Overview

Dataset statistics

Number of variables7
Number of observations506
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory29.3 KiB
Average record size in memory59.3 B

Variable types

Numeric3
Text2
Categorical2

Dataset

Description서대문구 의류수거함 위치 현황을 공유함으로써 주민들의 재활용품 정확한 분리배출 유도하고 옷이나 신발을 재이용하여 쓰레기를 줄이기 위함
Author서울특별시 서대문구
URLhttps://www.data.go.kr/data/15068863/fileData.do

Alerts

연번 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 관리단체 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
관리단체 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
행정동 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
연번 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-04-21 01:37:05.257419
Analysis finished2024-04-21 01:37:08.223617
Duration2.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct506
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.5
Minimum1
Maximum506
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-04-21T10:37:08.298996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.25
Q1127.25
median253.5
Q3379.75
95-th percentile480.75
Maximum506
Range505
Interquartile range (IQR)252.5

Descriptive statistics

Standard deviation146.21388
Coefficient of variation (CV)0.57678061
Kurtosis-1.2
Mean253.5
Median Absolute Deviation (MAD)126.5
Skewness0
Sum128271
Variance21378.5
MonotonicityStrictly increasing
2024-04-21T10:37:08.430344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
334 1
 
0.2%
347 1
 
0.2%
346 1
 
0.2%
345 1
 
0.2%
344 1
 
0.2%
343 1
 
0.2%
342 1
 
0.2%
341 1
 
0.2%
340 1
 
0.2%
Other values (496) 496
98.0%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
506 1
0.2%
505 1
0.2%
504 1
0.2%
503 1
0.2%
502 1
0.2%
501 1
0.2%
500 1
0.2%
499 1
0.2%
498 1
0.2%
497 1
0.2%

관리번호
Text

UNIQUE 

Distinct506
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2024-04-21T10:37:08.740278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4.5
Mean length4.4466403
Min length3

Characters and Unicode

Total characters2250
Distinct characters14
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

Unique506 ?
Unique (%)100.0%

Sample

1st row1-지
2nd row2-지
3rd row3-지
4th row4-지
5th row5-지
ValueCountFrequency (%)
1-지 1
 
0.2%
38-의 1
 
0.2%
51-의 1
 
0.2%
50-의 1
 
0.2%
49-의 1
 
0.2%
48-의 1
 
0.2%
47-의 1
 
0.2%
46-의 1
 
0.2%
45-의 1
 
0.2%
44-의 1
 
0.2%
Other values (496) 496
98.0%
2024-04-21T10:37:09.218930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 506
22.5%
지 293
13.0%
1 271
12.0%
2 206
9.2%
의 158
 
7.0%
3 112
 
5.0%
4 111
 
4.9%
5 106
 
4.7%
8 90
 
4.0%
6 90
 
4.0%
Other values (4) 307
13.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1238
55.0%
Dash Punctuation 506
22.5%
Other Letter 506
22.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 271
21.9%
2 206
16.6%
3 112
9.0%
4 111
9.0%
5 106
 
8.6%
8 90
 
7.3%
6 90
 
7.3%
7 90
 
7.3%
9 83
 
6.7%
0 79
 
6.4%
Other Letter
ValueCountFrequency (%)
지 293
57.9%
의 158
31.2%
고 55
 
10.9%
Dash Punctuation
ValueCountFrequency (%)
- 506
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1744
77.5%
Hangul 506
 
22.5%

Most frequent character per script

Common
ValueCountFrequency (%)
- 506
29.0%
1 271
15.5%
2 206
11.8%
3 112
 
6.4%
4 111
 
6.4%
5 106
 
6.1%
8 90
 
5.2%
6 90
 
5.2%
7 90
 
5.2%
9 83
 
4.8%
Hangul
ValueCountFrequency (%)
지 293
57.9%
의 158
31.2%
고 55
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1744
77.5%
Hangul 506
 
22.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 506
29.0%
1 271
15.5%
2 206
11.8%
3 112
 
6.4%
4 111
 
6.4%
5 106
 
6.1%
8 90
 
5.2%
6 90
 
5.2%
7 90
 
5.2%
9 83
 
4.8%
Hangul
ValueCountFrequency (%)
지 293
57.9%
의 158
31.2%
고 55
 
10.9%

관리단체
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
지체장애인협회
293 
의류협동조합
158 
고엽제전우회
55 

Length

Max length7
Median length7
Mean length6.5790514
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지체장애인협회 293
57.9%
의류협동조합 158
31.2%
고엽제전우회 55
 
10.9%

Length

2024-04-21T10:37:09.373490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:37:09.480897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지체장애인협회 293
57.9%
의류협동조합 158
31.2%
고엽제전우회 55
 
10.9%

행정동
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
홍은2동
116 
신촌동
58 
북가좌2동
53 
홍제1동
52 
연희동
46 
Other values (9)
181 

Length

Max length5
Median length4
Mean length3.9031621
Min length3

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
홍은2동 116
22.9%
신촌동 58
11.5%
북가좌2동 53
10.5%
홍제1동 52
10.3%
연희동 46
 
9.1%
홍은1동 35
 
6.9%
충현동 28
 
5.5%
홍제3동 26
 
5.1%
북아현동 24
 
4.7%
북가좌1동 22
 
4.3%
Other values (4) 46
 
9.1%

Length

2024-04-21T10:37:09.606168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
홍은2동 116
22.9%
신촌동 58
11.5%
북가좌2동 53
10.5%
홍제1동 52
10.3%
연희동 46
 
9.1%
홍은1동 35
 
6.9%
충현동 28
 
5.5%
홍제3동 26
 
5.1%
북아현동 24
 
4.7%
북가좌1동 22
 
4.3%
Other values (4) 46
 
9.1%

설치장소(도로명)
Text

Distinct498
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
2024-04-21T10:37:09.864463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length13.802372
Min length9

Characters and Unicode

Total characters6984
Distinct characters88
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

Unique490 ?
Unique (%)96.8%

Sample

1st row서대문구 북아현로14가길 43
2nd row서대문구 북아현로48
3rd row서대문구 북아현로16길 18-2
4th row서대문구 북아현로18길39
5th row서대문구 북아현로68-9
ValueCountFrequency (%)
서대문구 506
43.1%
앞 12
 
1.0%
성산로 5
 
0.4%
가좌로 5
 
0.4%
42 5
 
0.4%
거북골로22길 5
 
0.4%
홍제천로 5
 
0.4%
거북골로21길 4
 
0.3%
35 4
 
0.3%
독립문로 4
 
0.3%
Other values (544) 620
52.8%
2024-04-21T10:37:10.292205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
669
 
9.6%
대 552
 
7.9%
문 524
 
7.5%
서 512
 
7.3%
구 506
 
7.2%
길 450
 
6.4%
로 393
 
5.6%
2 346
 
5.0%
1 343
 
4.9%
3 214
 
3.1%
Other values (78) 2475
35.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4310
61.7%
Decimal Number 1851
26.5%
Space Separator 669
 
9.6%
Dash Punctuation 154
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 552
12.8%
문 524
12.2%
서 512
11.9%
구 506
11.7%
길 450
10.4%
로 393
 
9.1%
가 120
 
2.8%
연 93
 
2.2%
희 75
 
1.7%
북 70
 
1.6%
Other values (66) 1015
23.5%
Decimal Number
ValueCountFrequency (%)
2 346
18.7%
1 343
18.5%
3 214
11.6%
4 211
11.4%
6 134
 
7.2%
5 130
 
7.0%
7 129
 
7.0%
0 121
 
6.5%
8 117
 
6.3%
9 106
 
5.7%
Space Separator
ValueCountFrequency (%)
669
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4310
61.7%
Common 2674
38.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 552
12.8%
문 524
12.2%
서 512
11.9%
구 506
11.7%
길 450
10.4%
로 393
 
9.1%
가 120
 
2.8%
연 93
 
2.2%
희 75
 
1.7%
북 70
 
1.6%
Other values (66) 1015
23.5%
Common
ValueCountFrequency (%)
669
25.0%
2 346
12.9%
1 343
12.8%
3 214
 
8.0%
4 211
 
7.9%
- 154
 
5.8%
6 134
 
5.0%
5 130
 
4.9%
7 129
 
4.8%
0 121
 
4.5%
Other values (2) 223
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4310
61.7%
ASCII 2674
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
669
25.0%
2 346
12.9%
1 343
12.8%
3 214
 
8.0%
4 211
 
7.9%
- 154
 
5.8%
6 134
 
5.0%
5 130
 
4.9%
7 129
 
4.8%
0 121
 
4.5%
Other values (2) 223
 
8.3%
Hangul
ValueCountFrequency (%)
대 552
12.8%
문 524
12.2%
서 512
11.9%
구 506
11.7%
길 450
10.4%
로 393
 
9.1%
가 120
 
2.8%
연 93
 
2.2%
희 75
 
1.7%
북 70
 
1.6%
Other values (66) 1015
23.5%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct496
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93548
Minimum126.904
Maximum126.96441
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-04-21T10:37:10.449617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.904
5-th percentile126.90894
Q1126.92568
median126.93444
Q3126.94693
95-th percentile126.95873
Maximum126.96441
Range0.0604144
Interquartile range (IQR)0.02125805

Descriptive statistics

Standard deviation0.014485001
Coefficient of variation (CV)0.0001141131
Kurtosis-0.69545159
Mean126.93548
Median Absolute Deviation (MAD)0.0109566
Skewness-0.13729898
Sum64229.353
Variance0.00020981526
MonotonicityNot monotonic
2024-04-21T10:37:10.607289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9311345 2
 
0.4%
126.9454067 2
 
0.4%
126.9342382 2
 
0.4%
126.9437647 2
 
0.4%
126.9430407 2
 
0.4%
126.9241086 2
 
0.4%
126.9275118 2
 
0.4%
126.9315599 2
 
0.4%
126.9459186 2
 
0.4%
126.9270138 2
 
0.4%
Other values (486) 486
96.0%
ValueCountFrequency (%)
126.9039967 1
0.2%
126.9042142 1
0.2%
126.9049022 1
0.2%
126.9049902 1
0.2%
126.9054213 1
0.2%
126.9055947 1
0.2%
126.9057375 1
0.2%
126.9057478 1
0.2%
126.9058767 1
0.2%
126.90589 1
0.2%
ValueCountFrequency (%)
126.9644111 1
0.2%
126.9636448 1
0.2%
126.9630072 1
0.2%
126.9613185 1
0.2%
126.9613183 1
0.2%
126.9611767 1
0.2%
126.961074 1
0.2%
126.9608755 1
0.2%
126.9608096 1
0.2%
126.9607191 1
0.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct496
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.578502
Minimum37.556776
Maximum37.605467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2024-04-21T10:37:10.748223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.556776
5-th percentile37.55868
Q137.568107
median37.580599
Q337.586183
95-th percentile37.596977
Maximum37.605467
Range0.0486907
Interquartile range (IQR)0.01807605

Descriptive statistics

Standard deviation0.011738564
Coefficient of variation (CV)0.00031237445
Kurtosis-0.72229028
Mean37.578502
Median Absolute Deviation (MAD)0.00672495
Skewness-0.16524636
Sum19014.722
Variance0.00013779388
MonotonicityNot monotonic
2024-04-21T10:37:10.883032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5644018 2
 
0.4%
37.5580697 2
 
0.4%
37.5838453 2
 
0.4%
37.5581271 2
 
0.4%
37.5589045 2
 
0.4%
37.5739109 2
 
0.4%
37.5663158 2
 
0.4%
37.5769742 2
 
0.4%
37.5794297 2
 
0.4%
37.5675261 2
 
0.4%
Other values (486) 486
96.0%
ValueCountFrequency (%)
37.556776 1
0.2%
37.5571276 1
0.2%
37.5571554 1
0.2%
37.5572508 1
0.2%
37.5573718 1
0.2%
37.5573964 1
0.2%
37.5575907 1
0.2%
37.5576292 1
0.2%
37.5576438 1
0.2%
37.5576963 1
0.2%
ValueCountFrequency (%)
37.6054667 1
0.2%
37.6047186 1
0.2%
37.6046098 1
0.2%
37.6032351 1
0.2%
37.6030991 1
0.2%
37.6030075 1
0.2%
37.6018003 1
0.2%
37.6017301 1
0.2%
37.6015276 1
0.2%
37.6014602 1
0.2%

Interactions

2024-04-21T10:37:07.725996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:37:07.179662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:37:07.480672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:37:07.807693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:37:07.321604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:37:07.561662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:37:07.900276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:37:07.402613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:37:07.644008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:37:10.979546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번관리단체행정동경도위도
연번1.0000.9570.9340.9090.909
관리단체0.9571.0000.9480.6590.839
행정동0.9340.9481.0000.8880.888
경도0.9090.6590.8881.0000.802
위도0.9090.8390.8880.8021.000
2024-04-21T10:37:11.087803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동관리단체
행정동1.0000.895
관리단체0.8951.000
2024-04-21T10:37:11.192603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번경도위도관리단체행정동
연번1.000-0.450-0.5810.9480.740
경도-0.4501.0000.0390.5050.631
위도-0.5810.0391.0000.7450.629
관리단체0.9480.5050.7451.0000.895
행정동0.7400.6310.6290.8951.000

Missing values

2024-04-21T10:37:08.022253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:37:08.159323image/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

연번관리번호관리단체행정동설치장소(도로명)경도위도
011-지지체장애인협회충현동서대문구 북아현로14가길 43126.95861237.564008
122-지지체장애인협회충현동서대문구 북아현로48126.95602837.561759
233-지지체장애인협회충현동서대문구 북아현로16길 18-2126.95609837.563247
344-지지체장애인협회충현동서대문구 북아현로18길39126.95620237.563923
455-지지체장애인협회충현동서대문구 북아현로68-9126.95534637.563591
566-지지체장애인협회충현동서대문구 북아현로14길40126.95763837.563179
677-지지체장애인협회충현동서대문구 북아현로14길55126.95785537.563884
788-지지체장애인협회충현동서대문구 북아현로18길63-14126.95756137.563827
899-지지체장애인협회충현동서대문구 북아현로20길69126.95713737.564202
91010-지지체장애인협회충현동서대문구 북아현로20길45126.95591237.564443
연번관리번호관리단체행정동설치장소(도로명)경도위도
49649746-고고엽제전우회신촌동서대문구 신촌로7안길59-8126.93106137.560842
49749847-고고엽제전우회신촌동서대문구 성산로20길24126.93165537.561217
49849948-고고엽제전우회신촌동서대문구 연세로4길73126.94032837.558673
49950049-고고엽제전우회신촌동서대문구 연세로2나길 42126.93921137.557591
50050150-고고엽제전우회신촌동서대문구 연세로2라길51126.94129737.558132
50150251-고고엽제전우회신촌동서대문구 연세로2나길69126.94046837.558368
50250352-고고엽제전우회신촌동서대문구 연세로2나길34126.93890937.557372
50350453-고고엽제전우회신촌동서대문구 연세로2라길3126.93948737.557251
50450554-고고엽제전우회신촌동서대문구 연세로2길55126.93978937.556776
50550655-고고엽제전우회신촌동서대문구 명물1길 30126.93756637.558702