Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)0.1%
Total size in memory576.2 KiB
Average record size in memory59.0 B

Variable types

Numeric3
Categorical2
Text1

Dataset

Description부산광역시 영도구 옥외광고물에 관한 데이터 항목으로 우편번호와 시군구, 도로명, 건물번호본번, 건물번호부번 등의 사항을 분류하여 제공합니다.
Author부산광역시 영도구
URLhttps://www.data.go.kr/data/15072286/fileData.do

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
Dataset has 8 (0.1%) duplicate rowsDuplicates
건물번호부번 has 7105 (71.0%) zerosZeros

Reproduction

Analysis started2023-12-12 18:01:56.411912
Analysis finished2023-12-12 18:01:58.482785
Duration2.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

우편번호
Real number (ℝ)

Distinct128
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49057.086
Minimum49000
Maximum49128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:01:58.568798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49000
5-th percentile49009
Q149028
median49058
Q349079
95-th percentile49118
Maximum49128
Range128
Interquartile range (IQR)51

Descriptive statistics

Standard deviation32.553519
Coefficient of variation (CV)0.00066358445
Kurtosis-0.81568248
Mean49057.086
Median Absolute Deviation (MAD)26
Skewness0.25986647
Sum4.9057086 × 108
Variance1059.7316
MonotonicityNot monotonic
2023-12-13T03:01:58.771382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49079 414
 
4.1%
49024 252
 
2.5%
49076 233
 
2.3%
49027 223
 
2.2%
49014 210
 
2.1%
49061 203
 
2.0%
49017 201
 
2.0%
49102 184
 
1.8%
49074 182
 
1.8%
49126 169
 
1.7%
Other values (118) 7729
77.3%
ValueCountFrequency (%)
49000 61
0.6%
49001 1
 
< 0.1%
49002 2
 
< 0.1%
49003 68
0.7%
49004 30
 
0.3%
49005 110
1.1%
49006 28
 
0.3%
49007 93
0.9%
49008 69
0.7%
49009 62
0.6%
ValueCountFrequency (%)
49128 1
 
< 0.1%
49127 55
 
0.5%
49126 169
1.7%
49125 98
1.0%
49124 90
0.9%
49123 55
 
0.5%
49122 14
 
0.1%
49121 2
 
< 0.1%
49120 2
 
< 0.1%
49119 1
 
< 0.1%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부산광역시
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 10000
100.0%

Length

2023-12-13T03:01:58.988646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:01:59.436655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 10000
100.0%

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
영도구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영도구 10000
100.0%

Length

2023-12-13T03:01:59.533824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:01:59.637897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영도구 10000
100.0%

도로명
Text

Distinct431
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:01:59.893543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.227
Min length3

Characters and Unicode

Total characters52270
Distinct characters159
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row절영로77번길
2nd row청학동로61번길
3rd row남항시장길
4th row사택길
5th row대평로28번길
ValueCountFrequency (%)
태종로 360
 
3.6%
절영로 260
 
2.6%
하나길 213
 
2.1%
해양로 132
 
1.3%
중복길 118
 
1.2%
청학로 110
 
1.1%
청학동로 106
 
1.1%
웃서발로 95
 
0.9%
에움길 94
 
0.9%
새천년길 85
 
0.9%
Other values (421) 8427
84.3%
2023-12-13T03:02:00.369884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 7943
 
15.2%
로 5930
 
11.3%
번 3924
 
7.5%
남 1600
 
3.1%
1 1529
 
2.9%
청 1444
 
2.8%
학 1422
 
2.7%
3 1383
 
2.6%
2 1182
 
2.3%
영 1111
 
2.1%
Other values (149) 24802
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43190
82.6%
Decimal Number 9080
 
17.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 7943
18.4%
로 5930
 
13.7%
번 3924
 
9.1%
남 1600
 
3.7%
청 1444
 
3.3%
학 1422
 
3.3%
영 1111
 
2.6%
항 979
 
2.3%
태 950
 
2.2%
종 950
 
2.2%
Other values (139) 16937
39.2%
Decimal Number
ValueCountFrequency (%)
1 1529
16.8%
3 1383
15.2%
2 1182
13.0%
4 867
9.5%
9 840
9.3%
7 785
8.6%
6 762
8.4%
5 732
8.1%
0 521
 
5.7%
8 479
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43190
82.6%
Common 9080
 
17.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 7943
18.4%
로 5930
 
13.7%
번 3924
 
9.1%
남 1600
 
3.7%
청 1444
 
3.3%
학 1422
 
3.3%
영 1111
 
2.6%
항 979
 
2.3%
태 950
 
2.2%
종 950
 
2.2%
Other values (139) 16937
39.2%
Common
ValueCountFrequency (%)
1 1529
16.8%
3 1383
15.2%
2 1182
13.0%
4 867
9.5%
9 840
9.3%
7 785
8.6%
6 762
8.4%
5 732
8.1%
0 521
 
5.7%
8 479
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43190
82.6%
ASCII 9080
 
17.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 7943
18.4%
로 5930
 
13.7%
번 3924
 
9.1%
남 1600
 
3.7%
청 1444
 
3.3%
학 1422
 
3.3%
영 1111
 
2.6%
항 979
 
2.3%
태 950
 
2.2%
종 950
 
2.2%
Other values (139) 16937
39.2%
ASCII
ValueCountFrequency (%)
1 1529
16.8%
3 1383
15.2%
2 1182
13.0%
4 867
9.5%
9 840
9.3%
7 785
8.6%
6 762
8.4%
5 732
8.1%
0 521
 
5.7%
8 479
 
5.3%

건물번호본번
Real number (ℝ)

Distinct636
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.9702
Minimum1
Maximum948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:02:00.546009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median37
Q382
95-th percentile341
Maximum948
Range947
Interquartile range (IQR)66

Descriptive statistics

Standard deviation129.53424
Coefficient of variation (CV)1.5802601
Kurtosis13.379618
Mean81.9702
Median Absolute Deviation (MAD)25
Skewness3.3979465
Sum819702
Variance16779.118
MonotonicityNot monotonic
2023-12-13T03:02:00.700737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 204
 
2.0%
7 187
 
1.9%
6 186
 
1.9%
10 182
 
1.8%
8 180
 
1.8%
14 173
 
1.7%
18 172
 
1.7%
9 171
 
1.7%
11 170
 
1.7%
16 167
 
1.7%
Other values (626) 8208
82.1%
ValueCountFrequency (%)
1 83
0.8%
2 123
1.2%
3 135
1.4%
4 116
1.2%
5 153
1.5%
6 186
1.9%
7 187
1.9%
8 180
1.8%
9 171
1.7%
10 182
1.8%
ValueCountFrequency (%)
948 1
< 0.1%
946 1
< 0.1%
942 1
< 0.1%
934 1
< 0.1%
932 1
< 0.1%
930 1
< 0.1%
928 1
< 0.1%
906 1
< 0.1%
904 1
< 0.1%
902 1
< 0.1%

건물번호부번
Real number (ℝ)

ZEROS 

Distinct52
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0963
Minimum0
Maximum78
Zeros7105
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:02:00.849538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile12
Maximum78
Range78
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.1396369
Coefficient of variation (CV)2.4517659
Kurtosis25.281211
Mean2.0963
Median Absolute Deviation (MAD)0
Skewness4.1368882
Sum20963
Variance26.415868
MonotonicityNot monotonic
2023-12-13T03:02:01.017449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7105
71.0%
1 574
 
5.7%
4 265
 
2.6%
3 260
 
2.6%
6 242
 
2.4%
5 215
 
2.1%
2 200
 
2.0%
7 163
 
1.6%
8 161
 
1.6%
10 106
 
1.1%
Other values (42) 709
 
7.1%
ValueCountFrequency (%)
0 7105
71.0%
1 574
 
5.7%
2 200
 
2.0%
3 260
 
2.6%
4 265
 
2.6%
5 215
 
2.1%
6 242
 
2.4%
7 163
 
1.6%
8 161
 
1.6%
9 98
 
1.0%
ValueCountFrequency (%)
78 1
< 0.1%
66 1
< 0.1%
56 2
< 0.1%
53 1
< 0.1%
52 1
< 0.1%
51 1
< 0.1%
49 1
< 0.1%
48 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%

Interactions

2023-12-13T03:01:57.784225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:01:56.942027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:01:57.359162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:01:57.925420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:01:57.092598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:01:57.510284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:01:58.042988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:01:57.227285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:01:57.634839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:02:01.154809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호건물번호본번건물번호부번
우편번호1.0000.5080.252
건물번호본번0.5081.0000.179
건물번호부번0.2520.1791.000
2023-12-13T03:02:01.254970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호건물번호본번건물번호부번
우편번호1.0000.090-0.125
건물번호본번0.0901.000-0.174
건물번호부번-0.125-0.1741.000

Missing values

2023-12-13T03:01:58.231580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:01:58.412603image/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

우편번호시도시군구도로명건물번호본번건물번호부번
67549055부산광역시영도구절영로77번길180
1752949017부산광역시영도구청학동로61번길230
1196649068부산광역시영도구남항시장길1890
721649027부산광역시영도구사택길510
209549040부산광역시영도구대평로28번길917
720449029부산광역시영도구장미계단길1660
1107349077부산광역시영도구해오름2길860
867249062부산광역시영도구한결길1650
1153749036부산광역시영도구태종로1110
366149012부산광역시영도구태종로50710
우편번호시도시군구도로명건물번호본번건물번호부번
817949028부산광역시영도구나팔꽃길580
1565749014부산광역시영도구청학로76번길71
302049098부산광역시영도구동삼로710
148949048부산광역시영도구남항남로90
1584749018부산광역시영도구청학남로7번길70
1335449079부산광역시영도구남항새싹3길2360
200749041부산광역시영도구대평로81
1764149009부산광역시영도구태종로369번길106
654549005부산광역시영도구대교로14번길433
544149121부산광역시영도구절영로5040

Duplicate rows

Most frequently occurring

우편번호시도시군구도로명건물번호본번건물번호부번# duplicates
049012부산광역시영도구해양로24802
149049부산광역시영도구남항서로91번길5002
249050부산광역시영도구남항남로5002
349051부산광역시영도구영선대로2902
449079부산광역시영도구영선대로4002
549082부산광역시영도구절영로28102
649101부산광역시영도구웃서발로94번길2402
749127부산광역시영도구전망로31602