Overview

Dataset statistics

Number of variables20
Number of observations5041
Missing cells17039
Missing cells (%)16.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory861.6 KiB
Average record size in memory175.0 B

Variable types

Text4
Numeric6
Categorical7
Unsupported3

Dataset

Description중장기개방계획에따른 경상남도 경남도립남해대학 데이터자료입니다.(과목번호, 과목명(한글, 영어), 시간, 개설년도, 개설학기, 폐기년도등의 데이터를 포함하고있습니다.)
Author경상남도
URLhttps://www.data.go.kr/data/15067553/fileData.do

Alerts

실습과목구분 is highly overall correlated with 개설전공코드High correlation
개설학기 is highly overall correlated with 개설년도 and 1 other fieldsHigh correlation
등급구분 is highly overall correlated with 개설전공코드High correlation
개설학년 is highly overall correlated with 개설전공코드High correlation
폐기학기 is highly overall correlated with 폐기년도 and 1 other fieldsHigh correlation
개설전공코드 is highly overall correlated with 학점 and 11 other fieldsHigh correlation
폐기년도 is highly overall correlated with 폐기학기 and 1 other fieldsHigh correlation
학점 is highly overall correlated with 개설전공코드High correlation
이론시간 is highly overall correlated with 실습시간 and 1 other fieldsHigh correlation
실습시간 is highly overall correlated with 이론시간 and 1 other fieldsHigh correlation
개설년도 is highly overall correlated with 개설이수구분 and 2 other fieldsHigh correlation
개설학과코드 is highly overall correlated with 개설전공코드High correlation
개설이수구분 is highly overall correlated with 개설년도 and 1 other fieldsHigh correlation
개설학기 is highly imbalanced (55.0%)Imbalance
폐기년도 is highly imbalanced (51.0%)Imbalance
폐기학기 is highly imbalanced (58.1%)Imbalance
등급구분 is highly imbalanced (66.8%)Imbalance
과목명(영문) has 981 (19.5%) missing valuesMissing
대체과목1 has 5041 (100.0%) missing valuesMissing
대체과목2 has 5041 (100.0%) missing valuesMissing
대체과목3 has 5041 (100.0%) missing valuesMissing
사용날짜,시간 has 915 (18.2%) missing valuesMissing
과목 has unique valuesUnique
대체과목1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
대체과목2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
대체과목3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
이론시간 has 1158 (23.0%) zerosZeros
실습시간 has 2050 (40.7%) zerosZeros
개설이수구분 has 941 (18.7%) zerosZeros

Reproduction

Analysis started2023-12-12 17:31:20.891133
Analysis finished2023-12-12 17:31:26.327689
Duration5.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

과목
Text

UNIQUE 

Distinct5041
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
2023-12-13T02:31:26.607690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9986114
Min length5

Characters and Unicode

Total characters30239
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

Unique5041 ?
Unique (%)100.0%

Sample

1st row101102
2nd row102101
3rd row103101
4th row103102
5th row104101
ValueCountFrequency (%)
101102 1
 
< 0.1%
c30298 1
 
< 0.1%
c11137 1
 
< 0.1%
c11136 1
 
< 0.1%
c11135 1
 
< 0.1%
c11134 1
 
< 0.1%
c11133 1
 
< 0.1%
a11028 1
 
< 0.1%
e30004 1
 
< 0.1%
c30296 1
 
< 0.1%
Other values (5031) 5031
99.8%
2023-12-13T02:31:27.048946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8375
27.7%
1 4267
14.1%
2 3060
 
10.1%
C 2716
 
9.0%
3 1954
 
6.5%
6 1886
 
6.2%
4 1809
 
6.0%
7 1424
 
4.7%
5 1353
 
4.5%
8 1012
 
3.3%
Other values (7) 2383
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26140
86.4%
Uppercase Letter 4099
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8375
32.0%
1 4267
16.3%
2 3060
 
11.7%
3 1954
 
7.5%
6 1886
 
7.2%
4 1809
 
6.9%
7 1424
 
5.4%
5 1353
 
5.2%
8 1012
 
3.9%
9 1000
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
C 2716
66.3%
A 645
 
15.7%
N 377
 
9.2%
B 201
 
4.9%
E 74
 
1.8%
F 72
 
1.8%
D 14
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 26140
86.4%
Latin 4099
 
13.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8375
32.0%
1 4267
16.3%
2 3060
 
11.7%
3 1954
 
7.5%
6 1886
 
7.2%
4 1809
 
6.9%
7 1424
 
5.4%
5 1353
 
5.2%
8 1012
 
3.9%
9 1000
 
3.8%
Latin
ValueCountFrequency (%)
C 2716
66.3%
A 645
 
15.7%
N 377
 
9.2%
B 201
 
4.9%
E 74
 
1.8%
F 72
 
1.8%
D 14
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8375
27.7%
1 4267
14.1%
2 3060
 
10.1%
C 2716
 
9.0%
3 1954
 
6.5%
6 1886
 
6.2%
4 1809
 
6.0%
7 1424
 
4.7%
5 1353
 
4.5%
8 1012
 
3.3%
Other values (7) 2383
 
7.9%

과목명(한글)
Text

Distinct2912
Distinct (%)57.8%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
2023-12-13T02:31:27.301560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length7.0737949
Min length2

Characters and Unicode

Total characters35659
Distinct characters475
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1939 ?
Unique (%)38.5%

Sample

1st row전산개론
2nd row생활영어
3rd row생활영어
4th row전산개론
5th row생활영어
ValueCountFrequency (%)
및 72
 
1.3%
실습 46
 
0.8%
2 35
 
0.6%
현장실습 32
 
0.6%
1 31
 
0.6%
생활영어 29
 
0.5%
인성 28
 
0.5%
컴퓨터활용실습 28
 
0.5%
창업실무 25
 
0.5%
인턴실무실습 25
 
0.5%
Other values (2869) 5163
93.6%
2023-12-13T02:31:27.661860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
실 2073
 
5.8%
습 1209
 
3.4%
어 877
 
2.5%
무 848
 
2.4%
기 828
 
2.3%
리 688
 
1.9%
학 595
 
1.7%
전 556
 
1.6%
로 552
 
1.5%
회 521
 
1.5%
Other values (465) 26912
75.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30764
86.3%
Uppercase Letter 1782
 
5.0%
Decimal Number 1241
 
3.5%
Letter Number 492
 
1.4%
Space Separator 483
 
1.4%
Lowercase Letter 265
 
0.7%
Open Punctuation 252
 
0.7%
Close Punctuation 252
 
0.7%
Dash Punctuation 82
 
0.2%
Other Punctuation 36
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
실 2073
 
6.7%
습 1209
 
3.9%
어 877
 
2.9%
무 848
 
2.8%
기 828
 
2.7%
리 688
 
2.2%
학 595
 
1.9%
전 556
 
1.8%
로 552
 
1.8%
회 521
 
1.7%
Other values (399) 22017
71.6%
Uppercase Letter
ValueCountFrequency (%)
I 373
20.9%
C 268
15.0%
A 159
8.9%
D 134
 
7.5%
E 116
 
6.5%
T 101
 
5.7%
O 97
 
5.4%
P 96
 
5.4%
S 57
 
3.2%
R 54
 
3.0%
Other values (16) 327
18.4%
Lowercase Letter
ValueCountFrequency (%)
e 70
26.4%
a 35
13.2%
i 22
 
8.3%
r 17
 
6.4%
s 17
 
6.4%
o 14
 
5.3%
l 14
 
5.3%
c 13
 
4.9%
t 11
 
4.2%
n 9
 
3.4%
Other values (10) 43
16.2%
Letter Number
ValueCountFrequency (%)
Ⅰ 234
47.6%
Ⅱ 197
40.0%
Ⅲ 42
 
8.5%
Ⅳ 17
 
3.5%
Ⅵ 2
 
0.4%
Other Punctuation
ValueCountFrequency (%)
· 17
47.2%
. 7
19.4%
/ 5
 
13.9%
& 4
 
11.1%
, 3
 
8.3%
Decimal Number
ValueCountFrequency (%)
1 518
41.7%
2 504
40.6%
3 147
 
11.8%
4 72
 
5.8%
Math Symbol
ValueCountFrequency (%)
+ 6
60.0%
∥ 4
40.0%
Space Separator
ValueCountFrequency (%)
483
100.0%
Open Punctuation
ValueCountFrequency (%)
( 252
100.0%
Close Punctuation
ValueCountFrequency (%)
) 252
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30764
86.3%
Latin 2538
 
7.1%
Common 2356
 
6.6%
Greek 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
실 2073
 
6.7%
습 1209
 
3.9%
어 877
 
2.9%
무 848
 
2.8%
기 828
 
2.7%
리 688
 
2.2%
학 595
 
1.9%
전 556
 
1.8%
로 552
 
1.8%
회 521
 
1.7%
Other values (399) 22017
71.6%
Latin
ValueCountFrequency (%)
I 373
14.7%
C 268
 
10.6%
Ⅰ 234
 
9.2%
Ⅱ 197
 
7.8%
A 159
 
6.3%
D 134
 
5.3%
E 116
 
4.6%
T 101
 
4.0%
O 97
 
3.8%
P 96
 
3.8%
Other values (40) 763
30.1%
Common
ValueCountFrequency (%)
1 518
22.0%
2 504
21.4%
483
20.5%
( 252
10.7%
) 252
10.7%
3 147
 
6.2%
- 82
 
3.5%
4 72
 
3.1%
· 17
 
0.7%
. 7
 
0.3%
Other values (5) 22
 
0.9%
Greek
ValueCountFrequency (%)
Ι 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30761
86.3%
ASCII 4381
 
12.3%
Number Forms 492
 
1.4%
None 18
 
0.1%
Math Operators 4
 
< 0.1%
Compat Jamo 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
실 2073
 
6.7%
습 1209
 
3.9%
어 877
 
2.9%
무 848
 
2.8%
기 828
 
2.7%
리 688
 
2.2%
학 595
 
1.9%
전 556
 
1.8%
로 552
 
1.8%
회 521
 
1.7%
Other values (398) 22014
71.6%
ASCII
ValueCountFrequency (%)
1 518
11.8%
2 504
 
11.5%
483
 
11.0%
I 373
 
8.5%
C 268
 
6.1%
( 252
 
5.8%
) 252
 
5.8%
A 159
 
3.6%
3 147
 
3.4%
D 134
 
3.1%
Other values (48) 1291
29.5%
Number Forms
ValueCountFrequency (%)
Ⅰ 234
47.6%
Ⅱ 197
40.0%
Ⅲ 42
 
8.5%
Ⅳ 17
 
3.5%
Ⅵ 2
 
0.4%
None
ValueCountFrequency (%)
· 17
94.4%
Ι 1
 
5.6%
Math Operators
ValueCountFrequency (%)
∥ 4
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 3
100.0%

과목명(영문)
Text

MISSING 

Distinct2593
Distinct (%)63.9%
Missing981
Missing (%)19.5%
Memory size39.5 KiB
2023-12-13T02:31:27.971262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length58
Median length47
Mean length24.305911
Min length3

Characters and Unicode

Total characters98682
Distinct characters82
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1867 ?
Unique (%)46.0%

Sample

1st rowIntroduction To Computer System
2nd rowPractical English
3rd rowPractical English
4th rowIntroduction to Computation
5th rowPractical English
ValueCountFrequency (%)
practice 789
 
6.0%
and 366
 
2.8%
1 295
 
2.3%
2 279
 
2.1%
practical 244
 
1.9%
engineering 239
 
1.8%
design 235
 
1.8%
english 233
 
1.8%
management 194
 
1.5%
tourism 192
 
1.5%
Other values (1135) 9981
76.5%
2023-12-13T02:31:28.451976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9173
 
9.3%
i 8505
 
8.6%
e 8278
 
8.4%
n 8061
 
8.2%
a 6362
 
6.4%
t 6283
 
6.4%
r 6068
 
6.1%
c 5686
 
5.8%
o 5278
 
5.3%
s 3977
 
4.0%
Other values (72) 31011
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76479
77.5%
Uppercase Letter 11073
 
11.2%
Space Separator 9173
 
9.3%
Decimal Number 974
 
1.0%
Letter Number 337
 
0.3%
Other Punctuation 269
 
0.3%
Open Punctuation 123
 
0.1%
Close Punctuation 123
 
0.1%
Dash Punctuation 116
 
0.1%
Math Symbol 8
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 8505
11.1%
e 8278
10.8%
n 8061
10.5%
a 6362
8.3%
t 6283
8.2%
r 6068
7.9%
c 5686
 
7.4%
o 5278
 
6.9%
s 3977
 
5.2%
l 2987
 
3.9%
Other values (16) 14994
19.6%
Uppercase Letter
ValueCountFrequency (%)
P 1677
15.1%
C 1554
14.0%
E 1351
12.2%
I 959
8.7%
A 684
 
6.2%
T 635
 
5.7%
S 626
 
5.7%
D 521
 
4.7%
M 513
 
4.6%
B 436
 
3.9%
Other values (16) 2117
19.1%
Other Punctuation
ValueCountFrequency (%)
& 195
72.5%
. 56
 
20.8%
/ 8
 
3.0%
, 3
 
1.1%
· 2
 
0.7%
' 2
 
0.7%
: 2
 
0.7%
# 1
 
0.4%
Other Letter
ValueCountFrequency (%)
스 1
14.3%
마 1
14.3%
트 1
14.3%
식 1
14.3%
재 1
14.3%
관 1
14.3%
리 1
14.3%
Letter Number
ValueCountFrequency (%)
Ⅱ 145
43.0%
Ⅰ 135
40.1%
Ⅲ 41
 
12.2%
Ⅳ 14
 
4.2%
Ⅵ 2
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 410
42.1%
2 397
40.8%
3 113
 
11.6%
4 54
 
5.5%
Math Symbol
ValueCountFrequency (%)
+ 6
75.0%
∥ 2
 
25.0%
Space Separator
ValueCountFrequency (%)
9173
100.0%
Open Punctuation
ValueCountFrequency (%)
( 123
100.0%
Close Punctuation
ValueCountFrequency (%)
) 123
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87889
89.1%
Common 10786
 
10.9%
Hangul 7
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 8505
 
9.7%
e 8278
 
9.4%
n 8061
 
9.2%
a 6362
 
7.2%
t 6283
 
7.1%
r 6068
 
6.9%
c 5686
 
6.5%
o 5278
 
6.0%
s 3977
 
4.5%
l 2987
 
3.4%
Other values (47) 26404
30.0%
Common
ValueCountFrequency (%)
9173
85.0%
1 410
 
3.8%
2 397
 
3.7%
& 195
 
1.8%
( 123
 
1.1%
) 123
 
1.1%
- 116
 
1.1%
3 113
 
1.0%
. 56
 
0.5%
4 54
 
0.5%
Other values (8) 26
 
0.2%
Hangul
ValueCountFrequency (%)
스 1
14.3%
마 1
14.3%
트 1
14.3%
식 1
14.3%
재 1
14.3%
관 1
14.3%
리 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98334
99.6%
Number Forms 337
 
0.3%
Hangul 7
 
< 0.1%
None 2
 
< 0.1%
Math Operators 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9173
 
9.3%
i 8505
 
8.6%
e 8278
 
8.4%
n 8061
 
8.2%
a 6362
 
6.5%
t 6283
 
6.4%
r 6068
 
6.2%
c 5686
 
5.8%
o 5278
 
5.4%
s 3977
 
4.0%
Other values (58) 30663
31.2%
Number Forms
ValueCountFrequency (%)
Ⅱ 145
43.0%
Ⅰ 135
40.1%
Ⅲ 41
 
12.2%
Ⅳ 14
 
4.2%
Ⅵ 2
 
0.6%
None
ValueCountFrequency (%)
· 2
100.0%
Math Operators
ValueCountFrequency (%)
∥ 2
100.0%
Hangul
ValueCountFrequency (%)
스 1
14.3%
마 1
14.3%
트 1
14.3%
식 1
14.3%
재 1
14.3%
관 1
14.3%
리 1
14.3%

학점
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5348145
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 KiB
2023-12-13T02:31:28.583138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum24
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0140449
Coefficient of variation (CV)0.79455318
Kurtosis41.6812
Mean2.5348145
Median Absolute Deviation (MAD)0
Skewness5.9368528
Sum12778
Variance4.056377
MonotonicityNot monotonic
2023-12-13T02:31:28.708330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2 2900
57.5%
3 1350
26.8%
1 507
 
10.1%
4 95
 
1.9%
6 65
 
1.3%
8 35
 
0.7%
15 29
 
0.6%
18 20
 
0.4%
16 11
 
0.2%
20 7
 
0.1%
Other values (8) 22
 
0.4%
ValueCountFrequency (%)
1 507
 
10.1%
2 2900
57.5%
3 1350
26.8%
4 95
 
1.9%
5 5
 
0.1%
6 65
 
1.3%
7 1
 
< 0.1%
8 35
 
0.7%
9 1
 
< 0.1%
10 4
 
0.1%
ValueCountFrequency (%)
24 3
 
0.1%
20 7
 
0.1%
19 1
 
< 0.1%
18 20
0.4%
16 11
 
0.2%
15 29
0.6%
14 2
 
< 0.1%
12 5
 
0.1%
10 4
 
0.1%
9 1
 
< 0.1%

실습과목구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
3
1581 
1
1502 
2
1020 
<NA>
938 

Length

Max length4
Median length1
Mean length1.5582226
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 (%)
3 1581
31.4%
1 1502
29.8%
2 1020
20.2%
<NA> 938
18.6%

Length

2023-12-13T02:31:28.850016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:31:29.005817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1581
31.4%
1 1502
29.8%
2 1020
20.2%
na 938
18.6%

이론시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4320571
Minimum0
Maximum24
Zeros1158
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size44.4 KiB
2023-12-13T02:31:29.109958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2690594
Coefficient of variation (CV)0.88617933
Kurtosis49.02664
Mean1.4320571
Median Absolute Deviation (MAD)1
Skewness3.8171341
Sum7219
Variance1.6105118
MonotonicityNot monotonic
2023-12-13T02:31:29.250143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1642
32.6%
2 1469
29.1%
0 1158
23.0%
3 616
 
12.2%
4 117
 
2.3%
6 28
 
0.6%
8 3
 
0.1%
12 2
 
< 0.1%
20 2
 
< 0.1%
14 1
 
< 0.1%
Other values (3) 3
 
0.1%
ValueCountFrequency (%)
0 1158
23.0%
1 1642
32.6%
2 1469
29.1%
3 616
 
12.2%
4 117
 
2.3%
6 28
 
0.6%
8 3
 
0.1%
10 1
 
< 0.1%
12 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
20 2
 
< 0.1%
19 1
 
< 0.1%
14 1
 
< 0.1%
12 2
 
< 0.1%
10 1
 
< 0.1%
8 3
 
0.1%
6 28
 
0.6%
4 117
 
2.3%
3 616
12.2%

실습시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0230113
Minimum0
Maximum48
Zeros2050
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size44.4 KiB
2023-12-13T02:31:29.418130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q32
95-th percentile4
Maximum48
Range48
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.4862517
Coefficient of variation (CV)2.2176108
Kurtosis48.335762
Mean2.0230113
Median Absolute Deviation (MAD)1
Skewness6.5288943
Sum10198
Variance20.126455
MonotonicityNot monotonic
2023-12-13T02:31:29.597435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 2050
40.7%
2 2035
40.4%
3 418
 
8.3%
4 257
 
5.1%
1 93
 
1.8%
12 34
 
0.7%
40 27
 
0.5%
6 26
 
0.5%
36 19
 
0.4%
16 19
 
0.4%
Other values (13) 63
 
1.2%
ValueCountFrequency (%)
0 2050
40.7%
1 93
 
1.8%
2 2035
40.4%
3 418
 
8.3%
4 257
 
5.1%
5 2
 
< 0.1%
6 26
 
0.5%
7 1
 
< 0.1%
8 15
 
0.3%
9 6
 
0.1%
ValueCountFrequency (%)
48 2
 
< 0.1%
40 27
0.5%
36 19
0.4%
32 13
0.3%
30 2
 
< 0.1%
26 1
 
< 0.1%
24 1
 
< 0.1%
20 4
 
0.1%
18 1
 
< 0.1%
16 19
0.4%

개설년도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)0.6%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean2004.572
Minimum0
Maximum2208
Zeros13
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size44.4 KiB
2023-12-13T02:31:29.755200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1998
Q12002
median2012
Q32017
95-th percentile2020
Maximum2208
Range2208
Interquartile range (IQR)15

Descriptive statistics

Standard deviation106.13872
Coefficient of variation (CV)0.05294832
Kurtosis351.02065
Mean2004.572
Median Absolute Deviation (MAD)6
Skewness-18.728968
Sum10097029
Variance11265.428
MonotonicityNot monotonic
2023-12-13T02:31:29.928182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2015 433
 
8.6%
2018 411
 
8.2%
2019 397
 
7.9%
2002 373
 
7.4%
2020 328
 
6.5%
2016 297
 
5.9%
2017 295
 
5.9%
1999 294
 
5.8%
2008 252
 
5.0%
2000 202
 
4.0%
Other values (18) 1755
34.8%
ValueCountFrequency (%)
0 13
 
0.3%
2 1
 
< 0.1%
1996 34
 
0.7%
1997 180
3.6%
1998 114
 
2.3%
1999 294
5.8%
2000 202
4.0%
2001 113
 
2.2%
2002 373
7.4%
2003 109
 
2.2%
ValueCountFrequency (%)
2208 1
 
< 0.1%
2020 328
6.5%
2019 397
7.9%
2018 411
8.2%
2017 295
5.9%
2016 297
5.9%
2015 433
8.6%
2014 116
 
2.3%
2013 138
 
2.7%
2012 112
 
2.2%

개설학기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
1
2518 
2
2499 
0
 
13
<NA>
 
10
8
 
1

Length

Max length4
Median length1
Mean length1.0059512
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
1 2518
50.0%
2 2499
49.6%
0 13
 
0.3%
<NA> 10
 
0.2%
8 1
 
< 0.1%

Length

2023-12-13T02:31:30.081987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:31:30.224521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2518
50.0%
2 2499
49.6%
0 13
 
0.3%
na 10
 
0.2%
8 1
 
< 0.1%

폐기년도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
<NA>
3893 
0
1147 
2
 
1

Length

Max length4
Median length4
Mean length3.3168022
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3893
77.2%
0 1147
 
22.8%
2 1
 
< 0.1%

Length

2023-12-13T02:31:30.370867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:31:30.508830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3893
77.2%
0 1147
 
22.8%
2 1
 
< 0.1%

폐기학기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
<NA>
3824 
0
1191 
1
 
17
2
 
9

Length

Max length4
Median length4
Mean length3.2757389
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3824
75.9%
0 1191
 
23.6%
1 17
 
0.3%
2 9
 
0.2%

Length

2023-12-13T02:31:30.650359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:31:30.776325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3824
75.9%
0 1191
 
23.6%
1 17
 
0.3%
2 9
 
0.2%

등급구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
9
4733 
P
 
308

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 4733
93.9%
P 308
 
6.1%

Length

2023-12-13T02:31:30.922036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:31:31.034930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
9 4733
93.9%
p 308
 
6.1%

대체과목1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5041
Missing (%)100.0%
Memory size44.4 KiB

대체과목2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5041
Missing (%)100.0%
Memory size44.4 KiB

대체과목3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing5041
Missing (%)100.0%
Memory size44.4 KiB

개설학과코드
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.4%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean9.0747069
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size44.4 KiB
2023-12-13T02:31:31.140855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile15
Maximum99
Range98
Interquartile range (IQR)4

Descriptive statistics

Standard deviation18.952979
Coefficient of variation (CV)2.08855
Kurtosis17.404033
Mean9.0747069
Median Absolute Deviation (MAD)2
Skewness4.3345625
Sum45673
Variance359.2154
MonotonicityNot monotonic
2023-12-13T02:31:31.284156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4 655
13.0%
2 632
12.5%
7 629
12.5%
3 602
11.9%
6 523
10.4%
1 509
10.1%
5 478
9.5%
11 376
7.5%
98 124
 
2.5%
8 122
 
2.4%
Other values (9) 383
7.6%
ValueCountFrequency (%)
1 509
10.1%
2 632
12.5%
3 602
11.9%
4 655
13.0%
5 478
9.5%
6 523
10.4%
7 629
12.5%
8 122
 
2.4%
9 84
 
1.7%
10 25
 
0.5%
ValueCountFrequency (%)
99 17
 
0.3%
98 124
 
2.5%
97 73
 
1.4%
16 20
 
0.4%
15 27
 
0.5%
14 53
 
1.1%
13 34
 
0.7%
12 50
 
1.0%
11 376
7.5%
10 25
 
0.5%

개설전공코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
<NA>
3811 
0
1230 

Length

Max length4
Median length4
Mean length3.2680024
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 3811
75.6%
0 1230
 
24.4%

Length

2023-12-13T02:31:31.460017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:31:31.581793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3811
75.6%
0 1230
 
24.4%

개설이수구분
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)0.7%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean18.505663
Minimum0
Maximum60
Zeros941
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size44.4 KiB
2023-12-13T02:31:31.685573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q325
95-th percentile60
Maximum60
Range60
Interquartile range (IQR)23

Descriptive statistics

Standard deviation21.909964
Coefficient of variation (CV)1.18396
Kurtosis-0.56738804
Mean18.505663
Median Absolute Deviation (MAD)7
Skewness1.0327648
Sum93139
Variance480.04652
MonotonicityNot monotonic
2023-12-13T02:31:31.832926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7 1227
24.3%
0 941
18.7%
60 477
 
9.5%
59 285
 
5.7%
23 255
 
5.1%
1 252
 
5.0%
4 226
 
4.5%
3 204
 
4.0%
2 182
 
3.6%
22 176
 
3.5%
Other values (25) 808
16.0%
ValueCountFrequency (%)
0 941
18.7%
1 252
 
5.0%
2 182
 
3.6%
3 204
 
4.0%
4 226
 
4.5%
5 15
 
0.3%
7 1227
24.3%
11 105
 
2.1%
12 3
 
0.1%
13 10
 
0.2%
ValueCountFrequency (%)
60 477
9.5%
59 285
5.7%
58 20
 
0.4%
57 28
 
0.6%
56 6
 
0.1%
54 15
 
0.3%
53 100
 
2.0%
52 86
 
1.7%
51 27
 
0.5%
41 72
 
1.4%

개설학년
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.5 KiB
2
1882 
1
1615 
0
943 
<NA>
589 
3
 
7

Length

Max length4
Median length1
Mean length1.3505257
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
2 1882
37.3%
1 1615
32.0%
0 943
18.7%
<NA> 589
 
11.7%
3 7
 
0.1%
4 5
 
0.1%

Length

2023-12-13T02:31:32.000815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:31:32.125826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 1882
37.3%
1 1615
32.0%
0 943
18.7%
na 589
 
11.7%
3 7
 
0.1%
4 5
 
0.1%

사용날짜,시간
Text

MISSING 

Distinct4098
Distinct (%)99.3%
Missing915
Missing (%)18.2%
Memory size39.5 KiB
2023-12-13T02:31:32.480833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length21.31047
Min length21

Characters and Unicode

Total characters87927
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

Unique4077 ?
Unique (%)98.8%

Sample

1st row2005-04-01 오전 8:52:26
2nd row2005-01-24 오후 5:28:03
3rd row2005-04-01 오전 8:52:22
4th row2005-04-01 오전 8:52:38
5th row2005-04-01 오전 8:53:53
ValueCountFrequency (%)
오후 2668
 
21.6%
오전 1458
 
11.8%
2015-09-01 340
 
2.7%
2016-01-27 201
 
1.6%
2002-08-26 172
 
1.4%
2013-06-03 98
 
0.8%
2019-08-23 94
 
0.8%
2013-03-15 89
 
0.7%
2018-08-24 74
 
0.6%
2016-02-24 72
 
0.6%
Other values (4168) 7112
57.5%
2023-12-13T02:31:32.948005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13691
15.6%
2 11225
12.8%
1 9159
10.4%
- 8252
9.4%
8252
9.4%
: 8252
9.4%
오 4126
 
4.7%
3 3967
 
4.5%
5 3712
 
4.2%
4 3251
 
3.7%
Other values (6) 14040
16.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 54919
62.5%
Dash Punctuation 8252
 
9.4%
Space Separator 8252
 
9.4%
Other Punctuation 8252
 
9.4%
Other Letter 8252
 
9.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13691
24.9%
2 11225
20.4%
1 9159
16.7%
3 3967
 
7.2%
5 3712
 
6.8%
4 3251
 
5.9%
8 2955
 
5.4%
9 2459
 
4.5%
6 2446
 
4.5%
7 2054
 
3.7%
Other Letter
ValueCountFrequency (%)
오 4126
50.0%
후 2668
32.3%
전 1458
 
17.7%
Dash Punctuation
ValueCountFrequency (%)
- 8252
100.0%
Space Separator
ValueCountFrequency (%)
8252
100.0%
Other Punctuation
ValueCountFrequency (%)
: 8252
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79675
90.6%
Hangul 8252
 
9.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13691
17.2%
2 11225
14.1%
1 9159
11.5%
- 8252
10.4%
8252
10.4%
: 8252
10.4%
3 3967
 
5.0%
5 3712
 
4.7%
4 3251
 
4.1%
8 2955
 
3.7%
Other values (3) 6959
8.7%
Hangul
ValueCountFrequency (%)
오 4126
50.0%
후 2668
32.3%
전 1458
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79675
90.6%
Hangul 8252
 
9.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13691
17.2%
2 11225
14.1%
1 9159
11.5%
- 8252
10.4%
8252
10.4%
: 8252
10.4%
3 3967
 
5.0%
5 3712
 
4.7%
4 3251
 
4.1%
8 2955
 
3.7%
Other values (3) 6959
8.7%
Hangul
ValueCountFrequency (%)
오 4126
50.0%
후 2668
32.3%
전 1458
 
17.7%

Interactions

2023-12-13T02:31:25.109311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:22.402756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:22.962429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.498614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.112271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.653238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:25.189043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:22.494540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.061343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.585894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.189467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.735469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:25.272920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:22.582701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.145946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.682408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.266140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.808730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:25.354918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:22.673894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.238477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.775734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.381712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.880559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:25.424247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:22.760745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.321770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.894088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.479155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.948822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:25.503443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:22.865349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:23.408353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.009595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:24.555671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:31:25.027523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:31:33.050799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
학점실습과목구분이론시간실습시간개설년도개설학기폐기년도폐기학기등급구분개설학과코드개설이수구분개설학년
학점1.0000.2940.8030.9400.0000.1090.0000.4710.4790.3110.4730.185
실습과목구분0.2941.0000.5570.3210.0070.0180.0000.3590.1220.2990.6340.106
이론시간0.8030.5571.0000.3980.0000.0570.0000.0560.1150.2340.4950.247
실습시간0.9400.3210.3981.0000.0000.0690.0000.7240.4790.2210.4890.192
개설년도0.0000.0070.0000.0001.0001.0000.0000.0000.0000.0060.0390.010
개설학기0.1090.0180.0570.0691.0001.0000.0000.2250.0000.0430.1240.093
폐기년도0.0000.0000.0000.0000.0000.0001.000NaN0.0000.5250.1060.055
폐기학기0.4710.3590.0560.7240.0000.225NaN1.0000.1010.0000.3430.191
등급구분0.4790.1220.1150.4790.0000.0000.0000.1011.0000.0230.3970.092
개설학과코드0.3110.2990.2340.2210.0060.0430.5250.0000.0231.0000.7020.262
개설이수구분0.4730.6340.4950.4890.0390.1240.1060.3430.3970.7021.0000.564
개설학년0.1850.1060.2470.1920.0100.0930.0550.1910.0920.2620.5641.000
2023-12-13T02:31:33.179826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
실습과목구분개설학기등급구분개설학년폐기학기개설전공코드폐기년도
실습과목구분1.0000.0170.2020.0800.1271.0000.000
개설학기0.0171.0000.0000.0700.0711.0000.000
등급구분0.2020.0001.0000.1130.1671.0000.000
개설학년0.0800.0700.1131.0000.0591.0000.092
폐기학기0.1270.0710.1670.0591.0001.0001.000
개설전공코드1.0001.0001.0001.0001.0001.0001.000
폐기년도0.0000.0000.0000.0921.0001.0001.000
2023-12-13T02:31:33.289027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
학점이론시간실습시간개설년도개설학과코드개설이수구분실습과목구분개설학기폐기년도폐기학기등급구분개설전공코드개설학년
학점1.0000.2800.253-0.0650.0630.0400.1840.0650.0000.4040.3681.0000.078
이론시간0.2801.000-0.754-0.2900.080-0.2830.2930.0360.0000.0920.1151.0000.161
실습시간0.253-0.7541.0000.109-0.0960.1900.2040.0420.0000.4030.3671.0000.081
개설년도-0.065-0.2900.1091.0000.2180.9130.0121.0000.0000.0000.0001.0000.012
개설학과코드0.0630.080-0.0960.2181.0000.1970.1000.0410.3520.0000.0381.0000.204
개설이수구분0.040-0.2830.1900.9130.1971.0000.3520.0800.1750.2760.4961.0000.368
실습과목구분0.1840.2930.2040.0120.1000.3521.0000.0170.0000.1270.2021.0000.080
개설학기0.0650.0360.0421.0000.0410.0800.0171.0000.0000.0710.0001.0000.070
폐기년도0.0000.0000.0000.0000.3520.1750.0000.0001.0001.0000.0001.0000.092
폐기학기0.4040.0920.4030.0000.0000.2760.1270.0711.0001.0000.1671.0000.059
등급구분0.3680.1150.3670.0000.0380.4960.2020.0000.0000.1671.0001.0000.113
개설전공코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
개설학년0.0780.1610.0810.0120.2040.3680.0800.0700.0920.0590.1131.0001.000

Missing values

2023-12-13T02:31:25.845360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:31:26.039352image/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.
2023-12-13T02:31:26.209481image/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

과목과목명(한글)과목명(영문)학점실습과목구분이론시간실습시간개설년도개설학기폐기년도폐기학기등급구분대체과목1대체과목2대체과목3개설학과코드개설전공코드개설이수구분개설학년사용날짜,시간
0101102전산개론Introduction To Computer System3<NA>3019971009<NA><NA><NA>1000<NA>
1102101생활영어Practical English3<NA>3019971009<NA><NA><NA>2000<NA>
2103101생활영어Practical English3<NA>3019971009<NA><NA><NA>3000<NA>
3103102전산개론Introduction to Computation3<NA>3019971009<NA><NA><NA>3000<NA>
4104101생활영어Practical English2<NA>2019981009<NA><NA><NA>4000<NA>
5104102전산개론Introduction to Computer Theory3<NA>3019971009<NA><NA><NA>4000<NA>
6104105여가관리론Leisure Management3<NA>3019962009<NA><NA><NA>4000<NA>
7105101생활영어Living English3<NA>3019971009<NA><NA><NA>5000<NA>
8106101생활영어Practical English3<NA>3019971009<NA><NA><NA>6000<NA>
9106102전산개론Introduction to Computer System3<NA>3019971009<NA><NA><NA>6000<NA>
과목과목명(한글)과목명(영문)학점실습과목구분이론시간실습시간개설년도개설학기폐기년도폐기학기등급구분대체과목1대체과목2대체과목3개설학과코드개설전공코드개설이수구분개설학년사용날짜,시간
5031A04118e-대인관계능력<NA>111020202<NA><NA>P<NA><NA><NA>4<NA>5212020-08-31 오후 3:50:07
5032A04119소자본커피창업<NA>231220202<NA><NA>P<NA><NA><NA>4<NA>5422020-08-31 오후 3:50:39
5033C15903항공기도면실습<NA>220320202<NA><NA>9<NA><NA><NA>15<NA><NA>12020-08-31 오후 5:29:34
5034C15904항공기체실습Ⅰ<NA>220320202<NA><NA>9<NA><NA><NA>15<NA><NA>12020-08-31 오후 5:30:11
5035C15905왕복엔진작동실습Ⅰ<NA>120220202<NA><NA>9<NA><NA><NA>15<NA><NA>12020-08-31 오후 5:30:36
5036C14904비파괴검사<NA>112020202<NA><NA>9<NA><NA><NA>14<NA><NA>22020-08-31 오후 5:33:39
5037C14905가스터빈엔진정비실습Ⅱ<NA>120220202<NA><NA>9<NA><NA><NA>14<NA><NA>22020-08-31 오후 5:34:00
5038C14906항공기연료계통실습<NA>120220202<NA><NA>9<NA><NA><NA>14<NA><NA>22020-08-31 오후 5:34:27
5039C14907자동비행장치<NA>213020202<NA><NA>9<NA><NA><NA>14<NA><NA>22020-08-31 오후 5:34:48
5040A50055e대인관계능력(팀웍능력)Interpersonal Competency(Teamwork ability)111020202<NA><NA>9<NA><NA><NA>5<NA>5212020-09-01 오후 6:14:19