Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells30715
Missing cells (%)27.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory957.0 KiB
Average record size in memory98.0 B

Variable types

Numeric2
Categorical1
Text7
DateTime1

Dataset

Description평생학습계좌제에서 평가인정 받아 합격한 후 학습과정을 운영하였거나 운영중인 교육기관 및 연계 협력을 맺은 기관의 우편번호 정보를 제공합니다.
Author국가평생교육진흥원
URLhttps://www.data.go.kr/data/15090062/fileData.do

Alerts

우편번호 is highly overall correlated with 시도High correlation
시도 is highly overall correlated with 우편번호High correlation
리 has 6235 (62.4%) missing valuesMissing
도서 has 9969 (99.7%) missing valuesMissing
번지 has 7192 (71.9%) missing valuesMissing
건물이름 has 7250 (72.5%) missing valuesMissing
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:38:39.330618
Analysis finished2023-12-12 13:38:42.131560
Duration2.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct8475
Distinct (%)84.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460232.77
Minimum100012
Maximum799822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:38:42.251427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100012
5-th percentile133842.9
Q1325842.75
median472717.5
Q3616892.5
95-th percentile755804.35
Maximum799822
Range699810
Interquartile range (IQR)291049.75

Descriptive statistics

Standard deviation198287.13
Coefficient of variation (CV)0.43084096
Kurtosis-1.0222591
Mean460232.77
Median Absolute Deviation (MAD)145103.5
Skewness-0.24888143
Sum4.6023277 × 109
Variance3.9317786 × 1010
MonotonicityNot monotonic
2023-12-12T22:38:42.476395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
209819 12
 
0.1%
138820 8
 
0.1%
252829 8
 
0.1%
269849 8
 
0.1%
476809 7
 
0.1%
611839 7
 
0.1%
701819 7
 
0.1%
138873 7
 
0.1%
601803 7
 
0.1%
616835 6
 
0.1%
Other values (8465) 9923
99.2%
ValueCountFrequency (%)
100012 1
< 0.1%
100031 1
< 0.1%
100032 1
< 0.1%
100052 1
< 0.1%
100053 1
< 0.1%
100102 1
< 0.1%
100110 1
< 0.1%
100141 1
< 0.1%
100162 1
< 0.1%
100193 1
< 0.1%
ValueCountFrequency (%)
799822 1
 
< 0.1%
799803 1
 
< 0.1%
791948 1
 
< 0.1%
791945 3
< 0.1%
791944 2
< 0.1%
791942 1
 
< 0.1%
791941 1
 
< 0.1%
791940 1
 
< 0.1%
791923 2
< 0.1%
791922 2
< 0.1%

일련번호
Real number (ℝ)

Distinct89
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.2351
Minimum1
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:38:42.641360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median6
Q321
95-th percentile51
Maximum326
Range325
Interquartile range (IQR)20

Descriptive statistics

Standard deviation24.214691
Coefficient of variation (CV)1.7010552
Kurtosis37.098258
Mean14.2351
Median Absolute Deviation (MAD)5
Skewness4.9249173
Sum142351
Variance586.35126
MonotonicityNot monotonic
2023-12-12T22:38:42.798887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3333
33.3%
11 1537
15.4%
2 1105
 
11.1%
21 914
 
9.1%
31 591
 
5.9%
12 458
 
4.6%
3 336
 
3.4%
41 322
 
3.2%
51 226
 
2.3%
7 191
 
1.9%
Other values (79) 987
 
9.9%
ValueCountFrequency (%)
1 3333
33.3%
2 1105
 
11.1%
3 336
 
3.4%
4 77
 
0.8%
5 86
 
0.9%
6 77
 
0.8%
7 191
 
1.9%
9 20
 
0.2%
11 1537
15.4%
12 458
 
4.6%
ValueCountFrequency (%)
326 1
< 0.1%
306 1
< 0.1%
296 1
< 0.1%
291 1
< 0.1%
281 1
< 0.1%
276 1
< 0.1%
271 1
< 0.1%
266 2
< 0.1%
261 1
< 0.1%
256 1
< 0.1%

시도
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기
1586 
서울
1562 
경북
959 
전남
807 
경남
695 
Other values (11)
4391 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기
2nd row경기
3rd row서울
4th row광주
5th row충남

Common Values

ValueCountFrequency (%)
경기 1586
15.9%
서울 1562
15.6%
경북 959
9.6%
전남 807
8.1%
경남 695
7.0%
충남 639
6.4%
부산 593
 
5.9%
강원 587
 
5.9%
전북 568
 
5.7%
대구 544
 
5.4%
Other values (6) 1460
14.6%

Length

2023-12-12T22:38:42.940870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 1586
15.9%
서울 1562
15.6%
경북 959
9.6%
전남 807
8.1%
경남 695
7.0%
충남 639
6.4%
부산 593
 
5.9%
강원 587
 
5.9%
전북 568
 
5.7%
대구 544
 
5.4%
Other values (6) 1460
14.6%

시군구
Text

Distinct226
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:38:43.277225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.349
Min length2

Characters and Unicode

Total characters33490
Distinct characters140
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

Unique0 ?
Unique (%)0.0%

Sample

1st row수원시 권선구
2nd row연천군
3rd row은평구
4th row서구
5th row아산시
ValueCountFrequency (%)
남구 307
 
2.8%
북구 297
 
2.7%
동구 222
 
2.0%
서구 211
 
1.9%
중구 205
 
1.9%
전주시 132
 
1.2%
용인시 120
 
1.1%
포항시 114
 
1.0%
성남시 111
 
1.0%
강남구 108
 
1.0%
Other values (225) 9226
83.5%
2023-12-12T22:38:43.797879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 4707
 
14.1%
시 4028
 
12.0%
군 2675
 
8.0%
주 1092
 
3.3%
1053
 
3.1%
산 997
 
3.0%
천 965
 
2.9%
성 864
 
2.6%
남 808
 
2.4%
동 778
 
2.3%
Other values (130) 15523
46.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32437
96.9%
Space Separator 1053
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4707
 
14.5%
시 4028
 
12.4%
군 2675
 
8.2%
주 1092
 
3.4%
산 997
 
3.1%
천 965
 
3.0%
성 864
 
2.7%
남 808
 
2.5%
동 778
 
2.4%
양 722
 
2.2%
Other values (129) 14801
45.6%
Space Separator
ValueCountFrequency (%)
1053
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32437
96.9%
Common 1053
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4707
 
14.5%
시 4028
 
12.4%
군 2675
 
8.2%
주 1092
 
3.4%
산 997
 
3.1%
천 965
 
3.0%
성 864
 
2.7%
남 808
 
2.5%
동 778
 
2.4%
양 722
 
2.2%
Other values (129) 14801
45.6%
Common
ValueCountFrequency (%)
1053
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32437
96.9%
ASCII 1053
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 4707
 
14.5%
시 4028
 
12.4%
군 2675
 
8.2%
주 1092
 
3.4%
산 997
 
3.1%
천 965
 
3.0%
성 864
 
2.7%
남 808
 
2.5%
동 778
 
2.4%
양 722
 
2.2%
Other values (129) 14801
45.6%
ASCII
ValueCountFrequency (%)
1053
100.0%

읍면동
Text

Distinct3204
Distinct (%)32.3%
Missing69
Missing (%)0.7%
Memory size156.2 KiB
2023-12-12T22:38:44.166299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.3415567
Min length2

Characters and Unicode

Total characters33185
Distinct characters329
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

Unique1020 ?
Unique (%)10.3%

Sample

1st row금곡동
2nd row연천읍
3rd row신사1동
4th row양2동
5th row음봉면
ValueCountFrequency (%)
남면 46
 
0.5%
서면 38
 
0.4%
북면 29
 
0.3%
여의도동 24
 
0.2%
삼천동1가 21
 
0.2%
조례동 19
 
0.2%
송정동 17
 
0.2%
금곡동 17
 
0.2%
삼성동 15
 
0.2%
적성면 15
 
0.2%
Other values (3194) 9690
97.6%
2023-12-12T22:38:44.666939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 6012
 
18.1%
면 3136
 
9.5%
1 1145
 
3.5%
2 1123
 
3.4%
읍 1044
 
3.1%
산 778
 
2.3%
가 503
 
1.5%
천 447
 
1.3%
3 446
 
1.3%
성 439
 
1.3%
Other values (319) 18112
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30001
90.4%
Decimal Number 3142
 
9.5%
Other Punctuation 42
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6012
 
20.0%
면 3136
 
10.5%
읍 1044
 
3.5%
산 778
 
2.6%
가 503
 
1.7%
천 447
 
1.5%
성 439
 
1.5%
신 429
 
1.4%
정 379
 
1.3%
대 358
 
1.2%
Other values (308) 16476
54.9%
Decimal Number
ValueCountFrequency (%)
1 1145
36.4%
2 1123
35.7%
3 446
 
14.2%
4 191
 
6.1%
5 85
 
2.7%
6 65
 
2.1%
7 38
 
1.2%
8 24
 
0.8%
9 17
 
0.5%
0 8
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30001
90.4%
Common 3184
 
9.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6012
 
20.0%
면 3136
 
10.5%
읍 1044
 
3.5%
산 778
 
2.6%
가 503
 
1.7%
천 447
 
1.5%
성 439
 
1.5%
신 429
 
1.4%
정 379
 
1.3%
대 358
 
1.2%
Other values (308) 16476
54.9%
Common
ValueCountFrequency (%)
1 1145
36.0%
2 1123
35.3%
3 446
 
14.0%
4 191
 
6.0%
5 85
 
2.7%
6 65
 
2.0%
. 42
 
1.3%
7 38
 
1.2%
8 24
 
0.8%
9 17
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30001
90.4%
ASCII 3184
 
9.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 6012
 
20.0%
면 3136
 
10.5%
읍 1044
 
3.5%
산 778
 
2.6%
가 503
 
1.7%
천 447
 
1.5%
성 439
 
1.5%
신 429
 
1.4%
정 379
 
1.3%
대 358
 
1.2%
Other values (308) 16476
54.9%
ASCII
ValueCountFrequency (%)
1 1145
36.0%
2 1123
35.3%
3 446
 
14.0%
4 191
 
6.0%
5 85
 
2.7%
6 65
 
2.0%
. 42
 
1.3%
7 38
 
1.2%
8 24
 
0.8%
9 17
 
0.5%

리
Text

MISSING 

Distinct2744
Distinct (%)72.9%
Missing6235
Missing (%)62.4%
Memory size156.2 KiB
2023-12-12T22:38:45.243726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.1150066
Min length2

Characters and Unicode

Total characters11728
Distinct characters328
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

Unique2204 ?
Unique (%)58.5%

Sample

1st row원남리
2nd row덕림리
3rd row이연리
4th row죽암리
5th row희망4리
ValueCountFrequency (%)
대곡리 12
 
0.3%
신흥리 11
 
0.3%
신기리 11
 
0.3%
신대리 10
 
0.3%
구룡리 9
 
0.2%
금곡리 9
 
0.2%
송정리 9
 
0.2%
신원리 8
 
0.2%
부곡리 8
 
0.2%
우산리 8
 
0.2%
Other values (2735) 3671
97.5%
2023-12-12T22:38:45.689483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 3764
32.1%
산 287
 
2.4%
곡 246
 
2.1%
신 222
 
1.9%
동 175
 
1.5%
대 171
 
1.5%
정 146
 
1.2%
천 143
 
1.2%
덕 127
 
1.1%
성 120
 
1.0%
Other values (318) 6327
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11249
95.9%
Decimal Number 477
 
4.1%
Dash Punctuation 1
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 3764
33.5%
산 287
 
2.6%
곡 246
 
2.2%
신 222
 
2.0%
동 175
 
1.6%
대 171
 
1.5%
정 146
 
1.3%
천 143
 
1.3%
덕 127
 
1.1%
성 120
 
1.1%
Other values (306) 5848
52.0%
Decimal Number
ValueCountFrequency (%)
1 120
25.2%
2 108
22.6%
3 69
14.5%
4 47
 
9.9%
5 46
 
9.6%
6 34
 
7.1%
8 19
 
4.0%
7 19
 
4.0%
0 9
 
1.9%
9 6
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11249
95.9%
Common 479
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 3764
33.5%
산 287
 
2.6%
곡 246
 
2.2%
신 222
 
2.0%
동 175
 
1.6%
대 171
 
1.5%
정 146
 
1.3%
천 143
 
1.3%
덕 127
 
1.1%
성 120
 
1.1%
Other values (306) 5848
52.0%
Common
ValueCountFrequency (%)
1 120
25.1%
2 108
22.5%
3 69
14.4%
4 47
 
9.8%
5 46
 
9.6%
6 34
 
7.1%
8 19
 
4.0%
7 19
 
4.0%
0 9
 
1.9%
9 6
 
1.3%
Other values (2) 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11249
95.9%
ASCII 479
 
4.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 3764
33.5%
산 287
 
2.6%
곡 246
 
2.2%
신 222
 
2.0%
동 175
 
1.6%
대 171
 
1.5%
정 146
 
1.3%
천 143
 
1.3%
덕 127
 
1.1%
성 120
 
1.1%
Other values (306) 5848
52.0%
ASCII
ValueCountFrequency (%)
1 120
25.1%
2 108
22.5%
3 69
14.4%
4 47
 
9.8%
5 46
 
9.6%
6 34
 
7.1%
8 19
 
4.0%
7 19
 
4.0%
0 9
 
1.9%
9 6
 
1.3%
Other values (2) 2
 
0.4%

도서
Text

MISSING 

Distinct31
Distinct (%)100.0%
Missing9969
Missing (%)99.7%
Memory size156.2 KiB
2023-12-12T22:38:45.891993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.3870968
Min length2

Characters and Unicode

Total characters74
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row우실
2nd row도성
3rd row추도
4th row장병도
5th row진리
ValueCountFrequency (%)
우실 1
 
3.2%
신도 1
 
3.2%
넓도 1
 
3.2%
봉통 1
 
3.2%
두리도 1
 
3.2%
저도 1
 
3.2%
명도 1
 
3.2%
동리도 1
 
3.2%
대창 1
 
3.2%
원도 1
 
3.2%
Other values (21) 21
67.7%
2023-12-12T22:38:46.215597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 22
29.7%
리 3
 
4.1%
화 3
 
4.1%
성 2
 
2.7%
병 2
 
2.7%
서 2
 
2.7%
허 2
 
2.7%
동 2
 
2.7%
우 1
 
1.4%
원 1
 
1.4%
Other values (34) 34
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 74
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 22
29.7%
리 3
 
4.1%
화 3
 
4.1%
성 2
 
2.7%
병 2
 
2.7%
서 2
 
2.7%
허 2
 
2.7%
동 2
 
2.7%
우 1
 
1.4%
원 1
 
1.4%
Other values (34) 34
45.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 22
29.7%
리 3
 
4.1%
화 3
 
4.1%
성 2
 
2.7%
병 2
 
2.7%
서 2
 
2.7%
허 2
 
2.7%
동 2
 
2.7%
우 1
 
1.4%
원 1
 
1.4%
Other values (34) 34
45.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 74
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 22
29.7%
리 3
 
4.1%
화 3
 
4.1%
성 2
 
2.7%
병 2
 
2.7%
서 2
 
2.7%
허 2
 
2.7%
동 2
 
2.7%
우 1
 
1.4%
원 1
 
1.4%
Other values (34) 34
45.9%

번지
Text

MISSING 

Distinct2661
Distinct (%)94.8%
Missing7192
Missing (%)71.9%
Memory size156.2 KiB
2023-12-12T22:38:46.657390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length7.4451567
Min length1

Characters and Unicode

Total characters20906
Distinct characters33
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2558 ?
Unique (%)91.1%

Sample

1st row370
2nd row293 ~313
3rd row190-42~190-45
4th row656 ~694
5th row448 ~499
ValueCountFrequency (%)
1 226
 
4.5%
200 37
 
0.7%
300 35
 
0.7%
산1 33
 
0.7%
500 27
 
0.5%
1000 25
 
0.5%
299 23
 
0.5%
600 23
 
0.5%
400 21
 
0.4%
100 21
 
0.4%
Other values (1758) 4571
90.7%
2023-12-12T22:38:47.204879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2977
14.2%
~ 2352
11.3%
2234
10.7%
0 1974
9.4%
2 1678
8.0%
3 1554
7.4%
4 1415
6.8%
9 1403
6.7%
5 1352
6.5%
6 1252
6.0%
Other values (23) 2715
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15794
75.5%
Math Symbol 2352
 
11.3%
Space Separator 2234
 
10.7%
Dash Punctuation 374
 
1.8%
Other Letter 109
 
0.5%
Lowercase Letter 26
 
0.1%
Uppercase Letter 17
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2977
18.8%
0 1974
12.5%
2 1678
10.6%
3 1554
9.8%
4 1415
9.0%
9 1403
8.9%
5 1352
8.6%
6 1252
7.9%
7 1107
 
7.0%
8 1082
 
6.9%
Lowercase Letter
ValueCountFrequency (%)
e 5
19.2%
p 4
15.4%
c 4
15.4%
u 3
11.5%
r 3
11.5%
a 2
 
7.7%
n 2
 
7.7%
g 1
 
3.8%
l 1
 
3.8%
t 1
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
B 4
23.5%
D 3
17.6%
A 3
17.6%
J 3
17.6%
S 2
11.8%
O 1
 
5.9%
M 1
 
5.9%
Other Letter
ValueCountFrequency (%)
산 107
98.2%
일 1
 
0.9%
월 1
 
0.9%
Math Symbol
ValueCountFrequency (%)
~ 2352
100.0%
Space Separator
ValueCountFrequency (%)
2234
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20754
99.3%
Hangul 109
 
0.5%
Latin 43
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5
11.6%
p 4
9.3%
c 4
9.3%
B 4
9.3%
D 3
 
7.0%
A 3
 
7.0%
J 3
 
7.0%
u 3
 
7.0%
r 3
 
7.0%
a 2
 
4.7%
Other values (7) 9
20.9%
Common
ValueCountFrequency (%)
1 2977
14.3%
~ 2352
11.3%
2234
10.8%
0 1974
9.5%
2 1678
8.1%
3 1554
7.5%
4 1415
6.8%
9 1403
6.8%
5 1352
6.5%
6 1252
6.0%
Other values (3) 2563
12.3%
Hangul
ValueCountFrequency (%)
산 107
98.2%
일 1
 
0.9%
월 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20797
99.5%
Hangul 109
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2977
14.3%
~ 2352
11.3%
2234
10.7%
0 1974
9.5%
2 1678
8.1%
3 1554
7.5%
4 1415
6.8%
9 1403
6.7%
5 1352
6.5%
6 1252
6.0%
Other values (20) 2606
12.5%
Hangul
ValueCountFrequency (%)
산 107
98.2%
일 1
 
0.9%
월 1
 
0.9%

건물이름
Text

MISSING 

Distinct2120
Distinct (%)77.1%
Missing7250
Missing (%)72.5%
Memory size156.2 KiB
2023-12-12T22:38:47.498947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length6.9654545
Min length2

Characters and Unicode

Total characters19155
Distinct characters506
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

Unique1945 ?
Unique (%)70.7%

Sample

1st row연천군청
2nd row양동복개상가
3rd row호수빌딩
4th row춘천기능대학
5th row신성미소지움아파트
ValueCountFrequency (%)
사서함 121
 
4.4%
주공아파트 65
 
2.4%
현대아파트 46
 
1.7%
서울중앙우체국사서함 15
 
0.5%
삼성아파트 11
 
0.4%
쌍용아파트 10
 
0.4%
우성아파트 9
 
0.3%
삼성래미안아파트 9
 
0.3%
한신아파트 9
 
0.3%
두산아파트 8
 
0.3%
Other values (2111) 2448
89.0%
2023-12-12T22:38:47.915089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 1733
 
9.0%
파 1637
 
8.5%
트 1626
 
8.5%
대 452
 
2.4%
지 419
 
2.2%
서 356
 
1.9%
주 309
 
1.6%
차 295
 
1.5%
단 288
 
1.5%
공 283
 
1.5%
Other values (496) 11757
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18319
95.6%
Decimal Number 588
 
3.1%
Uppercase Letter 145
 
0.8%
Open Punctuation 29
 
0.2%
Close Punctuation 29
 
0.2%
Lowercase Letter 18
 
0.1%
Dash Punctuation 15
 
0.1%
Other Punctuation 8
 
< 0.1%
Letter Number 3
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 1733
 
9.5%
파 1637
 
8.9%
트 1626
 
8.9%
대 452
 
2.5%
지 419
 
2.3%
서 356
 
1.9%
주 309
 
1.7%
차 295
 
1.6%
단 288
 
1.6%
공 283
 
1.5%
Other values (454) 10921
59.6%
Uppercase Letter
ValueCountFrequency (%)
K 24
16.6%
S 19
13.1%
T 18
12.4%
G 14
9.7%
L 13
9.0%
I 8
 
5.5%
C 7
 
4.8%
A 7
 
4.8%
B 6
 
4.1%
E 6
 
4.1%
Other values (12) 23
15.9%
Decimal Number
ValueCountFrequency (%)
1 177
30.1%
2 174
29.6%
3 76
12.9%
4 46
 
7.8%
5 44
 
7.5%
6 21
 
3.6%
8 16
 
2.7%
7 13
 
2.2%
9 12
 
2.0%
0 9
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 6
75.0%
, 1
 
12.5%
& 1
 
12.5%
Letter Number
ValueCountFrequency (%)
Ⅳ 2
66.7%
Ⅱ 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18319
95.6%
Common 670
 
3.5%
Latin 166
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 1733
 
9.5%
파 1637
 
8.9%
트 1626
 
8.9%
대 452
 
2.5%
지 419
 
2.3%
서 356
 
1.9%
주 309
 
1.7%
차 295
 
1.6%
단 288
 
1.6%
공 283
 
1.5%
Other values (454) 10921
59.6%
Latin
ValueCountFrequency (%)
K 24
14.5%
S 19
11.4%
e 18
10.8%
T 18
10.8%
G 14
8.4%
L 13
7.8%
I 8
 
4.8%
C 7
 
4.2%
A 7
 
4.2%
B 6
 
3.6%
Other values (15) 32
19.3%
Common
ValueCountFrequency (%)
1 177
26.4%
2 174
26.0%
3 76
11.3%
4 46
 
6.9%
5 44
 
6.6%
( 29
 
4.3%
) 29
 
4.3%
6 21
 
3.1%
8 16
 
2.4%
- 15
 
2.2%
Other values (7) 43
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18319
95.6%
ASCII 833
 
4.3%
Number Forms 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 1733
 
9.5%
파 1637
 
8.9%
트 1626
 
8.9%
대 452
 
2.5%
지 419
 
2.3%
서 356
 
1.9%
주 309
 
1.7%
차 295
 
1.6%
단 288
 
1.6%
공 283
 
1.5%
Other values (454) 10921
59.6%
ASCII
ValueCountFrequency (%)
1 177
21.2%
2 174
20.9%
3 76
 
9.1%
4 46
 
5.5%
5 44
 
5.3%
( 29
 
3.5%
) 29
 
3.5%
K 24
 
2.9%
6 21
 
2.5%
S 19
 
2.3%
Other values (30) 194
23.3%
Number Forms
ValueCountFrequency (%)
Ⅳ 2
66.7%
Ⅱ 1
33.3%

수정일자
Date

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2004-05-17 00:00:00
Maximum2009-06-22 00:00:00
2023-12-12T22:38:48.041370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:48.188454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)

주소
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:38:48.604524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length39
Mean length17.2784
Min length8

Characters and Unicode

Total characters172784
Distinct characters580
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row경기 수원시 권선구 금곡동
2nd row경기 연천군 연천읍 연천군청
3rd row서울 은평구 신사1동 370
4th row광주 서구 양2동 양동복개상가
5th row충남 아산시 음봉면 원남리
ValueCountFrequency (%)
경기 1586
 
3.8%
서울 1562
 
3.8%
경북 959
 
2.3%
전남 807
 
2.0%
경남 695
 
1.7%
충남 639
 
1.5%
부산 593
 
1.4%
강원 587
 
1.4%
전북 568
 
1.4%
대구 544
 
1.3%
Other values (11315) 32720
79.3%
2023-12-12T22:38:49.147474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31263
 
18.1%
동 7204
 
4.2%
1 6503
 
3.8%
구 5599
 
3.2%
시 4195
 
2.4%
리 3976
 
2.3%
2 3571
 
2.1%
경 3562
 
2.1%
남 3476
 
2.0%
0 3451
 
2.0%
Other values (570) 99984
57.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 112201
64.9%
Space Separator 31263
 
18.1%
Decimal Number 25376
 
14.7%
Math Symbol 2352
 
1.4%
Dash Punctuation 1302
 
0.8%
Uppercase Letter 161
 
0.1%
Other Punctuation 50
 
< 0.1%
Open Punctuation 29
 
< 0.1%
Close Punctuation 29
 
< 0.1%
Lowercase Letter 18
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7204
 
6.4%
구 5599
 
5.0%
시 4195
 
3.7%
리 3976
 
3.5%
경 3562
 
3.2%
남 3476
 
3.1%
면 3139
 
2.8%
산 3099
 
2.8%
서 2952
 
2.6%
북 2787
 
2.5%
Other values (526) 72212
64.4%
Uppercase Letter
ValueCountFrequency (%)
K 24
14.9%
S 19
11.8%
T 18
11.2%
G 14
8.7%
B 13
8.1%
L 13
8.1%
A 13
8.1%
C 8
 
5.0%
I 8
 
5.0%
E 6
 
3.7%
Other values (13) 25
15.5%
Decimal Number
ValueCountFrequency (%)
1 6503
25.6%
2 3571
14.1%
0 3451
13.6%
3 2431
 
9.6%
4 1914
 
7.5%
5 1763
 
6.9%
6 1589
 
6.3%
9 1553
 
6.1%
7 1314
 
5.2%
8 1287
 
5.1%
Other Punctuation
ValueCountFrequency (%)
. 48
96.0%
, 1
 
2.0%
& 1
 
2.0%
Letter Number
ValueCountFrequency (%)
Ⅳ 2
66.7%
Ⅱ 1
33.3%
Space Separator
ValueCountFrequency (%)
31263
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2352
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1302
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 112201
64.9%
Common 60401
35.0%
Latin 182
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7204
 
6.4%
구 5599
 
5.0%
시 4195
 
3.7%
리 3976
 
3.5%
경 3562
 
3.2%
남 3476
 
3.1%
면 3139
 
2.8%
산 3099
 
2.8%
서 2952
 
2.6%
북 2787
 
2.5%
Other values (526) 72212
64.4%
Latin
ValueCountFrequency (%)
K 24
13.2%
S 19
10.4%
e 18
9.9%
T 18
9.9%
G 14
7.7%
B 13
 
7.1%
L 13
 
7.1%
A 13
 
7.1%
C 8
 
4.4%
I 8
 
4.4%
Other values (16) 34
18.7%
Common
ValueCountFrequency (%)
31263
51.8%
1 6503
 
10.8%
2 3571
 
5.9%
0 3451
 
5.7%
3 2431
 
4.0%
~ 2352
 
3.9%
4 1914
 
3.2%
5 1763
 
2.9%
6 1589
 
2.6%
9 1553
 
2.6%
Other values (8) 4011
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 112201
64.9%
ASCII 60580
35.1%
Number Forms 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31263
51.6%
1 6503
 
10.7%
2 3571
 
5.9%
0 3451
 
5.7%
3 2431
 
4.0%
~ 2352
 
3.9%
4 1914
 
3.2%
5 1763
 
2.9%
6 1589
 
2.6%
9 1553
 
2.6%
Other values (32) 4190
 
6.9%
Hangul
ValueCountFrequency (%)
동 7204
 
6.4%
구 5599
 
5.0%
시 4195
 
3.7%
리 3976
 
3.5%
경 3562
 
3.2%
남 3476
 
3.1%
면 3139
 
2.8%
산 3099
 
2.8%
서 2952
 
2.6%
북 2787
 
2.5%
Other values (526) 72212
64.4%
Number Forms
ValueCountFrequency (%)
Ⅳ 2
66.7%
Ⅱ 1
33.3%

Interactions

2023-12-12T22:38:41.371689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:41.117214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:41.502458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:41.252685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:38:49.255049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호일련번호시도도서수정일자
우편번호1.0000.1850.9691.0000.402
일련번호0.1851.0000.1761.0000.356
시도0.9690.1761.0001.0000.450
도서1.0001.0001.0001.000NaN
수정일자0.4020.3560.450NaN1.000
2023-12-12T22:38:49.388322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호일련번호시도
우편번호1.0000.0600.855
일련번호0.0601.0000.069
시도0.8550.0691.000

Missing values

2023-12-12T22:38:41.669450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:38:41.878253image/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.
2023-12-12T22:38:42.041791image/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

우편번호일련번호시도시군구읍면동리도서번지건물이름수정일자주소
168564414601경기수원시 권선구금곡동<NA><NA><NA><NA>2004-05-17경기 수원시 권선구 금곡동
1424867017경기연천군연천읍<NA><NA><NA>연천군청2004-05-17경기 연천군 연천읍 연천군청
4841112295011서울은평구신사1동<NA><NA>370<NA>2008-08-27서울 은평구 신사1동 370
132125027293광주서구양2동<NA><NA><NA>양동복개상가2004-05-17광주 서구 양2동 양동복개상가
3399633686431충남아산시음봉면원남리<NA><NA><NA>2004-05-17충남 아산시 음봉면 원남리
484861428821서울강북구인수동<NA><NA>293 ~313<NA>2008-08-27서울 강북구 인수동 293~313
367095258631전남함평군나산면덕림리<NA><NA><NA>2004-05-17전남 함평군 나산면 덕림리
39376138819121서울송파구마천2동<NA><NA>190-42~190-45<NA>2004-05-17서울 송파구 마천2동 190-42~190-45
383676178221부산사상구모라1동<NA><NA>656 ~694<NA>2004-05-17부산 사상구 모라1동 656~694
4611444651711경기용인시 기흥구상하동<NA><NA>448 ~499<NA>2007-11-29경기 용인시 기흥구 상하동 448~499
우편번호일련번호시도시군구읍면동리도서번지건물이름수정일자주소
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