Overview

Dataset statistics

Number of variables7
Number of observations57
Missing cells106
Missing cells (%)26.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory60.3 B

Variable types

Text3
DateTime2
Numeric2

Dataset

Description경기도 김포시 행정사사무소(상호명, 신고일자, 소재지도로명주소, 위도, 경도, 전화번호, 데이터 기준일자)의 데이터 현황을 제공하고 있습니다.
Author경기도 김포시
URLhttps://www.data.go.kr/data/15037677/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
위도 has 38 (66.7%) missing valuesMissing
경도 has 38 (66.7%) missing valuesMissing
전화번호 has 30 (52.6%) missing valuesMissing
상호명 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:08:58.535138
Analysis finished2024-04-06 08:09:01.602002
Duration3.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상호명
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-04-06T17:09:01.999680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length9.3157895
Min length5

Characters and Unicode

Total characters531
Distinct characters126
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st row길손행정사
2nd row홍충호행정사
3rd row유현진행정사
4th row조휘영행정사
5th row우리행정사
ValueCountFrequency (%)
행정사 12
 
12.5%
행정사사무소 11
 
11.5%
사무소 10
 
10.4%
로운행정사사무소 1
 
1.0%
재수 1
 
1.0%
경기사행정사 1
 
1.0%
비바행정사사무소 1
 
1.0%
공감 1
 
1.0%
위드행정사사무소 1
 
1.0%
김영기 1
 
1.0%
Other values (56) 56
58.3%
2024-04-06T17:09:02.935843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 103
19.4%
정 59
 
11.1%
행 58
 
10.9%
무 45
 
8.5%
소 43
 
8.1%
39
 
7.3%
성 6
 
1.1%
김 6
 
1.1%
이 5
 
0.9%
영 5
 
0.9%
Other values (116) 162
30.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 478
90.0%
Space Separator 39
 
7.3%
Uppercase Letter 10
 
1.9%
Decimal Number 2
 
0.4%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 103
21.5%
정 59
12.3%
행 58
12.1%
무 45
 
9.4%
소 43
 
9.0%
성 6
 
1.3%
김 6
 
1.3%
이 5
 
1.0%
영 5
 
1.0%
기 4
 
0.8%
Other values (104) 144
30.1%
Uppercase Letter
ValueCountFrequency (%)
O 2
20.0%
K 2
20.0%
E 2
20.0%
N 1
10.0%
W 1
10.0%
R 1
10.0%
A 1
10.0%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
39
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 477
89.8%
Common 43
 
8.1%
Latin 10
 
1.9%
Han 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 103
21.6%
정 59
12.4%
행 58
12.2%
무 45
 
9.4%
소 43
 
9.0%
성 6
 
1.3%
김 6
 
1.3%
이 5
 
1.0%
영 5
 
1.0%
기 4
 
0.8%
Other values (103) 143
30.0%
Latin
ValueCountFrequency (%)
O 2
20.0%
K 2
20.0%
E 2
20.0%
N 1
10.0%
W 1
10.0%
R 1
10.0%
A 1
10.0%
Common
ValueCountFrequency (%)
39
90.7%
) 1
 
2.3%
( 1
 
2.3%
1 1
 
2.3%
2 1
 
2.3%
Han
ValueCountFrequency (%)
民 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 477
89.8%
ASCII 53
 
10.0%
CJK 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 103
21.6%
정 59
12.4%
행 58
12.2%
무 45
 
9.4%
소 43
 
9.0%
성 6
 
1.3%
김 6
 
1.3%
이 5
 
1.0%
영 5
 
1.0%
기 4
 
0.8%
Other values (103) 143
30.0%
ASCII
ValueCountFrequency (%)
39
73.6%
O 2
 
3.8%
K 2
 
3.8%
E 2
 
3.8%
) 1
 
1.9%
( 1
 
1.9%
N 1
 
1.9%
1 1
 
1.9%
2 1
 
1.9%
W 1
 
1.9%
Other values (2) 2
 
3.8%
CJK
ValueCountFrequency (%)
民 1
100.0%

신고일자
Date

Distinct54
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size588.0 B
Minimum1999-12-24 00:00:00
Maximum2024-03-11 00:00:00
2024-04-06T17:09:03.308980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:09:03.652085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

소재지도로명주소
Text

Distinct56
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-04-06T17:09:04.156462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length19
Min length14

Characters and Unicode

Total characters1083
Distinct characters69
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

Unique55 ?
Unique (%)96.5%

Sample

1st row경기도 김포시 하성면 하성로 503
2nd row경기도 김포시 통진읍 서암로 127
3rd row경기도 김포시 풍무로 104 (풍무동)
4th row경기도 김포시 봉화로 72 (사우동)
5th row경기도 김포시 풍무로69번길 51
ValueCountFrequency (%)
경기도 57
22.8%
김포시 57
22.8%
통진읍 10
 
4.0%
태장로 4
 
1.6%
고촌읍 4
 
1.6%
사우중로 4
 
1.6%
풍무로 4
 
1.6%
서암로 3
 
1.2%
봉화로 3
 
1.2%
김포한강8로 3
 
1.2%
Other values (86) 101
40.4%
2024-04-06T17:09:04.927608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
193
17.8%
포 78
 
7.2%
김 77
 
7.1%
도 58
 
5.4%
경 57
 
5.3%
로 57
 
5.3%
시 57
 
5.3%
기 57
 
5.3%
1 43
 
4.0%
2 29
 
2.7%
Other values (59) 377
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 659
60.8%
Decimal Number 212
 
19.6%
Space Separator 193
 
17.8%
Dash Punctuation 7
 
0.6%
Close Punctuation 6
 
0.6%
Open Punctuation 6
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
포 78
11.8%
김 77
11.7%
도 58
 
8.8%
경 57
 
8.6%
로 57
 
8.6%
시 57
 
8.6%
기 57
 
8.6%
강 17
 
2.6%
한 17
 
2.6%
읍 16
 
2.4%
Other values (45) 168
25.5%
Decimal Number
ValueCountFrequency (%)
1 43
20.3%
2 29
13.7%
7 23
10.8%
4 21
9.9%
8 19
9.0%
3 19
9.0%
0 18
8.5%
5 17
 
8.0%
9 15
 
7.1%
6 8
 
3.8%
Space Separator
ValueCountFrequency (%)
193
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 659
60.8%
Common 424
39.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
포 78
11.8%
김 77
11.7%
도 58
 
8.8%
경 57
 
8.6%
로 57
 
8.6%
시 57
 
8.6%
기 57
 
8.6%
강 17
 
2.6%
한 17
 
2.6%
읍 16
 
2.4%
Other values (45) 168
25.5%
Common
ValueCountFrequency (%)
193
45.5%
1 43
 
10.1%
2 29
 
6.8%
7 23
 
5.4%
4 21
 
5.0%
8 19
 
4.5%
3 19
 
4.5%
0 18
 
4.2%
5 17
 
4.0%
9 15
 
3.5%
Other values (4) 27
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 659
60.8%
ASCII 424
39.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
193
45.5%
1 43
 
10.1%
2 29
 
6.8%
7 23
 
5.4%
4 21
 
5.0%
8 19
 
4.5%
3 19
 
4.5%
0 18
 
4.2%
5 17
 
4.0%
9 15
 
3.5%
Other values (4) 27
 
6.4%
Hangul
ValueCountFrequency (%)
포 78
11.8%
김 77
11.7%
도 58
 
8.8%
경 57
 
8.6%
로 57
 
8.6%
시 57
 
8.6%
기 57
 
8.6%
강 17
 
2.6%
한 17
 
2.6%
읍 16
 
2.4%
Other values (45) 168
25.5%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)94.7%
Missing38
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean37.640335
Minimum37.598512
Maximum37.719289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-06T17:09:05.161438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.598512
5-th percentile37.59866
Q137.611738
median37.641591
Q337.653806
95-th percentile37.698677
Maximum37.719289
Range0.12077756
Interquartile range (IQR)0.04206783

Descriptive statistics

Standard deviation0.036317285
Coefficient of variation (CV)0.00096485021
Kurtosis-0.33697466
Mean37.640335
Median Absolute Deviation (MAD)0.02296957
Skewness0.76057318
Sum715.16636
Variance0.0013189452
MonotonicityNot monotonic
2024-04-06T17:09:05.448245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
37.60485539 2
 
3.5%
37.64432673 1
 
1.8%
37.64159099 1
 
1.8%
37.62193317 1
 
1.8%
37.6448344 1
 
1.8%
37.61899398 1
 
1.8%
37.68516175 1
 
1.8%
37.6446967 1
 
1.8%
37.6927074 1
 
1.8%
37.71928914 1
 
1.8%
Other values (8) 8
 
14.0%
(Missing) 38
66.7%
ValueCountFrequency (%)
37.59851158 1
1.8%
37.59867698 1
1.8%
37.60199441 1
1.8%
37.60485539 2
3.5%
37.61862142 1
1.8%
37.61899398 1
1.8%
37.62130867 1
1.8%
37.62193317 1
1.8%
37.64159099 1
1.8%
37.64432673 1
1.8%
ValueCountFrequency (%)
37.71928914 1
1.8%
37.69638674 1
1.8%
37.6927074 1
1.8%
37.68516175 1
1.8%
37.66065028 1
1.8%
37.64696219 1
1.8%
37.6448344 1
1.8%
37.6446967 1
1.8%
37.64432673 1
1.8%
37.64159099 1
1.8%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)94.7%
Missing38
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean126.67543
Minimum126.59296
Maximum126.72258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-06T17:09:05.715538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.59296
5-th percentile126.60003
Q1126.62727
median126.70944
Q3126.71966
95-th percentile126.72258
Maximum126.72258
Range0.1296169
Interquartile range (IQR)0.09238305

Descriptive statistics

Standard deviation0.049254347
Coefficient of variation (CV)0.0003888232
Kurtosis-1.6340932
Mean126.67543
Median Absolute Deviation (MAD)0.0131373
Skewness-0.43082047
Sum2406.8332
Variance0.0024259907
MonotonicityNot monotonic
2024-04-06T17:09:05.937876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
126.7225768 2
 
3.5%
126.6677618 1
 
1.8%
126.7094395 1
 
1.8%
126.718999 1
 
1.8%
126.6271307 1
 
1.8%
126.7210665 1
 
1.8%
126.6131465 1
 
1.8%
126.6261361 1
 
1.8%
126.5929599 1
 
1.8%
126.6345948 1
 
1.8%
Other values (8) 8
 
14.0%
(Missing) 38
66.7%
ValueCountFrequency (%)
126.5929599 1
1.8%
126.6008164 1
1.8%
126.6131465 1
1.8%
126.6261361 1
1.8%
126.6271307 1
1.8%
126.6274178 1
1.8%
126.6345948 1
1.8%
126.6617885 1
1.8%
126.6677618 1
1.8%
126.7094395 1
1.8%
ValueCountFrequency (%)
126.7225768 2
3.5%
126.7210665 1
1.8%
126.7208506 1
1.8%
126.7203156 1
1.8%
126.718999 1
1.8%
126.7188092 1
1.8%
126.7157944 1
1.8%
126.7110359 1
1.8%
126.7094395 1
1.8%
126.6677618 1
1.8%

전화번호
Text

MISSING 

Distinct27
Distinct (%)100.0%
Missing30
Missing (%)52.6%
Memory size588.0 B
2024-04-06T17:09:06.303746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.074074
Min length12

Characters and Unicode

Total characters326
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

Unique27 ?
Unique (%)100.0%

Sample

1st row031-988-2607
2nd row031-981-0240
3rd row031-984-4238
4th row031-989-3100
5th row031-981-8100
ValueCountFrequency (%)
031-988-2607 1
 
3.7%
031-985-3515 1
 
3.7%
031-987-2473 1
 
3.7%
031-988-8800 1
 
3.7%
031-986-4725 1
 
3.7%
031-986-0060 1
 
3.7%
031-992-2703 1
 
3.7%
031-996-3549 1
 
3.7%
031-983-8260 1
 
3.7%
031-981-6500 1
 
3.7%
Other values (17) 17
63.0%
2024-04-06T17:09:06.999204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54
16.6%
- 54
16.6%
1 42
12.9%
9 41
12.6%
3 38
11.7%
8 29
8.9%
5 19
 
5.8%
2 14
 
4.3%
7 12
 
3.7%
4 12
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 272
83.4%
Dash Punctuation 54
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54
19.9%
1 42
15.4%
9 41
15.1%
3 38
14.0%
8 29
10.7%
5 19
 
7.0%
2 14
 
5.1%
7 12
 
4.4%
4 12
 
4.4%
6 11
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 326
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54
16.6%
- 54
16.6%
1 42
12.9%
9 41
12.6%
3 38
11.7%
8 29
8.9%
5 19
 
5.8%
2 14
 
4.3%
7 12
 
3.7%
4 12
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54
16.6%
- 54
16.6%
1 42
12.9%
9 41
12.6%
3 38
11.7%
8 29
8.9%
5 19
 
5.8%
2 14
 
4.3%
7 12
 
3.7%
4 12
 
3.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
Minimum2024-03-14 00:00:00
Maximum2024-03-14 00:00:00
2024-04-06T17:09:07.224094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:09:07.513729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-06T17:08:59.933539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:59.359665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:09:00.117659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:59.674531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:09:07.676514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상호명신고일자소재지도로명주소위도경도전화번호
상호명1.0001.0001.0001.0001.0001.000
신고일자1.0001.0000.9911.0001.0001.000
소재지도로명주소1.0000.9911.0001.0001.0001.000
위도1.0001.0001.0001.0000.8361.000
경도1.0001.0001.0000.8361.0001.000
전화번호1.0001.0001.0001.0001.0001.000
2024-04-06T17:09:07.935783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도
위도1.000-0.870
경도-0.8701.000

Missing values

2024-04-06T17:09:00.853954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:09:01.258342image/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.
2024-04-06T17:09:01.488024image/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

상호명신고일자소재지도로명주소위도경도전화번호데이터기준일자
0길손행정사1999-12-24경기도 김포시 하성면 하성로 50337.719289126.634595031-988-26072024-03-14
1홍충호행정사2000-09-18경기도 김포시 통진읍 서암로 12737.696387126.600816031-981-02402024-03-14
2유현진행정사2003-01-06경기도 김포시 풍무로 104 (풍무동)37.604855126.722577<NA>2024-03-14
3조휘영행정사2003-07-18경기도 김포시 봉화로 72 (사우동)37.621309126.711036031-984-42382024-03-14
4우리행정사2004-01-27경기도 김포시 풍무로69번길 5137.601994126.718809<NA>2024-03-14
5김진섭행정사2004-11-01경기도 김포시 양촌읍 양곡4로 17137.66065126.627418031-989-31002024-03-14
6양도마을행정사2004-12-30경기도 김포시 풍무로 104 (풍무동)37.604855126.722577<NA>2024-03-14
7행정사한명수사무소2006-07-13경기도 김포시 김포한강1로 247<NA><NA>031-981-81002024-03-14
8비젼21행정사2007-08-30경기도 김포시 풍무로 3337.598677126.720316<NA>2024-03-14
9박범희행정사2008-01-07경기도 김포시 풍무로 3437.598512126.720851031-997-98232024-03-14
상호명신고일자소재지도로명주소위도경도전화번호데이터기준일자
47예성행정사2022-06-08경기도 김포시 김포한강2로 273<NA><NA><NA>2024-03-14
48이재국행정사사무소2023-03-06경기도 김포시 양촌읍 김포한강4로 341-14<NA><NA>031-987-00992024-03-14
49다온행정사사무소2022-09-06경기도 김포시 김포한강10로133번길 127<NA><NA><NA>2024-03-14
50전왕희 행정사2022-09-16경기도 김포시 돌문로 48<NA><NA><NA>2024-03-14
51강신범 행정사사무소2022-12-02경기도 김포시 통진읍 옹정로185번길 91<NA><NA><NA>2024-03-14
52배춘영 행정사 사무소2023-01-27경기도 김포시 장릉로 56<NA><NA><NA>2024-03-14
53박노향행정사사무소2023-02-06경기도 김포시 고촌읍 장차로13번길 25<NA><NA><NA>2024-03-14
54우리동네 행정사 조성춘 사무소2023-02-24경기도 김포시 통진읍 검암2로 39-1<NA><NA><NA>2024-03-14
55행정사사무소 도율2024-03-08경기도 김포시 김포한강8로 410<NA><NA><NA>2024-03-14
56김영제 행정사 사무소2024-03-11경기도 김포시 통진읍 율마로384번길 78<NA><NA><NA>2024-03-14