Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory110.0 B

Variable types

Text4
Categorical2
Numeric6

Dataset

Description등록연도~전년도까지의 민간자격취득현황 데이터입니다. 취득현황은 민간자격정보서비스(www.pqi.or.kr)를 통하여 자격운영기관이 입력 및 관리하며, 취득현황 데이터의 입력 시점에 따라 실제 수치와 차이가 있을 수 있습니다. 또한 등록자격이더라도 미입력된 데이터가 존재할 수 있으므로 정확한 데이터는 해당 기관에 문의하시기 바랍니다.※ 공식적인 통계자료가 아니며, 별도의 검증이 이루어지지 않은 데이터이므로 민간자격취득 시 동향 분석 등 참고 데이터 용도로만 활용하시기 바랍니다.
Author한국직업능력연구원
URLhttps://www.data.go.kr/data/15090560/fileData.do

Alerts

총검정횟수 is highly overall correlated with 총접수자수 and 3 other fieldsHigh correlation
총접수자수 is highly overall correlated with 총검정횟수 and 3 other fieldsHigh correlation
총응시자수 is highly overall correlated with 총검정횟수 and 3 other fieldsHigh correlation
총취득자수 is highly overall correlated with 총검정횟수 and 3 other fieldsHigh correlation
합격률 is highly overall correlated with 총검정횟수 and 3 other fieldsHigh correlation
검정방식 is highly imbalanced (75.0%)Imbalance
총검정횟수 is highly skewed (γ1 = 22.7414965)Skewed
총접수자수 is highly skewed (γ1 = 29.66318585)Skewed
총응시자수 is highly skewed (γ1 = 31.10429673)Skewed
총취득자수 is highly skewed (γ1 = 33.79634681)Skewed
총검정횟수 has 3945 (39.5%) zerosZeros
총접수자수 has 4072 (40.7%) zerosZeros
총응시자수 has 4049 (40.5%) zerosZeros
총취득자수 has 3917 (39.2%) zerosZeros
합격률 has 3950 (39.5%) zerosZeros

Reproduction

Analysis started2023-12-12 19:21:07.427129
Analysis finished2023-12-12 19:21:14.112386
Duration6.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록번호
Text

Distinct4816
Distinct (%)48.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:21:14.352057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.5772
Min length11

Characters and Unicode

Total characters115772
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2364 ?
Unique (%)23.6%

Sample

1st row''2008-0631
2nd row''2015-003947
3rd row''2015-002050
4th row''2014-1987
5th row''2008-0634
ValueCountFrequency (%)
2009-0221 40
 
0.4%
2008-0141 34
 
0.3%
2008-0140 34
 
0.3%
2012-0404 29
 
0.3%
2010-0482 27
 
0.3%
2008-0645 25
 
0.2%
2008-0337 24
 
0.2%
2013-2732 22
 
0.2%
2012-0403 22
 
0.2%
2015-004354 20
 
0.2%
Other values (4806) 9723
97.2%
2023-12-13T04:21:14.823600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25662
22.2%
' 20000
17.3%
2 15232
13.2%
1 14335
12.4%
- 10000
 
8.6%
4 6384
 
5.5%
5 6086
 
5.3%
3 5373
 
4.6%
8 3650
 
3.2%
6 3372
 
2.9%
Other values (2) 5678
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 85772
74.1%
Other Punctuation 20000
 
17.3%
Dash Punctuation 10000
 
8.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25662
29.9%
2 15232
17.8%
1 14335
16.7%
4 6384
 
7.4%
5 6086
 
7.1%
3 5373
 
6.3%
8 3650
 
4.3%
6 3372
 
3.9%
9 3078
 
3.6%
7 2600
 
3.0%
Other Punctuation
ValueCountFrequency (%)
' 20000
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 115772
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25662
22.2%
' 20000
17.3%
2 15232
13.2%
1 14335
12.4%
- 10000
 
8.6%
4 6384
 
5.5%
5 6086
 
5.3%
3 5373
 
4.6%
8 3650
 
3.2%
6 3372
 
2.9%
Other values (2) 5678
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115772
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25662
22.2%
' 20000
17.3%
2 15232
13.2%
1 14335
12.4%
- 10000
 
8.6%
4 6384
 
5.5%
5 6086
 
5.3%
3 5373
 
4.6%
8 3650
 
3.2%
6 3372
 
2.9%
Other values (2) 5678
 
4.9%

자격명
Text

Distinct3297
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:21:15.152370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length26
Mean length7.4079
Min length2

Characters and Unicode

Total characters74079
Distinct characters681
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1376 ?
Unique (%)13.8%

Sample

1st row반려견지도사
2nd row미술심리상담사
3rd row아로마블렌딩사
4th row우쿨렐레지도사
5th row그린키퍼
ValueCountFrequency (%)
미술심리상담사 158
 
1.5%
심리상담사 154
 
1.5%
동화구연지도사 72
 
0.7%
주산 63
 
0.6%
독서지도사 57
 
0.6%
암산 56
 
0.5%
놀이심리상담사 48
 
0.5%
snult 47
 
0.5%
가족상담사 47
 
0.5%
음악심리상담사 45
 
0.4%
Other values (3322) 9518
92.7%
2023-12-13T04:21:15.711056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 5842
 
7.9%
지 4027
 
5.4%
도 3903
 
5.3%
리 2273
 
3.1%
상 1563
 
2.1%
담 1427
 
1.9%
자 1408
 
1.9%
스 1231
 
1.7%
이 1212
 
1.6%
심 1062
 
1.4%
Other values (671) 50131
67.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68745
92.8%
Uppercase Letter 2920
 
3.9%
Lowercase Letter 852
 
1.2%
Close Punctuation 481
 
0.6%
Open Punctuation 481
 
0.6%
Space Separator 276
 
0.4%
Other Punctuation 180
 
0.2%
Decimal Number 80
 
0.1%
Dash Punctuation 60
 
0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 5842
 
8.5%
지 4027
 
5.9%
도 3903
 
5.7%
리 2273
 
3.3%
상 1563
 
2.3%
담 1427
 
2.1%
자 1408
 
2.0%
스 1231
 
1.8%
이 1212
 
1.8%
심 1062
 
1.5%
Other values (603) 44797
65.2%
Uppercase Letter
ValueCountFrequency (%)
E 325
 
11.1%
P 294
 
10.1%
L 260
 
8.9%
S 257
 
8.8%
T 253
 
8.7%
F 187
 
6.4%
X 162
 
5.5%
A 140
 
4.8%
C 126
 
4.3%
I 103
 
3.5%
Other values (15) 813
27.8%
Lowercase Letter
ValueCountFrequency (%)
i 103
12.1%
a 98
11.5%
t 85
10.0%
e 80
9.4%
r 77
9.0%
n 68
8.0%
o 58
 
6.8%
c 41
 
4.8%
l 40
 
4.7%
s 39
 
4.6%
Other values (12) 163
19.1%
Decimal Number
ValueCountFrequency (%)
3 35
43.8%
6 17
21.2%
0 10
 
12.5%
1 6
 
7.5%
9 5
 
6.2%
4 4
 
5.0%
5 1
 
1.2%
2 1
 
1.2%
8 1
 
1.2%
Other Punctuation
ValueCountFrequency (%)
/ 55
30.6%
· 50
27.8%
& 36
20.0%
. 29
16.1%
＆ 5
 
2.8%
, 4
 
2.2%
′ 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 481
100.0%
Open Punctuation
ValueCountFrequency (%)
( 481
100.0%
Space Separator
ValueCountFrequency (%)
276
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68713
92.8%
Latin 3772
 
5.1%
Common 1562
 
2.1%
Han 32
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 5842
 
8.5%
지 4027
 
5.9%
도 3903
 
5.7%
리 2273
 
3.3%
상 1563
 
2.3%
담 1427
 
2.1%
자 1408
 
2.0%
스 1231
 
1.8%
이 1212
 
1.8%
심 1062
 
1.5%
Other values (590) 44765
65.1%
Latin
ValueCountFrequency (%)
E 325
 
8.6%
P 294
 
7.8%
L 260
 
6.9%
S 257
 
6.8%
T 253
 
6.7%
F 187
 
5.0%
X 162
 
4.3%
A 140
 
3.7%
C 126
 
3.3%
I 103
 
2.7%
Other values (37) 1665
44.1%
Common
ValueCountFrequency (%)
) 481
30.8%
( 481
30.8%
276
17.7%
- 60
 
3.8%
/ 55
 
3.5%
· 50
 
3.2%
& 36
 
2.3%
3 35
 
2.2%
. 29
 
1.9%
6 17
 
1.1%
Other values (11) 42
 
2.7%
Han
ValueCountFrequency (%)
漢 5
15.6%
檢 4
12.5%
務 4
12.5%
商 4
12.5%
茶 3
9.4%
健 2
 
6.2%
氣 2
 
6.2%
身 2
 
6.2%
功 2
 
6.2%
掌 1
 
3.1%
Other values (3) 3
9.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68710
92.8%
ASCII 5278
 
7.1%
None 55
 
0.1%
CJK 29
 
< 0.1%
CJK Compat Ideographs 3
 
< 0.1%
Compat Jamo 3
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 5842
 
8.5%
지 4027
 
5.9%
도 3903
 
5.7%
리 2273
 
3.3%
상 1563
 
2.3%
담 1427
 
2.1%
자 1408
 
2.0%
스 1231
 
1.8%
이 1212
 
1.8%
심 1062
 
1.5%
Other values (589) 44762
65.1%
ASCII
ValueCountFrequency (%)
) 481
 
9.1%
( 481
 
9.1%
E 325
 
6.2%
P 294
 
5.6%
276
 
5.2%
L 260
 
4.9%
S 257
 
4.9%
T 253
 
4.8%
F 187
 
3.5%
X 162
 
3.1%
Other values (55) 2302
43.6%
None
ValueCountFrequency (%)
· 50
90.9%
＆ 5
 
9.1%
CJK
ValueCountFrequency (%)
漢 5
17.2%
檢 4
13.8%
務 4
13.8%
商 4
13.8%
健 2
 
6.9%
氣 2
 
6.9%
身 2
 
6.9%
功 2
 
6.9%
掌 1
 
3.4%
極 1
 
3.4%
Other values (2) 2
 
6.9%
CJK Compat Ideographs
ValueCountFrequency (%)
茶 3
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 3
100.0%
Punctuation
ValueCountFrequency (%)
′ 1
100.0%

자격발급기관
Text

Distinct1721
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:21:15.995851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length24
Mean length10.9134
Min length2

Characters and Unicode

Total characters109134
Distinct characters607
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique498 ?
Unique (%)5.0%

Sample

1st row(사)한국애견협회
2nd row금산성폭력상담센터
3rd row한국미용산업학회
4th row인천음악사회교육원
5th row건국대학교
ValueCountFrequency (%)
사단법인 563
 
4.9%
주식회사 283
 
2.5%
대한상공회의소 171
 
1.5%
재)종이문화재단 144
 
1.3%
한국지식자원개발원 128
 
1.1%
사)한국국공립대학평생교육원협의회 119
 
1.0%
재단법인 117
 
1.0%
사)한국문화교육협회 112
 
1.0%
한국자격관리평가원 110
 
1.0%
키드잡아동문화협회 106
 
0.9%
Other values (1791) 9582
83.8%
2023-12-13T04:21:16.533818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 6266
 
5.7%
회 6186
 
5.7%
한 6078
 
5.6%
사 5253
 
4.8%
협 4097
 
3.8%
) 3199
 
2.9%
( 3166
 
2.9%
인 2834
 
2.6%
교 2640
 
2.4%
단 2448
 
2.2%
Other values (597) 66967
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99334
91.0%
Close Punctuation 3199
 
2.9%
Open Punctuation 3166
 
2.9%
Space Separator 1467
 
1.3%
Uppercase Letter 1092
 
1.0%
Lowercase Letter 684
 
0.6%
Other Punctuation 90
 
0.1%
Decimal Number 88
 
0.1%
Dash Punctuation 14
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 6266
 
6.3%
회 6186
 
6.2%
한 6078
 
6.1%
사 5253
 
5.3%
협 4097
 
4.1%
인 2834
 
2.9%
교 2640
 
2.7%
단 2448
 
2.5%
원 2404
 
2.4%
법 2114
 
2.1%
Other values (542) 59014
59.4%
Uppercase Letter
ValueCountFrequency (%)
A 209
19.1%
P 114
10.4%
M 112
10.3%
E 111
10.2%
C 102
9.3%
B 101
9.2%
K 90
8.2%
S 39
 
3.6%
D 33
 
3.0%
H 31
 
2.8%
Other values (12) 150
13.7%
Lowercase Letter
ValueCountFrequency (%)
o 98
14.3%
t 86
12.6%
n 83
12.1%
e 79
11.5%
a 51
7.5%
r 47
6.9%
i 39
 
5.7%
m 39
 
5.7%
s 32
 
4.7%
c 29
 
4.2%
Other values (10) 101
14.8%
Other Punctuation
ValueCountFrequency (%)
& 28
31.1%
. 24
26.7%
/ 23
25.6%
: 10
 
11.1%
／ 5
 
5.6%
Decimal Number
ValueCountFrequency (%)
2 33
37.5%
1 33
37.5%
5 20
22.7%
3 2
 
2.3%
Close Punctuation
ValueCountFrequency (%)
) 3199
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3166
100.0%
Space Separator
ValueCountFrequency (%)
1467
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99334
91.0%
Common 8024
 
7.4%
Latin 1776
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 6266
 
6.3%
회 6186
 
6.2%
한 6078
 
6.1%
사 5253
 
5.3%
협 4097
 
4.1%
인 2834
 
2.9%
교 2640
 
2.7%
단 2448
 
2.5%
원 2404
 
2.4%
법 2114
 
2.1%
Other values (542) 59014
59.4%
Latin
ValueCountFrequency (%)
A 209
 
11.8%
P 114
 
6.4%
M 112
 
6.3%
E 111
 
6.2%
C 102
 
5.7%
B 101
 
5.7%
o 98
 
5.5%
K 90
 
5.1%
t 86
 
4.8%
n 83
 
4.7%
Other values (32) 670
37.7%
Common
ValueCountFrequency (%)
) 3199
39.9%
( 3166
39.5%
1467
18.3%
2 33
 
0.4%
1 33
 
0.4%
& 28
 
0.3%
. 24
 
0.3%
/ 23
 
0.3%
5 20
 
0.2%
- 14
 
0.2%
Other values (3) 17
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99309
91.0%
ASCII 9795
 
9.0%
Compat Jamo 25
 
< 0.1%
None 5
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 6266
 
6.3%
회 6186
 
6.2%
한 6078
 
6.1%
사 5253
 
5.3%
협 4097
 
4.1%
인 2834
 
2.9%
교 2640
 
2.7%
단 2448
 
2.5%
원 2404
 
2.4%
법 2114
 
2.1%
Other values (541) 58989
59.4%
ASCII
ValueCountFrequency (%)
) 3199
32.7%
( 3166
32.3%
1467
15.0%
A 209
 
2.1%
P 114
 
1.2%
M 112
 
1.1%
E 111
 
1.1%
C 102
 
1.0%
B 101
 
1.0%
o 98
 
1.0%
Other values (44) 1116
 
11.4%
Compat Jamo
ValueCountFrequency (%)
ㆍ 25
100.0%
None
ValueCountFrequency (%)
／ 5
100.0%

주무부처
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
교육부
3168 
문화체육관광부
2856 
보건복지부
1506 
농림축산식품부
634 
산업통상자원부
569 
Other values (27)
1267 

Length

Max length9
Median length8
Mean length5.2437
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row농림축산식품부
2nd row보건복지부
3rd row산업통상자원부
4th row문화체육관광부
5th row문화체육관광부

Common Values

ValueCountFrequency (%)
교육부 3168
31.7%
문화체육관광부 2856
28.6%
보건복지부 1506
15.1%
농림축산식품부 634
 
6.3%
산업통상자원부 569
 
5.7%
여성가족부 237
 
2.4%
과학기술정보통신부 192
 
1.9%
고용노동부 156
 
1.6%
행정안전부 83
 
0.8%
금융위원회 73
 
0.7%
Other values (22) 526
 
5.3%

Length

2023-12-13T04:21:16.718333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
교육부 3168
31.7%
문화체육관광부 2856
28.6%
보건복지부 1506
15.1%
농림축산식품부 634
 
6.3%
산업통상자원부 569
 
5.7%
여성가족부 237
 
2.4%
과학기술정보통신부 192
 
1.9%
고용노동부 156
 
1.6%
행정안전부 83
 
0.8%
금융위원회 73
 
0.7%
Other values (22) 526
 
5.3%

검정방식
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
합격제
9584 
점수제
 
416

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
합격제 9584
95.8%
점수제 416
 
4.2%

Length

2023-12-13T04:21:16.860585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:21:16.995175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
합격제 9584
95.8%
점수제 416
 
4.2%

검정연도
Real number (ℝ)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.8451
Minimum2008
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:21:17.108382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2012
Q12015
median2017
Q32019
95-th percentile2021
Maximum2022
Range14
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7099152
Coefficient of variation (CV)0.0013436407
Kurtosis0.079666905
Mean2016.8451
Median Absolute Deviation (MAD)2
Skewness-0.40129008
Sum20168451
Variance7.3436404
MonotonicityNot monotonic
2023-12-13T04:21:17.244254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2016 1498
15.0%
2017 1448
14.5%
2018 1327
13.3%
2019 1185
11.8%
2015 1094
10.9%
2020 835
8.3%
2014 706
7.1%
2021 533
 
5.3%
2013 417
 
4.2%
2022 308
 
3.1%
Other values (5) 649
6.5%
ValueCountFrequency (%)
2008 30
 
0.3%
2009 74
 
0.7%
2010 82
 
0.8%
2011 200
 
2.0%
2012 263
 
2.6%
2013 417
 
4.2%
2014 706
7.1%
2015 1094
10.9%
2016 1498
15.0%
2017 1448
14.5%
ValueCountFrequency (%)
2022 308
 
3.1%
2021 533
 
5.3%
2020 835
8.3%
2019 1185
11.8%
2018 1327
13.3%
2017 1448
14.5%
2016 1498
15.0%
2015 1094
10.9%
2014 706
7.1%
2013 417
 
4.2%

등급
Text

Distinct787
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:21:17.637459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length2
Mean length3.0119
Min length1

Characters and Unicode

Total characters30119
Distinct characters350
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique406 ?
Unique (%)4.1%

Sample

1st row사범
2nd row1급
3rd row2급
4th row2급
5th row2급(매니저)
ValueCountFrequency (%)
2급 2557
25.0%
1급 2549
24.9%
3급 800
 
7.8%
등급없음 711
 
7.0%
단일등급 383
 
3.7%
전문가 183
 
1.8%
사범 120
 
1.2%
없음 101
 
1.0%
강사 88
 
0.9%
지도사 81
 
0.8%
Other values (721) 2644
25.9%
2023-12-13T04:21:18.171363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
급 8398
27.9%
1 3036
 
10.1%
2 2910
 
9.7%
등 1173
 
3.9%
3 1005
 
3.3%
음 846
 
2.8%
없 840
 
2.8%
사 738
 
2.5%
단 588
 
2.0%
) 430
 
1.4%
Other values (340) 10155
33.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19581
65.0%
Decimal Number 7431
 
24.7%
Lowercase Letter 1097
 
3.6%
Uppercase Letter 797
 
2.6%
Close Punctuation 443
 
1.5%
Open Punctuation 443
 
1.5%
Space Separator 226
 
0.8%
Math Symbol 53
 
0.2%
Other Punctuation 22
 
0.1%
Letter Number 17
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
급 8398
42.9%
등 1173
 
6.0%
음 846
 
4.3%
없 840
 
4.3%
사 738
 
3.8%
단 588
 
3.0%
전 427
 
2.2%
일 426
 
2.2%
문 396
 
2.0%
지 338
 
1.7%
Other values (264) 5411
27.6%
Uppercase Letter
ValueCountFrequency (%)
B 104
13.0%
A 101
12.7%
C 76
9.5%
I 75
9.4%
M 68
8.5%
E 60
7.5%
T 54
 
6.8%
L 53
 
6.6%
S 36
 
4.5%
P 35
 
4.4%
Other values (15) 135
16.9%
Lowercase Letter
ValueCountFrequency (%)
e 206
18.8%
r 173
15.8%
a 125
11.4%
t 106
9.7%
s 69
 
6.3%
n 67
 
6.1%
l 63
 
5.7%
i 50
 
4.6%
v 44
 
4.0%
o 37
 
3.4%
Other values (13) 157
14.3%
Decimal Number
ValueCountFrequency (%)
1 3036
40.9%
2 2910
39.2%
3 1005
 
13.5%
4 120
 
1.6%
5 99
 
1.3%
6 68
 
0.9%
7 62
 
0.8%
8 53
 
0.7%
9 43
 
0.6%
0 35
 
0.5%
Other Punctuation
ValueCountFrequency (%)
, 10
45.5%
. 6
27.3%
/ 3
 
13.6%
& 2
 
9.1%
· 1
 
4.5%
Math Symbol
ValueCountFrequency (%)
+ 41
77.4%
~ 10
 
18.9%
< 1
 
1.9%
> 1
 
1.9%
Letter Number
ValueCountFrequency (%)
Ⅱ 15
88.2%
Ⅲ 1
 
5.9%
Ⅰ 1
 
5.9%
Close Punctuation
ValueCountFrequency (%)
) 430
97.1%
] 13
 
2.9%
Open Punctuation
ValueCountFrequency (%)
( 430
97.1%
[ 13
 
2.9%
Space Separator
ValueCountFrequency (%)
226
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19581
65.0%
Common 8627
28.6%
Latin 1911
 
6.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
급 8398
42.9%
등 1173
 
6.0%
음 846
 
4.3%
없 840
 
4.3%
사 738
 
3.8%
단 588
 
3.0%
전 427
 
2.2%
일 426
 
2.2%
문 396
 
2.0%
지 338
 
1.7%
Other values (264) 5411
27.6%
Latin
ValueCountFrequency (%)
e 206
 
10.8%
r 173
 
9.1%
a 125
 
6.5%
t 106
 
5.5%
B 104
 
5.4%
A 101
 
5.3%
C 76
 
4.0%
I 75
 
3.9%
s 69
 
3.6%
M 68
 
3.6%
Other values (41) 808
42.3%
Common
ValueCountFrequency (%)
1 3036
35.2%
2 2910
33.7%
3 1005
 
11.6%
) 430
 
5.0%
( 430
 
5.0%
226
 
2.6%
4 120
 
1.4%
5 99
 
1.1%
6 68
 
0.8%
7 62
 
0.7%
Other values (15) 241
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19581
65.0%
ASCII 10520
34.9%
Number Forms 17
 
0.1%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
급 8398
42.9%
등 1173
 
6.0%
음 846
 
4.3%
없 840
 
4.3%
사 738
 
3.8%
단 588
 
3.0%
전 427
 
2.2%
일 426
 
2.2%
문 396
 
2.0%
지 338
 
1.7%
Other values (264) 5411
27.6%
ASCII
ValueCountFrequency (%)
1 3036
28.9%
2 2910
27.7%
3 1005
 
9.6%
) 430
 
4.1%
( 430
 
4.1%
226
 
2.1%
e 206
 
2.0%
r 173
 
1.6%
a 125
 
1.2%
4 120
 
1.1%
Other values (62) 1859
17.7%
Number Forms
ValueCountFrequency (%)
Ⅱ 15
88.2%
Ⅲ 1
 
5.9%
Ⅰ 1
 
5.9%
None
ValueCountFrequency (%)
· 1
100.0%

총검정횟수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct109
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2072
Minimum0
Maximum891
Zeros3945
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:21:18.343354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile12
Maximum891
Range891
Interquartile range (IQR)3

Descriptive statistics

Standard deviation19.326304
Coefficient of variation (CV)4.5936261
Kurtosis742.46772
Mean4.2072
Median Absolute Deviation (MAD)1
Skewness22.741497
Sum42072
Variance373.50602
MonotonicityNot monotonic
2023-12-13T04:21:18.491902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3945
39.5%
1 1784
17.8%
2 1362
 
13.6%
4 747
 
7.5%
3 471
 
4.7%
12 425
 
4.2%
6 252
 
2.5%
5 164
 
1.6%
7 119
 
1.2%
8 94
 
0.9%
Other values (99) 637
 
6.4%
ValueCountFrequency (%)
0 3945
39.5%
1 1784
17.8%
2 1362
 
13.6%
3 471
 
4.7%
4 747
 
7.5%
5 164
 
1.6%
6 252
 
2.5%
7 119
 
1.2%
8 94
 
0.9%
9 59
 
0.6%
ValueCountFrequency (%)
891 1
< 0.1%
564 1
< 0.1%
541 1
< 0.1%
523 1
< 0.1%
502 1
< 0.1%
477 1
< 0.1%
334 1
< 0.1%
322 1
< 0.1%
307 1
< 0.1%
257 2
< 0.1%

총접수자수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct815
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean292.8052
Minimum0
Maximum176561
Zeros4072
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:21:18.625722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q331
95-th percentile433.05
Maximum176561
Range176561
Interquartile range (IQR)31

Descriptive statistics

Standard deviation3303.7145
Coefficient of variation (CV)11.282977
Kurtosis1189.3399
Mean292.8052
Median Absolute Deviation (MAD)4
Skewness29.663186
Sum2928052
Variance10914529
MonotonicityNot monotonic
2023-12-13T04:21:18.774878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4072
40.7%
1 366
 
3.7%
2 272
 
2.7%
3 233
 
2.3%
5 210
 
2.1%
10 176
 
1.8%
4 168
 
1.7%
8 147
 
1.5%
6 143
 
1.4%
7 122
 
1.2%
Other values (805) 4091
40.9%
ValueCountFrequency (%)
0 4072
40.7%
1 366
 
3.7%
2 272
 
2.7%
3 233
 
2.3%
4 168
 
1.7%
5 210
 
2.1%
6 143
 
1.4%
7 122
 
1.2%
8 147
 
1.5%
9 115
 
1.1%
ValueCountFrequency (%)
176561 1
< 0.1%
121740 1
< 0.1%
88334 1
< 0.1%
85022 1
< 0.1%
79716 1
< 0.1%
76621 1
< 0.1%
68027 1
< 0.1%
47146 1
< 0.1%
43076 1
< 0.1%
42502 1
< 0.1%

총응시자수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct811
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.6133
Minimum0
Maximum156650
Zeros4049
Zeros (%)40.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:21:18.934818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q331
95-th percentile421.05
Maximum156650
Range156650
Interquartile range (IQR)31

Descriptive statistics

Standard deviation2745.7945
Coefficient of variation (CV)10.9563
Kurtosis1359.1367
Mean250.6133
Median Absolute Deviation (MAD)4
Skewness31.104297
Sum2506133
Variance7539387.7
MonotonicityNot monotonic
2023-12-13T04:21:19.440597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4049
40.5%
1 370
 
3.7%
2 297
 
3.0%
3 234
 
2.3%
5 201
 
2.0%
10 179
 
1.8%
4 177
 
1.8%
6 154
 
1.5%
8 145
 
1.5%
7 128
 
1.3%
Other values (801) 4066
40.7%
ValueCountFrequency (%)
0 4049
40.5%
1 370
 
3.7%
2 297
 
3.0%
3 234
 
2.3%
4 177
 
1.8%
5 201
 
2.0%
6 154
 
1.5%
7 128
 
1.3%
8 145
 
1.5%
9 117
 
1.2%
ValueCountFrequency (%)
156650 1
< 0.1%
93032 1
< 0.1%
78659 1
< 0.1%
66699 1
< 0.1%
58287 1
< 0.1%
55314 1
< 0.1%
55111 1
< 0.1%
39571 1
< 0.1%
37837 1
< 0.1%
34035 1
< 0.1%

총취득자수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct766
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.9848
Minimum0
Maximum128484
Zeros3917
Zeros (%)39.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:21:19.604578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q330
95-th percentile372
Maximum128484
Range128484
Interquartile range (IQR)30

Descriptive statistics

Standard deviation2044.2028
Coefficient of variation (CV)10.377464
Kurtosis1722.7903
Mean196.9848
Median Absolute Deviation (MAD)4
Skewness33.796347
Sum1969848
Variance4178764.9
MonotonicityNot monotonic
2023-12-13T04:21:19.786521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3917
39.2%
1 422
 
4.2%
2 325
 
3.2%
3 255
 
2.5%
5 211
 
2.1%
4 207
 
2.1%
6 171
 
1.7%
8 170
 
1.7%
10 159
 
1.6%
9 127
 
1.3%
Other values (756) 4036
40.4%
ValueCountFrequency (%)
0 3917
39.2%
1 422
 
4.2%
2 325
 
3.2%
3 255
 
2.5%
4 207
 
2.1%
5 211
 
2.1%
6 171
 
1.7%
7 127
 
1.3%
8 170
 
1.7%
9 127
 
1.3%
ValueCountFrequency (%)
128484 1
< 0.1%
57977 1
< 0.1%
45202 1
< 0.1%
43253 1
< 0.1%
38680 1
< 0.1%
38264 1
< 0.1%
37845 1
< 0.1%
29083 1
< 0.1%
27453 1
< 0.1%
26629 1
< 0.1%

합격률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1466
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.516612
Minimum0
Maximum113.33
Zeros3950
Zeros (%)39.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:21:19.952572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median73.515
Q3100
95-th percentile100
Maximum113.33
Range113.33
Interquartile range (IQR)100

Descriptive statistics

Standard deviation45.691948
Coefficient of variation (CV)0.87004753
Kurtosis-1.8516022
Mean52.516612
Median Absolute Deviation (MAD)26.485
Skewness-0.16937102
Sum525166.12
Variance2087.7541
MonotonicityNot monotonic
2023-12-13T04:21:20.136734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3950
39.5%
100.0 2844
28.4%
80.0 96
 
1.0%
50.0 78
 
0.8%
83.33 72
 
0.7%
75.0 63
 
0.6%
90.0 61
 
0.6%
66.67 57
 
0.6%
85.71 50
 
0.5%
88.89 43
 
0.4%
Other values (1456) 2686
26.9%
ValueCountFrequency (%)
0.0 3950
39.5%
0.01 1
 
< 0.1%
0.24 1
 
< 0.1%
0.39 2
 
< 0.1%
0.62 1
 
< 0.1%
0.74 1
 
< 0.1%
0.79 1
 
< 0.1%
0.82 1
 
< 0.1%
0.87 1
 
< 0.1%
1.02 1
 
< 0.1%
ValueCountFrequency (%)
113.33 1
 
< 0.1%
112.2 1
 
< 0.1%
110.0 1
 
< 0.1%
109.17 1
 
< 0.1%
106.03 1
 
< 0.1%
105.85 1
 
< 0.1%
100.0 2844
28.4%
99.85 2
 
< 0.1%
99.8 1
 
< 0.1%
99.79 2
 
< 0.1%

Interactions

2023-12-13T04:21:13.007403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:09.367799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:10.018420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:10.808619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:11.453034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:12.193002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:13.110604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:09.459310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:10.127958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:10.907279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:11.566040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:12.329346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:13.201008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:09.552088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:10.206079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:10.996446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:11.681063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:12.461467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:13.317393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:09.675932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:10.293040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:11.110895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:11.815043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:12.665980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:13.425800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:09.793574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:10.382560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:11.245139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:11.938584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:12.783760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:13.562227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:09.904323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:10.716522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:11.351197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:12.067054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:21:12.896277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:21:20.301301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주무부처검정방식검정연도총검정횟수총접수자수총응시자수총취득자수합격률
주무부처1.0000.2960.0980.0000.1650.1450.0540.333
검정방식0.2961.0000.0720.0000.0560.0400.1380.383
검정연도0.0980.0721.0000.0000.0610.0960.0660.212
총검정횟수0.0000.0000.0001.0000.0000.0000.0000.068
총접수자수0.1650.0560.0610.0001.0000.9540.6740.174
총응시자수0.1450.0400.0960.0000.9541.0000.6890.154
총취득자수0.0540.1380.0660.0000.6740.6891.0000.153
합격률0.3330.3830.2120.0680.1740.1540.1531.000
2023-12-13T04:21:20.429261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검정방식주무부처
검정방식1.0000.235
주무부처0.2351.000
2023-12-13T04:21:20.525328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검정연도총검정횟수총접수자수총응시자수총취득자수합격률주무부처검정방식
검정연도1.000-0.095-0.165-0.166-0.177-0.0920.0360.056
총검정횟수-0.0951.0000.8490.8530.8060.6890.0000.000
총접수자수-0.1650.8491.0000.9870.9360.6780.0590.042
총응시자수-0.1660.8530.9871.0000.9480.6870.0600.043
총취득자수-0.1770.8060.9360.9481.0000.6930.0240.099
합격률-0.0920.6890.6780.6870.6931.0000.1240.294
주무부처0.0360.0000.0590.0600.0240.1241.0000.235
검정방식0.0560.0000.0420.0430.0990.2940.2351.000

Missing values

2023-12-13T04:21:13.764650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:21:14.003179image/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

등록번호자격명자격발급기관주무부처검정방식검정연도등급총검정횟수총접수자수총응시자수총취득자수합격률
8739''2008-0631반려견지도사(사)한국애견협회농림축산식품부합격제2020사범00000.0
64096''2015-003947미술심리상담사금산성폭력상담센터보건복지부합격제20171급00000.0
73462''2015-002050아로마블렌딩사한국미용산업학회산업통상자원부합격제20222급3222222100.0
48029''2014-1987우쿨렐레지도사인천음악사회교육원문화체육관광부합격제20142급4222222100.0
8841''2008-0634그린키퍼건국대학교문화체육관광부합격제20222급(매니저)213131076.92
10821''2009-0134미술심리상담사(사)한국심성교육개발원보건복지부합격제20171급425625624495.31
15381''2010-0120유아아동놀이교육사단법인세계레크리에이션교육협회교육부합격제20151급218218217998.35
38790''2013-1192기타(guitar)지도자한국기타우쿨렐레교육협회교육부합격제20182급00000.0
68235''2015-001033문인화지도사사단법인평화예술재단문화체육관광부합격제20173급00000.0
91369''2015-005618주산활용수학교육사해밀영재교육교육부합격제20162단00000.0
등록번호자격명자격발급기관주무부처검정방식검정연도등급총검정횟수총접수자수총응시자수총취득자수합격률
52759''2014-0264브랜드관리지도사사단법인한국대학평생교육원협의회산업통상자원부합격제2020등급없음00000.0
67680''2015-000538가족상담사대전총신평생교육원여성가족부합격제20171급00000.0
63361''2014-5214가족상담사사단법인 부산청소년상담교육센터여성가족부합격제20141급00000.0
47084''2014-2359비즈아트지도사(사)한국종이접기협회교육부합격제2020지도사범1111100.0
13818''2009-0359독서능력검정급수주식회사 피알에스창의독서개발원교육부합격제20147급449945737080.96
14871''2010-0004스쿠버다이빙사단법인 대한수중핀수영협회해양수산부합격제2014상급강사0151515100.0
54408''2014-0456또래문화상담사(사)푸른청소년육성개발원교육부합격제20201급00000.0
91039''2015-005627창의과학교육지도사(주)한국창의교육연구원교육부합격제20211급00000.0
7916''2008-0496한국한자검정(사)한국평생교육평가원교육부합격제20146급416844151081108373.36
57101''2014-3614언어·표현력지도사사단법인한국청소년문화진흥협회교육부합격제20163급00000.0