Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells5056
Missing cells (%)5.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory927.7 KiB
Average record size in memory95.0 B

Variable types

Numeric5
Text2
Categorical3

Dataset

Description한국부동산원(구.한국감정원)의 청약홈에서 제공하는 APT(아파트) 경쟁률 데이터로 공고번호, 주택형, 공급세대수, 순위, 거주지역, 접수건수, 경쟁률 등의 데이터를 제공합니다.
Author한국부동산원
URLhttps://www.data.go.kr/data/15101048/fileData.do

Alerts

거주코드 is highly overall correlated with 거주지역High correlation
거주지역 is highly overall correlated with 거주코드High correlation
주택관리번호 is highly overall correlated with 공고번호High correlation
공고번호 is highly overall correlated with 주택관리번호High correlation
경쟁률 has 5056 (50.6%) missing valuesMissing
접수건수 has 4034 (40.3%) zerosZeros

Reproduction

Analysis started2024-03-14 20:34:37.427569
Analysis finished2024-03-14 20:34:45.849496
Duration8.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주택관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1640
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0213889 × 109
Minimum2.02 × 109
Maximum2.0240001 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T05:34:46.018940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.02 × 109
5-th percentile2.0200005 × 109
Q12.0200014 × 109
median2.0210009 × 109
Q32.0220008 × 109
95-th percentile2.0230006 × 109
Maximum2.0240001 × 109
Range4000081
Interquartile range (IQR)1999368

Descriptive statistics

Standard deviation1121004
Coefficient of variation (CV)0.00055457115
Kurtosis-0.99275887
Mean2.0213889 × 109
Median Absolute Deviation (MAD)999706
Skewness0.28852215
Sum2.0213889 × 1013
Variance1.2566499 × 1012
MonotonicityNot monotonic
2024-03-15T05:34:46.298354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021000540 40
 
0.4%
2020001464 29
 
0.3%
2023000617 29
 
0.3%
2022000092 26
 
0.3%
2020000415 26
 
0.3%
2023000548 25
 
0.2%
2023000633 25
 
0.2%
2021000057 25
 
0.2%
2020001022 25
 
0.2%
2023000100 25
 
0.2%
Other values (1630) 9725
97.2%
ValueCountFrequency (%)
2020000001 5
 
0.1%
2020000005 15
0.1%
2020000007 5
 
0.1%
2020000010 9
0.1%
2020000028 3
 
< 0.1%
2020000040 10
0.1%
2020000041 4
 
< 0.1%
2020000046 3
 
< 0.1%
2020000047 13
0.1%
2020000052 2
 
< 0.1%
ValueCountFrequency (%)
2024000082 3
 
< 0.1%
2024000079 4
 
< 0.1%
2024000078 3
 
< 0.1%
2024000075 2
 
< 0.1%
2024000074 4
 
< 0.1%
2024000067 10
0.1%
2024000064 15
0.1%
2024000063 4
 
< 0.1%
2024000061 4
 
< 0.1%
2024000060 4
 
< 0.1%

공고번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1640
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0213889 × 109
Minimum2.02 × 109
Maximum2.0240001 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T05:34:46.619443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.02 × 109
5-th percentile2.0200005 × 109
Q12.0200014 × 109
median2.0210009 × 109
Q32.0220008 × 109
95-th percentile2.0230006 × 109
Maximum2.0240001 × 109
Range4000081
Interquartile range (IQR)1999368

Descriptive statistics

Standard deviation1121004
Coefficient of variation (CV)0.00055457115
Kurtosis-0.99275887
Mean2.0213889 × 109
Median Absolute Deviation (MAD)999706
Skewness0.28852215
Sum2.0213889 × 1013
Variance1.2566499 × 1012
MonotonicityNot monotonic
2024-03-15T05:34:46.966454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021000540 40
 
0.4%
2020001464 29
 
0.3%
2023000617 29
 
0.3%
2022000092 26
 
0.3%
2020000415 26
 
0.3%
2023000548 25
 
0.2%
2023000633 25
 
0.2%
2021000057 25
 
0.2%
2020001022 25
 
0.2%
2023000100 25
 
0.2%
Other values (1630) 9725
97.2%
ValueCountFrequency (%)
2020000001 5
 
0.1%
2020000005 15
0.1%
2020000007 5
 
0.1%
2020000010 9
0.1%
2020000028 3
 
< 0.1%
2020000040 10
0.1%
2020000041 4
 
< 0.1%
2020000046 3
 
< 0.1%
2020000047 13
0.1%
2020000052 2
 
< 0.1%
ValueCountFrequency (%)
2024000082 3
 
< 0.1%
2024000079 4
 
< 0.1%
2024000078 3
 
< 0.1%
2024000075 2
 
< 0.1%
2024000074 4
 
< 0.1%
2024000067 10
0.1%
2024000064 15
0.1%
2024000063 4
 
< 0.1%
2024000061 4
 
< 0.1%
2024000060 4
 
< 0.1%

모델번호
Real number (ℝ)

Distinct35
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2263
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T05:34:47.235948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile11
Maximum38
Range37
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.5072665
Coefficient of variation (CV)0.82986691
Kurtosis11.307043
Mean4.2263
Median Absolute Deviation (MAD)2
Skewness2.5605131
Sum42263
Variance12.300918
MonotonicityNot monotonic
2024-03-15T05:34:47.474804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2 1842
18.4%
1 1811
18.1%
3 1640
16.4%
4 1276
12.8%
5 958
9.6%
6 735
 
7.3%
7 477
 
4.8%
8 354
 
3.5%
9 245
 
2.5%
10 157
 
1.6%
Other values (25) 505
 
5.1%
ValueCountFrequency (%)
1 1811
18.1%
2 1842
18.4%
3 1640
16.4%
4 1276
12.8%
5 958
9.6%
6 735
 
7.3%
7 477
 
4.8%
8 354
 
3.5%
9 245
 
2.5%
10 157
 
1.6%
ValueCountFrequency (%)
38 1
 
< 0.1%
37 3
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 2
< 0.1%
29 2
< 0.1%
28 2
< 0.1%
27 4
< 0.1%
26 1
 
< 0.1%

주택형
Text

Distinct5662
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-15T05:34:48.742307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters90000
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2985 ?
Unique (%)29.8%

Sample

1st row084.9702A
2nd row084.9815A
3rd row084.8812A
4th row074.8000
5th row068.0982
ValueCountFrequency (%)
084.9900b 27
 
0.3%
084.9800a 24
 
0.2%
059.9900a 22
 
0.2%
084.9800b 20
 
0.2%
084.9900a 19
 
0.2%
084.9600a 18
 
0.2%
059.9800b 18
 
0.2%
084.9900c 18
 
0.2%
084.9700a 13
 
0.1%
084.9400a 13
 
0.1%
Other values (5652) 9808
98.1%
2024-03-15T05:34:50.660266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15696
17.4%
9 10474
11.6%
. 10000
11.1%
8 8748
9.7%
4 8005
8.9%
5 5515
 
6.1%
7 5386
 
6.0%
1 5088
 
5.7%
6 4256
 
4.7%
3 3430
 
3.8%
Other values (25) 13402
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70000
77.8%
Other Punctuation 10000
 
11.1%
Uppercase Letter 7698
 
8.6%
Space Separator 2302
 
2.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2762
35.9%
B 2565
33.3%
C 1116
14.5%
D 461
 
6.0%
E 172
 
2.2%
P 170
 
2.2%
F 124
 
1.6%
T 91
 
1.2%
G 65
 
0.8%
H 55
 
0.7%
Other values (13) 117
 
1.5%
Decimal Number
ValueCountFrequency (%)
0 15696
22.4%
9 10474
15.0%
8 8748
12.5%
4 8005
11.4%
5 5515
 
7.9%
7 5386
 
7.7%
1 5088
 
7.3%
6 4256
 
6.1%
3 3430
 
4.9%
2 3402
 
4.9%
Other Punctuation
ValueCountFrequency (%)
. 10000
100.0%
Space Separator
ValueCountFrequency (%)
2302
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 82302
91.4%
Latin 7698
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2762
35.9%
B 2565
33.3%
C 1116
14.5%
D 461
 
6.0%
E 172
 
2.2%
P 170
 
2.2%
F 124
 
1.6%
T 91
 
1.2%
G 65
 
0.8%
H 55
 
0.7%
Other values (13) 117
 
1.5%
Common
ValueCountFrequency (%)
0 15696
19.1%
9 10474
12.7%
. 10000
12.2%
8 8748
10.6%
4 8005
9.7%
5 5515
 
6.7%
7 5386
 
6.5%
1 5088
 
6.2%
6 4256
 
5.2%
3 3430
 
4.2%
Other values (2) 5704
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15696
17.4%
9 10474
11.6%
. 10000
11.1%
8 8748
9.7%
4 8005
8.9%
5 5515
 
6.1%
7 5386
 
6.0%
1 5088
 
5.7%
6 4256
 
4.7%
3 3430
 
3.8%
Other values (25) 13402
14.9%

공급세대수
Real number (ℝ)

Distinct449
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.7778
Minimum1
Maximum1112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T05:34:51.110971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median33
Q386
95-th percentile251.05
Maximum1112
Range1111
Interquartile range (IQR)77

Descriptive statistics

Standard deviation95.744158
Coefficient of variation (CV)1.4337723
Kurtosis17.836917
Mean66.7778
Median Absolute Deviation (MAD)28
Skewness3.3965633
Sum667778
Variance9166.9437
MonotonicityNot monotonic
2024-03-15T05:34:51.738247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 641
 
6.4%
2 449
 
4.5%
6 263
 
2.6%
4 235
 
2.4%
5 231
 
2.3%
3 222
 
2.2%
9 187
 
1.9%
12 181
 
1.8%
7 170
 
1.7%
8 163
 
1.6%
Other values (439) 7258
72.6%
ValueCountFrequency (%)
1 641
6.4%
2 449
4.5%
3 222
 
2.2%
4 235
 
2.4%
5 231
 
2.3%
6 263
2.6%
7 170
 
1.7%
8 163
 
1.6%
9 187
 
1.9%
10 143
 
1.4%
ValueCountFrequency (%)
1112 1
 
< 0.1%
1031 3
< 0.1%
1017 2
< 0.1%
877 1
 
< 0.1%
862 3
< 0.1%
861 1
 
< 0.1%
842 1
 
< 0.1%
816 2
< 0.1%
803 1
 
< 0.1%
769 2
< 0.1%

순위
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5081 
2
4919 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 5081
50.8%
2 4919
49.2%

Length

2024-03-15T05:34:52.170691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:34:52.525788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5081
50.8%
2 4919
49.2%

거주코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
4934 
2
4766 
3
 
300

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4934
49.3%
2 4766
47.7%
3 300
 
3.0%

Length

2024-03-15T05:34:52.870170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:34:53.182259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4934
49.3%
2 4766
47.7%
3 300
 
3.0%

거주지역
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해당지역
4934 
기타지역
4766 
기타경기
 
300

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해당지역
2nd row기타지역
3rd row해당지역
4th row해당지역
5th row해당지역

Common Values

ValueCountFrequency (%)
해당지역 4934
49.3%
기타지역 4766
47.7%
기타경기 300
 
3.0%

Length

2024-03-15T05:34:53.363253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:34:53.530519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당지역 4934
49.3%
기타지역 4766
47.7%
기타경기 300
 
3.0%

접수건수
Real number (ℝ)

ZEROS 

Distinct1207
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean244.0302
Minimum0
Maximum31385
Zeros4034
Zeros (%)40.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-15T05:34:53.884962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q354
95-th percentile1098.15
Maximum31385
Range31385
Interquartile range (IQR)54

Descriptive statistics

Standard deviation1130.0317
Coefficient of variation (CV)4.6307043
Kurtosis180.96282
Mean244.0302
Median Absolute Deviation (MAD)3
Skewness11.200201
Sum2440302
Variance1276971.7
MonotonicityNot monotonic
2024-03-15T05:34:54.468623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4034
40.3%
1 450
 
4.5%
2 332
 
3.3%
3 231
 
2.3%
4 187
 
1.9%
6 141
 
1.4%
5 131
 
1.3%
7 129
 
1.3%
8 116
 
1.2%
11 99
 
1.0%
Other values (1197) 4150
41.5%
ValueCountFrequency (%)
0 4034
40.3%
1 450
 
4.5%
2 332
 
3.3%
3 231
 
2.3%
4 187
 
1.9%
5 131
 
1.3%
6 141
 
1.4%
7 129
 
1.3%
8 116
 
1.2%
9 96
 
1.0%
ValueCountFrequency (%)
31385 1
< 0.1%
23117 1
< 0.1%
22561 1
< 0.1%
21889 1
< 0.1%
21683 1
< 0.1%
21175 1
< 0.1%
19843 1
< 0.1%
18139 1
< 0.1%
16163 1
< 0.1%
15379 1
< 0.1%

경쟁률
Text

MISSING 

Distinct1783
Distinct (%)36.1%
Missing5056
Missing (%)50.6%
Memory size156.2 KiB
2024-03-15T05:34:56.563903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.7742718
Min length1

Characters and Unicode

Total characters23604
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1227 ?
Unique (%)24.8%

Sample

1st row(△69)
2nd row19.38
3rd row(△11)
4th row(△450)
5th row5.75
ValueCountFrequency (%)
△1 188
 
3.8%
△5 84
 
1.7%
△2 78
 
1.6%
△4 69
 
1.4%
1.00 67
 
1.4%
△6 65
 
1.3%
△7 61
 
1.2%
△3 55
 
1.1%
△11 53
 
1.1%
△8 52
 
1.1%
Other values (1773) 4172
84.4%
2024-03-15T05:34:58.840217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2603
11.0%
( 2552
10.8%
△ 2552
10.8%
) 2552
10.8%
. 2390
10.1%
0 1882
8.0%
2 1649
7.0%
3 1384
 
5.9%
5 1242
 
5.3%
4 1166
 
4.9%
Other values (4) 3632
15.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13558
57.4%
Open Punctuation 2552
 
10.8%
Other Symbol 2552
 
10.8%
Close Punctuation 2552
 
10.8%
Other Punctuation 2390
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2603
19.2%
0 1882
13.9%
2 1649
12.2%
3 1384
10.2%
5 1242
9.2%
4 1166
8.6%
6 1022
 
7.5%
7 976
 
7.2%
8 897
 
6.6%
9 737
 
5.4%
Open Punctuation
ValueCountFrequency (%)
( 2552
100.0%
Other Symbol
ValueCountFrequency (%)
△ 2552
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2552
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23604
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2603
11.0%
( 2552
10.8%
△ 2552
10.8%
) 2552
10.8%
. 2390
10.1%
0 1882
8.0%
2 1649
7.0%
3 1384
 
5.9%
5 1242
 
5.3%
4 1166
 
4.9%
Other values (4) 3632
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21052
89.2%
Geometric Shapes 2552
 
10.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2603
12.4%
( 2552
12.1%
) 2552
12.1%
. 2390
11.4%
0 1882
8.9%
2 1649
7.8%
3 1384
6.6%
5 1242
5.9%
4 1166
5.5%
6 1022
 
4.9%
Other values (3) 2610
12.4%
Geometric Shapes
ValueCountFrequency (%)
△ 2552
100.0%

Interactions

2024-03-15T05:34:44.143070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:38.327173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:39.821718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:41.273947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:42.718683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:44.438251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:38.642389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:40.129019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:41.651078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:43.002377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:44.732012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:38.950364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:40.432321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:41.927076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:43.294388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:44.967409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:39.240572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:40.745290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:42.364032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:43.603487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:45.132997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:39.522741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:40.991444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:42.529760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:34:43.871791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:34:59.116863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택관리번호공고번호모델번호공급세대수순위거주코드거주지역접수건수
주택관리번호1.0001.0000.1130.0480.0000.1100.1100.068
공고번호1.0001.0000.1130.0480.0000.1100.1100.068
모델번호0.1130.1131.0000.1160.0000.0300.0300.145
공급세대수0.0480.0480.1161.0000.0000.0890.0890.035
순위0.0000.0000.0000.0001.0000.0000.0000.130
거주코드0.1100.1100.0300.0890.0001.0001.0000.128
거주지역0.1100.1100.0300.0890.0001.0001.0000.128
접수건수0.0680.0680.1450.0350.1300.1280.1281.000
2024-03-15T05:34:59.368248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거주코드거주지역순위
거주코드1.0001.0000.000
거주지역1.0001.0000.000
순위0.0000.0001.000
2024-03-15T05:34:59.525751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택관리번호공고번호모델번호공급세대수접수건수순위거주코드거주지역
주택관리번호1.0001.0000.004-0.021-0.0080.0000.0450.045
공고번호1.0001.0000.004-0.021-0.0080.0000.0450.045
모델번호0.0040.0041.000-0.317-0.0960.0000.0180.018
공급세대수-0.021-0.021-0.3171.0000.2770.0000.0390.039
접수건수-0.008-0.008-0.0960.2771.0000.1290.0560.056
순위0.0000.0000.0000.0000.1291.0000.0000.000
거주코드0.0450.0450.0180.0390.0560.0001.0001.000
거주지역0.0450.0450.0180.0390.0560.0001.0001.000

Missing values

2024-03-15T05:34:45.391557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:34:45.732460image/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

주택관리번호공고번호모델번호주택형공급세대수순위거주코드거주지역접수건수경쟁률
11130202200056320220005631084.9702A15711해당지역88(△69)
36733202000027720200002774084.9815A3622기타지역0<NA>
9678202200072120220007213084.8812A8921해당지역0<NA>
3862202300041520230004157074.8000121해당지역0<NA>
22174202100053620210005362068.0982811해당지역15519.38
36983202000016720200001677156.4200212기타지역1<NA>
12086202200045220220004521041.7330A1411해당지역3(△11)
30758202000103020200010303084.9279B10422기타지역0<NA>
24701202100024520210002454068.9258A4012기타지역0<NA>
11638202200050620220005061084.8617A49011해당지역40(△450)
주택관리번호공고번호모델번호주택형공급세대수순위거주코드거주지역접수건수경쟁률
12930202200035820220003585084.9963E3111해당지역4(△27)
25308202100016520210001652059.9879B4811해당지역1693.52
6113202300014120230001411084.70763222기타지역0<NA>
21096202100062920210006292059.9370A3821해당지역1(△33)
24428202100029520210002956157.29535321해당지역0<NA>
34033202000069120200006914135.66003612기타지역8<NA>
75202400006720240000673084.9929C2422기타지역0(△23)
23998202100033020210003303059.5283C17611해당지역46(△130)
309152020001022202000102213124.7048D923기타경기0<NA>
17583202100094820210009486084.7733B2112기타지역0(△8)