How to aggregate one column by another column in Pandas? [duplicate]
I have a data frame with a hierarchical index in axis 1 (columns) (from a groupby.agg operation):
USAF WBAN year month day s_PC s_CL s_CD s_CNT tempf
sum sum sum sum amax amin
0 702730 26451 1993 1 1 1 0 12 13 30.92 24.98
1 702730 26451 1993 1 2 0 0 13 13 32.00 24.98
2 702730 26451 1993 1 3 1 10 2 13 23.00 6.98
3 702730 26451 1993 1 4 1 0 12 13 10.04 3.92
4 702730 26451 1993 1 5 3 0 10 13 19.94 10.94
I want to flatten it, so that it looks like this (names aren't critical - I could rename):
USAF WBAN year month day s_PC s_CL s_CD s_CNT tempf_amax tmpf_amin
0 702730 26451 1993 1 1 1 0 12 13 30.92 24.98
1 702730 26451 1993 1 2 0 0 13 13 32.00 24.98
2 702730 26451 1993 1 3 1 10 2 13 23.00 6.98
3 702730 26451 1993 1 4 1 0 12 13 10.04 3.92
4 702730 26451 1993 1 5 3 0 10 13 19.94 10.94
How do I do this? (I've tried a lot, to no avail.)
Per a suggestion, here is the head in dict form
{('USAF', ''): {0: '702730',
1: '702730',
2: '702730',
3: '702730',
4: '702730'},
('WBAN', ''): {0: '26451', 1: '26451', 2: '26451', 3: '26451', 4: '26451'},
('day', ''): {0: 1, 1: 2, 2: 3, 3: 4, 4: 5},
('month', ''): {0: 1, 1: 1, 2: 1, 3: 1, 4: 1},
('s_CD', 'sum'): {0: 12.0, 1: 13.0, 2: 2.0, 3: 12.0, 4: 10.0},
('s_CL', 'sum'): {0: 0.0, 1: 0.0, 2: 10.0, 3: 0.0, 4: 0.0},
('s_CNT', 'sum'): {0: 13.0, 1: 13.0, 2: 13.0, 3: 13.0, 4: 13.0},
('s_PC', 'sum'): {0: 1.0, 1: 0.0, 2: 1.0, 3: 1.0, 4: 3.0},
('tempf', 'amax'): {0: 30.920000000000002,
1: 32.0,
2: 23.0,
3: 10.039999999999999,
4: 19.939999999999998},
('tempf', 'amin'): {0: 24.98,
1: 24.98,
2: 6.9799999999999969,
3: 3.9199999999999982,
4: 10.940000000000001},
('year', ''): {0: 1993, 1: 1993, 2: 1993, 3: 1993, 4: 1993}}
I think the easiest way to do this would be to set the columns to the top level:
df.columns = df.columns.get_level_values(0)
Note: if the to level has a name you can also access it by this, rather than 0.
.
If you want to combine/join your MultiIndex into one Index (assuming you have just string entries in your columns) you could:
df.columns = [' '.join(col).strip() for col in df.columns.values]
Note: we must strip the whitespace for when there is no second index.
In [11]: [' '.join(col).strip() for col in df.columns.values]
Out[11]:
['USAF',
'WBAN',
'day',
'month',
's_CD sum',
's_CL sum',
's_CNT sum',
's_PC sum',
'tempf amax',
'tempf amin',
'year']
All of the current answers on this thread must have been a bit dated. As of pandas version 0.24.0, the .to_flat_index() does what you need.
From panda's own documentation:
MultiIndex.to_flat_index()
Convert a MultiIndex to an Index of Tuples containing the level values.
A simple example from its documentation:
import pandas as pd
print(pd.__version__) # '0.23.4'
index = pd.MultiIndex.from_product(
[['foo', 'bar'], ['baz', 'qux']],
names=['a', 'b'])
print(index)
# MultiIndex(levels=[['bar', 'foo'], ['baz', 'qux']],
# codes=[[1, 1, 0, 0], [0, 1, 0, 1]],
# names=['a', 'b'])
Applying to_flat_index():
index.to_flat_index()
# Index([('foo', 'baz'), ('foo', 'qux'), ('bar', 'baz'), ('bar', 'qux')], dtype='object')
Using it to replace existing pandas column
An example of how you'd use it on dat, which is a DataFrame with a MultiIndex column:
dat = df.loc[:,['name','workshop_period','class_size']].groupby(['name','workshop_period']).describe()
print(dat.columns)
# MultiIndex(levels=[['class_size'], ['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max']],
# codes=[[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5, 6, 7]])
dat.columns = dat.columns.to_flat_index()
print(dat.columns)
# Index([('class_size', 'count'), ('class_size', 'mean'),
# ('class_size', 'std'), ('class_size', 'min'),
# ('class_size', '25%'), ('class_size', '50%'),
# ('class_size', '75%'), ('class_size', 'max')],
# dtype='object')
Flattening and Renaming in-place
May be worth noting how you can combine that with a simple list comprehension (thanks @Skippy and @mmann1123) to join the elements so your resulting column names are simple strings separated by, for example, underscores:
dat.columns = ["_".join(a) for a in dat.columns.to_flat_index()]
pd.DataFrame(df.to_records()) # multiindex become columns and new index is integers only
Andy Hayden's answer is certainly the easiest way -- if you want to avoid duplicate column labels you need to tweak a bit
In [34]: df
Out[34]:
USAF WBAN day month s_CD s_CL s_CNT s_PC tempf year
sum sum sum sum amax amin
0 702730 26451 1 1 12 0 13 1 30.92 24.98 1993
1 702730 26451 2 1 13 0 13 0 32.00 24.98 1993
2 702730 26451 3 1 2 10 13 1 23.00 6.98 1993
3 702730 26451 4 1 12 0 13 1 10.04 3.92 1993
4 702730 26451 5 1 10 0 13 3 19.94 10.94 1993
In [35]: mi = df.columns
In [36]: mi
Out[36]:
MultiIndex
[(USAF, ), (WBAN, ), (day, ), (month, ), (s_CD, sum), (s_CL, sum), (s_CNT, sum), (s_PC, sum), (tempf, amax), (tempf, amin), (year, )]
In [37]: mi.tolist()
Out[37]:
[('USAF', ''),
('WBAN', ''),
('day', ''),
('month', ''),
('s_CD', 'sum'),
('s_CL', 'sum'),
('s_CNT', 'sum'),
('s_PC', 'sum'),
('tempf', 'amax'),
('tempf', 'amin'),
('year', '')]
In [38]: ind = pd.Index([e[0] + e[1] for e in mi.tolist()])
In [39]: ind
Out[39]: Index([USAF, WBAN, day, month, s_CDsum, s_CLsum, s_CNTsum, s_PCsum, tempfamax, tempfamin, year], dtype=object)
In [40]: df.columns = ind
In [46]: df
Out[46]:
USAF WBAN day month s_CDsum s_CLsum s_CNTsum s_PCsum tempfamax tempfamin \
0 702730 26451 1 1 12 0 13 1 30.92 24.98
1 702730 26451 2 1 13 0 13 0 32.00 24.98
2 702730 26451 3 1 2 10 13 1 23.00 6.98
3 702730 26451 4 1 12 0 13 1 10.04 3.92
4 702730 26451 5 1 10 0 13 3 19.94 10.94
year
0 1993
1 1993
2 1993
3 1993
4 1993