Overview

Dataset statistics

Number of variables10
Number of observations1007
Missing cells13
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.7 KiB
Average record size in memory83.1 B

Variable types

Numeric3
Text6
DateTime1

Dataset

Description전북특별자치도 전주시의 전문건설업을 제공하며, 연번, 업체명, 업체등록일자, 업종, 등록번호, 주소, 전화번호 등을 제공합니다.항목 : 연번, 업체명, 업체등록일자, 업종, 등록번호, 소재지도로명주소, 소재지지번주소, 위도, 경도, 전화번호제공부서 : 도시계획과
Author전북특별자치도 전주시
URLhttps://www.data.go.kr/data/15083125/fileData.do

Alerts

전화번호 has 13 (1.3%) missing valuesMissing
연번 has unique valuesUnique
업체명 has unique valuesUnique
등록번호 has unique valuesUnique

Reproduction

Analysis started2024-03-14 21:27:16.347769
Analysis finished2024-03-14 21:27:21.902483
Duration5.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct1007
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean504
Minimum1
Maximum1007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-03-15T06:27:22.110326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile51.3
Q1252.5
median504
Q3755.5
95-th percentile956.7
Maximum1007
Range1006
Interquartile range (IQR)503

Descriptive statistics

Standard deviation290.84016
Coefficient of variation (CV)0.57706381
Kurtosis-1.2
Mean504
Median Absolute Deviation (MAD)252
Skewness0
Sum507528
Variance84588
MonotonicityStrictly increasing
2024-03-15T06:27:22.389256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
678 1
 
0.1%
665 1
 
0.1%
666 1
 
0.1%
667 1
 
0.1%
668 1
 
0.1%
669 1
 
0.1%
670 1
 
0.1%
671 1
 
0.1%
672 1
 
0.1%
Other values (997) 997
99.0%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
1007 1
0.1%
1006 1
0.1%
1005 1
0.1%
1004 1
0.1%
1003 1
0.1%
1002 1
0.1%
1001 1
0.1%
1000 1
0.1%
999 1
0.1%
998 1
0.1%

업체명
Text

UNIQUE 

Distinct1007
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2024-03-15T06:27:23.459801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length7.4716981
Min length2

Characters and Unicode

Total characters7524
Distinct characters369
Distinct categories7 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1007 ?
Unique (%)100.0%

Sample

1st row(우)초원석면환경
2nd row(유)가람산업개발
3rd row(유)가람에너지
4th row(유)가림엔지니어링
5th row(유)가인건설
ValueCountFrequency (%)
우)초원석면환경 1
 
0.1%
주)케이피엠 1
 
0.1%
주)펀디자인 1
 
0.1%
주)청사건설 1
 
0.1%
주)청송이엔씨 1
 
0.1%
주)청운조경 1
 
0.1%
주)청원플러스 1
 
0.1%
주)청정건설 1
 
0.1%
주)체다카 1
 
0.1%
주)초록조경 1
 
0.1%
Other values (997) 997
99.0%
2024-03-15T06:27:24.937682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 814
 
10.8%
( 812
 
10.8%
주 489
 
6.5%
유 425
 
5.6%
건 385
 
5.1%
설 351
 
4.7%
이 178
 
2.4%
지 123
 
1.6%
엔 104
 
1.4%
스 101
 
1.3%
Other values (359) 3742
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5850
77.8%
Close Punctuation 814
 
10.8%
Open Punctuation 812
 
10.8%
Uppercase Letter 39
 
0.5%
Decimal Number 3
 
< 0.1%
Other Punctuation 3
 
< 0.1%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 489
 
8.4%
유 425
 
7.3%
건 385
 
6.6%
설 351
 
6.0%
이 178
 
3.0%
지 123
 
2.1%
엔 104
 
1.8%
스 101
 
1.7%
경 99
 
1.7%
산 94
 
1.6%
Other values (338) 3501
59.8%
Uppercase Letter
ValueCountFrequency (%)
N 9
23.1%
E 8
20.5%
G 8
20.5%
L 3
 
7.7%
S 2
 
5.1%
C 2
 
5.1%
J 2
 
5.1%
P 1
 
2.6%
K 1
 
2.6%
O 1
 
2.6%
Other values (2) 2
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
o 1
33.3%
r 1
33.3%
p 1
33.3%
Decimal Number
ValueCountFrequency (%)
3 2
66.7%
6 1
33.3%
Other Punctuation
ValueCountFrequency (%)
， 2
66.7%
, 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 814
100.0%
Open Punctuation
ValueCountFrequency (%)
( 812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5848
77.7%
Common 1632
 
21.7%
Latin 42
 
0.6%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 489
 
8.4%
유 425
 
7.3%
건 385
 
6.6%
설 351
 
6.0%
이 178
 
3.0%
지 123
 
2.1%
엔 104
 
1.8%
스 101
 
1.7%
경 99
 
1.7%
산 94
 
1.6%
Other values (336) 3499
59.8%
Latin
ValueCountFrequency (%)
N 9
21.4%
E 8
19.0%
G 8
19.0%
L 3
 
7.1%
S 2
 
4.8%
C 2
 
4.8%
J 2
 
4.8%
P 1
 
2.4%
K 1
 
2.4%
O 1
 
2.4%
Other values (5) 5
11.9%
Common
ValueCountFrequency (%)
) 814
49.9%
( 812
49.8%
3 2
 
0.1%
， 2
 
0.1%
6 1
 
0.1%
, 1
 
0.1%
Han
ValueCountFrequency (%)
昌 1
50.0%
延 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5848
77.7%
ASCII 1672
 
22.2%
None 2
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 814
48.7%
( 812
48.6%
N 9
 
0.5%
E 8
 
0.5%
G 8
 
0.5%
L 3
 
0.2%
3 2
 
0.1%
S 2
 
0.1%
C 2
 
0.1%
J 2
 
0.1%
Other values (10) 10
 
0.6%
Hangul
ValueCountFrequency (%)
주 489
 
8.4%
유 425
 
7.3%
건 385
 
6.6%
설 351
 
6.0%
이 178
 
3.0%
지 123
 
2.1%
엔 104
 
1.8%
스 101
 
1.7%
경 99
 
1.7%
산 94
 
1.6%
Other values (336) 3499
59.8%
None
ValueCountFrequency (%)
， 2
100.0%
CJK
ValueCountFrequency (%)
昌 1
50.0%
延 1
50.0%

업체등록일자
Date

Distinct824
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
Minimum1976-11-10 00:00:00
Maximum2023-04-03 00:00:00
2024-03-15T06:27:25.349482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:27:25.766885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업종
Text

Distinct305
Distinct (%)30.3%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2024-03-15T06:27:26.530810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length186
Median length152
Mean length38.085402
Min length7

Characters and Unicode

Total characters38352
Distinct characters88
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique230 ?
Unique (%)22.8%

Sample

1st row구조물해체ㆍ비계공사업 /비계ㆍ구조물해체공사업(대업종전환)
2nd row기계가스설비공사업 /기계설비공사업(대업종전환)
3rd row가스난방공사업 /가스시설시공업 제2종(대업종전환)
4th row기계가스설비공사업 /지반조성ㆍ포장공사업 /가스시설시공업 제1종(대업종전환) /포장공사업(대업종전환) /토공사업(대업종전환)
5th row시설물유지관리업 /도장ㆍ습식ㆍ방수ㆍ석공사업 /습식ㆍ방수공사업(대업종전환) /도장공사업(대업종전환)
ValueCountFrequency (%)
시설물유지관리업 187
 
6.2%
가스난방공사업 168
 
5.6%
가스시설시공업 141
 
4.7%
제2종(대업종전환 139
 
4.6%
실내건축공사업 136
 
4.5%
금속창호ㆍ지붕건축물조립공사업 136
 
4.5%
기계가스설비공사업 131
 
4.3%
도장ㆍ습식ㆍ방수ㆍ석공사업 128
 
4.2%
금속구조물ㆍ창호ㆍ온실공사업(대업종전환 120
 
4.0%
지반조성ㆍ포장공사업 120
 
4.0%
Other values (86) 1621
53.6%
2024-03-15T06:27:27.865479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 4116
 
10.7%
공 2572
 
6.7%
사 2312
 
6.0%
2020
 
5.3%
/ 1760
 
4.6%
ㆍ 1618
 
4.2%
( 1433
 
3.7%
) 1433
 
3.7%
종 1406
 
3.7%
환 1146
 
3.0%
Other values (78) 18536
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31446
82.0%
Space Separator 2020
 
5.3%
Other Punctuation 1760
 
4.6%
Open Punctuation 1433
 
3.7%
Close Punctuation 1433
 
3.7%
Decimal Number 260
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 4116
 
13.1%
공 2572
 
8.2%
사 2312
 
7.4%
ㆍ 1618
 
5.1%
종 1406
 
4.5%
환 1146
 
3.6%
전 1146
 
3.6%
대 1146
 
3.6%
설 966
 
3.1%
물 763
 
2.4%
Other values (71) 14255
45.3%
Decimal Number
ValueCountFrequency (%)
2 147
56.5%
1 61
23.5%
3 52
 
20.0%
Space Separator
ValueCountFrequency (%)
2020
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1760
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1433
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1433
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31446
82.0%
Common 6906
 
18.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 4116
 
13.1%
공 2572
 
8.2%
사 2312
 
7.4%
ㆍ 1618
 
5.1%
종 1406
 
4.5%
환 1146
 
3.6%
전 1146
 
3.6%
대 1146
 
3.6%
설 966
 
3.1%
물 763
 
2.4%
Other values (71) 14255
45.3%
Common
ValueCountFrequency (%)
2020
29.2%
/ 1760
25.5%
( 1433
20.8%
) 1433
20.8%
2 147
 
2.1%
1 61
 
0.9%
3 52
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29828
77.8%
ASCII 6906
 
18.0%
Compat Jamo 1618
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 4116
 
13.8%
공 2572
 
8.6%
사 2312
 
7.8%
종 1406
 
4.7%
환 1146
 
3.8%
전 1146
 
3.8%
대 1146
 
3.8%
설 966
 
3.2%
물 763
 
2.6%
시 762
 
2.6%
Other values (70) 13493
45.2%
ASCII
ValueCountFrequency (%)
2020
29.2%
/ 1760
25.5%
( 1433
20.8%
) 1433
20.8%
2 147
 
2.1%
1 61
 
0.9%
3 52
 
0.8%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1618
100.0%

등록번호
Text

UNIQUE 

Distinct1007
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2024-03-15T06:27:28.978861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length183
Median length157
Mean length37.562066
Min length9

Characters and Unicode

Total characters37825
Distinct characters107
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

Unique1007 ?
Unique (%)100.0%

Sample

1st row전북전주2008-06-1 /전북전주2008-06-1
2nd row전북전주2004-12-2 /전북전주2004-12-2
3rd row전북전주2015-24-4 /전북전주2015-24-4
4th row전북전주2006-27-1-1 /전북전주2016-14-1 /전북전주2006-27-1-1 /전북전주2016-14-1 /전북전주2020-2-7
5th row전북전주2014-29-1 /전북전주2012-3-1 /전북전주2012-3-1 /전북전주2012-5-1
ValueCountFrequency (%)
전북 28
 
1.0%
전북남원 15
 
0.5%
전북진안 14
 
0.5%
전북전주 12
 
0.4%
전북김제 9
 
0.3%
진안 6
 
0.2%
부안 4
 
0.1%
전북제35호 4
 
0.1%
대전중구 4
 
0.1%
제주특별자치 4
 
0.1%
Other values (1724) 2788
96.5%
2024-03-15T06:27:30.667255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 5522
14.6%
0 4749
12.6%
2 4506
11.9%
전 4242
11.2%
1 3859
10.2%
북 2524
6.7%
주 1952
 
5.2%
1881
 
5.0%
/ 1760
 
4.7%
9 1173
 
3.1%
Other values (97) 5657
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18481
48.9%
Other Letter 10169
26.9%
Dash Punctuation 5522
 
14.6%
Space Separator 1881
 
5.0%
Other Punctuation 1760
 
4.7%
Uppercase Letter 6
 
< 0.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 4242
41.7%
북 2524
24.8%
주 1952
19.2%
제 99
 
1.0%
완 93
 
0.9%
남 86
 
0.8%
산 78
 
0.8%
광 70
 
0.7%
안 64
 
0.6%
김 62
 
0.6%
Other values (81) 899
 
8.8%
Decimal Number
ValueCountFrequency (%)
0 4749
25.7%
2 4506
24.4%
1 3859
20.9%
9 1173
 
6.3%
7 820
 
4.4%
3 790
 
4.3%
4 686
 
3.7%
5 650
 
3.5%
6 644
 
3.5%
8 604
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 5522
100.0%
Space Separator
ValueCountFrequency (%)
1881
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1760
100.0%
Uppercase Letter
ValueCountFrequency (%)
G 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27650
73.1%
Hangul 10169
 
26.9%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 4242
41.7%
북 2524
24.8%
주 1952
19.2%
제 99
 
1.0%
완 93
 
0.9%
남 86
 
0.8%
산 78
 
0.8%
광 70
 
0.7%
안 64
 
0.6%
김 62
 
0.6%
Other values (81) 899
 
8.8%
Common
ValueCountFrequency (%)
- 5522
20.0%
0 4749
17.2%
2 4506
16.3%
1 3859
14.0%
1881
 
6.8%
/ 1760
 
6.4%
9 1173
 
4.2%
7 820
 
3.0%
3 790
 
2.9%
4 686
 
2.5%
Other values (5) 1904
 
6.9%
Latin
ValueCountFrequency (%)
G 6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27656
73.1%
Hangul 10169
 
26.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 5522
20.0%
0 4749
17.2%
2 4506
16.3%
1 3859
14.0%
1881
 
6.8%
/ 1760
 
6.4%
9 1173
 
4.2%
7 820
 
3.0%
3 790
 
2.9%
4 686
 
2.5%
Other values (6) 1910
 
6.9%
Hangul
ValueCountFrequency (%)
전 4242
41.7%
북 2524
24.8%
주 1952
19.2%
제 99
 
1.0%
완 93
 
0.9%
남 86
 
0.8%
산 78
 
0.8%
광 70
 
0.7%
안 64
 
0.6%
김 62
 
0.6%
Other values (81) 899
 
8.8%

소재지도로명주소
Text

Distinct966
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2024-03-15T06:27:32.559270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length69
Median length55
Mean length37.195631
Min length1

Characters and Unicode

Total characters37456
Distinct characters304
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

Unique927 ?
Unique (%)92.1%

Sample

1st row전북특별자치도 전주시 덕진구 팔달로 310-7 103호 진북동(진북동)
2nd row전북특별자치도 전주시 덕진구 한배미1길 57-5 (인후동1가)
3rd row전북특별자치도 전주시 덕진구 호성로 18-7 (우아동3가)
4th row전북특별자치도 전주시 덕진구 동부대로 733 (우아동3가)
5th row전북특별자치도 전주시 덕진구 견훤로 166 (인후동1가)
ValueCountFrequency (%)
전북특별자치도 1005
 
14.5%
전주시 1005
 
14.5%
덕진구 520
 
7.5%
완산구 485
 
7.0%
2층 91
 
1.3%
1층 87
 
1.3%
진북동 72
 
1.0%
중화산동2가 61
 
0.9%
3층 49
 
0.7%
효자동3가 47
 
0.7%
Other values (1264) 3528
50.8%
2024-03-15T06:27:34.656648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7422
 
19.8%
전 2131
 
5.7%
동 1451
 
3.9%
1 1235
 
3.3%
자 1208
 
3.2%
북 1114
 
3.0%
주 1040
 
2.8%
구 1027
 
2.7%
시 1022
 
2.7%
도 1015
 
2.7%
Other values (294) 18791
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22876
61.1%
Space Separator 7422
 
19.8%
Decimal Number 4772
 
12.7%
Open Punctuation 1005
 
2.7%
Close Punctuation 1005
 
2.7%
Dash Punctuation 309
 
0.8%
Other Punctuation 58
 
0.2%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 2131
 
9.3%
동 1451
 
6.3%
자 1208
 
5.3%
북 1114
 
4.9%
주 1040
 
4.5%
구 1027
 
4.5%
시 1022
 
4.5%
도 1015
 
4.4%
치 1007
 
4.4%
특 1006
 
4.4%
Other values (273) 10855
47.5%
Decimal Number
ValueCountFrequency (%)
1 1235
25.9%
2 931
19.5%
3 669
14.0%
0 378
 
7.9%
4 371
 
7.8%
5 324
 
6.8%
6 269
 
5.6%
7 247
 
5.2%
9 175
 
3.7%
8 173
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
K 3
33.3%
C 2
22.2%
A 1
 
11.1%
Y 1
 
11.1%
B 1
 
11.1%
T 1
 
11.1%
Space Separator
ValueCountFrequency (%)
7422
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1005
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1005
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 309
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22876
61.1%
Common 14571
38.9%
Latin 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 2131
 
9.3%
동 1451
 
6.3%
자 1208
 
5.3%
북 1114
 
4.9%
주 1040
 
4.5%
구 1027
 
4.5%
시 1022
 
4.5%
도 1015
 
4.4%
치 1007
 
4.4%
특 1006
 
4.4%
Other values (273) 10855
47.5%
Common
ValueCountFrequency (%)
7422
50.9%
1 1235
 
8.5%
( 1005
 
6.9%
) 1005
 
6.9%
2 931
 
6.4%
3 669
 
4.6%
0 378
 
2.6%
4 371
 
2.5%
5 324
 
2.2%
- 309
 
2.1%
Other values (5) 922
 
6.3%
Latin
ValueCountFrequency (%)
K 3
33.3%
C 2
22.2%
A 1
 
11.1%
Y 1
 
11.1%
B 1
 
11.1%
T 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22876
61.1%
ASCII 14580
38.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7422
50.9%
1 1235
 
8.5%
( 1005
 
6.9%
) 1005
 
6.9%
2 931
 
6.4%
3 669
 
4.6%
0 378
 
2.6%
4 371
 
2.5%
5 324
 
2.2%
- 309
 
2.1%
Other values (11) 931
 
6.4%
Hangul
ValueCountFrequency (%)
전 2131
 
9.3%
동 1451
 
6.3%
자 1208
 
5.3%
북 1114
 
4.9%
주 1040
 
4.5%
구 1027
 
4.5%
시 1022
 
4.5%
도 1015
 
4.4%
치 1007
 
4.4%
특 1006
 
4.4%
Other values (273) 10855
47.5%

소재지지번주소
Text

Distinct967
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2024-03-15T06:27:35.890184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length67
Median length57
Mean length34.087388
Min length26

Characters and Unicode

Total characters34326
Distinct characters217
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

Unique929 ?
Unique (%)92.3%

Sample

1st row전북특별자치도 전주시 덕진구 진북동 359-1번지 103호 진북동
2nd row전북특별자치도 전주시 덕진구 인후동1가 946-4번지
3rd row전북특별자치도 전주시 덕진구 우아동3가 602-69번지
4th row전북특별자치도 전주시 덕진구 우아동3가 743-46번지
5th row전북특별자치도 전주시 덕진구 인후동1가 892-16번지
ValueCountFrequency (%)
전북특별자치도 1007
 
16.1%
전주시 1007
 
16.1%
덕진구 521
 
8.3%
완산구 486
 
7.8%
가 183
 
2.9%
2층 90
 
1.4%
1층 86
 
1.4%
진북동 86
 
1.4%
효자동3가 85
 
1.4%
중화산동2가 85
 
1.4%
Other values (1168) 2606
41.7%
2024-03-15T06:27:37.597790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5980
 
17.4%
전 2060
 
6.0%
1 1404
 
4.1%
동 1379
 
4.0%
자 1190
 
3.5%
북 1095
 
3.2%
시 1019
 
3.0%
지 1016
 
3.0%
주 1016
 
3.0%
구 1015
 
3.0%
Other values (207) 17152
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21266
62.0%
Decimal Number 6094
 
17.8%
Space Separator 5980
 
17.4%
Dash Punctuation 919
 
2.7%
Other Punctuation 58
 
0.2%
Uppercase Letter 9
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 2060
 
9.7%
동 1379
 
6.5%
자 1190
 
5.6%
북 1095
 
5.1%
시 1019
 
4.8%
지 1016
 
4.8%
주 1016
 
4.8%
구 1015
 
4.8%
도 1010
 
4.7%
치 1009
 
4.7%
Other values (188) 9457
44.5%
Decimal Number
ValueCountFrequency (%)
1 1404
23.0%
2 953
15.6%
3 717
11.8%
4 529
 
8.7%
0 490
 
8.0%
6 480
 
7.9%
5 457
 
7.5%
7 428
 
7.0%
8 356
 
5.8%
9 280
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
K 3
33.3%
C 2
22.2%
Y 1
 
11.1%
A 1
 
11.1%
T 1
 
11.1%
B 1
 
11.1%
Space Separator
ValueCountFrequency (%)
5980
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 919
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21266
62.0%
Common 13051
38.0%
Latin 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 2060
 
9.7%
동 1379
 
6.5%
자 1190
 
5.6%
북 1095
 
5.1%
시 1019
 
4.8%
지 1016
 
4.8%
주 1016
 
4.8%
구 1015
 
4.8%
도 1010
 
4.7%
치 1009
 
4.7%
Other values (188) 9457
44.5%
Common
ValueCountFrequency (%)
5980
45.8%
1 1404
 
10.8%
2 953
 
7.3%
- 919
 
7.0%
3 717
 
5.5%
4 529
 
4.1%
0 490
 
3.8%
6 480
 
3.7%
5 457
 
3.5%
7 428
 
3.3%
Other values (3) 694
 
5.3%
Latin
ValueCountFrequency (%)
K 3
33.3%
C 2
22.2%
Y 1
 
11.1%
A 1
 
11.1%
T 1
 
11.1%
B 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21266
62.0%
ASCII 13060
38.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5980
45.8%
1 1404
 
10.8%
2 953
 
7.3%
- 919
 
7.0%
3 717
 
5.5%
4 529
 
4.1%
0 490
 
3.8%
6 480
 
3.7%
5 457
 
3.5%
7 428
 
3.3%
Other values (9) 703
 
5.4%
Hangul
ValueCountFrequency (%)
전 2060
 
9.7%
동 1379
 
6.5%
자 1190
 
5.6%
북 1095
 
5.1%
시 1019
 
4.8%
지 1016
 
4.8%
주 1016
 
4.8%
구 1015
 
4.8%
도 1010
 
4.7%
치 1009
 
4.7%
Other values (188) 9457
44.5%

위도
Real number (ℝ)

Distinct885
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.830826
Minimum35.760476
Maximum35.898693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-03-15T06:27:37.966671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.760476
5-th percentile35.786947
Q135.814341
median35.82892
Q335.849196
95-th percentile35.87753
Maximum35.898693
Range0.13821723
Interquartile range (IQR)0.03485531

Descriptive statistics

Standard deviation0.02656193
Coefficient of variation (CV)0.00074131505
Kurtosis-0.22259361
Mean35.830826
Median Absolute Deviation (MAD)0.01670703
Skewness0.074783624
Sum36081.642
Variance0.00070553614
MonotonicityNot monotonic
2024-03-15T06:27:38.414103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.83541465 9
 
0.9%
35.83880607 4
 
0.4%
35.81865177 4
 
0.4%
35.81340547 4
 
0.4%
35.76676158 3
 
0.3%
35.79340139 3
 
0.3%
35.85328171 3
 
0.3%
35.83350582 3
 
0.3%
35.82814055 3
 
0.3%
35.83628404 3
 
0.3%
Other values (875) 968
96.1%
ValueCountFrequency (%)
35.76047624 2
0.2%
35.76074323 1
 
0.1%
35.76598647 1
 
0.1%
35.76676158 3
0.3%
35.7669628 1
 
0.1%
35.76853515 1
 
0.1%
35.76890897 1
 
0.1%
35.76901158 1
 
0.1%
35.76994783 1
 
0.1%
35.77088195 1
 
0.1%
ValueCountFrequency (%)
35.89869347 1
0.1%
35.89718659 1
0.1%
35.89655385 1
0.1%
35.89469488 1
0.1%
35.89420076 1
0.1%
35.89384988 1
0.1%
35.89317094 1
0.1%
35.89310013 1
0.1%
35.89249544 2
0.2%
35.89233976 1
0.1%

경도
Real number (ℝ)

Distinct884
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.11902
Minimum127.01749
Maximum127.19903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2024-03-15T06:27:38.742926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.01749
5-th percentile127.066
Q1127.10148
median127.11945
Q3127.13895
95-th percentile127.16837
Maximum127.19903
Range0.1815403
Interquartile range (IQR)0.03746965

Descriptive statistics

Standard deviation0.030647803
Coefficient of variation (CV)0.00024109535
Kurtosis-0.032447991
Mean127.11902
Median Absolute Deviation (MAD)0.018616
Skewness-0.21787144
Sum128008.85
Variance0.00093928786
MonotonicityNot monotonic
2024-03-15T06:27:39.189909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1283974 9
 
0.9%
127.1064699 4
 
0.4%
127.0592894 4
 
0.4%
127.1235474 4
 
0.4%
127.1299125 3
 
0.3%
127.0764069 3
 
0.3%
127.1358257 3
 
0.3%
127.1283246 3
 
0.3%
127.118115 3
 
0.3%
127.093439 3
 
0.3%
Other values (874) 968
96.1%
ValueCountFrequency (%)
127.0174946 1
0.1%
127.0329001 1
0.1%
127.0330144 1
0.1%
127.033292 1
0.1%
127.0346278 1
0.1%
127.0367123 2
0.2%
127.0368925 1
0.1%
127.0374483 1
0.1%
127.0381234 2
0.2%
127.0383802 2
0.2%
ValueCountFrequency (%)
127.1990349 1
0.1%
127.1975554 1
0.1%
127.1960851 1
0.1%
127.194926 1
0.1%
127.1943598 1
0.1%
127.1938668 1
0.1%
127.1920431 1
0.1%
127.1893056 1
0.1%
127.1844013 1
0.1%
127.181441 1
0.1%

전화번호
Text

MISSING 

Distinct972
Distinct (%)97.8%
Missing13
Missing (%)1.3%
Memory size8.0 KiB
2024-03-15T06:27:39.976834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.019115
Min length12

Characters and Unicode

Total characters11947
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

Unique952 ?
Unique (%)95.8%

Sample

1st row063-276-1007
2nd row063-224-0488
3rd row063-244-0010
4th row063-241-2609
5th row063-274-0404
ValueCountFrequency (%)
063-000-0000 3
 
0.3%
063-276-4112 3
 
0.3%
063-635-7700 2
 
0.2%
063-242-0012 2
 
0.2%
063-227-8850 2
 
0.2%
063-236-0126 2
 
0.2%
063-273-7400 2
 
0.2%
063-227-8209 2
 
0.2%
063-291-6800 2
 
0.2%
063-253-5916 2
 
0.2%
Other values (962) 972
97.8%
2024-03-15T06:27:41.122840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1988
16.6%
2 1712
14.3%
0 1660
13.9%
3 1496
12.5%
6 1431
12.0%
1 767
 
6.4%
4 723
 
6.1%
5 656
 
5.5%
7 623
 
5.2%
8 507
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9959
83.4%
Dash Punctuation 1988
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1712
17.2%
0 1660
16.7%
3 1496
15.0%
6 1431
14.4%
1 767
7.7%
4 723
7.3%
5 656
 
6.6%
7 623
 
6.3%
8 507
 
5.1%
9 384
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 1988
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11947
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1988
16.6%
2 1712
14.3%
0 1660
13.9%
3 1496
12.5%
6 1431
12.0%
1 767
 
6.4%
4 723
 
6.1%
5 656
 
5.5%
7 623
 
5.2%
8 507
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1988
16.6%
2 1712
14.3%
0 1660
13.9%
3 1496
12.5%
6 1431
12.0%
1 767
 
6.4%
4 723
 
6.1%
5 656
 
5.5%
7 623
 
5.2%
8 507
 
4.2%

Interactions

2024-03-15T06:27:19.938730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:27:17.621747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:27:18.819252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:27:20.238252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:27:17.913100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:27:19.134977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:27:20.621879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:27:18.276728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:27:19.482179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T06:27:41.380910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.0000.193
위도0.0001.0000.633
경도0.1930.6331.000
2024-03-15T06:27:41.634037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.000-0.0610.062
위도-0.0611.000-0.163
경도0.062-0.1631.000

Missing values

2024-03-15T06:27:21.281419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T06:27:21.802754image/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

연번업체명업체등록일자업종등록번호소재지도로명주소소재지지번주소위도경도전화번호
01(우)초원석면환경2008-11-13구조물해체ㆍ비계공사업 /비계ㆍ구조물해체공사업(대업종전환)전북전주2008-06-1 /전북전주2008-06-1전북특별자치도 전주시 덕진구 팔달로 310-7 103호 진북동(진북동)전북특별자치도 전주시 덕진구 진북동 359-1번지 103호 진북동35.829489127.141688063-276-1007
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연번업체명업체등록일자업종등록번호소재지도로명주소소재지지번주소위도경도전화번호
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