Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Numeric4
Categorical9
Text2
DateTime2

Alerts

realm_cl has constant value ""Constant
edc_place_nm is highly overall correlated with city_do_cd and 8 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
signgu_nm is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
lnm_addr is highly overall correlated with city_do_cd and 8 other fieldsHigh correlation
rn_addr is highly overall correlated with city_do_cd and 8 other fieldsHigh correlation
adstrd_nm is highly overall correlated with city_do_cd and 8 other fieldsHigh correlation
yy is highly overall correlated with clHigh correlation
city_do_cd is highly overall correlated with signgu_cd and 7 other fieldsHigh correlation
signgu_cd is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
adstrd_cd is highly overall correlated with city_do_cd and 7 other fieldsHigh correlation
cl is highly overall correlated with yyHigh correlation
edc_time_dc is highly overall correlated with edc_place_nm and 3 other fieldsHigh correlation
edc_place_nm is highly imbalanced (69.6%)Imbalance
rn_addr is highly imbalanced (69.6%)Imbalance
lnm_addr is highly imbalanced (69.6%)Imbalance
ctprvn_nm is highly imbalanced (68.5%)Imbalance
signgu_nm is highly imbalanced (69.3%)Imbalance
adstrd_nm is highly imbalanced (69.6%)Imbalance
edc_begin_de has unique valuesUnique
edc_end_de has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:53:16.882722
Analysis finished2023-12-10 09:53:23.721313
Duration6.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

yy
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.22
Minimum2011
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:23.839757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12012
median2014
Q32016
95-th percentile2018
Maximum2019
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.2182894
Coefficient of variation (CV)0.0011013144
Kurtosis-0.68423117
Mean2014.22
Median Absolute Deviation (MAD)2
Skewness0.44134856
Sum201422
Variance4.9208081
MonotonicityIncreasing
2023-12-10T18:53:24.080498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2014 18
18.0%
2012 16
16.0%
2013 16
16.0%
2015 13
13.0%
2011 10
10.0%
2017 9
9.0%
2016 8
8.0%
2018 6
 
6.0%
2019 4
 
4.0%
ValueCountFrequency (%)
2011 10
10.0%
2012 16
16.0%
2013 16
16.0%
2014 18
18.0%
2015 13
13.0%
2016 8
8.0%
2017 9
9.0%
2018 6
 
6.0%
2019 4
 
4.0%
ValueCountFrequency (%)
2019 4
 
4.0%
2018 6
 
6.0%
2017 9
9.0%
2016 8
8.0%
2015 13
13.0%
2014 18
18.0%
2013 16
16.0%
2012 16
16.0%
2011 10
10.0%

realm_cl
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
무대예술전문교육
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무대예술전문교육 100
100.0%

Length

2023-12-10T18:53:24.320543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:24.505732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무대예술전문교육 100
100.0%

cl
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
무대조명
23 
무대음향
23 
무대장치
21 
무대,조명,음향,영상,공통
9 
무대장치,조명,음향,공통
8 
Other values (7)
16 

Length

Max length22
Median length4
Mean length6.1
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row무대조명
2nd row무대장치
3rd row무대음향
4th row무대음향
5th row무대장치

Common Values

ValueCountFrequency (%)
무대조명 23
23.0%
무대음향 23
23.0%
무대장치 21
21.0%
무대,조명,음향,영상,공통 9
 
9.0%
무대장치,조명,음향,공통 8
 
8.0%
조명, 음향, 영상 6
 
6.0%
무대공통 3
 
3.0%
극장경영 3
 
3.0%
조명, 음향, 무대, 하우스매니징, 영상 1
 
1.0%
음향 1
 
1.0%
Other values (2) 2
 
2.0%

Length

2023-12-10T18:53:24.718260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
무대조명 23
19.8%
무대음향 23
19.8%
무대장치 21
18.1%
무대,조명,음향,영상,공통 9
 
7.8%
무대장치,조명,음향,공통 8
 
6.9%
조명 8
 
6.9%
음향 8
 
6.9%
영상 7
 
6.0%
무대공통 3
 
2.6%
극장경영 3
 
2.6%
Other values (2) 3
 
2.6%

crclm_nm
Text

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:53:25.163866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length23
Mean length13.72
Min length4

Characters and Unicode

Total characters1372
Distinct characters149
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)87.0%

Sample

1st row이론운영과정
2nd row이론운영과정
3rd row이론운영과정
4th row디자인과정
5th row디자인과정
ValueCountFrequency (%)
1 11
 
5.3%
교육 9
 
4.3%
이해 6
 
2.9%
실무 6
 
2.9%
이해2 5
 
2.4%
무대예술현장전문가연수[2]-디자인 5
 
2.4%
무대조명 5
 
2.4%
음향 4
 
1.9%
무대예술현장전문가연수[1]-집중교육 4
 
1.9%
프로젝션 4
 
1.9%
Other values (101) 150
71.8%
2023-12-10T18:53:25.870779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
109
 
7.9%
무 63
 
4.6%
대 50
 
3.6%
술 38
 
2.8%
문 36
 
2.6%
장 36
 
2.6%
해 34
 
2.5%
기 33
 
2.4%
이 30
 
2.2%
1 29
 
2.1%
Other values (139) 914
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1071
78.1%
Space Separator 109
 
7.9%
Decimal Number 53
 
3.9%
Close Punctuation 39
 
2.8%
Open Punctuation 39
 
2.8%
Dash Punctuation 23
 
1.7%
Uppercase Letter 20
 
1.5%
Lowercase Letter 13
 
0.9%
Other Punctuation 3
 
0.2%
Letter Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
무 63
 
5.9%
대 50
 
4.7%
술 38
 
3.5%
문 36
 
3.4%
장 36
 
3.4%
해 34
 
3.2%
기 33
 
3.1%
이 30
 
2.8%
연 29
 
2.7%
수 27
 
2.5%
Other values (103) 695
64.9%
Uppercase Letter
ValueCountFrequency (%)
A 3
15.0%
N 3
15.0%
O 3
15.0%
D 2
10.0%
E 2
10.0%
W 1
 
5.0%
V 1
 
5.0%
I 1
 
5.0%
S 1
 
5.0%
G 1
 
5.0%
Other values (2) 2
10.0%
Lowercase Letter
ValueCountFrequency (%)
o 2
15.4%
r 2
15.4%
l 2
15.4%
n 1
7.7%
i 1
7.7%
e 1
7.7%
c 1
7.7%
t 1
7.7%
k 1
7.7%
s 1
7.7%
Decimal Number
ValueCountFrequency (%)
1 29
54.7%
2 22
41.5%
3 2
 
3.8%
Other Punctuation
ValueCountFrequency (%)
/ 1
33.3%
& 1
33.3%
· 1
33.3%
Close Punctuation
ValueCountFrequency (%)
] 23
59.0%
) 16
41.0%
Open Punctuation
ValueCountFrequency (%)
[ 23
59.0%
( 16
41.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%
Space Separator
ValueCountFrequency (%)
109
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1071
78.1%
Common 266
 
19.4%
Latin 35
 
2.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
무 63
 
5.9%
대 50
 
4.7%
술 38
 
3.5%
문 36
 
3.4%
장 36
 
3.4%
해 34
 
3.2%
기 33
 
3.1%
이 30
 
2.8%
연 29
 
2.7%
수 27
 
2.5%
Other values (103) 695
64.9%
Latin
ValueCountFrequency (%)
A 3
 
8.6%
N 3
 
8.6%
O 3
 
8.6%
D 2
 
5.7%
o 2
 
5.7%
r 2
 
5.7%
E 2
 
5.7%
l 2
 
5.7%
n 1
 
2.9%
Ⅱ 1
 
2.9%
Other values (14) 14
40.0%
Common
ValueCountFrequency (%)
109
41.0%
1 29
 
10.9%
] 23
 
8.6%
- 23
 
8.6%
[ 23
 
8.6%
2 22
 
8.3%
) 16
 
6.0%
( 16
 
6.0%
3 2
 
0.8%
/ 1
 
0.4%
Other values (2) 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1071
78.1%
ASCII 298
 
21.7%
Number Forms 2
 
0.1%
None 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
109
36.6%
1 29
 
9.7%
] 23
 
7.7%
- 23
 
7.7%
[ 23
 
7.7%
2 22
 
7.4%
) 16
 
5.4%
( 16
 
5.4%
A 3
 
1.0%
N 3
 
1.0%
Other values (23) 31
 
10.4%
Hangul
ValueCountFrequency (%)
무 63
 
5.9%
대 50
 
4.7%
술 38
 
3.5%
문 36
 
3.4%
장 36
 
3.4%
해 34
 
3.2%
기 33
 
3.1%
이 30
 
2.8%
연 29
 
2.7%
수 27
 
2.5%
Other values (103) 695
64.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%
None
ValueCountFrequency (%)
· 1
100.0%

edc_begin_de
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2011-04-06 00:00:00
Maximum2019-11-20 00:00:00
2023-12-10T18:53:26.156054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:26.496327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

edc_end_de
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2011-04-08 00:00:00
Maximum2019-11-22 00:00:00
2023-12-10T18:53:26.769485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:27.084452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

instrctr_nm
Text

Distinct51
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:53:27.470293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length3
Mean length7.33
Min length3

Characters and Unicode

Total characters733
Distinct characters84
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)32.0%

Sample

1st row정진덕
2nd row권용만
3rd row조현의, 이수용
4th row이재훈, 김도석, 권도경
5th row김종석
ValueCountFrequency (%)
이수용 10
 
7.5%
조현의 7
 
5.2%
손호성 6
 
4.5%
신성환 6
 
4.5%
최진근 5
 
3.7%
하종기 5
 
3.7%
이동훈 5
 
3.7%
김도석 5
 
3.7%
김방근 4
 
3.0%
김창기 4
 
3.0%
Other values (47) 77
57.5%
2023-12-10T18:53:28.108808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 100
 
13.6%
이 54
 
7.4%
34
 
4.6%
상 30
 
4.1%
호 30
 
4.1%
수 30
 
4.1%
김 30
 
4.1%
용 27
 
3.7%
윤 19
 
2.6%
성 18
 
2.5%
Other values (74) 361
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 599
81.7%
Other Punctuation 100
 
13.6%
Space Separator 34
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 54
 
9.0%
상 30
 
5.0%
호 30
 
5.0%
수 30
 
5.0%
김 30
 
5.0%
용 27
 
4.5%
윤 19
 
3.2%
성 18
 
3.0%
영 14
 
2.3%
경 14
 
2.3%
Other values (72) 333
55.6%
Other Punctuation
ValueCountFrequency (%)
, 100
100.0%
Space Separator
ValueCountFrequency (%)
34
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 599
81.7%
Common 134
 
18.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 54
 
9.0%
상 30
 
5.0%
호 30
 
5.0%
수 30
 
5.0%
김 30
 
5.0%
용 27
 
4.5%
윤 19
 
3.2%
성 18
 
3.0%
영 14
 
2.3%
경 14
 
2.3%
Other values (72) 333
55.6%
Common
ValueCountFrequency (%)
, 100
74.6%
34
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 599
81.7%
ASCII 134
 
18.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 100
74.6%
34
 
25.4%
Hangul
ValueCountFrequency (%)
이 54
 
9.0%
상 30
 
5.0%
호 30
 
5.0%
수 30
 
5.0%
김 30
 
5.0%
용 27
 
4.5%
윤 19
 
3.2%
성 18
 
3.0%
영 14
 
2.3%
경 14
 
2.3%
Other values (72) 333
55.6%

edc_time_dc
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
3일(24시간)
69 
4일(28시간)
11 
4일(32시간)
11 
3일(21시간)
 
4
5일(30시간)
 
2
Other values (3)
 
3

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row3일(24시간)
2nd row3일(24시간)
3rd row3일(24시간)
4th row3일(24시간)
5th row3일(24시간)

Common Values

ValueCountFrequency (%)
3일(24시간) 69
69.0%
4일(28시간) 11
 
11.0%
4일(32시간) 11
 
11.0%
3일(21시간) 4
 
4.0%
5일(30시간) 2
 
2.0%
2일(16시간) 1
 
1.0%
4일(26시간) 1
 
1.0%
3일(26시간) 1
 
1.0%

Length

2023-12-10T18:53:28.460928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:28.840348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3일(24시간 69
69.0%
4일(28시간 11
 
11.0%
4일(32시간 11
 
11.0%
3일(21시간 4
 
4.0%
5일(30시간 2
 
2.0%
2일(16시간 1
 
1.0%
4일(26시간 1
 
1.0%
3일(26시간 1
 
1.0%

edc_place_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
아르코예술인력개발원
85 
대구공연예술연습공간
 
2
청주공연예술연습공간
 
2
전주공연예술연습공간
 
1
국립부산국악원
 
1
Other values (9)
9 

Length

Max length10
Median length10
Mean length9.7
Min length5

Unique

Unique11 ?
Unique (%)11.0%

Sample

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

Common Values

ValueCountFrequency (%)
아르코예술인력개발원 85
85.0%
대구공연예술연습공간 2
 
2.0%
청주공연예술연습공간 2
 
2.0%
전주공연예술연습공간 1
 
1.0%
국립부산국악원 1
 
1.0%
광주문화예술회관 1
 
1.0%
단오문화관 1
 
1.0%
한라아트홀 1
 
1.0%
천안예술의전당 1
 
1.0%
국립민속국악원 1
 
1.0%
Other values (4) 4
 
4.0%

Length

2023-12-10T18:53:29.218219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아르코예술인력개발원 85
85.0%
대구공연예술연습공간 2
 
2.0%
청주공연예술연습공간 2
 
2.0%
전주공연예술연습공간 1
 
1.0%
국립부산국악원 1
 
1.0%
광주문화예술회관 1
 
1.0%
단오문화관 1
 
1.0%
한라아트홀 1
 
1.0%
천안예술의전당 1
 
1.0%
국립민속국악원 1
 
1.0%
Other values (4) 4
 
4.0%

rn_addr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기 고양시 일산동구 성현로513번길 10
85 
대구광역시 남구 명덕로 42
 
2
충청북도 청주시 청원구 상당로 314
 
2
전라북도 전주시 덕진구 여암 1길 19
 
1
부산 부산진구 국악로 2
 
1
Other values (9)
9 

Length

Max length25
Median length23
Mean length22.02
Min length12

Unique

Unique11 ?
Unique (%)11.0%

Sample

1st row경기 고양시 일산동구 성현로513번길 10
2nd row경기 고양시 일산동구 성현로513번길 10
3rd row경기 고양시 일산동구 성현로513번길 10
4th row경기 고양시 일산동구 성현로513번길 10
5th row경기 고양시 일산동구 성현로513번길 10

Common Values

ValueCountFrequency (%)
경기 고양시 일산동구 성현로513번길 10 85
85.0%
대구광역시 남구 명덕로 42 2
 
2.0%
충청북도 청주시 청원구 상당로 314 2
 
2.0%
전라북도 전주시 덕진구 여암 1길 19 1
 
1.0%
부산 부산진구 국악로 2 1
 
1.0%
광주 북구 북문대로 60 1
 
1.0%
강원 강릉시 단오장길 1 1
 
1.0%
제주시 한라대학로 38 1
 
1.0%
충남 천안시 동남구 성남면 종합휴양지로 185 1
 
1.0%
전북 남원시 양림길 54 1
 
1.0%
Other values (4) 4
 
4.0%

Length

2023-12-10T18:53:29.449831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 85
17.3%
일산동구 85
17.3%
성현로513번길 85
17.3%
10 85
17.3%
고양시 85
17.3%
청원구 2
 
0.4%
강원 2
 
0.4%
전북 2
 
0.4%
덕진구 2
 
0.4%
전주시 2
 
0.4%
Other values (49) 57
11.6%

lnm_addr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
고양시 일산동구 사리현동 464-2
85 
대구광역시 남구 대명동 2274-5
 
2
충청북도 청주시 청원구 내덕동 201-1
 
2
전라북도 전주시 덕진구 여의동 606-4
 
1
부산 연지동 219-2
 
1
Other values (9)
9 

Length

Max length22
Median length19
Mean length18.8
Min length12

Unique

Unique11 ?
Unique (%)11.0%

Sample

1st row고양시 일산동구 사리현동 464-2
2nd row고양시 일산동구 사리현동 464-2
3rd row고양시 일산동구 사리현동 464-2
4th row고양시 일산동구 사리현동 464-2
5th row고양시 일산동구 사리현동 464-2

Common Values

ValueCountFrequency (%)
고양시 일산동구 사리현동 464-2 85
85.0%
대구광역시 남구 대명동 2274-5 2
 
2.0%
충청북도 청주시 청원구 내덕동 201-1 2
 
2.0%
전라북도 전주시 덕진구 여의동 606-4 1
 
1.0%
부산 연지동 219-2 1
 
1.0%
광주 북구 운암동 328-16 1
 
1.0%
강원 강릉시 노암동 722-2 1
 
1.0%
제주시 노형동 1534 1
 
1.0%
천안시 동남구 성남면 용원리 710 1
 
1.0%
전북 남원시 어현동 37-40 1
 
1.0%
Other values (4) 4
 
4.0%

Length

2023-12-10T18:53:29.699111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 85
21.0%
사리현동 85
21.0%
464-2 85
21.0%
일산동구 85
21.0%
청원구 2
 
0.5%
덕진구 2
 
0.5%
전주시 2
 
0.5%
전북 2
 
0.5%
내덕동 2
 
0.5%
201-1 2
 
0.5%
Other values (45) 52
12.9%

city_do_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.07
Minimum21
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:29.891251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile31
Q131
median31
Q331
95-th percentile35
Maximum39
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3061402
Coefficient of variation (CV)0.074224018
Kurtosis10.047939
Mean31.07
Median Absolute Deviation (MAD)0
Skewness-1.3476528
Sum3107
Variance5.3182828
MonotonicityNot monotonic
2023-12-10T18:53:30.113377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
31 85
85.0%
35 3
 
3.0%
22 2
 
2.0%
33 2
 
2.0%
32 2
 
2.0%
21 1
 
1.0%
24 1
 
1.0%
39 1
 
1.0%
34 1
 
1.0%
37 1
 
1.0%
ValueCountFrequency (%)
21 1
 
1.0%
22 2
 
2.0%
24 1
 
1.0%
31 85
85.0%
32 2
 
2.0%
33 2
 
2.0%
34 1
 
1.0%
35 3
 
3.0%
37 1
 
1.0%
38 1
 
1.0%
ValueCountFrequency (%)
39 1
 
1.0%
38 1
 
1.0%
37 1
 
1.0%
35 3
 
3.0%
34 1
 
1.0%
33 2
 
2.0%
32 2
 
2.0%
31 85
85.0%
24 1
 
1.0%
22 2
 
2.0%

ctprvn_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
85 
전라북도
 
3
대구광역시
 
2
충청북도
 
2
강원도
 
2
Other values (6)
 
6

Length

Max length7
Median length3
Mean length3.2
Min length3

Unique

Unique6 ?
Unique (%)6.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 85
85.0%
전라북도 3
 
3.0%
대구광역시 2
 
2.0%
충청북도 2
 
2.0%
강원도 2
 
2.0%
부산광역시 1
 
1.0%
광주광역시 1
 
1.0%
제주특별자치도 1
 
1.0%
충청남도 1
 
1.0%
경상북도 1
 
1.0%

Length

2023-12-10T18:53:30.367564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 85
85.0%
전라북도 3
 
3.0%
대구광역시 2
 
2.0%
충청북도 2
 
2.0%
강원도 2
 
2.0%
부산광역시 1
 
1.0%
광주광역시 1
 
1.0%
제주특별자치도 1
 
1.0%
충청남도 1
 
1.0%
경상북도 1
 
1.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31165.68
Minimum21050
Maximum39010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:30.571853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21050
5-th percentile31103
Q131103
median31103
Q331103
95-th percentile35012
Maximum39010
Range17960
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2310.9956
Coefficient of variation (CV)0.074151939
Kurtosis10.150536
Mean31165.68
Median Absolute Deviation (MAD)0
Skewness-1.3850949
Sum3116568
Variance5340700.6
MonotonicityNot monotonic
2023-12-10T18:53:30.771729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
31103 85
85.0%
22040 2
 
2.0%
35012 2
 
2.0%
33044 2
 
2.0%
21050 1
 
1.0%
24040 1
 
1.0%
32030 1
 
1.0%
39010 1
 
1.0%
34011 1
 
1.0%
35050 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
21050 1
 
1.0%
22040 2
 
2.0%
24040 1
 
1.0%
31103 85
85.0%
32010 1
 
1.0%
32030 1
 
1.0%
33044 2
 
2.0%
34011 1
 
1.0%
35012 2
 
2.0%
35050 1
 
1.0%
ValueCountFrequency (%)
39010 1
 
1.0%
38070 1
 
1.0%
37350 1
 
1.0%
35050 1
 
1.0%
35012 2
 
2.0%
34011 1
 
1.0%
33044 2
 
2.0%
32030 1
 
1.0%
32010 1
 
1.0%
31103 85
85.0%

signgu_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
고양시 일산동구
85 
남구
 
2
전주시 덕진구
 
2
청주시 청원구
 
2
부산진구
 
1
Other values (8)
 
8

Length

Max length8
Median length8
Mean length7.43
Min length2

Unique

Unique9 ?
Unique (%)9.0%

Sample

1st row고양시 일산동구
2nd row고양시 일산동구
3rd row고양시 일산동구
4th row고양시 일산동구
5th row고양시 일산동구

Common Values

ValueCountFrequency (%)
고양시 일산동구 85
85.0%
남구 2
 
2.0%
전주시 덕진구 2
 
2.0%
청주시 청원구 2
 
2.0%
부산진구 1
 
1.0%
북구 1
 
1.0%
강릉시 1
 
1.0%
제주시 1
 
1.0%
천안시 동남구 1
 
1.0%
남원시 1
 
1.0%
Other values (3) 3
 
3.0%

Length

2023-12-10T18:53:31.027732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 85
44.7%
일산동구 85
44.7%
남구 2
 
1.1%
전주시 2
 
1.1%
덕진구 2
 
1.1%
청주시 2
 
1.1%
청원구 2
 
1.1%
동남구 1
 
0.5%
춘천시 1
 
0.5%
영덕군 1
 
0.5%
Other values (7) 7
 
3.7%

adstrd_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.1%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean3112742.5
Minimum2105082
Maximum3901066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:31.277929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2105082
5-th percentile3110360
Q13110360
median3110360
Q33110360
95-th percentile3411146.3
Maximum3901066
Range1795984
Interquartile range (IQR)0

Descriptive statistics

Standard deviation228968.18
Coefficient of variation (CV)0.073558344
Kurtosis10.608361
Mean3112742.5
Median Absolute Deviation (MAD)0
Skewness-1.4363507
Sum3.081615 × 108
Variance5.2426428 × 1010
MonotonicityNot monotonic
2023-12-10T18:53:31.579255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3110360 85
85.0%
2204056 2
 
2.0%
3304454 2
 
2.0%
2105082 1
 
1.0%
2404058 1
 
1.0%
3203062 1
 
1.0%
3901066 1
 
1.0%
3401134 1
 
1.0%
3505054 1
 
1.0%
3735036 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
2105082 1
 
1.0%
2204056 2
 
2.0%
2404058 1
 
1.0%
3110360 85
85.0%
3201071 1
 
1.0%
3203062 1
 
1.0%
3304454 2
 
2.0%
3401134 1
 
1.0%
3501257 1
 
1.0%
3505054 1
 
1.0%
ValueCountFrequency (%)
3901066 1
 
1.0%
3807063 1
 
1.0%
3735036 1
 
1.0%
3505054 1
 
1.0%
3501257 1
 
1.0%
3401134 1
 
1.0%
3304454 2
 
2.0%
3203062 1
 
1.0%
3201071 1
 
1.0%
3110360 85
85.0%

adstrd_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
고봉동
85 
대명1동
 
2
내덕2동
 
2
<NA>
 
1
부전1동
 
1
Other values (9)
9 

Length

Max length4
Median length3
Mean length3.09
Min length3

Unique

Unique11 ?
Unique (%)11.0%

Sample

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

Common Values

ValueCountFrequency (%)
고봉동 85
85.0%
대명1동 2
 
2.0%
내덕2동 2
 
2.0%
<NA> 1
 
1.0%
부전1동 1
 
1.0%
운암1동 1
 
1.0%
강남동 1
 
1.0%
노형동 1
 
1.0%
성남면 1
 
1.0%
노암동 1
 
1.0%
Other values (4) 4
 
4.0%

Length

2023-12-10T18:53:31.940739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고봉동 85
85.0%
대명1동 2
 
2.0%
내덕2동 2
 
2.0%
na 1
 
1.0%
부전1동 1
 
1.0%
운암1동 1
 
1.0%
강남동 1
 
1.0%
노형동 1
 
1.0%
성남면 1
 
1.0%
노암동 1
 
1.0%
Other values (4) 4
 
4.0%

Interactions

2023-12-10T18:53:22.154582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:19.197129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:20.440416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:21.291668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:22.349698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:19.461296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:20.719308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:21.490507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:22.538131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:19.754332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:20.922340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:21.754543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:22.727919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:20.209426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:21.103756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:53:21.946834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:53:32.129870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
yyclcrclm_nmedc_begin_deedc_end_deinstrctr_nmedc_time_dcedc_place_nmrn_addrlnm_addrcity_do_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nm
yy1.0000.9300.9321.0001.0000.7150.7570.5850.5850.5850.4950.4860.5150.5450.5460.578
cl0.9301.0000.9871.0001.0000.9390.7530.3110.3110.3110.4400.2410.5780.2690.6220.347
crclm_nm0.9320.9871.0001.0001.0000.3860.9981.0001.0001.0001.0001.0001.0001.0001.0001.000
edc_begin_de1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
edc_end_de1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
instrctr_nm0.7150.9390.3861.0001.0001.0000.9680.9860.9860.9860.9820.9801.0000.9841.0000.984
edc_time_dc0.7570.7530.9981.0001.0000.9681.0000.8110.8110.8110.5210.6770.5490.7740.5520.802
edc_place_nm0.5850.3111.0001.0001.0000.9860.8111.0001.0001.0001.0001.0001.0001.0001.0001.000
rn_addr0.5850.3111.0001.0001.0000.9860.8111.0001.0001.0001.0001.0001.0001.0001.0001.000
lnm_addr0.5850.3111.0001.0001.0000.9860.8111.0001.0001.0001.0001.0001.0001.0001.0001.000
city_do_cd0.4950.4401.0001.0001.0000.9820.5211.0001.0001.0001.0001.0001.0001.0001.0001.000
ctprvn_nm0.4860.2411.0001.0001.0000.9800.6771.0001.0001.0001.0001.0001.0001.0001.0001.000
signgu_cd0.5150.5781.0001.0001.0001.0000.5491.0001.0001.0001.0001.0001.0001.0001.0001.000
signgu_nm0.5450.2691.0001.0001.0000.9840.7741.0001.0001.0001.0001.0001.0001.0001.0001.000
adstrd_cd0.5460.6221.0001.0001.0001.0000.5521.0001.0001.0001.0001.0001.0001.0001.0001.000
adstrd_nm0.5780.3471.0001.0001.0000.9840.8021.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:53:32.787848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
edc_time_dcedc_place_nmctprvn_nmsigngu_nmlnm_addrrn_addrcladstrd_nm
edc_time_dc1.0000.5140.3960.4790.5140.5140.4270.516
edc_place_nm0.5141.0000.9830.9941.0001.0000.1151.000
ctprvn_nm0.3960.9831.0000.9890.9830.9830.0920.989
signgu_nm0.4790.9940.9891.0000.9940.9940.0981.000
lnm_addr0.5141.0000.9830.9941.0001.0000.1151.000
rn_addr0.5141.0000.9830.9941.0001.0000.1151.000
cl0.4270.1150.0920.0980.1150.1151.0000.133
adstrd_nm0.5161.0000.9891.0001.0001.0000.1331.000
2023-12-10T18:53:33.007616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
yycity_do_cdsigngu_cdadstrd_cdcledc_time_dcedc_place_nmrn_addrlnm_addrctprvn_nmsigngu_nmadstrd_nm
yy1.0000.2560.2560.2370.6610.4830.2670.2670.2670.2270.2500.271
city_do_cd0.2561.0001.0001.0000.2230.3090.9620.9620.9620.9780.9670.967
signgu_cd0.2561.0001.0001.0000.2700.3250.9570.9570.9570.9730.9620.962
adstrd_cd0.2371.0001.0001.0000.2970.3260.9620.9620.9620.9730.9620.962
cl0.6610.2230.2700.2971.0000.4270.1150.1150.1150.0920.0980.133
edc_time_dc0.4830.3090.3250.3260.4271.0000.5140.5140.5140.3960.4790.516
edc_place_nm0.2670.9620.9570.9620.1150.5141.0001.0001.0000.9830.9941.000
rn_addr0.2670.9620.9570.9620.1150.5141.0001.0001.0000.9830.9941.000
lnm_addr0.2670.9620.9570.9620.1150.5141.0001.0001.0000.9830.9941.000
ctprvn_nm0.2270.9780.9730.9730.0920.3960.9830.9830.9831.0000.9890.989
signgu_nm0.2500.9670.9620.9620.0980.4790.9940.9940.9940.9891.0001.000
adstrd_nm0.2710.9670.9620.9620.1330.5161.0001.0001.0000.9891.0001.000

Missing values

2023-12-10T18:53:23.037698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:53:23.558722image/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

yyrealm_clclcrclm_nmedc_begin_deedc_end_deinstrctr_nmedc_time_dcedc_place_nmrn_addrlnm_addrcity_do_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nm
02011무대예술전문교육무대조명이론운영과정2011.04.062011.04.08정진덕3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
12011무대예술전문교육무대장치이론운영과정2011.04.132011.04.15권용만3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
22011무대예술전문교육무대음향이론운영과정2011.04.202011.04.22조현의, 이수용3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
32011무대예술전문교육무대음향디자인과정2011.06.222011.06.24이재훈, 김도석, 권도경3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
42011무대예술전문교육무대장치디자인과정2011.06.292011.07.01김종석3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
52011무대예술전문교육무대장치기술개발과정2011.07.122011.07.15손호성4일(28시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
62011무대예술전문교육무대조명기술개발과정2011.07.192011.07.22최진근4일(28시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
72011무대예술전문교육무대음향기술개발과정2011.08.092011.08.12조현의4일(28시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
82011무대예술전문교육무대조명디자인과정2011.08.312011.09.02김창기3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
92011무대예술전문교육무대공통문제해결과정2011.11.072011.11.08이동훈, 안치운, 신성환, 이장원2일(16시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
yyrealm_clclcrclm_nmedc_begin_deedc_end_deinstrctr_nmedc_time_dcedc_place_nmrn_addrlnm_addrcity_do_cdctprvn_nmsigngu_cdsigngu_nmadstrd_cdadstrd_nm
902018무대예술전문교육조명, 음향, 영상무대예술현장전문가연수[1]-집중교육 조명Ⅱ2018.04.162018.04.18한희수3일(21시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
912018무대예술전문교육조명, 음향, 영상무대예술현장전문가연수[1]-집중교육 조명Ⅰ2018.05.022018.05.04이재만3일(21시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
922018무대예술전문교육조명, 음향, 영상무대예술현장전문가연수[1]-집중교육 음향2018.06.262018.06.28이수용3일(21시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
932018무대예술전문교육조명, 음향, 영상무대예술현장전문가연수[1]-집중교육 영상2018.07.092018.07.11배준호,정재진3일(21시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
942018무대예술전문교육조명, 음향, 영상무대예술현장전문가연수[1]-통합교육 서부권(전주)2018.07.162018.07.20한희수,김려원,이수용,배준호,정재진5일(30시간)전주소리문화의전당전북 전주시 덕진구 소리로 31전북 전주시 덕진구 덕진동1가 산1-135전라북도35012전주시 덕진구3501257덕진동
952018무대예술전문교육조명, 음향, 영상무대예술현장전문가연수[1]-통합교육 동부권(김해)2018.09.032018.09.07한희수,이재만,이수용,정재진5일(30시간)김해서부문화센터경남 김해시 율하2로 210경남 김해시 율하동 137738경상남도38070김해시3807063장유3동
962019무대예술전문교육조명, 음향, 무대, 하우스매니징, 영상문화예술기획자를위한무대기술AllinONE2019.08.212019.08.23최웅집,김영신,구종회,하종기3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
972019무대예술전문교육음향플러그인을사용한라이브사운드믹싱2019.09.182019.09.20신성환3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
982019무대예술전문교육조명콘솔실습2019.10.292019.10.31한희수3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동
992019무대예술전문교육무대공연무대3D디지털설계실습2019.11.202019.11.22어경준3일(24시간)아르코예술인력개발원경기 고양시 일산동구 성현로513번길 10고양시 일산동구 사리현동 464-231경기도31103고양시 일산동구3110360고봉동