Overview

Dataset statistics

Number of variables19
Number of observations6993
Missing cells21480
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory163.0 B

Variable types

Numeric11
Categorical3
Text2
DateTime3

Dataset

Description서울특별시 중랑구 관내 건축물의 연번, 건축구분, 대지위치, 면적, 구조, 허가일, 용도 등에 대한 데이터를 제공합니다.
Author서울특별시 중랑구
URLhttps://www.data.go.kr/data/15005072/fileData.do

Alerts

연번 is highly overall correlated with 세대수High correlation
대지면적(제곱미터) is highly overall correlated with 건축면적(제곱미터) and 4 other fieldsHigh correlation
건축면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 3 other fieldsHigh correlation
연면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 4 other fieldsHigh correlation
증축연면적(제곱미터) is highly overall correlated with 대지면적(제곱미터) and 4 other fieldsHigh correlation
최대지상층수 is highly overall correlated with 연면적(제곱미터) and 1 other fieldsHigh correlation
최대지하층수 is highly overall correlated with 호수High correlation
세대수 is highly overall correlated with 연번 and 6 other fieldsHigh correlation
호수 is highly overall correlated with 최대지하층수High correlation
가구수 is highly overall correlated with 세대수High correlation
건축구분 is highly overall correlated with 증축연면적(제곱미터)High correlation
주용도 is highly overall correlated with 대지면적(제곱미터)High correlation
건축구분 is highly imbalanced (75.2%)Imbalance
구조 is highly imbalanced (86.8%)Imbalance
주용도 is highly imbalanced (57.5%)Imbalance
증축연면적(제곱미터) has 6704 (95.9%) missing valuesMissing
착공처리일 has 351 (5.0%) missing valuesMissing
사용승인일 has 321 (4.6%) missing valuesMissing
최대지하층수 has 729 (10.4%) missing valuesMissing
부속용도 has 793 (11.3%) missing valuesMissing
세대수 has 2814 (40.2%) missing valuesMissing
호수 has 6204 (88.7%) missing valuesMissing
가구수 has 3552 (50.8%) missing valuesMissing
대지면적(제곱미터) is highly skewed (γ1 = 42.43603385)Skewed
건축면적(제곱미터) is highly skewed (γ1 = 24.0822643)Skewed
연면적(제곱미터) is highly skewed (γ1 = 28.78094608)Skewed
연번 has unique valuesUnique
최대지하층수 has 4656 (66.6%) zerosZeros
동수 has 417 (6.0%) zerosZeros
세대수 has 820 (11.7%) zerosZeros
가구수 has 865 (12.4%) zerosZeros

Reproduction

Analysis started2023-12-12 12:33:53.323766
Analysis finished2023-12-12 12:34:11.388306
Duration18.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct6993
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3497
Minimum1
Maximum6993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:11.830594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile350.6
Q11749
median3497
Q35245
95-th percentile6643.4
Maximum6993
Range6992
Interquartile range (IQR)3496

Descriptive statistics

Standard deviation2018.8495
Coefficient of variation (CV)0.57730899
Kurtosis-1.2
Mean3497
Median Absolute Deviation (MAD)1748
Skewness0
Sum24454521
Variance4075753.5
MonotonicityStrictly increasing
2023-12-12T21:34:12.011663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
4660 1
 
< 0.1%
4671 1
 
< 0.1%
4670 1
 
< 0.1%
4669 1
 
< 0.1%
4668 1
 
< 0.1%
4667 1
 
< 0.1%
4666 1
 
< 0.1%
4665 1
 
< 0.1%
4664 1
 
< 0.1%
Other values (6983) 6983
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
6993 1
< 0.1%
6992 1
< 0.1%
6991 1
< 0.1%
6990 1
< 0.1%
6989 1
< 0.1%
6988 1
< 0.1%
6987 1
< 0.1%
6986 1
< 0.1%
6985 1
< 0.1%
6984 1
< 0.1%

건축구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
신축
6704 
증축
 
289

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신축 6704
95.9%
증축 289
 
4.1%

Length

2023-12-12T21:34:12.174763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:34:12.298591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 6704
95.9%
증축 289
 
4.1%
Distinct6851
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
2023-12-12T21:34:12.456236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length41
Mean length20.974117
Min length1

Characters and Unicode

Total characters146672
Distinct characters87
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6724 ?
Unique (%)96.2%

Sample

1st row서울특별시 중랑구 면목동 183-79
2nd row서울특별시 중랑구 묵동 114-3 외1필지
3rd row서울특별시 중랑구 신내동 489-1 외3필지
4th row서울특별시 중랑구 망우동 526-15 외2필지
5th row서울특별시 중랑구 면목동 374-28 외1필지
ValueCountFrequency (%)
중랑구 6994
23.2%
서울특별시 6992
23.2%
면목동 2439
 
8.1%
외1필지 1400
 
4.7%
묵동 1155
 
3.8%
망우동 1138
 
3.8%
중화동 915
 
3.0%
상봉동 915
 
3.0%
신내동 430
 
1.4%
외2필지 371
 
1.2%
Other values (6453) 7353
24.4%
2023-12-12T21:34:12.819547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23113
 
15.8%
7909
 
5.4%
1 7717
 
5.3%
7064
 
4.8%
7022
 
4.8%
7022
 
4.8%
6994
 
4.8%
6993
 
4.8%
6993
 
4.8%
6992
 
4.8%
Other values (77) 58853
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82532
56.3%
Decimal Number 34245
23.3%
Space Separator 23113
 
15.8%
Dash Punctuation 6773
 
4.6%
Uppercase Letter 3
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Lowercase Letter 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7909
9.6%
7064
8.6%
7022
8.5%
7022
8.5%
6994
8.5%
6993
8.5%
6993
8.5%
6992
8.5%
6992
8.5%
2439
 
3.0%
Other values (60) 16112
19.5%
Decimal Number
ValueCountFrequency (%)
1 7717
22.5%
2 5088
14.9%
3 4238
12.4%
4 3647
10.6%
5 2770
 
8.1%
6 2412
 
7.0%
0 2191
 
6.4%
7 2185
 
6.4%
9 2007
 
5.9%
8 1990
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
H 1
33.3%
Space Separator
ValueCountFrequency (%)
23113
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6773
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
c 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82532
56.3%
Common 64135
43.7%
Latin 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7909
9.6%
7064
8.6%
7022
8.5%
7022
8.5%
6994
8.5%
6993
8.5%
6993
8.5%
6992
8.5%
6992
8.5%
2439
 
3.0%
Other values (60) 16112
19.5%
Common
ValueCountFrequency (%)
23113
36.0%
1 7717
 
12.0%
- 6773
 
10.6%
2 5088
 
7.9%
3 4238
 
6.6%
4 3647
 
5.7%
5 2770
 
4.3%
6 2412
 
3.8%
0 2191
 
3.4%
7 2185
 
3.4%
Other values (4) 4001
 
6.2%
Latin
ValueCountFrequency (%)
B 2
40.0%
c 2
40.0%
H 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82532
56.3%
ASCII 64140
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23113
36.0%
1 7717
 
12.0%
- 6773
 
10.6%
2 5088
 
7.9%
3 4238
 
6.6%
4 3647
 
5.7%
5 2770
 
4.3%
6 2412
 
3.8%
0 2191
 
3.4%
7 2185
 
3.4%
Other values (7) 4006
 
6.2%
Hangul
ValueCountFrequency (%)
7909
9.6%
7064
8.6%
7022
8.5%
7022
8.5%
6994
8.5%
6993
8.5%
6993
8.5%
6992
8.5%
6992
8.5%
2439
 
3.0%
Other values (60) 16112
19.5%

대지면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct3826
Distinct (%)54.7%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean442.33113
Minimum43.99
Maximum186884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:12.996042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43.99
5-th percentile105.7975
Q1146.4
median222.9
Q3325.05
95-th percentile860.45
Maximum186884
Range186840.01
Interquartile range (IQR)178.65

Descriptive statistics

Standard deviation3108.7536
Coefficient of variation (CV)7.0281141
Kurtosis2210.7468
Mean442.33113
Median Absolute Deviation (MAD)83.7
Skewness42.436034
Sum3092336.9
Variance9664349.1
MonotonicityNot monotonic
2023-12-12T21:34:13.167680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330.0 128
 
1.8%
116.0 20
 
0.3%
165.3 19
 
0.3%
126.0 18
 
0.3%
247.0 18
 
0.3%
264.0 17
 
0.2%
300.0 16
 
0.2%
329.0 16
 
0.2%
119.0 16
 
0.2%
156.0 15
 
0.2%
Other values (3816) 6708
95.9%
ValueCountFrequency (%)
43.99 1
< 0.1%
45.72 1
< 0.1%
53.6 1
< 0.1%
54.9 2
< 0.1%
58.0 1
< 0.1%
60.55 1
< 0.1%
61.9 1
< 0.1%
64.12 1
< 0.1%
64.4 1
< 0.1%
65.27 1
< 0.1%
ValueCountFrequency (%)
186884.0 1
< 0.1%
116826.0 1
< 0.1%
80349.7 1
< 0.1%
45028.0 1
< 0.1%
38012.1 2
< 0.1%
32770.0 1
< 0.1%
30945.7 1
< 0.1%
24509.7 2
< 0.1%
23728.0 1
< 0.1%
22411.1 1
< 0.1%

건축면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct5698
Distinct (%)81.5%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean190.40776
Minimum0
Maximum25077.71
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:13.349753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60.9955
Q184.83
median126.81
Q3175.1825
95-th percentile406.7165
Maximum25077.71
Range25077.71
Interquartile range (IQR)90.3525

Descriptive statistics

Standard deviation532.84339
Coefficient of variation (CV)2.7984332
Kurtosis846.09651
Mean190.40776
Median Absolute Deviation (MAD)43.83
Skewness24.082264
Sum1331331
Variance283922.08
MonotonicityNot monotonic
2023-12-12T21:34:13.529453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.84 6
 
0.1%
71.1 5
 
0.1%
82.68 5
 
0.1%
69.6 5
 
0.1%
99.0 5
 
0.1%
139.44 5
 
0.1%
92.4 5
 
0.1%
74.26 5
 
0.1%
82.8 5
 
0.1%
73.92 5
 
0.1%
Other values (5688) 6941
99.3%
ValueCountFrequency (%)
0.0 1
< 0.1%
25.53 1
< 0.1%
30.24 1
< 0.1%
34.75 1
< 0.1%
34.77 1
< 0.1%
34.94 1
< 0.1%
35.72 1
< 0.1%
36.12 1
< 0.1%
36.42 1
< 0.1%
37.04 2
< 0.1%
ValueCountFrequency (%)
25077.71 1
< 0.1%
12659.22 1
< 0.1%
11989.03 1
< 0.1%
10518.99 1
< 0.1%
10382.66 1
< 0.1%
9443.76 1
< 0.1%
8865.65 1
< 0.1%
7536.65 1
< 0.1%
7165.867 1
< 0.1%
6672.255 1
< 0.1%

연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct6493
Distinct (%)92.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean951.56211
Minimum45.12
Maximum258173.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:13.695183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45.12
5-th percentile173.465
Q1262.395
median427.81
Q3644.76
95-th percentile2020.26
Maximum258173.73
Range258128.61
Interquartile range (IQR)382.365

Descriptive statistics

Standard deviation5810.599
Coefficient of variation (CV)6.1063791
Kurtosis1043.0167
Mean951.56211
Median Absolute Deviation (MAD)182.57
Skewness28.780946
Sum6652370.7
Variance33763060
MonotonicityNot monotonic
2023-12-12T21:34:13.925733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
659.98 6
 
0.1%
659.85 5
 
0.1%
659.55 5
 
0.1%
659.88 5
 
0.1%
659.37 5
 
0.1%
659.26 5
 
0.1%
659.78 4
 
0.1%
659.11 4
 
0.1%
659.93 4
 
0.1%
659.8 4
 
0.1%
Other values (6483) 6944
99.3%
ValueCountFrequency (%)
45.12 1
< 0.1%
60.9 1
< 0.1%
61.0 2
< 0.1%
75.6 1
< 0.1%
77.34 1
< 0.1%
84.66 1
< 0.1%
85.52 1
< 0.1%
87.85 1
< 0.1%
88.83 1
< 0.1%
91.23 1
< 0.1%
ValueCountFrequency (%)
258173.73 1
< 0.1%
232942.93 1
< 0.1%
143708.001 1
< 0.1%
116792.419 1
< 0.1%
105041.37 1
< 0.1%
100120.53 1
< 0.1%
99871.25 1
< 0.1%
83043.47 1
< 0.1%
75324.46 1
< 0.1%
69805.86 1
< 0.1%

증축연면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct283
Distinct (%)97.9%
Missing6704
Missing (%)95.9%
Infinite0
Infinite (%)0.0%
Mean1160.1492
Minimum-30.6
Maximum39537.67
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size61.6 KiB
2023-12-12T21:34:14.231746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-30.6
5-th percentile13.896
Q199.12
median265.83
Q3776.1
95-th percentile3672.524
Maximum39537.67
Range39568.27
Interquartile range (IQR)676.98

Descriptive statistics

Standard deviation3774.2129
Coefficient of variation (CV)3.2532134
Kurtosis54.312895
Mean1160.1492
Median Absolute Deviation (MAD)221.26
Skewness6.8707528
Sum335283.12
Variance14244683
MonotonicityNot monotonic
2023-12-12T21:34:14.366430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.09 2
 
< 0.1%
45.04 2
 
< 0.1%
42.01 2
 
< 0.1%
32.48 2
 
< 0.1%
10.8 2
 
< 0.1%
26.18 2
 
< 0.1%
7.05 1
 
< 0.1%
688.01 1
 
< 0.1%
308.17 1
 
< 0.1%
301.31 1
 
< 0.1%
Other values (273) 273
 
3.9%
(Missing) 6704
95.9%
ValueCountFrequency (%)
-30.6 1
< 0.1%
-29.6 1
< 0.1%
3.06 1
< 0.1%
3.77 1
< 0.1%
4.4 1
< 0.1%
5.23 1
< 0.1%
6.4 1
< 0.1%
7.05 1
< 0.1%
8.9 1
< 0.1%
9.62 1
< 0.1%
ValueCountFrequency (%)
39537.67 1
< 0.1%
27797.25 1
< 0.1%
24450.95 1
< 0.1%
22791.92 1
< 0.1%
15638.03 1
< 0.1%
14549.08 1
< 0.1%
12421.23 1
< 0.1%
6659.3 1
< 0.1%
6395.0 1
< 0.1%
6197.42 1
< 0.1%

구조
Categorical

IMBALANCE 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
철근콘크리트구조
6491 
일반철골구조
 
261
벽돌구조
 
87
경량철골구조
 
72
철골철근콘크리트구조
 
32
Other values (11)
 
50

Length

Max length12
Median length8
Mean length7.8604319
Min length1

Unique

Unique5 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
철근콘크리트구조 6491
92.8%
일반철골구조 261
 
3.7%
벽돌구조 87
 
1.2%
경량철골구조 72
 
1.0%
철골철근콘크리트구조 32
 
0.5%
철골콘크리트구조 19
 
0.3%
일반목구조 7
 
0.1%
기타철골철근콘크리트구조 7
 
0.1%
기타조적구조 5
 
0.1%
기타콘크리트구조 4
 
0.1%
Other values (6) 8
 
0.1%

Length

2023-12-12T21:34:14.513001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
철근콘크리트구조 6491
92.8%
일반철골구조 261
 
3.7%
벽돌구조 87
 
1.2%
경량철골구조 72
 
1.0%
철골철근콘크리트구조 32
 
0.5%
철골콘크리트구조 19
 
0.3%
일반목구조 7
 
0.1%
기타철골철근콘크리트구조 7
 
0.1%
기타조적구조 5
 
0.1%
기타콘크리트구조 4
 
0.1%
Other values (5) 7
 
0.1%
Distinct3501
Distinct (%)50.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Minimum1983-06-09 00:00:00
Maximum2023-01-19 00:00:00
2023-12-12T21:34:14.668145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:14.869774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

착공처리일
Date

MISSING 

Distinct3418
Distinct (%)51.5%
Missing351
Missing (%)5.0%
Memory size54.8 KiB
Minimum1984-06-01 00:00:00
Maximum2022-12-27 00:00:00
2023-12-12T21:34:15.066841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:15.281145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

사용승인일
Date

MISSING 

Distinct3550
Distinct (%)53.2%
Missing321
Missing (%)4.6%
Memory size54.8 KiB
Minimum1989-05-16 00:00:00
Maximum2109-03-08 00:00:00
2023-12-12T21:34:15.467107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:15.664575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최대지상층수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.3%
Missing7
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean4.7369024
Minimum0
Maximum26
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:15.816945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q35
95-th percentile8
Maximum26
Range26
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.973425
Coefficient of variation (CV)0.41660664
Kurtosis14.877871
Mean4.7369024
Median Absolute Deviation (MAD)1
Skewness2.452288
Sum33092
Variance3.8944061
MonotonicityNot monotonic
2023-12-12T21:34:15.983868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5 2202
31.5%
4 1668
23.9%
6 987
14.1%
3 948
13.6%
2 337
 
4.8%
7 292
 
4.2%
1 187
 
2.7%
8 152
 
2.2%
9 60
 
0.9%
10 43
 
0.6%
Other values (13) 110
 
1.6%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 187
 
2.7%
2 337
 
4.8%
3 948
13.6%
4 1668
23.9%
5 2202
31.5%
6 987
14.1%
7 292
 
4.2%
8 152
 
2.2%
9 60
 
0.9%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 1
 
< 0.1%
20 9
0.1%
19 4
 
0.1%
18 5
 
0.1%
17 5
 
0.1%
16 4
 
0.1%
15 15
0.2%
14 8
0.1%
13 14
0.2%

최대지하층수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)0.1%
Missing729
Missing (%)10.4%
Infinite0
Infinite (%)0.0%
Mean0.30140485
Minimum0
Maximum7
Zeros4656
Zeros (%)66.6%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:16.134107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5984369
Coefficient of variation (CV)1.9854919
Kurtosis19.89621
Mean0.30140485
Median Absolute Deviation (MAD)0
Skewness3.2555284
Sum1888
Variance0.35812672
MonotonicityNot monotonic
2023-12-12T21:34:16.279391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4656
66.6%
1 1428
 
20.4%
2 127
 
1.8%
3 26
 
0.4%
4 18
 
0.3%
7 4
 
0.1%
6 3
 
< 0.1%
5 2
 
< 0.1%
(Missing) 729
 
10.4%
ValueCountFrequency (%)
0 4656
66.6%
1 1428
 
20.4%
2 127
 
1.8%
3 26
 
0.4%
4 18
 
0.3%
5 2
 
< 0.1%
6 3
 
< 0.1%
7 4
 
0.1%
ValueCountFrequency (%)
7 4
 
0.1%
6 3
 
< 0.1%
5 2
 
< 0.1%
4 18
 
0.3%
3 26
 
0.4%
2 127
 
1.8%
1 1428
 
20.4%
0 4656
66.6%

동수
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.013442
Minimum0
Maximum24
Zeros417
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:16.401321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile1
Maximum24
Range24
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.58620883
Coefficient of variation (CV)0.57843352
Kurtosis455.20886
Mean1.013442
Median Absolute Deviation (MAD)0
Skewness15.265288
Sum7087
Variance0.3436408
MonotonicityNot monotonic
2023-12-12T21:34:16.537030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 6258
89.5%
0 417
 
6.0%
2 234
 
3.3%
3 53
 
0.8%
4 15
 
0.2%
5 6
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
13 2
 
< 0.1%
10 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
0 417
 
6.0%
1 6258
89.5%
2 234
 
3.3%
3 53
 
0.8%
4 15
 
0.2%
5 6
 
0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
24 1
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
7 2
 
< 0.1%
6 2
 
< 0.1%
5 6
 
0.1%
4 15
 
0.2%
3 53
0.8%

주용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct27
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
공동주택
3223 
단독주택
2379 
제2종근린생활시설
662 
제1종근린생활시설
 
312
업무시설
 
114
Other values (22)
 
303

Length

Max length10
Median length4
Mean length4.7513228
Min length2

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st row단독주택
2nd row공동주택
3rd row공동주택
4th row공동주택
5th row공동주택

Common Values

ValueCountFrequency (%)
공동주택 3223
46.1%
단독주택 2379
34.0%
제2종근린생활시설 662
 
9.5%
제1종근린생활시설 312
 
4.5%
업무시설 114
 
1.6%
노유자시설 58
 
0.8%
종교시설 46
 
0.7%
창고시설 34
 
0.5%
문화및집회시설 25
 
0.4%
동.식물관련시설 24
 
0.3%
Other values (17) 116
 
1.7%

Length

2023-12-12T21:34:16.706888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택 3223
46.1%
단독주택 2379
34.0%
제2종근린생활시설 662
 
9.5%
제1종근린생활시설 312
 
4.5%
업무시설 114
 
1.6%
노유자시설 58
 
0.8%
종교시설 46
 
0.7%
창고시설 34
 
0.5%
문화및집회시설 25
 
0.4%
동.식물관련시설 24
 
0.3%
Other values (17) 116
 
1.7%

부속용도
Text

MISSING 

Distinct1571
Distinct (%)25.3%
Missing793
Missing (%)11.3%
Memory size54.8 KiB
2023-12-12T21:34:17.026238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length39
Mean length9.935
Min length1

Characters and Unicode

Total characters61597
Distinct characters237
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

Unique1209 ?
Unique (%)19.5%

Sample

1st row제2종근린생활시설(사무소) / 다가구 주택(3가구)
2nd row도시형생활주택-단지형 다세대주택
3rd row도시형생활주택
4th row도시형생활주책(단지형다세대)
5th row다세대주택
ValueCountFrequency (%)
다세대주택 1299
 
16.6%
다가구주택 994
 
12.7%
527
 
6.7%
근린생활시설 323
 
4.1%
도시형생활주택 212
 
2.7%
도시형생활주택(단지형다세대 209
 
2.7%
다중주택 205
 
2.6%
도시형생활주택(단지형다세대주택 160
 
2.0%
주택 125
 
1.6%
단독주택 123
 
1.6%
Other values (1245) 3669
46.8%
2023-12-12T21:34:17.863395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5686
 
9.2%
5645
 
9.2%
4714
 
7.7%
3004
 
4.9%
2951
 
4.8%
2948
 
4.8%
2904
 
4.7%
2585
 
4.2%
2201
 
3.6%
2031
 
3.3%
Other values (227) 26928
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53230
86.4%
Open Punctuation 1769
 
2.9%
Close Punctuation 1762
 
2.9%
Decimal Number 1738
 
2.8%
Space Separator 1664
 
2.7%
Other Punctuation 1217
 
2.0%
Dash Punctuation 189
 
0.3%
Connector Punctuation 10
 
< 0.1%
Math Symbol 9
 
< 0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5686
 
10.7%
5645
 
10.6%
4714
 
8.9%
3004
 
5.6%
2951
 
5.5%
2948
 
5.5%
2904
 
5.5%
2585
 
4.9%
2201
 
4.1%
2031
 
3.8%
Other values (193) 18561
34.9%
Decimal Number
ValueCountFrequency (%)
2 656
37.7%
1 516
29.7%
5 122
 
7.0%
8 106
 
6.1%
0 89
 
5.1%
3 67
 
3.9%
6 66
 
3.8%
4 57
 
3.3%
7 33
 
1.9%
9 26
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 618
50.8%
/ 452
37.1%
& 73
 
6.0%
: 38
 
3.1%
. 36
 
3.0%
Math Symbol
ValueCountFrequency (%)
~ 4
44.4%
> 2
22.2%
< 2
22.2%
+ 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 1760
99.5%
[ 8
 
0.5%
{ 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 1753
99.5%
] 8
 
0.5%
} 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 2
33.3%
T 2
33.3%
A 2
33.3%
Lowercase Letter
ValueCountFrequency (%)
t 1
33.3%
l 1
33.3%
f 1
33.3%
Space Separator
ValueCountFrequency (%)
1664
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 189
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53230
86.4%
Common 8358
 
13.6%
Latin 9
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5686
 
10.7%
5645
 
10.6%
4714
 
8.9%
3004
 
5.6%
2951
 
5.5%
2948
 
5.5%
2904
 
5.5%
2585
 
4.9%
2201
 
4.1%
2031
 
3.8%
Other values (193) 18561
34.9%
Common
ValueCountFrequency (%)
( 1760
21.1%
) 1753
21.0%
1664
19.9%
2 656
 
7.8%
, 618
 
7.4%
1 516
 
6.2%
/ 452
 
5.4%
- 189
 
2.3%
5 122
 
1.5%
8 106
 
1.3%
Other values (18) 522
 
6.2%
Latin
ValueCountFrequency (%)
P 2
22.2%
T 2
22.2%
A 2
22.2%
t 1
11.1%
l 1
11.1%
f 1
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53229
86.4%
ASCII 8367
 
13.6%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
5686
 
10.7%
5645
 
10.6%
4714
 
8.9%
3004
 
5.6%
2951
 
5.5%
2948
 
5.5%
2904
 
5.5%
2585
 
4.9%
2201
 
4.1%
2031
 
3.8%
Other values (192) 18560
34.9%
ASCII
ValueCountFrequency (%)
( 1760
21.0%
) 1753
21.0%
1664
19.9%
2 656
 
7.8%
, 618
 
7.4%
1 516
 
6.2%
/ 452
 
5.4%
- 189
 
2.3%
5 122
 
1.5%
8 106
 
1.3%
Other values (24) 531
 
6.3%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

세대수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct76
Distinct (%)1.8%
Missing2814
Missing (%)40.2%
Infinite0
Infinite (%)0.0%
Mean10.263221
Minimum0
Maximum719
Zeros820
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:18.032151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q312
95-th percentile23
Maximum719
Range719
Interquartile range (IQR)8

Descriptive statistics

Standard deviation21.342622
Coefficient of variation (CV)2.0795248
Kurtosis459.10068
Mean10.263221
Median Absolute Deviation (MAD)4
Skewness17.983455
Sum42890
Variance455.50753
MonotonicityNot monotonic
2023-12-12T21:34:18.213266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 820
 
11.7%
8 752
 
10.8%
10 527
 
7.5%
7 298
 
4.3%
12 189
 
2.7%
6 152
 
2.2%
4 140
 
2.0%
15 134
 
1.9%
11 131
 
1.9%
9 129
 
1.8%
Other values (66) 907
 
13.0%
(Missing) 2814
40.2%
ValueCountFrequency (%)
0 820
11.7%
1 38
 
0.5%
2 4
 
0.1%
3 61
 
0.9%
4 140
 
2.0%
5 68
 
1.0%
6 152
 
2.2%
7 298
 
4.3%
8 752
10.8%
9 129
 
1.8%
ValueCountFrequency (%)
719 1
< 0.1%
511 1
< 0.1%
497 1
< 0.1%
299 1
< 0.1%
265 2
< 0.1%
249 1
< 0.1%
238 1
< 0.1%
235 1
< 0.1%
176 1
< 0.1%
167 1
< 0.1%

호수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct59
Distinct (%)7.5%
Missing6204
Missing (%)88.7%
Infinite0
Infinite (%)0.0%
Mean10.365019
Minimum0
Maximum557
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:18.367685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q38
95-th percentile27.8
Maximum557
Range557
Interquartile range (IQR)7

Descriptive statistics

Standard deviation36.943345
Coefficient of variation (CV)3.5642332
Kurtosis124.60172
Mean10.365019
Median Absolute Deviation (MAD)2
Skewness10.316202
Sum8178
Variance1364.8108
MonotonicityNot monotonic
2023-12-12T21:34:18.508849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 244
 
3.5%
2 97
 
1.4%
3 62
 
0.9%
4 53
 
0.8%
6 49
 
0.7%
5 34
 
0.5%
7 28
 
0.4%
9 27
 
0.4%
8 26
 
0.4%
11 23
 
0.3%
Other values (49) 146
 
2.1%
(Missing) 6204
88.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 244
3.5%
2 97
 
1.4%
3 62
 
0.9%
4 53
 
0.8%
5 34
 
0.5%
6 49
 
0.7%
7 28
 
0.4%
8 26
 
0.4%
9 27
 
0.4%
ValueCountFrequency (%)
557 1
< 0.1%
503 1
< 0.1%
368 1
< 0.1%
325 1
< 0.1%
314 1
< 0.1%
196 1
< 0.1%
170 1
< 0.1%
143 1
< 0.1%
142 1
< 0.1%
140 1
< 0.1%

가구수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)0.6%
Missing3552
Missing (%)50.8%
Infinite0
Infinite (%)0.0%
Mean2.885789
Minimum0
Maximum19
Zeros865
Zeros (%)12.4%
Negative0
Negative (%)0.0%
Memory size61.6 KiB
2023-12-12T21:34:18.657387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile7
Maximum19
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5318962
Coefficient of variation (CV)0.87736707
Kurtosis1.4487827
Mean2.885789
Median Absolute Deviation (MAD)2
Skewness0.85316973
Sum9930
Variance6.4104986
MonotonicityNot monotonic
2023-12-12T21:34:18.803516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 865
 
12.4%
5 663
 
9.5%
3 567
 
8.1%
1 469
 
6.7%
4 249
 
3.6%
2 216
 
3.1%
6 205
 
2.9%
7 71
 
1.0%
8 63
 
0.9%
9 29
 
0.4%
Other values (9) 44
 
0.6%
(Missing) 3552
50.8%
ValueCountFrequency (%)
0 865
12.4%
1 469
6.7%
2 216
 
3.1%
3 567
8.1%
4 249
 
3.6%
5 663
9.5%
6 205
 
2.9%
7 71
 
1.0%
8 63
 
0.9%
9 29
 
0.4%
ValueCountFrequency (%)
19 1
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 3
 
< 0.1%
12 10
 
0.1%
11 9
 
0.1%
10 14
0.2%
9 29
0.4%

Interactions

2023-12-12T21:34:09.609164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:56.261651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:57.596674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:59.321661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:00.726042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:02.031529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:03.222333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:04.574317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:05.830371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:07.241097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.452882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:09.724044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:56.374969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:57.738688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:59.443990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:00.844357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:02.166083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:03.336603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:04.719968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:06.188854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:07.360505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.561904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:09.817349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:56.483849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:57.860302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:59.568117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:00.968777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:02.298712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:03.438412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:04.851007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:06.288338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:07.465057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.664389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:09.919667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:56.586544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:57.985749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:59.686931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:01.102613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:02.427436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:03.532170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:04.982943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:06.378852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:07.570711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.773050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:10.002796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:56.695143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:58.096694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:59.826097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:01.219610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:02.538669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:03.644237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:05.108471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:06.464440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:07.672075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.861408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:10.086980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:56.793942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:58.230845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:59.942847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:01.340463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:02.642221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:03.784286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:05.214685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:06.561534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:07.773731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.945440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:10.179411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:56.923074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:58.346998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:00.069077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:01.470322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:02.740792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:03.918965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:05.302895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:06.679687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:07.877963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:09.064464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:10.278023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:57.054958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:58.482021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:00.210457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:01.594260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:02.844552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:04.042886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:05.406865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:06.783455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.006390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:09.185859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:10.369279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:57.163659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:58.626934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:00.355406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:01.697014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:02.955696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:04.187230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:05.492403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:06.875679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.108792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:09.289419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:10.453931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:57.302621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:58.769218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:00.492254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:01.800630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:03.051038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:04.309584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:05.600865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:06.989356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.219464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:09.403463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:10.539139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:57.444478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:59.188112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:00.604232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:01.899935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:03.127082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:04.437372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:05.715571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:07.119409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:08.344923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:34:09.497737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:34:18.910379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번건축구분대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)구조최대지상층수최대지하층수동수주용도세대수호수가구수
연번1.0000.1230.0000.0000.0200.1160.2740.3260.2600.0490.2800.0380.1840.598
건축구분0.1231.0000.1490.1190.105NaN0.2970.0770.3190.2670.4560.0000.0000.169
대지면적(제곱미터)0.0000.1491.0000.7800.5930.6330.1900.2570.2620.7430.8660.8830.0000.000
건축면적(제곱미터)0.0000.1190.7801.0000.9200.9000.3900.2870.4350.6180.6290.7920.7590.000
연면적(제곱미터)0.0200.1050.5930.9201.0000.7090.3700.4370.5960.6560.6150.9110.8000.000
증축연면적(제곱미터)0.116NaN0.6330.9000.7091.0000.0000.7150.5940.4930.7641.000NaN0.052
구조0.2740.2970.1900.3900.3700.0001.0000.4560.2490.0930.5530.1260.2940.173
최대지상층수0.3260.0770.2570.2870.4370.7150.4561.0000.5090.5670.6030.6780.6700.313
최대지하층수0.2600.3190.2620.4350.5960.5940.2490.5091.0000.2750.5230.8640.7730.338
동수0.0490.2670.7430.6180.6560.4930.0930.5670.2751.0000.4000.7770.3870.120
주용도0.2800.4560.8660.6290.6150.7640.5530.6030.5230.4001.0000.1170.6740.685
세대수0.0380.0000.8830.7920.9111.0000.1260.6780.8640.7770.1171.0000.7750.000
호수0.1840.0000.0000.7590.800NaN0.2940.6700.7730.3870.6740.7751.000NaN
가구수0.5980.1690.0000.0000.0000.0520.1730.3130.3380.1200.6850.000NaN1.000
2023-12-12T21:34:19.090811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건축구분구조주용도
건축구분1.0000.2710.393
구조0.2711.0000.197
주용도0.3930.1971.000
2023-12-12T21:34:19.241470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)최대지상층수최대지하층수동수세대수호수가구수건축구분구조주용도
연번1.000-0.077-0.072-0.0890.130-0.3850.0390.049-0.545-0.089-0.4610.0940.1050.105
대지면적(제곱미터)-0.0771.0000.9770.8040.6400.3160.2510.2310.5690.180-0.3220.1070.0890.604
건축면적(제곱미터)-0.0720.9771.0000.8280.6610.3400.2400.2340.5810.180-0.3080.1270.1880.321
연면적(제곱미터)-0.0890.8040.8281.0000.6600.6340.4020.2140.6400.361-0.2220.1120.1770.310
증축연면적(제곱미터)0.1300.6400.6610.6601.0000.1100.1430.336-0.5630.496-0.4191.0000.0000.450
최대지상층수-0.3850.3160.3400.6340.1101.0000.1150.0410.6860.3690.0490.0770.2110.250
최대지하층수0.0390.2510.2400.4020.1430.1151.0000.1000.1280.609-0.1740.2400.1150.238
동수0.0490.2310.2340.2140.3360.0410.1001.0000.1450.065-0.1690.1950.0530.222
세대수-0.5450.5690.5810.640-0.5630.6860.1280.1451.0000.482-0.6530.0000.0530.048
호수-0.0890.1800.1800.3610.4960.3690.6090.0650.4821.0000.2260.0000.1790.444
가구수-0.461-0.322-0.308-0.222-0.4190.049-0.174-0.169-0.6530.2261.0000.1300.0740.331
건축구분0.0940.1070.1270.1121.0000.0770.2400.1950.0000.0000.1301.0000.2710.393
구조0.1050.0890.1880.1770.0000.2110.1150.0530.0530.1790.0740.2711.0000.197
주용도0.1050.6040.3210.3100.4500.2500.2380.2220.0480.4440.3310.3930.1971.000

Missing values

2023-12-12T21:34:10.708744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:34:10.949211image/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.
2023-12-12T21:34:11.196546image/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

연번건축구분대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)구조허가일착공처리일사용승인일최대지상층수최대지하층수동수주용도부속용도세대수호수가구수
01신축서울특별시 중랑구 면목동 183-79128.563.84198.89<NA>철근콘크리트구조2023-01-19<NA><NA>401단독주택제2종근린생활시설(사무소) / 다가구 주택(3가구)<NA><NA>3
12신축서울특별시 중랑구 묵동 114-3 외1필지328.1184.3650.18<NA>철근콘크리트구조2023-01-18<NA><NA>501공동주택도시형생활주택-단지형 다세대주택10<NA><NA>
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34신축서울특별시 중랑구 망우동 526-15 외2필지340.5170.12850.08<NA>철근콘크리트구조2022-12-30<NA><NA>801공동주택도시형생활주책(단지형다세대)105<NA>
45신축서울특별시 중랑구 면목동 374-28 외1필지224.8131.1399.78<NA>철근콘크리트구조2022-12-30<NA><NA>501공동주택다세대주택6<NA><NA>
56신축서울특별시 중랑구 망우동 349-24 외1필지193.09103.68385.03<NA>철근콘크리트구조2022-12-28<NA><NA>601공동주택도시형생활주택(단지형다세대)10<NA><NA>
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89신축서울특별시 중랑구 상봉동 124-89 외2필지396.23194.43659.25<NA>철근콘크리트구조2022-12-16<NA><NA>601공동주택다세대주택14<NA><NA>
910신축서울특별시 중랑구 면목동 636-4446.5224.582734.76<NA>철근콘크리트구조2022-12-15<NA><NA>1121제1종근린생활시설의원<NA><NA><NA>
연번건축구분대지위치대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)구조허가일착공처리일사용승인일최대지상층수최대지하층수동수주용도부속용도세대수호수가구수
69836984신축서울특별시 중랑구 면목동 456-3192.1845.06135.18<NA>벽돌구조1985-05-09<NA>2007-05-08211단독주택단독주택0<NA>1
69846985증축서울특별시 중랑구 상봉동 130-112144.887.83183.95183.95벽돌구조1985-03-25<NA><NA>211단독주택<NA>0<NA>1
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69866987증축서울특별시 중랑구 망우동 451-7105.79551.42179.5525.29벽돌구조1984-09-14<NA>2014-11-04311단독주택다가구주택<NA><NA>2
69876988신축서울특별시 중랑구 면목동 145-9 외4필지1278.85566.552502.95<NA>철근콘크리트구조1984-06-25<NA>2000-11-16411문화및집회시설<NA><NA><NA><NA>
69886989증축서울특별시 중랑구 면목동 374-55 외1필지396.7197.58667.69443.69철근콘크리트구조1984-05-291984-06-012005-02-25411문화및집회시설<NA>0<NA>0
69896990신축서울특별시 중랑구 묵동 296-31259.88453.82896.7<NA>철근콘크리트구조1984-02-01<NA>2011-06-28312종교시설교회<NA><NA><NA>
69906991신축서울특별시 중랑구 면목동 542-28146.887.99226.58<NA>벽돌구조1983-08-22<NA>2006-07-14211단독주택다가구주택0<NA>3
69916992신축서울특별시 중랑구 상봉동 125-28186.495.94320.34<NA>철근콘크리트구조1983-07-07<NA>2007-02-06311제1종근린생활시설<NA>0<NA>6
69926993증축서울특별시 중랑구 묵동 233-103523.2257.55742.55742.55철근콘크리트구조1983-06-09<NA>2006-12-27311제1종근린생활시설근생및주택<NA><NA>1