Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells140145
Missing cells (%)77.9%
Duplicate rows3
Duplicate rows (%)< 0.1%
Total size in memory1.5 MiB
Average record size in memory160.0 B

Variable types

Numeric5
Categorical3
Unsupported2
Text1
DateTime5
Boolean2

Dataset

Description학점은행제 정보공시 이후의 정정에 대한 신청 및 승인 등에 관한 정보이며 공시년도, 공시시기, 공시항목명, 정정차수, 입력순번, 공시입력시작일자, 공시입력시작시간, 공시입력종료일자, 공시입력종료시간, 공시정정사유, 공시정정신청일시, 관리자정정승인여부, 작성자마감여부, 작성자마감일시, 마감여부, 마감일시, 생성일시, 수정일시 항목의 정보를 제공한다.
Author국가평생교육진흥원
URLhttps://www.data.go.kr/data/15088011/fileData.do

Alerts

Dataset has 3 (< 0.1%) duplicate rowsDuplicates
작성자마감여부 is highly overall correlated with 관리자정정승인여부 and 1 other fieldsHigh correlation
마감여부 is highly overall correlated with 관리자정정승인여부 and 1 other fieldsHigh correlation
공시년도 is highly overall correlated with 입력순번 and 2 other fieldsHigh correlation
공시시기 is highly overall correlated with 공시항목명High correlation
입력순번 is highly overall correlated with 공시년도 and 2 other fieldsHigh correlation
공시입력시작일자 is highly overall correlated with 공시년도 and 2 other fieldsHigh correlation
공시입력종료일자 is highly overall correlated with 공시년도 and 2 other fieldsHigh correlation
공시항목명 is highly overall correlated with 공시시기High correlation
관리자정정승인여부 is highly overall correlated with 작성자마감여부 and 1 other fieldsHigh correlation
공시항목명 is highly imbalanced (83.2%)Imbalance
정정차수 is highly imbalanced (80.2%)Imbalance
관리자정정승인여부 is highly imbalanced (78.2%)Imbalance
공시년도 has 9211 (92.1%) missing valuesMissing
공시시기 has 9211 (92.1%) missing valuesMissing
입력순번 has 9211 (92.1%) missing valuesMissing
공시입력시작일자 has 9275 (92.8%) missing valuesMissing
공시입력시작시간 has 10000 (100.0%) missing valuesMissing
공시입력종료일자 has 9275 (92.8%) missing valuesMissing
공시입력종료시간 has 10000 (100.0%) missing valuesMissing
공시정정사유 has 9211 (92.1%) missing valuesMissing
공시정정신청일시 has 9211 (92.1%) missing valuesMissing
작성자마감여부 has 9211 (92.1%) missing valuesMissing
작성자마감일시 has 9348 (93.5%) missing valuesMissing
마감여부 has 9211 (92.1%) missing valuesMissing
마감일시 has 9348 (93.5%) missing valuesMissing
생성일시 has 9211 (92.1%) missing valuesMissing
수정일시 has 9211 (92.1%) missing valuesMissing
공시입력시작시간 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공시입력종료시간 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 10:17:14.388134
Analysis finished2023-12-12 10:17:19.965349
Duration5.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공시년도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.8%
Missing9211
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean2017.9632
Minimum2016
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:17:20.029593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12017
median2018
Q32019
95-th percentile2020
Maximum2021
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3988457
Coefficient of variation (CV)0.00069319679
Kurtosis-1.1212114
Mean2017.9632
Median Absolute Deviation (MAD)1
Skewness0.11855312
Sum1592173
Variance1.9567692
MonotonicityNot monotonic
2023-12-12T19:17:20.152388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 188
 
1.9%
2019 186
 
1.9%
2016 148
 
1.5%
2018 139
 
1.4%
2020 115
 
1.1%
2021 13
 
0.1%
(Missing) 9211
92.1%
ValueCountFrequency (%)
2016 148
1.5%
2017 188
1.9%
2018 139
1.4%
2019 186
1.9%
2020 115
1.1%
2021 13
 
0.1%
ValueCountFrequency (%)
2021 13
 
0.1%
2020 115
1.1%
2019 186
1.9%
2018 139
1.4%
2017 188
1.9%
2016 148
1.5%

공시시기
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.8%
Missing9211
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean5.6970849
Minimum0
Maximum9
Zeros34
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:17:20.262999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median6
Q39
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1736149
Coefficient of variation (CV)0.55705944
Kurtosis-1.6491481
Mean5.6970849
Median Absolute Deviation (MAD)3
Skewness-0.20923203
Sum4495
Variance10.071832
MonotonicityNot monotonic
2023-12-12T19:17:20.361062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9 288
 
2.9%
3 177
 
1.8%
2 139
 
1.4%
8 94
 
0.9%
6 57
 
0.6%
0 34
 
0.3%
(Missing) 9211
92.1%
ValueCountFrequency (%)
0 34
 
0.3%
2 139
1.4%
3 177
1.8%
6 57
 
0.6%
8 94
 
0.9%
9 288
2.9%
ValueCountFrequency (%)
9 288
2.9%
8 94
 
0.9%
6 57
 
0.6%
3 177
1.8%
2 139
1.4%
0 34
 
0.3%

공시항목명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9211 
예산 및 결산 [예산서]
 
127
교사(校舍) 등 시설 현황
 
107
장학금 지급 현황
 
84
교수 또는 강사의 수
 
73
Other values (11)
 
398

Length

Max length37
Median length4
Mean length4.8685
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9211
92.1%
예산 및 결산 [예산서] 127
 
1.3%
교사(校舍) 등 시설 현황 107
 
1.1%
장학금 지급 현황 84
 
0.8%
교수 또는 강사의 수 73
 
0.7%
교수 또는 강사 강의료 73
 
0.7%
기관 전체 교수 또는 강사 대비 해당 기관 소속 교수 또는 강사현황 62
 
0.6%
교수 또는 강사의 강의 담당 현황 59
 
0.6%
직원 수 59
 
0.6%
기관 운영규칙 및 평가인정 학습과정 운영에 관한 각종 규정 48
 
0.5%
Other values (6) 97
 
1.0%

Length

2023-12-12T19:17:20.468149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9211
71.1%
교수 329
 
2.5%
또는 329
 
2.5%
현황 297
 
2.3%
및 188
 
1.5%
기관 180
 
1.4%
수 142
 
1.1%
강사 135
 
1.0%
강사의 132
 
1.0%
예산 127
 
1.0%
Other values (37) 1891
 
14.6%

정정차수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9202 
1
 
673
2
 
105
3
 
19
4
 
1

Length

Max length4
Median length4
Mean length3.7606
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9202
92.0%
1 673
 
6.7%
2 105
 
1.1%
3 19
 
0.2%
4 1
 
< 0.1%

Length

2023-12-12T19:17:20.583451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:17:20.675761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9202
92.0%
1 673
 
6.7%
2 105
 
1.1%
3 19
 
0.2%
4 1
 
< 0.1%

입력순번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct789
Distinct (%)100.0%
Missing9211
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean2472.0976
Minimum8
Maximum5025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:17:20.795705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile240
Q11164
median2433
Q33731
95-th percentile4728
Maximum5025
Range5017
Interquartile range (IQR)2567

Descriptive statistics

Standard deviation1460.1316
Coefficient of variation (CV)0.59064481
Kurtosis-1.2136776
Mean2472.0976
Median Absolute Deviation (MAD)1291
Skewness0.039066345
Sum1950485
Variance2131984.3
MonotonicityNot monotonic
2023-12-12T19:17:20.953360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1630 1
 
< 0.1%
2713 1
 
< 0.1%
2987 1
 
< 0.1%
1325 1
 
< 0.1%
1454 1
 
< 0.1%
997 1
 
< 0.1%
28 1
 
< 0.1%
693 1
 
< 0.1%
3688 1
 
< 0.1%
4585 1
 
< 0.1%
Other values (779) 779
 
7.8%
(Missing) 9211
92.1%
ValueCountFrequency (%)
8 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
21 1
< 0.1%
25 1
< 0.1%
28 1
< 0.1%
58 1
< 0.1%
65 1
< 0.1%
82 1
< 0.1%
83 1
< 0.1%
ValueCountFrequency (%)
5025 1
< 0.1%
5023 1
< 0.1%
5012 1
< 0.1%
5010 1
< 0.1%
4998 1
< 0.1%
4988 1
< 0.1%
4987 1
< 0.1%
4981 1
< 0.1%
4980 1
< 0.1%
4973 1
< 0.1%

공시입력시작일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct247
Distinct (%)34.1%
Missing9275
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean20189815
Minimum20161128
Maximum20210818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:17:21.122939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20161128
5-th percentile20170510
Q120180605
median20191028
Q320200602
95-th percentile20210508
Maximum20210818
Range49690
Interquartile range (IQR)19997

Descriptive statistics

Standard deviation12986.304
Coefficient of variation (CV)0.00064321066
Kurtosis-0.75119764
Mean20189815
Median Absolute Deviation (MAD)9926
Skewness-0.36370839
Sum1.4637616 × 1010
Variance1.6864409 × 108
MonotonicityNot monotonic
2023-12-12T19:17:21.267217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20170602 18
 
0.2%
20191118 17
 
0.2%
20190807 14
 
0.1%
20190809 13
 
0.1%
20191213 13
 
0.1%
20180502 10
 
0.1%
20190812 9
 
0.1%
20180510 9
 
0.1%
20191121 9
 
0.1%
20200108 8
 
0.1%
Other values (237) 605
 
6.0%
(Missing) 9275
92.8%
ValueCountFrequency (%)
20161128 6
0.1%
20161206 7
0.1%
20161207 2
 
< 0.1%
20161209 3
< 0.1%
20161213 3
< 0.1%
20161214 1
 
< 0.1%
20170414 2
 
< 0.1%
20170417 1
 
< 0.1%
20170426 1
 
< 0.1%
20170428 1
 
< 0.1%
ValueCountFrequency (%)
20210818 1
 
< 0.1%
20210811 8
0.1%
20210805 1
 
< 0.1%
20210706 6
0.1%
20210701 6
0.1%
20210628 2
 
< 0.1%
20210623 1
 
< 0.1%
20210622 1
 
< 0.1%
20210617 2
 
< 0.1%
20210608 2
 
< 0.1%

공시입력시작시간
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

공시입력종료일자
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct207
Distinct (%)28.6%
Missing9275
Missing (%)92.8%
Infinite0
Infinite (%)0.0%
Mean20189837
Minimum20161128
Maximum20210827
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:17:21.431369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20161128
5-th percentile20170512
Q120180611
median20191028
Q320200611
95-th percentile20210510
Maximum20210827
Range49699
Interquartile range (IQR)20000

Descriptive statistics

Standard deviation12990.548
Coefficient of variation (CV)0.00064342016
Kurtosis-0.75365321
Mean20189837
Median Absolute Deviation (MAD)9923
Skewness-0.36502733
Sum1.4637632 × 1010
Variance1.6875434 × 108
MonotonicityNot monotonic
2023-12-12T19:17:21.581019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20191122 25
 
0.2%
20190813 21
 
0.2%
20191129 20
 
0.2%
20200522 19
 
0.2%
20201204 18
 
0.2%
20201127 16
 
0.2%
20190814 15
 
0.1%
20170609 13
 
0.1%
20191218 12
 
0.1%
20170608 12
 
0.1%
Other values (197) 554
 
5.5%
(Missing) 9275
92.8%
ValueCountFrequency (%)
20161128 1
 
< 0.1%
20161206 2
 
< 0.1%
20161207 2
 
< 0.1%
20161208 1
 
< 0.1%
20161209 5
0.1%
20161213 7
0.1%
20161214 2
 
< 0.1%
20161216 2
 
< 0.1%
20170417 2
 
< 0.1%
20170428 1
 
< 0.1%
ValueCountFrequency (%)
20210827 1
 
< 0.1%
20210820 8
0.1%
20210813 1
 
< 0.1%
20210716 6
0.1%
20210702 8
0.1%
20210625 2
 
< 0.1%
20210618 2
 
< 0.1%
20210611 6
0.1%
20210608 1
 
< 0.1%
20210604 1
 
< 0.1%

공시입력종료시간
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

공시정정사유
Text

MISSING 

Distinct618
Distinct (%)78.3%
Missing9211
Missing (%)92.1%
Memory size156.2 KiB
2023-12-12T19:17:22.117075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length323
Median length113
Mean length29.500634
Min length2

Characters and Unicode

Total characters23276
Distinct characters390
Distinct categories15 ?
Distinct scripts4 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique520 ?
Unique (%)65.9%

Sample

1st row2020년 교육부 특정감사 관련 정보공시 정정
2nd row담당자 공시입력 시 비고에 호수 및 면적 수용인원에 대한 입력 오류
3rd row오입력
4th row지급인원 및 지급액 수정
5th row계산 착오
ValueCountFrequency (%)
정정 208
 
3.8%
정보공시 168
 
3.1%
현장 94
 
1.7%
실태점검 90
 
1.6%
수정 88
 
1.6%
84
 
1.5%
및 80
 
1.5%
오류 72
 
1.3%
학점은행제 63
 
1.2%
합니다 62
 
1.1%
Other values (1599) 4452
81.5%
2023-12-12T19:17:23.073402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4843
 
20.8%
정 1423
 
6.1%
0 406
 
1.7%
수 393
 
1.7%
시 372
 
1.6%
2 370
 
1.6%
1 340
 
1.5%
. 315
 
1.4%
실 314
 
1.3%
학 310
 
1.3%
Other values (380) 14190
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15830
68.0%
Space Separator 4843
 
20.8%
Decimal Number 1631
 
7.0%
Other Punctuation 445
 
1.9%
Control 132
 
0.6%
Close Punctuation 124
 
0.5%
Open Punctuation 117
 
0.5%
Dash Punctuation 64
 
0.3%
Math Symbol 60
 
0.3%
Uppercase Letter 17
 
0.1%
Other values (5) 13
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 1423
 
9.0%
수 393
 
2.5%
시 372
 
2.3%
실 314
 
2.0%
학 310
 
2.0%
점 301
 
1.9%
청 288
 
1.8%
사 282
 
1.8%
오 264
 
1.7%
공 251
 
1.6%
Other values (334) 11632
73.5%
Decimal Number
ValueCountFrequency (%)
0 406
24.9%
2 370
22.7%
1 340
20.8%
9 106
 
6.5%
5 98
 
6.0%
3 73
 
4.5%
4 65
 
4.0%
8 59
 
3.6%
7 57
 
3.5%
6 57
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 315
70.8%
: 64
 
14.4%
/ 26
 
5.8%
' 25
 
5.6%
* 6
 
1.3%
" 6
 
1.3%
· 2
 
0.4%
※ 1
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
P 4
23.5%
F 4
23.5%
D 4
23.5%
B 2
11.8%
I 1
 
5.9%
G 1
 
5.9%
M 1
 
5.9%
Close Punctuation
ValueCountFrequency (%)
) 116
93.5%
] 7
 
5.6%
」 1
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 109
93.2%
[ 7
 
6.0%
「 1
 
0.9%
Math Symbol
ValueCountFrequency (%)
> 32
53.3%
→ 17
28.3%
~ 11
 
18.3%
Lowercase Letter
ValueCountFrequency (%)
p 1
33.3%
d 1
33.3%
f 1
33.3%
Other Symbol
ValueCountFrequency (%)
㎡ 2
50.0%
■ 2
50.0%
Other Number
ValueCountFrequency (%)
② 1
50.0%
① 1
50.0%
Space Separator
ValueCountFrequency (%)
4843
100.0%
Control
ValueCountFrequency (%)
132
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%
Modifier Symbol
ValueCountFrequency (%)
^ 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15828
68.0%
Common 7424
31.9%
Latin 22
 
0.1%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 1423
 
9.0%
수 393
 
2.5%
시 372
 
2.4%
실 314
 
2.0%
학 310
 
2.0%
점 301
 
1.9%
청 288
 
1.8%
사 282
 
1.8%
오 264
 
1.7%
공 251
 
1.6%
Other values (332) 11630
73.5%
Common
ValueCountFrequency (%)
4843
65.2%
0 406
 
5.5%
2 370
 
5.0%
1 340
 
4.6%
. 315
 
4.2%
132
 
1.8%
) 116
 
1.6%
( 109
 
1.5%
9 106
 
1.4%
5 98
 
1.3%
Other values (25) 589
 
7.9%
Latin
ValueCountFrequency (%)
P 4
18.2%
F 4
18.2%
D 4
18.2%
Ⅰ 2
9.1%
B 2
9.1%
p 1
 
4.5%
d 1
 
4.5%
f 1
 
4.5%
I 1
 
4.5%
G 1
 
4.5%
Han
ValueCountFrequency (%)
校 1
50.0%
舍 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15827
68.0%
ASCII 7416
31.9%
Arrows 17
 
0.1%
None 4
 
< 0.1%
Number Forms 2
 
< 0.1%
CJK Compat 2
 
< 0.1%
Geometric Shapes 2
 
< 0.1%
Enclosed Alphanum 2
 
< 0.1%
CJK 2
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4843
65.3%
0 406
 
5.5%
2 370
 
5.0%
1 340
 
4.6%
. 315
 
4.2%
132
 
1.8%
) 116
 
1.6%
( 109
 
1.5%
9 106
 
1.4%
5 98
 
1.3%
Other values (26) 581
 
7.8%
Hangul
ValueCountFrequency (%)
정 1423
 
9.0%
수 393
 
2.5%
시 372
 
2.4%
실 314
 
2.0%
학 310
 
2.0%
점 301
 
1.9%
청 288
 
1.8%
사 282
 
1.8%
오 264
 
1.7%
공 251
 
1.6%
Other values (331) 11629
73.5%
Arrows
ValueCountFrequency (%)
→ 17
100.0%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 2
100.0%
Geometric Shapes
ValueCountFrequency (%)
■ 2
100.0%
None
ValueCountFrequency (%)
· 2
50.0%
「 1
25.0%
」 1
25.0%
Punctuation
ValueCountFrequency (%)
※ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
② 1
50.0%
① 1
50.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%
CJK
ValueCountFrequency (%)
校 1
50.0%
舍 1
50.0%

공시정정신청일시
Date

MISSING 

Distinct780
Distinct (%)98.9%
Missing9211
Missing (%)92.1%
Memory size156.2 KiB
Minimum2016-11-24 10:50:57
Maximum2021-08-24 11:56:01
2023-12-12T19:17:23.278094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:23.502567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

관리자정정승인여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9211 
Y
 
716
R
 
70
N
 
3

Length

Max length4
Median length4
Mean length3.7633
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 9211
92.1%
Y 716
 
7.2%
R 70
 
0.7%
N 3
 
< 0.1%

Length

2023-12-12T19:17:23.678576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:17:23.781528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9211
92.1%
y 716
 
7.2%
r 70
 
0.7%
n 3
 
< 0.1%

작성자마감여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.3%
Missing9211
Missing (%)92.1%
Memory size97.7 KiB
True
 
646
False
 
143
(Missing)
9211 
ValueCountFrequency (%)
True 646
 
6.5%
False 143
 
1.4%
(Missing) 9211
92.1%
2023-12-12T19:17:23.877846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

작성자마감일시
Date

MISSING 

Distinct640
Distinct (%)98.2%
Missing9348
Missing (%)93.5%
Memory size156.2 KiB
Minimum2016-11-28 15:02:09
Maximum2021-08-18 11:53:57
2023-12-12T19:17:23.999417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:24.152327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

마감여부
Boolean

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.3%
Missing9211
Missing (%)92.1%
Memory size97.7 KiB
True
 
651
False
 
138
(Missing)
9211 
ValueCountFrequency (%)
True 651
 
6.5%
False 138
 
1.4%
(Missing) 9211
92.1%
2023-12-12T19:17:24.288552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

마감일시
Date

MISSING 

Distinct544
Distinct (%)83.4%
Missing9348
Missing (%)93.5%
Memory size156.2 KiB
Minimum2016-11-28 15:43:57
Maximum2021-08-18 11:33:33
2023-12-12T19:17:24.422035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:24.602263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

생성일시
Date

MISSING 

Distinct763
Distinct (%)96.7%
Missing9211
Missing (%)92.1%
Memory size156.2 KiB
Minimum2016-11-28 15:43:57
Maximum2021-08-24 11:56:01
2023-12-12T19:17:24.760594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:24.907153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

수정일시
Date

MISSING 

Distinct665
Distinct (%)84.3%
Missing9211
Missing (%)92.1%
Memory size156.2 KiB
Minimum2016-11-28 15:43:57
Maximum2021-08-24 11:56:01
2023-12-12T19:17:25.076551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:25.258286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-12T19:17:18.589989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:15.886062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:16.847800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:17.413523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:17.976368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:18.695401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:16.003130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:16.962240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:17.509632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:18.101137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:18.806264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:16.136471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:17.077013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:17.632464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:18.219613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:18.917756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:16.599413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:17.200572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:17.727862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:18.346687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:19.063659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:16.710091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:17.313527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:17.860632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:17:18.462863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:17:25.399217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공시년도공시시기공시항목명정정차수입력순번공시입력시작일자공시입력종료일자관리자정정승인여부작성자마감여부마감여부
공시년도1.0000.1890.4000.0950.7890.6370.6360.1700.0300.036
공시시기0.1891.0000.9290.1300.3680.4020.4080.0980.0000.000
공시항목명0.4000.9291.0000.0000.3820.4240.4190.2280.1300.131
정정차수0.0950.1300.0001.0000.2850.3280.3230.0000.0680.071
입력순번0.7890.3680.3820.2851.0000.9780.9780.2910.1140.106
공시입력시작일자0.6370.4020.4240.3280.9781.0001.0000.1070.2240.224
공시입력종료일자0.6360.4080.4190.3230.9781.0001.0000.1090.2280.228
관리자정정승인여부0.1700.0980.2280.0000.2910.1070.1091.0000.4350.446
작성자마감여부0.0300.0000.1300.0680.1140.2240.2280.4351.0000.999
마감여부0.0360.0000.1310.0710.1060.2240.2280.4460.9991.000
2023-12-12T19:17:25.530099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정정차수작성자마감여부공시항목명마감여부관리자정정승인여부
정정차수1.0000.0450.0000.0470.000
작성자마감여부0.0451.0000.1180.9660.677
공시항목명0.0000.1181.0000.1190.105
마감여부0.0470.9660.1191.0000.692
관리자정정승인여부0.0000.6770.1050.6921.000
2023-12-12T19:17:25.680284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공시년도공시시기입력순번공시입력시작일자공시입력종료일자공시항목명정정차수관리자정정승인여부작성자마감여부마감여부
공시년도1.000-0.4590.7440.7460.7460.1790.1010.1340.0230.047
공시시기-0.4591.000-0.260-0.271-0.2700.7530.0840.0400.0000.000
입력순번0.744-0.2601.0000.9980.9970.1520.1730.1810.0870.081
공시입력시작일자0.746-0.2710.9981.0001.0000.1700.1990.0820.1740.174
공시입력종료일자0.746-0.2700.9971.0001.0000.1700.1980.0830.1740.174
공시항목명0.1790.7530.1520.1700.1701.0000.0000.1050.1180.119
정정차수0.1010.0840.1730.1990.1980.0001.0000.0000.0450.047
관리자정정승인여부0.1340.0400.1810.0820.0830.1050.0001.0000.6770.692
작성자마감여부0.0230.0000.0870.1740.1740.1180.0450.6771.0000.966
마감여부0.0470.0000.0810.1740.1740.1190.0470.6920.9661.000

Missing values

2023-12-12T19:17:19.226839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:17:19.519876image/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.
2023-12-12T19:17:19.751436image/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

공시년도공시시기공시항목명정정차수입력순번공시입력시작일자공시입력시작시간공시입력종료일자공시입력종료시간공시정정사유공시정정신청일시관리자정정승인여부작성자마감여부작성자마감일시마감여부마감일시생성일시수정일시
21542<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
31337<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
19265<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
18369<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
53038<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8324<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
17599<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
40748<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
23310<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
49490<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
공시년도공시시기공시항목명정정차수입력순번공시입력시작일자공시입력시작시간공시입력종료일자공시입력종료시간공시정정사유공시정정신청일시관리자정정승인여부작성자마감여부작성자마감일시마감여부마감일시생성일시수정일시
63875<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
28525<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
33501<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
13820169연간 학습과정 운영일정174820170725<NA>20170727<NA>연간 학습일정 정정2017-07-20 09:48:32YY2017-07-26 14:25:04Y2017-08-01 09:14:422017-08-01 09:14:42.02017-08-01 09:14:42.0
19254<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
46678<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9554<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
61055<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
46737<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
61418<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

공시년도공시시기공시항목명정정차수입력순번공시입력시작일자공시입력종료일자공시정정사유공시정정신청일시관리자정정승인여부작성자마감여부작성자마감일시마감여부마감일시생성일시수정일시# duplicates
2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>9202
0<NA><NA><NA>1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>7
1<NA><NA><NA>2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2