Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells1917
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory131.0 B

Variable types

Numeric6
Categorical6
Text2

Dataset

Description중장기개방계획에따른 경상남도 경남도립남해대학 데이터자료입니다.(수강반, 학점, 이론시간)
Author경상남도
URLhttps://www.data.go.kr/data/15067551/fileData.do

Alerts

과목전공 has constant value ""Constant
년도 is highly overall correlated with 이수구분High correlation
이수구분 is highly overall correlated with 년도High correlation
과목학년 is highly overall correlated with 주야여부High correlation
주야여부 is highly overall correlated with 과목학년High correlation
수강반 is highly imbalanced (55.5%)Imbalance
주야여부 is highly imbalanced (54.7%)Imbalance
사용날짜,시간 has 1915 (19.1%) missing valuesMissing
이론시간 has 1782 (17.8%) zerosZeros
실습시간 has 3438 (34.4%) zerosZeros

Reproduction

Analysis started2023-12-12 18:23:37.969981
Analysis finished2023-12-12 18:23:45.695307
Duration7.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.5565
Minimum1997
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:23:45.778131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1997
5-th percentile1999
Q12004
median2010
Q32016
95-th percentile2019
Maximum2020
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.7904947
Coefficient of variation (CV)0.0033791012
Kurtosis-1.225236
Mean2009.5565
Median Absolute Deviation (MAD)6
Skewness-0.15551642
Sum20095565
Variance46.110819
MonotonicityNot monotonic
2023-12-13T03:23:45.932358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2019 516
 
5.2%
2016 515
 
5.1%
2018 507
 
5.1%
2017 505
 
5.1%
2015 502
 
5.0%
2014 483
 
4.8%
2013 480
 
4.8%
2020 472
 
4.7%
2012 446
 
4.5%
2001 438
 
4.4%
Other values (14) 5136
51.4%
ValueCountFrequency (%)
1997 124
 
1.2%
1998 324
3.2%
1999 389
3.9%
2000 436
4.4%
2001 438
4.4%
2002 405
4.0%
2003 376
3.8%
2004 373
3.7%
2005 367
3.7%
2006 361
3.6%
ValueCountFrequency (%)
2020 472
4.7%
2019 516
5.2%
2018 507
5.1%
2017 505
5.1%
2016 515
5.1%
2015 502
5.0%
2014 483
4.8%
2013 480
4.8%
2012 446
4.5%
2011 416
4.2%

학기
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5252 
2
4748 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 5252
52.5%
2 4748
47.5%

Length

2023-12-13T03:23:46.085312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:23:46.211590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5252
52.5%
2 4748
47.5%

과목학과
Real number (ℝ)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3228
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:23:46.336976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile11
Maximum99
Range98
Interquartile range (IQR)3

Descriptive statistics

Standard deviation12.460615
Coefficient of variation (CV)1.9707432
Kurtosis47.222906
Mean6.3228
Median Absolute Deviation (MAD)2
Skewness6.8292596
Sum63228
Variance155.26693
MonotonicityNot monotonic
2023-12-13T03:23:46.519188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4 1507
15.1%
5 1283
12.8%
1 1226
12.3%
7 1224
12.2%
3 1207
12.1%
6 1200
12.0%
2 1178
11.8%
11 419
 
4.2%
8 159
 
1.6%
9 136
 
1.4%
Other values (9) 461
 
4.6%
ValueCountFrequency (%)
1 1226
12.3%
2 1178
11.8%
3 1207
12.1%
4 1507
15.1%
5 1283
12.8%
6 1200
12.0%
7 1224
12.2%
8 159
 
1.6%
9 136
 
1.4%
10 24
 
0.2%
ValueCountFrequency (%)
99 5
 
0.1%
98 122
 
1.2%
97 46
 
0.5%
16 14
 
0.1%
15 46
 
0.5%
14 85
 
0.9%
13 24
 
0.2%
12 95
 
0.9%
11 419
4.2%
10 24
 
0.2%

과목전공
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-13T03:23:46.682066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:23:46.802349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

과목학년
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5514 
2
4424 
4
 
36
3
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5514
55.1%
2 4424
44.2%
4 36
 
0.4%
3 26
 
0.3%

Length

2023-12-13T03:23:46.933513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:23:47.065506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5514
55.1%
2 4424
44.2%
4 36
 
0.4%
3 26
 
0.3%

과목
Text

Distinct4214
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:23:47.487403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters60000
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1888 ?
Unique (%)18.9%

Sample

1st rowA30028
2nd rowB20013
3rd rowC10010
4th rowB50005
5th rowC40120
ValueCountFrequency (%)
c40001 20
 
0.2%
d10001 18
 
0.2%
d70002 17
 
0.2%
d70001 17
 
0.2%
c60009 16
 
0.2%
c40059 15
 
0.1%
c60082 15
 
0.1%
c70024 15
 
0.1%
c10016 15
 
0.1%
c70057 14
 
0.1%
Other values (4204) 9838
98.4%
2023-12-13T03:23:48.064745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17973
30.0%
1 8204
13.7%
C 5671
 
9.5%
2 5551
 
9.3%
6 3666
 
6.1%
3 3623
 
6.0%
4 3580
 
6.0%
5 2991
 
5.0%
7 2727
 
4.5%
8 1730
 
2.9%
Other values (7) 4284
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51711
86.2%
Uppercase Letter 8289
 
13.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17973
34.8%
1 8204
15.9%
2 5551
 
10.7%
6 3666
 
7.1%
3 3623
 
7.0%
4 3580
 
6.9%
5 2991
 
5.8%
7 2727
 
5.3%
8 1730
 
3.3%
9 1666
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
C 5671
68.4%
A 1462
 
17.6%
B 503
 
6.1%
N 426
 
5.1%
D 81
 
1.0%
E 76
 
0.9%
F 70
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 51711
86.2%
Latin 8289
 
13.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17973
34.8%
1 8204
15.9%
2 5551
 
10.7%
6 3666
 
7.1%
3 3623
 
7.0%
4 3580
 
6.9%
5 2991
 
5.8%
7 2727
 
5.3%
8 1730
 
3.3%
9 1666
 
3.2%
Latin
ValueCountFrequency (%)
C 5671
68.4%
A 1462
 
17.6%
B 503
 
6.1%
N 426
 
5.1%
D 81
 
1.0%
E 76
 
0.9%
F 70
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17973
30.0%
1 8204
13.7%
C 5671
 
9.5%
2 5551
 
9.3%
6 3666
 
6.1%
3 3623
 
6.0%
4 3580
 
6.0%
5 2991
 
5.0%
7 2727
 
4.5%
8 1730
 
2.9%
Other values (7) 4284
 
7.1%

수강반
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
A
6427 
B
3438 
C
 
116
D
 
13
E
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 6427
64.3%
B 3438
34.4%
C 116
 
1.2%
D 13
 
0.1%
E 6
 
0.1%

Length

2023-12-13T03:23:48.262993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:23:48.398766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 6427
64.3%
b 3438
34.4%
c 116
 
1.2%
d 13
 
0.1%
e 6
 
0.1%

주야여부
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8219 
<NA>
1711 
2
 
70

Length

Max length4
Median length1
Mean length1.5133
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8219
82.2%
<NA> 1711
 
17.1%
2 70
 
0.7%

Length

2023-12-13T03:23:48.549925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:23:48.666216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8219
82.2%
na 1711
 
17.1%
2 70
 
0.7%

이수구분
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)0.4%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14.504101
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:23:48.813615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q311
95-th percentile60
Maximum60
Range59
Interquartile range (IQR)7

Descriptive statistics

Standard deviation18.37096
Coefficient of variation (CV)1.2666046
Kurtosis1.3188986
Mean14.504101
Median Absolute Deviation (MAD)3
Skewness1.7007853
Sum145012
Variance337.49218
MonotonicityNot monotonic
2023-12-13T03:23:48.963892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7 4253
42.5%
3 772
 
7.7%
1 769
 
7.7%
4 746
 
7.5%
2 688
 
6.9%
60 502
 
5.0%
59 378
 
3.8%
23 333
 
3.3%
52 198
 
2.0%
11 194
 
1.9%
Other values (25) 1165
 
11.7%
ValueCountFrequency (%)
1 769
 
7.7%
2 688
 
6.9%
3 772
 
7.7%
4 746
 
7.5%
5 120
 
1.2%
6 2
 
< 0.1%
7 4253
42.5%
11 194
 
1.9%
12 4
 
< 0.1%
13 17
 
0.2%
ValueCountFrequency (%)
60 502
5.0%
59 378
3.8%
58 34
 
0.3%
57 46
 
0.5%
56 9
 
0.1%
54 10
 
0.1%
53 149
 
1.5%
52 198
 
2.0%
51 64
 
0.6%
41 70
 
0.7%

학점
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.501
Minimum0
Maximum24
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:23:49.127120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0799114
Coefficient of variation (CV)0.83163192
Kurtosis40.268413
Mean2.501
Median Absolute Deviation (MAD)0
Skewness5.9492491
Sum25010
Variance4.3260316
MonotonicityNot monotonic
2023-12-13T03:23:49.271930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 5978
59.8%
3 2507
25.1%
1 1056
 
10.6%
6 111
 
1.1%
4 109
 
1.1%
16 60
 
0.6%
8 60
 
0.6%
18 55
 
0.5%
15 38
 
0.4%
24 7
 
0.1%
Other values (8) 19
 
0.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 1056
 
10.6%
2 5978
59.8%
3 2507
25.1%
4 109
 
1.1%
5 3
 
< 0.1%
6 111
 
1.1%
8 60
 
0.6%
9 1
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
24 7
 
0.1%
20 3
 
< 0.1%
19 1
 
< 0.1%
18 55
0.5%
16 60
0.6%
15 38
0.4%
14 1
 
< 0.1%
12 5
 
0.1%
10 3
 
< 0.1%
9 1
 
< 0.1%

이론시간
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4857
Minimum0
Maximum20
Zeros1782
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:23:49.413093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1711245
Coefficient of variation (CV)0.78826447
Kurtosis50.474236
Mean1.4857
Median Absolute Deviation (MAD)1
Skewness3.7470633
Sum14857
Variance1.3715327
MonotonicityNot monotonic
2023-12-13T03:23:49.550272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 3535
35.4%
2 3234
32.3%
0 1782
17.8%
3 1199
 
12.0%
4 191
 
1.9%
6 44
 
0.4%
20 7
 
0.1%
12 3
 
< 0.1%
8 3
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
0 1782
17.8%
1 3535
35.4%
2 3234
32.3%
3 1199
 
12.0%
4 191
 
1.9%
6 44
 
0.4%
8 3
 
< 0.1%
10 1
 
< 0.1%
12 3
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
20 7
 
0.1%
19 1
 
< 0.1%
12 3
 
< 0.1%
10 1
 
< 0.1%
8 3
 
< 0.1%
6 44
 
0.4%
4 191
 
1.9%
3 1199
 
12.0%
2 3234
32.3%
1 3535
35.4%

실습시간
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9717
Minimum0
Maximum40
Zeros3438
Zeros (%)34.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:23:49.707209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32
95-th percentile4
Maximum40
Range40
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.9659894
Coefficient of variation (CV)2.0114568
Kurtosis53.24932
Mean1.9717
Median Absolute Deviation (MAD)1
Skewness6.8349724
Sum19717
Variance15.729072
MonotonicityNot monotonic
2023-12-13T03:23:49.863017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 4390
43.9%
0 3438
34.4%
3 968
 
9.7%
1 511
 
5.1%
4 429
 
4.3%
32 61
 
0.6%
16 58
 
0.6%
36 26
 
0.3%
12 26
 
0.3%
40 24
 
0.2%
Other values (10) 69
 
0.7%
ValueCountFrequency (%)
0 3438
34.4%
1 511
 
5.1%
2 4390
43.9%
3 968
 
9.7%
4 429
 
4.3%
5 2
 
< 0.1%
6 21
 
0.2%
8 18
 
0.2%
9 3
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
40 24
 
0.2%
36 26
0.3%
32 61
0.6%
30 1
 
< 0.1%
24 2
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
16 58
0.6%
15 19
 
0.2%
12 26
0.3%

실습과목구분
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
3371 
1
3145 
<NA>
1965 
2
1519 

Length

Max length4
Median length1
Mean length1.5895
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row<NA>
5th row3

Common Values

ValueCountFrequency (%)
3 3371
33.7%
1 3145
31.4%
<NA> 1965
19.7%
2 1519
15.2%

Length

2023-12-13T03:23:50.024901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:23:50.139849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 3371
33.7%
1 3145
31.4%
na 1965
19.7%
2 1519
15.2%

사용날짜,시간
Text

MISSING 

Distinct7644
Distinct (%)94.5%
Missing1915
Missing (%)19.1%
Memory size156.2 KiB
2023-12-13T03:23:50.509856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length18.946568
Min length18

Characters and Unicode

Total characters153183
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7490 ?
Unique (%)92.6%

Sample

1st row2010-08-16 13:37:33
2nd row2005-08-12 16:00:38
3rd row2003-02-14 9:50:34
4th row2009-08-12 17:16:46
5th row2010-08-12 14:29:32
ValueCountFrequency (%)
2002-08-15 169
 
1.0%
2008-02-27 161
 
1.0%
2004-02-19 159
 
1.0%
2010-02-19 150
 
0.9%
2006-02-20 150
 
0.9%
2011-08-17 132
 
0.8%
2005-08-12 131
 
0.8%
2013-02-14 128
 
0.8%
2003-06-16 126
 
0.8%
2009-08-12 122
 
0.8%
Other values (7322) 14742
91.2%
2023-12-13T03:23:51.040484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27184
17.7%
2 21692
14.2%
1 21525
14.1%
- 16170
10.6%
: 16170
10.6%
8085
 
5.3%
8 7639
 
5.0%
3 7392
 
4.8%
4 7333
 
4.8%
5 6467
 
4.2%
Other values (3) 13526
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 112758
73.6%
Dash Punctuation 16170
 
10.6%
Other Punctuation 16170
 
10.6%
Space Separator 8085
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 27184
24.1%
2 21692
19.2%
1 21525
19.1%
8 7639
 
6.8%
3 7392
 
6.6%
4 7333
 
6.5%
5 6467
 
5.7%
6 4781
 
4.2%
7 4578
 
4.1%
9 4167
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 16170
100.0%
Other Punctuation
ValueCountFrequency (%)
: 16170
100.0%
Space Separator
ValueCountFrequency (%)
8085
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 153183
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 27184
17.7%
2 21692
14.2%
1 21525
14.1%
- 16170
10.6%
: 16170
10.6%
8085
 
5.3%
8 7639
 
5.0%
3 7392
 
4.8%
4 7333
 
4.8%
5 6467
 
4.2%
Other values (3) 13526
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153183
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27184
17.7%
2 21692
14.2%
1 21525
14.1%
- 16170
10.6%
: 16170
10.6%
8085
 
5.3%
8 7639
 
5.0%
3 7392
 
4.8%
4 7333
 
4.8%
5 6467
 
4.2%
Other values (3) 13526
8.8%

Interactions

2023-12-13T03:23:44.252012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:40.024554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:40.776947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:41.671169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:42.410791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:43.109033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:44.369197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:40.148604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:40.906296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:41.803182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:42.534146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:43.563502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:44.509325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:40.281680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:41.024646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:41.943213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:42.635639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:43.670864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:44.643745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:40.409122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:41.158837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:42.062603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:42.743523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:43.792650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:44.773960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:40.513303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:41.287615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:42.165732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:42.840843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:43.938833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:44.919633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:40.636082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:41.470272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:42.285040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:42.977489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:23:44.095286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:23:51.194748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도학기과목학과과목학년수강반주야여부이수구분학점이론시간실습시간실습과목구분
년도1.0000.0610.3760.1550.2060.1450.7300.2150.3450.3040.423
학기0.0611.0000.0310.1800.0670.0000.1670.2020.1180.1630.045
과목학과0.3760.0311.0000.2280.1990.1870.7660.2390.2340.1910.240
과목학년0.1550.1800.2281.0000.0910.8870.1350.2070.1170.1590.120
수강반0.2060.0670.1990.0911.0000.0260.1770.1360.0630.1230.125
주야여부0.1450.0000.1870.8870.0261.0000.0640.1810.0560.0000.039
이수구분0.7300.1670.7660.1350.1770.0641.0000.4110.3720.4660.666
학점0.2150.2020.2390.2070.1360.1810.4111.0000.8820.9240.367
이론시간0.3450.1180.2340.1170.0630.0560.3720.8821.0000.6050.574
실습시간0.3040.1630.1910.1590.1230.0000.4660.9240.6051.0000.486
실습과목구분0.4230.0450.2400.1200.1250.0390.6660.3670.5740.4861.000
2023-12-13T03:23:51.383782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주야여부학기과목학년수강반실습과목구분
주야여부1.0000.0000.6960.0310.065
학기0.0001.0000.1190.0820.075
과목학년0.6960.1191.0000.0740.113
수강반0.0310.0820.0741.0000.094
실습과목구분0.0650.0750.1130.0941.000
2023-12-13T03:23:51.515078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도과목학과이수구분학점이론시간실습시간학기과목학년수강반주야여부실습과목구분
년도1.0000.1680.599-0.150-0.258-0.1920.0700.0930.0880.1080.298
과목학과0.1681.0000.1740.0770.107-0.1270.0510.2170.1520.3080.077
이수구분0.5990.1741.0000.071-0.2230.0340.1670.0870.1030.0640.378
학점-0.1500.0770.0711.0000.3760.3120.1560.1250.0570.1390.237
이론시간-0.2580.107-0.2230.3761.000-0.2940.0800.1180.0200.1740.276
실습시간-0.192-0.1270.0340.312-0.2941.0000.1220.0910.0490.0000.245
학기0.0700.0510.1670.1560.0800.1221.0000.1190.0820.0000.075
과목학년0.0930.2170.0870.1250.1180.0910.1191.0000.0740.6960.113
수강반0.0880.1520.1030.0570.0200.0490.0820.0741.0000.0310.094
주야여부0.1080.3080.0640.1390.1740.0000.0000.6960.0311.0000.065
실습과목구분0.2980.0770.3780.2370.2760.2450.0750.1130.0940.0651.000

Missing values

2023-12-13T03:23:45.147235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:23:45.406645image/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.
2023-12-13T03:23:45.596315image/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

년도학기과목학과과목전공과목학년과목수강반주야여부이수구분학점이론시간실습시간실습과목구분사용날짜,시간
725220102301A30028B1122012010-08-16 13:37:33
442920052201B20013B1122012005-08-12 16:00:38
323720031101C10010B1432232003-02-14 9:50:34
266420021502B50005B12222<NA><NA>
661320092401C40120A1721232009-08-12 17:16:46
726320102701A70019B1121232010-08-12 14:29:32
10050201511102C11090A1722012015-02-27 18:11:59
38419982302303207B<NA>3333<NA><NA>
682920101402C40113B1721232010-02-22 13:44:41
955920141402C40150A1721232014-02-17 11:21:00
년도학기과목학과과목전공과목학년과목수강반주야여부이수구분학점이론시간실습시간실습과목구분사용날짜,시간
968320142601C60118A1732232014-08-06 10:29:13
206820012302693214B<NA>7211<NA><NA>
42719982401304111A<NA>3222<NA><NA>
102019992502605209A<NA>7200<NA><NA>
53219981502305203B<NA>3244<NA><NA>
970220142102C10095A1432232014-08-06 10:47:22
352720032701B70002A1222012003-06-16 17:06:19
691720101302C30156A1732232010-02-19 15:01:14
298920022601C60025A1732232002-08-15 21:57:14
605020082301A30015A1111012008-08-08 15:40:27