Overview

Dataset statistics

Number of variables12
Number of observations103
Missing cells234
Missing cells (%)18.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.1 KiB
Average record size in memory100.3 B

Variable types

Categorical2
Text6
DateTime1
Numeric3

Alerts

허가면적(㎡) is highly overall correlated with 시군명High correlation
WGS84위도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 허가면적(㎡) and 3 other fieldsHigh correlation
토석채취용도 is highly overall correlated with WGS84위도 and 2 other fieldsHigh correlation
업체 전화번호 has 50 (48.5%) missing valuesMissing
허가면적(㎡) has 11 (10.7%) missing valuesMissing
허가지도로명주소 has 62 (60.2%) missing valuesMissing
WGS84위도 has 9 (8.7%) missing valuesMissing
WGS84경도 has 9 (8.7%) missing valuesMissing
비고 has 89 (86.4%) missing valuesMissing

Reproduction

Analysis started2024-04-11 02:12:53.229358
Analysis finished2024-04-11 02:12:56.954464
Duration3.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size956.0 B
여주시
30 
포천시
10 
가평군
9 
연천군
9 
평택시
7 
Other values (13)
38 

Length

Max length4
Median length3
Mean length3.0679612
Min length3

Unique

Unique3 ?
Unique (%)2.9%

Sample

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

Common Values

ValueCountFrequency (%)
여주시 30
29.1%
포천시 10
 
9.7%
가평군 9
 
8.7%
연천군 9
 
8.7%
평택시 7
 
6.8%
오산시 5
 
4.9%
용인시 5
 
4.9%
파주시 4
 
3.9%
광주시 4
 
3.9%
이천시 4
 
3.9%
Other values (8) 16
15.5%

Length

2024-04-11T11:12:57.014203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
여주시 30
29.1%
포천시 10
 
9.7%
가평군 9
 
8.7%
연천군 9
 
8.7%
평택시 7
 
6.8%
오산시 5
 
4.9%
용인시 5
 
4.9%
광주시 4
 
3.9%
이천시 4
 
3.9%
파주시 4
 
3.9%
Other values (8) 16
15.5%

상호명
Text

Distinct87
Distinct (%)85.3%
Missing1
Missing (%)1.0%
Memory size956.0 B
2024-04-11T11:12:57.218389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length13.5
Mean length7.0196078
Min length2

Characters and Unicode

Total characters716
Distinct characters170
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)72.5%

Sample

1st row㈜협신
2nd row유창산업㈜
3rd row㈜국제신소재
4th row㈜협신
5th row㈜KCC
ValueCountFrequency (%)
임의규 4
 
3.2%
서두산업㈜ 3
 
2.4%
개인 2
 
1.6%
학교법인 2
 
1.6%
여주에너지서비스㈜ 2
 
1.6%
㈜삼성종합물류 2
 
1.6%
oo산업개발㈜ 2
 
1.6%
박oo 2
 
1.6%
삼성물산㈜ 2
 
1.6%
현대건설㈜ 2
 
1.6%
Other values (93) 102
81.6%
2024-04-11T11:12:57.564485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 59
 
8.2%
산 26
 
3.6%
업 25
 
3.5%
23
 
3.2%
개 20
 
2.8%
발 19
 
2.7%
지 17
 
2.4%
사 15
 
2.1%
스 15
 
2.1%
주 14
 
2.0%
Other values (160) 483
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 601
83.9%
Other Symbol 59
 
8.2%
Space Separator 23
 
3.2%
Uppercase Letter 17
 
2.4%
Close Punctuation 5
 
0.7%
Open Punctuation 5
 
0.7%
Decimal Number 5
 
0.7%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 26
 
4.3%
업 25
 
4.2%
개 20
 
3.3%
발 19
 
3.2%
지 17
 
2.8%
사 15
 
2.5%
스 15
 
2.5%
주 14
 
2.3%
신 13
 
2.2%
합 10
 
1.7%
Other values (148) 427
71.0%
Uppercase Letter
ValueCountFrequency (%)
O 12
70.6%
K 2
 
11.8%
C 2
 
11.8%
H 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
3 1
 
20.0%
4 1
 
20.0%
Other Symbol
ValueCountFrequency (%)
㈜ 59
100.0%
Space Separator
ValueCountFrequency (%)
23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 660
92.2%
Common 39
 
5.4%
Latin 17
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 59
 
8.9%
산 26
 
3.9%
업 25
 
3.8%
개 20
 
3.0%
발 19
 
2.9%
지 17
 
2.6%
사 15
 
2.3%
스 15
 
2.3%
주 14
 
2.1%
신 13
 
2.0%
Other values (149) 437
66.2%
Common
ValueCountFrequency (%)
23
59.0%
) 5
 
12.8%
( 5
 
12.8%
1 3
 
7.7%
, 1
 
2.6%
3 1
 
2.6%
4 1
 
2.6%
Latin
ValueCountFrequency (%)
O 12
70.6%
K 2
 
11.8%
C 2
 
11.8%
H 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 601
83.9%
None 59
 
8.2%
ASCII 56
 
7.8%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 59
100.0%
Hangul
ValueCountFrequency (%)
산 26
 
4.3%
업 25
 
4.2%
개 20
 
3.3%
발 19
 
3.2%
지 17
 
2.8%
사 15
 
2.5%
스 15
 
2.5%
주 14
 
2.3%
신 13
 
2.2%
합 10
 
1.7%
Other values (148) 427
71.0%
ASCII
ValueCountFrequency (%)
23
41.1%
O 12
21.4%
) 5
 
8.9%
( 5
 
8.9%
1 3
 
5.4%
K 2
 
3.6%
C 2
 
3.6%
H 1
 
1.8%
, 1
 
1.8%
3 1
 
1.8%

허가시작일
Date

Distinct96
Distinct (%)94.1%
Missing1
Missing (%)1.0%
Memory size956.0 B
Minimum1991-05-03 00:00:00
Maximum2024-03-06 00:00:00
2024-04-11T11:12:57.686525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:12:57.808235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

허가종료일
Text

Distinct76
Distinct (%)74.5%
Missing1
Missing (%)1.0%
Memory size956.0 B
2024-04-11T11:12:58.034639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1020
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

Unique61 ?
Unique (%)59.8%

Sample

1st row2022-01-03
2nd row2024-06-16
3rd row2021-05-31
4th row2021-02-26
5th row2020-12-31
ValueCountFrequency (%)
2025-12-31 5
 
4.9%
2023-12-31 4
 
3.9%
2024-12-31 4
 
3.9%
2019-12-31 3
 
2.9%
2015-11-30 3
 
2.9%
2026-12-31 3
 
2.9%
2022-11-06 3
 
2.9%
2022-04-30 2
 
2.0%
2024-03-31 2
 
2.0%
2011-12-15 2
 
2.0%
Other values (66) 71
69.6%
2024-04-11T11:12:58.355566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 234
22.9%
0 216
21.2%
- 204
20.0%
1 149
14.6%
3 86
 
8.4%
4 34
 
3.3%
5 27
 
2.6%
6 23
 
2.3%
8 18
 
1.8%
9 15
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 816
80.0%
Dash Punctuation 204
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 234
28.7%
0 216
26.5%
1 149
18.3%
3 86
 
10.5%
4 34
 
4.2%
5 27
 
3.3%
6 23
 
2.8%
8 18
 
2.2%
9 15
 
1.8%
7 14
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1020
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 234
22.9%
0 216
21.2%
- 204
20.0%
1 149
14.6%
3 86
 
8.4%
4 34
 
3.3%
5 27
 
2.6%
6 23
 
2.3%
8 18
 
1.8%
9 15
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1020
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 234
22.9%
0 216
21.2%
- 204
20.0%
1 149
14.6%
3 86
 
8.4%
4 34
 
3.3%
5 27
 
2.6%
6 23
 
2.3%
8 18
 
1.8%
9 15
 
1.5%

업체 전화번호
Text

MISSING 

Distinct42
Distinct (%)79.2%
Missing50
Missing (%)48.5%
Memory size956.0 B
2024-04-11T11:12:58.539006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.867925
Min length11

Characters and Unicode

Total characters629
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

Unique34 ?
Unique (%)64.2%

Sample

1st row031-585-5511
2nd row031-584-5345
3rd row031-585-5995
4th row031-585-5511
5th row031-580-1520
ValueCountFrequency (%)
02-587-7735 5
 
9.4%
031-535-4264 2
 
3.8%
031-535-3507 2
 
3.8%
031-532-7989 2
 
3.8%
031-637-0036 2
 
3.8%
031-890-0013 2
 
3.8%
031-860-8000 2
 
3.8%
031-585-5511 2
 
3.8%
031-945-7015 1
 
1.9%
031-632-1118 1
 
1.9%
Other values (32) 32
60.4%
2024-04-11T11:12:58.841616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 108
17.2%
- 106
16.9%
3 93
14.8%
5 74
11.8%
1 59
9.4%
7 42
 
6.7%
8 40
 
6.4%
2 37
 
5.9%
6 27
 
4.3%
9 25
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 523
83.1%
Dash Punctuation 106
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 108
20.7%
3 93
17.8%
5 74
14.1%
1 59
11.3%
7 42
 
8.0%
8 40
 
7.6%
2 37
 
7.1%
6 27
 
5.2%
9 25
 
4.8%
4 18
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 629
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 108
17.2%
- 106
16.9%
3 93
14.8%
5 74
11.8%
1 59
9.4%
7 42
 
6.7%
8 40
 
6.4%
2 37
 
5.9%
6 27
 
4.3%
9 25
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 629
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 108
17.2%
- 106
16.9%
3 93
14.8%
5 74
11.8%
1 59
9.4%
7 42
 
6.7%
8 40
 
6.4%
2 37
 
5.9%
6 27
 
4.3%
9 25
 
4.0%

허가면적(㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct88
Distinct (%)95.7%
Missing11
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean67149.84
Minimum1490
Maximum1192913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-11T11:12:58.965972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1490
5-th percentile4064.95
Q120047.75
median30838.5
Q366735
95-th percentile220919.2
Maximum1192913
Range1191423
Interquartile range (IQR)46687.25

Descriptive statistics

Standard deviation136141.97
Coefficient of variation (CV)2.0274355
Kurtosis52.495236
Mean67149.84
Median Absolute Deviation (MAD)16422
Skewness6.6579113
Sum6177785.3
Variance1.8534636 × 1010
MonotonicityNot monotonic
2024-04-11T11:12:59.133835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97160.0 3
 
2.9%
54687.0 2
 
1.9%
94786.0 2
 
1.9%
21223.0 1
 
1.0%
66331.0 1
 
1.0%
203695.0 1
 
1.0%
25198.0 1
 
1.0%
19689.0 1
 
1.0%
41402.0 1
 
1.0%
27210.0 1
 
1.0%
Other values (78) 78
75.7%
(Missing) 11
 
10.7%
ValueCountFrequency (%)
1490.0 1
1.0%
2205.0 1
1.0%
3006.0 1
1.0%
3139.0 1
1.0%
3345.0 1
1.0%
4654.0 1
1.0%
5870.0 1
1.0%
10085.0 1
1.0%
10442.0 1
1.0%
13500.0 1
1.0%
ValueCountFrequency (%)
1192913.0 1
 
1.0%
402508.0 1
 
1.0%
314532.0 1
 
1.0%
295939.0 1
 
1.0%
241971.0 1
 
1.0%
203695.0 1
 
1.0%
191216.0 1
 
1.0%
99265.0 1
 
1.0%
97160.0 3
2.9%
94856.0 1
 
1.0%

토석채취용도
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Memory size956.0 B
쇄골재
14 
토사처리용
14 
토목용
12 
토목
8 
성토용
8 
Other values (25)
47 

Length

Max length19
Median length16
Mean length5.2038835
Min length2

Unique

Unique13 ?
Unique (%)12.6%

Sample

1st row쇄골재
2nd row건축
3rd row쇄골재
4th row쇄골재(복구를 위한 토석채취)
5th row쇄골재

Common Values

ValueCountFrequency (%)
쇄골재 14
13.6%
토사처리용 14
13.6%
토목용 12
11.7%
토목 8
 
7.8%
성토용 8
 
7.8%
부수적 토석채취 5
 
4.9%
조경토 4
 
3.9%
쇄골재용+토목용 4
 
3.9%
부수적토석채취 4
 
3.9%
건축 3
 
2.9%
Other values (20) 27
26.2%

Length

2024-04-11T11:12:59.260133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
쇄골재 15
 
12.1%
토사처리용 14
 
11.3%
토목용 12
 
9.7%
토목 9
 
7.3%
성토용 8
 
6.5%
부수적 8
 
6.5%
토석채취 7
 
5.6%
조경토 4
 
3.2%
쇄골재용+토목용 4
 
3.2%
부수적토석채취 4
 
3.2%
Other values (27) 39
31.5%

허가지도로명주소
Text

MISSING 

Distinct37
Distinct (%)90.2%
Missing62
Missing (%)60.2%
Memory size956.0 B
2024-04-11T11:12:59.476907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length22.219512
Min length14

Characters and Unicode

Total characters911
Distinct characters107
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

Unique33 ?
Unique (%)80.5%

Sample

1st row경기도 가평군 상면 물골길 84-50
2nd row경기도 가평군 조종면 운악청계로 333번길 211
3rd row경기도 가평군 북면 화악지암길 274번길 81
4th row경기도 가평군 상면 물골길 84-50
5th row경기도 가평군 가평읍 물안산길 42
ValueCountFrequency (%)
경기도 41
 
19.3%
가평군 9
 
4.2%
여주시 9
 
4.2%
포천시 5
 
2.4%
용인시 5
 
2.4%
처인구 5
 
2.4%
외 3
 
1.4%
양주시 3
 
1.4%
상면 3
 
1.4%
운학동 3
 
1.4%
Other values (103) 126
59.4%
2024-04-11T11:12:59.841910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
171
 
18.8%
경 41
 
4.5%
기 41
 
4.5%
도 41
 
4.5%
1 33
 
3.6%
시 31
 
3.4%
2 26
 
2.9%
로 25
 
2.7%
면 23
 
2.5%
길 19
 
2.1%
Other values (97) 460
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 551
60.5%
Decimal Number 173
 
19.0%
Space Separator 171
 
18.8%
Dash Punctuation 16
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 41
 
7.4%
기 41
 
7.4%
도 41
 
7.4%
시 31
 
5.6%
로 25
 
4.5%
면 23
 
4.2%
길 19
 
3.4%
번 16
 
2.9%
주 15
 
2.7%
가 14
 
2.5%
Other values (85) 285
51.7%
Decimal Number
ValueCountFrequency (%)
1 33
19.1%
2 26
15.0%
5 18
10.4%
7 18
10.4%
3 17
9.8%
0 14
8.1%
6 14
8.1%
4 13
 
7.5%
8 11
 
6.4%
9 9
 
5.2%
Space Separator
ValueCountFrequency (%)
171
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 551
60.5%
Common 360
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 41
 
7.4%
기 41
 
7.4%
도 41
 
7.4%
시 31
 
5.6%
로 25
 
4.5%
면 23
 
4.2%
길 19
 
3.4%
번 16
 
2.9%
주 15
 
2.7%
가 14
 
2.5%
Other values (85) 285
51.7%
Common
ValueCountFrequency (%)
171
47.5%
1 33
 
9.2%
2 26
 
7.2%
5 18
 
5.0%
7 18
 
5.0%
3 17
 
4.7%
- 16
 
4.4%
0 14
 
3.9%
6 14
 
3.9%
4 13
 
3.6%
Other values (2) 20
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 551
60.5%
ASCII 360
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
171
47.5%
1 33
 
9.2%
2 26
 
7.2%
5 18
 
5.0%
7 18
 
5.0%
3 17
 
4.7%
- 16
 
4.4%
0 14
 
3.9%
6 14
 
3.9%
4 13
 
3.6%
Other values (2) 20
 
5.6%
Hangul
ValueCountFrequency (%)
경 41
 
7.4%
기 41
 
7.4%
도 41
 
7.4%
시 31
 
5.6%
로 25
 
4.5%
면 23
 
4.2%
길 19
 
3.4%
번 16
 
2.9%
주 15
 
2.7%
가 14
 
2.5%
Other values (85) 285
51.7%

허가지지번주소
Text

Distinct95
Distinct (%)93.1%
Missing1
Missing (%)1.0%
Memory size956.0 B
2024-04-11T11:13:00.121694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length28
Mean length23.147059
Min length16

Characters and Unicode

Total characters2361
Distinct characters141
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique89 ?
Unique (%)87.3%

Sample

1st row경기도 가평군 상면 봉수리 산32-2 외 1필지
2nd row경기도 가평군 조종면 신상리 산1번지
3rd row경기도 가평군 북면 화악리 산69번지
4th row경기도 가평군 상면 봉수리 산31-2
5th row경기도 가평군 가평읍 개곡리 산280번지
ValueCountFrequency (%)
경기도 102
 
17.4%
여주시 30
 
5.1%
외 27
 
4.6%
산 15
 
2.6%
1필지 12
 
2.1%
포천시 10
 
1.7%
연천군 9
 
1.5%
가평군 9
 
1.5%
평택시 7
 
1.2%
용인시 5
 
0.9%
Other values (247) 359
61.4%
2024-04-11T11:13:00.547966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
483
20.5%
도 103
 
4.4%
경 102
 
4.3%
기 102
 
4.3%
1 89
 
3.8%
시 84
 
3.6%
산 83
 
3.5%
- 73
 
3.1%
리 72
 
3.0%
지 65
 
2.8%
Other values (131) 1105
46.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1394
59.0%
Space Separator 483
 
20.5%
Decimal Number 398
 
16.9%
Dash Punctuation 73
 
3.1%
Other Punctuation 13
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 103
 
7.4%
경 102
 
7.3%
기 102
 
7.3%
시 84
 
6.0%
산 83
 
6.0%
리 72
 
5.2%
지 65
 
4.7%
면 58
 
4.2%
주 42
 
3.0%
외 41
 
2.9%
Other values (118) 642
46.1%
Decimal Number
ValueCountFrequency (%)
1 89
22.4%
2 55
13.8%
5 44
11.1%
3 44
11.1%
8 36
9.0%
4 30
 
7.5%
6 27
 
6.8%
9 26
 
6.5%
7 25
 
6.3%
0 22
 
5.5%
Space Separator
ValueCountFrequency (%)
483
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 73
100.0%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1394
59.0%
Common 967
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 103
 
7.4%
경 102
 
7.3%
기 102
 
7.3%
시 84
 
6.0%
산 83
 
6.0%
리 72
 
5.2%
지 65
 
4.7%
면 58
 
4.2%
주 42
 
3.0%
외 41
 
2.9%
Other values (118) 642
46.1%
Common
ValueCountFrequency (%)
483
49.9%
1 89
 
9.2%
- 73
 
7.5%
2 55
 
5.7%
5 44
 
4.6%
3 44
 
4.6%
8 36
 
3.7%
4 30
 
3.1%
6 27
 
2.8%
9 26
 
2.7%
Other values (3) 60
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1394
59.0%
ASCII 967
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
483
49.9%
1 89
 
9.2%
- 73
 
7.5%
2 55
 
5.7%
5 44
 
4.6%
3 44
 
4.6%
8 36
 
3.7%
4 30
 
3.1%
6 27
 
2.8%
9 26
 
2.7%
Other values (3) 60
 
6.2%
Hangul
ValueCountFrequency (%)
도 103
 
7.4%
경 102
 
7.3%
기 102
 
7.3%
시 84
 
6.0%
산 83
 
6.0%
리 72
 
5.2%
지 65
 
4.7%
면 58
 
4.2%
주 42
 
3.0%
외 41
 
2.9%
Other values (118) 642
46.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)89.4%
Missing9
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean37.544053
Minimum37.027213
Maximum38.143329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-11T11:13:00.693324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.027213
5-th percentile37.098849
Q137.228897
median37.361456
Q337.89063
95-th percentile38.094917
Maximum38.143329
Range1.1161162
Interquartile range (IQR)0.66173372

Descriptive statistics

Standard deviation0.36009697
Coefficient of variation (CV)0.0095913185
Kurtosis-1.6084542
Mean37.544053
Median Absolute Deviation (MAD)0.29299487
Skewness0.21977498
Sum3529.1409
Variance0.12966983
MonotonicityNot monotonic
2024-04-11T11:13:00.829699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.0949172296 3
 
2.9%
37.8998521763 2
 
1.9%
37.9754457777 2
 
1.9%
37.1995581264 2
 
1.9%
37.3614562267 2
 
1.9%
37.2910889145 2
 
1.9%
37.3015790756 2
 
1.9%
37.8362195519 2
 
1.9%
37.9481448946 2
 
1.9%
37.3926706925 1
 
1.0%
Other values (74) 74
71.8%
(Missing) 9
 
8.7%
ValueCountFrequency (%)
37.0272130333 1
1.0%
37.0275487897 1
1.0%
37.0290221479 1
1.0%
37.0529367989 1
1.0%
37.0839859096 1
1.0%
37.1068527526 1
1.0%
37.1246437753 1
1.0%
37.1257017635 1
1.0%
37.1418963792 1
1.0%
37.1510841562 1
1.0%
ValueCountFrequency (%)
38.1433292041 1
 
1.0%
38.1204971073 1
 
1.0%
38.1166876633 1
 
1.0%
38.0949172296 3
2.9%
38.083668358 1
 
1.0%
38.0214195909 1
 
1.0%
38.0211757367 1
 
1.0%
38.0203931224 1
 
1.0%
38.0137253939 1
 
1.0%
38.0128427686 1
 
1.0%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct84
Distinct (%)89.4%
Missing9
Missing (%)8.7%
Infinite0
Infinite (%)0.0%
Mean127.29263
Minimum126.57084
Maximum127.7199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-11T11:13:01.171907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.57084
5-th percentile126.88501
Q1127.06822
median127.26042
Q3127.58522
95-th percentile127.68044
Maximum127.7199
Range1.149066
Interquartile range (IQR)0.51700248

Descriptive statistics

Standard deviation0.28845391
Coefficient of variation (CV)0.0022660693
Kurtosis-0.9874122
Mean127.29263
Median Absolute Deviation (MAD)0.24158348
Skewness-0.1593105
Sum11965.507
Variance0.083205661
MonotonicityNot monotonic
2024-04-11T11:13:01.312339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.3038376647 3
 
2.9%
127.0904740739 2
 
1.9%
127.214904441 2
 
1.9%
127.2640899254 2
 
1.9%
127.6587210349 2
 
1.9%
127.5853154952 2
 
1.9%
127.712767637 2
 
1.9%
127.3184613499 2
 
1.9%
127.5849428335 2
 
1.9%
127.1954555596 1
 
1.0%
Other values (74) 74
71.8%
(Missing) 9
 
8.7%
ValueCountFrequency (%)
126.5708380531 1
1.0%
126.7011800106 1
1.0%
126.713571188 1
1.0%
126.8650766681 1
1.0%
126.8754368384 1
1.0%
126.890159348 1
1.0%
126.8924912078 1
1.0%
126.8934302071 1
1.0%
126.9002952418 1
1.0%
126.9004427239 1
1.0%
ValueCountFrequency (%)
127.7199040902 1
1.0%
127.7178550098 1
1.0%
127.7156009111 1
1.0%
127.712767637 2
1.9%
127.6630279563 1
1.0%
127.6619431947 1
1.0%
127.6587210349 2
1.9%
127.6583834242 1
1.0%
127.6483587781 1
1.0%
127.6422216374 1
1.0%

비고
Text

MISSING 

Distinct11
Distinct (%)78.6%
Missing89
Missing (%)86.4%
Memory size956.0 B
2024-04-11T11:13:01.457514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length8.9285714
Min length3

Characters and Unicode

Total characters125
Distinct characters27
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)64.3%

Sample

1st row양주시청 허가과
2nd row양주시청 허가과
3rd row양주시청 허가과
4th row원측량
5th row원측량
ValueCountFrequency (%)
허가량 7
29.2%
양주시청 3
12.5%
허가과 3
12.5%
원측량 2
 
8.3%
성원측량 1
 
4.2%
우리측량 1
 
4.2%
89,421㎥ 1
 
4.2%
296,689㎥ 1
 
4.2%
89,240㎥ 1
 
4.2%
695,553㎥ 1
 
4.2%
Other values (3) 3
12.5%
2024-04-11T11:13:01.697754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
량 11
 
8.8%
10
 
8.0%
허 10
 
8.0%
가 10
 
8.0%
9 7
 
5.6%
㎥ 7
 
5.6%
, 7
 
5.6%
: 7
 
5.6%
2 6
 
4.8%
5 5
 
4.0%
Other values (17) 45
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 56
44.8%
Decimal Number 38
30.4%
Other Punctuation 14
 
11.2%
Space Separator 10
 
8.0%
Other Symbol 7
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
량 11
19.6%
허 10
17.9%
가 10
17.9%
측 4
 
7.1%
양 3
 
5.4%
주 3
 
5.4%
원 3
 
5.4%
과 3
 
5.4%
청 3
 
5.4%
시 3
 
5.4%
Other values (3) 3
 
5.4%
Decimal Number
ValueCountFrequency (%)
9 7
18.4%
2 6
15.8%
5 5
13.2%
4 4
10.5%
8 4
10.5%
0 3
7.9%
6 3
7.9%
7 3
7.9%
3 2
 
5.3%
1 1
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 7
50.0%
: 7
50.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Symbol
ValueCountFrequency (%)
㎥ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
55.2%
Hangul 56
44.8%

Most frequent character per script

Common
ValueCountFrequency (%)
10
14.5%
9 7
10.1%
㎥ 7
10.1%
, 7
10.1%
: 7
10.1%
2 6
8.7%
5 5
7.2%
4 4
 
5.8%
8 4
 
5.8%
0 3
 
4.3%
Other values (4) 9
13.0%
Hangul
ValueCountFrequency (%)
량 11
19.6%
허 10
17.9%
가 10
17.9%
측 4
 
7.1%
양 3
 
5.4%
주 3
 
5.4%
원 3
 
5.4%
과 3
 
5.4%
청 3
 
5.4%
시 3
 
5.4%
Other values (3) 3
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62
49.6%
Hangul 56
44.8%
CJK Compat 7
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
량 11
19.6%
허 10
17.9%
가 10
17.9%
측 4
 
7.1%
양 3
 
5.4%
주 3
 
5.4%
원 3
 
5.4%
과 3
 
5.4%
청 3
 
5.4%
시 3
 
5.4%
Other values (3) 3
 
5.4%
ASCII
ValueCountFrequency (%)
10
16.1%
9 7
11.3%
, 7
11.3%
: 7
11.3%
2 6
9.7%
5 5
8.1%
4 4
 
6.5%
8 4
 
6.5%
0 3
 
4.8%
6 3
 
4.8%
Other values (3) 6
9.7%
CJK Compat
ValueCountFrequency (%)
㎥ 7
100.0%

Interactions

2024-04-11T11:12:56.264770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:12:55.767997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:12:56.048556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:12:56.352844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:12:55.891173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:12:56.120646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:12:56.440555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:12:55.977246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-11T11:12:56.185839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-11T11:13:01.790043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명상호명허가시작일허가종료일업체 전화번호허가면적(㎡)토석채취용도허가지도로명주소허가지지번주소WGS84위도WGS84경도비고
시군명1.0000.9950.9990.9451.0000.8310.9691.0001.0000.9190.9061.000
상호명0.9951.0000.9980.9950.9990.0000.9920.9850.9980.9960.9881.000
허가시작일0.9990.9981.0000.9990.9921.0000.9981.0000.9960.9950.9971.000
허가종료일0.9450.9950.9991.0000.9430.0000.9510.9590.9880.7930.8631.000
업체 전화번호1.0000.9990.9920.9431.0000.4040.9850.9880.9951.0000.9991.000
허가면적(㎡)0.8310.0001.0000.0000.4041.0000.8021.0001.0000.2460.511NaN
토석채취용도0.9690.9920.9980.9510.9850.8021.0000.9780.9760.8960.8980.000
허가지도로명주소1.0000.9851.0000.9590.9881.0000.9781.0001.0001.0001.0001.000
허가지지번주소1.0000.9980.9960.9880.9951.0000.9761.0001.0001.0001.0001.000
WGS84위도0.9190.9960.9950.7931.0000.2460.8961.0001.0001.0000.5711.000
WGS84경도0.9060.9880.9970.8630.9990.5110.8981.0001.0000.5711.0000.931
비고1.0001.0001.0001.0001.000NaN0.0001.0001.0001.0000.9311.000
2024-04-11T11:13:01.901883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토석채취용도시군명
토석채취용도1.0000.683
시군명0.6831.000
2024-04-11T11:13:01.981259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
허가면적(㎡)WGS84위도WGS84경도시군명토석채취용도
허가면적(㎡)1.000-0.133-0.2330.5740.469
WGS84위도-0.1331.000-0.3030.6620.535
WGS84경도-0.233-0.3031.0000.6290.531
시군명0.5740.6620.6291.0000.683
토석채취용도0.4690.5350.5310.6831.000

Missing values

2024-04-11T11:12:56.542773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-11T11:12:56.678324image/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.
2024-04-11T11:12:56.848044image/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

시군명상호명허가시작일허가종료일업체 전화번호허가면적(㎡)토석채취용도허가지도로명주소허가지지번주소WGS84위도WGS84경도비고
0가평군㈜협신2012-01-042022-01-03031-585-551170894.0쇄골재경기도 가평군 상면 물골길 84-50경기도 가평군 상면 봉수리 산32-2 외 1필지37.83622127.318461<NA>
1가평군유창산업㈜2014-02-062024-06-16031-584-534570252.0건축경기도 가평군 조종면 운악청계로 333번길 211경기도 가평군 조종면 신상리 산1번지37.852369127.33545<NA>
2가평군㈜국제신소재2017-09-252021-05-31031-585-59951490.0쇄골재경기도 가평군 북면 화악지암길 274번길 81경기도 가평군 북면 화악리 산69번지37.948145127.584943<NA>
3가평군㈜협신2018-02-072021-02-26031-585-551137930.0쇄골재(복구를 위한 토석채취)경기도 가평군 상면 물골길 84-50경기도 가평군 상면 봉수리 산31-237.83622127.318461<NA>
4가평군㈜KCC2019-06-142020-12-31031-580-152029157.0쇄골재경기도 가평군 가평읍 물안산길 42경기도 가평군 가평읍 개곡리 산280번지37.861024127.532241<NA>
5가평군엘브스그라파이트2021-03-172024-12-3102-555-76322205.0쇄골재용(부수적)경기도 가평군 상면 연하리 산48경기도 가평군 상면 연하리 산4837.785125127.337598<NA>
6가평군국제흥업개발㈜2022-04-192024-05-31031-582-59953139.0쇄골재용(부수적)경기도 가평군 북면 화악지암길 274번길 81경기도 가평군 북면 화악리 산6937.948145127.584943<NA>
7가평군디엘본가평설악조합2022-07-132024-11-01031-585-240517845.0토목용(부수적)경기도 가평군 설악면 신천리 산45-27경기도 가평군 설악면 신천리 산45-2737.676533127.490173<NA>
8가평군동광종합토건㈜2023-07-102025-12-31031-887-537461088.0토목용(부수적)경기도 가평군 설악면 신천중앙로 107-11경기도 가평군 설악면 신천리 산45-1 외 1필지37.677122127.491234<NA>
9고양시㈜에스디산업개발2018-03-092023-12-3102-320-987337940.0토목<NA>경기도 고양시 덕양구 벽제동 97-1번지 외 23필지37.716903126.910719<NA>
시군명상호명허가시작일허가종료일업체 전화번호허가면적(㎡)토석채취용도허가지도로명주소허가지지번주소WGS84위도WGS84경도비고
93포천시㈜포천석산2019-01-012023-12-31031-532-7989<NA>건축+조경경기도 포천시 창수면 가영로150번길 159경기도 포천시 창수면 가양리 산55 외 1필지37.975446127.214904<NA>
94포천시㈜철원산업2021-03-132026-03-12031-535-3507<NA>건축+토목<NA>경기도 포천시 영북면 자일리 산59 외 1필지38.094917127.303838<NA>
95포천시㈜철원산업2021-06-012024-05-31031-535-3507<NA>건축+쇄골재<NA>경기도 포천시 영북면 자일리 산59 외 1필지38.094917127.303838<NA>
96포천시동아석재산업㈜2021-07-052031-06-28031-533-3795<NA>건축+토목경기도 포천시 영중면 금화봉길 358경기도 포천시 영중면 거사리 산60-3 외 4필지37.972176127.231039<NA>
97포천시화진석재2021-08-082024-08-07031-536-3935<NA>건축<NA>경기도 포천시 관인면 초과리 산57 외 1필지38.143329127.233037<NA>
98포천시삼양리소스㈜2021-08-312024-08-30031-533-1077<NA>쇄골재경기도 포천시 관인면 창동로1230번길 94경기도 포천시 관인면 삼율리 산143-1 외 1필지38.116688127.223391<NA>
99포천시한국종합개발㈜2022-09-212025-09-20031-535-4264<NA>쇄골재<NA>경기도 포천시 군내면 하성북리 산46 외 3필지37.914939127.246493<NA>
100포천시한국종합개발㈜2023-01-302026-01-29031-535-4264<NA>쇄골재경기도 포천시 군내면 틀못이길 300경기도 포천시 군내면 하성북리 산47 외 2필지37.911006127.243628<NA>
101포천시신덕기업2024-01-012026-12-31031-536-4000<NA>건축<NA>경기도 포천시 영북면 자일리 산59 외 1필지38.094917127.303838<NA>
102화성시㈜삼표산업1999-05-202028-05-31031-356-4376402508.0쇄골재 등<NA>경기도 화성시 비봉면 양노리 산161번지 일원37.213966126.865077<NA>