Overview

Dataset statistics

Number of variables20
Number of observations10000
Missing cells3966
Missing cells (%)2.0%
Duplicate rows11
Duplicate rows (%)0.1%
Total size in memory1.7 MiB
Average record size in memory180.0 B

Variable types

Text7
Categorical4
Numeric9

Dataset

Description나이스 대국민서비스의 학원서비스에서 대국민에게 공개된 울산광역시교육청 학원 및 교습소의 학원명, 교습정보, 정원, 교습비 등 종합정보 제공
Author울산광역시교육청
URLhttps://www.data.go.kr/data/15126426/fileData.do

Alerts

Dataset has 11 (0.1%) duplicate rowsDuplicates
기숙사비 is highly overall correlated with 모의고사비 and 1 other fieldsHigh correlation
교습계열 is highly overall correlated with 급식비High correlation
급식비 is highly overall correlated with 정원 and 11 other fieldsHigh correlation
피복비 is highly overall correlated with 급식비High correlation
정원 is highly overall correlated with 급식비High correlation
총교습시간(분) is highly overall correlated with 교습비 and 1 other fieldsHigh correlation
교습비 is highly overall correlated with 총교습시간(분) and 2 other fieldsHigh correlation
차량비 is highly overall correlated with 급식비High correlation
모의고사비 is highly overall correlated with 급식비 and 1 other fieldsHigh correlation
재료비 is highly overall correlated with 기타경비합계 and 1 other fieldsHigh correlation
기타경비합계 is highly overall correlated with 재료비 and 1 other fieldsHigh correlation
총교습비 is highly overall correlated with 교습비 and 1 other fieldsHigh correlation
강사수 is highly overall correlated with 급식비High correlation
피복비 is highly imbalanced (99.4%)Imbalance
기숙사비 is highly imbalanced (99.0%)Imbalance
전화번호 has 3681 (36.8%) missing valuesMissing
교습과정 has 225 (2.2%) missing valuesMissing
정원 is highly skewed (γ1 = 86.20451233)Skewed
총교습시간(분) is highly skewed (γ1 = 92.23896889)Skewed
차량비 is highly skewed (γ1 = 57.95399583)Skewed
모의고사비 is highly skewed (γ1 = 48.79724754)Skewed
총교습시간(분) has 215 (2.1%) zerosZeros
차량비 has 9985 (99.9%) zerosZeros
모의고사비 has 9973 (99.7%) zerosZeros
재료비 has 9842 (98.4%) zerosZeros
기타경비합계 has 9808 (98.1%) zerosZeros
총교습비 has 1818 (18.2%) zerosZeros
강사수 has 446 (4.5%) zerosZeros

Reproduction

Analysis started2024-03-14 17:14:59.374958
Analysis finished2024-03-14 17:15:27.564382
Duration28.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

학원명
Text

Distinct2474
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T02:15:28.658691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length27
Mean length9.5259
Min length3

Characters and Unicode

Total characters95259
Distinct characters687
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique523 ?
Unique (%)5.2%

Sample

1st row언양한솔학원
2nd row유알(UR)칸타빌레음악학원
3rd row이가자뷰티아카데미미용학원
4th row박쌤수학학원
5th row구영유앤아이영어학원
ValueCountFrequency (%)
남외점와와학습코칭학원 63
 
0.6%
반석성균관학원 55
 
0.5%
한솔커피바리스타제과제빵요리학원 47
 
0.5%
송정점와와학습코칭학원 46
 
0.5%
에스비에스(sbs)아카데미컴퓨터아트학원 42
 
0.4%
태화학원 38
 
0.4%
애니쿤만화학원 35
 
0.3%
미래직업기술학원 30
 
0.3%
눈높이러닝센터신정학원 29
 
0.3%
주)엠플러스단과학원 28
 
0.3%
Other values (2484) 9646
95.9%
2024-03-15T02:15:30.365292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
학 12101
 
12.7%
원 9921
 
10.4%
이 2439
 
2.6%
수 2217
 
2.3%
스 2060
 
2.2%
어 2014
 
2.1%
영 1466
 
1.5%
미 1405
 
1.5%
전 1353
 
1.4%
터 1310
 
1.4%
Other values (677) 58973
61.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89921
94.4%
Uppercase Letter 2013
 
2.1%
Lowercase Letter 1207
 
1.3%
Close Punctuation 779
 
0.8%
Open Punctuation 779
 
0.8%
Decimal Number 355
 
0.4%
Other Punctuation 107
 
0.1%
Space Separator 79
 
0.1%
Dash Punctuation 11
 
< 0.1%
Math Symbol 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 12101
 
13.5%
원 9921
 
11.0%
이 2439
 
2.7%
수 2217
 
2.5%
스 2060
 
2.3%
어 2014
 
2.2%
영 1466
 
1.6%
미 1405
 
1.6%
전 1353
 
1.5%
터 1310
 
1.5%
Other values (606) 53635
59.6%
Uppercase Letter
ValueCountFrequency (%)
S 276
13.7%
E 183
 
9.1%
N 138
 
6.9%
A 135
 
6.7%
B 127
 
6.3%
T 124
 
6.2%
C 111
 
5.5%
M 106
 
5.3%
D 87
 
4.3%
G 86
 
4.3%
Other values (15) 640
31.8%
Lowercase Letter
ValueCountFrequency (%)
e 175
14.5%
i 140
11.6%
a 104
 
8.6%
h 85
 
7.0%
s 85
 
7.0%
n 82
 
6.8%
t 58
 
4.8%
l 56
 
4.6%
c 56
 
4.6%
r 49
 
4.1%
Other values (13) 317
26.3%
Decimal Number
ValueCountFrequency (%)
1 105
29.6%
3 70
19.7%
2 67
18.9%
5 49
13.8%
0 30
 
8.5%
6 12
 
3.4%
7 8
 
2.3%
4 7
 
2.0%
8 4
 
1.1%
9 3
 
0.8%
Other Punctuation
ValueCountFrequency (%)
& 60
56.1%
· 16
 
15.0%
. 9
 
8.4%
! 7
 
6.5%
' 6
 
5.6%
/ 5
 
4.7%
: 4
 
3.7%
Close Punctuation
ValueCountFrequency (%)
) 779
100.0%
Open Punctuation
ValueCountFrequency (%)
( 779
100.0%
Space Separator
ValueCountFrequency (%)
79
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89908
94.4%
Latin 3220
 
3.4%
Common 2118
 
2.2%
Han 13
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 12101
 
13.5%
원 9921
 
11.0%
이 2439
 
2.7%
수 2217
 
2.5%
스 2060
 
2.3%
어 2014
 
2.2%
영 1466
 
1.6%
미 1405
 
1.6%
전 1353
 
1.5%
터 1310
 
1.5%
Other values (605) 53622
59.6%
Latin
ValueCountFrequency (%)
S 276
 
8.6%
E 183
 
5.7%
e 175
 
5.4%
i 140
 
4.3%
N 138
 
4.3%
A 135
 
4.2%
B 127
 
3.9%
T 124
 
3.9%
C 111
 
3.4%
M 106
 
3.3%
Other values (38) 1705
53.0%
Common
ValueCountFrequency (%)
) 779
36.8%
( 779
36.8%
1 105
 
5.0%
79
 
3.7%
3 70
 
3.3%
2 67
 
3.2%
& 60
 
2.8%
5 49
 
2.3%
0 30
 
1.4%
· 16
 
0.8%
Other values (13) 84
 
4.0%
Han
ValueCountFrequency (%)
秀 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89908
94.4%
ASCII 5322
 
5.6%
None 16
 
< 0.1%
CJK 13
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
학 12101
 
13.5%
원 9921
 
11.0%
이 2439
 
2.7%
수 2217
 
2.5%
스 2060
 
2.3%
어 2014
 
2.2%
영 1466
 
1.6%
미 1405
 
1.6%
전 1353
 
1.5%
터 1310
 
1.5%
Other values (605) 53622
59.6%
ASCII
ValueCountFrequency (%)
) 779
 
14.6%
( 779
 
14.6%
S 276
 
5.2%
E 183
 
3.4%
e 175
 
3.3%
i 140
 
2.6%
N 138
 
2.6%
A 135
 
2.5%
B 127
 
2.4%
T 124
 
2.3%
Other values (60) 2466
46.3%
None
ValueCountFrequency (%)
· 16
100.0%
CJK
ValueCountFrequency (%)
秀 13
100.0%

학원주소
Text

Distinct2467
Distinct (%)24.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T02:15:31.680818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length81
Median length60
Mean length32.1113
Min length18

Characters and Unicode

Total characters321113
Distinct characters383
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

Unique516 ?
Unique (%)5.2%

Sample

1st row울산광역시 울주군 언양읍 방천7길 27 , 2층 (언양읍)
2nd row울산광역시 중구 고복수길 6 , 1층 (서동)
3rd row울산광역시 남구 삼산로 254 , 18층 (달동)
4th row울산광역시 남구 돋질로 273 (삼산동) 2층
5th row울산광역시 울주군 범서읍 점촌3길 8-11 (범서읍)
ValueCountFrequency (%)
울산광역시 9951
 
13.4%
8711
 
11.7%
남구 3873
 
5.2%
2층 2969
 
4.0%
북구 2104
 
2.8%
3층 1763
 
2.4%
중구 1718
 
2.3%
범서읍 1648
 
2.2%
울주군 1443
 
1.9%
일부 917
 
1.2%
Other values (2019) 39207
52.8%
2024-03-15T02:15:33.582884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65340
20.3%
산 12880
 
4.0%
, 11601
 
3.6%
울 11587
 
3.6%
) 10824
 
3.4%
( 10822
 
3.4%
동 10579
 
3.3%
2 10358
 
3.2%
시 10086
 
3.1%
역 9970
 
3.1%
Other values (373) 157066
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 170559
53.1%
Space Separator 65340
 
20.3%
Decimal Number 50034
 
15.6%
Other Punctuation 11652
 
3.6%
Close Punctuation 10824
 
3.4%
Open Punctuation 10822
 
3.4%
Dash Punctuation 1450
 
0.5%
Uppercase Letter 178
 
0.1%
Lowercase Letter 123
 
< 0.1%
Math Symbol 74
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 12880
 
7.6%
울 11587
 
6.8%
동 10579
 
6.2%
시 10086
 
5.9%
역 9970
 
5.8%
광 9964
 
5.8%
구 9227
 
5.4%
층 8153
 
4.8%
로 6968
 
4.1%
길 4856
 
2.8%
Other values (329) 76289
44.7%
Uppercase Letter
ValueCountFrequency (%)
H 48
27.0%
B 37
20.8%
A 22
12.4%
M 19
 
10.7%
C 14
 
7.9%
W 10
 
5.6%
G 6
 
3.4%
F 4
 
2.2%
S 4
 
2.2%
Q 3
 
1.7%
Other values (5) 11
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 10358
20.7%
1 9656
19.3%
3 7424
14.8%
4 5040
10.1%
0 4659
9.3%
5 3865
 
7.7%
6 2953
 
5.9%
7 2241
 
4.5%
8 1996
 
4.0%
9 1842
 
3.7%
Lowercase Letter
ValueCountFrequency (%)
a 39
31.7%
b 26
21.1%
l 16
13.0%
p 15
 
12.2%
z 15
 
12.2%
e 9
 
7.3%
s 2
 
1.6%
r 1
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 11601
99.6%
. 20
 
0.2%
@ 17
 
0.1%
/ 8
 
0.1%
· 6
 
0.1%
Space Separator
ValueCountFrequency (%)
65340
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10824
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10822
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1450
100.0%
Math Symbol
ValueCountFrequency (%)
~ 74
100.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 170559
53.1%
Common 150196
46.8%
Latin 358
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 12880
 
7.6%
울 11587
 
6.8%
동 10579
 
6.2%
시 10086
 
5.9%
역 9970
 
5.8%
광 9964
 
5.8%
구 9227
 
5.4%
층 8153
 
4.8%
로 6968
 
4.1%
길 4856
 
2.8%
Other values (329) 76289
44.7%
Latin
ValueCountFrequency (%)
Ⅰ 57
15.9%
H 48
13.4%
a 39
10.9%
B 37
10.3%
b 26
7.3%
A 22
 
6.1%
M 19
 
5.3%
l 16
 
4.5%
p 15
 
4.2%
z 15
 
4.2%
Other values (14) 64
17.9%
Common
ValueCountFrequency (%)
65340
43.5%
, 11601
 
7.7%
) 10824
 
7.2%
( 10822
 
7.2%
2 10358
 
6.9%
1 9656
 
6.4%
3 7424
 
4.9%
4 5040
 
3.4%
0 4659
 
3.1%
5 3865
 
2.6%
Other values (10) 10607
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 170555
53.1%
ASCII 150491
46.9%
Number Forms 57
 
< 0.1%
None 6
 
< 0.1%
Compat Jamo 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65340
43.4%
, 11601
 
7.7%
) 10824
 
7.2%
( 10822
 
7.2%
2 10358
 
6.9%
1 9656
 
6.4%
3 7424
 
4.9%
4 5040
 
3.3%
0 4659
 
3.1%
5 3865
 
2.6%
Other values (32) 10902
 
7.2%
Hangul
ValueCountFrequency (%)
산 12880
 
7.6%
울 11587
 
6.8%
동 10579
 
6.2%
시 10086
 
5.9%
역 9970
 
5.8%
광 9964
 
5.8%
구 9227
 
5.4%
층 8153
 
4.8%
로 6968
 
4.1%
길 4856
 
2.8%
Other values (328) 76285
44.7%
Number Forms
ValueCountFrequency (%)
Ⅰ 57
100.0%
None
ValueCountFrequency (%)
· 6
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
100.0%

설립자-성명
Text

Distinct2089
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T02:15:34.846420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length3
Mean length4.2469
Min length2

Characters and Unicode

Total characters42469
Distinct characters362
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique399 ?
Unique (%)4.0%

Sample

1st row장주희
2nd row최유라
3rd row이나윤
4th row박은정
5th row최성혜
ValueCountFrequency (%)
주식회사 921
 
8.4%
대교 615
 
5.6%
주)웅진씽크빅 270
 
2.5%
주)동화세상에듀코 160
 
1.5%
김동욱 63
 
0.6%
이원규 55
 
0.5%
주)엠플러스단과학원 50
 
0.5%
더숲국어전문학원 49
 
0.4%
주)한솔요리학원 47
 
0.4%
주식회사에스씨에이아카데미울산 42
 
0.4%
Other values (2085) 8660
79.2%
2024-03-15T02:15:36.260565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 2169
 
5.1%
김 2090
 
4.9%
이 1494
 
3.5%
정 1244
 
2.9%
식 1104
 
2.6%
사 1029
 
2.4%
회 1027
 
2.4%
932
 
2.2%
영 897
 
2.1%
진 875
 
2.1%
Other values (352) 29608
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 38896
91.6%
Space Separator 932
 
2.2%
Close Punctuation 779
 
1.8%
Open Punctuation 724
 
1.7%
Uppercase Letter 698
 
1.6%
Other Punctuation 440
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 2169
 
5.6%
김 2090
 
5.4%
이 1494
 
3.8%
정 1244
 
3.2%
식 1104
 
2.8%
사 1029
 
2.6%
회 1027
 
2.6%
영 897
 
2.3%
진 875
 
2.2%
박 858
 
2.2%
Other values (324) 26109
67.1%
Uppercase Letter
ValueCountFrequency (%)
E 119
17.0%
A 102
14.6%
N 99
14.2%
L 60
8.6%
R 60
8.6%
D 44
 
6.3%
M 30
 
4.3%
K 30
 
4.3%
X 25
 
3.6%
W 21
 
3.0%
Other values (12) 108
15.5%
Other Punctuation
ValueCountFrequency (%)
, 438
99.5%
. 1
 
0.2%
& 1
 
0.2%
Space Separator
ValueCountFrequency (%)
932
100.0%
Close Punctuation
ValueCountFrequency (%)
) 779
100.0%
Open Punctuation
ValueCountFrequency (%)
( 724
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 38896
91.6%
Common 2875
 
6.8%
Latin 698
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 2169
 
5.6%
김 2090
 
5.4%
이 1494
 
3.8%
정 1244
 
3.2%
식 1104
 
2.8%
사 1029
 
2.6%
회 1027
 
2.6%
영 897
 
2.3%
진 875
 
2.2%
박 858
 
2.2%
Other values (324) 26109
67.1%
Latin
ValueCountFrequency (%)
E 119
17.0%
A 102
14.6%
N 99
14.2%
L 60
8.6%
R 60
8.6%
D 44
 
6.3%
M 30
 
4.3%
K 30
 
4.3%
X 25
 
3.6%
W 21
 
3.0%
Other values (12) 108
15.5%
Common
ValueCountFrequency (%)
932
32.4%
) 779
27.1%
( 724
25.2%
, 438
15.2%
. 1
 
< 0.1%
& 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 38896
91.6%
ASCII 3573
 
8.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 2169
 
5.6%
김 2090
 
5.4%
이 1494
 
3.8%
정 1244
 
3.2%
식 1104
 
2.8%
사 1029
 
2.6%
회 1027
 
2.6%
영 897
 
2.3%
진 875
 
2.2%
박 858
 
2.2%
Other values (324) 26109
67.1%
ASCII
ValueCountFrequency (%)
932
26.1%
) 779
21.8%
( 724
20.3%
, 438
12.3%
E 119
 
3.3%
A 102
 
2.9%
N 99
 
2.8%
L 60
 
1.7%
R 60
 
1.7%
D 44
 
1.2%
Other values (18) 216
 
6.0%

전화번호
Text

MISSING 

Distinct1500
Distinct (%)23.7%
Missing3681
Missing (%)36.8%
Memory size156.2 KiB
2024-03-15T02:15:37.121971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.010128
Min length11

Characters and Unicode

Total characters75892
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)4.8%

Sample

1st row052-258-1318
2nd row052-293-5650
3rd row052-265-5804
4th row052-249-2716
5th row052-233-2321
ValueCountFrequency (%)
052-266-9090 89
 
1.4%
052-276-6080 63
 
1.0%
052-973-2500 47
 
0.7%
052-276-6146 46
 
0.7%
052-922-8555 42
 
0.7%
052-267-8502 39
 
0.6%
052-243-0810 38
 
0.6%
052-246-1661 35
 
0.6%
052-294-8760 28
 
0.4%
052-700-1008 28
 
0.4%
Other values (1490) 5864
92.8%
2024-03-15T02:15:38.377710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 15729
20.7%
- 12638
16.7%
0 11745
15.5%
5 10058
13.3%
6 4212
 
5.5%
9 4192
 
5.5%
7 3823
 
5.0%
1 3803
 
5.0%
8 3618
 
4.8%
4 3098
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63254
83.3%
Dash Punctuation 12638
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15729
24.9%
0 11745
18.6%
5 10058
15.9%
6 4212
 
6.7%
9 4192
 
6.6%
7 3823
 
6.0%
1 3803
 
6.0%
8 3618
 
5.7%
4 3098
 
4.9%
3 2976
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 12638
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 75892
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15729
20.7%
- 12638
16.7%
0 11745
15.5%
5 10058
13.3%
6 4212
 
5.5%
9 4192
 
5.5%
7 3823
 
5.0%
1 3803
 
5.0%
8 3618
 
4.8%
4 3098
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 75892
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15729
20.7%
- 12638
16.7%
0 11745
15.5%
5 10058
13.3%
6 4212
 
5.5%
9 4192
 
5.5%
7 3823
 
5.0%
1 3803
 
5.0%
8 3618
 
4.8%
4 3098
 
4.1%

교습계열
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
보통교과
5379 
예능(중)
1947 
외국어
848 
산업응용기술
 
459
독서실
 
368
Other values (14)
999 

Length

Max length7
Median length4
Mean length4.2351
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row보통교과
2nd row예능(중)
3rd row산업응용기술
4th row<NA>
5th row외국어

Common Values

ValueCountFrequency (%)
보통교과 5379
53.8%
예능(중) 1947
 
19.5%
외국어 848
 
8.5%
산업응용기술 459
 
4.6%
독서실 368
 
3.7%
기타(중) 232
 
2.3%
<NA> 213
 
2.1%
산업기반기술 130
 
1.3%
기예(중) 127
 
1.3%
컴퓨터 117
 
1.2%
Other values (9) 180
 
1.8%

Length

2024-03-15T02:15:38.808810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
보통교과 5379
53.8%
예능(중 1947
 
19.5%
외국어 848
 
8.5%
산업응용기술 459
 
4.6%
독서실 368
 
3.7%
기타(중 232
 
2.3%
na 213
 
2.1%
산업기반기술 130
 
1.3%
기예(중 127
 
1.3%
컴퓨터 117
 
1.2%
Other values (9) 180
 
1.8%

교습과정
Text

MISSING 

Distinct63
Distinct (%)0.6%
Missing225
Missing (%)2.2%
Memory size156.2 KiB
2024-03-15T02:15:39.595068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length2
Mean length4.2189258
Min length2

Characters and Unicode

Total characters41240
Distinct characters116
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row보습
2nd row음악
3rd row이·미용
4th row실용외국어(유아/초·중·고)
5th row보습
ValueCountFrequency (%)
보습 4996
51.1%
음악 1093
 
11.2%
실용외국어(유아/초·중·고 846
 
8.7%
미술 707
 
7.2%
독서실(유아/초·중·고 368
 
3.8%
입시 353
 
3.6%
식음료품(바리스타,소믈리에 226
 
2.3%
이·미용 191
 
2.0%
무용 142
 
1.5%
기타(소 114
 
1.2%
Other values (53) 739
 
7.6%
2024-03-15T02:15:40.535657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
보 5101
 
12.4%
습 5001
 
12.1%
· 2636
 
6.4%
) 1765
 
4.3%
( 1765
 
4.3%
음 1385
 
3.4%
실 1282
 
3.1%
고 1281
 
3.1%
용 1256
 
3.0%
중 1221
 
3.0%
Other values (106) 18547
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33368
80.9%
Other Punctuation 4342
 
10.5%
Close Punctuation 1765
 
4.3%
Open Punctuation 1765
 
4.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
보 5101
 
15.3%
습 5001
 
15.0%
음 1385
 
4.2%
실 1282
 
3.8%
고 1281
 
3.8%
용 1256
 
3.8%
중 1221
 
3.7%
악 1219
 
3.7%
유 1215
 
3.6%
초 1214
 
3.6%
Other values (101) 13193
39.5%
Other Punctuation
ValueCountFrequency (%)
· 2636
60.7%
/ 1214
28.0%
, 492
 
11.3%
Close Punctuation
ValueCountFrequency (%)
) 1765
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33368
80.9%
Common 7872
 
19.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
보 5101
 
15.3%
습 5001
 
15.0%
음 1385
 
4.2%
실 1282
 
3.8%
고 1281
 
3.8%
용 1256
 
3.8%
중 1221
 
3.7%
악 1219
 
3.7%
유 1215
 
3.6%
초 1214
 
3.6%
Other values (101) 13193
39.5%
Common
ValueCountFrequency (%)
· 2636
33.5%
) 1765
22.4%
( 1765
22.4%
/ 1214
15.4%
, 492
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33368
80.9%
ASCII 5236
 
12.7%
None 2636
 
6.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
보 5101
 
15.3%
습 5001
 
15.0%
음 1385
 
4.2%
실 1282
 
3.8%
고 1281
 
3.8%
용 1256
 
3.8%
중 1221
 
3.7%
악 1219
 
3.7%
유 1215
 
3.6%
초 1214
 
3.6%
Other values (101) 13193
39.5%
None
ValueCountFrequency (%)
· 2636
100.0%
ASCII
ValueCountFrequency (%)
) 1765
33.7%
( 1765
33.7%
/ 1214
23.2%
, 492
 
9.4%

교습과목(반)
Text

Distinct5089
Distinct (%)50.9%
Missing6
Missing (%)0.1%
Memory size156.2 KiB
2024-03-15T02:15:41.492128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length31
Mean length6.5770462
Min length1

Characters and Unicode

Total characters65731
Distinct characters574
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4106 ?
Unique (%)41.1%

Sample

1st row수학(고등1)
2nd row중급취미반
3rd row속눈썹실무
4th row중등수학4
5th row중급(중등)
ValueCountFrequency (%)
중등수학 170
 
1.5%
초등수학 141
 
1.3%
초등 141
 
1.3%
초급 133
 
1.2%
고급 128
 
1.2%
중등 122
 
1.1%
중급 121
 
1.1%
고등수학 111
 
1.0%
중등영어 98
 
0.9%
고등영어 96
 
0.9%
Other values (4885) 9802
88.6%
2024-03-15T02:15:42.935094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
등 5101
 
7.8%
) 3733
 
5.7%
( 3643
 
5.5%
고 2984
 
4.5%
중 2901
 
4.4%
초 2864
 
4.4%
학 2813
 
4.3%
급 2557
 
3.9%
어 2351
 
3.6%
수 2259
 
3.4%
Other values (564) 34525
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 49129
74.7%
Decimal Number 3993
 
6.1%
Close Punctuation 3735
 
5.7%
Open Punctuation 3645
 
5.5%
Uppercase Letter 2014
 
3.1%
Other Punctuation 1256
 
1.9%
Space Separator 1082
 
1.6%
Lowercase Letter 370
 
0.6%
Dash Punctuation 220
 
0.3%
Math Symbol 118
 
0.2%
Other values (4) 169
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
등 5101
 
10.4%
고 2984
 
6.1%
중 2901
 
5.9%
초 2864
 
5.8%
학 2813
 
5.7%
급 2557
 
5.2%
어 2351
 
4.8%
수 2259
 
4.6%
영 1788
 
3.6%
과 1139
 
2.3%
Other values (483) 22372
45.5%
Uppercase Letter
ValueCountFrequency (%)
A 486
24.1%
B 439
21.8%
C 250
12.4%
E 133
 
6.6%
D 117
 
5.8%
T 90
 
4.5%
I 76
 
3.8%
M 61
 
3.0%
S 48
 
2.4%
G 46
 
2.3%
Other values (14) 268
13.3%
Lowercase Letter
ValueCountFrequency (%)
e 41
 
11.1%
i 31
 
8.4%
o 30
 
8.1%
a 30
 
8.1%
v 24
 
6.5%
c 24
 
6.5%
s 24
 
6.5%
n 21
 
5.7%
t 20
 
5.4%
p 19
 
5.1%
Other values (13) 106
28.6%
Decimal Number
ValueCountFrequency (%)
1 1350
33.8%
2 1050
26.3%
3 672
16.8%
4 283
 
7.1%
5 229
 
5.7%
6 144
 
3.6%
0 113
 
2.8%
7 65
 
1.6%
8 49
 
1.2%
9 38
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 1102
87.7%
. 70
 
5.6%
/ 63
 
5.0%
& 7
 
0.6%
: 7
 
0.6%
· 5
 
0.4%
! 1
 
0.1%
% 1
 
0.1%
Letter Number
ValueCountFrequency (%)
Ⅱ 42
67.7%
Ⅰ 17
27.4%
Ⅲ 2
 
3.2%
Ⅳ 1
 
1.6%
Close Punctuation
ValueCountFrequency (%)
) 3733
99.9%
] 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 3643
99.9%
[ 2
 
0.1%
Math Symbol
ValueCountFrequency (%)
+ 75
63.6%
~ 43
36.4%
Other Number
ValueCountFrequency (%)
① 1
50.0%
② 1
50.0%
Space Separator
ValueCountFrequency (%)
1082
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 220
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 100
100.0%
Control
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 49129
74.7%
Common 14156
 
21.5%
Latin 2446
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
등 5101
 
10.4%
고 2984
 
6.1%
중 2901
 
5.9%
초 2864
 
5.8%
학 2813
 
5.7%
급 2557
 
5.2%
어 2351
 
4.8%
수 2259
 
4.6%
영 1788
 
3.6%
과 1139
 
2.3%
Other values (483) 22372
45.5%
Latin
ValueCountFrequency (%)
A 486
19.9%
B 439
17.9%
C 250
 
10.2%
E 133
 
5.4%
D 117
 
4.8%
T 90
 
3.7%
I 76
 
3.1%
M 61
 
2.5%
S 48
 
2.0%
G 46
 
1.9%
Other values (41) 700
28.6%
Common
ValueCountFrequency (%)
) 3733
26.4%
( 3643
25.7%
1 1350
 
9.5%
, 1102
 
7.8%
1082
 
7.6%
2 1050
 
7.4%
3 672
 
4.7%
4 283
 
2.0%
5 229
 
1.6%
- 220
 
1.6%
Other values (20) 792
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 49124
74.7%
ASCII 16533
 
25.2%
Number Forms 62
 
0.1%
None 5
 
< 0.1%
Compat Jamo 5
 
< 0.1%
Enclosed Alphanum 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
등 5101
 
10.4%
고 2984
 
6.1%
중 2901
 
5.9%
초 2864
 
5.8%
학 2813
 
5.7%
급 2557
 
5.2%
어 2351
 
4.8%
수 2259
 
4.6%
영 1788
 
3.6%
과 1139
 
2.3%
Other values (481) 22367
45.5%
ASCII
ValueCountFrequency (%)
) 3733
22.6%
( 3643
22.0%
1 1350
 
8.2%
, 1102
 
6.7%
1082
 
6.5%
2 1050
 
6.4%
3 672
 
4.1%
A 486
 
2.9%
B 439
 
2.7%
4 283
 
1.7%
Other values (64) 2693
16.3%
Number Forms
ValueCountFrequency (%)
Ⅱ 42
67.7%
Ⅰ 17
27.4%
Ⅲ 2
 
3.2%
Ⅳ 1
 
1.6%
None
ValueCountFrequency (%)
· 5
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 4
80.0%
ㅅ 1
 
20.0%
Enclosed Alphanum
ValueCountFrequency (%)
① 1
50.0%
② 1
50.0%

정원
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct158
Distinct (%)1.6%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean22.909846
Minimum0
Maximum9999
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:15:43.335572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median14
Q327
95-th percentile62
Maximum9999
Range9999
Interquartile range (IQR)19

Descriptive statistics

Standard deviation105.22575
Coefficient of variation (CV)4.593036
Kurtosis8103.4763
Mean22.909846
Median Absolute Deviation (MAD)8
Skewness86.204512
Sum228961
Variance11072.458
MonotonicityNot monotonic
2024-03-15T02:15:43.686274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1267
 
12.7%
20 609
 
6.1%
7 437
 
4.4%
5 434
 
4.3%
8 376
 
3.8%
6 366
 
3.7%
9 346
 
3.5%
30 311
 
3.1%
15 309
 
3.1%
12 299
 
3.0%
Other values (148) 5240
52.4%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 285
2.9%
2 222
2.2%
3 274
2.7%
4 210
2.1%
5 434
4.3%
6 366
3.7%
7 437
4.4%
8 376
3.8%
9 346
3.5%
ValueCountFrequency (%)
9999 1
< 0.1%
2150 1
< 0.1%
710 1
< 0.1%
500 1
< 0.1%
399 1
< 0.1%
374 1
< 0.1%
373 1
< 0.1%
318 1
< 0.1%
284 1
< 0.1%
264 2
< 0.1%

교습기간
Text

Distinct80
Distinct (%)0.8%
Missing6
Missing (%)0.1%
Memory size156.2 KiB
2024-03-15T02:15:44.501053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0240144
Min length5

Characters and Unicode

Total characters50210
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)0.3%

Sample

1st row1개월0일
2nd row1개월0일
3rd row0개월8일
4th row1개월0일
5th row1개월0일
ValueCountFrequency (%)
1개월0일 9199
92.0%
0개월0일 257
 
2.6%
0개월1일 156
 
1.6%
0개월20일 38
 
0.4%
0개월10일 32
 
0.3%
0개월8일 25
 
0.3%
2개월0일 22
 
0.2%
0개월16일 22
 
0.2%
0개월7일 17
 
0.2%
0개월2일 15
 
0.2%
Other values (70) 211
 
2.1%
2024-03-15T02:15:46.075158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10336
20.6%
개 9994
19.9%
월 9994
19.9%
일 9994
19.9%
1 9511
18.9%
2 130
 
0.3%
6 52
 
0.1%
4 44
 
0.1%
3 43
 
0.1%
8 36
 
0.1%
Other values (3) 76
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29982
59.7%
Decimal Number 20228
40.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10336
51.1%
1 9511
47.0%
2 130
 
0.6%
6 52
 
0.3%
4 44
 
0.2%
3 43
 
0.2%
8 36
 
0.2%
5 34
 
0.2%
7 27
 
0.1%
9 15
 
0.1%
Other Letter
ValueCountFrequency (%)
개 9994
33.3%
월 9994
33.3%
일 9994
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29982
59.7%
Common 20228
40.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10336
51.1%
1 9511
47.0%
2 130
 
0.6%
6 52
 
0.3%
4 44
 
0.2%
3 43
 
0.2%
8 36
 
0.2%
5 34
 
0.2%
7 27
 
0.1%
9 15
 
0.1%
Hangul
ValueCountFrequency (%)
개 9994
33.3%
월 9994
33.3%
일 9994
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29982
59.7%
ASCII 20228
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10336
51.1%
1 9511
47.0%
2 130
 
0.6%
6 52
 
0.3%
4 44
 
0.2%
3 43
 
0.2%
8 36
 
0.2%
5 34
 
0.2%
7 27
 
0.1%
9 15
 
0.1%
Hangul
ValueCountFrequency (%)
개 9994
33.3%
월 9994
33.3%
일 9994
33.3%

총교습시간(분)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct844
Distinct (%)8.4%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4310.0504
Minimum0
Maximum20200205
Zeros215
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:15:46.684785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile260
Q1869
median1275
Q31564
95-th percentile3840
Maximum20200205
Range20200205
Interquartile range (IQR)695

Descriptive statistics

Standard deviation208713.67
Coefficient of variation (CV)48.424878
Kurtosis8814.1943
Mean4310.0504
Median Absolute Deviation (MAD)363
Skewness92.238969
Sum43074644
Variance4.3561394 × 1010
MonotonicityNot monotonic
2024-03-15T02:15:47.276890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1303 946
 
9.5%
1564 475
 
4.8%
1275 440
 
4.4%
260 436
 
4.4%
1173 366
 
3.7%
1042 350
 
3.5%
521 243
 
2.4%
782 222
 
2.2%
0 215
 
2.1%
1955 213
 
2.1%
Other values (834) 6088
60.9%
ValueCountFrequency (%)
0 215
2.1%
1 1
 
< 0.1%
40 1
 
< 0.1%
50 6
 
0.1%
60 13
 
0.1%
80 1
 
< 0.1%
95 1
 
< 0.1%
100 3
 
< 0.1%
117 1
 
< 0.1%
120 21
 
0.2%
ValueCountFrequency (%)
20200205 1
< 0.1%
5212607 1
< 0.1%
170000 1
< 0.1%
160000 1
< 0.1%
150000 1
< 0.1%
140000 1
< 0.1%
130000 1
< 0.1%
127300 1
< 0.1%
115000 1
< 0.1%
98300 1
< 0.1%

교습비
Real number (ℝ)

HIGH CORRELATION 

Distinct536
Distinct (%)5.4%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean263915.08
Minimum0
Maximum19900000
Zeros9
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:15:47.708089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile38000
Q1130000
median200000
Q3300000
95-th percentile600000
Maximum19900000
Range19900000
Interquartile range (IQR)170000

Descriptive statistics

Standard deviation412847.7
Coefficient of variation (CV)1.5643203
Kurtosis591.89799
Mean263915.08
Median Absolute Deviation (MAD)80000
Skewness17.214596
Sum2.6375673 × 109
Variance1.7044323 × 1011
MonotonicityNot monotonic
2024-03-15T02:15:48.103245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 618
 
6.2%
150000 525
 
5.2%
250000 502
 
5.0%
300000 454
 
4.5%
180000 335
 
3.4%
140000 298
 
3.0%
120000 294
 
2.9%
220000 279
 
2.8%
100000 278
 
2.8%
160000 266
 
2.7%
Other values (526) 6145
61.5%
ValueCountFrequency (%)
0 9
0.1%
1520 1
 
< 0.1%
2000 1
 
< 0.1%
3250 1
 
< 0.1%
3700 1
 
< 0.1%
4000 1
 
< 0.1%
4700 1
 
< 0.1%
5000 2
 
< 0.1%
5400 2
 
< 0.1%
6000 1
 
< 0.1%
ValueCountFrequency (%)
19900000 1
< 0.1%
8350000 1
< 0.1%
7911680 1
< 0.1%
7480200 1
< 0.1%
7200000 1
< 0.1%
6000000 1
< 0.1%
5678400 2
< 0.1%
5522160 1
< 0.1%
5006610 1
< 0.1%
5000000 1
< 0.1%

피복비
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9990 
<NA>
 
6
496000
 
2
530000
 
1
253800
 
1

Length

Max length6
Median length1
Mean length1.0038
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 9990
99.9%
<NA> 6
 
0.1%
496000 2
 
< 0.1%
530000 1
 
< 0.1%
253800 1
 
< 0.1%

Length

2024-03-15T02:15:48.653143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:15:48.970146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9990
99.9%
na 6
 
0.1%
496000 2
 
< 0.1%
530000 1
 
< 0.1%
253800 1
 
< 0.1%

차량비
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8
Distinct (%)0.1%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean30.778467
Minimum0
Maximum100000
Zeros9985
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:15:49.356569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100000
Range100000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1288.7016
Coefficient of variation (CV)41.870233
Kurtosis3971.7682
Mean30.778467
Median Absolute Deviation (MAD)0
Skewness57.953996
Sum307600
Variance1660751.8
MonotonicityNot monotonic
2024-03-15T02:15:49.735108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 9985
99.9%
30000 3
 
< 0.1%
30600 1
 
< 0.1%
50000 1
 
< 0.1%
10000 1
 
< 0.1%
100000 1
 
< 0.1%
12000 1
 
< 0.1%
15000 1
 
< 0.1%
(Missing) 6
 
0.1%
ValueCountFrequency (%)
0 9985
99.9%
10000 1
 
< 0.1%
12000 1
 
< 0.1%
15000 1
 
< 0.1%
30000 3
 
< 0.1%
30600 1
 
< 0.1%
50000 1
 
< 0.1%
100000 1
 
< 0.1%
ValueCountFrequency (%)
100000 1
 
< 0.1%
50000 1
 
< 0.1%
30600 1
 
< 0.1%
30000 3
 
< 0.1%
15000 1
 
< 0.1%
12000 1
 
< 0.1%
10000 1
 
< 0.1%
0 9985
99.9%

모의고사비
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct8
Distinct (%)0.1%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean47.629678
Minimum0
Maximum100000
Zeros9973
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:15:50.032104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100000
Range100000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1439.4385
Coefficient of variation (CV)30.221462
Kurtosis2935.5387
Mean47.629678
Median Absolute Deviation (MAD)0
Skewness48.797248
Sum476011
Variance2071983.2
MonotonicityNot monotonic
2024-03-15T02:15:50.480153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 9973
99.7%
20000 14
 
0.1%
8000 2
 
< 0.1%
6 1
 
< 0.1%
100000 1
 
< 0.1%
5 1
 
< 0.1%
10000 1
 
< 0.1%
70000 1
 
< 0.1%
(Missing) 6
 
0.1%
ValueCountFrequency (%)
0 9973
99.7%
5 1
 
< 0.1%
6 1
 
< 0.1%
8000 2
 
< 0.1%
10000 1
 
< 0.1%
20000 14
 
0.1%
70000 1
 
< 0.1%
100000 1
 
< 0.1%
ValueCountFrequency (%)
100000 1
 
< 0.1%
70000 1
 
< 0.1%
20000 14
 
0.1%
10000 1
 
< 0.1%
8000 2
 
< 0.1%
6 1
 
< 0.1%
5 1
 
< 0.1%
0 9973
99.7%

재료비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)0.6%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3182.3728
Minimum0
Maximum1300000
Zeros9842
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:15:51.177382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1300000
Range1300000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation47825.284
Coefficient of variation (CV)15.028184
Kurtosis372.26607
Mean3182.3728
Median Absolute Deviation (MAD)0
Skewness18.478046
Sum31804634
Variance2.2872578 × 109
MonotonicityNot monotonic
2024-03-15T02:15:51.666288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9842
98.4%
10000 21
 
0.2%
5000 15
 
0.1%
20000 11
 
0.1%
500000 8
 
0.1%
2022 7
 
0.1%
40000 6
 
0.1%
50000 6
 
0.1%
30000 5
 
0.1%
600000 4
 
< 0.1%
Other values (45) 69
 
0.7%
(Missing) 6
 
0.1%
ValueCountFrequency (%)
0 9842
98.4%
1000 2
 
< 0.1%
2000 2
 
< 0.1%
2022 7
 
0.1%
3000 1
 
< 0.1%
4000 1
 
< 0.1%
5000 15
 
0.1%
7000 1
 
< 0.1%
10000 21
 
0.2%
12000 1
 
< 0.1%
ValueCountFrequency (%)
1300000 1
 
< 0.1%
1200000 2
< 0.1%
1100000 1
 
< 0.1%
1090000 1
 
< 0.1%
1045000 1
 
< 0.1%
1000000 3
< 0.1%
950000 1
 
< 0.1%
900000 1
 
< 0.1%
862000 1
 
< 0.1%
820000 1
 
< 0.1%

급식비
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5317 
0
4683 

Length

Max length4
Median length4
Mean length2.5951
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5317
53.2%
0 4683
46.8%

Length

2024-03-15T02:15:52.127614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:15:52.436011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5317
53.2%
0 4683
46.8%

기숙사비
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9980 
347800
 
8
<NA>
 
6
376000
 
3
140000
 
2

Length

Max length6
Median length1
Mean length1.0088
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
0 9980
99.8%
347800 8
 
0.1%
<NA> 6
 
0.1%
376000 3
 
< 0.1%
140000 2
 
< 0.1%
100000 1
 
< 0.1%

Length

2024-03-15T02:15:52.909888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T02:15:53.366162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9980
99.8%
347800 8
 
0.1%
na 6
 
0.1%
376000 3
 
< 0.1%
140000 2
 
< 0.1%
100000 1
 
< 0.1%

기타경비합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)0.7%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4318.8358
Minimum0
Maximum1300000
Zeros9808
Zeros (%)98.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:15:53.748292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1300000
Range1300000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55243.331
Coefficient of variation (CV)12.791255
Kurtosis252.94627
Mean4318.8358
Median Absolute Deviation (MAD)0
Skewness15.298751
Sum43162445
Variance3.0518256 × 109
MonotonicityNot monotonic
2024-03-15T02:15:54.161478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9808
98.1%
10000 22
 
0.2%
5000 14
 
0.1%
20000 14
 
0.1%
500000 8
 
0.1%
30000 8
 
0.1%
784300 8
 
0.1%
50000 7
 
0.1%
2022 7
 
0.1%
40000 6
 
0.1%
Other values (56) 92
 
0.9%
(Missing) 6
 
0.1%
ValueCountFrequency (%)
0 9808
98.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
1000 2
 
< 0.1%
2000 2
 
< 0.1%
2022 7
 
0.1%
3000 1
 
< 0.1%
4000 1
 
< 0.1%
5000 14
 
0.1%
7000 1
 
< 0.1%
ValueCountFrequency (%)
1300000 1
 
< 0.1%
1200000 2
< 0.1%
1100000 1
 
< 0.1%
1090000 1
 
< 0.1%
1045000 1
 
< 0.1%
1000000 3
< 0.1%
950000 1
 
< 0.1%
900000 1
 
< 0.1%
862000 1
 
< 0.1%
820000 1
 
< 0.1%

총교습비
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct497
Distinct (%)5.0%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean214730.71
Minimum0
Maximum7911680
Zeros1818
Zeros (%)18.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:15:54.499974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150000
median160000
Q3250000
95-th percentile541960
Maximum7911680
Range7911680
Interquartile range (IQR)200000

Descriptive statistics

Standard deviation349772.01
Coefficient of variation (CV)1.6288867
Kurtosis115.2402
Mean214730.71
Median Absolute Deviation (MAD)100000
Skewness8.7249475
Sum2.1460187 × 109
Variance1.2234046 × 1011
MonotonicityNot monotonic
2024-03-15T02:15:54.969805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1818
 
18.2%
200000 574
 
5.7%
150000 456
 
4.6%
250000 426
 
4.3%
300000 358
 
3.6%
180000 286
 
2.9%
140000 267
 
2.7%
120000 260
 
2.6%
160000 247
 
2.5%
130000 242
 
2.4%
Other values (487) 5060
50.6%
ValueCountFrequency (%)
0 1818
18.2%
2000 1
 
< 0.1%
3700 1
 
< 0.1%
5000 1
 
< 0.1%
5400 2
 
< 0.1%
6000 1
 
< 0.1%
7000 17
 
0.2%
7100 14
 
0.1%
7300 11
 
0.1%
7500 1
 
< 0.1%
ValueCountFrequency (%)
7911680 1
< 0.1%
7200000 1
< 0.1%
6000000 1
< 0.1%
5678400 2
< 0.1%
5522160 1
< 0.1%
5362000 1
< 0.1%
5006610 1
< 0.1%
5000000 1
< 0.1%
4992000 1
< 0.1%
4840000 2
< 0.1%

강사수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1156
Minimum0
Maximum57
Zeros446
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T02:15:55.329795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q35
95-th percentile10
Maximum57
Range57
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.2263532
Coefficient of variation (CV)1.2698885
Kurtosis36.417037
Mean4.1156
Median Absolute Deviation (MAD)2
Skewness5.1055976
Sum41156
Variance27.314768
MonotonicityNot monotonic
2024-03-15T02:15:55.724087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 2138
21.4%
2 1939
19.4%
3 1360
13.6%
4 1147
11.5%
5 858
8.6%
6 600
 
6.0%
0 446
 
4.5%
7 390
 
3.9%
9 341
 
3.4%
8 236
 
2.4%
Other values (25) 545
 
5.5%
ValueCountFrequency (%)
0 446
 
4.5%
1 2138
21.4%
2 1939
19.4%
3 1360
13.6%
4 1147
11.5%
5 858
8.6%
6 600
 
6.0%
7 390
 
3.9%
8 236
 
2.4%
9 341
 
3.4%
ValueCountFrequency (%)
57 11
 
0.1%
51 28
0.3%
41 4
 
< 0.1%
36 9
 
0.1%
35 15
 
0.1%
33 7
 
0.1%
32 7
 
0.1%
31 14
 
0.1%
28 18
0.2%
26 42
0.4%

Interactions

2024-03-15T02:15:23.060181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:03.256382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:05.687802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:08.198449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:10.665981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:13.186714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:15.837853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:18.159078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:20.433043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:23.233049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:03.512751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:05.959691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:08.486745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:10.929799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:13.449905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:16.099917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:18.440816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:20.708004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:23.424278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:03.794108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:06.249116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:08.849827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:11.251961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:13.746715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:16.394218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:18.732282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:21.007752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:23.607131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:04.077844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:06.560539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:09.159543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:11.541927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:14.216410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:16.655393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:19.019309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:21.301061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:23.781594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:04.338261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:06.823220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:09.420507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:11.803380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:14.479741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:16.829367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:19.341444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:21.574222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:23.950034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:04.604306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:07.009217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:09.677010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:12.062402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:14.735958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:17.035838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:19.615125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:21.857059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:24.134835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:04.873454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:07.304241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:09.900537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:12.457935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:15.008984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:17.318125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:19.858621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:22.141160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:24.425041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:05.146771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:07.611854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:10.126379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:12.743799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:15.291293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:17.599180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:20.029455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:22.513053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:24.704186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:05.426056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:07.920319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:10.381506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:12.927872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:15.575178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:17.889956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:20.216774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:15:22.804870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:15:56.002419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교습계열교습과정정원교습기간총교습시간(분)교습비피복비차량비모의고사비재료비기숙사비기타경비합계총교습비강사수
교습계열1.0001.0000.0000.7430.0000.3270.0000.0000.0000.2270.0000.2010.5100.324
교습과정1.0001.0000.0000.8400.0000.4010.0000.0000.1610.3950.0990.3660.6210.556
정원0.0000.0001.0000.6460.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
교습기간0.7430.8400.6461.0000.0000.9160.0000.0000.0000.5920.0000.5090.9080.292
총교습시간(분)0.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
교습비0.3270.4010.0000.9160.0001.0000.0000.0000.0000.0800.0000.0000.8600.065
피복비0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.5260.2310.253
차량비0.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.6400.2570.0770.110
모의고사비0.0000.1610.0000.0000.0000.0000.0000.0001.0000.0000.5840.6070.2370.000
재료비0.2270.3950.0000.5920.0000.0800.0000.0000.0001.0000.0000.9980.3140.000
기숙사비0.0000.0990.0000.0000.0000.0000.0000.6400.5840.0001.0000.8260.3920.000
기타경비합계0.2010.3660.0000.5090.0000.0000.5260.2570.6070.9980.8261.0000.3790.071
총교습비0.5100.6210.0000.9080.0000.8600.2310.0770.2370.3140.3920.3791.0000.149
강사수0.3240.5560.0000.2920.0000.0650.2530.1100.0000.0000.0000.0710.1491.000
2024-03-15T02:15:56.296981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기숙사비교습계열급식비피복비
기숙사비1.0000.0001.0000.000
교습계열0.0001.0001.0000.000
급식비1.0001.0001.0001.000
피복비0.0000.0001.0001.000
2024-03-15T02:15:56.479910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정원총교습시간(분)교습비차량비모의고사비재료비기타경비합계총교습비강사수교습계열피복비급식비기숙사비
정원1.0000.1340.1180.0190.020-0.024-0.0060.146-0.0170.0000.0001.0000.000
총교습시간(분)0.1341.0000.7640.0130.0410.0460.0680.4930.0480.0000.0001.0000.000
교습비0.1180.7641.0000.0150.0280.0320.0510.6200.1400.1360.0001.0000.000
차량비0.0190.0130.0151.000-0.0010.0230.2170.0240.0290.0000.0001.0000.500
모의고사비0.0200.0410.028-0.0011.0000.0120.3340.048-0.0080.0000.0001.0000.511
재료비-0.0240.0460.0320.0230.0121.0000.9020.0620.0180.0890.0001.0000.000
기타경비합계-0.0060.0680.0510.2170.3340.9021.0000.0830.0250.0780.3421.0000.486
총교습비0.1460.4930.6200.0240.0480.0620.0831.0000.0550.1930.1501.0000.239
강사수-0.0170.0480.1400.029-0.0080.0180.0250.0551.0000.1300.1541.0000.000
교습계열0.0000.0000.1360.0000.0000.0890.0780.1930.1301.0000.0001.0000.000
피복비0.0000.0000.0000.0000.0000.0000.3420.1500.1540.0001.0001.0000.000
급식비1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
기숙사비0.0000.0000.0000.5000.5110.0000.4860.2390.0000.0000.0001.0001.000

Missing values

2024-03-15T02:15:25.111581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:15:25.925683image/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.
2024-03-15T02:15:27.026673image/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

학원명학원주소설립자-성명전화번호교습계열교습과정교습과목(반)정원교습기간총교습시간(분)교습비피복비차량비모의고사비재료비급식비기숙사비기타경비합계총교습비강사수
16415언양한솔학원울산광역시 울주군 언양읍 방천7길 27 , 2층 (언양읍)장주희052-258-1318보통교과보습수학(고등1)101개월0일11732700000000<NA>0001
172유알(UR)칸타빌레음악학원울산광역시 중구 고복수길 6 , 1층 (서동)최유라052-293-5650예능(중)음악중급취미반51개월0일1303140000000000002
23712이가자뷰티아카데미미용학원울산광역시 남구 삼산로 254 , 18층 (달동)이나윤<NA>산업응용기술이·미용속눈썹실무70개월8일120012000000000<NA>0012000006
24611박쌤수학학원울산광역시 남구 돋질로 273 (삼산동) 2층박은정<NA><NA><NA>중등수학451개월0일18243600000000<NA>0001
17168구영유앤아이영어학원울산광역시 울주군 범서읍 점촌3길 8-11 (범서읍)최성혜<NA>외국어실용외국어(유아/초·중·고)중급(중등)151개월0일11481900000000<NA>001900002
2780왕생이학원울산광역시 중구 중앙길 101-2 , 1~3층(성남동), 중앙길 101-3, 1층일부~2층일부 (성남동) (성남동)이민아052-265-5804보통교과보습국어,수학,사회,과학(단과)301개월0일114718000000000001800008
8412중산탑클래스영어수학학원울산광역시 북구 매곡2로 47 , 1층 일부, 2층 일부 (중산동)김수란<NA>보통교과보습수학(초등)1371개월0일108618000000000001800007
20389매쓰포유학원울산광역시 울주군 범서읍 굴화1길 65 , 2층 (범서읍)조성윤<NA>보통교과보습중등수학1201개월0일13032500000000<NA>0002
23480엔이능률엘리트영어학원울산광역시 울주군 온양읍 솔밭1길 12 , 204호, 205호 (온양읍, 울산온양대우아파트)설경주<NA>외국어실용외국어(유아/초·중·고)고급C271개월0일14332980000000<NA>002980005
20765재능스스로학습센터구영직영학원울산광역시 울주군 범서읍 점촌2길 2 , 5층 (범서읍)주식회사 재능교육052-249-2716보통교과보습사회,과학,셈수학141개월0일260380000000<NA>00380002
학원명학원주소설립자-성명전화번호교습계열교습과정교습과목(반)정원교습기간총교습시간(분)교습비피복비차량비모의고사비재료비급식비기숙사비기타경비합계총교습비강사수
7561미술나무미술학원울산광역시 중구 오산6길 18 , 1층 (태화동)이윤성<NA>예능(중)미술미술중급F91개월0일130316000000000001600002
16221눈높이러닝센터삼산아이비학원울산 남구 남중로 125 , 3층 (삼산동)주식회사 대교052-266-9090보통교과보습초등방학특강수학11개월0일304500000000<NA>0003
22581오하운폴댄스무거동학원울산광역시 남구 대학로 130 , 3층 (무거동, 주헌빌딩)(주)슬로우라이프052-225-8285기예(중)댄스전문 2급 연장 3개월203개월0일108025000000000<NA>0025000009
10322눈높이러닝센터송정학원울산광역시 북구 박상진12로 11 , 401호, 402호 (송정동, 지오타워)주식회사 대교052-707-9109보통교과보습독해트레이닝-2(초등중등결학)101개월0일912150000000000009
16827웅진씽크빅학습센터천상학원울산광역시 울주군 범서읍 천상1길 21 , 2층 (범서읍)(주)웅진씽크빅052-243-8520보통교과보습중등영어131개월0일7651000000000<NA>001000004
23518살롱드비숑뷰티아카데미피부전문학원울산광역시 남구 번영로250번길 18 , 2층 (삼산동)박영선<NA>산업응용기술이·미용몸매하체관리(다리)11개월0일7825000000000<NA>005000001
15639눈높이러닝센터옥현학원울산광역시 남구 옥현로46번길 5-9 , 4층 일부 (무거동,서영빌딩)주식회사 대교052-249-9509보통교과보습스쿨수학Ⅱ(초등)51개월0일304490000000<NA>00490005
14151키즈아일랜드학원울산광역시 남구 신복로46번길 35-11 , 1,2층 (무거동,연원학원)김해숙052-249-2996외국어실용외국어(유아/초·중·고)초급영어461개월0일12752000000000<NA>002000004
11319이화미술학원울산광역시 북구 신천로 22 4층김민서<NA>예능(중)미술고급미술9101개월0일1303170000000000001
4643이은석수학과학전문학원울산광역시 북구 동대중앙로 37 , 2층 (호계동)강정숙052-911-3231보통교과보습초등영어601개월0일91215000000000001200004

Duplicate rows

Most frequently occurring

학원명학원주소설립자-성명전화번호교습계열교습과정교습과목(반)정원교습기간총교습시간(분)교습비피복비차량비모의고사비재료비급식비기숙사비기타경비합계총교습비강사수# duplicates
3세인트조지아카데미어학원울산광역시 북구 호계매곡3로 8 , 3층 (호계동)최수빈<NA>외국어실용외국어(유아/초·중·고)초급영어61개월0일1742300000000000030000033
9카이로스영어전문학원울산광역시 북구 가재길 82 , 601호 (천곡동, 리더스빌딩)고현주<NA>보통교과보습영어중등451개월0일1303250000000000025000013
0김홍어학원울산광역시 남구 옥현로 19 , 2층, 3층 (무거동)김홍052-249-3494외국어실용외국어(유아/초·중·고)고급1501개월0일14403000000000<NA>0030000012
1남지향어학원울산광역시 중구 태화로 197 , 3층, 4층 (태화동)남지향052-900-3736외국어실용외국어(유아/초·중·고)영어(외국인)151개월0일1147230000000000023000012
2성보수학전문학원울산광역시 동구 학문로 57-2 , 1층 2층 (화정동)김형석052-235-5441보통교과보습수학고등561개월0일1173270000000000027000042
4수단과학원울산광역시 북구 아진로 102 3층 (천곡동)정진수052-286-0984보통교과보습고등수학51개월0일1147250000000000025000042
5엠베스트유곡학원울산광역시 중구 평동3길 37 , 3층 (유곡동)KELMANANDREWALEXANDER,김지연<NA>보통교과보습초등51개월0일782129000000000012900062
6윤선생아이지에스이(igse)아카데미호계어벤져스학원울산광역시 북구 당수골23길 8 , 102호 (호계동)한준익<NA>보통교과보습초등종합초급681개월0일2055320000000000032000042
7윤선생영어숲굴화어학원울산광역시 울주군 범서읍 굴화길 62-1 (범서읍)김영일<NA>외국어실용외국어(유아/초·중·고)중등151개월0일19122300000000<NA>0023000012
8전미정음악학원울산광역시 중구 약사로 20 , 205호 (약사동)전미정052-298-3790예능(중)음악피아노바이엘(초급)51개월0일1086120000000000012000022