Overview

Dataset statistics

Number of variables16
Number of observations31
Missing cells57
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory146.3 B

Variable types

Numeric13
Text1
Categorical2

Dataset

Description1994학년도부터 연도별로 시행된 대학수학능력시험(본수능) 접수현황(접수기간, 지원자 현황(인원, 성별, 자격별 등), 수수료)에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15098903/fileData.do

Alerts

6개 영역 응시수수료 is highly overall correlated with 학년도 and 10 other fieldsHigh correlation
기간(2) is highly overall correlated with 총 계 and 5 other fieldsHigh correlation
학년도 is highly overall correlated with 총 계 and 10 other fieldsHigh correlation
총 계 is highly overall correlated with 학년도 and 11 other fieldsHigh correlation
is highly overall correlated with 학년도 and 10 other fieldsHigh correlation
is highly overall correlated with 학년도 and 8 other fieldsHigh correlation
인문 is highly overall correlated with 총 계 and 4 other fieldsHigh correlation
자연 is highly overall correlated with 학년도 and 8 other fieldsHigh correlation
예체능 is highly overall correlated with 학년도 and 7 other fieldsHigh correlation
재학생 is highly overall correlated with 학년도 and 7 other fieldsHigh correlation
졸업생 is highly overall correlated with 학년도 and 10 other fieldsHigh correlation
검정등 is highly overall correlated with 예체능 and 2 other fieldsHigh correlation
3개 영역 이하 응시수수료 is highly overall correlated with 학년도 and 11 other fieldsHigh correlation
4개 영역 응시수수료 is highly overall correlated with 학년도 and 8 other fieldsHigh correlation
5개 영역 응시수수료 is highly overall correlated with 학년도 and 8 other fieldsHigh correlation
인문 has 19 (61.3%) missing valuesMissing
자연 has 19 (61.3%) missing valuesMissing
예체능 has 19 (61.3%) missing valuesMissing
기간(1) has unique valuesUnique
총 계 has unique valuesUnique
has unique valuesUnique
has unique valuesUnique
재학생 has unique valuesUnique
졸업생 has unique valuesUnique
검정등 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:01:55.818010
Analysis finished2023-12-12 16:02:15.361576
Duration19.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

학년도
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.0323
Minimum1994
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:15.424921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1994
5-th percentile1994.5
Q12000.5
median2008
Q32015.5
95-th percentile2021.5
Maximum2023
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0387457
Coefficient of variation (CV)0.0045012951
Kurtosis-1.2242473
Mean2008.0323
Median Absolute Deviation (MAD)8
Skewness0.019002164
Sum62249
Variance81.698925
MonotonicityIncreasing
2023-12-13T01:02:15.558180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1994 2
 
6.5%
2010 1
 
3.2%
2023 1
 
3.2%
2022 1
 
3.2%
2021 1
 
3.2%
2020 1
 
3.2%
2019 1
 
3.2%
2018 1
 
3.2%
2017 1
 
3.2%
2016 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
1994 2
6.5%
1995 1
3.2%
1996 1
3.2%
1997 1
3.2%
1998 1
3.2%
1999 1
3.2%
2000 1
3.2%
2001 1
3.2%
2002 1
3.2%
2003 1
3.2%
ValueCountFrequency (%)
2023 1
3.2%
2022 1
3.2%
2021 1
3.2%
2020 1
3.2%
2019 1
3.2%
2018 1
3.2%
2017 1
3.2%
2016 1
3.2%
2015 1
3.2%
2014 1
3.2%

기간(1)
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2023-12-13T01:02:15.775078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters496
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

Unique31 ?
Unique (%)100.0%

Sample

1st row1993-06-11~06-24
2nd row1993-10-02~10-11
3rd row1994-09-12~09-27
4th row1995-09-11~09-23
5th row1996-09-02~09-14
ValueCountFrequency (%)
1993-06-11~06-24 1
 
3.2%
2008-09-01~09-17 1
 
3.2%
2021-08-19~09-03 1
 
3.2%
2020-09-03~09-18 1
 
3.2%
2019-08-22~09-06 1
 
3.2%
2018-08-23~09-07 1
 
3.2%
2017-08-24~09-08 1
 
3.2%
2016-08-25~09-09 1
 
3.2%
2015-08-27~09-11 1
 
3.2%
2014-08-25~09-12 1
 
3.2%
Other values (21) 21
67.7%
2023-12-13T01:02:16.121406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
24.2%
- 93
18.8%
9 61
12.3%
2 57
11.5%
1 56
11.3%
~ 31
 
6.2%
8 30
 
6.0%
3 12
 
2.4%
6 11
 
2.2%
7 9
 
1.8%
Other values (2) 16
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 372
75.0%
Dash Punctuation 93
 
18.8%
Math Symbol 31
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120
32.3%
9 61
16.4%
2 57
15.3%
1 56
15.1%
8 30
 
8.1%
3 12
 
3.2%
6 11
 
3.0%
7 9
 
2.4%
4 8
 
2.2%
5 8
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 93
100.0%
Math Symbol
ValueCountFrequency (%)
~ 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 496
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 120
24.2%
- 93
18.8%
9 61
12.3%
2 57
11.5%
1 56
11.3%
~ 31
 
6.2%
8 30
 
6.0%
3 12
 
2.4%
6 11
 
2.2%
7 9
 
1.8%
Other values (2) 16
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
24.2%
- 93
18.8%
9 61
12.3%
2 57
11.5%
1 56
11.3%
~ 31
 
6.2%
8 30
 
6.0%
3 12
 
2.4%
6 11
 
2.2%
7 9
 
1.8%
Other values (2) 16
 
3.2%

기간(2)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size380.0 B
(12일간)
23 
(14일간)
(13일간)
 
2
(16일간)
 
1
(10일간)
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique2 ?
Unique (%)6.5%

Sample

1st row(14일간)
2nd row(14일간)
3rd row(16일간)
4th row(13일간)
5th row(13일간)

Common Values

ValueCountFrequency (%)
(12일간) 23
74.2%
(14일간) 4
 
12.9%
(13일간) 2
 
6.5%
(16일간) 1
 
3.2%
(10일간) 1
 
3.2%

Length

2023-12-13T01:02:16.258363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:02:16.379001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
12일간 23
74.2%
14일간 4
 
12.9%
13일간 2
 
6.5%
16일간 1
 
3.2%
10일간 1
 
3.2%

총 계
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean678941.35
Minimum493434
Maximum896122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:16.491393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum493434
5-th percentile508925.5
Q1593666.5
median668522
Q3746424.5
95-th percentile878809
Maximum896122
Range402688
Interquartile range (IQR)152758

Descriptive statistics

Standard deviation116284.1
Coefficient of variation (CV)0.17127267
Kurtosis-0.74198508
Mean678941.35
Median Absolute Deviation (MAD)74995
Skewness0.42857916
Sum21047182
Variance1.3521991 × 1010
MonotonicityNot monotonic
2023-12-13T01:02:16.645073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
742668 1
 
3.2%
750181 1
 
3.2%
508030 1
 
3.2%
509821 1
 
3.2%
493434 1
 
3.2%
548734 1
 
3.2%
594924 1
 
3.2%
593527 1
 
3.2%
605987 1
 
3.2%
631187 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
493434 1
3.2%
508030 1
3.2%
509821 1
3.2%
548734 1
3.2%
584934 1
3.2%
588839 1
3.2%
588899 1
3.2%
593527 1
3.2%
593806 1
3.2%
594924 1
3.2%
ValueCountFrequency (%)
896122 1
3.2%
885321 1
3.2%
872297 1
3.2%
868643 1
3.2%
840661 1
3.2%
824374 1
3.2%
781749 1
3.2%
750181 1
3.2%
742668 1
3.2%
739129 1
3.2%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365531.45
Minimum254027
Maximum495179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:16.808225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum254027
5-th percentile260738
Q1312533
median356418
Q3441038.5
95-th percentile481154.5
Maximum495179
Range241152
Interquartile range (IQR)128505.5

Descriptive statistics

Standard deviation74136.928
Coefficient of variation (CV)0.20281956
Kurtosis-1.0874909
Mean365531.45
Median Absolute Deviation (MAD)45967
Skewness0.41363368
Sum11331475
Variance5.4962841 × 109
MonotonicityNot monotonic
2023-12-13T01:02:16.953161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
437465 1
 
3.2%
444612 1
 
3.2%
260126 1
 
3.2%
261350 1
 
3.2%
254027 1
 
3.2%
282036 1
 
3.2%
306141 1
 
3.2%
303620 1
 
3.2%
310451 1
 
3.2%
323783 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
254027 1
3.2%
260126 1
3.2%
261350 1
3.2%
282036 1
3.2%
303620 1
3.2%
306141 1
3.2%
310451 1
3.2%
312064 1
3.2%
313002 1
3.2%
313715 1
3.2%
ValueCountFrequency (%)
495179 1
3.2%
483602 1
3.2%
478707 1
3.2%
475625 1
3.2%
472527 1
3.2%
465546 1
3.2%
452360 1
3.2%
444612 1
3.2%
437465 1
3.2%
390473 1
3.2%


Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313409.9
Minimum239407
Maximum412520
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:17.075137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum239407
5-th percentile248187.5
Q1282520
median307417
Q3331115.5
95-th percentile399884.5
Maximum412520
Range173113
Interquartile range (IQR)48595.5

Descriptive statistics

Standard deviation45074.251
Coefficient of variation (CV)0.14381885
Kurtosis0.0016584697
Mean313409.9
Median Absolute Deviation (MAD)25425
Skewness0.63694812
Sum9715707
Variance2.0316881 × 109
MonotonicityNot monotonic
2023-12-13T01:02:17.209445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
305203 1
 
3.2%
305569 1
 
3.2%
247904 1
 
3.2%
248471 1
 
3.2%
239407 1
 
3.2%
266698 1
 
3.2%
288783 1
 
3.2%
289907 1
 
3.2%
295536 1
 
3.2%
307404 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
239407 1
3.2%
247904 1
3.2%
248471 1
3.2%
266698 1
3.2%
272870 1
3.2%
275184 1
3.2%
275837 1
3.2%
279483 1
3.2%
285557 1
3.2%
288783 1
3.2%
ValueCountFrequency (%)
412520 1
3.2%
406751 1
3.2%
393018 1
3.2%
390142 1
3.2%
361954 1
3.2%
351847 1
3.2%
348656 1
3.2%
332842 1
3.2%
329389 1
3.2%
321860 1
3.2%

인문
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing19
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean400401.58
Minimum336941
Maximum481027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:17.388790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum336941
5-th percentile340028.7
Q1364669.5
median403626.5
Q3426833.25
95-th percentile473120.2
Maximum481027
Range144086
Interquartile range (IQR)62163.75

Descriptive statistics

Standard deviation46453.767
Coefficient of variation (CV)0.11601794
Kurtosis-0.83439527
Mean400401.58
Median Absolute Deviation (MAD)34525
Skewness0.31184133
Sum4804819
Variance2.1579525 × 109
MonotonicityNot monotonic
2023-12-13T01:02:17.518273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
336941 1
 
3.2%
342555 1
 
3.2%
372311 1
 
3.2%
413958 1
 
3.2%
393295 1
 
3.2%
428064 1
 
3.2%
426423 1
 
3.2%
466651 1
 
3.2%
481027 1
 
3.2%
416700 1
 
3.2%
Other values (2) 2
 
6.5%
(Missing) 19
61.3%
ValueCountFrequency (%)
336941 1
3.2%
342555 1
3.2%
361002 1
3.2%
365892 1
3.2%
372311 1
3.2%
393295 1
3.2%
413958 1
3.2%
416700 1
3.2%
426423 1
3.2%
428064 1
3.2%
ValueCountFrequency (%)
481027 1
3.2%
466651 1
3.2%
428064 1
3.2%
426423 1
3.2%
416700 1
3.2%
413958 1
3.2%
393295 1
3.2%
372311 1
3.2%
365892 1
3.2%
361002 1
3.2%

자연
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing19
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean302630.58
Minimum198963
Maximum375023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:17.702962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum198963
5-th percentile202167.85
Q1245269.25
median338321
Q3347981.75
95-th percentile364868.35
Maximum375023
Range176060
Interquartile range (IQR)102712.5

Descriptive statistics

Standard deviation65744.624
Coefficient of variation (CV)0.21724382
Kurtosis-1.2258839
Mean302630.58
Median Absolute Deviation (MAD)23227.5
Skewness-0.77243311
Sum3631567
Variance4.3223556 × 109
MonotonicityNot monotonic
2023-12-13T01:02:17.865626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
340252 1
 
3.2%
343168 1
 
3.2%
336390 1
 
3.2%
351719 1
 
3.2%
356560 1
 
3.2%
375023 1
 
3.2%
346736 1
 
3.2%
310105 1
 
3.2%
256608 1
 
3.2%
198963 1
 
3.2%
Other values (2) 2
 
6.5%
(Missing) 19
61.3%
ValueCountFrequency (%)
198963 1
3.2%
204790 1
3.2%
211253 1
3.2%
256608 1
3.2%
310105 1
3.2%
336390 1
3.2%
340252 1
3.2%
343168 1
3.2%
346736 1
3.2%
351719 1
3.2%
ValueCountFrequency (%)
375023 1
3.2%
356560 1
3.2%
351719 1
3.2%
346736 1
3.2%
343168 1
3.2%
340252 1
3.2%
336390 1
3.2%
310105 1
3.2%
256608 1
3.2%
211253 1
3.2%

예체능
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing19
Missing (%)61.3%
Infinite0
Infinite (%)0.0%
Mean92902.917
Minimum64458
Maximum134662
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:18.027484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64458
5-th percentile65017.35
Q174151.25
median88859
Q3108771.5
95-th percentile128504.2
Maximum134662
Range70204
Interquartile range (IQR)34620.25

Descriptive statistics

Standard deviation24019.909
Coefficient of variation (CV)0.25854849
Kurtosis-1.1741949
Mean92902.917
Median Absolute Deviation (MAD)16096
Skewness0.45551911
Sum1114835
Variance5.7695603 × 108
MonotonicityNot monotonic
2023-12-13T01:02:18.189211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
65475 1
 
3.2%
64458 1
 
3.2%
73048 1
 
3.2%
74984 1
 
3.2%
74519 1
 
3.2%
82234 1
 
3.2%
95484 1
 
3.2%
119366 1
 
3.2%
134662 1
 
3.2%
123466 1
 
3.2%
Other values (2) 2
 
6.5%
(Missing) 19
61.3%
ValueCountFrequency (%)
64458 1
3.2%
65475 1
3.2%
73048 1
3.2%
74519 1
3.2%
74984 1
3.2%
82234 1
3.2%
95484 1
3.2%
101899 1
3.2%
105240 1
3.2%
119366 1
3.2%
ValueCountFrequency (%)
134662 1
3.2%
123466 1
3.2%
119366 1
3.2%
105240 1
3.2%
101899 1
3.2%
95484 1
3.2%
82234 1
3.2%
74984 1
3.2%
74519 1
3.2%
73048 1
3.2%

재학생
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean487734.23
Minimum346673
Maximum631745
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:18.474270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum346673
5-th percentile355474.5
Q1445735
median482089
Q3529634.5
95-th percentile618253
Maximum631745
Range285072
Interquartile range (IQR)83899.5

Descriptive statistics

Standard deviation74606.582
Coefficient of variation (CV)0.15296565
Kurtosis-0.20343112
Mean487734.23
Median Absolute Deviation (MAD)44744
Skewness0.045178047
Sum15119761
Variance5.5661421 × 109
MonotonicityNot monotonic
2023-12-13T01:02:18.633121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
521806 1
 
3.2%
496617 1
 
3.2%
350239 1
 
3.2%
360710 1
 
3.2%
346673 1
 
3.2%
394024 1
 
3.2%
448111 1
 
3.2%
444873 1
 
3.2%
459342 1
 
3.2%
482054 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
346673 1
3.2%
350239 1
3.2%
360710 1
3.2%
394024 1
3.2%
422310 1
3.2%
425396 1
3.2%
435538 1
3.2%
444873 1
3.2%
446597 1
3.2%
448111 1
3.2%
ValueCountFrequency (%)
631745 1
3.2%
623130 1
3.2%
613376 1
3.2%
603238 1
3.2%
545023 1
3.2%
541880 1
3.2%
541662 1
3.2%
532436 1
3.2%
526833 1
3.2%
526418 1
3.2%

졸업생
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean177410.77
Minimum126729
Maximum300482
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:18.779554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126729
5-th percentile127610
Q1135301
median151887
Q3223676
95-th percentile264768
Maximum300482
Range173753
Interquartile range (IQR)88375

Descriptive statistics

Standard deviation53628.87
Coefficient of variation (CV)0.30228643
Kurtosis-0.64653648
Mean177410.77
Median Absolute Deviation (MAD)21229
Skewness0.91280248
Sum5499734
Variance2.8760557 × 109
MonotonicityNot monotonic
2023-12-13T01:02:18.973327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
213459 1
 
3.2%
243826 1
 
3.2%
142303 1
 
3.2%
134834 1
 
3.2%
133070 1
 
3.2%
142271 1
 
3.2%
135482 1
 
3.2%
137533 1
 
3.2%
135120 1
 
3.2%
136090 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
126729 1
3.2%
127586 1
3.2%
127634 1
3.2%
130658 1
3.2%
131539 1
3.2%
133070 1
3.2%
134834 1
3.2%
135120 1
3.2%
135482 1
3.2%
136090 1
3.2%
ValueCountFrequency (%)
300482 1
3.2%
268044 1
3.2%
261492 1
3.2%
261424 1
3.2%
254538 1
3.2%
250064 1
3.2%
243826 1
3.2%
233893 1
3.2%
213459 1
3.2%
185946 1
3.2%

검정등
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13796.355
Minimum7403
Maximum42297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:19.148736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7403
5-th percentile10129.5
Q111566.5
median13195
Q314295
95-th percentile15587
Maximum42297
Range34894
Interquartile range (IQR)2728.5

Descriptive statistics

Standard deviation5603.9156
Coefficient of variation (CV)0.40618813
Kurtosis24.016406
Mean13796.355
Median Absolute Deviation (MAD)1545
Skewness4.5954203
Sum427687
Variance31403870
MonotonicityNot monotonic
2023-12-13T01:02:19.316905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
7403 1
 
3.2%
9738 1
 
3.2%
15488 1
 
3.2%
14277 1
 
3.2%
13691 1
 
3.2%
12439 1
 
3.2%
11331 1
 
3.2%
11121 1
 
3.2%
11525 1
 
3.2%
13043 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
7403 1
3.2%
9738 1
3.2%
10521 1
3.2%
11121 1
3.2%
11307 1
3.2%
11331 1
3.2%
11521 1
3.2%
11525 1
3.2%
11608 1
3.2%
11620 1
3.2%
ValueCountFrequency (%)
42297 1
3.2%
15686 1
3.2%
15488 1
3.2%
15326 1
3.2%
14989 1
3.2%
14740 1
3.2%
14521 1
3.2%
14313 1
3.2%
14277 1
3.2%
14055 1
3.2%

3개 영역 이하 응시수수료
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28387.097
Minimum12000
Maximum37000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:19.464981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile12000
Q117500
median37000
Q337000
95-th percentile37000
Maximum37000
Range25000
Interquartile range (IQR)19500

Descriptive statistics

Standard deviation10989.927
Coefficient of variation (CV)0.38714515
Kurtosis-1.5127312
Mean28387.097
Median Absolute Deviation (MAD)0
Skewness-0.62777771
Sum880000
Variance1.2077849 × 108
MonotonicityIncreasing
2023-12-13T01:02:19.601652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
37000 18
58.1%
12000 7
 
22.6%
20000 2
 
6.5%
22000 2
 
6.5%
15000 1
 
3.2%
31000 1
 
3.2%
ValueCountFrequency (%)
12000 7
 
22.6%
15000 1
 
3.2%
20000 2
 
6.5%
22000 2
 
6.5%
31000 1
 
3.2%
37000 18
58.1%
ValueCountFrequency (%)
37000 18
58.1%
31000 1
 
3.2%
22000 2
 
6.5%
20000 2
 
6.5%
15000 1
 
3.2%
12000 7
 
22.6%

4개 영역 응시수수료
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30322.581
Minimum12000
Maximum42000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:19.724226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile12000
Q117500
median37000
Q342000
95-th percentile42000
Maximum42000
Range30000
Interquartile range (IQR)24500

Descriptive statistics

Standard deviation12623.753
Coefficient of variation (CV)0.41631526
Kurtosis-1.5678595
Mean30322.581
Median Absolute Deviation (MAD)5000
Skewness-0.53536731
Sum940000
Variance1.5935914 × 108
MonotonicityNot monotonic
2023-12-13T01:02:19.850321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
42000 11
35.5%
12000 7
22.6%
37000 7
22.6%
20000 2
 
6.5%
22000 2
 
6.5%
15000 1
 
3.2%
36000 1
 
3.2%
ValueCountFrequency (%)
12000 7
22.6%
15000 1
 
3.2%
20000 2
 
6.5%
22000 2
 
6.5%
36000 1
 
3.2%
37000 7
22.6%
42000 11
35.5%
ValueCountFrequency (%)
42000 11
35.5%
37000 7
22.6%
36000 1
 
3.2%
22000 2
 
6.5%
20000 2
 
6.5%
15000 1
 
3.2%
12000 7
22.6%

5개 영역 응시수수료
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33387.097
Minimum12000
Maximum47000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T01:02:20.003846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile12000
Q117500
median42000
Q347000
95-th percentile47000
Maximum47000
Range35000
Interquartile range (IQR)29500

Descriptive statistics

Standard deviation15025.927
Coefficient of variation (CV)0.45005193
Kurtosis-1.6504465
Mean33387.097
Median Absolute Deviation (MAD)5000
Skewness-0.52288374
Sum1035000
Variance2.2577849 × 108
MonotonicityNot monotonic
2023-12-13T01:02:20.144320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
47000 11
35.5%
12000 7
22.6%
42000 7
22.6%
20000 2
 
6.5%
22000 2
 
6.5%
15000 1
 
3.2%
41000 1
 
3.2%
ValueCountFrequency (%)
12000 7
22.6%
15000 1
 
3.2%
20000 2
 
6.5%
22000 2
 
6.5%
41000 1
 
3.2%
42000 7
22.6%
47000 11
35.5%
ValueCountFrequency (%)
47000 11
35.5%
42000 7
22.6%
41000 1
 
3.2%
22000 2
 
6.5%
20000 2
 
6.5%
15000 1
 
3.2%
12000 7
22.6%

6개 영역 응시수수료
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size380.0 B
<NA>
24 
47000

Length

Max length5
Median length4
Mean length4.2258065
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> 24
77.4%
47000 7
 
22.6%

Length

2023-12-13T01:02:20.293652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:02:20.423458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 24
77.4%
47000 7
 
22.6%

Interactions

2023-12-13T01:02:13.424977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:56.431830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.016062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.230159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.353222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.035121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:03.360764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:04.845567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.259581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:07.609823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:09.440231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:10.815431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:12.059430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.520966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:56.574111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.114848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.308589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.438802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.169198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:03.452952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:04.936526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.347171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:07.712662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:09.539802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:10.912042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:12.185703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.623338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:56.752463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.230992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.398077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.541096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.274751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:03.554950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:05.045125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.458052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:07.820570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:09.664462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.024453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:12.290470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.713352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:56.864166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.318622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.475141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.626389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.367812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:03.661435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:05.185770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.558526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:07.911439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:09.772819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.117607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:12.379000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.802881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:56.978354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.413439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.568755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.719993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.452110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:03.769846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:05.305413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.670800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:08.023801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:09.872119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.203946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:12.505494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.903900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:57.104629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.503896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.656370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.862189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.546263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:03.886096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:05.421414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.770953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:08.128374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:09.979540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.291522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:12.621580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.996716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:57.250381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.583786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.744707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.988921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.637918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:03.992766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:05.540701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.890187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:08.264274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:10.088797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.383174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:12.720028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:14.075750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:57.358821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.678078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.837908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:01.097733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.733080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:04.090796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:05.647487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.998651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:08.733294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:10.187511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.470335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:12.829574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:14.145976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:57.483320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.770646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.918186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:01.207917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.821285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:04.217388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:05.762007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:07.105337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:08.869262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:10.291414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.564485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:12.922055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:14.246895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:57.596984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.872370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.005155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:01.311661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.907224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:04.337794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:05.869979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:07.189213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:08.980542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:10.414622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.667347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.020126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:14.332485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:57.699842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:58.960821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.091369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:01.407541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:02.994169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:04.433888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:05.989069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:07.292270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:09.109975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:10.521747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.766203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.120792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:14.418220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:57.791385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.044832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.180037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:01.504581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:03.081998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:04.552590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.072779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:07.409360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:09.228055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:10.601408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.870667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.223976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:14.508539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:57.899649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:01:59.139210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:00.261997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:01.933667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:03.225662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:04.731280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:06.160452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:07.513812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:09.329097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:10.711447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:11.949988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:02:13.324982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:02:20.522621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
학년도기간(1)기간(2)총 계인문자연예체능재학생졸업생검정등3개 영역 이하 응시수수료4개 영역 응시수수료5개 영역 응시수수료
학년도1.0001.0000.6430.8990.7710.9340.7800.8870.8230.7990.7080.5540.7980.8110.926
기간(1)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기간(2)0.6431.0001.0000.9670.6800.8730.0000.8540.7260.1330.8120.6200.7050.9780.705
총 계0.8991.0000.9671.0000.9560.9740.7090.7290.7260.9040.6310.8190.5660.6480.853
0.7711.0000.6800.9561.0000.8750.6090.8380.8980.8630.6260.7190.8650.7550.943
0.9341.0000.8730.9740.8751.0000.9540.7280.7130.8280.6850.5680.6200.5930.777
인문0.7801.0000.0000.7090.6090.9541.0000.0000.0001.0000.6720.0000.0000.2610.000
자연0.8871.0000.8540.7290.8380.7280.0001.0000.8820.5420.0000.9251.0001.0001.000
예체능0.8231.0000.7260.7260.8980.7130.0000.8821.0000.7710.6340.7301.0001.0001.000
재학생0.7991.0000.1330.9040.8630.8281.0000.5420.7711.0000.4760.0000.5330.6610.879
졸업생0.7081.0000.8120.6310.6260.6850.6720.0000.6340.4761.0000.0000.8750.8660.732
검정등0.5541.0000.6200.8190.7190.5680.0000.9250.7300.0000.0001.0000.0000.0000.000
3개 영역 이하 응시수수료0.7981.0000.7050.5660.8650.6200.0001.0001.0000.5330.8750.0001.0001.0000.979
4개 영역 응시수수료0.8111.0000.9780.6480.7550.5930.2611.0001.0000.6610.8660.0001.0001.0001.000
5개 영역 응시수수료0.9261.0000.7050.8530.9430.7770.0001.0001.0000.8790.7320.0000.9791.0001.000
2023-12-13T01:02:20.727975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
6개 영역 응시수수료기간(2)
6개 영역 응시수수료1.0001.000
기간(2)1.0001.000
2023-12-13T01:02:20.858396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
학년도총 계인문자연예체능재학생졸업생검정등3개 영역 이하 응시수수료4개 영역 응시수수료5개 영역 응시수수료기간(2)6개 영역 응시수수료
학년도1.000-0.802-0.855-0.6370.347-0.6480.872-0.619-0.7940.2560.8910.6760.6760.0991.000
총 계-0.8021.0000.9810.9430.7900.5380.1400.9200.781-0.003-0.803-0.618-0.6180.6731.000
-0.8550.9811.0000.9140.6640.727-0.0490.9020.7840.000-0.811-0.585-0.5850.4411.000
-0.6370.9430.9141.0000.9650.1960.5730.9370.6660.145-0.641-0.457-0.4570.4931.000
인문0.3470.7900.6640.9651.0000.0770.6710.8110.3570.3990.0900.0900.0900.0000.000
자연-0.6480.5380.7270.1960.0771.000-0.6080.3570.706-0.469-0.862-0.862-0.8620.7100.000
예체능0.8720.140-0.0490.5730.671-0.6081.0000.364-0.3220.5310.7750.7750.7750.4230.000
재학생-0.6190.9200.9020.9370.8110.3570.3641.0000.5420.028-0.587-0.379-0.3790.0901.000
졸업생-0.7940.7810.7840.6660.3570.706-0.3220.5421.000-0.106-0.877-0.807-0.8070.5921.000
검정등0.256-0.0030.0000.1450.399-0.4690.5310.028-0.1061.0000.2730.3340.3340.5851.000
3개 영역 이하 응시수수료0.891-0.803-0.811-0.6410.090-0.8620.775-0.587-0.8770.2731.0000.9220.9220.5911.000
4개 영역 응시수수료0.676-0.618-0.585-0.4570.090-0.8620.775-0.379-0.8070.3340.9221.0001.0000.2281.000
5개 영역 응시수수료0.676-0.618-0.585-0.4570.090-0.8620.775-0.379-0.8070.3340.9221.0001.0000.3721.000
기간(2)0.0990.6730.4410.4930.0000.7100.4230.0900.5920.5850.5910.2280.3721.0001.000
6개 영역 응시수수료1.0001.0001.0001.0000.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T01:02:14.654865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:02:15.159467image/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.
2023-12-13T01:02:15.294240image/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

학년도기간(1)기간(2)총 계인문자연예체능재학생졸업생검정등3개 영역 이하 응시수수료4개 영역 응시수수료5개 영역 응시수수료6개 영역 응시수수료
019941993-06-11~06-24(14일간)742668437465305203336941340252654755218062134597403120001200012000<NA>
119941993-10-02~10-11(14일간)750181444612305569342555343168644584966172438269738120001200012000<NA>
219951994-09-12~09-27(16일간)7817494523603293893723113363907304847796026149242297120001200012000<NA>
319961995-09-11~09-23(13일간)8406614787073619544139583517197498452683330048213346120001200012000<NA>
419971996-09-02~09-14(13일간)8243744725273518473932953565607451954502326804411307120001200012000<NA>
519981997-09-01~09-13(12일간)8853214951793901424280643750238223461337626142410521120001200012000<NA>
619991998-09-01~09-15(14일간)8686434756253930184264233467369548462313023389311620120001200012000<NA>
720001999-09-01~09-11(10일간)89612248360241252046665131010511936663174525006414313150001500015000<NA>
820012000-09-01~09-16(14일간)87229746554640675148102725660813466260323825453814521200002000020000<NA>
920022001-08-27~09-08(12일간)73912939047334865641670019896312346654166218594611521200002000020000<NA>
학년도기간(1)기간(2)총 계인문자연예체능재학생졸업생검정등3개 영역 이하 응시수수료4개 영역 응시수수료5개 영역 응시수수료6개 영역 응시수수료
2120142013-08-22~09-06(12일간)650747342776307971<NA><NA><NA>50908112763414032370004200047000<NA>
2220152014-08-25~09-12(12일간)640621333204307417<NA><NA><NA>49502713153914055370004200047000<NA>
2320162015-08-27~09-11(12일간)631187323783307404<NA><NA><NA>48205413609013043370004200047000<NA>
2420172016-08-25~09-09(12일간)605987310451295536<NA><NA><NA>4593421351201152537000370004200047000
2520182017-08-24~09-08(12일간)593527303620289907<NA><NA><NA>4448731375331112137000370004200047000
2620192018-08-23~09-07(12일간)594924306141288783<NA><NA><NA>4481111354821133137000370004200047000
2720202019-08-22~09-06(12일간)548734282036266698<NA><NA><NA>3940241422711243937000370004200047000
2820212020-09-03~09-18(12일간)493434254027239407<NA><NA><NA>3466731330701369137000370004200047000
2920222021-08-19~09-03(12일간)509821261350248471<NA><NA><NA>3607101348341427737000370004200047000
3020232022-08-18~09-02(12일간)508030260126247904<NA><NA><NA>3502391423031548837000370004200047000