Overview

Dataset statistics

Number of variables18
Number of observations7216
Missing cells11043
Missing cells (%)8.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory161.0 B

Variable types

Numeric17
Text1

Dataset

Description기준_년분기_코드,행정동_코드,행정동_코드_명,아파트_단지_수,아파트_면적_66_제곱미터_미만_세대_수,아파트_면적_66_제곱미터_세대_수,아파트_면적_99_제곱미터_세대_수,아파트_면적_132_제곱미터_세대_수,아파트_면적_165_제곱미터_세대_수,아파트_가격_1_억_미만_세대_수,아파트_가격_1_억_세대_수,아파트_가격_2_억_세대_수,아파트_가격_3_억_세대_수,아파트_가격_4_억_세대_수,아파트_가격_5_억_세대_수,아파트_가격_6_억_이상_세대_수,아파트_평균_면적,아파트_평균_시가
Author서울신용보증재단
URLhttps://data.seoul.go.kr/dataList/OA-22163/S/1/datasetView.do

Alerts

기준_년분기_코드 is highly overall correlated with 아파트_면적_66_제곱미터_세대_수High correlation
아파트_단지_수 is highly overall correlated with 아파트_면적_66_제곱미터_미만_세대_수 and 4 other fieldsHigh correlation
아파트_면적_66_제곱미터_미만_세대_수 is highly overall correlated with 아파트_단지_수 and 6 other fieldsHigh correlation
아파트_면적_66_제곱미터_세대_수 is highly overall correlated with 기준_년분기_코드 and 6 other fieldsHigh correlation
아파트_면적_99_제곱미터_세대_수 is highly overall correlated with 아파트_면적_66_제곱미터_세대_수 and 6 other fieldsHigh correlation
아파트_면적_132_제곱미터_세대_수 is highly overall correlated with 아파트_면적_99_제곱미터_세대_수 and 4 other fieldsHigh correlation
아파트_면적_165_제곱미터_세대_수 is highly overall correlated with 아파트_면적_132_제곱미터_세대_수 and 3 other fieldsHigh correlation
아파트_가격_1_억_미만_세대_수 is highly overall correlated with 아파트_단지_수 and 4 other fieldsHigh correlation
아파트_가격_1_억_세대_수 is highly overall correlated with 아파트_단지_수 and 5 other fieldsHigh correlation
아파트_가격_2_억_세대_수 is highly overall correlated with 아파트_단지_수 and 4 other fieldsHigh correlation
아파트_가격_3_억_세대_수 is highly overall correlated with 아파트_면적_66_제곱미터_미만_세대_수 and 4 other fieldsHigh correlation
아파트_가격_4_억_세대_수 is highly overall correlated with 아파트_면적_66_제곱미터_세대_수 and 3 other fieldsHigh correlation
아파트_가격_5_억_세대_수 is highly overall correlated with 아파트_면적_66_제곱미터_세대_수 and 2 other fieldsHigh correlation
아파트_가격_6_억_이상_세대_수 is highly overall correlated with 아파트_면적_99_제곱미터_세대_수 and 4 other fieldsHigh correlation
아파트_평균_면적 is highly overall correlated with 아파트_면적_99_제곱미터_세대_수 and 6 other fieldsHigh correlation
아파트_평균_시가 is highly overall correlated with 아파트_단지_수 and 7 other fieldsHigh correlation
아파트_면적_66_제곱미터_미만_세대_수 has 86 (1.2%) missing valuesMissing
아파트_면적_99_제곱미터_세대_수 has 141 (2.0%) missing valuesMissing
아파트_면적_132_제곱미터_세대_수 has 1306 (18.1%) missing valuesMissing
아파트_면적_165_제곱미터_세대_수 has 3451 (47.8%) missing valuesMissing
아파트_가격_1_억_미만_세대_수 has 653 (9.0%) missing valuesMissing
아파트_가격_1_억_세대_수 has 342 (4.7%) missing valuesMissing
아파트_가격_2_억_세대_수 has 279 (3.9%) missing valuesMissing
아파트_가격_3_억_세대_수 has 280 (3.9%) missing valuesMissing
아파트_가격_4_억_세대_수 has 742 (10.3%) missing valuesMissing
아파트_가격_5_억_세대_수 has 1488 (20.6%) missing valuesMissing
아파트_가격_6_억_이상_세대_수 has 2270 (31.5%) missing valuesMissing

Reproduction

Analysis started2024-05-03 20:06:47.057770
Analysis finished2024-05-03 20:08:42.810608
Duration1 minute and 55.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준_년분기_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20216.105
Minimum20194
Maximum20234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:42.988169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20194
5-th percentile20194
Q120204
median20214
Q320224
95-th percentile20234
Maximum20234
Range40
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.224216
Coefficient of variation (CV)0.0006046771
Kurtosis-1.1881651
Mean20216.105
Median Absolute Deviation (MAD)10
Skewness-0.075840886
Sum1.4587942 × 108
Variance149.43146
MonotonicityNot monotonic
2024-05-03T20:08:43.489243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20211 425
 
5.9%
20194 425
 
5.9%
20201 425
 
5.9%
20202 425
 
5.9%
20203 425
 
5.9%
20204 425
 
5.9%
20212 425
 
5.9%
20213 425
 
5.9%
20221 424
 
5.9%
20224 424
 
5.9%
Other values (7) 2968
41.1%
ValueCountFrequency (%)
20194 425
5.9%
20201 425
5.9%
20202 425
5.9%
20203 425
5.9%
20204 425
5.9%
20211 425
5.9%
20212 425
5.9%
20213 425
5.9%
20214 424
5.9%
20221 424
5.9%
ValueCountFrequency (%)
20234 424
5.9%
20233 424
5.9%
20232 424
5.9%
20231 424
5.9%
20224 424
5.9%
20223 424
5.9%
20222 424
5.9%
20221 424
5.9%
20214 424
5.9%
20213 425
5.9%

행정동_코드
Real number (ℝ)

Distinct425
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11433042
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:43.959734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140580
Q111260655
median11440630
Q311590680
95-th percentile11710680
Maximum11740700
Range630185
Interquartile range (IQR)330025

Descriptive statistics

Standard deviation191325.35
Coefficient of variation (CV)0.016734423
Kurtosis-1.2625287
Mean11433042
Median Absolute Deviation (MAD)179940
Skewness-0.013888826
Sum8.2500828 × 1010
Variance3.6605389 × 1010
MonotonicityNot monotonic
2024-05-03T20:08:44.502741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11110515 17
 
0.2%
11590530 17
 
0.2%
11590510 17
 
0.2%
11560720 17
 
0.2%
11560710 17
 
0.2%
11560700 17
 
0.2%
11560690 17
 
0.2%
11560680 17
 
0.2%
11560670 17
 
0.2%
11560660 17
 
0.2%
Other values (415) 7046
97.6%
ValueCountFrequency (%)
11110515 17
0.2%
11110530 17
0.2%
11110540 17
0.2%
11110550 17
0.2%
11110560 17
0.2%
11110570 17
0.2%
11110580 17
0.2%
11110600 17
0.2%
11110615 17
0.2%
11110630 17
0.2%
ValueCountFrequency (%)
11740700 17
0.2%
11740690 8
0.1%
11740685 17
0.2%
11740660 17
0.2%
11740650 17
0.2%
11740640 17
0.2%
11740620 17
0.2%
11740610 17
0.2%
11740600 17
0.2%
11740590 17
0.2%
Distinct424
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size56.5 KiB
2024-05-03T20:08:45.328561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.7879712
Min length2

Characters and Unicode

Total characters27334
Distinct characters188
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row청운효자동
2nd row사직동
3rd row삼청동
4th row부암동
5th row평창동
ValueCountFrequency (%)
신사동 34
 
0.5%
노량진1동 17
 
0.2%
대림3동 17
 
0.2%
대림2동 17
 
0.2%
대림1동 17
 
0.2%
신길7동 17
 
0.2%
신길6동 17
 
0.2%
신길5동 17
 
0.2%
신길4동 17
 
0.2%
신길3동 17
 
0.2%
Other values (414) 7029
97.4%
2024-05-03T20:08:46.615205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7250
26.5%
2 1649
 
6.0%
1 1640
 
6.0%
3 731
 
2.7%
646
 
2.4%
4 442
 
1.6%
391
 
1.4%
306
 
1.1%
289
 
1.1%
289
 
1.1%
Other values (178) 13701
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22226
81.3%
Decimal Number 4955
 
18.1%
Other Punctuation 153
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
7250
32.6%
646
 
2.9%
391
 
1.8%
306
 
1.4%
289
 
1.3%
289
 
1.3%
272
 
1.2%
272
 
1.2%
272
 
1.2%
272
 
1.2%
Other values (167) 11967
53.8%
Decimal Number
ValueCountFrequency (%)
2 1649
33.3%
1 1640
33.1%
3 731
14.8%
4 442
 
8.9%
5 187
 
3.8%
6 119
 
2.4%
7 102
 
2.1%
8 51
 
1.0%
9 17
 
0.3%
0 17
 
0.3%
Other Punctuation
ValueCountFrequency (%)
? 153
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22226
81.3%
Common 5108
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
7250
32.6%
646
 
2.9%
391
 
1.8%
306
 
1.4%
289
 
1.3%
289
 
1.3%
272
 
1.2%
272
 
1.2%
272
 
1.2%
272
 
1.2%
Other values (167) 11967
53.8%
Common
ValueCountFrequency (%)
2 1649
32.3%
1 1640
32.1%
3 731
14.3%
4 442
 
8.7%
5 187
 
3.7%
? 153
 
3.0%
6 119
 
2.3%
7 102
 
2.0%
8 51
 
1.0%
9 17
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22226
81.3%
ASCII 5108
 
18.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
7250
32.6%
646
 
2.9%
391
 
1.8%
306
 
1.4%
289
 
1.3%
289
 
1.3%
272
 
1.2%
272
 
1.2%
272
 
1.2%
272
 
1.2%
Other values (167) 11967
53.8%
ASCII
ValueCountFrequency (%)
2 1649
32.3%
1 1640
32.1%
3 731
14.3%
4 442
 
8.7%
5 187
 
3.7%
? 153
 
3.0%
6 119
 
2.3%
7 102
 
2.0%
8 51
 
1.0%
9 17
 
0.3%

아파트_단지_수
Real number (ℝ)

HIGH CORRELATION 

Distinct522
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean267.50956
Minimum1
Maximum1296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:47.057929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q1116
median212.5
Q3375
95-th percentile671
Maximum1296
Range1295
Interquartile range (IQR)259

Descriptive statistics

Standard deviation204.69523
Coefficient of variation (CV)0.76518845
Kurtosis3.6156677
Mean267.50956
Median Absolute Deviation (MAD)115.5
Skewness1.5933613
Sum1930349
Variance41900.137
MonotonicityNot monotonic
2024-05-03T20:08:47.615439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131 88
 
1.2%
116 58
 
0.8%
122 51
 
0.7%
170 50
 
0.7%
4 46
 
0.6%
91 46
 
0.6%
125 45
 
0.6%
95 45
 
0.6%
180 44
 
0.6%
119 43
 
0.6%
Other values (512) 6700
92.8%
ValueCountFrequency (%)
1 9
 
0.1%
3 4
 
0.1%
4 46
0.6%
6 8
 
0.1%
7 9
 
0.1%
9 18
 
0.2%
10 14
 
0.2%
11 20
0.3%
14 8
 
0.1%
17 8
 
0.1%
ValueCountFrequency (%)
1296 8
0.1%
1243 4
 
0.1%
1242 5
 
0.1%
1205 4
 
0.1%
1203 8
0.1%
1192 5
 
0.1%
1170 9
0.1%
1160 8
0.1%
1133 8
0.1%
1112 16
0.2%

아파트_면적_66_제곱미터_미만_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct950
Distinct (%)13.3%
Missing86
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean2381.1582
Minimum8
Maximum12949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:48.128855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile297
Q1905
median1936
Q33293.5
95-th percentile5935
Maximum12949
Range12941
Interquartile range (IQR)2388.5

Descriptive statistics

Standard deviation1910.231
Coefficient of variation (CV)0.80222768
Kurtosis2.9460588
Mean2381.1582
Median Absolute Deviation (MAD)1153
Skewness1.4739073
Sum16977658
Variance3648982.6
MonotonicityNot monotonic
2024-05-03T20:08:49.005690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452 26
 
0.4%
712 23
 
0.3%
1759 22
 
0.3%
168 18
 
0.2%
463 18
 
0.2%
320 18
 
0.2%
455 18
 
0.2%
1869 18
 
0.2%
478 18
 
0.2%
427 18
 
0.2%
Other values (940) 6933
96.1%
(Missing) 86
 
1.2%
ValueCountFrequency (%)
8 8
0.1%
12 9
0.1%
13 8
0.1%
16 8
0.1%
32 9
0.1%
34 9
0.1%
43 17
0.2%
56 9
0.1%
61 9
0.1%
62 9
0.1%
ValueCountFrequency (%)
12949 8
0.1%
10613 8
0.1%
10485 8
0.1%
10133 8
0.1%
9717 4
0.1%
9634 5
0.1%
9591 8
0.1%
9471 8
0.1%
9394 4
0.1%
9392 5
0.1%

아파트_면적_66_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct799
Distinct (%)11.1%
Missing5
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1194.1384
Minimum1
Maximum7632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:49.627772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile143
Q1415
median765
Q31591
95-th percentile3579
Maximum7632
Range7631
Interquartile range (IQR)1176

Descriptive statistics

Standard deviation1163.5096
Coefficient of variation (CV)0.97435068
Kurtosis4.998419
Mean1194.1384
Median Absolute Deviation (MAD)457
Skewness2.007164
Sum8610932
Variance1353754.5
MonotonicityNot monotonic
2024-05-03T20:08:50.536305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
885 52
 
0.7%
424 36
 
0.5%
457 34
 
0.5%
430 34
 
0.5%
520 27
 
0.4%
761 26
 
0.4%
695 26
 
0.4%
496 25
 
0.3%
854 24
 
0.3%
711 23
 
0.3%
Other values (789) 6904
95.7%
ValueCountFrequency (%)
1 12
0.2%
4 8
0.1%
8 9
0.1%
11 9
0.1%
12 18
0.2%
14 8
0.1%
18 9
0.1%
19 9
0.1%
22 8
0.1%
45 9
0.1%
ValueCountFrequency (%)
7632 8
0.1%
7392 8
0.1%
7029 8
0.1%
6817 8
0.1%
6654 8
0.1%
5879 8
0.1%
5811 8
0.1%
5722 8
0.1%
5675 8
0.1%
5496 8
0.1%

아파트_면적_99_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct472
Distinct (%)6.7%
Missing141
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean315.73456
Minimum1
Maximum3182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:51.080311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q152
median134
Q3372
95-th percentile1325
Maximum3182
Range3181
Interquartile range (IQR)320

Descriptive statistics

Standard deviation451.61382
Coefficient of variation (CV)1.4303592
Kurtosis8.499402
Mean315.73456
Median Absolute Deviation (MAD)105
Skewness2.6825057
Sum2233822
Variance203955.05
MonotonicityNot monotonic
2024-05-03T20:08:51.625647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 93
 
1.3%
12 75
 
1.0%
92 62
 
0.9%
30 62
 
0.9%
6 62
 
0.9%
116 61
 
0.8%
19 61
 
0.8%
20 61
 
0.8%
38 60
 
0.8%
42 59
 
0.8%
Other values (462) 6419
89.0%
(Missing) 141
 
2.0%
ValueCountFrequency (%)
1 18
 
0.2%
2 93
1.3%
3 22
 
0.3%
4 9
 
0.1%
5 26
 
0.4%
6 62
0.9%
7 43
0.6%
8 43
0.6%
9 14
 
0.2%
10 55
0.8%
ValueCountFrequency (%)
3182 8
0.1%
2836 8
0.1%
2797 8
0.1%
2717 8
0.1%
2526 8
0.1%
2410 8
0.1%
2396 8
0.1%
2295 8
0.1%
2206 8
0.1%
2193 8
0.1%

아파트_면적_132_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct255
Distinct (%)4.3%
Missing1306
Missing (%)18.1%
Infinite0
Infinite (%)0.0%
Mean135.79577
Minimum1
Maximum3215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:52.337781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median33
Q3132
95-th percentile637.1
Maximum3215
Range3214
Interquartile range (IQR)126

Descriptive statistics

Standard deviation285.25258
Coefficient of variation (CV)2.1005999
Kurtosis30.703666
Mean135.79577
Median Absolute Deviation (MAD)31
Skewness4.6792207
Sum802553
Variance81369.034
MonotonicityNot monotonic
2024-05-03T20:08:52.797448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 516
 
7.2%
2 288
 
4.0%
3 266
 
3.7%
4 181
 
2.5%
6 137
 
1.9%
5 136
 
1.9%
7 135
 
1.9%
8 126
 
1.7%
12 106
 
1.5%
11 92
 
1.3%
Other values (245) 3927
54.4%
(Missing) 1306
 
18.1%
ValueCountFrequency (%)
1 516
7.2%
2 288
4.0%
3 266
3.7%
4 181
 
2.5%
5 136
 
1.9%
6 137
 
1.9%
7 135
 
1.9%
8 126
 
1.7%
9 75
 
1.0%
10 38
 
0.5%
ValueCountFrequency (%)
3215 8
0.1%
2186 8
0.1%
1980 8
0.1%
1911 8
0.1%
1785 8
0.1%
1659 8
0.1%
1445 8
0.1%
1432 8
0.1%
1351 8
0.1%
1303 8
0.1%

아파트_면적_165_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct173
Distinct (%)4.6%
Missing3451
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean104.3753
Minimum1
Maximum1619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:53.813566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q390
95-th percentile618
Maximum1619
Range1618
Interquartile range (IQR)87

Descriptive statistics

Standard deviation229.62588
Coefficient of variation (CV)2.2000022
Kurtosis14.811599
Mean104.3753
Median Absolute Deviation (MAD)14
Skewness3.6669346
Sum392973
Variance52728.046
MonotonicityNot monotonic
2024-05-03T20:08:54.281067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 603
 
8.4%
2 256
 
3.5%
4 180
 
2.5%
6 149
 
2.1%
3 133
 
1.8%
12 110
 
1.5%
5 96
 
1.3%
7 72
 
1.0%
24 69
 
1.0%
11 67
 
0.9%
Other values (163) 2030
28.1%
(Missing) 3451
47.8%
ValueCountFrequency (%)
1 603
8.4%
2 256
3.5%
3 133
 
1.8%
4 180
 
2.5%
5 96
 
1.3%
6 149
 
2.1%
7 72
 
1.0%
8 50
 
0.7%
9 33
 
0.5%
10 34
 
0.5%
ValueCountFrequency (%)
1619 8
0.1%
1460 8
0.1%
1381 8
0.1%
1273 8
0.1%
1264 8
0.1%
1226 8
0.1%
1115 8
0.1%
1114 8
0.1%
1031 8
0.1%
964 8
0.1%

아파트_가격_1_억_미만_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct555
Distinct (%)8.5%
Missing653
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean453.86134
Minimum1
Maximum4524
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:54.939820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q160
median246
Q3612
95-th percentile1584
Maximum4524
Range4523
Interquartile range (IQR)552

Descriptive statistics

Standard deviation583.25152
Coefficient of variation (CV)1.2850875
Kurtosis9.7957558
Mean453.86134
Median Absolute Deviation (MAD)217
Skewness2.632835
Sum2978692
Variance340182.34
MonotonicityNot monotonic
2024-05-03T20:08:55.382528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 114
 
1.6%
8 99
 
1.4%
4 77
 
1.1%
3 59
 
0.8%
22 54
 
0.7%
10 53
 
0.7%
15 51
 
0.7%
7 51
 
0.7%
25 51
 
0.7%
52 48
 
0.7%
Other values (545) 5906
81.8%
(Missing) 653
 
9.0%
ValueCountFrequency (%)
1 114
1.6%
2 17
 
0.2%
3 59
0.8%
4 77
1.1%
5 25
 
0.3%
6 30
 
0.4%
7 51
0.7%
8 99
1.4%
9 31
 
0.4%
10 53
0.7%
ValueCountFrequency (%)
4524 8
0.1%
4164 8
0.1%
3710 9
0.1%
3547 8
0.1%
3419 8
0.1%
3397 8
0.1%
3193 8
0.1%
2956 8
0.1%
2931 8
0.1%
2566 9
0.1%

아파트_가격_1_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct839
Distinct (%)12.2%
Missing342
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean1321.6158
Minimum1
Maximum10765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:55.811244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile41
Q1308
median904
Q31977
95-th percentile3699
Maximum10765
Range10764
Interquartile range (IQR)1669

Descriptive statistics

Standard deviation1335.8902
Coefficient of variation (CV)1.0108007
Kurtosis6.5961222
Mean1321.6158
Median Absolute Deviation (MAD)679
Skewness2.0008914
Sum9084787
Variance1784602.6
MonotonicityNot monotonic
2024-05-03T20:08:56.354617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 32
 
0.4%
237 26
 
0.4%
27 26
 
0.4%
143 26
 
0.4%
35 26
 
0.4%
762 26
 
0.4%
30 26
 
0.4%
158 25
 
0.3%
14 25
 
0.3%
268 25
 
0.3%
Other values (829) 6611
91.6%
(Missing) 342
 
4.7%
ValueCountFrequency (%)
1 18
0.2%
2 8
 
0.1%
3 18
0.2%
7 17
0.2%
8 17
0.2%
11 17
0.2%
12 8
 
0.1%
13 8
 
0.1%
14 25
0.3%
15 9
 
0.1%
ValueCountFrequency (%)
10765 8
0.1%
9078 4
0.1%
9037 5
0.1%
8327 8
0.1%
7799 8
0.1%
7537 4
0.1%
7533 5
0.1%
7477 9
0.1%
5909 8
0.1%
5891 8
0.1%

아파트_가격_2_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct705
Distinct (%)10.2%
Missing279
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean731.83163
Minimum1
Maximum7241
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:56.758173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37
Q1189
median459
Q31032
95-th percentile2272
Maximum7241
Range7240
Interquartile range (IQR)843

Descriptive statistics

Standard deviation847.96849
Coefficient of variation (CV)1.1586934
Kurtosis11.943829
Mean731.83163
Median Absolute Deviation (MAD)315
Skewness2.8602928
Sum5076716
Variance719050.56
MonotonicityNot monotonic
2024-05-03T20:08:57.196062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182 43
 
0.6%
198 42
 
0.6%
62 40
 
0.6%
579 39
 
0.5%
146 35
 
0.5%
193 34
 
0.5%
234 31
 
0.4%
37 31
 
0.4%
232 27
 
0.4%
55 27
 
0.4%
Other values (695) 6588
91.3%
(Missing) 279
 
3.9%
ValueCountFrequency (%)
1 13
0.2%
2 9
 
0.1%
4 18
0.2%
5 9
 
0.1%
6 17
0.2%
8 9
 
0.1%
10 18
0.2%
11 25
0.3%
12 17
0.2%
14 18
0.2%
ValueCountFrequency (%)
7241 8
0.1%
5925 8
0.1%
5543 8
0.1%
5485 8
0.1%
5269 8
0.1%
5217 8
0.1%
5104 8
0.1%
4914 8
0.1%
4669 8
0.1%
4543 8
0.1%

아파트_가격_3_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct551
Distinct (%)7.9%
Missing280
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean474.31747
Minimum1
Maximum4913
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:57.774123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q170
median208
Q3601
95-th percentile1840
Maximum4913
Range4912
Interquartile range (IQR)531

Descriptive statistics

Standard deviation687.40699
Coefficient of variation (CV)1.449255
Kurtosis9.8748809
Mean474.31747
Median Absolute Deviation (MAD)173
Skewness2.8240608
Sum3289866
Variance472528.38
MonotonicityNot monotonic
2024-05-03T20:08:58.268807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 94
 
1.3%
122 53
 
0.7%
38 52
 
0.7%
8 51
 
0.7%
6 50
 
0.7%
3 49
 
0.7%
104 48
 
0.7%
9 48
 
0.7%
28 44
 
0.6%
140 44
 
0.6%
Other values (541) 6403
88.7%
(Missing) 280
 
3.9%
ValueCountFrequency (%)
1 34
 
0.5%
2 94
1.3%
3 49
0.7%
4 34
 
0.5%
5 30
 
0.4%
6 50
0.7%
7 27
 
0.4%
8 51
0.7%
9 48
0.7%
10 39
0.5%
ValueCountFrequency (%)
4913 8
0.1%
4753 8
0.1%
4724 8
0.1%
4164 8
0.1%
3788 8
0.1%
3559 8
0.1%
3546 8
0.1%
3479 8
0.1%
3339 8
0.1%
3290 8
0.1%

아파트_가격_4_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct455
Distinct (%)7.0%
Missing742
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean375.63593
Minimum1
Maximum6248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:58.849554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q150
median148
Q3422
95-th percentile1626
Maximum6248
Range6247
Interquartile range (IQR)372

Descriptive statistics

Standard deviation595.03183
Coefficient of variation (CV)1.5840653
Kurtosis19.227144
Mean375.63593
Median Absolute Deviation (MAD)122
Skewness3.5489155
Sum2431867
Variance354062.87
MonotonicityNot monotonic
2024-05-03T20:08:59.274799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 116
 
1.6%
2 76
 
1.1%
105 68
 
0.9%
26 63
 
0.9%
3 61
 
0.8%
6 57
 
0.8%
35 51
 
0.7%
44 50
 
0.7%
37 49
 
0.7%
10 49
 
0.7%
Other values (445) 5834
80.8%
(Missing) 742
 
10.3%
ValueCountFrequency (%)
1 116
1.6%
2 76
1.1%
3 61
0.8%
4 35
 
0.5%
5 35
 
0.5%
6 57
0.8%
7 41
 
0.6%
8 42
 
0.6%
9 4
 
0.1%
10 49
0.7%
ValueCountFrequency (%)
6248 8
0.1%
4237 8
0.1%
4190 8
0.1%
3588 8
0.1%
3441 8
0.1%
3306 8
0.1%
2709 8
0.1%
2690 8
0.1%
2676 8
0.1%
2564 8
0.1%

아파트_가격_5_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct378
Distinct (%)6.6%
Missing1488
Missing (%)20.6%
Infinite0
Infinite (%)0.0%
Mean288.65381
Minimum1
Maximum3083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:08:59.719784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q137
median107.5
Q3274.75
95-th percentile1432
Maximum3083
Range3082
Interquartile range (IQR)237.75

Descriptive statistics

Standard deviation480.71983
Coefficient of variation (CV)1.6653854
Kurtosis9.1868017
Mean288.65381
Median Absolute Deviation (MAD)88.5
Skewness2.9302939
Sum1653409
Variance231091.56
MonotonicityNot monotonic
2024-05-03T20:09:00.224672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 199
 
2.8%
2 76
 
1.1%
12 74
 
1.0%
44 66
 
0.9%
24 63
 
0.9%
7 61
 
0.8%
3 61
 
0.8%
10 61
 
0.8%
16 54
 
0.7%
159 53
 
0.7%
Other values (368) 4960
68.7%
(Missing) 1488
 
20.6%
ValueCountFrequency (%)
1 199
2.8%
2 76
 
1.1%
3 61
 
0.8%
4 47
 
0.7%
6 53
 
0.7%
7 61
 
0.8%
8 43
 
0.6%
9 8
 
0.1%
10 61
 
0.8%
11 27
 
0.4%
ValueCountFrequency (%)
3083 8
0.1%
2826 8
0.1%
2818 8
0.1%
2771 8
0.1%
2632 8
0.1%
2611 8
0.1%
2390 8
0.1%
2312 8
0.1%
2214 8
0.1%
2198 8
0.1%

아파트_가격_6_억_이상_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct466
Distinct (%)9.4%
Missing2270
Missing (%)31.5%
Infinite0
Infinite (%)0.0%
Mean910.352
Minimum1
Maximum11045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:09:00.831806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q169
median278
Q3901
95-th percentile4559.25
Maximum11045
Range11044
Interquartile range (IQR)832

Descriptive statistics

Standard deviation1690.6513
Coefficient of variation (CV)1.8571402
Kurtosis12.920168
Mean910.352
Median Absolute Deviation (MAD)254
Skewness3.4080815
Sum4502601
Variance2858301.7
MonotonicityNot monotonic
2024-05-03T20:09:01.314715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 93
 
1.3%
12 89
 
1.2%
2 66
 
0.9%
4 65
 
0.9%
10 34
 
0.5%
20 34
 
0.5%
25 33
 
0.5%
11 27
 
0.4%
6 27
 
0.4%
22 27
 
0.4%
Other values (456) 4451
61.7%
(Missing) 2270
31.5%
ValueCountFrequency (%)
1 93
1.3%
2 66
0.9%
3 18
 
0.2%
4 65
0.9%
5 18
 
0.2%
6 27
 
0.4%
7 25
 
0.3%
8 26
 
0.4%
9 17
 
0.2%
10 34
 
0.5%
ValueCountFrequency (%)
11045 8
0.1%
10373 8
0.1%
10346 8
0.1%
9980 8
0.1%
9778 8
0.1%
9496 8
0.1%
9467 8
0.1%
9377 8
0.1%
9084 8
0.1%
8915 8
0.1%

아파트_평균_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.636502
Minimum34
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:09:01.637158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile48
Q153
median58
Q367.25
95-th percentile97
Maximum126
Range92
Interquartile range (IQR)14.25

Descriptive statistics

Standard deviation15.509904
Coefficient of variation (CV)0.24761765
Kurtosis3.2832334
Mean62.636502
Median Absolute Deviation (MAD)7
Skewness1.7481427
Sum451985
Variance240.55711
MonotonicityNot monotonic
2024-05-03T20:09:02.002445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 404
 
5.6%
53 376
 
5.2%
50 357
 
4.9%
54 352
 
4.9%
57 330
 
4.6%
56 329
 
4.6%
51 293
 
4.1%
58 281
 
3.9%
60 281
 
3.9%
52 279
 
3.9%
Other values (70) 3934
54.5%
ValueCountFrequency (%)
34 17
 
0.2%
38 12
 
0.2%
39 5
 
0.1%
41 14
 
0.2%
42 34
 
0.5%
44 22
 
0.3%
45 55
 
0.8%
46 91
1.3%
47 79
 
1.1%
48 213
3.0%
ValueCountFrequency (%)
126 25
0.3%
125 17
0.2%
124 9
 
0.1%
121 17
0.2%
120 17
0.2%
116 25
0.3%
115 5
 
0.1%
114 8
 
0.1%
112 21
0.3%
111 34
0.5%

아파트_평균_시가
Real number (ℝ)

HIGH CORRELATION 

Distinct1087
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4440814 × 108
Minimum81858513
Maximum2.6725539 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.6 KiB
2024-05-03T20:09:02.391710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81858513
5-th percentile1.2780076 × 108
Q11.7294392 × 108
median2.3820339 × 108
Q33.7710681 × 108
95-th percentile9.7239371 × 108
Maximum2.6725539 × 109
Range2.5906954 × 109
Interquartile range (IQR)2.0416288 × 108

Descriptive statistics

Standard deviation3.0985416 × 108
Coefficient of variation (CV)0.89967142
Kurtosis12.658735
Mean3.4440814 × 108
Median Absolute Deviation (MAD)80116986
Skewness3.1263388
Sum2.4852491 × 1012
Variance9.6009599 × 1016
MonotonicityNot monotonic
2024-05-03T20:09:02.665365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1320577273 9
 
0.1%
334857306 9
 
0.1%
314200953 9
 
0.1%
403325496 9
 
0.1%
179781904 9
 
0.1%
319439476 9
 
0.1%
430000223 9
 
0.1%
302852523 9
 
0.1%
588563712 9
 
0.1%
197056774 9
 
0.1%
Other values (1077) 7126
98.8%
ValueCountFrequency (%)
81858513 8
0.1%
84199868 8
0.1%
95872928 8
0.1%
99491166 8
0.1%
100738599 9
0.1%
101304348 9
0.1%
102305214 8
0.1%
107137629 9
0.1%
108518836 8
0.1%
110526592 8
0.1%
ValueCountFrequency (%)
2672553922 9
0.1%
2447858537 9
0.1%
2146752288 8
0.1%
2038380761 9
0.1%
1915929924 9
0.1%
1852605615 9
0.1%
1810545120 8
0.1%
1709243568 5
0.1%
1660707735 4
0.1%
1593767032 9
0.1%

Interactions

2024-05-03T20:08:33.524223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:04.022339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-05-03T20:08:38.130617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:08.233884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:13.430469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:19.301145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:24.349040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:29.015971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:32.977733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:38.295468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:45.419410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:51.214507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:57.792684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:03.921335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:08.933679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:13.075751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:18.567873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:25.055878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:31.564773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:38.632343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:08.573328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:13.708683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:19.841857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:24.614526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:29.290625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:33.246115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:38.555309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:45.701057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:51.588820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:58.065526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:04.274679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:09.121883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:13.346334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:18.909846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:25.495383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:31.905334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:39.019925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:08.910016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:13.991249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:20.071033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:24.916883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:29.574997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:33.521102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:38.943586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:46.031565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:51.859952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:58.418354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:04.626352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:09.299181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:13.844092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:19.443145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:26.012376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:32.257829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:39.358296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:09.194308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:14.309781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:20.322168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:25.209430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:29.774793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:33.818564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:39.262243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:46.396156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:52.150551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:58.812013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:04.911079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:09.607872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:14.119800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:19.806619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:26.408545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:32.577625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:39.719333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:09.486547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:14.604271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:20.588971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:25.464424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:29.965053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:34.101154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:39.605548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:46.760841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:52.550637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:59.097708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:05.186326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:09.810330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:14.438744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:20.100707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:26.797616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:32.906645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:40.172312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:09.763074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:14.932245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:20.863070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:25.729530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:30.202800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:34.329251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:39.951988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:47.056926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:52.966295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:07:59.470334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:05.456331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:10.077597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:14.714209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:20.403952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:27.151383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-03T20:08:33.191970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-03T20:09:02.870499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드행정동_코드아파트_단지_수아파트_면적_66_제곱미터_미만_세대_수아파트_면적_66_제곱미터_세대_수아파트_면적_99_제곱미터_세대_수아파트_면적_132_제곱미터_세대_수아파트_면적_165_제곱미터_세대_수아파트_가격_1_억_미만_세대_수아파트_가격_1_억_세대_수아파트_가격_2_억_세대_수아파트_가격_3_억_세대_수아파트_가격_4_억_세대_수아파트_가격_5_억_세대_수아파트_가격_6_억_이상_세대_수아파트_평균_면적아파트_평균_시가
기준_년분기_코드1.0000.0000.0000.2860.4500.3640.2020.1030.1900.1340.2460.3760.3210.3410.3290.0000.114
행정동_코드0.0001.0000.4080.5090.3890.3130.2600.4220.5630.5380.4450.3770.2580.3530.3130.4790.382
아파트_단지_수0.0000.4081.0000.7570.3510.1590.1520.2210.5110.7880.5110.2380.1340.2060.1620.3550.410
아파트_면적_66_제곱미터_미만_세대_수0.2860.5090.7571.0000.6130.3250.1000.1870.7680.9300.8290.7560.3720.3730.2230.4400.335
아파트_면적_66_제곱미터_세대_수0.4500.3890.3510.6131.0000.7520.4950.4890.4010.4670.7030.8160.6990.7290.6490.3890.249
아파트_면적_99_제곱미터_세대_수0.3640.3130.1590.3250.7521.0000.7070.7980.1800.1570.3550.6320.6200.6760.7000.6210.380
아파트_면적_132_제곱미터_세대_수0.2020.2600.1520.1000.4950.7071.0000.7110.1070.0970.1850.3600.6360.4180.6750.5280.451
아파트_면적_165_제곱미터_세대_수0.1030.4220.2210.1870.4890.7980.7111.0000.2000.1590.1520.2580.3680.4550.6160.6630.518
아파트_가격_1_억_미만_세대_수0.1900.5630.5110.7680.4010.1800.1070.2001.0000.7750.3460.3750.2320.2750.0900.5340.291
아파트_가격_1_억_세대_수0.1340.5380.7880.9300.4670.1570.0970.1590.7751.0000.5990.3030.1530.1360.1250.4610.353
아파트_가격_2_억_세대_수0.2460.4450.5110.8290.7030.3550.1850.1520.3460.5991.0000.8550.2290.1690.1010.2860.211
아파트_가격_3_억_세대_수0.3760.3770.2380.7560.8160.6320.3600.2580.3750.3030.8551.0000.6690.4210.2910.2990.175
아파트_가격_4_억_세대_수0.3210.2580.1340.3720.6990.6200.6360.3680.2320.1530.2290.6691.0000.5980.2640.2450.143
아파트_가격_5_억_세대_수0.3410.3530.2060.3730.7290.6760.4180.4550.2750.1360.1690.4210.5981.0000.4810.3850.200
아파트_가격_6_억_이상_세대_수0.3290.3130.1620.2230.6490.7000.6750.6160.0900.1250.1010.2910.2640.4811.0000.4840.700
아파트_평균_면적0.0000.4790.3550.4400.3890.6210.5280.6630.5340.4610.2860.2990.2450.3850.4841.0000.707
아파트_평균_시가0.1140.3820.4100.3350.2490.3800.4510.5180.2910.3530.2110.1750.1430.2000.7000.7071.000
2024-05-03T20:09:03.310783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드행정동_코드아파트_단지_수아파트_면적_66_제곱미터_미만_세대_수아파트_면적_66_제곱미터_세대_수아파트_면적_99_제곱미터_세대_수아파트_면적_132_제곱미터_세대_수아파트_면적_165_제곱미터_세대_수아파트_가격_1_억_미만_세대_수아파트_가격_1_억_세대_수아파트_가격_2_억_세대_수아파트_가격_3_억_세대_수아파트_가격_4_억_세대_수아파트_가격_5_억_세대_수아파트_가격_6_억_이상_세대_수아파트_평균_면적아파트_평균_시가
기준_년분기_코드1.000-0.002-0.013-0.347-0.541-0.414-0.192-0.025-0.218-0.189-0.307-0.436-0.412-0.308-0.219-0.0150.174
행정동_코드-0.0021.0000.1850.2030.2330.2020.2240.187-0.0160.1600.2260.2080.1680.1620.3100.0910.182
아파트_단지_수-0.0130.1851.0000.7460.3750.005-0.090-0.0730.5770.7790.5820.2470.0660.053-0.085-0.425-0.508
아파트_면적_66_제곱미터_미만_세대_수-0.3470.2030.7461.0000.6350.207-0.032-0.1550.7160.8600.7460.5060.3270.206-0.035-0.407-0.537
아파트_면적_66_제곱미터_세대_수-0.5410.2330.3750.6351.0000.6730.3600.1250.3390.4220.6270.6960.6240.5070.3790.161-0.076
아파트_면적_99_제곱미터_세대_수-0.4140.2020.0050.2070.6731.0000.5900.433-0.043-0.0200.3030.5570.6230.5510.6000.5370.364
아파트_면적_132_제곱미터_세대_수-0.1920.224-0.090-0.0320.3600.5901.0000.721-0.265-0.1620.1080.2870.3380.3540.5990.5640.522
아파트_면적_165_제곱미터_세대_수-0.0250.187-0.073-0.1550.1250.4330.7211.000-0.347-0.263-0.0640.0440.1390.2190.5080.6160.543
아파트_가격_1_억_미만_세대_수-0.218-0.0160.5770.7160.339-0.043-0.265-0.3471.0000.7910.4000.1840.041-0.085-0.366-0.539-0.808
아파트_가격_1_억_세대_수-0.1890.1600.7790.8600.422-0.020-0.162-0.2630.7911.0000.6530.2480.038-0.070-0.284-0.527-0.725
아파트_가격_2_억_세대_수-0.3070.2260.5820.7460.6270.3030.108-0.0640.4000.6531.0000.6350.2950.088-0.107-0.210-0.316
아파트_가격_3_억_세대_수-0.4360.2080.2470.5060.6960.5570.2870.0440.1840.2480.6351.0000.7100.3860.0730.086-0.038
아파트_가격_4_억_세대_수-0.4120.1680.0660.3270.6240.6230.3380.1390.0410.0380.2950.7101.0000.6860.2300.2190.138
아파트_가격_5_억_세대_수-0.3080.1620.0530.2060.5070.5510.3540.219-0.085-0.0700.0880.3860.6861.0000.4230.2570.258
아파트_가격_6_억_이상_세대_수-0.2190.310-0.085-0.0350.3790.6000.5990.508-0.366-0.284-0.1070.0730.2300.4231.0000.5570.705
아파트_평균_면적-0.0150.091-0.425-0.4070.1610.5370.5640.616-0.539-0.527-0.2100.0860.2190.2570.5571.0000.769
아파트_평균_시가0.1740.182-0.508-0.537-0.0760.3640.5220.543-0.808-0.725-0.316-0.0380.1380.2580.7050.7691.000

Missing values

2024-05-03T20:08:40.784778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T20:08:41.624061image/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.
2024-05-03T20:08:42.405160image/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

기준_년분기_코드행정동_코드행정동_코드_명아파트_단지_수아파트_면적_66_제곱미터_미만_세대_수아파트_면적_66_제곱미터_세대_수아파트_면적_99_제곱미터_세대_수아파트_면적_132_제곱미터_세대_수아파트_면적_165_제곱미터_세대_수아파트_가격_1_억_미만_세대_수아파트_가격_1_억_세대_수아파트_가격_2_억_세대_수아파트_가격_3_억_세대_수아파트_가격_4_억_세대_수아파트_가격_5_억_세대_수아파트_가격_6_억_이상_세대_수아파트_평균_면적아파트_평균_시가
02023411110515청운효자동32315335818391117231689834279846822069276146690
12023411110530사직동893091585481142517515570371613873371237211
22023411110540삼청동912195214<NA>111852313111401044841
32023411110550부암동18971266216246130158570553210268111482259634651
42023411110560평창동294527915314472538108379506607271347548124480839259
52023411110570무악동4711125610812<NA>143582122639082660258193
62023411110580교남동23112021942421368664516354225750235058041
72023411110600가회동1266837820841127452153271774767284759882
82023411110615종로1?2?3?4가동9628210<NA>1533142511249278382323
92023111110630종로5?6가동1056<NA><NA>3<NA>2432<NA>3<NA><NA><NA>42127248788
기준_년분기_코드행정동_코드행정동_코드_명아파트_단지_수아파트_면적_66_제곱미터_미만_세대_수아파트_면적_66_제곱미터_세대_수아파트_면적_99_제곱미터_세대_수아파트_면적_132_제곱미터_세대_수아파트_면적_165_제곱미터_세대_수아파트_가격_1_억_미만_세대_수아파트_가격_1_억_세대_수아파트_가격_2_억_세대_수아파트_가격_3_억_세대_수아파트_가격_4_억_세대_수아파트_가격_5_억_세대_수아파트_가격_6_억_이상_세대_수아파트_평균_면적아파트_평균_시가
72062023211740530명일1동1789736739211<NA>76504452141866043061354738270
72072023211740540명일2동8125131213388<NA>916081125351477606495793
72082023211740550고덕1동147760534562212<NA>6823442294980069636907618
72092023211740560고덕2동11572730612<NA><NA>1174232810<NA>62056566947021
72102023211740570암사1동67550609371281448934051596262663927350203840466
72112023211740580암사2동1147483082116213640514230387127353308204454
72122023211740590암사3동1196083522538374423424871254963272508041003
72132023211740600천호1동4643493926567<NA>20420421691330125682250206416585
72142023211740610천호2동52940517147323<NA>18322371925274565113548217529985
72152023211740620천호3동23016044788611102935637253904311051234414070