擅长:python、mysql、java
<p>使用<code>pandas</code>,正如您提到的<code>tsv</code>,希望它能有所帮助:</p>
<pre><code>df1=pd.read_csv('filepath/stock1',sep='\t')
df
Out[31]:
0 1
0 23 july 2009 10.03
1 24 july 2009 10.07
2 25 july 2009 NaN
</code></pre>
<p>与其他两个文件类似,如下所示:</p>
<pre><code>df2=pd.read_csv('filepath/stock2',sep='\t')
df2
Out[42]:
0 1
0 23 july 2009 NaN
1 24 july 2009 3.07
2 25 july 2009 3.10
df3=pd.read_csv('filepath/stock3',sep='\t')
df3
Out[43]:
0 1
0 23 july 2009 5.4
1 24 july 2009 NaN
2 25 july 2009 5.1
</code></pre>
<p>然后使用熊猫<a href="http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html" rel="nofollow">merge</a>:</p>
<pre><code>In[56]:df4=df1.merge(df2,on=0,how='left').merge(df3,on=0,how='left').rename(columns={0:'Date','1_x':'Stock1','1_y':'Stock2',1:'Stock3'}).fillna('No value')
df4
Out[57]:
Date Stock1 Stock2 Stock3
0 23 july 2009 10.03 No value 5.4
1 24 july 2009 10.07 3.07 No value
2 25 july 2009 No value 3.1 5.1
</code></pre>