Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells14861
Missing cells (%)12.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory110.0 B

Variable types

Numeric6
Categorical3
Text3

Dataset

Description부산광역시 동래구에 소재한 주소들에 대한 정보로서 시도, 구군, 읍면동, 도로명, 건물번호, 우편코드 등의 항목에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15086618/fileData.do

Alerts

시도 has constant value ""Constant
구군 has constant value ""Constant
순번 is highly overall correlated with 읍면동High correlation
행정동코드 is highly overall correlated with 읍면동High correlation
우편코드 is highly overall correlated with 읍면동High correlation
읍면동 is highly overall correlated with 순번 and 2 other fieldsHigh correlation
번지(부) has 430 (4.3%) missing valuesMissing
건물번호(부) has 5547 (55.5%) missing valuesMissing
건물명 has 8884 (88.8%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 04:51:19.274725
Analysis finished2023-12-12 04:51:26.287292
Duration7.01 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%
Mean9190.1416
Minimum3
Maximum18297
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:26.375732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile895.9
Q14649.75
median9156
Q313819.75
95-th percentile17361.05
Maximum18297
Range18294
Interquartile range (IQR)9170

Descriptive statistics

Standard deviation5286.1125
Coefficient of variation (CV)0.57519381
Kurtosis-1.2023817
Mean9190.1416
Median Absolute Deviation (MAD)4584.5
Skewness-0.0085085061
Sum91901416
Variance27942986
MonotonicityNot monotonic
2023-12-12T13:51:26.548414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15289 1
 
< 0.1%
17399 1
 
< 0.1%
6326 1
 
< 0.1%
4648 1
 
< 0.1%
1758 1
 
< 0.1%
8231 1
 
< 0.1%
17441 1
 
< 0.1%
6767 1
 
< 0.1%
1089 1
 
< 0.1%
1054 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
3 1
< 0.1%
6 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
14 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
22 1
< 0.1%
ValueCountFrequency (%)
18297 1
< 0.1%
18296 1
< 0.1%
18294 1
< 0.1%
18293 1
< 0.1%
18291 1
< 0.1%
18288 1
< 0.1%
18287 1
< 0.1%
18284 1
< 0.1%
18281 1
< 0.1%
18280 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

2023-12-12T13:51:26.722752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:51:26.824509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 10000
100.0%

구군
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동래구 10000
100.0%

Length

2023-12-12T13:51:26.928111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:51:27.006095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동래구 10000
100.0%

읍면동
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
온천동
2568 
안락동
1941 
사직동
1764 
명장동
1398 
명륜동
692 
Other values (4)
1637 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row온천동
2nd row사직동
3rd row명륜동
4th row사직동
5th row명장동

Common Values

ValueCountFrequency (%)
온천동 2568
25.7%
안락동 1941
19.4%
사직동 1764
17.6%
명장동 1398
14.0%
명륜동 692
 
6.9%
칠산동 552
 
5.5%
수안동 440
 
4.4%
복천동 373
 
3.7%
낙민동 272
 
2.7%

Length

2023-12-12T13:51:27.086963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:51:27.178058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
온천동 2568
25.7%
안락동 1941
19.4%
사직동 1764
17.6%
명장동 1398
14.0%
명륜동 692
 
6.9%
칠산동 552
 
5.5%
수안동 440
 
4.4%
복천동 373
 
3.7%
낙민동 272
 
2.7%

번지(주)
Text

Distinct1034
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:51:27.570178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9676
Min length1

Characters and Unicode

Total characters29676
Distinct characters12
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

Unique247 ?
Unique (%)2.5%

Sample

1st row1462
2nd row153
3rd row637
4th row124
5th row283
ValueCountFrequency (%)
147 95
 
0.9%
140 83
 
0.8%
148 79
 
0.8%
506 76
 
0.8%
151 74
 
0.7%
138 68
 
0.7%
144 68
 
0.7%
503 67
 
0.7%
632 66
 
0.7%
149 63
 
0.6%
Other values (998) 9316
92.7%
2023-12-12T13:51:28.130348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5847
19.7%
4 4647
15.7%
2 3144
10.6%
5 3092
10.4%
3 2919
9.8%
6 2445
8.2%
0 2221
 
7.5%
7 1989
 
6.7%
9 1774
 
6.0%
8 1488
 
5.0%
Other values (2) 110
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29566
99.6%
Other Letter 55
 
0.2%
Space Separator 55
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5847
19.8%
4 4647
15.7%
2 3144
10.6%
5 3092
10.5%
3 2919
9.9%
6 2445
8.3%
0 2221
 
7.5%
7 1989
 
6.7%
9 1774
 
6.0%
8 1488
 
5.0%
Other Letter
ValueCountFrequency (%)
산 55
100.0%
Space Separator
ValueCountFrequency (%)
55
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29621
99.8%
Hangul 55
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5847
19.7%
4 4647
15.7%
2 3144
10.6%
5 3092
10.4%
3 2919
9.9%
6 2445
8.3%
0 2221
 
7.5%
7 1989
 
6.7%
9 1774
 
6.0%
8 1488
 
5.0%
Hangul
ValueCountFrequency (%)
산 55
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29621
99.8%
Hangul 55
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5847
19.7%
4 4647
15.7%
2 3144
10.6%
5 3092
10.4%
3 2919
9.9%
6 2445
8.3%
0 2221
 
7.5%
7 1989
 
6.7%
9 1774
 
6.0%
8 1488
 
5.0%
Hangul
ValueCountFrequency (%)
산 55
100.0%

번지(부)
Real number (ℝ)

MISSING 

Distinct191
Distinct (%)2.0%
Missing430
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean26.290909
Minimum1
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:28.279421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median17
Q336
95-th percentile81
Maximum258
Range257
Interquartile range (IQR)30

Descriptive statistics

Standard deviation28.331193
Coefficient of variation (CV)1.0776042
Kurtosis7.2205401
Mean26.290909
Median Absolute Deviation (MAD)13
Skewness2.2144337
Sum251604
Variance802.65651
MonotonicityNot monotonic
2023-12-12T13:51:28.427343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 562
 
5.6%
2 454
 
4.5%
3 439
 
4.4%
5 359
 
3.6%
4 345
 
3.5%
6 320
 
3.2%
7 287
 
2.9%
8 263
 
2.6%
10 242
 
2.4%
9 231
 
2.3%
Other values (181) 6068
60.7%
(Missing) 430
 
4.3%
ValueCountFrequency (%)
1 562
5.6%
2 454
4.5%
3 439
4.4%
4 345
3.5%
5 359
3.6%
6 320
3.2%
7 287
2.9%
8 263
2.6%
9 231
2.3%
10 242
2.4%
ValueCountFrequency (%)
258 1
< 0.1%
248 1
< 0.1%
234 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%
216 1
< 0.1%
201 1
< 0.1%
200 1
< 0.1%
199 2
< 0.1%
197 1
< 0.1%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6260105 × 109
Minimum2.6260101 × 109
Maximum2.6260109 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:28.565859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6260101 × 109
5-th percentile2.6260101 × 109
Q12.6260102 × 109
median2.6260107 × 109
Q32.6260108 × 109
95-th percentile2.6260109 × 109
Maximum2.6260109 × 109
Range800
Interquartile range (IQR)600

Descriptive statistics

Standard deviation308.92047
Coefficient of variation (CV)1.176387 × 10-7
Kurtosis-1.6657199
Mean2.6260105 × 109
Median Absolute Deviation (MAD)200
Skewness-0.22898249
Sum2.6260105 × 1013
Variance95431.858
MonotonicityNot monotonic
2023-12-12T13:51:28.699320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2626010800 2568
25.7%
2626010200 1941
19.4%
2626010900 1764
17.6%
2626010100 1398
14.0%
2626010700 692
 
6.9%
2626010300 552
 
5.5%
2626010600 440
 
4.4%
2626010500 373
 
3.7%
2626010400 272
 
2.7%
ValueCountFrequency (%)
2626010100 1398
14.0%
2626010200 1941
19.4%
2626010300 552
 
5.5%
2626010400 272
 
2.7%
2626010500 373
 
3.7%
2626010600 440
 
4.4%
2626010700 692
 
6.9%
2626010800 2568
25.7%
2626010900 1764
17.6%
ValueCountFrequency (%)
2626010900 1764
17.6%
2626010800 2568
25.7%
2626010700 692
 
6.9%
2626010600 440
 
4.4%
2626010500 373
 
3.7%
2626010400 272
 
2.7%
2626010300 552
 
5.5%
2626010200 1941
19.4%
2626010100 1398
14.0%

도로명
Text

Distinct529
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:51:29.018546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length7.23
Min length3

Characters and Unicode

Total characters72300
Distinct characters85
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

Unique17 ?
Unique (%)0.2%

Sample

1st row충렬대로86번길
2nd row여고로75번길
3rd row명륜로
4th row아시아드대로154번길
5th row시실로211번길
ValueCountFrequency (%)
충렬대로 178
 
1.8%
안락로 135
 
1.4%
명장로 130
 
1.3%
명륜로 126
 
1.3%
여고북로 123
 
1.2%
온천천로 114
 
1.1%
동래로 102
 
1.0%
금강로 100
 
1.0%
아시아드대로 92
 
0.9%
중앙대로1367번길 91
 
0.9%
Other values (519) 8809
88.1%
2023-12-12T13:51:29.518436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 9919
 
13.7%
길 7727
 
10.7%
번 7646
 
10.6%
1 4975
 
6.9%
2 2666
 
3.7%
대 2470
 
3.4%
3 2319
 
3.2%
명 1988
 
2.7%
7 1854
 
2.6%
5 1841
 
2.5%
Other values (75) 28895
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52271
72.3%
Decimal Number 20029
 
27.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 9919
19.0%
길 7727
14.8%
번 7646
14.6%
대 2470
 
4.7%
명 1988
 
3.8%
안 1427
 
2.7%
충 1417
 
2.7%
렬 1417
 
2.7%
아 1366
 
2.6%
천 1203
 
2.3%
Other values (65) 15691
30.0%
Decimal Number
ValueCountFrequency (%)
1 4975
24.8%
2 2666
13.3%
3 2319
11.6%
7 1854
 
9.3%
5 1841
 
9.2%
4 1473
 
7.4%
6 1304
 
6.5%
9 1268
 
6.3%
8 1194
 
6.0%
0 1135
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52271
72.3%
Common 20029
 
27.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 9919
19.0%
길 7727
14.8%
번 7646
14.6%
대 2470
 
4.7%
명 1988
 
3.8%
안 1427
 
2.7%
충 1417
 
2.7%
렬 1417
 
2.7%
아 1366
 
2.6%
천 1203
 
2.3%
Other values (65) 15691
30.0%
Common
ValueCountFrequency (%)
1 4975
24.8%
2 2666
13.3%
3 2319
11.6%
7 1854
 
9.3%
5 1841
 
9.2%
4 1473
 
7.4%
6 1304
 
6.5%
9 1268
 
6.3%
8 1194
 
6.0%
0 1135
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52271
72.3%
ASCII 20029
 
27.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 9919
19.0%
길 7727
14.8%
번 7646
14.6%
대 2470
 
4.7%
명 1988
 
3.8%
안 1427
 
2.7%
충 1417
 
2.7%
렬 1417
 
2.7%
아 1366
 
2.6%
천 1203
 
2.3%
Other values (65) 15691
30.0%
ASCII
ValueCountFrequency (%)
1 4975
24.8%
2 2666
13.3%
3 2319
11.6%
7 1854
 
9.3%
5 1841
 
9.2%
4 1473
 
7.4%
6 1304
 
6.5%
9 1268
 
6.3%
8 1194
 
6.0%
0 1135
 
5.7%

건물번호(주)
Real number (ℝ)

Distinct423
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.5225
Minimum1
Maximum1523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:29.716135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q117
median37
Q372
95-th percentile193
Maximum1523
Range1522
Interquartile range (IQR)55

Descriptive statistics

Standard deviation109.12144
Coefficient of variation (CV)1.7178392
Kurtosis95.109398
Mean63.5225
Median Absolute Deviation (MAD)24
Skewness8.330798
Sum635225
Variance11907.489
MonotonicityNot monotonic
2023-12-12T13:51:29.897570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 200
 
2.0%
13 190
 
1.9%
14 189
 
1.9%
15 186
 
1.9%
7 185
 
1.8%
10 183
 
1.8%
21 177
 
1.8%
17 176
 
1.8%
6 174
 
1.7%
20 174
 
1.7%
Other values (413) 8166
81.7%
ValueCountFrequency (%)
1 56
 
0.6%
2 33
 
0.3%
3 97
1.0%
4 80
0.8%
5 155
1.6%
6 174
1.7%
7 185
1.8%
8 167
1.7%
9 172
1.7%
10 183
1.8%
ValueCountFrequency (%)
1523 1
 
< 0.1%
1509 1
 
< 0.1%
1495 11
0.1%
1483 1
 
< 0.1%
1481 1
 
< 0.1%
1473 1
 
< 0.1%
1459 1
 
< 0.1%
1455 1
 
< 0.1%
1439 1
 
< 0.1%
1437 1
 
< 0.1%

건물번호(부)
Real number (ℝ)

MISSING 

Distinct43
Distinct (%)1.0%
Missing5547
Missing (%)55.5%
Infinite0
Infinite (%)0.0%
Mean5.8728947
Minimum1
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:30.084017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q37
95-th percentile16
Maximum146
Range145
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.1791184
Coefficient of variation (CV)1.0521419
Kurtosis130.00716
Mean5.8728947
Median Absolute Deviation (MAD)2
Skewness7.3675293
Sum26152
Variance38.181505
MonotonicityNot monotonic
2023-12-12T13:51:30.248039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 775
 
7.8%
3 595
 
5.9%
4 566
 
5.7%
6 432
 
4.3%
5 415
 
4.2%
2 349
 
3.5%
7 267
 
2.7%
8 267
 
2.7%
9 142
 
1.4%
11 102
 
1.0%
Other values (33) 543
 
5.4%
(Missing) 5547
55.5%
ValueCountFrequency (%)
1 775
7.8%
2 349
3.5%
3 595
5.9%
4 566
5.7%
5 415
4.2%
6 432
4.3%
7 267
 
2.7%
8 267
 
2.7%
9 142
 
1.4%
11 102
 
1.0%
ValueCountFrequency (%)
146 1
< 0.1%
145 1
< 0.1%
87 1
< 0.1%
67 1
< 0.1%
58 1
< 0.1%
56 1
< 0.1%
52 1
< 0.1%
43 1
< 0.1%
41 2
< 0.1%
39 1
< 0.1%

건물명
Text

MISSING 

Distinct1027
Distinct (%)92.0%
Missing8884
Missing (%)88.8%
Memory size156.2 KiB
2023-12-12T13:51:30.609817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length5.4426523
Min length1

Characters and Unicode

Total characters6074
Distinct characters441
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique966 ?
Unique (%)86.6%

Sample

1st row네오앤바하
2nd row온천3동어린이집
3rd row라비앙로즈Ⅶ
4th row온천파크장
5th row남호빌딩
ValueCountFrequency (%)
동래 9
 
0.7%
a동 8
 
0.6%
온천동 8
 
0.6%
아파트 7
 
0.5%
b동 7
 
0.5%
연립주택 6
 
0.5%
주민센터 6
 
0.5%
사직동 5
 
0.4%
명륜동 5
 
0.4%
더 5
 
0.4%
Other values (1096) 1226
94.9%
2023-12-12T13:51:31.410024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
빌 518
 
8.5%
라 245
 
4.0%
177
 
2.9%
동 173
 
2.8%
아 152
 
2.5%
트 145
 
2.4%
스 139
 
2.3%
이 122
 
2.0%
파 111
 
1.8%
리 98
 
1.6%
Other values (431) 4194
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5683
93.6%
Space Separator 177
 
2.9%
Uppercase Letter 106
 
1.7%
Decimal Number 72
 
1.2%
Lowercase Letter 17
 
0.3%
Dash Punctuation 5
 
0.1%
Other Punctuation 5
 
0.1%
Close Punctuation 4
 
0.1%
Open Punctuation 4
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
빌 518
 
9.1%
라 245
 
4.3%
동 173
 
3.0%
아 152
 
2.7%
트 145
 
2.6%
스 139
 
2.4%
이 122
 
2.1%
파 111
 
2.0%
리 98
 
1.7%
하 83
 
1.5%
Other values (382) 3897
68.6%
Uppercase Letter
ValueCountFrequency (%)
B 16
15.1%
A 14
13.2%
S 12
11.3%
K 7
 
6.6%
H 7
 
6.6%
T 7
 
6.6%
E 5
 
4.7%
I 5
 
4.7%
O 5
 
4.7%
W 4
 
3.8%
Other values (12) 24
22.6%
Decimal Number
ValueCountFrequency (%)
2 29
40.3%
1 14
19.4%
3 12
16.7%
4 5
 
6.9%
7 4
 
5.6%
5 3
 
4.2%
0 3
 
4.2%
8 1
 
1.4%
6 1
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
e 6
35.3%
v 2
 
11.8%
i 2
 
11.8%
o 2
 
11.8%
u 1
 
5.9%
s 1
 
5.9%
l 1
 
5.9%
n 1
 
5.9%
t 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 2
40.0%
& 1
20.0%
, 1
20.0%
# 1
20.0%
Space Separator
ValueCountFrequency (%)
177
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Letter Number
ValueCountFrequency (%)
Ⅶ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5683
93.6%
Common 267
 
4.4%
Latin 124
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
빌 518
 
9.1%
라 245
 
4.3%
동 173
 
3.0%
아 152
 
2.7%
트 145
 
2.6%
스 139
 
2.4%
이 122
 
2.1%
파 111
 
2.0%
리 98
 
1.7%
하 83
 
1.5%
Other values (382) 3897
68.6%
Latin
ValueCountFrequency (%)
B 16
 
12.9%
A 14
 
11.3%
S 12
 
9.7%
K 7
 
5.6%
H 7
 
5.6%
T 7
 
5.6%
e 6
 
4.8%
E 5
 
4.0%
I 5
 
4.0%
O 5
 
4.0%
Other values (22) 40
32.3%
Common
ValueCountFrequency (%)
177
66.3%
2 29
 
10.9%
1 14
 
5.2%
3 12
 
4.5%
- 5
 
1.9%
4 5
 
1.9%
) 4
 
1.5%
( 4
 
1.5%
7 4
 
1.5%
5 3
 
1.1%
Other values (7) 10
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5683
93.6%
ASCII 390
 
6.4%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
빌 518
 
9.1%
라 245
 
4.3%
동 173
 
3.0%
아 152
 
2.7%
트 145
 
2.6%
스 139
 
2.4%
이 122
 
2.1%
파 111
 
2.0%
리 98
 
1.7%
하 83
 
1.5%
Other values (382) 3897
68.6%
ASCII
ValueCountFrequency (%)
177
45.4%
2 29
 
7.4%
B 16
 
4.1%
1 14
 
3.6%
A 14
 
3.6%
S 12
 
3.1%
3 12
 
3.1%
K 7
 
1.8%
H 7
 
1.8%
T 7
 
1.8%
Other values (38) 95
24.4%
Number Forms
ValueCountFrequency (%)
Ⅶ 1
100.0%

우편코드
Real number (ℝ)

HIGH CORRELATION 

Distinct200
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47803.833
Minimum47700
Maximum47905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:51:31.535666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47700
5-th percentile47712
Q147760
median47807
Q347843
95-th percentile47895
Maximum47905
Range205
Interquartile range (IQR)83

Descriptive statistics

Standard deviation54.145095
Coefficient of variation (CV)0.0011326517
Kurtosis-0.89430584
Mean47803.833
Median Absolute Deviation (MAD)41
Skewness-0.046337933
Sum4.7803833 × 108
Variance2931.6913
MonotonicityNot monotonic
2023-12-12T13:51:31.649968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47755 175
 
1.8%
47718 139
 
1.4%
47810 138
 
1.4%
47809 130
 
1.3%
47826 130
 
1.3%
47905 127
 
1.3%
47840 127
 
1.3%
47813 126
 
1.3%
47830 124
 
1.2%
47728 123
 
1.2%
Other values (190) 8661
86.6%
ValueCountFrequency (%)
47700 27
 
0.3%
47701 8
 
0.1%
47702 2
 
< 0.1%
47703 4
 
< 0.1%
47704 8
 
0.1%
47705 45
0.4%
47706 58
0.6%
47707 96
1.0%
47708 100
1.0%
47709 79
0.8%
ValueCountFrequency (%)
47905 127
1.3%
47904 1
 
< 0.1%
47903 11
 
0.1%
47902 1
 
< 0.1%
47901 74
0.7%
47900 123
1.2%
47899 10
 
0.1%
47898 75
0.8%
47896 38
 
0.4%
47895 73
0.7%

Interactions

2023-12-12T13:51:25.030546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.018630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.657570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:22.321243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:23.481513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:24.284772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:25.141130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.127119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.755064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:22.463382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:23.631252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:24.428708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:25.232646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.224792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.851674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:22.567724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:23.757346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:24.549691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:25.367235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.349458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.972673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:23.065268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:23.897296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:24.673078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:25.485775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.461704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:22.063369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:23.216095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:24.013104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:24.781883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:25.603594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:21.564716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:22.181799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:23.366440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:24.148579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:51:24.909742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:51:31.723809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번읍면동번지(부)행정동코드건물번호(주)건물번호(부)우편코드
순번1.0000.9270.3300.9200.2190.1350.900
읍면동0.9271.0000.2561.0000.2270.1060.840
번지(부)0.3300.2561.0000.2760.0400.0000.361
행정동코드0.9201.0000.2761.0000.2170.1110.805
건물번호(주)0.2190.2270.0400.2171.0000.1700.243
건물번호(부)0.1350.1060.0000.1110.1701.0000.170
우편코드0.9000.8400.3610.8050.2430.1701.000
2023-12-12T13:51:31.818134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번번지(부)행정동코드건물번호(주)건물번호(부)우편코드읍면동
순번1.000-0.0530.2260.0070.070-0.0620.763
번지(부)-0.0531.000-0.0450.0180.064-0.0150.119
행정동코드0.226-0.0451.000-0.005-0.0230.2511.000
건물번호(주)0.0070.018-0.0051.0000.0200.0430.115
건물번호(부)0.0700.064-0.0230.0201.000-0.1070.056
우편코드-0.062-0.0150.2510.043-0.1071.0000.588
읍면동0.7630.1191.0000.1150.0560.5881.000

Missing values

2023-12-12T13:51:25.800404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:51:26.063741image/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.
2023-12-12T13:51:26.210015image/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

순번시도구군읍면동번지(주)번지(부)행정동코드도로명건물번호(주)건물번호(부)건물명우편코드
1528815289부산광역시동래구온천동1462592626010800충렬대로86번길107<NA><NA>47827
67746775부산광역시동래구사직동153212626010900여고로75번길111<NA>47836
14171418부산광역시동래구명륜동63752626010700명륜로161<NA>네오앤바하47743
57945795부산광역시동래구사직동12442626010900아시아드대로154번길721<NA>47841
30053006부산광역시동래구명장동28342626010100시실로211번길6918<NA>47762
53095310부산광역시동래구사직동64102626010900아시아드대로164번가길12<NA><NA>47842
1201512016부산광역시동래구안락동630172626010200안남로943<NA>47900
10431044부산광역시동래구명륜동46772626010700명륜로123<NA><NA>47738
734735부산광역시동래구명륜동2882626010700명륜로252번길265<NA>47744
84168417부산광역시동래구수안동4062626010600수안로8번길17<NA><NA>47887
순번시도구군읍면동번지(주)번지(부)행정동코드도로명건물번호(주)건물번호(부)건물명우편코드
1529315294부산광역시동래구온천동1462722626010800여고북로123번길459<NA>47827
1347113472부산광역시동래구온천동1243112626010800사직북로48번길154<NA>궁전빌라47849
1188611887부산광역시동래구안락동628262626010200연안로59번길834<NA>47895
1317113172부산광역시동래구온천동1213122626010800아시아드대로247번길5211무학빌라47846
71167117부산광역시동래구사직동18212626010900사직북로48번길366<NA>47856
60476048부산광역시동래구사직동137222626010900미남로682<NA>47829
50105011부산광역시동래구복천동500402626010500복천로5번길753<NA>47799
1020610207부산광역시동래구안락동429292626010200명안로25번길80<NA><NA>47787
1194311944부산광역시동래구안락동6291092626010200충렬대로428번길76<NA><NA>47895
1540115402부산광역시동래구온천동146652626010800여고북로123번가길193<NA>47827