Overview

Dataset statistics

Number of variables8
Number of observations38
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory70.5 B

Variable types

Text3
Categorical1
Numeric2
DateTime2

Dataset

Description수도권매리비지관리공사 내 건축물 임대 및 사용자 정보 제공항목 : 관리번호, 임대구분코드, 시설구분명,사용자명(마스킹처리),임대용도,사용시작일자,사용종료일자,사용일수를 제공 합니다
URLhttps://www.data.go.kr/data/15118891/fileData.do

Alerts

임대용도 is highly overall correlated with 사용일수 and 1 other fieldsHigh correlation
사용일수 is highly overall correlated with 임대용도High correlation
임대구분코드 is highly overall correlated with 임대용도High correlation
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:48:44.739928
Analysis finished2023-12-12 21:48:45.843738
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T06:48:45.993747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters456
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row2015U0000001
2nd row2015U0000002
3rd row2015U0000003
4th row2015U0000004
5th row2015U0000005
ValueCountFrequency (%)
2015u0000001 1
 
2.6%
2015u0000115 1
 
2.6%
2015u0000205 1
 
2.6%
2015u0000109 1
 
2.6%
2015u0000110 1
 
2.6%
2015u0000111 1
 
2.6%
2015u0000112 1
 
2.6%
2015u0000113 1
 
2.6%
2015u0000114 1
 
2.6%
2015u0000117 1
 
2.6%
Other values (28) 28
73.7%
2023-12-13T06:48:46.395483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 229
50.2%
1 76
 
16.7%
2 49
 
10.7%
5 42
 
9.2%
U 38
 
8.3%
3 5
 
1.1%
4 4
 
0.9%
6 4
 
0.9%
9 3
 
0.7%
7 3
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 418
91.7%
Uppercase Letter 38
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 229
54.8%
1 76
 
18.2%
2 49
 
11.7%
5 42
 
10.0%
3 5
 
1.2%
4 4
 
1.0%
6 4
 
1.0%
9 3
 
0.7%
7 3
 
0.7%
8 3
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
U 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 418
91.7%
Latin 38
 
8.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 229
54.8%
1 76
 
18.2%
2 49
 
11.7%
5 42
 
10.0%
3 5
 
1.2%
4 4
 
1.0%
6 4
 
1.0%
9 3
 
0.7%
7 3
 
0.7%
8 3
 
0.7%
Latin
ValueCountFrequency (%)
U 38
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 229
50.2%
1 76
 
16.7%
2 49
 
10.7%
5 42
 
9.2%
U 38
 
8.3%
3 5
 
1.1%
4 4
 
0.9%
6 4
 
0.9%
9 3
 
0.7%
7 3
 
0.7%

임대구분코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size436.0 B
224002
19 
224001
13 
224003
6 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row224001
2nd row224001
3rd row224001
4th row224001
5th row224001

Common Values

ValueCountFrequency (%)
224002 19
50.0%
224001 13
34.2%
224003 6
 
15.8%

Length

2023-12-13T06:48:46.561148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:48:46.668818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
224002 19
50.0%
224001 13
34.2%
224003 6
 
15.8%

시설구분명
Text

Distinct27
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T06:48:46.842806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.0263158
Min length3

Characters and Unicode

Total characters191
Distinct characters66
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

Unique21 ?
Unique (%)55.3%

Sample

1st row조경식재
2nd row주차장
3rd row진출입로
4th row진출입로
5th row진출입로
ValueCountFrequency (%)
진출입로 6
 
13.6%
버스차고지 3
 
6.8%
공장용지 3
 
6.8%
주민복지타운 2
 
4.5%
공영주차장 2
 
4.5%
통신기지국 2
 
4.5%
주차장 2
 
4.5%
및 2
 
4.5%
관로 1
 
2.3%
야립광고물 1
 
2.3%
Other values (20) 20
45.5%
2023-12-13T06:48:47.192344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 12
 
6.3%
지 12
 
6.3%
로 9
 
4.7%
차 8
 
4.2%
주 8
 
4.2%
공 7
 
3.7%
진 6
 
3.1%
선 6
 
3.1%
6
 
3.1%
출 6
 
3.1%
Other values (56) 111
58.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 185
96.9%
Space Separator 6
 
3.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 12
 
6.5%
지 12
 
6.5%
로 9
 
4.9%
차 8
 
4.3%
주 8
 
4.3%
공 7
 
3.8%
진 6
 
3.2%
선 6
 
3.2%
출 6
 
3.2%
입 6
 
3.2%
Other values (55) 105
56.8%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 185
96.9%
Common 6
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 12
 
6.5%
지 12
 
6.5%
로 9
 
4.9%
차 8
 
4.3%
주 8
 
4.3%
공 7
 
3.8%
진 6
 
3.2%
선 6
 
3.2%
출 6
 
3.2%
입 6
 
3.2%
Other values (55) 105
56.8%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 185
96.9%
ASCII 6
 
3.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 12
 
6.5%
지 12
 
6.5%
로 9
 
4.9%
차 8
 
4.3%
주 8
 
4.3%
공 7
 
3.8%
진 6
 
3.2%
선 6
 
3.2%
출 6
 
3.2%
입 6
 
3.2%
Other values (55) 105
56.8%
ASCII
ValueCountFrequency (%)
6
100.0%

사용자명
Text

Distinct34
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Memory size436.0 B
2023-12-13T06:48:47.431420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length10.5
Mean length7.9736842
Min length3

Characters and Unicode

Total characters303
Distinct characters106
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)81.6%

Sample

1st row정창*
2nd row신용화물
3rd row유경목재
4th row온누리컵
5th row원진특수
ValueCountFrequency (%)
인천 4
 
9.1%
한국전력공사(인천지역본부 3
 
6.8%
서구청(교통민원과 2
 
4.5%
미래복지재단 2
 
4.5%
청라에너지㈜ 1
 
2.3%
인천광역시 1
 
2.3%
서부청 1
 
2.3%
㈜삼환교통 1
 
2.3%
신동아교통(합 1
 
2.3%
제물포교통㈜ 1
 
2.3%
Other values (27) 27
61.4%
2023-12-13T06:48:48.078510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 17
 
5.6%
) 17
 
5.6%
천 10
 
3.3%
국 10
 
3.3%
인 10
 
3.3%
지 9
 
3.0%
한 9
 
3.0%
사 9
 
3.0%
전 8
 
2.6%
서 8
 
2.6%
Other values (96) 196
64.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 248
81.8%
Open Punctuation 17
 
5.6%
Close Punctuation 17
 
5.6%
Other Symbol 6
 
2.0%
Space Separator 6
 
2.0%
Uppercase Letter 6
 
2.0%
Other Punctuation 3
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 10
 
4.0%
국 10
 
4.0%
인 10
 
4.0%
지 9
 
3.6%
한 9
 
3.6%
사 9
 
3.6%
전 8
 
3.2%
서 8
 
3.2%
공 7
 
2.8%
청 6
 
2.4%
Other values (87) 162
65.3%
Uppercase Letter
ValueCountFrequency (%)
K 3
50.0%
T 2
33.3%
S 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
* 2
66.7%
, 1
33.3%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 6
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 254
83.8%
Common 43
 
14.2%
Latin 6
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 10
 
3.9%
국 10
 
3.9%
인 10
 
3.9%
지 9
 
3.5%
한 9
 
3.5%
사 9
 
3.5%
전 8
 
3.1%
서 8
 
3.1%
공 7
 
2.8%
청 6
 
2.4%
Other values (88) 168
66.1%
Common
ValueCountFrequency (%)
( 17
39.5%
) 17
39.5%
6
 
14.0%
* 2
 
4.7%
, 1
 
2.3%
Latin
ValueCountFrequency (%)
K 3
50.0%
T 2
33.3%
S 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 248
81.8%
ASCII 49
 
16.2%
None 6
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 17
34.7%
) 17
34.7%
6
 
12.2%
K 3
 
6.1%
T 2
 
4.1%
* 2
 
4.1%
S 1
 
2.0%
, 1
 
2.0%
Hangul
ValueCountFrequency (%)
천 10
 
4.0%
국 10
 
4.0%
인 10
 
4.0%
지 9
 
3.6%
한 9
 
3.6%
사 9
 
3.6%
전 8
 
3.2%
서 8
 
3.2%
공 7
 
2.8%
청 6
 
2.4%
Other values (87) 162
65.3%
None
ValueCountFrequency (%)
㈜ 6
100.0%

임대용도
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227185.05
Minimum227101
Maximum227304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T06:48:48.196976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum227101
5-th percentile227102.7
Q1227105
median227202
Q3227205
95-th percentile227302.15
Maximum227304
Range203
Interquartile range (IQR)100

Descriptive statistics

Standard deviation68.754537
Coefficient of variation (CV)0.00030263671
Kurtosis-0.84668366
Mean227185.05
Median Absolute Deviation (MAD)51.5
Skewness0.23893594
Sum8633032
Variance4727.1863
MonotonicityNot monotonic
2023-12-13T06:48:48.389145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
227205 7
18.4%
227105 5
13.2%
227201 5
13.2%
227301 3
7.9%
227104 3
7.9%
227103 3
7.9%
227101 2
 
5.3%
227202 2
 
5.3%
227303 1
 
2.6%
227302 1
 
2.6%
Other values (6) 6
15.8%
ValueCountFrequency (%)
227101 2
 
5.3%
227103 3
7.9%
227104 3
7.9%
227105 5
13.2%
227201 5
13.2%
227202 2
 
5.3%
227203 1
 
2.6%
227204 1
 
2.6%
227205 7
18.4%
227206 1
 
2.6%
ValueCountFrequency (%)
227304 1
 
2.6%
227303 1
 
2.6%
227302 1
 
2.6%
227301 3
7.9%
227208 1
 
2.6%
227207 1
 
2.6%
227206 1
 
2.6%
227205 7
18.4%
227204 1
 
2.6%
227203 1
 
2.6%

사용시작일자
Date

Distinct7
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size436.0 B
Minimum2013-12-06 00:00:00
Maximum2016-03-01 00:00:00
2023-12-13T06:48:48.536303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:48.670058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)

사용종료일자
Date

Distinct3
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size436.0 B
Minimum2015-12-31 00:00:00
Maximum2018-12-05 00:00:00
2023-12-13T06:48:48.780014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:48.961889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

사용일수
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean414.39474
Minimum306
Maximum1826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size474.0 B
2023-12-13T06:48:49.081877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum306
5-th percentile365
Q1366
median366
Q3366
95-th percentile500.95
Maximum1826
Range1520
Interquartile range (IQR)0

Descriptive statistics

Standard deviation239.79141
Coefficient of variation (CV)0.57865458
Kurtosis34.935389
Mean414.39474
Median Absolute Deviation (MAD)0
Skewness5.8266269
Sum15747
Variance57499.921
MonotonicityNot monotonic
2023-12-13T06:48:49.199576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
366 28
73.7%
365 5
 
13.2%
447 1
 
2.6%
1826 1
 
2.6%
481 1
 
2.6%
614 1
 
2.6%
306 1
 
2.6%
ValueCountFrequency (%)
306 1
 
2.6%
365 5
 
13.2%
366 28
73.7%
447 1
 
2.6%
481 1
 
2.6%
614 1
 
2.6%
1826 1
 
2.6%
ValueCountFrequency (%)
1826 1
 
2.6%
614 1
 
2.6%
481 1
 
2.6%
447 1
 
2.6%
366 28
73.7%
365 5
 
13.2%
306 1
 
2.6%

Interactions

2023-12-13T06:48:45.324287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:45.070550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:45.482826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:45.204682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:48:49.308469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호임대구분코드시설구분명사용자명임대용도사용시작일자사용종료일자사용일수
관리번호1.0001.0001.0001.0001.0001.0001.0001.000
임대구분코드1.0001.0000.9421.0001.0000.3760.5230.000
시설구분명1.0000.9421.0000.8470.9310.9390.0001.000
사용자명1.0001.0000.8471.0001.0000.8700.0000.000
임대용도1.0001.0000.9311.0001.0000.3520.1550.321
사용시작일자1.0000.3760.9390.8700.3521.0001.0001.000
사용종료일자1.0000.5230.0000.0000.1551.0001.0000.651
사용일수1.0000.0001.0000.0000.3211.0000.6511.000
2023-12-13T06:48:49.427591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임대용도사용일수임대구분코드
임대용도1.000-0.5430.986
사용일수-0.5431.0000.000
임대구분코드0.9860.0001.000

Missing values

2023-12-13T06:48:45.654137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:48:45.790292image/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

관리번호임대구분코드시설구분명사용자명임대용도사용시작일자사용종료일자사용일수
02015U0000001224001조경식재정창*2271012016-01-012016-12-31366
12015U0000002224001주차장신용화물2271032016-01-012016-12-31366
22015U0000003224001진출입로유경목재2271032016-01-012016-12-31366
32015U0000004224001진출입로온누리컵2271042016-01-012016-12-31366
42015U0000005224001진출입로원진특수2271042016-01-012016-12-31366
52015U0000006224001진출입로 및 공장부지건설철강2271042016-01-012016-12-31366
62015U0000007224001주차장현대종합정비2271032016-01-012016-12-31366
72015U0000008224001공영주차장인천 서구청(교통민원과)2271012016-01-012016-12-31366
82015U0000009224001공영주차장인천 서구청(교통민원과)2271052016-01-012016-12-31366
92015U0000010224001하수관로인천 서구청(건설과)2271052015-10-122016-12-31447
관리번호임대구분코드시설구분명사용자명임대용도사용시작일자사용종료일자사용일수
282015U0000116224002공장용지(주)신애철재2272012016-01-012016-12-31366
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