Overview

Dataset statistics

Number of variables19
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory81.2 KiB
Average record size in memory166.3 B

Variable types

Numeric13
Text4
Categorical2

Dataset

Description샘플 데이터
Author빅밸류
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=325

Alerts

사례_부번(SR_BUN2) has 32 (6.4%) zerosZeros

Reproduction

Analysis started2023-12-10 15:04:14.092414
Analysis finished2023-12-10 15:04:53.367838
Duration39.28 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

PNU코드(PNU)
Real number (ℝ)

Distinct458
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1496297 × 1018
Minimum1.1110104 × 1018
Maximum1.1740109 × 1018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:53.500081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110104 × 1018
5-th percentile1.1215103 × 1018
Q11.1380105 × 1018
median1.1500103 × 1018
Q31.1680101 × 1018
95-th percentile1.1740107 × 1018
Maximum1.1740109 × 1018
Range6.30005 × 1016
Interquartile range (IQR)2.99996 × 1016

Descriptive statistics

Standard deviation1.7001859 × 1016
Coefficient of variation (CV)0.014788987
Kurtosis-0.98900977
Mean1.1496297 × 1018
Median Absolute Deviation (MAD)1.49998 × 1016
Skewness-0.25059939
Sum2.9657724 × 1018
Variance2.890632 × 1032
MonotonicityNot monotonic
2023-12-11T00:04:53.799973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1153010900102130021 7
 
1.4%
1153011000100440001 4
 
0.8%
1150010300111300007 3
 
0.6%
1123010600103930004 3
 
0.6%
1150010200105330009 3
 
0.6%
1120010700101680002 2
 
0.4%
1153010800100680021 2
 
0.4%
1171010800100220008 2
 
0.4%
1121510100101750105 2
 
0.4%
1168011800109550007 2
 
0.4%
Other values (448) 470
94.0%
ValueCountFrequency (%)
1111010400100500015 1
0.2%
1111010800100810000 1
0.2%
1111017000100050099 1
0.2%
1111018400101290044 1
0.2%
1111018400101820027 1
0.2%
1117010100102440102 1
0.2%
1117011100101180162 1
0.2%
1117011900100050190 1
0.2%
1120010500107670005 1
0.2%
1120010500107950001 1
0.2%
ValueCountFrequency (%)
1174010900105740000 1
0.2%
1174010900103090016 1
0.2%
1174010900102330032 1
0.2%
1174010900102210051 1
0.2%
1174010900102170061 1
0.2%
1174010900102140005 1
0.2%
1174010900101130022 1
0.2%
1174010900100820027 1
0.2%
1174010900100520015 1
0.2%
1174010900100480008 1
0.2%

기준년월(KEYMONTH)
Real number (ℝ)

Distinct17
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202037.05
Minimum202001
Maximum202105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:54.060486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum202001
5-th percentile202001
Q1202005
median202009
Q3202102
95-th percentile202105
Maximum202105
Range104
Interquartile range (IQR)97

Descriptive statistics

Standard deviation45.055669
Coefficient of variation (CV)0.00022300696
Kurtosis-1.3739791
Mean202037.05
Median Absolute Deviation (MAD)5
Skewness0.78285464
Sum1.0101853 × 108
Variance2030.0133
MonotonicityNot monotonic
2023-12-11T00:04:54.326700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
202104 39
 
7.8%
202009 38
 
7.6%
202006 37
 
7.4%
202005 35
 
7.0%
202105 34
 
6.8%
202004 31
 
6.2%
202012 30
 
6.0%
202101 29
 
5.8%
202103 29
 
5.8%
202102 27
 
5.4%
Other values (7) 171
34.2%
ValueCountFrequency (%)
202001 26
5.2%
202002 22
4.4%
202003 27
5.4%
202004 31
6.2%
202005 35
7.0%
202006 37
7.4%
202007 25
5.0%
202008 26
5.2%
202009 38
7.6%
202010 22
4.4%
ValueCountFrequency (%)
202105 34
6.8%
202104 39
7.8%
202103 29
5.8%
202102 27
5.4%
202101 29
5.8%
202012 30
6.0%
202011 23
4.6%
202010 22
4.4%
202009 38
7.6%
202008 26
5.2%
Distinct483
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:04:54.731881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length13.68
Min length9

Characters and Unicode

Total characters6840
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique467 ?
Unique (%)93.4%

Sample

1st row11740-100226711
2nd row11440-100248038
3rd row11305-13282
4th row11545-1922
5th row11530-100227724
ValueCountFrequency (%)
11500-3589 3
 
0.6%
11110-29070 2
 
0.4%
11320-100210664 2
 
0.4%
11620-24786 2
 
0.4%
11560-100218216 2
 
0.4%
11440-100184969 2
 
0.4%
11500-25151 2
 
0.4%
11530-3619 2
 
0.4%
11680-3270 2
 
0.4%
11710-100469422 2
 
0.4%
Other values (473) 479
95.8%
2023-12-11T00:04:55.451124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1790
26.2%
0 1414
20.7%
2 652
 
9.5%
- 500
 
7.3%
5 480
 
7.0%
4 408
 
6.0%
3 391
 
5.7%
7 329
 
4.8%
6 320
 
4.7%
8 286
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6340
92.7%
Dash Punctuation 500
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1790
28.2%
0 1414
22.3%
2 652
 
10.3%
5 480
 
7.6%
4 408
 
6.4%
3 391
 
6.2%
7 329
 
5.2%
6 320
 
5.0%
8 286
 
4.5%
9 270
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1790
26.2%
0 1414
20.7%
2 652
 
9.5%
- 500
 
7.3%
5 480
 
7.0%
4 408
 
6.0%
3 391
 
5.7%
7 329
 
4.8%
6 320
 
4.7%
8 286
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1790
26.2%
0 1414
20.7%
2 652
 
9.5%
- 500
 
7.3%
5 480
 
7.0%
4 408
 
6.0%
3 391
 
5.7%
7 329
 
4.8%
6 320
 
4.7%
8 286
 
4.2%
Distinct499
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:04:55.885177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length13.926
Min length11

Characters and Unicode

Total characters6963
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique498 ?
Unique (%)99.6%

Sample

1st row11710-100528577
2nd row11650-100276623
3rd row11470-123003
4th row11530-115740
5th row11215-100226433
ValueCountFrequency (%)
11500-75206 2
 
0.4%
11710-100503909 1
 
0.2%
11470-108741 1
 
0.2%
11500-88568 1
 
0.2%
11545-100240640 1
 
0.2%
11740-100216463 1
 
0.2%
11740-100250545 1
 
0.2%
11620-100233107 1
 
0.2%
11110-32368 1
 
0.2%
11215-100181900 1
 
0.2%
Other values (489) 489
97.8%
2023-12-11T00:04:56.637787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1834
26.3%
0 1485
21.3%
2 646
 
9.3%
- 500
 
7.2%
5 445
 
6.4%
4 391
 
5.6%
3 373
 
5.4%
7 371
 
5.3%
6 351
 
5.0%
8 308
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6463
92.8%
Dash Punctuation 500
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1834
28.4%
0 1485
23.0%
2 646
 
10.0%
5 445
 
6.9%
4 391
 
6.0%
3 373
 
5.8%
7 371
 
5.7%
6 351
 
5.4%
8 308
 
4.8%
9 259
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6963
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1834
26.3%
0 1485
21.3%
2 646
 
9.3%
- 500
 
7.2%
5 445
 
6.4%
4 391
 
5.6%
3 373
 
5.4%
7 371
 
5.3%
6 351
 
5.0%
8 308
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6963
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1834
26.3%
0 1485
21.3%
2 646
 
9.3%
- 500
 
7.2%
5 445
 
6.4%
4 391
 
5.6%
3 373
 
5.4%
7 371
 
5.3%
6 351
 
5.0%
8 308
 
4.4%
Distinct388
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:04:57.148025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length22.5
Min length18

Characters and Unicode

Total characters11250
Distinct characters45
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique310 ?
Unique (%)62.0%

Sample

1st row서**별**송** **동**2**1**
2nd row서**별**광** **동**9**번**
3rd row서**별**강** **동**6**1**
4th row서**별**관** **동**4**1**번**
5th row서**별**은** **동**5**2**지**
ValueCountFrequency (%)
서**별**강 138
 
13.5%
서**별**송 54
 
5.3%
서**별**은 43
 
4.2%
39
 
3.8%
서**별**구 34
 
3.3%
서**별**광 28
 
2.7%
서**별**양 26
 
2.5%
서**별**관 24
 
2.3%
서**별**마 22
 
2.1%
서**별**도 18
 
1.8%
Other values (234) 599
58.4%
2023-12-11T00:04:57.868273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 7500
66.7%
525
 
4.7%
520
 
4.6%
500
 
4.4%
456
 
4.1%
217
 
1.9%
1 163
 
1.4%
138
 
1.2%
2 119
 
1.1%
3 100
 
0.9%
Other values (35) 1012
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 7500
66.7%
Other Letter 2296
 
20.4%
Decimal Number 883
 
7.8%
Space Separator 525
 
4.7%
Dash Punctuation 46
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
520
22.6%
500
21.8%
456
19.9%
217
9.5%
138
 
6.0%
93
 
4.1%
58
 
2.5%
54
 
2.4%
45
 
2.0%
28
 
1.2%
Other values (22) 187
 
8.1%
Decimal Number
ValueCountFrequency (%)
1 163
18.5%
2 119
13.5%
3 100
11.3%
4 90
10.2%
7 87
9.9%
6 75
8.5%
5 73
8.3%
0 67
7.6%
9 65
 
7.4%
8 44
 
5.0%
Other Punctuation
ValueCountFrequency (%)
* 7500
100.0%
Space Separator
ValueCountFrequency (%)
525
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8954
79.6%
Hangul 2296
 
20.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
520
22.6%
500
21.8%
456
19.9%
217
9.5%
138
 
6.0%
93
 
4.1%
58
 
2.5%
54
 
2.4%
45
 
2.0%
28
 
1.2%
Other values (22) 187
 
8.1%
Common
ValueCountFrequency (%)
* 7500
83.8%
525
 
5.9%
1 163
 
1.8%
2 119
 
1.3%
3 100
 
1.1%
4 90
 
1.0%
7 87
 
1.0%
6 75
 
0.8%
5 73
 
0.8%
0 67
 
0.7%
Other values (3) 155
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8954
79.6%
Hangul 2296
 
20.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 7500
83.8%
525
 
5.9%
1 163
 
1.8%
2 119
 
1.3%
3 100
 
1.1%
4 90
 
1.0%
7 87
 
1.0%
6 75
 
0.8%
5 73
 
0.8%
0 67
 
0.7%
Other values (3) 155
 
1.7%
Hangul
ValueCountFrequency (%)
520
22.6%
500
21.8%
456
19.9%
217
9.5%
138
 
6.0%
93
 
4.1%
58
 
2.5%
54
 
2.4%
45
 
2.0%
28
 
1.2%
Other values (22) 187
 
8.1%
Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
1
163 
2
122 
3
90 
4
72 
5
53 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row3
3rd row3
4th row3
5th row1

Common Values

ValueCountFrequency (%)
1 163
32.6%
2 122
24.4%
3 90
18.0%
4 72
14.4%
5 53
 
10.6%

Length

2023-12-11T00:04:58.114578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:04:58.335755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 163
32.6%
2 122
24.4%
3 90
18.0%
4 72
14.4%
5 53
 
10.6%
Distinct455
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.857372
Minimum12.57
Maximum244.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:58.609957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.57
5-th percentile23.219
Q130
median44.18
Q354.2
95-th percentile72.7795
Maximum244.96
Range232.39
Interquartile range (IQR)24.2

Descriptive statistics

Standard deviation21.049268
Coefficient of variation (CV)0.45901602
Kurtosis25.878694
Mean45.857372
Median Absolute Deviation (MAD)11.925
Skewness3.6522122
Sum22928.686
Variance443.07169
MonotonicityNot monotonic
2023-12-11T00:04:58.896752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.95 4
 
0.8%
29.98 4
 
0.8%
59.76 3
 
0.6%
43.92 3
 
0.6%
29.97 3
 
0.6%
54.99 2
 
0.4%
26.93 2
 
0.4%
29.87 2
 
0.4%
27.67 2
 
0.4%
59.04 2
 
0.4%
Other values (445) 473
94.6%
ValueCountFrequency (%)
12.57 1
0.2%
12.98 1
0.2%
13.37 1
0.2%
14.04 1
0.2%
14.13 1
0.2%
14.52 1
0.2%
15.29 1
0.2%
15.54 1
0.2%
16.03 1
0.2%
16.35 1
0.2%
ValueCountFrequency (%)
244.96 1
0.2%
190.5 1
0.2%
186.65 1
0.2%
160.44 1
0.2%
142.76 1
0.2%
130.29 1
0.2%
117.22 1
0.2%
84.898 1
0.2%
83.16 1
0.2%
81.81 1
0.2%
Distinct36
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
서**별**강** **동**
115 
서**별**송** **동**
58 
서**별**은** **동**
33 
서**별**금** **동**
32 
서**별**구** **동**
 
23
Other values (31)
239 

Length

Max length18
Median length15
Mean length14.79
Min length12

Unique

Unique10 ?
Unique (%)2.0%

Sample

1st row서**별**금** **동**
2nd row서**별**강** **동**
3rd row서**별**송** **동**
4th row서**별**강** **동**
5th row서**별**구** **동**

Common Values

ValueCountFrequency (%)
서**별**강** **동** 115
23.0%
서**별**송** **동** 58
11.6%
서**별**은** **동** 33
 
6.6%
서**별**금** **동** 32
 
6.4%
서**별**구** **동** 23
 
4.6%
서**별**서** **동** 22
 
4.4%
서**별**광** **동** 21
 
4.2%
서**별**성** **동** 21
 
4.2%
서**별**마** **동** 19
 
3.8%
서**별**동** **동** 19
 
3.8%
Other values (26) 137
27.4%

Length

2023-12-11T00:04:59.175031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
436
44.6%
서**별**강 121
 
12.4%
서**별**송 58
 
5.9%
37
 
3.8%
서**별**구 36
 
3.7%
서**별**은 33
 
3.4%
서**별**금 32
 
3.3%
서**별**양 30
 
3.1%
서**별**성 23
 
2.4%
서**별**서 22
 
2.3%
Other values (22) 149
 
15.3%

사례_본번(SR_BUN1)
Real number (ℝ)

Distinct335
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean393.96
Minimum1
Maximum1652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:04:59.871008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q1119.75
median313.5
Q3595.5
95-th percentile1006.4
Maximum1652
Range1651
Interquartile range (IQR)475.75

Descriptive statistics

Standard deviation334.90545
Coefficient of variation (CV)0.85010014
Kurtosis0.46677897
Mean393.96
Median Absolute Deviation (MAD)213.5
Skewness0.98197651
Sum196980
Variance112161.66
MonotonicityNot monotonic
2023-12-11T00:05:00.191387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 6
 
1.2%
56 5
 
1.0%
115 5
 
1.0%
94 5
 
1.0%
293 5
 
1.0%
158 5
 
1.0%
616 4
 
0.8%
299 4
 
0.8%
533 4
 
0.8%
220 4
 
0.8%
Other values (325) 453
90.6%
ValueCountFrequency (%)
1 1
 
0.2%
3 4
0.8%
4 4
0.8%
5 1
 
0.2%
6 1
 
0.2%
7 2
0.4%
9 2
0.4%
10 1
 
0.2%
11 2
0.4%
12 1
 
0.2%
ValueCountFrequency (%)
1652 1
0.2%
1559 2
0.4%
1481 1
0.2%
1477 1
0.2%
1291 1
0.2%
1271 1
0.2%
1233 1
0.2%
1231 1
0.2%
1219 2
0.4%
1209 1
0.2%

사례_부번(SR_BUN2)
Real number (ℝ)

ZEROS 

Distinct111
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.002
Minimum0
Maximum1984
Zeros32
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:00.499718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median13
Q332
95-th percentile138
Maximum1984
Range1984
Interquartile range (IQR)28

Descriptive statistics

Standard deviation119.05371
Coefficient of variation (CV)3.1328274
Kurtosis161.18
Mean38.002
Median Absolute Deviation (MAD)10
Skewness11.311084
Sum19001
Variance14173.786
MonotonicityNot monotonic
2023-12-11T00:05:00.777647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 32
 
6.4%
0 32
 
6.4%
1 28
 
5.6%
3 22
 
4.4%
6 21
 
4.2%
15 20
 
4.0%
7 18
 
3.6%
4 18
 
3.6%
13 18
 
3.6%
8 17
 
3.4%
Other values (101) 274
54.8%
ValueCountFrequency (%)
0 32
6.4%
1 28
5.6%
2 32
6.4%
3 22
4.4%
4 18
3.6%
5 11
 
2.2%
6 21
4.2%
7 18
3.6%
8 17
3.4%
9 11
 
2.2%
ValueCountFrequency (%)
1984 1
0.2%
1080 1
0.2%
940 1
0.2%
480 1
0.2%
386 1
0.2%
353 1
0.2%
304 1
0.2%
253 1
0.2%
249 2
0.4%
244 1
0.2%
Distinct395
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
2023-12-11T00:05:01.344002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length6.678
Min length2

Characters and Unicode

Total characters3339
Distinct characters235
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique324 ?
Unique (%)64.8%

Sample

1st row콘*르*
2nd row포*빌
3rd row인*하*빌
4th row애*하*스
5th row에*캐*
ValueCountFrequency (%)
대*빌 8
 
1.6%
로*빌 6
 
1.2%
g*n*g*n*1*t 6
 
1.2%
제*드 6
 
1.2%
금*빌 5
 
1.0%
선*하*츠 4
 
0.8%
4
 
0.8%
4
 
0.8%
인*시*빌*5*3*1 4
 
0.8%
정*운 3
 
0.6%
Other values (382) 462
90.2%
2023-12-11T00:05:02.182510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 1542
46.2%
180
 
5.4%
( 81
 
2.4%
1 80
 
2.4%
) 75
 
2.2%
- 67
 
2.0%
51
 
1.5%
2 49
 
1.5%
3 47
 
1.4%
41
 
1.2%
Other values (225) 1126
33.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 1544
46.2%
Other Letter 1125
33.7%
Decimal Number 354
 
10.6%
Open Punctuation 81
 
2.4%
Close Punctuation 75
 
2.2%
Dash Punctuation 67
 
2.0%
Lowercase Letter 42
 
1.3%
Uppercase Letter 38
 
1.1%
Space Separator 12
 
0.4%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
180
 
16.0%
51
 
4.5%
41
 
3.6%
33
 
2.9%
27
 
2.4%
27
 
2.4%
25
 
2.2%
24
 
2.1%
20
 
1.8%
19
 
1.7%
Other values (183) 678
60.3%
Uppercase Letter
ValueCountFrequency (%)
A 7
18.4%
G 6
15.8%
B 4
10.5%
V 4
10.5%
J 3
7.9%
I 3
7.9%
C 2
 
5.3%
M 2
 
5.3%
E 2
 
5.3%
U 1
 
2.6%
Other values (4) 4
10.5%
Lowercase Letter
ValueCountFrequency (%)
n 12
28.6%
g 7
16.7%
t 6
14.3%
o 4
 
9.5%
u 3
 
7.1%
e 3
 
7.1%
l 2
 
4.8%
a 2
 
4.8%
h 1
 
2.4%
s 1
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 80
22.6%
2 49
13.8%
3 47
13.3%
4 32
 
9.0%
0 28
 
7.9%
5 28
 
7.9%
6 25
 
7.1%
7 24
 
6.8%
8 22
 
6.2%
9 19
 
5.4%
Other Punctuation
ValueCountFrequency (%)
* 1542
99.9%
, 2
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 81
100.0%
Close Punctuation
ValueCountFrequency (%)
) 75
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 67
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2133
63.9%
Hangul 1123
33.6%
Latin 81
 
2.4%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
180
 
16.0%
51
 
4.5%
41
 
3.7%
33
 
2.9%
27
 
2.4%
27
 
2.4%
25
 
2.2%
24
 
2.1%
20
 
1.8%
19
 
1.7%
Other values (181) 676
60.2%
Latin
ValueCountFrequency (%)
n 12
14.8%
g 7
 
8.6%
A 7
 
8.6%
G 6
 
7.4%
t 6
 
7.4%
B 4
 
4.9%
o 4
 
4.9%
V 4
 
4.9%
u 3
 
3.7%
J 3
 
3.7%
Other values (16) 25
30.9%
Common
ValueCountFrequency (%)
* 1542
72.3%
( 81
 
3.8%
1 80
 
3.8%
) 75
 
3.5%
- 67
 
3.1%
2 49
 
2.3%
3 47
 
2.2%
4 32
 
1.5%
0 28
 
1.3%
5 28
 
1.3%
Other values (6) 104
 
4.9%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2213
66.3%
Hangul 1123
33.6%
CJK 2
 
0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 1542
69.7%
( 81
 
3.7%
1 80
 
3.6%
) 75
 
3.4%
- 67
 
3.0%
2 49
 
2.2%
3 47
 
2.1%
4 32
 
1.4%
0 28
 
1.3%
5 28
 
1.3%
Other values (31) 184
 
8.3%
Hangul
ValueCountFrequency (%)
180
 
16.0%
51
 
4.5%
41
 
3.7%
33
 
2.9%
27
 
2.4%
27
 
2.4%
25
 
2.2%
24
 
2.1%
20
 
1.8%
19
 
1.7%
Other values (181) 676
60.2%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Distinct24
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202006.73
Minimum201907
Maximum202106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:02.476915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum201907
5-th percentile201908
Q1202001
median202006
Q3202012
95-th percentile202105
Maximum202106
Range199
Interquartile range (IQR)11

Descriptive statistics

Standard deviation67.791921
Coefficient of variation (CV)0.00033559239
Kurtosis-0.9433366
Mean202006.73
Median Absolute Deviation (MAD)6
Skewness0.00076747585
Sum1.0100336 × 108
Variance4595.7446
MonotonicityNot monotonic
2023-12-11T00:05:02.727177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
202001 32
 
6.4%
202002 29
 
5.8%
202102 27
 
5.4%
201908 25
 
5.0%
202009 24
 
4.8%
202104 24
 
4.8%
202008 22
 
4.4%
201910 22
 
4.4%
202004 22
 
4.4%
201907 22
 
4.4%
Other values (14) 251
50.2%
ValueCountFrequency (%)
201907 22
4.4%
201908 25
5.0%
201909 17
3.4%
201910 22
4.4%
201911 21
4.2%
201912 13
2.6%
202001 32
6.4%
202002 29
5.8%
202003 20
4.0%
202004 22
4.4%
ValueCountFrequency (%)
202106 16
3.2%
202105 20
4.0%
202104 24
4.8%
202103 19
3.8%
202102 27
5.4%
202101 17
3.4%
202012 18
3.6%
202011 16
3.2%
202010 16
3.2%
202009 24
4.8%
Distinct63
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7866.18
Minimum0
Maximum50000
Zeros4
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:03.058161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1000
Q12000
median5000
Q312000
95-th percentile25000
Maximum50000
Range50000
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation7894.2
Coefficient of variation (CV)1.0035621
Kurtosis3.229397
Mean7866.18
Median Absolute Deviation (MAD)3000
Skewness1.7088394
Sum3933090
Variance62318394
MonotonicityNot monotonic
2023-12-11T00:05:03.353591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000 70
14.0%
5000 61
12.2%
3000 59
 
11.8%
1000 37
 
7.4%
10000 35
 
7.0%
4000 24
 
4.8%
12000 21
 
4.2%
15000 20
 
4.0%
7000 11
 
2.2%
9000 10
 
2.0%
Other values (53) 152
30.4%
ValueCountFrequency (%)
0 4
 
0.8%
80 1
 
0.2%
100 1
 
0.2%
200 4
 
0.8%
300 3
 
0.6%
500 7
 
1.4%
950 1
 
0.2%
1000 37
7.4%
1400 2
 
0.4%
1500 10
 
2.0%
ValueCountFrequency (%)
50000 1
 
0.2%
40000 1
 
0.2%
36500 1
 
0.2%
35000 2
0.4%
34000 2
0.4%
33000 1
 
0.2%
32000 2
0.4%
30000 4
0.8%
29400 1
 
0.2%
29000 2
0.4%

사례_월세(SR_RENT)
Real number (ℝ)

Distinct67
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.108
Minimum1
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:03.671325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q139.75
median50
Q370
95-th percentile100
Maximum180
Range179
Interquartile range (IQR)30.25

Descriptive statistics

Standard deviation25.968386
Coefficient of variation (CV)0.48897314
Kurtosis0.95211619
Mean53.108
Median Absolute Deviation (MAD)18
Skewness0.41012712
Sum26554
Variance674.35705
MonotonicityNot monotonic
2023-12-11T00:05:03.982875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 61
 
12.2%
50 57
 
11.4%
70 41
 
8.2%
40 37
 
7.4%
80 27
 
5.4%
30 24
 
4.8%
45 24
 
4.8%
20 18
 
3.6%
65 17
 
3.4%
55 16
 
3.2%
Other values (57) 178
35.6%
ValueCountFrequency (%)
1 1
 
0.2%
2 2
 
0.4%
4 3
 
0.6%
5 6
 
1.2%
6 2
 
0.4%
7 3
 
0.6%
8 2
 
0.4%
10 16
3.2%
12 1
 
0.2%
13 1
 
0.2%
ValueCountFrequency (%)
180 1
 
0.2%
130 1
 
0.2%
125 3
 
0.6%
122 1
 
0.2%
120 3
 
0.6%
115 2
 
0.4%
110 4
 
0.8%
105 3
 
0.6%
104 1
 
0.2%
100 10
2.0%
Distinct475
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5826.472
Minimum1516
Maximum36979
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:04.316037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1516
5-th percentile2924
Q14426.25
median5374
Q36615.75
95-th percentile9120.65
Maximum36979
Range35463
Interquartile range (IQR)2189.5

Descriptive statistics

Standard deviation2882.214
Coefficient of variation (CV)0.49467568
Kurtosis46.216871
Mean5826.472
Median Absolute Deviation (MAD)1038
Skewness5.3712739
Sum2913236
Variance8307157.4
MonotonicityNot monotonic
2023-12-11T00:05:04.690995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6112 2
 
0.4%
4431 2
 
0.4%
2924 2
 
0.4%
4774 2
 
0.4%
5127 2
 
0.4%
7995 2
 
0.4%
5615 2
 
0.4%
5567 2
 
0.4%
2286 2
 
0.4%
3828 2
 
0.4%
Other values (465) 480
96.0%
ValueCountFrequency (%)
1516 1
0.2%
2060 1
0.2%
2098 1
0.2%
2206 1
0.2%
2269 1
0.2%
2286 2
0.4%
2396 1
0.2%
2431 1
0.2%
2471 1
0.2%
2490 1
0.2%
ValueCountFrequency (%)
36979 1
0.2%
30525 1
0.2%
28160 1
0.2%
22270 1
0.2%
16313 1
0.2%
14755 1
0.2%
14137 1
0.2%
12004 1
0.2%
11611 1
0.2%
11559 1
0.2%
Distinct92
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.42
Minimum18
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:05.026214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile36
Q148
median60
Q371.25
95-th percentile100.05
Maximum200
Range182
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation21.868426
Coefficient of variation (CV)0.35034326
Kurtosis6.3476068
Mean62.42
Median Absolute Deviation (MAD)12
Skewness1.7607668
Sum31210
Variance478.22806
MonotonicityNot monotonic
2023-12-11T00:05:05.357038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 17
 
3.4%
39 15
 
3.0%
61 15
 
3.0%
45 14
 
2.8%
60 14
 
2.8%
64 14
 
2.8%
41 14
 
2.8%
50 13
 
2.6%
55 13
 
2.6%
58 12
 
2.4%
Other values (82) 359
71.8%
ValueCountFrequency (%)
18 1
 
0.2%
22 1
 
0.2%
24 2
 
0.4%
28 1
 
0.2%
30 3
 
0.6%
31 3
 
0.6%
32 3
 
0.6%
33 1
 
0.2%
35 5
1.0%
36 8
1.6%
ValueCountFrequency (%)
200 1
0.2%
189 1
0.2%
156 1
0.2%
142 1
0.2%
141 1
0.2%
138 1
0.2%
136 1
0.2%
134 1
0.2%
128 1
0.2%
127 1
0.2%
Distinct40
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.57
Minimum1979
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:05.709812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1979
5-th percentile1987
Q12002
median2012
Q32016
95-th percentile2019
Maximum2020
Range41
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.7036809
Coefficient of variation (CV)0.004831139
Kurtosis0.3639269
Mean2008.57
Median Absolute Deviation (MAD)4
Skewness-1.1413813
Sum1004285
Variance94.161423
MonotonicityNot monotonic
2023-12-11T00:05:06.023616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2016 76
15.2%
2015 44
 
8.8%
2002 38
 
7.6%
2012 34
 
6.8%
2014 31
 
6.2%
2017 31
 
6.2%
2008 24
 
4.8%
2019 23
 
4.6%
2013 21
 
4.2%
2003 20
 
4.0%
Other values (30) 158
31.6%
ValueCountFrequency (%)
1979 1
 
0.2%
1981 2
 
0.4%
1982 3
 
0.6%
1983 1
 
0.2%
1984 2
 
0.4%
1985 11
2.2%
1986 1
 
0.2%
1987 9
1.8%
1988 7
1.4%
1989 2
 
0.4%
ValueCountFrequency (%)
2020 4
 
0.8%
2019 23
 
4.6%
2018 19
 
3.8%
2017 31
6.2%
2016 76
15.2%
2015 44
8.8%
2014 31
6.2%
2013 21
 
4.2%
2012 34
6.8%
2011 12
 
2.4%
Distinct388
Distinct (%)77.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.425582
Minimum13.7
Maximum158.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:06.351704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13.7
5-th percentile24.22
Q129.94375
median44.26
Q353.5675
95-th percentile70.421
Maximum158.55
Range144.85
Interquartile range (IQR)23.62375

Descriptive statistics

Standard deviation16.405829
Coefficient of variation (CV)0.36928788
Kurtosis5.447186
Mean44.425582
Median Absolute Deviation (MAD)12.38
Skewness1.3082243
Sum22212.791
Variance269.15122
MonotonicityNot monotonic
2023-12-11T00:05:06.626323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.99 7
 
1.4%
29.58 5
 
1.0%
51.39 5
 
1.0%
51.27 5
 
1.0%
29.96 5
 
1.0%
50.2 5
 
1.0%
28.7 4
 
0.8%
29.94 4
 
0.8%
29.82 4
 
0.8%
46.56 4
 
0.8%
Other values (378) 452
90.4%
ValueCountFrequency (%)
13.7 1
0.2%
14.04 2
0.4%
14.46 1
0.2%
15.6 1
0.2%
15.885 1
0.2%
16.02 1
0.2%
16.5 1
0.2%
17.36 1
0.2%
17.46 1
0.2%
17.93 1
0.2%
ValueCountFrequency (%)
158.55 1
0.2%
129.84 1
0.2%
93.57 2
0.4%
90.24 1
0.2%
86.92 2
0.4%
84.84 2
0.4%
84.73 1
0.2%
84.06 1
0.2%
81.96 1
0.2%
79.56 2
0.4%

사례_층수(SR_FLOOR)
Real number (ℝ)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.178
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.5 KiB
2023-12-11T00:05:06.867220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum17
Range16
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.476033
Coefficient of variation (CV)0.46445342
Kurtosis15.22557
Mean3.178
Median Absolute Deviation (MAD)1
Skewness2.2434601
Sum1589
Variance2.1786733
MonotonicityNot monotonic
2023-12-11T00:05:07.115998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 160
32.0%
3 145
29.0%
4 84
16.8%
5 52
 
10.4%
1 27
 
5.4%
6 26
 
5.2%
7 4
 
0.8%
10 1
 
0.2%
17 1
 
0.2%
ValueCountFrequency (%)
1 27
 
5.4%
2 160
32.0%
3 145
29.0%
4 84
16.8%
5 52
 
10.4%
6 26
 
5.2%
7 4
 
0.8%
10 1
 
0.2%
17 1
 
0.2%
ValueCountFrequency (%)
17 1
 
0.2%
10 1
 
0.2%
7 4
 
0.8%
6 26
 
5.2%
5 52
 
10.4%
4 84
16.8%
3 145
29.0%
2 160
32.0%
1 27
 
5.4%

Interactions

2023-12-11T00:04:49.994234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:16.041999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:18.745874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:22.446400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:25.115465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:27.948776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:30.404795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:33.394934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:36.035604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:39.131699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:41.868731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:44.397794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:46.932425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:50.162613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:16.236483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:18.956742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:22.695283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:25.735014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:28.131997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:30.597004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:33.609926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:36.652872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:39.330303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:42.078759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:44.562248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:47.231615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:50.332396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:16.443147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:19.128392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:22.899849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:25.921764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:28.315258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:30.791442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:33.806591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:36.820468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:39.549326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:42.296587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:44.715088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:47.430823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:50.491506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:16.638032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:19.297700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:23.100930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:26.099293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:28.489367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:30.995770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:34.001353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:36.993930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:39.752559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:42.496479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:44.892903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:47.597169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:50.668352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:16.852232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:19.495479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:23.299059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:26.257369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:28.672237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:31.198104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:34.195811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:37.199564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:39.952272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:42.706350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:45.090049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:48.189819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:50.849637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:17.075257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:19.699317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:23.563208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:26.442302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:28.893363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:31.422118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:34.410420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:37.438938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:40.156272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:42.909579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:45.299094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:48.405140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:51.035083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:17.304340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:20.167746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:23.768825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:26.651206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:29.085315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:31.640516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:34.656251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:37.696050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:40.378076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:43.104407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:45.501157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:48.622127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:51.248217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:17.487613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:20.579558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:23.953343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:26.816829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:29.228869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:31.876790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:34.854320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:37.899435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:40.615711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:43.259957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:45.688849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:48.827461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:51.453873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:17.687374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:20.977357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:24.141614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:27.009485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:29.418856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:32.093980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:35.059307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:38.164515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:40.820652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:43.463291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:45.872803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:49.034861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:51.627809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:17.904727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:21.246778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:24.339199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:27.211792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:29.629747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:32.477501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:35.248505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:38.384464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:41.014864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:43.682406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:46.069298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:49.238642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:51.837080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:18.123765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:21.722106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:24.545336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:27.411667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:29.827762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:32.694626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:35.441895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:38.584068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:41.233254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:43.878125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:46.295861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:49.456381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:52.012465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:18.344866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:22.016883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:24.722096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:27.584557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:30.013375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:32.904052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:35.634788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:38.756789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:41.424589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:44.069858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:46.505961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:49.631828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:52.208851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:18.550570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:22.238897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:24.910481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:27.751088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:30.221255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:33.119345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:35.830204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:38.945131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:41.637893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:44.244381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:46.730935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:04:49.825350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:05:07.346445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PNU코드(PNU)기준년월(KEYMONTH)사례번호(SRNUM)본건_전용면적(BON_HO_AREA)사례_주소(SR_ADDRESS)사례_본번(SR_BUN1)사례_부번(SR_BUN2)사례_거래년월(SR_YYYYMM)사례_보증금(SR_DEPOSIT)사례_월세(SR_RENT)사례_조정_보증금(SR_MOD_DEPO)사례_조정_월세(SR_MOD_RENT)사례_건축연도(SR_CON_YEAR)사례_전용면적(SR_AREA)사례_층수(SR_FLOOR)
PNU코드(PNU)1.0000.0790.0000.0000.0000.0000.0000.0880.0000.0460.0000.0370.2350.0180.095
기준년월(KEYMONTH)0.0791.0000.0910.2150.0000.0000.0000.0240.0000.0000.2330.0740.0840.1680.000
사례번호(SRNUM)0.0000.0911.0000.0860.2220.0000.0000.0350.0940.0000.0000.0860.0360.0000.000
본건_전용면적(BON_HO_AREA)0.0000.2150.0861.0000.6220.0000.0000.0000.0000.0000.0000.0000.0000.0000.544
사례_주소(SR_ADDRESS)0.0000.0000.2220.6221.0000.0000.0000.2360.0000.0000.0000.7170.0920.0000.000
사례_본번(SR_BUN1)0.0000.0000.0000.0000.0001.0000.0000.0000.1680.0000.0000.0000.2910.0000.031
사례_부번(SR_BUN2)0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.6970.2680.0000.1230.000
사례_거래년월(SR_YYYYMM)0.0880.0240.0350.0000.2360.0000.0001.0000.0750.0000.0250.0000.0000.1730.049
사례_보증금(SR_DEPOSIT)0.0000.0000.0940.0000.0000.1680.0000.0751.0000.0880.1340.0000.0000.0000.000
사례_월세(SR_RENT)0.0460.0000.0000.0000.0000.0000.0000.0000.0881.0000.0000.0000.1510.0000.000
사례_조정_보증금(SR_MOD_DEPO)0.0000.2330.0000.0000.0000.0000.6970.0250.1340.0001.0000.2470.0130.1450.274
사례_조정_월세(SR_MOD_RENT)0.0370.0740.0860.0000.7170.0000.2680.0000.0000.0000.2471.0000.0000.1150.110
사례_건축연도(SR_CON_YEAR)0.2350.0840.0360.0000.0920.2910.0000.0000.0000.1510.0130.0001.0000.0000.000
사례_전용면적(SR_AREA)0.0180.1680.0000.0000.0000.0000.1230.1730.0000.0000.1450.1150.0001.0000.000
사례_층수(SR_FLOOR)0.0950.0000.0000.5440.0000.0310.0000.0490.0000.0000.2740.1100.0000.0001.000
2023-12-11T00:05:07.649036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사례_주소(SR_ADDRESS)사례번호(SRNUM)
사례_주소(SR_ADDRESS)1.0000.101
사례번호(SRNUM)0.1011.000
2023-12-11T00:05:07.833138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PNU코드(PNU)기준년월(KEYMONTH)본건_전용면적(BON_HO_AREA)사례_본번(SR_BUN1)사례_부번(SR_BUN2)사례_거래년월(SR_YYYYMM)사례_보증금(SR_DEPOSIT)사례_월세(SR_RENT)사례_조정_보증금(SR_MOD_DEPO)사례_조정_월세(SR_MOD_RENT)사례_건축연도(SR_CON_YEAR)사례_전용면적(SR_AREA)사례_층수(SR_FLOOR)사례번호(SRNUM)사례_주소(SR_ADDRESS)
PNU코드(PNU)1.0000.0480.0080.0890.003-0.000-0.0080.0260.0610.034-0.0480.0190.0150.0000.000
기준년월(KEYMONTH)0.0481.0000.0140.058-0.0260.051-0.0640.0390.070-0.023-0.009-0.039-0.0630.0660.000
본건_전용면적(BON_HO_AREA)0.0080.0141.0000.0090.0180.034-0.034-0.067-0.0080.0640.037-0.0180.0720.0490.251
사례_본번(SR_BUN1)0.0890.0580.0091.000-0.005-0.001-0.0080.0060.0210.077-0.0200.0120.0030.0000.000
사례_부번(SR_BUN2)0.003-0.0260.018-0.0051.0000.030-0.0230.071-0.0210.0970.0710.016-0.0450.0000.000
사례_거래년월(SR_YYYYMM)-0.0000.0510.034-0.0010.0301.000-0.014-0.0570.0550.018-0.017-0.0060.0130.0310.086
사례_보증금(SR_DEPOSIT)-0.008-0.064-0.034-0.008-0.023-0.0141.000-0.047-0.007-0.057-0.039-0.0180.0130.0530.000
사례_월세(SR_RENT)0.0260.039-0.0670.0060.071-0.057-0.0471.000-0.0320.0060.0300.0120.0020.0000.000
사례_조정_보증금(SR_MOD_DEPO)0.0610.070-0.0080.021-0.0210.055-0.007-0.0321.0000.068-0.0090.0080.0580.0000.000
사례_조정_월세(SR_MOD_RENT)0.034-0.0230.0640.0770.0970.018-0.0570.0060.0681.0000.0280.043-0.0340.0610.321
사례_건축연도(SR_CON_YEAR)-0.048-0.0090.037-0.0200.071-0.017-0.0390.030-0.0090.0281.0000.008-0.0740.0320.029
사례_전용면적(SR_AREA)0.019-0.039-0.0180.0120.016-0.006-0.0180.0120.0080.0430.0081.000-0.0440.0000.000
사례_층수(SR_FLOOR)0.015-0.0630.0720.003-0.0450.0130.0130.0020.058-0.034-0.074-0.0441.0000.0000.000
사례번호(SRNUM)0.0000.0660.0490.0000.0000.0310.0530.0000.0000.0610.0320.0000.0001.0000.101
사례_주소(SR_ADDRESS)0.0000.0000.2510.0000.0000.0860.0000.0000.0000.3210.0290.0000.0000.1011.000

Missing values

2023-12-11T00:04:52.570406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:04:53.168807image/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

PNU코드(PNU)기준년월(KEYMONTH)표제부_키코드(PKCODE1)전유부_키코드(PKCODE2)본건_주소(ADDRESS)사례번호(SRNUM)본건_전용면적(BON_HO_AREA)사례_주소(SR_ADDRESS)사례_본번(SR_BUN1)사례_부번(SR_BUN2)사례_건물이름(SR_BLDNAME)사례_거래년월(SR_YYYYMM)사례_보증금(SR_DEPOSIT)사례_월세(SR_RENT)사례_조정_보증금(SR_MOD_DEPO)사례_조정_월세(SR_MOD_RENT)사례_건축연도(SR_CON_YEAR)사례_전용면적(SR_AREA)사례_층수(SR_FLOOR)
0116801080010136001920200111740-10022671111710-100528577서**별**송** **동**2**1**159.91서**별**금** **동**12529콘*르*202009200040523439200240.796
1117101120010076000620210411440-10024803811650-100276623서**별**광** **동**9**번**363.245서**별**강** **동**9825포*빌2020051900079428089198329.945
2115451030010849001120200511305-1328211470-123003서**별**강** **동**6**1**313.37서**별**송** **동**83105인*하*빌202102200059650656201629.582
3116801010010774001020200811545-192211530-115740서**별**관** **동**4**1**번**376.02서**별**강** **동**15592애*하*스2020022000401004267201457.66
4116501020010392000320210511530-10022772411215-100226433서**별**은** **동**5**2**지**153.64서**별**구** **동**125에*캐*20200125001045218127201751.275
5112901380010219014620200911650-10025953411680-100202765서**별**금** **동**6**1**지**137.16서**별**강** **동**2012도*빌*트*5*3*202106300050615038200513.74
6116801030011263000120201011710-1281111230-100241014서**별**강** **동**9**3**지**162.61서**별**구** **동**79418올*푸*(*동*202001150080672771201556.562
7116801180010413000920210211740-10022337011740-100258316서**별**구** **동**4**번**527.71서**별**성** **곡**96644*-*2021021580070723384201627.393
8113801080010073000220200311380-3702311380-94097서**별**강** **동**1**-**지**428.4서**별**강** **동**3915파*플*이*(*1*4*2020112300075640750201114.045
9115301100010044000120210111710-10047340211530-100201632서**별**송** **동**0**1**지**253.76서**별**강** **동**15213제*드*202102100010391644201662.146
PNU코드(PNU)기준년월(KEYMONTH)표제부_키코드(PKCODE1)전유부_키코드(PKCODE2)본건_주소(ADDRESS)사례번호(SRNUM)본건_전용면적(BON_HO_AREA)사례_주소(SR_ADDRESS)사례_본번(SR_BUN1)사례_부번(SR_BUN2)사례_건물이름(SR_BLDNAME)사례_거래년월(SR_YYYYMM)사례_보증금(SR_DEPOSIT)사례_월세(SR_RENT)사례_조정_보증금(SR_MOD_DEPO)사례_조정_월세(SR_MOD_RENT)사례_건축연도(SR_CON_YEAR)사례_전용면적(SR_AREA)사례_층수(SR_FLOOR)
490112301060010426000320210511710-10047506011680-100263502서**별**동**구**안**4**-**지**454.2서**별**송** **동**3640나*스*빌201908340090519847201929.913
491114701030010507001220201211590-10021910511650-100276401서**별**강** **동**0**7**129.95서**별**송** **동**4267드*하*츠202009200065505342200245.143
492115301030010125001020210311680-10026197011710-62047서**별**광** **동**2**3**514.13서**별**강** **동**2121(*7*-*2*2020031500050612936197926.914
493115001030010805001320210211740-10024440411215-100234960서**별**서** **동**1**1**549.03서**별**동** **동**58724성*빌*(*1*7*)2021031200030382835201637.422
494113201070010608014620210411710-10046088611590-70647서**별**양** ** **1**번**172.39서**별**금** **동**40912우*하*츠*2021061000040763146199447.452
495116801010010745002320210511320-10021066411440-100258098서**별**도** **동**5**4**129.97서**별**강** **동**14816(*7*-*2*2021021000050607785198124.334
496112601030010328003520200911740-966511260-100208629서**별**강** **동**9**3**지**541.28서**별**송** **동**46437응*하*스20191216000100682866201764.522
497116801080010251000720200211500-10031986811620-100242287서**별**송** **동**4**1**지**526.95서**별**양** **동**17138정*홈*운20210215000601475557201631.926
498117401090010309001620210211320-10021492511680-168727서**별**강** **동**7**2**142.38서**별**송** **동**2441동*에*빌*4*-*3*202101500060282722199324.224
499117101050010279001220200611680-10025555011545-100217930서**별**구** **동**9**7**377.4서**별**중** **동**5434네*트*2*202103400080228651201633.873