
import pandas


import seaborn as sns

tips = sns.load_dataset("tips")
tips


tips.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 244 entries, 0 to 243
Data columns (total 7 columns):
 #   Column      Non-Null Count  Dtype   
---  ------      --------------  -----   
 0   total_bill  244 non-null    float64 
 1   tip         244 non-null    float64 
 2   sex         244 non-null    category
 3   smoker      244 non-null    category
 4   day         244 non-null    category
 5   time        244 non-null    category
 6   size        244 non-null    int64   
dtypes: category(4), float64(2), int64(1)
memory usage: 7.4 KB


tips["sex"].unique()

['Female', 'Male']
Categories (2, object): ['Male', 'Female']


tips["day"].unique()

['Sun', 'Sat', 'Thur', 'Fri']
Categories (4, object): ['Thur', 'Fri', 'Sat', 'Sun']


tips["time"].unique()

['Dinner', 'Lunch']
Categories (2, object): ['Lunch', 'Dinner']


tips["tip"].mean()

2.9982786885245902


tips["tip"].max()

10.0


tips[tips["smoker"]== "Yes"]


tips.sort_values(by="total_bill", ascending=False)


grouped = tips.groupby("sex")
grouped

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7ff30a2a3ee0>


grouped.get_group("Male")


for k, df in grouped:
    print(k)
    display(df.head(3))
    print("\n")

Male


Female


grouped.count()


grouped.sum()


grouped["total_bill"].max()

sex
Male      50.81
Female    44.30
Name: total_bill, dtype: float64


grouped[["total_bill", "tip"]].mean()


def large_amounts(values, min_value=5):
    n = 0
    for t in values:
        if t >= min_value:
            n += 1
    return n

grouped[["tip", "total_bill"]].agg(large_amounts)


grouped[["tip", "total_bill"]].agg({"tip": lambda x: large_amounts(x, min_value=3),
                                    "total_bill": lambda x: large_amounts(x, min_value=15)
    
})


def large_tips(df):
    df = df.sort_values(by="tip", ascending=False)
    return df.head(3)

big_tips = grouped.apply(large_tips)
big_tips


tips


means = tips.groupby(by=["day", "time"]).mean()
means


tips[(tips["day"]=="Sat") & (tips["time"] == "Lunch") ]


means.index

MultiIndex([('Thur',  'Lunch'),
            ('Thur', 'Dinner'),
            ( 'Fri',  'Lunch'),
            ( 'Fri', 'Dinner'),
            ( 'Sat',  'Lunch'),
            ( 'Sat', 'Dinner'),
            ( 'Sun',  'Lunch'),
            ( 'Sun', 'Dinner')],
           names=['day', 'time'])


means.loc[('Thur', 'Lunch')]

total_bill    17.664754
tip            2.767705
size           2.459016
Name: (Thur, Lunch), dtype: float64


flights = sns.load_dataset("flights")
flights


flights = flights.set_index("year")
flights


flights.loc["1950"]


flights = flights.reset_index()
flights


flights = flights.set_index(["year", "month"])
flights


flights.index

MultiIndex([(1949, 'Jan'),
            (1949, 'Feb'),
            (1949, 'Mar'),
            (1949, 'Apr'),
            (1949, 'May'),
            (1949, 'Jun'),
            (1949, 'Jul'),
            (1949, 'Aug'),
            (1949, 'Sep'),
            (1949, 'Oct'),
            ...
            (1960, 'Mar'),
            (1960, 'Apr'),
            (1960, 'May'),
            (1960, 'Jun'),
            (1960, 'Jul'),
            (1960, 'Aug'),
            (1960, 'Sep'),
            (1960, 'Oct'),
            (1960, 'Nov'),
            (1960, 'Dec')],
           names=['year', 'month'], length=144)


flights.loc[(1960, 'Mar')]

passengers    419
Name: (1960, Mar), dtype: int64


flights = flights.unstack()
flights


flights["Jan"]

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3360             try:
-> 3361                 return self._engine.get_loc(casted_key)
   3362             except KeyError as err:

~/opt/anaconda3/lib/python3.8/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

~/opt/anaconda3/lib/python3.8/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'Jan'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
/var/folders/vd/9gpvwb493r52y4sgtl_fvtvm0000gn/T/ipykernel_12352/3691583124.py in <module>
----> 1 flights["Jan"]

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py in __getitem__(self, key)
   3455         if is_single_key:
   3456             if self.columns.nlevels > 1:
-> 3457                 return self._getitem_multilevel(key)
   3458             indexer = self.columns.get_loc(key)
   3459             if is_integer(indexer):

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py in _getitem_multilevel(self, key)
   3506     def _getitem_multilevel(self, key):
   3507         # self.columns is a MultiIndex
-> 3508         loc = self.columns.get_loc(key)
   3509         if isinstance(loc, (slice, np.ndarray)):
   3510             new_columns = self.columns[loc]

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/multi.py in get_loc(self, key, method)
   2920 
   2921         if not isinstance(key, tuple):
-> 2922             loc = self._get_level_indexer(key, level=0)
   2923             return _maybe_to_slice(loc)
   2924 

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/multi.py in _get_level_indexer(self, key, level, indexer)
   3202         else:
   3203 
-> 3204             idx = self._get_loc_single_level_index(level_index, key)
   3205 
   3206             if level > 0 or self._lexsort_depth == 0:

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/multi.py in _get_loc_single_level_index(self, level_index, key)
   2853             return -1
   2854         else:
-> 2855             return level_index.get_loc(key)
   2856 
   2857     def get_loc(self, key, method=None):

~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3361                 return self._engine.get_loc(casted_key)
   3362             except KeyError as err:
-> 3363                 raise KeyError(key) from err
   3364 
   3365         if is_scalar(key) and isna(key) and not self.hasnans:

KeyError: 'Jan'


flights.columns

MultiIndex([('passengers', 'Jan'),
            ('passengers', 'Feb'),
            ('passengers', 'Mar'),
            ('passengers', 'Apr'),
            ('passengers', 'May'),
            ('passengers', 'Jun'),
            ('passengers', 'Jul'),
            ('passengers', 'Aug'),
            ('passengers', 'Sep'),
            ('passengers', 'Oct'),
            ('passengers', 'Nov'),
            ('passengers', 'Dec')],
           names=[None, 'month'])


flights[('passengers', 'Jan')]

year
1949    112
1950    115
1951    145
1952    171
1953    196
1954    204
1955    242
1956    284
1957    315
1958    340
1959    360
1960    417
Name: (passengers, Jan), dtype: int64


flights = flights.droplevel(0, axis=1)
flights


flights.columns

CategoricalIndex(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug',
                  'Sep', 'Oct', 'Nov', 'Dec'],
                 categories=['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', ...], ordered=False, dtype='category', name='month')


flights["Jan"]

year
1949    112
1950    115
1951    145
1952    171
1953    196
1954    204
1955    242
1956    284
1957    315
1958    340
1959    360
1960    417
Name: Jan, dtype: int64


flights


flights = flights.stack()
flights

year  month
1949  Jan      112
      Feb      118
      Mar      132
      Apr      129
      May      121
              ... 
1960  Aug      606
      Sep      508
      Oct      461
      Nov      390
      Dec      432
Length: 144, dtype: int64

	total_bill	tip	smoker	day	time	size
sex
Male	157	157	157	157	157	157
Female	87	87	87	87	87	87

	passengers
month	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
year
1949	112	118	132	129	121	135	148	148	136	119	104	118
1950	115	126	141	135	125	149	170	170	158	133	114	140
1951	145	150	178	163	172	178	199	199	184	162	146	166
1952	171	180	193	181	183	218	230	242	209	191	172	194
1953	196	196	236	235	229	243	264	272	237	211	180	201
1954	204	188	235	227	234	264	302	293	259	229	203	229
1955	242	233	267	269	270	315	364	347	312	274	237	278
1956	284	277	317	313	318	374	413	405	355	306	271	306
1957	315	301	356	348	355	422	465	467	404	347	305	336
1958	340	318	362	348	363	435	491	505	404	359	310	337
1959	360	342	406	396	420	472	548	559	463	407	362	405
1960	417	391	419	461	472	535	622	606	508	461	390	432

month	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
year
1949	112	118	132	129	121	135	148	148	136	119	104	118
1950	115	126	141	135	125	149	170	170	158	133	114	140
1951	145	150	178	163	172	178	199	199	184	162	146	166
1952	171	180	193	181	183	218	230	242	209	191	172	194
1953	196	196	236	235	229	243	264	272	237	211	180	201
1954	204	188	235	227	234	264	302	293	259	229	203	229
1955	242	233	267	269	270	315	364	347	312	274	237	278
1956	284	277	317	313	318	374	413	405	355	306	271	306
1957	315	301	356	348	355	422	465	467	404	347	305	336
1958	340	318	362	348	363	435	491	505	404	359	310	337
1959	360	342	406	396	420	472	548	559	463	407	362	405
1960	417	391	419	461	472	535	622	606	508	461	390	432

month	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
year
1949	112	118	132	129	121	135	148	148	136	119	104	118
1950	115	126	141	135	125	149	170	170	158	133	114	140
1951	145	150	178	163	172	178	199	199	184	162	146	166
1952	171	180	193	181	183	218	230	242	209	191	172	194
1953	196	196	236	235	229	243	264	272	237	211	180	201
1954	204	188	235	227	234	264	302	293	259	229	203	229
1955	242	233	267	269	270	315	364	347	312	274	237	278
1956	284	277	317	313	318	374	413	405	355	306	271	306
1957	315	301	356	348	355	422	465	467	404	347	305	336
1958	340	318	362	348	363	435	491	505	404	359	310	337
1959	360	342	406	396	420	472	548	559	463	407	362	405
1960	417	391	419	461	472	535	622	606	508	461	390	432

Week 5¶

Groupby¶

Aggregations¶

groupby.agg¶

groupby.apply¶

Gouping by multiple columns¶

Reindexing¶

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

	total_bill	tip	sex	smoker	day	time	size
56	38.01	3.00	Male	Yes	Sat	Dinner	4
58	11.24	1.76	Male	Yes	Sat	Dinner	2
60	20.29	3.21	Male	Yes	Sat	Dinner	2
61	13.81	2.00	Male	Yes	Sat	Dinner	2
62	11.02	1.98	Male	Yes	Sat	Dinner	2
...	...	...	...	...	...	...	...
234	15.53	3.00	Male	Yes	Sat	Dinner	2
236	12.60	1.00	Male	Yes	Sat	Dinner	2
237	32.83	1.17	Male	Yes	Sat	Dinner	2
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2

	total_bill	tip	sex	smoker	day	time	size
170	50.81	10.00	Male	Yes	Sat	Dinner	3
212	48.33	9.00	Male	No	Sat	Dinner	4
59	48.27	6.73	Male	No	Sat	Dinner	4
156	48.17	5.00	Male	No	Sun	Dinner	6
182	45.35	3.50	Male	Yes	Sun	Dinner	3
...	...	...	...	...	...	...	...
149	7.51	2.00	Male	No	Thur	Lunch	2
111	7.25	1.00	Female	No	Sat	Dinner	1
172	7.25	5.15	Male	Yes	Sun	Dinner	2
92	5.75	1.00	Female	Yes	Fri	Dinner	2
67	3.07	1.00	Female	Yes	Sat	Dinner	1