Overview

Dataset statistics

Number of variables7
Number of observations412
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.1 KiB
Average record size in memory62.3 B

Variable types

Text1
Numeric6

Dataset

Description구분,방향표지,이정표지,경계표지,노선표지,기타,계
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15016/S/1/datasetView.do

Alerts

방향표지 is highly overall correlated with 계High correlation
기타 is highly overall correlated with 계High correlation
계 is highly overall correlated with 방향표지 and 1 other fieldsHigh correlation
구분 has unique valuesUnique
이정표지 has 343 (83.3%) zerosZeros
경계표지 has 369 (89.6%) zerosZeros
노선표지 has 365 (88.6%) zerosZeros
기타 has 319 (77.4%) zerosZeros

Reproduction

Analysis started2023-12-11 09:36:12.205898
Analysis finished2023-12-11 09:36:16.232155
Duration4.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct412
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-11T18:36:16.363263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length13.597087
Min length11

Characters and Unicode

Total characters5602
Distinct characters209
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

Unique412 ?
Unique (%)100.0%

Sample

1st row서울특별시 종로구 청운동
2nd row서울특별시 종로구 궁정동
3rd row서울특별시 종로구 통의동
4th row서울특별시 종로구 적선동
5th row서울특별시 종로구 통인동
ValueCountFrequency (%)
서울특별시 412
33.3%
중구 68
 
5.5%
종로구 60
 
4.9%
영등포구 34
 
2.8%
용산구 31
 
2.5%
성북구 28
 
2.3%
마포구 26
 
2.1%
성동구 17
 
1.4%
서대문구 17
 
1.4%
강남구 14
 
1.1%
Other values (426) 529
42.8%
2023-12-11T18:36:16.666075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
824
14.7%
서 456
 
8.1%
동 428
 
7.6%
구 427
 
7.6%
시 413
 
7.4%
특 412
 
7.4%
별 412
 
7.4%
울 412
 
7.4%
가 125
 
2.2%
로 110
 
2.0%
Other values (199) 1583
28.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4659
83.2%
Space Separator 824
 
14.7%
Decimal Number 119
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 456
 
9.8%
동 428
 
9.2%
구 427
 
9.2%
시 413
 
8.9%
특 412
 
8.8%
별 412
 
8.8%
울 412
 
8.8%
가 125
 
2.7%
로 110
 
2.4%
중 81
 
1.7%
Other values (190) 1383
29.7%
Decimal Number
ValueCountFrequency (%)
1 33
27.7%
2 30
25.2%
3 16
13.4%
4 15
12.6%
5 13
 
10.9%
6 8
 
6.7%
7 3
 
2.5%
8 1
 
0.8%
Space Separator
ValueCountFrequency (%)
824
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4659
83.2%
Common 943
 
16.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 456
 
9.8%
동 428
 
9.2%
구 427
 
9.2%
시 413
 
8.9%
특 412
 
8.8%
별 412
 
8.8%
울 412
 
8.8%
가 125
 
2.7%
로 110
 
2.4%
중 81
 
1.7%
Other values (190) 1383
29.7%
Common
ValueCountFrequency (%)
824
87.4%
1 33
 
3.5%
2 30
 
3.2%
3 16
 
1.7%
4 15
 
1.6%
5 13
 
1.4%
6 8
 
0.8%
7 3
 
0.3%
8 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4659
83.2%
ASCII 943
 
16.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
824
87.4%
1 33
 
3.5%
2 30
 
3.2%
3 16
 
1.7%
4 15
 
1.6%
5 13
 
1.4%
6 8
 
0.8%
7 3
 
0.3%
8 1
 
0.1%
Hangul
ValueCountFrequency (%)
서 456
 
9.8%
동 428
 
9.2%
구 427
 
9.2%
시 413
 
8.9%
특 412
 
8.8%
별 412
 
8.8%
울 412
 
8.8%
가 125
 
2.7%
로 110
 
2.4%
중 81
 
1.7%
Other values (190) 1383
29.7%

방향표지
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.167476
Minimum0
Maximum258
Zeros3
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-11T18:36:16.809023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median9
Q331
95-th percentile107.9
Maximum258
Range258
Interquartile range (IQR)28

Descriptive statistics

Standard deviation37.122715
Coefficient of variation (CV)1.4750274
Kurtosis8.9344867
Mean25.167476
Median Absolute Deviation (MAD)7
Skewness2.6922779
Sum10369
Variance1378.096
MonotonicityNot monotonic
2023-12-11T18:36:16.956875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 38
 
9.2%
1 38
 
9.2%
2 28
 
6.8%
4 23
 
5.6%
6 21
 
5.1%
5 20
 
4.9%
7 14
 
3.4%
9 13
 
3.2%
8 12
 
2.9%
10 10
 
2.4%
Other values (85) 195
47.3%
ValueCountFrequency (%)
0 3
 
0.7%
1 38
9.2%
2 28
6.8%
3 38
9.2%
4 23
5.6%
5 20
4.9%
6 21
5.1%
7 14
 
3.4%
8 12
 
2.9%
9 13
 
3.2%
ValueCountFrequency (%)
258 1
0.2%
225 1
0.2%
206 1
0.2%
189 1
0.2%
171 1
0.2%
164 1
0.2%
152 1
0.2%
141 1
0.2%
133 1
0.2%
130 1
0.2%

이정표지
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26941748
Minimum0
Maximum5
Zeros343
Zeros (%)83.3%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-11T18:36:17.079333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70984502
Coefficient of variation (CV)2.6347401
Kurtosis11.837599
Mean0.26941748
Median Absolute Deviation (MAD)0
Skewness3.2507578
Sum111
Variance0.50387995
MonotonicityNot monotonic
2023-12-11T18:36:17.205527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 343
83.3%
1 43
 
10.4%
2 14
 
3.4%
3 9
 
2.2%
4 2
 
0.5%
5 1
 
0.2%
ValueCountFrequency (%)
0 343
83.3%
1 43
 
10.4%
2 14
 
3.4%
3 9
 
2.2%
4 2
 
0.5%
5 1
 
0.2%
ValueCountFrequency (%)
5 1
 
0.2%
4 2
 
0.5%
3 9
 
2.2%
2 14
 
3.4%
1 43
 
10.4%
0 343
83.3%

경계표지
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29368932
Minimum0
Maximum10
Zeros369
Zeros (%)89.6%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-11T18:36:17.316503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0733031
Coefficient of variation (CV)3.6545527
Kurtosis29.734799
Mean0.29368932
Median Absolute Deviation (MAD)0
Skewness4.9582645
Sum121
Variance1.1519795
MonotonicityNot monotonic
2023-12-11T18:36:17.503211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 369
89.6%
2 15
 
3.6%
1 11
 
2.7%
4 6
 
1.5%
3 5
 
1.2%
6 3
 
0.7%
10 1
 
0.2%
8 1
 
0.2%
5 1
 
0.2%
ValueCountFrequency (%)
0 369
89.6%
1 11
 
2.7%
2 15
 
3.6%
3 5
 
1.2%
4 6
 
1.5%
5 1
 
0.2%
6 3
 
0.7%
8 1
 
0.2%
10 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.2%
8 1
 
0.2%
6 3
 
0.7%
5 1
 
0.2%
4 6
 
1.5%
3 5
 
1.2%
2 15
 
3.6%
1 11
 
2.7%
0 369
89.6%

노선표지
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2184466
Minimum0
Maximum9
Zeros365
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-11T18:36:17.621762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84048967
Coefficient of variation (CV)3.8475749
Kurtosis51.554451
Mean0.2184466
Median Absolute Deviation (MAD)0
Skewness6.3639224
Sum90
Variance0.70642289
MonotonicityNot monotonic
2023-12-11T18:36:17.730409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 365
88.6%
1 29
 
7.0%
2 9
 
2.2%
3 3
 
0.7%
4 3
 
0.7%
5 1
 
0.2%
9 1
 
0.2%
8 1
 
0.2%
ValueCountFrequency (%)
0 365
88.6%
1 29
 
7.0%
2 9
 
2.2%
3 3
 
0.7%
4 3
 
0.7%
5 1
 
0.2%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
8 1
 
0.2%
5 1
 
0.2%
4 3
 
0.7%
3 3
 
0.7%
2 9
 
2.2%
1 29
 
7.0%
0 365
88.6%

기타
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60679612
Minimum0
Maximum10
Zeros319
Zeros (%)77.4%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-11T18:36:17.896903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.4898693
Coefficient of variation (CV)2.4553045
Kurtosis11.64173
Mean0.60679612
Median Absolute Deviation (MAD)0
Skewness3.2361169
Sum250
Variance2.2197104
MonotonicityNot monotonic
2023-12-11T18:36:18.048219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 319
77.4%
1 36
 
8.7%
2 20
 
4.9%
3 13
 
3.2%
4 10
 
2.4%
6 5
 
1.2%
7 4
 
1.0%
5 2
 
0.5%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
0 319
77.4%
1 36
 
8.7%
2 20
 
4.9%
3 13
 
3.2%
4 10
 
2.4%
5 2
 
0.5%
6 5
 
1.2%
7 4
 
1.0%
8 1
 
0.2%
9 1
 
0.2%
ValueCountFrequency (%)
10 1
 
0.2%
9 1
 
0.2%
8 1
 
0.2%
7 4
 
1.0%
6 5
 
1.2%
5 2
 
0.5%
4 10
 
2.4%
3 13
 
3.2%
2 20
4.9%
1 36
8.7%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.555825
Minimum1
Maximum271
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2023-12-11T18:36:18.225993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median9.5
Q333
95-th percentile114.9
Maximum271
Range270
Interquartile range (IQR)29

Descriptive statistics

Standard deviation39.045351
Coefficient of variation (CV)1.4703121
Kurtosis8.74949
Mean26.555825
Median Absolute Deviation (MAD)7.5
Skewness2.6603433
Sum10941
Variance1524.5395
MonotonicityNot monotonic
2023-12-11T18:36:18.399712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 37
 
9.0%
3 36
 
8.7%
2 28
 
6.8%
4 26
 
6.3%
6 23
 
5.6%
5 19
 
4.6%
12 14
 
3.4%
9 13
 
3.2%
7 12
 
2.9%
8 12
 
2.9%
Other values (89) 192
46.6%
ValueCountFrequency (%)
1 37
9.0%
2 28
6.8%
3 36
8.7%
4 26
6.3%
5 19
4.6%
6 23
5.6%
7 12
 
2.9%
8 12
 
2.9%
9 13
 
3.2%
10 9
 
2.2%
ValueCountFrequency (%)
271 1
0.2%
232 1
0.2%
225 1
0.2%
193 1
0.2%
178 1
0.2%
169 1
0.2%
156 1
0.2%
148 1
0.2%
143 1
0.2%
136 1
0.2%

Interactions

2023-12-11T18:36:15.458190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:12.528185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.148491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.650988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:14.152946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:14.928682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.548711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:12.609132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.224080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.739160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:14.227389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.025753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.635139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:12.705736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.313195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.817027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:14.581661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.108361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.739916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:12.827984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.393139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.897668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:14.654305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.211647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.839950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:12.945035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.486516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.971802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:14.766545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.289974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.924517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.043451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:13.572098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:14.062626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:14.848128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:36:15.377751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:36:18.515496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향표지이정표지경계표지노선표지기타계
방향표지1.0000.5680.5870.6340.8480.996
이정표지0.5681.0000.2530.3730.5080.563
경계표지0.5870.2531.0000.5640.5830.643
노선표지0.6340.3730.5641.0000.5400.652
기타0.8480.5080.5830.5401.0000.858
계0.9960.5630.6430.6520.8581.000
2023-12-11T18:36:18.634648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
방향표지이정표지경계표지노선표지기타계
방향표지1.0000.4270.3000.3810.4950.996
이정표지0.4271.0000.2040.4070.4210.450
경계표지0.3000.2041.0000.2850.2730.331
노선표지0.3810.4070.2851.0000.3560.399
기타0.4950.4210.2730.3561.0000.523
계0.9960.4500.3310.3990.5231.000

Missing values

2023-12-11T18:36:16.049000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:36:16.178145image/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

구분방향표지이정표지경계표지노선표지기타계
0서울특별시 종로구 청운동400004
1서울특별시 종로구 궁정동100001
2서울특별시 종로구 통의동100001
3서울특별시 종로구 적선동600006
4서울특별시 종로구 통인동100001
5서울특별시 종로구 옥인동200002
6서울특별시 종로구 체부동100001
7서울특별시 종로구 필운동300003
8서울특별시 종로구 내자동200002
9서울특별시 종로구 사직동300003
구분방향표지이정표지경계표지노선표지기타계
402서울특별시 송파구 마천동24000024
403서울특별시 강동구 명일동33000033
404서울특별시 강동구 고덕동55300563
405서울특별시 강동구 상일동62023269
406서울특별시 강동구 길동35001036
407서울특별시 강동구 둔촌동41000142
408서울특별시 강동구 암사동58000260
409서울특별시 강동구 성내동43100145
410서울특별시 강동구 천호동48000351
411서울특별시 강동구 강일동38024751