决办法是使用<a href="https://docs.python.org/3/library/io.html#io.StringIO" rel="nofollow noreferrer">^{<cd1>}</a>和<a href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html" rel="nofollow noreferrer">^{<cd2>}</a>。仅使用^{cd4>}字符<a href="https://docs.python.org/2/library/stdtypes.html#str.join" rel="nofollow noreferrer">^{<cd3>}</a>数组中的每个元素:</p>
<pre><code>from io import StringIO
# Setup
a = np.array([['1;"Female";133;132;124;"118";"64.5";816932'],
['2;"Male";140;150;124;".";"72.5";1001121'],
['3;"Male";139;123;150;"143";"73.3";1038437'],
['4;"Male";133;129;128;"172";"68.8";965353'],
['5;"Female";137;132;134;"147";"65.0";951545'],
['6;"Female";99;90;110;"146";"69.0";928799'],
['7;"Female";138;136;131;"138";"64.5";991305']])
columns = ["Gender", "FSIQ", "VIQ", "PIQ", "Weight", "Height", "MRI_Count"]
df = pd.read_csv(StringIO('\n'.join(a.ravel())), header=None,
sep=';', names=columns, na_values=['.'])
</code></pre>
<p>[外]</p>
<pre><code> Gender FSIQ VIQ PIQ Weight Height MRI_Count
1 Female 133 132 124 118.0 64.5 816932
2 Male 140 150 124 NaN 72.5 1001121
3 Male 139 123 150 143.0 73.3 1038437
4 Male 133 129 128 172.0 68.8 965353
5 Female 137 132 134 147.0 65.0 951545
6 Female 99 90 110 146.0 69.0 928799
7 Female 138 136 131 138.0 64.5 991305
</code></pre>
<hr/>
<p><code>pandas</code>应该能很好地解释数据类型</p>
<pre><code>print(df.info())
<class 'pandas.core.frame.DataFrame'>
Int64Index: 7 entries, 1 to 7
Data columns (total 7 columns):
# Column Non-Null Count Dtype
- -
0 Gender 7 non-null object
1 FSIQ 7 non-null int64
2 VIQ 7 non-null int64
3 PIQ 7 non-null int64
4 Weight 6 non-null float64
5 Height 7 non-null float64
6 MRI_Count 7 non-null int64
dtypes: float64(2), int64(4), object(1)
memory usage: 448.0+ bytes
</code></pre>