Transforming I

R for Data Science by Wickham & Grolemund

Author

Sungkyun Cho

Published

February 11, 2023

Load Packages

# numerical calculation & data frames
import numpy as np
import pandas as pd

# visualization
import matplotlib.pyplot as plt
import seaborn as sns
import seaborn.objects as so

# statistics
import statsmodels.api as sm

Options

# pandas options
pd.set_option("mode.copy_on_write", True)
pd.options.display.precision = 2
pd.options.display.float_format = '{:.2f}'.format  # pd.reset_option('display.float_format')
pd.options.display.max_rows = 7

# Numpy options
np.set_printoptions(precision = 2, suppress=True)

이번 장에서는 시각화를 하기 전후로, 중요한 데이터 패턴을 보기 위해서 새로운 변수를 만들거나 요약한 통계치를 만들 필요가 있는데 이를 다루는 핵심적인 함수들에 대해 익힙니다.

대략 다음과 같은 transform들을 조합하여 분석에 필요한 상태로 바꿉니다.

변수들의 선택: subsetting
조건에 맞는 부분(관측치, 행)만 필터링: query()
조건에 맞도록 행을 재정렬: sort_values()
변수들과 함수들을 이용하여 새로운 변수를 생성: assign()
카테고리별로 나뉘어진 데이터에 대한 통계치를 생성: groupby(), agg(), apply()

On-time data for all flights that departed NYC (i.e. JFK, LGA or EWR) in 2013

# import the dataset
flights_data = sm.datasets.get_rdataset("flights", "nycflights13")
flights = flights_data.data
flights = flights.drop(columns="time_hour") # drop the "time_hour" column

# Description
print(flights_data.__doc__)

flights

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
0	2013	1	1	517.00	515	2.00	830.00	819	11.00	UA	1545	N14228	EWR	IAH	227.00	1400	5	15
1	2013	1	1	533.00	529	4.00	850.00	830	20.00	UA	1714	N24211	LGA	IAH	227.00	1416	5	29
2	2013	1	1	542.00	540	2.00	923.00	850	33.00	AA	1141	N619AA	JFK	MIA	160.00	1089	5	40
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336773	2013	9	30	NaN	1210	NaN	NaN	1330	NaN	MQ	3461	N535MQ	LGA	BNA	NaN	764	12	10
336774	2013	9	30	NaN	1159	NaN	NaN	1344	NaN	MQ	3572	N511MQ	LGA	CLE	NaN	419	11	59
336775	2013	9	30	NaN	840	NaN	NaN	1020	NaN	MQ	3531	N839MQ	LGA	RDU	NaN	431	8	40

336776 rows × 18 columns

flights.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 336776 entries, 0 to 336775
Data columns (total 18 columns):
 #   Column          Non-Null Count   Dtype  
---  ------          --------------   -----  
 0   year            336776 non-null  int64  
 1   month           336776 non-null  int64  
 2   day             336776 non-null  int64  
 3   dep_time        328521 non-null  float64
 4   sched_dep_time  336776 non-null  int64  
 5   dep_delay       328521 non-null  float64
 6   arr_time        328063 non-null  float64
 7   sched_arr_time  336776 non-null  int64  
 8   arr_delay       327346 non-null  float64
 9   carrier         336776 non-null  object 
 10  flight          336776 non-null  int64  
 11  tailnum         334264 non-null  object 
 12  origin          336776 non-null  object 
 13  dest            336776 non-null  object 
 14  air_time        327346 non-null  float64
 15  distance        336776 non-null  int64  
 16  hour            336776 non-null  int64  
 17  minute          336776 non-null  int64  
dtypes: float64(5), int64(9), object(4)
memory usage: 46.2+ MB

Rows

`query()`

Conditional operators
>, >=, <, <=,
== (equal to), != (not equal to)
&, and (and)
|, or (or)
~, not (not)
in (includes), not in (not included)

# Flights that arrived more than 120 minutes (two hours) late
flights.query('arr_delay > 120')

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
119	2013	1	1	811.00	630	101.00	1047.00	830	137.00	MQ	4576	N531MQ	LGA	CLT	118.00	544	6	30
151	2013	1	1	848.00	1835	853.00	1001.00	1950	851.00	MQ	3944	N942MQ	JFK	BWI	41.00	184	18	35
218	2013	1	1	957.00	733	144.00	1056.00	853	123.00	UA	856	N534UA	EWR	BOS	37.00	200	7	33
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336724	2013	9	30	2053.00	1815	158.00	2310.00	2054	136.00	EV	5292	N600QX	EWR	ATL	91.00	746	18	15
336757	2013	9	30	2159.00	1845	194.00	2344.00	2030	194.00	9E	3320	N906XJ	JFK	BUF	50.00	301	18	45
336763	2013	9	30	2235.00	2001	154.00	59.00	2249	130.00	B6	1083	N804JB	JFK	MCO	123.00	944	20	1

10034 rows × 18 columns

Note

외부 변수/함수를 참조하려면 @와 함께

delay_cutoff = 120
flights.query('arr_delay > @delay_cutoff')

def cut_off(df):
    return df["dep_delay"].min()

flights.query('arr_delay < @cut_off(@flights)')

위의 query 방식의 filtering은 다음과 같은 boolean indexing의 결과와 같음
flights[flights["arr_delay"] > 120]

# Flights that departed on January 1
flights.query('month == 1 & day == 1')  # == 과 = 을 혼동하지 말것!

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
0	2013	1	1	517.00	515	2.00	830.00	819	11.00	UA	1545	N14228	EWR	IAH	227.00	1400	5	15
1	2013	1	1	533.00	529	4.00	850.00	830	20.00	UA	1714	N24211	LGA	IAH	227.00	1416	5	29
2	2013	1	1	542.00	540	2.00	923.00	850	33.00	AA	1141	N619AA	JFK	MIA	160.00	1089	5	40
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
839	2013	1	1	NaN	1935	NaN	NaN	2240	NaN	AA	791	N3EHAA	LGA	DFW	NaN	1389	19	35
840	2013	1	1	NaN	1500	NaN	NaN	1825	NaN	AA	1925	N3EVAA	LGA	MIA	NaN	1096	15	0
841	2013	1	1	NaN	600	NaN	NaN	901	NaN	B6	125	N618JB	JFK	FLL	NaN	1069	6	0

842 rows × 18 columns

# Flights that departed in January or February
flights.query('month == 1 | month == 2')

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
0	2013	1	1	517.00	515	2.00	830.00	819	11.00	UA	1545	N14228	EWR	IAH	227.00	1400	5	15
1	2013	1	1	533.00	529	4.00	850.00	830	20.00	UA	1714	N24211	LGA	IAH	227.00	1416	5	29
2	2013	1	1	542.00	540	2.00	923.00	850	33.00	AA	1141	N619AA	JFK	MIA	160.00	1089	5	40
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
136244	2013	2	28	NaN	1115	NaN	NaN	1310	NaN	MQ	4485	N725MQ	LGA	CMH	NaN	479	11	15
136245	2013	2	28	NaN	830	NaN	NaN	1205	NaN	UA	1480	NaN	EWR	SFO	NaN	2565	8	30
136246	2013	2	28	NaN	840	NaN	NaN	1147	NaN	UA	443	NaN	JFK	LAX	NaN	2475	8	40

51955 rows × 18 columns

# A shorter way to select flights that departed in January or February
flights.query('month in [1, 2]')

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
0	2013	1	1	517.00	515	2.00	830.00	819	11.00	UA	1545	N14228	EWR	IAH	227.00	1400	5	15
1	2013	1	1	533.00	529	4.00	850.00	830	20.00	UA	1714	N24211	LGA	IAH	227.00	1416	5	29
2	2013	1	1	542.00	540	2.00	923.00	850	33.00	AA	1141	N619AA	JFK	MIA	160.00	1089	5	40
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
136244	2013	2	28	NaN	1115	NaN	NaN	1310	NaN	MQ	4485	N725MQ	LGA	CMH	NaN	479	11	15
136245	2013	2	28	NaN	830	NaN	NaN	1205	NaN	UA	1480	NaN	EWR	SFO	NaN	2565	8	30
136246	2013	2	28	NaN	840	NaN	NaN	1147	NaN	UA	443	NaN	JFK	LAX	NaN	2475	8	40

51955 rows × 18 columns

flights.query('arr_delay > 120 & ~(origin == "JFK")')

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
119	2013	1	1	811.00	630	101.00	1047.00	830	137.00	MQ	4576	N531MQ	LGA	CLT	118.00	544	6	30
218	2013	1	1	957.00	733	144.00	1056.00	853	123.00	UA	856	N534UA	EWR	BOS	37.00	200	7	33
268	2013	1	1	1114.00	900	134.00	1447.00	1222	145.00	UA	1086	N76502	LGA	IAH	248.00	1416	9	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336529	2013	9	30	1738.00	1529	129.00	1906.00	1649	137.00	EV	4580	N12563	EWR	MKE	110.00	725	15	29
336668	2013	9	30	1951.00	1649	182.00	2157.00	1903	174.00	EV	4294	N13988	EWR	SAV	95.00	708	16	49
336724	2013	9	30	2053.00	1815	158.00	2310.00	2054	136.00	EV	5292	N600QX	EWR	ATL	91.00	746	18	15

6868 rows × 18 columns

flights.query('dep_time < sched_dep_time')

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
3	2013	1	1	544.00	545	-1.00	1004.00	1022	-18.00	B6	725	N804JB	JFK	BQN	183.00	1576	5	45
4	2013	1	1	554.00	600	-6.00	812.00	837	-25.00	DL	461	N668DN	LGA	ATL	116.00	762	6	0
5	2013	1	1	554.00	558	-4.00	740.00	728	12.00	UA	1696	N39463	EWR	ORD	150.00	719	5	58
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336766	2013	9	30	2240.00	2250	-10.00	2347.00	7	-20.00	B6	2002	N281JB	JFK	BUF	52.00	301	22	50
336767	2013	9	30	2241.00	2246	-5.00	2345.00	1	-16.00	B6	486	N346JB	JFK	ROC	47.00	264	22	46
336769	2013	9	30	2349.00	2359	-10.00	325.00	350	-25.00	B6	745	N516JB	JFK	PSE	196.00	1617	23	59

184782 rows × 18 columns

flights.query('arr_delay + dep_delay < 0')

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
3	2013	1	1	544.00	545	-1.00	1004.00	1022	-18.00	B6	725	N804JB	JFK	BQN	183.00	1576	5	45
4	2013	1	1	554.00	600	-6.00	812.00	837	-25.00	DL	461	N668DN	LGA	ATL	116.00	762	6	0
7	2013	1	1	557.00	600	-3.00	709.00	723	-14.00	EV	5708	N829AS	LGA	IAD	53.00	229	6	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336766	2013	9	30	2240.00	2250	-10.00	2347.00	7	-20.00	B6	2002	N281JB	JFK	BUF	52.00	301	22	50
336767	2013	9	30	2241.00	2246	-5.00	2345.00	1	-16.00	B6	486	N346JB	JFK	ROC	47.00	264	22	46
336769	2013	9	30	2349.00	2359	-10.00	325.00	350	-25.00	B6	745	N516JB	JFK	PSE	196.00	1617	23	59

188401 rows × 18 columns

flights.query('~dep_delay.isna() & arr_delay.isna()')

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
471	2013	1	1	1525.00	1530	-5.00	1934.00	1805	NaN	MQ	4525	N719MQ	LGA	XNA	NaN	1147	15	30
477	2013	1	1	1528.00	1459	29.00	2002.00	1647	NaN	EV	3806	N17108	EWR	STL	NaN	872	14	59
615	2013	1	1	1740.00	1745	-5.00	2158.00	2020	NaN	MQ	4413	N739MQ	LGA	XNA	NaN	1147	17	45
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
334495	2013	9	28	1214.00	1225	-11.00	1801.00	1510	NaN	AA	300	N488AA	EWR	DFW	NaN	1372	12	25
335534	2013	9	29	1734.00	1711	23.00	2159.00	2020	NaN	UA	327	N463UA	EWR	PDX	NaN	2434	17	11
335805	2013	9	30	559.00	600	-1.00	NaN	715	NaN	WN	464	N411WN	EWR	MDW	NaN	711	6	0

1175 rows × 18 columns

delay_time = 900
flights.query('arr_delay > @delay_time')

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
7072	2013	1	9	641.00	900	1301.00	1242.00	1530	1272.00	HA	51	N384HA	JFK	HNL	640.00	4983	9	0
8239	2013	1	10	1121.00	1635	1126.00	1239.00	1810	1109.00	MQ	3695	N517MQ	EWR	ORD	111.00	719	16	35
151974	2013	3	17	2321.00	810	911.00	135.00	1020	915.00	DL	2119	N927DA	LGA	MSP	167.00	1020	8	10
173992	2013	4	10	1100.00	1900	960.00	1342.00	2211	931.00	DL	2391	N959DL	JFK	TPA	139.00	1005	19	0
235778	2013	6	15	1432.00	1935	1137.00	1607.00	2120	1127.00	MQ	3535	N504MQ	JFK	CMH	74.00	483	19	35
270376	2013	7	22	845.00	1600	1005.00	1044.00	1815	989.00	MQ	3075	N665MQ	JFK	CVG	96.00	589	16	0
327043	2013	9	20	1139.00	1845	1014.00	1457.00	2210	1007.00	AA	177	N338AA	JFK	SFO	354.00	2586	18	45

Note

.query()의 결과는 view이므로 수정하려면 SettingWithCopyWarning; .copy() 후 사용 권장
Python에서 유효하지 않은 변수명의 경우, 예를 들어, 빈칸이나 .이 있는 경우; income total, income.total
Backtick으로 감싸줘야 함; `income total`, `income.total`, `class`

query() 조건의 참거짓에 상관없이 NA값은 모두 제외함

# df가 다음과 같을 때,
#      A     B
# 0 1.00  2.00
# 1  NaN  5.00
# 2 3.00  3.00
# 3 4.00   NaN

df.query('A > 1')
#      A     B
# 2 3.00  3.00
# 3 4.00   NaN

# NA를 포함하고자 할 때,
df.query('A > 1 | A.isna()') # .isna() : NA인지 여부
#      A     B
# 1  NaN  5.00
# 2 3.00  3.00
# 3 4.00   NaN

NA(missing)에 대해서는 뒤에서 자세히

`sort_values()`

# "year", "month", "day", "dep_time" 순서대로 내림차순으로 정렬
flights.sort_values(by=["year", "month", "day", "dep_time"], ascending=False) # default: ascending=True

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
111279	2013	12	31	2356.00	2359	-3.00	436.00	445	-9.00	B6	745	N665JB	JFK	PSE	200.00	1617	23	59
111278	2013	12	31	2355.00	2359	-4.00	430.00	440	-10.00	B6	1503	N509JB	JFK	SJU	195.00	1598	23	59
111277	2013	12	31	2332.00	2245	47.00	58.00	3	55.00	B6	486	N334JB	JFK	ROC	60.00	264	22	45
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
839	2013	1	1	NaN	1935	NaN	NaN	2240	NaN	AA	791	N3EHAA	LGA	DFW	NaN	1389	19	35
840	2013	1	1	NaN	1500	NaN	NaN	1825	NaN	AA	1925	N3EVAA	LGA	MIA	NaN	1096	15	0
841	2013	1	1	NaN	600	NaN	NaN	901	NaN	B6	125	N618JB	JFK	FLL	NaN	1069	6	0

336776 rows × 18 columns

# "dep_time"은 내림차순으로, "arr_delay"는 오름차순으로
flights.sort_values(by=["dep_time", "arr_delay"], ascending=[False, True])

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
150301	2013	3	15	2400.00	2359	1.00	324.00	338	-14.00	B6	727	N636JB	JFK	BQN	186.00	1576	23	59
87893	2013	12	5	2400.00	2359	1.00	427.00	440	-13.00	B6	1503	N587JB	JFK	SJU	182.00	1598	23	59
212941	2013	5	21	2400.00	2359	1.00	339.00	350	-11.00	B6	739	N527JB	JFK	PSE	199.00	1617	23	59
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336773	2013	9	30	NaN	1210	NaN	NaN	1330	NaN	MQ	3461	N535MQ	LGA	BNA	NaN	764	12	10
336774	2013	9	30	NaN	1159	NaN	NaN	1344	NaN	MQ	3572	N511MQ	LGA	CLE	NaN	419	11	59
336775	2013	9	30	NaN	840	NaN	NaN	1020	NaN	MQ	3531	N839MQ	LGA	RDU	NaN	431	8	40

336776 rows × 18 columns

query()와 sort_values()을 함께 이용하여 좀 더 복잡한 문제를 해결할 수 있음
예를 들어, 다음과 같이 거의 제시간에 출발한(+- 10분) 항공편들 중 가장 도착 지연이 큰 항공편을 찾을 수 있음

(
    flights
    .query('dep_delay <= 10 & dep_delay >= -10')
    .sort_values("arr_delay", ascending=False)
)

	year	month	day	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
55985	2013	11	1	658.00	700	-2.00	1329.00	1015	194.00	VX	399	N629VA	JFK	LAX	336.00	2475	7	0
181270	2013	4	18	558.00	600	-2.00	1149.00	850	179.00	AA	707	N3EXAA	LGA	DFW	234.00	1389	6	0
256340	2013	7	7	1659.00	1700	-1.00	2050.00	1823	147.00	US	2183	N948UW	LGA	DCA	64.00	214	17	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
334354	2013	9	28	847.00	839	8.00	1130.00	959	NaN	EV	4510	N14542	EWR	MKE	NaN	725	8	39
334412	2013	9	28	1010.00	1020	-10.00	1344.00	1222	NaN	EV	4412	N12175	EWR	DSM	NaN	1017	10	20
335805	2013	9	30	559.00	600	-1.00	NaN	715	NaN	WN	464	N411WN	EWR	MDW	NaN	711	6	0

239109 rows × 18 columns

Note

Row가 재정렬되는 operation을 한 후에는 index 순서가 바뀌는데, 이를 reset하려면,
.sort_values(ignore_index=True)
DataFrame updated: inplace=True
NA는 sort 후 맨 뒤로
맨 앞으로 오게하려면 na_position='first'

`unique()`

Series method

flights["origin"].unique() # return as a NumPy array, but depends on the dtypes

array(['EWR', 'LGA', 'JFK'], dtype=object)

# finds all unique origin and destination pairs.
flights[["origin", "dest"]].value_counts() # default: dropna=True

origin  dest
JFK     LAX     11262
LGA     ATL     10263
        ORD      8857
                ...  
        LEX         1
JFK     MEM         1
        BHM         1
Length: 224, dtype: int64

flights[["origin", "dest"]].value_counts().reset_index(name="n")

    origin dest      n
0      JFK  LAX  11262
1      LGA  ATL  10263
2      LGA  ORD   8857
..     ...  ...    ...
221    LGA  LEX      1
222    JFK  MEM      1
223    JFK  BHM      1

[224 rows x 3 columns]

Columns

Select

기본적인 column selection은 이전 섹션 참고: subsetting

# Select columns by name
flights[["year", "month", "day"]]

        year  month  day
0       2013      1    1
1       2013      1    1
2       2013      1    1
...      ...    ...  ...
336773  2013      9   30
336774  2013      9   30
336775  2013      9   30

[336776 rows x 3 columns]

# Select all columns between year and day (inclusive)
flights.loc[:, "year":"day"]

        year  month  day
0       2013      1    1
1       2013      1    1
2       2013      1    1
...      ...    ...  ...
336773  2013      9   30
336774  2013      9   30
336775  2013      9   30

[336776 rows x 3 columns]

# Select all columns except those from year to day (inclusive)
# .isin(): includes
flights.loc[:, ~flights.columns.isin(["year", "month", "day"])]  # Boolean indexing

	dep_time	sched_dep_time	dep_delay	arr_time	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
0	517.00	515	2.00	830.00	819	11.00	UA	1545	N14228	EWR	IAH	227.00	1400	5	15
1	533.00	529	4.00	850.00	830	20.00	UA	1714	N24211	LGA	IAH	227.00	1416	5	29
2	542.00	540	2.00	923.00	850	33.00	AA	1141	N619AA	JFK	MIA	160.00	1089	5	40
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336773	NaN	1210	NaN	NaN	1330	NaN	MQ	3461	N535MQ	LGA	BNA	NaN	764	12	10
336774	NaN	1159	NaN	NaN	1344	NaN	MQ	3572	N511MQ	LGA	CLE	NaN	419	11	59
336775	NaN	840	NaN	NaN	1020	NaN	MQ	3531	N839MQ	LGA	RDU	NaN	431	8	40

336776 rows × 15 columns

Series/Index object의 method와 함께 (string method와는 구별)
.str.contains(), .str.startswith(), .str.endswith() ; True/False

# Select all columns that begin with “dep”.
flights.loc[:, flights.columns.str.startswith("dep")]  # Boolean indexing

        dep_time  dep_delay
0         517.00       2.00
1         533.00       4.00
2         542.00       2.00
...          ...        ...
336773       NaN        NaN
336774       NaN        NaN
336775       NaN        NaN

[336776 rows x 2 columns]

# Select all columns that are characters
flights.select_dtypes("object") # dtype: object, number, ...

       carrier tailnum origin dest
0           UA  N14228    EWR  IAH
1           UA  N24211    LGA  IAH
2           AA  N619AA    JFK  MIA
...        ...     ...    ...  ...
336773      MQ  N535MQ    LGA  BNA
336774      MQ  N511MQ    LGA  CLE
336775      MQ  N839MQ    LGA  RDU

[336776 rows x 4 columns]

index selection: reindex

`rename()`

flights.rename(
    columns={"dep_time": "dep_t", "arr_time": "arr_t"},  # default: index=
  # inplace=True   # dataframe is updated
)

	year	month	day	dep_t	sched_dep_time	dep_delay	arr_t	sched_arr_time	arr_delay	carrier	flight	tailnum	origin	dest	air_time	distance	hour	minute
0	2013	1	1	517.00	515	2.00	830.00	819	11.00	UA	1545	N14228	EWR	IAH	227.00	1400	5	15
1	2013	1	1	533.00	529	4.00	850.00	830	20.00	UA	1714	N24211	LGA	IAH	227.00	1416	5	29
2	2013	1	1	542.00	540	2.00	923.00	850	33.00	AA	1141	N619AA	JFK	MIA	160.00	1089	5	40
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
336773	2013	9	30	NaN	1210	NaN	NaN	1330	NaN	MQ	3461	N535MQ	LGA	BNA	NaN	764	12	10
336774	2013	9	30	NaN	1159	NaN	NaN	1344	NaN	MQ	3572	N511MQ	LGA	CLE	NaN	419	11	59
336775	2013	9	30	NaN	840	NaN	NaN	1020	NaN	MQ	3531	N839MQ	LGA	RDU	NaN	431	8	40

336776 rows × 18 columns

Tip

Series의 경우

s = flights.dep_delay.head(3)
# 0   2.00
# 1   4.00
# 2   2.00
# Name: dep_delay, dtype: float64

s.rename("what")
# 0   2.00
# 1   4.00
# 2   2.00
# Name: what, dtype: float64

함수 str.capitalize, str.lower, str.upper과 함께

flights.rename(str.capitalize, axis="columns").head(3) # axis=1

	Year	Month	Day	Dep_time	Sched_dep_time	Dep_delay	Arr_time	Sched_arr_time	Arr_delay	Carrier	Flight	Tailnum	Origin	Dest	Air_time	Distance	Hour	Minute
0	2013	1	1	517.00	515	2.00	830.00	819	11.00	UA	1545	N14228	EWR	IAH	227.00	1400	5	15
1	2013	1	1	533.00	529	4.00	850.00	830	20.00	UA	1714	N24211	LGA	IAH	227.00	1416	5	29
2	2013	1	1	542.00	540	2.00	923.00	850	33.00	AA	1141	N619AA	JFK	MIA	160.00	1089	5	40

Note

index를 rename할 수도 있음

flights.rename(
    index={0: "a", 1: "b"},
).head(3)
#    year  month  day  dep_time  ...
# a  2013      1    1    517.00  ...
# b  2013      1    1    533.00  ...
# 2  2013      1    1    542.00  ...

`assign()`

cols = ["year", "month", "day", "distance", "air_time"] + \
        [col for col in flights.columns if col.endswith("delay")]  # string method .endswith
flights_sml = flights[cols].copy()
flights_sml

        year  month  day  distance  air_time  dep_delay  arr_delay
0       2013      1    1      1400    227.00       2.00      11.00
1       2013      1    1      1416    227.00       4.00      20.00
2       2013      1    1      1089    160.00       2.00      33.00
...      ...    ...  ...       ...       ...        ...        ...
336773  2013      9   30       764       NaN        NaN        NaN
336774  2013      9   30       419       NaN        NaN        NaN
336775  2013      9   30       431       NaN        NaN        NaN

[336776 rows x 7 columns]

# 새로 만들어진 변수는 맨 뒤로
flights_sml.assign(
    gain=lambda x: x.dep_delay - x.arr_delay,   # x: DataFrame, flights_sml
    speed=flights_sml["distance"] / flights_sml["air_time"] * 60    # 직접 DataFrame 참조할 수도 있음
)

        year  month  day  distance  air_time  dep_delay  arr_delay   gain  \
0       2013      1    1      1400    227.00       2.00      11.00  -9.00   
1       2013      1    1      1416    227.00       4.00      20.00 -16.00   
2       2013      1    1      1089    160.00       2.00      33.00 -31.00   
...      ...    ...  ...       ...       ...        ...        ...    ...   
336773  2013      9   30       764       NaN        NaN        NaN    NaN   
336774  2013      9   30       419       NaN        NaN        NaN    NaN   
336775  2013      9   30       431       NaN        NaN        NaN    NaN   

        speed  
0      370.04  
1      374.27  
2      408.38  
...       ...  
336773    NaN  
336774    NaN  
336775    NaN  

[336776 rows x 9 columns]

# 앞에서 만든 변수나 함수를 이용할 수 있음
flights_sml.assign(
    gain=lambda x: x.dep_delay - x.arr_delay,
    hours=lambda x: x.air_time / 60,
    gain_per_hour=lambda x: x.gain / x.hours,
    rounded=lambda x: np.round(x.gain_per_hour, 1)  # use a numpy function
)

        year  month  day  distance  air_time  dep_delay  arr_delay   gain  \
0       2013      1    1      1400    227.00       2.00      11.00  -9.00   
1       2013      1    1      1416    227.00       4.00      20.00 -16.00   
2       2013      1    1      1089    160.00       2.00      33.00 -31.00   
...      ...    ...  ...       ...       ...        ...        ...    ...   
336773  2013      9   30       764       NaN        NaN        NaN    NaN   
336774  2013      9   30       419       NaN        NaN        NaN    NaN   
336775  2013      9   30       431       NaN        NaN        NaN    NaN   

        hours  gain_per_hour  rounded  
0        3.78          -2.38    -2.40  
1        3.78          -4.23    -4.20  
2        2.67         -11.62   -11.60  
...       ...            ...      ...  
336773    NaN            NaN      NaN  
336774    NaN            NaN      NaN  
336775    NaN            NaN      NaN  

[336776 rows x 11 columns]

# Find the fastest flights
(
    flights_sml
    .assign(speed=lambda x: x.distance / x.air_time)
    .sort_values(by="speed", ascending=False)
    .head(5)
)

        year  month  day  distance  air_time  dep_delay  arr_delay  speed
216447  2013      5   25       762     65.00       9.00     -14.00  11.72
251999  2013      7    2      1008     93.00      45.00      26.00  10.84
205388  2013      5   13       594     55.00      15.00      -1.00  10.80
157516  2013      3   23       748     70.00       4.00       2.00  10.69
10223   2013      1   12      1035    105.00      -1.00     -28.00   9.86

Groups

`groupby()`

groupby()는 데이터를 의미있는 그룹으로 나누어 분석할 수 있도록 해줌
.count(), .sum(), .mean(), .min(), .max()과 같은 통계치를 구하는 methods와 함께 효과적으로, 자주 활용됨

Source: Ch.10 in Python for Data Analysis (3e) by Wes McKinney

아래 표는 groupby()와 함께 자주 쓰이는 효율적인 methods

Source: Ch.10 in Python for Data Analysis (3e) by Wes McKinney

flights.groupby("month") # “GroupBy” object

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x28001c810>

flights_sml.groupby("month").mean()

         year   day  distance  air_time  dep_delay  arr_delay
month                                                        
1     2013.00 15.99   1006.84    154.19      10.04       6.13
2     2013.00 14.74   1000.98    151.35      10.82       5.61
3     2013.00 16.00   1011.99    149.08      13.23       5.81
...       ...   ...       ...       ...        ...        ...
10    2013.00 15.98   1038.88    148.89       6.24      -0.17
11    2013.00 15.29   1050.31    155.47       5.44       0.46
12    2013.00 15.72   1064.66    162.59      16.58      14.87

[12 rows x 6 columns]

# 보통은 다음과 같이 특정 columns을 선택
flights.groupby("month")["dep_delay"]  # [["dep_delay"]] 처럼 list로 입력하면 DataFrameGroupBy object

<pandas.core.groupby.generic.SeriesGroupBy object at 0x13d0126d0>

flights.groupby("month")["dep_delay"].mean() # Series GroupBy object에 적용된 결과는 Series

month
1    10.04
2    10.82
3    13.23
      ... 
10    6.24
11    5.44
12   16.58
Name: dep_delay, Length: 12, dtype: float64

flights.groupby("month")[["dep_delay", "arr_delay"]].mean().head(3)

       dep_delay  arr_delay
month                      
1          10.04       6.13
2          10.82       5.61
3          13.23       5.81

# 2 levels의 grouping
flights.groupby(["month", "day"])["arr_delay"].nsmallest(1)

month  day        
1      1    696      -48.00
       2    919      -59.00
       3    2035     -65.00
                      ...  
12     29   108914   -60.00
       30   110330   -45.00
       31   111113   -44.00
Name: arr_delay, Length: 365, dtype: float64

Tip

Multi-index의 level을 drop하려면 droplevel()

flights.groupby(["month", "day"])["arr_delay"].nsmallest(1).droplevel(2)
# month  day
# 1      1     -48.00
#        2     -59.00
#        3     -65.00
#               ...  
# 12     29    -60.00
#        30    -45.00
#        31    -44.00
# Name: arr_delay, Length: 365, dtype: float64

flights.groupby(["origin", "dest"])["arr_delay"].count()

origin  dest
EWR     ALB      418
        ANC        8
        ATL     4876
                ... 
LGA     TVC       73
        TYS      265
        XNA      709
Name: arr_delay, Length: 224, dtype: int64

Tip

as_index=False: grouping 변수들을 index가 아닌 columns으로

flights.groupby(["month", "day"], as_index=False)["arr_delay"].mean().head(3)
#    month  day  arr_delay
# 0      1    1      12.65
# 1      1    2      12.69
# 2      1    3       5.73

물론, 결과물에 .reset_index() method를 사용해도 됨

Note

원칙적으로 grouping은 같은 DataFrame의 변수일 필요없이 match만 되면 됨
Source: Wes McKinney’s

# df
#    key1  key2  data1  data2
# 0     a     1   0.36  -0.42
# 1     a     2  -1.51   0.04
# 2  None     1   0.75  -0.28
# 3     b     2   0.57   0.25
# 4     b     1   1.30  -0.77
# 5     a  <NA>  -0.53  -0.73
# 6  None     1   2.04  -0.37

states = np.array(["OH", "CA", "CA", "OH", "OH", "CA", "OH"])
years = [2005, 2005, 2006, 2005, 2006, 2005, 2006]

df["data1"].groupby([states, years]).mean()
# CA  2005   -1.02
#     2006    0.75
# OH  2005    0.46
#     2006    1.67
# Name: data1, dtype: float64

flights.groupby("dest").size() # .size(): group의 사이즈/열의 갯수

dest
ABQ     254
ACK     265
ALB     439
       ... 
TVC     101
TYS     631
XNA    1036
Length: 105, dtype: int64

flights.groupby("tailnum", dropna=False).size() # groupby는 기본적으로 NA 무시

tailnum
D942DN       4
N0EGMQ     371
N10156     153
          ... 
N999DN      61
N9EAMQ     248
NaN       2512
Length: 4044, dtype: int64

Note

.size()는 .value_counts()와 유사하게 사용될 수 있음

flights["tailnum"].value_counts(dropna=False)
# NaN       2512
# N725MQ     575
# N722MQ     513
#           ... 
# N318AS       1
# N651UA       1
# N557AS       1
# Name: tailnum, Length: 4044, dtype: int64

Note

Index에 grouping하는 방식에 대해서는 Wes McKineey’s Chapter 10 참고

Grouping with Dictionaries and Series
Grouping with Functions
Grouping by Index Levels

Note

Time series 데이터의 경우 다양한 grouping 방식이 존재
Stephanie Molin의 ch.4 Working with time series data 참고

예를 들어, facebook stock에 대한 자료에서 2018년 4분기에 해당하는 날을 week단위로 그룹핑하여 volume을 다음과 같이 계산할 수 있음

fb.loc['2018-Q4'].groupby(pd.Grouper(freq='W')).volume.sum()

Tip

groupby filtering

flights.groupby(["year", "month", "day"]).filter(lambda x: x["arr_delay"].mean() < 0)

`agg()`

Aggregations: data transformation that produces scalar values from arrays

앞서 GroupBy object에 직접 stats function을 적용하였는데, agg()를 이용하여 더 확장, 일반화할 수 있음

# 모두 동일
flights_sml.groupby("month").mean()
flights_sml.groupby("month").agg("mean")  # function names
flights_sml.groupby("month").agg(np.mean)  # general functions

         year   day  distance  air_time  dep_delay  arr_delay
month                                                        
1     2013.00 15.99   1006.84    154.19      10.04       6.13
2     2013.00 14.74   1000.98    151.35      10.82       5.61
3     2013.00 16.00   1011.99    149.08      13.23       5.81
...       ...   ...       ...       ...        ...        ...
10    2013.00 15.98   1038.88    148.89       6.24      -0.17
11    2013.00 15.29   1050.31    155.47       5.44       0.46
12    2013.00 15.72   1064.66    162.59      16.58      14.87

[12 rows x 6 columns]

Tip

차이 참고

flights_sml.groupby("month")["arr_delay"].agg(["mean"])
flights_sml.groupby("month")[["arr_delay"]].agg("mean")

flights.groupby("month")["dep_delay"].agg(["mean", "count"])

       mean  count
month             
1     10.04  26483
2     10.82  23690
3     13.23  27973
...     ...    ...
10     6.24  28653
11     5.44  27035
12    16.58  27110

[12 rows x 2 columns]

flights_sml.groupby("month")[["arr_delay", "dep_delay"]].agg(["mean", "count"])

      arr_delay        dep_delay       
           mean  count      mean  count
month                                  
1          6.13  26398     10.04  26483
2          5.61  23611     10.82  23690
3          5.81  27902     13.23  27973
...         ...    ...       ...    ...
10        -0.17  28618      6.24  28653
11         0.46  26971      5.44  27035
12        14.87  27020     16.58  27110

[12 rows x 4 columns]

# Apply different funtions to different columns
flights_agg = flights.groupby("month").agg({
    "air_time": ["min", "max"],
    "dep_delay": "mean",
    "arr_delay": "median"
})
flights_agg.head(3)

      air_time        dep_delay arr_delay
           min    max      mean    median
month                                    
1        20.00 667.00     10.04     -3.00
2        21.00 691.00     10.82     -3.00
3        21.00 695.00     13.23     -6.00

Tip

MultiIndex를 collapse하는 팁

flights_agg.columns
# MultiIndex([( 'air_time',    'min'),
#             ( 'air_time',    'max'),
#             ('dep_delay',   'mean'),
#             ('arr_delay', 'median')],
#            )

flights_agg.columns = ['_'.join(col_agg) for col_agg in flights_agg.columns]
flights_agg.head(3)
#        air_time_min  air_time_max  dep_delay_mean  arr_delay_median
# month                                                              
# 1             20.00        667.00           10.04             -3.00
# 2             21.00        691.00           10.82             -3.00
# 3             21.00        695.00           13.23             -6.00

agg()에는 custom function을 pass할 수 있음
단, the optimized functions (Table 10-1)에 비해 일반적으로 훨씬 느림

def peak_to_peak(arr):
    return arr.max() - arr.min()

grouped = flights_sml.groupby(["month", "day"])
grouped_dist = flights_sml.groupby(["month", "day"])["distance"]

grouped_dist.agg(["std", peak_to_peak]) # a list of functions

             std  peak_to_peak
month day                     
1     1   727.73          4889
      2   721.72          4889
      3   714.95          4903
...          ...           ...
12    29  728.78          4887
      30  723.88          4887
      31  731.36          4887

[365 rows x 2 columns]

# Naming a function as a tuple
grouped_dist.agg([("sd", "std"), ("range", peak_to_peak)])

              sd  range
month day              
1     1   727.73   4889
      2   721.72   4889
      3   714.95   4903
...          ...    ...
12    29  728.78   4887
      30  723.88   4887
      31  731.36   4887

[365 rows x 2 columns]

Tip

.describe()는 aggregation은 아니나 grouped objects에 적용가능

grouped[["dep_delay", "arr_delay"]].describe()
#       dep_delay                                              arr_delay        \
#           count  mean   std    min   25%   50%   75%     max     count  mean   
# month                                                                          
# 1      26483.00 10.04 36.39 -30.00 -5.00 -2.00  8.00 1301.00  26398.00  6.13   
# 2      23690.00 10.82 36.27 -33.00 -5.00 -2.00  9.00  853.00  23611.00  5.61   
# 3      27973.00 13.23 40.13 -25.00 -5.00 -1.00 12.00  911.00  27902.00  5.81   
# ...         ...   ...   ...    ...   ...   ...   ...     ...       ...   ...    
                                               
#         std    min    25%   50%   75%     max  
# month                                          
# 1     40.42 -70.00 -15.00 -3.00 13.00 1272.00  
# 2     39.53 -70.00 -15.00 -3.00 13.00  834.00  
# 3     44.12 -68.00 -18.00 -6.00 13.00  915.00  
# ...     ...    ...    ...   ...   ...     ...  

# [12 rows x 16 columns]

`transform()`

앞서 group별로 통계치가 summary되어 원래 reduced 데이터를 변형됐다면,
transform()은 group별로 얻은 통계치가 원래 데이터의 형태를 그대로 보존하면서 출력
만약, 전달되는 함수가 Series를 반환하려면, 동일한 사이즈로 반환되어야 함.

flights_sml.groupby(["month"])["arr_delay"].mean()

month
1     6.13
2     5.61
3     5.81
      ... 
10   -0.17
11    0.46
12   14.87
Name: arr_delay, Length: 12, dtype: float64

grouped_delay = flights_sml.groupby(["month"])["arr_delay"].transform("mean")
grouped_delay

0         6.13
1         6.13
2         6.13
          ... 
336773   -4.02
336774   -4.02
336775   -4.02
Name: arr_delay, Length: 336776, dtype: float64

flights_sml["monthly_delay"] = grouped_delay
flights_sml

        year  month  day  distance  air_time  dep_delay  arr_delay  \
0       2013      1    1      1400    227.00       2.00      11.00   
1       2013      1    1      1416    227.00       4.00      20.00   
2       2013      1    1      1089    160.00       2.00      33.00   
...      ...    ...  ...       ...       ...        ...        ...   
336773  2013      9   30       764       NaN        NaN        NaN   
336774  2013      9   30       419       NaN        NaN        NaN   
336775  2013      9   30       431       NaN        NaN        NaN   

        monthly_delay  
0                6.13  
1                6.13  
2                6.13  
...               ...  
336773          -4.02  
336774          -4.02  
336775          -4.02  

[336776 rows x 8 columns]

Q: 1년에 10000편 이상 운항편이 있는 도착지로 가는 항공편들만 추리면,

dest_size =  flights.groupby("dest").transform("size")
dest_size

# 또는 flights.groupby("dest")["dest"].transform("count")

0          7198
1          7198
2         11728
          ...  
336773     6333
336774     4573
336775     8163
Length: 336776, dtype: int64

# 1년에 10000편 이상 운항편이 있는 도착지에 대한 항공편
flights[dest_size > 10000]

        year  month  day  dep_time  sched_dep_time  dep_delay  arr_time  \
2       2013      1    1    542.00             540       2.00    923.00   
4       2013      1    1    554.00             600      -6.00    812.00   
5       2013      1    1    554.00             558      -4.00    740.00   
...      ...    ...  ...       ...             ...        ...       ...   
336762  2013      9   30   2233.00            2113      80.00    112.00   
336763  2013      9   30   2235.00            2001     154.00     59.00   
336768  2013      9   30   2307.00            2255      12.00   2359.00   

        sched_arr_time  arr_delay carrier  flight tailnum origin dest  \
2                  850      33.00      AA    1141  N619AA    JFK  MIA   
4                  837     -25.00      DL     461  N668DN    LGA  ATL   
5                  728      12.00      UA    1696  N39463    EWR  ORD   
...                ...        ...     ...     ...     ...    ...  ...   
336762              30      42.00      UA     471  N578UA    EWR  SFO   
336763            2249     130.00      B6    1083  N804JB    JFK  MCO   
336768            2358       1.00      B6     718  N565JB    JFK  BOS   

        air_time  distance  hour  minute  
2         160.00      1089     5      40  
4         116.00       762     6       0  
5         150.00       719     5      58  
...          ...       ...   ...     ...  
336762    318.00      2565    21      13  
336763    123.00       944    20       1  
336768     33.00       187    22      55  

[131440 rows x 18 columns]

Q: 하루 중 출발 지연이 가장 늦은 두 항공편들을 매일 각각 구하면,

def get_ranks(group):
    return group.rank(ascending=False, method="min") # method: 동일 등수에 대한 처리방식

delay_rank = flights.groupby(["month", "day"])["dep_delay"].transform(get_ranks)
# 또는 .transform("rank", ascending=False, method="min")

delay_rank

0        302.00
1        269.00
2        302.00
          ...  
336773      NaN
336774      NaN
336775      NaN
Name: dep_delay, Length: 336776, dtype: float64

flights[delay_rank < 3].head(6)

      year  month  day  dep_time  sched_dep_time  dep_delay  arr_time  \
151   2013      1    1    848.00            1835     853.00   1001.00   
834   2013      1    1   2343.00            1724     379.00    314.00   
1440  2013      1    2   1607.00            1030     337.00   2003.00   
1749  2013      1    2   2131.00            1512     379.00   2340.00   
2598  2013      1    3   2008.00            1540     268.00   2339.00   
2637  2013      1    3   2056.00            1605     291.00   2239.00   

      sched_arr_time  arr_delay carrier  flight tailnum origin dest  air_time  \
151             1950     851.00      MQ    3944  N942MQ    JFK  BWI     41.00   
834             1938     456.00      EV    4321  N21197    EWR  MCI    222.00   
1440            1355     368.00      AA     179  N324AA    JFK  SFO    346.00   
1749            1741     359.00      UA     488  N593UA    LGA  DEN    228.00   
2598            1909     270.00      DL    2027  N338NW    JFK  FLL    158.00   
2637            1754     285.00      9E    3459  N928XJ    JFK  BNA    125.00   

      distance  hour  minute  
151        184    18      35  
834       1092    17      24  
1440      2586    10      30  
1749      1620    15      12  
2598      1069    15      40  
2637       765    16       5

Q: Normalize air time by destination

dest_air = flights.groupby("dest")["air_time"]

# Z = (x - mean) / std
(flights['air_time'] - dest_air.transform('mean')) / dest_air.transform('std')

0        1.73
1        1.73
2        0.61
         ... 
336773    NaN
336774    NaN
336775    NaN
Name: air_time, Length: 336776, dtype: float64

def normalize(x):
    return (x - x.mean()) / x.std()

dest_air.transform(normalize)

0        1.73
1        1.73
2        0.61
         ... 
336773    NaN
336774    NaN
336775    NaN
Name: air_time, Length: 336776, dtype: float64

`apply()`

Apply: General split-apply-combine in McKineey’s Chapter 10.3 참고

The most general-purpose GroupBy method is apply, which is the subject of this section. apply splits the object being manipulated into pieces, invokes the passed function on each piece, and then attempts to concatenate the pieces.

tips = sns.load_dataset("tips")
tips = tips.assign(tip_pct = lambda x: x.tip / x.total_bill)
tips.head(3)

   total_bill  tip     sex smoker  day    time  size  tip_pct
0       16.99 1.01  Female     No  Sun  Dinner     2     0.06
1       10.34 1.66    Male     No  Sun  Dinner     3     0.16
2       21.01 3.50    Male     No  Sun  Dinner     3     0.17

def top(df, n=5, column="tip_pct"):
    return df.sort_values(column, ascending=False)[:n]

top(tips, n=4)

     total_bill  tip     sex smoker  day    time  size  tip_pct
172        7.25 5.15    Male    Yes  Sun  Dinner     2     0.71
178        9.60 4.00  Female    Yes  Sun  Dinner     2     0.42
67         3.07 1.00  Female    Yes  Sat  Dinner     1     0.33
232       11.61 3.39    Male     No  Sat  Dinner     2     0.29

tips.groupby("time").apply(top)

            total_bill  tip     sex smoker   day    time  size  tip_pct
time                                                                   
Lunch  149        7.51 2.00    Male     No  Thur   Lunch     2     0.27
       221       13.42 3.48  Female    Yes   Fri   Lunch     2     0.26
       194       16.58 4.00    Male    Yes  Thur   Lunch     2     0.24
...                ...  ...     ...    ...   ...     ...   ...      ...
Dinner 67         3.07 1.00  Female    Yes   Sat  Dinner     1     0.33
       232       11.61 3.39    Male     No   Sat  Dinner     2     0.29
       183       23.17 6.50    Male    Yes   Sun  Dinner     4     0.28

[10 rows x 8 columns]

tips.groupby(["time", "day"]).apply(top, n=1, column="total_bill")

                 total_bill   tip     sex smoker   day    time  size  tip_pct
time   day                                                                   
Lunch  Thur 197       43.11  5.00  Female    Yes  Thur   Lunch     4     0.12
       Fri  225       16.27  2.50  Female    Yes   Fri   Lunch     2     0.15
Dinner Thur 243       18.78  3.00  Female     No  Thur  Dinner     2     0.16
       Fri  95        40.17  4.73    Male    Yes   Fri  Dinner     4     0.12
       Sat  170       50.81 10.00    Male    Yes   Sat  Dinner     3     0.20
       Sun  156       48.17  5.00    Male     No   Sun  Dinner     6     0.10

Important

What occurs inside the function passed is up to you;
it must either return a pandas object or a scalar value.

tips.groupby("sex")["tip_pct"].describe()

        count  mean  std  min  25%  50%  75%  max
sex                                              
Male   157.00  0.16 0.06 0.04 0.12 0.15 0.19 0.71
Female  87.00  0.17 0.05 0.06 0.14 0.16 0.19 0.42

Inside GroupBy, when you invoke a method like describe, it is actually just a shortcut for:

def f(group):
    return group.describe()

Tip

Suppressing the Group Keys

tips.groupby("time", group_keys=False).apply(top)
#      total_bill  tip     sex smoker   day    time  size  tip_pct
# 149        7.51 2.00    Male     No  Thur   Lunch     2     0.27
# 221       13.42 3.48  Female    Yes   Fri   Lunch     2     0.26
# 194       16.58 4.00    Male    Yes  Thur   Lunch     2     0.24
# 88        24.71 5.85    Male     No  Thur   Lunch     2     0.24
# 222        8.58 1.92    Male    Yes   Fri   Lunch     1     0.22
# 172        7.25 5.15    Male    Yes   Sun  Dinner     2     0.71
# 178        9.60 4.00  Female    Yes   Sun  Dinner     2     0.42
# 67         3.07 1.00  Female    Yes   Sat  Dinner     1     0.33
# 232       11.61 3.39    Male     No   Sat  Dinner     2     0.29
# 183       23.17 6.50    Male    Yes   Sun  Dinner     4     0.28

비교

applymap: element-wise, DataFrame method
map: element-wise, Series method
apply: column/row-wise, DataFrame method, 또는 element-wise, Series method

Source, 비교 참고

def my_format(x):
    return f"{x:.1f}"

tips_num = tips.select_dtypes("number")

# element-wise, DataFrame method
tips_num.applymap(my_format)

    total_bill  tip size tip_pct
0         17.0  1.0  2.0     0.1
1         10.3  1.7  3.0     0.2
2         21.0  3.5  3.0     0.2
..         ...  ...  ...     ...
241       22.7  2.0  2.0     0.1
242       17.8  1.8  2.0     0.1
243       18.8  3.0  2.0     0.2

[244 rows x 4 columns]

# element-wise, Series method
tips["tip"].map(my_format)

0      1.0
1      1.7
2      3.5
      ... 
241    2.0
242    1.8
243    3.0
Name: tip, Length: 244, dtype: object

def peak_to_peak(arr):
    return arr.max() - arr.min()

# column-wise operation
tips_num.apply(peak_to_peak)

total_bill   47.74
tip           9.00
size          5.00
tip_pct       0.67
dtype: float64

# row-wise operation
tips_num.apply(peak_to_peak, axis="columns")

0     16.93
1     10.18
2     20.84
       ... 
241   22.58
242   17.72
243   18.62
Length: 244, dtype: float64

def f2(x):
    return pd.Series([x.min(), x.max()], index=["min", "max"])

# apply에 패스되는 함수는 scalar 값이 아닌 Series를 반환해도 됨
tips_num.apply(f2)

     total_bill   tip  size  tip_pct
min        3.07  1.00     1     0.04
max       50.81 10.00     6     0.71

# Series Groupby object의 경우
tips.groupby("time")["tip"].apply(f2)

time       
Lunch   min    1.25
        max    6.70
Dinner  min    1.00
        max   10.00
Name: tip, dtype: float64

# DataFrame GroupBy object의 경우
def f3(g):
    x = g["tip"]
    return pd.Series([x.min(), x.max()], index=["min", "max"])

tips.groupby("time").apply(f3)

        min   max
time             
Lunch  1.25  6.70
Dinner 1.00 10.00