Overview

Dataset statistics

Number of variables7
Number of observations243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.4 KiB
Average record size in memory56.5 B

Variable types

Categorical4
Text3

Dataset

Description인천광역시 부평구 제설함 현황 데이터는 규격, 관리청, 관리번호, 관리부서, 전화번호, 상세위치 등에 대한 데이터를 제공합니다.
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15102665&srcSe=7661IVAWM27C61E190

Alerts

규격 has constant value ""Constant
관리청 has constant value ""Constant
구분 is highly overall correlated with 관리부서High correlation
관리부서 is highly overall correlated with 구분High correlation
구분 is highly imbalanced (66.6%)Imbalance
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-03-18 05:07:01.677419
Analysis finished2024-03-18 05:07:02.618004
Duration0.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
제설함
228 
제설함
 
15

Length

Max length4
Median length3
Mean length3.0617284
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제설함 228
93.8%
제설함 15
 
6.2%

Length

2024-03-18T14:07:02.685899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:07:02.796343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제설함 243
100.0%

규격
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
1170*720*770
243 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1170*720*770
2nd row1170*720*770
3rd row1170*720*770
4th row1170*720*770
5th row1170*720*770

Common Values

ValueCountFrequency (%)
1170*720*770 243
100.0%

Length

2024-03-18T14:07:02.884109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:07:02.969368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1170*720*770 243
100.0%

관리청
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
인천광역시 부평구청
243 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천광역시 부평구청
2nd row인천광역시 부평구청
3rd row인천광역시 부평구청
4th row인천광역시 부평구청
5th row인천광역시 부평구청

Common Values

ValueCountFrequency (%)
인천광역시 부평구청 243
100.0%

Length

2024-03-18T14:07:03.060042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:07:03.135959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 243
50.0%
부평구청 243
50.0%

관리번호
Text

UNIQUE 

Distinct243
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-18T14:07:03.391152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1604938
Min length3

Characters and Unicode

Total characters1254
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique243 ?
Unique (%)100.0%

Sample

1st row구청-1
2nd row구청-2
3rd row구청-3
4th row구청-4
5th row구청-5
ValueCountFrequency (%)
구청-1 1
 
0.4%
부평6-2 1
 
0.4%
산곡4-3 1
 
0.4%
산곡3-2 1
 
0.4%
산곡3-3 1
 
0.4%
산곡3-4 1
 
0.4%
산곡3-5 1
 
0.4%
산곡3-6 1
 
0.4%
산곡3-7 1
 
0.4%
산곡3-8 1
 
0.4%
Other values (233) 233
95.9%
2024-03-18T14:07:03.807591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 238
19.0%
1 166
13.2%
2 126
10.0%
부 90
 
7.2%
평 67
 
5.3%
3 65
 
5.2%
산 64
 
5.1%
청 44
 
3.5%
곡 42
 
3.3%
4 41
 
3.3%
Other values (15) 311
24.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 530
42.3%
Other Letter 486
38.8%
Dash Punctuation 238
19.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 90
18.5%
평 67
13.8%
산 64
13.2%
청 44
9.1%
곡 42
8.6%
십 40
8.2%
정 40
8.2%
구 25
 
5.1%
개 23
 
4.7%
천 19
 
3.9%
Other values (4) 32
 
6.6%
Decimal Number
ValueCountFrequency (%)
1 166
31.3%
2 126
23.8%
3 65
 
12.3%
4 41
 
7.7%
6 32
 
6.0%
5 30
 
5.7%
7 23
 
4.3%
8 20
 
3.8%
9 15
 
2.8%
0 12
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 238
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 768
61.2%
Hangul 486
38.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 90
18.5%
평 67
13.8%
산 64
13.2%
청 44
9.1%
곡 42
8.6%
십 40
8.2%
정 40
8.2%
구 25
 
5.1%
개 23
 
4.7%
천 19
 
3.9%
Other values (4) 32
 
6.6%
Common
ValueCountFrequency (%)
- 238
31.0%
1 166
21.6%
2 126
16.4%
3 65
 
8.5%
4 41
 
5.3%
6 32
 
4.2%
5 30
 
3.9%
7 23
 
3.0%
8 20
 
2.6%
9 15
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 768
61.2%
Hangul 486
38.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 238
31.0%
1 166
21.6%
2 126
16.4%
3 65
 
8.5%
4 41
 
5.3%
6 32
 
4.2%
5 30
 
3.9%
7 23
 
3.0%
8 20
 
2.6%
9 15
 
2.0%
Hangul
ValueCountFrequency (%)
부 90
18.5%
평 67
13.8%
산 64
13.2%
청 44
9.1%
곡 42
8.6%
십 40
8.2%
정 40
8.2%
구 25
 
5.1%
개 23
 
4.7%
천 19
 
3.9%
Other values (4) 32
 
6.6%

관리부서
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
십정1동
28 
부평2동
28 
도로과
25 
부평3동
17 
산곡1동
14 
Other values (17)
131 

Length

Max length4
Median length4
Mean length3.8765432
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
십정1동 28
 
11.5%
부평2동 28
 
11.5%
도로과 25
 
10.3%
부평3동 17
 
7.0%
산곡1동 14
 
5.8%
십정2동 12
 
4.9%
산곡3동 11
 
4.5%
산곡2동 10
 
4.1%
청천2동 10
 
4.1%
부개1동 9
 
3.7%
Other values (12) 79
32.5%

Length

2024-03-18T14:07:03.962971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
십정1동 28
 
11.5%
부평2동 28
 
11.5%
도로과 25
 
10.3%
부평3동 17
 
7.0%
산곡1동 14
 
5.8%
십정2동 12
 
4.9%
산곡3동 11
 
4.5%
산곡2동 10
 
4.1%
청천2동 10
 
4.1%
청천1동 9
 
3.7%
Other values (12) 79
32.5%

전화번호
Text

Distinct58
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-18T14:07:04.145474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2916
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

Unique40 ?
Unique (%)16.5%

Sample

1st row032-509-6835
2nd row032-509-6835
3rd row032-509-6835
4th row032-509-6835
5th row032-509-6835
ValueCountFrequency (%)
032-509-8314 28
 
11.5%
032-509-8001 28
 
11.5%
032-509-6835 25
 
10.3%
032-509-8435 14
 
5.8%
032-509-8613 12
 
4.9%
032-509-8385 11
 
4.5%
032-509-7923 10
 
4.1%
032-509-7878 10
 
4.1%
032-509-8404 9
 
3.7%
032-509-8487 8
 
3.3%
Other values (48) 88
36.2%
2024-03-18T14:07:04.429429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 563
19.3%
- 486
16.7%
3 435
14.9%
5 322
11.0%
9 285
9.8%
2 268
9.2%
8 259
8.9%
4 96
 
3.3%
1 81
 
2.8%
7 78
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2430
83.3%
Dash Punctuation 486
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 563
23.2%
3 435
17.9%
5 322
13.3%
9 285
11.7%
2 268
11.0%
8 259
10.7%
4 96
 
4.0%
1 81
 
3.3%
7 78
 
3.2%
6 43
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2916
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 563
19.3%
- 486
16.7%
3 435
14.9%
5 322
11.0%
9 285
9.8%
2 268
9.2%
8 259
8.9%
4 96
 
3.3%
1 81
 
2.8%
7 78
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2916
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 563
19.3%
- 486
16.7%
3 435
14.9%
5 322
11.0%
9 285
9.8%
2 268
9.2%
8 259
8.9%
4 96
 
3.3%
1 81
 
2.8%
7 78
 
2.7%

상세위치
Text

Distinct237
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2024-03-18T14:07:04.734349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length19.641975
Min length15

Characters and Unicode

Total characters4773
Distinct characters85
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique231 ?
Unique (%)95.1%

Sample

1st row인천광역시 부평구 갈산동 423
2nd row인천광역시 부평구 갈산동 181
3rd row인천광역시 부평구 삼산동 215-1
4th row인천광역시 부평구 삼산동 426-1
5th row인천광역시 부평구 청천동 375
ValueCountFrequency (%)
인천광역시 243
24.2%
부평구 243
24.2%
산곡동 26
 
2.6%
부평동 19
 
1.9%
부개동 12
 
1.2%
마장로 9
 
0.9%
경인로 8
 
0.8%
십정동 6
 
0.6%
11 6
 
0.6%
이규보로 5
 
0.5%
Other values (308) 429
42.6%
2024-03-18T14:07:05.157593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
768
16.1%
부 296
 
6.2%
평 273
 
5.7%
인 260
 
5.4%
천 253
 
5.3%
광 245
 
5.1%
역 243
 
5.1%
시 243
 
5.1%
구 243
 
5.1%
1 194
 
4.1%
Other values (75) 1755
36.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2935
61.5%
Decimal Number 968
 
20.3%
Space Separator 768
 
16.1%
Dash Punctuation 102
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 296
10.1%
평 273
9.3%
인 260
8.9%
천 253
8.6%
광 245
8.3%
역 243
8.3%
시 243
8.3%
구 243
8.3%
로 168
 
5.7%
길 107
 
3.6%
Other values (63) 604
20.6%
Decimal Number
ValueCountFrequency (%)
1 194
20.0%
2 122
12.6%
3 111
11.5%
4 109
11.3%
6 83
8.6%
7 79
8.2%
5 77
 
8.0%
8 76
 
7.9%
0 61
 
6.3%
9 56
 
5.8%
Space Separator
ValueCountFrequency (%)
768
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 102
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2935
61.5%
Common 1838
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 296
10.1%
평 273
9.3%
인 260
8.9%
천 253
8.6%
광 245
8.3%
역 243
8.3%
시 243
8.3%
구 243
8.3%
로 168
 
5.7%
길 107
 
3.6%
Other values (63) 604
20.6%
Common
ValueCountFrequency (%)
768
41.8%
1 194
 
10.6%
2 122
 
6.6%
3 111
 
6.0%
4 109
 
5.9%
- 102
 
5.5%
6 83
 
4.5%
7 79
 
4.3%
5 77
 
4.2%
8 76
 
4.1%
Other values (2) 117
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2935
61.5%
ASCII 1838
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
768
41.8%
1 194
 
10.6%
2 122
 
6.6%
3 111
 
6.0%
4 109
 
5.9%
- 102
 
5.5%
6 83
 
4.5%
7 79
 
4.3%
5 77
 
4.2%
8 76
 
4.1%
Other values (2) 117
 
6.4%
Hangul
ValueCountFrequency (%)
부 296
10.1%
평 273
9.3%
인 260
8.9%
천 253
8.6%
광 245
8.3%
역 243
8.3%
시 243
8.3%
구 243
8.3%
로 168
 
5.7%
길 107
 
3.6%
Other values (63) 604
20.6%

Correlations

2024-03-18T14:07:05.245995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분관리부서전화번호
구분1.0001.0000.946
관리부서1.0001.0000.999
전화번호0.9460.9991.000
2024-03-18T14:07:05.328797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분관리부서
구분1.0000.958
관리부서0.9581.000
2024-03-18T14:07:05.405939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분관리부서
구분1.0000.958
관리부서0.9581.000

Missing values

2024-03-18T14:07:02.454233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:07:02.573894image/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

구분규격관리청관리번호관리부서전화번호상세위치
0제설함1170*720*770인천광역시 부평구청구청-1도로과032-509-6835인천광역시 부평구 갈산동 423
1제설함1170*720*770인천광역시 부평구청구청-2도로과032-509-6835인천광역시 부평구 갈산동 181
2제설함1170*720*770인천광역시 부평구청구청-3도로과032-509-6835인천광역시 부평구 삼산동 215-1
3제설함1170*720*770인천광역시 부평구청구청-4도로과032-509-6835인천광역시 부평구 삼산동 426-1
4제설함1170*720*770인천광역시 부평구청구청-5도로과032-509-6835인천광역시 부평구 청천동 375
5제설함1170*720*770인천광역시 부평구청구청-6도로과032-509-6835인천광역시 부평구 일신동 398
6제설함1170*720*770인천광역시 부평구청구청-7도로과032-509-6835인천광역시 부평구 일신동 126-64
7제설함1170*720*770인천광역시 부평구청구청-8도로과032-509-6835인천광역시 부평구 갈산동 74-1
8제설함1170*720*770인천광역시 부평구청구청-9도로과032-509-6835인천광역시 부평구 산곡동 180-95
9제설함1170*720*770인천광역시 부평구청구청-10도로과032-509-6835인천광역시 부평구 산곡동 159-52
구분규격관리청관리번호관리부서전화번호상세위치
233제설함1170*720*770인천광역시 부평구청청천2-1청천2동032-509-7917인천광역시 부평구 안남로 434번길 11
234제설함1170*720*770인천광역시 부평구청청천2-2청천2동032-509-7918인천광역시 부평구 평천로 118
235제설함1170*720*770인천광역시 부평구청청천2-3청천2동032-509-7919인천광역시 부평구 평천로 136번길 28-3
236제설함1170*720*770인천광역시 부평구청청천2-4청천2동032-509-7920인천광역시 부평구 마장로 412
237제설함1170*720*770인천광역시 부평구청청천2-5청천2동032-509-7921인천광역시 부평구 세월천로 40번길 34
238제설함1170*720*770인천광역시 부평구청청천2-6청천2동032-509-7922인천광역시 부평구 마장로 384번길 5
239제설함1170*720*770인천광역시 부평구청청천2-7청천2동032-509-7923인천광역시 부평구 마장로 364번길 23
240제설함1170*720*770인천광역시 부평구청청천2-8청천2동032-509-7924인천광역시 부평구 안남로 272
241제설함1170*720*770인천광역시 부평구청청천2-9청천2동032-509-7925인천광역시 부평구 원길로 23
242제설함1170*720*770인천광역시 부평구청청천2-10청천2동032-509-7926인천광역시 부평구 부평대로 167번길 43