Overview

Dataset statistics

Number of variables12
Number of observations100
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory101.3 B

Variable types

Categorical5
Text4
Numeric3

Alerts

item_cd is highly overall correlated with item_nmHigh correlation
game_gbn is highly overall correlated with gbn and 2 other fieldsHigh correlation
gbn is highly overall correlated with game_gbn and 2 other fieldsHigh correlation
gbn_nm is highly overall correlated with game_gbn and 2 other fieldsHigh correlation
item_nm is highly overall correlated with item_cdHigh correlation
game_gbn_nm is highly overall correlated with game_gbn and 2 other fieldsHigh correlation
gbn is highly imbalanced (80.6%)Imbalance
gbn_nm is highly imbalanced (80.6%)Imbalance

Reproduction

Analysis started2023-12-10 10:04:25.693142
Analysis finished2023-12-10 10:04:29.465595
Duration3.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

gbn
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
B
97 
F
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowF
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B 97
97.0%
F 3
 
3.0%

Length

2023-12-10T19:04:29.608710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:29.831327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 97
97.0%
f 3
 
3.0%

gbn_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
체육인
97 
장애체육인
 
3

Length

Max length5
Median length3
Mean length3.06
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row체육인
2nd row장애체육인
3rd row체육인
4th row체육인
5th row체육인

Common Values

ValueCountFrequency (%)
체육인 97
97.0%
장애체육인 3
 
3.0%

Length

2023-12-10T19:04:30.128226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:30.297316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
체육인 97
97.0%
장애체육인 3
 
3.0%

racer_nm
Text

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:04:30.720544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters300
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)66.0%

Sample

1st row장미란
2nd row김민규
3rd row장용호
4th row박해정
5th row이호응
ValueCountFrequency (%)
이호석 5
 
5.0%
진선유 4
 
4.0%
김동문 3
 
3.0%
김세진 2
 
2.0%
이원희 2
 
2.0%
최원종 2
 
2.0%
조남석 2
 
2.0%
황경선 2
 
2.0%
오세종 2
 
2.0%
여수연 2
 
2.0%
Other values (70) 74
74.0%
2023-12-10T19:04:31.634295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 23
 
7.7%
김 20
 
6.7%
석 11
 
3.7%
선 10
 
3.3%
정 10
 
3.3%
진 9
 
3.0%
호 8
 
2.7%
상 7
 
2.3%
희 7
 
2.3%
경 7
 
2.3%
Other values (78) 188
62.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 300
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 23
 
7.7%
김 20
 
6.7%
석 11
 
3.7%
선 10
 
3.3%
정 10
 
3.3%
진 9
 
3.0%
호 8
 
2.7%
상 7
 
2.3%
희 7
 
2.3%
경 7
 
2.3%
Other values (78) 188
62.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 300
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 23
 
7.7%
김 20
 
6.7%
석 11
 
3.7%
선 10
 
3.3%
정 10
 
3.3%
진 9
 
3.0%
호 8
 
2.7%
상 7
 
2.3%
희 7
 
2.3%
경 7
 
2.3%
Other values (78) 188
62.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 300
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 23
 
7.7%
김 20
 
6.7%
석 11
 
3.7%
선 10
 
3.3%
정 10
 
3.3%
진 9
 
3.0%
호 8
 
2.7%
상 7
 
2.3%
희 7
 
2.3%
경 7
 
2.3%
Other values (78) 188
62.7%

seq
Real number (ℝ)

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.21
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:31.866801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile14.1
Maximum23
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.4478913
Coefficient of variation (CV)1.0565063
Kurtosis4.2396845
Mean4.21
Median Absolute Deviation (MAD)1
Skewness2.0217204
Sum421
Variance19.783737
MonotonicityNot monotonic
2023-12-10T19:04:32.054921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 33
33.0%
2 20
20.0%
6 10
 
10.0%
3 9
 
9.0%
4 6
 
6.0%
5 5
 
5.0%
8 3
 
3.0%
9 3
 
3.0%
10 2
 
2.0%
16 2
 
2.0%
Other values (7) 7
 
7.0%
ValueCountFrequency (%)
1 33
33.0%
2 20
20.0%
3 9
 
9.0%
4 6
 
6.0%
5 5
 
5.0%
6 10
 
10.0%
8 3
 
3.0%
9 3
 
3.0%
10 2
 
2.0%
11 1
 
1.0%
ValueCountFrequency (%)
23 1
 
1.0%
19 1
 
1.0%
17 1
 
1.0%
16 2
2.0%
14 1
 
1.0%
13 1
 
1.0%
12 1
 
1.0%
11 1
 
1.0%
10 2
2.0%
9 3
3.0%

win_pr_dt
Text

Distinct68
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:04:32.469747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)53.0%

Sample

1st row
2nd row20120910
3rd row20030720
4th row19950510
5th row19970126
ValueCountFrequency (%)
20130717 7
 
7.1%
20021011 6
 
6.1%
20030830 4
 
4.0%
20050626 3
 
3.0%
20021014 3
 
3.0%
20060401 3
 
3.0%
20050819 3
 
3.0%
20031206 3
 
3.0%
20061206 3
 
3.0%
20050313 2
 
2.0%
Other values (57) 62
62.6%
2023-12-10T19:04:33.232273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 274
34.2%
2 151
18.9%
1 129
16.1%
3 55
 
6.9%
9 42
 
5.2%
6 37
 
4.6%
7 34
 
4.2%
5 28
 
3.5%
8 26
 
3.2%
4 16
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 792
99.0%
Space Separator 8
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 274
34.6%
2 151
19.1%
1 129
16.3%
3 55
 
6.9%
9 42
 
5.3%
6 37
 
4.7%
7 34
 
4.3%
5 28
 
3.5%
8 26
 
3.3%
4 16
 
2.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 274
34.2%
2 151
18.9%
1 129
16.1%
3 55
 
6.9%
9 42
 
5.2%
6 37
 
4.6%
7 34
 
4.2%
5 28
 
3.5%
8 26
 
3.2%
4 16
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 274
34.2%
2 151
18.9%
1 129
16.1%
3 55
 
6.9%
9 42
 
5.2%
6 37
 
4.6%
7 34
 
4.2%
5 28
 
3.5%
8 26
 
3.2%
4 16
 
2.0%

item_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.69
Minimum4
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:33.455190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6.95
Q116
median23.5
Q327
95-th percentile36
Maximum50
Range46
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.8804575
Coefficient of variation (CV)0.42921496
Kurtosis0.61675868
Mean20.69
Median Absolute Deviation (MAD)4.5
Skewness0.25762687
Sum2069
Variance78.862525
MonotonicityNot monotonic
2023-12-10T19:04:33.666273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
27 26
26.0%
24 13
13.0%
16 10
 
10.0%
18 7
 
7.0%
8 6
 
6.0%
9 5
 
5.0%
19 4
 
4.0%
36 4
 
4.0%
25 4
 
4.0%
23 3
 
3.0%
Other values (12) 18
18.0%
ValueCountFrequency (%)
4 1
 
1.0%
5 2
 
2.0%
6 2
 
2.0%
7 2
 
2.0%
8 6
6.0%
9 5
5.0%
10 2
 
2.0%
12 1
 
1.0%
14 1
 
1.0%
15 2
 
2.0%
ValueCountFrequency (%)
50 1
 
1.0%
47 1
 
1.0%
36 4
 
4.0%
32 1
 
1.0%
27 26
26.0%
25 4
 
4.0%
24 13
13.0%
23 3
 
3.0%
21 2
 
2.0%
19 4
 
4.0%

item_nm
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
빙상
26 
배드민턴
13 
유도
10 
양궁
7 
배구
6 
Other values (17)
38 

Length

Max length4
Median length2
Mean length2.44
Min length2

Unique

Unique6 ?
Unique (%)6.0%

Sample

1st row역도
2nd row탁구
3rd row양궁
4th row탁구
5th row빙상

Common Values

ValueCountFrequency (%)
빙상 26
26.0%
배드민턴 13
13.0%
유도 10
 
10.0%
양궁 7
 
7.0%
배구 6
 
6.0%
탁구 5
 
5.0%
사격 4
 
4.0%
우슈쿵푸 4
 
4.0%
태권도 4
 
4.0%
펜싱 3
 
3.0%
Other values (12) 18
18.0%

Length

2023-12-10T19:04:33.892670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
빙상 26
26.0%
배드민턴 13
13.0%
유도 10
 
10.0%
양궁 7
 
7.0%
배구 6
 
6.0%
탁구 5
 
5.0%
사격 4
 
4.0%
우슈쿵푸 4
 
4.0%
태권도 4
 
4.0%
펜싱 3
 
3.0%
Other values (12) 18
18.0%

game_gbn
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.8
Minimum10
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:34.162468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q122
median30
Q340
95-th percentile40
Maximum50
Range40
Interquartile range (IQR)18

Descriptive statistics

Standard deviation9.6200548
Coefficient of variation (CV)0.33402968
Kurtosis-0.43541672
Mean28.8
Median Absolute Deviation (MAD)8
Skewness0.025701535
Sum2880
Variance92.545455
MonotonicityNot monotonic
2023-12-10T19:04:34.471774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
40 27
27.0%
30 22
22.0%
22 19
19.0%
24 19
19.0%
10 8
 
8.0%
50 3
 
3.0%
21 1
 
1.0%
15 1
 
1.0%
ValueCountFrequency (%)
10 8
 
8.0%
15 1
 
1.0%
21 1
 
1.0%
22 19
19.0%
24 19
19.0%
30 22
22.0%
40 27
27.0%
50 3
 
3.0%
ValueCountFrequency (%)
50 3
 
3.0%
40 27
27.0%
30 22
22.0%
24 19
19.0%
22 19
19.0%
21 1
 
1.0%
15 1
 
1.0%
10 8
 
8.0%

game_gbn_nm
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
아시아경기대회
27 
세계대학생경기대회
22 
세계선수권대회(2~3년주기)
19 
세계선수권대회(1년주기)
19 
올림픽대회
8 
Other values (3)
5 

Length

Max length15
Median length9
Mean length10.01
Min length5

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row올림픽대회
2nd row올림픽대회
3rd row세계선수권대회(2~3년주기)
4th row세계선수권대회(2~3년주기)
5th row세계대학생경기대회

Common Values

ValueCountFrequency (%)
아시아경기대회 27
27.0%
세계대학생경기대회 22
22.0%
세계선수권대회(2~3년주기) 19
19.0%
세계선수권대회(1년주기) 19
19.0%
올림픽대회 8
 
8.0%
세계군인체육대회 3
 
3.0%
세계선수권대회(4년주기) 1
 
1.0%
농아올림픽 1
 
1.0%

Length

2023-12-10T19:04:34.705575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:34.940767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아시아경기대회 27
27.0%
세계대학생경기대회 22
22.0%
세계선수권대회(2~3년주기 19
19.0%
세계선수권대회(1년주기 19
19.0%
올림픽대회 8
 
8.0%
세계군인체육대회 3
 
3.0%
세계선수권대회(4년주기 1
 
1.0%
농아올림픽 1
 
1.0%

game_nm
Text

Distinct61
Distinct (%)61.6%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T19:04:35.386024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length14.585859
Min length9

Characters and Unicode

Total characters1444
Distinct characters108
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)43.4%

Sample

1st row제30회 런던올림픽
2nd row12런던올림픽대회
3rd row03.제42회세계양궁선수권대회
4th row제43회세계탁구선수권대회
5th row'97동계유니버시아드
ValueCountFrequency (%)
06 13
 
8.3%
02.제14회부산아시아경기대회 10
 
6.4%
아시아경기대회 8
 
5.1%
13 8
 
5.1%
2005년 7
 
4.5%
도하 7
 
4.5%
카잔하계유니버시아드대회 6
 
3.8%
세계선수권대회 5
 
3.2%
03.제3회세계군인체육대회 3
 
1.9%
15회 3
 
1.9%
Other values (67) 86
55.1%
2023-12-10T19:04:36.152174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 133
 
9.2%
대 92
 
6.4%
0 82
 
5.7%
아 72
 
5.0%
계 71
 
4.9%
57
 
3.9%
제 47
 
3.3%
세 46
 
3.2%
시 45
 
3.1%
2 45
 
3.1%
Other values (98) 754
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1045
72.4%
Decimal Number 292
 
20.2%
Space Separator 57
 
3.9%
Other Punctuation 45
 
3.1%
Close Punctuation 2
 
0.1%
Open Punctuation 2
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 133
 
12.7%
대 92
 
8.8%
아 72
 
6.9%
계 71
 
6.8%
제 47
 
4.5%
세 46
 
4.4%
시 45
 
4.3%
권 41
 
3.9%
수 39
 
3.7%
선 39
 
3.7%
Other values (82) 420
40.2%
Decimal Number
ValueCountFrequency (%)
0 82
28.1%
2 45
15.4%
1 40
13.7%
3 39
13.4%
4 21
 
7.2%
5 19
 
6.5%
6 16
 
5.5%
9 12
 
4.1%
8 9
 
3.1%
7 9
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 33
73.3%
' 12
 
26.7%
Space Separator
ValueCountFrequency (%)
57
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1045
72.4%
Common 398
 
27.6%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 133
 
12.7%
대 92
 
8.8%
아 72
 
6.9%
계 71
 
6.8%
제 47
 
4.5%
세 46
 
4.4%
시 45
 
4.3%
권 41
 
3.9%
수 39
 
3.7%
선 39
 
3.7%
Other values (82) 420
40.2%
Common
ValueCountFrequency (%)
0 82
20.6%
57
14.3%
2 45
11.3%
1 40
10.1%
3 39
9.8%
. 33
8.3%
4 21
 
5.3%
5 19
 
4.8%
6 16
 
4.0%
9 12
 
3.0%
Other values (5) 34
8.5%
Latin
ValueCountFrequency (%)
m 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1045
72.4%
ASCII 399
 
27.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 133
 
12.7%
대 92
 
8.8%
아 72
 
6.9%
계 71
 
6.8%
제 47
 
4.5%
세 46
 
4.4%
시 45
 
4.3%
권 41
 
3.9%
수 39
 
3.7%
선 39
 
3.7%
Other values (82) 420
40.2%
ASCII
ValueCountFrequency (%)
0 82
20.6%
57
14.3%
2 45
11.3%
1 40
10.0%
3 39
9.8%
. 33
8.3%
4 21
 
5.3%
5 19
 
4.8%
6 16
 
4.0%
9 12
 
3.0%
Other values (6) 35
8.8%

dtl_item_nm
Text

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:04:36.744583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length12
Mean length5.44
Min length2

Characters and Unicode

Total characters544
Distinct characters101
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)48.0%

Sample

1st row여자 75kg+ 동메달
2nd rowTT1~2 단체
3rd row남자단체
4th row여자단체
5th row5000m 계주
ValueCountFrequency (%)
남자단체 12
 
9.9%
여자 8
 
6.6%
여자단체 7
 
5.8%
남자단체전 4
 
3.3%
유도 4
 
3.3%
1000m 4
 
3.3%
단체 4
 
3.3%
5000m 3
 
2.5%
계주 3
 
2.5%
남자복식 3
 
2.5%
Other values (56) 69
57.0%
2023-12-10T19:04:37.746633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 63
 
11.6%
자 59
 
10.8%
체 41
 
7.5%
단 40
 
7.4%
남 36
 
6.6%
여 23
 
4.2%
m 23
 
4.2%
21
 
3.9%
5 17
 
3.1%
1 10
 
1.8%
Other values (91) 211
38.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 362
66.5%
Decimal Number 109
 
20.0%
Lowercase Letter 37
 
6.8%
Space Separator 21
 
3.9%
Uppercase Letter 5
 
0.9%
Other Punctuation 3
 
0.6%
Math Symbol 3
 
0.6%
Dash Punctuation 2
 
0.4%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 59
16.3%
체 41
 
11.3%
단 40
 
11.0%
남 36
 
9.9%
여 23
 
6.4%
합 7
 
1.9%
계 6
 
1.7%
전 6
 
1.7%
복 6
 
1.7%
주 6
 
1.7%
Other values (70) 132
36.5%
Decimal Number
ValueCountFrequency (%)
0 63
57.8%
5 17
 
15.6%
1 10
 
9.2%
3 7
 
6.4%
6 5
 
4.6%
2 3
 
2.8%
9 2
 
1.8%
4 1
 
0.9%
7 1
 
0.9%
Lowercase Letter
ValueCountFrequency (%)
m 23
62.2%
k 7
 
18.9%
g 7
 
18.9%
Uppercase Letter
ValueCountFrequency (%)
T 4
80.0%
M 1
 
20.0%
Math Symbol
ValueCountFrequency (%)
~ 2
66.7%
+ 1
33.3%
Space Separator
ValueCountFrequency (%)
21
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 362
66.5%
Common 140
 
25.7%
Latin 42
 
7.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 59
16.3%
체 41
 
11.3%
단 40
 
11.0%
남 36
 
9.9%
여 23
 
6.4%
합 7
 
1.9%
계 6
 
1.7%
전 6
 
1.7%
복 6
 
1.7%
주 6
 
1.7%
Other values (70) 132
36.5%
Common
ValueCountFrequency (%)
0 63
45.0%
21
 
15.0%
5 17
 
12.1%
1 10
 
7.1%
3 7
 
5.0%
6 5
 
3.6%
2 3
 
2.1%
, 3
 
2.1%
~ 2
 
1.4%
- 2
 
1.4%
Other values (6) 7
 
5.0%
Latin
ValueCountFrequency (%)
m 23
54.8%
k 7
 
16.7%
g 7
 
16.7%
T 4
 
9.5%
M 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 362
66.5%
ASCII 182
33.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 63
34.6%
m 23
 
12.6%
21
 
11.5%
5 17
 
9.3%
1 10
 
5.5%
k 7
 
3.8%
3 7
 
3.8%
g 7
 
3.8%
6 5
 
2.7%
T 4
 
2.2%
Other values (11) 18
 
9.9%
Hangul
ValueCountFrequency (%)
자 59
16.3%
체 41
 
11.3%
단 40
 
11.0%
남 36
 
9.9%
여 23
 
6.4%
합 7
 
1.9%
계 6
 
1.7%
전 6
 
1.7%
복 6
 
1.7%
주 6
 
1.7%
Other values (70) 132
36.5%

rank
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
54 
2
22 
3
21 
4
 
2
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row3
2nd row3
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 54
54.0%
2 22
22.0%
3 21
 
21.0%
4 2
 
2.0%
5 1
 
1.0%

Length

2023-12-10T19:04:38.030257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:38.237078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 54
54.0%
2 22
22.0%
3 21
 
21.0%
4 2
 
2.0%
5 1
 
1.0%

Interactions

2023-12-10T19:04:28.074552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:27.210668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:27.638430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:28.234761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:27.338300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:27.786737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:28.387321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:27.469519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:27.923585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:04:38.418999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
gbngbn_nmracer_nmseqwin_pr_dtitem_cditem_nmgame_gbngame_gbn_nmgame_nmdtl_item_nmrank
gbn1.0000.9631.0000.0001.0000.4090.4900.6240.8471.0001.0000.025
gbn_nm0.9631.0001.0000.0001.0000.4090.4900.6240.8471.0001.0000.025
racer_nm1.0001.0001.0000.0000.9701.0001.0000.9750.9840.8690.0000.937
seq0.0000.0000.0001.0000.7680.2230.0000.1240.3100.9300.8390.000
win_pr_dt1.0001.0000.9700.7681.0000.9590.8721.0001.0000.9990.9550.957
item_cd0.4090.4091.0000.2230.9591.0001.0000.4630.7770.9390.9770.287
item_nm0.4900.4901.0000.0000.8721.0001.0000.7240.7890.8140.9650.079
game_gbn0.6240.6240.9750.1241.0000.4630.7241.0001.0001.0000.0000.392
game_gbn_nm0.8470.8470.9840.3101.0000.7770.7891.0001.0001.0000.5210.395
game_nm1.0001.0000.8690.9300.9990.9390.8141.0001.0001.0000.9510.944
dtl_item_nm1.0001.0000.0000.8390.9550.9770.9650.0000.5210.9511.0000.571
rank0.0250.0250.9370.0000.9570.2870.0790.3920.3950.9440.5711.000
2023-12-10T19:04:38.656385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
game_gbn_nmrankitem_nmgbngbn_nm
game_gbn_nm1.0000.2500.4320.6470.647
rank0.2501.0000.0000.0220.022
item_nm0.4320.0001.0000.3450.345
gbn0.6470.0220.3451.0000.826
gbn_nm0.6470.0220.3450.8261.000
2023-12-10T19:04:38.864485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seqitem_cdgame_gbngbngbn_nmitem_nmgame_gbn_nmrank
seq1.0000.333-0.3220.0000.0000.0000.1440.000
item_cd0.3331.000-0.2530.2980.2980.9160.3060.178
game_gbn-0.322-0.2531.0000.6540.6540.4040.9950.265
gbn0.0000.2980.6541.0000.8260.3450.6470.022
gbn_nm0.0000.2980.6540.8261.0000.3450.6470.022
item_nm0.0000.9160.4040.3450.3451.0000.4320.000
game_gbn_nm0.1440.3060.9950.6470.6470.4321.0000.250
rank0.0000.1780.2650.0220.0220.0000.2501.000

Missing values

2023-12-10T19:04:28.688759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:04:29.157680image/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.

Sample

gbngbn_nmracer_nmseqwin_pr_dtitem_cditem_nmgame_gbngame_gbn_nmgame_nmdtl_item_nmrank
0B체육인장미란2315역도10올림픽대회제30회 런던올림픽여자 75kg+ 동메달3
1F장애체육인김민규1201209109탁구10올림픽대회12런던올림픽대회TT1~2 단체3
2B체육인장용호62003072018양궁22세계선수권대회(2~3년주기)03.제42회세계양궁선수권대회남자단체1
3B체육인박해정4199505109탁구22세계선수권대회(2~3년주기)제43회세계탁구선수권대회여자단체2
4B체육인이호응21997012627빙상30세계대학생경기대회'97동계유니버시아드5000m 계주1
5B체육인천희주11996021027빙상40아시아경기대회'96(제3회)동계아시아경기대회1000m1
6B체육인천희주21997021427빙상30세계대학생경기대회'97동계유니버시아드5000m 계주1
7F장애체육인정영아1201211209탁구10올림픽대회12런던올림픽대회여자 TT4~5단체3
8B체육인이준환81998032027빙상24세계선수권대회(1년주기)'98세계남녀숏트랙스피드스케이팅선수권5000m 계주2
9B체육인김세진1199408158배구40아시아경기대회94.제12회히로시마아시아경기대회남자단체3
gbngbn_nmracer_nmseqwin_pr_dtitem_cditem_nmgame_gbngame_gbn_nmgame_nmdtl_item_nmrank
90B체육인노상우2201307175테니스30세계대학생경기대회'13 카잔하계유니버시아드대회남자복식3
91B체육인이대명162013071719사격30세계대학생경기대회'13카잔하계유니버시아드대회남자단체10m 공기권총1
92B체육인김승현1200210117농구40아시아경기대회02.제14회부산아시아경기대회남자단체1
93B체육인조상현1199812057농구40아시아경기대회98.제13회방콕아시아경기대회남자단체2
94B체육인이미경12002101119사격40아시아경기대회02.제14회부산아시아경기대회여자50m소총복사단체1
95B체육인김상우1200210118배구40아시아경기대회02.제14회부산아시아경기대회남자단체1
96B체육인여오현2200612138배구40아시아경기대회06 도하 아시아경기대회배구1
97B체육인박재한1200210118배구40아시아경기대회02.제14회부산아시아경기대회남자단체1
98B체육인서석규12002101112사이클40아시아경기대회02.제14회부산아시아경기대회남자메디슨1
99B체육인이상훈1200210114야구40아시아경기대회02.제14회부산아시아경기대회남자단체1