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.)
URLhttps://www.data.go.kr/data/15049414/fileData.do

Alerts

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

Reproduction

Analysis started2023-12-12 10:45:59.066949
Analysis finished2023-12-12 10:46:03.369486
Duration4.3 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%
Mean25000.798
Minimum1
Maximum492019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:46:03.480162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2403.45
Q112187.75
median24757.5
Q336878
95-th percentile46937.05
Maximum492019
Range492018
Interquartile range (IQR)24690.25

Descriptive statistics

Standard deviation19715.236
Coefficient of variation (CV)0.78858426
Kurtosis229.41546
Mean25000.798
Median Absolute Deviation (MAD)12369.5
Skewness10.449019
Sum2.5000798 × 108
Variance3.8869051 × 108
MonotonicityNot monotonic
2023-12-12T19:46:03.675133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12084 1
 
< 0.1%
20235 1
 
< 0.1%
43274 1
 
< 0.1%
6163 1
 
< 0.1%
15739 1
 
< 0.1%
25751 1
 
< 0.1%
19673 1
 
< 0.1%
18602 1
 
< 0.1%
18348 1
 
< 0.1%
21061 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
29 1
< 0.1%
32 1
< 0.1%
ValueCountFrequency (%)
492019 1
< 0.1%
492017 1
< 0.1%
492009 1
< 0.1%
492006 1
< 0.1%
429031 1
< 0.1%
429030 1
< 0.1%
429023 1
< 0.1%
429022 1
< 0.1%
429021 1
< 0.1%
429014 1
< 0.1%

우편번호1
Real number (ℝ)

HIGH CORRELATION 

Distinct254
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean459.2415
Minimum100
Maximum799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:46:03.847555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile133
Q1325
median472
Q3617
95-th percentile750
Maximum799
Range699
Interquartile range (IQR)292

Descriptive statistics

Standard deviation199.10884
Coefficient of variation (CV)0.4335602
Kurtosis-1.0331437
Mean459.2415
Median Absolute Deviation (MAD)147
Skewness-0.26269304
Sum4592415
Variance39644.329
MonotonicityNot monotonic
2023-12-12T19:46:04.014901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 132
 
1.3%
472 104
 
1.0%
151 99
 
1.0%
702 97
 
1.0%
560 89
 
0.9%
706 87
 
0.9%
139 85
 
0.9%
100 84
 
0.8%
704 84
 
0.8%
140 82
 
0.8%
Other values (244) 9057
90.6%
ValueCountFrequency (%)
100 84
0.8%
110 67
0.7%
120 46
0.5%
121 53
0.5%
122 48
0.5%
130 62
0.6%
131 66
0.7%
132 56
0.6%
133 44
0.4%
134 69
0.7%
ValueCountFrequency (%)
799 5
 
0.1%
791 54
0.5%
790 49
0.5%
780 68
0.7%
770 59
0.6%
769 52
0.5%
767 26
 
0.3%
766 30
0.3%
764 15
 
0.1%
763 20
 
0.2%

우편번호2
Real number (ℝ)

Distinct564
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean758.7337
Minimum3
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:46:04.188903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile120
Q1760
median821
Q3861
95-th percentile931
Maximum999
Range996
Interquartile range (IQR)101

Descriptive statistics

Standard deviation213.90496
Coefficient of variation (CV)0.28192364
Kurtosis4.8634057
Mean758.7337
Median Absolute Deviation (MAD)48
Skewness-2.4102093
Sum7587337
Variance45755.333
MonotonicityNot monotonic
2023-12-12T19:46:04.395865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
811 153
 
1.5%
841 149
 
1.5%
831 147
 
1.5%
822 138
 
1.4%
821 130
 
1.3%
812 126
 
1.3%
801 121
 
1.2%
842 117
 
1.2%
803 115
 
1.1%
851 115
 
1.1%
Other values (554) 8689
86.9%
ValueCountFrequency (%)
3 8
 
0.1%
10 38
0.4%
11 10
 
0.1%
12 9
 
0.1%
13 8
 
0.1%
14 6
 
0.1%
15 5
 
0.1%
16 2
 
< 0.1%
17 2
 
< 0.1%
19 2
 
< 0.1%
ValueCountFrequency (%)
999 2
 
< 0.1%
998 3
< 0.1%
994 2
 
< 0.1%
993 3
< 0.1%
992 3
< 0.1%
991 1
 
< 0.1%
990 5
0.1%
989 3
< 0.1%
988 2
 
< 0.1%
987 3
< 0.1%

우편번호
Text

Distinct8468
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T19:46:04.767932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9168
Min length4

Characters and Unicode

Total characters69168
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

Unique7277 ?
Unique (%)72.8%

Sample

1st row666-970
2nd row515-805
3rd row343-884
4th row742-753
5th row142-872
ValueCountFrequency (%)
487-839 8
 
0.1%
701-819 7
 
0.1%
611-839 7
 
0.1%
601-803 7
 
0.1%
138-873 7
 
0.1%
138-820 6
 
0.1%
482-839 6
 
0.1%
476-809 6
 
0.1%
537-874 6
 
0.1%
616-844 5
 
< 0.1%
Other values (8458) 9935
99.4%
2023-12-12T19:46:05.385051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 9588
13.9%
8 7520
10.9%
1 7346
10.6%
0 7164
10.4%
3 6297
9.1%
7 6286
9.1%
2 5538
8.0%
4 5443
7.9%
5 5377
7.8%
6 5334
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59580
86.1%
Dash Punctuation 9588
 
13.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 7520
12.6%
1 7346
12.3%
0 7164
12.0%
3 6297
10.6%
7 6286
10.6%
2 5538
9.3%
4 5443
9.1%
5 5377
9.0%
6 5334
9.0%
9 3275
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 9588
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69168
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 9588
13.9%
8 7520
10.9%
1 7346
10.6%
0 7164
10.4%
3 6297
9.1%
7 6286
9.1%
2 5538
8.0%
4 5443
7.9%
5 5377
7.8%
6 5334
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69168
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 9588
13.9%
8 7520
10.9%
1 7346
10.6%
0 7164
10.4%
3 6297
9.1%
7 6286
9.1%
2 5538
8.0%
4 5443
7.9%
5 5377
7.8%
6 5334
7.7%

주소
Text

Distinct8355
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T19:46:05.823779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length41
Median length38
Mean length14.9328
Min length8

Characters and Unicode

Total characters149328
Distinct characters558
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7523 ?
Unique (%)75.2%

Sample

1st row경남 산청군 생비량면
2nd row전남 장성군 장성읍 영천리
3rd row충남 당진군 면천면 성상리
4th row경북 상주시 냉림동 주공4단지아파트
5th row서울 강북구 수유3동
ValueCountFrequency (%)
서울 1610
 
4.2%
경기 1605
 
4.2%
경북 964
 
2.5%
전남 783
 
2.1%
경남 734
 
1.9%
부산 630
 
1.7%
충남 609
 
1.6%
전북 573
 
1.5%
강원 560
 
1.5%
대구 507
 
1.3%
Other values (8588) 29330
77.4%
2023-12-12T19:46:06.683188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
30198
 
20.2%
동 7126
 
4.8%
구 5411
 
3.6%
시 4254
 
2.8%
리 3979
 
2.7%
경 3612
 
2.4%
남 3457
 
2.3%
면 3196
 
2.1%
산 3097
 
2.1%
1 3036
 
2.0%
Other values (548) 81962
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109707
73.5%
Space Separator 30198
 
20.2%
Decimal Number 8478
 
5.7%
Dash Punctuation 700
 
0.5%
Uppercase Letter 172
 
0.1%
Open Punctuation 28
 
< 0.1%
Close Punctuation 28
 
< 0.1%
Other Punctuation 12
 
< 0.1%
Lowercase Letter 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7126
 
6.5%
구 5411
 
4.9%
시 4254
 
3.9%
리 3979
 
3.6%
경 3612
 
3.3%
남 3457
 
3.2%
면 3196
 
2.9%
산 3097
 
2.8%
서 2913
 
2.7%
북 2732
 
2.5%
Other values (508) 69930
63.7%
Uppercase Letter
ValueCountFrequency (%)
S 27
15.7%
K 22
12.8%
L 19
11.0%
G 18
10.5%
T 14
8.1%
A 12
 
7.0%
B 9
 
5.2%
C 9
 
5.2%
D 6
 
3.5%
I 5
 
2.9%
Other values (13) 31
18.0%
Decimal Number
ValueCountFrequency (%)
1 3036
35.8%
2 1797
21.2%
0 1097
 
12.9%
3 877
 
10.3%
4 511
 
6.0%
5 376
 
4.4%
6 294
 
3.5%
7 200
 
2.4%
8 158
 
1.9%
9 132
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 11
91.7%
& 1
 
8.3%
Space Separator
ValueCountFrequency (%)
30198
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 700
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109707
73.5%
Common 39444
 
26.4%
Latin 177
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7126
 
6.5%
구 5411
 
4.9%
시 4254
 
3.9%
리 3979
 
3.6%
경 3612
 
3.3%
남 3457
 
3.2%
면 3196
 
2.9%
산 3097
 
2.8%
서 2913
 
2.7%
북 2732
 
2.5%
Other values (508) 69930
63.7%
Latin
ValueCountFrequency (%)
S 27
15.3%
K 22
12.4%
L 19
10.7%
G 18
10.2%
T 14
 
7.9%
A 12
 
6.8%
B 9
 
5.1%
C 9
 
5.1%
D 6
 
3.4%
e 5
 
2.8%
Other values (14) 36
20.3%
Common
ValueCountFrequency (%)
30198
76.6%
1 3036
 
7.7%
2 1797
 
4.6%
0 1097
 
2.8%
3 877
 
2.2%
- 700
 
1.8%
4 511
 
1.3%
5 376
 
1.0%
6 294
 
0.7%
7 200
 
0.5%
Other values (6) 358
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109707
73.5%
ASCII 39621
 
26.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
30198
76.2%
1 3036
 
7.7%
2 1797
 
4.5%
0 1097
 
2.8%
3 877
 
2.2%
- 700
 
1.8%
4 511
 
1.3%
5 376
 
0.9%
6 294
 
0.7%
7 200
 
0.5%
Other values (30) 535
 
1.4%
Hangul
ValueCountFrequency (%)
동 7126
 
6.5%
구 5411
 
4.9%
시 4254
 
3.9%
리 3979
 
3.6%
경 3612
 
3.3%
남 3457
 
3.2%
면 3196
 
2.9%
산 3097
 
2.8%
서 2913
 
2.7%
북 2732
 
2.5%
Other values (508) 69930
63.7%

시도
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.0342
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T19:46:06.887322image/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 deviation6.1432894
Coefficient of variation (CV)0.61223509
Kurtosis-1.4072111
Mean10.0342
Median Absolute Deviation (MAD)6
Skewness-0.3171108
Sum100342
Variance37.740004
MonotonicityNot monotonic
2023-12-12T19:46:07.056862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 1610
16.1%
11 1605
16.1%
18 964
9.6%
15 783
7.8%
17 734
7.3%
2 630
 
6.3%
13 609
 
6.1%
16 573
 
5.7%
12 560
 
5.6%
3 507
 
5.1%
Other values (6) 1425
14.2%
ValueCountFrequency (%)
1 1610
16.1%
2 630
 
6.3%
3 507
 
5.1%
4 284
 
2.8%
5 234
 
2.3%
6 164
 
1.6%
7 168
 
1.7%
11 1605
16.1%
12 560
 
5.6%
13 609
 
6.1%
ValueCountFrequency (%)
19 80
 
0.8%
18 964
9.6%
17 734
7.3%
16 573
 
5.7%
15 783
7.8%
14 495
 
5.0%
13 609
 
6.1%
12 560
 
5.6%
11 1605
16.1%
7 168
 
1.7%

Interactions

2023-12-12T19:46:02.505775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:00.254334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:01.234303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:01.887621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:02.651618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:00.394635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:01.395517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:02.052359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:02.819391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:00.888330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:01.565880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:02.214813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:02.966986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:01.025764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:01.719770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:46:02.363266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:46:07.213417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호우편번호1우편번호2시도
번호1.0000.0780.0960.103
우편번호10.0781.0000.3080.889
우편번호20.0960.3081.0000.265
시도0.1030.8890.2651.000
2023-12-12T19:46:07.372364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호우편번호1우편번호2시도
번호1.000-0.1670.093-0.009
우편번호1-0.1671.0000.0730.594
우편번호20.0930.0731.0000.219
시도-0.0090.5940.2191.000

Missing values

2023-12-12T19:46:03.133992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:46:03.297724image/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
4455912084666970666-970경남 산청군 생비량면17
990039673515805515-805전남 장성군 장성읍 영천리15
848444503343884343-884충남 당진군 면천면 성상리13
3830215962742753742-753경북 상주시 냉림동 주공4단지아파트18
2842027841142872142-872서울 강북구 수유3동1
22095895443756443-756경기 수원시 영통구 원천동 주공아파트 201-22111
4447311998664951664-951경남 사천시 용현면 통양리17
1486745635336823336-823충남 아산시 영인면 아산리13
25560358814016040160인천 동구 금곡동4
1765446995367847367-847충북 괴산군 청천면 삼송리14
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