Overview

Dataset statistics

Number of variables17
Number of observations168
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.6 KiB
Average record size in memory149.8 B

Variable types

Text1
Categorical3
Numeric13

Dataset

Description충청남도 산업단지 내 입주업체 현황 정보를 단지명, 유형, 시도, 시군구, 지정면적, 지정일자 등 데이터로 개방하고자 합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=404&beforeMenuCd=DOM_000000201001001000&publicdatapk=15032201

Alerts

시도 has constant value ""Constant
지정면적 is highly overall correlated with 분양현황 계(분양대상) and 5 other fieldsHigh correlation
분양현황 계(분양대상) is highly overall correlated with 지정면적 and 5 other fieldsHigh correlation
본양공고 소계 is highly overall correlated with 지정면적 and 5 other fieldsHigh correlation
분양 is highly overall correlated with 지정면적 and 5 other fieldsHigh correlation
미분양 is highly overall correlated with 개발용지 미분양 and 1 other fieldsHigh correlation
개발용지 계(분양대상) is highly overall correlated with 지정면적 and 5 other fieldsHigh correlation
개발용지 분양공고 소계 is highly overall correlated with 지정면적 and 5 other fieldsHigh correlation
개발용지 분양 is highly overall correlated with 지정면적 and 5 other fieldsHigh correlation
개발용지 미분양 is highly overall correlated with 미분양High correlation
미개발용지 계(분양대상) is highly overall correlated with 미개발용지 분양공고 소계 and 1 other fieldsHigh correlation
미개발용지 분양공고 소계 is highly overall correlated with 미개발용지 계(분양대상) and 2 other fieldsHigh correlation
미개발용지 분양 is highly overall correlated with 미개발용지 계(분양대상) and 1 other fieldsHigh correlation
미개발용지 미분양 is highly overall correlated with 미분양 and 1 other fieldsHigh correlation
단지명 has unique valuesUnique
지정면적 has unique valuesUnique
분양현황 계(분양대상) has unique valuesUnique
본양공고 소계 has 11 (6.5%) zerosZeros
분양 has 12 (7.1%) zerosZeros
미분양 has 146 (86.9%) zerosZeros
개발용지 계(분양대상) has 17 (10.1%) zerosZeros
개발용지 분양공고 소계 has 18 (10.7%) zerosZeros
개발용지 분양 has 18 (10.7%) zerosZeros
개발용지 미분양 has 149 (88.7%) zerosZeros
미개발용지 계(분양대상) has 133 (79.2%) zerosZeros
미개발용지 분양공고 소계 has 148 (88.1%) zerosZeros
미개발용지 분양 has 149 (88.7%) zerosZeros
미개발용지 미분양 has 161 (95.8%) zerosZeros

Reproduction

Analysis started2024-01-09 21:25:09.137975
Analysis finished2024-01-09 21:25:23.663080
Duration14.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단지명
Text

UNIQUE 

Distinct168
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-01-10T06:25:23.796858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length23
Mean length9.2321429
Min length6

Characters and Unicode

Total characters1551
Distinct characters193
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique168 ?
Unique (%)100.0%

Sample

1st row아산(고대지구)
2nd row아산(부곡지구)
3rd row고정국가산업단지
4th row대죽자원비축산업단지
5th row석문국가산업단지
ValueCountFrequency (%)
아산(고대지구 1
 
0.6%
구:홍산농공 1
 
0.6%
합덕농공단지 1
 
0.6%
은산산업단지 1
 
0.6%
구:은산농공 1
 
0.6%
임천산업단지 1
 
0.6%
구:임천농공 1
 
0.6%
부여장암산업단지 1
 
0.6%
구:장암농공 1
 
0.6%
홍산산업단지 1
 
0.6%
Other values (164) 164
94.3%
2024-01-10T06:25:24.095314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
174
 
11.2%
169
 
10.9%
130
 
8.4%
93
 
6.0%
90
 
5.8%
83
 
5.4%
62
 
4.0%
62
 
4.0%
29
 
1.9%
27
 
1.7%
Other values (183) 632
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1436
92.6%
Decimal Number 36
 
2.3%
Close Punctuation 24
 
1.5%
Open Punctuation 24
 
1.5%
Other Punctuation 20
 
1.3%
Space Separator 6
 
0.4%
Uppercase Letter 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
174
 
12.1%
169
 
11.8%
130
 
9.1%
93
 
6.5%
90
 
6.3%
83
 
5.8%
62
 
4.3%
62
 
4.3%
29
 
2.0%
27
 
1.9%
Other values (166) 517
36.0%
Decimal Number
ValueCountFrequency (%)
2 25
69.4%
1 5
 
13.9%
3 3
 
8.3%
4 1
 
2.8%
5 1
 
2.8%
6 1
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
G 1
20.0%
L 1
20.0%
T 1
20.0%
I 1
20.0%
B 1
20.0%
Close Punctuation
ValueCountFrequency (%)
] 21
87.5%
) 3
 
12.5%
Open Punctuation
ValueCountFrequency (%)
[ 21
87.5%
( 3
 
12.5%
Other Punctuation
ValueCountFrequency (%)
: 20
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1436
92.6%
Common 110
 
7.1%
Latin 5
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
174
 
12.1%
169
 
11.8%
130
 
9.1%
93
 
6.5%
90
 
6.3%
83
 
5.8%
62
 
4.3%
62
 
4.3%
29
 
2.0%
27
 
1.9%
Other values (166) 517
36.0%
Common
ValueCountFrequency (%)
2 25
22.7%
] 21
19.1%
[ 21
19.1%
: 20
18.2%
6
 
5.5%
1 5
 
4.5%
) 3
 
2.7%
3 3
 
2.7%
( 3
 
2.7%
4 1
 
0.9%
Other values (2) 2
 
1.8%
Latin
ValueCountFrequency (%)
G 1
20.0%
L 1
20.0%
T 1
20.0%
I 1
20.0%
B 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1436
92.6%
ASCII 115
 
7.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
174
 
12.1%
169
 
11.8%
130
 
9.1%
93
 
6.5%
90
 
6.3%
83
 
5.8%
62
 
4.3%
62
 
4.3%
29
 
2.0%
27
 
1.9%
Other values (166) 517
36.0%
ASCII
ValueCountFrequency (%)
2 25
21.7%
] 21
18.3%
[ 21
18.3%
: 20
17.4%
6
 
5.2%
1 5
 
4.3%
) 3
 
2.6%
3 3
 
2.6%
( 3
 
2.6%
G 1
 
0.9%
Other values (7) 7
 
6.1%

유형
Categorical

Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
농공
93 
일반
66 
국가
 
6
도시첨단
 
3

Length

Max length4
Median length2
Mean length2.0357143
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농공 93
55.4%
일반 66
39.3%
국가 6
 
3.6%
도시첨단 3
 
1.8%

Length

2024-01-10T06:25:24.206291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:25:24.296068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농공 93
55.4%
일반 66
39.3%
국가 6
 
3.6%
도시첨단 3
 
1.8%

시도
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
충남
168 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충남 168
100.0%

Length

2024-01-10T06:25:24.382450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:25:24.459411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충남 168
100.0%

시군구
Categorical

Distinct15
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
아산시
22 
천안시
20 
공주시
19 
서산시
16 
당진시
15 
Other values (10)
76 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row당진시
2nd row당진시
3rd row보령시
4th row서산시
5th row당진시

Common Values

ValueCountFrequency (%)
아산시 22
13.1%
천안시 20
11.9%
공주시 19
11.3%
서산시 16
9.5%
당진시 15
8.9%
논산시 13
7.7%
예산군 13
7.7%
보령시 12
7.1%
홍성군 10
6.0%
부여군 7
 
4.2%
Other values (5) 21
12.5%

Length

2024-01-10T06:25:24.536531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
아산시 22
13.1%
천안시 20
11.9%
공주시 19
11.3%
서산시 16
9.5%
당진시 15
8.9%
논산시 13
7.7%
예산군 13
7.7%
보령시 12
7.1%
홍성군 10
6.0%
부여군 7
 
4.2%
Other values (5) 21
12.5%

지정면적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct168
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean709812.57
Minimum38900
Maximum12011613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:24.639453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38900
5-th percentile68699.6
Q1138792.25
median215142
Q3730058.25
95-th percentile2902348.9
Maximum12011613
Range11972713
Interquartile range (IQR)591266

Descriptive statistics

Standard deviation1323856.9
Coefficient of variation (CV)1.8650796
Kurtosis34.296764
Mean709812.57
Median Absolute Deviation (MAD)122648
Skewness4.9989932
Sum1.1924851 × 108
Variance1.7525971 × 1012
MonotonicityNot monotonic
2024-01-10T06:25:24.764903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3036397 1
 
0.6%
159500 1
 
0.6%
137866 1
 
0.6%
106212 1
 
0.6%
123326 1
 
0.6%
166287 1
 
0.6%
157642 1
 
0.6%
127875 1
 
0.6%
480561 1
 
0.6%
250418 1
 
0.6%
Other values (158) 158
94.0%
ValueCountFrequency (%)
38900 1
0.6%
45005 1
0.6%
50350 1
0.6%
55908 1
0.6%
56040 1
0.6%
56772 1
0.6%
64067 1
0.6%
67472 1
0.6%
68533 1
0.6%
69009 1
0.6%
ValueCountFrequency (%)
12011613 1
0.6%
6304110 1
0.6%
5825332 1
0.6%
4288401 1
0.6%
3989547 1
0.6%
3528930 1
0.6%
3118889 1
0.6%
3036397 1
0.6%
2983902 1
0.6%
2750893 1
0.6%

분양현황 계(분양대상)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct168
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean516209.99
Minimum30201
Maximum7758986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:24.873331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30201
5-th percentile54761.55
Q1101604
median163699
Q3536606.75
95-th percentile1942307.4
Maximum7758986
Range7728785
Interquartile range (IQR)435002.75

Descriptive statistics

Standard deviation943953.84
Coefficient of variation (CV)1.8286237
Kurtosis25.604596
Mean516209.99
Median Absolute Deviation (MAD)90989.5
Skewness4.4537163
Sum86723279
Variance8.9104885 × 1011
MonotonicityNot monotonic
2024-01-10T06:25:24.986887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2491776 1
 
0.6%
136334 1
 
0.6%
103996 1
 
0.6%
73167 1
 
0.6%
91543 1
 
0.6%
142179 1
 
0.6%
133331 1
 
0.6%
105490 1
 
0.6%
360899 1
 
0.6%
208854 1
 
0.6%
Other values (158) 158
94.0%
ValueCountFrequency (%)
30201 1
0.6%
33467 1
0.6%
41869 1
0.6%
42849 1
0.6%
44936 1
0.6%
45622 1
0.6%
46601 1
0.6%
46862 1
0.6%
54379 1
0.6%
55472 1
0.6%
ValueCountFrequency (%)
7758986 1
0.6%
5132877 1
0.6%
4708324 1
0.6%
3094054 1
0.6%
2876866 1
0.6%
2702293 1
0.6%
2491776 1
0.6%
2372900 1
0.6%
1946134 1
0.6%
1935201 1
0.6%

본양공고 소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean471509.68
Minimum0
Maximum7758986
Zeros11
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:25.101206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q188586.5
median135829.5
Q3476346
95-th percentile1942307.4
Maximum7758986
Range7758986
Interquartile range (IQR)387759.5

Descriptive statistics

Standard deviation943337.58
Coefficient of variation (CV)2.0006749
Kurtosis26.501264
Mean471509.68
Median Absolute Deviation (MAD)77854.5
Skewness4.5664918
Sum79213627
Variance8.8988579 × 1011
MonotonicityNot monotonic
2024-01-10T06:25:25.212885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
6.5%
123629 1
 
0.6%
142179 1
 
0.6%
133331 1
 
0.6%
105490 1
 
0.6%
360899 1
 
0.6%
208854 1
 
0.6%
136334 1
 
0.6%
125039 1
 
0.6%
153236 1
 
0.6%
Other values (148) 148
88.1%
ValueCountFrequency (%)
0 11
6.5%
30201 1
 
0.6%
32193 1
 
0.6%
33467 1
 
0.6%
41593 1
 
0.6%
41869 1
 
0.6%
42849 1
 
0.6%
45514 1
 
0.6%
45622 1
 
0.6%
46601 1
 
0.6%
ValueCountFrequency (%)
7758986 1
0.6%
5132877 1
0.6%
4708324 1
0.6%
3037349 1
0.6%
2876866 1
0.6%
2702293 1
0.6%
2491776 1
0.6%
2372900 1
0.6%
1946134 1
0.6%
1935201 1
0.6%

분양
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct157
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446462.29
Minimum0
Maximum5460922
Zeros12
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:25.324899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q187742.25
median133971
Q3451217.25
95-th percentile1942307.4
Maximum5460922
Range5460922
Interquartile range (IQR)363475

Descriptive statistics

Standard deviation847176.1
Coefficient of variation (CV)1.8975312
Kurtosis16.229883
Mean446462.29
Median Absolute Deviation (MAD)75464
Skewness3.7500656
Sum75005664
Variance7.1770734 × 1011
MonotonicityNot monotonic
2024-01-10T06:25:25.436353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12
 
7.1%
153955 1
 
0.6%
133331 1
 
0.6%
105490 1
 
0.6%
360899 1
 
0.6%
208854 1
 
0.6%
136334 1
 
0.6%
125039 1
 
0.6%
153236 1
 
0.6%
134611 1
 
0.6%
Other values (147) 147
87.5%
ValueCountFrequency (%)
0 12
7.1%
10718 1
 
0.6%
30201 1
 
0.6%
32193 1
 
0.6%
33370 1
 
0.6%
33467 1
 
0.6%
41593 1
 
0.6%
41869 1
 
0.6%
42849 1
 
0.6%
45622 1
 
0.6%
ValueCountFrequency (%)
5460922 1
0.6%
5132877 1
0.6%
4708324 1
0.6%
2927917 1
0.6%
2876866 1
0.6%
2702293 1
0.6%
2491776 1
0.6%
2372900 1
0.6%
1946134 1
0.6%
1935201 1
0.6%

미분양
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25047.399
Minimum0
Maximum2298064
Zeros146
Zeros (%)86.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:25.532724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile65518.5
Maximum2298064
Range2298064
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186036.04
Coefficient of variation (CV)7.4273597
Kurtosis135.83721
Mean25047.399
Median Absolute Deviation (MAD)0
Skewness11.264118
Sum4207963
Variance3.4609408 × 1010
MonotonicityNot monotonic
2024-01-10T06:25:25.631489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 146
86.9%
2298064 1
 
0.6%
6753 1
 
0.6%
20944 1
 
0.6%
39169 1
 
0.6%
12144 1
 
0.6%
85132 1
 
0.6%
40786 1
 
0.6%
86933 1
 
0.6%
570691 1
 
0.6%
Other values (13) 13
 
7.7%
ValueCountFrequency (%)
0 146
86.9%
658 1
 
0.6%
4035 1
 
0.6%
6753 1
 
0.6%
6914 1
 
0.6%
8389 1
 
0.6%
9065 1
 
0.6%
10110 1
 
0.6%
11142 1
 
0.6%
12144 1
 
0.6%
ValueCountFrequency (%)
2298064 1
0.6%
570691 1
0.6%
377327 1
0.6%
284334 1
0.6%
133024 1
0.6%
109432 1
0.6%
86933 1
0.6%
85132 1
0.6%
78836 1
0.6%
40786 1
0.6%

개발용지 계(분양대상)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct152
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451373.24
Minimum0
Maximum7758986
Zeros17
Zeros (%)10.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:25.734766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q176367
median124334
Q3396408.25
95-th percentile1924467.9
Maximum7758986
Range7758986
Interquartile range (IQR)320041.25

Descriptive statistics

Standard deviation936075.8
Coefficient of variation (CV)2.0738398
Kurtosis26.939884
Mean451373.24
Median Absolute Deviation (MAD)67984.5
Skewness4.5893455
Sum75830705
Variance8.762379 × 1011
MonotonicityNot monotonic
2024-01-10T06:25:25.870928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
10.1%
2491776 1
 
0.6%
153236 1
 
0.6%
142179 1
 
0.6%
133331 1
 
0.6%
105490 1
 
0.6%
360899 1
 
0.6%
208854 1
 
0.6%
136334 1
 
0.6%
125039 1
 
0.6%
Other values (142) 142
84.5%
ValueCountFrequency (%)
0 17
10.1%
16035 1
 
0.6%
33467 1
 
0.6%
40655 1
 
0.6%
40949 1
 
0.6%
41869 1
 
0.6%
42849 1
 
0.6%
44936 1
 
0.6%
45622 1
 
0.6%
46601 1
 
0.6%
ValueCountFrequency (%)
7758986 1
0.6%
4842797 1
0.6%
4708324 1
0.6%
3037349 1
0.6%
2876866 1
0.6%
2702293 1
0.6%
2491776 1
0.6%
2372900 1
0.6%
1935201 1
0.6%
1904535 1
0.6%

개발용지 분양공고 소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct151
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean445748.52
Minimum0
Maximum7758986
Zeros18
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:25.989943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q176367
median121292.5
Q3396408.25
95-th percentile1924467.9
Maximum7758986
Range7758986
Interquartile range (IQR)320041.25

Descriptive statistics

Standard deviation933994.62
Coefficient of variation (CV)2.0953398
Kurtosis27.316803
Mean445748.52
Median Absolute Deviation (MAD)66367
Skewness4.6320722
Sum74885751
Variance8.7234594 × 1011
MonotonicityNot monotonic
2024-01-10T06:25:26.112362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
10.7%
153236 1
 
0.6%
142179 1
 
0.6%
133331 1
 
0.6%
105490 1
 
0.6%
360899 1
 
0.6%
208854 1
 
0.6%
136334 1
 
0.6%
125039 1
 
0.6%
153955 1
 
0.6%
Other values (141) 141
83.9%
ValueCountFrequency (%)
0 18
10.7%
12459 1
 
0.6%
16035 1
 
0.6%
29457 1
 
0.6%
33467 1
 
0.6%
41869 1
 
0.6%
42849 1
 
0.6%
45514 1
 
0.6%
45622 1
 
0.6%
46601 1
 
0.6%
ValueCountFrequency (%)
7758986 1
0.6%
4842797 1
0.6%
4708324 1
0.6%
3037349 1
0.6%
2876866 1
0.6%
2702293 1
0.6%
2491776 1
0.6%
2372900 1
0.6%
1935201 1
0.6%
1904535 1
0.6%

개발용지 분양
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct151
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean426420.02
Minimum0
Maximum5460922
Zeros18
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:26.257345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q175834.5
median119966
Q3375459
95-th percentile1924467.9
Maximum5460922
Range5460922
Interquartile range (IQR)299624.5

Descriptive statistics

Standard deviation835925.23
Coefficient of variation (CV)1.960333
Kurtosis16.220207
Mean426420.02
Median Absolute Deviation (MAD)65149.5
Skewness3.7583691
Sum71638563
Variance6.9877099 × 1011
MonotonicityNot monotonic
2024-01-10T06:25:26.395258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18
 
10.7%
153236 1
 
0.6%
142179 1
 
0.6%
133331 1
 
0.6%
105490 1
 
0.6%
360899 1
 
0.6%
208854 1
 
0.6%
136334 1
 
0.6%
125039 1
 
0.6%
153955 1
 
0.6%
Other values (141) 141
83.9%
ValueCountFrequency (%)
0 18
10.7%
1393 1
 
0.6%
12459 1
 
0.6%
16035 1
 
0.6%
33370 1
 
0.6%
33467 1
 
0.6%
41869 1
 
0.6%
42849 1
 
0.6%
45622 1
 
0.6%
46601 1
 
0.6%
ValueCountFrequency (%)
5460922 1
0.6%
4842797 1
0.6%
4708324 1
0.6%
2927917 1
0.6%
2876866 1
0.6%
2702293 1
0.6%
2491776 1
0.6%
2372900 1
0.6%
1935201 1
0.6%
1904535 1
0.6%

개발용지 미분양
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19328.5
Minimum0
Maximum2298064
Zeros149
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:26.497995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23831.95
Maximum2298064
Range2298064
Interquartile range (IQR)0

Descriptive statistics

Standard deviation179407.82
Coefficient of variation (CV)9.2820355
Kurtosis158.53111
Mean19328.5
Median Absolute Deviation (MAD)0
Skewness12.445398
Sum3247188
Variance3.2187167 × 1010
MonotonicityNot monotonic
2024-01-10T06:25:26.585499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 149
88.7%
658 1
 
0.6%
6753 1
 
0.6%
20944 1
 
0.6%
39169 1
 
0.6%
12144 1
 
0.6%
85132 1
 
0.6%
28064 1
 
0.6%
1634 1
 
0.6%
6914 1
 
0.6%
Other values (10) 10
 
6.0%
ValueCountFrequency (%)
0 149
88.7%
658 1
 
0.6%
1634 1
 
0.6%
4035 1
 
0.6%
6753 1
 
0.6%
6914 1
 
0.6%
8389 1
 
0.6%
10110 1
 
0.6%
11142 1
 
0.6%
12144 1
 
0.6%
ValueCountFrequency (%)
2298064 1
0.6%
284334 1
0.6%
216047 1
0.6%
109432 1
0.6%
85132 1
0.6%
78836 1
0.6%
39169 1
0.6%
28064 1
0.6%
25387 1
0.6%
20944 1
0.6%

미개발용지 계(분양대상)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64836.75
Minimum0
Maximum1204682
Zeros133
Zeros (%)79.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:26.913254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile469822.95
Maximum1204682
Range1204682
Interquartile range (IQR)0

Descriptive statistics

Standard deviation172916.33
Coefficient of variation (CV)2.6669494
Kurtosis14.630675
Mean64836.75
Median Absolute Deviation (MAD)0
Skewness3.5209032
Sum10892574
Variance2.9900058 × 1010
MonotonicityNot monotonic
2024-01-10T06:25:27.053794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 133
79.2%
1204682 1
 
0.6%
13647 1
 
0.6%
766715 1
 
0.6%
663973 1
 
0.6%
274031 1
 
0.6%
248974 1
 
0.6%
681757 1
 
0.6%
608255 1
 
0.6%
510679 1
 
0.6%
Other values (26) 26
 
15.5%
ValueCountFrequency (%)
0 133
79.2%
13647 1
 
0.6%
29199 1
 
0.6%
30201 1
 
0.6%
49808 1
 
0.6%
56705 1
 
0.6%
60982 1
 
0.6%
99436 1
 
0.6%
107595 1
 
0.6%
118278 1
 
0.6%
ValueCountFrequency (%)
1204682 1
0.6%
766715 1
0.6%
681757 1
0.6%
663973 1
0.6%
616823 1
0.6%
608255 1
0.6%
510679 1
0.6%
485639 1
0.6%
476530 1
0.6%
457367 1
0.6%

미개발용지 분양공고 소계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25761.167
Minimum0
Maximum616386
Zeros148
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:27.173195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile163155.1
Maximum616386
Range616386
Interquartile range (IQR)0

Descriptive statistics

Standard deviation97635.642
Coefficient of variation (CV)3.7900318
Kurtosis22.535787
Mean25761.167
Median Absolute Deviation (MAD)0
Skewness4.6430095
Sum4327876
Variance9.5327186 × 109
MonotonicityNot monotonic
2024-01-10T06:25:27.289805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 148
88.1%
163790 1
 
0.6%
30201 1
 
0.6%
41593 1
 
0.6%
220232 1
 
0.6%
19734 1
 
0.6%
608255 1
 
0.6%
22047 1
 
0.6%
616386 1
 
0.6%
570691 1
 
0.6%
Other values (11) 11
 
6.5%
ValueCountFrequency (%)
0 148
88.1%
13647 1
 
0.6%
19734 1
 
0.6%
22047 1
 
0.6%
30201 1
 
0.6%
41593 1
 
0.6%
49808 1
 
0.6%
99436 1
 
0.6%
107595 1
 
0.6%
128888 1
 
0.6%
ValueCountFrequency (%)
616386 1
0.6%
608255 1
0.6%
570691 1
0.6%
413834 1
0.6%
392329 1
0.6%
290080 1
0.6%
220232 1
0.6%
216150 1
0.6%
163790 1
0.6%
161976 1
0.6%

미개발용지 분양
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20042.268
Minimum0
Maximum608255
Zeros149
Zeros (%)88.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:27.408024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile139585.2
Maximum608255
Range608255
Interquartile range (IQR)0

Descriptive statistics

Standard deviation83531.254
Coefficient of variation (CV)4.1677546
Kurtosis27.495171
Mean20042.268
Median Absolute Deviation (MAD)0
Skewness5.0856306
Sum3367101
Variance6.9774704 × 109
MonotonicityNot monotonic
2024-01-10T06:25:27.523968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 149
88.7%
413834 1
 
0.6%
30201 1
 
0.6%
41593 1
 
0.6%
220232 1
 
0.6%
19734 1
 
0.6%
608255 1
 
0.6%
9325 1
 
0.6%
529453 1
 
0.6%
1200 1
 
0.6%
Other values (10) 10
 
6.0%
ValueCountFrequency (%)
0 149
88.7%
1200 1
 
0.6%
2510 1
 
0.6%
9325 1
 
0.6%
19734 1
 
0.6%
21251 1
 
0.6%
30201 1
 
0.6%
41593 1
 
0.6%
49808 1
 
0.6%
98530 1
 
0.6%
ValueCountFrequency (%)
608255 1
0.6%
529453 1
0.6%
413834 1
0.6%
392329 1
0.6%
290080 1
0.6%
220232 1
0.6%
216150 1
0.6%
161976 1
0.6%
161204 1
0.6%
99436 1
0.6%

미개발용지 미분양
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5718.8988
Minimum0
Maximum570691
Zeros161
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-10T06:25:27.629382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum570691
Range570691
Interquartile range (IQR)0

Descriptive statistics

Standard deviation46798.517
Coefficient of variation (CV)8.1831343
Kurtosis129.74536
Mean5718.8988
Median Absolute Deviation (MAD)0
Skewness10.977866
Sum960775
Variance2.1901012 × 109
MonotonicityNot monotonic
2024-01-10T06:25:27.728605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 161
95.8%
161280 1
 
0.6%
9065 1
 
0.6%
107637 1
 
0.6%
12447 1
 
0.6%
570691 1
 
0.6%
86933 1
 
0.6%
12722 1
 
0.6%
ValueCountFrequency (%)
0 161
95.8%
9065 1
 
0.6%
12447 1
 
0.6%
12722 1
 
0.6%
86933 1
 
0.6%
107637 1
 
0.6%
161280 1
 
0.6%
570691 1
 
0.6%
ValueCountFrequency (%)
570691 1
 
0.6%
161280 1
 
0.6%
107637 1
 
0.6%
86933 1
 
0.6%
12722 1
 
0.6%
12447 1
 
0.6%
9065 1
 
0.6%
0 161
95.8%

Interactions

2024-01-10T06:25:22.141909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:09.681978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.581623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.762955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.723507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.863100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.864977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.846522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.787616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.919977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.977113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.972873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.938351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:22.230177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:09.751274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.656659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.837263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.789264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.948449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.942036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.922901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.856424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.987248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.068457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.047442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.036567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:22.326695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:09.828085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.748048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.920557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.082330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.031047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.021946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.998197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.928941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.068946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.147831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.126843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.119228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:22.421313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:09.908796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.856471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.999366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.150929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.098931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.096601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.074080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.006192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.163987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.222610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.204836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.228812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:22.507541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:09.973346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.945708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.069591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.224090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.163438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.165853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.141400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.073349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.245104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.287409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.270940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.315839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:22.587276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.037147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.035640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.137707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.288328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.239224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.233271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.208330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.136591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.324886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.349607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.333632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.398226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:22.916618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.109789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.138664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.215059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.362063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.334320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.315047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.284534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.206681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.418473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.431867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.411355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.500906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:22.991760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.183265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.239859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.288744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.433366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.429090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.390516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.359176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.278568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.513047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.518914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.487428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.596142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:23.056878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.247884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.327029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.357860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.499290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.511859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.460562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.428331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.347901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.594491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.606643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.554852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.682580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:23.117011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.309357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.415567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.427095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.560772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.587924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.537942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.495200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.410221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.654828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.689285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.618293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.765543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:23.186457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.380901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.508018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.506470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.629328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.673304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.633225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.566686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.700580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.723667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.769876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.697221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.860100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:23.261198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.447922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.601410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.579073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.695285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.739017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.703947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.642320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.767205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.806376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.836379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.765632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:21.954730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:23.329454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:10.516507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:11.689103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:12.653327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:13.781967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:14.801841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:15.776513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:16.718701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:17.837168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:18.894706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:19.907381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:20.843479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:25:22.049826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:25:27.821785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형시군구지정면적분양현황 계(분양대상)본양공고 소계분양미분양개발용지 계(분양대상)개발용지 분양공고 소계개발용지 분양개발용지 미분양미개발용지 계(분양대상)미개발용지 분양공고 소계미개발용지 분양미개발용지 미분양
유형1.0000.5330.5980.5730.5620.7920.5830.5680.5620.7880.2740.5000.1410.0000.503
시군구0.5331.0000.0000.0000.0000.0000.1230.0000.0000.0000.0000.0000.0000.0000.240
지정면적0.5980.0001.0000.9190.8990.9310.7070.9040.8990.9250.7590.2960.8050.6200.154
분양현황 계(분양대상)0.5730.0000.9191.0000.9990.9200.7420.9990.9990.9580.9370.2640.4660.4760.203
본양공고 소계0.5620.0000.8990.9991.0000.9320.7371.0001.0000.9690.9370.0000.4880.4820.082
분양0.7920.0000.9310.9200.9321.0000.6930.9340.9320.9970.5970.0000.4700.6550.000
미분양0.5830.1230.7070.7420.7370.6931.0000.7360.7370.8740.8000.9140.4710.0000.950
개발용지 계(분양대상)0.5680.0000.9040.9991.0000.9340.7361.0001.0000.9710.9370.0000.4840.4800.000
개발용지 분양공고 소계0.5620.0000.8990.9991.0000.9320.7371.0001.0000.9690.9370.0000.4880.4820.082
개발용지 분양0.7880.0000.9250.9580.9690.9970.8740.9710.9691.0000.7780.0000.4640.6220.000
개발용지 미분양0.2740.0000.7590.9370.9370.5970.8000.9370.9370.7781.0000.0000.0000.0000.000
미개발용지 계(분양대상)0.5000.0000.2960.2640.0000.0000.9140.0000.0000.0000.0001.0000.7480.8500.962
미개발용지 분양공고 소계0.1410.0000.8050.4660.4880.4700.4710.4840.4880.4640.0000.7481.0000.9690.651
미개발용지 분양0.0000.0000.6200.4760.4820.6550.0000.4800.4820.6220.0000.8500.9691.0000.681
미개발용지 미분양0.5030.2400.1540.2030.0820.0000.9500.0000.0820.0000.0000.9620.6510.6811.000
2024-01-10T06:25:27.934785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유형시군구
유형1.0000.320
시군구0.3201.000
2024-01-10T06:25:28.010075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지정면적분양현황 계(분양대상)본양공고 소계분양미분양개발용지 계(분양대상)개발용지 분양공고 소계개발용지 분양개발용지 미분양미개발용지 계(분양대상)미개발용지 분양공고 소계미개발용지 분양미개발용지 미분양유형시군구
지정면적1.0000.9890.7860.7440.2970.6860.6760.6650.2560.3920.2540.2330.1880.4480.000
분양현황 계(분양대상)0.9891.0000.7980.7600.2730.6970.6880.6800.2310.3880.2470.2250.1810.4040.000
본양공고 소계0.7860.7981.0000.9730.2450.8950.8940.8880.202-0.0340.2080.1900.1250.3950.000
분양0.7440.7600.9731.0000.1350.9090.9140.9170.141-0.0960.1360.171-0.0010.4530.000
미분양0.2970.2730.2450.1351.0000.1760.1470.1030.9140.2690.2520.1890.5610.2600.064
개발용지 계(분양대상)0.6860.6970.8950.9090.1761.0000.9950.9890.236-0.244-0.084-0.058-0.0340.4000.000
개발용지 분양공고 소계0.6760.6880.8940.9140.1470.9951.0000.9970.205-0.269-0.103-0.076-0.0620.3950.000
개발용지 분양0.6650.6800.8880.9170.1030.9890.9971.0000.158-0.276-0.109-0.083-0.0770.4490.000
개발용지 미분양0.2560.2310.2020.1410.9140.2360.2050.1581.0000.1580.0910.0900.3040.2610.000
미개발용지 계(분양대상)0.3920.388-0.034-0.0960.269-0.244-0.269-0.2760.1581.0000.7010.6690.4200.2380.000
미개발용지 분양공고 소계0.2540.2470.2080.1360.252-0.084-0.103-0.1090.0910.7011.0000.9660.5640.0950.000
미개발용지 분양0.2330.2250.1900.1710.189-0.058-0.076-0.0830.0900.6690.9661.0000.4610.0000.000
미개발용지 미분양0.1880.1810.125-0.0010.561-0.034-0.062-0.0770.3040.4200.5640.4611.0000.2150.132
유형0.4480.4040.3950.4530.2600.4000.3950.4490.2610.2380.0950.0000.2151.0000.320
시군구0.0000.0000.0000.0000.0640.0000.0000.0000.0000.0000.0000.0000.1320.3201.000

Missing values

2024-01-10T06:25:23.426992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:25:23.599300image/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

단지명유형시도시군구지정면적분양현황 계(분양대상)본양공고 소계분양미분양개발용지 계(분양대상)개발용지 분양공고 소계개발용지 분양개발용지 미분양미개발용지 계(분양대상)미개발용지 분양공고 소계미개발용지 분양미개발용지 미분양
0아산(고대지구)국가충남당진시3036397249177624917762491776024917762491776249177600000
1아산(부곡지구)국가충남당진시3118889237290023729002372900023729002372900237290000000
2고정국가산업단지국가충남보령시6304110470832447083244708324047083244708324470832400000
3대죽자원비축산업단지국가충남서산시911653449340449340449340044934044934044934000000
4석문국가산업단지국가충남당진시12011613775898677589865460922229806477589867758986546092222980640000
5장항국가생태산업단지국가충남서천군27508931853654105853468120737732713680158947446786972160474856391637902510161280
6금산일반산업단지일반충남금산군920450818610818610818610081861081861081861000000
7논산일반산업단지일반충남논산시253354169659169659169659016965916965916965900000
8관창일반산업단지일반충남보령시2442223194613419461341946134015538051553805155380503923293923293923290
9서산오토밸리일반산업단지[구:서산]일반충남서산시3989547287686628768662876866028768662876866287686600000
단지명유형시도시군구지정면적분양현황 계(분양대상)본양공고 소계분양미분양개발용지 계(분양대상)개발용지 분양공고 소계개발용지 분양개발용지 미분양미개발용지 계(분양대상)미개발용지 분양공고 소계미개발용지 분양미개발용지 미분양
158종천2농공단지농공충남서천군197478117704117704785353916911770411770478535391690000
159청양운곡2농공단지농공충남청양군146109887178871788717088717887178871700000
160월미2스마트산업단지[구:월미2농공]농공충남공주시69009572275722757227057227572275722700000
161계룡제2산업단지농공충남계룡시19231512799812799810705420944127998127998107054209440000
162강경농공단지농공충남논산시129657913399133991339091339913399133900000
163노성특화농공단지농공충남논산시174797116918116918110165675311691811691811016567530000
164가야곡2농공단지농공충남논산시308350220232220232220232000002202322202322202320
165정산2농공단지농공충남청양군18669412496141593415930000012496141593415930
166청라농공단지농공충남보령시6747244936000449360000000
167제이팜스농공단지농공충남공주시45005302013020130201000003020130201302010