Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory585.9 KiB
Average record size in memory60.0 B

Variable types

Numeric4
Text2

Dataset

Description도립거창대학교 시스템 DB 내 우편번호(신주소)데이터입니다 .(번호, 우편번호, 주소, 시도 데이터를 포함하고 있습니다.)
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15049414

Alerts

우편번호1 is highly overall correlated with 시도High correlation
시도 is highly overall correlated with 우편번호1High correlation
번호 has unique valuesUnique

Reproduction

Analysis started2024-04-16 22:38:12.028072
Analysis finished2024-04-16 22:38:14.585891
Duration2.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25063.939
Minimum5
Maximum492019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T07:38:14.653738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile2572.85
Q112446
median24861
Q337119.5
95-th percentile46956.45
Maximum492019
Range492014
Interquartile range (IQR)24673.5

Descriptive statistics

Standard deviation18521.1
Coefficient of variation (CV)0.73895409
Kurtosis245.06809
Mean25063.939
Median Absolute Deviation (MAD)12330
Skewness10.058258
Sum2.5063939 × 108
Variance3.4303116 × 108
MonotonicityNot monotonic
2024-04-17T07:38:14.830448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16316 1
 
< 0.1%
46470 1
 
< 0.1%
33807 1
 
< 0.1%
16824 1
 
< 0.1%
18699 1
 
< 0.1%
36202 1
 
< 0.1%
31911 1
 
< 0.1%
2910 1
 
< 0.1%
44826 1
 
< 0.1%
31626 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
7 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
15 1
< 0.1%
21 1
< 0.1%
22 1
< 0.1%
26 1
< 0.1%
37 1
< 0.1%
ValueCountFrequency (%)
492019 1
< 0.1%
492018 1
< 0.1%
492015 1
< 0.1%
492008 1
< 0.1%
492005 1
< 0.1%
429022 1
< 0.1%
429019 1
< 0.1%
49199 1
< 0.1%
49194 1
< 0.1%
49181 1
< 0.1%

우편번호1
Real number (ℝ)

HIGH CORRELATION 

Distinct254
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460.7341
Minimum100
Maximum799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T07:38:14.988145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile133
Q1325
median472
Q3617
95-th percentile755
Maximum799
Range699
Interquartile range (IQR)292

Descriptive statistics

Standard deviation198.20943
Coefficient of variation (CV)0.43020352
Kurtosis-1.0134427
Mean460.7341
Median Absolute Deviation (MAD)145
Skewness-0.26832714
Sum4607341
Variance39286.979
MonotonicityNot monotonic
2024-04-17T07:38:15.196999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 117
 
1.2%
702 96
 
1.0%
139 95
 
0.9%
706 88
 
0.9%
472 85
 
0.9%
151 85
 
0.9%
100 82
 
0.8%
704 80
 
0.8%
363 79
 
0.8%
132 76
 
0.8%
Other values (244) 9117
91.2%
ValueCountFrequency (%)
100 82
0.8%
110 55
0.5%
120 49
0.5%
121 69
0.7%
122 50
0.5%
130 47
0.5%
131 56
0.6%
132 76
0.8%
133 39
0.4%
134 76
0.8%
ValueCountFrequency (%)
799 3
 
< 0.1%
791 59
0.6%
790 41
0.4%
780 68
0.7%
770 63
0.6%
769 56
0.6%
767 21
 
0.2%
766 38
0.4%
764 26
 
0.3%
763 21
 
0.2%

우편번호2
Real number (ℝ)

Distinct577
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean760.2692
Minimum3
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T07:38:15.374206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile112
Q1762
median821
Q3862
95-th percentile931
Maximum999
Range996
Interquartile range (IQR)100

Descriptive statistics

Standard deviation213.61543
Coefficient of variation (CV)0.28097341
Kurtosis4.9617542
Mean760.2692
Median Absolute Deviation (MAD)49
Skewness-2.430658
Sum7602692
Variance45631.553
MonotonicityNot monotonic
2024-04-17T07:38:15.559620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
821 150
 
1.5%
822 146
 
1.5%
841 137
 
1.4%
832 128
 
1.3%
831 127
 
1.3%
811 125
 
1.2%
842 116
 
1.2%
812 113
 
1.1%
801 113
 
1.1%
804 113
 
1.1%
Other values (567) 8732
87.3%
ValueCountFrequency (%)
3 4
 
< 0.1%
10 23
0.2%
11 11
0.1%
12 10
0.1%
13 9
 
0.1%
14 3
 
< 0.1%
15 1
 
< 0.1%
16 2
 
< 0.1%
17 3
 
< 0.1%
18 3
 
< 0.1%
ValueCountFrequency (%)
999 1
 
< 0.1%
998 1
 
< 0.1%
997 1
 
< 0.1%
996 1
 
< 0.1%
992 3
< 0.1%
991 4
< 0.1%
990 3
< 0.1%
989 1
 
< 0.1%
988 2
< 0.1%
987 2
< 0.1%

우편번호
Text

Distinct8500
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T07:38:15.847101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9118
Min length4

Characters and Unicode

Total characters69118
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7313 ?
Unique (%)73.1%

Sample

1st row719-814
2nd row13461
3rd row415-854
4th row157-854
5th row441-855
ValueCountFrequency (%)
611-839 8
 
0.1%
536-911 6
 
0.1%
138-873 6
 
0.1%
537-873 6
 
0.1%
487-839 6
 
0.1%
601-803 6
 
0.1%
535-841 5
 
< 0.1%
413-911 5
 
< 0.1%
395-812 5
 
< 0.1%
703-816 5
 
< 0.1%
Other values (8490) 9942
99.4%
2024-04-17T07:38:16.242695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 9561
13.8%
8 7586
11.0%
1 7211
10.4%
0 7063
10.2%
7 6341
9.2%
3 6294
9.1%
2 5643
8.2%
5 5417
7.8%
6 5368
7.8%
4 5319
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59557
86.2%
Dash Punctuation 9561
 
13.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 7586
12.7%
1 7211
12.1%
0 7063
11.9%
7 6341
10.6%
3 6294
10.6%
2 5643
9.5%
5 5417
9.1%
6 5368
9.0%
4 5319
8.9%
9 3315
5.6%
Dash Punctuation
ValueCountFrequency (%)
- 9561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69118
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 9561
13.8%
8 7586
11.0%
1 7211
10.4%
0 7063
10.2%
7 6341
9.2%
3 6294
9.1%
2 5643
8.2%
5 5417
7.8%
6 5368
7.8%
4 5319
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69118
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 9561
13.8%
8 7586
11.0%
1 7211
10.4%
0 7063
10.2%
7 6341
9.2%
3 6294
9.1%
2 5643
8.2%
5 5417
7.8%
6 5368
7.8%
4 5319
7.7%

주소
Text

Distinct8399
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-17T07:38:16.535126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length38
Mean length14.8534
Min length8

Characters and Unicode

Total characters148534
Distinct characters571
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7585 ?
Unique (%)75.8%

Sample

1st row경북 성주군 초전면 월곡2리
2nd row서울 강동구 둔촌1동
3rd row경기 김포시 대곶면 상마리
4th row서울 강서구 방화2동
5th row경기 수원시 권선구 서둔동
ValueCountFrequency (%)
서울 1577
 
4.2%
경기 1576
 
4.2%
경북 973
 
2.6%
전남 798
 
2.1%
경남 728
 
1.9%
부산 654
 
1.7%
충남 618
 
1.6%
전북 579
 
1.5%
강원 526
 
1.4%
충북 509
 
1.3%
Other values (8564) 29317
77.4%
2024-04-17T07:38:16.949776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30113
 
20.3%
동 7104
 
4.8%
구 5389
 
3.6%
시 4131
 
2.8%
리 4041
 
2.7%
경 3613
 
2.4%
남 3427
 
2.3%
면 3299
 
2.2%
산 2948
 
2.0%
1 2935
 
2.0%
Other values (561) 81534
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109426
73.7%
Space Separator 30113
 
20.3%
Decimal Number 8119
 
5.5%
Dash Punctuation 668
 
0.4%
Uppercase Letter 131
 
0.1%
Close Punctuation 28
 
< 0.1%
Open Punctuation 28
 
< 0.1%
Other Punctuation 13
 
< 0.1%
Lowercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7104
 
6.5%
구 5389
 
4.9%
시 4131
 
3.8%
리 4041
 
3.7%
경 3613
 
3.3%
남 3427
 
3.1%
면 3299
 
3.0%
산 2948
 
2.7%
서 2883
 
2.6%
군 2825
 
2.6%
Other values (525) 69766
63.8%
Uppercase Letter
ValueCountFrequency (%)
K 23
17.6%
S 20
15.3%
L 18
13.7%
G 18
13.7%
T 14
10.7%
C 11
8.4%
A 7
 
5.3%
B 6
 
4.6%
D 3
 
2.3%
N 2
 
1.5%
Other values (8) 9
 
6.9%
Decimal Number
ValueCountFrequency (%)
1 2935
36.1%
2 1702
21.0%
0 1038
 
12.8%
3 865
 
10.7%
4 509
 
6.3%
5 299
 
3.7%
6 275
 
3.4%
8 185
 
2.3%
7 179
 
2.2%
9 132
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 8
61.5%
· 3
 
23.1%
& 2
 
15.4%
Space Separator
ValueCountFrequency (%)
30113
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 668
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109426
73.7%
Common 38969
 
26.2%
Latin 139
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7104
 
6.5%
구 5389
 
4.9%
시 4131
 
3.8%
리 4041
 
3.7%
경 3613
 
3.3%
남 3427
 
3.1%
면 3299
 
3.0%
산 2948
 
2.7%
서 2883
 
2.6%
군 2825
 
2.6%
Other values (525) 69766
63.8%
Latin
ValueCountFrequency (%)
K 23
16.5%
S 20
14.4%
L 18
12.9%
G 18
12.9%
T 14
10.1%
C 11
7.9%
e 8
 
5.8%
A 7
 
5.0%
B 6
 
4.3%
D 3
 
2.2%
Other values (9) 11
7.9%
Common
ValueCountFrequency (%)
30113
77.3%
1 2935
 
7.5%
2 1702
 
4.4%
0 1038
 
2.7%
3 865
 
2.2%
- 668
 
1.7%
4 509
 
1.3%
5 299
 
0.8%
6 275
 
0.7%
8 185
 
0.5%
Other values (7) 380
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109426
73.7%
ASCII 39105
 
26.3%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30113
77.0%
1 2935
 
7.5%
2 1702
 
4.4%
0 1038
 
2.7%
3 865
 
2.2%
- 668
 
1.7%
4 509
 
1.3%
5 299
 
0.8%
6 275
 
0.7%
8 185
 
0.5%
Other values (25) 516
 
1.3%
Hangul
ValueCountFrequency (%)
동 7104
 
6.5%
구 5389
 
4.9%
시 4131
 
3.8%
리 4041
 
3.7%
경 3613
 
3.3%
남 3427
 
3.1%
면 3299
 
3.0%
산 2948
 
2.7%
서 2883
 
2.6%
군 2825
 
2.6%
Other values (525) 69766
63.8%
None
ValueCountFrequency (%)
· 3
100.0%

시도
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.0519
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-17T07:38:17.058878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11
Q315
95-th percentile18
Maximum19
Range18
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.1491127
Coefficient of variation (CV)0.61173636
Kurtosis-1.4123621
Mean10.0519
Median Absolute Deviation (MAD)6
Skewness-0.31620787
Sum100519
Variance37.811588
MonotonicityNot monotonic
2024-04-17T07:38:17.167789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 1577
15.8%
11 1576
15.8%
18 973
9.7%
15 798
8.0%
17 728
7.3%
2 654
6.5%
13 618
 
6.2%
16 579
 
5.8%
12 526
 
5.3%
14 509
 
5.1%
Other values (6) 1462
14.6%
ValueCountFrequency (%)
1 1577
15.8%
2 654
6.5%
3 502
 
5.0%
4 290
 
2.9%
5 241
 
2.4%
6 192
 
1.9%
7 152
 
1.5%
11 1576
15.8%
12 526
 
5.3%
13 618
 
6.2%
ValueCountFrequency (%)
19 85
 
0.9%
18 973
9.7%
17 728
7.3%
16 579
 
5.8%
15 798
8.0%
14 509
 
5.1%
13 618
 
6.2%
12 526
 
5.3%
11 1576
15.8%
7 152
 
1.5%

Interactions

2024-04-17T07:38:14.062662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.011822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.347946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.714539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:14.141708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.100825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.426810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.797587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:14.225247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.195704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.532324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.899228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:14.298657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.270032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.622016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T07:38:13.982234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T07:38:17.241300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호우편번호1우편번호2시도
번호1.0000.0530.1080.072
우편번호10.0531.0000.3250.890
우편번호20.1080.3251.0000.280
시도0.0720.8900.2801.000
2024-04-17T07:38:17.315972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호우편번호1우편번호2시도
번호1.000-0.1790.115-0.008
우편번호1-0.1791.0000.0700.593
우편번호20.1150.0701.0000.241
시도-0.0080.5930.2411.000

Missing values

2024-04-17T07:38:14.398633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T07:38:14.535185image/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

번호우편번호1우편번호2우편번호주소시도
4544516316719814719-814경북 성주군 초전면 월곡2리18
30279273491346113461서울 강동구 둔촌1동1
12754149415854415-854경기 김포시 대곶면 상마리11
2832328041157854157-854서울 강서구 방화2동1
34185739441855441-855경기 수원시 권선구 서둔동11
3667320990711820711-820대구 달성군 하빈면3
3199127109135794135-794서울 강남구 압구정2동 한양아파트 1-111
1311745828339801339-801충남 연기군 조치원읍 교리13
1007446813350892350-892충남 홍성군 장곡면 광성리13
953946735350870350-870충남 홍성군 결성면13
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