Overview

Dataset statistics

Number of variables10
Number of observations165
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory85.8 B

Variable types

Text1
Categorical4
Numeric5

Dataset

Description경상북도 내 조성완료 된 산업단지 및 조성중인 산업단지에 대하여 단지별(국가산업단지, 일반산업단지, 농공단지) 현황 자료 입니다.
Author경상북도
URLhttps://www.data.go.kr/data/15056118/fileData.do

Alerts

시도 has constant value ""Constant
지정면적 is highly overall correlated with 분양대상면적 and 3 other fieldsHigh correlation
분양대상면적 is highly overall correlated with 지정면적 and 2 other fieldsHigh correlation
분양공고면적 is highly overall correlated with 지정면적 and 2 other fieldsHigh correlation
분양 is highly overall correlated with 지정면적 and 2 other fieldsHigh correlation
미분양 is highly overall correlated with 미분양율 High correlation
미분양율 is highly overall correlated with 지정면적 and 1 other fieldsHigh correlation
미분양율 is highly imbalanced (62.4%)Imbalance
단지명 has unique valuesUnique
분양공고면적 has 13 (7.9%) zerosZeros
분양 has 14 (8.5%) zerosZeros
미분양 has 128 (77.6%) zerosZeros

Reproduction

Analysis started2024-03-14 15:24:52.338425
Analysis finished2024-03-14 15:25:00.421544
Duration8.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단지명
Text

UNIQUE 

Distinct165
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-15T00:25:01.196297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length23
Mean length8.9212121
Min length6

Characters and Unicode

Total characters1472
Distinct characters169
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)100.0%

Sample

1st row구미제1국가산업단지[재생사업지구]
2nd row구미국가산업단지(제2·3·4·확장단지)
3rd row ◇구미(2·3단지)
4th row ◇구미(4단지)
5th row ▷구미4(산업)
ValueCountFrequency (%)
구미제1국가산업단지[재생사업지구 1
 
0.6%
명계3일반산업단지 1
 
0.6%
감문농공단지 1
 
0.6%
대광농공단지 1
 
0.6%
아포농공단지 1
 
0.6%
지례농공단지 1
 
0.6%
가은농공단지 1
 
0.6%
마성농공단지 1
 
0.6%
산양농공단지 1
 
0.6%
영순농공단지 1
 
0.6%
Other values (155) 155
93.9%
2024-03-15T00:25:02.618918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
단 168
 
11.4%
지 163
 
11.1%
산 104
 
7.1%
업 85
 
5.8%
일 81
 
5.5%
반 76
 
5.2%
공 68
 
4.6%
농 68
 
4.6%
41
 
2.8%
2 28
 
1.9%
Other values (159) 590
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1302
88.5%
Decimal Number 59
 
4.0%
Space Separator 41
 
2.8%
Close Punctuation 22
 
1.5%
Open Punctuation 22
 
1.5%
Other Punctuation 10
 
0.7%
Other Symbol 9
 
0.6%
Uppercase Letter 4
 
0.3%
Math Symbol 2
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
단 168
 
12.9%
지 163
 
12.5%
산 104
 
8.0%
업 85
 
6.5%
일 81
 
6.2%
반 76
 
5.8%
공 68
 
5.2%
농 68
 
5.2%
구 21
 
1.6%
경 19
 
1.5%
Other values (142) 449
34.5%
Decimal Number
ValueCountFrequency (%)
2 28
47.5%
1 11
 
18.6%
3 10
 
16.9%
4 9
 
15.3%
5 1
 
1.7%
Close Punctuation
ValueCountFrequency (%)
) 15
68.2%
] 7
31.8%
Open Punctuation
ValueCountFrequency (%)
( 15
68.2%
[ 7
31.8%
Other Punctuation
ValueCountFrequency (%)
: 6
60.0%
· 4
40.0%
Uppercase Letter
ValueCountFrequency (%)
K 2
50.0%
S 2
50.0%
Space Separator
ValueCountFrequency (%)
41
100.0%
Other Symbol
ValueCountFrequency (%)
◇ 9
100.0%
Math Symbol
ValueCountFrequency (%)
▷ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1302
88.5%
Common 166
 
11.3%
Latin 4
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
단 168
 
12.9%
지 163
 
12.5%
산 104
 
8.0%
업 85
 
6.5%
일 81
 
6.2%
반 76
 
5.8%
공 68
 
5.2%
농 68
 
5.2%
구 21
 
1.6%
경 19
 
1.5%
Other values (142) 449
34.5%
Common
ValueCountFrequency (%)
41
24.7%
2 28
16.9%
) 15
 
9.0%
( 15
 
9.0%
1 11
 
6.6%
3 10
 
6.0%
◇ 9
 
5.4%
4 9
 
5.4%
] 7
 
4.2%
[ 7
 
4.2%
Other values (5) 14
 
8.4%
Latin
ValueCountFrequency (%)
K 2
50.0%
S 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1302
88.5%
ASCII 155
 
10.5%
Geometric Shapes 11
 
0.7%
None 4
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
단 168
 
12.9%
지 163
 
12.5%
산 104
 
8.0%
업 85
 
6.5%
일 81
 
6.2%
반 76
 
5.8%
공 68
 
5.2%
농 68
 
5.2%
구 21
 
1.6%
경 19
 
1.5%
Other values (142) 449
34.5%
ASCII
ValueCountFrequency (%)
41
26.5%
2 28
18.1%
) 15
 
9.7%
( 15
 
9.7%
1 11
 
7.1%
3 10
 
6.5%
4 9
 
5.8%
] 7
 
4.5%
[ 7
 
4.5%
: 6
 
3.9%
Other values (4) 6
 
3.9%
Geometric Shapes
ValueCountFrequency (%)
◇ 9
81.8%
▷ 2
 
18.2%
None
ValueCountFrequency (%)
· 4
100.0%

유형
Categorical

Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
일반
85 
농공
67 
국가
12 
도시첨단
 
1

Length

Max length4
Median length2
Mean length2.0121212
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
일반 85
51.5%
농공 67
40.6%
국가 12
 
7.3%
도시첨단 1
 
0.6%

Length

2024-03-15T00:25:03.273845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:25:03.633709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 85
51.5%
농공 67
40.6%
국가 12
 
7.3%
도시첨단 1
 
0.6%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
경북
165 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경북 165
100.0%

Length

2024-03-15T00:25:04.002868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:25:04.318496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경북 165
100.0%

시군구
Categorical

Distinct20
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
경주시
36 
포항시
13 
구미시
12 
영주시
12 
고령군
10 
Other values (15)
82 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
경주시 36
21.8%
포항시 13
 
7.9%
구미시 12
 
7.3%
영주시 12
 
7.3%
고령군 10
 
6.1%
상주시 10
 
6.1%
김천시 9
 
5.5%
문경시 9
 
5.5%
영천시 9
 
5.5%
경산시 7
 
4.2%
Other values (10) 38
23.0%

Length

2024-03-15T00:25:04.645761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경주시 36
21.8%
포항시 13
 
7.9%
구미시 12
 
7.3%
영주시 12
 
7.3%
고령군 10
 
6.1%
상주시 10
 
6.1%
김천시 9
 
5.5%
문경시 9
 
5.5%
영천시 9
 
5.5%
칠곡군 7
 
4.2%
Other values (10) 38
23.0%

지정면적
Real number (ℝ)

HIGH CORRELATION 

Distinct138
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1020.5697
Minimum30
Maximum28755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-03-15T00:25:05.009568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile69.8
Q1137
median226
Q3766
95-th percentile3469.8
Maximum28755
Range28725
Interquartile range (IQR)629

Descriptive statistics

Standard deviation2907.5296
Coefficient of variation (CV)2.848928
Kurtosis55.951096
Mean1020.5697
Median Absolute Deviation (MAD)121
Skewness6.8031528
Sum168394
Variance8453728.5
MonotonicityNot monotonic
2024-03-15T00:25:05.444225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149 5
 
3.0%
150 4
 
2.4%
113 3
 
1.8%
126 3
 
1.8%
195 3
 
1.8%
146 3
 
1.8%
105 2
 
1.2%
206 2
 
1.2%
104 2
 
1.2%
766 2
 
1.2%
Other values (128) 136
82.4%
ValueCountFrequency (%)
30 1
0.6%
39 1
0.6%
52 1
0.6%
55 1
0.6%
57 2
1.2%
60 1
0.6%
67 1
0.6%
69 1
0.6%
73 1
0.6%
76 1
0.6%
ValueCountFrequency (%)
28755 1
0.6%
16652 1
0.6%
10089 1
0.6%
9325 1
0.6%
7410 1
0.6%
6766 1
0.6%
6079 1
0.6%
5175 1
0.6%
3690 1
0.6%
2589 1
0.6%

분양대상면적
Real number (ℝ)

HIGH CORRELATION 

Distinct147
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean691.78788
Minimum14
Maximum16801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-03-15T00:25:05.855340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile53.8
Q199
median164
Q3562
95-th percentile1762.4
Maximum16801
Range16787
Interquartile range (IQR)463

Descriptive statistics

Standard deviation1842.7975
Coefficient of variation (CV)2.6638186
Kurtosis43.555668
Mean691.78788
Median Absolute Deviation (MAD)92
Skewness6.082469
Sum114145
Variance3395902.4
MonotonicityNot monotonic
2024-03-15T00:25:06.293727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 3
 
1.8%
102 3
 
1.8%
119 3
 
1.8%
105 2
 
1.2%
63 2
 
1.2%
132 2
 
1.2%
91 2
 
1.2%
139 2
 
1.2%
184 2
 
1.2%
65 2
 
1.2%
Other values (137) 142
86.1%
ValueCountFrequency (%)
14 1
0.6%
24 1
0.6%
37 1
0.6%
41 1
0.6%
43 1
0.6%
44 1
0.6%
45 1
0.6%
49 1
0.6%
53 1
0.6%
57 1
0.6%
ValueCountFrequency (%)
16801 1
0.6%
11184 1
0.6%
8589 1
0.6%
5987 1
0.6%
5397 1
0.6%
4467 1
0.6%
3963 1
0.6%
2876 1
0.6%
1774 1
0.6%
1716 1
0.6%

분양공고면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct138
Distinct (%)83.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean594.46061
Minimum0
Maximum16097
Zeros13
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-03-15T00:25:06.712068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q178
median132
Q3457
95-th percentile1663.6
Maximum16097
Range16097
Interquartile range (IQR)379

Descriptive statistics

Standard deviation1745.9444
Coefficient of variation (CV)2.9370229
Kurtosis47.905238
Mean594.46061
Median Absolute Deviation (MAD)82
Skewness6.4707835
Sum98086
Variance3048321.9
MonotonicityNot monotonic
2024-03-15T00:25:07.160536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13
 
7.9%
196 2
 
1.2%
134 2
 
1.2%
139 2
 
1.2%
103 2
 
1.2%
132 2
 
1.2%
184 2
 
1.2%
119 2
 
1.2%
102 2
 
1.2%
52 2
 
1.2%
Other values (128) 134
81.2%
ValueCountFrequency (%)
0 13
7.9%
3 1
 
0.6%
14 1
 
0.6%
30 1
 
0.6%
33 1
 
0.6%
37 1
 
0.6%
41 1
 
0.6%
43 1
 
0.6%
44 2
 
1.2%
45 1
 
0.6%
ValueCountFrequency (%)
16097 1
0.6%
11108 1
0.6%
8589 1
0.6%
5397 1
0.6%
4467 1
0.6%
2876 1
0.6%
2391 1
0.6%
1774 1
0.6%
1681 1
0.6%
1594 1
0.6%

분양
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct134
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean578.49091
Minimum0
Maximum16097
Zeros14
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-03-15T00:25:07.579165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q167
median125
Q3453
95-th percentile1663
Maximum16097
Range16097
Interquartile range (IQR)386

Descriptive statistics

Standard deviation1731.8778
Coefficient of variation (CV)2.9937856
Kurtosis48.786394
Mean578.49091
Median Absolute Deviation (MAD)88
Skewness6.5258221
Sum95451
Variance2999400.6
MonotonicityNot monotonic
2024-03-15T00:25:08.036019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14
 
8.5%
49 3
 
1.8%
89 3
 
1.8%
119 3
 
1.8%
77 2
 
1.2%
196 2
 
1.2%
139 2
 
1.2%
37 2
 
1.2%
1565 2
 
1.2%
103 2
 
1.2%
Other values (124) 130
78.8%
ValueCountFrequency (%)
0 14
8.5%
3 1
 
0.6%
7 1
 
0.6%
9 1
 
0.6%
11 1
 
0.6%
12 1
 
0.6%
14 1
 
0.6%
20 1
 
0.6%
21 1
 
0.6%
30 1
 
0.6%
ValueCountFrequency (%)
16097 1
0.6%
10776 1
0.6%
8589 1
0.6%
5397 1
0.6%
4447 1
0.6%
2856 1
0.6%
2275 1
0.6%
1774 1
0.6%
1681 1
0.6%
1591 1
0.6%

미분양
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.963636
Minimum0
Maximum370
Zeros128
Zeros (%)77.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-03-15T00:25:08.435667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile66.6
Maximum370
Range370
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.843192
Coefficient of variation (CV)3.4981498
Kurtosis26.194821
Mean15.963636
Median Absolute Deviation (MAD)0
Skewness4.9989265
Sum2634
Variance3118.4621
MonotonicityNot monotonic
2024-03-15T00:25:08.845824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 128
77.6%
3 4
 
2.4%
20 2
 
1.2%
29 2
 
1.2%
33 2
 
1.2%
370 1
 
0.6%
142 1
 
0.6%
70 1
 
0.6%
16 1
 
0.6%
146 1
 
0.6%
Other values (22) 22
 
13.3%
ValueCountFrequency (%)
0 128
77.6%
3 4
 
2.4%
4 1
 
0.6%
5 1
 
0.6%
9 1
 
0.6%
11 1
 
0.6%
12 1
 
0.6%
13 1
 
0.6%
14 1
 
0.6%
16 1
 
0.6%
ValueCountFrequency (%)
370 1
0.6%
341 1
0.6%
331 1
0.6%
311 1
0.6%
146 1
0.6%
142 1
0.6%
115 1
0.6%
70 1
0.6%
68 1
0.6%
61 1
0.6%

미분양율
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct38
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
0
127 
0.7
 
2
17.7
 
1
43.8
 
1
0.4
 
1
Other values (33)
33 

Length

Max length4
Median length1
Mean length1.4969697
Min length1

Unique

Unique36 ?
Unique (%)21.8%

Sample

1st row0
2nd row3
3rd row0
4th row0.4
5th row0.7

Common Values

ValueCountFrequency (%)
0 127
77.0%
0.7 2
 
1.2%
17.7 1
 
0.6%
43.8 1
 
0.6%
0.4 1
 
0.6%
25 1
 
0.6%
26 1
 
0.6%
4.8 1
 
0.6%
4.3 1
 
0.6%
11 1
 
0.6%
Other values (28) 28
 
17.0%

Length

2024-03-15T00:25:09.191569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 127
77.0%
0.7 2
 
1.2%
82.6 1
 
0.6%
83.8 1
 
0.6%
11.6 1
 
0.6%
90.4 1
 
0.6%
5.8 1
 
0.6%
1
 
0.6%
3.2 1
 
0.6%
26.1 1
 
0.6%
Other values (28) 28
 
17.0%

Interactions

2024-03-15T00:24:58.311378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:52.998646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:54.303112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:55.621738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:56.964057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:58.570827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:53.248734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:54.557997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:55.880661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:57.226547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:58.835003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:53.505147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:54.818331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:56.148231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:57.496565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:59.106560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:53.771703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:55.087956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:56.423553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:57.773876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:59.374254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:54.043503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:55.358654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:56.697600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:24:58.043338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:25:09.347549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형시군구지정면적분양대상면적분양공고면적분양미분양미분양율
유형1.0000.7550.6420.5490.5430.5430.2970.000
시군구0.7551.0000.0000.0000.0000.0000.4370.720
지정면적0.6420.0001.0000.9310.9330.9330.5630.851
분양대상면적0.5490.0000.9311.0000.9920.9920.6030.835
분양공고면적0.5430.0000.9330.9921.0001.0000.4810.790
분양0.5430.0000.9330.9921.0001.0000.4810.790
미분양0.2970.4370.5630.6030.4810.4811.0001.000
미분양율0.0000.7200.8510.8350.7900.7901.0001.000
2024-03-15T00:25:09.539661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형시군구미분양율
유형1.0000.4320.000
시군구0.4321.0000.240
미분양율0.0000.2401.000
2024-03-15T00:25:09.694909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정면적분양대상면적분양공고면적분양미분양유형시군구미분양율
지정면적1.0000.9900.7610.7310.1310.4680.0000.514
분양대상면적0.9901.0000.7900.7700.0850.4070.0000.476
분양공고면적0.7610.7901.0000.9770.1670.4010.0000.420
분양0.7310.7700.9771.0000.0420.4010.0000.420
미분양0.1310.0850.1670.0421.0000.2450.1920.891
유형0.4680.4070.4010.4010.2451.0000.4320.000
시군구0.0000.0000.0000.0000.1920.4321.0000.240
미분양율0.5140.4760.4200.4200.8910.0000.2401.000

Missing values

2024-03-15T00:24:59.747833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:25:00.239096image/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

단지명유형시도시군구지정면적분양대상면적분양공고면적분양미분양미분양율
0구미제1국가산업단지[재생사업지구]국가경북구미시1008985898589858900
1구미국가산업단지(제2·3·4·확장단지)국가경북구미시166521118411108107763313
2◇구미(2·3단지)국가경북구미시741053975397539700
3◇구미(4단지)국가경북구미시6766446744674447200.4
4▷구미4(산업)국가경북구미시5175287628762856200.7
5▷구미4(구미외국인)국가경북구미시159115911591159100
6◇구미(확장단지)국가경북구미시24761320124493231125
7월성전원단지국가경북경주시369016811681168100
8포항국가산업단지국가경북포항시2875516801160971609700
9포항블루밸리국가경북포항시60793963131297134126
단지명유형시도시군구지정면적분양대상면적분양공고면적분양미분양미분양율
155산양제2농공단지농공경북문경시1349191771415.3
156평해농공단지농공경북울진군1509694613334.9
157유곡농공단지농공경북봉화군248134134765843.5
158영덕제2농공단지(영덕신재생에너지혁신단지)농공경북영덕군3271951954914675
159영덕로하스특화농공단지농공경북영덕군14810110110100
160예천제2농공단지농공경북예천군257185185169168.8
161칠곡농기계특화농공단지농공경북칠곡군24518418418400
162남영양농공단지농공경북영양군30141411320.4
163죽변농공단지[구:죽변해양바이오]농공경북울진군1498383147083.8
164고아제2농공단지농공경북구미시2631741541214292.2