Overview

Dataset statistics

Number of variables6
Number of observations245
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 KiB
Average record size in memory50.5 B

Variable types

Categorical2
Text2
Numeric2

Dataset

Description서울시 종로구에 위치한 의류수거함 위치현황 정보를 담은 파일입니다, 동, 위경도, 도로명주소, 지번주소를 담고있습니다.
URLhttps://www.data.go.kr/data/15104622/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
행정동 is highly overall correlated with 위도 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 22:13:45.123811
Analysis finished2023-12-12 22:13:45.845933
Duration0.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
평창동
39 
창신동
27 
숭인동
24 
명륜동
22 
부암동
16 
Other values (24)
117 

Length

Max length3
Median length3
Mean length2.9918367
Min length2

Unique

Unique6 ?
Unique (%)2.4%

Sample

1st row청운동
2nd row청운동
3rd row청운동
4th row청운동
5th row창성동

Common Values

ValueCountFrequency (%)
평창동 39
15.9%
창신동 27
11.0%
숭인동 24
 
9.8%
명륜동 22
 
9.0%
부암동 16
 
6.5%
구기동 14
 
5.7%
혜화동 12
 
4.9%
행촌동 12
 
4.9%
신영동 10
 
4.1%
동숭동 8
 
3.3%
Other values (19) 61
24.9%

Length

2023-12-13T07:13:45.925126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
평창동 39
15.9%
창신동 27
11.0%
숭인동 24
 
9.8%
명륜동 22
 
9.0%
부암동 16
 
6.5%
구기동 14
 
5.7%
혜화동 12
 
4.9%
행촌동 12
 
4.9%
신영동 10
 
4.1%
동숭동 8
 
3.3%
Other values (19) 61
24.9%

도로명주소
Text

Distinct244
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T07:13:46.176995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length36
Mean length26.040816
Min length17

Characters and Unicode

Total characters6380
Distinct characters171
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique243 ?
Unique (%)99.2%

Sample

1st row서울특별시 종로구 자하문로33길 12 (청운동)
2nd row서울특별시 종로구 자하문로33길 22-2 (청운동)
3rd row서울특별시 종로구 자하문로33길 90 (청운동)
4th row서울특별시 종로구 자하문로33다길 47-4 (청운동)
5th row서울특별시 종로구 자하문로12길 33 (창성동)
ValueCountFrequency (%)
서울특별시 245
 
19.2%
종로구 245
 
19.2%
평창동 39
 
3.1%
창신동 27
 
2.1%
숭인동 24
 
1.9%
부암동 16
 
1.3%
구기동 14
 
1.1%
혜화동 12
 
0.9%
행촌동 11
 
0.9%
평창길 10
 
0.8%
Other values (360) 634
49.6%
2023-12-13T07:13:46.587882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1032
 
16.2%
로 375
 
5.9%
구 260
 
4.1%
종 255
 
4.0%
동 252
 
3.9%
서 251
 
3.9%
시 246
 
3.9%
특 245
 
3.8%
별 245
 
3.8%
울 245
 
3.8%
Other values (161) 2974
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3918
61.4%
Space Separator 1032
 
16.2%
Decimal Number 841
 
13.2%
Close Punctuation 244
 
3.8%
Open Punctuation 244
 
3.8%
Other Punctuation 52
 
0.8%
Dash Punctuation 48
 
0.8%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 375
 
9.6%
구 260
 
6.6%
종 255
 
6.5%
동 252
 
6.4%
서 251
 
6.4%
시 246
 
6.3%
특 245
 
6.3%
별 245
 
6.3%
울 245
 
6.3%
길 225
 
5.7%
Other values (145) 1319
33.7%
Decimal Number
ValueCountFrequency (%)
1 192
22.8%
2 134
15.9%
3 108
12.8%
4 94
11.2%
6 80
9.5%
5 57
 
6.8%
7 49
 
5.8%
0 46
 
5.5%
9 42
 
5.0%
8 39
 
4.6%
Space Separator
ValueCountFrequency (%)
1032
100.0%
Close Punctuation
ValueCountFrequency (%)
) 244
100.0%
Open Punctuation
ValueCountFrequency (%)
( 244
100.0%
Other Punctuation
ValueCountFrequency (%)
, 52
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 48
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3918
61.4%
Common 2461
38.6%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 375
 
9.6%
구 260
 
6.6%
종 255
 
6.5%
동 252
 
6.4%
서 251
 
6.4%
시 246
 
6.3%
특 245
 
6.3%
별 245
 
6.3%
울 245
 
6.3%
길 225
 
5.7%
Other values (145) 1319
33.7%
Common
ValueCountFrequency (%)
1032
41.9%
) 244
 
9.9%
( 244
 
9.9%
1 192
 
7.8%
2 134
 
5.4%
3 108
 
4.4%
4 94
 
3.8%
6 80
 
3.3%
5 57
 
2.3%
, 52
 
2.1%
Other values (5) 224
 
9.1%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3918
61.4%
ASCII 2462
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1032
41.9%
) 244
 
9.9%
( 244
 
9.9%
1 192
 
7.8%
2 134
 
5.4%
3 108
 
4.4%
4 94
 
3.8%
6 80
 
3.2%
5 57
 
2.3%
, 52
 
2.1%
Other values (6) 225
 
9.1%
Hangul
ValueCountFrequency (%)
로 375
 
9.6%
구 260
 
6.6%
종 255
 
6.5%
동 252
 
6.4%
서 251
 
6.4%
시 246
 
6.3%
특 245
 
6.3%
별 245
 
6.3%
울 245
 
6.3%
길 225
 
5.7%
Other values (145) 1319
33.7%

지번주소
Text

Distinct244
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T07:13:47.080580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length18.885714
Min length15

Characters and Unicode

Total characters4627
Distinct characters72
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

Unique243 ?
Unique (%)99.2%

Sample

1st row서울특별시 종로구 청운동 59-3
2nd row서울특별시?종로구?청운동 52-82
3rd row서울특별시 종로구 청운동 56-75
4th row서울특별시 종로구 청운동 52-111
5th row서울특별시 종로구 창성동 109-6
ValueCountFrequency (%)
서울특별시 244
24.9%
종로구 244
24.9%
평창동 39
 
4.0%
창신동 27
 
2.8%
숭인동 24
 
2.5%
부암동 16
 
1.6%
구기동 14
 
1.4%
혜화동 12
 
1.2%
행촌동 11
 
1.1%
명륜1가 10
 
1.0%
Other values (270) 337
34.5%
2023-12-13T07:13:47.644710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
733
15.8%
구 259
 
5.6%
서 251
 
5.4%
로 247
 
5.3%
특 245
 
5.3%
별 245
 
5.3%
시 245
 
5.3%
종 245
 
5.3%
울 245
 
5.3%
동 229
 
4.9%
Other values (62) 1683
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2697
58.3%
Decimal Number 975
 
21.1%
Space Separator 733
 
15.8%
Dash Punctuation 220
 
4.8%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 259
9.6%
서 251
9.3%
로 247
9.2%
특 245
9.1%
별 245
9.1%
시 245
9.1%
종 245
9.1%
울 245
9.1%
동 229
8.5%
창 67
 
2.5%
Other values (49) 419
15.5%
Decimal Number
ValueCountFrequency (%)
1 205
21.0%
2 156
16.0%
6 112
11.5%
4 89
9.1%
3 89
9.1%
5 83
8.5%
0 71
 
7.3%
7 65
 
6.7%
8 53
 
5.4%
9 52
 
5.3%
Space Separator
ValueCountFrequency (%)
733
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 220
100.0%
Other Punctuation
ValueCountFrequency (%)
? 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2697
58.3%
Common 1930
41.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 259
9.6%
서 251
9.3%
로 247
9.2%
특 245
9.1%
별 245
9.1%
시 245
9.1%
종 245
9.1%
울 245
9.1%
동 229
8.5%
창 67
 
2.5%
Other values (49) 419
15.5%
Common
ValueCountFrequency (%)
733
38.0%
- 220
 
11.4%
1 205
 
10.6%
2 156
 
8.1%
6 112
 
5.8%
4 89
 
4.6%
3 89
 
4.6%
5 83
 
4.3%
0 71
 
3.7%
7 65
 
3.4%
Other values (3) 107
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2697
58.3%
ASCII 1930
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
733
38.0%
- 220
 
11.4%
1 205
 
10.6%
2 156
 
8.1%
6 112
 
5.8%
4 89
 
4.6%
3 89
 
4.6%
5 83
 
4.3%
0 71
 
3.7%
7 65
 
3.4%
Other values (3) 107
 
5.5%
Hangul
ValueCountFrequency (%)
구 259
9.6%
서 251
9.3%
로 247
9.2%
특 245
9.1%
별 245
9.1%
시 245
9.1%
종 245
9.1%
울 245
9.1%
동 229
8.5%
창 67
 
2.5%
Other values (49) 419
15.5%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct243
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.588975
Minimum37.571909
Maximum37.617151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T07:13:47.794159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.571909
5-th percentile37.573535
Q137.577237
median37.584893
Q337.601973
95-th percentile37.612902
Maximum37.617151
Range0.04524209
Interquartile range (IQR)0.02473518

Descriptive statistics

Standard deviation0.013351175
Coefficient of variation (CV)0.00035518859
Kurtosis-1.0255272
Mean37.588975
Median Absolute Deviation (MAD)0.00882464
Skewness0.60818248
Sum9209.299
Variance0.00017825388
MonotonicityNot monotonic
2023-12-13T07:13:47.950581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.61129317 2
 
0.8%
37.60237091 2
 
0.8%
37.58226603 1
 
0.4%
37.58501017 1
 
0.4%
37.57676356 1
 
0.4%
37.57752692 1
 
0.4%
37.58455447 1
 
0.4%
37.57825728 1
 
0.4%
37.57704085 1
 
0.4%
37.57792628 1
 
0.4%
Other values (233) 233
95.1%
ValueCountFrequency (%)
37.57190916 1
0.4%
37.572234 1
0.4%
37.57247474 1
0.4%
37.57258331 1
0.4%
37.5727216 1
0.4%
37.5728231 1
0.4%
37.57301595 1
0.4%
37.57309497 1
0.4%
37.57331654 1
0.4%
37.57335578 1
0.4%
ValueCountFrequency (%)
37.61715125 1
0.4%
37.61651656 1
0.4%
37.61596155 1
0.4%
37.6150836 1
0.4%
37.61498754 1
0.4%
37.61473822 1
0.4%
37.61454596 1
0.4%
37.61408018 1
0.4%
37.61392194 1
0.4%
37.61367399 1
0.4%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct243
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98439
Minimum126.95446
Maximum127.02188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2023-12-13T07:13:48.086540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.95446
5-th percentile126.95791
Q1126.96509
median126.97652
Q3127.00454
95-th percentile127.01757
Maximum127.02188
Range0.0674146
Interquartile range (IQR)0.039447

Descriptive statistics

Standard deviation0.021202847
Coefficient of variation (CV)0.00016697207
Kurtosis-1.4763968
Mean126.98439
Median Absolute Deviation (MAD)0.0166911
Skewness0.29421037
Sum31111.176
Variance0.00044956072
MonotonicityNot monotonic
2023-12-13T07:13:48.256728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9764537 2
 
0.8%
126.9602349 2
 
0.8%
127.0045416 1
 
0.4%
126.9950714 1
 
0.4%
127.0066643 1
 
0.4%
127.0059547 1
 
0.4%
126.9957224 1
 
0.4%
127.0056488 1
 
0.4%
127.0057314 1
 
0.4%
127.0052458 1
 
0.4%
Other values (233) 233
95.1%
ValueCountFrequency (%)
126.9544616 1
0.4%
126.9545549 1
0.4%
126.9562703 1
0.4%
126.9568555 1
0.4%
126.9569704 1
0.4%
126.9569872 1
0.4%
126.9571365 1
0.4%
126.9574689 1
0.4%
126.9575113 1
0.4%
126.9575349 1
0.4%
ValueCountFrequency (%)
127.0218762 1
0.4%
127.0215822 1
0.4%
127.021301 1
0.4%
127.0210604 1
0.4%
127.0208386 1
0.4%
127.0207011 1
0.4%
127.0200716 1
0.4%
127.0199617 1
0.4%
127.01991 1
0.4%
127.0190433 1
0.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-06-05
245 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-05
2nd row2023-06-05
3rd row2023-06-05
4th row2023-06-05
5th row2023-06-05

Common Values

ValueCountFrequency (%)
2023-06-05 245
100.0%

Length

2023-12-13T07:13:48.400713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:13:48.501094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-05 245
100.0%

Interactions

2023-12-13T07:13:45.512350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:45.347224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:45.597601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:13:45.422744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:13:48.552629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동위도경도
행정동1.0000.8800.948
위도0.8801.0000.854
경도0.9480.8541.000
2023-12-13T07:13:48.637446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동
위도1.000-0.5130.535
경도-0.5131.0000.702
행정동0.5350.7021.000

Missing values

2023-12-13T07:13:45.707548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:13:45.809424image/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

행정동도로명주소지번주소위도경도데이터기준일자
0청운동서울특별시 종로구 자하문로33길 12 (청운동)서울특별시 종로구 청운동 59-337.586242126.9692562023-06-05
1청운동서울특별시 종로구 자하문로33길 22-2 (청운동)서울특별시?종로구?청운동 52-8237.586346126.9688112023-06-05
2청운동서울특별시 종로구 자하문로33길 90 (청운동)서울특별시 종로구 청운동 56-7537.585751126.9662212023-06-05
3청운동서울특별시 종로구 자하문로33다길 47-4 (청운동)서울특별시 종로구 청운동 52-11137.58674126.967092023-06-05
4창성동서울특별시 종로구 자하문로12길 33 (창성동)서울특별시 종로구 창성동 109-637.584438126.9705342023-06-05
5청운동서울특별시 종로구 자하문로26길 1 (청운동)서울특별시 종로구 청운동 144-137.580723126.9720582023-06-05
6효자동서울특별시 종로구 자하문로24길 24 (효자동)서울특별시 종로구 효자동 6237.582855126.9711262023-06-05
7청운동서울특별시 종로구 자하문로30길 14 (청운동)서울특별시 종로구 청운동 10637.575657126.9620372023-06-05
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