Overview

Dataset statistics

Number of variables5
Number of observations667
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.1 KiB
Average record size in memory43.2 B

Variable types

Numeric3
Text2

Dataset

Description서울특별시 강남구 의류수거함 위치 데이터입니다. 도로명주소, 지번주소, 위도와 경도를 포함하고 있으며, 기타 문의사항은 강남구 자원순환과(02-3423-5968)로 문의주시기 바랍니다.
Author서울특별시 강남구
URLhttps://www.data.go.kr/data/15127131/fileData.do

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2024-04-21 02:31:23.916160
Analysis finished2024-04-21 02:31:26.267245
Duration2.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct667
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean334
Minimum1
Maximum667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-04-21T11:31:26.339560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34.3
Q1167.5
median334
Q3500.5
95-th percentile633.7
Maximum667
Range666
Interquartile range (IQR)333

Descriptive statistics

Standard deviation192.6906
Coefficient of variation (CV)0.57691796
Kurtosis-1.2
Mean334
Median Absolute Deviation (MAD)167
Skewness0
Sum222778
Variance37129.667
MonotonicityStrictly increasing
2024-04-21T11:31:26.468254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
440 1
 
0.1%
442 1
 
0.1%
443 1
 
0.1%
444 1
 
0.1%
445 1
 
0.1%
446 1
 
0.1%
447 1
 
0.1%
448 1
 
0.1%
449 1
 
0.1%
Other values (657) 657
98.5%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
667 1
0.1%
666 1
0.1%
665 1
0.1%
664 1
0.1%
663 1
0.1%
662 1
0.1%
661 1
0.1%
660 1
0.1%
659 1
0.1%
658 1
0.1%

지번주소
Text

Distinct654
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size5.3 KiB
2024-04-21T11:31:26.659914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length26
Mean length19.7991
Min length16

Characters and Unicode

Total characters13206
Distinct characters58
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

Unique641 ?
Unique (%)96.1%

Sample

1st row서울특별시 강남구 역삼1동 694-6
2nd row서울특별시 강남구 역삼1동 698-14
3rd row서울특별시 강남구 역삼1동 697-4
4th row서울특별시 강남구 역삼1동 695-20
5th row서울특별시 강남구 역삼1동683-29
ValueCountFrequency (%)
서울특별시 667
24.8%
강남구 667
24.8%
역삼동 95
 
3.5%
개포4동 72
 
2.7%
논현1동 68
 
2.5%
논현2동 65
 
2.4%
청담동 57
 
2.1%
신사동 48
 
1.8%
삼성2동 37
 
1.4%
대치4동 31
 
1.2%
Other values (663) 878
32.7%
2024-04-21T11:31:26.943301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2023
 
15.3%
1 709
 
5.4%
서 667
 
5.1%
동 667
 
5.1%
특 667
 
5.1%
별 667
 
5.1%
시 667
 
5.1%
강 667
 
5.1%
남 667
 
5.1%
구 667
 
5.1%
Other values (48) 5138
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7381
55.9%
Decimal Number 3214
24.3%
Space Separator 2023
 
15.3%
Dash Punctuation 572
 
4.3%
Close Punctuation 8
 
0.1%
Open Punctuation 8
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 667
9.0%
동 667
9.0%
특 667
9.0%
별 667
9.0%
시 667
9.0%
강 667
9.0%
남 667
9.0%
구 667
9.0%
울 667
9.0%
삼 183
 
2.5%
Other values (34) 1195
16.2%
Decimal Number
ValueCountFrequency (%)
1 709
22.1%
2 563
17.5%
4 319
9.9%
6 311
9.7%
5 263
 
8.2%
9 246
 
7.7%
3 239
 
7.4%
7 225
 
7.0%
0 209
 
6.5%
8 130
 
4.0%
Space Separator
ValueCountFrequency (%)
2023
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 572
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7381
55.9%
Common 5825
44.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 667
9.0%
동 667
9.0%
특 667
9.0%
별 667
9.0%
시 667
9.0%
강 667
9.0%
남 667
9.0%
구 667
9.0%
울 667
9.0%
삼 183
 
2.5%
Other values (34) 1195
16.2%
Common
ValueCountFrequency (%)
2023
34.7%
1 709
 
12.2%
- 572
 
9.8%
2 563
 
9.7%
4 319
 
5.5%
6 311
 
5.3%
5 263
 
4.5%
9 246
 
4.2%
3 239
 
4.1%
7 225
 
3.9%
Other values (4) 355
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7381
55.9%
ASCII 5825
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2023
34.7%
1 709
 
12.2%
- 572
 
9.8%
2 563
 
9.7%
4 319
 
5.5%
6 311
 
5.3%
5 263
 
4.5%
9 246
 
4.2%
3 239
 
4.1%
7 225
 
3.9%
Other values (4) 355
 
6.1%
Hangul
ValueCountFrequency (%)
서 667
9.0%
동 667
9.0%
특 667
9.0%
별 667
9.0%
시 667
9.0%
강 667
9.0%
남 667
9.0%
구 667
9.0%
울 667
9.0%
삼 183
 
2.5%
Other values (34) 1195
16.2%

도로명 주소
Text

Distinct648
Distinct (%)97.3%
Missing1
Missing (%)0.1%
Memory size5.3 KiB
2024-04-21T11:31:27.170423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length46
Median length40
Mean length26.327327
Min length17

Characters and Unicode

Total characters17534
Distinct characters173
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique630 ?
Unique (%)94.6%

Sample

1st row서울특별시 강남구 테헤란로57길 38 (역삼동, 동우빌라)
2nd row서울특별시 강남구 테헤란로 53길 51 (역삼동)
3rd row서울특별시 강남구 테헤란로 53길 37 (역삼동)
4th row서울특별시 강남구 테헤란로55길50 (역삼동)
5th row서울특별시 강남구봉은사로50길41 (역삼동)
ValueCountFrequency (%)
서울특별시 666
19.8%
강남구 659
19.6%
논현동 133
 
4.0%
개포동 77
 
2.3%
역삼동 61
 
1.8%
삼성동 58
 
1.7%
청담동 57
 
1.7%
대치동 52
 
1.5%
신사동 48
 
1.4%
일원동 34
 
1.0%
Other values (699) 1520
45.2%
2024-04-21T11:31:27.538157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2709
 
15.4%
남 707
 
4.0%
강 691
 
3.9%
구 679
 
3.9%
동 671
 
3.8%
서 669
 
3.8%
로 669
 
3.8%
시 667
 
3.8%
울 667
 
3.8%
특 666
 
3.8%
Other values (163) 8739
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10578
60.3%
Decimal Number 2834
 
16.2%
Space Separator 2709
 
15.4%
Open Punctuation 593
 
3.4%
Close Punctuation 593
 
3.4%
Dash Punctuation 109
 
0.6%
Other Punctuation 109
 
0.6%
Uppercase Letter 7
 
< 0.1%
Letter Number 1
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 707
 
6.7%
강 691
 
6.5%
구 679
 
6.4%
동 671
 
6.3%
서 669
 
6.3%
로 669
 
6.3%
시 667
 
6.3%
울 667
 
6.3%
특 666
 
6.3%
별 666
 
6.3%
Other values (140) 3826
36.2%
Decimal Number
ValueCountFrequency (%)
1 607
21.4%
2 421
14.9%
3 330
11.6%
4 276
9.7%
6 265
9.4%
5 249
8.8%
7 191
 
6.7%
8 175
 
6.2%
9 163
 
5.8%
0 157
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
S 2
28.6%
M 1
14.3%
H 1
14.3%
O 1
14.3%
D 1
14.3%
J 1
14.3%
Space Separator
ValueCountFrequency (%)
2709
100.0%
Open Punctuation
ValueCountFrequency (%)
( 593
100.0%
Close Punctuation
ValueCountFrequency (%)
) 593
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 109
100.0%
Other Punctuation
ValueCountFrequency (%)
, 109
100.0%
Letter Number
ValueCountFrequency (%)
Ⅲ 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
i 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10578
60.3%
Common 6947
39.6%
Latin 9
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 707
 
6.7%
강 691
 
6.5%
구 679
 
6.4%
동 671
 
6.3%
서 669
 
6.3%
로 669
 
6.3%
시 667
 
6.3%
울 667
 
6.3%
특 666
 
6.3%
별 666
 
6.3%
Other values (140) 3826
36.2%
Common
ValueCountFrequency (%)
2709
39.0%
1 607
 
8.7%
( 593
 
8.5%
) 593
 
8.5%
2 421
 
6.1%
3 330
 
4.8%
4 276
 
4.0%
6 265
 
3.8%
5 249
 
3.6%
7 191
 
2.7%
Other values (5) 713
 
10.3%
Latin
ValueCountFrequency (%)
S 2
22.2%
Ⅲ 1
11.1%
M 1
11.1%
H 1
11.1%
i 1
11.1%
O 1
11.1%
D 1
11.1%
J 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10578
60.3%
ASCII 6955
39.7%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2709
39.0%
1 607
 
8.7%
( 593
 
8.5%
) 593
 
8.5%
2 421
 
6.1%
3 330
 
4.7%
4 276
 
4.0%
6 265
 
3.8%
5 249
 
3.6%
7 191
 
2.7%
Other values (12) 721
 
10.4%
Hangul
ValueCountFrequency (%)
남 707
 
6.7%
강 691
 
6.5%
구 679
 
6.4%
동 671
 
6.3%
서 669
 
6.3%
로 669
 
6.3%
시 667
 
6.3%
울 667
 
6.3%
특 666
 
6.3%
별 666
 
6.3%
Other values (140) 3826
36.2%
Number Forms
ValueCountFrequency (%)
Ⅲ 1
100.0%

위도
Real number (ℝ)

Distinct643
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.50254
Minimum37.463063
Maximum37.528443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-04-21T11:31:27.672198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.463063
5-th percentile37.473355
Q137.49086
median37.505692
Q337.516549
95-th percentile37.523763
Maximum37.528443
Range0.065379995
Interquartile range (IQR)0.025689261

Descriptive statistics

Standard deviation0.016599563
Coefficient of variation (CV)0.00044262503
Kurtosis-0.87267984
Mean37.50254
Median Absolute Deviation (MAD)0.012342598
Skewness-0.51729368
Sum25014.194
Variance0.00027554549
MonotonicityNot monotonic
2024-04-21T11:31:27.804611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4762126909 2
 
0.3%
37.498586161 2
 
0.3%
37.485759051 2
 
0.3%
37.4868058902 2
 
0.3%
37.4868646241 2
 
0.3%
37.4761743271 2
 
0.3%
37.491817728 2
 
0.3%
37.4911636898 2
 
0.3%
37.5041706329 2
 
0.3%
37.501382881 2
 
0.3%
Other values (633) 647
97.0%
ValueCountFrequency (%)
37.4630633293 1
0.1%
37.4631755863 1
0.1%
37.4636471716 1
0.1%
37.4657949625 1
0.1%
37.4659514479 1
0.1%
37.466362955 1
0.1%
37.4675757453 1
0.1%
37.4679124772 1
0.1%
37.4680243837 1
0.1%
37.4682577914 1
0.1%
ValueCountFrequency (%)
37.5284433245 1
0.1%
37.5282696741 1
0.1%
37.5277648875 1
0.1%
37.5276059297 1
0.1%
37.527191949 1
0.1%
37.527140512 1
0.1%
37.5270768201 1
0.1%
37.526973384 1
0.1%
37.5269495847 1
0.1%
37.5263422262 2
0.3%

경도
Real number (ℝ)

Distinct643
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.04755
Minimum127.01941
Maximum127.10961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.0 KiB
2024-04-21T11:31:27.940799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.01941
5-th percentile127.02524
Q1127.03351
median127.04436
Q3127.05333
95-th percentile127.09794
Maximum127.10961
Range0.090193924
Interquartile range (IQR)0.019812862

Descriptive statistics

Standard deviation0.019424951
Coefficient of variation (CV)0.00015289513
Kurtosis1.9563041
Mean127.04755
Median Absolute Deviation (MAD)0.0098589612
Skewness1.4506141
Sum84740.715
Variance0.00037732873
MonotonicityNot monotonic
2024-04-21T11:31:28.070705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.0495723797 2
 
0.3%
127.0577066207 2
 
0.3%
127.0364877702 2
 
0.3%
127.0391527674 2
 
0.3%
127.0394591042 2
 
0.3%
127.050167356 2
 
0.3%
127.0868370129 2
 
0.3%
127.0873221445 2
 
0.3%
127.0284469013 2
 
0.3%
127.0274755168 2
 
0.3%
Other values (633) 647
97.0%
ValueCountFrequency (%)
127.019413922 1
0.1%
127.0196359303 1
0.1%
127.0200373983 1
0.1%
127.0202535616 1
0.1%
127.0206000226 1
0.1%
127.0206205229 1
0.1%
127.0211460325 1
0.1%
127.0213837318 1
0.1%
127.0220538214 1
0.1%
127.0227057327 1
0.1%
ValueCountFrequency (%)
127.1096078459 1
0.1%
127.1096027212 1
0.1%
127.1090402452 1
0.1%
127.108848244 1
0.1%
127.108693074 1
0.1%
127.1084049081 1
0.1%
127.1080902803 1
0.1%
127.1078636941 1
0.1%
127.1068959298 1
0.1%
127.106889763 1
0.1%

Interactions

2024-04-21T11:31:25.839981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:31:25.238387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:31:25.569931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:31:25.924204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:31:25.381044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:31:25.655621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:31:26.028094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:31:25.485115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:31:25.756709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:31:28.147000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.9210.755
위도0.9211.0000.733
경도0.7550.7331.000
2024-04-21T11:31:28.224499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.109-0.061
위도0.1091.000-0.436
경도-0.061-0.4361.000

Missing values

2024-04-21T11:31:26.129565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:31:26.224534image/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

연번지번주소도로명 주소위도경도
01서울특별시 강남구 역삼1동 694-6서울특별시 강남구 테헤란로57길 38 (역삼동, 동우빌라)37.506749127.046375
12서울특별시 강남구 역삼1동 698-14서울특별시 강남구 테헤란로 53길 51 (역삼동)37.506841127.044369
23서울특별시 강남구 역삼1동 697-4서울특별시 강남구 테헤란로 53길 37 (역삼동)37.506241127.045348
34서울특별시 강남구 역삼1동 695-20서울특별시 강남구 테헤란로55길50 (역삼동)37.507094127.045051
45서울특별시 강남구 역삼1동683-29서울특별시 강남구봉은사로50길41 (역삼동)37.507862127.044635
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