Overview

Dataset statistics

Number of variables17
Number of observations5428
Missing cells153
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory768.7 KiB
Average record size in memory145.0 B

Variable types

Numeric8
Categorical7
Text2

Dataset

Description생활체육 활성화를 위한 제공 정보(강습회 등 각종 생활체육 활동 및 행사 관련정보)의 데이터로 종목코드, 종목명, 참가구분, 선정방법코드, 선정방법, 접수시작일 접수종료일, 급수명, 참가비, 모집인원, 강습시작일, 강습종료일, 장소 등의 정보가 있음
Author대한체육회
URLhttps://www.data.go.kr/data/15021207/fileData.do

Alerts

종목명 is highly overall correlated with 모집인원 and 6 other fieldsHigh correlation
선정방법 is highly overall correlated with 참가비 and 4 other fieldsHigh correlation
구분코드 is highly overall correlated with 참가비 and 3 other fieldsHigh correlation
종목코드 is highly overall correlated with 모집인원 and 6 other fieldsHigh correlation
구분명 is highly overall correlated with 참가비 and 3 other fieldsHigh correlation
선정방법코드 is highly overall correlated with 참가비 and 4 other fieldsHigh correlation
연번 is highly overall correlated with 년도 and 4 other fieldsHigh correlation
년도 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
접수시작일 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
접수종료일 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
참가비 is highly overall correlated with 구분코드 and 4 other fieldsHigh correlation
모집인원 is highly overall correlated with 종목코드 and 1 other fieldsHigh correlation
강습시작일 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
강습종료일 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
참가구분 is highly overall correlated with 참가비 and 4 other fieldsHigh correlation
구분코드 is highly imbalanced (82.6%)Imbalance
구분명 is highly imbalanced (82.6%)Imbalance
종목코드 is highly imbalanced (82.0%)Imbalance
종목명 is highly imbalanced (81.8%)Imbalance
년도 has 146 (2.7%) missing valuesMissing
모집인원 is highly skewed (γ1 = 30.63440327)Skewed
강습종료일 is highly skewed (γ1 = -36.735303)Skewed
참가비 has 164 (3.0%) zerosZeros
모집인원 has 75 (1.4%) zerosZeros

Reproduction

Analysis started2023-12-12 07:52:40.482463
Analysis finished2023-12-12 07:52:52.423902
Duration11.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION 

Distinct4090
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3021.7898
Minimum95
Maximum4995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2023-12-12T16:52:52.500726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95
5-th percentile471.35
Q11719.75
median3290.5
Q34429
95-th percentile4853
Maximum4995
Range4900
Interquartile range (IQR)2709.25

Descriptive statistics

Standard deviation1494.716
Coefficient of variation (CV)0.49464593
Kurtosis-1.2294453
Mean3021.7898
Median Absolute Deviation (MAD)1209.5
Skewness-0.37197702
Sum16402275
Variance2234176
MonotonicityNot monotonic
2023-12-12T16:52:52.672729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4813 19
 
0.4%
4846 12
 
0.2%
4809 11
 
0.2%
4774 10
 
0.2%
4835 10
 
0.2%
4803 10
 
0.2%
4821 9
 
0.2%
4857 9
 
0.2%
4856 9
 
0.2%
4855 9
 
0.2%
Other values (4080) 5320
98.0%
ValueCountFrequency (%)
95 1
< 0.1%
96 1
< 0.1%
97 1
< 0.1%
98 1
< 0.1%
99 1
< 0.1%
100 1
< 0.1%
103 1
< 0.1%
104 1
< 0.1%
105 1
< 0.1%
106 1
< 0.1%
ValueCountFrequency (%)
4995 1
< 0.1%
4994 1
< 0.1%
4968 1
< 0.1%
4967 1
< 0.1%
4966 1
< 0.1%
4965 1
< 0.1%
4964 1
< 0.1%
4963 1
< 0.1%
4962 1
< 0.1%
4950 1
< 0.1%

구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
2
5287 
3
 
141

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 5287
97.4%
3 141
 
2.6%

Length

2023-12-12T16:52:52.828331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:52:52.938829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5287
97.4%
3 141
 
2.6%

구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
동계스포츠보급
5287 
행복나눔생활체육교실
 
141

Length

Max length10
Median length7
Mean length7.0779293
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동계스포츠보급 5287
97.4%
행복나눔생활체육교실 141
 
2.6%

Length

2023-12-12T16:52:53.034349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:52:53.134928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동계스포츠보급 5287
97.4%
행복나눔생활체육교실 141
 
2.6%

년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.1%
Missing146
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean2017.0638
Minimum2014
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2023-12-12T16:52:53.233460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2017
Q32018
95-th percentile2020
Maximum2020
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7857978
Coefficient of variation (CV)0.00088534523
Kurtosis-0.97731714
Mean2017.0638
Median Absolute Deviation (MAD)1
Skewness0.090143487
Sum10654131
Variance3.1890738
MonotonicityNot monotonic
2023-12-12T16:52:53.356260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2016 1147
21.1%
2017 950
17.5%
2018 788
14.5%
2015 666
12.3%
2019 663
12.2%
2020 651
12.0%
2014 417
 
7.7%
(Missing) 146
 
2.7%
ValueCountFrequency (%)
2014 417
 
7.7%
2015 666
12.3%
2016 1147
21.1%
2017 950
17.5%
2018 788
14.5%
2019 663
12.2%
2020 651
12.0%
ValueCountFrequency (%)
2020 651
12.0%
2019 663
12.2%
2018 788
14.5%
2017 950
17.5%
2016 1147
21.1%
2015 666
12.3%
2014 417
 
7.7%

종목코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct34
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
SI
4256 
SK
1081 
VB
 
6
AT
 
6
KG
 
6
Other values (29)
 
73

Length

Max length4
Median length2
Mean length2.0036846
Min length2

Unique

Unique9 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
SI 4256
78.4%
SK 1081
 
19.9%
VB 6
 
0.1%
AT 6
 
0.1%
KG 6
 
0.1%
BB 5
 
0.1%
286 5
 
0.1%
SH 5
 
0.1%
KD 5
 
0.1%
BM 4
 
0.1%
Other values (24) 49
 
0.9%

Length

2023-12-12T16:52:53.515489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
si 4256
78.4%
sk 1081
 
19.9%
vb 6
 
0.1%
at 6
 
0.1%
kg 6
 
0.1%
bb 5
 
0.1%
286 5
 
0.1%
sh 5
 
0.1%
kd 5
 
0.1%
284 4
 
0.1%
Other values (24) 49
 
0.9%

종목명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct33
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
스키
4256 
빙상
1081 
육상
 
6
국학기공
 
6
배구
 
6
Other values (28)
 
73

Length

Max length10
Median length2
Mean length2.0169492
Min length2

Unique

Unique7 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
스키 4256
78.4%
빙상 1081
 
19.9%
육상 6
 
0.1%
국학기공 6
 
0.1%
배구 6
 
0.1%
올림피언 5
 
0.1%
사격 5
 
0.1%
검도 5
 
0.1%
농구 5
 
0.1%
배드민턴 4
 
0.1%
Other values (23) 49
 
0.9%

Length

2023-12-12T16:52:53.677834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
스키 4256
78.4%
빙상 1081
 
19.9%
육상 6
 
0.1%
국학기공 6
 
0.1%
배구 6
 
0.1%
올림피언 5
 
0.1%
사격 5
 
0.1%
검도 5
 
0.1%
농구 5
 
0.1%
배드민턴 4
 
0.1%
Other values (23) 49
 
0.9%

참가구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
개인
4437 
단체
991 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
개인 4437
81.7%
단체 991
 
18.3%

Length

2023-12-12T16:52:53.832137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:52:53.924190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 4437
81.7%
단체 991
 
18.3%

선정방법코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
A
4496 
B
932 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 4496
82.8%
B 932
 
17.2%

Length

2023-12-12T16:52:54.014481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:52:54.099920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 4496
82.8%
b 932
 
17.2%

선정방법
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
선착순
4496 
심사/추첨
932 

Length

Max length5
Median length3
Mean length3.3434046
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row심사/추첨
2nd row심사/추첨
3rd row심사/추첨
4th row심사/추첨
5th row심사/추첨

Common Values

ValueCountFrequency (%)
선착순 4496
82.8%
심사/추첨 932
 
17.2%

Length

2023-12-12T16:52:54.227634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:52:54.326754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
선착순 4496
82.8%
심사/추첨 932
 
17.2%

접수시작일
Real number (ℝ)

HIGH CORRELATION 

Distinct977
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20171826
Minimum20140405
Maximum20201212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2023-12-12T16:52:54.447454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20140405
5-th percentile20140920
Q120160221
median20170706
Q320190109
95-th percentile20200207
Maximum20201212
Range60807
Interquartile range (IQR)29888

Descriptive statistics

Standard deviation17913.297
Coefficient of variation (CV)0.00088803548
Kurtosis-1.0159582
Mean20171826
Median Absolute Deviation (MAD)10516
Skewness0.053285268
Sum1.0949267 × 1011
Variance3.2088623 × 108
MonotonicityNot monotonic
2023-12-12T16:52:54.613139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200129 42
 
0.8%
20200108 41
 
0.8%
20200116 38
 
0.7%
20200130 38
 
0.7%
20190221 35
 
0.6%
20181201 28
 
0.5%
20190226 27
 
0.5%
20161211 25
 
0.5%
20200211 25
 
0.5%
20200120 25
 
0.5%
Other values (967) 5104
94.0%
ValueCountFrequency (%)
20140405 5
0.1%
20140407 4
0.1%
20140412 3
0.1%
20140414 2
 
< 0.1%
20140415 1
 
< 0.1%
20140416 1
 
< 0.1%
20140417 1
 
< 0.1%
20140418 2
 
< 0.1%
20140423 2
 
< 0.1%
20140424 1
 
< 0.1%
ValueCountFrequency (%)
20201212 1
 
< 0.1%
20201207 1
 
< 0.1%
20201206 2
< 0.1%
20201205 1
 
< 0.1%
20201201 2
< 0.1%
20201130 1
 
< 0.1%
20201129 2
< 0.1%
20201125 2
< 0.1%
20201122 3
0.1%
20201118 1
 
< 0.1%

접수종료일
Real number (ℝ)

HIGH CORRELATION 

Distinct946
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20171846
Minimum20140419
Maximum20210102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2023-12-12T16:52:55.074894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20140419
5-th percentile20140924
Q120160221
median20170813
Q320190108
95-th percentile20200210
Maximum20210102
Range69683
Interquartile range (IQR)29887

Descriptive statistics

Standard deviation17913.503
Coefficient of variation (CV)0.0008880448
Kurtosis-1.0160141
Mean20171846
Median Absolute Deviation (MAD)10599.5
Skewness0.053340226
Sum1.0949278 × 1011
Variance3.2089359 × 108
MonotonicityNot monotonic
2023-12-12T16:52:55.209697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200117 72
 
1.3%
20200131 62
 
1.1%
20200129 37
 
0.7%
20200212 33
 
0.6%
20200119 31
 
0.6%
20171130 29
 
0.5%
20190221 28
 
0.5%
20190226 27
 
0.5%
20190131 26
 
0.5%
20190129 25
 
0.5%
Other values (936) 5058
93.2%
ValueCountFrequency (%)
20140419 4
0.1%
20140424 1
 
< 0.1%
20140426 1
 
< 0.1%
20140430 4
0.1%
20140504 2
< 0.1%
20140510 4
0.1%
20140512 1
 
< 0.1%
20140523 4
0.1%
20140524 2
< 0.1%
20140528 1
 
< 0.1%
ValueCountFrequency (%)
20210102 1
 
< 0.1%
20201228 1
 
< 0.1%
20201227 2
< 0.1%
20201226 1
 
< 0.1%
20201222 1
 
< 0.1%
20201221 1
 
< 0.1%
20201220 2
< 0.1%
20201213 3
0.1%
20201211 1
 
< 0.1%
20201210 2
< 0.1%
Distinct107
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size42.5 KiB
2023-12-12T16:52:55.406800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length4
Mean length4.8089536
Min length2

Characters and Unicode

Total characters26103
Distinct characters79
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

Unique46 ?
Unique (%)0.8%

Sample

1st row공통과정
2nd row공통과정
3rd row공통과정
4th row공통과정
5th row공통과정
ValueCountFrequency (%)
공통과정 3497
56.7%
2급 215
 
3.5%
1:10 176
 
2.9%
공통 166
 
2.7%
4급 164
 
2.7%
입문 142
 
2.3%
1급(처음 116
 
1.9%
3급 104
 
1.7%
3급(3회이상 100
 
1.6%
5급(숙련자 88
 
1.4%
Other values (72) 1396
 
22.6%
2023-12-12T16:52:55.731132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3682
14.1%
3663
14.0%
3520
13.5%
3497
13.4%
1510
 
5.8%
) 1128
 
4.3%
( 1125
 
4.3%
1 1025
 
3.9%
736
 
2.8%
3 620
 
2.4%
Other values (69) 5597
21.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19060
73.0%
Decimal Number 2957
 
11.3%
Close Punctuation 1128
 
4.3%
Open Punctuation 1125
 
4.3%
Space Separator 736
 
2.8%
Other Punctuation 506
 
1.9%
Lowercase Letter 241
 
0.9%
Uppercase Letter 198
 
0.8%
Math Symbol 84
 
0.3%
Dash Punctuation 68
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3682
19.3%
3663
19.2%
3520
18.5%
3497
18.3%
1510
7.9%
444
 
2.3%
277
 
1.5%
181
 
0.9%
163
 
0.9%
142
 
0.7%
Other values (45) 1981
10.4%
Decimal Number
ValueCountFrequency (%)
1 1025
34.7%
3 620
21.0%
2 465
15.7%
5 335
 
11.3%
4 261
 
8.8%
0 230
 
7.8%
9 13
 
0.4%
8 4
 
0.1%
6 3
 
0.1%
7 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 158
65.6%
r 79
32.8%
s 2
 
0.8%
t 2
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
F 79
39.9%
A 71
35.9%
S 48
24.2%
Other Punctuation
ValueCountFrequency (%)
: 268
53.0%
' 238
47.0%
Close Punctuation
ValueCountFrequency (%)
) 1128
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1125
100.0%
Space Separator
ValueCountFrequency (%)
736
100.0%
Math Symbol
ValueCountFrequency (%)
~ 84
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19060
73.0%
Common 6604
 
25.3%
Latin 439
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3682
19.3%
3663
19.2%
3520
18.5%
3497
18.3%
1510
7.9%
444
 
2.3%
277
 
1.5%
181
 
0.9%
163
 
0.9%
142
 
0.7%
Other values (45) 1981
10.4%
Common
ValueCountFrequency (%)
) 1128
17.1%
( 1125
17.0%
1 1025
15.5%
736
11.1%
3 620
9.4%
2 465
7.0%
5 335
 
5.1%
: 268
 
4.1%
4 261
 
4.0%
' 238
 
3.6%
Other values (7) 403
 
6.1%
Latin
ValueCountFrequency (%)
e 158
36.0%
r 79
18.0%
F 79
18.0%
A 71
16.2%
S 48
 
10.9%
s 2
 
0.5%
t 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19060
73.0%
ASCII 7043
 
27.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3682
19.3%
3663
19.2%
3520
18.5%
3497
18.3%
1510
7.9%
444
 
2.3%
277
 
1.5%
181
 
0.9%
163
 
0.9%
142
 
0.7%
Other values (45) 1981
10.4%
ASCII
ValueCountFrequency (%)
) 1128
16.0%
( 1125
16.0%
1 1025
14.6%
736
10.5%
3 620
8.8%
2 465
6.6%
5 335
 
4.8%
: 268
 
3.8%
4 261
 
3.7%
' 238
 
3.4%
Other values (14) 842
12.0%

참가비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41010.875
Minimum0
Maximum180000
Zeros164
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2023-12-12T16:52:55.883440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20000
Q120000
median40000
Q350000
95-th percentile90000
Maximum180000
Range180000
Interquartile range (IQR)30000

Descriptive statistics

Standard deviation24295.915
Coefficient of variation (CV)0.59242617
Kurtosis8.4547605
Mean41010.875
Median Absolute Deviation (MAD)10000
Skewness2.0737367
Sum2.2260703 × 108
Variance5.902915 × 108
MonotonicityNot monotonic
2023-12-12T16:52:56.011412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
50000 1382
25.5%
20000 1327
24.4%
40000 688
12.7%
60000 639
11.8%
30000 584
10.8%
25000 247
 
4.6%
0 164
 
3.0%
100000 119
 
2.2%
45000 69
 
1.3%
110000 67
 
1.2%
Other values (17) 142
 
2.6%
ValueCountFrequency (%)
0 164
 
3.0%
10 1
 
< 0.1%
15 1
 
< 0.1%
1004 1
 
< 0.1%
2000 1
 
< 0.1%
4000 1
 
< 0.1%
5000 1
 
< 0.1%
10000 21
 
0.4%
20000 1327
24.4%
25000 247
 
4.6%
ValueCountFrequency (%)
180000 42
 
0.8%
170000 4
 
0.1%
150000 1
 
< 0.1%
120000 15
 
0.3%
115000 1
 
< 0.1%
110000 67
1.2%
100000 119
2.2%
95000 17
 
0.3%
90000 17
 
0.3%
85000 1
 
< 0.1%

모집인원
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct82
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.806927
Minimum0
Maximum3600
Zeros75
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2023-12-12T16:52:56.187535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median12
Q315
95-th percentile30
Maximum3600
Range3600
Interquartile range (IQR)5

Descriptive statistics

Standard deviation68.989812
Coefficient of variation (CV)3.4831154
Kurtosis1395.8321
Mean19.806927
Median Absolute Deviation (MAD)3
Skewness30.634403
Sum107512
Variance4759.5942
MonotonicityNot monotonic
2023-12-12T16:52:56.307398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2174
40.1%
15 1342
24.7%
20 491
 
9.0%
12 216
 
4.0%
18 119
 
2.2%
16 112
 
2.1%
6 111
 
2.0%
14 75
 
1.4%
0 75
 
1.4%
30 65
 
1.2%
Other values (72) 648
 
11.9%
ValueCountFrequency (%)
0 75
1.4%
1 14
 
0.3%
2 28
 
0.5%
3 34
 
0.6%
4 23
 
0.4%
5 35
 
0.6%
6 111
2.0%
7 22
 
0.4%
8 28
 
0.5%
9 12
 
0.2%
ValueCountFrequency (%)
3600 1
 
< 0.1%
1200 1
 
< 0.1%
1000 4
0.1%
900 1
 
< 0.1%
760 1
 
< 0.1%
600 4
0.1%
500 4
0.1%
450 1
 
< 0.1%
440 2
< 0.1%
400 3
0.1%

강습시작일
Real number (ℝ)

HIGH CORRELATION 

Distinct967
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20171676
Minimum20140405
Maximum20201212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2023-12-12T16:52:56.435887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20140405
5-th percentile20140920
Q120160221
median20170702
Q320190105
95-th percentile20200203
Maximum20201212
Range60807
Interquartile range (IQR)29884

Descriptive statistics

Standard deviation17751.794
Coefficient of variation (CV)0.00088003567
Kurtosis-0.9977531
Mean20171676
Median Absolute Deviation (MAD)10513
Skewness0.046933796
Sum1.0949186 × 1011
Variance3.151262 × 108
MonotonicityNot monotonic
2023-12-12T16:52:56.574114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200108 114
 
2.1%
20181201 53
 
1.0%
20200130 41
 
0.8%
20180526 40
 
0.7%
20190110 34
 
0.6%
20200116 32
 
0.6%
20200203 28
 
0.5%
20190201 27
 
0.5%
20160228 25
 
0.5%
20190221 25
 
0.5%
Other values (957) 5009
92.3%
ValueCountFrequency (%)
20140405 5
0.1%
20140407 4
0.1%
20140412 3
0.1%
20140414 2
 
< 0.1%
20140415 1
 
< 0.1%
20140416 1
 
< 0.1%
20140417 1
 
< 0.1%
20140418 2
 
< 0.1%
20140423 2
 
< 0.1%
20140424 1
 
< 0.1%
ValueCountFrequency (%)
20201212 1
 
< 0.1%
20201207 1
 
< 0.1%
20201206 2
< 0.1%
20201205 1
 
< 0.1%
20201201 1
 
< 0.1%
20201130 1
 
< 0.1%
20201129 2
< 0.1%
20201125 2
< 0.1%
20201122 3
0.1%
20201118 1
 
< 0.1%

강습종료일
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct937
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20158316
Minimum2018122
Maximum20210102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.8 KiB
2023-12-12T16:52:56.702463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018122
5-th percentile20140921
Q120160221
median20170811
Q320190105
95-th percentile20200204
Maximum20210102
Range18191980
Interquartile range (IQR)29884

Descriptive statistics

Standard deviation492984.86
Coefficient of variation (CV)0.024455657
Kurtosis1349.7359
Mean20158316
Median Absolute Deviation (MAD)10591
Skewness-36.735303
Sum1.0941934 × 1011
Variance2.4303407 × 1011
MonotonicityNot monotonic
2023-12-12T16:52:56.843138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20200108 114
 
2.1%
20181203 44
 
0.8%
20200131 41
 
0.8%
20180526 38
 
0.7%
20200129 36
 
0.7%
20200117 31
 
0.6%
20190131 30
 
0.6%
20190111 29
 
0.5%
20171130 29
 
0.5%
20200204 28
 
0.5%
Other values (927) 5008
92.3%
ValueCountFrequency (%)
2018122 1
 
< 0.1%
2018123 2
< 0.1%
2018127 1
 
< 0.1%
20140419 4
0.1%
20140424 1
 
< 0.1%
20140426 1
 
< 0.1%
20140430 4
0.1%
20140504 2
< 0.1%
20140510 4
0.1%
20140512 1
 
< 0.1%
ValueCountFrequency (%)
20210102 1
 
< 0.1%
20201228 1
 
< 0.1%
20201227 2
< 0.1%
20201226 1
 
< 0.1%
20201222 1
 
< 0.1%
20201221 1
 
< 0.1%
20201220 2
< 0.1%
20201213 3
0.1%
20201210 1
 
< 0.1%
20201209 3
0.1%

장소
Text

Distinct214
Distinct (%)3.9%
Missing7
Missing (%)0.1%
Memory size42.5 KiB
2023-12-12T16:52:57.092841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length38
Mean length11.020845
Min length2

Characters and Unicode

Total characters59744
Distinct characters243
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique105 ?
Unique (%)1.9%

Sample

1st row분당미필스키랜드
2nd row분당미필스키랜드
3rd row분당미필스키랜드
4th row분당미필스키랜드
5th row분당미필스키랜드
ValueCountFrequency (%)
1090
 
10.2%
양지파인리조트 803
 
7.5%
베어스타운리조트 676
 
6.3%
베어스타운 668
 
6.2%
유스호스텔 641
 
6.0%
광장 522
 
4.9%
단체렌탈장 522
 
4.9%
웅진플레이도시 516
 
4.8%
부천웅진플레이도시 494
 
4.6%
웰리힐리파크 373
 
3.5%
Other values (294) 4387
41.0%
2023-12-12T16:52:57.491662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5277
 
8.8%
4527
 
7.6%
2376
 
4.0%
2135
 
3.6%
1937
 
3.2%
1762
 
2.9%
1552
 
2.6%
1416
 
2.4%
1365
 
2.3%
1361
 
2.3%
Other values (233) 36036
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53685
89.9%
Space Separator 5277
 
8.8%
Decimal Number 674
 
1.1%
Other Punctuation 49
 
0.1%
Open Punctuation 29
 
< 0.1%
Close Punctuation 29
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4527
 
8.4%
2376
 
4.4%
2135
 
4.0%
1937
 
3.6%
1762
 
3.3%
1552
 
2.9%
1416
 
2.6%
1365
 
2.5%
1361
 
2.5%
1361
 
2.5%
Other values (216) 33893
63.1%
Decimal Number
ValueCountFrequency (%)
2 261
38.7%
5 195
28.9%
3 131
19.4%
1 81
 
12.0%
4 3
 
0.4%
0 1
 
0.1%
8 1
 
0.1%
7 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 41
83.7%
/ 5
 
10.2%
' 1
 
2.0%
. 1
 
2.0%
& 1
 
2.0%
Space Separator
ValueCountFrequency (%)
5277
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53685
89.9%
Common 6059
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4527
 
8.4%
2376
 
4.4%
2135
 
4.0%
1937
 
3.6%
1762
 
3.3%
1552
 
2.9%
1416
 
2.6%
1365
 
2.5%
1361
 
2.5%
1361
 
2.5%
Other values (216) 33893
63.1%
Common
ValueCountFrequency (%)
5277
87.1%
2 261
 
4.3%
5 195
 
3.2%
3 131
 
2.2%
1 81
 
1.3%
, 41
 
0.7%
( 29
 
0.5%
) 29
 
0.5%
/ 5
 
0.1%
4 3
 
< 0.1%
Other values (7) 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53683
89.9%
ASCII 6059
 
10.1%
Compat Jamo 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5277
87.1%
2 261
 
4.3%
5 195
 
3.2%
3 131
 
2.2%
1 81
 
1.3%
, 41
 
0.7%
( 29
 
0.5%
) 29
 
0.5%
/ 5
 
0.1%
4 3
 
< 0.1%
Other values (7) 7
 
0.1%
Hangul
ValueCountFrequency (%)
4527
 
8.4%
2376
 
4.4%
2135
 
4.0%
1937
 
3.6%
1762
 
3.3%
1552
 
2.9%
1416
 
2.6%
1365
 
2.5%
1361
 
2.5%
1361
 
2.5%
Other values (215) 33891
63.1%
Compat Jamo
ValueCountFrequency (%)
2
100.0%

Interactions

2023-12-12T16:52:50.826835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:44.057717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:45.011415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:45.751813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.448198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:47.379981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:48.783999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:49.846497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:50.953491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:44.231176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:45.106273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:45.834646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.533467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:47.533639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:48.889573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:49.941903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:51.063549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:44.319942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:52:45.926726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.631452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:47.670021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:49.014432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:50.045655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:51.193555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:44.431924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:45.295701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.013272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.723881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:47.820012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:52:45.389164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.103289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.821641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:47.941388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T16:52:45.478512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.189267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.944725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:48.409975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:49.436413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:50.455806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:51.596372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:44.815867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:45.571504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.277694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:47.076421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:48.560909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:49.563778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:50.563915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:51.732111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:44.909094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:45.660467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:46.359871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:47.222866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:48.674037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:49.708025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:52:50.696687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:52:57.595881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분코드구분명년도종목코드종목명참가구분선정방법코드선정방법접수시작일접수종료일참가비모집인원강습시작일강습종료일
연번1.0000.3150.3150.9380.3190.3180.4980.2260.2260.9270.9130.4610.1820.9250.036
구분코드0.3151.0001.0000.1850.8740.9150.5100.5240.5240.1950.1820.8280.3120.1940.000
구분명0.3151.0001.0000.1850.8740.9150.5100.5240.5240.1950.1820.8280.3120.1940.000
년도0.9380.1850.1851.0000.1900.1870.3290.2360.2360.9951.0000.4010.0570.9840.046
종목코드0.3190.8740.8740.1901.0001.0000.6920.6010.6010.2790.2230.6620.8550.2760.000
종목명0.3180.9150.9150.1871.0001.0000.7340.6390.6390.3010.2430.6570.8550.2980.000
참가구분0.4980.5100.5100.3290.6920.7341.0000.8010.8010.3700.3710.5480.1050.3690.000
선정방법코드0.2260.5240.5240.2360.6010.6390.8011.0001.0000.2420.2400.5390.1090.2150.000
선정방법0.2260.5240.5240.2360.6010.6390.8011.0001.0000.2420.2400.5390.1090.2150.000
접수시작일0.9270.1950.1950.9950.2790.3010.3700.2420.2421.0000.9980.4030.0801.0000.056
접수종료일0.9130.1820.1821.0000.2230.2430.3710.2400.2400.9981.0000.3790.0600.9970.056
참가비0.4610.8280.8280.4010.6620.6570.5480.5390.5390.4030.3791.0000.2690.4320.000
모집인원0.1820.3120.3120.0570.8550.8550.1050.1090.1090.0800.0600.2691.0000.0800.000
강습시작일0.9250.1940.1940.9840.2760.2980.3690.2150.2151.0000.9970.4320.0801.0000.054
강습종료일0.0360.0000.0000.0460.0000.0000.0000.0000.0000.0560.0560.0000.0000.0541.000
2023-12-12T16:52:57.731702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종목명선정방법구분코드종목코드참가구분구분명선정방법코드
종목명1.0000.5140.7871.0000.5980.7870.514
선정방법0.5141.0000.3510.5140.5910.3510.999
구분코드0.7870.3511.0000.7870.3410.9960.351
종목코드1.0000.5140.7871.0000.5970.7870.514
참가구분0.5980.5910.3410.5971.0000.3410.591
구분명0.7870.3510.9960.7870.3411.0000.351
선정방법코드0.5140.9990.3510.5140.5910.3511.000
2023-12-12T16:52:57.839051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번년도접수시작일접수종료일참가비모집인원강습시작일강습종료일구분코드구분명종목코드종목명참가구분선정방법코드선정방법
연번1.0000.9870.9990.9980.374-0.2610.9970.9940.2410.2410.1180.1180.3830.1730.173
년도0.9871.0000.9870.9870.406-0.2720.9860.9850.1360.1360.0960.0970.2730.1810.181
접수시작일0.9990.9871.0000.9990.379-0.2610.9980.9950.1460.1460.1120.1120.2810.1800.180
접수종료일0.9980.9870.9991.0000.374-0.2550.9970.9950.1360.1360.0890.0890.2810.1790.179
참가비0.3740.4060.3790.3741.000-0.3110.3760.3720.8540.8540.3110.3110.5500.5410.541
모집인원-0.261-0.272-0.261-0.255-0.3111.000-0.265-0.2610.3810.3810.6120.6120.1280.1330.133
강습시작일0.9970.9860.9980.9970.376-0.2651.0000.9970.1460.1460.1110.1110.2800.1600.160
강습종료일0.9940.9850.9950.9950.372-0.2610.9971.0000.0000.0000.0000.0000.0000.0000.000
구분코드0.2410.1360.1460.1360.8540.3810.1460.0001.0000.9960.7870.7870.3410.3510.351
구분명0.2410.1360.1460.1360.8540.3810.1460.0000.9961.0000.7870.7870.3410.3510.351
종목코드0.1180.0960.1120.0890.3110.6120.1110.0000.7870.7871.0001.0000.5970.5140.514
종목명0.1180.0970.1120.0890.3110.6120.1110.0000.7870.7871.0001.0000.5980.5140.514
참가구분0.3830.2730.2810.2810.5500.1280.2800.0000.3410.3410.5970.5981.0000.5910.591
선정방법코드0.1730.1810.1800.1790.5410.1330.1600.0000.3510.3510.5140.5140.5911.0000.999
선정방법0.1730.1810.1800.1790.5410.1330.1600.0000.3510.3510.5140.5140.5910.9991.000

Missing values

2023-12-12T16:52:51.927902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:52:52.177995image/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.
2023-12-12T16:52:52.357023image/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

연번구분코드구분명년도종목코드종목명참가구분선정방법코드선정방법접수시작일접수종료일급수명참가비모집인원강습시작일강습종료일장소
02322동계스포츠보급2014SI스키개인B심사/추첨2014061720140624공통과정20000202014061720140624분당미필스키랜드
12332동계스포츠보급2014SI스키개인B심사/추첨2014061720140624공통과정20000202014061720140624분당미필스키랜드
22342동계스포츠보급2014SI스키개인B심사/추첨2014061820140625공통과정20000202014061820140625분당미필스키랜드
32352동계스포츠보급2014SI스키개인B심사/추첨2014061820140625공통과정20000202014061820140625분당미필스키랜드
42362동계스포츠보급2014SI스키개인B심사/추첨2014061820140625공통과정20000202014061820140625분당미필스키랜드
52382동계스포츠보급2014SI스키개인B심사/추첨2014061920140626공통과정20000202014061920140626분당미필스키랜드
62392동계스포츠보급2014SI스키개인B심사/추첨2014061920140626공통과정20000202014061920140626분당미필스키랜드
72402동계스포츠보급2014SI스키개인B심사/추첨2014061920140626공통과정20000202014061920140626분당미필스키랜드
82412동계스포츠보급2014SI스키개인B심사/추첨2014062720140704공통과정20000202014062720140704분당미필스키랜드
92422동계스포츠보급2014SI스키개인B심사/추첨2014062720140704공통과정20000202014062720140704분당미필스키랜드
연번구분코드구분명년도종목코드종목명참가구분선정방법코드선정방법접수시작일접수종료일급수명참가비모집인원강습시작일강습종료일장소
541848742동계스포츠보급2020SI스키개인A선착순20200220202002204~5급 통합50000152020022020200220양지파인리조트
541948752동계스포츠보급2020SI스키개인A선착순20200221202002211급60000102020022120200221양지파인리조트
542048752동계스포츠보급2020SI스키개인A선착순20200221202002211급(2회)60000102020022120200221양지파인리조트
542148752동계스포츠보급2020SI스키개인A선착순20200221202002212급50000152020022120200221양지파인리조트
542248752동계스포츠보급2020SI스키개인A선착순20200221202002213급(1~2회)50000152020022120200221양지파인리조트
542348752동계스포츠보급2020SI스키개인A선착순20200221202002213급(2회)50000102020010820200108양지파인리조트
542448752동계스포츠보급2020SI스키개인A선착순20200221202002213급(3회이상)50000102020010820200108양지파인리조트
542548752동계스포츠보급2020SI스키개인A선착순20200221202002214~5급50000102020010820200108양지파인리조트
542648752동계스포츠보급2020SI스키개인A선착순20200221202002213급(3회 이상)50000152020022120200221양지파인리조트
542748752동계스포츠보급2020SI스키개인A선착순20200221202002214~5급 통합50000152020022120200221양지파인리조트