python - delete a row from dataframe when the index (DateTime) is Sunday -


sample data

                              open     high      low    close datetime                                                      2016-01-03 00:00:00+00:00  1.08701  1.08723  1.08451  1.08515 2016-01-04 00:00:00+00:00  1.08701  1.09464  1.07811  1.08239 2016-01-05 00:00:00+00:00  1.08238  1.08388  1.07106  1.07502 2016-01-06 00:00:00+00:00  1.07504  1.07994  1.07185  1.07766 2016-01-07 00:00:00+00:00  1.07767  1.09401  1.07710  1.09256 2016-01-08 00:00:00+00:00  1.09255  1.09300  1.08030  1.09218

datetime index, need delete row has datetime sunday or saturday (2016-01-03).

i reading data cvs file 

df = pd.read_csv(filename, names=['datetime','open','high','low','close'],                  parse_dates = [0], index_col = 'datetime')

tried below did not work.

df = df.drop(df[df.weekday() == 6].index) #delete sundays

you use asfreq('b') reindex df rows business days. note, however, if business day missing df.index, asfreq return dataframe row of nans indicate missing row. note df.index must datetimeindex.

in [106]: df.asfreq('b') out[106]:                 open     high      low    close 2016-01-04  1.08701  1.09464  1.07811  1.08239 2016-01-05  1.08238  1.08388  1.07106  1.07502 2016-01-06  1.07504  1.07994  1.07185  1.07766 2016-01-07  1.07767  1.09401  1.07710  1.09256 2016-01-08  1.09255  1.09300  1.08030  1.09218

here setup used produce result above:

import pandas pd df = pd.dataframe(     {'close': [1.0851500000000001, 1.08239, 1.0750200000000001, 1.0776600000000001, 1.09256, 1.0921799999999999], 'datetime': ['2016-01-03 00:00:00+00:00', '2016-01-04 00:00:00+00:00', '2016-01-05 00:00:00+00:00', '2016-01-06 00:00:00+00:00', '2016-01-07 00:00:00+00:00', '2016-01-08 00:00:00+00:00'], 'high': [1.0872299999999999, 1.0946400000000001, 1.08388, 1.0799399999999999, 1.0940099999999999, 1.093], 'low': [1.0845100000000001, 1.0781100000000001, 1.0710600000000001, 1.07185, 1.0770999999999999, 1.0803], 'open': [1.08701, 1.08701, 1.0823799999999999, 1.07504, 1.0776700000000001, 1.0925499999999999]}) df['datetime'] = pd.to_datetime(df['datetime']) df = df.set_index('datetime') print(df.asfreq('b'))

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