Re: How to read csv file into pandas DataFrame with multiple row index level?

[Bob]

On Saturday, April 20, 2013 9:15:15 AM UTC-6, Terence wrote:

The original csv file data is like that:

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

How to either read the csv file into DataFrame and add multiple row index level, or add multiple row index into csv and import into DataFrame as following:

                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

Given that no one has responded there must not be an obvious solution to your question. Maybe you could rephrase your question by explaining why you would need mutli-row index level? How would you use this?

Seems to me that multi-row index would be handled by a python variable name not a DataFrame:

data_JAS = pandas.read_csv('JAS.csv', index_col=0, names=['bid', 'ask'], parse_dates=True)

>>> data_JAS

Out[3]: 

               bid     ask

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

[Terence Ng]

It should be:

df = pd.read_csv('JAS.csv', index_col=0, header=None, dayfirst=True, parse_dates=True)

df.columns = pd.MultiIndex.from_tuples([('JAS','bid'),('JAS','ask')])

df.index.name = 'date'