Overview

Dataset statistics

Number of variables5
Number of observations192
Missing cells18
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.0 KiB
Average record size in memory42.7 B

Variable types

Numeric2
Text3

Dataset

Description인천광역시 부평구 세탁업 현황 데이터는 세탁업 업소명, 영업소 주소지, 우편번호, 소재지전화번호에 대한 데이터를 제공합니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15103819&srcSe=7661IVAWM27C61E190

Alerts

우편번호(도로명) has 2 (1.0%) missing valuesMissing
소재지전화 has 16 (8.3%) missing valuesMissing
순번 has unique valuesUnique
영업소 주소(도로명) has unique valuesUnique

Reproduction

Analysis started2024-04-17 17:31:44.482780
Analysis finished2024-04-17 17:31:45.203354
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct192
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.5
Minimum1
Maximum192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T02:31:45.257466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.55
Q148.75
median96.5
Q3144.25
95-th percentile182.45
Maximum192
Range191
Interquartile range (IQR)95.5

Descriptive statistics

Standard deviation55.569776
Coefficient of variation (CV)0.5758526
Kurtosis-1.2
Mean96.5
Median Absolute Deviation (MAD)48
Skewness0
Sum18528
Variance3088
MonotonicityStrictly increasing
2024-04-18T02:31:45.382936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
98 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
126 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
Other values (182) 182
94.8%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
192 1
0.5%
191 1
0.5%
190 1
0.5%
189 1
0.5%
188 1
0.5%
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%

업소명
Text

Distinct165
Distinct (%)85.9%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-18T02:31:45.593571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length4.8645833
Min length2

Characters and Unicode

Total characters934
Distinct characters165
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)76.0%

Sample

1st row명품세탁
2nd row미림세탁소
3rd row충남사
4th row현대세탁소
5th row단비사세탁소
ValueCountFrequency (%)
현대세탁소 5
 
2.5%
주공세탁소 4
 
2.0%
그린세탁소 3
 
1.5%
광명세탁소 3
 
1.5%
명품세탁소 3
 
1.5%
대우사 2
 
1.0%
빨다 2
 
1.0%
우리세탁소 2
 
1.0%
삼성세탁 2
 
1.0%
금호세탁 2
 
1.0%
Other values (159) 169
85.8%
2024-04-18T02:31:45.879391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 146
 
15.6%
탁 145
 
15.5%
소 93
 
10.0%
사 34
 
3.6%
대 20
 
2.1%
린 16
 
1.7%
성 14
 
1.5%
명 14
 
1.5%
현 14
 
1.5%
동 13
 
1.4%
Other values (155) 425
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 926
99.1%
Space Separator 5
 
0.5%
Uppercase Letter 2
 
0.2%
Decimal Number 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 146
 
15.8%
탁 145
 
15.7%
소 93
 
10.0%
사 34
 
3.7%
대 20
 
2.2%
린 16
 
1.7%
성 14
 
1.5%
명 14
 
1.5%
현 14
 
1.5%
동 13
 
1.4%
Other values (151) 417
45.0%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
O 1
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Decimal Number
ValueCountFrequency (%)
3 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 926
99.1%
Common 6
 
0.6%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 146
 
15.8%
탁 145
 
15.7%
소 93
 
10.0%
사 34
 
3.7%
대 20
 
2.2%
린 16
 
1.7%
성 14
 
1.5%
명 14
 
1.5%
현 14
 
1.5%
동 13
 
1.4%
Other values (151) 417
45.0%
Common
ValueCountFrequency (%)
5
83.3%
3 1
 
16.7%
Latin
ValueCountFrequency (%)
K 1
50.0%
O 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 926
99.1%
ASCII 8
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 146
 
15.8%
탁 145
 
15.7%
소 93
 
10.0%
사 34
 
3.7%
대 20
 
2.2%
린 16
 
1.7%
성 14
 
1.5%
명 14
 
1.5%
현 14
 
1.5%
동 13
 
1.4%
Other values (151) 417
45.0%
ASCII
ValueCountFrequency (%)
5
62.5%
K 1
 
12.5%
O 1
 
12.5%
3 1
 
12.5%

영업소 주소(도로명)
Text

UNIQUE 

Distinct192
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-04-18T02:31:46.107877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length41
Mean length33.583333
Min length21

Characters and Unicode

Total characters6448
Distinct characters167
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

Unique192 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 체육관로 111, B동 101호 (삼산동, 주공삼산타운@ B상가동)
2nd row인천광역시 부평구 주부토로262번길 34, 1층 (갈산동)
3rd row인천광역시 부평구 부흥로376번길 18 (부평동)
4th row인천광역시 부평구 부평북로 407 (삼산동,현대A상가동)
5th row인천광역시 부평구 부흥로366번길 10 (부평동)
ValueCountFrequency (%)
인천광역시 192
 
16.3%
부평구 192
 
16.3%
부평동 51
 
4.3%
1층 38
 
3.2%
부개동 19
 
1.6%
산곡동 18
 
1.5%
십정동 15
 
1.3%
상가동 13
 
1.1%
갈산동 12
 
1.0%
청천동 11
 
0.9%
Other values (402) 617
52.4%
2024-04-18T02:31:46.684921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
987
 
15.3%
부 368
 
5.7%
1 315
 
4.9%
평 271
 
4.2%
동 263
 
4.1%
천 218
 
3.4%
인 209
 
3.2%
( 203
 
3.1%
) 203
 
3.1%
광 195
 
3.0%
Other values (157) 3216
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3775
58.5%
Decimal Number 1072
 
16.6%
Space Separator 987
 
15.3%
Open Punctuation 203
 
3.1%
Close Punctuation 203
 
3.1%
Other Punctuation 165
 
2.6%
Dash Punctuation 36
 
0.6%
Uppercase Letter 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 368
 
9.7%
평 271
 
7.2%
동 263
 
7.0%
천 218
 
5.8%
인 209
 
5.5%
광 195
 
5.2%
역 195
 
5.2%
시 193
 
5.1%
구 193
 
5.1%
로 192
 
5.1%
Other values (139) 1478
39.2%
Decimal Number
ValueCountFrequency (%)
1 315
29.4%
2 158
14.7%
0 132
12.3%
3 100
 
9.3%
4 80
 
7.5%
6 66
 
6.2%
5 65
 
6.1%
7 56
 
5.2%
8 52
 
4.9%
9 48
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 156
94.5%
@ 9
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
B 4
57.1%
A 3
42.9%
Space Separator
ValueCountFrequency (%)
987
100.0%
Open Punctuation
ValueCountFrequency (%)
( 203
100.0%
Close Punctuation
ValueCountFrequency (%)
) 203
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3775
58.5%
Common 2666
41.3%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 368
 
9.7%
평 271
 
7.2%
동 263
 
7.0%
천 218
 
5.8%
인 209
 
5.5%
광 195
 
5.2%
역 195
 
5.2%
시 193
 
5.1%
구 193
 
5.1%
로 192
 
5.1%
Other values (139) 1478
39.2%
Common
ValueCountFrequency (%)
987
37.0%
1 315
 
11.8%
( 203
 
7.6%
) 203
 
7.6%
2 158
 
5.9%
, 156
 
5.9%
0 132
 
5.0%
3 100
 
3.8%
4 80
 
3.0%
6 66
 
2.5%
Other values (6) 266
 
10.0%
Latin
ValueCountFrequency (%)
B 4
57.1%
A 3
42.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3775
58.5%
ASCII 2673
41.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
987
36.9%
1 315
 
11.8%
( 203
 
7.6%
) 203
 
7.6%
2 158
 
5.9%
, 156
 
5.8%
0 132
 
4.9%
3 100
 
3.7%
4 80
 
3.0%
6 66
 
2.5%
Other values (8) 273
 
10.2%
Hangul
ValueCountFrequency (%)
부 368
 
9.7%
평 271
 
7.2%
동 263
 
7.0%
천 218
 
5.8%
인 209
 
5.5%
광 195
 
5.2%
역 195
 
5.2%
시 193
 
5.1%
구 193
 
5.1%
로 192
 
5.1%
Other values (139) 1478
39.2%

우편번호(도로명)
Real number (ℝ)

MISSING 

Distinct95
Distinct (%)50.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean21378.221
Minimum21302
Maximum21458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-04-18T02:31:46.796075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21302
5-th percentile21311
Q121345
median21376.5
Q321414
95-th percentile21450
Maximum21458
Range156
Interquartile range (IQR)69

Descriptive statistics

Standard deviation42.95012
Coefficient of variation (CV)0.0020090596
Kurtosis-1.0532771
Mean21378.221
Median Absolute Deviation (MAD)34.5
Skewness0.062522765
Sum4061862
Variance1844.7128
MonotonicityNot monotonic
2024-04-18T02:31:46.907378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21450 6
 
3.1%
21351 5
 
2.6%
21311 5
 
2.6%
21376 5
 
2.6%
21414 5
 
2.6%
21369 4
 
2.1%
21355 4
 
2.1%
21318 4
 
2.1%
21397 4
 
2.1%
21405 4
 
2.1%
Other values (85) 144
75.0%
ValueCountFrequency (%)
21302 1
 
0.5%
21304 3
1.6%
21310 2
 
1.0%
21311 5
2.6%
21312 2
 
1.0%
21313 1
 
0.5%
21316 1
 
0.5%
21317 3
1.6%
21318 4
2.1%
21319 1
 
0.5%
ValueCountFrequency (%)
21458 3
1.6%
21457 1
 
0.5%
21455 1
 
0.5%
21454 1
 
0.5%
21451 1
 
0.5%
21450 6
3.1%
21445 2
 
1.0%
21444 1
 
0.5%
21441 2
 
1.0%
21438 1
 
0.5%

소재지전화
Text

MISSING 

Distinct176
Distinct (%)100.0%
Missing16
Missing (%)8.3%
Memory size1.6 KiB
2024-04-18T02:31:47.118078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2112
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

Unique176 ?
Unique (%)100.0%

Sample

1st row032-511-7243
2nd row032-549-0482
3rd row032-523-4059
4th row032-502-3406
5th row032-501-2048
ValueCountFrequency (%)
032-518-9894 1
 
0.6%
032-528-2522 1
 
0.6%
032-514-3234 1
 
0.6%
032-511-7895 1
 
0.6%
032-524-5520 1
 
0.6%
032-888-9977 1
 
0.6%
032-506-7811 1
 
0.6%
032-511-4548 1
 
0.6%
032-435-7381 1
 
0.6%
032-427-6505 1
 
0.6%
Other values (166) 166
94.3%
2024-04-18T02:31:47.427332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 352
16.7%
2 321
15.2%
0 301
14.3%
3 287
13.6%
5 245
11.6%
1 159
7.5%
7 103
 
4.9%
6 94
 
4.5%
9 87
 
4.1%
4 86
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1760
83.3%
Dash Punctuation 352
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 321
18.2%
0 301
17.1%
3 287
16.3%
5 245
13.9%
1 159
9.0%
7 103
 
5.9%
6 94
 
5.3%
9 87
 
4.9%
4 86
 
4.9%
8 77
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 352
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2112
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 352
16.7%
2 321
15.2%
0 301
14.3%
3 287
13.6%
5 245
11.6%
1 159
7.5%
7 103
 
4.9%
6 94
 
4.5%
9 87
 
4.1%
4 86
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 352
16.7%
2 321
15.2%
0 301
14.3%
3 287
13.6%
5 245
11.6%
1 159
7.5%
7 103
 
4.9%
6 94
 
4.5%
9 87
 
4.1%
4 86
 
4.1%

Interactions

2024-04-18T02:31:44.837905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:31:44.690493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:31:44.916605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T02:31:44.764296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T02:31:47.508027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번우편번호(도로명)
순번1.0000.239
우편번호(도로명)0.2391.000
2024-04-18T02:31:47.569149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번우편번호(도로명)
순번1.0000.025
우편번호(도로명)0.0251.000

Missing values

2024-04-18T02:31:45.027566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T02:31:45.102122image/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.
2024-04-18T02:31:45.169042image/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

순번업소명영업소 주소(도로명)우편번호(도로명)소재지전화
01명품세탁인천광역시 부평구 체육관로 111, B동 101호 (삼산동, 주공삼산타운@ B상가동)21342032-511-7243
12미림세탁소인천광역시 부평구 주부토로262번길 34, 1층 (갈산동)21317032-549-0482
23충남사인천광역시 부평구 부흥로376번길 18 (부평동)21397032-523-4059
34현대세탁소인천광역시 부평구 부평북로 407 (삼산동,현대A상가동)21318032-502-3406
45단비사세탁소인천광역시 부평구 부흥로366번길 10 (부평동)21397032-501-2048
56한국세탁인천광역시 부평구 동수로120번길 11 (부개동,한국아파트상가 204호)21458032-523-3393
67일오삼세탁인천광역시 부평구 경인로1045번길 11 (부개동)21415032-462-7766
78욱일세탁인천광역시 부평구 부흥로243번길 39 (부평동)21378032-518-4903
89백합세탁인천광역시 부평구 길주로 344 (산곡동)21369032-503-3584
910동아세탁소인천광역시 부평구 부흥로 246, 상가동 1층 104호 (부평동, 동아아파트2단지)21387032-504-1888
순번업소명영업소 주소(도로명)우편번호(도로명)소재지전화
182183뽀얀세탁인천광역시 부평구 부영로 15-23, 1층 (부평동)21409032-529-7773
183184바로콜운동화세탁인천광역시 부평구 부영로 196, 2층 209호 (부평동, 대림아파트)21378<NA>
184185삼성세탁인천광역시 부평구 주부토로262번길 30, 1층 일부호 (갈산동)21317<NA>
185186포베이비인천광역시 부평구 경인로1092번길 17-15, 1층 일부호 (부개동)21419<NA>
186187맘스카클리닝인천광역시 부평구 길주로565번길 7-4, 1층 일부호 (갈산동)21338<NA>
187188빨다인천광역시 부평구 장제로 36, 부일빌딩 1층 일부호 (부평동)21414<NA>
188189신동아세탁소인천광역시 부평구 아트센터로 118, 상가 나동 307호 (십정동, 동암신동아아파트)21434032-502-0089
189190형제세탁소인천광역시 부평구 안남로417번길 35, 상가동 103호 (청천동, 동양아파트)21310<NA>
190191빨다 세탁소인천광역시 부평구 장제로 36, 부일빌딩 2층 202호 (부평동)21414<NA>
191192복음세탁인천광역시 부평구 장제로228번길 28, 1층 일부호 (부개동)21351032-503-7073