Overview

Dataset statistics

Number of variables13
Number of observations10000
Missing cells5767
Missing cells (%)4.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory124.0 B

Variable types

Text1
Numeric11
Categorical1

Dataset

Description관리_허가대장_PK,허가번호_년,허가번호_기관_코드,허가번호_구분_코드,허가번호_일련번호,건축_구분_코드,건축_허가_일,대지_면적,건폐_율,연면적,용적_율,주_용도_코드,외필지_수
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15400/S/1/datasetView.do

Alerts

외필지_수 has constant value ""Constant
허가번호_년 is highly overall correlated with 건축_허가_일High correlation
허가번호_구분_코드 is highly overall correlated with 건축_구분_코드 and 3 other fieldsHigh correlation
건축_구분_코드 is highly overall correlated with 허가번호_구분_코드High correlation
건축_허가_일 is highly overall correlated with 허가번호_년High correlation
대지_면적 is highly overall correlated with 연면적High correlation
건폐_율 is highly overall correlated with 허가번호_구분_코드 and 2 other fieldsHigh correlation
연면적 is highly overall correlated with 허가번호_구분_코드 and 3 other fieldsHigh correlation
용적_율 is highly overall correlated with 허가번호_구분_코드 and 2 other fieldsHigh correlation
건축_구분_코드 has 2882 (28.8%) missing valuesMissing
주_용도_코드 has 2885 (28.8%) missing valuesMissing
대지_면적 is highly skewed (γ1 = 37.49358473)Skewed
건폐_율 is highly skewed (γ1 = 69.3968301)Skewed
연면적 is highly skewed (γ1 = 82.68928223)Skewed
용적_율 is highly skewed (γ1 = 95.74149657)Skewed
관리_허가대장_PK has unique valuesUnique
대지_면적 has 1506 (15.1%) zerosZeros
건폐_율 has 2967 (29.7%) zerosZeros
연면적 has 1423 (14.2%) zerosZeros
용적_율 has 2961 (29.6%) zerosZeros

Reproduction

Analysis started2024-05-18 04:09:23.578802
Analysis finished2024-05-18 04:10:13.166924
Duration49.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T13:10:13.477792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length17.639
Min length15

Characters and Unicode

Total characters176390
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 row11650-100117192
2nd row11260-100040896
3rd row11170-1000000000000000063299
4th row11305-100083961
5th row11500-100106104
ValueCountFrequency (%)
11650-100117192 1
 
< 0.1%
11440-100108539 1
 
< 0.1%
11560-100090851 1
 
< 0.1%
11740-1000000000000000120334 1
 
< 0.1%
11680-100118949 1
 
< 0.1%
11545-100084919 1
 
< 0.1%
11230-100105732 1
 
< 0.1%
11170-100090073 1
 
< 0.1%
11440-100111922 1
 
< 0.1%
11590-100105401 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-18T13:10:14.229375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 68016
38.6%
1 40348
22.9%
- 10000
 
5.7%
5 8128
 
4.6%
6 7969
 
4.5%
2 7839
 
4.4%
4 7421
 
4.2%
3 7119
 
4.0%
7 6877
 
3.9%
8 6436
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 166390
94.3%
Dash Punctuation 10000
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 68016
40.9%
1 40348
24.2%
5 8128
 
4.9%
6 7969
 
4.8%
2 7839
 
4.7%
4 7421
 
4.5%
3 7119
 
4.3%
7 6877
 
4.1%
8 6436
 
3.9%
9 6237
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 176390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 68016
38.6%
1 40348
22.9%
- 10000
 
5.7%
5 8128
 
4.6%
6 7969
 
4.5%
2 7839
 
4.4%
4 7421
 
4.2%
3 7119
 
4.0%
7 6877
 
3.9%
8 6436
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 176390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 68016
38.6%
1 40348
22.9%
- 10000
 
5.7%
5 8128
 
4.6%
6 7969
 
4.5%
2 7839
 
4.4%
4 7421
 
4.2%
3 7119
 
4.0%
7 6877
 
3.9%
8 6436
 
3.6%

허가번호_년
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.684
Minimum2018
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:14.691835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2021
Q32022
95-th percentile2023
Maximum2024
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5654357
Coefficient of variation (CV)0.00077470586
Kurtosis-0.8344364
Mean2020.684
Median Absolute Deviation (MAD)1
Skewness0.24507401
Sum20206840
Variance2.4505891
MonotonicityNot monotonic
2024-05-18T13:10:14.962843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2019 2279
22.8%
2020 1998
20.0%
2021 1991
19.9%
2022 1740
17.4%
2023 1056
10.6%
2018 544
 
5.4%
2024 392
 
3.9%
ValueCountFrequency (%)
2018 544
 
5.4%
2019 2279
22.8%
2020 1998
20.0%
2021 1991
19.9%
2022 1740
17.4%
2023 1056
10.6%
2024 392
 
3.9%
ValueCountFrequency (%)
2024 392
 
3.9%
2023 1056
10.6%
2022 1740
17.4%
2021 1991
19.9%
2020 1998
20.0%
2019 2279
22.8%
2018 544
 
5.4%
Distinct170
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3136827.9
Minimum3000000
Maximum6114031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:15.345981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3010178
Q13060180
median3150029
Q33210141
95-th percentile3230304
Maximum6114031
Range3114031
Interquartile range (IQR)149961

Descriptive statistics

Standard deviation117395.99
Coefficient of variation (CV)0.037425066
Kurtosis369.81542
Mean3136827.9
Median Absolute Deviation (MAD)69855
Skewness14.577078
Sum3.1368279 × 1010
Variance1.3781818 × 1010
MonotonicityNot monotonic
2024-05-18T13:10:15.776737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3220175 958
 
9.6%
3200245 534
 
5.3%
3150029 503
 
5.0%
3180176 452
 
4.5%
3130160 447
 
4.5%
3210141 445
 
4.5%
3230165 402
 
4.0%
3240079 392
 
3.9%
3060180 342
 
3.4%
3000082 335
 
3.4%
Other values (160) 5190
51.9%
ValueCountFrequency (%)
3000000 1
 
< 0.1%
3000082 335
3.4%
3000148 30
 
0.3%
3000220 10
 
0.1%
3000221 84
 
0.8%
3010000 2
 
< 0.1%
3010075 7
 
0.1%
3010134 2
 
< 0.1%
3010178 47
 
0.5%
3010180 174
1.7%
ValueCountFrequency (%)
6114031 3
 
< 0.1%
6113930 6
 
0.1%
3240295 3
 
< 0.1%
3240172 40
 
0.4%
3240159 28
 
0.3%
3240079 392
3.9%
3230304 99
 
1.0%
3230301 9
 
0.1%
3230263 1
 
< 0.1%
3230262 24
 
0.2%

허가번호_구분_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1275.713
Minimum1101
Maximum5804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:16.189788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1101
5-th percentile1101
Q11102
median1108
Q31208
95-th percentile1501
Maximum5804
Range4703
Interquartile range (IQR)106

Descriptive statistics

Standard deviation597.12611
Coefficient of variation (CV)0.46807245
Kurtosis37.4506
Mean1275.713
Median Absolute Deviation (MAD)7
Skewness6.1373234
Sum12757130
Variance356559.59
MonotonicityNot monotonic
2024-05-18T13:10:16.549862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1101 2441
24.4%
1108 2058
20.6%
1208 1491
14.9%
1501 1218
12.2%
1207 941
 
9.4%
1107 513
 
5.1%
1202 429
 
4.3%
1102 246
 
2.5%
5200 147
 
1.5%
1210 122
 
1.2%
Other values (16) 394
 
3.9%
ValueCountFrequency (%)
1101 2441
24.4%
1102 246
 
2.5%
1103 11
 
0.1%
1106 8
 
0.1%
1107 513
 
5.1%
1108 2058
20.6%
1201 85
 
0.9%
1202 429
 
4.3%
1203 7
 
0.1%
1206 106
 
1.1%
ValueCountFrequency (%)
5804 1
 
< 0.1%
5803 2
 
< 0.1%
5802 1
 
< 0.1%
5801 3
 
< 0.1%
5510 1
 
< 0.1%
5200 147
 
1.5%
5100 62
 
0.6%
1502 17
 
0.2%
1501 1218
12.2%
1403 1
 
< 0.1%

허가번호_일련번호
Real number (ℝ)

Distinct446
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.4662
Minimum1
Maximum635
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:16.950436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median36
Q389
95-th percentile229.05
Maximum635
Range634
Interquartile range (IQR)78

Descriptive statistics

Standard deviation81.073654
Coefficient of variation (CV)1.2384048
Kurtosis7.4985161
Mean65.4662
Median Absolute Deviation (MAD)30
Skewness2.3639509
Sum654662
Variance6572.9374
MonotonicityNot monotonic
2024-05-18T13:10:17.386026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 416
 
4.2%
2 318
 
3.2%
4 280
 
2.8%
3 268
 
2.7%
5 245
 
2.5%
6 219
 
2.2%
7 194
 
1.9%
8 164
 
1.6%
10 162
 
1.6%
11 159
 
1.6%
Other values (436) 7575
75.8%
ValueCountFrequency (%)
1 416
4.2%
2 318
3.2%
3 268
2.7%
4 280
2.8%
5 245
2.5%
6 219
2.2%
7 194
1.9%
8 164
 
1.6%
9 154
 
1.5%
10 162
 
1.6%
ValueCountFrequency (%)
635 1
< 0.1%
627 1
< 0.1%
626 1
< 0.1%
623 1
< 0.1%
620 1
< 0.1%
604 1
< 0.1%
600 1
< 0.1%
598 1
< 0.1%
595 1
< 0.1%
590 1
< 0.1%

건축_구분_코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)0.1%
Missing2882
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean427.29699
Minimum100
Maximum3000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:17.749776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100
median600
Q3700
95-th percentile700
Maximum3000
Range2900
Interquartile range (IQR)600

Descriptive statistics

Standard deviation304.74819
Coefficient of variation (CV)0.71319995
Kurtosis7.1191378
Mean427.29699
Median Absolute Deviation (MAD)100
Skewness0.95895845
Sum3041500
Variance92871.462
MonotonicityNot monotonic
2024-05-18T13:10:18.126507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
700 3017
30.2%
100 2583
25.8%
200 724
 
7.2%
600 639
 
6.4%
800 123
 
1.2%
300 19
 
0.2%
3000 13
 
0.1%
(Missing) 2882
28.8%
ValueCountFrequency (%)
100 2583
25.8%
200 724
 
7.2%
300 19
 
0.2%
600 639
 
6.4%
700 3017
30.2%
800 123
 
1.2%
3000 13
 
0.1%
ValueCountFrequency (%)
3000 13
 
0.1%
800 123
 
1.2%
700 3017
30.2%
600 639
 
6.4%
300 19
 
0.2%
200 724
 
7.2%
100 2583
25.8%

건축_허가_일
Real number (ℝ)

HIGH CORRELATION 

Distinct1445
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20207409
Minimum19871016
Maximum20240513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:18.536389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19871016
5-th percentile20181221
Q120191108
median20210208
Q320220510
95-th percentile20231123
Maximum20240513
Range369497
Interquartile range (IQR)29402

Descriptive statistics

Standard deviation16224.035
Coefficient of variation (CV)0.00080287555
Kurtosis27.161195
Mean20207409
Median Absolute Deviation (MAD)10499
Skewness-1.2213681
Sum2.0207409 × 1011
Variance2.632193 × 108
MonotonicityNot monotonic
2024-05-18T13:10:19.069444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190307 24
 
0.2%
20190305 23
 
0.2%
20200429 22
 
0.2%
20190228 19
 
0.2%
20190313 18
 
0.2%
20200406 18
 
0.2%
20190118 18
 
0.2%
20200114 18
 
0.2%
20190416 17
 
0.2%
20210521 17
 
0.2%
Other values (1435) 9806
98.1%
ValueCountFrequency (%)
19871016 1
 
< 0.1%
19920307 1
 
< 0.1%
20150727 1
 
< 0.1%
20161021 1
 
< 0.1%
20170911 1
 
< 0.1%
20170913 1
 
< 0.1%
20180227 1
 
< 0.1%
20180410 1
 
< 0.1%
20181008 5
0.1%
20181010 11
0.1%
ValueCountFrequency (%)
20240513 3
< 0.1%
20240511 1
 
< 0.1%
20240510 4
< 0.1%
20240509 2
 
< 0.1%
20240508 6
0.1%
20240507 3
< 0.1%
20240503 7
0.1%
20240502 3
< 0.1%
20240501 7
0.1%
20240430 7
0.1%

대지_면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5388
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12589.517
Minimum0
Maximum11440144
Zeros1506
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:19.517858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1122.3
median263.6
Q3795.025
95-th percentile17323.872
Maximum11440144
Range11440144
Interquartile range (IQR)672.725

Descriptive statistics

Standard deviation177513.35
Coefficient of variation (CV)14.100092
Kurtosis1939.998
Mean12589.517
Median Absolute Deviation (MAD)211.4
Skewness37.493585
Sum1.2589517 × 108
Variance3.1510989 × 1010
MonotonicityNot monotonic
2024-05-18T13:10:19.990806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1506
 
15.1%
18.0 28
 
0.3%
165.0 23
 
0.2%
132.0 20
 
0.2%
162.0 19
 
0.2%
231.0 18
 
0.2%
149.0 18
 
0.2%
152.0 17
 
0.2%
330.0 16
 
0.2%
202.0 15
 
0.1%
Other values (5378) 8320
83.2%
ValueCountFrequency (%)
0.0 1506
15.1%
1.0 1
 
< 0.1%
4.1 1
 
< 0.1%
9.0 3
 
< 0.1%
9.2 1
 
< 0.1%
10.75 1
 
< 0.1%
12.0 1
 
< 0.1%
12.9 1
 
< 0.1%
15.0 1
 
< 0.1%
17.5 1
 
< 0.1%
ValueCountFrequency (%)
11440144.0 1
 
< 0.1%
4108394.0 1
 
< 0.1%
3895659.0 6
0.1%
3890567.0 1
 
< 0.1%
3425949.9 2
 
< 0.1%
2693724.0 1
 
< 0.1%
1612459.0 1
 
< 0.1%
1569434.3 1
 
< 0.1%
1414246.0 2
 
< 0.1%
1230943.3 1
 
< 0.1%

건폐_율
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct3646
Distinct (%)36.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.100851
Minimum0
Maximum39316.049
Zeros2967
Zeros (%)29.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:20.426658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median49.76685
Q358.97
95-th percentile59.99
Maximum39316.049
Range39316.049
Interquartile range (IQR)58.97

Descriptive statistics

Standard deviation559.21714
Coefficient of variation (CV)12.1303
Kurtosis4865.1616
Mean46.100851
Median Absolute Deviation (MAD)9.99315
Skewness69.39683
Sum461008.51
Variance312723.81
MonotonicityNot monotonic
2024-05-18T13:10:20.882843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2967
29.7%
59.96 58
 
0.6%
59.98 56
 
0.6%
59.94 51
 
0.5%
59.99 51
 
0.5%
59.95 49
 
0.5%
59.85 37
 
0.4%
59.78 35
 
0.4%
59.97 34
 
0.3%
59.91 33
 
0.3%
Other values (3636) 6629
66.3%
ValueCountFrequency (%)
0.0 2967
29.7%
0.0075 1
 
< 0.1%
0.0216 1
 
< 0.1%
0.0252 1
 
< 0.1%
0.0368 1
 
< 0.1%
0.0402 1
 
< 0.1%
0.0479 1
 
< 0.1%
0.0549 1
 
< 0.1%
0.0644 1
 
< 0.1%
0.0728 1
 
< 0.1%
ValueCountFrequency (%)
39316.049 2
< 0.1%
5959.2042 1
< 0.1%
220.78 1
< 0.1%
206.2509 1
< 0.1%
165.56 1
< 0.1%
143.3978 2
< 0.1%
138.6295 1
< 0.1%
128.6237 1
< 0.1%
111.1 1
< 0.1%
105.9818 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7042
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14949.818
Minimum0
Maximum49967875
Zeros1423
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:21.536073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q154
median363.375
Q3999.7
95-th percentile22575.057
Maximum49967875
Range49967875
Interquartile range (IQR)945.7

Descriptive statistics

Standard deviation541641.64
Coefficient of variation (CV)36.230653
Kurtosis7366.4601
Mean14949.818
Median Absolute Deviation (MAD)345.375
Skewness82.689282
Sum1.4949818 × 108
Variance2.9337567 × 1011
MonotonicityNot monotonic
2024-05-18T13:10:22.039232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1423
 
14.2%
18.0 258
 
2.6%
36.0 122
 
1.2%
54.0 93
 
0.9%
27.0 88
 
0.9%
72.0 48
 
0.5%
45.0 48
 
0.5%
9.0 40
 
0.4%
108.0 36
 
0.4%
81.0 30
 
0.3%
Other values (7032) 7814
78.1%
ValueCountFrequency (%)
0.0 1423
14.2%
0.95 1
 
< 0.1%
1.0 2
 
< 0.1%
1.17 1
 
< 0.1%
1.2 1
 
< 0.1%
1.5 1
 
< 0.1%
1.52 1
 
< 0.1%
1.55 1
 
< 0.1%
1.57 1
 
< 0.1%
1.62 1
 
< 0.1%
ValueCountFrequency (%)
49967875.0 1
< 0.1%
17884439.0 1
< 0.1%
9999344.0 1
< 0.1%
915921.58 1
< 0.1%
882279.87 1
< 0.1%
824351.71 1
< 0.1%
806049.78 1
< 0.1%
805927.36 1
< 0.1%
805872.45 1
< 0.1%
792584.63 1
< 0.1%

용적_율
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5952
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean767.81169
Minimum0
Maximum5037330.8
Zeros2961
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:22.405449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median149.64
Q3207.8513
95-th percentile583.89719
Maximum5037330.8
Range5037330.8
Interquartile range (IQR)207.8513

Descriptive statistics

Standard deviation51211.094
Coefficient of variation (CV)66.697466
Kurtosis9369.8793
Mean767.81169
Median Absolute Deviation (MAD)102.375
Skewness95.741497
Sum7678116.9
Variance2.6225761 × 109
MonotonicityNot monotonic
2024-05-18T13:10:22.671785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2961
29.6%
199.98 22
 
0.2%
199.88 18
 
0.2%
199.96 14
 
0.1%
199.91 14
 
0.1%
199.94 14
 
0.1%
199.93 14
 
0.1%
199.9 13
 
0.1%
199.92 13
 
0.1%
199.97 12
 
0.1%
Other values (5942) 6905
69.0%
ValueCountFrequency (%)
0.0 2961
29.6%
0.0075 1
 
< 0.1%
0.0252 1
 
< 0.1%
0.0479 1
 
< 0.1%
0.0489 1
 
< 0.1%
0.0644 1
 
< 0.1%
0.0728 1
 
< 0.1%
0.0783 1
 
< 0.1%
0.0945 1
 
< 0.1%
0.1122 1
 
< 0.1%
ValueCountFrequency (%)
5037330.7617 1
< 0.1%
923113.53 1
< 0.1%
39345.5539 1
< 0.1%
1542.5582 1
< 0.1%
1471.9992 1
< 0.1%
1372.33 1
< 0.1%
1335.2621 1
< 0.1%
1315.99 1
< 0.1%
1249.69 2
< 0.1%
1199.75 2
< 0.1%

주_용도_코드
Real number (ℝ)

MISSING 

Distinct29
Distinct (%)0.4%
Missing2885
Missing (%)28.8%
Infinite0
Infinite (%)0.0%
Mean4874.0689
Minimum1000
Maximum31000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:10:23.002294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q12000
median4000
Q34000
95-th percentile14000
Maximum31000
Range30000
Interquartile range (IQR)2000

Descriptive statistics

Standard deviation4675.8456
Coefficient of variation (CV)0.95933104
Kurtosis2.463997
Mean4874.0689
Median Absolute Deviation (MAD)2000
Skewness1.7431702
Sum34679000
Variance21863532
MonotonicityNot monotonic
2024-05-18T13:10:23.389360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4000 2047
20.5%
2000 1346
13.5%
1000 1231
12.3%
3000 955
 
9.6%
14000 752
 
7.5%
10000 175
 
1.8%
7000 98
 
1.0%
17000 88
 
0.9%
9000 70
 
0.7%
5000 62
 
0.6%
Other values (19) 291
 
2.9%
(Missing) 2885
28.8%
ValueCountFrequency (%)
1000 1231
12.3%
2000 1346
13.5%
3000 955
9.6%
4000 2047
20.5%
5000 62
 
0.6%
6000 48
 
0.5%
7000 98
 
1.0%
8000 8
 
0.1%
9000 70
 
0.7%
10000 175
 
1.8%
ValueCountFrequency (%)
31000 1
 
< 0.1%
30000 2
 
< 0.1%
29000 1
 
< 0.1%
28000 8
 
0.1%
27000 3
 
< 0.1%
26000 3
 
< 0.1%
24000 8
 
0.1%
23000 6
 
0.1%
21000 1
 
< 0.1%
20000 40
0.4%

외필지_수
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2024-05-18T13:10:23.784532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:10:24.016863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

Interactions

2024-05-18T13:10:08.945476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:33.518690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:37.732823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:41.387316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:45.132400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:49.889699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:53.737072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:56.616558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:59.779370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:02.844644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:05.460846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:09.207149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:34.039312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:38.096565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:41.723835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:45.647623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:50.253874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:54.036202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:56.893347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:00.067869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:03.030378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:05.828338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:09.495478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:34.327251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:38.603608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:42.038154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:46.123696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:50.566323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:54.393338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:57.176875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:00.367275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:03.213110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:06.123002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:09.770682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:34.830158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:38.943587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:42.319667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:46.492527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:50.947479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:54.676299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:57.451447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:00.645729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:03.395491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:06.400715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:10.062912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:35.191935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:39.320306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:42.611347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:46.857451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:51.588263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:54.909303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:57.731666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:00.934212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:03.574668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:06.892250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:10.295569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:35.586302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:39.626156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:42.987690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:47.225640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:51.988974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:55.107256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:58.014931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:01.216812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:03.849261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:07.172382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:10.493373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:35.911311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:39.948182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:43.379587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:47.623741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:52.337735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:55.353369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:58.309402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:01.510432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:04.138892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:07.466303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:10.790491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:36.194701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:40.237226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:43.683193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:48.122337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:52.742731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:55.550596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:58.649415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:01.704804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:04.488606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:07.780013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:11.069839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:36.584604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:40.535347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:44.105941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:48.610004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:53.046973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:55.777677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:58.944561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:02.000715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:04.681419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:08.073000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:11.341329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:36.919917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:40.811019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:44.379019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:49.005725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:53.260863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:56.059457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:59.216229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:02.276528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:04.923164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:08.349972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:11.610869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:37.369116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:41.107337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:44.759449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:49.479706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:53.487534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:56.323810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:09:59.491735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:02.561949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:05.188405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:10:08.578864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T13:10:24.168074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
허가번호_년허가번호_기관_코드허가번호_구분_코드허가번호_일련번호건축_구분_코드건축_허가_일대지_면적건폐_율연면적용적_율주_용도_코드
허가번호_년1.0000.0000.0640.2220.1160.7400.0040.0000.0270.0710.086
허가번호_기관_코드0.0001.000NaN0.0000.0480.0000.0000.0000.0000.0000.000
허가번호_구분_코드0.064NaN1.0000.1090.1940.2610.0670.0000.0000.0000.348
허가번호_일련번호0.2220.0000.1091.0000.2180.1370.0000.0000.0000.0000.107
건축_구분_코드0.1160.0480.1940.2181.0000.1310.0350.0000.0000.0210.570
건축_허가_일0.7400.0000.2610.1370.1311.0000.0000.0000.0000.0000.000
대지_면적0.0040.0000.0670.0000.0350.0001.0000.0000.0000.0000.180
건폐_율0.0000.0000.0000.0000.0000.0000.0001.0000.0000.8270.000
연면적0.0270.0000.0000.0000.0000.0000.0000.0001.0000.0000.053
용적_율0.0710.0000.0000.0000.0210.0000.0000.8270.0001.0000.087
주_용도_코드0.0860.0000.3480.1070.5700.0000.1800.0000.0530.0871.000
2024-05-18T13:10:24.525672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
허가번호_년허가번호_기관_코드허가번호_구분_코드허가번호_일련번호건축_구분_코드건축_허가_일대지_면적건폐_율연면적용적_율주_용도_코드
허가번호_년1.0000.039-0.140-0.2330.1200.9810.2610.1340.2010.1470.072
허가번호_기관_코드0.0391.000-0.0190.164-0.0270.0350.132-0.0210.1120.0850.033
허가번호_구분_코드-0.140-0.0191.000-0.1800.776-0.143-0.127-0.654-0.521-0.5970.251
허가번호_일련번호-0.2330.164-0.1801.000-0.156-0.141-0.2030.046-0.0760.031-0.076
건축_구분_코드0.120-0.0270.776-0.1561.0000.1280.131-0.1310.1290.0460.288
건축_허가_일0.9810.035-0.143-0.1410.1281.0000.2670.1380.2060.1520.078
대지_면적0.2610.132-0.127-0.2030.1310.2671.0000.0340.5990.3050.417
건폐_율0.134-0.021-0.6540.046-0.1310.1380.0341.0000.5520.736-0.133
연면적0.2010.112-0.521-0.0760.1290.2060.5990.5521.0000.8470.453
용적_율0.1470.085-0.5970.0310.0460.1520.3050.7360.8471.0000.291
주_용도_코드0.0720.0330.251-0.0760.2880.0780.417-0.1330.4530.2911.000

Missing values

2024-05-18T13:10:12.010639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T13:10:12.578117image/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.
2024-05-18T13:10:12.953051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

관리_허가대장_PK허가번호_년허가번호_기관_코드허가번호_구분_코드허가번호_일련번호건축_구분_코드건축_허가_일대지_면적건폐_율연면적용적_율주_용도_코드외필지_수
5103711650-100117192202032101411102620020200305346.659.3191914.83191.806140000
4867411260-1000408962020306018011016910020200417306.856.22719.64186.3340000
1407311170-100000000000000006329920223020171110111010020221007535.958.98322442.174407.2043140000
3742211305-1000839612021308007712082<NA>20210209162.00.018.00.0<NA>0
2263011500-100106104202231500291202320020220216570.249.93731.29128.2540000
5080911110-100043191202030000821206260020200310257.237.4496.2937.4440000
2462511110-10005669620213000082110821270020211222470.433.26324.7663.4230000
1494711560-10000000000000000299652022318017612074870020220907103.050.83157.05101.6510000
2747311680-100164111202132201751210538002021101333696.149.9251457994.318919.650120000
4762011680-10013887020203220175150195<NA>202005110.00.00.00.0<NA>0
관리_허가대장_PK허가번호_년허가번호_기관_코드허가번호_구분_코드허가번호_일련번호건축_구분_코드건축_허가_일대지_면적건폐_율연면적용적_율주_용도_코드외필지_수
7269011410-100056114201831201591501145<NA>201812030.00.00.00.0<NA>0
3757811440-1001245192021313016011011410020210204157.159.5481313.85199.777240000
4657711650-10012010920203210141150115<NA>202006050.00.00.00.0<NA>0
2362911170-100089193202230201711108570020220114601.020.86309.42520.8650000
5827311230-10008577120193050088120828<NA>20190927132.00.09.660.0<NA>0
5369211560-100071511202031801761101510020200109165.057.72467.2283.1540000
188511260-1000000000000000463829202430601801101810020240221239.759.77478.35199.5620000
5019111290-1000705232020307027152005200202003203903.027.266678.84146.47100000
6515311470-1000563482019314023111014910020190424209.1659.02470.07199.9720000
535411500-10000000000000003753752023315008012086<NA>202309185366.90.0105.60.0<NA>0