Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells20147
Missing cells (%)14.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory127.0 B

Variable types

Numeric5
Categorical4
Text4
Unsupported1

Dataset

Description부산광역시영도구_옥외광고물새주소관리_20211231
Author부산광역시 영도구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15072284

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
읍면동 is highly overall correlated with 순번 and 3 other fieldsHigh correlation
우편번호1 is highly overall correlated with 순번 and 5 other fieldsHigh correlation
순번 is highly overall correlated with 우편번호2 and 2 other fieldsHigh correlation
우편번호2 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
건물번호 is highly overall correlated with 우편번호1High correlation
건물번호2 is highly overall correlated with 우편번호1High correlation
신우편코드 is highly overall correlated with 우편번호1 and 1 other fieldsHigh correlation
우편번호1 is highly imbalanced (92.7%)Imbalance
건물명 has 9264 (92.6%) missing valuesMissing
신우편코드 has 499 (5.0%) missing valuesMissing
Unnamed: 13 has 10000 (100.0%) missing valuesMissing
순번 has unique valuesUnique
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported
건물번호2 has 7151 (71.5%) zerosZeros

Reproduction

Analysis started2023-12-10 17:08:19.054528
Analysis finished2023-12-10 17:08:25.796202
Duration6.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12189.779
Minimum1
Maximum24468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:08:25.919765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1243.95
Q16087.75
median12150.5
Q318300.5
95-th percentile23212.05
Maximum24468
Range24467
Interquartile range (IQR)12212.75

Descriptive statistics

Standard deviation7040.4615
Coefficient of variation (CV)0.5775709
Kurtosis-1.1910282
Mean12189.779
Median Absolute Deviation (MAD)6108.5
Skewness0.0092008237
Sum1.2189779 × 108
Variance49568098
MonotonicityNot monotonic
2023-12-11T02:08:26.143792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184 1
 
< 0.1%
6281 1
 
< 0.1%
107 1
 
< 0.1%
15909 1
 
< 0.1%
20492 1
 
< 0.1%
5656 1
 
< 0.1%
8486 1
 
< 0.1%
13883 1
 
< 0.1%
12616 1
 
< 0.1%
8912 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
10 1
< 0.1%
13 1
< 0.1%
17 1
< 0.1%
22 1
< 0.1%
25 1
< 0.1%
26 1
< 0.1%
ValueCountFrequency (%)
24468 1
< 0.1%
24466 1
< 0.1%
24460 1
< 0.1%
24459 1
< 0.1%
24458 1
< 0.1%
24457 1
< 0.1%
24450 1
< 0.1%
24448 1
< 0.1%
24443 1
< 0.1%
24439 1
< 0.1%

우편번호1
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
606
9912 
<NA>
 
88

Length

Max length4
Median length3
Mean length3.0088
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row606
2nd row606
3rd row606
4th row606
5th row606

Common Values

ValueCountFrequency (%)
606 9912
99.1%
<NA> 88
 
0.9%

Length

2023-12-11T02:08:26.371393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:26.531984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
606 9912
99.1%
na 88
 
0.9%

우편번호2
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)0.7%
Missing88
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean580.94653
Minimum11
Maximum825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:08:26.708571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile22
Q161
median808
Q3818
95-th percentile822
Maximum825
Range814
Interquartile range (IQR)757

Descriptive statistics

Standard deviation353.136
Coefficient of variation (CV)0.60786317
Kurtosis-1.2468566
Mean580.94653
Median Absolute Deviation (MAD)11
Skewness-0.86493229
Sum5758342
Variance124705.03
MonotonicityNot monotonic
2023-12-11T02:08:26.921017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
822 699
 
7.0%
53 614
 
6.1%
806 508
 
5.1%
812 480
 
4.8%
814 449
 
4.5%
818 432
 
4.3%
823 409
 
4.1%
804 389
 
3.9%
820 389
 
3.9%
51 365
 
3.6%
Other values (61) 5178
51.8%
ValueCountFrequency (%)
11 115
 
1.1%
12 116
 
1.2%
21 155
1.6%
22 197
2.0%
33 333
3.3%
41 145
 
1.5%
42 194
1.9%
43 171
1.7%
44 3
 
< 0.1%
51 365
3.6%
ValueCountFrequency (%)
825 13
 
0.1%
823 409
4.1%
822 699
7.0%
821 268
 
2.7%
820 389
3.9%
819 273
 
2.7%
818 432
4.3%
817 324
3.2%
816 262
 
2.6%
815 177
 
1.8%

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

2023-12-11T02:08:27.154487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:27.311222image/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

2023-12-11T02:08:27.470049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:08:27.637101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영도구 10000
100.0%

읍면동
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
청학동
2579 
동삼동
1774 
신선동2가
627 
신선동3가
615 
영선동4가
598 
Other values (16)
3807 

Length

Max length5
Median length5
Mean length4.1294
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남항동1가
2nd row청학동
3rd row대평동2가
4th row동삼동
5th row청학동

Common Values

ValueCountFrequency (%)
청학동 2579
25.8%
동삼동 1774
17.7%
신선동2가 627
 
6.3%
신선동3가 615
 
6.2%
영선동4가 598
 
6.0%
봉래동5가 524
 
5.2%
봉래동4가 505
 
5.1%
신선동1가 364
 
3.6%
남항동3가 336
 
3.4%
남항동1가 246
 
2.5%
Other values (11) 1832
18.3%

Length

2023-12-11T02:08:27.833759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청학동 2579
25.8%
동삼동 1774
17.7%
신선동2가 627
 
6.3%
신선동3가 615
 
6.2%
영선동4가 598
 
6.0%
봉래동5가 524
 
5.2%
봉래동4가 505
 
5.1%
신선동1가 364
 
3.6%
남항동3가 336
 
3.4%
남항동1가 246
 
2.5%
Other values (11) 1832
18.3%

번지
Text

Distinct6964
Distinct (%)69.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T02:08:28.355262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.4343
Min length1

Characters and Unicode

Total characters54343
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5227 ?
Unique (%)52.3%

Sample

1st row201-12
2nd row21-30
3rd row4
4th row1148
5th row34912
ValueCountFrequency (%)
01월 112
 
1.1%
04월 63
 
0.6%
12월 58
 
0.5%
01일 56
 
0.5%
05월 56
 
0.5%
02월 52
 
0.5%
06월 52
 
0.5%
03월 50
 
0.5%
279-2 38
 
0.4%
07월 37
 
0.3%
Other values (6753) 10007
94.6%
2023-12-11T02:08:29.076131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9795
18.0%
- 7498
13.8%
2 7423
13.7%
3 4971
9.1%
4 4036
7.4%
0 3425
 
6.3%
5 3337
 
6.1%
6 3242
 
6.0%
7 2978
 
5.5%
8 2953
 
5.4%
Other values (5) 4685
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45101
83.0%
Dash Punctuation 7498
 
13.8%
Other Letter 1163
 
2.1%
Space Separator 581
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9795
21.7%
2 7423
16.5%
3 4971
11.0%
4 4036
8.9%
0 3425
 
7.6%
5 3337
 
7.4%
6 3242
 
7.2%
7 2978
 
6.6%
8 2953
 
6.5%
9 2941
 
6.5%
Other Letter
ValueCountFrequency (%)
월 581
50.0%
일 581
50.0%
산 1
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 7498
100.0%
Space Separator
ValueCountFrequency (%)
581
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53180
97.9%
Hangul 1163
 
2.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9795
18.4%
- 7498
14.1%
2 7423
14.0%
3 4971
9.3%
4 4036
7.6%
0 3425
 
6.4%
5 3337
 
6.3%
6 3242
 
6.1%
7 2978
 
5.6%
8 2953
 
5.6%
Other values (2) 3522
 
6.6%
Hangul
ValueCountFrequency (%)
월 581
50.0%
일 581
50.0%
산 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53180
97.9%
Hangul 1163
 
2.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9795
18.4%
- 7498
14.1%
2 7423
14.0%
3 4971
9.3%
4 4036
7.6%
0 3425
 
6.4%
5 3337
 
6.3%
6 3242
 
6.1%
7 2978
 
5.6%
8 2953
 
5.6%
Other values (2) 3522
 
6.6%
Hangul
ValueCountFrequency (%)
월 581
50.0%
일 581
50.0%
산 1
 
0.1%

도로명
Text

Distinct430
Distinct (%)4.3%
Missing74
Missing (%)0.7%
Memory size156.2 KiB
2023-12-11T02:08:29.521323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.1587749
Min length3

Characters and Unicode

Total characters51206
Distinct characters158
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

Unique8 ?
Unique (%)0.1%

Sample

1st row남항로31번길
2nd row청학동로
3rd row대평남로
4th row하리동길
5th row청학남로60번길
ValueCountFrequency (%)
태종로 418
 
4.2%
절영로 274
 
2.8%
하나길 248
 
2.5%
해양로 112
 
1.1%
중복길 111
 
1.1%
청학로 108
 
1.1%
웃서발로 104
 
1.0%
청학동로 103
 
1.0%
새천년길 88
 
0.9%
아리랑길 87
 
0.9%
Other values (420) 8273
83.3%
2023-12-11T02:08:30.195967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 7795
 
15.2%
로 5782
 
11.3%
번 3781
 
7.4%
남 1529
 
3.0%
1 1425
 
2.8%
3 1384
 
2.7%
학 1315
 
2.6%
청 1307
 
2.6%
2 1178
 
2.3%
영 1147
 
2.2%
Other values (148) 24563
48.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42533
83.1%
Decimal Number 8673
 
16.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 7795
18.3%
로 5782
 
13.6%
번 3781
 
8.9%
남 1529
 
3.6%
학 1315
 
3.1%
청 1307
 
3.1%
영 1147
 
2.7%
태 991
 
2.3%
종 991
 
2.3%
항 951
 
2.2%
Other values (138) 16944
39.8%
Decimal Number
ValueCountFrequency (%)
1 1425
16.4%
3 1384
16.0%
2 1178
13.6%
4 814
9.4%
9 804
9.3%
7 729
8.4%
6 689
7.9%
5 673
7.8%
0 528
 
6.1%
8 449
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42533
83.1%
Common 8673
 
16.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 7795
18.3%
로 5782
 
13.6%
번 3781
 
8.9%
남 1529
 
3.6%
학 1315
 
3.1%
청 1307
 
3.1%
영 1147
 
2.7%
태 991
 
2.3%
종 991
 
2.3%
항 951
 
2.2%
Other values (138) 16944
39.8%
Common
ValueCountFrequency (%)
1 1425
16.4%
3 1384
16.0%
2 1178
13.6%
4 814
9.4%
9 804
9.3%
7 729
8.4%
6 689
7.9%
5 673
7.8%
0 528
 
6.1%
8 449
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42533
83.1%
ASCII 8673
 
16.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 7795
18.3%
로 5782
 
13.6%
번 3781
 
8.9%
남 1529
 
3.6%
학 1315
 
3.1%
청 1307
 
3.1%
영 1147
 
2.7%
태 991
 
2.3%
종 991
 
2.3%
항 951
 
2.2%
Other values (138) 16944
39.8%
ASCII
ValueCountFrequency (%)
1 1425
16.4%
3 1384
16.0%
2 1178
13.6%
4 814
9.4%
9 804
9.3%
7 729
8.4%
6 689
7.9%
5 673
7.8%
0 528
 
6.1%
8 449
 
5.2%

건물번호
Real number (ℝ)

HIGH CORRELATION 

Distinct621
Distinct (%)6.3%
Missing74
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean86.88374
Minimum1
Maximum950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:08:30.408845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q117
median39
Q387
95-th percentile371.5
Maximum950
Range949
Interquartile range (IQR)70

Descriptive statistics

Standard deviation136.75852
Coefficient of variation (CV)1.5740404
Kurtosis11.526463
Mean86.88374
Median Absolute Deviation (MAD)27
Skewness3.2165036
Sum862408
Variance18702.892
MonotonicityNot monotonic
2023-12-11T02:08:30.622506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 188
 
1.9%
14 185
 
1.8%
6 180
 
1.8%
10 178
 
1.8%
16 174
 
1.7%
12 174
 
1.7%
8 173
 
1.7%
11 167
 
1.7%
5 159
 
1.6%
13 157
 
1.6%
Other values (611) 8191
81.9%
ValueCountFrequency (%)
1 92
0.9%
2 120
1.2%
3 118
1.2%
4 122
1.2%
5 159
1.6%
6 180
1.8%
7 188
1.9%
8 173
1.7%
9 152
1.5%
10 178
1.8%
ValueCountFrequency (%)
950 1
< 0.1%
948 1
< 0.1%
946 1
< 0.1%
942 1
< 0.1%
928 1
< 0.1%
922 1
< 0.1%
904 1
< 0.1%
898 1
< 0.1%
894 1
< 0.1%
890 1
< 0.1%

건물번호2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)0.5%
Missing74
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean2.083518
Minimum0
Maximum70
Zeros7151
Zeros (%)71.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:08:30.804750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile12
Maximum70
Range70
Interquartile range (IQR)1

Descriptive statistics

Standard deviation5.1670313
Coefficient of variation (CV)2.4799552
Kurtosis25.88065
Mean2.083518
Median Absolute Deviation (MAD)0
Skewness4.2120576
Sum20681
Variance26.698213
MonotonicityNot monotonic
2023-12-11T02:08:30.943111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7151
71.5%
1 500
 
5.0%
3 255
 
2.5%
4 250
 
2.5%
5 248
 
2.5%
6 225
 
2.2%
2 168
 
1.7%
7 158
 
1.6%
8 158
 
1.6%
9 114
 
1.1%
Other values (42) 699
 
7.0%
ValueCountFrequency (%)
0 7151
71.5%
1 500
 
5.0%
2 168
 
1.7%
3 255
 
2.5%
4 250
 
2.5%
5 248
 
2.5%
6 225
 
2.2%
7 158
 
1.6%
8 158
 
1.6%
9 114
 
1.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
68 1
 
< 0.1%
64 1
 
< 0.1%
60 1
 
< 0.1%
55 1
 
< 0.1%
53 1
 
< 0.1%
52 1
 
< 0.1%
51 1
 
< 0.1%
43 3
< 0.1%
42 1
 
< 0.1%

건물명
Text

MISSING 

Distinct505
Distinct (%)68.6%
Missing9264
Missing (%)92.6%
Memory size156.2 KiB
2023-12-11T02:08:31.215849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length6.25
Min length2

Characters and Unicode

Total characters4600
Distinct characters328
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

Unique363 ?
Unique (%)49.3%

Sample

1st row신영도 롯데낙천대 아파트
2nd row(주)포코엔지니어링
3rd row(주)한진중공업
4th row주차관리소
5th row절영초등학교
ValueCountFrequency (%)
영선미니아파트 13
 
1.6%
주)한진중공업 11
 
1.4%
한국해양대학교 8
 
1.0%
동삼그린힐아파트 7
 
0.9%
절영아파트 6
 
0.7%
조양비취맨션 6
 
0.7%
주식회사 5
 
0.6%
국보 5
 
0.6%
동삼주공영구임대아파트 5
 
0.6%
a동 5
 
0.6%
Other values (528) 737
91.2%
2023-12-11T02:08:31.763957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 159
 
3.5%
영 152
 
3.3%
아 142
 
3.1%
빌 137
 
3.0%
트 132
 
2.9%
파 124
 
2.7%
라 113
 
2.5%
교 103
 
2.2%
도 101
 
2.2%
주 95
 
2.1%
Other values (318) 3342
72.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4351
94.6%
Space Separator 72
 
1.6%
Open Punctuation 48
 
1.0%
Close Punctuation 48
 
1.0%
Decimal Number 45
 
1.0%
Uppercase Letter 32
 
0.7%
Other Punctuation 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 159
 
3.7%
영 152
 
3.5%
아 142
 
3.3%
빌 137
 
3.1%
트 132
 
3.0%
파 124
 
2.8%
라 113
 
2.6%
교 103
 
2.4%
도 101
 
2.3%
주 95
 
2.2%
Other values (294) 3093
71.1%
Uppercase Letter
ValueCountFrequency (%)
C 6
18.8%
A 6
18.8%
S 5
15.6%
B 5
15.6%
K 4
12.5%
T 2
 
6.2%
G 1
 
3.1%
I 1
 
3.1%
X 1
 
3.1%
Z 1
 
3.1%
Decimal Number
ValueCountFrequency (%)
1 19
42.2%
2 15
33.3%
3 4
 
8.9%
5 2
 
4.4%
4 2
 
4.4%
0 1
 
2.2%
8 1
 
2.2%
9 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
t 1
50.0%
o 1
50.0%
Space Separator
ValueCountFrequency (%)
72
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4351
94.6%
Common 215
 
4.7%
Latin 34
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 159
 
3.7%
영 152
 
3.5%
아 142
 
3.3%
빌 137
 
3.1%
트 132
 
3.0%
파 124
 
2.8%
라 113
 
2.6%
교 103
 
2.4%
도 101
 
2.3%
주 95
 
2.2%
Other values (294) 3093
71.1%
Common
ValueCountFrequency (%)
72
33.5%
( 48
22.3%
) 48
22.3%
1 19
 
8.8%
2 15
 
7.0%
3 4
 
1.9%
5 2
 
0.9%
4 2
 
0.9%
. 2
 
0.9%
0 1
 
0.5%
Other values (2) 2
 
0.9%
Latin
ValueCountFrequency (%)
C 6
17.6%
A 6
17.6%
S 5
14.7%
B 5
14.7%
K 4
11.8%
T 2
 
5.9%
G 1
 
2.9%
I 1
 
2.9%
X 1
 
2.9%
t 1
 
2.9%
Other values (2) 2
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4351
94.6%
ASCII 249
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 159
 
3.7%
영 152
 
3.5%
아 142
 
3.3%
빌 137
 
3.1%
트 132
 
3.0%
파 124
 
2.8%
라 113
 
2.6%
교 103
 
2.4%
도 101
 
2.3%
주 95
 
2.2%
Other values (294) 3093
71.1%
ASCII
ValueCountFrequency (%)
72
28.9%
( 48
19.3%
) 48
19.3%
1 19
 
7.6%
2 15
 
6.0%
C 6
 
2.4%
A 6
 
2.4%
S 5
 
2.0%
B 5
 
2.0%
3 4
 
1.6%
Other values (14) 21
 
8.4%

지번
Text

Distinct8615
Distinct (%)86.8%
Missing74
Missing (%)0.7%
Memory size156.2 KiB
2023-12-11T02:08:32.318934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.294076
Min length5

Characters and Unicode

Total characters102179
Distinct characters28
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7656 ?
Unique (%)77.1%

Sample

1st row남항동1가 201-12
2nd row청학동 21-30
3rd row대평동2가 4
4th row동삼동 1148
5th row청학동 95-8
ValueCountFrequency (%)
청학동 2536
 
12.8%
동삼동 1762
 
8.9%
신선동2가 627
 
3.2%
신선동3가 615
 
3.1%
영선동4가 598
 
3.0%
봉래동5가 523
 
2.6%
봉래동4가 505
 
2.5%
신선동1가 362
 
1.8%
남항동3가 332
 
1.7%
남항동1가 246
 
1.2%
Other values (6964) 11746
59.2%
2023-12-11T02:08:33.510849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 11688
 
11.4%
1 10585
 
10.4%
9926
 
9.7%
- 9100
 
8.9%
2 8315
 
8.1%
3 5804
 
5.7%
가 5628
 
5.5%
4 4984
 
4.9%
5 3703
 
3.6%
6 3130
 
3.1%
Other values (18) 29316
28.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47669
46.7%
Other Letter 35484
34.7%
Space Separator 9926
 
9.7%
Dash Punctuation 9100
 
8.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11688
32.9%
가 5628
15.9%
선 2716
 
7.7%
청 2536
 
7.1%
학 2536
 
7.1%
삼 1762
 
5.0%
신 1604
 
4.5%
래 1519
 
4.3%
봉 1519
 
4.3%
영 1112
 
3.1%
Other values (6) 2864
 
8.1%
Decimal Number
ValueCountFrequency (%)
1 10585
22.2%
2 8315
17.4%
3 5804
12.2%
4 4984
10.5%
5 3703
 
7.8%
6 3130
 
6.6%
7 2892
 
6.1%
9 2880
 
6.0%
8 2878
 
6.0%
0 2498
 
5.2%
Space Separator
ValueCountFrequency (%)
9926
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66695
65.3%
Hangul 35484
34.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11688
32.9%
가 5628
15.9%
선 2716
 
7.7%
청 2536
 
7.1%
학 2536
 
7.1%
삼 1762
 
5.0%
신 1604
 
4.5%
래 1519
 
4.3%
봉 1519
 
4.3%
영 1112
 
3.1%
Other values (6) 2864
 
8.1%
Common
ValueCountFrequency (%)
1 10585
15.9%
9926
14.9%
- 9100
13.6%
2 8315
12.5%
3 5804
8.7%
4 4984
7.5%
5 3703
 
5.6%
6 3130
 
4.7%
7 2892
 
4.3%
9 2880
 
4.3%
Other values (2) 5376
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66695
65.3%
Hangul 35484
34.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 11688
32.9%
가 5628
15.9%
선 2716
 
7.7%
청 2536
 
7.1%
학 2536
 
7.1%
삼 1762
 
5.0%
신 1604
 
4.5%
래 1519
 
4.3%
봉 1519
 
4.3%
영 1112
 
3.1%
Other values (6) 2864
 
8.1%
ASCII
ValueCountFrequency (%)
1 10585
15.9%
9926
14.9%
- 9100
13.6%
2 8315
12.5%
3 5804
8.7%
4 4984
7.5%
5 3703
 
5.6%
6 3130
 
4.7%
7 2892
 
4.3%
9 2880
 
4.3%
Other values (2) 5376
8.1%

신우편코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct126
Distinct (%)1.3%
Missing499
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean49057.827
Minimum49000
Maximum49127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T02:08:33.770425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49000
5-th percentile49010
Q149029
median49058
Q349079
95-th percentile49121
Maximum49127
Range127
Interquartile range (IQR)50

Descriptive statistics

Standard deviation32.535063
Coefficient of variation (CV)0.00066319821
Kurtosis-0.78773997
Mean49057.827
Median Absolute Deviation (MAD)26
Skewness0.27629532
Sum4.6609842 × 108
Variance1058.5303
MonotonicityNot monotonic
2023-12-11T02:08:34.031574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49079 346
 
3.5%
49024 215
 
2.1%
49061 197
 
2.0%
49076 194
 
1.9%
49027 194
 
1.9%
49031 180
 
1.8%
49102 173
 
1.7%
49014 166
 
1.7%
49126 164
 
1.6%
49017 159
 
1.6%
Other values (116) 7513
75.1%
(Missing) 499
 
5.0%
ValueCountFrequency (%)
49000 44
0.4%
49001 7
 
0.1%
49002 1
 
< 0.1%
49003 72
0.7%
49004 27
 
0.3%
49005 101
1.0%
49006 30
 
0.3%
49007 44
0.4%
49008 69
0.7%
49009 59
0.6%
ValueCountFrequency (%)
49127 50
 
0.5%
49126 164
1.6%
49125 105
1.1%
49124 84
0.8%
49123 56
 
0.6%
49122 14
 
0.1%
49121 8
 
0.1%
49120 2
 
< 0.1%
49119 4
 
< 0.1%
49118 84
0.8%

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Interactions

2023-12-11T02:08:24.166897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:21.303446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:21.947927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:22.627344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:23.217057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:24.316078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:21.439837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:22.073013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:22.731192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:23.637682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:24.486922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:21.609675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:22.220632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:22.845080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:23.772374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:24.630610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:21.716609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:22.322195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:22.939940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:23.895809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:24.843697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:21.835137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:22.464251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:23.073751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:08:24.040261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:08:34.200979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번우편번호2읍면동건물번호건물번호2신우편코드
순번1.0000.5420.9550.5610.2770.932
우편번호20.5421.0000.9080.1630.0180.522
읍면동0.9550.9081.0000.4570.2340.918
건물번호0.5610.1630.4571.0000.2510.505
건물번호20.2770.0180.2340.2511.0000.267
신우편코드0.9320.5220.9180.5050.2671.000
2023-12-11T02:08:34.350007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동우편번호1
읍면동1.0001.000
우편번호11.0001.000
2023-12-11T02:08:34.504985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번우편번호2건물번호건물번호2신우편코드우편번호1읍면동
순번1.0000.7000.078-0.016-0.3721.0000.775
우편번호20.7001.000-0.0210.043-0.2691.0000.689
건물번호0.078-0.0211.000-0.1620.0901.0000.187
건물번호2-0.0160.043-0.1621.000-0.1151.0000.082
신우편코드-0.372-0.2690.090-0.1151.0001.0000.659
우편번호11.0001.0001.0001.0001.0001.0001.000
읍면동0.7750.6890.1870.0820.6591.0001.000

Missing values

2023-12-11T02:08:25.073917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:08:25.391923image/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.
2023-12-11T02:08:25.641195image/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

순번우편번호1우편번호2시도시군구읍면동번지도로명건물번호건물번호2건물명지번신우편코드Unnamed: 13
278184606801부산광역시영도구남항동1가201-12남항로31번길143<NA>남항동1가 201-1249053<NA>
1932720133606818부산광역시영도구청학동21-30청학동로761<NA>청학동 21-3049016<NA>
771299560622부산광역시영도구대평동2가4대평남로590<NA>대평동2가 449044<NA>
9378748660680부산광역시영도구동삼동1148하리동길370<NA>동삼동 114849125<NA>
2004319733606819부산광역시영도구청학동34912청학남로60번길100<NA>청학동 95-8<NA><NA>
1504617201606817부산광역시영도구영선동4가238-16에움길1400<NA>영선동4가 238-1649078<NA>
162871596560642부산광역시영도구영선동2가16528절영로101번길320<NA>영선동2가 45-449056<NA>
55794376606806부산광역시영도구동삼동227-205동삼서로65<NA>동삼동 227-20549098<NA>
2074318894606823부산광역시영도구청학동391-679우정길290<NA>청학동 391-67949030<NA>
2073718888606823부산광역시영도구청학동391-95우정길200<NA>청학동 391-95<NA><NA>
순번우편번호1우편번호2시도시군구읍면동번지도로명건물번호건물번호2건물명지번신우편코드Unnamed: 13
175101555160641부산광역시영도구영선동1가08월 02일태종로113번길130<NA>영선동1가 8-249036<NA>
2278623191606821부산광역시영도구청학동126-49태종로352번길404<NA>청학동 126-4949015<NA>
101769033606811부산광역시영도구봉래동4가260-6개량2길370<NA>봉래동4가 260-649065<NA>
2107421317606821부산광역시영도구청학동159-12청학북로453<NA>청학동 159-1249014<NA>
120941184860651부산광역시영도구신선동1가218-11진달래길100<NA>신선동1가 218-1149061<NA>
2018117274606816부산광역시영도구영선동4가58영선대로310<NA>영선동4가 5849051<NA>
756286060621부산광역시영도구대평동1가11355대평로28번길94<NA>대평동1가 31-249040<NA>
5680414860680부산광역시영도구동삼동116-41동삼로43번길1610<NA>동삼동 산116-4149106<NA>
139281411560653부산광역시영도구신선동3가131-12상록수길860<NA>신선동3가 131-1249073<NA>
2262823828606823부산광역시영도구청학동468-429해돋이3길2440<NA>청학동 468-42949032<NA>