Overview

Dataset statistics

Number of variables15
Number of observations7653
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory986.6 KiB
Average record size in memory132.0 B

Variable types

Numeric6
Categorical7
Text2

Dataset

Description중장기개방계획에따른 경상남도 경남도립남해대학 데이터자료입니다.(학점, 이론시간, 실습시간, 사용낭짜 및 시간, 성적분포비율사용 여부등의 데이터를 포함하고있습니다.)
Author경상남도
URLhttps://www.data.go.kr/data/15067560/fileData.do

Alerts

과목전공코드 has constant value ""Constant
년도 is highly overall correlated with 이수구분 and 1 other fieldsHigh correlation
이수구분 is highly overall correlated with 년도High correlation
이론시간 is highly overall correlated with 실습시간High correlation
실습시간 is highly overall correlated with 이론시간High correlation
성적분포비율사용 is highly overall correlated with 년도High correlation
시작과목반 is highly imbalanced (89.3%)Imbalance
분반수 is highly imbalanced (53.4%)Imbalance
이론시간 has 1471 (19.2%) zerosZeros
실습시간 has 2788 (36.4%) zerosZeros

Reproduction

Analysis started2023-12-12 22:12:35.485105
Analysis finished2023-12-12 22:12:41.281798
Duration5.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.806
Minimum2002
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-12-13T07:12:41.348364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2003
Q12007
median2012
Q32017
95-th percentile2020
Maximum2020
Range18
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.3799551
Coefficient of variation (CV)0.0026741918
Kurtosis-1.1731647
Mean2011.806
Median Absolute Deviation (MAD)5
Skewness-0.14311098
Sum15396351
Variance28.943916
MonotonicityNot monotonic
2023-12-13T07:12:41.518502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2019 557
 
7.3%
2018 510
 
6.7%
2017 503
 
6.6%
2015 467
 
6.1%
2020 466
 
6.1%
2014 450
 
5.9%
2013 449
 
5.9%
2012 438
 
5.7%
2011 432
 
5.6%
2010 417
 
5.4%
Other values (9) 2964
38.7%
ValueCountFrequency (%)
2002 189
2.5%
2003 377
4.9%
2004 375
4.9%
2005 364
4.8%
2006 347
4.5%
2007 336
4.4%
2008 358
4.7%
2009 374
4.9%
2010 417
5.4%
2011 432
5.6%
ValueCountFrequency (%)
2020 466
6.1%
2019 557
7.3%
2018 510
6.7%
2017 503
6.6%
2016 244
3.2%
2015 467
6.1%
2014 450
5.9%
2013 449
5.9%
2012 438
5.7%
2011 432
5.6%

학기
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
2
4028 
1
3625 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 4028
52.6%
1 3625
47.4%

Length

2023-12-13T07:12:41.657130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:12:41.791983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 4028
52.6%
1 3625
47.4%

과목계열코드
Real number (ℝ)

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6539919
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-12-13T07:12:41.911899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile12
Maximum99
Range98
Interquartile range (IQR)4

Descriptive statistics

Standard deviation15.684486
Coefficient of variation (CV)2.0491903
Kurtosis27.895
Mean7.6539919
Median Absolute Deviation (MAD)2
Skewness5.3578489
Sum58576
Variance246.00311
MonotonicityNot monotonic
2023-12-13T07:12:42.037367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3 1023
13.4%
7 990
12.9%
4 979
12.8%
6 962
12.6%
1 875
11.4%
5 816
10.7%
2 768
10.0%
11 523
6.8%
98 152
 
2.0%
12 118
 
1.5%
Other values (9) 447
5.8%
ValueCountFrequency (%)
1 875
11.4%
2 768
10.0%
3 1023
13.4%
4 979
12.8%
5 816
10.7%
6 962
12.6%
7 990
12.9%
8 112
 
1.5%
9 111
 
1.5%
10 33
 
0.4%
ValueCountFrequency (%)
99 4
 
0.1%
98 152
 
2.0%
97 61
 
0.8%
16 20
 
0.3%
15 24
 
0.3%
14 49
 
0.6%
13 33
 
0.4%
12 118
 
1.5%
11 523
6.8%
10 33
 
0.4%

과목전공코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
0
7653 

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 7653
100.0%

Length

2023-12-13T07:12:42.194276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:12:42.322891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 7653
100.0%

과목학년
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
1
3824 
2
3764 
4
 
38
3
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 3824
50.0%
2 3764
49.2%
4 38
 
0.5%
3 27
 
0.4%

Length

2023-12-13T07:12:42.425425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:12:42.538708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3824
50.0%
2 3764
49.2%
4 38
 
0.5%
3 27
 
0.4%

과목
Text

Distinct3818
Distinct (%)49.9%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
2023-12-13T07:12:42.934024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters45918
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

Unique2201 ?
Unique (%)28.8%

Sample

1st rowA10002
2nd rowB10001
3rd rowC10004
4th rowC10005
5th rowC10006
ValueCountFrequency (%)
c60033 13
 
0.2%
c60016 13
 
0.2%
c60032 13
 
0.2%
d70002 13
 
0.2%
c30077 12
 
0.2%
c70060 12
 
0.2%
c60009 12
 
0.2%
d10001 12
 
0.2%
c30127 12
 
0.2%
d70001 12
 
0.2%
Other values (3808) 7529
98.4%
2023-12-13T07:12:43.540128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13994
30.5%
1 5915
12.9%
C 5272
 
11.5%
2 3563
 
7.8%
3 2847
 
6.2%
4 2490
 
5.4%
7 2251
 
4.9%
5 2209
 
4.8%
6 2191
 
4.8%
9 1432
 
3.1%
Other values (7) 3754
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38265
83.3%
Uppercase Letter 7653
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13994
36.6%
1 5915
15.5%
2 3563
 
9.3%
3 2847
 
7.4%
4 2490
 
6.5%
7 2251
 
5.9%
5 2209
 
5.8%
6 2191
 
5.7%
9 1432
 
3.7%
8 1373
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
C 5272
68.9%
A 1217
 
15.9%
N 474
 
6.2%
B 418
 
5.5%
E 104
 
1.4%
F 96
 
1.3%
D 72
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 38265
83.3%
Latin 7653
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13994
36.6%
1 5915
15.5%
2 3563
 
9.3%
3 2847
 
7.4%
4 2490
 
6.5%
7 2251
 
5.9%
5 2209
 
5.8%
6 2191
 
5.7%
9 1432
 
3.7%
8 1373
 
3.6%
Latin
ValueCountFrequency (%)
C 5272
68.9%
A 1217
 
15.9%
N 474
 
6.2%
B 418
 
5.5%
E 104
 
1.4%
F 96
 
1.3%
D 72
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13994
30.5%
1 5915
12.9%
C 5272
 
11.5%
2 3563
 
7.8%
3 2847
 
6.2%
4 2490
 
5.4%
7 2251
 
4.9%
5 2209
 
4.8%
6 2191
 
4.8%
9 1432
 
3.1%
Other values (7) 3754
 
8.2%

시작과목반
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
A
7477 
B
 
158
C
 
18

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 7477
97.7%
B 158
 
2.1%
C 18
 
0.2%

Length

2023-12-13T07:12:43.701823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:12:43.811070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 7477
97.7%
b 158
 
2.1%
c 18
 
0.2%

분반수
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
1
4306 
2
3251 
3
 
80
4
 
10
5
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 4306
56.3%
2 3251
42.5%
3 80
 
1.0%
4 10
 
0.1%
5 6
 
0.1%

Length

2023-12-13T07:12:43.934960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:12:44.061944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4306
56.3%
2 3251
42.5%
3 80
 
1.0%
4 10
 
0.1%
5 6
 
0.1%

이수구분
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.296485
Minimum1
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-12-13T07:12:44.528031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q323
95-th percentile60
Maximum60
Range59
Interquartile range (IQR)19

Descriptive statistics

Standard deviation20.283099
Coefficient of variation (CV)1.1726717
Kurtosis0.017481588
Mean17.296485
Median Absolute Deviation (MAD)4
Skewness1.3033468
Sum132370
Variance411.40411
MonotonicityNot monotonic
2023-12-13T07:12:44.686468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7 3073
40.2%
4 534
 
7.0%
60 534
 
7.0%
3 530
 
6.9%
1 506
 
6.6%
2 453
 
5.9%
59 368
 
4.8%
23 270
 
3.5%
52 167
 
2.2%
53 155
 
2.0%
Other values (25) 1063
 
13.9%
ValueCountFrequency (%)
1 506
 
6.6%
2 453
 
5.9%
3 530
 
6.9%
4 534
 
7.0%
5 80
 
1.0%
6 1
 
< 0.1%
7 3073
40.2%
11 144
 
1.9%
12 2
 
< 0.1%
13 7
 
0.1%
ValueCountFrequency (%)
60 534
7.0%
59 368
4.8%
58 36
 
0.5%
57 45
 
0.6%
56 11
 
0.1%
54 15
 
0.2%
53 155
 
2.0%
52 167
 
2.2%
51 68
 
0.9%
41 96
 
1.3%

학점
Real number (ℝ)

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6711094
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-12-13T07:12:44.834246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile6
Maximum24
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4570855
Coefficient of variation (CV)0.91987453
Kurtosis27.557691
Mean2.6711094
Median Absolute Deviation (MAD)0
Skewness4.9886053
Sum20442
Variance6.037269
MonotonicityNot monotonic
2023-12-13T07:12:44.964594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 4365
57.0%
3 1942
25.4%
1 826
 
10.8%
4 123
 
1.6%
6 113
 
1.5%
8 82
 
1.1%
16 81
 
1.1%
15 54
 
0.7%
18 31
 
0.4%
24 10
 
0.1%
Other values (8) 26
 
0.3%
ValueCountFrequency (%)
1 826
 
10.8%
2 4365
57.0%
3 1942
25.4%
4 123
 
1.6%
5 6
 
0.1%
6 113
 
1.5%
7 1
 
< 0.1%
8 82
 
1.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
24 10
 
0.1%
20 7
 
0.1%
19 2
 
< 0.1%
18 31
 
0.4%
16 81
1.1%
15 54
0.7%
14 1
 
< 0.1%
12 6
 
0.1%
10 2
 
< 0.1%
9 1
 
< 0.1%

이론시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4010192
Minimum0
Maximum20
Zeros1471
Zeros (%)19.2%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-12-13T07:12:45.087637image/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 deviation1.2884074
Coefficient of variation (CV)0.91962154
Kurtosis70.649361
Mean1.4010192
Median Absolute Deviation (MAD)1
Skewness5.6537795
Sum10722
Variance1.6599937
MonotonicityNot monotonic
2023-12-13T07:12:45.223320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 2965
38.7%
2 2394
31.3%
0 1471
19.2%
3 717
 
9.4%
6 60
 
0.8%
4 22
 
0.3%
20 10
 
0.1%
12 7
 
0.1%
8 3
 
< 0.1%
19 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 1471
19.2%
1 2965
38.7%
2 2394
31.3%
3 717
 
9.4%
4 22
 
0.3%
6 60
 
0.8%
8 3
 
< 0.1%
10 1
 
< 0.1%
12 7
 
0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
20 10
 
0.1%
19 2
 
< 0.1%
14 1
 
< 0.1%
12 7
 
0.1%
10 1
 
< 0.1%
8 3
 
< 0.1%
6 60
 
0.8%
4 22
 
0.3%
3 717
 
9.4%
2 2394
31.3%

실습시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.32876
Minimum0
Maximum40
Zeros2788
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size67.4 KiB
2023-12-13T07:12:45.376812image/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 deviation5.1353459
Coefficient of variation (CV)2.2051847
Kurtosis30.575975
Mean2.32876
Median Absolute Deviation (MAD)1
Skewness5.3321801
Sum17822
Variance26.371777
MonotonicityNot monotonic
2023-12-13T07:12:45.507664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 3661
47.8%
0 2788
36.4%
3 445
 
5.8%
4 277
 
3.6%
1 121
 
1.6%
16 84
 
1.1%
32 83
 
1.1%
12 42
 
0.5%
40 35
 
0.5%
36 29
 
0.4%
Other values (12) 88
 
1.1%
ValueCountFrequency (%)
0 2788
36.4%
1 121
 
1.6%
2 3661
47.8%
3 445
 
5.8%
4 277
 
3.6%
5 2
 
< 0.1%
6 22
 
0.3%
7 1
 
< 0.1%
8 22
 
0.3%
9 3
 
< 0.1%
ValueCountFrequency (%)
40 35
0.5%
36 29
 
0.4%
32 83
1.1%
30 2
 
< 0.1%
26 1
 
< 0.1%
24 3
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
16 84
1.1%
15 28
 
0.4%

실습과목구분
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
3
3389 
1
2756 
2
1471 
<NA>
 
37

Length

Max length4
Median length1
Mean length1.0145041
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 3389
44.3%
1 2756
36.0%
2 1471
19.2%
<NA> 37
 
0.5%

Length

2023-12-13T07:12:45.685451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:12:45.799836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 3389
44.3%
1 2756
36.0%
2 1471
19.2%
na 37
 
0.5%

사용날짜,시간
Text

Distinct7079
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
2023-12-13T07:12:46.083648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length21.257415
Min length21

Characters and Unicode

Total characters162683
Distinct characters16
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6874 ?
Unique (%)89.8%

Sample

1st row2002-08-13 오후 10:56:57
2nd row2002-08-14 오후 5:54:56
3rd row2002-08-14 오후 5:55:40
4th row2002-08-14 오후 5:56:14
5th row2002-08-14 오후 5:56:25
ValueCountFrequency (%)
오후 5560
 
24.2%
오전 2093
 
9.1%
2012-08-16 195
 
0.8%
2002-08-15 172
 
0.7%
2006-02-20 167
 
0.7%
2010-02-19 163
 
0.7%
2012-02-17 156
 
0.7%
2004-08-10 155
 
0.7%
2003-06-16 152
 
0.7%
2005-08-12 149
 
0.6%
Other values (6716) 13997
61.0%
2023-12-13T07:12:46.550744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25699
15.8%
2 20112
12.4%
1 16614
10.2%
- 15306
9.4%
15306
9.4%
: 15306
9.4%
오 7653
 
4.7%
8 7102
 
4.4%
3 7101
 
4.4%
4 6755
 
4.2%
Other values (6) 25729
15.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101459
62.4%
Dash Punctuation 15306
 
9.4%
Space Separator 15306
 
9.4%
Other Punctuation 15306
 
9.4%
Other Letter 15306
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25699
25.3%
2 20112
19.8%
1 16614
16.4%
8 7102
 
7.0%
3 7101
 
7.0%
4 6755
 
6.7%
5 6409
 
6.3%
6 4371
 
4.3%
9 3713
 
3.7%
7 3583
 
3.5%
Other Letter
ValueCountFrequency (%)
오 7653
50.0%
후 5560
36.3%
전 2093
 
13.7%
Dash Punctuation
ValueCountFrequency (%)
- 15306
100.0%
Space Separator
ValueCountFrequency (%)
15306
100.0%
Other Punctuation
ValueCountFrequency (%)
: 15306
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147377
90.6%
Hangul 15306
 
9.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25699
17.4%
2 20112
13.6%
1 16614
11.3%
- 15306
10.4%
15306
10.4%
: 15306
10.4%
8 7102
 
4.8%
3 7101
 
4.8%
4 6755
 
4.6%
5 6409
 
4.3%
Other values (3) 11667
7.9%
Hangul
ValueCountFrequency (%)
오 7653
50.0%
후 5560
36.3%
전 2093
 
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147377
90.6%
Hangul 15306
 
9.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25699
17.4%
2 20112
13.6%
1 16614
11.3%
- 15306
10.4%
15306
10.4%
: 15306
10.4%
8 7102
 
4.8%
3 7101
 
4.8%
4 6755
 
4.6%
5 6409
 
4.3%
Other values (3) 11667
7.9%
Hangul
ValueCountFrequency (%)
오 7653
50.0%
후 5560
36.3%
전 2093
 
13.7%

성적분포비율사용
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size59.9 KiB
0
5998 
1
1655 

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 5998
78.4%
1 1655
 
21.6%

Length

2023-12-13T07:12:46.725405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:12:46.847631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5998
78.4%
1 1655
 
21.6%

Interactions

2023-12-13T07:12:40.278935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:37.266647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:37.875081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.420620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.965728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:39.508715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:40.380265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:37.361410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:37.972557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.514403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:39.060217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:39.619171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:40.478896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:37.449159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.052341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.601397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:39.145863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:39.713420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:40.592379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:37.544527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.134910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.684240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:39.230669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:39.811559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:40.683954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:37.647678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.214870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.771035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:39.315256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:40.010270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:40.797858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:37.757438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.318366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:38.873051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:39.406584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:12:40.164071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:12:46.939080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도학기과목계열코드과목학년시작과목반분반수이수구분학점이론시간실습시간실습과목구분성적분포비율사용
년도1.0000.1310.3740.1470.2740.2150.6820.2480.1510.2580.4270.845
학기0.1311.0000.0470.1730.0070.1570.2600.2070.0820.1850.0470.000
과목계열코드0.3740.0471.0000.2180.1110.2570.7560.2490.3800.1860.3000.086
과목학년0.1470.1730.2181.0000.0430.2310.1400.1800.0990.1920.1360.280
시작과목반0.2740.0070.1110.0431.0000.1230.3790.0000.1030.0000.1550.000
분반수0.2150.1570.2570.2310.1231.0000.2640.2280.0920.2260.1700.045
이수구분0.6820.2600.7560.1400.3790.2641.0000.4320.4900.4840.6460.443
학점0.2480.2070.2490.1800.0000.2280.4321.0000.8510.9290.4190.159
이론시간0.1510.0820.3800.0990.1030.0920.4900.8511.0000.6060.7380.047
실습시간0.2580.1850.1860.1920.0000.2260.4840.9290.6061.0000.5300.138
실습과목구분0.4270.0470.3000.1360.1550.1700.6460.4190.7380.5301.0000.105
성적분포비율사용0.8450.0000.0860.2800.0000.0450.4430.1590.0470.1380.1051.000
2023-12-13T07:12:47.138107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분반수실습과목구분과목학년학기시작과목반성적분포비율사용
분반수1.0000.1290.1900.1920.0930.055
실습과목구분0.1291.0000.1290.0770.0470.174
과목학년0.1900.1291.0000.1150.0410.186
학기0.1920.0770.1151.0000.0110.000
시작과목반0.0930.0470.0410.0111.0000.000
성적분포비율사용0.0550.1740.1860.0000.0001.000
2023-12-13T07:12:47.292177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도과목계열코드이수구분학점이론시간실습시간학기과목학년시작과목반분반수실습과목구분성적분포비율사용
년도1.0000.1610.698-0.089-0.149-0.0740.1240.0890.1710.0910.2800.680
과목계열코드0.1611.0000.1610.1170.173-0.1550.0770.2080.0330.2000.1010.143
이수구분0.6980.1611.0000.107-0.1690.1190.2600.0900.1810.1550.3620.444
학점-0.0890.1170.1071.0000.3230.3060.1590.1080.0000.0970.2770.122
이론시간-0.1490.173-0.1690.3231.000-0.6900.0830.1650.0000.0420.3010.045
실습시간-0.074-0.1550.1190.306-0.6901.0000.1420.1090.0000.0810.2930.092
학기0.1240.0770.2600.1590.0830.1421.0000.1150.0110.1920.0770.000
과목학년0.0890.2080.0900.1080.1650.1090.1151.0000.0410.1900.1290.186
시작과목반0.1710.0330.1810.0000.0000.0000.0110.0411.0000.0930.0470.000
분반수0.0910.2000.1550.0970.0420.0810.1920.1900.0931.0000.1290.055
실습과목구분0.2800.1010.3620.2770.3010.2930.0770.1290.0470.1291.0000.174
성적분포비율사용0.6800.1430.4440.1220.0450.0920.0000.1860.0000.0550.1741.000

Missing values

2023-12-13T07:12:40.955948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:12:41.177577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

년도학기과목계열코드과목전공코드과목학년과목시작과목반분반수이수구분학점이론시간실습시간실습과목구분사용날짜,시간성적분포비율사용
020022101A10002A2133012002-08-13 오후 10:56:570
120022101B10001A2221232002-08-14 오후 5:54:560
220022101C10004A2721232002-08-14 오후 5:55:400
320022101C10005A2732232002-08-14 오후 5:56:140
420022101C10006A2732232002-08-14 오후 5:56:250
520022101C10007A2732232002-08-14 오후 5:56:400
620022101C10016A2721232002-08-14 오후 5:56:530
720022101C10023A2721232002-08-14 오후 5:57:080
820022101C10025A2721232002-08-14 오후 5:57:230
920022102B10004A1222012002-08-14 오후 8:50:340
년도학기과목계열코드과목전공코드과목학년과목시작과목반분반수이수구분학점이론시간실습시간실습과목구분사용날짜,시간성적분포비율사용
764320202202C20332A15830322020-08-21 오전 10:36:360
764420202202C20337A15830322020-08-21 오전 10:36:580
764520202701A07099A15211012020-08-21 오후 3:01:230
764620202701A07098A15211012020-08-21 오후 3:02:070
764720202701E07004A131404022020-08-21 오후 3:02:230
764820202702E07004A131404022020-08-21 오후 3:11:480
764920202602A60070A25422012020-08-21 오후 5:52:400
765020202702C07271A25933012020-08-28 오후 5:43:530
765120202402A04119A15421232020-08-31 오후 4:25:070
765220202501A50055A15211012020-09-02 오전 9:13:220