Overview

Dataset statistics

Number of variables19
Number of observations2759
Missing cells11810
Missing cells (%)22.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory452.8 KiB
Average record size in memory168.0 B

Variable types

Text3
Categorical1
Unsupported4
Numeric11

Dataset

Description관리_공급_대상_pk,관리_입주자모집_공고_승인_pk,주택_구분_코드,주택_관리_번호,모델_번호,주택_형별_면적,전용_면적,주거_공용_면적,공급_면적_계,기타_공용_면적,지하_주차장_면적,계약_면적,대지_지분,공급_세대_수,층_수,공급방식_구분_코드,형별_공급_총_세대_수,특별_공급_세대_수,작업_일자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15684/S/1/datasetView.do

Alerts

전용_면적 is highly overall correlated with 주거_공용_면적 and 3 other fieldsHigh correlation
주거_공용_면적 is highly overall correlated with 전용_면적 and 4 other fieldsHigh correlation
공급_면적_계 is highly overall correlated with 전용_면적 and 4 other fieldsHigh correlation
기타_공용_면적 is highly overall correlated with 계약_면적High correlation
지하_주차장_면적 is highly overall correlated with 주거_공용_면적 and 2 other fieldsHigh correlation
계약_면적 is highly overall correlated with 전용_면적 and 5 other fieldsHigh correlation
대지_지분 is highly overall correlated with 전용_면적 and 3 other fieldsHigh correlation
주택_관리_번호 has 774 (28.1%) missing valuesMissing
모델_번호 has 2759 (100.0%) missing valuesMissing
공급방식_구분_코드 has 2759 (100.0%) missing valuesMissing
형별_공급_총_세대_수 has 2759 (100.0%) missing valuesMissing
특별_공급_세대_수 has 2759 (100.0%) missing valuesMissing
전용_면적 is highly skewed (γ1 = 49.82806827)Skewed
주거_공용_면적 is highly skewed (γ1 = 49.99872972)Skewed
공급_면적_계 is highly skewed (γ1 = 44.98844043)Skewed
지하_주차장_면적 is highly skewed (γ1 = 34.37207313)Skewed
계약_면적 is highly skewed (γ1 = 46.44267014)Skewed
대지_지분 is highly skewed (γ1 = 39.99743689)Skewed
층_수 is highly skewed (γ1 = 34.50365074)Skewed
관리_공급_대상_pk has unique valuesUnique
모델_번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공급방식_구분_코드 is an unsupported type, check if it needs cleaning or further analysisUnsupported
형별_공급_총_세대_수 is an unsupported type, check if it needs cleaning or further analysisUnsupported
특별_공급_세대_수 is an unsupported type, check if it needs cleaning or further analysisUnsupported
주택_형별_면적 has 2749 (99.6%) zerosZeros
주거_공용_면적 has 93 (3.4%) zerosZeros
공급_면적_계 has 32 (1.2%) zerosZeros
기타_공용_면적 has 265 (9.6%) zerosZeros
지하_주차장_면적 has 881 (31.9%) zerosZeros
층_수 has 858 (31.1%) zerosZeros

Reproduction

Analysis started2024-05-11 05:28:30.385705
Analysis finished2024-05-11 05:29:23.962764
Duration53.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2759
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.7 KiB
2024-05-11T05:29:24.677768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length15.047119
Min length15

Characters and Unicode

Total characters41515
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

Unique2759 ?
Unique (%)100.0%

Sample

1st row11110-100000141
2nd row11110-100000142
3rd row11110-100000143
4th row11110-100000144
5th row11140-100000141
ValueCountFrequency (%)
11110-100000141 1
 
< 0.1%
11650-100000188 1
 
< 0.1%
11650-100000198 1
 
< 0.1%
11650-100000181 1
 
< 0.1%
11650-100000182 1
 
< 0.1%
11650-100000183 1
 
< 0.1%
11650-100000184 1
 
< 0.1%
11650-100000185 1
 
< 0.1%
11650-100000186 1
 
< 0.1%
11650-100000187 1
 
< 0.1%
Other values (2749) 2749
99.6%
2024-05-11T05:29:26.101551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17417
42.0%
1 10079
24.3%
- 2759
 
6.6%
2 2067
 
5.0%
4 2021
 
4.9%
5 1672
 
4.0%
3 1490
 
3.6%
6 1407
 
3.4%
7 1296
 
3.1%
8 816
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38756
93.4%
Dash Punctuation 2759
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17417
44.9%
1 10079
26.0%
2 2067
 
5.3%
4 2021
 
5.2%
5 1672
 
4.3%
3 1490
 
3.8%
6 1407
 
3.6%
7 1296
 
3.3%
8 816
 
2.1%
9 491
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
- 2759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41515
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17417
42.0%
1 10079
24.3%
- 2759
 
6.6%
2 2067
 
5.0%
4 2021
 
4.9%
5 1672
 
4.0%
3 1490
 
3.6%
6 1407
 
3.4%
7 1296
 
3.1%
8 816
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41515
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17417
42.0%
1 10079
24.3%
- 2759
 
6.6%
2 2067
 
5.0%
4 2021
 
4.9%
5 1672
 
4.0%
3 1490
 
3.6%
6 1407
 
3.4%
7 1296
 
3.1%
8 816
 
2.0%
Distinct219
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size21.7 KiB
2024-05-11T05:29:27.030004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length15.501993
Min length8

Characters and Unicode

Total characters42770
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

Unique12 ?
Unique (%)0.4%

Sample

1st row11110-100007026
2nd row11110-100007026
3rd row11110-100007026
4th row11110-100007026
5th row11140-15
ValueCountFrequency (%)
11440-100009861 123
 
4.5%
11200-100042354 88
 
3.2%
11710-100000100000001 58
 
2.1%
11500-100015410 58
 
2.1%
11710-100046311 58
 
2.1%
11440-100007581 53
 
1.9%
11740-100019542 50
 
1.8%
11260-100012474 49
 
1.8%
11140-100006842 46
 
1.7%
11710-100045030 43
 
1.6%
Other values (209) 2133
77.3%
2024-05-11T05:29:28.553900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14809
34.6%
1 11421
26.7%
- 2759
 
6.5%
4 2535
 
5.9%
5 2234
 
5.2%
2 2013
 
4.7%
6 1900
 
4.4%
3 1542
 
3.6%
7 1422
 
3.3%
8 1391
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40011
93.5%
Dash Punctuation 2759
 
6.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14809
37.0%
1 11421
28.5%
4 2535
 
6.3%
5 2234
 
5.6%
2 2013
 
5.0%
6 1900
 
4.7%
3 1542
 
3.9%
7 1422
 
3.6%
8 1391
 
3.5%
9 744
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 2759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42770
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14809
34.6%
1 11421
26.7%
- 2759
 
6.5%
4 2535
 
5.9%
5 2234
 
5.2%
2 2013
 
4.7%
6 1900
 
4.4%
3 1542
 
3.6%
7 1422
 
3.3%
8 1391
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42770
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14809
34.6%
1 11421
26.7%
- 2759
 
6.5%
4 2535
 
5.9%
5 2234
 
5.2%
2 2013
 
4.7%
6 1900
 
4.4%
3 1542
 
3.6%
7 1422
 
3.3%
8 1391
 
3.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.7 KiB
1
2317 
2
442 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 2317
84.0%
2 442
 
16.0%

Length

2024-05-11T05:29:29.128069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T05:29:29.646004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2317
84.0%
2 442
 
16.0%

주택_관리_번호
Text

MISSING 

Distinct1267
Distinct (%)63.8%
Missing774
Missing (%)28.1%
Memory size21.7 KiB
2024-05-11T05:29:30.513107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length5.7309824
Min length1

Characters and Unicode

Total characters11376
Distinct characters109
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1065 ?
Unique (%)53.7%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row84.95B
ValueCountFrequency (%)
도시형생활주택 131
 
5.9%
a 31
 
1.4%
b 30
 
1.3%
도시형생활주택-원룸형 23
 
1.0%
아파트 22
 
1.0%
101동 22
 
1.0%
106동 21
 
0.9%
102동 21
 
0.9%
103동 20
 
0.9%
107동 20
 
0.9%
Other values (1095) 1894
84.7%
2024-05-11T05:29:32.171428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1215
 
10.7%
0 1185
 
10.4%
2 759
 
6.7%
4 542
 
4.8%
3 506
 
4.4%
- 423
 
3.7%
5 357
 
3.1%
336
 
3.0%
304
 
2.7%
A 277
 
2.4%
Other values (99) 5472
48.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5411
47.6%
Other Letter 3021
26.6%
Uppercase Letter 1498
 
13.2%
Dash Punctuation 423
 
3.7%
Other Punctuation 287
 
2.5%
Space Separator 250
 
2.2%
Lowercase Letter 171
 
1.5%
Close Punctuation 148
 
1.3%
Open Punctuation 148
 
1.3%
Math Symbol 15
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
336
11.1%
304
 
10.1%
230
 
7.6%
230
 
7.6%
228
 
7.5%
206
 
6.8%
206
 
6.8%
206
 
6.8%
196
 
6.5%
87
 
2.9%
Other values (44) 792
26.2%
Uppercase Letter
ValueCountFrequency (%)
A 277
18.5%
B 273
18.2%
C 146
9.7%
T 145
9.7%
D 106
 
7.1%
E 105
 
7.0%
P 70
 
4.7%
F 66
 
4.4%
G 53
 
3.5%
H 50
 
3.3%
Other values (16) 207
13.8%
Decimal Number
ValueCountFrequency (%)
1 1215
22.5%
0 1185
21.9%
2 759
14.0%
4 542
10.0%
3 506
9.4%
5 357
 
6.6%
6 252
 
4.7%
8 223
 
4.1%
9 212
 
3.9%
7 160
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
e 51
29.8%
p 50
29.2%
y 50
29.2%
a 8
 
4.7%
b 4
 
2.3%
c 3
 
1.8%
d 2
 
1.2%
h 1
 
0.6%
f 1
 
0.6%
g 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 235
81.9%
, 49
 
17.1%
' 3
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
- 423
100.0%
Space Separator
ValueCountFrequency (%)
250
100.0%
Close Punctuation
ValueCountFrequency (%)
) 148
100.0%
Open Punctuation
ValueCountFrequency (%)
( 148
100.0%
Math Symbol
ValueCountFrequency (%)
~ 15
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6686
58.8%
Hangul 3021
26.6%
Latin 1669
 
14.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
336
11.1%
304
 
10.1%
230
 
7.6%
230
 
7.6%
228
 
7.5%
206
 
6.8%
206
 
6.8%
206
 
6.8%
196
 
6.5%
87
 
2.9%
Other values (44) 792
26.2%
Latin
ValueCountFrequency (%)
A 277
16.6%
B 273
16.4%
C 146
 
8.7%
T 145
 
8.7%
D 106
 
6.4%
E 105
 
6.3%
P 70
 
4.2%
F 66
 
4.0%
G 53
 
3.2%
e 51
 
3.1%
Other values (26) 377
22.6%
Common
ValueCountFrequency (%)
1 1215
18.2%
0 1185
17.7%
2 759
11.4%
4 542
8.1%
3 506
7.6%
- 423
 
6.3%
5 357
 
5.3%
6 252
 
3.8%
250
 
3.7%
. 235
 
3.5%
Other values (9) 962
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8355
73.4%
Hangul 3021
 
26.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1215
14.5%
0 1185
14.2%
2 759
 
9.1%
4 542
 
6.5%
3 506
 
6.1%
- 423
 
5.1%
5 357
 
4.3%
A 277
 
3.3%
B 273
 
3.3%
6 252
 
3.0%
Other values (45) 2566
30.7%
Hangul
ValueCountFrequency (%)
336
11.1%
304
 
10.1%
230
 
7.6%
230
 
7.6%
228
 
7.5%
206
 
6.8%
206
 
6.8%
206
 
6.8%
196
 
6.5%
87
 
2.9%
Other values (44) 792
26.2%

모델_번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2759
Missing (%)100.0%
Memory size24.4 KiB

주택_형별_면적
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32477408
Minimum0
Maximum126.39
Zeros2749
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:32.798785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum126.39
Range126.39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.5605069
Coefficient of variation (CV)17.121153
Kurtosis330.35959
Mean0.32477408
Median Absolute Deviation (MAD)0
Skewness17.889153
Sum896.0517
Variance30.919237
MonotonicityNot monotonic
2024-05-11T05:29:33.334645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 2749
99.6%
40.66 1
 
< 0.1%
102.49 1
 
< 0.1%
116.47 1
 
< 0.1%
69.41 1
 
< 0.1%
126.39 1
 
< 0.1%
84.6921 1
 
< 0.1%
101.3957 1
 
< 0.1%
84.918 1
 
< 0.1%
84.8935 1
 
< 0.1%
ValueCountFrequency (%)
0.0 2749
99.6%
40.66 1
 
< 0.1%
69.41 1
 
< 0.1%
84.6921 1
 
< 0.1%
84.7324 1
 
< 0.1%
84.8935 1
 
< 0.1%
84.918 1
 
< 0.1%
101.3957 1
 
< 0.1%
102.49 1
 
< 0.1%
116.47 1
 
< 0.1%
ValueCountFrequency (%)
126.39 1
< 0.1%
116.47 1
< 0.1%
102.49 1
< 0.1%
101.3957 1
< 0.1%
84.918 1
< 0.1%
84.8935 1
< 0.1%
84.7324 1
< 0.1%
84.6921 1
< 0.1%
69.41 1
< 0.1%
40.66 1
< 0.1%

전용_면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1654
Distinct (%)59.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.021244
Minimum11.32
Maximum19685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:33.927831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.32
5-th percentile14.598
Q124.33
median38.98
Q354.22
95-th percentile111.5354
Maximum19685
Range19673.68
Interquartile range (IQR)29.89

Descriptive statistics

Standard deviation381.09484
Coefficient of variation (CV)6.8026844
Kurtosis2556.6087
Mean56.021244
Median Absolute Deviation (MAD)14.84
Skewness49.828068
Sum154562.61
Variance145233.28
MonotonicityNot monotonic
2024-05-11T05:29:34.582823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.51 28
 
1.0%
28.0 19
 
0.7%
19.685 15
 
0.5%
16.959 14
 
0.5%
51.08 13
 
0.5%
22.6 12
 
0.4%
12.49 12
 
0.4%
13.25 11
 
0.4%
26.77 11
 
0.4%
13.35 11
 
0.4%
Other values (1644) 2613
94.7%
ValueCountFrequency (%)
11.32 1
< 0.1%
12.01 2
0.1%
12.0192 1
< 0.1%
12.03 1
< 0.1%
12.0478 1
< 0.1%
12.05 1
< 0.1%
12.1 1
< 0.1%
12.1192 2
0.1%
12.16 1
< 0.1%
12.1722 1
< 0.1%
ValueCountFrequency (%)
19685.0 1
< 0.1%
3397.9 1
< 0.1%
532.62 1
< 0.1%
340.34 1
< 0.1%
315.02 1
< 0.1%
303.24 1
< 0.1%
245.09 1
< 0.1%
244.783 1
< 0.1%
244.749 1
< 0.1%
244.2033 1
< 0.1%

주거_공용_면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1564
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.427464
Minimum0
Maximum11614
Zeros93
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:35.318580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.42
Q18.1735
median11.197
Q318.718
95-th percentile33.06694
Maximum11614
Range11614
Interquartile range (IQR)10.5445

Descriptive statistics

Standard deviation224.98685
Coefficient of variation (CV)11.580866
Kurtosis2563.2563
Mean19.427464
Median Absolute Deviation (MAD)4.037
Skewness49.99873
Sum53600.374
Variance50619.084
MonotonicityNot monotonic
2024-05-11T05:29:35.878499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 93
 
3.4%
7.16 18
 
0.7%
9.15 16
 
0.6%
9.5 15
 
0.5%
7.6 15
 
0.5%
8.764 14
 
0.5%
8.53 14
 
0.5%
6.74 11
 
0.4%
6.43 11
 
0.4%
10.24 11
 
0.4%
Other values (1554) 2541
92.1%
ValueCountFrequency (%)
0.0 93
3.4%
1.13 1
 
< 0.1%
1.965 1
 
< 0.1%
2.15 1
 
< 0.1%
2.205 2
 
0.1%
2.32 1
 
< 0.1%
2.35 1
 
< 0.1%
2.36 2
 
0.1%
2.37 1
 
< 0.1%
2.39 2
 
0.1%
ValueCountFrequency (%)
11614.0 1
< 0.1%
2216.56 1
< 0.1%
87.983 1
< 0.1%
87.4 1
< 0.1%
87.292 1
< 0.1%
87.281 1
< 0.1%
75.7011 1
< 0.1%
64.78 1
< 0.1%
63.79 1
< 0.1%
62.898 2
0.1%

공급_면적_계
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1805
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.74955
Minimum0
Maximum281593
Zeros32
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:36.559346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.95
Q134.83
median50.55
Q369.475
95-th percentile141.86798
Maximum281593
Range281593
Interquartile range (IQR)34.645

Descriptive statistics

Standard deviation5751.3328
Coefficient of variation (CV)27.953077
Kurtosis2126.5517
Mean205.74955
Median Absolute Deviation (MAD)16.6328
Skewness44.98844
Sum567662.99
Variance33077830
MonotonicityNot monotonic
2024-05-11T05:29:37.218080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 32
 
1.2%
37.5 18
 
0.7%
34.83 15
 
0.5%
25.723 14
 
0.5%
36.66 14
 
0.5%
37.12 11
 
0.4%
22.842 10
 
0.4%
23.57 10
 
0.4%
35.4 10
 
0.4%
32.86 10
 
0.4%
Other values (1795) 2615
94.8%
ValueCountFrequency (%)
0.0 32
1.2%
8.24 1
 
< 0.1%
8.25 4
 
0.1%
8.58 1
 
< 0.1%
8.87 1
 
< 0.1%
9.37 1
 
< 0.1%
11.32 1
 
< 0.1%
12.05 1
 
< 0.1%
12.79 1
 
< 0.1%
13.21 1
 
< 0.1%
ValueCountFrequency (%)
281593.0 1
< 0.1%
109559.0 1
< 0.1%
5614.46 1
< 0.1%
532.62 1
< 0.1%
355.6 1
< 0.1%
332.766 1
< 0.1%
332.03 1
< 0.1%
331.042 1
< 0.1%
330.493 1
< 0.1%
316.84 1
< 0.1%

기타_공용_면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1354
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9081553
Minimum0
Maximum273.5651
Zeros265
Zeros (%)9.6%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:37.728453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.76
median2.64
Q37.065
95-th percentile50.31722
Maximum273.5651
Range273.5651
Interquartile range (IQR)6.305

Descriptive statistics

Standard deviation19.747989
Coefficient of variation (CV)2.2168438
Kurtosis37.133763
Mean8.9081553
Median Absolute Deviation (MAD)2.417
Skewness4.9769002
Sum24577.601
Variance389.98308
MonotonicityNot monotonic
2024-05-11T05:29:38.230998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 265
 
9.6%
0.17 54
 
2.0%
0.18 35
 
1.3%
0.75 26
 
0.9%
0.42 24
 
0.9%
0.78 20
 
0.7%
1.27 19
 
0.7%
0.76 18
 
0.7%
0.74 16
 
0.6%
3.857 16
 
0.6%
Other values (1344) 2266
82.1%
ValueCountFrequency (%)
0.0 265
9.6%
0.081 2
 
0.1%
0.11 10
 
0.4%
0.12 2
 
0.1%
0.13 1
 
< 0.1%
0.1312 1
 
< 0.1%
0.1336 2
 
0.1%
0.14 1
 
< 0.1%
0.15 12
 
0.4%
0.17 54
 
2.0%
ValueCountFrequency (%)
273.5651 1
< 0.1%
270.49 1
< 0.1%
202.89 1
< 0.1%
154.92 1
< 0.1%
146.77 1
< 0.1%
144.35 1
< 0.1%
136.11 1
< 0.1%
127.2164 1
< 0.1%
127.1787 1
< 0.1%
126.9024 1
< 0.1%

지하_주차장_면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1173
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.882579
Minimum0
Maximum2387.21
Zeros881
Zeros (%)31.9%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:39.136857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.34
Q327.5206
95-th percentile77.77191
Maximum2387.21
Range2387.21
Interquartile range (IQR)27.5206

Descriptive statistics

Standard deviation52.029046
Coefficient of variation (CV)2.4915048
Kurtosis1552.2593
Mean20.882579
Median Absolute Deviation (MAD)11.34
Skewness34.372073
Sum57615.036
Variance2707.0216
MonotonicityNot monotonic
2024-05-11T05:29:39.603022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 881
31.9%
4.42 24
 
0.9%
12.3 17
 
0.6%
37.895 16
 
0.6%
7.189 14
 
0.5%
4.38 12
 
0.4%
4.33 12
 
0.4%
6.99 12
 
0.4%
4.5 11
 
0.4%
9.77 11
 
0.4%
Other values (1163) 1749
63.4%
ValueCountFrequency (%)
0.0 881
31.9%
1.58 3
 
0.1%
1.59 1
 
< 0.1%
1.604 5
 
0.2%
1.64 1
 
< 0.1%
1.65 1
 
< 0.1%
1.67 1
 
< 0.1%
1.677 4
 
0.1%
1.68 1
 
< 0.1%
1.698 5
 
0.2%
ValueCountFrequency (%)
2387.21 1
< 0.1%
241.29 1
< 0.1%
141.7539 1
< 0.1%
139.7012 1
< 0.1%
134.1352 1
< 0.1%
132.6848 1
< 0.1%
132.024 1
< 0.1%
132.006 1
< 0.1%
131.409 1
< 0.1%
131.171 1
< 0.1%

계약_면적
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1844
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.32394
Minimum0
Maximum28302
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:40.111819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.36
Q139.805
median64.51
Q3106.46
95-th percentile228.5583
Maximum28302
Range28302
Interquartile range (IQR)66.655

Descriptive statistics

Standard deviation564.08331
Coefficient of variation (CV)5.512721
Kurtosis2282.6395
Mean102.32394
Median Absolute Deviation (MAD)27.68
Skewness46.44267
Sum282311.74
Variance318189.98
MonotonicityNot monotonic
2024-05-11T05:29:40.551321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.014 14
 
0.5%
36.83 13
 
0.5%
37.68 13
 
0.5%
33.01 12
 
0.4%
35.4 11
 
0.4%
37.12 11
 
0.4%
55.83 11
 
0.4%
23.68 10
 
0.4%
33.76 10
 
0.4%
36.64 10
 
0.4%
Other values (1834) 2644
95.8%
ValueCountFrequency (%)
0.0 5
0.2%
7.41 1
 
< 0.1%
10.08 1
 
< 0.1%
17.06 1
 
< 0.1%
17.87 1
 
< 0.1%
18.7 1
 
< 0.1%
18.83 1
 
< 0.1%
20.08 1
 
< 0.1%
20.13 1
 
< 0.1%
20.26 1
 
< 0.1%
ValueCountFrequency (%)
28302.0 1
< 0.1%
8275.2351 1
< 0.1%
1044.4 1
< 0.1%
517.91 1
< 0.1%
478.457 1
< 0.1%
477.7 1
< 0.1%
476.054 1
< 0.1%
475.243 1
< 0.1%
451.4716 1
< 0.1%
447.68 1
< 0.1%

대지_지분
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1669
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.524435
Minimum2.33
Maximum24063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:41.025588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.33
5-th percentile8.616
Q114.7
median22.55
Q332.5205
95-th percentile68.31533
Maximum24063
Range24060.67
Interquartile range (IQR)17.8205

Descriptive statistics

Standard deviation532.83087
Coefficient of variation (CV)12.242109
Kurtosis1676.2383
Mean43.524435
Median Absolute Deviation (MAD)8.36
Skewness39.997437
Sum120083.92
Variance283908.73
MonotonicityNot monotonic
2024-05-11T05:29:41.545058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.45 27
 
1.0%
14.73 20
 
0.7%
18.78 19
 
0.7%
14.86 18
 
0.7%
10.328 14
 
0.5%
22.8 11
 
0.4%
12.61 11
 
0.4%
14.29 11
 
0.4%
9.92 11
 
0.4%
17.96 11
 
0.4%
Other values (1659) 2606
94.5%
ValueCountFrequency (%)
2.33 1
< 0.1%
2.36 1
< 0.1%
2.77 1
< 0.1%
2.79 1
< 0.1%
2.877 2
0.1%
3.54 1
< 0.1%
3.61 1
< 0.1%
3.7 2
0.1%
3.77 1
< 0.1%
3.84 2
0.1%
ValueCountFrequency (%)
24063.0 1
< 0.1%
14152.0 1
< 0.1%
2156.8009 1
< 0.1%
239.374 1
< 0.1%
239.34 1
< 0.1%
238.258 1
< 0.1%
237.827 1
< 0.1%
235.725 1
< 0.1%
234.995 2
0.1%
234.921 2
0.1%

공급_세대_수
Real number (ℝ)

Distinct145
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.56506
Minimum0
Maximum756
Zeros5
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:41.984750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median3
Q310
95-th percentile62
Maximum756
Range756
Interquartile range (IQR)9

Descriptive statistics

Standard deviation38.603195
Coefficient of variation (CV)2.8457814
Kurtosis124.97594
Mean13.56506
Median Absolute Deviation (MAD)2
Skewness8.9963083
Sum37426
Variance1490.2067
MonotonicityNot monotonic
2024-05-11T05:29:42.455321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1128
40.9%
2 216
 
7.8%
4 196
 
7.1%
5 168
 
6.1%
3 117
 
4.2%
8 83
 
3.0%
12 75
 
2.7%
6 67
 
2.4%
10 65
 
2.4%
7 36
 
1.3%
Other values (135) 608
22.0%
ValueCountFrequency (%)
0 5
 
0.2%
1 1128
40.9%
2 216
 
7.8%
3 117
 
4.2%
4 196
 
7.1%
5 168
 
6.1%
6 67
 
2.4%
7 36
 
1.3%
8 83
 
3.0%
9 28
 
1.0%
ValueCountFrequency (%)
756 1
 
< 0.1%
735 1
 
< 0.1%
507 1
 
< 0.1%
474 1
 
< 0.1%
440 1
 
< 0.1%
294 1
 
< 0.1%
290 1
 
< 0.1%
281 1
 
< 0.1%
280 3
0.1%
262 1
 
< 0.1%

층_수
Real number (ℝ)

SKEWED  ZEROS 

Distinct47
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0264589
Minimum0
Maximum2345
Zeros858
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:42.940604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q36
95-th percentile23
Maximum2345
Range2345
Interquartile range (IQR)6

Descriptive statistics

Standard deviation64.6371
Coefficient of variation (CV)8.0530033
Kurtosis1240.062
Mean8.0264589
Median Absolute Deviation (MAD)4
Skewness34.503651
Sum22145
Variance4177.9547
MonotonicityNot monotonic
2024-05-11T05:29:43.357800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 858
31.1%
6 326
 
11.8%
5 244
 
8.8%
4 196
 
7.1%
2 189
 
6.9%
3 147
 
5.3%
1 134
 
4.9%
7 86
 
3.1%
20 57
 
2.1%
12 54
 
2.0%
Other values (37) 468
17.0%
ValueCountFrequency (%)
0 858
31.1%
1 134
 
4.9%
2 189
 
6.9%
3 147
 
5.3%
4 196
 
7.1%
5 244
 
8.8%
6 326
 
11.8%
7 86
 
3.1%
8 15
 
0.5%
9 34
 
1.2%
ValueCountFrequency (%)
2345 2
 
0.1%
456 1
 
< 0.1%
345 1
 
< 0.1%
235 1
 
< 0.1%
234 1
 
< 0.1%
47 11
0.4%
45 4
 
0.1%
39 4
 
0.1%
38 4
 
0.1%
37 1
 
< 0.1%

공급방식_구분_코드
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2759
Missing (%)100.0%
Memory size24.4 KiB

형별_공급_총_세대_수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2759
Missing (%)100.0%
Memory size24.4 KiB

특별_공급_세대_수
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2759
Missing (%)100.0%
Memory size24.4 KiB

작업_일자
Real number (ℝ)

Distinct57
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20198448
Minimum20180927
Maximum20240507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.4 KiB
2024-05-11T05:29:43.799411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20180927
5-th percentile20180927
Q120180927
median20180927
Q320221115
95-th percentile20240208
Maximum20240507
Range59580
Interquartile range (IQR)40188

Descriptive statistics

Standard deviation23388.223
Coefficient of variation (CV)0.0011579218
Kurtosis-1.1434873
Mean20198448
Median Absolute Deviation (MAD)0
Skewness0.79689496
Sum5.5727518 × 1010
Variance5.4700897 × 108
MonotonicityNot monotonic
2024-05-11T05:29:44.306691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20180927 1565
56.7%
20240102 106
 
3.8%
20240208 100
 
3.6%
20230411 88
 
3.2%
20230310 64
 
2.3%
20230104 58
 
2.1%
20191016 56
 
2.0%
20211029 49
 
1.8%
20240420 33
 
1.2%
20190629 32
 
1.2%
Other values (47) 608
 
22.0%
ValueCountFrequency (%)
20180927 1565
56.7%
20181114 5
 
0.2%
20181215 14
 
0.5%
20190108 13
 
0.5%
20190214 6
 
0.2%
20190219 19
 
0.7%
20190307 20
 
0.7%
20190328 8
 
0.3%
20190529 9
 
0.3%
20190629 32
 
1.2%
ValueCountFrequency (%)
20240507 22
 
0.8%
20240425 5
 
0.2%
20240420 33
 
1.2%
20240227 26
 
0.9%
20240221 30
 
1.1%
20240208 100
3.6%
20240102 106
3.8%
20230929 11
 
0.4%
20230912 30
 
1.1%
20230831 15
 
0.5%

Interactions

2024-05-11T05:29:17.376153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:37.895081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:41.980754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:45.896367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:51.992499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:55.711725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:59.863715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:03.884794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:06.963502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:09.762727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:13.734289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:17.778485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:38.329345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:42.300708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:46.287184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:52.290786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:56.232661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:00.305296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:04.186540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:07.249961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:10.010709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:14.062531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:18.290834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:38.681959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:42.730509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:46.873486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:52.736955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:56.555636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:00.756972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:04.470214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:07.550120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:10.337316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:14.395726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:18.629838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:39.035008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:43.084594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:47.310908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:53.162672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:56.913772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:01.213322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:04.772826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:07.818158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:10.634099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:14.790931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:18.963509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:39.365356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:43.402013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:47.824970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:53.437049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:57.317369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:01.649595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:05.050525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:08.052652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:10.969721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:15.076230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:19.376478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:39.723799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:43.726835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:48.261911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:53.716834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:57.692753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:02.008646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:05.328140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:08.256305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:11.425368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:15.353694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:19.795399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:40.028859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:44.030935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:48.773375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:54.226769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:58.029367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:02.353359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:05.582871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:08.471555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:11.729877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:15.649476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:20.129457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:40.438012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:44.335002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:49.147650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:54.612856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:58.337925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:02.652099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:05.852286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:08.695108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:12.477383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:16.020829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:20.467167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:40.789407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:44.632227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:49.740999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:54.874483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:58.664669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:03.000802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:06.123989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:08.869776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:12.778017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:16.335365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:21.095653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:41.286368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:45.020472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:50.934701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:55.092669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:59.006218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:03.335312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:06.408748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:09.124551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:13.101226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:16.697609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:21.519649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:41.659590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:45.416977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:51.392566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:55.331728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:28:59.442840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:03.597944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:06.680827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:09.452588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:13.414461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T05:29:16.997030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T05:29:44.613476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택_구분_코드주택_형별_면적전용_면적주거_공용_면적공급_면적_계기타_공용_면적지하_주차장_면적계약_면적대지_지분공급_세대_수층_수작업_일자
주택_구분_코드1.0000.0000.0000.0000.0000.1050.0000.0000.0000.0410.0000.520
주택_형별_면적0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
전용_면적0.0000.0001.0000.9430.0000.6260.9430.9430.0000.1040.0000.225
주거_공용_면적0.0000.0000.9431.0000.0000.6260.9430.9430.0000.1040.0000.286
공급_면적_계0.0000.0000.0000.0001.0000.0640.0000.0000.0000.1040.0000.000
기타_공용_면적0.1050.0000.6260.6260.0641.0000.7880.6260.0000.1190.0000.462
지하_주차장_면적0.0000.0000.9430.9430.0000.7881.0000.9430.0000.1040.0000.286
계약_면적0.0000.0000.9430.9430.0000.6260.9431.0000.9430.1040.0000.286
대지_지분0.0000.0000.0000.0000.0000.0000.0000.9431.0000.0000.000NaN
공급_세대_수0.0410.0000.1040.1040.1040.1190.1040.1040.0001.0000.0000.159
층_수0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.000NaN
작업_일자0.5200.0000.2250.2860.0000.4620.2860.286NaN0.159NaN1.000
2024-05-11T05:29:45.055225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택_형별_면적전용_면적주거_공용_면적공급_면적_계기타_공용_면적지하_주차장_면적계약_면적대지_지분공급_세대_수층_수작업_일자주택_구분_코드
주택_형별_면적1.0000.076-0.0100.0680.0370.0620.0640.083-0.057-0.0200.1140.000
전용_면적0.0761.0000.6740.9440.4070.4840.9010.8940.3320.1500.0620.000
주거_공용_면적-0.0100.6741.0000.7720.4570.5770.7830.5820.4780.3010.2250.000
공급_면적_계0.0680.9440.7721.0000.4240.5390.9020.8480.3650.1930.1020.000
기타_공용_면적0.0370.4070.4570.4241.0000.3990.6050.2460.3520.1630.3130.079
지하_주차장_면적0.0620.4840.5770.5390.3991.0000.6780.4000.3440.2270.2420.000
계약_면적0.0640.9010.7830.9020.6050.6781.0000.7650.3970.1760.2610.000
대지_지분0.0830.8940.5820.8480.2460.4000.7651.0000.2250.0970.0130.000
공급_세대_수-0.0570.3320.4780.3650.3520.3440.3970.2251.0000.2140.1580.044
층_수-0.0200.1500.3010.1930.1630.2270.1760.0970.2141.0000.1000.000
작업_일자0.1140.0620.2250.1020.3130.2420.2610.0130.1580.1001.0000.271
주택_구분_코드0.0000.0000.0000.0000.0790.0000.0000.0000.0440.0000.2711.000

Missing values

2024-05-11T05:29:22.534659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T05:29:23.668972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

관리_공급_대상_pk관리_입주자모집_공고_승인_pk주택_구분_코드주택_관리_번호모델_번호주택_형별_면적전용_면적주거_공용_면적공급_면적_계기타_공용_면적지하_주차장_면적계약_면적대지_지분공급_세대_수층_수공급방식_구분_코드형별_공급_총_세대_수특별_공급_세대_수작업_일자
011110-10000014111110-10000702610<NA>0.082.224322.0105104.23486.872349.8192160.9263112.8867423<NA><NA><NA>20221026
111110-10000014211110-10000702610<NA>0.084.975122.2912107.26637.102151.4859165.8543116.663321<NA><NA><NA>20221026
211110-10000014311110-10000702610<NA>0.084.997423.1502108.14767.10451.4994166.751116.693922<NA><NA><NA>20221026
311110-10000014411110-10000702610<NA>0.084.982622.5711107.55377.102751.4905165.1469116.673662<NA><NA><NA>20221026
411140-10000014111140-15184.95B<NA>0.084.9526.01110.964.5547.33162.8548.46580<NA><NA><NA>20180927
511140-10000014211140-15184.98A<NA>0.084.9827.12112.14.5547.35164.0148.8840<NA><NA><NA>20180927
611140-10000014311140-15184.97C<NA>0.084.9727.45112.424.5547.34164.3348.9130<NA><NA><NA>20180927
711140-10000014411140-15184.95D<NA>0.084.9526.49111.454.5547.33163.3448.6130<NA><NA><NA>20180927
811140-10000014511140-15184.98E<NA>0.084.9826.31111.34.5547.35163.248.56130<NA><NA><NA>20180927
911140-10000016111140-1000059821D<NA>0.031.0217.2848.32.1717.8468.3113.51760<NA><NA><NA>20180927
관리_공급_대상_pk관리_입주자모집_공고_승인_pk주택_구분_코드주택_관리_번호모델_번호주택_형별_면적전용_면적주거_공용_면적공급_면적_계기타_공용_면적지하_주차장_면적계약_면적대지_지분공급_세대_수층_수공급방식_구분_코드형별_공급_총_세대_수특별_공급_세대_수작업_일자
274911740-10000071711740-100025383184A<NA>0.084.096426.9471111.04358.908146.0234165.97555.44395611<NA><NA><NA>20240227
275011740-10000071811740-100025383184T<NA>0.084.094127.8453111.93948.90846.0221166.869555.4424510<NA><NA><NA>20240227
275111740-10000071911740-100025383184M<NA>0.084.416226.9541111.37038.94246.1984166.510755.65471112<NA><NA><NA>20240227
275211740-10000072011740-1000253831101E<NA>0.0101.080431.8332132.913610.707355.3182198.939166.64131624<NA><NA><NA>20240227
275311740-10000072111740-100025383184I<NA>0.084.050525.3018109.35238.903345.9983164.253955.4136359<NA><NA><NA>20240227
275411740-10000072211740-100025383184H<NA>0.084.059325.9062109.96558.904246.0031164.872855.419414<NA><NA><NA>20240227
275511740-10000072311740-100025383184G<NA>0.084.127725.9654110.09318.911546.0405165.045155.46452211<NA><NA><NA>20240227
275611740-10000072411740-100025383184Q<NA>0.084.24130.2844114.52548.923546.1025169.551455.539224<NA><NA><NA>20240227
275711740-10000072511740-1000253831101F<NA>0.0101.079733.2587134.338410.707255.3179200.363566.6408623<NA><NA><NA>20240227
275811740-10000072611740-1000253831101C<NA>0.0101.045932.159133.204910.703655.2994199.207966.61854827<NA><NA><NA>20240227