Overview

Dataset statistics

Number of variables8
Number of observations102
Missing cells0
Missing cells (%)0.0%
Duplicate rows51
Duplicate rows (%)50.0%
Total size in memory7.3 KiB
Average record size in memory73.3 B

Variable types

Categorical1
Numeric7

Dataset

Description대전광역시 2021년 대전지식산업센터 운영 현황입니다. 2022년 공공데이터 기업매칭지원사업으로 수행되었습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15111078/fileData.do

Alerts

기준년도 has constant value ""Constant
Dataset has 51 (50.0%) duplicate rowsDuplicates
전용면적(제곱미터) is highly overall correlated with 공용면적(제곱미터) and 4 other fieldsHigh correlation
공용면적(제곱미터) is highly overall correlated with 전용면적(제곱미터) and 4 other fieldsHigh correlation
임대면적(제곱미터) is highly overall correlated with 전용면적(제곱미터) and 4 other fieldsHigh correlation
연간임대료(원) is highly overall correlated with 전용면적(제곱미터) and 4 other fieldsHigh correlation
연간관리비(원) is highly overall correlated with 전용면적(제곱미터) and 4 other fieldsHigh correlation
월임대관리비(원) is highly overall correlated with 전용면적(제곱미터) and 4 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 04:18:32.747882
Analysis finished2023-12-12 04:18:37.936027
Duration5.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size948.0 B
2021
102 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 102
100.0%

Length

2023-12-12T13:18:38.012444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:18:38.121693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 102
100.0%

호수
Real number (ℝ)

Distinct51
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean429.03922
Minimum101
Maximum701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T13:18:38.256451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile202.05
Q1307.25
median409
Q3510.75
95-th percentile609.95
Maximum701
Range600
Interquartile range (IQR)203.5

Descriptive statistics

Standard deviation142.08308
Coefficient of variation (CV)0.33116572
Kurtosis-0.83077009
Mean429.03922
Median Absolute Deviation (MAD)102
Skewness-0.19403457
Sum43762
Variance20187.602
MonotonicityNot monotonic
2023-12-12T13:18:38.445002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 2
 
2.0%
201 2
 
2.0%
501 2
 
2.0%
502 2
 
2.0%
503 2
 
2.0%
504 2
 
2.0%
505 2
 
2.0%
506 2
 
2.0%
507 2
 
2.0%
508 2
 
2.0%
Other values (41) 82
80.4%
ValueCountFrequency (%)
101 2
2.0%
201 2
2.0%
202 2
2.0%
203 2
2.0%
204 2
2.0%
205 2
2.0%
301 2
2.0%
302 2
2.0%
303 2
2.0%
304 2
2.0%
ValueCountFrequency (%)
701 2
2.0%
611 2
2.0%
610 2
2.0%
609 2
2.0%
608 2
2.0%
607 2
2.0%
606 2
2.0%
605 2
2.0%
604 2
2.0%
603 2
2.0%

전용면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)32.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.06157
Minimum53.7
Maximum285.65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T13:18:38.591962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53.7
5-th percentile64.7865
Q178.345
median80.68
Q3133.47
95-th percentile160.8065
Maximum285.65
Range231.95
Interquartile range (IQR)55.125

Descriptive statistics

Standard deviation46.643076
Coefficient of variation (CV)0.44395945
Kurtosis4.2820719
Mean105.06157
Median Absolute Deviation (MAD)6.49
Skewness1.9395545
Sum10716.28
Variance2175.5765
MonotonicityNot monotonic
2023-12-12T13:18:38.751724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
79.42 10
 
9.8%
87.17 8
 
7.8%
77.49 6
 
5.9%
78.45 6
 
5.9%
77.48 6
 
5.9%
80.97 4
 
3.9%
160.26 4
 
3.9%
159.54 4
 
3.9%
141.1 4
 
3.9%
78.48 4
 
3.9%
Other values (23) 46
45.1%
ValueCountFrequency (%)
53.7 2
 
2.0%
60.59 2
 
2.0%
64.78 2
 
2.0%
64.91 2
 
2.0%
71.33 2
 
2.0%
71.59 2
 
2.0%
77.48 6
5.9%
77.49 6
5.9%
78.31 2
 
2.0%
78.45 6
5.9%
ValueCountFrequency (%)
285.65 2
2.0%
254.25 2
2.0%
160.82 2
2.0%
160.55 2
2.0%
160.26 4
3.9%
159.54 4
3.9%
154.51 2
2.0%
154.39 2
2.0%
141.1 4
3.9%
133.51 2
2.0%

공용면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.516863
Minimum36.09
Maximum275.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T13:18:39.235908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.09
5-th percentile39.797
Q144.03
median47.07
Q385.24
95-th percentile102.529
Maximum275.42
Range239.33
Interquartile range (IQR)41.21

Descriptive statistics

Standard deviation39.147587
Coefficient of variation (CV)0.60678069
Kurtosis15.683898
Mean64.516863
Median Absolute Deviation (MAD)4.14
Skewness3.5042913
Sum6580.72
Variance1532.5336
MonotonicityNot monotonic
2023-12-12T13:18:39.386923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
42.4 6
 
5.9%
44.03 6
 
5.9%
44.16 4
 
3.9%
47.71 4
 
3.9%
43.63 4
 
3.9%
49.53 4
 
3.9%
166.08 2
 
2.0%
45.02 2
 
2.0%
88.7 2
 
2.0%
78.45 2
 
2.0%
Other values (33) 66
64.7%
ValueCountFrequency (%)
36.09 2
 
2.0%
38.39 2
 
2.0%
39.66 2
 
2.0%
42.4 6
5.9%
42.43 2
 
2.0%
42.93 2
 
2.0%
43.46 2
 
2.0%
43.63 4
3.9%
44.03 6
5.9%
44.15 2
 
2.0%
ValueCountFrequency (%)
275.42 2
2.0%
166.08 2
2.0%
102.68 2
2.0%
99.66 2
2.0%
94.98 2
2.0%
91.5 2
2.0%
91.37 2
2.0%
89.65 2
2.0%
89.1 2
2.0%
88.7 2
2.0%

임대면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169.57843
Minimum98.18
Maximum561.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T13:18:39.527827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum98.18
5-th percentile103.5505
Q1121.6675
median126.25
Q3219.2425
95-th percentile255.0875
Maximum561.07
Range462.89
Interquartile range (IQR)97.575

Descriptive statistics

Standard deviation84.274626
Coefficient of variation (CV)0.49696548
Kurtosis8.5752809
Mean169.57843
Median Absolute Deviation (MAD)10.45
Skewness2.5817874
Sum17297
Variance7102.2126
MonotonicityNot monotonic
2023-12-12T13:18:39.727707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
119.88 6
 
5.9%
121.52 6
 
5.9%
123.58 4
 
3.9%
134.88 4
 
3.9%
122.11 4
 
3.9%
136.7 4
 
3.9%
420.33 2
 
2.0%
125.99 2
 
2.0%
248.24 2
 
2.0%
219.55 2
 
2.0%
Other values (33) 66
64.7%
ValueCountFrequency (%)
98.18 2
 
2.0%
101.0 2
 
2.0%
103.17 2
 
2.0%
110.78 2
 
2.0%
110.99 2
 
2.0%
114.02 2
 
2.0%
119.88 6
5.9%
121.38 2
 
2.0%
121.52 6
5.9%
122.11 4
3.9%
ValueCountFrequency (%)
561.07 2
2.0%
420.33 2
2.0%
255.24 2
2.0%
252.19 2
2.0%
249.36 2
2.0%
248.41 2
2.0%
248.24 2
2.0%
246.85 2
2.0%
245.89 2
2.0%
242.3 2
2.0%

연간임대료(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5007733.2
Minimum2899284
Maximum16568487
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T13:18:39.908172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2899284
5-th percentile3057967.9
Q13592845.5
median3728237
Q36474357.5
95-th percentile7532865.2
Maximum16568487
Range13669203
Interquartile range (IQR)2881512

Descriptive statistics

Standard deviation2488656.8
Coefficient of variation (CV)0.49696274
Kurtosis8.5748505
Mean5007733.2
Median Absolute Deviation (MAD)308453
Skewness2.5817243
Sum5.1078879 × 108
Variance6.1934127 × 1012
MonotonicityNot monotonic
2023-12-12T13:18:40.078479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3540189 6
 
5.9%
3588427 6
 
5.9%
3649293 4
 
3.9%
3982941 4
 
3.9%
3606101 4
 
3.9%
4036690 4
 
3.9%
12412457 2
 
2.0%
3720515 2
 
2.0%
7330751 2
 
2.0%
6483446 2
 
2.0%
Other values (33) 66
64.7%
ValueCountFrequency (%)
2899284 2
 
2.0%
2982569 2
 
2.0%
3046741 2
 
2.0%
3271278 2
 
2.0%
3277563 2
 
2.0%
3367030 2
 
2.0%
3540189 6
5.9%
3584510 2
 
2.0%
3588427 6
5.9%
3606101 4
3.9%
ValueCountFrequency (%)
16568487 2
2.0%
12412457 2
2.0%
7537369 2
2.0%
7447293 2
2.0%
7363834 2
2.0%
7335794 2
2.0%
7330751 2
2.0%
7289646 2
2.0%
7261290 2
2.0%
7155088 2
2.0%

연간관리비(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6166503.4
Minimum3570168
Maximum20402371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T13:18:40.247309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3570168
5-th percentile3765569.7
Q14424216
median4590936
Q37972498.5
95-th percentile9275941
Maximum20402371
Range16832203
Interquartile range (IQR)3548282.5

Descriptive statistics

Standard deviation3064522.4
Coefficient of variation (CV)0.49696273
Kurtosis8.5748509
Mean6166503.4
Median Absolute Deviation (MAD)379829
Skewness2.5817243
Sum6.2898335 × 108
Variance9.3912974 × 1012
MonotonicityNot monotonic
2023-12-12T13:18:40.426533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4359375 6
 
5.9%
4418775 6
 
5.9%
4493726 4
 
3.9%
4904578 4
 
3.9%
4440539 4
 
3.9%
4970765 4
 
3.9%
15284652 2
 
2.0%
4581427 2
 
2.0%
9027058 2
 
2.0%
7983690 2
 
2.0%
Other values (33) 66
64.7%
ValueCountFrequency (%)
3570168 2
 
2.0%
3672724 2
 
2.0%
3751745 2
 
2.0%
4028239 2
 
2.0%
4035979 2
 
2.0%
4146148 2
 
2.0%
4359375 6
5.9%
4413952 2
 
2.0%
4418775 6
5.9%
4440539 4
3.9%
ValueCountFrequency (%)
20402371 2
2.0%
15284652 2
2.0%
9281487 2
2.0%
9170568 2
2.0%
9067797 2
2.0%
9033269 2
2.0%
9027058 2
2.0%
8976442 2
2.0%
8941525 2
2.0%
8810748 2
2.0%

월임대관리비(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)42.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean931186.39
Minimum539121
Maximum3080905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T13:18:40.575569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum539121
5-th percentile568628.6
Q1668088.5
median693264
Q31203905
95-th percentile1400733.5
Maximum3080905
Range2541784
Interquartile range (IQR)535816.5

Descriptive statistics

Standard deviation462764.93
Coefficient of variation (CV)0.49696273
Kurtosis8.5748517
Mean931186.39
Median Absolute Deviation (MAD)57357
Skewness2.5817243
Sum94981012
Variance2.1415138 × 1011
MonotonicityNot monotonic
2023-12-12T13:18:40.713148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
658297 6
 
5.9%
667267 6
 
5.9%
678585 4
 
3.9%
740627 4
 
3.9%
670553 4
 
3.9%
750621 4
 
3.9%
2308092 2
 
2.0%
691828 2
 
2.0%
1363151 2
 
2.0%
1205595 2
 
2.0%
Other values (33) 66
64.7%
ValueCountFrequency (%)
539121 2
 
2.0%
554608 2
 
2.0%
566541 2
 
2.0%
608293 2
 
2.0%
609462 2
 
2.0%
626098 2
 
2.0%
658297 6
5.9%
666538 2
 
2.0%
667267 6
5.9%
670553 4
3.9%
ValueCountFrequency (%)
3080905 2
2.0%
2308092 2
2.0%
1401571 2
2.0%
1384822 2
2.0%
1369303 2
2.0%
1364089 2
2.0%
1363151 2
2.0%
1355507 2
2.0%
1350235 2
2.0%
1330486 2
2.0%

Interactions

2023-12-12T13:18:36.982982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:32.967241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.696385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.341788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.952589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.584630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:36.237575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:37.090362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.091255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.792485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.421194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.034929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.668265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:36.343833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:37.186411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.211318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.894214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.495241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.118726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.750776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:36.452866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:37.274209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.320610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.994143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.576449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.220806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.832729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:36.550299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:37.374246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.400181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.076355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.651177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.309749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.913081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:36.652273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:37.469648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.500753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.163494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.749569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.406740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.995803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:36.782364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:37.568495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:33.602225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.258677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:34.843595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:35.506332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:36.088148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:18:36.897423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:18:40.837712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호수전용면적(제곱미터)공용면적(제곱미터)임대면적(제곱미터)연간임대료(원)연간관리비(원)월임대관리비(원)
호수1.0000.8330.8370.8510.8580.8580.858
전용면적(제곱미터)0.8331.0000.9641.0001.0001.0001.000
공용면적(제곱미터)0.8370.9641.0000.9970.9970.9970.997
임대면적(제곱미터)0.8511.0000.9971.0001.0001.0001.000
연간임대료(원)0.8581.0000.9971.0001.0001.0001.000
연간관리비(원)0.8581.0000.9971.0001.0001.0001.000
월임대관리비(원)0.8581.0000.9971.0001.0001.0001.000
2023-12-12T13:18:41.024273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호수전용면적(제곱미터)공용면적(제곱미터)임대면적(제곱미터)연간임대료(원)연간관리비(원)월임대관리비(원)
호수1.0000.016-0.296-0.064-0.064-0.064-0.064
전용면적(제곱미터)0.0161.0000.8770.9810.9810.9810.981
공용면적(제곱미터)-0.2960.8771.0000.9240.9240.9240.924
임대면적(제곱미터)-0.0640.9810.9241.0001.0001.0001.000
연간임대료(원)-0.0640.9810.9241.0001.0001.0001.000
연간관리비(원)-0.0640.9810.9241.0001.0001.0001.000
월임대관리비(원)-0.0640.9810.9241.0001.0001.0001.000

Missing values

2023-12-12T13:18:37.710577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:18:37.876899image/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

기준년도호수전용면적(제곱미터)공용면적(제곱미터)임대면적(제곱미터)연간임대료(원)연간관리비(원)월임대관리비(원)
02021101254.25166.08420.3312412457152846522308092
12021201120.3299.66219.98649612579993031207952
22021202108.2389.65197.88584338171955171086575
32021203123.97102.68226.65669319082419681244596
4202120453.744.4898.1828992843570168539121
5202120560.5950.19110.7832712784028239608293
62021301154.5187.79242.3715508888107481330486
72021302133.5175.85209.36618261576132481149655
8202130379.4245.12124.5436778014528830683886
9202130477.4944.03121.5235884274418775667267
기준년도호수전용면적(제곱미터)공용면적(제곱미터)임대면적(제곱미터)연간임대료(원)연간관리비(원)월임대관리비(원)
92202160379.4243.46122.8836288314468528674780
93202160477.4842.4119.8835401894359375658297
94202160587.1747.71134.8839829414904578740627
95202160677.4842.4119.8835401894359375658297
96202160777.4842.4119.8835401894359375658297
97202160878.4542.93121.3835845104413952666538
98202160987.1747.71134.8839829414904578740627
99202161080.6844.15124.8336864024539422685485
1002021611160.5587.86248.41733579490332691364089
1012021701285.65275.42561.0716568487204023713080905

Duplicate rows

Most frequently occurring

기준년도호수전용면적(제곱미터)공용면적(제곱미터)임대면적(제곱미터)연간임대료(원)연간관리비(원)월임대관리비(원)# duplicates
02021101254.25166.08420.33124124571528465223080922
12021201120.3299.66219.986496125799930312079522
22021202108.2389.65197.885843381719551710865752
32021203123.97102.68226.656693190824196812445962
4202120453.744.4898.18289928435701685391212
5202120560.5950.19110.78327127840282396082932
62021301154.5187.79242.37155088881074813304862
72021302133.5175.85209.366182615761324811496552
8202130379.4245.12124.54367780145288306838862
9202130477.4944.03121.52358842744187756672672