Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells40000
Missing cells (%)30.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory119.0 B

Variable types

Text1
Numeric7
Categorical5

Dataset

Description2020년1월1일이후 당사에 등록된 고객에게 제공된 서비스 내역을 호텔, 운송, 기타로 분류한 통계정보 입니다. 고객번호는 가상번호이며 데이터 추가 개방이 변경될 수 있습니다.
Author그랜드코리아레저(주)
URLhttps://www.data.go.kr/data/15116857/fileData.do

Alerts

고객여권 발급 국가코드 is highly overall correlated with 고객여권 발급 국가명High correlation
고객여권 발급 국가명 is highly overall correlated with 고객여권 발급 국가코드High correlation
호텔 체류일 수 is highly overall correlated with 평균 숙박제공 금액High correlation
평균 숙박제공 금액 is highly overall correlated with 호텔 체류일 수High correlation
고객여권 발급 국가코드 is highly imbalanced (60.7%)Imbalance
고객여권 발급 국가명 is highly imbalanced (60.7%)Imbalance
호텔명 is highly imbalanced (55.9%)Imbalance
운송 구분명 is highly imbalanced (59.9%)Imbalance
호텔 체류일 수 has 6851 (68.5%) missing valuesMissing
평균 숙박제공 금액 has 6851 (68.5%) missing valuesMissing
운송 제공 건수 has 6935 (69.3%) missing valuesMissing
평균 운송 제공금액 has 6935 (69.3%) missing valuesMissing
기타 서비스 제공 건수 has 6214 (62.1%) missing valuesMissing
평균 기타서비스 제공 금액 has 6214 (62.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 02:17:21.593810
Analysis finished2023-12-12 02:17:29.355933
Duration7.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

가상고객 번호
Text

Distinct5851
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T11:17:29.609192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters110000
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3442 ?
Unique (%)34.4%

Sample

1st rowV0004643017
2nd rowV0038913001
3rd rowV0029714495
4th rowV0064080154
5th rowV0011388861
ValueCountFrequency (%)
v0057811884 10
 
0.1%
v0042706537 8
 
0.1%
v0003185820 8
 
0.1%
v0003185642 8
 
0.1%
v0069811398 7
 
0.1%
v0020256465 7
 
0.1%
v0000923885 7
 
0.1%
v0072773941 7
 
0.1%
v0032637878 7
 
0.1%
v0017778438 7
 
0.1%
Other values (5841) 9924
99.2%
2023-12-12T11:17:30.139164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29192
26.5%
V 10000
 
9.1%
7 9350
 
8.5%
3 8459
 
7.7%
2 8337
 
7.6%
4 8271
 
7.5%
1 7955
 
7.2%
5 7436
 
6.8%
6 7408
 
6.7%
8 6897
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 100000
90.9%
Uppercase Letter 10000
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29192
29.2%
7 9350
 
9.3%
3 8459
 
8.5%
2 8337
 
8.3%
4 8271
 
8.3%
1 7955
 
8.0%
5 7436
 
7.4%
6 7408
 
7.4%
8 6897
 
6.9%
9 6695
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
V 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 100000
90.9%
Latin 10000
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29192
29.2%
7 9350
 
9.3%
3 8459
 
8.5%
2 8337
 
8.3%
4 8271
 
8.3%
1 7955
 
8.0%
5 7436
 
7.4%
6 7408
 
7.4%
8 6897
 
6.9%
9 6695
 
6.7%
Latin
ValueCountFrequency (%)
V 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29192
26.5%
V 10000
 
9.1%
7 9350
 
8.5%
3 8459
 
7.7%
2 8337
 
7.6%
4 8271
 
7.5%
1 7955
 
7.2%
5 7436
 
6.8%
6 7408
 
6.7%
8 6897
 
6.3%

출생연도
Real number (ℝ)

Distinct97
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1974.5699
Minimum1911
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:17:30.346514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1911
5-th percentile1950
Q11966
median1975
Q31984
95-th percentile1997
Maximum2022
Range111
Interquartile range (IQR)18

Descriptive statistics

Standard deviation14.15997
Coefficient of variation (CV)0.007171167
Kurtosis0.2595016
Mean1974.5699
Median Absolute Deviation (MAD)9
Skewness0.0036275678
Sum19745699
Variance200.50476
MonotonicityNot monotonic
2023-12-12T11:17:30.531308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1972 346
 
3.5%
1973 317
 
3.2%
1982 308
 
3.1%
1968 296
 
3.0%
1976 295
 
2.9%
1969 294
 
2.9%
1975 287
 
2.9%
1981 285
 
2.9%
1974 275
 
2.8%
1971 275
 
2.8%
Other values (87) 7022
70.2%
ValueCountFrequency (%)
1911 1
 
< 0.1%
1916 1
 
< 0.1%
1917 1
 
< 0.1%
1921 1
 
< 0.1%
1922 1
 
< 0.1%
1930 3
< 0.1%
1931 1
 
< 0.1%
1932 1
 
< 0.1%
1934 2
< 0.1%
1935 3
< 0.1%
ValueCountFrequency (%)
2022 7
0.1%
2021 7
0.1%
2020 2
 
< 0.1%
2019 7
0.1%
2018 10
0.1%
2017 9
0.1%
2016 10
0.1%
2015 10
0.1%
2014 10
0.1%
2013 15
0.1%

고객여권 발급 국가코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
JPN
6203 
CHN
1640 
KOR
624 
USA
 
373
TWN
 
357
Other values (31)
803 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowUSA
2nd rowJPN
3rd rowJPN
4th rowCHN
5th rowJPN

Common Values

ValueCountFrequency (%)
JPN 6203
62.0%
CHN 1640
 
16.4%
KOR 624
 
6.2%
USA 373
 
3.7%
TWN 357
 
3.6%
THA 175
 
1.8%
MNG 153
 
1.5%
HKG 80
 
0.8%
MYS 70
 
0.7%
SGP 70
 
0.7%
Other values (26) 255
 
2.5%

Length

2023-12-12T11:17:30.720292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
jpn 6203
62.0%
chn 1640
 
16.4%
kor 624
 
6.2%
usa 373
 
3.7%
twn 357
 
3.6%
tha 175
 
1.8%
mng 153
 
1.5%
hkg 80
 
0.8%
sgp 70
 
0.7%
mys 70
 
0.7%
Other values (26) 255
 
2.5%

고객여권 발급 국가명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일본
6203 
중국
1640 
대한민국(영주권자)
624 
미국
 
373
대만
 
357
Other values (31)
803 

Length

Max length10
Median length2
Mean length2.5582
Min length2

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row미국
2nd row일본
3rd row일본
4th row중국
5th row일본

Common Values

ValueCountFrequency (%)
일본 6203
62.0%
중국 1640
 
16.4%
대한민국(영주권자) 624
 
6.2%
미국 373
 
3.7%
대만 357
 
3.6%
태국 175
 
1.8%
몽골 153
 
1.5%
홍콩 80
 
0.8%
말레이시아 70
 
0.7%
싱가폴 70
 
0.7%
Other values (26) 255
 
2.5%

Length

2023-12-12T11:17:31.223169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
일본 6203
62.0%
중국 1640
 
16.4%
대한민국(영주권자 624
 
6.2%
미국 373
 
3.7%
대만 357
 
3.6%
태국 175
 
1.8%
몽골 153
 
1.5%
홍콩 80
 
0.8%
싱가폴 70
 
0.7%
말레이시아 70
 
0.7%
Other values (26) 255
 
2.5%

호텔명
Categorical

IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6851 
Coex Inter
 
683
Lotte Busan
 
622
Oakwood
 
585
M. Hilton
 
433
Other values (12)
826 

Length

Max length13
Median length4
Mean length5.6066
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd rowCoex Inter
3rd row<NA>
4th row<NA>
5th rowM. Hilton

Common Values

ValueCountFrequency (%)
<NA> 6851
68.5%
Coex Inter 683
 
6.8%
Lotte Busan 622
 
6.2%
Oakwood 585
 
5.9%
M. Hilton 433
 
4.3%
Novotel 381
 
3.8%
Novotel Suite 201
 
2.0%
부산비즈니스 112
 
1.1%
Grand Inter 44
 
0.4%
기타(서울) 31
 
0.3%
Other values (7) 57
 
0.6%

Length

2023-12-12T11:17:31.384789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6851
57.1%
inter 727
 
6.1%
coex 683
 
5.7%
lotte 624
 
5.2%
busan 622
 
5.2%
oakwood 585
 
4.9%
novotel 582
 
4.9%
m 433
 
3.6%
hilton 433
 
3.6%
suite 201
 
1.7%
Other values (10) 247
 
2.1%

호텔 체류일 수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)1.9%
Missing6851
Missing (%)68.5%
Infinite0
Infinite (%)0.0%
Mean4.8135916
Minimum0
Maximum366
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:17:31.544584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q35
95-th percentile16
Maximum366
Range366
Interquartile range (IQR)3

Descriptive statistics

Standard deviation9.8503346
Coefficient of variation (CV)2.0463586
Kurtosis589.53398
Mean4.8135916
Median Absolute Deviation (MAD)1
Skewness18.123322
Sum15158
Variance97.029091
MonotonicityNot monotonic
2023-12-12T11:17:31.755152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 944
 
9.4%
1 735
 
7.3%
3 380
 
3.8%
4 265
 
2.6%
5 131
 
1.3%
6 120
 
1.2%
7 84
 
0.8%
8 69
 
0.7%
10 49
 
0.5%
9 43
 
0.4%
Other values (51) 329
 
3.3%
(Missing) 6851
68.5%
ValueCountFrequency (%)
0 25
 
0.2%
1 735
7.3%
2 944
9.4%
3 380
3.8%
4 265
 
2.6%
5 131
 
1.3%
6 120
 
1.2%
7 84
 
0.8%
8 69
 
0.7%
9 43
 
0.4%
ValueCountFrequency (%)
366 1
< 0.1%
123 1
< 0.1%
90 1
< 0.1%
87 1
< 0.1%
76 1
< 0.1%
71 1
< 0.1%
69 2
< 0.1%
67 2
< 0.1%
60 1
< 0.1%
57 1
< 0.1%

평균 숙박제공 금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1010
Distinct (%)32.1%
Missing6851
Missing (%)68.5%
Infinite0
Infinite (%)0.0%
Mean765545.5
Minimum1134
Maximum7949700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:17:31.933025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1134
5-th percentile286000
Q1381150
median650375
Q3871200
95-th percentile1834360
Maximum7949700
Range7948566
Interquartile range (IQR)490050

Descriptive statistics

Standard deviation607302.35
Coefficient of variation (CV)0.79329362
Kurtosis18.730441
Mean765545.5
Median Absolute Deviation (MAD)265595
Skewness3.3427729
Sum2.4107028 × 109
Variance3.6881615 × 1011
MonotonicityNot monotonic
2023-12-12T11:17:32.133878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
713900 132
 
1.3%
572000 126
 
1.3%
286000 119
 
1.2%
356950 87
 
0.9%
381150 72
 
0.7%
363000 72
 
0.7%
648560 69
 
0.7%
762300 59
 
0.6%
726000 56
 
0.6%
1070850 56
 
0.6%
Other values (1000) 2301
 
23.0%
(Missing) 6851
68.5%
ValueCountFrequency (%)
1134 1
 
< 0.1%
26000 1
 
< 0.1%
51000 1
 
< 0.1%
56666 1
 
< 0.1%
59500 23
0.2%
60000 3
 
< 0.1%
61500 1
 
< 0.1%
63600 1
 
< 0.1%
66130 1
 
< 0.1%
67000 1
 
< 0.1%
ValueCountFrequency (%)
7949700 1
< 0.1%
6177792 1
< 0.1%
5818890 1
< 0.1%
5084823 1
< 0.1%
5082000 1
< 0.1%
4993800 1
< 0.1%
4864200 1
< 0.1%
4796300 1
< 0.1%
4586505 1
< 0.1%
4440700 1
< 0.1%

운송 구분명
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6935 
항공
2612 
택시
 
303
열차
 
136
렌트카
 
5
Other values (2)
 
9

Length

Max length4
Median length4
Mean length3.3885
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row항공
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6935
69.3%
항공 2612
 
26.1%
택시 303
 
3.0%
열차 136
 
1.4%
렌트카 5
 
0.1%
수시차량 5
 
0.1%
선박 4
 
< 0.1%

Length

2023-12-12T11:17:32.348652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:17:32.495266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6935
69.3%
항공 2612
 
26.1%
택시 303
 
3.0%
열차 136
 
1.4%
렌트카 5
 
< 0.1%
수시차량 5
 
< 0.1%
선박 4
 
< 0.1%

운송 제공 건수
Real number (ℝ)

MISSING 

Distinct94
Distinct (%)3.1%
Missing6935
Missing (%)69.3%
Infinite0
Infinite (%)0.0%
Mean8.2763458
Minimum1
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:17:32.664627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q38
95-th percentile27
Maximum288
Range287
Interquartile range (IQR)6

Descriptive statistics

Standard deviation15.699081
Coefficient of variation (CV)1.8968614
Kurtosis72.900741
Mean8.2763458
Median Absolute Deviation (MAD)2
Skewness6.9269049
Sum25367
Variance246.46114
MonotonicityNot monotonic
2023-12-12T11:17:32.807220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 677
 
6.8%
1 496
 
5.0%
2 422
 
4.2%
5 214
 
2.1%
3 189
 
1.9%
6 187
 
1.9%
8 116
 
1.2%
7 78
 
0.8%
10 69
 
0.7%
9 62
 
0.6%
Other values (84) 555
 
5.5%
(Missing) 6935
69.3%
ValueCountFrequency (%)
1 496
5.0%
2 422
4.2%
3 189
 
1.9%
4 677
6.8%
5 214
 
2.1%
6 187
 
1.9%
7 78
 
0.8%
8 116
 
1.2%
9 62
 
0.6%
10 69
 
0.7%
ValueCountFrequency (%)
288 1
< 0.1%
237 1
< 0.1%
169 1
< 0.1%
164 1
< 0.1%
155 1
< 0.1%
145 1
< 0.1%
142 1
< 0.1%
136 1
< 0.1%
132 1
< 0.1%
125 1
< 0.1%

평균 운송 제공금액
Real number (ℝ)

MISSING 

Distinct2670
Distinct (%)87.1%
Missing6935
Missing (%)69.3%
Infinite0
Infinite (%)0.0%
Mean400955.87
Minimum400
Maximum3785750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:17:32.952921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum400
5-th percentile18240
Q1161238
median351729
Q3556385
95-th percentile952362
Maximum3785750
Range3785350
Interquartile range (IQR)395147

Descriptive statistics

Standard deviation326236.72
Coefficient of variation (CV)0.81364745
Kurtosis16.173008
Mean400955.87
Median Absolute Deviation (MAD)196823
Skewness2.5368344
Sum1.2289297 × 109
Variance1.064304 × 1011
MonotonicityNot monotonic
2023-12-12T11:17:33.136208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 16
 
0.2%
83700 9
 
0.1%
30000 8
 
0.1%
15000 7
 
0.1%
360000 6
 
0.1%
504200 6
 
0.1%
10000 6
 
0.1%
100000 5
 
0.1%
287700 5
 
0.1%
390450 5
 
0.1%
Other values (2660) 2992
29.9%
(Missing) 6935
69.3%
ValueCountFrequency (%)
400 1
 
< 0.1%
3000 1
 
< 0.1%
3400 1
 
< 0.1%
3800 1
 
< 0.1%
3900 1
 
< 0.1%
4200 4
< 0.1%
4400 2
< 0.1%
4700 2
< 0.1%
4800 2
< 0.1%
5000 1
 
< 0.1%
ValueCountFrequency (%)
3785750 1
< 0.1%
3509000 1
< 0.1%
3374233 1
< 0.1%
3302363 1
< 0.1%
3153300 1
< 0.1%
3018402 1
< 0.1%
2810409 1
< 0.1%
2490000 1
< 0.1%
2327000 1
< 0.1%
2312100 1
< 0.1%

기타 서비스명
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6214 
일반음식점
1498 
기타(10만원미만)
905 
선물대
635 
기타(10만원이상)
 
477
Other values (2)
 
271

Length

Max length10
Median length4
Mean length4.8854
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반음식점
2nd row<NA>
3rd row선물대
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6214
62.1%
일반음식점 1498
 
15.0%
기타(10만원미만) 905
 
9.0%
선물대 635
 
6.3%
기타(10만원이상) 477
 
4.8%
경조사 241
 
2.4%
골프 30
 
0.3%

Length

2023-12-12T11:17:33.311323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:17:33.448238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 6214
62.1%
일반음식점 1498
 
15.0%
기타(10만원미만 905
 
9.0%
선물대 635
 
6.3%
기타(10만원이상 477
 
4.8%
경조사 241
 
2.4%
골프 30
 
0.3%

기타 서비스 제공 건수
Real number (ℝ)

MISSING 

Distinct64
Distinct (%)1.7%
Missing6214
Missing (%)62.1%
Infinite0
Infinite (%)0.0%
Mean3.9403064
Minimum1
Maximum236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:17:33.596265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile14
Maximum236
Range235
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.4935364
Coefficient of variation (CV)2.1555523
Kurtosis192.20584
Mean3.9403064
Median Absolute Deviation (MAD)1
Skewness10.285768
Sum14918
Variance72.140161
MonotonicityNot monotonic
2023-12-12T11:17:33.758669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1845
 
18.4%
2 703
 
7.0%
3 339
 
3.4%
4 185
 
1.8%
5 129
 
1.3%
6 85
 
0.9%
7 71
 
0.7%
8 58
 
0.6%
9 50
 
0.5%
10 39
 
0.4%
Other values (54) 282
 
2.8%
(Missing) 6214
62.1%
ValueCountFrequency (%)
1 1845
18.4%
2 703
 
7.0%
3 339
 
3.4%
4 185
 
1.8%
5 129
 
1.3%
6 85
 
0.9%
7 71
 
0.7%
8 58
 
0.6%
9 50
 
0.5%
10 39
 
0.4%
ValueCountFrequency (%)
236 1
 
< 0.1%
144 1
 
< 0.1%
106 1
 
< 0.1%
99 1
 
< 0.1%
90 1
 
< 0.1%
83 1
 
< 0.1%
81 1
 
< 0.1%
79 1
 
< 0.1%
67 1
 
< 0.1%
64 3
< 0.1%

평균 기타서비스 제공 금액
Real number (ℝ)

MISSING 

Distinct2509
Distinct (%)66.3%
Missing6214
Missing (%)62.1%
Infinite0
Infinite (%)0.0%
Mean248177.43
Minimum2000
Maximum15550388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:17:33.924625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile36557.75
Q181000
median156913
Q3309652.25
95-th percentile701365
Maximum15550388
Range15548388
Interquartile range (IQR)228652.25

Descriptive statistics

Standard deviation373488.55
Coefficient of variation (CV)1.5049255
Kurtosis750.06692
Mean248177.43
Median Absolute Deviation (MAD)91913
Skewness19.694321
Sum9.3959975 × 108
Variance1.394937 × 1011
MonotonicityNot monotonic
2023-12-12T11:17:34.090492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65000 303
 
3.0%
89000 166
 
1.7%
104000 59
 
0.6%
110000 53
 
0.5%
120000 34
 
0.3%
90590 33
 
0.3%
73557 24
 
0.2%
240000 18
 
0.2%
77000 18
 
0.2%
63559 16
 
0.2%
Other values (2499) 3062
30.6%
(Missing) 6214
62.1%
ValueCountFrequency (%)
2000 1
< 0.1%
2500 1
< 0.1%
2600 1
< 0.1%
3000 2
< 0.1%
3200 1
< 0.1%
3500 1
< 0.1%
3600 1
< 0.1%
3700 1
< 0.1%
4000 1
< 0.1%
4500 1
< 0.1%
ValueCountFrequency (%)
15550388 1
< 0.1%
3513700 1
< 0.1%
2863500 1
< 0.1%
2710000 1
< 0.1%
2525000 1
< 0.1%
2430143 1
< 0.1%
2423333 1
< 0.1%
2415000 1
< 0.1%
2209758 1
< 0.1%
2197600 2
< 0.1%

Interactions

2023-12-12T11:17:27.800300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:22.850907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:23.983664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:24.698590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.376341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:26.118792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:27.009207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:27.919935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:22.949990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:24.098765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:24.805355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.475691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:26.241403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:27.129807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:28.036480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:23.066922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:24.203244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:24.926462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.596931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:26.360495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:27.228059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:28.131551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:23.546268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:24.347298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.039182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.690780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:26.497906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:27.306682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:28.213578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:23.666008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:24.464773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.115988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.826194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:26.636222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:27.422629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:28.315820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:23.789457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:24.547921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.205936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.950063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:26.802033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:27.537118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:28.483853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:23.878645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:24.619055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:25.287338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:26.033014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:26.902259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:17:27.650300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:17:34.221890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출생연도고객여권 발급 국가코드고객여권 발급 국가명호텔명호텔 체류일 수평균 숙박제공 금액운송 구분명운송 제공 건수평균 운송 제공금액기타 서비스명기타 서비스 제공 건수평균 기타서비스 제공 금액
출생연도1.0000.2460.2460.0300.0000.0790.1160.1290.1340.2830.0000.000
고객여권 발급 국가코드0.2461.0001.0000.4800.1650.4780.4910.0000.3500.2570.2900.661
고객여권 발급 국가명0.2461.0001.0000.4800.1650.4780.4910.0000.3500.2570.2900.661
호텔명0.0300.4800.4801.0000.0000.340NaNNaNNaNNaNNaNNaN
호텔 체류일 수0.0000.1650.1650.0001.0000.254NaNNaNNaNNaNNaNNaN
평균 숙박제공 금액0.0790.4780.4780.3400.2541.000NaNNaNNaNNaNNaNNaN
운송 구분명0.1160.4910.491NaNNaNNaN1.0000.0000.410NaNNaNNaN
운송 제공 건수0.1290.0000.000NaNNaNNaN0.0001.0000.000NaNNaNNaN
평균 운송 제공금액0.1340.3500.350NaNNaNNaN0.4100.0001.000NaNNaNNaN
기타 서비스명0.2830.2570.257NaNNaNNaNNaNNaNNaN1.0000.0730.121
기타 서비스 제공 건수0.0000.2900.290NaNNaNNaNNaNNaNNaN0.0731.0000.012
평균 기타서비스 제공 금액0.0000.6610.661NaNNaNNaNNaNNaNNaN0.1210.0121.000
2023-12-12T11:17:34.373561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객여권 발급 국가코드고객여권 발급 국가명호텔명운송 구분명기타 서비스명
고객여권 발급 국가코드1.0001.0000.1550.2410.116
고객여권 발급 국가명1.0001.0000.1550.2410.116
호텔명0.1550.1551.000NaNNaN
운송 구분명0.2410.241NaN1.000NaN
기타 서비스명0.1160.116NaNNaN1.000
2023-12-12T11:17:34.509204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
출생연도호텔 체류일 수평균 숙박제공 금액운송 제공 건수평균 운송 제공금액기타 서비스 제공 건수평균 기타서비스 제공 금액고객여권 발급 국가코드고객여권 발급 국가명호텔명운송 구분명기타 서비스명
출생연도1.000-0.171-0.132-0.0750.011-0.130-0.1080.0880.0880.0100.0610.152
호텔 체류일 수-0.1711.0000.562NaNNaNNaNNaN0.0780.0780.0000.0000.000
평균 숙박제공 금액-0.1320.5621.000NaNNaNNaNNaN0.1980.1980.1470.0000.000
운송 제공 건수-0.075NaNNaN1.0000.034NaNNaN0.0000.0000.0000.0000.000
평균 운송 제공금액0.011NaNNaN0.0341.000NaNNaN0.1310.1310.0000.2300.000
기타 서비스 제공 건수-0.130NaNNaNNaNNaN1.0000.2460.1230.1230.0000.0000.043
평균 기타서비스 제공 금액-0.108NaNNaNNaNNaN0.2461.0000.4070.4070.0000.0000.078
고객여권 발급 국가코드0.0880.0780.1980.0000.1310.1230.4071.0001.0000.1550.2410.116
고객여권 발급 국가명0.0880.0780.1980.0000.1310.1230.4071.0001.0000.1550.2410.116
호텔명0.0100.0000.1470.0000.0000.0000.0000.1550.1551.0000.0000.000
운송 구분명0.0610.0000.0000.0000.2300.0000.0000.2410.2410.0001.0000.000
기타 서비스명0.1520.0000.0000.0000.0000.0430.0780.1160.1160.0000.0001.000

Missing values

2023-12-12T11:17:28.706277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:17:28.938427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T11:17:29.162879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

가상고객 번호출생연도고객여권 발급 국가코드고객여권 발급 국가명호텔명호텔 체류일 수평균 숙박제공 금액운송 구분명운송 제공 건수평균 운송 제공금액기타 서비스명기타 서비스 제공 건수평균 기타서비스 제공 금액
2956V00046430171974USA미국<NA><NA><NA><NA><NA><NA>일반음식점6275333
10994V00389130011973JPN일본Coex Inter3535425<NA><NA><NA><NA><NA><NA>
8825V00297144951974JPN일본<NA><NA><NA><NA><NA><NA>선물대1130531
15764V00640801541985CHN중국<NA><NA><NA>항공2505700<NA><NA><NA>
4978V00113888611973JPN일본M. Hilton7668827<NA><NA><NA><NA><NA><NA>
524V00004002091950USA미국Oakwood385084823<NA><NA><NA><NA><NA><NA>
10493V00371330901963JPN일본Coex Inter5892375<NA><NA><NA><NA><NA><NA>
21770V00748825291999JPN일본<NA><NA><NA>항공4320451<NA><NA><NA>
26V00000513291944KOR대한민국(영주권자)<NA><NA><NA>항공1251500<NA><NA><NA>
10051V00349178801987CHN중국Coex Inter3556600<NA><NA><NA><NA><NA><NA>
가상고객 번호출생연도고객여권 발급 국가코드고객여권 발급 국가명호텔명호텔 체류일 수평균 숙박제공 금액운송 구분명운송 제공 건수평균 운송 제공금액기타 서비스명기타 서비스 제공 건수평균 기타서비스 제공 금액
22504V00761580771964CHN중국<NA><NA><NA>항공4306163<NA><NA><NA>
2395V00031858201954JPN일본<NA><NA><NA><NA><NA><NA>선물대2349671
13584V00508455871983JPN일본<NA><NA><NA><NA><NA><NA>기타(10만원미만)165000
2926V00044848641974JPN일본M. Hilton3599555<NA><NA><NA><NA><NA><NA>
8541V00284223931965KOR대한민국(영주권자)<NA><NA><NA>항공2365031<NA><NA><NA>
9971V00346400221976CHN중국Coex Inter31070850<NA><NA><NA><NA><NA><NA>
9381V00321888731977JPN일본Coex Inter2363000<NA><NA><NA><NA><NA><NA>
14613V00572510951977JPN일본<NA><NA><NA>항공2736400<NA><NA><NA>
6607V00185768571957TWN대만<NA><NA><NA>택시2559637<NA><NA><NA>
12720V00465840891975CHN중국<NA><NA><NA>항공3513200<NA><NA><NA>