Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows6
Duplicate rows (%)0.1%
Total size in memory576.2 KiB
Average record size in memory59.0 B

Variable types

Numeric3
Categorical2
Text1

Dataset

Description부산광역시영도구_옥외광고물우편코드관리_20211231
Author부산광역시 영도구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15072286

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
Dataset has 6 (0.1%) duplicate rowsDuplicates
건물번호부번 has 7095 (71.0%) zerosZeros

Reproduction

Analysis started2024-04-21 11:17:59.770580
Analysis finished2024-04-21 11:18:02.211915
Duration2.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

우편번호
Real number (ℝ)

Distinct127
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49056.814
Minimum49000
Maximum49128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:18:02.344849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49000
5-th percentile49009
Q149028
median49055
Q349079
95-th percentile49122
Maximum49128
Range128
Interquartile range (IQR)51

Descriptive statistics

Standard deviation32.626208
Coefficient of variation (CV)0.00066506985
Kurtosis-0.79417442
Mean49056.814
Median Absolute Deviation (MAD)26
Skewness0.29700242
Sum4.9056814 × 108
Variance1064.4695
MonotonicityNot monotonic
2024-04-21T20:18:02.610638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49079 381
 
3.8%
49024 249
 
2.5%
49076 246
 
2.5%
49027 234
 
2.3%
49014 206
 
2.1%
49061 201
 
2.0%
49017 188
 
1.9%
49102 183
 
1.8%
49074 179
 
1.8%
49053 175
 
1.8%
Other values (117) 7758
77.6%
ValueCountFrequency (%)
49000 57
0.6%
49002 2
 
< 0.1%
49003 63
0.6%
49004 28
 
0.3%
49005 108
1.1%
49006 35
 
0.4%
49007 84
0.8%
49008 62
0.6%
49009 63
0.6%
49010 53
0.5%
ValueCountFrequency (%)
49128 1
 
< 0.1%
49127 46
 
0.5%
49126 165
1.7%
49125 110
1.1%
49124 113
1.1%
49123 53
 
0.5%
49122 16
 
0.2%
49121 3
 
< 0.1%
49120 1
 
< 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

2024-04-21T20:18:02.843566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:18:02.999876image/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

2024-04-21T20:18:03.160169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T20:18:03.313509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영도구 10000
100.0%

도로명
Text

Distinct432
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T20:18:04.241516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.2391
Min length3

Characters and Unicode

Total characters52391
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

Unique11 ?
Unique (%)0.1%

Sample

1st row중리북로
2nd row하나길
3rd row놀이터길
4th row봉래언덕길
5th row하나길
ValueCountFrequency (%)
태종로 338
 
3.4%
절영로 246
 
2.5%
하나길 216
 
2.2%
중복길 127
 
1.3%
해양로 126
 
1.3%
청학동로 119
 
1.2%
청학로 116
 
1.2%
청학북로 88
 
0.9%
새천년길 87
 
0.9%
봉래길 86
 
0.9%
Other values (422) 8451
84.5%
2024-04-21T20:18:05.256575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 7967
 
15.2%
로 5917
 
11.3%
번 3938
 
7.5%
남 1608
 
3.1%
1 1476
 
2.8%
청 1441
 
2.8%
학 1421
 
2.7%
3 1413
 
2.7%
2 1259
 
2.4%
영 1110
 
2.1%
Other values (149) 24841
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 43234
82.5%
Decimal Number 9157
 
17.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 7967
18.4%
로 5917
 
13.7%
번 3938
 
9.1%
남 1608
 
3.7%
청 1441
 
3.3%
학 1421
 
3.3%
영 1110
 
2.6%
항 994
 
2.3%
태 979
 
2.3%
종 979
 
2.3%
Other values (139) 16880
39.0%
Decimal Number
ValueCountFrequency (%)
1 1476
16.1%
3 1413
15.4%
2 1259
13.7%
4 872
9.5%
9 858
9.4%
7 794
8.7%
6 754
8.2%
5 686
7.5%
0 550
 
6.0%
8 495
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 43234
82.5%
Common 9157
 
17.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 7967
18.4%
로 5917
 
13.7%
번 3938
 
9.1%
남 1608
 
3.7%
청 1441
 
3.3%
학 1421
 
3.3%
영 1110
 
2.6%
항 994
 
2.3%
태 979
 
2.3%
종 979
 
2.3%
Other values (139) 16880
39.0%
Common
ValueCountFrequency (%)
1 1476
16.1%
3 1413
15.4%
2 1259
13.7%
4 872
9.5%
9 858
9.4%
7 794
8.7%
6 754
8.2%
5 686
7.5%
0 550
 
6.0%
8 495
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 43234
82.5%
ASCII 9157
 
17.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 7967
18.4%
로 5917
 
13.7%
번 3938
 
9.1%
남 1608
 
3.7%
청 1441
 
3.3%
학 1421
 
3.3%
영 1110
 
2.6%
항 994
 
2.3%
태 979
 
2.3%
종 979
 
2.3%
Other values (139) 16880
39.0%
ASCII
ValueCountFrequency (%)
1 1476
16.1%
3 1413
15.4%
2 1259
13.7%
4 872
9.5%
9 858
9.4%
7 794
8.7%
6 754
8.2%
5 686
7.5%
0 550
 
6.0%
8 495
 
5.4%

건물번호본번
Real number (ℝ)

Distinct636
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.1128
Minimum1
Maximum946
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:18:05.498799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q117
median37
Q382
95-th percentile338
Maximum946
Range945
Interquartile range (IQR)65

Descriptive statistics

Standard deviation130.65808
Coefficient of variation (CV)1.5912024
Kurtosis13.3599
Mean82.1128
Median Absolute Deviation (MAD)25
Skewness3.4203986
Sum821128
Variance17071.534
MonotonicityNot monotonic
2024-04-21T20:18:05.744656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 188
 
1.9%
6 185
 
1.8%
12 182
 
1.8%
7 181
 
1.8%
11 180
 
1.8%
10 180
 
1.8%
14 176
 
1.8%
5 165
 
1.7%
21 164
 
1.6%
17 156
 
1.6%
Other values (626) 8243
82.4%
ValueCountFrequency (%)
1 84
0.8%
2 106
1.1%
3 127
1.3%
4 134
1.3%
5 165
1.7%
6 185
1.8%
7 181
1.8%
8 156
1.6%
9 152
1.5%
10 180
1.8%
ValueCountFrequency (%)
946 1
< 0.1%
938 1
< 0.1%
934 1
< 0.1%
932 1
< 0.1%
910 1
< 0.1%
906 1
< 0.1%
904 1
< 0.1%
902 1
< 0.1%
898 1
< 0.1%
896 1
< 0.1%

건물번호부번
Real number (ℝ)

ZEROS 

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1589
Minimum0
Maximum78
Zeros7095
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T20:18:05.989015image/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 deviation5.2227175
Coefficient of variation (CV)2.4191568
Kurtosis24.773135
Mean2.1589
Median Absolute Deviation (MAD)0
Skewness4.0944461
Sum21589
Variance27.276778
MonotonicityNot monotonic
2024-04-21T20:18:06.248174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7095
71.0%
1 543
 
5.4%
3 259
 
2.6%
4 247
 
2.5%
6 240
 
2.4%
5 233
 
2.3%
2 189
 
1.9%
7 165
 
1.7%
8 146
 
1.5%
9 116
 
1.2%
Other values (43) 767
 
7.7%
ValueCountFrequency (%)
0 7095
71.0%
1 543
 
5.4%
2 189
 
1.9%
3 259
 
2.6%
4 247
 
2.5%
5 233
 
2.3%
6 240
 
2.4%
7 165
 
1.7%
8 146
 
1.5%
9 116
 
1.2%
ValueCountFrequency (%)
78 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%
56 2
< 0.1%
53 1
< 0.1%
52 2
< 0.1%
51 2
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
43 1
< 0.1%

Interactions

2024-04-21T20:18:01.380635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:00.341432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:00.897342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:01.559560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:00.561513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:01.058827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:01.717778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:00.716637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T20:18:01.210552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T20:18:06.417925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호건물번호본번건물번호부번
우편번호1.0000.4990.183
건물번호본번0.4991.0000.115
건물번호부번0.1830.1151.000
2024-04-21T20:18:06.563053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호건물번호본번건물번호부번
우편번호1.0000.097-0.116
건물번호본번0.0971.000-0.176
건물번호부번-0.116-0.1761.000

Missing values

2024-04-21T20:18:01.949476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T20:18:02.133300image/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
562849118부산광역시영도구중리북로50
993849067부산광역시영도구하나길4820
1060849076부산광역시영도구놀이터길80
767349027부산광역시영도구봉래언덕길1590
1075449074부산광역시영도구하나길3240
44049054부산광역시영도구남항로19번길160
560649092부산광역시영도구동삼서로5118
1807849084부산광역시영도구청학동로54
1396349017부산광역시영도구청학로460
1692749016부산광역시영도구태종로392번길173
우편번호시도시군구도로명건물번호본번건물번호부번
537349118부산광역시영도구중리북로210
450749095부산광역시영도구와치로167번길530
920649068부산광역시영도구남항시장길1930
555649125부산광역시영도구태종로765번길40
595849033부산광역시영도구절영로140
1337349079부산광역시영도구남항새싹6길320
1297449078부산광역시영도구에움길470
1670149017부산광역시영도구청학로544
1504649030부산광역시영도구까치길120
1840849086부산광역시영도구조내기로460

Duplicate rows

Most frequently occurring

우편번호시도시군구도로명건물번호본번건물번호부번# duplicates
049012부산광역시영도구해양로246번길802
149038부산광역시영도구대평로6002
249051부산광역시영도구영선대로2902
349082부산광역시영도구절영로28102
449092부산광역시영도구동삼로11502
549127부산광역시영도구전망로31602