Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells24666
Missing cells (%)19.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory116.0 B

Variable types

Numeric4
Text7
Categorical2

Dataset

Description토지수용관련 이해관계인들에게 재결서 정본 송달을 하고자 우정사업본부와 연계하여 사용하는 법정동 정보로써 시도코드, 동코드, 시도명, 우편번호 등이 포함됨
Author국토교통부 중앙토지수용위원회
URLhttps://www.data.go.kr/data/15049201/fileData.do

Alerts

시도코드 is highly overall correlated with 이전시도코드 and 1 other fieldsHigh correlation
이전시도코드 is highly overall correlated with 시도코드 and 1 other fieldsHigh correlation
시도명 is highly overall correlated with 시도코드 and 1 other fieldsHigh correlation
수정일자 has 3647 (36.5%) missing valuesMissing
이전시도코드 has 5596 (56.0%) missing valuesMissing
이전동코드 has 5596 (56.0%) missing valuesMissing
이전동명 has 5470 (54.7%) missing valuesMissing
읍면명 has 1901 (19.0%) missing valuesMissing
동명 has 802 (8.0%) missing valuesMissing
우편번호 has 1579 (15.8%) missing valuesMissing
동코드 has 129 (1.3%) zerosZeros
수정일자 has 184 (1.8%) zerosZeros
이전시도코드 has 230 (2.3%) zerosZeros

Reproduction

Analysis started2023-12-12 19:55:58.763509
Analysis finished2023-12-12 19:56:02.982332
Duration4.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도코드
Real number (ℝ)

HIGH CORRELATION 

Distinct402
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43217.562
Minimum10002
Maximum99000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:56:03.068602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10002
5-th percentile26320
Q141850
median44840
Q347290
95-th percentile48800
Maximum99000
Range88998
Interquartile range (IQR)5440

Descriptive statistics

Standard deviation7062.2291
Coefficient of variation (CV)0.16341109
Kurtosis6.9151881
Mean43217.562
Median Absolute Deviation (MAD)2890
Skewness-2.4385454
Sum4.3217562 × 108
Variance49875079
MonotonicityNot monotonic
2023-12-13T04:56:03.249440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43710 120
 
1.2%
41590 91
 
0.9%
46790 72
 
0.7%
41750 72
 
0.7%
47170 72
 
0.7%
45190 71
 
0.7%
44150 71
 
0.7%
48890 70
 
0.7%
41860 69
 
0.7%
46170 68
 
0.7%
Other values (392) 9224
92.2%
ValueCountFrequency (%)
10002 1
 
< 0.1%
10003 1
 
< 0.1%
11000 1
 
< 0.1%
11110 23
0.2%
11140 20
0.2%
11170 12
0.1%
11200 6
 
0.1%
11210 3
 
< 0.1%
11215 5
 
0.1%
11230 1
 
< 0.1%
ValueCountFrequency (%)
99000 1
 
< 0.1%
50130 26
 
0.3%
50110 57
0.6%
49720 15
 
0.1%
49710 31
0.3%
49130 5
 
0.1%
49110 17
 
0.2%
48890 70
0.7%
48880 38
0.4%
48870 33
0.3%

동코드
Real number (ℝ)

ZEROS 

Distinct793
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29322.203
Minimum0
Maximum47028
Zeros129
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:56:03.690772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10500
Q125030
median33022
Q336033
95-th percentile41030
Maximum47028
Range47028
Interquartile range (IQR)11003

Descriptive statistics

Standard deviation10279.617
Coefficient of variation (CV)0.35057451
Kurtosis-0.0106194
Mean29322.203
Median Absolute Deviation (MAD)5003
Skewness-0.9816256
Sum2.9322203 × 108
Variance1.0567052 × 108
MonotonicityNot monotonic
2023-12-13T04:56:03.848186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 129
 
1.3%
10400 76
 
0.8%
10300 75
 
0.8%
33023 71
 
0.7%
10600 71
 
0.7%
32000 69
 
0.7%
10100 69
 
0.7%
25024 67
 
0.7%
10200 67
 
0.7%
32021 65
 
0.7%
Other values (783) 9241
92.4%
ValueCountFrequency (%)
0 129
1.3%
100 1
 
< 0.1%
400 2
 
< 0.1%
500 3
 
< 0.1%
600 3
 
< 0.1%
700 2
 
< 0.1%
800 1
 
< 0.1%
900 3
 
< 0.1%
1000 2
 
< 0.1%
1100 1
 
< 0.1%
ValueCountFrequency (%)
47028 1
< 0.1%
47027 1
< 0.1%
47026 1
< 0.1%
47024 2
< 0.1%
47023 2
< 0.1%
47022 1
< 0.1%
46045 1
< 0.1%
46042 1
< 0.1%
46038 1
< 0.1%
46036 1
< 0.1%

출력동명
Text

Distinct5924
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T04:56:04.266210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.027
Min length2

Characters and Unicode

Total characters30270
Distinct characters384
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4050 ?
Unique (%)40.5%

Sample

1st row계남리
2nd row서구
3rd row석천리
4th row수산리
5th row용전리
ValueCountFrequency (%)
용암리 22
 
0.2%
대곡리 22
 
0.2%
신흥리 20
 
0.2%
금곡리 18
 
0.2%
오산리 18
 
0.2%
용산리 18
 
0.2%
신촌리 17
 
0.2%
교촌리 17
 
0.2%
가산리 17
 
0.2%
마산리 15
 
0.1%
Other values (5912) 9816
98.2%
2023-12-13T04:56:04.840450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 7471
24.7%
동 2172
 
7.2%
산 815
 
2.7%
면 580
 
1.9%
곡 559
 
1.8%
신 475
 
1.6%
천 457
 
1.5%
가 404
 
1.3%
대 399
 
1.3%
성 350
 
1.2%
Other values (374) 16588
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30029
99.2%
Decimal Number 231
 
0.8%
Space Separator 8
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 7471
24.9%
동 2172
 
7.2%
산 815
 
2.7%
면 580
 
1.9%
곡 559
 
1.9%
신 475
 
1.6%
천 457
 
1.5%
가 404
 
1.3%
대 399
 
1.3%
성 350
 
1.2%
Other values (364) 16347
54.4%
Decimal Number
ValueCountFrequency (%)
1 73
31.6%
2 69
29.9%
3 42
18.2%
4 24
 
10.4%
5 13
 
5.6%
6 5
 
2.2%
7 5
 
2.2%
Space Separator
ValueCountFrequency (%)
8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30027
99.2%
Common 241
 
0.8%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 7471
24.9%
동 2172
 
7.2%
산 815
 
2.7%
면 580
 
1.9%
곡 559
 
1.9%
신 475
 
1.6%
천 457
 
1.5%
가 404
 
1.3%
대 399
 
1.3%
성 350
 
1.2%
Other values (362) 16345
54.4%
Common
ValueCountFrequency (%)
1 73
30.3%
2 69
28.6%
3 42
17.4%
4 24
 
10.0%
5 13
 
5.4%
8
 
3.3%
6 5
 
2.1%
7 5
 
2.1%
( 1
 
0.4%
) 1
 
0.4%
Han
ValueCountFrequency (%)
華 1
50.0%
山 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30027
99.2%
ASCII 241
 
0.8%
CJK 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 7471
24.9%
동 2172
 
7.2%
산 815
 
2.7%
면 580
 
1.9%
곡 559
 
1.9%
신 475
 
1.6%
천 457
 
1.5%
가 404
 
1.3%
대 399
 
1.3%
성 350
 
1.2%
Other values (362) 16345
54.4%
ASCII
ValueCountFrequency (%)
1 73
30.3%
2 69
28.6%
3 42
17.4%
4 24
 
10.0%
5 13
 
5.4%
8
 
3.3%
6 5
 
2.1%
7 5
 
2.1%
( 1
 
0.4%
) 1
 
0.4%
CJK
ValueCountFrequency (%)
華 1
50.0%
山 1
50.0%

수정일자
Real number (ℝ)

MISSING  ZEROS 

Distinct141
Distinct (%)2.2%
Missing3647
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean730956.65
Minimum0
Maximum980501
Zeros184
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:56:05.032946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10105
Q1810701
median950101
Q3950101
95-th percentile960301
Maximum980501
Range980501
Interquartile range (IQR)139400

Descriptive statistics

Standard deviation373863.33
Coefficient of variation (CV)0.51147128
Kurtosis-0.47584993
Mean730956.65
Median Absolute Deviation (MAD)0
Skewness-1.2112165
Sum4.6437676 × 109
Variance1.3977379 × 1011
MonotonicityNot monotonic
2023-12-13T04:56:05.235609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
950101 3257
32.6%
880701 414
 
4.1%
960301 215
 
2.1%
950510 201
 
2.0%
140701 188
 
1.9%
0 184
 
1.8%
10105 164
 
1.6%
950301 150
 
1.5%
100701 108
 
1.1%
10322 106
 
1.1%
Other values (131) 1366
 
13.7%
(Missing) 3647
36.5%
ValueCountFrequency (%)
0 184
1.8%
1 1
 
< 0.1%
101 1
 
< 0.1%
1001 5
 
0.1%
1231 52
 
0.5%
10105 164
1.6%
10310 1
 
< 0.1%
10322 106
1.1%
11010 1
 
< 0.1%
11128 2
 
< 0.1%
ValueCountFrequency (%)
980501 5
 
0.1%
980401 95
0.9%
980214 1
 
< 0.1%
980101 1
 
< 0.1%
971101 13
 
0.1%
970715 72
0.7%
970606 1
 
< 0.1%
961130 9
 
0.1%
960901 1
 
< 0.1%
960501 1
 
< 0.1%

이전시도코드
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct257
Distinct (%)5.8%
Missing5596
Missing (%)56.0%
Infinite0
Infinite (%)0.0%
Mean41116.311
Minimum0
Maximum82608
Zeros230
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T04:56:05.423571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141740
median44740
Q347800
95-th percentile48790
Maximum82608
Range82608
Interquartile range (IQR)6060

Descriptive statistics

Standard deviation11617.786
Coefficient of variation (CV)0.28255905
Kurtosis5.8247248
Mean41116.311
Median Absolute Deviation (MAD)3030
Skewness-2.5272736
Sum1.8107623 × 108
Variance1.3497295 × 108
MonotonicityNot monotonic
2023-12-13T04:56:05.613717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 230
 
2.3%
41750 84
 
0.8%
47780 74
 
0.7%
44750 74
 
0.7%
44130 71
 
0.7%
44820 71
 
0.7%
44740 58
 
0.6%
44730 58
 
0.6%
44850 57
 
0.6%
41730 56
 
0.6%
Other values (247) 3571
35.7%
(Missing) 5596
56.0%
ValueCountFrequency (%)
0 230
2.3%
11200 2
 
< 0.1%
11210 5
 
0.1%
11300 2
 
< 0.1%
11380 2
 
< 0.1%
11530 1
 
< 0.1%
11650 1
 
< 0.1%
11680 2
 
< 0.1%
21000 1
 
< 0.1%
21110 13
 
0.1%
ValueCountFrequency (%)
82608 1
 
< 0.1%
49720 20
0.2%
49710 39
0.4%
49130 6
 
0.1%
49110 19
0.2%
48890 1
 
< 0.1%
48880 1
 
< 0.1%
48850 2
 
< 0.1%
48830 30
0.3%
48810 27
0.3%

이전동코드
Text

MISSING 

Distinct654
Distinct (%)14.9%
Missing5596
Missing (%)56.0%
Memory size156.2 KiB
2023-12-13T04:56:06.156614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters22020
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique210 ?
Unique (%)4.8%

Sample

1st row37027
2nd row31022
3rd row33033
4th row32022
5th row25037
ValueCountFrequency (%)
00000 267
 
6.1%
33023 35
 
0.8%
31000 35
 
0.8%
32021 33
 
0.7%
31021 32
 
0.7%
31022 32
 
0.7%
32023 31
 
0.7%
32027 29
 
0.7%
37000 28
 
0.6%
10200 28
 
0.6%
Other values (645) 3855
87.5%
2023-12-13T04:56:06.770723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 7215
32.8%
3 4306
19.6%
2 3637
16.5%
1 1871
 
8.5%
4 1376
 
6.2%
5 1189
 
5.4%
6 710
 
3.2%
7 657
 
3.0%
8 546
 
2.5%
9 506
 
2.3%
Other values (4) 7
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22013
> 99.9%
Lowercase Letter 4
 
< 0.1%
Space Separator 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7215
32.8%
3 4306
19.6%
2 3637
16.5%
1 1871
 
8.5%
4 1376
 
6.3%
5 1189
 
5.4%
6 710
 
3.2%
7 657
 
3.0%
8 546
 
2.5%
9 506
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
l 2
50.0%
n 1
25.0%
u 1
25.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22016
> 99.9%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7215
32.8%
3 4306
19.6%
2 3637
16.5%
1 1871
 
8.5%
4 1376
 
6.2%
5 1189
 
5.4%
6 710
 
3.2%
7 657
 
3.0%
8 546
 
2.5%
9 506
 
2.3%
Latin
ValueCountFrequency (%)
l 2
50.0%
n 1
25.0%
u 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7215
32.8%
3 4306
19.6%
2 3637
16.5%
1 1871
 
8.5%
4 1376
 
6.2%
5 1189
 
5.4%
6 710
 
3.2%
7 657
 
3.0%
8 546
 
2.5%
9 506
 
2.3%
Other values (4) 7
 
< 0.1%

이전동명
Text

MISSING 

Distinct3154
Distinct (%)69.6%
Missing5470
Missing (%)54.7%
Memory size156.2 KiB
2023-12-13T04:56:07.184762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0147903
Min length2

Characters and Unicode

Total characters13657
Distinct characters345
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2396 ?
Unique (%)52.9%

Sample

1st row석천리
2nd row용전리
3rd row상성리
4th row상야리
5th row동연리
ValueCountFrequency (%)
신흥리 14
 
0.3%
용암리 13
 
0.3%
중리 12
 
0.3%
동산리 10
 
0.2%
교촌리 10
 
0.2%
금곡리 10
 
0.2%
남산리 10
 
0.2%
대곡리 10
 
0.2%
가산리 10
 
0.2%
신촌리 9
 
0.2%
Other values (3144) 4422
97.6%
2023-12-13T04:56:07.732032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 3477
25.5%
동 913
 
6.7%
산 379
 
2.8%
면 278
 
2.0%
곡 270
 
2.0%
신 248
 
1.8%
천 208
 
1.5%
대 190
 
1.4%
가 173
 
1.3%
성 158
 
1.2%
Other values (335) 7363
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13563
99.3%
Decimal Number 84
 
0.6%
Space Separator 6
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 3477
25.6%
동 913
 
6.7%
산 379
 
2.8%
면 278
 
2.0%
곡 270
 
2.0%
신 248
 
1.8%
천 208
 
1.5%
대 190
 
1.4%
가 173
 
1.3%
성 158
 
1.2%
Other values (326) 7269
53.6%
Decimal Number
ValueCountFrequency (%)
2 34
40.5%
1 25
29.8%
3 15
17.9%
5 5
 
6.0%
4 4
 
4.8%
7 1
 
1.2%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13559
99.3%
Common 94
 
0.7%
Han 4
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 3477
25.6%
동 913
 
6.7%
산 379
 
2.8%
면 278
 
2.1%
곡 270
 
2.0%
신 248
 
1.8%
천 208
 
1.5%
대 190
 
1.4%
가 173
 
1.3%
성 158
 
1.2%
Other values (322) 7265
53.6%
Common
ValueCountFrequency (%)
2 34
36.2%
1 25
26.6%
3 15
16.0%
6
 
6.4%
5 5
 
5.3%
4 4
 
4.3%
) 2
 
2.1%
( 2
 
2.1%
7 1
 
1.1%
Han
ValueCountFrequency (%)
基 1
25.0%
岩 1
25.0%
華 1
25.0%
山 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13559
99.3%
ASCII 94
 
0.7%
CJK 4
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 3477
25.6%
동 913
 
6.7%
산 379
 
2.8%
면 278
 
2.1%
곡 270
 
2.0%
신 248
 
1.8%
천 208
 
1.5%
대 190
 
1.4%
가 173
 
1.3%
성 158
 
1.2%
Other values (322) 7265
53.6%
ASCII
ValueCountFrequency (%)
2 34
36.2%
1 25
26.6%
3 15
16.0%
6
 
6.4%
5 5
 
5.3%
4 4
 
4.3%
) 2
 
2.1%
( 2
 
2.1%
7 1
 
1.1%
CJK
ValueCountFrequency (%)
基 1
25.0%
岩 1
25.0%
華 1
25.0%
山 1
25.0%

사용여부(fuse)
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
사용
6020 
미사용
3980 

Length

Max length3
Median length2
Mean length2.398
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사용
2nd row미사용
3rd row미사용
4th row사용
5th row미사용

Common Values

ValueCountFrequency (%)
사용 6020
60.2%
미사용 3980
39.8%

Length

2023-12-13T04:56:07.909614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:56:08.028337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사용 6020
60.2%
미사용 3980
39.8%

시도명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
1592 
경상북도
1555 
경상남도
1340 
충청남도
1154 
전라남도
1069 
Other values (21)
3290 

Length

Max length7
Median length4
Mean length3.9229
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row경상남도
2nd row대구직할시
3rd row경기도
4th row전라남도
5th row경상남도

Common Values

ValueCountFrequency (%)
경기도 1592
15.9%
경상북도 1555
15.6%
경상남도 1340
13.4%
충청남도 1154
11.5%
전라남도 1069
10.7%
전라북도 807
8.1%
충청북도 742
7.4%
강원도 537
 
5.4%
서울특별시 150
 
1.5%
인천광역시 123
 
1.2%
Other values (16) 931
9.3%

Length

2023-12-13T04:56:08.165460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1592
15.9%
경상북도 1555
15.6%
경상남도 1340
13.4%
충청남도 1154
11.5%
전라남도 1069
10.7%
전라북도 807
8.1%
충청북도 742
7.4%
강원도 537
 
5.4%
서울특별시 150
 
1.5%
인천광역시 123
 
1.2%
Other values (16) 931
9.3%

시군구명
Text

Distinct320
Distinct (%)3.2%
Missing75
Missing (%)0.8%
Memory size156.2 KiB
2023-12-13T04:56:08.493312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.1122418
Min length2

Characters and Unicode

Total characters30889
Distinct characters147
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

Unique8 ?
Unique (%)0.1%

Sample

1st row산청군
2nd row서구
3rd row용인시
4th row영암군
5th row창원시
ValueCountFrequency (%)
중구 137
 
1.4%
청원군 120
 
1.2%
이천시 92
 
0.9%
화성시 91
 
0.9%
북구 90
 
0.9%
달성군 88
 
0.9%
파주시 87
 
0.9%
동구 83
 
0.8%
강화군 77
 
0.8%
제주시 74
 
0.7%
Other values (310) 8986
90.5%
2023-12-13T04:56:09.007483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 5618
18.2%
시 2920
 
9.5%
주 1674
 
5.4%
구 1596
 
5.2%
산 1280
 
4.1%
천 1247
 
4.0%
성 835
 
2.7%
양 761
 
2.5%
원 667
 
2.2%
안 657
 
2.1%
Other values (137) 13634
44.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30889
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 5618
18.2%
시 2920
 
9.5%
주 1674
 
5.4%
구 1596
 
5.2%
산 1280
 
4.1%
천 1247
 
4.0%
성 835
 
2.7%
양 761
 
2.5%
원 667
 
2.2%
안 657
 
2.1%
Other values (137) 13634
44.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30889
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 5618
18.2%
시 2920
 
9.5%
주 1674
 
5.4%
구 1596
 
5.2%
산 1280
 
4.1%
천 1247
 
4.0%
성 835
 
2.7%
양 761
 
2.5%
원 667
 
2.2%
안 657
 
2.1%
Other values (137) 13634
44.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30889
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 5618
18.2%
시 2920
 
9.5%
주 1674
 
5.4%
구 1596
 
5.2%
산 1280
 
4.1%
천 1247
 
4.0%
성 835
 
2.7%
양 761
 
2.5%
원 667
 
2.2%
안 657
 
2.1%
Other values (137) 13634
44.1%

읍면명
Text

MISSING 

Distinct1333
Distinct (%)16.5%
Missing1901
Missing (%)19.0%
Memory size156.2 KiB
2023-12-13T04:56:09.418432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.9928386
Min length2

Characters and Unicode

Total characters24239
Distinct characters282
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

Unique109 ?
Unique (%)1.3%

Sample

1st row생초면
2nd row백암면
3rd row도포면
4th row동읍
5th row앙성면
ValueCountFrequency (%)
동면 59
 
0.7%
북면 57
 
0.7%
서면 51
 
0.6%
남면 43
 
0.5%
상북면 27
 
0.3%
북이면 25
 
0.3%
오창읍 25
 
0.3%
진동면 24
 
0.3%
진북면 24
 
0.3%
봉담읍 23
 
0.3%
Other values (1322) 7741
95.6%
2023-12-13T04:56:10.010069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 6625
27.3%
읍 1482
 
6.1%
산 814
 
3.4%
성 459
 
1.9%
동 451
 
1.9%
남 441
 
1.8%
북 389
 
1.6%
양 362
 
1.5%
천 348
 
1.4%
서 295
 
1.2%
Other values (272) 12573
51.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24229
> 99.9%
Space Separator 10
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 6625
27.3%
읍 1482
 
6.1%
산 814
 
3.4%
성 459
 
1.9%
동 451
 
1.9%
남 441
 
1.8%
북 389
 
1.6%
양 362
 
1.5%
천 348
 
1.4%
서 295
 
1.2%
Other values (271) 12563
51.9%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 24229
> 99.9%
Common 10
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 6625
27.3%
읍 1482
 
6.1%
산 814
 
3.4%
성 459
 
1.9%
동 451
 
1.9%
남 441
 
1.8%
북 389
 
1.6%
양 362
 
1.5%
천 348
 
1.4%
서 295
 
1.2%
Other values (271) 12563
51.9%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 24229
> 99.9%
ASCII 10
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 6625
27.3%
읍 1482
 
6.1%
산 814
 
3.4%
성 459
 
1.9%
동 451
 
1.9%
남 441
 
1.8%
북 389
 
1.6%
양 362
 
1.5%
천 348
 
1.4%
서 295
 
1.2%
Other values (271) 12563
51.9%
ASCII
ValueCountFrequency (%)
10
100.0%

동명
Text

MISSING 

Distinct5236
Distinct (%)56.9%
Missing802
Missing (%)8.0%
Memory size156.2 KiB
2023-12-13T04:56:10.407454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.0271798
Min length2

Characters and Unicode

Total characters27844
Distinct characters380
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3453 ?
Unique (%)37.5%

Sample

1st row계남리
2nd row석천리
3rd row수산리
4th row용전리
5th row용포리
ValueCountFrequency (%)
대곡리 22
 
0.2%
용암리 22
 
0.2%
신흥리 20
 
0.2%
금곡리 18
 
0.2%
용산리 18
 
0.2%
오산리 18
 
0.2%
신촌리 17
 
0.2%
교촌리 17
 
0.2%
가산리 17
 
0.2%
마산리 15
 
0.2%
Other values (5225) 9014
98.0%
2023-12-13T04:56:10.960497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 7462
26.8%
동 2124
 
7.6%
산 729
 
2.6%
곡 548
 
2.0%
신 457
 
1.6%
천 413
 
1.5%
가 390
 
1.4%
대 377
 
1.4%
정 326
 
1.2%
성 311
 
1.1%
Other values (370) 14707
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 27605
99.1%
Decimal Number 231
 
0.8%
Space Separator 6
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 7462
27.0%
동 2124
 
7.7%
산 729
 
2.6%
곡 548
 
2.0%
신 457
 
1.7%
천 413
 
1.5%
가 390
 
1.4%
대 377
 
1.4%
정 326
 
1.2%
성 311
 
1.1%
Other values (360) 14468
52.4%
Decimal Number
ValueCountFrequency (%)
1 73
31.6%
2 69
29.9%
3 42
18.2%
4 24
 
10.4%
5 13
 
5.6%
6 5
 
2.2%
7 5
 
2.2%
Space Separator
ValueCountFrequency (%)
6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 27603
99.1%
Common 239
 
0.9%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 7462
27.0%
동 2124
 
7.7%
산 729
 
2.6%
곡 548
 
2.0%
신 457
 
1.7%
천 413
 
1.5%
가 390
 
1.4%
대 377
 
1.4%
정 326
 
1.2%
성 311
 
1.1%
Other values (358) 14466
52.4%
Common
ValueCountFrequency (%)
1 73
30.5%
2 69
28.9%
3 42
17.6%
4 24
 
10.0%
5 13
 
5.4%
6
 
2.5%
6 5
 
2.1%
7 5
 
2.1%
( 1
 
0.4%
) 1
 
0.4%
Han
ValueCountFrequency (%)
華 1
50.0%
山 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 27603
99.1%
ASCII 239
 
0.9%
CJK 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 7462
27.0%
동 2124
 
7.7%
산 729
 
2.6%
곡 548
 
2.0%
신 457
 
1.7%
천 413
 
1.5%
가 390
 
1.4%
대 377
 
1.4%
정 326
 
1.2%
성 311
 
1.1%
Other values (358) 14466
52.4%
ASCII
ValueCountFrequency (%)
1 73
30.5%
2 69
28.9%
3 42
17.6%
4 24
 
10.0%
5 13
 
5.4%
6
 
2.5%
6 5
 
2.1%
7 5
 
2.1%
( 1
 
0.4%
) 1
 
0.4%
CJK
ValueCountFrequency (%)
華 1
50.0%
山 1
50.0%

우편번호
Text

MISSING 

Distinct5305
Distinct (%)63.0%
Missing1579
Missing (%)15.8%
Memory size156.2 KiB
2023-12-13T04:56:11.351326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters58947
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

Unique3454 ?
Unique (%)41.0%

Sample

1st row666-912
2nd row449-864
3rd row526-833
4th row641-862
5th row413-810
ValueCountFrequency (%)
363-883 15
 
0.2%
445-892 11
 
0.1%
486-880 10
 
0.1%
413-830 10
 
0.1%
467-850 10
 
0.1%
641-872 9
 
0.1%
330-842 9
 
0.1%
467-860 9
 
0.1%
445-921 8
 
0.1%
451-802 8
 
0.1%
Other values (5295) 8322
98.8%
2023-12-13T04:56:11.855819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 8421
14.3%
8 6827
11.6%
0 6111
10.4%
3 6014
10.2%
1 5348
9.1%
2 5000
8.5%
5 4939
8.4%
6 4711
8.0%
4 4640
7.9%
7 3743
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50526
85.7%
Dash Punctuation 8421
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 6827
13.5%
0 6111
12.1%
3 6014
11.9%
1 5348
10.6%
2 5000
9.9%
5 4939
9.8%
6 4711
9.3%
4 4640
9.2%
7 3743
7.4%
9 3193
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 8421
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58947
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 8421
14.3%
8 6827
11.6%
0 6111
10.4%
3 6014
10.2%
1 5348
9.1%
2 5000
8.5%
5 4939
8.4%
6 4711
8.0%
4 4640
7.9%
7 3743
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 8421
14.3%
8 6827
11.6%
0 6111
10.4%
3 6014
10.2%
1 5348
9.1%
2 5000
8.5%
5 4939
8.4%
6 4711
8.0%
4 4640
7.9%
7 3743
6.3%

Interactions

2023-12-13T04:56:01.975154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:00.633488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:01.105359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:01.542530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:02.088060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:00.744187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:01.227906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:01.657682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:02.192836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:00.846932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:01.334950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:01.762561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:02.308199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:00.959234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:01.443929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:56:01.864339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:56:11.968047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드동코드수정일자이전시도코드사용여부(fuse)시도명
시도코드1.0000.4290.2930.7600.2550.997
동코드0.4291.0000.4320.5150.1380.561
수정일자0.2930.4321.0000.5320.3310.736
이전시도코드0.7600.5150.5321.0000.4250.899
사용여부(fuse)0.2550.1380.3310.4251.0000.475
시도명0.9970.5610.7360.8990.4751.000
2023-12-13T04:56:12.090069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사용여부(fuse)시도명
사용여부(fuse)1.0000.378
시도명0.3781.000
2023-12-13T04:56:12.186079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도코드동코드수정일자이전시도코드사용여부(fuse)시도명
시도코드1.0000.2660.0540.7980.1840.987
동코드0.2661.0000.1560.2960.1050.240
수정일자0.0540.1561.0000.2560.2380.443
이전시도코드0.7980.2960.2561.0000.3190.644
사용여부(fuse)0.1840.1050.2380.3191.0000.378
시도명0.9870.2400.4430.6440.3781.000

Missing values

2023-12-13T04:56:02.473058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:56:02.676379image/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.
2023-12-13T04:56:02.855766image/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

시도코드동코드출력동명수정일자이전시도코드이전동코드이전동명사용여부(fuse)시도명시군구명읍면명동명우편번호
318094886033032계남리<NA><NA><NA><NA>사용경상남도산청군생초면계남리666-912
33377221700서구950101<NA><NA><NA>미사용대구직할시서구<NA><NA><NA>
45304146037027석천리101054149037027석천리미사용경기도용인시백암면석천리449-864
209874683035028수산리<NA><NA><NA><NA>사용전라남도영암군도포면수산리526-833
289154811025022용전리9503024811031022용전리미사용경상남도창원시동읍용전리641-862
265974378037021용포리950101<NA><NA><NA>미사용충청북도중원군앙성면용포리<NA>
52934177031025영태리<NA><NA><NA><NA>미사용경기도파주군월롱면영태리413-810
48204173036021후포리<NA><NA><NA><NA>미사용경기도여주군대신면후포리469-843
262934771037039오산리<NA><NA><NA><NA>미사용경상북도달성군현풍면오산리<NA>
302084780036033삼귀리950101<NA><NA><NA>미사용경상북도영천군자양면삼귀리770-871
시도코드동코드출력동명수정일자이전시도코드이전동코드이전동명사용여부(fuse)시도명시군구명읍면명동명우편번호
121024579041027동산리<NA><NA><NA><NA>사용전라북도고창군성내면동산리585-811
34694155025000공도읍111284155037000공도면사용경기도안성시공도읍<NA>456-820
10904159025300봉담읍103224175025300봉담면사용경기도화성시봉담읍<NA>445-890
3724173039000산북면<NA><NA><NA><NA>미사용경기도여주군산북면<NA>469-890
33849481600마산시12314815300000마산시미사용경상남도마산시<NA><NA><NA>
144552417014000대촌동950101<NA><NA><NA>미사용광주직할시북구<NA>대촌동<NA>
288484788043022관동리950101<NA><NA><NA>미사용경상북도상주군외서면관동리742-922
224274678039021율포리<NA><NA><NA><NA>사용전라남도보성군회천면율포리546-873
265794378035026모남리950101<NA><NA><NA>미사용충청북도중원군신니면모남리<NA>
285004671036024영천리<NA><NA><NA><NA>사용전라남도담양군무정면영천리517-843