原始的csv文件数据是这样的:
06/04/2011,104.64,105.17
07/04/2011,104.98,105.51
08/04/2011,105.43,105.96
11/04/2011,104.47,104.99
如何将csv文件读入DataFrame并添加多行索引级别,或者将多行索引添加到csv并导入到DataFrame中,如下所示:
JAS
date bid ask
06/04/2011 104.64 105.17
07/04/2011 104.98 105.51
08/04/2011 105.43 105.96
11/04/2011 104.47 104.99
答案 0 :(得分:4)
读取CSV,将第一(第0)列设置为索引。
In [8]: df = pd.read_csv(StringIO("""06/04/2011,104.64,105.17
07/04/2011,104.98,105.51
08/04/2011,105.43,105.96
11/04/2011,104.47,104.99"""), index_col=0, header=None)
创建一个新的MultiIndex,并将其分配给列。
In [11]: df.columns = pd.MultiIndex.from_tuples([('JAS', 'bid'), ('JAS', 'ask')])
最后,为索引命名,我们得到您想要的结果。
In [12]: df.index.name = 'date'
In [13]: df
Out[13]:
JAS
bid ask
date
06/04/2011 104.64 105.17
07/04/2011 104.98 105.51
08/04/2011 105.43 105.96
11/04/2011 104.47 104.99
答案 1 :(得分:0)
简短回答:
df = pd.read_csv('file.csv', parse_dates=True, index_col=0, header=None).rename_axis(
index='date').rename(columns={1: 'bid', 2: 'ask'}).reindex(
columns=pd.MultiIndex.from_product([['JAS'], ['bid', 'ask']]), level=1)
Out[1]:
JAS
bid ask
date
2011-06-04 104.64 105.17
2011-07-04 104.98 105.51
2011-08-04 105.43 105.96
2011-11-04 104.47 104.99