Overview

Dataset statistics

Number of variables6
Number of observations55
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory54.4 B

Variable types

Numeric4
Text2

Dataset

Description인천광역시 미추홀구 관내에 위치한 나트륨줄이기 참여업소현황 데이터로 업소명, 지정연도, 소재지 등의 정보를 제공합니다.
Author인천광역시
URLhttps://www.incheon.go.kr/data/DATA010201/view?docId=15100153

Alerts

연번 has unique valuesUnique
업소명 has unique valuesUnique
소재지 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2024-01-28 09:46:32.616615
Analysis finished2024-01-28 09:46:34.148282
Duration1.53 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-01-28T18:46:34.204034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q114.5
median28
Q341.5
95-th percentile52.3
Maximum55
Range54
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.02082
Coefficient of variation (CV)0.57217214
Kurtosis-1.2
Mean28
Median Absolute Deviation (MAD)14
Skewness0
Sum1540
Variance256.66667
MonotonicityStrictly increasing
2024-01-28T18:46:34.306161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.8%
2 1
 
1.8%
31 1
 
1.8%
32 1
 
1.8%
33 1
 
1.8%
34 1
 
1.8%
35 1
 
1.8%
36 1
 
1.8%
37 1
 
1.8%
38 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
55 1
1.8%
54 1
1.8%
53 1
1.8%
52 1
1.8%
51 1
1.8%
50 1
1.8%
49 1
1.8%
48 1
1.8%
47 1
1.8%
46 1
1.8%

업소명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-01-28T18:46:34.496772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.2363636
Min length3

Characters and Unicode

Total characters398
Distinct characters142
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row(주)서울엔지니어링
2nd rowSK아이숲어린이집
3rd row가마솥손두부
4th row개나리어린이집
5th row경복궁불고기학익점
ValueCountFrequency (%)
주)서울엔지니어링 1
 
1.8%
도화e편한어린이집 1
 
1.8%
성진물텀벙이 1
 
1.8%
세종유치원 1
 
1.8%
소울림(林)소고기 1
 
1.8%
국밥 1
 
1.8%
송도갈매기(주안점 1
 
1.8%
숭의가든 1
 
1.8%
숭의유치원 1
 
1.8%
신동아어린이집 1
 
1.8%
Other values (46) 46
82.1%
2024-01-28T18:46:34.824481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 44
 
11.1%
어 41
 
10.3%
집 40
 
10.1%
린 39
 
9.8%
원 5
 
1.3%
리 5
 
1.3%
마 5
 
1.3%
( 4
 
1.0%
한 4
 
1.0%
미 4
 
1.0%
Other values (132) 207
52.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 386
97.0%
Open Punctuation 4
 
1.0%
Close Punctuation 4
 
1.0%
Uppercase Letter 2
 
0.5%
Space Separator 1
 
0.3%
Lowercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 44
 
11.4%
어 41
 
10.6%
집 40
 
10.4%
린 39
 
10.1%
원 5
 
1.3%
리 5
 
1.3%
마 5
 
1.3%
한 4
 
1.0%
미 4
 
1.0%
나 4
 
1.0%
Other values (126) 195
50.5%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
S 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 385
96.7%
Common 9
 
2.3%
Latin 3
 
0.8%
Han 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 44
 
11.4%
어 41
 
10.6%
집 40
 
10.4%
린 39
 
10.1%
원 5
 
1.3%
리 5
 
1.3%
마 5
 
1.3%
한 4
 
1.0%
미 4
 
1.0%
나 4
 
1.0%
Other values (125) 194
50.4%
Common
ValueCountFrequency (%)
( 4
44.4%
) 4
44.4%
1
 
11.1%
Latin
ValueCountFrequency (%)
e 1
33.3%
K 1
33.3%
S 1
33.3%
Han
ValueCountFrequency (%)
林 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 385
96.7%
ASCII 12
 
3.0%
CJK 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 44
 
11.4%
어 41
 
10.6%
집 40
 
10.4%
린 39
 
10.1%
원 5
 
1.3%
리 5
 
1.3%
마 5
 
1.3%
한 4
 
1.0%
미 4
 
1.0%
나 4
 
1.0%
Other values (125) 194
50.4%
ASCII
ValueCountFrequency (%)
( 4
33.3%
) 4
33.3%
1
 
8.3%
e 1
 
8.3%
K 1
 
8.3%
S 1
 
8.3%
CJK
ValueCountFrequency (%)
林 1
100.0%

지정연도
Real number (ℝ)

Distinct12
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.3818
Minimum2010
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-01-28T18:46:34.921359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2012.7
Q12015
median2016
Q32017.5
95-th percentile2021
Maximum2022
Range12
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.5566972
Coefficient of variation (CV)0.0012679628
Kurtosis0.42051044
Mean2016.3818
Median Absolute Deviation (MAD)1
Skewness0.1697393
Sum110901
Variance6.5367003
MonotonicityNot monotonic
2024-01-28T18:46:35.015874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2016 18
32.7%
2017 8
14.5%
2014 8
14.5%
2021 4
 
7.3%
2018 4
 
7.3%
2019 4
 
7.3%
2013 2
 
3.6%
2015 2
 
3.6%
2022 2
 
3.6%
2010 1
 
1.8%
Other values (2) 2
 
3.6%
ValueCountFrequency (%)
2010 1
 
1.8%
2011 1
 
1.8%
2012 1
 
1.8%
2013 2
 
3.6%
2014 8
14.5%
2015 2
 
3.6%
2016 18
32.7%
2017 8
14.5%
2018 4
 
7.3%
2019 4
 
7.3%
ValueCountFrequency (%)
2022 2
 
3.6%
2021 4
 
7.3%
2019 4
 
7.3%
2018 4
 
7.3%
2017 8
14.5%
2016 18
32.7%
2015 2
 
3.6%
2014 8
14.5%
2013 2
 
3.6%
2012 1
 
1.8%

소재지
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2024-01-28T18:46:35.186917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length44
Mean length29.672727
Min length23

Characters and Unicode

Total characters1632
Distinct characters118
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row인천광역시 미추홀구 방축로 332 (주안동)
2nd row인천광역시 미추홀구 용정공원로 33, 106동 101호(용현동, 인천SK스카이뷰)
3rd row인천광역시 미추홀구 소성로326번길 4 (문학동)
4th row인천광역시 미추홀구 재넘이길19번길 18 (학익동)
5th row인천광역시 미추홀구 매소홀로 378 (학익동)
ValueCountFrequency (%)
인천광역시 55
18.6%
미추홀구 55
18.6%
주안동 13
 
4.4%
용현동 13
 
4.4%
학익동 10
 
3.4%
도화동 7
 
2.4%
숭의동 5
 
1.7%
소성로 3
 
1.0%
매소홀로 3
 
1.0%
22 2
 
0.7%
Other values (118) 130
43.9%
2024-01-28T18:46:35.470755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
242
 
14.8%
인 65
 
4.0%
홀 63
 
3.9%
동 63
 
3.9%
미 58
 
3.6%
추 58
 
3.6%
천 57
 
3.5%
구 56
 
3.4%
( 55
 
3.4%
광 55
 
3.4%
Other values (108) 860
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1016
62.3%
Space Separator 242
 
14.8%
Decimal Number 242
 
14.8%
Open Punctuation 55
 
3.4%
Close Punctuation 55
 
3.4%
Dash Punctuation 10
 
0.6%
Other Punctuation 9
 
0.6%
Uppercase Letter 2
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 65
 
6.4%
홀 63
 
6.2%
동 63
 
6.2%
미 58
 
5.7%
추 58
 
5.7%
천 57
 
5.6%
구 56
 
5.5%
광 55
 
5.4%
역 55
 
5.4%
시 55
 
5.4%
Other values (90) 431
42.4%
Decimal Number
ValueCountFrequency (%)
1 49
20.2%
2 32
13.2%
8 28
11.6%
7 25
10.3%
4 24
9.9%
3 20
8.3%
6 19
 
7.9%
0 18
 
7.4%
5 14
 
5.8%
9 13
 
5.4%
Uppercase Letter
ValueCountFrequency (%)
S 1
50.0%
K 1
50.0%
Space Separator
ValueCountFrequency (%)
242
100.0%
Open Punctuation
ValueCountFrequency (%)
( 55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1016
62.3%
Common 613
37.6%
Latin 3
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 65
 
6.4%
홀 63
 
6.2%
동 63
 
6.2%
미 58
 
5.7%
추 58
 
5.7%
천 57
 
5.6%
구 56
 
5.5%
광 55
 
5.4%
역 55
 
5.4%
시 55
 
5.4%
Other values (90) 431
42.4%
Common
ValueCountFrequency (%)
242
39.5%
( 55
 
9.0%
) 55
 
9.0%
1 49
 
8.0%
2 32
 
5.2%
8 28
 
4.6%
7 25
 
4.1%
4 24
 
3.9%
3 20
 
3.3%
6 19
 
3.1%
Other values (5) 64
 
10.4%
Latin
ValueCountFrequency (%)
S 1
33.3%
K 1
33.3%
e 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1016
62.3%
ASCII 616
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
242
39.3%
( 55
 
8.9%
) 55
 
8.9%
1 49
 
8.0%
2 32
 
5.2%
8 28
 
4.5%
7 25
 
4.1%
4 24
 
3.9%
3 20
 
3.2%
6 19
 
3.1%
Other values (8) 67
 
10.9%
Hangul
ValueCountFrequency (%)
인 65
 
6.4%
홀 63
 
6.2%
동 63
 
6.2%
미 58
 
5.7%
추 58
 
5.7%
천 57
 
5.6%
구 56
 
5.5%
광 55
 
5.4%
역 55
 
5.4%
시 55
 
5.4%
Other values (90) 431
42.4%

위도
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.455221
Minimum37.437384
Maximum37.47687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-01-28T18:46:35.583422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.437384
5-th percentile37.43948
Q137.444939
median37.454207
Q337.465295
95-th percentile37.474815
Maximum37.47687
Range0.03948572
Interquartile range (IQR)0.020356205

Descriptive statistics

Standard deviation0.011617604
Coefficient of variation (CV)0.00031017316
Kurtosis-1.1625063
Mean37.455221
Median Absolute Deviation (MAD)0.00986607
Skewness0.24168267
Sum2060.0372
Variance0.00013496872
MonotonicityNot monotonic
2024-01-28T18:46:35.689230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.47340597 1
 
1.8%
37.45201495 1
 
1.8%
37.45601429 1
 
1.8%
37.4559415 1
 
1.8%
37.45458701 1
 
1.8%
37.45789582 1
 
1.8%
37.46539379 1
 
1.8%
37.46067917 1
 
1.8%
37.44146492 1
 
1.8%
37.44515359 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
37.43738389 1
1.8%
37.43866722 1
1.8%
37.43928549 1
1.8%
37.43956361 1
1.8%
37.43988652 1
1.8%
37.44146492 1
1.8%
37.441612 1
1.8%
37.44164691 1
1.8%
37.44170089 1
1.8%
37.44179636 1
1.8%
ValueCountFrequency (%)
37.47686961 1
1.8%
37.47559651 1
1.8%
37.47501371 1
1.8%
37.47472939 1
1.8%
37.47340597 1
1.8%
37.47273375 1
1.8%
37.47176204 1
1.8%
37.46886321 1
1.8%
37.46847313 1
1.8%
37.46802658 1
1.8%

경도
Real number (ℝ)

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.6643
Minimum126.63268
Maximum126.69445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2024-01-28T18:46:35.808664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63268
5-th percentile126.63617
Q1126.65109
median126.66494
Q3126.67832
95-th percentile126.69238
Maximum126.69445
Range0.0617718
Interquartile range (IQR)0.02722775

Descriptive statistics

Standard deviation0.017301636
Coefficient of variation (CV)0.00013659441
Kurtosis-0.86332049
Mean126.6643
Median Absolute Deviation (MAD)0.0138448
Skewness-0.047891252
Sum6966.5368
Variance0.00029934661
MonotonicityNot monotonic
2024-01-28T18:46:35.918554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.67963 1
 
1.8%
126.6450629 1
 
1.8%
126.6491775 1
 
1.8%
126.6376266 1
 
1.8%
126.6641934 1
 
1.8%
126.692997 1
 
1.8%
126.652139 1
 
1.8%
126.6447166 1
 
1.8%
126.6752028 1
 
1.8%
126.6421979 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
126.6326813 1
1.8%
126.6350757 1
1.8%
126.6361011 1
1.8%
126.6361965 1
1.8%
126.6367798 1
1.8%
126.6376266 1
1.8%
126.6397288 1
1.8%
126.6421979 1
1.8%
126.6447166 1
1.8%
126.6450629 1
1.8%
ValueCountFrequency (%)
126.694453100301 1
1.8%
126.6939106 1
1.8%
126.692997 1
1.8%
126.692114 1
1.8%
126.6905797 1
1.8%
126.6887847 1
1.8%
126.6868903 1
1.8%
126.6857555 1
1.8%
126.6849558 1
1.8%
126.6832391 1
1.8%

Interactions

2024-01-28T18:46:33.746401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:32.883809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.139134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.426332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.810742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:32.940997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.198830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.501699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.876707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.004503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.274955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.586815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.947815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.072472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.348979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:46:33.674114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T18:46:35.997597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업소명지정연도소재지위도경도
연번1.0001.0000.0001.0000.7380.342
업소명1.0001.0001.0001.0001.0001.000
지정연도0.0001.0001.0001.0000.0000.105
소재지1.0001.0001.0001.0001.0001.000
위도0.7381.0000.0001.0001.0000.630
경도0.3421.0000.1051.0000.6301.000
2024-01-28T18:46:36.078015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번지정연도위도경도
연번1.000-0.0290.060-0.063
지정연도-0.0291.0000.091-0.098
위도0.0600.0911.000-0.113
경도-0.063-0.098-0.1131.000

Missing values

2024-01-28T18:46:34.045680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T18:46:34.119603image/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

연번업소명지정연도소재지위도경도
01(주)서울엔지니어링2013인천광역시 미추홀구 방축로 332 (주안동)37.473406126.67963
12SK아이숲어린이집2021인천광역시 미추홀구 용정공원로 33, 106동 101호(용현동, 인천SK스카이뷰)37.452015126.645063
23가마솥손두부2016인천광역시 미추홀구 소성로326번길 4 (문학동)37.437384126.683239
34개나리어린이집2018인천광역시 미추홀구 재넘이길19번길 18 (학익동)37.446726126.665504
45경복궁불고기학익점2010인천광역시 미추홀구 매소홀로 378 (학익동)37.439887126.663823
56관교어린이집2016인천광역시 미추홀구 경원대로658번길 29-12 (관교동)37.443063126.693911
67금빛어린이집2016인천광역시 미추홀구 낙섬서로20번길 28 (용현동)37.451167126.636101
78금호어린이집2017인천광역시 미추홀구 매소홀로 68 금호2차아파트내 관리동 1층 금호어린이집 (용현동)37.447394126.632681
89꼬마대통령어린이집2016인천광역시 미추홀구 한나루로357번길 5-42 (학익동)37.439564126.661522
910꼬마별하어린이집2016인천광역시 미추홀구 미추홀대로587번길 15 (주안동)37.449668126.67867
연번업소명지정연도소재지위도경도
4546청송어린이집2014인천광역시 미추홀구 수봉남로17번길 40-16 (용현동)37.458921126.655731
4647피카소어린이집2016인천광역시 미추홀구 낙섬서로18번길 13 (용현동)37.451062126.635076
4748한결어린이집2016인천광역시 미추홀구 경인남길102번길 146 (용현동)37.450481126.665573
4849한별어린이집2014인천광역시 미추홀구 연송로 78 (도화동)37.47687126.657091
4950한울타리어린이집2017인천광역시 미추홀구 주승로 94 (주안동)37.444679126.680744
5051해솔어린이집2014인천광역시 미추홀구 토금남로27번길 22 (용현동)37.452631126.636196
5152해오름어린이집2014인천광역시 미추홀구 인하로330번길 29 (주안동)37.446411126.685755
5253효담채요양원2017인천광역시 미추홀구 경인로 109 (숭의동)37.466487126.654684
5354목련집2022인천광역시 미추홀구 소성로 185번길 16-13 (학익동)37.441796126.669757
5455어주곰탕2022인천광역시 미추홀구 소성로 185번길 16-8 (학익동)37.441612126.66943