Overview

Dataset statistics

Number of variables12
Number of observations10000
Missing cells7285
Missing cells (%)6.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory114.0 B

Variable types

Text2
Numeric10

Dataset

Description관리_주택대장_PK,승인번호_년,승인번호_기관_코드,승인번호_구분_코드,승인번호_일련번호,승인_일,대지_면적,건폐_율,연면적,용적_율,기타_용도,작업_일자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15404/S/1/datasetView.do

Alerts

승인번호_년 is highly overall correlated with 승인_일 and 1 other fieldsHigh correlation
승인_일 is highly overall correlated with 승인번호_년 and 1 other fieldsHigh correlation
대지_면적 is highly overall correlated with 건폐_율 and 2 other fieldsHigh correlation
건폐_율 is highly overall correlated with 대지_면적 and 2 other fieldsHigh correlation
연면적 is highly overall correlated with 대지_면적 and 2 other fieldsHigh correlation
용적_율 is highly overall correlated with 대지_면적 and 2 other fieldsHigh correlation
작업_일자 is highly overall correlated with 승인번호_년 and 1 other fieldsHigh correlation
기타_용도 has 7285 (72.9%) missing valuesMissing
승인번호_년 is highly skewed (γ1 = -83.65294151)Skewed
건폐_율 is highly skewed (γ1 = 94.37027062)Skewed
연면적 is highly skewed (γ1 = 46.11278155)Skewed
용적_율 is highly skewed (γ1 = 99.9999391)Skewed
관리_주택대장_PK has unique valuesUnique
대지_면적 has 9379 (93.8%) zerosZeros
건폐_율 has 9380 (93.8%) zerosZeros
연면적 has 8212 (82.1%) zerosZeros
용적_율 has 9398 (94.0%) zerosZeros

Reproduction

Analysis started2024-05-11 05:39:57.001763
Analysis finished2024-05-11 05:40:21.846635
Duration24.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T14:40:22.110068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length16.2167
Min length7

Characters and Unicode

Total characters162167
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

Unique10000 ?
Unique (%)100.0%

Sample

1st row11650-100028992
2nd row11545-1000000000000000125803
3rd row11470-100023711
4th row11710-100037789
5th row11290-100025183
ValueCountFrequency (%)
11650-100028992 1
 
< 0.1%
11200-100029794 1
 
< 0.1%
11470-2284 1
 
< 0.1%
11650-100030637 1
 
< 0.1%
11680-1000000000000000083055 1
 
< 0.1%
11470-100027115 1
 
< 0.1%
11500-1000000000000000120058 1
 
< 0.1%
11680-100046865 1
 
< 0.1%
11350-100041831 1
 
< 0.1%
11710-9617 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-11T14:40:22.737646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 60071
37.0%
1 39024
24.1%
- 10000
 
6.2%
7 8219
 
5.1%
2 7539
 
4.6%
5 7496
 
4.6%
4 7279
 
4.5%
6 6803
 
4.2%
3 6352
 
3.9%
8 5417
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 152167
93.8%
Dash Punctuation 10000
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 60071
39.5%
1 39024
25.6%
7 8219
 
5.4%
2 7539
 
5.0%
5 7496
 
4.9%
4 7279
 
4.8%
6 6803
 
4.5%
3 6352
 
4.2%
8 5417
 
3.6%
9 3967
 
2.6%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 162167
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 60071
37.0%
1 39024
24.1%
- 10000
 
6.2%
7 8219
 
5.1%
2 7539
 
4.6%
5 7496
 
4.6%
4 7279
 
4.5%
6 6803
 
4.2%
3 6352
 
3.9%
8 5417
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 162167
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 60071
37.0%
1 39024
24.1%
- 10000
 
6.2%
7 8219
 
5.1%
2 7539
 
4.6%
5 7496
 
4.6%
4 7279
 
4.5%
6 6803
 
4.2%
3 6352
 
3.9%
8 5417
 
3.3%

승인번호_년
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.0063
Minimum199
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:22.953297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum199
5-th percentile2003
Q12012
median2019
Q32021
95-th percentile2023
Maximum2024
Range1825
Interquartile range (IQR)9

Descriptive statistics

Standard deviation19.288096
Coefficient of variation (CV)0.0095674782
Kurtosis7877.8663
Mean2016.0063
Median Absolute Deviation (MAD)3
Skewness-83.652942
Sum20160063
Variance372.03066
MonotonicityNot monotonic
2024-05-11T14:40:23.190006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2021 1289
12.9%
2022 989
 
9.9%
2020 977
 
9.8%
2023 937
 
9.4%
2007 692
 
6.9%
2008 530
 
5.3%
2019 499
 
5.0%
2018 486
 
4.9%
2017 447
 
4.5%
2016 405
 
4.0%
Other values (19) 2749
27.5%
ValueCountFrequency (%)
199 1
 
< 0.1%
1995 1
 
< 0.1%
1997 1
 
< 0.1%
1999 2
 
< 0.1%
2000 75
 
0.8%
2001 199
2.0%
2002 145
1.5%
2003 170
1.7%
2004 86
0.9%
2005 80
0.8%
ValueCountFrequency (%)
2024 312
 
3.1%
2023 937
9.4%
2022 989
9.9%
2021 1289
12.9%
2020 977
9.8%
2019 499
 
5.0%
2018 486
 
4.9%
2017 447
 
4.5%
2016 405
 
4.0%
2015 381
 
3.8%
Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3175588.9
Minimum3000080
Maximum6114262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:23.435978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000080
5-th percentile3030158
Q13100164
median3180173
Q33230139
95-th percentile3230263
Maximum6114262
Range3114182
Interquartile range (IQR)129975

Descriptive statistics

Standard deviation232134.53
Coefficient of variation (CV)0.07309968
Kurtosis140.78388
Mean3175588.9
Median Absolute Deviation (MAD)49990
Skewness11.331603
Sum3.1755889 × 1010
Variance5.3886439 × 1010
MonotonicityNot monotonic
2024-05-11T14:40:23.668445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3100164 715
 
7.1%
3230139 664
 
6.6%
3230163 593
 
5.9%
3230089 496
 
5.0%
3230263 395
 
4.0%
3230211 379
 
3.8%
3220226 372
 
3.7%
3030158 337
 
3.4%
3220173 316
 
3.2%
3210163 298
 
3.0%
Other values (137) 5435
54.4%
ValueCountFrequency (%)
3000080 13
 
0.1%
3000082 1
 
< 0.1%
3000148 22
 
0.2%
3000185 2
 
< 0.1%
3000219 14
 
0.1%
3010000 1
 
< 0.1%
3010107 25
 
0.2%
3010179 68
0.7%
3010217 24
 
0.2%
3020076 161
1.6%
ValueCountFrequency (%)
6114262 1
 
< 0.1%
6114261 1
 
< 0.1%
6114031 5
0.1%
6113933 4
< 0.1%
6113930 6
0.1%
6113929 1
 
< 0.1%
6113486 9
0.1%
6113485 2
 
< 0.1%
6112999 7
0.1%
6112779 4
< 0.1%
Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2257.5468
Minimum1501
Maximum5812
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:23.882396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1501
5-th percentile2229
Q12230
median2230
Q32232
95-th percentile2250
Maximum5812
Range4311
Interquartile range (IQR)2

Descriptive statistics

Standard deviation318.38198
Coefficient of variation (CV)0.14103007
Kurtosis119.50764
Mean2257.5468
Median Absolute Deviation (MAD)0
Skewness10.954889
Sum22575468
Variance101367.08
MonotonicityNot monotonic
2024-05-11T14:40:24.084404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2230 6250
62.5%
2232 1486
 
14.9%
2229 733
 
7.3%
2241 516
 
5.2%
2251 399
 
4.0%
2101 133
 
1.3%
2233 131
 
1.3%
2226 82
 
0.8%
2248 56
 
0.6%
5809 42
 
0.4%
Other values (19) 172
 
1.7%
ValueCountFrequency (%)
1501 8
 
0.1%
1502 4
 
< 0.1%
2101 133
1.3%
2221 1
 
< 0.1%
2222 22
 
0.2%
2223 5
 
0.1%
2225 14
 
0.1%
2226 82
0.8%
2227 9
 
0.1%
2228 17
 
0.2%
ValueCountFrequency (%)
5812 1
 
< 0.1%
5811 4
 
< 0.1%
5810 32
 
0.3%
5809 42
 
0.4%
2301 10
 
0.1%
2260 1
 
< 0.1%
2251 399
4.0%
2250 31
 
0.3%
2249 5
 
0.1%
2248 56
 
0.6%

승인번호_일련번호
Real number (ℝ)

Distinct1601
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean351.0173
Minimum1
Maximum5353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:24.348328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q122
median85
Q3243
95-th percentile2160.4
Maximum5353
Range5352
Interquartile range (IQR)221

Descriptive statistics

Standard deviation799.07205
Coefficient of variation (CV)2.2764463
Kurtosis15.233275
Mean351.0173
Median Absolute Deviation (MAD)75
Skewness3.8043065
Sum3510173
Variance638516.14
MonotonicityNot monotonic
2024-05-11T14:40:24.580825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 338
 
3.4%
2 219
 
2.2%
3 205
 
2.1%
5 136
 
1.4%
4 134
 
1.3%
6 120
 
1.2%
10 107
 
1.1%
8 106
 
1.1%
7 100
 
1.0%
15 97
 
1.0%
Other values (1591) 8438
84.4%
ValueCountFrequency (%)
1 338
3.4%
2 219
2.2%
3 205
2.1%
4 134
 
1.3%
5 136
1.4%
6 120
 
1.2%
7 100
 
1.0%
8 106
 
1.1%
9 94
 
0.9%
10 107
 
1.1%
ValueCountFrequency (%)
5353 1
< 0.1%
5338 1
< 0.1%
5319 1
< 0.1%
5292 1
< 0.1%
5276 1
< 0.1%
5275 1
< 0.1%
5255 1
< 0.1%
5243 1
< 0.1%
5226 1
< 0.1%
5223 1
< 0.1%

승인_일
Real number (ℝ)

HIGH CORRELATION 

Distinct3622
Distinct (%)36.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20162401
Minimum19950426
Maximum20240508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:24.849769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19950426
5-th percentile20030620
Q120121030
median20181229
Q320211021
95-th percentile20231024
Maximum20240508
Range290082
Interquartile range (IQR)89991

Descriptive statistics

Standard deviation64707.31
Coefficient of variation (CV)0.0032093058
Kurtosis-0.45150573
Mean20162401
Median Absolute Deviation (MAD)39393.5
Skewness-0.84651418
Sum2.0162401 × 1011
Variance4.187036 × 109
MonotonicityNot monotonic
2024-05-11T14:40:25.089319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20070828 46
 
0.5%
20070903 34
 
0.3%
20070827 26
 
0.3%
20070831 23
 
0.2%
20071005 22
 
0.2%
20071116 21
 
0.2%
20070830 21
 
0.2%
20070920 20
 
0.2%
20070824 20
 
0.2%
20070905 19
 
0.2%
Other values (3612) 9748
97.5%
ValueCountFrequency (%)
19950426 1
< 0.1%
19960625 1
< 0.1%
19970402 1
< 0.1%
19990528 1
< 0.1%
19991230 1
< 0.1%
20000223 1
< 0.1%
20000303 1
< 0.1%
20000308 1
< 0.1%
20000314 1
< 0.1%
20000328 1
< 0.1%
ValueCountFrequency (%)
20240508 2
 
< 0.1%
20240507 8
0.1%
20240503 6
0.1%
20240502 8
0.1%
20240501 1
 
< 0.1%
20240430 2
 
< 0.1%
20240429 3
 
< 0.1%
20240426 5
0.1%
20240425 2
 
< 0.1%
20240424 4
< 0.1%

대지_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct469
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3288.64
Minimum0
Maximum311810
Zeros9379
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:25.347220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4032.9
Maximum311810
Range311810
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20112.276
Coefficient of variation (CV)6.1156818
Kurtosis67.23311
Mean3288.64
Median Absolute Deviation (MAD)0
Skewness7.7865051
Sum32886400
Variance4.0450363 × 108
MonotonicityNot monotonic
2024-05-11T14:40:25.957914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9379
93.8%
155041.0 20
 
0.2%
175403.0 14
 
0.1%
144209.0 12
 
0.1%
163197.0 10
 
0.1%
96634.0 10
 
0.1%
21067.4 9
 
0.1%
161345.0 8
 
0.1%
146538.0 7
 
0.1%
33018.0 7
 
0.1%
Other values (459) 524
 
5.2%
ValueCountFrequency (%)
0.0 9379
93.8%
110.0 1
 
< 0.1%
112.87 1
 
< 0.1%
144.2 1
 
< 0.1%
152.0 1
 
< 0.1%
175.0 1
 
< 0.1%
193.73 1
 
< 0.1%
283.97 1
 
< 0.1%
290.76 1
 
< 0.1%
385.3 1
 
< 0.1%
ValueCountFrequency (%)
311810.0 1
 
< 0.1%
304375.3 2
 
< 0.1%
237830.7 1
 
< 0.1%
219217.9 2
 
< 0.1%
215214.0 5
0.1%
195080.4 1
 
< 0.1%
183281.0 2
 
< 0.1%
181627.0 7
0.1%
180149.9 1
 
< 0.1%
179339.0 5
0.1%

건폐_율
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct453
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0272807
Minimum0
Maximum3838.41
Zeros9380
Zeros (%)93.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:26.221718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.4205
Maximum3838.41
Range3838.41
Interquartile range (IQR)0

Descriptive statistics

Standard deviation39.121949
Coefficient of variation (CV)19.297746
Kurtosis9250.6456
Mean2.0272807
Median Absolute Deviation (MAD)0
Skewness94.370271
Sum20272.807
Variance1530.5269
MonotonicityNot monotonic
2024-05-11T14:40:26.499586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9380
93.8%
17.54 19
 
0.2%
12.35 14
 
0.1%
13.21 12
 
0.1%
12.67 10
 
0.1%
16.83 10
 
0.1%
10.28 9
 
0.1%
16.0 8
 
0.1%
18.13 7
 
0.1%
12.41 7
 
0.1%
Other values (443) 524
 
5.2%
ValueCountFrequency (%)
0.0 9380
93.8%
0.02 3
 
< 0.1%
0.3 1
 
< 0.1%
0.34 1
 
< 0.1%
0.39 1
 
< 0.1%
0.61 1
 
< 0.1%
0.71 1
 
< 0.1%
0.78 1
 
< 0.1%
0.86 3
 
< 0.1%
0.89 1
 
< 0.1%
ValueCountFrequency (%)
3838.41 1
 
< 0.1%
100.0 6
0.1%
87.91 1
 
< 0.1%
74.64 1
 
< 0.1%
68.92 1
 
< 0.1%
66.32 1
 
< 0.1%
61.05 1
 
< 0.1%
59.99 1
 
< 0.1%
59.96 1
 
< 0.1%
59.93 2
 
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1334
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123377.42
Minimum0
Maximum2.376202 × 108
Zeros8212
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:26.761047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile137014.9
Maximum2.376202 × 108
Range2.376202 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4491028
Coefficient of variation (CV)36.40073
Kurtosis2174.0685
Mean123377.42
Median Absolute Deviation (MAD)0
Skewness46.112782
Sum1.2337742 × 109
Variance2.0169333 × 1013
MonotonicityNot monotonic
2024-05-11T14:40:27.039936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8212
82.1%
208958.0 20
 
0.2%
565619.28 17
 
0.2%
241842.0 14
 
0.1%
405203.72 11
 
0.1%
218969.0 10
 
0.1%
142544.0 10
 
0.1%
6222.3586 9
 
0.1%
137014.9 9
 
0.1%
117492.28 9
 
0.1%
Other values (1324) 1679
 
16.8%
ValueCountFrequency (%)
0.0 8212
82.1%
8.0 1
 
< 0.1%
11.89 1
 
< 0.1%
16.989 1
 
< 0.1%
22.897 1
 
< 0.1%
36.88 1
 
< 0.1%
49.94 1
 
< 0.1%
53.59 1
 
< 0.1%
53.78 1
 
< 0.1%
58.65 1
 
< 0.1%
ValueCountFrequency (%)
237620205.0 1
< 0.1%
222238584.0 1
< 0.1%
208026305.0 1
< 0.1%
170725215.0 1
< 0.1%
151064192.0 1
< 0.1%
22412776.0 1
< 0.1%
8624786.0 1
< 0.1%
8200562.0 1
< 0.1%
3700892.598 1
< 0.1%
2733103.0 1
< 0.1%

용적_율
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct444
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108316.77
Minimum0
Maximum1.0820891 × 109
Zeros9398
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:27.282166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile124.16
Maximum1.0820891 × 109
Range1.0820891 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10820892
Coefficient of variation (CV)99.900437
Kurtosis9999.9919
Mean108316.77
Median Absolute Deviation (MAD)0
Skewness99.999939
Sum1.0831677 × 109
Variance1.1709171 × 1014
MonotonicityNot monotonic
2024-05-11T14:40:27.535507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9398
94.0%
124.16 20
 
0.2%
127.35 14
 
0.1%
276.95 12
 
0.1%
128.9 10
 
0.1%
139.08 10
 
0.1%
20.31 9
 
0.1%
161.25 8
 
0.1%
122.05 7
 
0.1%
124.76 7
 
0.1%
Other values (434) 505
 
5.1%
ValueCountFrequency (%)
0.0 9398
94.0%
0.5 1
 
< 0.1%
0.64 1
 
< 0.1%
1.22 1
 
< 0.1%
1.44 1
 
< 0.1%
5.86 1
 
< 0.1%
6.0 1
 
< 0.1%
6.71 1
 
< 0.1%
7.05 1
 
< 0.1%
7.14 1
 
< 0.1%
ValueCountFrequency (%)
1082089127.0 1
< 0.1%
599885.96 1
< 0.1%
340072.12 1
< 0.1%
999.34 1
< 0.1%
991.65 1
< 0.1%
959.94 1
< 0.1%
959.16 2
< 0.1%
921.44 1
< 0.1%
899.76 1
< 0.1%
799.64 1
< 0.1%

기타_용도
Text

MISSING 

Distinct189
Distinct (%)7.0%
Missing7285
Missing (%)72.9%
Memory size156.2 KiB
2024-05-11T14:40:27.812055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length3
Mean length4.4069982
Min length2

Characters and Unicode

Total characters11965
Distinct characters221
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique152 ?
Unique (%)5.6%

Sample

1st row아파트
2nd row아파트
3rd row아파트
4th row비내력벽 철거
5th row아파트
ValueCountFrequency (%)
아파트 1808
62.2%
비내력벽철거 391
 
13.5%
발코니확장 153
 
5.3%
공동주택(아파트 47
 
1.6%
철거 46
 
1.6%
비내력벽 37
 
1.3%
승강기 24
 
0.8%
공동주택 22
 
0.8%
16
 
0.6%
부대시설(승강기 13
 
0.4%
Other values (234) 348
 
12.0%
2024-05-11T14:40:28.329498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1889
15.8%
1888
15.8%
1888
15.8%
452
 
3.8%
445
 
3.7%
433
 
3.6%
433
 
3.6%
430
 
3.6%
430
 
3.6%
191
 
1.6%
Other values (211) 3486
29.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11138
93.1%
Decimal Number 275
 
2.3%
Space Separator 191
 
1.6%
Open Punctuation 128
 
1.1%
Close Punctuation 128
 
1.1%
Other Punctuation 55
 
0.5%
Uppercase Letter 33
 
0.3%
Dash Punctuation 7
 
0.1%
Lowercase Letter 6
 
0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1889
17.0%
1888
17.0%
1888
17.0%
452
 
4.1%
445
 
4.0%
433
 
3.9%
433
 
3.9%
430
 
3.9%
430
 
3.9%
179
 
1.6%
Other values (178) 2671
24.0%
Decimal Number
ValueCountFrequency (%)
1 76
27.6%
0 65
23.6%
2 34
12.4%
5 25
 
9.1%
4 24
 
8.7%
3 19
 
6.9%
8 9
 
3.3%
7 8
 
2.9%
6 8
 
2.9%
9 7
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
C 12
36.4%
V 7
21.2%
T 7
21.2%
K 2
 
6.1%
D 1
 
3.0%
X 1
 
3.0%
M 1
 
3.0%
P 1
 
3.0%
A 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 47
85.5%
/ 4
 
7.3%
. 3
 
5.5%
? 1
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
c 2
33.3%
m 2
33.3%
t 1
16.7%
v 1
16.7%
Space Separator
ValueCountFrequency (%)
191
100.0%
Open Punctuation
ValueCountFrequency (%)
( 128
100.0%
Close Punctuation
ValueCountFrequency (%)
) 128
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11138
93.1%
Common 788
 
6.6%
Latin 39
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1889
17.0%
1888
17.0%
1888
17.0%
452
 
4.1%
445
 
4.0%
433
 
3.9%
433
 
3.9%
430
 
3.9%
430
 
3.9%
179
 
1.6%
Other values (178) 2671
24.0%
Common
ValueCountFrequency (%)
191
24.2%
( 128
16.2%
) 128
16.2%
1 76
 
9.6%
0 65
 
8.2%
, 47
 
6.0%
2 34
 
4.3%
5 25
 
3.2%
4 24
 
3.0%
3 19
 
2.4%
Other values (10) 51
 
6.5%
Latin
ValueCountFrequency (%)
C 12
30.8%
V 7
17.9%
T 7
17.9%
K 2
 
5.1%
c 2
 
5.1%
m 2
 
5.1%
D 1
 
2.6%
X 1
 
2.6%
M 1
 
2.6%
t 1
 
2.6%
Other values (3) 3
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11138
93.1%
ASCII 825
 
6.9%
CJK Compat 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1889
17.0%
1888
17.0%
1888
17.0%
452
 
4.1%
445
 
4.0%
433
 
3.9%
433
 
3.9%
430
 
3.9%
430
 
3.9%
179
 
1.6%
Other values (178) 2671
24.0%
ASCII
ValueCountFrequency (%)
191
23.2%
( 128
15.5%
) 128
15.5%
1 76
 
9.2%
0 65
 
7.9%
, 47
 
5.7%
2 34
 
4.1%
5 25
 
3.0%
4 24
 
2.9%
3 19
 
2.3%
Other values (22) 88
10.7%
CJK Compat
ValueCountFrequency (%)
2
100.0%

작업_일자
Real number (ℝ)

HIGH CORRELATION 

Distinct904
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20181148
Minimum20120222
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T14:40:28.558937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20120222
5-th percentile20120222
Q120140521
median20191203
Q320211204
95-th percentile20240102
Maximum20240510
Range120288
Interquartile range (IQR)70683

Descriptive statistics

Standard deviation40810.238
Coefficient of variation (CV)0.002022196
Kurtosis-1.3188226
Mean20181148
Median Absolute Deviation (MAD)29915
Skewness-0.32482683
Sum2.0181148 × 1011
Variance1.6654756 × 109
MonotonicityNot monotonic
2024-05-11T14:40:28.809501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20120222 1762
 
17.6%
20211029 937
 
9.4%
20191203 437
 
4.4%
20240102 117
 
1.2%
20240208 107
 
1.1%
20170406 89
 
0.9%
20170928 74
 
0.7%
20141115 65
 
0.7%
20200304 62
 
0.6%
20240510 61
 
0.6%
Other values (894) 6289
62.9%
ValueCountFrequency (%)
20120222 1762
17.6%
20120223 1
 
< 0.1%
20120229 5
 
0.1%
20120301 2
 
< 0.1%
20120303 1
 
< 0.1%
20120306 5
 
0.1%
20120309 2
 
< 0.1%
20120315 2
 
< 0.1%
20120317 2
 
< 0.1%
20120320 1
 
< 0.1%
ValueCountFrequency (%)
20240510 61
0.6%
20240507 28
0.3%
20240425 11
 
0.1%
20240420 22
 
0.2%
20240417 16
 
0.2%
20240411 4
 
< 0.1%
20240406 11
 
0.1%
20240402 12
 
0.1%
20240330 20
 
0.2%
20240327 57
0.6%

Interactions

2024-05-11T14:40:19.400656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:02.433479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:04.289025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:06.509164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:08.200337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:09.894694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:11.602531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:13.714872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:15.571665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:17.620468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:19.662311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:02.604502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:04.475112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:06.679110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:08.433444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:10.110818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:11.781866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:13.918113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:16.085075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:17.835027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:19.890195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:02.771164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:04.945916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:06.814892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:08.625596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:10.315258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:11.965635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:14.110859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:16.234273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:18.017877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:20.089805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:02.927360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:05.112934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:06.955117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:08.775731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:10.479430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:12.176767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:14.278717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:16.385939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:18.177678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:20.270815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:03.087522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:05.344594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:07.148321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:08.923064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:10.620578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:12.563663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:14.454910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:16.559059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:18.351140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:20.450283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:03.273372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:05.540961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:07.325432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:09.080192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:10.807495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:12.756853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:14.644729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:16.751392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:18.530800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:20.643342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:03.496852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:05.736213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:07.484322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:09.202956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:10.954907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:12.957950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:14.852087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:16.929526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:18.700137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:20.841097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:03.694754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:05.969440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:07.688076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:09.367266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:11.115428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:13.178575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:15.051708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:17.119001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:18.874999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:20.984618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:03.945618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:06.151894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:07.867321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:09.525711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:11.255685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:13.326432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:15.214629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:17.294122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:19.041341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:21.164445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:04.092669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:06.335031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:08.036677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:09.705888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:11.427438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:13.532932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:15.396118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:17.444088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:40:19.199727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:40:29.025849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승인번호_년승인번호_기관_코드승인번호_구분_코드승인번호_일련번호승인_일대지_면적건폐_율연면적용적_율작업_일자
승인번호_년1.000NaNNaNNaNNaNNaNNaNNaNNaNNaN
승인번호_기관_코드NaN1.0000.0000.0000.1500.1160.0000.0000.0000.151
승인번호_구분_코드NaN0.0001.0000.0000.0890.3010.0000.0000.0000.049
승인번호_일련번호NaN0.0000.0001.0000.6800.0000.0000.0000.0000.272
승인_일NaN0.1500.0890.6801.0000.2390.0000.0000.0000.938
대지_면적NaN0.1160.3010.0000.2391.0000.0000.0440.0000.172
건폐_율NaN0.0000.0000.0000.0000.0001.0000.0000.0000.000
연면적NaN0.0000.0000.0000.0000.0440.0001.0000.0000.016
용적_율NaN0.0000.0000.0000.0000.0000.0000.0001.0000.000
작업_일자NaN0.1510.0490.2720.9380.1720.0000.0160.0001.000
2024-05-11T14:40:29.250987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승인번호_년승인번호_기관_코드승인번호_구분_코드승인번호_일련번호승인_일대지_면적건폐_율연면적용적_율작업_일자
승인번호_년1.000-0.269-0.047-0.3040.996-0.048-0.0440.138-0.0430.918
승인번호_기관_코드-0.2691.0000.0050.347-0.266-0.028-0.026-0.113-0.024-0.301
승인번호_구분_코드-0.0470.0051.000-0.167-0.047-0.003-0.008-0.082-0.010-0.139
승인번호_일련번호-0.3040.347-0.1671.000-0.273-0.215-0.224-0.179-0.213-0.282
승인_일0.996-0.266-0.047-0.2731.000-0.053-0.0490.133-0.0490.918
대지_면적-0.048-0.028-0.003-0.215-0.0531.0000.9880.5470.9660.100
건폐_율-0.044-0.026-0.008-0.224-0.0490.9881.0000.5410.9710.102
연면적0.138-0.113-0.082-0.1790.1330.5470.5411.0000.5340.208
용적_율-0.043-0.024-0.010-0.213-0.0490.9660.9710.5341.0000.103
작업_일자0.918-0.301-0.139-0.2820.9180.1000.1020.2080.1031.000

Missing values

2024-05-11T14:40:21.405969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:40:21.689698image/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

관리_주택대장_PK승인번호_년승인번호_기관_코드승인번호_구분_코드승인번호_일련번호승인_일대지_면적건폐_율연면적용적_율기타_용도작업_일자
6162311650-100028992201532101632230153201510300.00.00.00.0<NA>20151121
847411545-10000000000000001258032023317026122294202307030.00.00.00.0아파트20230708
5901211470-100023711201631401742230153201608220.00.00.00.0<NA>20160827
6389711710-10003778920153230211223258201503250.00.00.00.0아파트20150402
1577211290-10002518320223070267222935202207260.00.00.00.0<NA>20220728
7498211710-3364200332300892230698200308250.00.00.00.0<NA>20120222
8233211710-100013658200932301632232425200905060.00.00.00.0<NA>20120222
4848911230-10001570520193050000581012019030729850.330.8295116772.6617249.9711아파트20190309
4347211470-45120013140079222950200103120.00.00.00.0<NA>20191203
4293911470-213820053140104223037020050324175403.012.35241842.0127.35<NA>20191203
관리_주택대장_PK승인번호_년승인번호_기관_코드승인번호_구분_코드승인번호_일련번호승인_일대지_면적건폐_율연면적용적_율기타_용도작업_일자
6351211650-1000275702015321016322417201505074799.549.9621924.13243.91<NA>20150512
4653811320-1000163262019309007522326201907290.00.00.00.0<NA>20190907
2224611590-1000190442021319022622416202111050.00.00.00.0<NA>20211204
2926111320-100022866202130900752230166202109230.00.08880.00.0아파트20211029
8598411710-10001369120093230163223082200905070.00.00.00.0아파트20120222
8310611710-1000078482008323016322321131200809180.00.00.00.0<NA>20120222
4838811200-10003391420193030158223041201903150.00.00.00.0<NA>20190321
2427611710-10007915820213230263222991202106020.00.00.00.0<NA>20211029
8475511710-4698200732301392226172200711130.00.00.00.0<NA>20120222
1229411740-10000000000000000763042023324017222291202301020.00.023616.540.0<NA>20230110