Overview

Dataset statistics

Number of variables13
Number of observations100
Missing cells40
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory109.3 B

Variable types

Categorical9
Numeric3
Text1

Alerts

game_year is highly overall correlated with game_begin_de and 9 other fieldsHigh correlation
game_nm is highly overall correlated with game_begin_de and 10 other fieldsHigh correlation
item_nm is highly overall correlated with game_begin_de and 9 other fieldsHigh correlation
partcpt_scale_value is highly overall correlated with game_begin_de and 10 other fieldsHigh correlation
game_place_nm is highly overall correlated with game_begin_de and 10 other fieldsHigh correlation
game_flag_nm is highly overall correlated with match_de and 7 other fieldsHigh correlation
rnd_nm is highly overall correlated with game_begin_de and 8 other fieldsHigh correlation
match_knd_nm is highly overall correlated with game_begin_de and 7 other fieldsHigh correlation
match_detail_item_nm is highly overall correlated with game_begin_de and 8 other fieldsHigh correlation
game_begin_de is highly overall correlated with game_end_de and 9 other fieldsHigh correlation
game_end_de is highly overall correlated with game_begin_de and 9 other fieldsHigh correlation
match_de is highly overall correlated with game_begin_de and 10 other fieldsHigh correlation
item_nm is highly imbalanced (80.6%)Imbalance
game_flag_nm is highly imbalanced (75.6%)Imbalance
match_de has 40 (40.0%) missing valuesMissing
vido_url has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:51:19.766352
Analysis finished2023-12-10 09:51:24.045597
Duration4.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

item_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
검도
97 
근대5종
 
3

Length

Max length4
Median length2
Mean length2.06
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row검도
2nd row근대5종
3rd row검도
4th row검도
5th row검도

Common Values

ValueCountFrequency (%)
검도 97
97.0%
근대5종 3
 
3.0%

Length

2023-12-10T18:51:24.195129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:24.381776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
검도 97
97.0%
근대5종 3
 
3.0%

game_flag_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
총리배
94 
<NA>
 
3
소년체전
 
3

Length

Max length4
Median length3
Mean length3.06
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
총리배 94
94.0%
<NA> 3
 
3.0%
소년체전 3
 
3.0%

Length

2023-12-10T18:51:24.600947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:24.780044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
총리배 94
94.0%
na 3
 
3.0%
소년체전 3
 
3.0%

partcpt_scale_value
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
700명 이하
45 
1000명 이상
40 
<NA>
15 

Length

Max length8
Median length7
Mean length6.95
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row700명 이하
2nd row<NA>
3rd row1000명 이상
4th row1000명 이상
5th row1000명 이상

Common Values

ValueCountFrequency (%)
700명 이하 45
45.0%
1000명 이상 40
40.0%
<NA> 15
 
15.0%

Length

2023-12-10T18:51:25.048562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:25.338145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
700명 45
24.3%
이하 45
24.3%
1000명 40
21.6%
이상 40
21.6%
na 15
 
8.1%

game_year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2018
43 
2019
40 
2016
9 
2020
5 
2021
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2021
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2018 43
43.0%
2019 40
40.0%
2016 9
 
9.0%
2020 5
 
5.0%
2021 3
 
3.0%

Length

2023-12-10T18:51:25.750852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:25.946098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 43
43.0%
2019 40
40.0%
2016 9
 
9.0%
2020 5
 
5.0%
2021 3
 
3.0%

game_nm
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제36회 추계 전국중·고등학교검도대회
40 
제27회 회장기 전국 중.고등학교검도대회
40 
제13회 추계 전국실업검도대회
8 
회장기 제60회 전국검도단별선수권대회
5 
제102회 전국체육대회
 
3
Other values (2)
 
4

Length

Max length22
Median length20
Mean length19.93
Min length10

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row회장기 제60회 전국검도단별선수권대회
2nd row제102회 전국체육대회
3rd row제36회 추계 전국중·고등학교검도대회
4th row제36회 추계 전국중·고등학교검도대회
5th row제36회 추계 전국중·고등학교검도대회

Common Values

ValueCountFrequency (%)
제36회 추계 전국중·고등학교검도대회 40
40.0%
제27회 회장기 전국 중.고등학교검도대회 40
40.0%
제13회 추계 전국실업검도대회 8
 
8.0%
회장기 제60회 전국검도단별선수권대회 5
 
5.0%
제102회 전국체육대회 3
 
3.0%
제47회 전국소년체육대회 3
 
3.0%
하계전국실업검도리그 1
 
1.0%

Length

2023-12-10T18:51:26.194869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:26.433566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
추계 48
14.5%
회장기 45
13.6%
제36회 40
12.0%
전국중·고등학교검도대회 40
12.0%
제27회 40
12.0%
전국 40
12.0%
중.고등학교검도대회 40
12.0%
제13회 8
 
2.4%
전국실업검도대회 8
 
2.4%
제60회 5
 
1.5%
Other values (6) 18
 
5.4%

game_place_nm
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전남, 해남군 우슬체육관
40 
경북, 청송국민체육센터
40 
옥천체육센터
8 
경남 고성군, 국민체육센터
5 
<NA>
 
3
Other values (2)
 
4

Length

Max length29
Median length14
Mean length11.65
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row경남 고성군, 국민체육센터
2nd row<NA>
3rd row전남, 해남군 우슬체육관
4th row전남, 해남군 우슬체육관
5th row전남, 해남군 우슬체육관

Common Values

ValueCountFrequency (%)
전남, 해남군 우슬체육관 40
40.0%
경북, 청송국민체육센터 40
40.0%
옥천체육센터 8
 
8.0%
경남 고성군, 국민체육센터 5
 
5.0%
<NA> 3
 
3.0%
충북 3
 
3.0%
유관순 체육관(충남 천안시 서북구 백석동 255-1) 1
 
1.0%

Length

2023-12-10T18:51:26.702522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:26.915626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전남 40
17.0%
우슬체육관 40
17.0%
경북 40
17.0%
청송국민체육센터 40
17.0%
해남군 40
17.0%
옥천체육센터 8
 
3.4%
고성군 5
 
2.1%
국민체육센터 5
 
2.1%
경남 5
 
2.1%
na 3
 
1.3%
Other values (7) 9
 
3.8%

game_begin_de
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20184962
Minimum20160817
Maximum20211009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:27.137247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160817
5-th percentile20160908
Q120180608
median20180608
Q320191101
95-th percentile20201031
Maximum20211009
Range50192
Interquartile range (IQR)10493

Descriptive statistics

Standard deviation10475.073
Coefficient of variation (CV)0.00051895433
Kurtosis1.1896224
Mean20184962
Median Absolute Deviation (MAD)10493
Skewness-0.41218022
Sum2.0184962 × 109
Variance1.0972716 × 108
MonotonicityNot monotonic
2023-12-10T18:51:27.381520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20191101 40
40.0%
20180608 40
40.0%
20160908 8
 
8.0%
20201031 5
 
5.0%
20211009 3
 
3.0%
20180525 3
 
3.0%
20160817 1
 
1.0%
ValueCountFrequency (%)
20160817 1
 
1.0%
20160908 8
 
8.0%
20180525 3
 
3.0%
20180608 40
40.0%
20191101 40
40.0%
20201031 5
 
5.0%
20211009 3
 
3.0%
ValueCountFrequency (%)
20211009 3
 
3.0%
20201031 5
 
5.0%
20191101 40
40.0%
20180608 40
40.0%
20180525 3
 
3.0%
20160908 8
 
8.0%
20160817 1
 
1.0%

game_end_de
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20184968
Minimum20160820
Maximum20211013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:27.606053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160820
5-th percentile20160911
Q120180610
median20180610
Q320191103
95-th percentile20201101
Maximum20211013
Range50193
Interquartile range (IQR)10493

Descriptive statistics

Standard deviation10480.267
Coefficient of variation (CV)0.0005192115
Kurtosis1.1867077
Mean20184968
Median Absolute Deviation (MAD)10493
Skewness-0.41000135
Sum2.0184968 × 109
Variance1.09836 × 108
MonotonicityNot monotonic
2023-12-10T18:51:27.853722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20191103 40
40.0%
20180610 40
40.0%
20160911 8
 
8.0%
20201101 5
 
5.0%
20211013 3
 
3.0%
20180529 3
 
3.0%
20160820 1
 
1.0%
ValueCountFrequency (%)
20160820 1
 
1.0%
20160911 8
 
8.0%
20180529 3
 
3.0%
20180610 40
40.0%
20191103 40
40.0%
20201101 5
 
5.0%
20211013 3
 
3.0%
ValueCountFrequency (%)
20211013 3
 
3.0%
20201101 5
 
5.0%
20191103 40
40.0%
20180610 40
40.0%
20180529 3
 
3.0%
20160911 8
 
8.0%
20160820 1
 
1.0%

match_de
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)15.0%
Missing40
Missing (%)40.0%
Infinite0
Infinite (%)0.0%
Mean20187866
Minimum20160819
Maximum20211011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:28.129409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20160819
5-th percentile20160908
Q120191102
median20191102
Q320191103
95-th percentile20201530
Maximum20211011
Range50192
Interquartile range (IQR)1

Descriptive statistics

Standard deviation12755.219
Coefficient of variation (CV)0.000631826
Kurtosis0.94110045
Mean20187866
Median Absolute Deviation (MAD)0
Skewness-1.0790978
Sum1.2112719 × 109
Variance1.626956 × 108
MonotonicityNot monotonic
2023-12-10T18:51:28.338777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
20191102 32
32.0%
20191103 8
 
8.0%
20201031 5
 
5.0%
20160908 5
 
5.0%
20160911 3
 
3.0%
20180527 3
 
3.0%
20211011 2
 
2.0%
20211010 1
 
1.0%
20160819 1
 
1.0%
(Missing) 40
40.0%
ValueCountFrequency (%)
20160819 1
 
1.0%
20160908 5
 
5.0%
20160911 3
 
3.0%
20180527 3
 
3.0%
20191102 32
32.0%
20191103 8
 
8.0%
20201031 5
 
5.0%
20211010 1
 
1.0%
20211011 2
 
2.0%
ValueCountFrequency (%)
20211011 2
 
2.0%
20211010 1
 
1.0%
20201031 5
 
5.0%
20191103 8
 
8.0%
20191102 32
32.0%
20180527 3
 
3.0%
20160911 3
 
3.0%
20160908 5
 
5.0%
20160819 1
 
1.0%

match_knd_nm
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
남자고등부
36 
여자고등부
16 
<NA>
12 
남자중학부
11 
여자중등부
11 
Other values (5)
14 

Length

Max length5
Median length5
Mean length4.88
Min length4

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row남자초단부
2nd row남자고등부
3rd row남자중학부
4th row남자중학부
5th row남자고등부

Common Values

ValueCountFrequency (%)
남자고등부 36
36.0%
여자고등부 16
16.0%
<NA> 12
 
12.0%
남자중학부 11
 
11.0%
여자중등부 11
 
11.0%
남자중등부 9
 
9.0%
남자초단부 2
 
2.0%
남자2단부 1
 
1.0%
여자초단부 1
 
1.0%
여자2단부 1
 
1.0%

Length

2023-12-10T18:51:28.600222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:28.833616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남자고등부 36
36.0%
여자고등부 16
16.0%
na 12
 
12.0%
남자중학부 11
 
11.0%
여자중등부 11
 
11.0%
남자중등부 9
 
9.0%
남자초단부 2
 
2.0%
남자2단부 1
 
1.0%
여자초단부 1
 
1.0%
여자2단부 1
 
1.0%

match_detail_item_nm
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
단체전
24 
개인(단식)
22 
개인전
21 
단체
18 
<NA>
12 
Other values (2)
3 

Length

Max length6
Median length4
Mean length3.57
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row개인전
2nd row수영
3rd row단체전
4th row단체전
5th row개인전

Common Values

ValueCountFrequency (%)
단체전 24
24.0%
개인(단식) 22
22.0%
개인전 21
21.0%
단체 18
18.0%
<NA> 12
12.0%
수영 2
 
2.0%
펜싱 1
 
1.0%

Length

2023-12-10T18:51:29.129817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:29.330438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단체전 24
24.0%
개인(단식 22
22.0%
개인전 21
21.0%
단체 18
18.0%
na 12
12.0%
수영 2
 
2.0%
펜싱 1
 
1.0%

rnd_nm
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
12 
결승
9 
4강-1경기
 
5
8강-1경기
 
5
8강1경기
 
4
Other values (27)
65 

Length

Max length7
Median length6
Mean length5.14
Min length1

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row8강
2nd row-
3rd row결승
4th row4강-1경기
5th row4강-1경기

Common Values

ValueCountFrequency (%)
<NA> 12
 
12.0%
결승 9
 
9.0%
4강-1경기 5
 
5.0%
8강-1경기 5
 
5.0%
8강1경기 4
 
4.0%
8강2경기 4
 
4.0%
8강-4경기 4
 
4.0%
8강-2경기 4
 
4.0%
4강1경기 3
 
3.0%
16강-4경기 3
 
3.0%
Other values (22) 47
47.0%

Length

2023-12-10T18:51:29.581623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 12
 
12.0%
결승 9
 
9.0%
4강-1경기 5
 
5.0%
8강-1경기 5
 
5.0%
8강1경기 4
 
4.0%
8강2경기 4
 
4.0%
8강-4경기 4
 
4.0%
8강-2경기 4
 
4.0%
4강-2경기 3
 
3.0%
16강2경기 3
 
3.0%
Other values (22) 47
47.0%

vido_url
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:30.268927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length79
Median length78
Mean length78.23
Min length71

Characters and Unicode

Total characters7823
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowhttps://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=202005337&movSeq=51946
2nd rowhttps://vod.sports.or.kr/vod/view.do?pclassCd=MP&eventCd=202106529&movSeq=57745
3rd rowhttps://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=43036
4th rowhttps://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=43033
5th rowhttps://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=42522
ValueCountFrequency (%)
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=202005337&movseq=51946 1
 
1.0%
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=201904721&movseq=42471 1
 
1.0%
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=201904721&movseq=42489 1
 
1.0%
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=201904721&movseq=42488 1
 
1.0%
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=201904721&movseq=42487 1
 
1.0%
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=201904721&movseq=42486 1
 
1.0%
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=201904721&movseq=42485 1
 
1.0%
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=201904721&movseq=42484 1
 
1.0%
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=201904721&movseq=42483 1
 
1.0%
https://vod.sports.or.kr/vod/view.do?pclasscd=kd&eventcd=201600199&movseq=1039 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:51:31.594742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 600
 
7.7%
s 500
 
6.4%
v 500
 
6.4%
d 500
 
6.4%
. 400
 
5.1%
t 400
 
5.1%
e 400
 
5.1%
/ 400
 
5.1%
r 300
 
3.8%
= 300
 
3.8%
Other values (30) 3523
45.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4500
57.5%
Decimal Number 1323
 
16.9%
Other Punctuation 1200
 
15.3%
Uppercase Letter 500
 
6.4%
Math Symbol 300
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 600
13.3%
s 500
11.1%
v 500
11.1%
d 500
11.1%
t 400
8.9%
e 400
8.9%
r 300
 
6.7%
p 300
 
6.7%
m 100
 
2.2%
q 100
 
2.2%
Other values (8) 800
17.8%
Decimal Number
ValueCountFrequency (%)
0 268
20.3%
1 215
16.3%
2 202
15.3%
8 141
10.7%
9 133
10.1%
4 118
8.9%
7 86
 
6.5%
6 80
 
6.0%
5 46
 
3.5%
3 34
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
C 200
40.0%
S 100
20.0%
D 97
19.4%
K 97
19.4%
M 3
 
0.6%
P 3
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 400
33.3%
/ 400
33.3%
& 200
16.7%
? 100
 
8.3%
: 100
 
8.3%
Math Symbol
ValueCountFrequency (%)
= 300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5000
63.9%
Common 2823
36.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 600
12.0%
s 500
10.0%
v 500
10.0%
d 500
10.0%
t 400
 
8.0%
e 400
 
8.0%
r 300
 
6.0%
p 300
 
6.0%
C 200
 
4.0%
m 100
 
2.0%
Other values (14) 1200
24.0%
Common
ValueCountFrequency (%)
. 400
14.2%
/ 400
14.2%
= 300
10.6%
0 268
9.5%
1 215
7.6%
2 202
7.2%
& 200
7.1%
8 141
 
5.0%
9 133
 
4.7%
4 118
 
4.2%
Other values (6) 446
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 600
 
7.7%
s 500
 
6.4%
v 500
 
6.4%
d 500
 
6.4%
. 400
 
5.1%
t 400
 
5.1%
e 400
 
5.1%
/ 400
 
5.1%
r 300
 
3.8%
= 300
 
3.8%
Other values (30) 3523
45.0%

Interactions

2023-12-10T18:51:22.661174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:21.722814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:22.204912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:22.826599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:21.879703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:22.351841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:23.025635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:22.036751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:51:22.506348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:51:31.831324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
item_nmgame_flag_nmpartcpt_scale_valuegame_yeargame_nmgame_place_nmgame_begin_degame_end_dematch_dematch_knd_nmmatch_detail_item_nmrnd_nmvido_url
item_nm1.000NaNNaN1.0001.000NaN1.0001.0001.0000.0001.0000.8211.000
game_flag_nmNaN1.000NaN0.1471.0001.0000.1410.1411.000NaNNaNNaN1.000
partcpt_scale_valueNaNNaN1.0001.0001.0001.0001.0001.0000.9840.7400.9880.9921.000
game_year1.0000.1471.0001.0001.0001.0001.0001.0001.0000.8240.9380.9421.000
game_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0000.8240.9380.9421.000
game_place_nmNaN1.0001.0001.0001.0001.0001.0001.0001.0000.9900.7070.9741.000
game_begin_de1.0000.1411.0001.0001.0001.0001.0001.0001.0000.8240.9380.9421.000
game_end_de1.0000.1411.0001.0001.0001.0001.0001.0001.0000.8240.9380.9421.000
match_de1.0001.0000.9841.0001.0001.0001.0001.0001.0000.7420.7000.8791.000
match_knd_nm0.000NaN0.7400.8240.8240.9900.8240.8240.7421.0000.7070.7121.000
match_detail_item_nm1.000NaN0.9880.9380.9380.7070.9380.9380.7000.7071.0000.7401.000
rnd_nm0.821NaN0.9920.9420.9420.9740.9420.9420.8790.7120.7401.0001.000
vido_url1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:51:32.319045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
game_yeargame_nmitem_nmpartcpt_scale_valuegame_place_nmgame_flag_nmrnd_nmmatch_knd_nmmatch_detail_item_nm
game_year1.0000.9890.9850.9940.9890.0940.6590.6760.825
game_nm0.9891.0000.9740.9941.0000.9790.6590.6760.825
item_nm0.9850.9741.0001.0001.0001.0000.5940.0000.976
partcpt_scale_value0.9940.9941.0001.0000.9941.0000.7570.7230.891
game_place_nm0.9891.0001.0000.9941.0000.9790.6850.8460.741
game_flag_nm0.0940.9791.0001.0000.9791.0001.0001.0001.000
rnd_nm0.6590.6590.5940.7570.6851.0001.0000.2980.359
match_knd_nm0.6760.6760.0000.7230.8461.0000.2981.0000.435
match_detail_item_nm0.8250.8250.9760.8910.7411.0000.3590.4351.000
2023-12-10T18:51:32.671388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
game_begin_degame_end_dematch_deitem_nmgame_flag_nmpartcpt_scale_valuegame_yeargame_nmgame_place_nmmatch_knd_nmmatch_detail_item_nmrnd_nm
game_begin_de1.0001.0000.9110.9850.0940.9941.0000.9890.9890.6760.8250.659
game_end_de1.0001.0000.9110.9850.0940.9941.0000.9890.9890.6760.8250.659
match_de0.9110.9111.0000.9740.9820.8851.0000.9910.9910.6390.7280.620
item_nm0.9850.9850.9741.0001.0001.0000.9850.9741.0000.0000.9760.594
game_flag_nm0.0940.0940.9821.0001.0001.0000.0940.9790.9791.0001.0001.000
partcpt_scale_value0.9940.9940.8851.0001.0001.0000.9940.9940.9940.7230.8910.757
game_year1.0001.0001.0000.9850.0940.9941.0000.9890.9890.6760.8250.659
game_nm0.9890.9890.9910.9740.9790.9940.9891.0001.0000.6760.8250.659
game_place_nm0.9890.9890.9911.0000.9790.9940.9891.0001.0000.8460.7410.685
match_knd_nm0.6760.6760.6390.0001.0000.7230.6760.6760.8461.0000.4350.298
match_detail_item_nm0.8250.8250.7280.9761.0000.8910.8250.8250.7410.4351.0000.359
rnd_nm0.6590.6590.6200.5941.0000.7570.6590.6590.6850.2980.3591.000

Missing values

2023-12-10T18:51:23.400322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:51:23.899166image/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

item_nmgame_flag_nmpartcpt_scale_valuegame_yeargame_nmgame_place_nmgame_begin_degame_end_dematch_dematch_knd_nmmatch_detail_item_nmrnd_nmvido_url
0검도총리배700명 이하2020회장기 제60회 전국검도단별선수권대회경남 고성군, 국민체육센터202010312020110120201031남자초단부개인전8강https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=202005337&movSeq=51946
1근대5종<NA><NA>2021제102회 전국체육대회<NA>202110092021101320211011남자고등부수영-https://vod.sports.or.kr/vod/view.do?pclassCd=MP&eventCd=202106529&movSeq=57745
2검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191102남자중학부단체전결승https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=43036
3검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191102남자중학부단체전4강-1경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=43033
4검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191102남자고등부개인전4강-1경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=42522
5검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191102남자중학부단체전8강-4경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=43028
6검도총리배700명 이하2020회장기 제60회 전국검도단별선수권대회경남 고성군, 국민체육센터202010312020110120201031남자2단부개인전16강https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=202005337&movSeq=51915
7근대5종<NA><NA>2021제102회 전국체육대회<NA>202110092021101320211011여자고등부수영-https://vod.sports.or.kr/vod/view.do?pclassCd=MP&eventCd=202106529&movSeq=57761
8검도총리배700명 이하2020회장기 제60회 전국검도단별선수권대회경남 고성군, 국민체육센터202010312020110120201031남자초단부개인전8강https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=202005337&movSeq=51944
9검도총리배700명 이하2018제27회 회장기 전국 중.고등학교검도대회경북, 청송국민체육센터2018060820180610<NA>남자고등부개인(단식)16강1경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201800189&movSeq=6866
item_nmgame_flag_nmpartcpt_scale_valuegame_yeargame_nmgame_place_nmgame_begin_degame_end_dematch_dematch_knd_nmmatch_detail_item_nmrnd_nmvido_url
90검도총리배<NA>2016제13회 추계 전국실업검도대회옥천체육센터201609082016091120160908<NA><NA><NA>https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201600199&movSeq=1041
91검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191102남자중학부단체전8강-2경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=42666
92검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191102남자중학부단체전8강-3경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=42670
93검도총리배<NA>2016제13회 추계 전국실업검도대회옥천체육센터201609082016091120160908<NA><NA><NA>https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201600199&movSeq=1102
94검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191103남자고등부단체전16강-4경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=42708
95검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191103남자고등부단체전8강-1경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=42741
96검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191103남자고등부단체전8강-2경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=42743
97검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191103남자고등부단체전8강-3경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=42745
98검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191102남자고등부개인전8강-4경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=42520
99검도총리배1000명 이상2019제36회 추계 전국중·고등학교검도대회전남, 해남군 우슬체육관201911012019110320191102여자고등부단체전8강-4경기https://vod.sports.or.kr/vod/view.do?pclassCd=KD&eventCd=201904721&movSeq=43042