Overview

Dataset statistics

Number of variables6
Number of observations24
Missing cells70
Missing cells (%)48.6%
Duplicate rows1
Duplicate rows (%)4.2%
Total size in memory1.3 KiB
Average record size in memory54.5 B

Variable types

Numeric1
DateTime1
Text3
Categorical1

Dataset

Description경상남도 함안군 행정사 사무소 현황을 제공합니다.
Author경상남도 함안군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15030337

Alerts

Dataset has 1 (4.2%) duplicate rowsDuplicates
연번 is highly overall correlated with 비 고High correlation
비 고 is highly overall correlated with 연번High correlation
연번 has 14 (58.3%) missing valuesMissing
신고연월일 has 14 (58.3%) missing valuesMissing
성 명 has 14 (58.3%) missing valuesMissing
명 칭 has 14 (58.3%) missing valuesMissing
소재지 has 14 (58.3%) missing valuesMissing

Reproduction

Analysis started2024-04-06 08:03:07.112563
Analysis finished2024-04-06 08:03:08.624015
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing14
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean5.5
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-04-06T17:03:08.714994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.45
Q13.25
median5.5
Q37.75
95-th percentile9.55
Maximum10
Range9
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.0276504
Coefficient of variation (CV)0.55048188
Kurtosis-1.2
Mean5.5
Median Absolute Deviation (MAD)2.5
Skewness0
Sum55
Variance9.1666667
MonotonicityStrictly increasing
2024-04-06T17:03:08.953291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1
 
4.2%
2 1
 
4.2%
3 1
 
4.2%
4 1
 
4.2%
5 1
 
4.2%
6 1
 
4.2%
7 1
 
4.2%
8 1
 
4.2%
9 1
 
4.2%
10 1
 
4.2%
(Missing) 14
58.3%
ValueCountFrequency (%)
1 1
4.2%
2 1
4.2%
3 1
4.2%
4 1
4.2%
5 1
4.2%
6 1
4.2%
7 1
4.2%
8 1
4.2%
9 1
4.2%
10 1
4.2%
ValueCountFrequency (%)
10 1
4.2%
9 1
4.2%
8 1
4.2%
7 1
4.2%
6 1
4.2%
5 1
4.2%
4 1
4.2%
3 1
4.2%
2 1
4.2%
1 1
4.2%

신고연월일
Date

MISSING 

Distinct10
Distinct (%)100.0%
Missing14
Missing (%)58.3%
Memory size324.0 B
Minimum2001-08-24 00:00:00
Maximum2022-02-18 00:00:00
2024-04-06T17:03:09.142331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:09.332219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)

성 명
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing14
Missing (%)58.3%
Memory size324.0 B
2024-04-06T17:03:09.604470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters30
Distinct characters28
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

Unique10 ?
Unique (%)100.0%

Sample

1st row이봉환
2nd row류점철
3rd row장동성
4th row박지원
5th row홍종갑
ValueCountFrequency (%)
이봉환 1
10.0%
류점철 1
10.0%
장동성 1
10.0%
박지원 1
10.0%
홍종갑 1
10.0%
박상곤 1
10.0%
조문규 1
10.0%
조방제 1
10.0%
차신희 1
10.0%
최수암 1
10.0%
2024-04-06T17:03:10.070376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
박 2
 
6.7%
조 2
 
6.7%
이 1
 
3.3%
상 1
 
3.3%
수 1
 
3.3%
최 1
 
3.3%
희 1
 
3.3%
신 1
 
3.3%
차 1
 
3.3%
제 1
 
3.3%
Other values (18) 18
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
박 2
 
6.7%
조 2
 
6.7%
이 1
 
3.3%
상 1
 
3.3%
수 1
 
3.3%
최 1
 
3.3%
희 1
 
3.3%
신 1
 
3.3%
차 1
 
3.3%
제 1
 
3.3%
Other values (18) 18
60.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
박 2
 
6.7%
조 2
 
6.7%
이 1
 
3.3%
상 1
 
3.3%
수 1
 
3.3%
최 1
 
3.3%
희 1
 
3.3%
신 1
 
3.3%
차 1
 
3.3%
제 1
 
3.3%
Other values (18) 18
60.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
박 2
 
6.7%
조 2
 
6.7%
이 1
 
3.3%
상 1
 
3.3%
수 1
 
3.3%
최 1
 
3.3%
희 1
 
3.3%
신 1
 
3.3%
차 1
 
3.3%
제 1
 
3.3%
Other values (18) 18
60.0%

명 칭
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing14
Missing (%)58.3%
Memory size324.0 B
2024-04-06T17:03:10.750498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10.5
Mean length9.5
Min length6

Characters and Unicode

Total characters95
Distinct characters33
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

Unique10 ?
Unique (%)100.0%

Sample

1st row함안 행정사
2nd row행정사 류점철 사무소
3rd row장동성 행정사 사무소
4th row정 행정사사무소
5th row도남 행정사사무소
ValueCountFrequency (%)
행정사 6
28.6%
행정사사무소 3
14.3%
사무소 2
 
9.5%
함안 1
 
4.8%
류점철 1
 
4.8%
장동성 1
 
4.8%
정 1
 
4.8%
도남 1
 
4.8%
박상곤사랑방 1
 
4.8%
조문규 1
 
4.8%
Other values (3) 3
14.3%
2024-04-06T17:03:11.466032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 18
18.9%
11
11.6%
정 11
11.6%
행 10
10.5%
소 7
 
7.4%
무 7
 
7.4%
제 2
 
2.1%
남 2
 
2.1%
방 2
 
2.1%
조 2
 
2.1%
Other values (23) 23
24.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 84
88.4%
Space Separator 11
 
11.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 18
21.4%
정 11
13.1%
행 10
11.9%
소 7
 
8.3%
무 7
 
8.3%
제 2
 
2.4%
남 2
 
2.4%
방 2
 
2.4%
조 2
 
2.4%
희 1
 
1.2%
Other values (22) 22
26.2%
Space Separator
ValueCountFrequency (%)
11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 84
88.4%
Common 11
 
11.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 18
21.4%
정 11
13.1%
행 10
11.9%
소 7
 
8.3%
무 7
 
8.3%
제 2
 
2.4%
남 2
 
2.4%
방 2
 
2.4%
조 2
 
2.4%
희 1
 
1.2%
Other values (22) 22
26.2%
Common
ValueCountFrequency (%)
11
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 84
88.4%
ASCII 11
 
11.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 18
21.4%
정 11
13.1%
행 10
11.9%
소 7
 
8.3%
무 7
 
8.3%
제 2
 
2.4%
남 2
 
2.4%
방 2
 
2.4%
조 2
 
2.4%
희 1
 
1.2%
Other values (22) 22
26.2%
ASCII
ValueCountFrequency (%)
11
100.0%

소재지
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing14
Missing (%)58.3%
Memory size324.0 B
2024-04-06T17:03:11.786603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length20
Min length17

Characters and Unicode

Total characters200
Distinct characters56
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

Unique10 ?
Unique (%)100.0%

Sample

1st row경남 함안군 산인면 내인리 274
2nd row경상남도 함안군 가야읍 가야로 97
3rd row경남 함안군 가야읍 함안대로 512
4th row경남 함안군 칠원읍 오곡로 54
5th row경남 함안군 가야읍 말산로 22
ValueCountFrequency (%)
함안군 10
19.2%
경남 9
17.3%
가야읍 5
 
9.6%
동촌3길 1
 
1.9%
대산중앙로 1
 
1.9%
대산면 1
 
1.9%
대현기계공업사 1
 
1.9%
65 1
 
1.9%
충무길 1
 
1.9%
1호 1
 
1.9%
Other values (21) 21
40.4%
2024-04-06T17:03:12.397617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
21.0%
함 12
 
6.0%
안 11
 
5.5%
군 11
 
5.5%
경 10
 
5.0%
남 10
 
5.0%
2 7
 
3.5%
읍 6
 
3.0%
야 6
 
3.0%
가 6
 
3.0%
Other values (46) 79
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126
63.0%
Space Separator 42
 
21.0%
Decimal Number 29
 
14.5%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%
Dash Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
함 12
 
9.5%
안 11
 
8.7%
군 11
 
8.7%
경 10
 
7.9%
남 10
 
7.9%
읍 6
 
4.8%
야 6
 
4.8%
가 6
 
4.8%
로 6
 
4.8%
대 5
 
4.0%
Other values (32) 43
34.1%
Decimal Number
ValueCountFrequency (%)
2 7
24.1%
1 6
20.7%
7 4
13.8%
5 3
10.3%
6 2
 
6.9%
0 2
 
6.9%
4 2
 
6.9%
8 1
 
3.4%
3 1
 
3.4%
9 1
 
3.4%
Space Separator
ValueCountFrequency (%)
42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 126
63.0%
Common 74
37.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
함 12
 
9.5%
안 11
 
8.7%
군 11
 
8.7%
경 10
 
7.9%
남 10
 
7.9%
읍 6
 
4.8%
야 6
 
4.8%
가 6
 
4.8%
로 6
 
4.8%
대 5
 
4.0%
Other values (32) 43
34.1%
Common
ValueCountFrequency (%)
42
56.8%
2 7
 
9.5%
1 6
 
8.1%
7 4
 
5.4%
5 3
 
4.1%
6 2
 
2.7%
0 2
 
2.7%
4 2
 
2.7%
( 1
 
1.4%
) 1
 
1.4%
Other values (4) 4
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 126
63.0%
ASCII 74
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42
56.8%
2 7
 
9.5%
1 6
 
8.1%
7 4
 
5.4%
5 3
 
4.1%
6 2
 
2.7%
0 2
 
2.7%
4 2
 
2.7%
( 1
 
1.4%
) 1
 
1.4%
Other values (4) 4
 
5.4%
Hangul
ValueCountFrequency (%)
함 12
 
9.5%
안 11
 
8.7%
군 11
 
8.7%
경 10
 
7.9%
남 10
 
7.9%
읍 6
 
4.8%
야 6
 
4.8%
가 6
 
4.8%
로 6
 
4.8%
대 5
 
4.0%
Other values (32) 43
34.1%

비 고
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
<NA>
14 
영업중
8 
휴 업
2 

Length

Max length5
Median length4
Mean length3.75
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 14
58.3%
영업중 8
33.3%
휴 업 2
 
8.3%

Length

2024-04-06T17:03:12.772161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:03:12.972435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 14
53.8%
영업중 8
30.8%
휴 2
 
7.7%
업 2
 
7.7%

Interactions

2024-04-06T17:03:07.634494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:03:13.103145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번신고연월일성 명명 칭소재지비 고
연번1.0001.0001.0001.0001.0001.000
신고연월일1.0001.0001.0001.0001.0001.000
성 명1.0001.0001.0001.0001.0001.000
명 칭1.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.000
비 고1.0001.0001.0001.0001.0001.000
2024-04-06T17:03:13.331568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번비 고
연번1.0001.000
비 고1.0001.000

Missing values

2024-04-06T17:03:07.979436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:03:08.290536image/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.
2024-04-06T17:03:08.498285image/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

연번신고연월일성 명명 칭소재지비 고
012020-11-25이봉환함안 행정사경남 함안군 산인면 내인리 274영업중
122021-12-03류점철행정사 류점철 사무소경상남도 함안군 가야읍 가야로 97영업중
232021-03-15장동성장동성 행정사 사무소경남 함안군 가야읍 함안대로 512영업중
342022-02-18박지원정 행정사사무소경남 함안군 칠원읍 오곡로 54영업중
452006-11-22홍종갑도남 행정사사무소경남 함안군 가야읍 말산로 22영업중
562011-07-22박상곤행정사 박상곤사랑방경남 함안군 군북면 동촌3길 82영업중
672001-08-24조문규조문규 행정사사무소경남 함안군 가야읍 함마대로 1600-1영업중
782005-12-16조방제행정사 조방제사무소경남 함안군 칠서면 태곡리 122번지 1호영업중
892016-10-17차신희경남국제차신희행정사경남 함안군 가야읍 충무길 65 (대현기계공업사)휴 업
9102012-11-30최수암행정사 최수암사무소경남 함안군 대산면 대산중앙로 177휴 업
연번신고연월일성 명명 칭소재지비 고
14<NA><NA><NA><NA><NA><NA>
15<NA><NA><NA><NA><NA><NA>
16<NA><NA><NA><NA><NA><NA>
17<NA><NA><NA><NA><NA><NA>
18<NA><NA><NA><NA><NA><NA>
19<NA><NA><NA><NA><NA><NA>
20<NA><NA><NA><NA><NA><NA>
21<NA><NA><NA><NA><NA><NA>
22<NA><NA><NA><NA><NA><NA>
23<NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

연번신고연월일성 명명 칭소재지비 고# duplicates
0<NA><NA><NA><NA><NA><NA>14