Overview

Dataset statistics

Number of variables11
Number of observations323
Missing cells2
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.5 KiB
Average record size in memory90.4 B

Variable types

Numeric2
Categorical1
Text7
DateTime1

Dataset

Description제1종 환경영향평가업 등록현황(2019-12-27 기준 / 지방청, 등록번호, 업체명, 대표자명, 소재지, 전화번호 등)
Author한국환경산업기술원
URLhttps://www.data.go.kr/data/15068356/fileData.do

Alerts

일련 번호 is highly overall correlated with 우편번호 and 1 other fieldsHigh correlation
우편번호 is highly overall correlated with 일련 번호 and 1 other fieldsHigh correlation
지방청 is highly overall correlated with 일련 번호 and 1 other fieldsHigh correlation
일련 번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:32:52.050672
Analysis finished2023-12-12 06:32:53.625456
Duration1.57 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일련 번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct323
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162
Minimum1
Maximum323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-12T15:32:53.731691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17.1
Q181.5
median162
Q3242.5
95-th percentile306.9
Maximum323
Range322
Interquartile range (IQR)161

Descriptive statistics

Standard deviation93.386294
Coefficient of variation (CV)0.57645861
Kurtosis-1.2
Mean162
Median Absolute Deviation (MAD)81
Skewness0
Sum52326
Variance8721
MonotonicityStrictly increasing
2023-12-12T15:32:53.892055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
204 1
 
0.3%
222 1
 
0.3%
221 1
 
0.3%
220 1
 
0.3%
219 1
 
0.3%
218 1
 
0.3%
217 1
 
0.3%
216 1
 
0.3%
215 1
 
0.3%
Other values (313) 313
96.9%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
323 1
0.3%
322 1
0.3%
321 1
0.3%
320 1
0.3%
319 1
0.3%
318 1
0.3%
317 1
0.3%
316 1
0.3%
315 1
0.3%
314 1
0.3%

지방청
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
한강청
149 
영산강청
42 
낙동강청
41 
대구청
39 
금강청
19 
Other values (2)
33 

Length

Max length4
Median length3
Mean length3.2569659
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한강청 149
46.1%
영산강청 42
 
13.0%
낙동강청 41
 
12.7%
대구청 39
 
12.1%
금강청 19
 
5.9%
원주청 19
 
5.9%
전북청 14
 
4.3%

Length

2023-12-12T15:32:54.029714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:32:54.148945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한강청 149
46.1%
영산강청 42
 
13.0%
낙동강청 41
 
12.7%
대구청 39
 
12.1%
금강청 19
 
5.9%
원주청 19
 
5.9%
전북청 14
 
4.3%

등록번호
Text

Distinct322
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T15:32:54.423099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length7
Mean length7.377709
Min length6

Characters and Unicode

Total characters2383
Distinct characters24
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)99.4%

Sample

1st row제서-002호
2nd row제서-003호
3rd row제서-004호
4th row제서-006호
5th row제서-008호
ValueCountFrequency (%)
제광-104호 2
 
0.6%
제대-065호 1
 
0.3%
제광-023호 1
 
0.3%
제광-018호 1
 
0.3%
제광-042호 1
 
0.3%
제광-017호 1
 
0.3%
제광-047호 1
 
0.3%
제광-013호 1
 
0.3%
012호 1
 
0.3%
제광 1
 
0.3%
Other values (326) 326
96.7%
2023-12-12T15:32:54.965962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 335
14.1%
- 335
14.1%
호 334
14.0%
0 272
11.4%
서 145
 
6.1%
1 134
 
5.6%
3 110
 
4.6%
2 102
 
4.3%
5 76
 
3.2%
6 72
 
3.0%
Other values (14) 468
19.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1005
42.2%
Other Letter 1004
42.1%
Dash Punctuation 335
 
14.1%
Space Separator 15
 
0.6%
Open Punctuation 12
 
0.5%
Close Punctuation 12
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 335
33.4%
호 334
33.3%
서 145
14.4%
광 42
 
4.2%
부 41
 
4.1%
구 39
 
3.9%
대 21
 
2.1%
원 19
 
1.9%
한 14
 
1.4%
전 14
 
1.4%
Decimal Number
ValueCountFrequency (%)
0 272
27.1%
1 134
13.3%
3 110
10.9%
2 102
 
10.1%
5 76
 
7.6%
6 72
 
7.2%
4 70
 
7.0%
8 61
 
6.1%
7 60
 
6.0%
9 48
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 335
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1379
57.9%
Hangul 1004
42.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 335
24.3%
0 272
19.7%
1 134
 
9.7%
3 110
 
8.0%
2 102
 
7.4%
5 76
 
5.5%
6 72
 
5.2%
4 70
 
5.1%
8 61
 
4.4%
7 60
 
4.4%
Other values (4) 87
 
6.3%
Hangul
ValueCountFrequency (%)
제 335
33.4%
호 334
33.3%
서 145
14.4%
광 42
 
4.2%
부 41
 
4.1%
구 39
 
3.9%
대 21
 
2.1%
원 19
 
1.9%
한 14
 
1.4%
전 14
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1379
57.9%
Hangul 1004
42.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 335
33.4%
호 334
33.3%
서 145
14.4%
광 42
 
4.2%
부 41
 
4.1%
구 39
 
3.9%
대 21
 
2.1%
원 19
 
1.9%
한 14
 
1.4%
전 14
 
1.4%
ASCII
ValueCountFrequency (%)
- 335
24.3%
0 272
19.7%
1 134
 
9.7%
3 110
 
8.0%
2 102
 
7.4%
5 76
 
5.5%
6 72
 
5.2%
4 70
 
5.1%
8 61
 
4.4%
7 60
 
4.4%
Other values (4) 87
 
6.3%

등록일자
Date

Distinct288
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
Minimum1994-02-16 00:00:00
Maximum2019-12-27 00:00:00
2023-12-12T15:32:55.162747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:55.644961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업 체 명
Text

Distinct320
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T15:32:56.001748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length17
Mean length7.4210526
Min length3

Characters and Unicode

Total characters2397
Distinct characters217
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique317 ?
Unique (%)98.1%

Sample

1st row㈜신성엔지니어링
2nd row㈜대한콘설탄트
3rd row㈜삼안
4th row㈜제일엔지니어링 종합건축사사무소
5th row㈜우대기술단
ValueCountFrequency (%)
주식회사 13
 
3.7%
종합건축사사무소 4
 
1.1%
㈜한가람 2
 
0.6%
㈜그린환경 2
 
0.6%
㈜유일 2
 
0.6%
종합건축사무소 2
 
0.6%
건축사사무소 2
 
0.6%
㈜일월이엔씨 1
 
0.3%
도시종합건설㈜ 1
 
0.3%
㈜신명건설기술공사 1
 
0.3%
Other values (322) 322
91.5%
2023-12-12T15:32:56.468032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 294
 
12.3%
엔 145
 
6.0%
지 120
 
5.0%
어 98
 
4.1%
니 98
 
4.1%
링 97
 
4.0%
이 87
 
3.6%
사 70
 
2.9%
기 68
 
2.8%
술 66
 
2.8%
Other values (207) 1254
52.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2035
84.9%
Other Symbol 294
 
12.3%
Space Separator 29
 
1.2%
Open Punctuation 13
 
0.5%
Close Punctuation 12
 
0.5%
Decimal Number 6
 
0.3%
Uppercase Letter 5
 
0.2%
Other Punctuation 2
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
엔 145
 
7.1%
지 120
 
5.9%
어 98
 
4.8%
니 98
 
4.8%
링 97
 
4.8%
이 87
 
4.3%
사 70
 
3.4%
기 68
 
3.3%
술 66
 
3.2%
씨 44
 
2.2%
Other values (190) 1142
56.1%
Decimal Number
ValueCountFrequency (%)
0 1
16.7%
5 1
16.7%
2 1
16.7%
3 1
16.7%
1 1
16.7%
9 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
N 1
20.0%
E 1
20.0%
G 1
20.0%
S 1
20.0%
L 1
20.0%
Other Symbol
ValueCountFrequency (%)
㈜ 294
100.0%
Space Separator
ValueCountFrequency (%)
29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2329
97.2%
Common 63
 
2.6%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 294
 
12.6%
엔 145
 
6.2%
지 120
 
5.2%
어 98
 
4.2%
니 98
 
4.2%
링 97
 
4.2%
이 87
 
3.7%
사 70
 
3.0%
기 68
 
2.9%
술 66
 
2.8%
Other values (191) 1186
50.9%
Common
ValueCountFrequency (%)
29
46.0%
( 13
20.6%
) 12
19.0%
. 2
 
3.2%
0 1
 
1.6%
5 1
 
1.6%
2 1
 
1.6%
3 1
 
1.6%
1 1
 
1.6%
9 1
 
1.6%
Latin
ValueCountFrequency (%)
N 1
20.0%
E 1
20.0%
G 1
20.0%
S 1
20.0%
L 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2035
84.9%
None 294
 
12.3%
ASCII 68
 
2.8%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 294
100.0%
Hangul
ValueCountFrequency (%)
엔 145
 
7.1%
지 120
 
5.9%
어 98
 
4.8%
니 98
 
4.8%
링 97
 
4.8%
이 87
 
4.3%
사 70
 
3.4%
기 68
 
3.3%
술 66
 
3.2%
씨 44
 
2.2%
Other values (190) 1142
56.1%
ASCII
ValueCountFrequency (%)
29
42.6%
( 13
19.1%
) 12
17.6%
. 2
 
2.9%
0 1
 
1.5%
5 1
 
1.5%
2 1
 
1.5%
N 1
 
1.5%
E 1
 
1.5%
3 1
 
1.5%
Other values (6) 6
 
8.8%

대표자
Text

Distinct321
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T15:32:56.850056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length3
Mean length3.8142415
Min length2

Characters and Unicode

Total characters1232
Distinct characters168
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

Unique319 ?
Unique (%)98.8%

Sample

1st row정찬섭 정태섭
2nd row이영민
3rd row최동식
4th row임종선 임은영
5th row안치섭
ValueCountFrequency (%)
이상현 2
 
0.5%
김명철 2
 
0.5%
이상수 2
 
0.5%
이재호 2
 
0.5%
오상호 2
 
0.5%
현훈철 1
 
0.3%
김장흥 1
 
0.3%
정병연 1
 
0.3%
이정용 1
 
0.3%
김정민 1
 
0.3%
Other values (367) 367
96.1%
2023-12-12T15:32:57.424176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80
 
6.5%
김 75
 
6.1%
이 60
 
4.9%
정 45
 
3.7%
박 32
 
2.6%
영 32
 
2.6%
상 31
 
2.5%
호 26
 
2.1%
수 22
 
1.8%
현 20
 
1.6%
Other values (158) 809
65.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1149
93.3%
Space Separator 80
 
6.5%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 75
 
6.5%
이 60
 
5.2%
정 45
 
3.9%
박 32
 
2.8%
영 32
 
2.8%
상 31
 
2.7%
호 26
 
2.3%
수 22
 
1.9%
현 20
 
1.7%
최 19
 
1.7%
Other values (156) 787
68.5%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1149
93.3%
Common 83
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 75
 
6.5%
이 60
 
5.2%
정 45
 
3.9%
박 32
 
2.8%
영 32
 
2.8%
상 31
 
2.7%
호 26
 
2.3%
수 22
 
1.9%
현 20
 
1.7%
최 19
 
1.7%
Other values (156) 787
68.5%
Common
ValueCountFrequency (%)
80
96.4%
, 3
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1149
93.3%
ASCII 83
 
6.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
80
96.4%
, 3
 
3.6%
Hangul
ValueCountFrequency (%)
김 75
 
6.5%
이 60
 
5.2%
정 45
 
3.9%
박 32
 
2.8%
영 32
 
2.8%
상 31
 
2.7%
호 26
 
2.3%
수 22
 
1.9%
현 20
 
1.7%
최 19
 
1.7%
Other values (156) 787
68.5%

주사무소소재지
Text

Distinct321
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T15:32:57.910508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length44
Mean length27.402477
Min length14

Characters and Unicode

Total characters8851
Distinct characters334
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique319 ?
Unique (%)98.8%

Sample

1st row경기도 과천시 별양상가로 7(별양동, 414호)
2nd row서울특별시 종로구 필운대로 9
3rd row경기도 과천시 별양상가3로 5(별양동)
4th row서울시 서초구 강남대로16길 22-6(양재동)
5th row강원도 춘천시 요선동 4-9
ValueCountFrequency (%)
경기도 52
 
2.9%
경상북도 41
 
2.3%
강원도 28
 
1.6%
경상남도 25
 
1.4%
전라남도 23
 
1.3%
창원시 17
 
1.0%
충청북도 14
 
0.8%
서울특별시 14
 
0.8%
전남 13
 
0.7%
3층 13
 
0.7%
Other values (1059) 1539
86.5%
2023-12-12T15:32:58.567236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1458
 
16.5%
1 325
 
3.7%
동 277
 
3.1%
시 271
 
3.1%
로 231
 
2.6%
도 228
 
2.6%
2 224
 
2.5%
, 205
 
2.3%
( 191
 
2.2%
) 190
 
2.1%
Other values (324) 5251
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5173
58.4%
Decimal Number 1501
 
17.0%
Space Separator 1458
 
16.5%
Other Punctuation 206
 
2.3%
Open Punctuation 191
 
2.2%
Close Punctuation 190
 
2.1%
Dash Punctuation 110
 
1.2%
Uppercase Letter 16
 
0.2%
Lowercase Letter 4
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 277
 
5.4%
시 271
 
5.2%
로 231
 
4.5%
도 228
 
4.4%
남 157
 
3.0%
구 152
 
2.9%
경 150
 
2.9%
길 131
 
2.5%
층 104
 
2.0%
북 103
 
2.0%
Other values (296) 3369
65.1%
Decimal Number
ValueCountFrequency (%)
1 325
21.7%
2 224
14.9%
3 183
12.2%
4 171
11.4%
0 152
10.1%
5 121
 
8.1%
6 106
 
7.1%
7 76
 
5.1%
9 76
 
5.1%
8 67
 
4.5%
Uppercase Letter
ValueCountFrequency (%)
B 3
18.8%
T 3
18.8%
I 3
18.8%
C 2
12.5%
A 2
12.5%
S 2
12.5%
D 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
z 1
25.0%
i 1
25.0%
b 1
25.0%
e 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 205
99.5%
. 1
 
0.5%
Space Separator
ValueCountFrequency (%)
1458
100.0%
Open Punctuation
ValueCountFrequency (%)
( 191
100.0%
Close Punctuation
ValueCountFrequency (%)
) 190
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 110
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5173
58.4%
Common 3658
41.3%
Latin 20
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 277
 
5.4%
시 271
 
5.2%
로 231
 
4.5%
도 228
 
4.4%
남 157
 
3.0%
구 152
 
2.9%
경 150
 
2.9%
길 131
 
2.5%
층 104
 
2.0%
북 103
 
2.0%
Other values (296) 3369
65.1%
Common
ValueCountFrequency (%)
1458
39.9%
1 325
 
8.9%
2 224
 
6.1%
, 205
 
5.6%
( 191
 
5.2%
) 190
 
5.2%
3 183
 
5.0%
4 171
 
4.7%
0 152
 
4.2%
5 121
 
3.3%
Other values (7) 438
 
12.0%
Latin
ValueCountFrequency (%)
B 3
15.0%
T 3
15.0%
I 3
15.0%
C 2
10.0%
A 2
10.0%
S 2
10.0%
z 1
 
5.0%
i 1
 
5.0%
b 1
 
5.0%
e 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5173
58.4%
ASCII 3678
41.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1458
39.6%
1 325
 
8.8%
2 224
 
6.1%
, 205
 
5.6%
( 191
 
5.2%
) 190
 
5.2%
3 183
 
5.0%
4 171
 
4.6%
0 152
 
4.1%
5 121
 
3.3%
Other values (18) 458
 
12.5%
Hangul
ValueCountFrequency (%)
동 277
 
5.4%
시 271
 
5.2%
로 231
 
4.5%
도 228
 
4.4%
남 157
 
3.0%
구 152
 
2.9%
경 150
 
2.9%
길 131
 
2.5%
층 104
 
2.0%
북 103
 
2.0%
Other values (296) 3369
65.1%

평가담당부서소재지
Text

Distinct319
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T15:32:58.929253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length42
Mean length30.956656
Min length14

Characters and Unicode

Total characters9999
Distinct characters339
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

Unique315 ?
Unique (%)97.5%

Sample

1st row서울특별시 서초구 언남길 5(양재동, 신성빌딩 2층)
2nd row서울특별시 종로구 사직로 113번지 사학회관 302호
3rd row경기도 과천시 별양상가로 13(영덕빌딩 7층)
4th row서울시 서초구 강남대로16길 22-6(양재동)
5th row경기도 안양시 동안구 호계동 1043-1 다운타운빌딩 5층 505호
ValueCountFrequency (%)
경기도 75
 
3.9%
서울특별시 48
 
2.5%
안양시 37
 
1.9%
동안구 36
 
1.9%
광주광역시 29
 
1.5%
대구광역시 27
 
1.4%
2층 26
 
1.3%
부산광역시 23
 
1.2%
3층 23
 
1.2%
서울시 19
 
1.0%
Other values (1000) 1586
82.2%
2023-12-12T15:32:59.486641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1614
 
16.1%
1 407
 
4.1%
동 353
 
3.5%
시 329
 
3.3%
구 315
 
3.2%
2 280
 
2.8%
로 271
 
2.7%
, 250
 
2.5%
0 208
 
2.1%
( 200
 
2.0%
Other values (329) 5772
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5743
57.4%
Decimal Number 1825
 
18.3%
Space Separator 1614
 
16.1%
Other Punctuation 251
 
2.5%
Open Punctuation 200
 
2.0%
Close Punctuation 200
 
2.0%
Dash Punctuation 100
 
1.0%
Uppercase Letter 50
 
0.5%
Math Symbol 9
 
0.1%
Lowercase Letter 6
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 353
 
6.1%
시 329
 
5.7%
구 315
 
5.5%
로 271
 
4.7%
호 155
 
2.7%
광 138
 
2.4%
도 135
 
2.4%
층 134
 
2.3%
대 130
 
2.3%
길 126
 
2.2%
Other values (293) 3657
63.7%
Uppercase Letter
ValueCountFrequency (%)
T 10
20.0%
I 10
20.0%
A 7
14.0%
B 6
12.0%
C 5
10.0%
S 3
 
6.0%
E 2
 
4.0%
K 2
 
4.0%
V 2
 
4.0%
Y 1
 
2.0%
Other values (2) 2
 
4.0%
Decimal Number
ValueCountFrequency (%)
1 407
22.3%
2 280
15.3%
0 208
11.4%
3 195
10.7%
4 165
9.0%
5 164
9.0%
6 138
 
7.6%
7 110
 
6.0%
8 87
 
4.8%
9 71
 
3.9%
Lowercase Letter
ValueCountFrequency (%)
b 1
16.7%
i 1
16.7%
z 1
16.7%
e 1
16.7%
m 1
16.7%
j 1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 250
99.6%
& 1
 
0.4%
Space Separator
ValueCountFrequency (%)
1614
100.0%
Open Punctuation
ValueCountFrequency (%)
( 200
100.0%
Close Punctuation
ValueCountFrequency (%)
) 200
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Math Symbol
ValueCountFrequency (%)
~ 9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5744
57.4%
Common 4199
42.0%
Latin 56
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 353
 
6.1%
시 329
 
5.7%
구 315
 
5.5%
로 271
 
4.7%
호 155
 
2.7%
광 138
 
2.4%
도 135
 
2.4%
층 134
 
2.3%
대 130
 
2.3%
길 126
 
2.2%
Other values (294) 3658
63.7%
Latin
ValueCountFrequency (%)
T 10
17.9%
I 10
17.9%
A 7
12.5%
B 6
10.7%
C 5
8.9%
S 3
 
5.4%
E 2
 
3.6%
K 2
 
3.6%
V 2
 
3.6%
b 1
 
1.8%
Other values (8) 8
14.3%
Common
ValueCountFrequency (%)
1614
38.4%
1 407
 
9.7%
2 280
 
6.7%
, 250
 
6.0%
0 208
 
5.0%
( 200
 
4.8%
) 200
 
4.8%
3 195
 
4.6%
4 165
 
3.9%
5 164
 
3.9%
Other values (7) 516
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5743
57.4%
ASCII 4255
42.6%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1614
37.9%
1 407
 
9.6%
2 280
 
6.6%
, 250
 
5.9%
0 208
 
4.9%
( 200
 
4.7%
) 200
 
4.7%
3 195
 
4.6%
4 165
 
3.9%
5 164
 
3.9%
Other values (25) 572
 
13.4%
Hangul
ValueCountFrequency (%)
동 353
 
6.1%
시 329
 
5.7%
구 315
 
5.5%
로 271
 
4.7%
호 155
 
2.7%
광 138
 
2.4%
도 135
 
2.4%
층 134
 
2.3%
대 130
 
2.3%
길 126
 
2.2%
Other values (293) 3657
63.7%
None
ValueCountFrequency (%)
㈜ 1
100.0%

전화번호
Text

Distinct320
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T15:32:59.825124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.996904
Min length11

Characters and Unicode

Total characters3875
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

Unique317 ?
Unique (%)98.1%

Sample

1st row02-3497-6600
2nd row02-739-9201
3rd row02-397-2399
4th row02-3498-2487
5th row031-423-8040
ValueCountFrequency (%)
053-563-6806 2
 
0.6%
031-689-5759 2
 
0.6%
02-575-1199 2
 
0.6%
051-851-6906 1
 
0.3%
062-512-5612 1
 
0.3%
062-515-4664 1
 
0.3%
062-676-9261 1
 
0.3%
061-750-8011 1
 
0.3%
062-600-3112 1
 
0.3%
062-971-1919 1
 
0.3%
Other values (310) 310
96.0%
2023-12-12T15:33:00.353702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 646
16.7%
0 644
16.6%
3 392
10.1%
2 387
10.0%
1 333
8.6%
5 320
8.3%
6 274
7.1%
4 267
6.9%
7 261
6.7%
8 206
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3229
83.3%
Dash Punctuation 646
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 644
19.9%
3 392
12.1%
2 387
12.0%
1 333
10.3%
5 320
9.9%
6 274
8.5%
4 267
8.3%
7 261
8.1%
8 206
 
6.4%
9 145
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3875
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 646
16.7%
0 644
16.6%
3 392
10.1%
2 387
10.0%
1 333
8.6%
5 320
8.3%
6 274
7.1%
4 267
6.9%
7 261
6.7%
8 206
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 646
16.7%
0 644
16.6%
3 392
10.1%
2 387
10.0%
1 333
8.6%
5 320
8.3%
6 274
7.1%
4 267
6.9%
7 261
6.7%
8 206
 
5.3%

fax
Text

Distinct311
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size2.7 KiB
2023-12-12T15:33:00.738553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.928793
Min length11

Characters and Unicode

Total characters3853
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

Unique299 ?
Unique (%)92.6%

Sample

1st row02-3497-6677
2nd row02-739-9201
3rd row02-735-2791
4th row02-574-7364
5th row031-423-7615
ValueCountFrequency (%)
051-520-4008 2
 
0.6%
031-627-5588 2
 
0.6%
031-345-6755 2
 
0.6%
02-469-1945 2
 
0.6%
031-730-0411 2
 
0.6%
02-6219-3000 2
 
0.6%
051-628-0509 2
 
0.6%
051-760-7500 2
 
0.6%
053-564-2110 2
 
0.6%
053-214-3153 2
 
0.6%
Other values (301) 303
93.8%
2023-12-12T15:33:01.301856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 646
16.8%
0 556
14.4%
3 381
9.9%
2 371
9.6%
1 356
9.2%
5 351
9.1%
6 296
7.7%
4 273
7.1%
8 222
 
5.8%
7 215
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3207
83.2%
Dash Punctuation 646
 
16.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 556
17.3%
3 381
11.9%
2 371
11.6%
1 356
11.1%
5 351
10.9%
6 296
9.2%
4 273
8.5%
8 222
 
6.9%
7 215
 
6.7%
9 186
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 646
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3853
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 646
16.8%
0 556
14.4%
3 381
9.9%
2 371
9.6%
1 356
9.2%
5 351
9.1%
6 296
7.7%
4 273
7.1%
8 222
 
5.8%
7 215
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 646
16.8%
0 556
14.4%
3 381
9.9%
2 371
9.6%
1 356
9.2%
5 351
9.1%
6 296
7.7%
4 273
7.1%
8 222
 
5.8%
7 215
 
5.6%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct249
Distinct (%)77.6%
Missing2
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean30224.287
Minimum2589
Maximum63223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-12-12T15:33:01.505140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2589
5-th percentile5711
Q113837
median27339
Q347297
95-th percentile61965
Maximum63223
Range60634
Interquartile range (IQR)33460

Descriptive statistics

Standard deviation19672.617
Coefficient of variation (CV)0.65088772
Kurtosis-1.413456
Mean30224.287
Median Absolute Deviation (MAD)17351
Skewness0.24619077
Sum9701996
Variance3.8701186 × 108
MonotonicityNot monotonic
2023-12-12T15:33:01.685681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14056 8
 
2.5%
16006 7
 
2.2%
14059 7
 
2.2%
14057 7
 
2.2%
48059 4
 
1.2%
41251 4
 
1.2%
6779 4
 
1.2%
5836 3
 
0.9%
61965 3
 
0.9%
61011 3
 
0.9%
Other values (239) 271
83.9%
ValueCountFrequency (%)
2589 1
0.3%
3027 1
0.3%
3058 1
0.3%
4323 1
0.3%
4552 1
0.3%
4607 1
0.3%
4795 2
0.6%
4799 2
0.6%
5288 1
0.3%
5312 1
0.3%
ValueCountFrequency (%)
63223 1
0.3%
63222 1
0.3%
63184 1
0.3%
63145 1
0.3%
63102 1
0.3%
63082 1
0.3%
62374 1
0.3%
62305 1
0.3%
62274 1
0.3%
62073 1
0.3%

Interactions

2023-12-12T15:32:53.032168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:52.793573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:53.162020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:32:52.905137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:33:01.798865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련 번호지방청우편번호
일련 번호1.0000.8790.909
지방청0.8791.0000.951
우편번호0.9090.9511.000
2023-12-12T15:33:01.938412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일련 번호우편번호지방청
일련 번호1.0000.7500.704
우편번호0.7501.0000.869
지방청0.7040.8691.000

Missing values

2023-12-12T15:32:53.345474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:32:53.544045image/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

일련 번호지방청등록번호등록일자업 체 명대표자주사무소소재지평가담당부서소재지전화번호fax우편번호
01한강청제서-002호1994-02-16㈜신성엔지니어링정찬섭 정태섭경기도 과천시 별양상가로 7(별양동, 414호)서울특별시 서초구 언남길 5(양재동, 신성빌딩 2층)02-3497-660002-3497-66776779
12한강청제서-003호1994-02-16㈜대한콘설탄트이영민서울특별시 종로구 필운대로 9서울특별시 종로구 사직로 113번지 사학회관 302호02-739-920102-739-92013027
23한강청제서-004호1994-02-16㈜삼안최동식경기도 과천시 별양상가3로 5(별양동)경기도 과천시 별양상가로 13(영덕빌딩 7층)02-397-239902-735-279113837
34한강청제서-006호1994-02-16㈜제일엔지니어링 종합건축사사무소임종선 임은영서울시 서초구 강남대로16길 22-6(양재동)서울시 서초구 강남대로16길 22-6(양재동)02-3498-248702-574-73646779
45한강청제서-008호1994-02-16㈜우대기술단안치섭강원도 춘천시 요선동 4-9경기도 안양시 동안구 호계동 1043-1 다운타운빌딩 5층 505호031-423-8040031-423-761514072
56한강청제서-009호1994-02-16㈜이산이원찬경기도 안양시 동안구 부림로 121경기도 안양시 동안구 부림로 121031-436-8275031-382-606914066
67한강청제서-014호1994-02-22㈜유신성낙일 전경수서울시 강남구 역삼로4길 8(역삼동)서울특별시 강남구 역삼로 114, 4층 (역삼동, 현죽빌딩)02-6202-068602-6202-11526252
78한강청제서-016호1994-02-21㈜동명기술공단 종합건축사무소신완수 신희정경기도 화성시 노작로 4길 8(반송동, 4층)서울특별시 동대문구 왕산로 117, 5층(제기동, 불로장생타워상가)02-6211-751902-6211-75302589
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