Overview

Dataset statistics

Number of variables12
Number of observations133
Missing cells49
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.0 KiB
Average record size in memory100.0 B

Variable types

Numeric3
Categorical6
Text2
Boolean1

Dataset

Description부산광역시연제구폐·공가현황_20230816
Author부산광역시 연제구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15025129

Alerts

정비여부 has constant value ""Constant
연번 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
경도 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 4 other fieldsHigh correlation
입력일자 is highly overall correlated with 연번 and 7 other fieldsHigh correlation
건물구조 is highly overall correlated with 경도 and 4 other fieldsHigh correlation
건물용도 is highly overall correlated with 입력일자 and 2 other fieldsHigh correlation
정비일자 is highly overall correlated with 연번 and 7 other fieldsHigh correlation
정비방법 is highly overall correlated with 경도 and 5 other fieldsHigh correlation
철거 후 활용 is highly overall correlated with 경도 and 4 other fieldsHigh correlation
건물규모 has 49 (36.8%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:10:03.403995
Analysis finished2023-12-10 16:10:04.770616
Duration1.37 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67
Minimum1
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T01:10:04.827440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.6
Q134
median67
Q3100
95-th percentile126.4
Maximum133
Range132
Interquartile range (IQR)66

Descriptive statistics

Standard deviation38.53786
Coefficient of variation (CV)0.57519194
Kurtosis-1.2
Mean67
Median Absolute Deviation (MAD)33
Skewness0
Sum8911
Variance1485.1667
MonotonicityStrictly increasing
2023-12-11T01:10:04.938071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
85 1
 
0.8%
99 1
 
0.8%
98 1
 
0.8%
97 1
 
0.8%
96 1
 
0.8%
95 1
 
0.8%
94 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
Other values (123) 123
92.5%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
133 1
0.8%
132 1
0.8%
131 1
0.8%
130 1
0.8%
129 1
0.8%
128 1
0.8%
127 1
0.8%
126 1
0.8%
125 1
0.8%
124 1
0.8%

입력일자
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2014-06-02
46 
2015-06-02
36 
2015-02-12
8 
2013-04-18
7 
2015-05-06
6 
Other values (20)
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique14 ?
Unique (%)10.5%

Sample

1st row2021-04-09
2nd row2021-04-09
3rd row2021-04-09
4th row2020-11-01
5th row2020-07-09

Common Values

ValueCountFrequency (%)
2014-06-02 46
34.6%
2015-06-02 36
27.1%
2015-02-12 8
 
6.0%
2013-04-18 7
 
5.3%
2015-05-06 6
 
4.5%
2015-06-01 4
 
3.0%
2021-04-09 3
 
2.3%
2015-03-12 3
 
2.3%
2015-04-01 2
 
1.5%
2014-07-07 2
 
1.5%
Other values (15) 16
 
12.0%

Length

2023-12-11T01:10:05.047355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-06-02 46
34.6%
2015-06-02 36
27.1%
2015-02-12 8
 
6.0%
2013-04-18 7
 
5.3%
2015-05-06 6
 
4.5%
2015-06-01 4
 
3.0%
2021-04-09 3
 
2.3%
2015-03-12 3
 
2.3%
2015-05-02 2
 
1.5%
2015-04-01 2
 
1.5%
Other values (15) 16
 
12.0%

건물주소
Text

Distinct132
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-11T01:10:05.292697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length10.511278
Min length7

Characters and Unicode

Total characters1398
Distinct characters29
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)98.5%

Sample

1st row거제동 765-8
2nd row거제동 766-9
3rd row거제동 766-15
4th row거제동 760-6
5th row거제동 산 64-2
ValueCountFrequency (%)
연산동 71
25.9%
거제동 55
20.1%
거제1동 4
 
1.5%
외 2
 
0.7%
1필지 2
 
0.7%
1811-581번지 2
 
0.7%
1575-12 2
 
0.7%
1584-13 1
 
0.4%
1584-30 1
 
0.4%
1584-28 1
 
0.4%
Other values (133) 133
48.5%
2023-12-11T01:10:05.664672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 157
11.2%
141
 
10.1%
동 133
 
9.5%
- 133
 
9.5%
0 86
 
6.2%
5 83
 
5.9%
3 82
 
5.9%
산 76
 
5.4%
연 75
 
5.4%
8 63
 
4.5%
Other values (19) 369
26.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 699
50.0%
Other Letter 422
30.2%
Space Separator 141
 
10.1%
Dash Punctuation 133
 
9.5%
Other Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%
Close Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 133
31.5%
산 76
18.0%
연 75
17.8%
제 60
14.2%
거 59
14.0%
지 6
 
1.4%
번 4
 
0.9%
외 2
 
0.5%
필 2
 
0.5%
쌍 1
 
0.2%
Other values (4) 4
 
0.9%
Decimal Number
ValueCountFrequency (%)
1 157
22.5%
0 86
12.3%
5 83
11.9%
3 82
11.7%
8 63
9.0%
7 53
 
7.6%
2 49
 
7.0%
6 46
 
6.6%
4 43
 
6.2%
9 37
 
5.3%
Space Separator
ValueCountFrequency (%)
141
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 133
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 976
69.8%
Hangul 422
30.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 157
16.1%
141
14.4%
- 133
13.6%
0 86
8.8%
5 83
8.5%
3 82
8.4%
8 63
6.5%
7 53
 
5.4%
2 49
 
5.0%
6 46
 
4.7%
Other values (5) 83
8.5%
Hangul
ValueCountFrequency (%)
동 133
31.5%
산 76
18.0%
연 75
17.8%
제 60
14.2%
거 59
14.0%
지 6
 
1.4%
번 4
 
0.9%
외 2
 
0.5%
필 2
 
0.5%
쌍 1
 
0.2%
Other values (4) 4
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 976
69.8%
Hangul 422
30.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 157
16.1%
141
14.4%
- 133
13.6%
0 86
8.8%
5 83
8.5%
3 82
8.4%
8 63
6.5%
7 53
 
5.4%
2 49
 
5.0%
6 46
 
4.7%
Other values (5) 83
8.5%
Hangul
ValueCountFrequency (%)
동 133
31.5%
산 76
18.0%
연 75
17.8%
제 60
14.2%
거 59
14.0%
지 6
 
1.4%
번 4
 
0.9%
외 2
 
0.5%
필 2
 
0.5%
쌍 1
 
0.2%
Other values (4) 4
 
0.9%

건물구조
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
조적조(블럭)
54 
<NA>
51 
블럭조
8 
블럭스레트
7 
조적조(벽돌)
 
5
Other values (6)
8 

Length

Max length18
Median length12
Mean length5.5789474
Min length3

Unique

Unique5 ?
Unique (%)3.8%

Sample

1st row조적조(블럭)
2nd row조적조(블럭)
3rd row조적조(블럭)
4th row조적조(철근)
5th row블럭조

Common Values

ValueCountFrequency (%)
조적조(블럭) 54
40.6%
<NA> 51
38.3%
블럭조 8
 
6.0%
블럭스레트 7
 
5.3%
조적조(벽돌) 5
 
3.8%
블럭슬레이트 3
 
2.3%
조적조(철근) 1
 
0.8%
블럭조 기와지붕 1
 
0.8%
목조 루빙지붕 단층주택 1
 
0.8%
시멘트블록조 기와지붕, 지상 2층 1
 
0.8%

Length

2023-12-11T01:10:05.780275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
조적조(블럭 54
38.8%
na 51
36.7%
블럭조 9
 
6.5%
블럭스레트 7
 
5.0%
조적조(벽돌 5
 
3.6%
블럭슬레이트 3
 
2.2%
기와지붕 2
 
1.4%
조적조(철근 1
 
0.7%
목조 1
 
0.7%
루빙지붕 1
 
0.7%
Other values (5) 5
 
3.6%

건물용도
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
단독주택
75 
<NA>
51 
주택
 
3
단독주택 및 근린생활시설
 
2
단독주택, 근린생활시설
 
1

Length

Max length13
Median length4
Mean length4.1879699
Min length2

Unique

Unique2 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
단독주택 75
56.4%
<NA> 51
38.3%
주택 3
 
2.3%
단독주택 및 근린생활시설 2
 
1.5%
단독주택, 근린생활시설 1
 
0.8%
단독주택 및 근생 1
 
0.8%

Length

2023-12-11T01:10:05.888819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:10:05.976748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단독주택 79
56.4%
na 51
36.4%
주택 3
 
2.1%
및 3
 
2.1%
근린생활시설 3
 
2.1%
근생 1
 
0.7%

건물규모
Text

MISSING 

Distinct64
Distinct (%)76.2%
Missing49
Missing (%)36.8%
Memory size1.2 KiB
2023-12-11T01:10:06.157515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length13.702381
Min length4

Characters and Unicode

Total characters1151
Distinct characters25
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

Unique55 ?
Unique (%)65.5%

Sample

1st row연면적 29.75㎡, 지상1층
2nd row연면적 36.2㎡, 지상1층
3rd row연면적 71.96㎡, 지상1층
4th row연면적 300.59㎡, 지하1층/지상2층
5th row연면적 44㎡, 지상 1층
ValueCountFrequency (%)
연면적 64
23.7%
지상 54
20.0%
1층 46
17.0%
지상1층 15
 
5.6%
2층 9
 
3.3%
30.1㎡ 6
 
2.2%
2개동 3
 
1.1%
대지면적 3
 
1.1%
19.83㎡ 3
 
1.1%
44.3㎡ 2
 
0.7%
Other values (60) 65
24.1%
2023-12-11T01:10:06.462579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
186
16.2%
1 91
 
7.9%
지 76
 
6.6%
층 74
 
6.4%
상 71
 
6.2%
㎡ 69
 
6.0%
적 67
 
5.8%
면 67
 
5.8%
연 64
 
5.6%
. 63
 
5.5%
Other values (15) 323
28.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 434
37.7%
Decimal Number 336
29.2%
Space Separator 186
16.2%
Other Punctuation 126
 
10.9%
Other Symbol 69
 
6.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 91
27.1%
3 40
11.9%
2 34
 
10.1%
9 30
 
8.9%
5 29
 
8.6%
6 26
 
7.7%
4 24
 
7.1%
8 23
 
6.8%
7 22
 
6.5%
0 17
 
5.1%
Other Letter
ValueCountFrequency (%)
지 76
17.5%
층 74
17.1%
상 71
16.4%
적 67
15.4%
면 67
15.4%
연 64
14.7%
개 5
 
1.2%
동 5
 
1.2%
대 3
 
0.7%
하 2
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 63
50.0%
, 62
49.2%
/ 1
 
0.8%
Space Separator
ValueCountFrequency (%)
186
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 69
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 717
62.3%
Hangul 434
37.7%

Most frequent character per script

Common
ValueCountFrequency (%)
186
25.9%
1 91
12.7%
㎡ 69
 
9.6%
. 63
 
8.8%
, 62
 
8.6%
3 40
 
5.6%
2 34
 
4.7%
9 30
 
4.2%
5 29
 
4.0%
6 26
 
3.6%
Other values (5) 87
12.1%
Hangul
ValueCountFrequency (%)
지 76
17.5%
층 74
17.1%
상 71
16.4%
적 67
15.4%
면 67
15.4%
연 64
14.7%
개 5
 
1.2%
동 5
 
1.2%
대 3
 
0.7%
하 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 648
56.3%
Hangul 434
37.7%
CJK Compat 69
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
186
28.7%
1 91
14.0%
. 63
 
9.7%
, 62
 
9.6%
3 40
 
6.2%
2 34
 
5.2%
9 30
 
4.6%
5 29
 
4.5%
6 26
 
4.0%
4 24
 
3.7%
Other values (4) 63
 
9.7%
Hangul
ValueCountFrequency (%)
지 76
17.5%
층 74
17.1%
상 71
16.4%
적 67
15.4%
면 67
15.4%
연 64
14.7%
개 5
 
1.2%
동 5
 
1.2%
대 3
 
0.7%
하 2
 
0.5%
CJK Compat
ValueCountFrequency (%)
㎡ 69
100.0%

정비여부
Boolean

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size265.0 B
True
133 
ValueCountFrequency (%)
True 133
100.0%
2023-12-11T01:10:06.560862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

정비일자
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2015-09-25
28 
2014-04-01
18 
2014-04-02
15 
2014-03-31
13 
2016-02-12
10 
Other values (29)
49 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique23 ?
Unique (%)17.3%

Sample

1st row2021-06-03
2nd row2021-06-03
3rd row2021-06-03
4th row2021-07-06
5th row2020-07-28

Common Values

ValueCountFrequency (%)
2015-09-25 28
21.1%
2014-04-01 18
13.5%
2014-04-02 15
11.3%
2014-03-31 13
9.8%
2016-02-12 10
 
7.5%
2016-02-05 9
 
6.8%
2015-05-07 6
 
4.5%
2015-03-31 4
 
3.0%
2021-06-03 3
 
2.3%
2014-04-09 2
 
1.5%
Other values (24) 25
18.8%

Length

2023-12-11T01:10:06.637234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-09-25 28
21.1%
2014-04-01 18
13.5%
2014-04-02 15
11.3%
2014-03-31 13
9.8%
2016-02-12 10
 
7.5%
2016-02-05 9
 
6.8%
2015-05-07 6
 
4.5%
2015-03-31 4
 
3.0%
2021-06-03 3
 
2.3%
2014-04-09 2
 
1.5%
Other values (24) 25
18.8%

정비방법
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
기타 ()
63 
자진철거
63 
예산지원철거
 
6
예산지원
 
1

Length

Max length6
Median length5
Mean length4.5639098
Min length4

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row기타 ()
2nd row기타 ()
3rd row기타 ()
4th row기타 ()
5th row기타 ()

Common Values

ValueCountFrequency (%)
기타 () 63
47.4%
자진철거 63
47.4%
예산지원철거 6
 
4.5%
예산지원 1
 
0.8%

Length

2023-12-11T01:10:06.744600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:10:06.838468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 63
32.1%
63
32.1%
자진철거 63
32.1%
예산지원철거 6
 
3.1%
예산지원 1
 
0.5%

철거 후 활용
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
<NA>
59 
기타 (.)
50 
기타 ()
15 
기타 (-)
 
2
쉼터(녹지, 운동시설 등)
 
2
Other values (3)
 
5

Length

Max length14
Median length7
Mean length5.0827068
Min length3

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 59
44.4%
기타 (.) 50
37.6%
기타 () 15
 
11.3%
기타 (-) 2
 
1.5%
쉼터(녹지, 운동시설 등) 2
 
1.5%
기타 (0) 2
 
1.5%
주차장 2
 
1.5%
기타 (기타) 1
 
0.8%

Length

2023-12-11T01:10:07.142542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:10:07.234514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기타 71
34.3%
67
32.4%
na 59
28.5%
쉼터(녹지 2
 
1.0%
운동시설 2
 
1.0%
등 2
 
1.0%
0 2
 
1.0%
주차장 2
 
1.0%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.07823
Minimum129.06473
Maximum129.09586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T01:10:07.351062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum129.06473
5-th percentile129.06828
Q1129.07651
median129.07761
Q3129.07956
95-th percentile129.08755
Maximum129.09586
Range0.0311297
Interquartile range (IQR)0.0030565

Descriptive statistics

Standard deviation0.0053031608
Coefficient of variation (CV)4.1084858 × 10-5
Kurtosis4.5076722
Mean129.07823
Median Absolute Deviation (MAD)0.0012229
Skewness0.97884408
Sum17167.405
Variance2.8123515 × 10-5
MonotonicityNot monotonic
2023-12-11T01:10:07.474889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0958488 2
 
1.5%
129.0867053 2
 
1.5%
129.0796208 2
 
1.5%
129.0779053 1
 
0.8%
129.0780872 1
 
0.8%
129.0779222 1
 
0.8%
129.0777099 1
 
0.8%
129.0784875 1
 
0.8%
129.0779734 1
 
0.8%
129.06549 1
 
0.8%
Other values (120) 120
90.2%
ValueCountFrequency (%)
129.0647319 1
0.8%
129.0652695 1
0.8%
129.0652817 1
0.8%
129.06549 1
0.8%
129.0656822 1
0.8%
129.0657131 1
0.8%
129.0658823 1
0.8%
129.0698853 1
0.8%
129.0752246 1
0.8%
129.0753103 1
0.8%
ValueCountFrequency (%)
129.0958616 1
0.8%
129.0958488 2
1.5%
129.095232 1
0.8%
129.0949509 1
0.8%
129.0948005 1
0.8%
129.0885266 1
0.8%
129.0868951 1
0.8%
129.0868703 1
0.8%
129.0867053 2
1.5%
129.0851454 1
0.8%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.183605
Minimum35.168393
Maximum35.193808
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-11T01:10:07.672186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.168393
5-th percentile35.174348
Q135.177297
median35.17897
Q335.19275
95-th percentile35.193603
Maximum35.193808
Range0.02541479
Interquartile range (IQR)0.01545342

Descriptive statistics

Standard deviation0.007838738
Coefficient of variation (CV)0.00022279519
Kurtosis-1.6012042
Mean35.183605
Median Absolute Deviation (MAD)0.00229914
Skewness0.2320589
Sum4679.4194
Variance6.1445814 × 10-5
MonotonicityNot monotonic
2023-12-11T01:10:07.937154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1706747 2
 
1.5%
35.18803677 2
 
1.5%
35.17706724 2
 
1.5%
35.17768647 1
 
0.8%
35.17732429 1
 
0.8%
35.17729872 1
 
0.8%
35.17785046 1
 
0.8%
35.17770832 1
 
0.8%
35.17784811 1
 
0.8%
35.18114964 1
 
0.8%
Other values (120) 120
90.2%
ValueCountFrequency (%)
35.16839331 1
0.8%
35.17052705 1
0.8%
35.1706747 2
1.5%
35.17080979 1
0.8%
35.17191008 1
0.8%
35.17323452 1
0.8%
35.17509115 1
0.8%
35.1764146 1
0.8%
35.17657192 1
0.8%
35.1765825 1
0.8%
ValueCountFrequency (%)
35.1938081 1
0.8%
35.19373307 1
0.8%
35.19370466 1
0.8%
35.1936788 1
0.8%
35.19367479 1
0.8%
35.19365813 1
0.8%
35.1936211 1
0.8%
35.19359152 1
0.8%
35.19358847 1
0.8%
35.19356859 1
0.8%

Interactions

2023-12-11T01:10:04.340317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:03.895093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:04.117859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:04.413607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:03.958924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:04.190001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:04.491550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:04.038430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:10:04.268447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:10:08.081193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번입력일자건물구조건물용도건물규모정비일자정비방법철거 후 활용경도위도
연번1.0000.9000.6860.3350.9530.9050.6540.6980.7300.729
입력일자0.9001.0000.9780.8550.9440.9910.9190.9190.9900.996
건물구조0.6860.9781.0000.8950.9360.9860.7290.4800.7630.696
건물용도0.3350.8550.8951.0000.8790.8750.4950.2810.2610.502
건물규모0.9530.9440.9360.8791.0000.0000.8590.0000.9230.000
정비일자0.9050.9910.9860.8750.0001.0000.9970.9960.9930.997
정비방법0.6540.9190.7290.4950.8590.9971.0000.6570.9220.953
철거 후 활용0.6980.9190.4800.2810.0000.9960.6571.0000.9410.841
경도0.7300.9900.7630.2610.9230.9930.9220.9411.0000.947
위도0.7290.9960.6960.5020.0000.9970.9530.8410.9471.000
2023-12-11T01:10:08.192354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정비일자철거 후 활용정비방법건물구조입력일자건물용도
정비일자1.0000.8000.8590.7540.8050.520
철거 후 활용0.8001.0000.5070.2180.7160.136
정비방법0.8590.5071.0000.5120.6950.420
건물구조0.7540.2180.5121.0000.7950.560
입력일자0.8050.7160.6950.7951.0000.552
건물용도0.5200.1360.4200.5600.5521.000
2023-12-11T01:10:08.281774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번경도위도입력일자건물구조건물용도정비일자정비방법철거 후 활용
연번1.0000.503-0.4640.5420.4240.2350.5470.4430.438
경도0.5031.000-0.7650.7910.5000.1680.8400.6310.633
위도-0.464-0.7651.0000.9060.4190.3300.8730.7030.733
입력일자0.5420.7910.9061.0000.7950.5520.8050.6950.716
건물구조0.4240.5000.4190.7951.0000.5600.7540.5120.218
건물용도0.2350.1680.3300.5520.5601.0000.5200.4200.136
정비일자0.5470.8400.8730.8050.7540.5201.0000.8590.800
정비방법0.4430.6310.7030.6950.5120.4200.8591.0000.507
철거 후 활용0.4380.6330.7330.7160.2180.1360.8000.5071.000

Missing values

2023-12-11T01:10:04.591397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:10:04.722236image/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

연번입력일자건물주소건물구조건물용도건물규모정비여부정비일자정비방법철거 후 활용경도위도
012021-04-09거제동 765-8조적조(블럭)단독주택연면적 29.75㎡, 지상1층Y2021-06-03기타 ()<NA>129.0654935.18115
122021-04-09거제동 766-9조적조(블럭)단독주택연면적 36.2㎡, 지상1층Y2021-06-03기타 ()<NA>129.06528235.18117
232021-04-09거제동 766-15조적조(블럭)단독주택연면적 71.96㎡, 지상1층Y2021-06-03기타 ()<NA>129.06526935.181079
342020-11-01거제동 760-6조적조(철근)단독주택연면적 300.59㎡, 지하1층/지상2층Y2021-07-06기타 ()<NA>129.06588235.180083
452020-07-09거제동 산 64-2블럭조단독주택연면적 44㎡, 지상 1층Y2020-07-28기타 ()<NA>129.06473235.18195
562020-04-23연산동 1207-2블럭조단독주택연면적 50㎡, 지상1층Y2020-05-14기타 ()<NA>129.08514535.182201
672019-07-11연산동 산161-1조적조(블럭)단독주택연면적 52㎡, 지상1층Y2019-11-27기타 ()<NA>129.09495135.168393
782018-06-04연산동 1919-26블럭조단독주택연면적 39.67㎡, 지상 1층Y2018-12-14기타 ()<NA>129.08689535.175091
892017-08-18연산동 1941-142블럭조단독주택연면적 19.83㎡, 지상1층Y2017-09-12기타 ()<NA>129.0868735.173235
9102016-11-15연산동 1811-771블럭조단독주택연면적 40㎡, 지상1층Y2016-11-29기타 ()<NA>129.094835.17191
연번입력일자건물주소건물구조건물용도건물규모정비여부정비일자정비방법철거 후 활용경도위도
1231242014-01-06연산동 615-71<NA><NA><NA>Y2014-04-09예산지원철거주차장129.08670535.188037
1241252013-07-29연제구 거제동 751-37번지조적조단독주택지하1층, 지상2층, 연면적 127.14㎡Y2013-11-12자진철거기타 ()129.06988535.176415
1251262013-04-18거제1동 300-4블럭스레트단독주택지상1층Y2016-02-12기타 ()<NA>129.07638935.193348
1261272013-04-18거제동 318-10블럭스레트단독주택연면적 68.43㎡, 지상1층, 2개동Y2015-09-25기타 ()<NA>129.07559735.193319
1271282013-04-18연산7동 667-73블럭스레트단독주택지상1층 34.05㎡Y2013-07-08예산지원기타 ()129.08852735.17724
1281292013-04-18거제1동 303-3블럭스레트단독주택지상1층Y2016-02-12자진철거기타 (기타)129.07669235.193057
1291302013-04-18거제1동 303-2블럭스레트단독주택지상1층Y2016-02-20기타 ()<NA>129.07681335.193074
1301312013-04-18거제1동 300-60블럭스레트단독주택지상1층 31.40㎡Y2015-09-25기타 ()<NA>129.07699735.193658
1311322013-04-18거제동 309-12블럭스레트단독주택연면적 45.66㎡, 지상 1층Y2015-09-25기타 ()<NA>129.07617735.192313
1321332013-03-15연산2동 1575-12블럭조단독주택지상1층Y2014-01-02기타 ()<NA>129.07962135.177067