Overview

Dataset statistics

Number of variables12
Number of observations40
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory104.3 B

Variable types

Text4
Categorical4
Numeric4

Dataset

Description대전광역시 유성구 대기배출사업장 현황에 대한 데이터로 사업장명, 종별, 업종, 전화번호, 소재지도로명주소, 소재지지번주소, 위도, 경도, 행정동코드, 행정동이름, 법정동코드, 법정동이름 항목을 제공합니다.
Author대전광역시 유성구
URLhttps://www.data.go.kr/data/15080486/fileData.do

Alerts

위도 is highly overall correlated with 행정동코드 and 3 other fieldsHigh correlation
행정동코드 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
법정동코드 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
행정동이름 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
법정동이름 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
전화번호 has 1 (2.5%) missing valuesMissing
사업장명 has unique valuesUnique
소재지도로명주소 has unique valuesUnique
소재지지번주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:15:07.043565
Analysis finished2023-12-12 16:15:09.977700
Duration2.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업장명
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-13T01:15:10.179610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length8.8
Min length3

Characters and Unicode

Total characters352
Distinct characters128
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

Unique40 ?
Unique (%)100.0%

Sample

1st row한국지엠서대전서비스센타㈜
2nd row신대광㈜
3rd row㈜케이에스모터스
4th row㈜유성현대서비스
5th row오토월드자동차공업사
ValueCountFrequency (%)
주식회사 2
 
4.1%
한국지엠서대전서비스센타㈜ 1
 
2.0%
신대광㈜ 1
 
2.0%
국군복지단 1
 
2.0%
자운대쇼핑타운 1
 
2.0%
일레븐 1
 
2.0%
대온장 1
 
2.0%
계룡스파텔 1
 
2.0%
뉴월드마스터 1
 
2.0%
아크로모터스 1
 
2.0%
Other values (38) 38
77.6%
2023-12-13T01:15:10.589647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 19
 
5.4%
㈜ 19
 
5.4%
대 16
 
4.5%
터 11
 
3.1%
9
 
2.6%
사 9
 
2.6%
성 9
 
2.6%
전 9
 
2.6%
서 9
 
2.6%
업 8
 
2.3%
Other values (118) 234
66.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 318
90.3%
Other Symbol 19
 
5.4%
Space Separator 9
 
2.6%
Close Punctuation 3
 
0.9%
Open Punctuation 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 19
 
6.0%
대 16
 
5.0%
터 11
 
3.5%
사 9
 
2.8%
성 9
 
2.8%
전 9
 
2.8%
서 9
 
2.8%
업 8
 
2.5%
유 7
 
2.2%
자 7
 
2.2%
Other values (114) 214
67.3%
Other Symbol
ValueCountFrequency (%)
㈜ 19
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 337
95.7%
Common 15
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 19
 
5.6%
㈜ 19
 
5.6%
대 16
 
4.7%
터 11
 
3.3%
사 9
 
2.7%
성 9
 
2.7%
전 9
 
2.7%
서 9
 
2.7%
업 8
 
2.4%
유 7
 
2.1%
Other values (115) 221
65.6%
Common
ValueCountFrequency (%)
9
60.0%
) 3
 
20.0%
( 3
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 318
90.3%
None 19
 
5.4%
ASCII 15
 
4.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 19
 
6.0%
대 16
 
5.0%
터 11
 
3.5%
사 9
 
2.8%
성 9
 
2.8%
전 9
 
2.8%
서 9
 
2.8%
업 8
 
2.5%
유 7
 
2.2%
자 7
 
2.2%
Other values (114) 214
67.3%
None
ValueCountFrequency (%)
㈜ 19
100.0%
ASCII
ValueCountFrequency (%)
9
60.0%
) 3
 
20.0%
( 3
 
20.0%

종별
Categorical

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
5
24 
4
16 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 24
60.0%
4 16
40.0%

Length

2023-12-13T01:15:10.722471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:15:10.853884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 24
60.0%
4 16
40.0%

업종
Categorical

Distinct20
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
자동차종합수리업
11 
자동차정비업
5 
대형종합할인매장
3 
부동산업
2 
교육서비스
2 
Other values (15)
17 

Length

Max length15
Median length14
Mean length7.15
Min length3

Unique

Unique13 ?
Unique (%)32.5%

Sample

1st row운수시설및장비수선
2nd row자동차정비업
3rd row자동차정비업
4th row자동차정비업
5th row자동차정비업

Common Values

ValueCountFrequency (%)
자동차종합수리업 11
27.5%
자동차정비업 5
12.5%
대형종합할인매장 3
 
7.5%
부동산업 2
 
5.0%
교육서비스 2
 
5.0%
음식 및 숙박업 2
 
5.0%
서비스업 2
 
5.0%
호텔업 1
 
2.5%
자동차종합정비업 1
 
2.5%
금속조립구조재제조업 1
 
2.5%
Other values (10) 10
25.0%

Length

2023-12-13T01:15:11.018829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차종합수리업 11
22.4%
자동차정비업 5
 
10.2%
대형종합할인매장 3
 
6.1%
및 3
 
6.1%
숙박업 3
 
6.1%
부동산업 2
 
4.1%
교육서비스 2
 
4.1%
음식 2
 
4.1%
서비스업 2
 
4.1%
운수시설및장비수선 1
 
2.0%
Other values (15) 15
30.6%

전화번호
Text

MISSING 

Distinct39
Distinct (%)100.0%
Missing1
Missing (%)2.5%
Memory size452.0 B
2023-12-13T01:15:11.272752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.025641
Min length12

Characters and Unicode

Total characters469
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

Unique39 ?
Unique (%)100.0%

Sample

1st row042-543-4464
2nd row042-545-8297
3rd row042-822-7000
4th row042-824-4972
5th row042-604-7878
ValueCountFrequency (%)
042-543-4464 1
 
2.6%
042-533-6841 1
 
2.6%
042-716-7335 1
 
2.6%
042-828-6608 1
 
2.6%
042-863-0930 1
 
2.6%
042-824-2466 1
 
2.6%
042-822-0211 1
 
2.6%
042-602-1161 1
 
2.6%
042-257-3100 1
 
2.6%
042-825-3183 1
 
2.6%
Other values (29) 29
74.4%
2023-12-13T01:15:11.993464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 85
18.1%
2 85
18.1%
- 78
16.6%
4 62
13.2%
8 37
7.9%
5 30
 
6.4%
6 23
 
4.9%
1 23
 
4.9%
3 21
 
4.5%
7 20
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 391
83.4%
Dash Punctuation 78
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85
21.7%
2 85
21.7%
4 62
15.9%
8 37
9.5%
5 30
 
7.7%
6 23
 
5.9%
1 23
 
5.9%
3 21
 
5.4%
7 20
 
5.1%
9 5
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 469
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85
18.1%
2 85
18.1%
- 78
16.6%
4 62
13.2%
8 37
7.9%
5 30
 
6.4%
6 23
 
4.9%
1 23
 
4.9%
3 21
 
4.5%
7 20
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85
18.1%
2 85
18.1%
- 78
16.6%
4 62
13.2%
8 37
7.9%
5 30
 
6.4%
6 23
 
4.9%
1 23
 
4.9%
3 21
 
4.5%
7 20
 
4.3%

소재지도로명주소
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-13T01:15:12.231059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length23
Mean length18.75
Min length15

Characters and Unicode

Total characters750
Distinct characters64
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row대전광역시 유성구 계백로 809
2nd row대전광역시 유성구 진잠로 67
3rd row대전광역시 유성구 대학로76번길 99
4th row대전광역시 유성구 한밭대로 398
5th row대전광역시 유성구 유성대로 488
ValueCountFrequency (%)
대전광역시 40
24.2%
유성구 40
24.2%
계백로 5
 
3.0%
유성대로 5
 
3.0%
온천로 5
 
3.0%
35 3
 
1.8%
복용동로 3
 
1.8%
한밭대로 3
 
1.8%
북유성대로 2
 
1.2%
지하1층 2
 
1.2%
Other values (55) 57
34.5%
2023-12-13T01:15:12.667365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
125
16.7%
대 57
 
7.6%
유 47
 
6.3%
성 47
 
6.3%
전 40
 
5.3%
구 40
 
5.3%
로 40
 
5.3%
시 40
 
5.3%
역 40
 
5.3%
광 40
 
5.3%
Other values (54) 234
31.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 483
64.4%
Decimal Number 130
 
17.3%
Space Separator 125
 
16.7%
Dash Punctuation 7
 
0.9%
Uppercase Letter 2
 
0.3%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 57
11.8%
유 47
9.7%
성 47
9.7%
전 40
8.3%
구 40
8.3%
로 40
8.3%
시 40
8.3%
역 40
8.3%
광 40
8.3%
동 8
 
1.7%
Other values (38) 84
17.4%
Decimal Number
ValueCountFrequency (%)
9 21
16.2%
3 19
14.6%
2 19
14.6%
1 15
11.5%
6 14
10.8%
8 12
9.2%
7 9
6.9%
5 9
6.9%
4 6
 
4.6%
0 6
 
4.6%
Space Separator
ValueCountFrequency (%)
125
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 483
64.4%
Common 265
35.3%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 57
11.8%
유 47
9.7%
성 47
9.7%
전 40
8.3%
구 40
8.3%
로 40
8.3%
시 40
8.3%
역 40
8.3%
광 40
8.3%
동 8
 
1.7%
Other values (38) 84
17.4%
Common
ValueCountFrequency (%)
125
47.2%
9 21
 
7.9%
3 19
 
7.2%
2 19
 
7.2%
1 15
 
5.7%
6 14
 
5.3%
8 12
 
4.5%
7 9
 
3.4%
5 9
 
3.4%
- 7
 
2.6%
Other values (5) 15
 
5.7%
Latin
ValueCountFrequency (%)
B 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 483
64.4%
ASCII 267
35.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
125
46.8%
9 21
 
7.9%
3 19
 
7.1%
2 19
 
7.1%
1 15
 
5.6%
6 14
 
5.2%
8 12
 
4.5%
7 9
 
3.4%
5 9
 
3.4%
- 7
 
2.6%
Other values (6) 17
 
6.4%
Hangul
ValueCountFrequency (%)
대 57
11.8%
유 47
9.7%
성 47
9.7%
전 40
8.3%
구 40
8.3%
로 40
8.3%
시 40
8.3%
역 40
8.3%
광 40
8.3%
동 8
 
1.7%
Other values (38) 84
17.4%

소재지지번주소
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-13T01:15:12.965446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length19.15
Min length15

Characters and Unicode

Total characters766
Distinct characters49
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

Unique40 ?
Unique (%)100.0%

Sample

1st row대전광역시 유성구 원내동 425-15
2nd row대전광역시 유성구 원내동 291-7
3rd row대전광역시 유성구 궁동 497-5
4th row대전광역시 유성구 궁동 452-2
5th row대전광역시 유성구 복용동 225-6
ValueCountFrequency (%)
대전광역시 40
24.2%
유성구 40
24.2%
봉명동 10
 
6.1%
원내동 10
 
6.1%
복용동 4
 
2.4%
236 3
 
1.8%
반석동 2
 
1.2%
덕명동 2
 
1.2%
궁동 2
 
1.2%
지하1층 2
 
1.2%
Other values (50) 50
30.3%
2023-12-13T01:15:13.389978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
125
16.3%
대 42
 
5.5%
구 41
 
5.4%
유 40
 
5.2%
동 40
 
5.2%
성 40
 
5.2%
전 40
 
5.2%
시 40
 
5.2%
광 40
 
5.2%
역 40
 
5.2%
Other values (39) 278
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 448
58.5%
Decimal Number 161
 
21.0%
Space Separator 125
 
16.3%
Dash Punctuation 29
 
3.8%
Uppercase Letter 2
 
0.3%
Math Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 42
9.4%
구 41
9.2%
유 40
8.9%
동 40
8.9%
성 40
8.9%
전 40
8.9%
시 40
8.9%
광 40
8.9%
역 40
8.9%
명 12
 
2.7%
Other values (25) 73
16.3%
Decimal Number
ValueCountFrequency (%)
1 27
16.8%
5 26
16.1%
3 22
13.7%
2 19
11.8%
4 17
10.6%
6 16
9.9%
9 11
6.8%
7 9
 
5.6%
0 9
 
5.6%
8 5
 
3.1%
Space Separator
ValueCountFrequency (%)
125
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 448
58.5%
Common 316
41.3%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 42
9.4%
구 41
9.2%
유 40
8.9%
동 40
8.9%
성 40
8.9%
전 40
8.9%
시 40
8.9%
광 40
8.9%
역 40
8.9%
명 12
 
2.7%
Other values (25) 73
16.3%
Common
ValueCountFrequency (%)
125
39.6%
- 29
 
9.2%
1 27
 
8.5%
5 26
 
8.2%
3 22
 
7.0%
2 19
 
6.0%
4 17
 
5.4%
6 16
 
5.1%
9 11
 
3.5%
7 9
 
2.8%
Other values (3) 15
 
4.7%
Latin
ValueCountFrequency (%)
B 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 448
58.5%
ASCII 318
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
125
39.3%
- 29
 
9.1%
1 27
 
8.5%
5 26
 
8.2%
3 22
 
6.9%
2 19
 
6.0%
4 17
 
5.3%
6 16
 
5.0%
9 11
 
3.5%
7 9
 
2.8%
Other values (4) 17
 
5.3%
Hangul
ValueCountFrequency (%)
대 42
9.4%
구 41
9.2%
유 40
8.9%
동 40
8.9%
성 40
8.9%
전 40
8.9%
시 40
8.9%
광 40
8.9%
역 40
8.9%
명 12
 
2.7%
Other values (25) 73
16.3%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.341363
Minimum36.2861
Maximum36.414017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:15:13.533602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.2861
5-th percentile36.297574
Q136.305906
median36.352782
Q336.358774
95-th percentile36.390661
Maximum36.414017
Range0.12791731
Interquartile range (IQR)0.052868045

Descriptive statistics

Standard deviation0.03168226
Coefficient of variation (CV)0.00087179613
Kurtosis-0.59292046
Mean36.341363
Median Absolute Deviation (MAD)0.01438507
Skewness-0.00081506616
Sum1453.6545
Variance0.0010037656
MonotonicityNot monotonic
2023-12-13T01:15:13.701563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
36.33839656 3
 
7.5%
36.29229363 1
 
2.5%
36.35319569 1
 
2.5%
36.3562326 1
 
2.5%
36.41401744 1
 
2.5%
36.35458153 1
 
2.5%
36.35587312 1
 
2.5%
36.35513271 1
 
2.5%
36.30012155 1
 
2.5%
36.36203267 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
36.28610013 1
2.5%
36.29229363 1
2.5%
36.29785152 1
2.5%
36.30012155 1
2.5%
36.30043516 1
2.5%
36.30051535 1
2.5%
36.30105052 1
2.5%
36.30158242 1
2.5%
36.30196006 1
2.5%
36.30197327 1
2.5%
ValueCountFrequency (%)
36.41401744 1
2.5%
36.39560035 1
2.5%
36.39040055 1
2.5%
36.38473592 1
2.5%
36.38358216 1
2.5%
36.36281774 1
2.5%
36.36203267 1
2.5%
36.36200294 1
2.5%
36.36029003 1
2.5%
36.35988994 1
2.5%

경도
Real number (ℝ)

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.3271
Minimum127.29424
Maximum127.35417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:15:13.852211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.29424
5-th percentile127.30056
Q1127.31883
median127.32426
Q3127.34191
95-th percentile127.34825
Maximum127.35417
Range0.0599336
Interquartile range (IQR)0.023084875

Descriptive statistics

Standard deviation0.015458307
Coefficient of variation (CV)0.00012140626
Kurtosis-0.69310973
Mean127.3271
Median Absolute Deviation (MAD)0.0113893
Skewness-0.11784132
Sum5093.0839
Variance0.00023895925
MonotonicityNot monotonic
2023-12-13T01:15:13.983601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
127.3203275 3
 
7.5%
127.3196357 1
 
2.5%
127.3446996 1
 
2.5%
127.3430029 1
 
2.5%
127.3375892 1
 
2.5%
127.3443203 1
 
2.5%
127.3415463 1
 
2.5%
127.3478811 1
 
2.5%
127.3237098 1
 
2.5%
127.3055698 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
127.2942381 1
2.5%
127.2981531 1
2.5%
127.3006829 1
2.5%
127.3051391 1
2.5%
127.3055698 1
2.5%
127.3127632 1
2.5%
127.3129878 1
2.5%
127.3159691 1
2.5%
127.3182523 1
2.5%
127.3182882 1
2.5%
ValueCountFrequency (%)
127.3541717 1
2.5%
127.3492901 1
2.5%
127.3481917 1
2.5%
127.3478811 1
2.5%
127.3478658 1
2.5%
127.34755 1
2.5%
127.3446996 1
2.5%
127.3443203 1
2.5%
127.3434336 1
2.5%
127.3430029 1
2.5%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200521 × 109
Minimum3.0200119 × 109
Maximum3.020061 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:15:14.102876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0200119 × 109
5-th percentile3.020052 × 109
Q13.020052 × 109
median3.0200528 × 109
Q33.020053 × 109
95-th percentile3.0200548 × 109
Maximum3.020061 × 109
Range49100
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation6704.9083
Coefficient of variation (CV)2.22013 × 10-6
Kurtosis35.537987
Mean3.0200521 × 109
Median Absolute Deviation (MAD)850
Skewness-5.7474119
Sum1.2080208 × 1011
Variance44955795
MonotonicityNot monotonic
2023-12-13T01:15:14.230960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3020052000 12
30.0%
3020053000 11
27.5%
3020052700 4
 
10.0%
3020054000 3
 
7.5%
3020054700 3
 
7.5%
3020052600 3
 
7.5%
3020011900 1
 
2.5%
3020055000 1
 
2.5%
3020054800 1
 
2.5%
3020061000 1
 
2.5%
ValueCountFrequency (%)
3020011900 1
 
2.5%
3020052000 12
30.0%
3020052600 3
 
7.5%
3020052700 4
 
10.0%
3020053000 11
27.5%
3020054000 3
 
7.5%
3020054700 3
 
7.5%
3020054800 1
 
2.5%
3020055000 1
 
2.5%
3020061000 1
 
2.5%
ValueCountFrequency (%)
3020061000 1
 
2.5%
3020055000 1
 
2.5%
3020054800 1
 
2.5%
3020054700 3
 
7.5%
3020054000 3
 
7.5%
3020053000 11
27.5%
3020052700 4
 
10.0%
3020052600 3
 
7.5%
3020052000 12
30.0%
3020011900 1
 
2.5%

행정동이름
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
진잠동
12 
온천1동
9 
상대동
4 
온천2동
3 
노은2동
3 
Other values (6)
9 

Length

Max length5
Median length4.5
Mean length3.6
Min length3

Unique

Unique4 ?
Unique (%)10.0%

Sample

1st row진잠동
2nd row진잠동
3rd row온천2동
4th row온천2동
5th row상대동

Common Values

ValueCountFrequency (%)
진잠동 12
30.0%
온천1동 9
22.5%
상대동 4
 
10.0%
온천2동 3
 
7.5%
노은2동 3
 
7.5%
학하동 3
 
7.5%
온천1동 2
 
5.0%
노은1동 1
 
2.5%
신성동 1
 
2.5%
노은3동 1
 
2.5%

Length

2023-12-13T01:15:14.398272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진잠동 12
30.0%
온천1동 11
27.5%
상대동 4
 
10.0%
온천2동 3
 
7.5%
노은2동 3
 
7.5%
학하동 3
 
7.5%
노은1동 1
 
2.5%
신성동 1
 
2.5%
노은3동 1
 
2.5%
원신흥동 1
 
2.5%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200112 × 109
Minimum3.0200101 × 109
Maximum3.0200139 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:15:14.556153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0200101 × 109
5-th percentile3.0200101 × 109
Q13.0200102 × 109
median3.0200111 × 109
Q33.0200116 × 109
95-th percentile3.0200135 × 109
Maximum3.0200139 × 109
Range3800
Interquartile range (IQR)1350

Descriptive statistics

Standard deviation1028.5663
Coefficient of variation (CV)3.4058361 × 10-7
Kurtosis1.0150419
Mean3.0200112 × 109
Median Absolute Deviation (MAD)550
Skewness1.0494903
Sum1.2080045 × 1011
Variance1057948.7
MonotonicityNot monotonic
2023-12-13T01:15:14.699286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3020010100 10
25.0%
3020011100 10
25.0%
3020011600 4
 
10.0%
3020012200 2
 
5.0%
3020011300 2
 
5.0%
3020013900 2
 
5.0%
3020011900 1
 
2.5%
3020011700 1
 
2.5%
3020010600 1
 
2.5%
3020012000 1
 
2.5%
Other values (6) 6
15.0%
ValueCountFrequency (%)
3020010100 10
25.0%
3020010300 1
 
2.5%
3020010600 1
 
2.5%
3020011000 1
 
2.5%
3020011100 10
25.0%
3020011200 1
 
2.5%
3020011300 2
 
5.0%
3020011400 1
 
2.5%
3020011600 4
 
10.0%
3020011700 1
 
2.5%
ValueCountFrequency (%)
3020013900 2
5.0%
3020013500 1
 
2.5%
3020013200 1
 
2.5%
3020012200 2
5.0%
3020012000 1
 
2.5%
3020011900 1
 
2.5%
3020011700 1
 
2.5%
3020011600 4
10.0%
3020011400 1
 
2.5%
3020011300 2
5.0%

법정동이름
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
원내동
10 
봉명동
10 
복용동
4 
궁동
2 
반석동
2 
Other values (11)
12 

Length

Max length4
Median length3
Mean length3.025
Min length3

Unique

Unique10 ?
Unique (%)25.0%

Sample

1st row원내동
2nd row원내동
3rd row궁동
4th row궁동
5th row복용동

Common Values

ValueCountFrequency (%)
원내동 10
25.0%
봉명동 10
25.0%
복용동 4
 
10.0%
궁동 2
 
5.0%
반석동 2
 
5.0%
덕명동 2
 
5.0%
노은동 1
 
2.5%
장대동 1
 
2.5%
계산동 1
 
2.5%
지족동 1
 
2.5%
Other values (6) 6
15.0%

Length

2023-12-13T01:15:14.846286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
원내동 10
25.0%
봉명동 10
25.0%
복용동 4
 
10.0%
궁동 2
 
5.0%
반석동 2
 
5.0%
덕명동 2
 
5.0%
노은동 1
 
2.5%
장대동 1
 
2.5%
계산동 1
 
2.5%
지족동 1
 
2.5%
Other values (6) 6
15.0%

Interactions

2023-12-13T01:15:09.120601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:07.767162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:08.201216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:08.636004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:09.256765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:07.868484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:08.319408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:08.760821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:09.382493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:07.963830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:08.416015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:08.878804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:09.512402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:08.085158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:08.518497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:15:08.985758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:15:14.941734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장명종별업종전화번호소재지도로명주소소재지지번주소위도경도행정동코드행정동이름법정동코드법정동이름
사업장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
종별1.0001.0000.3461.0001.0001.0000.2870.2700.0000.0000.4850.000
업종1.0000.3461.0001.0001.0001.0000.7610.6291.0000.4050.5110.682
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0000.2870.7611.0001.0001.0001.0000.6590.5910.8900.9600.978
경도1.0000.2700.6291.0001.0001.0000.6591.0000.0000.7320.6250.845
행정동코드1.0000.0001.0001.0001.0001.0000.5910.0001.0001.0000.0001.000
행정동이름1.0000.0000.4051.0001.0001.0000.8900.7321.0001.0000.8590.972
법정동코드1.0000.4850.5111.0001.0001.0000.9600.6250.0000.8591.0001.000
법정동이름1.0000.0000.6821.0001.0001.0000.9780.8451.0000.9721.0001.000
2023-12-13T01:15:15.086147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동이름종별행정동이름업종
법정동이름1.0000.0000.7790.231
종별0.0001.0000.0000.199
행정동이름0.7790.0001.0000.055
업종0.2310.1990.0551.000
2023-12-13T01:15:15.205370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동코드법정동코드종별업종행정동이름법정동이름
위도1.0000.1020.7700.8270.2490.3270.6600.799
경도0.1021.0000.326-0.0830.1740.2330.4310.397
행정동코드0.7700.3261.0000.7470.0000.2510.8850.805
법정동코드0.827-0.0830.7471.0000.2780.1150.7030.866
종별0.2490.1740.0000.2781.0000.1990.0000.000
업종0.3270.2330.2510.1150.1991.0000.0550.231
행정동이름0.6600.4310.8850.7030.0000.0551.0000.779
법정동이름0.7990.3970.8050.8660.0000.2310.7791.000

Missing values

2023-12-13T01:15:09.691446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:15:09.888237image/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

사업장명종별업종전화번호소재지도로명주소소재지지번주소위도경도행정동코드행정동이름법정동코드법정동이름
0한국지엠서대전서비스센타㈜4운수시설및장비수선042-543-4464대전광역시 유성구 계백로 809대전광역시 유성구 원내동 425-1536.292294127.3196363020052000진잠동3020010100원내동
1신대광㈜4자동차정비업042-545-8297대전광역시 유성구 진잠로 67대전광역시 유성구 원내동 291-736.297852127.3159693020052000진잠동3020010100원내동
2㈜케이에스모터스5자동차정비업042-822-7000대전광역시 유성구 대학로76번길 99대전광역시 유성구 궁동 497-536.35989127.349293020054000온천2동3020012200궁동
3㈜유성현대서비스5자동차정비업042-824-4972대전광역시 유성구 한밭대로 398대전광역시 유성구 궁동 452-236.362003127.3434343020054000온천2동3020012200궁동
4오토월드자동차공업사5자동차정비업042-604-7878대전광역시 유성구 유성대로 488대전광역시 유성구 복용동 225-636.339048127.3207123020052700상대동3020011600복용동
5㈜르노삼성자동차서대전정비사업소5자동차종합수리업042-560-0100대전광역시 유성구 유성대로 34대전광역시 유성구 원내동 35-336.301973127.3228483020052000진잠동3020010100원내동
6한성자동차㈜대전유성서비스센터5자동차종합정비업042-602-2050대전광역시 유성구 북유성대로 352대전광역시 유성구 반석동 66336.3956127.3127633020054700노은2동3020013900반석동
7㈜노블레스모터스5자동차종합수리업042-826-2221대전광역시 유성구 노은로 36대전광역시 유성구 노은동 567-136.362818127.3182883020011900노은1동3020011900노은동
8신나는 모터스5자동차종합수리업042-826-8572대전광역시 유성구 온천동로 5대전광역시 유성구 봉명동 542-736.352629127.3481923020053000온천1동3020011100봉명동
9(주)서일모터스5자동차정비업042-541-2282대전광역시 유성구 유성대로 26-26대전광역시 유성구 원내동 360-436.300435127.3233843020052000진잠동3020010100원내동
사업장명종별업종전화번호소재지도로명주소소재지지번주소위도경도행정동코드행정동이름법정동코드법정동이름
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31한국가스공사 대전충청지역본부4연료용가스제조 및 배관공급업042-229-3583대전광역시 유성구 계백로 689대전광역시 유성구 방동 7-336.2861127.3129883020052000진잠동3020011000방동
32㈜디오토몰모터스4자동차종합수리업042-536-2500대전광역시 유성구 복용동로 35대전광역시 유성구 복용동 23636.338397127.3203283020052700상대동3020011600복용동
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35㈜제이엘모터스5자동차종합수리업042-620-7020대전광역시 유성구 복용동로 35 지하1층 B121~126호대전광역시 유성구 복용동 236 지하1층 B121~126호36.338397127.3203283020052700상대동3020011600복용동
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38주식회사 에르코스 농업회사법인5식품제조업등070-4711-2327대전광역시 유성구 도안동로 329대전광역시 유성구 원신흥동 524-136.334682127.347553020061000원신흥동3020011400원신흥동
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