Overview

Dataset statistics

Number of variables11
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory100.8 B

Variable types

Categorical2
Numeric8
Text1

Dataset

Description대전도시공사 임대아파트 상가는 저소득층 및 취약계층 등 복지를 위한 영구임대아파트 단지내 상가임대 현황 자료 입니다. 자료 입니다.
URLhttps://www.data.go.kr/data/15103602/fileData.do

Alerts

임대면적 is highly overall correlated with 전용면적 and 5 other fieldsHigh correlation
전용면적 is highly overall correlated with 임대면적 and 6 other fieldsHigh correlation
공용면적 is highly overall correlated with 임대면적 and 6 other fieldsHigh correlation
보증금 is highly overall correlated with 전용면적 and 5 other fieldsHigh correlation
임대료_공급가액 is highly overall correlated with 임대면적 and 6 other fieldsHigh correlation
임대료_부가세 is highly overall correlated with 임대면적 and 6 other fieldsHigh correlation
임대료계 is highly overall correlated with 임대면적 and 6 other fieldsHigh correlation
층 is highly overall correlated with 임대면적 and 6 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 07:11:08.475064
Analysis finished2023-12-12 07:11:15.893924
Duration7.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

임대대상명
Categorical

Distinct4
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size412.0 B
한마음상가
15 
보라상가
8 
송강마을상가
6 
오류동(누리보듬상가)
6 

Length

Max length11
Median length6
Mean length5.9714286
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한마음상가 15
42.9%
보라상가 8
22.9%
송강마을상가 6
 
17.1%
오류동(누리보듬상가) 6
 
17.1%

Length

2023-12-12T16:11:15.966306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:11:16.113865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한마음상가 15
42.9%
보라상가 8
22.9%
송강마을상가 6
 
17.1%
오류동(누리보듬상가 6
 
17.1%

순번
Real number (ℝ)

Distinct15
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7428571
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T16:11:16.244406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37.5
95-th percentile13.3
Maximum15
Range14
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.8527519
Coefficient of variation (CV)0.6708772
Kurtosis0.0012981115
Mean5.7428571
Median Absolute Deviation (MAD)2
Skewness0.83614711
Sum201
Variance14.843697
MonotonicityNot monotonic
2023-12-12T16:11:16.345698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 4
11.4%
2 4
11.4%
3 4
11.4%
5 4
11.4%
6 4
11.4%
4 3
8.6%
7 3
8.6%
8 2
 
5.7%
9 1
 
2.9%
10 1
 
2.9%
Other values (5) 5
14.3%
ValueCountFrequency (%)
1 4
11.4%
2 4
11.4%
3 4
11.4%
4 3
8.6%
5 4
11.4%
6 4
11.4%
7 3
8.6%
8 2
5.7%
9 1
 
2.9%
10 1
 
2.9%
ValueCountFrequency (%)
15 1
 
2.9%
14 1
 
2.9%
13 1
 
2.9%
12 1
 
2.9%
11 1
 
2.9%
10 1
 
2.9%
9 1
 
2.9%
8 2
5.7%
7 3
8.6%
6 4
11.4%

층
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
1
23 
2
10 
-1
 
2

Length

Max length2
Median length1
Mean length1.0571429
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 23
65.7%
2 10
28.6%
-1 2
 
5.7%

Length

2023-12-12T16:11:16.466702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:11:16.584401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 25
71.4%
2 10
 
28.6%

호
Text

Distinct20
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Memory size412.0 B
2023-12-12T16:11:16.775799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0571429
Min length3

Characters and Unicode

Total characters107
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)25.7%

Sample

1st row102
2nd row103
3rd row106
4th row107
5th row109
ValueCountFrequency (%)
102 3
 
8.6%
201 3
 
8.6%
103 3
 
8.6%
109 3
 
8.6%
106 2
 
5.7%
205 2
 
5.7%
104 2
 
5.7%
101 2
 
5.7%
203 2
 
5.7%
107 2
 
5.7%
Other values (10) 11
31.4%
2023-12-12T16:11:17.111950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 33
30.8%
0 31
29.0%
2 15
14.0%
3 5
 
4.7%
9 3
 
2.8%
7 3
 
2.8%
6 3
 
2.8%
4 3
 
2.8%
5 3
 
2.8%
8 2
 
1.9%
Other values (3) 6
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 101
94.4%
Other Letter 6
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33
32.7%
0 31
30.7%
2 15
14.9%
3 5
 
5.0%
9 3
 
3.0%
7 3
 
3.0%
6 3
 
3.0%
4 3
 
3.0%
5 3
 
3.0%
8 2
 
2.0%
Other Letter
ValueCountFrequency (%)
지 2
33.3%
하 2
33.3%
호 2
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 101
94.4%
Hangul 6
 
5.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33
32.7%
0 31
30.7%
2 15
14.9%
3 5
 
5.0%
9 3
 
3.0%
7 3
 
3.0%
6 3
 
3.0%
4 3
 
3.0%
5 3
 
3.0%
8 2
 
2.0%
Hangul
ValueCountFrequency (%)
지 2
33.3%
하 2
33.3%
호 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101
94.4%
Hangul 6
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33
32.7%
0 31
30.7%
2 15
14.9%
3 5
 
5.0%
9 3
 
3.0%
7 3
 
3.0%
6 3
 
3.0%
4 3
 
3.0%
5 3
 
3.0%
8 2
 
2.0%
Hangul
ValueCountFrequency (%)
지 2
33.3%
하 2
33.3%
호 2
33.3%

임대면적
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.890143
Minimum11.781
Maximum531.642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T16:11:17.253780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.781
5-th percentile21.174
Q139.259
median55.474
Q385.624
95-th percentile225.269
Maximum531.642
Range519.861
Interquartile range (IQR)46.365

Descriptive statistics

Standard deviation99.16094
Coefficient of variation (CV)1.168109
Kurtosis13.293607
Mean84.890143
Median Absolute Deviation (MAD)17.858
Skewness3.4793819
Sum2971.155
Variance9832.892
MonotonicityNot monotonic
2023-12-12T16:11:17.402818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
37.616 4
 
11.4%
42.548 3
 
8.6%
55.474 2
 
5.7%
21.174 2
 
5.7%
68.855 2
 
5.7%
39.259 2
 
5.7%
48.509 1
 
2.9%
74.373 1
 
2.9%
96.469 1
 
2.9%
168.02 1
 
2.9%
Other values (16) 16
45.7%
ValueCountFrequency (%)
11.781 1
 
2.9%
21.174 2
5.7%
35.704 1
 
2.9%
37.616 4
11.4%
39.259 2
5.7%
42.548 3
8.6%
44.381 1
 
2.9%
48.509 1
 
2.9%
49.841 1
 
2.9%
53.899 1
 
2.9%
ValueCountFrequency (%)
531.642 1
2.9%
358.85 1
2.9%
168.02 1
2.9%
159.234 1
2.9%
142.72 1
2.9%
98.356 1
2.9%
96.469 1
2.9%
92.358 1
2.9%
87.648 1
2.9%
83.6 1
2.9%

전용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.761143
Minimum7.65
Maximum442.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T16:11:17.547016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.65
5-th percentile13.75
Q127.6
median35
Q346.007
95-th percentile174.014
Maximum442.66
Range435.01
Interquartile range (IQR)18.407

Descriptive statistics

Standard deviation82.250289
Coefficient of variation (CV)1.4239727
Kurtosis15.883766
Mean57.761143
Median Absolute Deviation (MAD)9.989
Skewness3.8914822
Sum2021.64
Variance6765.1101
MonotonicityNot monotonic
2023-12-12T16:11:17.662757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
23.6 4
 
11.4%
33.0 3
 
8.6%
39.0 2
 
5.7%
13.75 2
 
5.7%
43.2 2
 
5.7%
27.6 2
 
5.7%
31.5 1
 
2.9%
34.296 1
 
2.9%
35.263 1
 
2.9%
77.48 1
 
2.9%
Other values (16) 16
45.7%
ValueCountFrequency (%)
7.65 1
 
2.9%
13.75 2
5.7%
22.4 1
 
2.9%
23.6 4
11.4%
27.6 2
5.7%
31.2 1
 
2.9%
31.27 1
 
2.9%
31.5 1
 
2.9%
33.0 3
8.6%
34.296 1
 
2.9%
ValueCountFrequency (%)
442.66 1
2.9%
291.88 1
2.9%
123.5 1
2.9%
77.48 1
2.9%
70.325 1
2.9%
67.98 1
2.9%
65.282 1
2.9%
48.0 1
2.9%
47.025 1
2.9%
44.989 1
2.9%

공용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.557486
Minimum4.131
Maximum90.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T16:11:17.787808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.131
5-th percentile7.424
Q113.2425
median17.009
Q330.6945
95-th percentile80.9012
Maximum90.54
Range86.409
Interquartile range (IQR)17.452

Descriptive statistics

Standard deviation23.005653
Coefficient of variation (CV)0.86625871
Kurtosis2.119979
Mean26.557486
Median Absolute Deviation (MAD)5.35
Skewness1.715349
Sum929.512
Variance529.26009
MonotonicityNot monotonic
2023-12-12T16:11:17.896199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
14.016 4
 
11.4%
9.548 3
 
8.6%
16.474 2
 
5.7%
7.424 2
 
5.7%
25.655 2
 
5.7%
11.659 2
 
5.7%
17.009 1
 
2.9%
40.076 1
 
2.9%
41.206 1
 
2.9%
90.54 1
 
2.9%
Other values (16) 16
45.7%
ValueCountFrequency (%)
4.131 1
 
2.9%
7.424 2
5.7%
9.548 3
8.6%
11.659 2
5.7%
13.181 1
 
2.9%
13.304 1
 
2.9%
14.016 4
11.4%
14.733 1
 
2.9%
16.474 2
5.7%
17.009 1
 
2.9%
ValueCountFrequency (%)
90.54 1
2.9%
88.982 1
2.9%
77.438 1
2.9%
66.97 1
2.9%
53.366 1
2.9%
45.359 1
2.9%
41.206 1
2.9%
40.076 1
2.9%
35.734 1
2.9%
25.655 2
5.7%

보증금
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14503829
Minimum3100000
Maximum88520000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T16:11:18.001695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3100000
5-th percentile4059600
Q17436000
median9960000
Q316675000
95-th percentile32326000
Maximum88520000
Range85420000
Interquartile range (IQR)9239000

Descriptive statistics

Standard deviation15738831
Coefficient of variation (CV)1.0851501
Kurtosis15.372621
Mean14503829
Median Absolute Deviation (MAD)3160000
Skewness3.6848071
Sum5.07634 × 108
Variance2.4771081 × 1014
MonotonicityNot monotonic
2023-12-12T16:11:18.113254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
9960000 4
 
11.4%
6800000 3
 
8.6%
8800000 2
 
5.7%
18600000 2
 
5.7%
10118000 1
 
2.9%
3100000 1
 
2.9%
3200000 1
 
2.9%
10269000 1
 
2.9%
21700000 1
 
2.9%
8368000 1
 
2.9%
Other values (18) 18
51.4%
ValueCountFrequency (%)
3100000 1
 
2.9%
3200000 1
 
2.9%
4428000 1
 
2.9%
4711000 1
 
2.9%
5325000 1
 
2.9%
6800000 3
8.6%
7322000 1
 
2.9%
7550000 1
 
2.9%
7560000 1
 
2.9%
8193000 1
 
2.9%
ValueCountFrequency (%)
88520000 1
2.9%
54320000 1
2.9%
22900000 1
2.9%
21700000 1
2.9%
19740000 1
2.9%
19240000 1
2.9%
18600000 2
5.7%
18200000 1
2.9%
15150000 1
2.9%
14800000 1
2.9%

임대료_공급가액
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean562072.74
Minimum107000
Maximum2597000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T16:11:18.221618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum107000
5-th percentile149000
Q1220000
median294000
Q3506500
95-th percentile2083227.4
Maximum2597000
Range2490000
Interquartile range (IQR)286500

Descriptive statistics

Standard deviation634282.1
Coefficient of variation (CV)1.1284698
Kurtosis4.2590164
Mean562072.74
Median Absolute Deviation (MAD)125000
Skewness2.2323967
Sum19672546
Variance4.0231378 × 1011
MonotonicityNot monotonic
2023-12-12T16:11:18.340394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
287000 3
 
8.6%
149000 3
 
8.6%
292000 2
 
5.7%
169000 2
 
5.7%
536000 2
 
5.7%
376000 1
 
2.9%
464000 1
 
2.9%
477000 1
 
2.9%
1556000 1
 
2.9%
2445000 1
 
2.9%
Other values (18) 18
51.4%
ValueCountFrequency (%)
107000 1
 
2.9%
149000 3
8.6%
169000 2
5.7%
172000 1
 
2.9%
175000 1
 
2.9%
211000 1
 
2.9%
229000 1
 
2.9%
252000 1
 
2.9%
266000 1
 
2.9%
287000 3
8.6%
ValueCountFrequency (%)
2597000 1
2.9%
2445000 1
2.9%
1928182 1
2.9%
1556000 1
2.9%
1268000 1
2.9%
1050364 1
2.9%
537000 1
2.9%
536000 2
5.7%
477000 1
2.9%
464000 1
2.9%

임대료_부가세
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56207.257
Minimum10700
Maximum259700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T16:11:18.460871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10700
5-th percentile14900
Q122000
median29400
Q350650
95-th percentile208322.6
Maximum259700
Range249000
Interquartile range (IQR)28650

Descriptive statistics

Standard deviation63428.188
Coefficient of variation (CV)1.1284697
Kurtosis4.2590246
Mean56207.257
Median Absolute Deviation (MAD)12500
Skewness2.2323982
Sum1967254
Variance4.023135 × 109
MonotonicityNot monotonic
2023-12-12T16:11:18.572838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
28700 3
 
8.6%
14900 3
 
8.6%
29200 2
 
5.7%
16900 2
 
5.7%
53600 2
 
5.7%
37600 1
 
2.9%
46400 1
 
2.9%
47700 1
 
2.9%
155600 1
 
2.9%
244500 1
 
2.9%
Other values (18) 18
51.4%
ValueCountFrequency (%)
10700 1
 
2.9%
14900 3
8.6%
16900 2
5.7%
17200 1
 
2.9%
17500 1
 
2.9%
21100 1
 
2.9%
22900 1
 
2.9%
25200 1
 
2.9%
26600 1
 
2.9%
28700 3
8.6%
ValueCountFrequency (%)
259700 1
2.9%
244500 1
2.9%
192818 1
2.9%
155600 1
2.9%
126800 1
2.9%
105036 1
2.9%
53700 1
2.9%
53600 2
5.7%
47700 1
2.9%
46400 1
2.9%

임대료계
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean618280
Minimum117700
Maximum2856700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-12T16:11:18.700769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum117700
5-th percentile163900
Q1242000
median323400
Q3557150
95-th percentile2291550
Maximum2856700
Range2739000
Interquartile range (IQR)315150

Descriptive statistics

Standard deviation697710.28
Coefficient of variation (CV)1.1284698
Kurtosis4.2590172
Mean618280
Median Absolute Deviation (MAD)137500
Skewness2.2323969
Sum21639800
Variance4.8679964 × 1011
MonotonicityNot monotonic
2023-12-12T16:11:18.826202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
315700 3
 
8.6%
163900 3
 
8.6%
321200 2
 
5.7%
185900 2
 
5.7%
589600 2
 
5.7%
413600 1
 
2.9%
510400 1
 
2.9%
524700 1
 
2.9%
1711600 1
 
2.9%
2689500 1
 
2.9%
Other values (18) 18
51.4%
ValueCountFrequency (%)
117700 1
 
2.9%
163900 3
8.6%
185900 2
5.7%
189200 1
 
2.9%
192500 1
 
2.9%
232100 1
 
2.9%
251900 1
 
2.9%
277200 1
 
2.9%
292600 1
 
2.9%
315700 3
8.6%
ValueCountFrequency (%)
2856700 1
2.9%
2689500 1
2.9%
2121000 1
2.9%
1711600 1
2.9%
1394800 1
2.9%
1155400 1
2.9%
590700 1
2.9%
589600 2
5.7%
524700 1
2.9%
510400 1
2.9%

Interactions

2023-12-12T16:11:14.860520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:08.964453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:09.871404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:10.835845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.624472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:12.367151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.345354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:14.072306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:14.974111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:09.090556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:10.017601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:10.938500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.730005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:12.459960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.431505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:14.172342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:15.082108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:09.209874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:10.137223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.040205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.823745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:12.545770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.508962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:14.268158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:15.178787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:09.319448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:10.251602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.130840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.915190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:12.627646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.584311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:14.365216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:15.271751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:09.424423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:10.361815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.225122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.996399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:12.707753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.678309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:14.459925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:15.370652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:09.531223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:10.481632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.333443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:12.088171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:12.795513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.777067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:14.560328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:15.461435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:09.644702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:10.601017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.428961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:12.172948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.193144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.861908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:14.660789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:15.546737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:09.761169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:10.702559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:11.518041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:12.269642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.267428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:13.959032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:11:14.754267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:11:18.925420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임대대상명순번층호임대면적전용면적공용면적보증금임대료_공급가액임대료_부가세임대료계
임대대상명1.0000.0000.0000.0000.3870.0000.5960.4230.3950.3950.395
순번0.0001.0000.6560.8340.4810.8780.5150.2000.0000.0000.000
층0.0000.6561.0001.0000.9420.7470.8670.6970.6430.6430.643
호0.0000.8341.0001.0000.7290.9000.8190.8740.8220.8220.822
임대면적0.3870.4810.9420.7291.0000.8930.9540.8280.8750.8750.875
전용면적0.0000.8780.7470.9000.8931.0000.9060.9550.7680.7680.768
공용면적0.5960.5150.8670.8190.9540.9061.0000.8770.9060.9060.906
보증금0.4230.2000.6970.8740.8280.9550.8771.0000.8520.8520.852
임대료_공급가액0.3950.0000.6430.8220.8750.7680.9060.8521.0001.0001.000
임대료_부가세0.3950.0000.6430.8220.8750.7680.9060.8521.0001.0001.000
임대료계0.3950.0000.6430.8220.8750.7680.9060.8521.0001.0001.000
2023-12-12T16:11:19.368201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
임대대상명층
임대대상명1.0000.000
층0.0001.000
2023-12-12T16:11:19.482600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번임대면적전용면적공용면적보증금임대료_공급가액임대료_부가세임대료계임대대상명층
순번1.0000.0440.120-0.125-0.105-0.292-0.292-0.2920.0000.392
임대면적0.0441.0000.9470.9210.4970.8220.8220.8220.2410.672
전용면적0.1200.9471.0000.8110.5330.6950.6950.6950.0000.722
공용면적-0.1250.9210.8111.0000.5870.9610.9610.9610.3840.526
보증금-0.1050.4970.5330.5871.0000.6480.6480.6480.3210.654
임대료_공급가액-0.2920.8220.6950.9610.6481.0001.0001.0000.2570.508
임대료_부가세-0.2920.8220.6950.9610.6481.0001.0001.0000.2570.508
임대료계-0.2920.8220.6950.9610.6481.0001.0001.0000.2570.508
임대대상명0.0000.2410.0000.3840.3210.2570.2570.2571.0000.000
층0.3920.6720.7220.5260.6540.5080.5080.5080.0001.000

Missing values

2023-12-12T16:11:15.688081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:11:15.832851image/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

임대대상명순번층호임대면적전용면적공용면적보증금임대료_공급가액임대료_부가세임대료계
0보라상가1110248.50931.517.0091515000043900043900482900
1보라상가2110353.89935.018.8991924000037700037700414700
2보라상가3110621.17413.757.424755000016900016900185900
3보라상가4110721.17413.757.424756000016900016900185900
4보라상가5110911.7817.654.131880000010700010700117700
5보라상가6220192.35870.32522.0331480000029400029400323400
6보라상가7220361.75847.02514.7331252000022900022900251900
7보라상가8-1지하1호358.85291.8866.975432000010503641050361155400
8한마음상가1110156.10435.220.9041820000053700053700590700
9한마음상가2110235.70422.413.304880000025200025200277200
임대대상명순번층호임대면적전용면적공용면적보증금임대료_공급가액임대료_부가세임대료계
25송강마을상가3110344.38131.213.181532500021100021100232100
26송강마을상가5110639.25927.611.659442800017200017200189200
27송강마을상가6110739.25927.611.659471100017500017500192500
28송강마을상가7110868.27648.020.276819300032500032500357500
29오류동(누리보듬상가)11104142.7265.28277.4382290000019281821928182121000
30오류동(누리보듬상가)2110598.35644.98953.366836800012680001268001394800
31오류동(누리보듬상가)3110983.638.2445.3592170000024450002445002689500
32오류동(누리보듬상가)42201168.0277.4890.541026900015560001556001711600
33오류동(누리보듬상가)5220596.46935.26341.206320000047700047700524700
34오류동(누리보듬상가)6220774.37334.29640.076310000046400046400510400