Overview

Dataset statistics

Number of variables9
Number of observations349
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.1 KiB
Average record size in memory79.4 B

Variable types

Numeric7
Text1
Categorical1

Dataset

Description경기주택도시공사 GH주택청약센터의 주택유형정보로써 사업코드, 주택유형코드, 주거공용면적, 주거전용면적, 지하주차장면적 등의 정보를 포함하고 있습니다.
URLhttps://www.data.go.kr/data/15119422/fileData.do

Alerts

기타유의사항 has constant value ""Constant
주거전용면적 is highly overall correlated with 주거공용면적 and 1 other fieldsHigh correlation
주거공용면적 is highly overall correlated with 주거전용면적 and 1 other fieldsHigh correlation
방수 is highly overall correlated with 주거전용면적 and 1 other fieldsHigh correlation
구분 has unique valuesUnique
기타공용면적 has 16 (4.6%) zerosZeros
지하주차장면적 has 121 (34.7%) zerosZeros
방수 has 12 (3.4%) zerosZeros

Reproduction

Analysis started2023-12-12 18:07:50.930711
Analysis finished2023-12-12 18:07:57.032432
Duration6.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

UNIQUE 

Distinct349
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175
Minimum1
Maximum349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T03:07:57.110440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.4
Q188
median175
Q3262
95-th percentile331.6
Maximum349
Range348
Interquartile range (IQR)174

Descriptive statistics

Standard deviation100.89186
Coefficient of variation (CV)0.57652489
Kurtosis-1.2
Mean175
Median Absolute Deviation (MAD)87
Skewness0
Sum61075
Variance10179.167
MonotonicityStrictly increasing
2023-12-13T03:07:57.261404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
231 1
 
0.3%
239 1
 
0.3%
238 1
 
0.3%
237 1
 
0.3%
236 1
 
0.3%
235 1
 
0.3%
234 1
 
0.3%
233 1
 
0.3%
232 1
 
0.3%
Other values (339) 339
97.1%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
349 1
0.3%
348 1
0.3%
347 1
0.3%
346 1
0.3%
345 1
0.3%
344 1
0.3%
343 1
0.3%
342 1
0.3%
341 1
0.3%
340 1
0.3%

사업코드
Real number (ℝ)

Distinct72
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2367761.6
Minimum0
Maximum9999903
Zeros3
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T03:07:57.441109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile201701
Q12012108
median2017003
Q32017016
95-th percentile9999004
Maximum9999903
Range9999903
Interquartile range (IQR)4908

Descriptive statistics

Standard deviation2230572
Coefficient of variation (CV)0.94205939
Kurtosis7.117556
Mean2367761.6
Median Absolute Deviation (MAD)1852
Skewness2.770978
Sum8.2634878 × 108
Variance4.9754515 × 1012
MonotonicityNot monotonic
2023-12-13T03:07:57.610077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2017003 22
 
6.3%
2017030 19
 
5.4%
2019013 15
 
4.3%
9999022 14
 
4.0%
2017004 13
 
3.7%
2015023 13
 
3.7%
2017012 12
 
3.4%
2017007 12
 
3.4%
2016017 10
 
2.9%
2016011 9
 
2.6%
Other values (62) 210
60.2%
ValueCountFrequency (%)
0 3
0.9%
19031 1
 
0.3%
19032 1
 
0.3%
201106 6
1.7%
201121 5
1.4%
201701 5
1.4%
211029 6
1.7%
211109 6
1.7%
221029 5
1.4%
221109 5
1.4%
ValueCountFrequency (%)
9999903 1
 
0.3%
9999902 1
 
0.3%
9999901 1
 
0.3%
9999022 14
4.0%
9999004 2
 
0.6%
9999002 5
 
1.4%
9999001 1
 
0.3%
4017006 6
1.7%
2207291 1
 
0.3%
2106181 3
 
0.9%

주택유형코드
Text

Distinct147
Distinct (%)42.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-13T03:07:58.078855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.4297994
Min length2

Characters and Unicode

Total characters1197
Distinct characters25
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)23.2%

Sample

1st row49B
2nd row84A
3rd row84B
4th row84C1
5th row84C2
ValueCountFrequency (%)
51a 11
 
3.2%
26a 11
 
3.2%
59형 11
 
3.2%
36a 9
 
2.6%
59a 9
 
2.6%
26a1 8
 
2.3%
26b 8
 
2.3%
59b 8
 
2.3%
59c 8
 
2.3%
44a 8
 
2.3%
Other values (137) 258
73.9%
2023-12-13T03:07:58.614493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 179
15.0%
2 155
12.9%
1 144
12.0%
6 124
10.4%
B 106
8.9%
3 95
7.9%
4 94
7.9%
5 81
6.8%
9 65
 
5.4%
C 42
 
3.5%
Other values (15) 112
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 818
68.3%
Uppercase Letter 336
28.1%
Other Letter 26
 
2.2%
Other Punctuation 10
 
0.8%
Dash Punctuation 7
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 155
18.9%
1 144
17.6%
6 124
15.2%
3 95
11.6%
4 94
11.5%
5 81
9.9%
9 65
7.9%
8 38
 
4.6%
7 16
 
2.0%
0 6
 
0.7%
Other Letter
ValueCountFrequency (%)
형 19
73.1%
주 1
 
3.8%
거 1
 
3.8%
약 1
 
3.8%
자 1
 
3.8%
아 1
 
3.8%
파 1
 
3.8%
트 1
 
3.8%
Uppercase Letter
ValueCountFrequency (%)
A 179
53.3%
B 106
31.5%
C 42
 
12.5%
D 7
 
2.1%
E 2
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 835
69.8%
Latin 336
28.1%
Hangul 26
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 155
18.6%
1 144
17.2%
6 124
14.9%
3 95
11.4%
4 94
11.3%
5 81
9.7%
9 65
7.8%
8 38
 
4.6%
7 16
 
1.9%
, 10
 
1.2%
Other values (2) 13
 
1.6%
Hangul
ValueCountFrequency (%)
형 19
73.1%
주 1
 
3.8%
거 1
 
3.8%
약 1
 
3.8%
자 1
 
3.8%
아 1
 
3.8%
파 1
 
3.8%
트 1
 
3.8%
Latin
ValueCountFrequency (%)
A 179
53.3%
B 106
31.5%
C 42
 
12.5%
D 7
 
2.1%
E 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1171
97.8%
Hangul 26
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 179
15.3%
2 155
13.2%
1 144
12.3%
6 124
10.6%
B 106
9.1%
3 95
8.1%
4 94
8.0%
5 81
6.9%
9 65
 
5.6%
C 42
 
3.6%
Other values (7) 86
7.3%
Hangul
ValueCountFrequency (%)
형 19
73.1%
주 1
 
3.8%
거 1
 
3.8%
약 1
 
3.8%
자 1
 
3.8%
아 1
 
3.8%
파 1
 
3.8%
트 1
 
3.8%

주거전용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct161
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.542969
Minimum16.39
Maximum84.9876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T03:07:58.809605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.39
5-th percentile17.59602
Q126.25
median36.05
Q349.944
95-th percentile72.98348
Maximum84.9876
Range68.5976
Interquartile range (IQR)23.694

Descriptive statistics

Standard deviation17.155741
Coefficient of variation (CV)0.4451069
Kurtosis0.27171064
Mean38.542969
Median Absolute Deviation (MAD)11.08
Skewness0.88406912
Sum13451.496
Variance294.31947
MonotonicityNot monotonic
2023-12-13T03:07:59.004206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.9625 10
 
2.9%
18.9415 8
 
2.3%
21.84 7
 
2.0%
36.9618 5
 
1.4%
51.9567 5
 
1.4%
17.4867 5
 
1.4%
26.9294 5
 
1.4%
26.73 5
 
1.4%
26.25 5
 
1.4%
35.02 5
 
1.4%
Other values (151) 289
82.8%
ValueCountFrequency (%)
16.39 2
 
0.6%
16.61 3
0.9%
16.74 3
0.9%
16.9 4
1.1%
17.12 1
 
0.3%
17.4867 5
1.4%
17.76 2
 
0.6%
17.83 1
 
0.3%
17.92 3
0.9%
17.97 1
 
0.3%
ValueCountFrequency (%)
84.9876 2
0.6%
84.9825 4
1.1%
84.98 3
0.9%
84.9585 2
0.6%
84.9511 2
0.6%
84.95 2
0.6%
72.9858 3
0.9%
72.98 1
 
0.3%
59.993 1
 
0.3%
59.9925 3
0.9%

주거공용면적
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)49.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.905273
Minimum0
Maximum33.5678
Zeros2
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T03:07:59.231390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.6433
Q114.6914
median18.67
Q322.98
95-th percentile26.2327
Maximum33.5678
Range33.5678
Interquartile range (IQR)8.2886

Descriptive statistics

Standard deviation5.171381
Coefficient of variation (CV)0.27354172
Kurtosis-0.2141669
Mean18.905273
Median Absolute Deviation (MAD)4.31
Skewness-0.2267113
Sum6597.9404
Variance26.743181
MonotonicityNot monotonic
2023-12-13T03:07:59.439805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.1249 8
 
2.3%
13.1504 6
 
1.7%
22.9156 6
 
1.7%
24.48 5
 
1.4%
13.3652 5
 
1.4%
16.7005 5
 
1.4%
13.0781 5
 
1.4%
26.2327 4
 
1.1%
10.6433 4
 
1.1%
16.3687 4
 
1.1%
Other values (163) 297
85.1%
ValueCountFrequency (%)
0.0 2
0.6%
9.0808 1
 
0.3%
9.0857 2
0.6%
9.9229 4
1.1%
9.9466 4
1.1%
10.0013 3
0.9%
10.6433 4
1.1%
11.6791 2
0.6%
11.68 3
0.9%
11.8015 2
0.6%
ValueCountFrequency (%)
33.5678 1
0.3%
29.1192 2
0.6%
28.14 1
0.3%
27.82 1
0.3%
27.2754 2
0.6%
27.27 1
0.3%
27.04 1
0.3%
26.77 2
0.6%
26.7472 1
0.3%
26.437 2
0.6%

기타공용면적
Real number (ℝ)

ZEROS 

Distinct163
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.787434
Minimum0
Maximum48.8
Zeros16
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T03:07:59.612681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9003
Q13.47
median5.5141
Q310.386
95-th percentile41.6492
Maximum48.8
Range48.8
Interquartile range (IQR)6.916

Descriptive statistics

Standard deviation12.761651
Coefficient of variation (CV)1.1830108
Kurtosis2.0081313
Mean10.787434
Median Absolute Deviation (MAD)2.7531
Skewness1.8334258
Sum3764.8143
Variance162.85973
MonotonicityNot monotonic
2023-12-13T03:07:59.825319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
4.6%
2.7051 8
 
2.3%
6.2921 6
 
1.7%
3.4225 6
 
1.7%
9.54 5
 
1.4%
2.1134 5
 
1.4%
3.7889 5
 
1.4%
4.0 5
 
1.4%
11.2315 5
 
1.4%
13.34 4
 
1.1%
Other values (153) 284
81.4%
ValueCountFrequency (%)
0.0 16
4.6%
1.9003 4
 
1.1%
1.9698 1
 
0.3%
2.1134 5
 
1.4%
2.13 4
 
1.1%
2.1505 1
 
0.3%
2.174 1
 
0.3%
2.175 2
 
0.6%
2.307 4
 
1.1%
2.31 3
 
0.9%
ValueCountFrequency (%)
48.8 1
 
0.3%
46.8957 2
0.6%
46.8929 4
1.1%
46.89 3
0.9%
46.8797 2
0.6%
46.8756 2
0.6%
46.87 2
0.6%
43.81 1
 
0.3%
42.122 1
 
0.3%
40.94 1
 
0.3%

지하주차장면적
Real number (ℝ)

ZEROS 

Distinct107
Distinct (%)30.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.708269
Minimum0
Maximum38.078
Zeros121
Zeros (%)34.7%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T03:08:00.008631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13.4855
Q321.3107
95-th percentile32.24
Maximum38.078
Range38.078
Interquartile range (IQR)21.3107

Descriptive statistics

Standard deviation11.082388
Coefficient of variation (CV)0.87206121
Kurtosis-1.1049265
Mean12.708269
Median Absolute Deviation (MAD)11.6045
Skewness0.24136935
Sum4435.1858
Variance122.81933
MonotonicityNot monotonic
2023-12-13T03:08:00.211533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 121
34.7%
13.5694 8
 
2.3%
7.4051 6
 
1.7%
22.4105 6
 
1.7%
14.4479 5
 
1.4%
15.6023 5
 
1.4%
13.1819 5
 
1.4%
17.63 4
 
1.1%
5.3439 4
 
1.1%
10.7263 4
 
1.1%
Other values (97) 181
51.9%
ValueCountFrequency (%)
0.0 121
34.7%
5.3439 4
 
1.1%
5.6318 3
 
0.9%
7.4051 6
 
1.7%
8.7996 4
 
1.1%
9.1529 1
 
0.3%
9.1578 2
 
0.6%
9.5327 4
 
1.1%
10.24 1
 
0.3%
10.2903 2
 
0.6%
ValueCountFrequency (%)
38.078 2
0.6%
37.1864 1
0.3%
37.1862 1
0.3%
37.1611 1
0.3%
37.0 1
0.3%
33.6884 1
0.3%
33.627 2
0.6%
33.207 2
0.6%
33.184 2
0.6%
33.101 2
0.6%

방수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)6.6%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean15.816092
Minimum0
Maximum742
Zeros12
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-13T03:08:00.377445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q32
95-th percentile28.3
Maximum742
Range742
Interquartile range (IQR)1

Descriptive statistics

Standard deviation75.107612
Coefficient of variation (CV)4.7488098
Kurtosis45.952717
Mean15.816092
Median Absolute Deviation (MAD)1
Skewness6.4943126
Sum5504
Variance5641.1534
MonotonicityNot monotonic
2023-12-13T03:08:00.532306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 145
41.5%
2 109
31.2%
3 61
17.5%
0 12
 
3.4%
162 2
 
0.6%
490 2
 
0.6%
29 1
 
0.3%
493 1
 
0.3%
213 1
 
0.3%
274 1
 
0.3%
Other values (13) 13
 
3.7%
ValueCountFrequency (%)
0 12
 
3.4%
1 145
41.5%
2 109
31.2%
3 61
17.5%
5 1
 
0.3%
26 1
 
0.3%
27 1
 
0.3%
29 1
 
0.3%
53 1
 
0.3%
62 1
 
0.3%
ValueCountFrequency (%)
742 1
0.3%
522 1
0.3%
493 1
0.3%
490 2
0.6%
386 1
0.3%
320 1
0.3%
274 1
0.3%
213 1
0.3%
162 2
0.6%
142 1
0.3%

기타유의사항
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
공란은 데이터 미존재
349 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공란은 데이터 미존재
2nd row공란은 데이터 미존재
3rd row공란은 데이터 미존재
4th row공란은 데이터 미존재
5th row공란은 데이터 미존재

Common Values

ValueCountFrequency (%)
공란은 데이터 미존재 349
100.0%

Length

2023-12-13T03:08:00.693513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:08:00.805094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공란은 349
33.3%
데이터 349
33.3%
미존재 349
33.3%

Interactions

2023-12-13T03:07:56.028942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:51.211879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:51.903799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.559399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:53.435974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.184080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.987693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:56.116213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:51.320073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:51.996312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.647832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:53.546704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.290619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.119666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:56.223056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:51.410365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.079697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.752750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:53.662653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.387469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.213385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:56.335320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:51.502419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.168962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.860301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:53.752810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.509316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.615782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:56.442608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:51.608151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.259093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.976819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:53.854511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.623299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.716614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:56.540619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:51.705112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.355256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:53.068781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:53.954652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.725104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.815635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:56.665689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:51.799537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:52.453698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:53.219570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.076200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:54.849660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:07:55.916310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:08:00.896928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분사업코드주거전용면적주거공용면적기타공용면적지하주차장면적방수
구분1.0000.6500.3570.1960.4360.4900.249
사업코드0.6501.0000.6140.4770.6510.6510.118
주거전용면적0.3570.6141.0000.8190.9000.6770.402
주거공용면적0.1960.4770.8191.0000.6910.6590.000
기타공용면적0.4360.6510.9000.6911.0000.4480.122
지하주차장면적0.4900.6510.6770.6590.4481.0000.000
방수0.2490.1180.4020.0000.1220.0001.000
2023-12-13T03:08:01.038262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분사업코드주거전용면적주거공용면적기타공용면적지하주차장면적방수
구분1.0000.1400.0980.0890.0570.0430.031
사업코드0.1401.000-0.356-0.225-0.2100.413-0.370
주거전용면적0.098-0.3561.0000.8090.2830.1180.834
주거공용면적0.089-0.2250.8091.0000.3920.2830.702
기타공용면적0.057-0.2100.2830.3921.000-0.2670.231
지하주차장면적0.0430.4130.1180.283-0.2671.0000.057
방수0.031-0.3700.8340.7020.2310.0571.000

Missing values

2023-12-13T03:07:56.814596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:07:56.962711image/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

구분사업코드주택유형코드주거전용면적주거공용면적기타공용면적지하주차장면적방수기타유의사항
0122120549B49.9117.7632.540.02공란은 데이터 미존재
1220110684A84.9825.946.890.0320공란은 데이터 미존재
2320110684B84.9527.2746.870.0110공란은 데이터 미존재
3420110684C184.9826.2346.890.027공란은 데이터 미존재
4520110684C284.9826.2346.890.029공란은 데이터 미존재
5620110684D84.9525.8446.870.053공란은 데이터 미존재
6722120549C49.9417.632.570.02공란은 데이터 미존재
7822120549A49.9317.4232.560.02공란은 데이터 미존재
89201700616A116.99.94664.657110.72631공란은 데이터 미존재
910201700616A216.99.94664.657110.72631공란은 데이터 미존재
구분사업코드주택유형코드주거전용면적주거공용면적기타공용면적지하주차장면적방수기타유의사항
339340140430159형59.9427.8238.310.03공란은 데이터 미존재
340341201701321A1B121.513.367.1717.631공란은 데이터 미존재
34134220020513939.5814.9714.180.01공란은 데이터 미존재
34234320020514949.5218.3616.550.02공란은 데이터 미존재
34334420020515959.9922.720.050.03공란은 데이터 미존재
344345201703036A336.9218.06385.233418.20711공란은 데이터 미존재
345346210618149형49.94422.04435.0780.02공란은 데이터 미존재
346347210618159형59.97421.67442.1220.03공란은 데이터 미존재
347348210618159B형59.980824.02237.97950.03공란은 데이터 미존재
34834915120715959.9928.1439.00.03공란은 데이터 미존재