Overview

Dataset statistics

Number of variables11
Number of observations1747
Missing cells247
Missing cells (%)1.3%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory150.3 KiB
Average record size in memory88.1 B

Variable types

Categorical6
Text4
DateTime1

Dataset

Description역대전국장애인체육대회 신기록 명세(대회구분, 종목, 구분별, 세부종목, 경기구분, 경기일정, 소속시도, 소속명, 기록, 기준기록, 신기록구분)에 대한 데이터
Author대한장애인체육회
URLhttps://www.data.go.kr/data/15072756/fileData.do

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
종목 is highly overall correlated with 종별High correlation
종별 is highly overall correlated with 종목High correlation
경기구분 is highly imbalanced (86.7%)Imbalance
신기록 is highly imbalanced (63.1%)Imbalance
소속명 has 179 (10.2%) missing valuesMissing
기준기록 has 68 (3.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 15:43:56.699589
Analysis finished2023-12-12 15:43:58.072213
Duration1.37 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대회구분
Categorical

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
37회 전국장애인체육대회
306 
42회 전국장애인체육대회
292 
36회 전국장애인체육대회
270 
39회 전국장애인체육대회
254 
43회 전국장애인체육대회
220 
Other values (2)
405 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36회 전국장애인체육대회
2nd row36회 전국장애인체육대회
3rd row36회 전국장애인체육대회
4th row36회 전국장애인체육대회
5th row36회 전국장애인체육대회

Common Values

ValueCountFrequency (%)
37회 전국장애인체육대회 306
17.5%
42회 전국장애인체육대회 292
16.7%
36회 전국장애인체육대회 270
15.5%
39회 전국장애인체육대회 254
14.5%
43회 전국장애인체육대회 220
12.6%
38회 전국장애인체육대회 218
12.5%
41회 전국장애인체육대회 187
10.7%

Length

2023-12-13T00:43:58.164437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:43:58.339978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전국장애인체육대회 1747
50.0%
37회 306
 
8.8%
42회 292
 
8.4%
36회 270
 
7.7%
39회 254
 
7.3%
43회 220
 
6.3%
38회 218
 
6.2%
41회 187
 
5.4%

종목
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
역도
790 
수영
288 
육상트랙
226 
육상필드
200 
사격
129 
Other values (2)
114 

Length

Max length4
Median length2
Mean length2.5283343
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사격
2nd row사격
3rd row사격
4th row사격
5th row사격

Common Values

ValueCountFrequency (%)
역도 790
45.2%
수영 288
 
16.5%
육상트랙 226
 
12.9%
육상필드 200
 
11.4%
사격 129
 
7.4%
사이클 71
 
4.1%
양궁 43
 
2.5%

Length

2023-12-13T00:43:58.532812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:43:58.725398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
역도 790
45.2%
수영 288
 
16.5%
육상트랙 226
 
12.9%
육상필드 200
 
11.4%
사격 129
 
7.4%
사이클 71
 
4.1%
양궁 43
 
2.5%

종별
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
선수부
1093 
동호인부
654 

Length

Max length4
Median length3
Mean length3.374356
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row선수부
2nd row선수부
3rd row선수부
4th row선수부
5th row선수부

Common Values

ValueCountFrequency (%)
선수부 1093
62.6%
동호인부 654
37.4%

Length

2023-12-13T00:43:58.927219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:43:59.051960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
선수부 1093
62.6%
동호인부 654
37.4%

세부종목
Text

Distinct561
Distinct (%)32.1%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2023-12-13T00:43:59.389876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length23.793932
Min length15

Characters and Unicode

Total characters41568
Distinct characters126
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

Unique185 ?
Unique (%)10.6%

Sample

1st row남자 공기권총 P1 개인전 SH1(선수부)
2nd row남자 공기권총 P1 개인전 SH1(선수부)
3rd row남자 공기권총 P1 단체전 SH1(선수부)
4th row여자 50m소총 3자세 R8 개인전 SH1(선수부)
5th row여자 공기소총 입사 R2 개인전 SH1(선수부)
ValueCountFrequency (%)
남자 977
 
14.1%
여자 686
 
9.9%
open(지적,동호인부 398
 
5.7%
스쿼트 211
 
3.0%
데드리프트 209
 
3.0%
파워리프트종합 186
 
2.7%
open(선수부 184
 
2.7%
100m 145
 
2.1%
자유형 128
 
1.8%
open(청각,동호인부 124
 
1.8%
Other values (220) 3681
53.1%
2023-12-13T00:43:59.991509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5182
 
12.5%
) 1812
 
4.4%
( 1812
 
4.4%
자 1800
 
4.3%
부 1744
 
4.2%
0 1244
 
3.0%
수 1090
 
2.6%
선 1090
 
2.6%
남 977
 
2.4%
k 907
 
2.2%
Other values (116) 23910
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19151
46.1%
Space Separator 5182
 
12.5%
Decimal Number 4830
 
11.6%
Uppercase Letter 4765
 
11.5%
Lowercase Letter 2487
 
6.0%
Close Punctuation 1812
 
4.4%
Open Punctuation 1812
 
4.4%
Dash Punctuation 716
 
1.7%
Other Punctuation 698
 
1.7%
Math Symbol 115
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 1800
 
9.4%
부 1744
 
9.1%
수 1090
 
5.7%
선 1090
 
5.7%
남 977
 
5.1%
인 840
 
4.4%
급 790
 
4.1%
트 757
 
4.0%
여 686
 
3.6%
동 654
 
3.4%
Other values (73) 8723
45.5%
Uppercase Letter
ValueCountFrequency (%)
P 876
18.4%
O 819
17.2%
N 819
17.2%
E 819
17.2%
S 416
8.7%
T 264
 
5.5%
F 207
 
4.3%
B 161
 
3.4%
H 111
 
2.3%
D 82
 
1.7%
Other values (4) 191
 
4.0%
Lowercase Letter
ValueCountFrequency (%)
k 907
36.5%
g 790
31.8%
m 622
25.0%
n 41
 
1.6%
t 21
 
0.8%
o 21
 
0.8%
i 21
 
0.8%
a 20
 
0.8%
d 20
 
0.8%
e 20
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 1244
25.8%
1 881
18.2%
5 569
11.8%
2 481
 
10.0%
4 391
 
8.1%
6 307
 
6.4%
3 296
 
6.1%
7 264
 
5.5%
8 234
 
4.8%
9 163
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 695
99.6%
/ 3
 
0.4%
Math Symbol
ValueCountFrequency (%)
+ 110
95.7%
~ 5
 
4.3%
Space Separator
ValueCountFrequency (%)
5182
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1812
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1812
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19151
46.1%
Common 15165
36.5%
Latin 7252
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 1800
 
9.4%
부 1744
 
9.1%
수 1090
 
5.7%
선 1090
 
5.7%
남 977
 
5.1%
인 840
 
4.4%
급 790
 
4.1%
트 757
 
4.0%
여 686
 
3.6%
동 654
 
3.4%
Other values (73) 8723
45.5%
Latin
ValueCountFrequency (%)
k 907
12.5%
P 876
12.1%
O 819
11.3%
N 819
11.3%
E 819
11.3%
g 790
10.9%
m 622
8.6%
S 416
5.7%
T 264
 
3.6%
F 207
 
2.9%
Other values (15) 713
9.8%
Common
ValueCountFrequency (%)
5182
34.2%
) 1812
 
11.9%
( 1812
 
11.9%
0 1244
 
8.2%
1 881
 
5.8%
- 716
 
4.7%
, 695
 
4.6%
5 569
 
3.8%
2 481
 
3.2%
4 391
 
2.6%
Other values (8) 1382
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22417
53.9%
Hangul 19151
46.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5182
23.1%
) 1812
 
8.1%
( 1812
 
8.1%
0 1244
 
5.5%
k 907
 
4.0%
1 881
 
3.9%
P 876
 
3.9%
O 819
 
3.7%
N 819
 
3.7%
E 819
 
3.7%
Other values (33) 7246
32.3%
Hangul
ValueCountFrequency (%)
자 1800
 
9.4%
부 1744
 
9.1%
수 1090
 
5.7%
선 1090
 
5.7%
남 977
 
5.1%
인 840
 
4.4%
급 790
 
4.1%
트 757
 
4.0%
여 686
 
3.6%
동 654
 
3.4%
Other values (73) 8723
45.5%

경기구분
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
결승
1664 
예선
 
59
본선
 
10
3-4위전
 
6
4강
 
5

Length

Max length5
Median length2
Mean length2.0103034
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row결승
2nd row결승
3rd row결승
4th row결승
5th row결승

Common Values

ValueCountFrequency (%)
결승 1664
95.2%
예선 59
 
3.4%
본선 10
 
0.6%
3-4위전 6
 
0.3%
4강 5
 
0.3%
8강 3
 
0.2%

Length

2023-12-13T00:44:00.190943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:44:00.361791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
결승 1664
95.2%
예선 59
 
3.4%
본선 10
 
0.6%
3-4위전 6
 
0.3%
4강 5
 
0.3%
8강 3
 
0.2%

경기일자
Date

Distinct51
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
Minimum2016-10-21 00:00:00
Maximum2023-11-08 00:00:00
2023-12-13T00:44:00.500357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:44:00.652859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

소속시도
Categorical

Distinct17
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
충북
324 
경기
211 
울산
177 
부산
175 
서울
164 
Other values (12)
696 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충북
2nd row충북
3rd row충북
4th row전남
5th row전남

Common Values

ValueCountFrequency (%)
충북 324
18.5%
경기 211
12.1%
울산 177
10.1%
부산 175
10.0%
서울 164
9.4%
인천 109
 
6.2%
광주 94
 
5.4%
대전 87
 
5.0%
경북 65
 
3.7%
제주 56
 
3.2%
Other values (7) 285
16.3%

Length

2023-12-13T00:44:00.823016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 324
18.5%
경기 211
12.1%
울산 177
10.1%
부산 175
10.0%
서울 164
9.4%
인천 109
 
6.2%
광주 94
 
5.4%
대전 87
 
5.0%
경북 65
 
3.7%
제주 56
 
3.2%
Other values (7) 285
16.3%

소속명
Text

MISSING 

Distinct166
Distinct (%)10.6%
Missing179
Missing (%)10.2%
Memory size13.8 KiB
2023-12-13T00:44:01.385387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length8.6753827
Min length2

Characters and Unicode

Total characters13603
Distinct characters180
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)2.1%

Sample

1st row청주시청
2nd row청주시청
3rd row전남장애인사격연맹
4th row전남장애인사격연맹
5th row정선군청
ValueCountFrequency (%)
일반(개인 238
 
14.3%
충북장애인역도연맹 101
 
6.1%
충청북도장애인체육회 94
 
5.6%
부산장애인역도연맹 88
 
5.3%
울산광역시장애인역도연맹 71
 
4.3%
경기도장애인역도연맹 51
 
3.1%
울산광역시동구청 35
 
2.1%
울산광역시장애인육상연맹팀 33
 
2.0%
서울특별시장애인역도연맹 33
 
2.0%
소속팀없음 31
 
1.9%
Other values (172) 889
53.4%
2023-12-13T00:44:01.711251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
인 1217
 
8.9%
장 934
 
6.9%
애 920
 
6.8%
도 699
 
5.1%
연 675
 
5.0%
맹 672
 
4.9%
역 643
 
4.7%
육 438
 
3.2%
시 401
 
2.9%
산 312
 
2.3%
Other values (170) 6692
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12954
95.2%
Close Punctuation 260
 
1.9%
Open Punctuation 260
 
1.9%
Space Separator 96
 
0.7%
Uppercase Letter 16
 
0.1%
Lowercase Letter 15
 
0.1%
Decimal Number 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 1217
 
9.4%
장 934
 
7.2%
애 920
 
7.1%
도 699
 
5.4%
연 675
 
5.2%
맹 672
 
5.2%
역 643
 
5.0%
육 438
 
3.4%
시 401
 
3.1%
산 312
 
2.4%
Other values (149) 6043
46.6%
Lowercase Letter
ValueCountFrequency (%)
m 2
13.3%
i 2
13.3%
c 2
13.3%
u 1
6.7%
l 1
6.7%
g 1
6.7%
n 1
6.7%
w 1
6.7%
s 1
6.7%
d 1
6.7%
Other values (2) 2
13.3%
Uppercase Letter
ValueCountFrequency (%)
K 6
37.5%
S 6
37.5%
T 3
18.8%
N 1
 
6.2%
Close Punctuation
ValueCountFrequency (%)
) 260
100.0%
Open Punctuation
ValueCountFrequency (%)
( 260
100.0%
Space Separator
ValueCountFrequency (%)
96
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12954
95.2%
Common 618
 
4.5%
Latin 31
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 1217
 
9.4%
장 934
 
7.2%
애 920
 
7.1%
도 699
 
5.4%
연 675
 
5.2%
맹 672
 
5.2%
역 643
 
5.0%
육 438
 
3.4%
시 401
 
3.1%
산 312
 
2.4%
Other values (149) 6043
46.6%
Latin
ValueCountFrequency (%)
K 6
19.4%
S 6
19.4%
T 3
9.7%
m 2
 
6.5%
i 2
 
6.5%
c 2
 
6.5%
u 1
 
3.2%
l 1
 
3.2%
g 1
 
3.2%
n 1
 
3.2%
Other values (6) 6
19.4%
Common
ValueCountFrequency (%)
) 260
42.1%
( 260
42.1%
96
 
15.5%
1 1
 
0.2%
. 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12954
95.2%
ASCII 649
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
인 1217
 
9.4%
장 934
 
7.2%
애 920
 
7.1%
도 699
 
5.4%
연 675
 
5.2%
맹 672
 
5.2%
역 643
 
5.0%
육 438
 
3.4%
시 401
 
3.1%
산 312
 
2.4%
Other values (149) 6043
46.6%
ASCII
ValueCountFrequency (%)
) 260
40.1%
( 260
40.1%
96
 
14.8%
K 6
 
0.9%
S 6
 
0.9%
T 3
 
0.5%
m 2
 
0.3%
i 2
 
0.3%
c 2
 
0.3%
u 1
 
0.2%
Other values (11) 11
 
1.7%

기록
Text

Distinct1154
Distinct (%)66.1%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
2023-12-13T00:44:02.082753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.276474
Min length1

Characters and Unicode

Total characters7471
Distinct characters12
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

Unique880 ?
Unique (%)50.4%

Sample

1st row197.9
2nd row199
3rd row1714
4th row447.4
5th row205.6
ValueCountFrequency (%)
182 10
 
0.6%
194 10
 
0.6%
161 9
 
0.5%
183 9
 
0.5%
201 8
 
0.5%
180 8
 
0.5%
181 8
 
0.5%
150 8
 
0.5%
145 8
 
0.5%
149 7
 
0.4%
Other values (1144) 1662
95.1%
2023-12-13T00:44:02.541943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1195
16.0%
. 871
11.7%
2 820
11.0%
0 774
10.4%
3 680
9.1%
5 613
8.2%
4 552
7.4%
6 440
 
5.9%
8 426
 
5.7%
7 406
 
5.4%
Other values (2) 694
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6264
83.8%
Other Punctuation 1207
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1195
19.1%
2 820
13.1%
0 774
12.4%
3 680
10.9%
5 613
9.8%
4 552
8.8%
6 440
 
7.0%
8 426
 
6.8%
7 406
 
6.5%
9 358
 
5.7%
Other Punctuation
ValueCountFrequency (%)
. 871
72.2%
: 336
 
27.8%

Most occurring scripts

ValueCountFrequency (%)
Common 7471
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1195
16.0%
. 871
11.7%
2 820
11.0%
0 774
10.4%
3 680
9.1%
5 613
8.2%
4 552
7.4%
6 440
 
5.9%
8 426
 
5.7%
7 406
 
5.4%
Other values (2) 694
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1195
16.0%
. 871
11.7%
2 820
11.0%
0 774
10.4%
3 680
9.1%
5 613
8.2%
4 552
7.4%
6 440
 
5.9%
8 426
 
5.7%
7 406
 
5.4%
Other values (2) 694
9.3%

기준기록
Text

MISSING 

Distinct1005
Distinct (%)59.9%
Missing68
Missing (%)3.9%
Memory size13.8 KiB
2023-12-13T00:44:02.858685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.5068493
Min length2

Characters and Unicode

Total characters9246
Distinct characters24
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique627 ?
Unique (%)37.3%

Sample

1st row195.3
2nd row198.2
3rd row1713
4th row444.7
5th row205
ValueCountFrequency (%)
35.3 13
 
0.8%
194.0kg 9
 
0.5%
182.0kg 9
 
0.5%
181.0kg 9
 
0.5%
180 8
 
0.5%
33분29초21 8
 
0.5%
186.0kg 7
 
0.4%
205 7
 
0.4%
257.0kg 7
 
0.4%
102 6
 
0.4%
Other values (995) 1600
95.1%
2023-12-13T00:44:03.283343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1258
13.6%
1 1152
12.5%
. 843
9.1%
2 801
8.7%
3 723
 
7.8%
5 572
 
6.2%
4 536
 
5.8%
k 470
 
5.1%
g 470
 
5.1%
6 420
 
4.5%
Other values (14) 2001
21.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6613
71.5%
Other Punctuation 1178
 
12.7%
Lowercase Letter 1154
 
12.5%
Other Letter 297
 
3.2%
Space Separator 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1258
19.0%
1 1152
17.4%
2 801
12.1%
3 723
10.9%
5 572
8.6%
4 536
8.1%
6 420
 
6.4%
8 412
 
6.2%
7 383
 
5.8%
9 356
 
5.4%
Other Punctuation
ValueCountFrequency (%)
. 843
71.6%
: 189
 
16.0%
' 82
 
7.0%
" 46
 
3.9%
, 16
 
1.4%
/ 2
 
0.2%
Other Letter
ValueCountFrequency (%)
초 193
65.0%
분 102
34.3%
시 1
 
0.3%
간 1
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
k 470
40.7%
g 470
40.7%
m 214
18.5%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7795
84.3%
Latin 1154
 
12.5%
Hangul 297
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1258
16.1%
1 1152
14.8%
. 843
10.8%
2 801
10.3%
3 723
9.3%
5 572
7.3%
4 536
6.9%
6 420
 
5.4%
8 412
 
5.3%
7 383
 
4.9%
Other values (7) 695
8.9%
Hangul
ValueCountFrequency (%)
초 193
65.0%
분 102
34.3%
시 1
 
0.3%
간 1
 
0.3%
Latin
ValueCountFrequency (%)
k 470
40.7%
g 470
40.7%
m 214
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8949
96.8%
Hangul 297
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1258
14.1%
1 1152
12.9%
. 843
9.4%
2 801
9.0%
3 723
8.1%
5 572
 
6.4%
4 536
 
6.0%
k 470
 
5.3%
g 470
 
5.3%
6 420
 
4.7%
Other values (10) 1704
19.0%
Hangul
ValueCountFrequency (%)
초 193
65.0%
분 102
34.3%
시 1
 
0.3%
간 1
 
0.3%

신기록
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
KR(New)
1344 
GR(New)
352 
KR(Tie)
 
25
GR(Tie)
 
16
WR(New)
 
7

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGR(New)
2nd rowPR(New)
3rd rowWR(New)
4th rowGR(New)
5th rowGR(New)

Common Values

ValueCountFrequency (%)
KR(New) 1344
76.9%
GR(New) 352
 
20.1%
KR(Tie) 25
 
1.4%
GR(Tie) 16
 
0.9%
WR(New) 7
 
0.4%
PR(New) 3
 
0.2%

Length

2023-12-13T00:44:03.419676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:44:03.530570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kr(new 1344
76.9%
gr(new 352
 
20.1%
kr(tie 25
 
1.4%
gr(tie 16
 
0.9%
wr(new 7
 
0.4%
pr(new 3
 
0.2%

Correlations

2023-12-13T00:44:03.616229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대회구분종목종별경기구분경기일자소속시도신기록
대회구분1.0000.3180.0950.2001.0000.1610.100
종목0.3181.0000.6840.4770.7860.5520.304
종별0.0950.6841.0000.1340.3870.3870.303
경기구분0.2000.4770.1341.0000.6460.3510.282
경기일자1.0000.7860.3870.6461.0000.5400.303
소속시도0.1610.5520.3870.3510.5401.0000.156
신기록0.1000.3040.3030.2820.3030.1561.000
2023-12-13T00:44:03.722321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경기구분소속시도종목종별신기록대회구분
경기구분1.0000.1730.3080.0960.1050.120
소속시도0.1731.0000.2860.3470.0740.073
종목0.3080.2861.0000.7380.1860.116
종별0.0960.3470.7381.0000.2180.102
신기록0.1050.0740.1860.2181.0000.059
대회구분0.1200.0730.1160.1020.0591.000
2023-12-13T00:44:03.814220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대회구분종목종별경기구분소속시도신기록
대회구분1.0000.1160.1020.1200.0730.059
종목0.1161.0000.7380.3080.2860.186
종별0.1020.7381.0000.0960.3470.218
경기구분0.1200.3080.0961.0000.1730.105
소속시도0.0730.2860.3470.1731.0000.074
신기록0.0590.1860.2180.1050.0741.000

Missing values

2023-12-13T00:43:57.666036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:43:57.893903image/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.
2023-12-13T00:43:58.020914image/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

대회구분종목종별세부종목경기구분경기일자소속시도소속명기록기준기록신기록
036회 전국장애인체육대회사격선수부남자 공기권총 P1 개인전 SH1(선수부)결승2016-10-24충북청주시청197.9195.3GR(New)
136회 전국장애인체육대회사격선수부남자 공기권총 P1 개인전 SH1(선수부)결승2016-10-24충북청주시청199198.2PR(New)
236회 전국장애인체육대회사격선수부남자 공기권총 P1 단체전 SH1(선수부)결승2016-10-24충북<NA>17141713WR(New)
336회 전국장애인체육대회사격선수부여자 50m소총 3자세 R8 개인전 SH1(선수부)결승2016-10-24전남전남장애인사격연맹447.4444.7GR(New)
436회 전국장애인체육대회사격선수부여자 공기소총 입사 R2 개인전 SH1(선수부)결승2016-10-22전남전남장애인사격연맹205.6205GR(New)
536회 전국장애인체육대회사격선수부혼성 25m권총 P3 개인전 SH1(선수부)예선2016-10-23강원정선군청581580GR(New)
636회 전국장애인체육대회사격선수부혼성 25m권총 P3 단체전 SH1(선수부)결승2016-10-23충북<NA>16951678GR(New)
736회 전국장애인체육대회사격선수부혼성 25m권총 P3 단체전 SH1(선수부)결승2016-10-23경남<NA>16901678GR(New)
836회 전국장애인체육대회사격선수부혼성 50m권총 P4 개인전 SH1(선수부)예선2016-10-22충북청주시청558552WR(New)
936회 전국장애인체육대회사격선수부혼성 50m권총 P4 단체전 SH1(선수부)결승2016-10-22충북<NA>16581591WR(New)
대회구분종목종별세부종목경기구분경기일자소속시도소속명기록기준기록신기록
173743회 전국장애인체육대회육상필드선수부여자 포환던지기 F12(선수부)결승2023-11-07부산부산육상7.567m42KR(New)
173843회 전국장애인체육대회육상필드선수부여자 포환던지기 F12(선수부)결승2023-11-07서울잠실육상클럽7.757m42KR(New)
173943회 전국장애인체육대회육상필드선수부여자 포환던지기 F13(선수부)결승2023-11-07광주광주광역시장애인육상연맹7.066m65KR(New)
174043회 전국장애인체육대회육상필드선수부여자 포환던지기 F20(선수부)결승2023-11-03대전대전장애인육상연맹10.259m89GR(New)
174143회 전국장애인체육대회육상필드선수부여자 포환던지기 F34(선수부)결승2023-11-04충북충청북도장애인체육회2.992m91KR(New)
174243회 전국장애인체육대회육상필드선수부여자 포환던지기 F34(선수부)결승2023-11-04경남일반(개인)3.422m91KR(New)
174343회 전국장애인체육대회육상필드선수부여자 포환던지기 F37(선수부)결승2023-11-06제주제주특별자치도장애인체육회7.837m66KR(New)
174443회 전국장애인체육대회육상필드선수부여자 포환던지기 F37(선수부)결승2023-11-06광주광주광역시장애인육상연맹8.097m66KR(New)
174543회 전국장애인체육대회육상필드선수부여자 포환던지기 F57(선수부)결승2023-11-05경기경기도장애인육상연맹4.33m90KR(New)
174643회 전국장애인체육대회육상필드선수부여자 포환던지기 F57(선수부)결승2023-11-05부산부산육상5.013m90KR(New)

Duplicate rows

Most frequently occurring

대회구분종목종별세부종목경기구분경기일자소속시도소속명기록기준기록신기록# duplicates
039회 전국장애인체육대회역도동호인부남자 -60kg급 스쿼트 OPEN(지적,동호인부)결승2019-10-16서울서울특별시장애인역도연맹144143.0kgKR(New)2