Overview

Dataset statistics

Number of variables15
Number of observations47
Missing cells145
Missing cells (%)20.6%
Duplicate rows1
Duplicate rows (%)2.1%
Total size in memory5.9 KiB
Average record size in memory128.8 B

Variable types

Categorical5
Text3
Numeric4
DateTime3

Dataset

Description2018년부터 2023년 8월까지 건축허가, 신고, 사업승인된 주용도가 숙박시설, 공동주택, 운동시설, 제2종근린생활시설인 미준공 건축물 현황 공개
URLhttps://www.data.go.kr/data/15121587/fileData.do

Alerts

건축구분 has constant value ""Constant
데이터기준일 has constant value ""Constant
Dataset has 1 (2.1%) duplicate rowsDuplicates
연면적(제곱미터) is highly overall correlated with 최대지상층수 and 4 other fieldsHigh correlation
최대지상층수 is highly overall correlated with 연면적(제곱미터) and 3 other fieldsHigh correlation
총주차대수 is highly overall correlated with 연면적(제곱미터) and 4 other fieldsHigh correlation
세대수 is highly overall correlated with 연면적(제곱미터) and 3 other fieldsHigh correlation
최대지하층수 is highly overall correlated with 연면적(제곱미터) and 2 other fieldsHigh correlation
주용도 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
가구수 is highly overall correlated with 연면적(제곱미터) and 2 other fieldsHigh correlation
가구수 is highly imbalanced (59.4%)Imbalance
착공처리일 has 24 (51.1%) missing valuesMissing
준공예정일(사용승인예정일) has 24 (51.1%) missing valuesMissing
부속용도 has 15 (31.9%) missing valuesMissing
총주차대수 has 9 (19.1%) missing valuesMissing
세대수 has 40 (85.1%) missing valuesMissing
시공자사무소명 has 33 (70.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 09:02:37.502904
Analysis finished2023-12-12 09:02:40.877707
Duration3.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

건축구분
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
신축
47 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신축 47
100.0%

Length

2023-12-12T18:02:40.973602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:02:41.130340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 47
100.0%

대지위치
Text

Distinct43
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-12-12T18:02:41.476758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length25
Mean length23.319149
Min length16

Characters and Unicode

Total characters1096
Distinct characters58
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)87.2%

Sample

1st row경상남도 통영시 용남면 삼화리 934 외1필지
2nd row경상남도 통영시 광도면 노산리 5 외1필지
3rd row경상남도 통영시 용남면 화삼리 76-3 외6필지
4th row경상남도 통영시 광도면 죽림리 380-1 외1필지
5th row경상남도 통영시 산양읍 남평리 1293-3 외1필지
ValueCountFrequency (%)
경상남도 47
18.4%
통영시 47
18.4%
용남면 17
 
6.6%
외1필지 12
 
4.7%
산양읍 10
 
3.9%
광도면 8
 
3.1%
동달리 6
 
2.3%
죽림리 6
 
2.3%
신전리 5
 
2.0%
274 5
 
2.0%
Other values (72) 93
36.3%
2023-12-12T18:02:41.971005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
209
19.1%
남 66
 
6.0%
도 56
 
5.1%
영 48
 
4.4%
경 47
 
4.3%
통 47
 
4.3%
시 47
 
4.3%
상 47
 
4.3%
1 39
 
3.6%
리 38
 
3.5%
Other values (48) 452
41.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 663
60.5%
Space Separator 209
 
19.1%
Decimal Number 197
 
18.0%
Dash Punctuation 27
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 66
 
10.0%
도 56
 
8.4%
영 48
 
7.2%
경 47
 
7.1%
통 47
 
7.1%
시 47
 
7.1%
상 47
 
7.1%
리 38
 
5.7%
면 28
 
4.2%
지 25
 
3.8%
Other values (36) 214
32.3%
Decimal Number
ValueCountFrequency (%)
1 39
19.8%
2 28
14.2%
3 22
11.2%
5 21
10.7%
4 20
10.2%
7 16
8.1%
6 15
 
7.6%
8 13
 
6.6%
0 12
 
6.1%
9 11
 
5.6%
Space Separator
ValueCountFrequency (%)
209
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 663
60.5%
Common 433
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 66
 
10.0%
도 56
 
8.4%
영 48
 
7.2%
경 47
 
7.1%
통 47
 
7.1%
시 47
 
7.1%
상 47
 
7.1%
리 38
 
5.7%
면 28
 
4.2%
지 25
 
3.8%
Other values (36) 214
32.3%
Common
ValueCountFrequency (%)
209
48.3%
1 39
 
9.0%
2 28
 
6.5%
- 27
 
6.2%
3 22
 
5.1%
5 21
 
4.8%
4 20
 
4.6%
7 16
 
3.7%
6 15
 
3.5%
8 13
 
3.0%
Other values (2) 23
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 663
60.5%
ASCII 433
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
209
48.3%
1 39
 
9.0%
2 28
 
6.5%
- 27
 
6.2%
3 22
 
5.1%
5 21
 
4.8%
4 20
 
4.6%
7 16
 
3.7%
6 15
 
3.5%
8 13
 
3.0%
Other values (2) 23
 
5.3%
Hangul
ValueCountFrequency (%)
남 66
 
10.0%
도 56
 
8.4%
영 48
 
7.2%
경 47
 
7.1%
통 47
 
7.1%
시 47
 
7.1%
상 47
 
7.1%
리 38
 
5.7%
면 28
 
4.2%
지 25
 
3.8%
Other values (36) 214
32.3%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7383.5436
Minimum99.33
Maximum129853.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T18:02:42.118711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99.33
5-th percentile161.508
Q1265.354
median467.82
Q31308.265
95-th percentile56412.042
Maximum129853.13
Range129753.8
Interquartile range (IQR)1042.911

Descriptive statistics

Standard deviation23207.271
Coefficient of variation (CV)3.1431075
Kurtosis18.214041
Mean7383.5436
Median Absolute Deviation (MAD)272.65
Skewness4.1208188
Sum347026.55
Variance5.3857742 × 108
MonotonicityNot monotonic
2023-12-12T18:02:42.290136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
265.354 2
 
4.3%
446.77 1
 
2.1%
369.0 1
 
2.1%
740.34 1
 
2.1%
175.96 1
 
2.1%
467.82 1
 
2.1%
168.48 1
 
2.1%
490.33 1
 
2.1%
177.5 1
 
2.1%
1110.6 1
 
2.1%
Other values (36) 36
76.6%
ValueCountFrequency (%)
99.33 1
2.1%
148.5 1
2.1%
158.52 1
2.1%
168.48 1
2.1%
175.96 1
2.1%
177.5 1
2.1%
186.76 1
2.1%
195.17 1
2.1%
196.0 1
2.1%
198.61 1
2.1%
ValueCountFrequency (%)
129853.1278 1
2.1%
68262.7137 1
2.1%
65576.4121 1
2.1%
35028.5104 1
2.1%
14895.23 1
2.1%
4540.16 1
2.1%
3354.5 1
2.1%
3100.61 1
2.1%
2718.11 1
2.1%
1889.15 1
2.1%

허가일(사업승인일)
Date

Distinct41
Distinct (%)87.2%
Missing0
Missing (%)0.0%
Memory size508.0 B
Minimum2018-01-19 00:00:00
Maximum2023-07-07 00:00:00
2023-12-12T18:02:42.444417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:42.598427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)

착공처리일
Date

MISSING 

Distinct21
Distinct (%)91.3%
Missing24
Missing (%)51.1%
Memory size508.0 B
Minimum2018-04-05 00:00:00
Maximum2023-07-21 00:00:00
2023-12-12T18:02:42.724927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:42.846394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)

준공예정일(사용승인예정일)
Date

MISSING 

Distinct20
Distinct (%)87.0%
Missing24
Missing (%)51.1%
Memory size508.0 B
Minimum2018-11-09 00:00:00
Maximum2023-12-31 00:00:00
2023-12-12T18:02:42.971101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:43.104079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)

최대지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1914894
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T18:02:43.214378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile20
Maximum29
Range28
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.127764
Coefficient of variation (CV)1.4619538
Kurtosis8.3798199
Mean4.1914894
Median Absolute Deviation (MAD)1
Skewness2.9965036
Sum197
Variance37.549491
MonotonicityNot monotonic
2023-12-12T18:02:43.342136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 15
31.9%
3 10
21.3%
2 9
19.1%
4 6
 
12.8%
5 2
 
4.3%
20 2
 
4.3%
7 1
 
2.1%
24 1
 
2.1%
29 1
 
2.1%
ValueCountFrequency (%)
1 15
31.9%
2 9
19.1%
3 10
21.3%
4 6
 
12.8%
5 2
 
4.3%
7 1
 
2.1%
20 2
 
4.3%
24 1
 
2.1%
29 1
 
2.1%
ValueCountFrequency (%)
29 1
 
2.1%
24 1
 
2.1%
20 2
 
4.3%
7 1
 
2.1%
5 2
 
4.3%
4 6
 
12.8%
3 10
21.3%
2 9
19.1%
1 15
31.9%

최대지하층수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size508.0 B
0
31 
1
10 
2
 
3
4
 
2
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 31
66.0%
1 10
 
21.3%
2 3
 
6.4%
4 2
 
4.3%
3 1
 
2.1%

Length

2023-12-12T18:02:43.540207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:02:43.664505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
66.0%
1 10
 
21.3%
2 3
 
6.4%
4 2
 
4.3%
3 1
 
2.1%

주용도
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size508.0 B
제2종근린생활시설
26 
제1종근린생활시설
5 
숙박시설
4 
공동주택(아파트)
4 
공동주택
3 
Other values (3)
5 

Length

Max length9
Median length9
Mean length7.7234043
Min length4

Unique

Unique2 ?
Unique (%)4.3%

Sample

1st row제2종근린생활시설
2nd row제1종근린생활시설
3rd row제2종근린생활시설
4th row제2종근린생활시설
5th row제2종근린생활시설

Common Values

ValueCountFrequency (%)
제2종근린생활시설 26
55.3%
제1종근린생활시설 5
 
10.6%
숙박시설 4
 
8.5%
공동주택(아파트) 4
 
8.5%
공동주택 3
 
6.4%
단독주택 3
 
6.4%
운동시설 1
 
2.1%
창고시설 1
 
2.1%

Length

2023-12-12T18:02:43.942265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:02:44.150167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제2종근린생활시설 26
55.3%
제1종근린생활시설 5
 
10.6%
숙박시설 4
 
8.5%
공동주택(아파트 4
 
8.5%
공동주택 3
 
6.4%
단독주택 3
 
6.4%
운동시설 1
 
2.1%
창고시설 1
 
2.1%

부속용도
Text

MISSING 

Distinct27
Distinct (%)84.4%
Missing15
Missing (%)31.9%
Memory size508.0 B
2023-12-12T18:02:44.393039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length16
Mean length10.4375
Min length1

Characters and Unicode

Total characters334
Distinct characters72
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

Unique23 ?
Unique (%)71.9%

Sample

1st row제조업소(수산물 작업장)
2nd row휴게음식점 및 2종(사무소)
3rd row제조업소
4th row사무소
5th row일반음식점
ValueCountFrequency (%)
일반음식점 4
 
9.5%
사무소 4
 
9.5%
다가구주택,제2종근린생활시설(사무소 2
 
4.8%
제조업소 2
 
4.8%
및 2
 
4.8%
휴게음식점 2
 
4.8%
종교집회장 1
 
2.4%
제2종근린생활시설(사무소 1
 
2.4%
부속창고 1
 
2.4%
외 1
 
2.4%
Other values (22) 22
52.4%
2023-12-12T18:02:44.875079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
소 20
 
6.0%
, 15
 
4.5%
점 15
 
4.5%
) 13
 
3.9%
사 13
 
3.9%
무 13
 
3.9%
( 13
 
3.9%
음 11
 
3.3%
식 11
 
3.3%
종 11
 
3.3%
Other values (62) 199
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 269
80.5%
Other Punctuation 16
 
4.8%
Close Punctuation 13
 
3.9%
Open Punctuation 13
 
3.9%
Decimal Number 12
 
3.6%
Space Separator 10
 
3.0%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 20
 
7.4%
점 15
 
5.6%
사 13
 
4.8%
무 13
 
4.8%
음 11
 
4.1%
식 11
 
4.1%
종 11
 
4.1%
제 10
 
3.7%
시 9
 
3.3%
설 9
 
3.3%
Other values (54) 147
54.6%
Other Punctuation
ValueCountFrequency (%)
, 15
93.8%
. 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 9
75.0%
1 3
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 269
80.5%
Common 65
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 20
 
7.4%
점 15
 
5.6%
사 13
 
4.8%
무 13
 
4.8%
음 11
 
4.1%
식 11
 
4.1%
종 11
 
4.1%
제 10
 
3.7%
시 9
 
3.3%
설 9
 
3.3%
Other values (54) 147
54.6%
Common
ValueCountFrequency (%)
, 15
23.1%
) 13
20.0%
( 13
20.0%
10
15.4%
2 9
13.8%
1 3
 
4.6%
. 1
 
1.5%
- 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 269
80.5%
ASCII 65
 
19.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
소 20
 
7.4%
점 15
 
5.6%
사 13
 
4.8%
무 13
 
4.8%
음 11
 
4.1%
식 11
 
4.1%
종 11
 
4.1%
제 10
 
3.7%
시 9
 
3.3%
설 9
 
3.3%
Other values (54) 147
54.6%
ASCII
ValueCountFrequency (%)
, 15
23.1%
) 13
20.0%
( 13
20.0%
10
15.4%
2 9
13.8%
1 3
 
4.6%
. 1
 
1.5%
- 1
 
1.5%

총주차대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)57.9%
Missing9
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean76.710526
Minimum1
Maximum983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T18:02:45.062624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q330.75
95-th percentile581.75
Maximum983
Range982
Interquartile range (IQR)27.75

Descriptive statistics

Standard deviation204.2718
Coefficient of variation (CV)2.6628914
Kurtosis11.663858
Mean76.710526
Median Absolute Deviation (MAD)4.5
Skewness3.3998046
Sum2915
Variance41726.968
MonotonicityNot monotonic
2023-12-12T18:02:45.238268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 6
12.8%
1 4
 
8.5%
2 4
 
8.5%
6 4
 
8.5%
4 3
 
6.4%
92 1
 
2.1%
12 1
 
2.1%
603 1
 
2.1%
292 1
 
2.1%
983 1
 
2.1%
Other values (12) 12
25.5%
(Missing) 9
19.1%
ValueCountFrequency (%)
1 4
8.5%
2 4
8.5%
3 6
12.8%
4 3
6.4%
5 1
 
2.1%
6 4
8.5%
8 1
 
2.1%
12 1
 
2.1%
16 1
 
2.1%
17 1
 
2.1%
ValueCountFrequency (%)
983 1
2.1%
603 1
2.1%
578 1
2.1%
292 1
2.1%
92 1
2.1%
44 1
2.1%
43 1
2.1%
38 1
2.1%
33 1
2.1%
31 1
2.1%

세대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)100.0%
Missing40
Missing (%)85.1%
Infinite0
Infinite (%)0.0%
Mean303.14286
Minimum24
Maximum784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size555.0 B
2023-12-12T18:02:45.390285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile25.8
Q139
median240
Q3498
95-th percentile700.6
Maximum784
Range760
Interquartile range (IQR)459

Descriptive statistics

Standard deviation296.90146
Coefficient of variation (CV)0.97941103
Kurtosis-1.0598425
Mean303.14286
Median Absolute Deviation (MAD)216
Skewness0.59792877
Sum2122
Variance88150.476
MonotonicityNot monotonic
2023-12-12T18:02:45.552796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
48 1
 
2.1%
30 1
 
2.1%
24 1
 
2.1%
506 1
 
2.1%
784 1
 
2.1%
240 1
 
2.1%
490 1
 
2.1%
(Missing) 40
85.1%
ValueCountFrequency (%)
24 1
2.1%
30 1
2.1%
48 1
2.1%
240 1
2.1%
490 1
2.1%
506 1
2.1%
784 1
2.1%
ValueCountFrequency (%)
784 1
2.1%
506 1
2.1%
490 1
2.1%
240 1
2.1%
48 1
2.1%
30 1
2.1%
24 1
2.1%

가구수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size508.0 B
<NA>
40 
5
 
4
1
 
2
3
 
1

Length

Max length4
Median length4
Mean length3.5531915
Min length1

Unique

Unique1 ?
Unique (%)2.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 40
85.1%
5 4
 
8.5%
1 2
 
4.3%
3 1
 
2.1%

Length

2023-12-12T18:02:45.753150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:02:45.884213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 40
85.1%
5 4
 
8.5%
1 2
 
4.3%
3 1
 
2.1%

시공자사무소명
Text

MISSING 

Distinct11
Distinct (%)78.6%
Missing33
Missing (%)70.2%
Memory size508.0 B
2023-12-12T18:02:46.068791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.5714286
Min length1

Characters and Unicode

Total characters120
Distinct characters37
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

Unique8 ?
Unique (%)57.1%

Sample

1st row(주)상명종합건설
2nd row주식회사 이룸종합건설
3rd row주식회사 이룸종합건설
4th row가야종합건설주식회사
5th row(주)부광종합건설
ValueCountFrequency (%)
주식회사 3
18.8%
주)상명종합건설 2
12.5%
이룸종합건설 2
12.5%
주)부광종합건설 2
12.5%
가야종합건설주식회사 1
 
6.2%
경인종합건설(주 1
 
6.2%
덕정종합건설(주 1
 
6.2%
서일건설 1
 
6.2%
라인건설(주 1
 
6.2%
주식회사신태양건설 1
 
6.2%
2023-12-12T18:02:46.446979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 12
 
10.0%
건 12
 
10.0%
설 12
 
10.0%
종 9
 
7.5%
합 9
 
7.5%
( 7
 
5.8%
) 7
 
5.8%
식 5
 
4.2%
회 5
 
4.2%
사 5
 
4.2%
Other values (27) 37
30.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 101
84.2%
Open Punctuation 7
 
5.8%
Close Punctuation 7
 
5.8%
Space Separator 4
 
3.3%
Other Symbol 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 12
11.9%
건 12
11.9%
설 12
11.9%
종 9
 
8.9%
합 9
 
8.9%
식 5
 
5.0%
회 5
 
5.0%
사 5
 
5.0%
부 2
 
2.0%
인 2
 
2.0%
Other values (23) 28
27.7%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102
85.0%
Common 18
 
15.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 12
11.8%
건 12
11.8%
설 12
11.8%
종 9
 
8.8%
합 9
 
8.8%
식 5
 
4.9%
회 5
 
4.9%
사 5
 
4.9%
부 2
 
2.0%
인 2
 
2.0%
Other values (24) 29
28.4%
Common
ValueCountFrequency (%)
( 7
38.9%
) 7
38.9%
4
22.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 101
84.2%
ASCII 18
 
15.0%
None 1
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 12
11.9%
건 12
11.9%
설 12
11.9%
종 9
 
8.9%
합 9
 
8.9%
식 5
 
5.0%
회 5
 
5.0%
사 5
 
5.0%
부 2
 
2.0%
인 2
 
2.0%
Other values (23) 28
27.7%
ASCII
ValueCountFrequency (%)
( 7
38.9%
) 7
38.9%
4
22.2%
None
ValueCountFrequency (%)
㈜ 1
100.0%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size508.0 B
2023-08-30
47 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-30
2nd row2023-08-30
3rd row2023-08-30
4th row2023-08-30
5th row2023-08-30

Common Values

ValueCountFrequency (%)
2023-08-30 47
100.0%

Length

2023-12-12T18:02:46.622892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:02:46.749220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-30 47
100.0%

Interactions

2023-12-12T18:02:39.173303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:37.988915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.367041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.745284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:39.293746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.073313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.462605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.830744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:39.410740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.174093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.570967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.934275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:39.541028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.264306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:38.657077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:02:39.076298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:02:46.829071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대지위치연면적(제곱미터)허가일(사업승인일)착공처리일준공예정일(사용승인예정일)최대지상층수최대지하층수주용도부속용도총주차대수세대수가구수시공자사무소명
대지위치1.0001.0000.9971.0001.0000.9810.7700.8610.9281.0001.0001.0001.000
연면적(제곱미터)1.0001.0001.0001.0001.0000.8330.9450.609NaN0.9891.000NaN1.000
허가일(사업승인일)0.9971.0001.0000.9940.9750.9700.8990.9600.9681.0001.0001.0001.000
착공처리일1.0001.0000.9941.0001.0001.0001.0000.0000.9691.0001.0001.0001.000
준공예정일(사용승인예정일)1.0001.0000.9751.0001.0000.0000.9360.0000.9001.0001.0001.0000.956
최대지상층수0.9810.8330.9701.0000.0001.0000.7290.6860.9590.9010.7080.3200.917
최대지하층수0.7700.9450.8991.0000.9360.7291.0000.6670.9900.9720.7080.6170.902
주용도0.8610.6090.9600.0000.0000.6860.6671.0001.0000.4771.0000.8600.688
부속용도0.928NaN0.9680.9690.9000.9590.9901.0001.000NaNNaN1.0001.000
총주차대수1.0000.9891.0001.0001.0000.9010.9720.477NaN1.0001.000NaN1.000
세대수1.0001.0001.0001.0001.0000.7080.7081.000NaN1.0001.000NaN1.000
가구수1.000NaN1.0001.0001.0000.3200.6170.8601.000NaNNaN1.000NaN
시공자사무소명1.0001.0001.0001.0000.9560.9170.9020.6881.0001.0001.000NaN1.000
2023-12-12T18:02:47.032537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최대지하층수가구수주용도
최대지하층수1.0000.1250.471
가구수0.1251.0000.500
주용도0.4710.5001.000
2023-12-12T18:02:47.137546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연면적(제곱미터)최대지상층수총주차대수세대수최대지하층수주용도가구수
연면적(제곱미터)1.0000.7890.9180.9640.6690.4131.000
최대지상층수0.7891.0000.7330.9270.5890.4560.412
총주차대수0.9180.7331.0000.9640.7590.3141.000
세대수0.9640.9270.9641.0000.4330.7750.000
최대지하층수0.6690.5890.7590.4331.0000.4710.125
주용도0.4130.4560.3140.7750.4711.0000.500
가구수1.0000.4121.0000.0000.1250.5001.000

Missing values

2023-12-12T18:02:39.719897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:02:40.069662image/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.
2023-12-12T18:02:40.260028image/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

건축구분대지위치연면적(제곱미터)허가일(사업승인일)착공처리일준공예정일(사용승인예정일)최대지상층수최대지하층수주용도부속용도총주차대수세대수가구수시공자사무소명데이터기준일
0신축경상남도 통영시 용남면 삼화리 934 외1필지478.872023-07-072023-07-212023-12-3110제2종근린생활시설제조업소(수산물 작업장)4<NA><NA>(주)상명종합건설2023-08-30
1신축경상남도 통영시 광도면 노산리 5 외1필지393.82023-07-06<NA><NA>10제1종근린생활시설휴게음식점 및 2종(사무소)3<NA><NA><NA>2023-08-30
2신축경상남도 통영시 용남면 화삼리 76-3 외6필지302.12023-06-14<NA><NA>10제2종근린생활시설제조업소3<NA><NA><NA>2023-08-30
3신축경상남도 통영시 광도면 죽림리 380-1 외1필지148.52023-06-14<NA><NA>10제2종근린생활시설사무소1<NA><NA><NA>2023-08-30
4신축경상남도 통영시 산양읍 남평리 1293-3 외1필지186.762023-05-242023-07-132023-10-0210제2종근린생활시설일반음식점2<NA><NA><NA>2023-08-30
5신축경상남도 통영시 미수동 291-13689.782023-01-182023-02-162023-07-3120제2종근린생활시설일반음식점,소매점,사무소6<NA><NA>주식회사 이룸종합건설2023-08-30
6신축경상남도 통영시 용남면 삼화리 297-3 외1필지684.972023-01-162023-02-202023-05-3110제2종근린생활시설일반음식점6<NA><NA><NA>2023-08-30
7신축경상남도 통영시 미수동 291-13414.522023-01-132023-02-162023-07-3120제1종근린생활시설소매점,휴게음식점,일반음식점3<NA><NA>주식회사 이룸종합건설2023-08-30
8신축경상남도 통영시 산양읍 신전리 171-2 외4필지1733.432023-01-11<NA><NA>30제1종근린생활시설제2종근린생활시설,교육연구시설31<NA><NA><NA>2023-08-30
9신축경상남도 통영시 용남면 동달리 105-4 외3필지441.862022-12-142022-12-202023-12-1421제2종근린생활시설골프연습장 외24<NA>1가야종합건설주식회사2023-08-30
건축구분대지위치연면적(제곱미터)허가일(사업승인일)착공처리일준공예정일(사용승인예정일)최대지상층수최대지하층수주용도부속용도총주차대수세대수가구수시공자사무소명데이터기준일
37신축경상남도 통영시 무전동 355-5 외2필지531.022018-10-192022-01-112023-01-2540제2종근린생활시설사무소.단독주택(다가구주택)5<NA>3<NA>2023-08-30
38신축경상남도 통영시 용남면 장평리 350-5198.92018-10-192019-10-232020-09-3010제2종근린생활시설사무소, 일반음식점<NA><NA><NA><NA>2023-08-30
39신축경상남도 통영시 용남면 장평리 348-7 외1필지195.172018-10-192019-10-232020-09-3010제2종근린생활시설<NA><NA><NA><NA><NA>2023-08-30
40신축경상남도 통영시 인평동 357 외1필지1889.152018-10-182019-11-012020-11-0550공동주택<NA>2424<NA>라인건설(주)2023-08-30
41신축경상남도 통영시 산양읍 삼덕리 50-299.332018-03-122018-04-052018-11-0910제2종근린생활시설<NA><NA><NA><NA><NA>2023-08-30
42신축경상남도 통영시 광도면 죽림리 1475158.522018-01-19<NA><NA>10제2종근린생활시설종교집회장(기도원)<NA><NA><NA><NA>2023-08-30
43신축경상남도 통영시 용남면 원평리 260 일원65576.41212018-12-242020-04-092023-09-23242공동주택(아파트)<NA>578506<NA>주식회사신태양건설2023-08-30
44신축경상남도 통영시 광도면 죽림리 산 266-10 일원129853.12782020-12-042021-09-012023-04-25294공동주택(아파트)<NA>983784<NA>현대엔지니어링㈜2023-08-30
45신축경상남도 통영시 용남면 동달리 133-4 일원35028.51042021-03-19<NA><NA>202공동주택(아파트)<NA>292240<NA><NA>2023-08-30
46신축경상남도 통영시 용남면 원평리 48-3 일원68262.71372022-01-27<NA><NA>203공동주택(아파트)<NA>603490<NA>2023-08-30

Duplicate rows

Most frequently occurring

건축구분대지위치연면적(제곱미터)허가일(사업승인일)착공처리일준공예정일(사용승인예정일)최대지상층수최대지하층수주용도부속용도총주차대수세대수가구수시공자사무소명데이터기준일# duplicates
0신축경상남도 통영시 산양읍 신전리 274265.3542021-06-15<NA><NA>31단독주택다가구주택,제2종근린생활시설(사무소)<NA><NA>5<NA>2023-08-302