Overview

Dataset statistics

Number of variables5
Number of observations488
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.1 KiB
Average record size in memory42.3 B

Variable types

Text2
Categorical1
Numeric2

Dataset

Description서울특별시 성동구 내에 위치한 제설함 현황 자료입니다. 시설명, 도로명주소, 행정동, 위도, 경도 등의 정보를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15064477/fileData.do

Alerts

위도 is highly overall correlated with 행정동High correlation
경도 is highly overall correlated with 행정동High correlation
행정동 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
시설명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:10:53.733270
Analysis finished2023-12-12 17:10:54.660946
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct488
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-13T02:10:54.942176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.2192623
Min length5

Characters and Unicode

Total characters3523
Distinct characters15
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

Unique488 ?
Unique (%)100.0%

Sample

1st row제설함 1
2nd row제설함 2
3rd row제설함 3
4th row제설함 4
5th row제설함 5
ValueCountFrequency (%)
제설함 303
38.3%
제설함1-032 1
 
0.1%
제설함1-030 1
 
0.1%
제설함1-029 1
 
0.1%
제설함1-028 1
 
0.1%
제설함1-027 1
 
0.1%
제설함1-026 1
 
0.1%
제설함1-025 1
 
0.1%
제설함1-024 1
 
0.1%
제설함1-023 1
 
0.1%
Other values (479) 479
60.6%
2023-12-13T02:10:55.508700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 488
13.9%
설 488
13.9%
함 488
13.9%
1 481
13.7%
303
8.6%
- 224
6.4%
2 214
6.1%
0 185
 
5.3%
3 110
 
3.1%
4 100
 
2.8%
Other values (5) 442
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1532
43.5%
Other Letter 1464
41.6%
Space Separator 303
 
8.6%
Dash Punctuation 224
 
6.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 481
31.4%
2 214
14.0%
0 185
 
12.1%
3 110
 
7.2%
4 100
 
6.5%
5 99
 
6.5%
6 93
 
6.1%
7 88
 
5.7%
8 84
 
5.5%
9 78
 
5.1%
Other Letter
ValueCountFrequency (%)
제 488
33.3%
설 488
33.3%
함 488
33.3%
Space Separator
ValueCountFrequency (%)
303
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2059
58.4%
Hangul 1464
41.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 481
23.4%
303
14.7%
- 224
10.9%
2 214
10.4%
0 185
 
9.0%
3 110
 
5.3%
4 100
 
4.9%
5 99
 
4.8%
6 93
 
4.5%
7 88
 
4.3%
Other values (2) 162
 
7.9%
Hangul
ValueCountFrequency (%)
제 488
33.3%
설 488
33.3%
함 488
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2059
58.4%
Hangul 1464
41.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 488
33.3%
설 488
33.3%
함 488
33.3%
ASCII
ValueCountFrequency (%)
1 481
23.4%
303
14.7%
- 224
10.9%
2 214
10.4%
0 185
 
9.0%
3 110
 
5.3%
4 100
 
4.9%
5 99
 
4.8%
6 93
 
4.5%
7 88
 
4.3%
Other values (2) 162
 
7.9%

도로명주소
Text

Distinct463
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-13T02:10:55.868726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length28
Mean length19.010246
Min length13

Characters and Unicode

Total characters9277
Distinct characters135
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique440 ?
Unique (%)90.2%

Sample

1st row서울특별시 성동구 무학봉7길 27
2nd row서울특별시 성동구 무학봉7길 23
3rd row서울특별시 성동구 무학봉7길 22-17
4th row서울특별시 성동구 무학봉7길 13-1
5th row서울특별시 성동구 무학봉길 88
ValueCountFrequency (%)
서울특별시 488
25.2%
성동구 488
25.2%
독서당로 20
 
1.0%
고산자로 15
 
0.8%
행당로 12
 
0.6%
왕십리로 12
 
0.6%
난계로 10
 
0.5%
15 9
 
0.5%
금호로16길 9
 
0.5%
앞 9
 
0.5%
Other values (457) 868
44.7%
2023-12-13T02:10:56.443966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1452
15.7%
서 565
 
6.1%
동 525
 
5.7%
성 495
 
5.3%
구 492
 
5.3%
울 491
 
5.3%
시 490
 
5.3%
특 488
 
5.3%
별 488
 
5.3%
1 404
 
4.4%
Other values (125) 3387
36.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6110
65.9%
Decimal Number 1610
 
17.4%
Space Separator 1452
 
15.7%
Dash Punctuation 99
 
1.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 565
9.2%
동 525
 
8.6%
성 495
 
8.1%
구 492
 
8.1%
울 491
 
8.0%
시 490
 
8.0%
특 488
 
8.0%
별 488
 
8.0%
길 345
 
5.6%
로 326
 
5.3%
Other values (111) 1405
23.0%
Decimal Number
ValueCountFrequency (%)
1 404
25.1%
2 240
14.9%
3 202
12.5%
5 149
 
9.3%
4 147
 
9.1%
6 122
 
7.6%
7 99
 
6.1%
9 96
 
6.0%
8 84
 
5.2%
0 67
 
4.2%
Space Separator
ValueCountFrequency (%)
1452
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 99
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6110
65.9%
Common 3167
34.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 565
9.2%
동 525
 
8.6%
성 495
 
8.1%
구 492
 
8.1%
울 491
 
8.0%
시 490
 
8.0%
특 488
 
8.0%
별 488
 
8.0%
길 345
 
5.6%
로 326
 
5.3%
Other values (111) 1405
23.0%
Common
ValueCountFrequency (%)
1452
45.8%
1 404
 
12.8%
2 240
 
7.6%
3 202
 
6.4%
5 149
 
4.7%
4 147
 
4.6%
6 122
 
3.9%
- 99
 
3.1%
7 99
 
3.1%
9 96
 
3.0%
Other values (4) 157
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6110
65.9%
ASCII 3167
34.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1452
45.8%
1 404
 
12.8%
2 240
 
7.6%
3 202
 
6.4%
5 149
 
4.7%
4 147
 
4.6%
6 122
 
3.9%
- 99
 
3.1%
7 99
 
3.1%
9 96
 
3.0%
Other values (4) 157
 
5.0%
Hangul
ValueCountFrequency (%)
서 565
9.2%
동 525
 
8.6%
성 495
 
8.1%
구 492
 
8.1%
울 491
 
8.0%
시 490
 
8.0%
특 488
 
8.0%
별 488
 
8.0%
길 345
 
5.6%
로 326
 
5.3%
Other values (111) 1405
23.0%

행정동
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
금호2-3가동
68 
행당제1동
50 
왕십리제2동
43 
마장동
39 
옥수동
38 
Other values (12)
250 

Length

Max length7
Median length6
Mean length4.920082
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row왕십리제2동
2nd row왕십리제2동
3rd row왕십리제2동
4th row왕십리제2동
5th row왕십리제2동

Common Values

ValueCountFrequency (%)
금호2-3가동 68
13.9%
행당제1동 50
10.2%
왕십리제2동 43
8.8%
마장동 39
8.0%
옥수동 38
7.8%
금호1가동 37
7.6%
왕십리도선동 36
7.4%
행당제2동 33
 
6.8%
응봉동 30
 
6.1%
사근동 25
 
5.1%
Other values (7) 89
18.2%

Length

2023-12-13T02:10:56.643641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금호2-3가동 68
13.9%
행당제1동 50
10.2%
왕십리제2동 43
8.8%
마장동 39
8.0%
옥수동 38
7.8%
금호1가동 37
7.6%
왕십리도선동 36
7.4%
행당제2동 33
 
6.8%
응봉동 30
 
6.1%
사근동 25
 
5.1%
Other values (7) 89
18.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct483
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.555412
Minimum37.536373
Maximum37.571694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-13T02:10:56.809138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.536373
5-th percentile37.541238
Q137.549402
median37.556082
Q337.562033
95-th percentile37.567891
Maximum37.571694
Range0.035321
Interquartile range (IQR)0.0126315

Descriptive statistics

Standard deviation0.0078410184
Coefficient of variation (CV)0.00020878531
Kurtosis-0.72011688
Mean37.555412
Median Absolute Deviation (MAD)0.006303
Skewness-0.16683855
Sum18327.041
Variance6.1481569 × 10-5
MonotonicityNot monotonic
2023-12-13T02:10:56.991295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.548188 2
 
0.4%
37.56468 2
 
0.4%
37.558386 2
 
0.4%
37.549839 2
 
0.4%
37.549726 2
 
0.4%
37.565283 1
 
0.2%
37.570998 1
 
0.2%
37.569562 1
 
0.2%
37.570553 1
 
0.2%
37.571694 1
 
0.2%
Other values (473) 473
96.9%
ValueCountFrequency (%)
37.536373 1
0.2%
37.536584 1
0.2%
37.53682 1
0.2%
37.53711 1
0.2%
37.538062 1
0.2%
37.538733 1
0.2%
37.539211 1
0.2%
37.539239 1
0.2%
37.53938 1
0.2%
37.539467 1
0.2%
ValueCountFrequency (%)
37.571694 1
0.2%
37.57169 1
0.2%
37.571327 1
0.2%
37.570998 1
0.2%
37.570553 1
0.2%
37.570104 1
0.2%
37.569986 1
0.2%
37.569966 1
0.2%
37.569658 1
0.2%
37.569562 1
0.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct482
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.03203
Minimum127.00956
Maximum127.07302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-13T02:10:57.146116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.00956
5-th percentile127.01538
Q1127.02251
median127.03056
Q3127.03773
95-th percentile127.05748
Maximum127.07302
Range0.063465
Interquartile range (IQR)0.0152195

Descriptive statistics

Standard deviation0.012965042
Coefficient of variation (CV)0.00010206121
Kurtosis0.78088554
Mean127.03203
Median Absolute Deviation (MAD)0.007609
Skewness0.89556948
Sum61991.631
Variance0.00016809232
MonotonicityNot monotonic
2023-12-13T02:10:57.304059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.024985 2
 
0.4%
127.01878 2
 
0.4%
127.031817 2
 
0.4%
127.036231 2
 
0.4%
127.029085 2
 
0.4%
127.029288 2
 
0.4%
127.059754 1
 
0.2%
127.030378 1
 
0.2%
127.03871 1
 
0.2%
127.037848 1
 
0.2%
Other values (472) 472
96.7%
ValueCountFrequency (%)
127.009558 1
0.2%
127.00967 1
0.2%
127.009966 1
0.2%
127.010204 1
0.2%
127.010308 1
0.2%
127.010739 1
0.2%
127.010791 1
0.2%
127.010987 1
0.2%
127.011051 1
0.2%
127.011315 1
0.2%
ValueCountFrequency (%)
127.073023 1
0.2%
127.070686 1
0.2%
127.070033 1
0.2%
127.069805 1
0.2%
127.069789 1
0.2%
127.069415 1
0.2%
127.069367 1
0.2%
127.069366 1
0.2%
127.0688 1
0.2%
127.068732 1
0.2%

Interactions

2023-12-13T02:10:54.237404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:10:53.947221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:10:54.372159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:10:54.115330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:10:57.418787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동위도경도
행정동1.0000.8780.895
위도0.8781.0000.762
경도0.8950.7621.000
2023-12-13T02:10:57.510900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동
위도1.0000.3930.592
경도0.3931.0000.629
행정동0.5920.6291.000

Missing values

2023-12-13T02:10:54.503119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:10:54.619560image/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

시설명도로명주소행정동위도경도
0제설함 1서울특별시 성동구 무학봉7길 27왕십리제2동37.561681127.02506
1제설함 2서울특별시 성동구 무학봉7길 23왕십리제2동37.561074127.02504
2제설함 3서울특별시 성동구 무학봉7길 22-17왕십리제2동37.560374127.025477
3제설함 4서울특별시 성동구 무학봉7길 13-1왕십리제2동37.560293127.025889
4제설함 5서울특별시 성동구 무학봉길 88왕십리제2동37.562044127.031986
5제설함 6서울특별시 성동구 왕십리로33길 18-2왕십리제2동37.563401127.026963
6제설함 7서울특별시 성동구 왕십리로33길 15왕십리제2동37.563656127.026841
7제설함 8서울특별시 성동구 왕십리로33길 11왕십리제2동37.563838127.027002
8제설함 9서울특별시 성동구 무학봉15라길 15왕십리제2동37.562056127.029519
9제설함 10서울특별시 성동구 무학봉11길 14왕십리제2동37.561552127.028419
시설명도로명주소행정동위도경도
478제설함1-176서울특별시 성동구 천호대로 454용답동37.560493127.069367
479제설함1-177서울특별시 성동구 가람길 267용답동37.558056127.068062
480제설함1-178서울특별시 성동구 천호대로 342용답동37.563431127.056146
481제설함1-179서울특별시 성동구 마장로299마장동37.566146127.042862
482제설함1-180서울특별시 성동구 마장로302마장동37.565941127.043042
483제설함1-181서울특별시 성동구 살곶이길40마장동37.571327127.041257
484제설함1-182서울특별시 성동구 독서당로187옥수동37.542507127.012042
485제설함1-183서울특별시 성동구 매봉길 숲속도서관 입구옥수동37.545945127.010987
486제설함1-184서울특별시 성동구 매봉길 동호초 입구옥수동37.546432127.010739
487제설함1-185서울특별시 성동구 천호대로78용답동37.562493127.05578