Overview

Dataset statistics

Number of variables9
Number of observations311
Missing cells2
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.2 KiB
Average record size in memory76.4 B

Variable types

Numeric4
DateTime1
Categorical1
Text3

Dataset

Description서울월드컵경기장 주경기장 연간 사용자 통계로 경기별 행사구분, 행사종류, 주최, 관람인원, 수입금, 사용일수 자료 제공
Author서울시설공단
URLhttps://www.data.go.kr/data/15044128/fileData.do

Alerts

관람인원(명) is highly overall correlated with 수입금(원)High correlation
수입금(원) is highly overall correlated with 관람인원(명) High correlation
사용일수(일) is highly overall correlated with 구 분High correlation
구 분 is highly overall correlated with 사용일수(일)High correlation
연번 has unique valuesUnique
관람인원(명) has 25 (8.0%) zerosZeros
수입금(원) has 27 (8.7%) zerosZeros
사용일수(일) has 5 (1.6%) zerosZeros

Reproduction

Analysis started2024-04-21 01:10:12.556632
Analysis finished2024-04-21 01:10:16.188839
Duration3.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct311
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156
Minimum1
Maximum311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T10:10:16.255502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.5
Q178.5
median156
Q3233.5
95-th percentile295.5
Maximum311
Range310
Interquartile range (IQR)155

Descriptive statistics

Standard deviation89.922189
Coefficient of variation (CV)0.57642429
Kurtosis-1.2
Mean156
Median Absolute Deviation (MAD)78
Skewness0
Sum48516
Variance8086
MonotonicityStrictly increasing
2024-04-21T10:10:16.397501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
206 1
 
0.3%
213 1
 
0.3%
212 1
 
0.3%
211 1
 
0.3%
210 1
 
0.3%
209 1
 
0.3%
208 1
 
0.3%
207 1
 
0.3%
205 1
 
0.3%
Other values (301) 301
96.8%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
311 1
0.3%
310 1
0.3%
309 1
0.3%
308 1
0.3%
307 1
0.3%
306 1
0.3%
305 1
0.3%
304 1
0.3%
303 1
0.3%
302 1
0.3%

경기(행사) 일시
Date

Distinct310
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum2015-02-17 19:30:00
Maximum2023-11-25 16:30:00
2024-04-21T10:10:16.542108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:16.648506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

구 분
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
K리그
168 
기타축구
79 
일반행사
39 
국가대표
24 
공공행사
 
1

Length

Max length4
Median length3
Mean length3.4598071
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row기타축구
2nd row기타축구
3rd rowK리그
4th row기타축구
5th row국가대표

Common Values

ValueCountFrequency (%)
K리그 168
54.0%
기타축구 79
25.4%
일반행사 39
 
12.5%
국가대표 24
 
7.7%
공공행사 1
 
0.3%

Length

2024-04-21T10:10:16.756468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:10:16.858352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
k리그 168
54.0%
기타축구 79
25.4%
일반행사 39
 
12.5%
국가대표 24
 
7.7%
공공행사 1
 
0.3%

경기(행사) 종류
Text

Distinct59
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-21T10:10:17.077655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length27
Mean length6.9003215
Min length3

Characters and Unicode

Total characters2146
Distinct characters174
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)14.1%

Sample

1st rowACL
2nd rowACL
3rd rowK리그 클래식
4th rowACL
5th row국가대표팀 친선경기
ValueCountFrequency (%)
k리그1 89
17.5%
k리그 78
15.3%
클래식 77
15.1%
acl 25
 
4.9%
콘서트 18
 
3.5%
축구경기 16
 
3.1%
fa컵 15
 
2.9%
친선경기 13
 
2.5%
일반시민 10
 
2.0%
국가대표팀 7
 
1.4%
Other values (92) 162
31.8%
2024-04-21T10:10:17.441074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
200
 
9.3%
리 183
 
8.5%
그 173
 
8.1%
K 169
 
7.9%
1 103
 
4.8%
식 81
 
3.8%
클 77
 
3.6%
래 77
 
3.6%
기 41
 
1.9%
A 41
 
1.9%
Other values (164) 1001
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1430
66.6%
Uppercase Letter 288
 
13.4%
Space Separator 200
 
9.3%
Decimal Number 176
 
8.2%
Lowercase Letter 21
 
1.0%
Close Punctuation 13
 
0.6%
Open Punctuation 13
 
0.6%
Dash Punctuation 3
 
0.1%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 183
 
12.8%
그 173
 
12.1%
식 81
 
5.7%
클 77
 
5.4%
래 77
 
5.4%
기 41
 
2.9%
경 40
 
2.8%
컵 35
 
2.4%
축 28
 
2.0%
구 27
 
1.9%
Other values (132) 668
46.7%
Uppercase Letter
ValueCountFrequency (%)
K 169
58.7%
A 41
 
14.2%
C 27
 
9.4%
L 25
 
8.7%
F 19
 
6.6%
S 2
 
0.7%
I 1
 
0.3%
U 1
 
0.3%
E 1
 
0.3%
O 1
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 103
58.5%
2 23
 
13.1%
0 22
 
12.5%
3 11
 
6.2%
9 8
 
4.5%
5 4
 
2.3%
6 3
 
1.7%
7 2
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
v 8
38.1%
s 8
38.1%
a 1
 
4.8%
f 1
 
4.8%
t 1
 
4.8%
e 1
 
4.8%
r 1
 
4.8%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
200
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1430
66.6%
Common 407
 
19.0%
Latin 309
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 183
 
12.8%
그 173
 
12.1%
식 81
 
5.7%
클 77
 
5.4%
래 77
 
5.4%
기 41
 
2.9%
경 40
 
2.8%
컵 35
 
2.4%
축 28
 
2.0%
구 27
 
1.9%
Other values (132) 668
46.7%
Latin
ValueCountFrequency (%)
K 169
54.7%
A 41
 
13.3%
C 27
 
8.7%
L 25
 
8.1%
F 19
 
6.1%
v 8
 
2.6%
s 8
 
2.6%
S 2
 
0.6%
I 1
 
0.3%
U 1
 
0.3%
Other values (8) 8
 
2.6%
Common
ValueCountFrequency (%)
200
49.1%
1 103
25.3%
2 23
 
5.7%
0 22
 
5.4%
) 13
 
3.2%
( 13
 
3.2%
3 11
 
2.7%
9 8
 
2.0%
5 4
 
1.0%
6 3
 
0.7%
Other values (4) 7
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1430
66.6%
ASCII 716
33.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
200
27.9%
K 169
23.6%
1 103
14.4%
A 41
 
5.7%
C 27
 
3.8%
L 25
 
3.5%
2 23
 
3.2%
0 22
 
3.1%
F 19
 
2.7%
) 13
 
1.8%
Other values (22) 74
 
10.3%
Hangul
ValueCountFrequency (%)
리 183
 
12.8%
그 173
 
12.1%
식 81
 
5.7%
클 77
 
5.4%
래 77
 
5.4%
기 41
 
2.9%
경 40
 
2.8%
컵 35
 
2.4%
축 28
 
2.0%
구 27
 
1.9%
Other values (132) 668
46.7%

경기(행사) 내용
Text

Distinct236
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-21T10:10:17.700382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length32
Mean length16.096463
Min length6

Characters and Unicode

Total characters5006
Distinct characters262
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)61.1%

Sample

1st rowACL(vs하노이T&T FC)
2nd rowACL(vs가시마앤틀러스)
3rd rowK리그 클래식(vs전북)
4th rowACL(vs웨스턴시드니)
5th row친선경기(vs뉴질랜드)
ValueCountFrequency (%)
k리그1 105
 
10.7%
vs 69
 
7.0%
k리그 53
 
5.4%
서울 37
 
3.8%
2023 25
 
2.5%
2022 24
 
2.4%
2018 23
 
2.3%
fc서울 22
 
2.2%
2019 20
 
2.0%
2021 19
 
1.9%
Other values (264) 586
59.6%
2024-04-21T10:10:18.075972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
678
 
13.5%
2 261
 
5.2%
s 217
 
4.3%
v 217
 
4.3%
1 199
 
4.0%
리 184
 
3.7%
0 170
 
3.4%
그 170
 
3.4%
K 169
 
3.4%
울 142
 
2.8%
Other values (252) 2599
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2479
49.5%
Decimal Number 758
 
15.1%
Space Separator 678
 
13.5%
Lowercase Letter 442
 
8.8%
Uppercase Letter 403
 
8.1%
Open Punctuation 112
 
2.2%
Close Punctuation 112
 
2.2%
Dash Punctuation 17
 
0.3%
Other Punctuation 3
 
0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 184
 
7.4%
그 170
 
6.9%
울 142
 
5.7%
서 138
 
5.6%
전 67
 
2.7%
대 51
 
2.1%
래 51
 
2.1%
식 51
 
2.1%
기 49
 
2.0%
클 49
 
2.0%
Other values (206) 1527
61.6%
Uppercase Letter
ValueCountFrequency (%)
K 169
41.9%
C 60
 
14.9%
F 52
 
12.9%
A 45
 
11.2%
L 28
 
6.9%
S 9
 
2.2%
R 6
 
1.5%
V 6
 
1.5%
E 5
 
1.2%
T 5
 
1.2%
Other values (7) 18
 
4.5%
Decimal Number
ValueCountFrequency (%)
2 261
34.4%
1 199
26.3%
0 170
22.4%
3 36
 
4.7%
8 31
 
4.1%
9 28
 
3.7%
6 11
 
1.5%
4 9
 
1.2%
5 8
 
1.1%
7 5
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
s 217
49.1%
v 217
49.1%
b 1
 
0.2%
m 1
 
0.2%
c 1
 
0.2%
r 1
 
0.2%
e 1
 
0.2%
t 1
 
0.2%
f 1
 
0.2%
a 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
, 1
33.3%
. 1
33.3%
& 1
33.3%
Space Separator
ValueCountFrequency (%)
678
100.0%
Open Punctuation
ValueCountFrequency (%)
( 112
100.0%
Close Punctuation
ValueCountFrequency (%)
) 112
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2479
49.5%
Common 1682
33.6%
Latin 845
 
16.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 184
 
7.4%
그 170
 
6.9%
울 142
 
5.7%
서 138
 
5.6%
전 67
 
2.7%
대 51
 
2.1%
래 51
 
2.1%
식 51
 
2.1%
기 49
 
2.0%
클 49
 
2.0%
Other values (206) 1527
61.6%
Latin
ValueCountFrequency (%)
s 217
25.7%
v 217
25.7%
K 169
20.0%
C 60
 
7.1%
F 52
 
6.2%
A 45
 
5.3%
L 28
 
3.3%
S 9
 
1.1%
R 6
 
0.7%
V 6
 
0.7%
Other values (17) 36
 
4.3%
Common
ValueCountFrequency (%)
678
40.3%
2 261
 
15.5%
1 199
 
11.8%
0 170
 
10.1%
( 112
 
6.7%
) 112
 
6.7%
3 36
 
2.1%
8 31
 
1.8%
9 28
 
1.7%
- 17
 
1.0%
Other values (9) 38
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2525
50.4%
Hangul 2479
49.5%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
678
26.9%
2 261
 
10.3%
s 217
 
8.6%
v 217
 
8.6%
1 199
 
7.9%
0 170
 
6.7%
K 169
 
6.7%
( 112
 
4.4%
) 112
 
4.4%
C 60
 
2.4%
Other values (34) 330
13.1%
Hangul
ValueCountFrequency (%)
리 184
 
7.4%
그 170
 
6.9%
울 142
 
5.7%
서 138
 
5.6%
전 67
 
2.7%
대 51
 
2.1%
래 51
 
2.1%
식 51
 
2.1%
기 49
 
2.0%
클 49
 
2.0%
Other values (206) 1527
61.6%
Punctuation
ValueCountFrequency (%)
’ 1
50.0%
‘ 1
50.0%

주 최
Text

Distinct58
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-04-21T10:10:18.309645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length17
Mean length7.2025723
Min length3

Characters and Unicode

Total characters2240
Distinct characters182
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)14.1%

Sample

1st rowGS스포츠
2nd rowGS스포츠
3rd rowGS스포츠
4th rowGS스포츠
5th row대한축구협회
ValueCountFrequency (%)
주)gs스포츠 116
33.4%
gs스포츠 94
27.1%
대한축구협회 14
 
4.0%
서울특별시 11
 
3.2%
사)대한축구협회 10
 
2.9%
한국연예제작자협회 5
 
1.4%
한국프로축구연맹 5
 
1.4%
쿠팡주식회사 4
 
1.2%
공단 4
 
1.2%
프 4
 
1.2%
Other values (66) 80
23.1%
2024-04-21T10:10:18.651634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 218
 
9.7%
G 215
 
9.6%
포 215
 
9.6%
S 214
 
9.6%
츠 213
 
9.5%
) 140
 
6.2%
( 139
 
6.2%
주 126
 
5.6%
회 40
 
1.8%
한 38
 
1.7%
Other values (172) 682
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1448
64.6%
Uppercase Letter 442
 
19.7%
Close Punctuation 140
 
6.2%
Open Punctuation 139
 
6.2%
Space Separator 36
 
1.6%
Other Punctuation 16
 
0.7%
Lowercase Letter 13
 
0.6%
Decimal Number 4
 
0.2%
Other Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 218
15.1%
포 215
14.8%
츠 213
14.7%
주 126
 
8.7%
회 40
 
2.8%
한 38
 
2.6%
구 37
 
2.6%
축 35
 
2.4%
협 30
 
2.1%
대 26
 
1.8%
Other values (142) 470
32.5%
Lowercase Letter
ValueCountFrequency (%)
n 2
15.4%
g 2
15.4%
j 1
7.7%
c 1
7.7%
b 1
7.7%
m 1
7.7%
h 1
7.7%
z 1
7.7%
a 1
7.7%
u 1
7.7%
Uppercase Letter
ValueCountFrequency (%)
G 215
48.6%
S 214
48.4%
C 3
 
0.7%
F 3
 
0.7%
Y 2
 
0.5%
W 1
 
0.2%
B 1
 
0.2%
K 1
 
0.2%
T 1
 
0.2%
O 1
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 2
50.0%
3 1
25.0%
0 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
/ 1
 
6.2%
Close Punctuation
ValueCountFrequency (%)
) 140
100.0%
Open Punctuation
ValueCountFrequency (%)
( 139
100.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1450
64.7%
Latin 455
 
20.3%
Common 335
 
15.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 218
15.0%
포 215
14.8%
츠 213
14.7%
주 126
 
8.7%
회 40
 
2.8%
한 38
 
2.6%
구 37
 
2.6%
축 35
 
2.4%
협 30
 
2.1%
대 26
 
1.8%
Other values (143) 472
32.6%
Latin
ValueCountFrequency (%)
G 215
47.3%
S 214
47.0%
C 3
 
0.7%
F 3
 
0.7%
n 2
 
0.4%
g 2
 
0.4%
Y 2
 
0.4%
W 1
 
0.2%
B 1
 
0.2%
K 1
 
0.2%
Other values (11) 11
 
2.4%
Common
ValueCountFrequency (%)
) 140
41.8%
( 139
41.5%
36
 
10.7%
, 15
 
4.5%
2 2
 
0.6%
/ 1
 
0.3%
3 1
 
0.3%
0 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1448
64.6%
ASCII 790
35.3%
None 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 218
15.1%
포 215
14.8%
츠 213
14.7%
주 126
 
8.7%
회 40
 
2.8%
한 38
 
2.6%
구 37
 
2.6%
축 35
 
2.4%
협 30
 
2.1%
대 26
 
1.8%
Other values (142) 470
32.5%
ASCII
ValueCountFrequency (%)
G 215
27.2%
S 214
27.1%
) 140
17.7%
( 139
17.6%
36
 
4.6%
, 15
 
1.9%
C 3
 
0.4%
F 3
 
0.4%
n 2
 
0.3%
g 2
 
0.3%
Other values (19) 21
 
2.7%
None
ValueCountFrequency (%)
㈜ 2
100.0%

관람인원(명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct239
Distinct (%)77.1%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean14863.594
Minimum0
Maximum65608
Zeros25
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T10:10:18.773245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1350
median10035
Q318941.25
95-th percentile56861.1
Maximum65608
Range65608
Interquartile range (IQR)18591.25

Descriptive statistics

Standard deviation16864.531
Coefficient of variation (CV)1.1346201
Kurtosis1.5658355
Mean14863.594
Median Absolute Deviation (MAD)9535
Skewness1.4856171
Sum4607714
Variance2.8441242 × 108
MonotonicityNot monotonic
2024-04-21T10:10:18.895809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 26
 
8.4%
0 25
 
8.0%
200 6
 
1.9%
100 5
 
1.6%
60 5
 
1.6%
350 5
 
1.6%
500 3
 
1.0%
35481 2
 
0.6%
300 2
 
0.6%
150 2
 
0.6%
Other values (229) 229
73.6%
ValueCountFrequency (%)
0 25
8.0%
40 1
 
0.3%
50 26
8.4%
60 5
 
1.6%
100 5
 
1.6%
120 1
 
0.3%
150 2
 
0.6%
200 6
 
1.9%
240 1
 
0.3%
300 2
 
0.6%
ValueCountFrequency (%)
65608 1
0.3%
64816 1
0.3%
64412 1
0.3%
64185 1
0.3%
64170 1
0.3%
64149 1
0.3%
63336 1
0.3%
63234 1
0.3%
62689 1
0.3%
61633 1
0.3%

수입금(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct262
Distinct (%)84.5%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean73500906
Minimum0
Maximum3.4002999 × 109
Zeros27
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T10:10:19.198707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q119073755
median39275525
Q354564400
95-th percentile2.5753988 × 108
Maximum3.4002999 × 109
Range3.4002999 × 109
Interquartile range (IQR)35490645

Descriptive statistics

Standard deviation2.2455578 × 108
Coefficient of variation (CV)3.055143
Kurtosis159.2335
Mean73500906
Median Absolute Deviation (MAD)19502005
Skewness11.39938
Sum2.2785281 × 1010
Variance5.0425299 × 1016
MonotonicityNot monotonic
2024-04-21T10:10:19.317021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
8.7%
897000 13
 
4.2%
598000 7
 
2.3%
460000 4
 
1.3%
714000 2
 
0.6%
21223160 1
 
0.3%
21376390 1
 
0.3%
30671040 1
 
0.3%
26975750 1
 
0.3%
21260110 1
 
0.3%
Other values (252) 252
81.0%
ValueCountFrequency (%)
0 27
8.7%
354510 1
 
0.3%
411580 1
 
0.3%
460000 4
 
1.3%
598000 7
 
2.3%
649060 1
 
0.3%
690000 1
 
0.3%
714000 2
 
0.6%
897000 13
4.2%
1196000 1
 
0.3%
ValueCountFrequency (%)
3400299930 1
0.3%
1220458130 1
0.3%
912828350 1
0.3%
597568450 1
0.3%
575270560 1
0.3%
503839130 1
0.3%
493347130 1
0.3%
485448330 1
0.3%
484131730 1
0.3%
478920270 1
0.3%

사용일수(일)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.607717
Minimum0
Maximum12
Zeros5
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-04-21T10:10:19.422891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile6
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6560169
Coefficient of variation (CV)1.0300425
Kurtosis10.656407
Mean1.607717
Median Absolute Deviation (MAD)0
Skewness3.1936505
Sum500
Variance2.7423919
MonotonicityNot monotonic
2024-04-21T10:10:19.513160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 240
77.2%
2 33
 
10.6%
7 9
 
2.9%
3 6
 
1.9%
6 5
 
1.6%
5 5
 
1.6%
0 5
 
1.6%
4 3
 
1.0%
8 2
 
0.6%
9 2
 
0.6%
ValueCountFrequency (%)
0 5
 
1.6%
1 240
77.2%
2 33
 
10.6%
3 6
 
1.9%
4 3
 
1.0%
5 5
 
1.6%
6 5
 
1.6%
7 9
 
2.9%
8 2
 
0.6%
9 2
 
0.6%
ValueCountFrequency (%)
12 1
 
0.3%
9 2
 
0.6%
8 2
 
0.6%
7 9
 
2.9%
6 5
 
1.6%
5 5
 
1.6%
4 3
 
1.0%
3 6
 
1.9%
2 33
 
10.6%
1 240
77.2%

Interactions

2024-04-21T10:10:15.600514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:14.535647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:14.903441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:15.235531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:15.675139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:14.669562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:14.980686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:15.315465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:15.752956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:14.744436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:15.055376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:15.405268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:15.832393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:14.822821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:15.152843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:10:15.516865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:10:19.599453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구 분경기(행사) 종류주 최관람인원(명)수입금(원)사용일수(일)
연번1.0000.4850.7870.7840.5480.2470.189
구 분0.4851.0000.9680.9890.7880.6290.736
경기(행사) 종류0.7870.9681.0000.9920.7690.9620.897
주 최0.7840.9890.9921.0000.7990.9570.974
관람인원(명)0.5480.7880.7690.7991.0000.6660.595
수입금(원)0.2470.6290.9620.9570.6661.0000.602
사용일수(일)0.1890.7360.8970.9740.5950.6021.000
2024-04-21T10:10:19.708050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번관람인원(명)수입금(원)사용일수(일)구 분
연번1.0000.0350.191-0.0680.228
관람인원(명)0.0351.0000.8260.3390.440
수입금(원)0.1910.8261.0000.3330.279
사용일수(일)-0.0680.3390.3331.0000.514
구 분0.2280.4400.2790.5141.000

Missing values

2024-04-21T10:10:15.932278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:10:16.049804image/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.
2024-04-21T10:10:16.145654image/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

연번경기(행사) 일시구 분경기(행사) 종류경기(행사) 내용주 최관람인원(명)수입금(원)사용일수(일)
012015-02-17 19:30:00기타축구ACLACL(vs하노이T&T FC)GS스포츠6718238351503
122015-03-04 19:30:00기타축구ACLACL(vs가시마앤틀러스)GS스포츠5790275799902
232015-03-14 14:00:00K리그K리그 클래식K리그 클래식(vs전북)GS스포츠32516530330201
342015-03-18 19:30:00기타축구ACLACL(vs웨스턴시드니)GS스포츠5645269554702
452015-03-31 20:00:00국가대표국가대표팀 친선경기친선경기(vs뉴질랜드)대한축구협회285741529079703
562015-04-04 14:00:00K리그K리그 클래식K리그 클래식-4R(vs제주)GS스포츠22155443834201
672015-04-15 19:30:00K리그K리그 클래식K리그 클래식-6R(vs대전)GS스포츠7186320392551
782015-04-21 19:30:00기타축구ACLACL(vs광저우에버그란데)GS스포츠17157438921702
892015-04-25 18:00:00일반행사콘서트토요일을 즐겨라(주)월드쇼마켓152461033470606
9102015-04-29 19:30:00기타축구FA컵FA컵(vs경주 한수원)GS스포츠157075933701
연번경기(행사) 일시구 분경기(행사) 종류경기(행사) 내용주 최관람인원(명)수입금(원)사용일수(일)
3013022023-08-11 19:00:00공공행사콘서트2023 세계스카우트 잼버리 K-POP SUPER LIVE문화체육관공부, 2023새만금세계스카우트잼버리조직위4300005
3023032023-08-19 19:30:00K리그K리그12023 K리그1 FC서울 vs 대구(주)GS스포츠18592668298801
3033042023-08-27 19:00:00K리그K리그12023 K리그1 FC서울 vs 울산(주)GS스포츠27621847281301
3043052023-09-17 14:00:00K리그K리그12023 K리그1 FC서울 vs 광주(주)GS스포츠21014721843801
3053062023-09-23 18:00:00일반행사콘서트MBC 아이돌라디오 ‘2023 LIVE IN SEOUL’(주)문화방송205462325056907
3063072023-10-08 15:00:00K리그K리그12023 K리그1 FC서울 vs 전북(주)GS스포츠34082890704201
3073082023-10-13 20:00:00국가대표국가대표팀 친선경기하나은행 초청 축구 국가대표팀 친선경기 대한민국vs튀니지(사)대한축구협회575735038391302
3083092023-10-22 14:00:00K리그K리그12023 K리그1 파이널 FC서울 vs 강원(주)GS스포츠12188519089301
3093102023-11-16 20:00:00국가대표월드컵 아시아 예선경기2026 북중미월드컵 아시아 예선경기 대한민국vs싱가포르(사)대한축구협회641494789202702
3103112023-11-25 16:30:00K리그K리그12023 K리그1 파이널 FC서울 vs 수원(주)GS스포츠36872973991001