Overview

Dataset statistics

Number of variables7
Number of observations35
Missing cells3
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory62.8 B

Variable types

Numeric3
Categorical1
Text3

Dataset

Description인천광역시 미추홀구 관내에 소재한 목욕장업에 대한 데이터로 연번, 업종명, 상호명, 도로명주소, 전화번호, 좌표값 항목을 제공합니다.
Author인천광역시 미추홀구
URLhttps://www.data.go.kr/data/15061011/fileData.do

Alerts

업종명 has constant value ""Constant
전화번호 has 3 (8.6%) missing valuesMissing
연번 has unique valuesUnique
상호명 has unique valuesUnique
도로명주소 has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:36:49.621428
Analysis finished2024-04-29 22:36:52.482501
Duration2.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-30T07:36:52.546386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.7
Q19.5
median18
Q326.5
95-th percentile33.3
Maximum35
Range34
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.246951
Coefficient of variation (CV)0.56927504
Kurtosis-1.2
Mean18
Median Absolute Deviation (MAD)9
Skewness0
Sum630
Variance105
MonotonicityStrictly increasing
2024-04-30T07:36:52.678167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 1
 
2.9%
2 1
 
2.9%
21 1
 
2.9%
22 1
 
2.9%
23 1
 
2.9%
24 1
 
2.9%
25 1
 
2.9%
26 1
 
2.9%
27 1
 
2.9%
28 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 1
2.9%
6 1
2.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
ValueCountFrequency (%)
35 1
2.9%
34 1
2.9%
33 1
2.9%
32 1
2.9%
31 1
2.9%
30 1
2.9%
29 1
2.9%
28 1
2.9%
27 1
2.9%
26 1
2.9%

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
목욕장업
35 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
목욕장업 35
100.0%

Length

2024-04-30T07:36:52.818374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-30T07:36:52.922343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
목욕장업 35
100.0%

상호명
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2024-04-30T07:36:53.091687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.2857143
Min length2

Characters and Unicode

Total characters220
Distinct characters92
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row수봉목욕탕
2nd row보성목욕탕
3rd row대왕목욕탕
4th row용수탕
5th row백조여성사우나
ValueCountFrequency (%)
사우나 2
 
4.5%
수봉목욕탕 1
 
2.3%
금양해수헬스사우나 1
 
2.3%
진성레져 1
 
2.3%
더 1
 
2.3%
힐 1
 
2.3%
스토리 1
 
2.3%
스파시스 1
 
2.3%
보석자수정해수탕 1
 
2.3%
월미랜드 1
 
2.3%
Other values (33) 33
75.0%
2024-04-30T07:36:53.402796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
우 15
 
6.8%
나 14
 
6.4%
사 14
 
6.4%
탕 9
 
4.1%
스 9
 
4.1%
9
 
4.1%
수 7
 
3.2%
목 7
 
3.2%
욕 6
 
2.7%
가 5
 
2.3%
Other values (82) 125
56.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 205
93.2%
Space Separator 9
 
4.1%
Decimal Number 4
 
1.8%
Close Punctuation 1
 
0.5%
Open Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
우 15
 
7.3%
나 14
 
6.8%
사 14
 
6.8%
탕 9
 
4.4%
스 9
 
4.4%
수 7
 
3.4%
목 7
 
3.4%
욕 6
 
2.9%
가 5
 
2.4%
성 5
 
2.4%
Other values (77) 114
55.6%
Decimal Number
ValueCountFrequency (%)
4 2
50.0%
2 2
50.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 205
93.2%
Common 15
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
우 15
 
7.3%
나 14
 
6.8%
사 14
 
6.8%
탕 9
 
4.4%
스 9
 
4.4%
수 7
 
3.4%
목 7
 
3.4%
욕 6
 
2.9%
가 5
 
2.4%
성 5
 
2.4%
Other values (77) 114
55.6%
Common
ValueCountFrequency (%)
9
60.0%
4 2
 
13.3%
2 2
 
13.3%
) 1
 
6.7%
( 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 205
93.2%
ASCII 15
 
6.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
우 15
 
7.3%
나 14
 
6.8%
사 14
 
6.8%
탕 9
 
4.4%
스 9
 
4.4%
수 7
 
3.4%
목 7
 
3.4%
욕 6
 
2.9%
가 5
 
2.4%
성 5
 
2.4%
Other values (77) 114
55.6%
ASCII
ValueCountFrequency (%)
9
60.0%
4 2
 
13.3%
2 2
 
13.3%
) 1
 
6.7%
( 1
 
6.7%

도로명주소
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
2024-04-30T07:36:53.650775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length36
Mean length29.6
Min length23

Characters and Unicode

Total characters1036
Distinct characters95
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)100.0%

Sample

1st row인천광역시 미추홀구 수봉로 49 (숭의동)
2nd row인천광역시 미추홀구 소성로 89 (학익동)
3rd row인천광역시 미추홀구 석정로 129 (숭의동)
4th row인천광역시 미추홀구 경원대로852번길 67 (주안동)
5th row인천광역시 미추홀구 독정이로 28-1 (용현동)
ValueCountFrequency (%)
인천광역시 35
18.0%
미추홀구 35
18.0%
주안동 11
 
5.7%
용현동 8
 
4.1%
2층 4
 
2.1%
학익동 4
 
2.1%
도화동 3
 
1.5%
경인로 3
 
1.5%
미추홀대로 3
 
1.5%
석정로 3
 
1.5%
Other values (78) 85
43.8%
2024-04-30T07:36:54.019428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
159
 
15.3%
홀 41
 
4.0%
인 40
 
3.9%
추 39
 
3.8%
미 39
 
3.8%
( 36
 
3.5%
) 36
 
3.5%
구 36
 
3.5%
시 36
 
3.5%
동 36
 
3.5%
Other values (85) 538
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 626
60.4%
Space Separator 159
 
15.3%
Decimal Number 152
 
14.7%
Open Punctuation 36
 
3.5%
Close Punctuation 36
 
3.5%
Other Punctuation 23
 
2.2%
Dash Punctuation 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
홀 41
 
6.5%
인 40
 
6.4%
추 39
 
6.2%
미 39
 
6.2%
구 36
 
5.8%
시 36
 
5.8%
동 36
 
5.8%
역 35
 
5.6%
광 35
 
5.6%
천 35
 
5.6%
Other values (70) 254
40.6%
Decimal Number
ValueCountFrequency (%)
2 26
17.1%
1 23
15.1%
4 19
12.5%
3 16
10.5%
0 15
9.9%
5 13
8.6%
6 13
8.6%
7 11
7.2%
8 10
 
6.6%
9 6
 
3.9%
Space Separator
ValueCountFrequency (%)
159
100.0%
Open Punctuation
ValueCountFrequency (%)
( 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%
Other Punctuation
ValueCountFrequency (%)
, 23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 626
60.4%
Common 410
39.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
홀 41
 
6.5%
인 40
 
6.4%
추 39
 
6.2%
미 39
 
6.2%
구 36
 
5.8%
시 36
 
5.8%
동 36
 
5.8%
역 35
 
5.6%
광 35
 
5.6%
천 35
 
5.6%
Other values (70) 254
40.6%
Common
ValueCountFrequency (%)
159
38.8%
( 36
 
8.8%
) 36
 
8.8%
2 26
 
6.3%
1 23
 
5.6%
, 23
 
5.6%
4 19
 
4.6%
3 16
 
3.9%
0 15
 
3.7%
5 13
 
3.2%
Other values (5) 44
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 626
60.4%
ASCII 410
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
159
38.8%
( 36
 
8.8%
) 36
 
8.8%
2 26
 
6.3%
1 23
 
5.6%
, 23
 
5.6%
4 19
 
4.6%
3 16
 
3.9%
0 15
 
3.7%
5 13
 
3.2%
Other values (5) 44
 
10.7%
Hangul
ValueCountFrequency (%)
홀 41
 
6.5%
인 40
 
6.4%
추 39
 
6.2%
미 39
 
6.2%
구 36
 
5.8%
시 36
 
5.8%
동 36
 
5.8%
역 35
 
5.6%
광 35
 
5.6%
천 35
 
5.6%
Other values (70) 254
40.6%

전화번호
Text

MISSING 

Distinct32
Distinct (%)100.0%
Missing3
Missing (%)8.6%
Memory size412.0 B
2024-04-30T07:36:54.206534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.03125
Min length12

Characters and Unicode

Total characters385
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

Unique32 ?
Unique (%)100.0%

Sample

1st row032-875-5693
2nd row032-868-0036
3rd row032-766-2091
4th row032-427-2991
5th row032-882-8267
ValueCountFrequency (%)
032-868-0036 1
 
3.1%
032-766-2091 1
 
3.1%
032-766-6080 1
 
3.1%
032-292-7272 1
 
3.1%
070-8851-3927 1
 
3.1%
032-422-4420 1
 
3.1%
032-426-3373 1
 
3.1%
032-424-6960 1
 
3.1%
032-881-8603 1
 
3.1%
032-866-4545 1
 
3.1%
Other values (22) 22
68.8%
2024-04-30T07:36:54.527078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 64
16.6%
2 56
14.5%
0 52
13.5%
3 47
12.2%
8 38
9.9%
6 32
8.3%
7 26
6.8%
4 26
6.8%
9 17
 
4.4%
1 17
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 321
83.4%
Dash Punctuation 64
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 56
17.4%
0 52
16.2%
3 47
14.6%
8 38
11.8%
6 32
10.0%
7 26
8.1%
4 26
8.1%
9 17
 
5.3%
1 17
 
5.3%
5 10
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 385
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 64
16.6%
2 56
14.5%
0 52
13.5%
3 47
12.2%
8 38
9.9%
6 32
8.3%
7 26
6.8%
4 26
6.8%
9 17
 
4.4%
1 17
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 64
16.6%
2 56
14.5%
0 52
13.5%
3 47
12.2%
8 38
9.9%
6 32
8.3%
7 26
6.8%
4 26
6.8%
9 17
 
4.4%
1 17
 
4.4%

위도
Real number (ℝ)

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.455202
Minimum37.436545
Maximum37.469938
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-30T07:36:54.657033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.436545
5-th percentile37.441521
Q137.448162
median37.457361
Q337.460316
95-th percentile37.46824
Maximum37.469938
Range0.03339317
Interquartile range (IQR)0.012154215

Descriptive statistics

Standard deviation0.0087434061
Coefficient of variation (CV)0.00023343636
Kurtosis-0.6286748
Mean37.455202
Median Absolute Deviation (MAD)0.00541785
Skewness-0.27693893
Sum1310.9321
Variance7.644715 × 10-5
MonotonicityNot monotonic
2024-04-30T07:36:54.795539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
37.45752977 2
 
5.7%
37.46215483 1
 
2.9%
37.45704019 1
 
2.9%
37.44278689 1
 
2.9%
37.46057865 1
 
2.9%
37.45829703 1
 
2.9%
37.43654516 1
 
2.9%
37.46277933 1
 
2.9%
37.44979085 1
 
2.9%
37.4570674 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
37.43654516 1
2.9%
37.43856703 1
2.9%
37.44278689 1
2.9%
37.444259455475 1
2.9%
37.44478424 1
2.9%
37.44519519 1
2.9%
37.44571084 1
2.9%
37.44572643 1
2.9%
37.44779308 1
2.9%
37.44853011 1
2.9%
ValueCountFrequency (%)
37.46993833 1
2.9%
37.46888386 1
2.9%
37.4679645 1
2.9%
37.46757466 1
2.9%
37.46676252 1
2.9%
37.46330616 1
2.9%
37.46277933 1
2.9%
37.46215483 1
2.9%
37.46057865 1
2.9%
37.46005297 1
2.9%

경도
Real number (ℝ)

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.66831
Minimum126.63543
Maximum126.69291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2024-04-30T07:36:54.938806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63543
5-th percentile126.6377
Q1126.6546
median126.67195
Q3126.68151
95-th percentile126.69094
Maximum126.69291
Range0.0574779
Interquartile range (IQR)0.02691515

Descriptive statistics

Standard deviation0.01747349
Coefficient of variation (CV)0.00013794681
Kurtosis-1.0200523
Mean126.66831
Median Absolute Deviation (MAD)0.0145074
Skewness-0.39238555
Sum4433.3909
Variance0.00030532284
MonotonicityNot monotonic
2024-04-30T07:36:55.079001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
126.6881872 2
 
5.7%
126.6578112 1
 
2.9%
126.6550892 1
 
2.9%
126.6789952 1
 
2.9%
126.6822513 1
 
2.9%
126.6748019 1
 
2.9%
126.6864606 1
 
2.9%
126.6719532 1
 
2.9%
126.6354289 1
 
2.9%
126.6916324 1
 
2.9%
Other values (24) 24
68.6%
ValueCountFrequency (%)
126.6354289 1
2.9%
126.6356716 1
2.9%
126.6385628 1
2.9%
126.6402635 1
2.9%
126.6481512 1
2.9%
126.6496739 1
2.9%
126.6505495 1
2.9%
126.652473650634 1
2.9%
126.6541043 1
2.9%
126.6550892 1
2.9%
ValueCountFrequency (%)
126.6929068 1
2.9%
126.6916324 1
2.9%
126.6906483 1
2.9%
126.6903431 1
2.9%
126.6881872 2
5.7%
126.6864606 1
2.9%
126.683723 1
2.9%
126.6822513 1
2.9%
126.6807725 1
2.9%
126.6798341 1
2.9%

Interactions

2024-04-30T07:36:52.036010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:36:51.490579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:36:51.786841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:36:52.120436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:36:51.624388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:36:51.869112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:36:52.202929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:36:51.707601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:36:51.954771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:36:55.171612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호명도로명주소전화번호위도경도
연번1.0001.0001.0001.0000.0000.000
상호명1.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
위도0.0001.0001.0001.0001.0000.333
경도0.0001.0001.0001.0000.3331.000
2024-04-30T07:36:55.267997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.1220.053
위도0.1221.000-0.037
경도0.053-0.0371.000

Missing values

2024-04-30T07:36:52.304453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:36:52.429097image/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

연번업종명상호명도로명주소전화번호위도경도
01목욕장업수봉목욕탕인천광역시 미추홀구 수봉로 49 (숭의동)032-875-569337.462155126.657811
12목욕장업보성목욕탕인천광역시 미추홀구 소성로 89 (학익동)032-868-003637.445195126.659168
23목욕장업대왕목욕탕인천광역시 미추홀구 석정로 129 (숭의동)032-766-209137.468884126.649674
34목욕장업용수탕인천광역시 미추홀구 경원대로852번길 67 (주안동)032-427-299137.460053126.692907
45목욕장업백조여성사우나인천광역시 미추홀구 독정이로 28-1 (용현동)032-882-826737.458477126.654104
56목욕장업한일목욕탕인천광역시 미추홀구 신기길58번길 26 (주안동)032-862-167237.445726126.6753
67목욕장업삼화목욕탕인천광역시 미추홀구 토금북로 58 (용현동)032-884-799137.452807126.640264
78목욕장업부흥한증막인천광역시 미추홀구 인주대로123번길 20 (용현동)032-891-185737.457361126.650549
89목욕장업관교탕인천광역시 미추홀구 경원대로712번길 6-15 (관교동)032-431-232337.445711126.690648
910목욕장업조흥해수사우나인천광역시 미추홀구 한나루로 505 (용현동)032-875-799037.452019126.66717
연번업종명상호명도로명주소전화번호위도경도
2526목욕장업보석자수정해수탕인천광역시 미추홀구 낙섬서로10번길 31 (용현동)032-881-860337.449791126.635429
2627목욕장업세종황토사우나인천광역시 미추홀구 구월로8번길 16 (주안동)032-424-696037.457067126.691632
2728목욕장업주안24시인천광역시 미추홀구 미추홀대로 736 (주안동, 아이하니 8층1호, 7층9호)032-426-337337.463306126.680773
2829목욕장업에이스 스포츠센터(사우나)인천광역시 미추홀구 경인로 434, 지하3층 (주안동, 제일빌딩)032-422-442037.45753126.688187
2930목욕장업주안보석사우나인천광역시 미추홀구 미추홀대로697번길 22, 지하1층 (주안동, 롯데하이텔)070-8851-392737.45994126.678581
3031목욕장업토로미인천광역시 미추홀구 미추홀대로 677, 2층 (주안동)032-292-727237.4579126.679834
3132목욕장업올가힐링인천광역시 미추홀구 석정로162번길 55-13, 2층 (도화동)032-766-608037.467575126.656103
3233목욕장업혼목쌀롱인천광역시 미추홀구 토금북로 40-1, 2층 (용현동)<NA>37.45371126.638563
3334목욕장업세신샵 바디인천광역시 미추홀구 매소홀로 262, 시티필드 4층 4001,4002,4003호 (학익동)<NA>37.444259126.652474
3435목욕장업밀다인천광역시 미추홀구 숙골로87번길 28, 다온프라자 2층 203호 (도화동)<NA>37.469938126.660485