Overview

Dataset statistics

Number of variables11
Number of observations51
Missing cells2
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory94.6 B

Variable types

Categorical2
Text5
Numeric4

Alerts

업종명 has constant value ""Constant
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
사업자등록번호 has 1 (2.0%) missing valuesMissing
전화번호 has 1 (2.0%) missing valuesMissing
소재지지번주소 has unique valuesUnique

Reproduction

Analysis started2024-03-12 23:22:11.810366
Analysis finished2024-03-12 23:22:13.627001
Duration1.82 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)41.2%
Missing0
Missing (%)0.0%
Memory size540.0 B
고양시
9 
평택시
7 
안양시
6 
하남시
3 
포천시
3 
Other values (16)
23 

Length

Max length4
Median length3
Mean length3.0392157
Min length3

Unique

Unique11 ?
Unique (%)21.6%

Sample

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

Common Values

ValueCountFrequency (%)
고양시 9
17.6%
평택시 7
13.7%
안양시 6
11.8%
하남시 3
 
5.9%
포천시 3
 
5.9%
용인시 3
 
5.9%
화성시 3
 
5.9%
수원시 2
 
3.9%
남양주시 2
 
3.9%
김포시 2
 
3.9%
Other values (11) 11
21.6%

Length

2024-03-13T08:22:13.708310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고양시 9
17.6%
평택시 7
13.7%
안양시 6
11.8%
하남시 3
 
5.9%
포천시 3
 
5.9%
용인시 3
 
5.9%
화성시 3
 
5.9%
수원시 2
 
3.9%
남양주시 2
 
3.9%
김포시 2
 
3.9%
Other values (11) 11
21.6%

상호명
Text

Distinct49
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024-03-13T08:22:13.876184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length9.1372549
Min length5

Characters and Unicode

Total characters466
Distinct characters102
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)92.2%

Sample

1st row(주)아세아항측
2nd row한국항공촬영 주식회사
3rd row(주)일도엔지니어링
4th row지오엠 주식회사
5th row(주)유민지적
ValueCountFrequency (%)
주식회사 9
 
15.0%
주)대명지적측량기술공사 2
 
3.3%
주)고려기술단 2
 
3.3%
서일엔지니어링 1
 
1.7%
주)삼인공간정보 1
 
1.7%
주)진성이엔씨 1
 
1.7%
주)케이에스 1
 
1.7%
주)아세아항측 1
 
1.7%
한성이엔지 1
 
1.7%
주)하나엔지니어링 1
 
1.7%
Other values (40) 40
66.7%
2024-03-13T08:22:14.150859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 40
 
8.6%
지 30
 
6.4%
( 29
 
6.2%
) 29
 
6.2%
사 16
 
3.4%
적 16
 
3.4%
식 11
 
2.4%
측 11
 
2.4%
회 11
 
2.4%
량 10
 
2.1%
Other values (92) 263
56.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 390
83.7%
Open Punctuation 29
 
6.2%
Close Punctuation 29
 
6.2%
Other Symbol 9
 
1.9%
Space Separator 9
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 40
 
10.3%
지 30
 
7.7%
사 16
 
4.1%
적 16
 
4.1%
식 11
 
2.8%
측 11
 
2.8%
회 11
 
2.8%
량 10
 
2.6%
보 9
 
2.3%
한 9
 
2.3%
Other values (88) 227
58.2%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 9
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 399
85.6%
Common 67
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 40
 
10.0%
지 30
 
7.5%
사 16
 
4.0%
적 16
 
4.0%
식 11
 
2.8%
측 11
 
2.8%
회 11
 
2.8%
량 10
 
2.5%
㈜ 9
 
2.3%
보 9
 
2.3%
Other values (89) 236
59.1%
Common
ValueCountFrequency (%)
( 29
43.3%
) 29
43.3%
9
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 390
83.7%
ASCII 67
 
14.4%
None 9
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 40
 
10.3%
지 30
 
7.7%
사 16
 
4.1%
적 16
 
4.1%
식 11
 
2.8%
측 11
 
2.8%
회 11
 
2.8%
량 10
 
2.6%
보 9
 
2.3%
한 9
 
2.3%
Other values (88) 227
58.2%
ASCII
ValueCountFrequency (%)
( 29
43.3%
) 29
43.3%
9
 
13.4%
None
ValueCountFrequency (%)
㈜ 9
100.0%

사업자등록번호
Real number (ℝ)

MISSING 

Distinct50
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean2.3241305 × 109
Minimum1.0581752 × 109
Maximum8.1587013 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-03-13T08:22:14.451336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0581752 × 109
5-th percentile1.1641174 × 109
Q11.2881231 × 109
median1.4281247 × 109
Q32.1487294 × 109
95-th percentile6.6969162 × 109
Maximum8.1587013 × 109
Range7.1005261 × 109
Interquartile range (IQR)8.6060626 × 108

Descriptive statistics

Standard deviation1.8160191 × 109
Coefficient of variation (CV)0.7813757
Kurtosis3.034564
Mean2.3241305 × 109
Median Absolute Deviation (MAD)2.6000872 × 108
Skewness2.011586
Sum1.1620652 × 1011
Variance3.2979252 × 1018
MonotonicityNot monotonic
2024-03-13T08:22:14.566828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1058175163 1
 
2.0%
1128130524 1
 
2.0%
3958801720 1
 
2.0%
2358601822 1
 
2.0%
1278640112 1
 
2.0%
1428136058 1
 
2.0%
1428120838 1
 
2.0%
1428128543 1
 
2.0%
1388129301 1
 
2.0%
1308675733 1
 
2.0%
Other values (40) 40
78.4%
ValueCountFrequency (%)
1058175163 1
2.0%
1078174705 1
2.0%
1128130524 1
2.0%
1208101409 1
2.0%
1248110327 1
2.0%
1248600746 1
2.0%
1248687286 1
2.0%
1258176623 1
2.0%
1258191774 1
2.0%
1268675511 1
2.0%
ValueCountFrequency (%)
8158701265 1
2.0%
7438601584 1
2.0%
6818101184 1
2.0%
6548801274 1
2.0%
6198702782 1
2.0%
5308502205 1
2.0%
4458101443 1
2.0%
3958801720 1
2.0%
3128617470 1
2.0%
3118138249 1
2.0%

전화번호
Text

MISSING 

Distinct48
Distinct (%)96.0%
Missing1
Missing (%)2.0%
Memory size540.0 B
2024-03-13T08:22:14.755586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12
Min length11

Characters and Unicode

Total characters600
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

Unique46 ?
Unique (%)92.0%

Sample

1st row02-3660-6400
2nd row031-926-2307
3rd row031-967-0380
4th row031-994-7677
5th row031-812-0266
ValueCountFrequency (%)
032-677-1275 2
 
4.0%
031-667-5670 2
 
4.0%
031-354-7048 1
 
2.0%
031-458-3227 1
 
2.0%
02-3660-6400 1
 
2.0%
031-658-6656 1
 
2.0%
031-423-6533 1
 
2.0%
031-859-6512 1
 
2.0%
031-883-7979 1
 
2.0%
031-834-9100 1
 
2.0%
Other values (38) 38
76.0%
2024-03-13T08:22:15.046164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
16.7%
0 94
15.7%
3 84
14.0%
1 63
10.5%
6 47
7.8%
7 41
6.8%
2 38
 
6.3%
5 35
 
5.8%
4 35
 
5.8%
8 32
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
83.3%
Dash Punctuation 100
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94
18.8%
3 84
16.8%
1 63
12.6%
6 47
9.4%
7 41
8.2%
2 38
7.6%
5 35
 
7.0%
4 35
 
7.0%
8 32
 
6.4%
9 31
 
6.2%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 100
16.7%
0 94
15.7%
3 84
14.0%
1 63
10.5%
6 47
7.8%
7 41
6.8%
2 38
 
6.3%
5 35
 
5.8%
4 35
 
5.8%
8 32
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
16.7%
0 94
15.7%
3 84
14.0%
1 63
10.5%
6 47
7.8%
7 41
6.8%
2 38
 
6.3%
5 35
 
5.8%
4 35
 
5.8%
8 32
 
5.3%

대표자명
Text

Distinct49
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024-03-13T08:22:15.233362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9803922
Min length2

Characters and Unicode

Total characters152
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)92.2%

Sample

1st row임은성
2nd row박복용
3rd row조영원
4th row김진목
5th row곽윤영
ValueCountFrequency (%)
황운식 2
 
3.9%
이춘희 2
 
3.9%
김산 1
 
2.0%
최태혁 1
 
2.0%
진선미 1
 
2.0%
최봉구 1
 
2.0%
임은성 1
 
2.0%
구문환 1
 
2.0%
조창희 1
 
2.0%
배윤정 1
 
2.0%
Other values (39) 39
76.5%
2024-03-13T08:22:15.503427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 10
 
6.6%
이 9
 
5.9%
성 6
 
3.9%
경 6
 
3.9%
희 5
 
3.3%
박 5
 
3.3%
최 4
 
2.6%
조 4
 
2.6%
기 3
 
2.0%
운 3
 
2.0%
Other values (62) 97
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 152
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 10
 
6.6%
이 9
 
5.9%
성 6
 
3.9%
경 6
 
3.9%
희 5
 
3.3%
박 5
 
3.3%
최 4
 
2.6%
조 4
 
2.6%
기 3
 
2.0%
운 3
 
2.0%
Other values (62) 97
63.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 152
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 10
 
6.6%
이 9
 
5.9%
성 6
 
3.9%
경 6
 
3.9%
희 5
 
3.3%
박 5
 
3.3%
최 4
 
2.6%
조 4
 
2.6%
기 3
 
2.0%
운 3
 
2.0%
Other values (62) 97
63.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 152
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 10
 
6.6%
이 9
 
5.9%
성 6
 
3.9%
경 6
 
3.9%
희 5
 
3.3%
박 5
 
3.3%
최 4
 
2.6%
조 4
 
2.6%
기 3
 
2.0%
운 3
 
2.0%
Other values (62) 97
63.8%

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
지적측량업
51 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지적측량업 51
100.0%

Length

2024-03-13T08:22:15.604314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T08:22:15.673742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지적측량업 51
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13889.275
Minimum10109
Maximum18598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-03-13T08:22:15.752929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10109
5-th percentile10407
Q111145
median13840
Q316758.5
95-th percentile18209
Maximum18598
Range8489
Interquartile range (IQR)5613.5

Descriptive statistics

Standard deviation2870.3795
Coefficient of variation (CV)0.20666158
Kurtosis-1.3687199
Mean13889.275
Median Absolute Deviation (MAD)2701
Skewness0.25394667
Sum708353
Variance8239078.2
MonotonicityNot monotonic
2024-03-13T08:22:15.870350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
10442 2
 
3.9%
17922 2
 
3.9%
10477 2
 
3.9%
14056 2
 
3.9%
11014 1
 
2.0%
17091 1
 
2.0%
17049 1
 
2.0%
16976 1
 
2.0%
16069 1
 
2.0%
17721 1
 
2.0%
Other values (37) 37
72.5%
ValueCountFrequency (%)
10109 1
2.0%
10126 1
2.0%
10402 1
2.0%
10412 1
2.0%
10442 2
3.9%
10477 2
3.9%
10497 1
2.0%
10515 1
2.0%
10550 1
2.0%
11014 1
2.0%
ValueCountFrequency (%)
18598 1
2.0%
18412 1
2.0%
18404 1
2.0%
18014 1
2.0%
17922 2
3.9%
17892 1
2.0%
17885 1
2.0%
17879 1
2.0%
17721 1
2.0%
17091 1
2.0%

소재지지번주소
Text

UNIQUE 

Distinct51
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024-03-13T08:22:16.080157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length38
Mean length30.54902
Min length17

Characters and Unicode

Total characters1558
Distinct characters177
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

Unique51 ?
Unique (%)100.0%

Sample

1st row경기도 고양시 덕양구 원흥동 706번지 광양프런티어밸리6차 1305호
2nd row경기도 고양시 일산동구 백석동 1141-2번지 유니테크빌 912호
3rd row경기도 고양시 덕양구 화정동 967-1번지 한솔타워 7층
4th row경기도 고양시 덕양구 화정동 902-3번지 아성프라자 503호
5th row경기도 고양시 덕양구 화정동 909-3번지 605-2호
ValueCountFrequency (%)
경기도 51
 
15.9%
고양시 9
 
2.8%
평택시 7
 
2.2%
동안구 6
 
1.9%
안양시 6
 
1.9%
2층 5
 
1.6%
덕양구 5
 
1.6%
일산동구 4
 
1.2%
관양동 4
 
1.2%
1층 3
 
0.9%
Other values (191) 220
68.8%
2024-03-13T08:22:16.462294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
269
 
17.3%
1 72
 
4.6%
동 67
 
4.3%
지 54
 
3.5%
기 52
 
3.3%
경 51
 
3.3%
번 51
 
3.3%
시 51
 
3.3%
도 51
 
3.3%
2 46
 
3.0%
Other values (167) 794
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 896
57.5%
Decimal Number 339
 
21.8%
Space Separator 269
 
17.3%
Dash Punctuation 38
 
2.4%
Uppercase Letter 14
 
0.9%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 67
 
7.5%
지 54
 
6.0%
기 52
 
5.8%
경 51
 
5.7%
번 51
 
5.7%
시 51
 
5.7%
도 51
 
5.7%
호 33
 
3.7%
양 31
 
3.5%
구 25
 
2.8%
Other values (142) 430
48.0%
Uppercase Letter
ValueCountFrequency (%)
E 2
14.3%
A 2
14.3%
C 1
7.1%
W 1
7.1%
B 1
7.1%
R 1
7.1%
T 1
7.1%
N 1
7.1%
U 1
7.1%
S 1
7.1%
Other values (2) 2
14.3%
Decimal Number
ValueCountFrequency (%)
1 72
21.2%
2 46
13.6%
0 40
11.8%
3 40
11.8%
5 30
8.8%
4 28
 
8.3%
6 26
 
7.7%
8 22
 
6.5%
9 18
 
5.3%
7 17
 
5.0%
Space Separator
ValueCountFrequency (%)
269
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 896
57.5%
Common 648
41.6%
Latin 14
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 67
 
7.5%
지 54
 
6.0%
기 52
 
5.8%
경 51
 
5.7%
번 51
 
5.7%
시 51
 
5.7%
도 51
 
5.7%
호 33
 
3.7%
양 31
 
3.5%
구 25
 
2.8%
Other values (142) 430
48.0%
Common
ValueCountFrequency (%)
269
41.5%
1 72
 
11.1%
2 46
 
7.1%
0 40
 
6.2%
3 40
 
6.2%
- 38
 
5.9%
5 30
 
4.6%
4 28
 
4.3%
6 26
 
4.0%
8 22
 
3.4%
Other values (3) 37
 
5.7%
Latin
ValueCountFrequency (%)
E 2
14.3%
A 2
14.3%
C 1
7.1%
W 1
7.1%
B 1
7.1%
R 1
7.1%
T 1
7.1%
N 1
7.1%
U 1
7.1%
S 1
7.1%
Other values (2) 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 896
57.5%
ASCII 662
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
269
40.6%
1 72
 
10.9%
2 46
 
6.9%
0 40
 
6.0%
3 40
 
6.0%
- 38
 
5.7%
5 30
 
4.5%
4 28
 
4.2%
6 26
 
3.9%
8 22
 
3.3%
Other values (15) 51
 
7.7%
Hangul
ValueCountFrequency (%)
동 67
 
7.5%
지 54
 
6.0%
기 52
 
5.8%
경 51
 
5.7%
번 51
 
5.7%
시 51
 
5.7%
도 51
 
5.7%
호 33
 
3.7%
양 31
 
3.5%
구 25
 
2.8%
Other values (142) 430
48.0%

소재지도로명주소
Text

Distinct50
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size540.0 B
2024-03-13T08:22:16.750144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length18.647059
Min length14

Characters and Unicode

Total characters951
Distinct characters113
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)96.1%

Sample

1st row경기도 고양시 덕양구 삼원로 83
2nd row경기도 고양시 일산동구 일산로 142
3rd row경기도 고양시 덕양구 화중로104번길 26
4th row경기도 고양시 덕양구 화중로130번길 14
5th row경기도 고양시 덕양구 은빛로 45
ValueCountFrequency (%)
경기도 51
 
22.2%
고양시 9
 
3.9%
평택시 7
 
3.0%
안양시 6
 
2.6%
동안구 6
 
2.6%
덕양구 5
 
2.2%
일산동구 4
 
1.7%
화성시 3
 
1.3%
하남시 3
 
1.3%
용인시 3
 
1.3%
Other values (119) 133
57.8%
2024-03-13T08:22:17.164294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
179
18.8%
시 55
 
5.8%
경 53
 
5.6%
기 53
 
5.6%
도 51
 
5.4%
로 48
 
5.0%
1 48
 
5.0%
양 23
 
2.4%
구 23
 
2.4%
3 19
 
2.0%
Other values (103) 399
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 598
62.9%
Space Separator 179
 
18.8%
Decimal Number 165
 
17.4%
Dash Punctuation 9
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 55
 
9.2%
경 53
 
8.9%
기 53
 
8.9%
도 51
 
8.5%
로 48
 
8.0%
양 23
 
3.8%
구 23
 
3.8%
안 16
 
2.7%
길 16
 
2.7%
동 12
 
2.0%
Other values (91) 248
41.5%
Decimal Number
ValueCountFrequency (%)
1 48
29.1%
3 19
 
11.5%
2 16
 
9.7%
0 15
 
9.1%
4 15
 
9.1%
8 12
 
7.3%
5 11
 
6.7%
9 10
 
6.1%
6 10
 
6.1%
7 9
 
5.5%
Space Separator
ValueCountFrequency (%)
179
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 598
62.9%
Common 353
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 55
 
9.2%
경 53
 
8.9%
기 53
 
8.9%
도 51
 
8.5%
로 48
 
8.0%
양 23
 
3.8%
구 23
 
3.8%
안 16
 
2.7%
길 16
 
2.7%
동 12
 
2.0%
Other values (91) 248
41.5%
Common
ValueCountFrequency (%)
179
50.7%
1 48
 
13.6%
3 19
 
5.4%
2 16
 
4.5%
0 15
 
4.2%
4 15
 
4.2%
8 12
 
3.4%
5 11
 
3.1%
9 10
 
2.8%
6 10
 
2.8%
Other values (2) 18
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 598
62.9%
ASCII 353
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
179
50.7%
1 48
 
13.6%
3 19
 
5.4%
2 16
 
4.5%
0 15
 
4.2%
4 15
 
4.2%
8 12
 
3.4%
5 11
 
3.1%
9 10
 
2.8%
6 10
 
2.8%
Other values (2) 18
 
5.1%
Hangul
ValueCountFrequency (%)
시 55
 
9.2%
경 53
 
8.9%
기 53
 
8.9%
도 51
 
8.5%
로 48
 
8.0%
양 23
 
3.8%
구 23
 
3.8%
안 16
 
2.7%
길 16
 
2.7%
동 12
 
2.0%
Other values (91) 248
41.5%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.440322
Minimum36.987548
Maximum38.096079
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-03-13T08:22:17.305127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.987548
5-th percentile36.996222
Q137.272312
median37.413787
Q337.627693
95-th percentile37.897006
Maximum38.096079
Range1.1085312
Interquartile range (IQR)0.35538067

Descriptive statistics

Standard deviation0.26347909
Coefficient of variation (CV)0.0070373084
Kurtosis-0.29628996
Mean37.440322
Median Absolute Deviation (MAD)0.19890643
Skewness0.085678222
Sum1909.4564
Variance0.06942123
MonotonicityNot monotonic
2024-03-13T08:22:17.466771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6499050791 2
 
3.9%
37.3988682462 1
 
2.0%
37.8108057553 1
 
2.0%
37.2980159336 1
 
2.0%
38.0960787378 1
 
2.0%
37.2380046539 1
 
2.0%
37.233656259 1
 
2.0%
37.2800243826 1
 
2.0%
37.3464980618 1
 
2.0%
37.0802985763 1
 
2.0%
Other values (40) 40
78.4%
ValueCountFrequency (%)
36.9875475595 1
2.0%
36.9880246968 1
2.0%
36.9929897486 1
2.0%
36.9994545779 1
2.0%
37.0005244744 1
2.0%
37.0436840815 1
2.0%
37.0802985763 1
2.0%
37.1260297871 1
2.0%
37.205381584 1
2.0%
37.2118960038 1
2.0%
ValueCountFrequency (%)
38.0960787378 1
2.0%
37.9005186098 1
2.0%
37.8990371516 1
2.0%
37.8949757404 1
2.0%
37.8108057553 1
2.0%
37.6624704694 1
2.0%
37.6565580392 1
2.0%
37.6499050791 2
3.9%
37.6384764656 1
2.0%
37.6381778451 1
2.0%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.01961
Minimum126.71702
Maximum127.63808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size591.0 B
2024-03-13T08:22:17.575176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.71702
5-th percentile126.76975
Q1126.88862
median127.04041
Q3127.1334
95-th percentile127.21162
Maximum127.63808
Range0.92106541
Interquartile range (IQR)0.24477297

Descriptive statistics

Standard deviation0.17410308
Coefficient of variation (CV)0.0013706787
Kurtosis1.6382364
Mean127.01961
Median Absolute Deviation (MAD)0.12628428
Skewness0.5977776
Sum6478.0004
Variance0.030311881
MonotonicityNot monotonic
2024-03-13T08:22:17.679007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7936807093 2
 
3.9%
126.9707572784 1
 
2.0%
127.0722803941 1
 
2.0%
127.6380833721 1
 
2.0%
127.0737593379 1
 
2.0%
127.1782682221 1
 
2.0%
127.2009577543 1
 
2.0%
127.1193522953 1
 
2.0%
126.9767815154 1
 
2.0%
127.0766890116 1
 
2.0%
Other values (40) 40
78.4%
ValueCountFrequency (%)
126.7170179659 1
2.0%
126.767192498 1
2.0%
126.7693138824 1
2.0%
126.7701841668 1
2.0%
126.7810622235 1
2.0%
126.7908292354 1
2.0%
126.7936807093 2
3.9%
126.827859868 1
2.0%
126.8324160198 1
2.0%
126.8325305558 1
2.0%
ValueCountFrequency (%)
127.6380833721 1
2.0%
127.2535076229 1
2.0%
127.2166395108 1
2.0%
127.2066059826 1
2.0%
127.2016981856 1
2.0%
127.2009577543 1
2.0%
127.1946802895 1
2.0%
127.1936926063 1
2.0%
127.1925360584 1
2.0%
127.191758588 1
2.0%

Interactions

2024-03-13T08:22:13.104751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.298983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.599202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.859130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:13.173666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.368767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.665825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.928467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:13.229442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.450615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.723977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.986051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:13.289502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.520997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:12.790603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T08:22:13.045723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T08:22:17.751464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명상호명사업자등록번호전화번호대표자명소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
시군명1.0000.0000.6160.9770.0000.9961.0001.0000.9790.971
상호명0.0001.0000.0000.9991.0000.8851.0000.9890.0000.903
사업자등록번호0.6160.0001.0001.0000.0000.4041.0000.5870.1850.000
전화번호0.9770.9991.0001.0000.9990.8511.0000.9890.9220.863
대표자명0.0001.0000.0000.9991.0000.8851.0000.9890.0000.903
소재지우편번호0.9960.8850.4040.8510.8851.0001.0001.0000.9430.801
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0000.9890.5870.9890.9891.0001.0001.0001.0001.000
WGS84위도0.9790.0000.1850.9220.0000.9431.0001.0001.0000.718
WGS84경도0.9710.9030.0000.8630.9030.8011.0001.0000.7181.000
2024-03-13T08:22:17.845208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업자등록번호소재지우편번호WGS84위도WGS84경도시군명
사업자등록번호1.000-0.1540.1250.0420.255
소재지우편번호-0.1541.000-0.9090.2810.831
WGS84위도0.125-0.9091.000-0.2130.746
WGS84경도0.0420.281-0.2131.0000.626
시군명0.2550.8310.7460.6261.000

Missing values

2024-03-13T08:22:13.374132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T08:22:13.491525image/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.
2024-03-13T08:22:13.585733image/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

시군명상호명사업자등록번호전화번호대표자명업종명소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
0고양시(주)아세아항측105817516302-3660-6400임은성지적측량업10550경기도 고양시 덕양구 원흥동 706번지 광양프런티어밸리6차 1305호경기도 고양시 덕양구 삼원로 8337.638178126.874979
1고양시한국항공촬영 주식회사6548801274031-926-2307박복용지적측량업10442경기도 고양시 일산동구 백석동 1141-2번지 유니테크빌 912호경기도 고양시 일산동구 일산로 14237.649905126.793681
2고양시(주)일도엔지니어링2148608468031-967-0380조영원지적측량업10497경기도 고양시 덕양구 화정동 967-1번지 한솔타워 7층경기도 고양시 덕양구 화중로104번길 2637.635809126.832531
3고양시지오엠 주식회사1538601893031-994-7677김진목지적측량업10477경기도 고양시 덕양구 화정동 902-3번지 아성프라자 503호경기도 고양시 덕양구 화중로130번길 1437.638476126.832416
4고양시(주)유민지적1728602739031-812-0266곽윤영지적측량업10477경기도 고양시 덕양구 화정동 909-3번지 605-2호경기도 고양시 덕양구 은빛로 4537.63811126.83302
5고양시명화지리정보㈜1288125496031-978-4057이명헌지적측량업10402경기도 고양시 일산동구 장항동 863-2번지 일호골든타워 601호경기도 고양시 일산동구 정발산로 1137.656558126.770184
6고양시㈜스페이스2068151523031-904-0694태웅성지적측량업10412경기도 고양시 일산동구 마두동 862-2번지 타임빌딩 4층경기도 고양시 일산동구 경의로 36937.66247126.790829
7고양시삼아항업(주)1288122335031-925-9236강형기지적측량업10442경기도 고양시 일산동구 백석동 1141-2번지 유니테크빌 1024호경기도 고양시 일산동구 일산로 14237.649905126.793681
8고양시제일항업㈜1288156136031-970-3791이경호지적측량업10515경기도 고양시 덕양구 토당동 881-3번지 윤창주상복합 3층경기도 고양시 덕양구 행당로33번길 7-937.619577126.82786
9과천시(주)신한항업107817470502-2108-3700배상태지적측량업13840경기도 과천시 갈현동 554번지 디테크타워과천 A동 1002호경기도 과천시 과천대로7길 3337.413787126.977127
시군명상호명사업자등록번호전화번호대표자명업종명소재지우편번호소재지지번주소소재지도로명주소WGS84위도WGS84경도
41평택시(주)선진지적공사3118138249031-667-5670황금자지적측량업17922경기도 평택시 평택동 206-19번지 101호경기도 평택시 원평로35번길 3736.988025127.08337
42포천시㈜미도지리정보127860827602-866-6866장용호지적측량업11144경기도 포천시 신읍동 202-4번지 302호경기도 포천시 중앙로 17037.900519127.206606
43포천시(주)대진공간정보엔지니어링6198702782031-536-7363김지희지적측량업11148경기도 포천시 신읍동 318-4번지 3층경기도 포천시 왕방로 13037.894976127.192536
44포천시(주)대명지적측량기술공사5308502205031-533-9101황운식지적측량업11146경기도 포천시 신읍동 176-16번지 1층경기도 포천시 원앙로 2337.899037127.201698
45하남시뉴올린지적(주)1298617335031-790-0505김천탁지적측량업12930경기도 하남시 덕풍동 762번지 아이테코 815호경기도 하남시 조정대로 15037.553504127.19468
46하남시주식회사 서일엔지니어링743860158402-404-3889김화경지적측량업12939경기도 하남시 풍산동 599-2번지 두산 더프론트 미사 W동 315호경기도 하남시 미사강변중앙로 1137.548187127.191759
47하남시(주)삼경지적측량사업단206819138802-446-5556김산지적측량업12982경기도 하남시 풍산동 618번지 하남테크노밸리 U1 CENTER B동 1109호경기도 하남시 하남대로 94737.545867127.193693
48화성시주식회사 효성지적토지보상1248600746031-225-6503이옥수지적측량업18404경기도 화성시 진안동 884-12번지 승원빌딩 2층경기도 화성시 병점중앙로156번길 15-1137.211896127.040409
49화성시(주)진성이엔씨1248687286031-354-7048진선미지적측량업18598경기도 화성시 향남읍 행정리 456-9번지 1층경기도 화성시 향남읍 행정서로1길 13-637.12603126.914124
50화성시바로지적측량1923201110031-224-8272차성복지적측량업18412경기도 화성시 병점동 871번지 병점역효성해링턴플레이스 106동 121호경기도 화성시 떡전골로 6037.205382127.036481