Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows8
Duplicate rows (%)0.1%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Numeric8
Categorical6
Text1

Dataset

Description서울특별시 성동구 무지개 장난감세상 프로그램에서 보유하고 있는 우편번호 정보입니다. 우편번호,시도,시군구,도로명코드,도로명,지하여부,건물번호본번,건물번호부번,법정동코드,법정동명,행정동명,산여부,지번본번,읍면동일련번호,지번부번 등의 정보를 포함하고 있습니다.
Author서울특별시 성동구
URLhttps://www.data.go.kr/data/15084489/fileData.do

Alerts

시도 has constant value ""Constant
시군구 has constant value ""Constant
Dataset has 8 (0.1%) duplicate rowsDuplicates
우편번호 is highly overall correlated with 법정동코드 and 2 other fieldsHigh correlation
법정동코드 is highly overall correlated with 우편번호 and 2 other fieldsHigh correlation
지번본번 is highly overall correlated with 법정동명 and 1 other fieldsHigh correlation
법정동명 is highly overall correlated with 우편번호 and 3 other fieldsHigh correlation
행정동명 is highly overall correlated with 우편번호 and 3 other fieldsHigh correlation
지하여부 is highly imbalanced (99.5%)Imbalance
산여부 is highly imbalanced (95.9%)Imbalance
건물번호부번 has 5331 (53.3%) zerosZeros
지번부번 has 1562 (15.6%) zerosZeros

Reproduction

Analysis started2023-12-12 00:26:32.560196
Analysis finished2023-12-12 00:26:41.749332
Duration9.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4757.9717
Minimum4700
Maximum4809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:26:41.840296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4700
5-th percentile4705
Q14725
median4762
Q34788
95-th percentile4805
Maximum4809
Range109
Interquartile range (IQR)63

Descriptive statistics

Standard deviation34.089328
Coefficient of variation (CV)0.0071646766
Kurtosis-1.370653
Mean4757.9717
Median Absolute Deviation (MAD)33
Skewness-0.18054423
Sum47579717
Variance1162.0823
MonotonicityNot monotonic
2023-12-12T09:26:41.972051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4801 425
 
4.2%
4775 420
 
4.2%
4805 414
 
4.1%
4714 355
 
3.5%
4710 334
 
3.3%
4761 314
 
3.1%
4803 294
 
2.9%
4704 293
 
2.9%
4774 276
 
2.8%
4804 274
 
2.7%
Other values (98) 6601
66.0%
ValueCountFrequency (%)
4700 9
 
0.1%
4701 8
 
0.1%
4702 8
 
0.1%
4703 7
 
0.1%
4704 293
2.9%
4705 198
2.0%
4706 72
 
0.7%
4707 183
1.8%
4708 187
1.9%
4709 96
 
1.0%
ValueCountFrequency (%)
4809 4
 
< 0.1%
4808 74
 
0.7%
4807 3
 
< 0.1%
4806 8
 
0.1%
4805 414
4.1%
4804 274
2.7%
4803 294
2.9%
4802 34
 
0.3%
4801 425
4.2%
4800 208
2.1%

시도
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 10000
100.0%

Length

2023-12-12T09:26:42.110116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:42.490194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 10000
100.0%

시군구
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
성동구 10000
100.0%

Length

2023-12-12T09:26:42.579953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:42.686668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성동구 10000
100.0%

도로명코드
Real number (ℝ)

Distinct494
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1200395 × 1011
Minimum1.12002 × 1011
Maximum1.1200486 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:26:42.798605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.12002 × 1011
5-th percentile1.1200301 × 1011
Q11.1200411 × 1011
median1.1200411 × 1011
Q31.1200411 × 1011
95-th percentile1.1200411 × 1011
Maximum1.1200486 × 1011
Range2859664
Interquartile range (IQR)303.25

Descriptive statistics

Standard deviation392824.27
Coefficient of variation (CV)3.5072357 × 10-6
Kurtosis4.3305538
Mean1.1200395 × 1011
Median Absolute Deviation (MAD)151
Skewness-2.3185066
Sum1.1200395 × 1015
Variance1.5431091 × 1011
MonotonicityNot monotonic
2023-12-12T09:26:42.949138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112003005014 189
 
1.9%
112004109340 176
 
1.8%
112003005011 160
 
1.6%
112003005030 131
 
1.3%
112003005024 118
 
1.2%
112004109413 114
 
1.1%
112003005009 104
 
1.0%
112003103003 101
 
1.0%
112004109487 93
 
0.9%
112003103010 91
 
0.9%
Other values (484) 8723
87.2%
ValueCountFrequency (%)
112002000008 53
 
0.5%
112003000001 88
0.9%
112003000002 61
 
0.6%
112003005001 14
 
0.1%
112003005009 104
1.0%
112003005011 160
1.6%
112003005014 189
1.9%
112003005024 118
1.2%
112003005030 131
1.3%
112003005033 28
 
0.3%
ValueCountFrequency (%)
112004859672 1
 
< 0.1%
112004859671 2
 
< 0.1%
112004109549 29
0.3%
112004109547 2
 
< 0.1%
112004109545 1
 
< 0.1%
112004109544 5
 
0.1%
112004109543 8
 
0.1%
112004109542 6
 
0.1%
112004109541 15
0.1%
112004109540 19
0.2%

도로명
Text

Distinct494
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T09:26:43.271839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.4932
Min length3

Characters and Unicode

Total characters54932
Distinct characters83
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

Unique10 ?
Unique (%)0.1%

Sample

1st row금호산10길
2nd row마장로29길
3rd row용답1길
4th row용답23가길
5th row용답중앙11길
ValueCountFrequency (%)
독서당로 189
 
1.9%
성덕정길 176
 
1.8%
왕십리로 160
 
1.6%
고산자로 131
 
1.3%
광나루로 118
 
1.2%
연무장길 114
 
1.1%
마장로 104
 
1.0%
성수이로 101
 
1.0%
용답길 93
 
0.9%
뚝섬로 91
 
0.9%
Other values (484) 8723
87.2%
2023-12-12T09:26:43.830072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 8605
 
15.7%
로 5302
 
9.7%
1 3848
 
7.0%
2 2196
 
4.0%
가 1396
 
2.5%
3 1196
 
2.2%
무 1164
 
2.1%
5 1157
 
2.1%
성 994
 
1.8%
답 953
 
1.7%
Other values (73) 28121
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42732
77.8%
Decimal Number 12200
 
22.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 8605
20.1%
로 5302
 
12.4%
가 1396
 
3.3%
무 1164
 
2.7%
성 994
 
2.3%
답 953
 
2.2%
용 953
 
2.2%
장 917
 
2.1%
마 904
 
2.1%
산 887
 
2.1%
Other values (63) 20657
48.3%
Decimal Number
ValueCountFrequency (%)
1 3848
31.5%
2 2196
18.0%
3 1196
 
9.8%
5 1157
 
9.5%
9 803
 
6.6%
4 766
 
6.3%
6 675
 
5.5%
0 600
 
4.9%
7 543
 
4.5%
8 416
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42732
77.8%
Common 12200
 
22.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 8605
20.1%
로 5302
 
12.4%
가 1396
 
3.3%
무 1164
 
2.7%
성 994
 
2.3%
답 953
 
2.2%
용 953
 
2.2%
장 917
 
2.1%
마 904
 
2.1%
산 887
 
2.1%
Other values (63) 20657
48.3%
Common
ValueCountFrequency (%)
1 3848
31.5%
2 2196
18.0%
3 1196
 
9.8%
5 1157
 
9.5%
9 803
 
6.6%
4 766
 
6.3%
6 675
 
5.5%
0 600
 
4.9%
7 543
 
4.5%
8 416
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42732
77.8%
ASCII 12200
 
22.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 8605
20.1%
로 5302
 
12.4%
가 1396
 
3.3%
무 1164
 
2.7%
성 994
 
2.3%
답 953
 
2.2%
용 953
 
2.2%
장 917
 
2.1%
마 904
 
2.1%
산 887
 
2.1%
Other values (63) 20657
48.3%
ASCII
ValueCountFrequency (%)
1 3848
31.5%
2 2196
18.0%
3 1196
 
9.8%
5 1157
 
9.5%
9 803
 
6.6%
4 766
 
6.3%
6 675
 
5.5%
0 600
 
4.9%
7 543
 
4.5%
8 416
 
3.4%

지하여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9996 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9996
> 99.9%
1 4
 
< 0.1%

Length

2023-12-12T09:26:43.971570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:44.083710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9996
> 99.9%
1 4
 
< 0.1%

건물번호본번
Real number (ℝ)

Distinct413
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.5685
Minimum1
Maximum598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:26:44.186119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q19
median18
Q340
95-th percentile284
Maximum598
Range597
Interquartile range (IQR)31

Descriptive statistics

Standard deviation82.302556
Coefficient of variation (CV)1.6945666
Kurtosis7.2954877
Mean48.5685
Median Absolute Deviation (MAD)12
Skewness2.7905105
Sum485685
Variance6773.7107
MonotonicityNot monotonic
2023-12-12T09:26:44.328899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 422
 
4.2%
5 345
 
3.5%
8 340
 
3.4%
10 336
 
3.4%
9 330
 
3.3%
12 323
 
3.2%
4 322
 
3.2%
7 308
 
3.1%
3 300
 
3.0%
11 295
 
2.9%
Other values (403) 6679
66.8%
ValueCountFrequency (%)
1 164
 
1.6%
2 177
1.8%
3 300
3.0%
4 322
3.2%
5 345
3.5%
6 422
4.2%
7 308
3.1%
8 340
3.4%
9 330
3.3%
10 336
3.4%
ValueCountFrequency (%)
598 1
 
< 0.1%
530 1
 
< 0.1%
500 1
 
< 0.1%
484 1
 
< 0.1%
474 1
 
< 0.1%
466 1
 
< 0.1%
462 1
 
< 0.1%
452 1
 
< 0.1%
450 1
 
< 0.1%
448 3
< 0.1%

건물번호부번
Real number (ℝ)

ZEROS 

Distinct99
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1221
Minimum0
Maximum145
Zeros5331
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:26:44.473713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile25
Maximum145
Range145
Interquartile range (IQR)6

Descriptive statistics

Standard deviation10.806316
Coefficient of variation (CV)2.1097433
Kurtosis26.689622
Mean5.1221
Median Absolute Deviation (MAD)0
Skewness4.1513279
Sum51221
Variance116.77647
MonotonicityNot monotonic
2023-12-12T09:26:44.637837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5331
53.3%
1 1298
 
13.0%
2 242
 
2.4%
7 212
 
2.1%
5 211
 
2.1%
8 210
 
2.1%
6 205
 
2.1%
9 179
 
1.8%
10 171
 
1.7%
12 143
 
1.4%
Other values (89) 1798
 
18.0%
ValueCountFrequency (%)
0 5331
53.3%
1 1298
 
13.0%
2 242
 
2.4%
3 142
 
1.4%
4 96
 
1.0%
5 211
 
2.1%
6 205
 
2.1%
7 212
 
2.1%
8 210
 
2.1%
9 179
 
1.8%
ValueCountFrequency (%)
145 1
< 0.1%
141 1
< 0.1%
132 1
< 0.1%
126 1
< 0.1%
124 1
< 0.1%
120 1
< 0.1%
116 1
< 0.1%
102 1
< 0.1%
101 2
< 0.1%
98 1
< 0.1%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1200112 × 109
Minimum1.1200101 × 109
Maximum1.1200122 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:26:44.767228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1200101 × 109
5-th percentile1.1200103 × 109
Q11.1200106 × 109
median1.1200112 × 109
Q31.1200115 × 109
95-th percentile1.1200122 × 109
Maximum1.1200122 × 109
Range2100
Interquartile range (IQR)900

Descriptive statistics

Standard deviation581.71187
Coefficient of variation (CV)5.1938042 × 10-7
Kurtosis-0.94188352
Mean1.1200112 × 109
Median Absolute Deviation (MAD)500
Skewness0.12992926
Sum1.1200112 × 1013
Variance338388.69
MonotonicityNot monotonic
2023-12-12T09:26:44.938267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1120011500 1698
17.0%
1120011400 1326
13.3%
1120010500 1174
11.7%
1120012200 1067
10.7%
1120010700 790
7.9%
1120011800 673
 
6.7%
1120011100 562
 
5.6%
1120010200 484
 
4.8%
1120010600 425
 
4.2%
1120011200 384
 
3.8%
Other values (7) 1417
14.2%
ValueCountFrequency (%)
1120010100 12
 
0.1%
1120010200 484
4.8%
1120010300 253
 
2.5%
1120010400 286
 
2.9%
1120010500 1174
11.7%
1120010600 425
 
4.2%
1120010700 790
7.9%
1120010800 241
 
2.4%
1120010900 207
 
2.1%
1120011000 247
 
2.5%
ValueCountFrequency (%)
1120012200 1067
10.7%
1120011800 673
 
6.7%
1120011500 1698
17.0%
1120011400 1326
13.3%
1120011300 171
 
1.7%
1120011200 384
 
3.8%
1120011100 562
 
5.6%
1120011000 247
 
2.5%
1120010900 207
 
2.1%
1120010800 241
 
2.4%

법정동명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
성수동2가
1698 
성수동1가
1326 
마장동
1174 
용답동
1067 
행당동
790 
Other values (12)
3945 

Length

Max length5
Median length3
Mean length3.984
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금호동3가
2nd row마장동
3rd row용답동
4th row용답동
5th row용답동

Common Values

ValueCountFrequency (%)
성수동2가 1698
17.0%
성수동1가 1326
13.3%
마장동 1174
11.7%
용답동 1067
10.7%
행당동 790
7.9%
송정동 673
 
6.7%
금호동3가 562
 
5.6%
하왕십리동 484
 
4.8%
사근동 425
 
4.2%
금호동4가 384
 
3.8%
Other values (7) 1417
14.2%

Length

2023-12-12T09:26:45.075404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성수동2가 1698
17.0%
성수동1가 1326
13.3%
마장동 1174
11.7%
용답동 1067
10.7%
행당동 790
7.9%
송정동 673
 
6.7%
금호동3가 562
 
5.6%
하왕십리동 484
 
4.8%
사근동 425
 
4.2%
금호동4가 384
 
3.8%
Other values (7) 1417
14.2%

행정동명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
마장동
1149 
용답동
1068 
성수2가제1동
986 
금호2.3가동
796 
성수1가제2동
685 
Other values (13)
5316 

Length

Max length7
Median length6
Mean length5.0416
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금호2.3가동
2nd row마장동
3rd row용답동
4th row용답동
5th row용답동

Common Values

ValueCountFrequency (%)
마장동 1149
11.5%
용답동 1068
10.7%
성수2가제1동 986
9.9%
금호2.3가동 796
 
8.0%
성수1가제2동 685
 
6.9%
성수2가제3동 669
 
6.7%
송정동 660
 
6.6%
사근동 656
 
6.6%
성수1가제1동 624
 
6.2%
왕십리도선동 559
 
5.6%
Other values (8) 2148
21.5%

Length

2023-12-12T09:26:45.219426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
마장동 1149
11.5%
용답동 1068
10.7%
성수2가제1동 986
9.9%
금호2.3가동 796
 
8.0%
성수1가제2동 685
 
6.9%
성수2가제3동 669
 
6.7%
송정동 660
 
6.6%
사근동 656
 
6.6%
성수1가제1동 624
 
6.2%
왕십리도선동 559
 
5.6%
Other values (8) 2148
21.5%

산여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9956 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9956
99.6%
1 44
 
0.4%

Length

2023-12-12T09:26:45.335936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:26:45.443293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9956
99.6%
1 44
 
0.4%

지번본번
Real number (ℝ)

HIGH CORRELATION 

Distinct1167
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean380.9592
Minimum1
Maximum1836
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:26:45.564746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q1128
median299
Q3532
95-th percentile995.2
Maximum1836
Range1835
Interquartile range (IQR)404

Descriptive statistics

Standard deviation329.8582
Coefficient of variation (CV)0.86586228
Kurtosis1.965361
Mean380.9592
Median Absolute Deviation (MAD)202
Skewness1.3555803
Sum3809592
Variance108806.43
MonotonicityNot monotonic
2023-12-12T09:26:45.693980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
656 361
 
3.6%
73 280
 
2.8%
13 178
 
1.8%
66 145
 
1.5%
299 125
 
1.2%
289 104
 
1.0%
265 100
 
1.0%
193 99
 
1.0%
685 98
 
1.0%
301 97
 
1.0%
Other values (1157) 8413
84.1%
ValueCountFrequency (%)
1 73
0.7%
2 10
 
0.1%
3 25
 
0.2%
4 21
 
0.2%
5 15
 
0.1%
6 14
 
0.1%
7 20
 
0.2%
8 33
0.3%
9 37
0.4%
10 19
 
0.2%
ValueCountFrequency (%)
1836 1
< 0.1%
1835 1
< 0.1%
1828 1
< 0.1%
1824 1
< 0.1%
1823 1
< 0.1%
1822 1
< 0.1%
1818 1
< 0.1%
1815 2
< 0.1%
1806 1
< 0.1%
1805 1
< 0.1%

읍면동일련번호
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3236
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:26:45.797718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum7
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.89328766
Coefficient of variation (CV)0.67489246
Kurtosis16.073827
Mean1.3236
Median Absolute Deviation (MAD)0
Skewness3.7779145
Sum13236
Variance0.79796284
MonotonicityNot monotonic
2023-12-12T09:26:45.908698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 8214
82.1%
2 1120
 
11.2%
3 299
 
3.0%
4 144
 
1.4%
6 94
 
0.9%
5 79
 
0.8%
7 50
 
0.5%
ValueCountFrequency (%)
1 8214
82.1%
2 1120
 
11.2%
3 299
 
3.0%
4 144
 
1.4%
5 79
 
0.8%
6 94
 
0.9%
7 50
 
0.5%
ValueCountFrequency (%)
7 50
 
0.5%
6 94
 
0.9%
5 79
 
0.8%
4 144
 
1.4%
3 299
 
3.0%
2 1120
 
11.2%
1 8214
82.1%

지번부번
Real number (ℝ)

ZEROS 

Distinct871
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.5547
Minimum0
Maximum2004
Zeros1562
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T09:26:46.059844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q351
95-th percentile513.05
Maximum2004
Range2004
Interquartile range (IQR)49

Descriptive statistics

Standard deviation214.82485
Coefficient of variation (CV)2.5710685
Kurtosis22.370131
Mean83.5547
Median Absolute Deviation (MAD)11
Skewness4.3443121
Sum835547
Variance46149.718
MonotonicityNot monotonic
2023-12-12T09:26:46.226031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1562
 
15.6%
1 761
 
7.6%
2 560
 
5.6%
3 385
 
3.9%
4 318
 
3.2%
5 265
 
2.6%
6 249
 
2.5%
8 226
 
2.3%
7 203
 
2.0%
9 194
 
1.9%
Other values (861) 5277
52.8%
ValueCountFrequency (%)
0 1562
15.6%
1 761
7.6%
2 560
 
5.6%
3 385
 
3.9%
4 318
 
3.2%
5 265
 
2.6%
6 249
 
2.5%
7 203
 
2.0%
8 226
 
2.3%
9 194
 
1.9%
ValueCountFrequency (%)
2004 1
< 0.1%
2002 1
< 0.1%
1994 1
< 0.1%
1960 1
< 0.1%
1928 1
< 0.1%
1927 1
< 0.1%
1887 1
< 0.1%
1869 1
< 0.1%
1868 1
< 0.1%
1866 1
< 0.1%

Interactions

2023-12-12T09:26:40.597396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:34.584817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:35.389483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:36.292724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:37.470010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.328856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.150196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.855614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:40.728453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:34.682339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:35.482718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:36.387007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:37.591335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.417025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.234563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.939806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:40.843072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:34.800733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:35.581241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:36.788128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:37.696239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.507597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.317403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:40.032517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:40.933506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:34.898349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:35.688564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:36.895908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:37.801064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.622938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.409493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:40.126648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:41.047878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:34.993477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:35.812178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:37.014942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:37.912533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.738653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.517707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:40.236509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:41.140985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:35.082624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:35.908812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:37.118708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.012896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.824193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.594130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:40.317807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:41.236302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:35.179443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:36.024335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:37.228635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.110919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.929675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.676268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:40.403261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:41.343831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:35.292532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:36.169858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:37.350969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:38.228057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.039908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:39.767739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:26:40.496350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:26:46.335768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호도로명코드지하여부건물번호본번건물번호부번법정동코드법정동명행정동명산여부지번본번읍면동일련번호지번부번
우편번호1.0000.1580.0000.3400.1440.9030.9250.9440.1410.8040.3550.453
도로명코드0.1581.0000.0610.6600.0000.1400.2440.2330.0030.1230.5300.024
지하여부0.0000.0611.0000.0830.0000.0000.0000.0410.0000.0000.0770.039
건물번호본번0.3400.6600.0831.0000.0860.2310.4180.3620.0000.2190.6330.111
건물번호부번0.1440.0000.0000.0861.0000.1030.1500.1300.0350.0940.1210.058
법정동코드0.9030.1400.0000.2310.1031.0001.0000.9960.1140.7360.3830.398
법정동명0.9250.2440.0000.4180.1501.0001.0000.9970.2250.8420.5580.490
행정동명0.9440.2330.0410.3620.1300.9960.9971.0000.1250.8430.5600.535
산여부0.1410.0030.0000.0000.0350.1140.2250.1251.0000.1240.0000.000
지번본번0.8040.1230.0000.2190.0940.7360.8420.8430.1241.0000.2800.451
읍면동일련번호0.3550.5300.0770.6330.1210.3830.5580.5600.0000.2801.0000.160
지번부번0.4530.0240.0390.1110.0580.3980.4900.5350.0000.4510.1601.000
2023-12-12T09:26:46.484312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산여부법정동명지하여부행정동명
산여부1.0000.2020.0000.112
법정동명0.2021.0000.0000.877
지하여부0.0000.0001.0000.037
행정동명0.1120.8770.0371.000
2023-12-12T09:26:46.600639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호도로명코드건물번호본번건물번호부번법정동코드지번본번읍면동일련번호지번부번지하여부법정동명행정동명산여부
우편번호1.0000.210-0.079-0.0790.891-0.486-0.1450.3010.0000.7070.7660.108
도로명코드0.2101.000-0.3040.0070.216-0.146-0.3560.1180.0750.1570.1520.000
건물번호본번-0.079-0.3041.000-0.014-0.0520.0370.449-0.0670.0640.1760.1530.000
건물번호부번-0.0790.007-0.0141.000-0.0540.0740.0220.0160.0000.0590.0510.030
법정동코드0.8910.216-0.052-0.0541.000-0.413-0.0990.1740.0001.0000.9790.114
지번본번-0.486-0.1460.0370.074-0.4131.0000.036-0.1080.0000.5320.5340.095
읍면동일련번호-0.145-0.3560.4490.022-0.0990.0361.000-0.0910.0820.2910.2920.000
지번부번0.3010.118-0.0670.0160.174-0.108-0.0911.0000.0300.2140.2410.000
지하여부0.0000.0750.0640.0000.0000.0000.0820.0301.0000.0000.0370.000
법정동명0.7070.1570.1760.0591.0000.5320.2910.2140.0001.0000.8770.202
행정동명0.7660.1520.1530.0510.9790.5340.2920.2410.0370.8771.0000.112
산여부0.1080.0000.0000.0300.1140.0950.0000.0000.0000.2020.1121.000

Missing values

2023-12-12T09:26:41.490956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:26:41.660156image/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

우편번호시도시군구도로명코드도로명지하여부건물번호본번건물번호부번법정동코드법정동명행정동명산여부지번본번읍면동일련번호지번부번
15964726서울특별시성동구112004109061금호산10길09361120011100금호동3가금호2.3가동0116013
38054704서울특별시성동구112004109173마장로29길053401120010500마장동마장동046611
124774803서울특별시성동구112004109464용답1길07181120012200용답동용답동012211
128144805서울특별시성동구112004109466용답23가길02001120012200용답동용답동03613
130914803서울특별시성동구112004109489용답중앙11길0901120012200용답동용답동090111
49454762서울특별시성동구112004109297사근동길02091120010600사근동사근동03091146
116534733서울특별시성동구112004109229매봉길0601120011300옥수동옥수동0517217
87104776서울특별시성동구112004109133둘레17길0311120011500성수동2가성수2가제1동064611
70034774서울특별시성동구112004109340성덕정길03611120011400성수동1가성수1가제1동017513
28364704서울특별시성동구112004109526청계천로10다길05001120010500마장동<NA>0464111
우편번호시도시군구도로명코드도로명지하여부건물번호본번건물번호부번법정동코드법정동명행정동명산여부지번본번읍면동일련번호지번부번
27974758서울특별시성동구112003103002마조로07201120010500마장동마장동079611
145164714서울특별시성동구112004109280무학봉길064141120010200하왕십리동왕십리제2동0979113
62744774서울특별시성동구112004109340성덕정길03901120011400성수동1가성수1가제1동09010
106004801서울특별시성동구112004109371송정12나길04601120011800송정동송정동0731888
108064801서울특별시성동구112004109373송정12라길03801120011800송정동송정동0731712
50004761서울특별시성동구112004109291사근동7길01201120010600사근동사근동021214
26494708서울특별시성동구112004109249무학로2나길02401120010400도선동왕십리도선동019410
73484788서울특별시성동구112004109421왕십리로14가길02161120011400성수동1가성수1가제2동06561794
18544725서울특별시성동구112004109125동호로2길01791120011200금호동4가금호4가동0139911
24214708서울특별시성동구112004109439왕십리로24길01511120010400도선동왕십리도선동019922

Duplicate rows

Most frequently occurring

우편번호시도시군구도로명코드도로명지하여부건물번호본번건물번호부번법정동코드법정동명행정동명산여부지번본번읍면동일련번호지번부번# duplicates
04708서울특별시성동구112004109439왕십리로24길02711120010300홍익동왕십리도선동0274162
14714서울특별시성동구112004109280무학봉길06801120010200하왕십리동왕십리제2동0979112
24759서울특별시성동구112004109192마조로11길01801120010500마장동마장동04051452
34776서울특별시성동구112004109332성덕정21길0301120011500성수동2가성수2가제1동0572122
44785서울특별시성동구112004109153뚝섬로17가길04901120011500성수동2가성수2가제1동02691622
54792서울특별시성동구112003005024광나루로021201120011500성수동2가성수2가제3동029922362
64792서울특별시성동구112003005024광나루로021401120011500성수동2가성수2가제3동02992732
74794서울특별시성동구112003000002아차산로010701120011500성수동2가성수2가제3동0300232