Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells12366
Missing cells (%)13.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory849.6 KiB
Average record size in memory87.0 B

Variable types

Numeric6
Text1
Categorical2

Dataset

Description한국부동산원(구.한국감정원)의 청약홈에서 제공하는 APT(아파트) 경쟁률 가점정보 데이터로 공고번호, 모델번호, 주택형, 최저당첨가점, 최고당첨가점, 평균당첨가점 등의 데이터를 제공합니다.
Author한국부동산원
URLhttps://www.data.go.kr/data/15126242/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
최저당첨가점 is highly overall correlated with 최고당첨가점 and 1 other fieldsHigh correlation
최고당첨가점 is highly overall correlated with 최저당첨가점 and 1 other fieldsHigh correlation
평균당첨가점 is highly overall correlated with 최저당첨가점 and 1 other fieldsHigh correlation
최저당첨가점 has 4122 (41.2%) missing valuesMissing
최고당첨가점 has 4122 (41.2%) missing valuesMissing
평균당첨가점 has 4122 (41.2%) missing valuesMissing
최저당첨가점 has 2356 (23.6%) zerosZeros
최고당첨가점 has 2356 (23.6%) zerosZeros
평균당첨가점 has 2356 (23.6%) zerosZeros

Reproduction

Analysis started2024-03-14 10:21:26.133594
Analysis finished2024-03-14 10:21:38.110489
Duration11.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주택관리번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1648
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.021248 × 109
Minimum2.02 × 109
Maximum2.0230006 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T19:21:38.335876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.02 × 109
5-th percentile2.0200004 × 109
Q12.0200012 × 109
median2.0210008 × 109
Q32.0220006 × 109
95-th percentile2.0230004 × 109
Maximum2.0230006 × 109
Range3000608
Interquartile range (IQR)1999347

Descriptive statistics

Standard deviation1033404.5
Coefficient of variation (CV)0.00051127051
Kurtosis-1.1465685
Mean2.021248 × 109
Median Absolute Deviation (MAD)999674.5
Skewness0.24902628
Sum2.021248 × 1013
Variance1.0679249 × 1012
MonotonicityNot monotonic
2024-03-14T19:21:38.673416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021000026 37
 
0.4%
2021000540 33
 
0.3%
2021000057 30
 
0.3%
2020000415 28
 
0.3%
2021000025 27
 
0.3%
2023000408 27
 
0.3%
2020001464 27
 
0.3%
2020000451 26
 
0.3%
2021000516 25
 
0.2%
2020001190 24
 
0.2%
Other values (1638) 9716
97.2%
ValueCountFrequency (%)
2020000001 5
 
0.1%
2020000005 11
0.1%
2020000007 4
 
< 0.1%
2020000009 3
 
< 0.1%
2020000010 3
 
< 0.1%
2020000028 1
 
< 0.1%
2020000040 8
0.1%
2020000041 5
 
0.1%
2020000046 6
0.1%
2020000047 13
0.1%
ValueCountFrequency (%)
2023000609 4
< 0.1%
2023000607 6
0.1%
2023000603 3
 
< 0.1%
2023000593 5
0.1%
2023000585 1
 
< 0.1%
2023000583 5
0.1%
2023000579 8
0.1%
2023000570 7
0.1%
2023000568 3
 
< 0.1%
2023000566 4
< 0.1%

공고번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1648
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.021248 × 109
Minimum2.02 × 109
Maximum2.0230006 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T19:21:38.943052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.02 × 109
5-th percentile2.0200004 × 109
Q12.0200012 × 109
median2.0210008 × 109
Q32.0220006 × 109
95-th percentile2.0230004 × 109
Maximum2.0230006 × 109
Range3000608
Interquartile range (IQR)1999347

Descriptive statistics

Standard deviation1033404.5
Coefficient of variation (CV)0.00051127051
Kurtosis-1.1465685
Mean2.021248 × 109
Median Absolute Deviation (MAD)999674.5
Skewness0.24902628
Sum2.021248 × 1013
Variance1.0679249 × 1012
MonotonicityNot monotonic
2024-03-14T19:21:39.206061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2021000026 37
 
0.4%
2021000540 33
 
0.3%
2021000057 30
 
0.3%
2020000415 28
 
0.3%
2021000025 27
 
0.3%
2023000408 27
 
0.3%
2020001464 27
 
0.3%
2020000451 26
 
0.3%
2021000516 25
 
0.2%
2020001190 24
 
0.2%
Other values (1638) 9716
97.2%
ValueCountFrequency (%)
2020000001 5
 
0.1%
2020000005 11
0.1%
2020000007 4
 
< 0.1%
2020000009 3
 
< 0.1%
2020000010 3
 
< 0.1%
2020000028 1
 
< 0.1%
2020000040 8
0.1%
2020000041 5
 
0.1%
2020000046 6
0.1%
2020000047 13
0.1%
ValueCountFrequency (%)
2023000609 4
< 0.1%
2023000607 6
0.1%
2023000603 3
 
< 0.1%
2023000593 5
0.1%
2023000585 1
 
< 0.1%
2023000583 5
0.1%
2023000579 8
0.1%
2023000570 7
0.1%
2023000568 3
 
< 0.1%
2023000566 4
< 0.1%

모델번호
Real number (ℝ)

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1667
Minimum1
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T19:21:39.561781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile10
Maximum38
Range37
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.4549247
Coefficient of variation (CV)0.8291753
Kurtosis10.830949
Mean4.1667
Median Absolute Deviation (MAD)2
Skewness2.5318486
Sum41667
Variance11.936505
MonotonicityNot monotonic
2024-03-14T19:21:39.995068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2 1862
18.6%
1 1851
18.5%
3 1634
16.3%
4 1318
13.2%
5 959
9.6%
6 694
 
6.9%
7 455
 
4.5%
8 354
 
3.5%
9 235
 
2.4%
10 144
 
1.4%
Other values (26) 494
 
4.9%
ValueCountFrequency (%)
1 1851
18.5%
2 1862
18.6%
3 1634
16.3%
4 1318
13.2%
5 959
9.6%
6 694
 
6.9%
7 455
 
4.5%
8 354
 
3.5%
9 235
 
2.4%
10 144
 
1.4%
ValueCountFrequency (%)
38 1
 
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
34 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
30 2
< 0.1%
29 2
< 0.1%
28 2
< 0.1%
27 4
< 0.1%

주택형
Text

Distinct6010
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T19:21:40.955091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.5181
Min length2

Characters and Unicode

Total characters85181
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3255 ?
Unique (%)32.6%

Sample

1st row084.7571A
2nd row059.9786A
3rd row084.7783A
4th row59.4482
5th row084.6635C
ValueCountFrequency (%)
084.9900a 32
 
0.3%
084.9600a 20
 
0.2%
084.9900c 20
 
0.2%
084.9800a 20
 
0.2%
084.9900b 19
 
0.2%
084.9700a 18
 
0.2%
059.9900a 17
 
0.2%
059.9900b 17
 
0.2%
059.9700c 16
 
0.2%
084.9400a 14
 
0.1%
Other values (6000) 9807
98.1%
2024-03-14T19:21:42.059988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13476
15.8%
9 10560
12.4%
. 9998
11.7%
8 8784
10.3%
4 7924
9.3%
5 5572
6.5%
7 5392
6.3%
1 4996
 
5.9%
6 4203
 
4.9%
3 3310
 
3.9%
Other values (24) 10966
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67416
79.1%
Other Punctuation 9998
 
11.7%
Uppercase Letter 7767
 
9.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2799
36.0%
B 2537
32.7%
C 1149
14.8%
D 454
 
5.8%
E 193
 
2.5%
P 170
 
2.2%
F 106
 
1.4%
T 88
 
1.1%
H 63
 
0.8%
G 63
 
0.8%
Other values (13) 145
 
1.9%
Decimal Number
ValueCountFrequency (%)
0 13476
20.0%
9 10560
15.7%
8 8784
13.0%
4 7924
11.8%
5 5572
8.3%
7 5392
8.0%
1 4996
 
7.4%
6 4203
 
6.2%
3 3310
 
4.9%
2 3199
 
4.7%
Other Punctuation
ValueCountFrequency (%)
. 9998
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 77414
90.9%
Latin 7767
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2799
36.0%
B 2537
32.7%
C 1149
14.8%
D 454
 
5.8%
E 193
 
2.5%
P 170
 
2.2%
F 106
 
1.4%
T 88
 
1.1%
H 63
 
0.8%
G 63
 
0.8%
Other values (13) 145
 
1.9%
Common
ValueCountFrequency (%)
0 13476
17.4%
9 10560
13.6%
. 9998
12.9%
8 8784
11.3%
4 7924
10.2%
5 5572
7.2%
7 5392
7.0%
1 4996
 
6.5%
6 4203
 
5.4%
3 3310
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85181
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13476
15.8%
9 10560
12.4%
. 9998
11.7%
8 8784
10.3%
4 7924
9.3%
5 5572
6.5%
7 5392
6.3%
1 4996
 
5.9%
6 4203
 
4.9%
3 3310
 
3.9%
Other values (24) 10966
12.9%

거주코드
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
4854 
2
4741 
3
 
405

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 4854
48.5%
2 4741
47.4%
3 405
 
4.0%

Length

2024-03-14T19:21:42.289019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:21:42.453674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4854
48.5%
2 4741
47.4%
3 405
 
4.0%

거주지역
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
해당지역
4854 
기타지역
4741 
기타경기
 
405

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해당지역 4854
48.5%
기타지역 4741
47.4%
기타경기 405
 
4.0%

Length

2024-03-14T19:21:42.628774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T19:21:43.047827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해당지역 4854
48.5%
기타지역 4741
47.4%
기타경기 405
 
4.0%

최저당첨가점
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct70
Distinct (%)1.2%
Missing4122
Missing (%)41.2%
Infinite0
Infinite (%)0.0%
Mean27.781728
Minimum0
Maximum79
Zeros2356
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T19:21:43.400047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29
Q352
95-th percentile67
Maximum79
Range79
Interquartile range (IQR)52

Descriptive statistics

Standard deviation25.546692
Coefficient of variation (CV)0.91955013
Kurtosis-1.5709028
Mean27.781728
Median Absolute Deviation (MAD)29
Skewness0.12688434
Sum163301
Variance652.63348
MonotonicityNot monotonic
2024-03-14T19:21:43.851825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2356
23.6%
69 180
 
1.8%
54 113
 
1.1%
51 113
 
1.1%
56 111
 
1.1%
55 107
 
1.1%
50 98
 
1.0%
59 98
 
1.0%
58 98
 
1.0%
57 97
 
1.0%
Other values (60) 2507
25.1%
(Missing) 4122
41.2%
ValueCountFrequency (%)
0 2356
23.6%
9 8
 
0.1%
10 12
 
0.1%
11 10
 
0.1%
12 8
 
0.1%
13 14
 
0.1%
14 15
 
0.1%
15 19
 
0.2%
16 21
 
0.2%
17 18
 
0.2%
ValueCountFrequency (%)
79 1
 
< 0.1%
76 1
 
< 0.1%
75 2
 
< 0.1%
74 22
 
0.2%
73 7
 
0.1%
72 9
 
0.1%
71 10
 
0.1%
70 17
 
0.2%
69 180
1.8%
68 27
 
0.3%

최고당첨가점
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct74
Distinct (%)1.3%
Missing4122
Missing (%)41.2%
Infinite0
Infinite (%)0.0%
Mean36.826812
Minimum0
Maximum84
Zeros2356
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T19:21:44.281681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median53
Q365
95-th percentile74
Maximum84
Range84
Interquartile range (IQR)65

Descriptive statistics

Standard deviation31.162371
Coefficient of variation (CV)0.84618704
Kurtosis-1.7790082
Mean36.826812
Median Absolute Deviation (MAD)18
Skewness-0.2349399
Sum216468
Variance971.09336
MonotonicityNot monotonic
2024-03-14T19:21:44.697902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2356
23.6%
69 427
 
4.3%
64 213
 
2.1%
74 190
 
1.9%
61 154
 
1.5%
59 150
 
1.5%
60 146
 
1.5%
65 136
 
1.4%
66 130
 
1.3%
63 128
 
1.3%
Other values (64) 1848
18.5%
(Missing) 4122
41.2%
ValueCountFrequency (%)
0 2356
23.6%
11 1
 
< 0.1%
12 2
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
17 2
 
< 0.1%
18 5
 
0.1%
19 3
 
< 0.1%
20 2
 
< 0.1%
ValueCountFrequency (%)
84 3
 
< 0.1%
83 3
 
< 0.1%
82 2
 
< 0.1%
81 5
 
0.1%
80 9
 
0.1%
79 46
0.5%
78 15
 
0.1%
77 19
0.2%
76 16
 
0.2%
75 36
0.4%

평균당첨가점
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct1893
Distinct (%)32.2%
Missing4122
Missing (%)41.2%
Infinite0
Infinite (%)0.0%
Mean30.825189
Minimum0
Maximum79
Zeros2356
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T19:21:45.044022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.71
Q355.585
95-th percentile68.67
Maximum79
Range79
Interquartile range (IQR)55.585

Descriptive statistics

Standard deviation26.985672
Coefficient of variation (CV)0.87544224
Kurtosis-1.6738486
Mean30.825189
Median Absolute Deviation (MAD)26.34
Skewness-0.063365846
Sum181190.46
Variance728.22652
MonotonicityNot monotonic
2024-03-14T19:21:45.304269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2356
23.6%
69.0 61
 
0.6%
55.0 34
 
0.3%
58.0 28
 
0.3%
57.0 27
 
0.3%
60.0 25
 
0.2%
54.0 24
 
0.2%
59.0 24
 
0.2%
56.0 24
 
0.2%
51.0 23
 
0.2%
Other values (1883) 3252
32.5%
(Missing) 4122
41.2%
ValueCountFrequency (%)
0.0 2356
23.6%
11.0 1
 
< 0.1%
11.67 1
 
< 0.1%
12.0 1
 
< 0.1%
14.0 1
 
< 0.1%
15.0 1
 
< 0.1%
16.0 1
 
< 0.1%
16.48 1
 
< 0.1%
16.5 2
 
< 0.1%
17.0 3
 
< 0.1%
ValueCountFrequency (%)
79.0 1
 
< 0.1%
77.88 1
 
< 0.1%
76.5 1
 
< 0.1%
76.33 1
 
< 0.1%
76.0 1
 
< 0.1%
75.88 1
 
< 0.1%
75.71 2
< 0.1%
75.54 1
 
< 0.1%
75.44 1
 
< 0.1%
75.0 3
< 0.1%

Interactions

2024-03-14T19:21:35.362624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:26.856549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:28.548935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:30.221244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:32.086368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:33.760017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:35.646845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:27.135962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:28.826754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:30.502742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:32.370274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:34.029986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:35.927312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:27.415495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:29.107614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:30.781437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:32.654835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:34.298004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:36.214684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:27.701703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:29.388633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:31.055055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:32.930447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:34.568619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:36.497635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:27.990416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:29.675241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:31.545829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:33.213516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:34.843019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:36.759985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:28.256675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:29.933963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:31.802579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:33.473947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T19:21:35.090758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T19:21:45.586376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택관리번호공고번호모델번호거주코드거주지역최저당첨가점최고당첨가점평균당첨가점
주택관리번호1.0001.0000.0700.2540.2540.2040.1410.176
공고번호1.0001.0000.0700.2540.2540.2040.1410.176
모델번호0.0700.0701.0000.0000.0000.2590.0890.212
거주코드0.2540.2540.0001.0001.0000.6480.6410.646
거주지역0.2540.2540.0001.0001.0000.6480.6410.646
최저당첨가점0.2040.2040.2590.6480.6481.0000.8560.968
최고당첨가점0.1410.1410.0890.6410.6410.8561.0000.936
평균당첨가점0.1760.1760.2120.6460.6460.9680.9361.000
2024-03-14T19:21:45.869532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거주코드거주지역
거주코드1.0001.000
거주지역1.0001.000
2024-03-14T19:21:46.118072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택관리번호공고번호모델번호최저당첨가점최고당첨가점평균당첨가점거주코드거주지역
주택관리번호1.0001.000-0.003-0.051-0.036-0.0500.1080.108
공고번호1.0001.000-0.003-0.051-0.036-0.0500.1080.108
모델번호-0.003-0.0031.0000.062-0.0030.0520.0000.000
최저당첨가점-0.051-0.0510.0621.0000.9160.9960.4960.496
최고당첨가점-0.036-0.036-0.0030.9161.0000.9380.4880.488
평균당첨가점-0.050-0.0500.0520.9960.9381.0000.4940.494
거주코드0.1080.1080.0000.4960.4880.4941.0001.000
거주지역0.1080.1080.0000.4960.4880.4941.0001.000

Missing values

2024-03-14T19:21:37.143130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T19:21:37.603578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-14T19:21:37.954814image/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

주택관리번호공고번호모델번호주택형거주코드거주지역최저당첨가점최고당첨가점평균당첨가점
10210202100091120210009111084.7571A2기타지역667468.27
7218202100033420210003341059.9786A1해당지역355242.49
7715202100043620210004365084.7783A2기타지역000.0
135220200005532020000553159.44822기타지역000.0
13619202200053720220005373084.6635C2기타지역000.0
5879202100005720210000571084.1500A2기타지역747974.31
8989202100069920210006993053.2800D2기타지역516455.0
12191202200023020220002306084.9853B2기타지역000.0
16713202300016020230001604115.29121해당지역<NA><NA><NA>
25020200001922020000192447.4122기타지역<NA><NA><NA>
주택관리번호공고번호모델번호주택형거주코드거주지역최저당첨가점최고당첨가점평균당첨가점
12498202200027220220002723074.9740B1해당지역<NA><NA><NA>
1699920230002282023000228374.95572기타지역000.0
206720200006862020000686484.90871해당지역596059.33
3488202000098220200009822075.1931B1해당지역<NA><NA><NA>
12357202200024720220002479168.14432기타지역<NA><NA><NA>
16752202300017420230001744084.9800A2기타지역000.0
18231202300051920230005193056.2836B2기타지역<NA><NA><NA>
2474202000077520200007756084.9602A1해당지역506854.42
1673620230001652023000165159.99322기타지역295441.5
15179202200083320220008332120.5450B1해당지역<NA><NA><NA>