Overview

Dataset statistics

Number of variables17
Number of observations1373
Missing cells500
Missing cells (%)2.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory195.9 KiB
Average record size in memory146.1 B

Variable types

Text4
DateTime1
Categorical2
Numeric10

Dataset

Description국토안전관리원에서 제공하는 데이터이며 시설물의 안전관리에 관한 특별법에서 규정한 광역시/도의 안전진단전문기관에 대한 정보를 제공합니다.
Author국토안전관리원
URLhttps://www.data.go.kr/data/15083310/fileData.do

Alerts

초급 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
특급 is highly overall correlated with 합계 and 1 other fieldsHigh correlation
합계 is highly overall correlated with 초급 and 2 other fieldsHigh correlation
매출액(2019) is highly overall correlated with 매출액(2020) and 2 other fieldsHigh correlation
매출액(2020) is highly overall correlated with 매출액(2019) and 2 other fieldsHigh correlation
매출액(2021) is highly overall correlated with 매출액(2019) and 2 other fieldsHigh correlation
매출액(2022) is highly overall correlated with 매출액(2019) and 2 other fieldsHigh correlation
기술사 또는 건축사 보유유무 is highly overall correlated with 초급 and 2 other fieldsHigh correlation
매출액(2019) has 119 (8.7%) missing valuesMissing
매출액(2020) has 119 (8.7%) missing valuesMissing
매출액(2021) has 119 (8.7%) missing valuesMissing
매출액(2022) has 119 (8.7%) missing valuesMissing
소재지 has unique valuesUnique
초급 has 46 (3.4%) zerosZeros
중급 has 362 (26.4%) zerosZeros
고급 has 248 (18.1%) zerosZeros
기타 has 1276 (92.9%) zerosZeros
매출액(2019) has 401 (29.2%) zerosZeros
매출액(2020) has 284 (20.7%) zerosZeros
매출액(2021) has 182 (13.3%) zerosZeros
매출액(2022) has 136 (9.9%) zerosZeros

Reproduction

Analysis started2023-12-12 04:01:20.096982
Analysis finished2023-12-12 04:01:36.653228
Duration16.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1366
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2023-12-12T13:01:36.862670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length18
Mean length10.158048
Min length4

Characters and Unicode

Total characters13947
Distinct characters356
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1359 ?
Unique (%)99.0%

Sample

1st row에이앤티엔지니어링주식회사
2nd row주식회사 범산
3rd row(주)한국구조물성능평가원
4th row(주)다음기술단
5th row(주)미래원씨앤엠
ValueCountFrequency (%)
주식회사 202
 
12.6%
7
 
0.4%
유한회사 5
 
0.3%
건축사사무소 4
 
0.2%
주)수이앤씨 2
 
0.1%
가람이엔씨 2
 
0.1%
주)에스디이엔지 2
 
0.1%
주)진성엔지니어링 2
 
0.1%
주)도원엔지니어링 2
 
0.1%
주)하이콘엔지니어링 2
 
0.1%
Other values (1371) 1373
85.7%
2023-12-12T13:01:37.324810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1319
 
9.5%
( 1061
 
7.6%
) 1061
 
7.6%
625
 
4.5%
529
 
3.8%
480
 
3.4%
422
 
3.0%
362
 
2.6%
357
 
2.6%
356
 
2.6%
Other values (346) 7375
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11552
82.8%
Open Punctuation 1061
 
7.6%
Close Punctuation 1061
 
7.6%
Space Separator 230
 
1.6%
Uppercase Letter 19
 
0.1%
Lowercase Letter 15
 
0.1%
Other Punctuation 4
 
< 0.1%
Decimal Number 3
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1319
 
11.4%
625
 
5.4%
529
 
4.6%
480
 
4.2%
422
 
3.7%
362
 
3.1%
357
 
3.1%
356
 
3.1%
305
 
2.6%
292
 
2.5%
Other values (318) 6505
56.3%
Lowercase Letter
ValueCountFrequency (%)
n 3
20.0%
e 2
13.3%
i 2
13.3%
g 2
13.3%
o 1
 
6.7%
t 1
 
6.7%
d 1
 
6.7%
r 1
 
6.7%
w 1
 
6.7%
j 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
E 5
26.3%
S 5
26.3%
C 3
15.8%
N 2
 
10.5%
O 1
 
5.3%
L 1
 
5.3%
H 1
 
5.3%
G 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
& 2
50.0%
. 1
25.0%
, 1
25.0%
Decimal Number
ValueCountFrequency (%)
5 1
33.3%
6 1
33.3%
3 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 1061
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1061
100.0%
Space Separator
ValueCountFrequency (%)
230
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11554
82.8%
Common 2359
 
16.9%
Latin 34
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1319
 
11.4%
625
 
5.4%
529
 
4.6%
480
 
4.2%
422
 
3.7%
362
 
3.1%
357
 
3.1%
356
 
3.1%
305
 
2.6%
292
 
2.5%
Other values (319) 6507
56.3%
Latin
ValueCountFrequency (%)
E 5
14.7%
S 5
14.7%
n 3
 
8.8%
C 3
 
8.8%
e 2
 
5.9%
i 2
 
5.9%
g 2
 
5.9%
N 2
 
5.9%
O 1
 
2.9%
o 1
 
2.9%
Other values (8) 8
23.5%
Common
ValueCountFrequency (%)
( 1061
45.0%
) 1061
45.0%
230
 
9.7%
& 2
 
0.1%
. 1
 
< 0.1%
, 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11552
82.8%
ASCII 2393
 
17.2%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1319
 
11.4%
625
 
5.4%
529
 
4.6%
480
 
4.2%
422
 
3.7%
362
 
3.1%
357
 
3.1%
356
 
3.1%
305
 
2.6%
292
 
2.5%
Other values (318) 6505
56.3%
ASCII
ValueCountFrequency (%)
( 1061
44.3%
) 1061
44.3%
230
 
9.6%
E 5
 
0.2%
S 5
 
0.2%
n 3
 
0.1%
C 3
 
0.1%
e 2
 
0.1%
i 2
 
0.1%
g 2
 
0.1%
Other values (17) 19
 
0.8%
None
ValueCountFrequency (%)
2
100.0%
Distinct1341
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2023-12-12T13:01:37.780131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length3
Mean length3.8055353
Min length2

Characters and Unicode

Total characters5225
Distinct characters226
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1312 ?
Unique (%)95.6%

Sample

1st row송현담,김미진
2nd row이철상, 이승규
3rd row이원구
4th row박철
5th row이창열
ValueCountFrequency (%)
김경민 3
 
0.2%
김광호 3
 
0.2%
3
 
0.2%
이준우 3
 
0.2%
김대호 3
 
0.2%
김영미 3
 
0.2%
정승호 2
 
0.1%
김형균 2
 
0.1%
이상진 2
 
0.1%
박창제 2
 
0.1%
Other values (1488) 1525
98.3%
2023-12-12T13:01:38.508961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
324
 
6.2%
242
 
4.6%
, 221
 
4.2%
181
 
3.5%
174
 
3.3%
145
 
2.8%
140
 
2.7%
105
 
2.0%
100
 
1.9%
93
 
1.8%
Other values (216) 3500
67.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4817
92.2%
Other Punctuation 222
 
4.2%
Space Separator 181
 
3.5%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
324
 
6.7%
242
 
5.0%
174
 
3.6%
145
 
3.0%
140
 
2.9%
105
 
2.2%
100
 
2.1%
93
 
1.9%
89
 
1.8%
80
 
1.7%
Other values (210) 3325
69.0%
Other Punctuation
ValueCountFrequency (%)
, 221
99.5%
/ 1
 
0.5%
Space Separator
ValueCountFrequency (%)
181
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4814
92.1%
Common 408
 
7.8%
Han 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
324
 
6.7%
242
 
5.0%
174
 
3.6%
145
 
3.0%
140
 
2.9%
105
 
2.2%
100
 
2.1%
93
 
1.9%
89
 
1.8%
80
 
1.7%
Other values (207) 3322
69.0%
Common
ValueCountFrequency (%)
, 221
54.2%
181
44.4%
) 2
 
0.5%
( 2
 
0.5%
/ 1
 
0.2%
1 1
 
0.2%
Han
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4814
92.1%
ASCII 408
 
7.8%
CJK 3
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
324
 
6.7%
242
 
5.0%
174
 
3.6%
145
 
3.0%
140
 
2.9%
105
 
2.2%
100
 
2.1%
93
 
1.9%
89
 
1.8%
80
 
1.7%
Other values (207) 3322
69.0%
ASCII
ValueCountFrequency (%)
, 221
54.2%
181
44.4%
) 2
 
0.5%
( 2
 
0.5%
/ 1
 
0.2%
1 1
 
0.2%
CJK
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Distinct1125
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Minimum1995-08-29 00:00:00
Maximum2022-12-21 00:00:00
2023-12-12T13:01:38.727834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:38.955027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

등록분야
Categorical

Distinct13
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
건축
546 
교량/터널
423 
교량/터널,수리시설
205 
교량/터널,건축,수리시설
55 
교량/터널,건축
 
46
Other values (8)
98 

Length

Max length16
Median length13
Mean length5.1828114
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건축
2nd row교량/터널,수리시설
3rd row건축
4th row교량/터널,건축,항만,수리시설
5th row건축

Common Values

ValueCountFrequency (%)
건축 546
39.8%
교량/터널 423
30.8%
교량/터널,수리시설 205
 
14.9%
교량/터널,건축,수리시설 55
 
4.0%
교량/터널,건축 46
 
3.4%
교량/터널,건축,항만,수리시설 24
 
1.7%
종합 21
 
1.5%
수리시설 16
 
1.2%
교량/터널,항만,수리시설 11
 
0.8%
교량/터널,항만 10
 
0.7%
Other values (3) 16
 
1.2%

Length

2023-12-12T13:01:39.142630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
건축 546
39.8%
교량/터널 423
30.8%
교량/터널,수리시설 205
 
14.9%
교량/터널,건축,수리시설 55
 
4.0%
교량/터널,건축 46
 
3.4%
교량/터널,건축,항만,수리시설 24
 
1.7%
종합 21
 
1.5%
수리시설 16
 
1.2%
교량/터널,항만,수리시설 11
 
0.8%
교량/터널,항만 10
 
0.7%
Other values (3) 16
 
1.2%
Distinct1357
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2023-12-12T13:01:39.521317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.963583
Min length11

Characters and Unicode

Total characters16426
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

Unique1341 ?
Unique (%)97.7%

Sample

1st row02-598-8808
2nd row063-247-4438
3rd row02-2201-3882
4th row031-698-2288
5th row02-527-0220
ValueCountFrequency (%)
053-525-5758 2
 
0.1%
031-722-3316 2
 
0.1%
062-381-5360 2
 
0.1%
02-2082-2992 2
 
0.1%
062-351-0404 2
 
0.1%
063-236-0480 2
 
0.1%
031-422-0274 2
 
0.1%
062-376-0449 2
 
0.1%
053-428-9800 2
 
0.1%
063-236-2713 2
 
0.1%
Other values (1347) 1353
98.5%
2023-12-12T13:01:40.068312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2746
16.7%
0 2680
16.3%
2 1652
10.1%
3 1650
10.0%
5 1422
8.7%
1 1335
8.1%
4 1202
7.3%
7 1079
 
6.6%
6 1041
 
6.3%
8 886
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13680
83.3%
Dash Punctuation 2746
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2680
19.6%
2 1652
12.1%
3 1650
12.1%
5 1422
10.4%
1 1335
9.8%
4 1202
8.8%
7 1079
7.9%
6 1041
 
7.6%
8 886
 
6.5%
9 733
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 2746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16426
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2746
16.7%
0 2680
16.3%
2 1652
10.1%
3 1650
10.0%
5 1422
8.7%
1 1335
8.1%
4 1202
7.3%
7 1079
 
6.6%
6 1041
 
6.3%
8 886
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16426
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2746
16.7%
0 2680
16.3%
2 1652
10.1%
3 1650
10.0%
5 1422
8.7%
1 1335
8.1%
4 1202
7.3%
7 1079
 
6.6%
6 1041
 
6.3%
8 886
 
5.4%

소재지
Text

UNIQUE 

Distinct1373
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
2023-12-12T13:01:40.509379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length51
Mean length32.28405
Min length15

Characters and Unicode

Total characters44326
Distinct characters509
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1373 ?
Unique (%)100.0%

Sample

1st row서울특별시 서초구 서초대로 280, 4층(서초동, 태양빌딩)
2nd row전라북도 무주군 무주읍 단천로1길 12, 2층
3rd row서울특별시 광진구 자양로 39, 2층 (자양동)
4th row경기도 성남시 분당구 판교역로240, 에이동 309호 삼평동,삼환하이펙스
5th row서울특별시 송파구 법원로 128, 씨518호, 씨519호, 씨520호(문정동, 문정에스케이브이원지엘메트로시티)
ValueCountFrequency (%)
서울특별시 276
 
3.2%
경기도 243
 
2.9%
2층 143
 
1.7%
경상북도 110
 
1.3%
강원특별자치도 106
 
1.2%
3층 102
 
1.2%
전라남도 87
 
1.0%
경상남도 81
 
1.0%
전라북도 74
 
0.9%
충청남도 73
 
0.9%
Other values (3468) 7225
84.8%
2023-12-12T13:01:41.036210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7206
 
16.3%
1 1621
 
3.7%
1577
 
3.6%
, 1395
 
3.1%
1294
 
2.9%
2 1212
 
2.7%
1209
 
2.7%
) 1183
 
2.7%
( 1183
 
2.7%
944
 
2.1%
Other values (499) 25502
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25396
57.3%
Decimal Number 7512
 
16.9%
Space Separator 7206
 
16.3%
Other Punctuation 1403
 
3.2%
Close Punctuation 1183
 
2.7%
Open Punctuation 1183
 
2.7%
Dash Punctuation 296
 
0.7%
Uppercase Letter 127
 
0.3%
Lowercase Letter 13
 
< 0.1%
Math Symbol 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1577
 
6.2%
1294
 
5.1%
1209
 
4.8%
944
 
3.7%
921
 
3.6%
714
 
2.8%
578
 
2.3%
522
 
2.1%
520
 
2.0%
506
 
2.0%
Other values (453) 16611
65.4%
Uppercase Letter
ValueCountFrequency (%)
A 27
21.3%
B 26
20.5%
C 14
11.0%
D 8
 
6.3%
S 7
 
5.5%
F 7
 
5.5%
T 6
 
4.7%
I 6
 
4.7%
H 4
 
3.1%
K 4
 
3.1%
Other values (9) 18
14.2%
Decimal Number
ValueCountFrequency (%)
1 1621
21.6%
2 1212
16.1%
0 914
12.2%
3 878
11.7%
4 662
8.8%
5 583
 
7.8%
6 521
 
6.9%
7 408
 
5.4%
8 377
 
5.0%
9 336
 
4.5%
Lowercase Letter
ValueCountFrequency (%)
c 3
23.1%
k 2
15.4%
t 2
15.4%
n 2
15.4%
e 2
15.4%
i 1
 
7.7%
r 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 1395
99.4%
. 5
 
0.4%
: 1
 
0.1%
· 1
 
0.1%
/ 1
 
0.1%
Space Separator
ValueCountFrequency (%)
7206
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1183
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1183
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 296
100.0%
Math Symbol
ValueCountFrequency (%)
~ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25396
57.3%
Common 18790
42.4%
Latin 140
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1577
 
6.2%
1294
 
5.1%
1209
 
4.8%
944
 
3.7%
921
 
3.6%
714
 
2.8%
578
 
2.3%
522
 
2.1%
520
 
2.0%
506
 
2.0%
Other values (453) 16611
65.4%
Latin
ValueCountFrequency (%)
A 27
19.3%
B 26
18.6%
C 14
10.0%
D 8
 
5.7%
S 7
 
5.0%
F 7
 
5.0%
T 6
 
4.3%
I 6
 
4.3%
H 4
 
2.9%
K 4
 
2.9%
Other values (16) 31
22.1%
Common
ValueCountFrequency (%)
7206
38.4%
1 1621
 
8.6%
, 1395
 
7.4%
2 1212
 
6.5%
) 1183
 
6.3%
( 1183
 
6.3%
0 914
 
4.9%
3 878
 
4.7%
4 662
 
3.5%
5 583
 
3.1%
Other values (10) 1953
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25396
57.3%
ASCII 18929
42.7%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7206
38.1%
1 1621
 
8.6%
, 1395
 
7.4%
2 1212
 
6.4%
) 1183
 
6.2%
( 1183
 
6.2%
0 914
 
4.8%
3 878
 
4.6%
4 662
 
3.5%
5 583
 
3.1%
Other values (35) 2092
 
11.1%
Hangul
ValueCountFrequency (%)
1577
 
6.2%
1294
 
5.1%
1209
 
4.8%
944
 
3.7%
921
 
3.6%
714
 
2.8%
578
 
2.3%
522
 
2.1%
520
 
2.0%
506
 
2.0%
Other values (453) 16611
65.4%
None
ValueCountFrequency (%)
· 1
100.0%

초급
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)2.2%
Missing4
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean3.6471877
Minimum0
Maximum60
Zeros46
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:41.173888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile8
Maximum60
Range60
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.708946
Coefficient of variation (CV)1.0169331
Kurtosis60.428577
Mean3.6471877
Median Absolute Deviation (MAD)1
Skewness6.0817901
Sum4993
Variance13.75628
MonotonicityNot monotonic
2023-12-12T13:01:41.310536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3 418
30.4%
2 303
22.1%
4 175
12.7%
1 146
 
10.6%
5 109
 
7.9%
6 57
 
4.2%
0 46
 
3.4%
7 27
 
2.0%
8 21
 
1.5%
10 18
 
1.3%
Other values (20) 49
 
3.6%
ValueCountFrequency (%)
0 46
 
3.4%
1 146
 
10.6%
2 303
22.1%
3 418
30.4%
4 175
12.7%
5 109
 
7.9%
6 57
 
4.2%
7 27
 
2.0%
8 21
 
1.5%
9 11
 
0.8%
ValueCountFrequency (%)
60 1
 
0.1%
39 1
 
0.1%
36 1
 
0.1%
35 1
 
0.1%
32 1
 
0.1%
26 1
 
0.1%
24 3
0.2%
23 2
0.1%
22 2
0.1%
21 2
0.1%

중급
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.9%
Missing4
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.4799123
Minimum0
Maximum13
Zeros362
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:41.459790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4644165
Coefficient of variation (CV)0.98952924
Kurtosis8.7488511
Mean1.4799123
Median Absolute Deviation (MAD)1
Skewness2.0760328
Sum2026
Variance2.1445158
MonotonicityNot monotonic
2023-12-12T13:01:41.591749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 444
32.3%
0 362
26.4%
2 304
22.1%
3 170
 
12.4%
4 48
 
3.5%
5 17
 
1.2%
6 8
 
0.6%
7 6
 
0.4%
8 3
 
0.2%
10 3
 
0.2%
Other values (3) 4
 
0.3%
(Missing) 4
 
0.3%
ValueCountFrequency (%)
0 362
26.4%
1 444
32.3%
2 304
22.1%
3 170
 
12.4%
4 48
 
3.5%
5 17
 
1.2%
6 8
 
0.6%
7 6
 
0.4%
8 3
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
13 1
 
0.1%
12 1
 
0.1%
10 3
 
0.2%
9 2
 
0.1%
8 3
 
0.2%
7 6
 
0.4%
6 8
 
0.6%
5 17
 
1.2%
4 48
 
3.5%
3 170
12.4%

고급
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)1.2%
Missing4
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1.9452155
Minimum0
Maximum25
Zeros248
Zeros (%)18.1%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:41.705670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum25
Range25
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9994318
Coefficient of variation (CV)1.0278716
Kurtosis24.486179
Mean1.9452155
Median Absolute Deviation (MAD)1
Skewness3.5044517
Sum2663
Variance3.9977275
MonotonicityNot monotonic
2023-12-12T13:01:41.811342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 418
30.4%
2 344
25.1%
0 248
18.1%
3 173
12.6%
4 89
 
6.5%
5 43
 
3.1%
6 16
 
1.2%
7 14
 
1.0%
8 8
 
0.6%
10 4
 
0.3%
Other values (7) 12
 
0.9%
(Missing) 4
 
0.3%
ValueCountFrequency (%)
0 248
18.1%
1 418
30.4%
2 344
25.1%
3 173
12.6%
4 89
 
6.5%
5 43
 
3.1%
6 16
 
1.2%
7 14
 
1.0%
8 8
 
0.6%
9 2
 
0.1%
ValueCountFrequency (%)
25 1
 
0.1%
19 1
 
0.1%
17 2
 
0.1%
15 1
 
0.1%
12 3
 
0.2%
11 2
 
0.1%
10 4
 
0.3%
9 2
 
0.1%
8 8
0.6%
7 14
1.0%

특급
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)3.2%
Missing4
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean6.1263696
Minimum0
Maximum124
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:41.934689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q37
95-th percentile17
Maximum124
Range124
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.8918726
Coefficient of variation (CV)1.1249521
Kurtosis74.124038
Mean6.1263696
Median Absolute Deviation (MAD)2
Skewness6.274182
Sum8387
Variance47.497908
MonotonicityNot monotonic
2023-12-12T13:01:42.077865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3 274
20.0%
2 256
18.6%
4 226
16.5%
5 144
10.5%
6 87
 
6.3%
7 56
 
4.1%
8 55
 
4.0%
9 47
 
3.4%
10 31
 
2.3%
11 29
 
2.1%
Other values (34) 164
11.9%
ValueCountFrequency (%)
0 1
 
0.1%
1 15
 
1.1%
2 256
18.6%
3 274
20.0%
4 226
16.5%
5 144
10.5%
6 87
 
6.3%
7 56
 
4.1%
8 55
 
4.0%
9 47
 
3.4%
ValueCountFrequency (%)
124 1
0.1%
64 1
0.1%
51 1
0.1%
50 1
0.1%
48 1
0.1%
47 2
0.1%
45 2
0.1%
42 1
0.1%
40 1
0.1%
36 1
0.1%

기타
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.4%
Missing4
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean0.096420745
Minimum0
Maximum8
Zeros1276
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:42.184686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.43027978
Coefficient of variation (CV)4.4625229
Kurtosis98.48944
Mean0.096420745
Median Absolute Deviation (MAD)0
Skewness7.751515
Sum132
Variance0.18514069
MonotonicityNot monotonic
2023-12-12T13:01:42.287194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1276
92.9%
1 66
 
4.8%
2 21
 
1.5%
3 4
 
0.3%
8 1
 
0.1%
4 1
 
0.1%
(Missing) 4
 
0.3%
ValueCountFrequency (%)
0 1276
92.9%
1 66
 
4.8%
2 21
 
1.5%
3 4
 
0.3%
4 1
 
0.1%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
4 1
 
0.1%
3 4
 
0.3%
2 21
 
1.5%
1 66
 
4.8%
0 1276
92.9%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)4.5%
Missing4
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean13.295106
Minimum1
Maximum208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:42.444730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q18
median9
Q314
95-th percentile31
Maximum208
Range207
Interquartile range (IQR)6

Descriptive statistics

Standard deviation11.942503
Coefficient of variation (CV)0.89826309
Kurtosis69.623367
Mean13.295106
Median Absolute Deviation (MAD)1
Skewness6.5262578
Sum18201
Variance142.62338
MonotonicityNot monotonic
2023-12-12T13:01:42.597464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 557
40.6%
9 187
 
13.6%
10 91
 
6.6%
14 83
 
6.0%
11 54
 
3.9%
15 52
 
3.8%
16 48
 
3.5%
12 40
 
2.9%
17 36
 
2.6%
18 22
 
1.6%
Other values (51) 199
 
14.5%
ValueCountFrequency (%)
1 1
 
0.1%
6 3
 
0.2%
7 4
 
0.3%
8 557
40.6%
9 187
 
13.6%
10 91
 
6.6%
11 54
 
3.9%
12 40
 
2.9%
13 14
 
1.0%
14 83
 
6.0%
ValueCountFrequency (%)
208 1
 
0.1%
115 1
 
0.1%
111 1
 
0.1%
106 1
 
0.1%
91 1
 
0.1%
89 2
0.1%
87 1
 
0.1%
85 1
 
0.1%
79 1
 
0.1%
76 3
0.2%

매출액(2019)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct851
Distinct (%)67.9%
Missing119
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean428750.46
Minimum0
Maximum26330426
Zeros401
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:42.736341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median67524.2
Q3227481.38
95-th percentile1466530.7
Maximum26330426
Range26330426
Interquartile range (IQR)227481.38

Descriptive statistics

Standard deviation1722564.1
Coefficient of variation (CV)4.0176378
Kurtosis104.36793
Mean428750.46
Median Absolute Deviation (MAD)67524.2
Skewness9.102218
Sum5.3765308 × 108
Variance2.967227 × 1012
MonotonicityNot monotonic
2023-12-12T13:01:42.886834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 401
29.2%
5500.0 2
 
0.1%
15675.0 2
 
0.1%
19800.0 2
 
0.1%
86132.0 1
 
0.1%
37336.0 1
 
0.1%
411012.0 1
 
0.1%
102355.0 1
 
0.1%
1067953.2 1
 
0.1%
41816.0 1
 
0.1%
Other values (841) 841
61.3%
(Missing) 119
 
8.7%
ValueCountFrequency (%)
0.0 401
29.2%
550.0 1
 
0.1%
600.0 1
 
0.1%
1540.0 1
 
0.1%
1650.0 1
 
0.1%
1705.0 1
 
0.1%
1800.0 1
 
0.1%
2090.0 1
 
0.1%
2860.0 1
 
0.1%
3410.0 1
 
0.1%
ValueCountFrequency (%)
26330426.0 1
0.1%
25813105.0 1
0.1%
18652508.0 1
0.1%
15087224.0 1
0.1%
14354082.0 1
0.1%
12093022.0 1
0.1%
11936777.2 1
0.1%
11081767.0 1
0.1%
11054136.4 1
0.1%
10584377.3 1
0.1%

매출액(2020)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct963
Distinct (%)76.8%
Missing119
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean411664.51
Minimum0
Maximum24702126
Zeros284
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:43.087880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15392.5
median91902.5
Q3247532.4
95-th percentile1397657.5
Maximum24702126
Range24702126
Interquartile range (IQR)242139.9

Descriptive statistics

Standard deviation1527242.9
Coefficient of variation (CV)3.7099211
Kurtosis102.23404
Mean411664.51
Median Absolute Deviation (MAD)91902.5
Skewness8.981786
Sum5.162273 × 108
Variance2.3324708 × 1012
MonotonicityNot monotonic
2023-12-12T13:01:43.276780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 284
 
20.7%
4400.0 3
 
0.2%
28730.0 2
 
0.1%
30500.0 2
 
0.1%
20000.0 2
 
0.1%
8900.0 2
 
0.1%
12100.0 2
 
0.1%
11000.0 2
 
0.1%
38536.0 1
 
0.1%
242324.0 1
 
0.1%
Other values (953) 953
69.4%
(Missing) 119
 
8.7%
ValueCountFrequency (%)
0.0 284
20.7%
520.0 1
 
0.1%
825.0 1
 
0.1%
880.0 1
 
0.1%
990.0 1
 
0.1%
1000.0 1
 
0.1%
1100.0 1
 
0.1%
1150.0 1
 
0.1%
1200.0 1
 
0.1%
1540.0 1
 
0.1%
ValueCountFrequency (%)
24702126.4 1
0.1%
19791557.0 1
0.1%
17193018.0 1
0.1%
15850460.71 1
0.1%
12546749.0 1
0.1%
10981198.08 1
0.1%
10235205.0 1
0.1%
10189189.82 1
0.1%
9094368.0 1
0.1%
9068035.0 1
0.1%

매출액(2021)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1066
Distinct (%)85.0%
Missing119
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean499860.64
Minimum0
Maximum43699453
Zeros182
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:43.507528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q124150
median105458.39
Q3271874.98
95-th percentile1438478.9
Maximum43699453
Range43699453
Interquartile range (IQR)247724.98

Descriptive statistics

Standard deviation2183797.7
Coefficient of variation (CV)4.368813
Kurtosis177.23973
Mean499860.64
Median Absolute Deviation (MAD)97758.395
Skewness11.560228
Sum6.2682525 × 108
Variance4.7689723 × 1012
MonotonicityNot monotonic
2023-12-12T13:01:43.691383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 182
 
13.3%
6600.0 3
 
0.2%
7700.0 3
 
0.2%
16800.0 2
 
0.1%
6000.0 2
 
0.1%
2200.0 2
 
0.1%
300736.8 1
 
0.1%
46717.0 1
 
0.1%
47859.0 1
 
0.1%
3300.0 1
 
0.1%
Other values (1056) 1056
76.9%
(Missing) 119
 
8.7%
ValueCountFrequency (%)
0.0 182
13.3%
825.0 1
 
0.1%
880.0 1
 
0.1%
1100.0 1
 
0.1%
1353.0 1
 
0.1%
1540.0 1
 
0.1%
1650.0 1
 
0.1%
2000.0 1
 
0.1%
2200.0 2
 
0.1%
2310.0 1
 
0.1%
ValueCountFrequency (%)
43699452.9 1
0.1%
33119748.48 1
0.1%
17953250.6 1
0.1%
17853926.0 1
0.1%
17678898.1 1
0.1%
16107295.0 1
0.1%
13515997.0 1
0.1%
13285422.5 1
0.1%
13049677.0 1
0.1%
13030226.5 1
0.1%

매출액(2022)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1107
Distinct (%)88.3%
Missing119
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean348482.6
Minimum0
Maximum11658934
Zeros136
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2023-12-12T13:01:43.895412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q132704.5
median105353.49
Q3256318.75
95-th percentile1140540.7
Maximum11658934
Range11658934
Interquartile range (IQR)223614.25

Descriptive statistics

Standard deviation1042286.4
Coefficient of variation (CV)2.9909282
Kurtosis56.092934
Mean348482.6
Median Absolute Deviation (MAD)91789.5
Skewness6.9247042
Sum4.3699718 × 108
Variance1.086361 × 1012
MonotonicityNot monotonic
2023-12-12T13:01:44.128815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 136
 
9.9%
5500.0 5
 
0.4%
6600.0 3
 
0.2%
1980.0 2
 
0.1%
880.0 2
 
0.1%
3300.0 2
 
0.1%
4950.0 2
 
0.1%
3600.0 2
 
0.1%
59840.0 2
 
0.1%
1530216.8 1
 
0.1%
Other values (1097) 1097
79.9%
(Missing) 119
 
8.7%
ValueCountFrequency (%)
0.0 136
9.9%
880.0 2
 
0.1%
895.0 1
 
0.1%
1000.0 1
 
0.1%
1045.0 1
 
0.1%
1100.0 1
 
0.1%
1430.0 1
 
0.1%
1485.0 1
 
0.1%
1575.0 1
 
0.1%
1980.0 2
 
0.1%
ValueCountFrequency (%)
11658933.77 1
0.1%
11590192.11 1
0.1%
11211411.0 1
0.1%
9991920.81 1
0.1%
9109281.0 1
0.1%
8449370.0 1
0.1%
8362532.0 1
0.1%
8182478.0 1
0.1%
7447688.72 1
0.1%
7052059.6 1
0.1%

기술사 또는 건축사 보유유무
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Y(1)
453 
N
366 
Y(2)
275 
Y(3)
101 
Y(4)
64 
Other values (14)
114 

Length

Max length5
Median length4
Mean length3.2184996
Min length1

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowY(1)
2nd rowY(1)
3rd rowN
4th rowY(7)
5th rowY(1)

Common Values

ValueCountFrequency (%)
Y(1) 453
33.0%
N 366
26.7%
Y(2) 275
20.0%
Y(3) 101
 
7.4%
Y(4) 64
 
4.7%
Y(5) 31
 
2.3%
Y(7) 21
 
1.5%
Y(6) 21
 
1.5%
Y(8) 9
 
0.7%
Y(9) 7
 
0.5%
Other values (9) 25
 
1.8%

Length

2023-12-12T13:01:44.366183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
y(1 453
33.0%
n 366
26.7%
y(2 275
20.0%
y(3 101
 
7.4%
y(4 64
 
4.7%
y(5 31
 
2.3%
y(7 21
 
1.5%
y(6 21
 
1.5%
y(8 9
 
0.7%
y(9 7
 
0.5%
Other values (9) 25
 
1.8%

Interactions

2023-12-12T13:01:34.286204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:21.814087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:23.223818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:24.576956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:26.011101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:27.438602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:29.248878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:30.486665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:31.636491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:32.963268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:34.405155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:22.017171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:23.370767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:24.693577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:26.186008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:27.952276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:29.368527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:30.607506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:31.788479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:33.122839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:34.527951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:22.115324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:23.507127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:24.833767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:26.343932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:28.080597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:29.501685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:30.723038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:31.939076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:33.260880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:34.648906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:22.225466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:23.637899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:24.955572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:26.500079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:28.205416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:29.623054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:30.839472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:32.053817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:33.394754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:34.767836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:22.359815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:23.767210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:25.088578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:26.646301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:28.352250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:29.752912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:30.941670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:32.185399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:33.515599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:34.920444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:22.498572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:23.898420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:25.226705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:26.794795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:28.504038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:29.891352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:31.072225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:32.301347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:33.664029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:35.078420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:22.624806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:24.006619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:25.357224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:26.917099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:28.627172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:30.017429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:31.180958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:32.404969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:33.787051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:35.221079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:22.761675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:24.170864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:25.496405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:27.041350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:28.775950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:30.134702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:31.278027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:32.526871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:33.914196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:35.340792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:22.895866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:24.309896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:25.661470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:27.175882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:28.954182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:30.243246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:31.391533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:32.662327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:34.047964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:35.458159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:23.051059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:24.434135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:25.833831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:27.304649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:29.121390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:30.359840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:31.495393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:32.813115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:01:34.152216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:01:44.857134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록분야초급중급고급특급기타합계매출액(2019)매출액(2020)매출액(2021)매출액(2022)기술사 또는 건축사 보유유무
등록분야1.0000.6040.4870.5430.6280.1900.7010.5030.4550.4770.4880.601
초급0.6041.0000.7080.8610.8320.4390.8940.8840.7370.6230.6840.835
중급0.4870.7081.0000.6330.6360.4840.7110.7310.8440.6160.5800.687
고급0.5430.8610.6331.0000.7170.2840.7610.9110.8100.6570.7130.702
특급0.6280.8320.6360.7171.0000.2870.9660.7270.7540.8240.7260.908
기타0.1900.4390.4840.2840.2871.0000.4220.5220.7130.1840.4590.308
합계0.7010.8940.7110.7610.9660.4221.0000.7790.7840.8230.7440.860
매출액(2019)0.5030.8840.7310.9110.7270.5220.7791.0000.9010.7960.7880.750
매출액(2020)0.4550.7370.8440.8100.7540.7130.7840.9011.0000.8130.8210.724
매출액(2021)0.4770.6230.6160.6570.8240.1840.8230.7960.8131.0000.8330.651
매출액(2022)0.4880.6840.5800.7130.7260.4590.7440.7880.8210.8331.0000.733
기술사 또는 건축사 보유유무0.6010.8350.6870.7020.9080.3080.8600.7500.7240.6510.7331.000
2023-12-12T13:01:45.016483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기술사 또는 건축사 보유유무등록분야
기술사 또는 건축사 보유유무1.0000.250
등록분야0.2501.000
2023-12-12T13:01:45.152936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
초급중급고급특급기타합계매출액(2019)매출액(2020)매출액(2021)매출액(2022)등록분야기술사 또는 건축사 보유유무
초급1.0000.1420.1170.2030.0740.5720.2380.2590.2390.2620.3100.550
중급0.1421.000-0.056-0.0280.0450.2330.1440.1730.1690.2120.2330.350
고급0.117-0.0561.0000.1620.0590.4090.1940.1910.1440.1590.2560.473
특급0.203-0.0280.1621.0000.0350.7180.3600.3920.3750.3580.3530.705
기타0.0740.0450.0590.0351.0000.1300.0490.0560.0480.0740.0950.147
합계0.5720.2330.4090.7180.1301.0000.3810.4160.3830.4050.4190.608
매출액(2019)0.2380.1440.1940.3600.0490.3811.0000.7560.6490.5510.2550.434
매출액(2020)0.2590.1730.1910.3920.0560.4160.7561.0000.7540.6440.2140.384
매출액(2021)0.2390.1690.1440.3750.0480.3830.6490.7541.0000.7200.2440.357
매출액(2022)0.2620.2120.1590.3580.0740.4050.5510.6440.7201.0000.2260.379
등록분야0.3100.2330.2560.3530.0950.4190.2550.2140.2440.2261.0000.250
기술사 또는 건축사 보유유무0.5500.3500.4730.7050.1470.6080.4340.3840.3570.3790.2501.000

Missing values

2023-12-12T13:01:35.658953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:01:36.278099image/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.
2023-12-12T13:01:36.503774image/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

업체명대표자등록일자등록분야전화번호소재지초급중급고급특급기타합계매출액(2019)매출액(2020)매출액(2021)매출액(2022)기술사 또는 건축사 보유유무
0에이앤티엔지니어링주식회사송현담,김미진2011-03-15건축02-598-8808서울특별시 서초구 서초대로 280, 4층(서초동, 태양빌딩)6013111600.01200.00.00.0Y(1)
1주식회사 범산이철상, 이승규2011-10-26교량/터널,수리시설063-247-4438전라북도 무주군 무주읍 단천로1길 12, 2층31012016608520.0207936.0606243.0144384.0Y(1)
2(주)한국구조물성능평가원이원구2002-03-05건축02-2201-3882서울특별시 광진구 자양로 39, 2층 (자양동)41120818590.017160.010120.03960.0N
3(주)다음기술단박철2004-08-17교량/터널,건축,항만,수리시설031-698-2288경기도 성남시 분당구 판교역로240, 에이동 309호 삼평동,삼환하이펙스22210420769896178.0310981198.0833119748.489991920.81Y(7)
4(주)미래원씨앤엠이창열2004-12-22건축02-527-0220서울특별시 송파구 법원로 128, 씨518호, 씨519호, 씨520호(문정동, 문정에스케이브이원지엘메트로시티)3234012876644.0723936.0172042.73297506.0Y(1)
5(주)세종이엔씨이인안2013-07-22교량/터널,수리시설041-541-2990충청남도 아산시 번영로 113-3, 302호 (온천동)4217014109830.0184841.0108184.0156763.0Y(4)
6(사)대한산업안전협회박종선1996-01-19종합02-860-4700서울특별시 구로구 공원로 70 (구로동)24825280859856778.010189189.829462697.335814936.15Y(8)
7(주)건설방재기술연구원박동웅1998-11-19교량/터널,건축,수리시설063-225-0550전라북도 김제시 죽산면 해학로 236661120251610605.0885320.21758316.0665824.5Y(1)
8(주)사림엔지니어링한용섭2009-07-22건축02-838-3600서울특별시 송파구 법원로 127, 6층 601호 (문정동)312208348766.063800.089188.0100653.0Y(1)
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