Overview

Dataset statistics

Number of variables6
Number of observations253
Missing cells21
Missing cells (%)1.4%
Duplicate rows2
Duplicate rows (%)0.8%
Total size in memory12.5 KiB
Average record size in memory50.5 B

Variable types

Categorical2
Text2
Numeric2

Dataset

Description서울특별시 영등포구 의류수거함 위치현황입니다. 제공데이터: 행정동, 도로명주소, 지번주소, 위도, 경도, 데이터기준일자
Author서울특별시 영등포구
URLhttps://www.data.go.kr/data/15106473/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
Dataset has 2 (0.8%) duplicate rowsDuplicates
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
위도 has 9 (3.6%) missing valuesMissing
경도 has 9 (3.6%) missing valuesMissing

Reproduction

Analysis started2024-05-16 09:03:42.485670
Analysis finished2024-05-16 09:03:47.533550
Duration5.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
양평제2동
23 
영등포동
21 
당산제2동
20 
영등포본동
19 
대림제3동
19 
Other values (12)
151 

Length

Max length5
Median length5
Mean length4.7509881
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
양평제2동 23
 
9.1%
영등포동 21
 
8.3%
당산제2동 20
 
7.9%
영등포본동 19
 
7.5%
대림제3동 19
 
7.5%
대림제2동 18
 
7.1%
도림동 16
 
6.3%
신길제3동 16
 
6.3%
신길제6동 14
 
5.5%
양평제1동 13
 
5.1%
Other values (7) 74
29.2%

Length

2024-05-16T18:03:47.672383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
양평제2동 23
 
9.1%
영등포동 21
 
8.3%
당산제2동 20
 
7.9%
영등포본동 19
 
7.5%
대림제3동 19
 
7.5%
대림제2동 18
 
7.1%
도림동 16
 
6.3%
신길제3동 16
 
6.3%
신길제6동 14
 
5.5%
양평제1동 13
 
5.1%
Other values (7) 74
29.2%

도로명주소
Text

Distinct249
Distinct (%)98.8%
Missing1
Missing (%)0.4%
Memory size2.1 KiB
2024-05-16T18:03:48.833990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length20.702381
Min length16

Characters and Unicode

Total characters5217
Distinct characters73
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique246 ?
Unique (%)97.6%

Sample

1st row서울특별시 영등포구 도신로51길 9
2nd row서울특별시 영등포구 영신로9라길 6
3rd row서울특별시 영등포구 신길로61길 17
4th row서울특별시 영등포구 신길로 276
5th row서울특별시 영등포구 영등포로60길 29
ValueCountFrequency (%)
서울특별시 252
25.0%
영등포구 252
25.0%
11 10
 
1.0%
7 10
 
1.0%
9 9
 
0.9%
1 9
 
0.9%
6 9
 
0.9%
17 8
 
0.8%
2 8
 
0.8%
3 7
 
0.7%
Other values (299) 434
43.1%
2024-05-16T18:03:50.114662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
756
 
14.5%
영 281
 
5.4%
포 268
 
5.1%
등 268
 
5.1%
서 259
 
5.0%
시 256
 
4.9%
구 253
 
4.8%
로 252
 
4.8%
울 252
 
4.8%
별 252
 
4.8%
Other values (63) 2120
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3424
65.6%
Decimal Number 965
 
18.5%
Space Separator 756
 
14.5%
Dash Punctuation 69
 
1.3%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 281
 
8.2%
포 268
 
7.8%
등 268
 
7.8%
서 259
 
7.6%
시 256
 
7.5%
구 253
 
7.4%
로 252
 
7.4%
울 252
 
7.4%
별 252
 
7.4%
특 252
 
7.4%
Other values (48) 831
24.3%
Decimal Number
ValueCountFrequency (%)
1 233
24.1%
2 138
14.3%
3 122
12.6%
4 100
10.4%
5 90
 
9.3%
6 64
 
6.6%
7 61
 
6.3%
8 58
 
6.0%
0 54
 
5.6%
9 45
 
4.7%
Space Separator
ValueCountFrequency (%)
756
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 69
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3424
65.6%
Common 1793
34.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 281
 
8.2%
포 268
 
7.8%
등 268
 
7.8%
서 259
 
7.6%
시 256
 
7.5%
구 253
 
7.4%
로 252
 
7.4%
울 252
 
7.4%
별 252
 
7.4%
특 252
 
7.4%
Other values (48) 831
24.3%
Common
ValueCountFrequency (%)
756
42.2%
1 233
 
13.0%
2 138
 
7.7%
3 122
 
6.8%
4 100
 
5.6%
5 90
 
5.0%
- 69
 
3.8%
6 64
 
3.6%
7 61
 
3.4%
8 58
 
3.2%
Other values (5) 102
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3424
65.6%
ASCII 1793
34.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
756
42.2%
1 233
 
13.0%
2 138
 
7.7%
3 122
 
6.8%
4 100
 
5.6%
5 90
 
5.0%
- 69
 
3.8%
6 64
 
3.6%
7 61
 
3.4%
8 58
 
3.2%
Other values (5) 102
 
5.7%
Hangul
ValueCountFrequency (%)
영 281
 
8.2%
포 268
 
7.8%
등 268
 
7.8%
서 259
 
7.6%
시 256
 
7.5%
구 253
 
7.4%
로 252
 
7.4%
울 252
 
7.4%
별 252
 
7.4%
특 252
 
7.4%
Other values (48) 831
24.3%

지번주소
Text

Distinct248
Distinct (%)98.8%
Missing2
Missing (%)0.8%
Memory size2.1 KiB
2024-05-16T18:03:51.250253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length20.844622
Min length17

Characters and Unicode

Total characters5232
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique245 ?
Unique (%)97.6%

Sample

1st row서울특별시 영등포구 신길동 186-230
2nd row서울특별시 영등포구 영등포동 631-8
3rd row서울특별시 영등포구 영등포동 592-70
4th row서울특별시 영등포구 영등포동 585-25
5th row서울특별시 영등포구 신길동 35
ValueCountFrequency (%)
서울특별시 251
25.0%
영등포구 249
24.9%
신길동 83
 
8.3%
대림동 49
 
4.9%
도림동 16
 
1.6%
양평동4가 10
 
1.0%
당산동6가 9
 
0.9%
양평동6가 8
 
0.8%
당산동3가 8
 
0.8%
영등포동7가 7
 
0.7%
Other values (269) 312
31.1%
2024-05-16T18:03:52.597061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
751
 
14.4%
포 276
 
5.3%
등 276
 
5.3%
영 276
 
5.3%
구 252
 
4.8%
울 251
 
4.8%
동 251
 
4.8%
서 251
 
4.8%
시 251
 
4.8%
별 251
 
4.8%
Other values (27) 2146
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3129
59.8%
Decimal Number 1147
 
21.9%
Space Separator 751
 
14.4%
Dash Punctuation 204
 
3.9%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
포 276
8.8%
등 276
8.8%
영 276
8.8%
구 252
8.1%
울 251
8.0%
동 251
8.0%
서 251
8.0%
시 251
8.0%
별 251
8.0%
특 251
8.0%
Other values (14) 543
17.4%
Decimal Number
ValueCountFrequency (%)
1 221
19.3%
2 156
13.6%
3 130
11.3%
4 121
10.5%
7 97
8.5%
5 96
8.4%
6 92
8.0%
0 81
 
7.1%
8 81
 
7.1%
9 72
 
6.3%
Space Separator
ValueCountFrequency (%)
751
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 204
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3129
59.8%
Common 2103
40.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
포 276
8.8%
등 276
8.8%
영 276
8.8%
구 252
8.1%
울 251
8.0%
동 251
8.0%
서 251
8.0%
시 251
8.0%
별 251
8.0%
특 251
8.0%
Other values (14) 543
17.4%
Common
ValueCountFrequency (%)
751
35.7%
1 221
 
10.5%
- 204
 
9.7%
2 156
 
7.4%
3 130
 
6.2%
4 121
 
5.8%
7 97
 
4.6%
5 96
 
4.6%
6 92
 
4.4%
0 81
 
3.9%
Other values (3) 154
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3129
59.8%
ASCII 2103
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
751
35.7%
1 221
 
10.5%
- 204
 
9.7%
2 156
 
7.4%
3 130
 
6.2%
4 121
 
5.8%
7 97
 
4.6%
5 96
 
4.6%
6 92
 
4.4%
0 81
 
3.9%
Other values (3) 154
 
7.3%
Hangul
ValueCountFrequency (%)
포 276
8.8%
등 276
8.8%
영 276
8.8%
구 252
8.1%
울 251
8.0%
동 251
8.0%
서 251
8.0%
시 251
8.0%
별 251
8.0%
특 251
8.0%
Other values (14) 543
17.4%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct242
Distinct (%)99.2%
Missing9
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean37.512535
Minimum37.486899
Maximum37.544192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-16T18:03:52.869757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.486899
5-th percentile37.49032
Q137.501127
median37.5103
Q337.523795
95-th percentile37.538891
Maximum37.544192
Range0.05729315
Interquartile range (IQR)0.02266723

Descriptive statistics

Standard deviation0.014509877
Coefficient of variation (CV)0.00038680075
Kurtosis-0.66928789
Mean37.512535
Median Absolute Deviation (MAD)0.0110823
Skewness0.28747354
Sum9153.0585
Variance0.00021053652
MonotonicityNot monotonic
2024-05-16T18:03:53.126185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.49708188 2
 
0.8%
37.5024725 2
 
0.8%
37.49572404 1
 
0.4%
37.50778558 1
 
0.4%
37.50765282 1
 
0.4%
37.50684191 1
 
0.4%
37.50632241 1
 
0.4%
37.50553053 1
 
0.4%
37.50508109 1
 
0.4%
37.5060683 1
 
0.4%
Other values (232) 232
91.7%
(Missing) 9
 
3.6%
ValueCountFrequency (%)
37.48689875 1
0.4%
37.48711904 1
0.4%
37.48724436 1
0.4%
37.48749246 1
0.4%
37.4880166 1
0.4%
37.4881429 1
0.4%
37.48815068 1
0.4%
37.4884 1
0.4%
37.48859399 1
0.4%
37.48881784 1
0.4%
ValueCountFrequency (%)
37.5441919 1
0.4%
37.54387763 1
0.4%
37.54320674 1
0.4%
37.54296634 1
0.4%
37.54286644 1
0.4%
37.542441 1
0.4%
37.54222583 1
0.4%
37.54177201 1
0.4%
37.54084659 1
0.4%
37.54042075 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct242
Distinct (%)99.2%
Missing9
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean126.90441
Minimum126.88333
Maximum126.92339
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-05-16T18:03:53.383569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.88333
5-th percentile126.88903
Q1126.89763
median126.90358
Q3126.91235
95-th percentile126.92181
Maximum126.92339
Range0.0400602
Interquartile range (IQR)0.01471935

Descriptive statistics

Standard deviation0.0096221865
Coefficient of variation (CV)7.5822317 × 10-5
Kurtosis-0.6863267
Mean126.90441
Median Absolute Deviation (MAD)0.00655505
Skewness0.11834476
Sum30964.676
Variance9.2586472 × 10-5
MonotonicityNot monotonic
2024-05-16T18:03:53.810958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9070321 2
 
0.8%
126.90157 2
 
0.8%
126.9093229 1
 
0.4%
126.9137185 1
 
0.4%
126.9127992 1
 
0.4%
126.9137717 1
 
0.4%
126.9126932 1
 
0.4%
126.9130873 1
 
0.4%
126.9142922 1
 
0.4%
126.9148799 1
 
0.4%
Other values (232) 232
91.7%
(Missing) 9
 
3.6%
ValueCountFrequency (%)
126.8833253 1
0.4%
126.8837386 1
0.4%
126.8840878 1
0.4%
126.8850352 1
0.4%
126.8852923 1
0.4%
126.8857593 1
0.4%
126.8861656 1
0.4%
126.8864387 1
0.4%
126.8875694 1
0.4%
126.8883802 1
0.4%
ValueCountFrequency (%)
126.9233855 1
0.4%
126.9232004 1
0.4%
126.922959 1
0.4%
126.92273 1
0.4%
126.9225666 1
0.4%
126.9225161 1
0.4%
126.9223382 1
0.4%
126.922133 1
0.4%
126.9221306 1
0.4%
126.9220354 1
0.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-09-01
253 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-01
2nd row2023-09-01
3rd row2023-09-01
4th row2023-09-01
5th row2023-09-01

Common Values

ValueCountFrequency (%)
2023-09-01 253
100.0%

Length

2024-05-16T18:03:54.057858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-16T18:03:54.234557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-01 253
100.0%

Interactions

2024-05-16T18:03:46.656239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-16T18:03:46.157044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-16T18:03:46.819424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-16T18:03:46.476584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-16T18:03:54.333136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동위도경도
행정동1.0000.9220.873
위도0.9221.0000.736
경도0.8730.7361.000
2024-05-16T18:03:54.479708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동
위도1.000-0.2500.689
경도-0.2501.0000.579
행정동0.6890.5791.000

Missing values

2024-05-16T18:03:47.049005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-16T18:03:47.254676image/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.
2024-05-16T18:03:47.434197image/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

행정동도로명주소지번주소위도경도데이터기준일자
0영등포본동서울특별시 영등포구 도신로51길 9서울특별시 영등포구 신길동 186-23037.512462126.9133782023-09-01
1영등포본동서울특별시 영등포구 영신로9라길 6서울특별시 영등포구 영등포동 631-837.51284126.9058892023-09-01
2영등포본동서울특별시 영등포구 신길로61길 17서울특별시 영등포구 영등포동 592-7037.514666126.9093392023-09-01
3영등포본동서울특별시 영등포구 신길로 276서울특별시 영등포구 영등포동 585-2537.516106126.9127312023-09-01
4영등포본동서울특별시 영등포구 영등포로60길 29서울특별시 영등포구 신길동 3537.516798126.9150172023-09-01
5영등포본동서울특별시 영등포구 영등포로60길 17서울특별시 영등포구 신길동 31-2037.516807126.9164272023-09-01
6영등포본동서울특별시 영등포구 영등포로62길 34서울특별시 영등포구 영등포동 576-937.516405126.9143572023-09-01
7영등포본동서울특별시 영등포구 신길로62나길 9서울특별시 영등포구 신길동 184-1337.514758126.9160182023-09-01
8영등포본동서울특별시 영등포구 신길로62길 37서울특별시 영등포구 신길동 187-1437.514285126.9154622023-09-01
9영등포본동서울특별시 영등포구 신길로60다길 11서울특별시 영등포구 신길동 189-2537.5138126.9139522023-09-01
행정동도로명주소지번주소위도경도데이터기준일자
243대림제3동서울특별시 영등포구 도신로4길 17서울특별시 영등포구 대림동 783-1337.502105126.8961712023-09-01
244대림제3동서울특별시 영등포구 도신로8길 17서울특별시 영등포구 대림동 65237.503219126.8972612023-09-01
245대림제3동서울특별시 영등포구 도신로12길 6서울특별시 영등포구 대림동 1118-437.504606126.8980942023-09-01
246대림제3동서울특별시 영등포구 대림로34마길 13서울특별시 영등포구 대림동 687-437.500191126.899532023-09-01
247대림제3동서울특별시 영등포구 대림로34나길 1서울특별시 영등포구 대림동 689-1837.498502126.8990582023-09-01
248대림제3동서울특별시 영등포구 도림로47가길 14서울특별시 영등포구 대림동 754-837.497528126.901062023-09-01
249대림제3동서울특별시 영등포구 도림로47길 23서울특별시 영등포구 대림동 691-837.497361126.8994222023-09-01
250대림제3동서울특별시 영등포구 대림로35길(동심어린이공원)<NA><NA><NA>2023-09-01
251대림제3동서울특별시 영등포구 도림로39길 21서울특별시 영등포구 대림동 713-7<NA><NA>2023-09-01
252대림제3동서울특별시 영등포구 가마산로47길 신우어린이공원<NA><NA><NA>2023-09-01

Duplicate rows

Most frequently occurring

행정동도로명주소지번주소위도경도데이터기준일자# duplicates
0신길제5동서울특별시 영등포구 가마산로50길 9서울특별시 영등포구 신길동 355-12837.502473126.901572023-09-012
1신길제5동서울특별시 영등포구 신길로13길 11서울특별시 영등포구 대림동 907-3537.497082126.9070322023-09-012