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df = pd.DataFrame({'A': ['A0'] * 5 + ['A1']*5 + ['A2']*5,
'B': ['B0','B0','B1','B1','B2'] * 3,
'DATE': ["2013-06-11",
"2013-07-02",
"2013-07-09",
"2013-07-30",
"2013-08-06",
"2013-06-11",
"2013-07-02",
"2013-07-09",
"2013-07-30",
"2013-08-06",
"2013-09-03",
"2013-10-01",
"2013-07-09",
"2013-08-06",
"2013-09-03"],
'VALUES': [22, 35, 14, 9, 4, 40, 18, 4, 2, 5, 1, 2, 3,4, 2]})
df.DATE = df['DATE'].apply(lambda x: pd.to_datetime(x))
df1 = df.set_index(['A', 'B', 'DATE'])
df1 = df1.sortlevel()
df2 = df.set_index('DATE')
# A1 - Works - Get all values under "A0" and "A1"
df1.loc[(slice('A1')),:]
# A2 - Works - Get all values from the start to "A2"
df1.loc[(slice('A2')),:]
# A3 - Works - Get all values under "B1" or "B2"
df1.loc[(slice(None),slice('B1','B2')),:]
# A4 - Works - Get all values between 2013-07-02 and 2013-07-09
df1.loc[(slice(None),slice(None),slice('20130702','20130709')),:]
##############################################
# These do not work and I'm wondering why... #
##############################################
# B1 - Does not work - Get all values in B0 that are also under A0, A1 and A2
df1.loc[(slice('A2'),slice('B0')),:]
# B2 - Does not work - Get all values in B0, B1 and B2 (similar to what #2 is doing for the As)
df1.loc[(slice(None),slice('B2')),:]
# B3 - Does not work - Get all values from B1 to B2 and up to 2013-08-06
df1.loc[(slice(None),slice('B1','B2'),slice('2013-08-06')),:]
# B4 - Does not work - Same as A4 but the start of the date slice is not a key.
# Would have thought the behavior would be similar to something like df2['20130701':]
# In other words, date indexing allowed starting on non-key points
df1.loc[(slice(None),slice(None),slice('20130701','20130709')),:]
The text was updated successfully, but these errors were encountered:
from SO
The text was updated successfully, but these errors were encountered: