Overview

Dataset statistics

Number of variables18
Number of observations425
Missing cells8
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.0 KiB
Average record size in memory161.3 B

Variable types

Numeric17
Categorical1

Dataset

Description기준_년분기_코드,자치구_코드,자치구_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-22164/S/1/datasetView.do

Alerts

기준_년분기_코드 is highly overall correlated with 아파트_면적_66_제곱미터_세대_수 and 4 other fieldsHigh correlation
자치구_코드 is highly overall correlated with 아파트_단지_수 and 1 other fieldsHigh correlation
아파트_단지_수 is highly overall correlated with 자치구_코드 and 5 other fieldsHigh correlation
아파트_면적_66_제곱미터_미만_세대_수 is highly overall correlated with 아파트_단지_수 and 8 other fieldsHigh correlation
아파트_면적_66_제곱미터_세대_수 is highly overall correlated with 기준_년분기_코드 and 10 other fieldsHigh correlation
아파트_면적_99_제곱미터_세대_수 is highly overall correlated with 기준_년분기_코드 and 8 other fieldsHigh correlation
아파트_면적_132_제곱미터_세대_수 is highly overall correlated with 기준_년분기_코드 and 9 other fieldsHigh correlation
아파트_면적_165_제곱미터_세대_수 is highly overall correlated with 아파트_면적_132_제곱미터_세대_수 and 4 other fieldsHigh correlation
아파트_가격_1_억_미만_세대_수 is highly overall correlated with 아파트_면적_66_제곱미터_미만_세대_수 and 4 other fieldsHigh correlation
아파트_가격_1_억_세대_수 is highly overall correlated with 아파트_단지_수 and 7 other fieldsHigh correlation
아파트_가격_2_억_세대_수 is highly overall correlated with 아파트_단지_수 and 8 other fieldsHigh correlation
아파트_가격_3_억_세대_수 is highly overall correlated with 기준_년분기_코드 and 8 other fieldsHigh correlation
아파트_가격_4_억_세대_수 is highly overall correlated with 기준_년분기_코드 and 8 other fieldsHigh correlation
아파트_가격_5_억_세대_수 is highly overall correlated with 아파트_면적_66_제곱미터_세대_수 and 7 other fieldsHigh correlation
아파트_가격_6_억_이상_세대_수 is highly overall correlated with 아파트_면적_132_제곱미터_세대_수 and 4 other fieldsHigh correlation
아파트_평균_면적 is highly overall correlated with 아파트_면적_165_제곱미터_세대_수 and 4 other fieldsHigh correlation
아파트_평균_시가 is highly overall correlated with 아파트_가격_1_억_미만_세대_수 and 4 other fieldsHigh correlation
자치구_코드_명 is highly overall correlated with 자치구_코드 and 15 other fieldsHigh correlation
아파트_가격_6_억_이상_세대_수 has 8 (1.9%) missing valuesMissing

Reproduction

Analysis started2024-05-04 02:53:30.387613
Analysis finished2024-05-04 02:55:09.569908
Duration1 minute and 39.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준_년분기_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20216.118
Minimum20194
Maximum20234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:09.731706image/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 deviation12.23725
Coefficient of variation (CV)0.00060532145
Kurtosis-1.1877657
Mean20216.118
Median Absolute Deviation (MAD)10
Skewness-0.07759927
Sum8591850
Variance149.75028
MonotonicityNot monotonic
2024-05-04T02:55:10.089250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20231 25
 
5.9%
20232 25
 
5.9%
20194 25
 
5.9%
20201 25
 
5.9%
20202 25
 
5.9%
20203 25
 
5.9%
20204 25
 
5.9%
20211 25
 
5.9%
20212 25
 
5.9%
20213 25
 
5.9%
Other values (7) 175
41.2%
ValueCountFrequency (%)
20194 25
5.9%
20201 25
5.9%
20202 25
5.9%
20203 25
5.9%
20204 25
5.9%
20211 25
5.9%
20212 25
5.9%
20213 25
5.9%
20214 25
5.9%
20221 25
5.9%
ValueCountFrequency (%)
20234 25
5.9%
20233 25
5.9%
20232 25
5.9%
20231 25
5.9%
20224 25
5.9%
20223 25
5.9%
20222 25
5.9%
20221 25
5.9%
20214 25
5.9%
20213 25
5.9%

자치구_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11416.6
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:10.621593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111260
median11410
Q311560
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)300

Descriptive statistics

Standard deviation186.56188
Coefficient of variation (CV)0.016341282
Kurtosis-1.2006364
Mean11416.6
Median Absolute Deviation (MAD)150
Skewness0.078352341
Sum4852055
Variance34805.335
MonotonicityNot monotonic
2024-05-04T02:55:11.023700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11110 17
 
4.0%
11140 17
 
4.0%
11530 17
 
4.0%
11500 17
 
4.0%
11440 17
 
4.0%
11410 17
 
4.0%
11740 17
 
4.0%
11710 17
 
4.0%
11680 17
 
4.0%
11650 17
 
4.0%
Other values (15) 255
60.0%
ValueCountFrequency (%)
11110 17
4.0%
11140 17
4.0%
11170 17
4.0%
11200 17
4.0%
11215 17
4.0%
11230 17
4.0%
11260 17
4.0%
11290 17
4.0%
11305 17
4.0%
11320 17
4.0%
ValueCountFrequency (%)
11740 17
4.0%
11710 17
4.0%
11680 17
4.0%
11650 17
4.0%
11620 17
4.0%
11590 17
4.0%
11560 17
4.0%
11545 17
4.0%
11530 17
4.0%
11500 17
4.0%

자치구_코드_명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
종로구
 
17
중구
 
17
용산구
 
17
성동구
 
17
광진구
 
17
Other values (20)
340 

Length

Max length4
Median length3
Mean length3.08
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종로구
2nd row중구
3rd row용산구
4th row성동구
5th row광진구

Common Values

ValueCountFrequency (%)
종로구 17
 
4.0%
중구 17
 
4.0%
용산구 17
 
4.0%
성동구 17
 
4.0%
광진구 17
 
4.0%
동대문구 17
 
4.0%
중랑구 17
 
4.0%
성북구 17
 
4.0%
강북구 17
 
4.0%
도봉구 17
 
4.0%
Other values (15) 255
60.0%

Length

2024-05-04T02:55:11.473291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 17
 
4.0%
금천구 17
 
4.0%
강서구 17
 
4.0%
마포구 17
 
4.0%
서대문구 17
 
4.0%
강동구 17
 
4.0%
송파구 17
 
4.0%
강남구 17
 
4.0%
서초구 17
 
4.0%
관악구 17
 
4.0%
Other values (15) 255
60.0%

아파트_단지_수
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4541.9976
Minimum1081
Maximum9575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:11.939140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1081
5-th percentile1938
Q13042
median4592
Q35205
95-th percentile9184
Maximum9575
Range8494
Interquartile range (IQR)2163

Descriptive statistics

Standard deviation2052.9054
Coefficient of variation (CV)0.45198294
Kurtosis0.44266194
Mean4541.9976
Median Absolute Deviation (MAD)1162
Skewness0.82640548
Sum1930349
Variance4214420.8
MonotonicityNot monotonic
2024-05-04T02:55:12.326925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2461 8
 
1.9%
4337 8
 
1.9%
2088 8
 
1.9%
5153 8
 
1.9%
9441 8
 
1.9%
5085 8
 
1.9%
3327 8
 
1.9%
4554 8
 
1.9%
5836 8
 
1.9%
5893 8
 
1.9%
Other values (65) 345
81.2%
ValueCountFrequency (%)
1081 5
1.2%
1082 4
0.9%
1183 8
1.9%
1928 5
1.2%
1978 4
0.9%
2088 8
1.9%
2275 4
0.9%
2283 5
1.2%
2293 8
1.9%
2335 4
0.9%
ValueCountFrequency (%)
9575 8
1.9%
9441 8
1.9%
9284 4
0.9%
9184 5
1.2%
9039 5
1.2%
9008 4
0.9%
8339 8
1.9%
8293 4
0.9%
8285 5
1.2%
5893 8
1.9%

아파트_면적_66_제곱미터_미만_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39947.431
Minimum6261
Maximum116197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:12.803894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6261
5-th percentile10999
Q124617
median34148
Q355198
95-th percentile90763
Maximum116197
Range109936
Interquartile range (IQR)30581

Descriptive statistics

Standard deviation23424.638
Coefficient of variation (CV)0.5863866
Kurtosis1.6607145
Mean39947.431
Median Absolute Deviation (MAD)13442
Skewness1.2191355
Sum16977658
Variance5.4871366 × 108
MonotonicityNot monotonic
2024-05-04T02:55:13.446121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16568 8
 
1.9%
38499 8
 
1.9%
27662 8
 
1.9%
50237 8
 
1.9%
90763 8
 
1.9%
41985 8
 
1.9%
22051 8
 
1.9%
37675 8
 
1.9%
68370 8
 
1.9%
57628 8
 
1.9%
Other values (65) 345
81.2%
ValueCountFrequency (%)
6261 5
1.2%
6282 4
0.9%
10647 5
1.2%
10948 4
0.9%
10999 5
1.2%
11021 4
0.9%
13399 8
1.9%
13605 5
1.2%
13650 4
0.9%
13924 5
1.2%
ValueCountFrequency (%)
116197 8
1.9%
109235 8
1.9%
90763 8
1.9%
75987 8
1.9%
68953 8
1.9%
68370 8
1.9%
62892 8
1.9%
61634 4
0.9%
61249 5
1.2%
60489 4
0.9%

아파트_면적_66_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20261.016
Minimum2179
Maximum64007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:13.856850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2179
5-th percentile5488
Q18913
median13013
Q331149
95-th percentile47353
Maximum64007
Range61828
Interquartile range (IQR)22236

Descriptive statistics

Standard deviation14624.338
Coefficient of variation (CV)0.72179686
Kurtosis-0.098676219
Mean20261.016
Median Absolute Deviation (MAD)7051
Skewness0.90697955
Sum8610932
Variance2.1387127 × 108
MonotonicityNot monotonic
2024-05-04T02:55:14.311615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9382 8
 
1.9%
26407 8
 
1.9%
28394 8
 
1.9%
17917 8
 
1.9%
64007 8
 
1.9%
40549 8
 
1.9%
14358 8
 
1.9%
24426 8
 
1.9%
42377 8
 
1.9%
27713 8
 
1.9%
Other values (65) 345
81.2%
ValueCountFrequency (%)
2179 5
1.2%
2192 4
0.9%
4185 5
1.2%
4188 4
0.9%
5488 5
1.2%
5494 4
0.9%
5508 5
1.2%
5527 4
0.9%
5542 5
1.2%
5600 4
0.9%
ValueCountFrequency (%)
64007 8
1.9%
47570 8
1.9%
47353 8
1.9%
45499 8
1.9%
42903 8
1.9%
42377 8
1.9%
40549 8
1.9%
39743 8
1.9%
38762 8
1.9%
36624 8
1.9%

아파트_면적_99_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5256.0518
Minimum523
Maximum22439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:14.929949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum523
5-th percentile880
Q11759
median3100
Q37950
95-th percentile19234
Maximum22439
Range21916
Interquartile range (IQR)6191

Descriptive statistics

Standard deviation4985.8656
Coefficient of variation (CV)0.94859522
Kurtosis2.5984715
Mean5256.0518
Median Absolute Deviation (MAD)1964
Skewness1.6465103
Sum2233822
Variance24858855
MonotonicityNot monotonic
2024-05-04T02:55:15.340110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880 9
 
2.1%
1800 9
 
2.1%
2458 9
 
2.1%
19234 8
 
1.9%
10843 8
 
1.9%
3894 8
 
1.9%
6850 8
 
1.9%
19765 8
 
1.9%
2765 8
 
1.9%
7950 8
 
1.9%
Other values (62) 342
80.5%
ValueCountFrequency (%)
523 5
1.2%
527 4
0.9%
740 5
1.2%
742 4
0.9%
880 9
2.1%
965 5
1.2%
966 4
0.9%
985 5
1.2%
988 4
0.9%
1084 5
1.2%
ValueCountFrequency (%)
22439 8
1.9%
19765 8
1.9%
19234 8
1.9%
12095 8
1.9%
10843 8
1.9%
10527 8
1.9%
10470 8
1.9%
10016 8
1.9%
9939 8
1.9%
9551 8
1.9%

아파트_면적_132_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1888.36
Minimum57
Maximum13274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:15.724487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile148
Q1469
median810
Q32138
95-th percentile9722
Maximum13274
Range13217
Interquartile range (IQR)1669

Descriptive statistics

Standard deviation2698.7334
Coefficient of variation (CV)1.4291414
Kurtosis8.5180333
Mean1888.36
Median Absolute Deviation (MAD)653
Skewness2.9076458
Sum802553
Variance7283161.9
MonotonicityNot monotonic
2024-05-04T02:55:16.159457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
775 14
 
3.3%
148 9
 
2.1%
782 9
 
2.1%
157 9
 
2.1%
835 9
 
2.1%
793 9
 
2.1%
539 9
 
2.1%
469 9
 
2.1%
519 9
 
2.1%
274 9
 
2.1%
Other values (53) 330
77.6%
ValueCountFrequency (%)
57 5
1.2%
58 4
0.9%
144 5
1.2%
145 4
0.9%
148 9
2.1%
149 5
1.2%
150 4
0.9%
157 9
2.1%
235 5
1.2%
238 4
0.9%
ValueCountFrequency (%)
13274 8
1.9%
12152 8
1.9%
9722 8
1.9%
5057 8
1.9%
4447 8
1.9%
4148 8
1.9%
3450 4
0.9%
3334 5
1.2%
3101 4
0.9%
3076 5
1.2%

아파트_면적_165_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean924.64235
Minimum2
Maximum9435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:16.580881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q186
median318
Q3880
95-th percentile4284
Maximum9435
Range9433
Interquartile range (IQR)794

Descriptive statistics

Standard deviation1844.885
Coefficient of variation (CV)1.9952417
Kurtosis11.525405
Mean924.64235
Median Absolute Deviation (MAD)234
Skewness3.3706332
Sum392973
Variance3403600.5
MonotonicityNot monotonic
2024-05-04T02:55:17.093531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 12
 
2.8%
569 9
 
2.1%
32 9
 
2.1%
103 9
 
2.1%
170 9
 
2.1%
321 9
 
2.1%
84 9
 
2.1%
2 9
 
2.1%
300 9
 
2.1%
31 9
 
2.1%
Other values (49) 332
78.1%
ValueCountFrequency (%)
2 9
2.1%
3 5
1.2%
4 4
 
0.9%
9 5
1.2%
13 12
2.8%
15 8
1.9%
29 8
1.9%
31 9
2.1%
32 9
2.1%
39 9
2.1%
ValueCountFrequency (%)
9435 8
1.9%
8509 8
1.9%
4284 8
1.9%
3883 4
0.9%
3833 5
1.2%
3012 4
0.9%
3004 5
1.2%
2461 8
1.9%
1767 4
0.9%
1762 5
1.2%

아파트_가격_1_억_미만_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7008.6871
Minimum189
Maximum23374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:17.572497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum189
5-th percentile314
Q12886
median5372
Q310586
95-th percentile20034
Maximum23374
Range23185
Interquartile range (IQR)7700

Descriptive statistics

Standard deviation5771.2717
Coefficient of variation (CV)0.82344548
Kurtosis0.32978403
Mean7008.6871
Median Absolute Deviation (MAD)3055
Skewness1.0347047
Sum2978692
Variance33307577
MonotonicityNot monotonic
2024-05-04T02:55:18.039195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4976 13
 
3.1%
314 9
 
2.1%
7837 8
 
1.9%
20034 8
 
1.9%
5279 8
 
1.9%
3028 8
 
1.9%
17961 8
 
1.9%
6115 8
 
1.9%
5715 8
 
1.9%
670 8
 
1.9%
Other values (63) 339
79.8%
ValueCountFrequency (%)
189 5
1.2%
203 4
0.9%
220 5
1.2%
224 4
0.9%
314 9
2.1%
670 8
1.9%
815 8
1.9%
934 8
1.9%
1143 5
1.2%
1145 4
0.9%
ValueCountFrequency (%)
23374 8
1.9%
20163 8
1.9%
20034 8
1.9%
18939 8
1.9%
17961 8
1.9%
14882 8
1.9%
14393 8
1.9%
12641 8
1.9%
12426 4
0.9%
12386 5
1.2%

아파트_가격_1_억_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21375.969
Minimum3552
Maximum66484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:18.645869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3552
5-th percentile3826
Q19924
median19541
Q327522
95-th percentile48044
Maximum66484
Range62932
Interquartile range (IQR)17598

Descriptive statistics

Standard deviation13551.11
Coefficient of variation (CV)0.63394131
Kurtosis1.2002555
Mean21375.969
Median Absolute Deviation (MAD)8367
Skewness1.0254735
Sum9084787
Variance1.8363258 × 108
MonotonicityNot monotonic
2024-05-04T02:55:19.027582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8660 8
 
1.9%
20485 8
 
1.9%
5779 8
 
1.9%
30591 8
 
1.9%
35743 8
 
1.9%
11572 8
 
1.9%
6818 8
 
1.9%
22188 8
 
1.9%
33920 8
 
1.9%
33218 8
 
1.9%
Other values (65) 345
81.2%
ValueCountFrequency (%)
3552 5
1.2%
3563 4
0.9%
3591 5
1.2%
3619 4
0.9%
3826 5
1.2%
3847 4
0.9%
5738 5
1.2%
5779 8
1.9%
5802 4
0.9%
6242 5
1.2%
ValueCountFrequency (%)
66484 8
1.9%
55366 8
1.9%
48214 4
0.9%
48044 5
1.2%
44027 4
0.9%
43936 5
1.2%
40934 8
1.9%
35743 8
1.9%
33967 8
1.9%
33920 8
1.9%

아파트_가격_2_억_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11945.214
Minimum1975
Maximum60361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:19.447190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1975
5-th percentile2719
Q15757
median10148
Q314897
95-th percentile28249
Maximum60361
Range58386
Interquartile range (IQR)9140

Descriptive statistics

Standard deviation9748.4024
Coefficient of variation (CV)0.81609273
Kurtosis9.3632127
Mean11945.214
Median Absolute Deviation (MAD)4749
Skewness2.4986317
Sum5076716
Variance95031349
MonotonicityNot monotonic
2024-05-04T02:55:19.863640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2719 9
 
2.1%
4764 9
 
2.1%
14897 8
 
1.9%
12069 8
 
1.9%
13204 8
 
1.9%
4428 8
 
1.9%
10094 8
 
1.9%
14302 8
 
1.9%
29531 8
 
1.9%
12089 8
 
1.9%
Other values (63) 343
80.7%
ValueCountFrequency (%)
1975 5
1.2%
1992 4
0.9%
2200 5
1.2%
2271 4
0.9%
2719 9
2.1%
2896 8
1.9%
2918 5
1.2%
2940 4
0.9%
3459 5
1.2%
3469 4
0.9%
ValueCountFrequency (%)
60361 8
1.9%
29531 8
1.9%
28249 8
1.9%
25574 4
0.9%
25551 8
1.9%
25220 5
1.2%
22882 8
1.9%
21136 8
1.9%
19528 8
1.9%
18542 8
1.9%

아파트_가격_3_억_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct71
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7740.8612
Minimum710
Maximum33404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:20.273242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum710
5-th percentile1068
Q12399
median4184
Q310189
95-th percentile22450
Maximum33404
Range32694
Interquartile range (IQR)7790

Descriptive statistics

Standard deviation7448.9658
Coefficient of variation (CV)0.96229161
Kurtosis1.4017907
Mean7740.8612
Median Absolute Deviation (MAD)2771
Skewness1.384117
Sum3289866
Variance55487091
MonotonicityNot monotonic
2024-05-04T02:55:20.706634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1068 9
 
2.1%
2683 9
 
2.1%
5149 9
 
2.1%
2502 9
 
2.1%
9841 8
 
1.9%
16782 8
 
1.9%
10189 8
 
1.9%
16974 8
 
1.9%
4184 8
 
1.9%
8862 8
 
1.9%
Other values (61) 341
80.2%
ValueCountFrequency (%)
710 5
1.2%
714 4
0.9%
721 5
1.2%
778 4
0.9%
1068 9
2.1%
1111 5
1.2%
1115 4
0.9%
1309 5
1.2%
1413 4
0.9%
1427 5
1.2%
ValueCountFrequency (%)
33404 8
1.9%
25257 8
1.9%
22450 8
1.9%
22318 8
1.9%
19128 8
1.9%
19059 8
1.9%
17893 8
1.9%
16974 8
1.9%
16782 8
1.9%
14696 8
1.9%

아파트_가격_4_억_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5722.04
Minimum226
Maximum19637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:21.141431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum226
5-th percentile722
Q11767
median3105
Q39756
95-th percentile16041
Maximum19637
Range19411
Interquartile range (IQR)7989

Descriptive statistics

Standard deviation5219.3845
Coefficient of variation (CV)0.9121545
Kurtosis-0.20449557
Mean5722.04
Median Absolute Deviation (MAD)1764
Skewness1.04267
Sum2431867
Variance27241975
MonotonicityNot monotonic
2024-05-04T02:55:21.700848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1703 13
 
3.1%
1837 9
 
2.1%
226 9
 
2.1%
711 9
 
2.1%
2993 9
 
2.1%
3381 9
 
2.1%
1341 9
 
2.1%
4499 9
 
2.1%
8988 8
 
1.9%
10628 8
 
1.9%
Other values (56) 333
78.4%
ValueCountFrequency (%)
226 9
2.1%
711 9
2.1%
722 5
1.2%
723 4
0.9%
794 5
1.2%
795 4
0.9%
836 5
1.2%
924 4
0.9%
1289 5
1.2%
1313 4
0.9%
ValueCountFrequency (%)
19637 8
1.9%
17304 8
1.9%
16041 8
1.9%
15207 8
1.9%
14808 8
1.9%
14404 8
1.9%
13705 8
1.9%
13419 8
1.9%
13245 8
1.9%
11637 8
1.9%

아파트_가격_5_억_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3890.3741
Minimum257
Maximum14990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:22.126400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum257
5-th percentile492
Q11159
median2136
Q34498
95-th percentile13220
Maximum14990
Range14733
Interquartile range (IQR)3339

Descriptive statistics

Standard deviation4021.2987
Coefficient of variation (CV)1.0336535
Kurtosis1.0015319
Mean3890.3741
Median Absolute Deviation (MAD)1309
Skewness1.4772923
Sum1653409
Variance16170843
MonotonicityNot monotonic
2024-05-04T02:55:22.550306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2285 9
 
2.1%
257 9
 
2.1%
2090 9
 
2.1%
544 9
 
2.1%
1128 9
 
2.1%
701 9
 
2.1%
1813 9
 
2.1%
2684 9
 
2.1%
1035 9
 
2.1%
310 9
 
2.1%
Other values (55) 335
78.8%
ValueCountFrequency (%)
257 9
2.1%
310 9
2.1%
492 8
1.9%
543 8
1.9%
544 9
2.1%
701 9
2.1%
761 5
1.2%
763 4
0.9%
861 8
1.9%
865 8
1.9%
ValueCountFrequency (%)
14990 8
1.9%
14362 8
1.9%
13220 8
1.9%
12979 8
1.9%
12724 8
1.9%
11384 8
1.9%
9722 8
1.9%
8987 8
1.9%
7530 8
1.9%
7265 8
1.9%

아파트_가격_6_억_이상_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct59
Distinct (%)14.1%
Missing8
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean10797.604
Minimum10
Maximum97515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:23.019173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile46
Q11296
median4574
Q312410
95-th percentile68558
Maximum97515
Range97505
Interquartile range (IQR)11114

Descriptive statistics

Standard deviation18859.136
Coefficient of variation (CV)1.7466037
Kurtosis10.252892
Mean10797.604
Median Absolute Deviation (MAD)3736
Skewness3.1917357
Sum4502601
Variance3.5566701 × 108
MonotonicityNot monotonic
2024-05-04T02:55:23.627043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7561 9
 
2.1%
1700 9
 
2.1%
1052 9
 
2.1%
6163 9
 
2.1%
5185 9
 
2.1%
213 9
 
2.1%
1437 9
 
2.1%
2385 9
 
2.1%
422 9
 
2.1%
684 9
 
2.1%
Other values (49) 327
76.9%
ValueCountFrequency (%)
10 8
1.9%
12 8
1.9%
46 8
1.9%
102 8
1.9%
213 9
2.1%
304 8
1.9%
308 9
2.1%
419 8
1.9%
422 9
2.1%
684 9
2.1%
ValueCountFrequency (%)
97515 8
1.9%
75966 8
1.9%
68558 8
1.9%
26366 8
1.9%
25278 8
1.9%
20655 4
0.9%
20336 5
1.2%
18939 4
0.9%
18778 8
1.9%
18694 8
1.9%

아파트_평균_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.672941
Minimum52
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:24.005347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile53
Q155
median57
Q361
95-th percentile73
Maximum81
Range29
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.9968893
Coefficient of variation (CV)0.11725397
Kurtosis1.7212121
Mean59.672941
Median Absolute Deviation (MAD)3
Skewness1.5365715
Sum25361
Variance48.956459
MonotonicityNot monotonic
2024-05-04T02:55:24.339387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
56 77
18.1%
55 42
9.9%
54 42
9.9%
60 40
9.4%
61 37
8.7%
53 35
8.2%
71 30
 
7.1%
62 26
 
6.1%
59 25
 
5.9%
81 17
 
4.0%
Other values (5) 54
12.7%
ValueCountFrequency (%)
52 9
 
2.1%
53 35
8.2%
54 42
9.9%
55 42
9.9%
56 77
18.1%
57 16
 
3.8%
58 8
 
1.9%
59 25
 
5.9%
60 40
9.4%
61 37
8.7%
ValueCountFrequency (%)
81 17
4.0%
73 9
 
2.1%
72 12
 
2.8%
71 30
7.1%
62 26
6.1%
61 37
8.7%
60 40
9.4%
59 25
5.9%
58 8
 
1.9%
57 16
 
3.8%

아파트_평균_시가
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7358393 × 108
Minimum1.307841 × 108
Maximum7.4073498 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-05-04T02:55:24.794379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.307841 × 108
5-th percentile1.4989574 × 108
Q11.8610077 × 108
median2.3439929 × 108
Q32.8996902 × 108
95-th percentile5.7840524 × 108
Maximum7.4073498 × 108
Range6.0995088 × 108
Interquartile range (IQR)1.0386826 × 108

Descriptive statistics

Standard deviation1.4025263 × 108
Coefficient of variation (CV)0.51264938
Kurtosis2.7934605
Mean2.7358393 × 108
Median Absolute Deviation (MAD)54830616
Skewness1.8261186
Sum1.1627317 × 1011
Variance1.9670801 × 1016
MonotonicityNot monotonic
2024-05-04T02:55:25.334001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
234399288 8
 
1.9%
183347922 8
 
1.9%
302518487 8
 
1.9%
130784103 8
 
1.9%
289969021 8
 
1.9%
542748152 8
 
1.9%
457642448 8
 
1.9%
205010694 8
 
1.9%
201858093 8
 
1.9%
162549763 8
 
1.9%
Other values (65) 345
81.2%
ValueCountFrequency (%)
130784103 8
1.9%
139271590 8
1.9%
149895742 8
1.9%
151355915 5
1.2%
151372000 4
0.9%
158713151 8
1.9%
160234494 8
1.9%
161726614 8
1.9%
162549763 8
1.9%
167945971 5
1.2%
ValueCountFrequency (%)
740734978 5
1.2%
737092638 4
0.9%
705455212 4
0.9%
699516368 5
1.2%
578405236 5
1.2%
578338340 4
0.9%
557171127 8
1.9%
542748152 8
1.9%
459388770 5
1.2%
457642448 8
1.9%

Interactions

2024-05-04T02:55:02.083498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:32.179379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:36.526308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:41.118283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:47.298319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:53.722721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:59.453489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:04.865213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:10.529904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:15.255729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:20.345827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:25.675992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:30.783649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:35.916261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:41.743902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:47.934893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:55.655668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:02.384917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2024-05-04T02:54:53.039291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:00.731011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:06.787588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:35.499802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:39.597359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:45.750384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:52.236081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:58.293523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:03.604185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:09.352068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:14.215521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:19.155660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:24.528787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:29.717049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:34.788949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:40.220932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:46.218350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:53.629040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:01.027616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:07.195874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:35.772807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:39.928563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:46.217373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:52.635708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:58.593333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:03.878168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:09.636383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:14.495463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:19.491693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:24.820884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:29.985424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:35.067328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:40.518291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:46.657559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:54.122605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:01.306805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:07.537700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:36.082362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:40.189812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:46.687018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:53.013537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:58.863342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:04.140365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:09.899771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:14.743716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:19.741194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:25.126861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:30.247065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:35.331893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:41.078836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:47.189259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:54.690427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:01.586227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:07.779228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:36.331180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:40.785612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:46.968852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:53.351615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:53:59.136042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:04.479262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:10.182831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:15.000031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:20.050543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:25.406232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:30.509468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:35.629339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:41.423891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:47.658599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:54:55.144579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:55:01.828132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T02:55:25.679932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드자치구_코드자치구_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
기준_년분기_코드1.0000.0000.0000.0000.3500.5550.5430.6000.2100.3460.2950.5440.5330.5260.4450.2100.0000.231
자치구_코드0.0001.0001.0000.8400.6970.6350.6170.5810.6290.8210.8460.6200.6630.8740.8480.7310.7810.720
자치구_코드_명0.0001.0001.0000.9930.9090.8950.8860.8730.8960.9230.9330.8900.8950.9200.9340.9100.9840.918
아파트_단지_수0.0000.8400.9931.0000.7640.7120.7240.5630.5010.6840.8010.6850.6860.7120.6420.5000.6770.766
아파트_면적_66_제곱미터_미만_세대_수0.3500.6970.9090.7641.0000.9480.7800.8420.8100.7680.8940.8590.9050.7970.7620.7470.6290.638
아파트_면적_66_제곱미터_세대_수0.5550.6350.8950.7120.9481.0000.8450.8530.7860.7170.8000.7960.9150.8560.8220.7510.5170.626
아파트_면적_99_제곱미터_세대_수0.5430.6170.8860.7240.7800.8451.0000.8620.7730.6870.6790.6810.7600.8290.8390.9010.6040.862
아파트_면적_132_제곱미터_세대_수0.6000.5810.8730.5630.8420.8530.8621.0000.9700.6690.7100.7710.7680.8340.8200.8380.6300.809
아파트_면적_165_제곱미터_세대_수0.2100.6290.8960.5010.8100.7860.7730.9701.0000.5440.6290.6670.6860.7280.7440.7840.7850.844
아파트_가격_1_억_미만_세대_수0.3460.8210.9230.6840.7680.7170.6870.6690.5441.0000.9300.6980.7850.9340.8680.6630.6160.663
아파트_가격_1_억_세대_수0.2950.8460.9330.8010.8940.8000.6790.7100.6290.9301.0000.8050.7740.9030.8530.7200.6670.628
아파트_가격_2_억_세대_수0.5440.6200.8900.6850.8590.7960.6810.7710.6670.6980.8051.0000.8690.7380.6370.4440.5180.471
아파트_가격_3_억_세대_수0.5330.6630.8950.6860.9050.9150.7600.7680.6860.7850.7740.8691.0000.8460.7800.6690.5340.632
아파트_가격_4_억_세대_수0.5260.8740.9200.7120.7970.8560.8290.8340.7280.9340.9030.7380.8461.0000.9510.8740.5630.711
아파트_가격_5_억_세대_수0.4450.8480.9340.6420.7620.8220.8390.8200.7440.8680.8530.6370.7800.9511.0000.9100.6280.665
아파트_가격_6_억_이상_세대_수0.2100.7310.9100.5000.7470.7510.9010.8380.7840.6630.7200.4440.6690.8740.9101.0000.6230.711
아파트_평균_면적0.0000.7810.9840.6770.6290.5170.6040.6300.7850.6160.6670.5180.5340.5630.6280.6231.0000.726
아파트_평균_시가0.2310.7200.9180.7660.6380.6260.8620.8090.8440.6630.6280.4710.6320.7110.6650.7110.7261.000
2024-05-04T02:55:26.251752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드자치구_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_시가자치구_코드_명
기준_년분기_코드1.0000.000-0.037-0.452-0.672-0.710-0.519-0.286-0.327-0.276-0.477-0.669-0.690-0.477-0.117-0.0400.2740.000
자치구_코드0.0001.0000.5880.4630.4240.3220.2730.1070.0270.3430.4300.3100.2540.2170.305-0.1180.1170.982
아파트_단지_수-0.0370.5881.0000.7510.5030.3420.2990.1870.4070.6980.5620.3390.2760.2020.201-0.404-0.1470.941
아파트_면적_66_제곱미터_미만_세대_수-0.4520.4630.7511.0000.8570.6500.4740.1290.6930.8880.8100.7090.6410.3990.078-0.424-0.4240.642
아파트_면적_66_제곱미터_세대_수-0.6720.4240.5030.8571.0000.9040.7140.3470.4910.6150.8220.8710.8590.6600.306-0.069-0.2110.611
아파트_면적_99_제곱미터_세대_수-0.7100.3220.3420.6500.9041.0000.8180.4970.2340.3310.6870.8560.8870.7320.4590.2060.0260.597
아파트_면적_132_제곱미터_세대_수-0.5190.2730.2990.4740.7140.8181.0000.8010.0090.1410.5170.6770.7280.7130.6760.4140.2830.615
아파트_면적_165_제곱미터_세대_수-0.2860.1070.1870.1290.3470.4970.8011.000-0.282-0.1540.2390.3260.3890.5240.7010.5450.4440.656
아파트_가격_1_억_미만_세대_수-0.3270.0270.4070.6930.4910.2340.009-0.2821.0000.8370.4260.4460.3840.084-0.437-0.579-0.8350.640
아파트_가격_1_억_세대_수-0.2760.3430.6980.8880.6150.3310.141-0.1540.8371.0000.6680.4710.3950.115-0.257-0.648-0.6830.665
아파트_가격_2_억_세대_수-0.4770.4300.5620.8100.8220.6870.5170.2390.4260.6681.0000.7610.5980.3280.131-0.262-0.2330.646
아파트_가격_3_억_세대_수-0.6690.3100.3390.7090.8710.8560.6770.3260.4460.4710.7611.0000.8770.5260.172-0.017-0.2250.611
아파트_가격_4_억_세대_수-0.6900.2540.2760.6410.8590.8870.7280.3890.3840.3950.5980.8771.0000.7900.2930.089-0.1700.618
아파트_가격_5_억_세대_수-0.4770.2170.2020.3990.6600.7320.7130.5240.0840.1150.3280.5260.7901.0000.5920.2930.1830.658
아파트_가격_6_억_이상_세대_수-0.1170.3050.2010.0780.3060.4590.6760.701-0.437-0.2570.1310.1720.2930.5921.0000.4320.7350.631
아파트_평균_면적-0.040-0.118-0.404-0.424-0.0690.2060.4140.545-0.579-0.648-0.262-0.0170.0890.2930.4321.0000.6510.903
아파트_평균_시가0.2740.117-0.147-0.424-0.2110.0260.2830.444-0.835-0.683-0.233-0.225-0.1700.1830.7350.6511.0000.671
자치구_코드_명0.0000.9820.9410.6420.6110.5970.6150.6560.6400.6650.6460.6110.6180.6580.6310.9030.6711.000

Missing values

2024-05-04T02:55:08.268083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T02:55:09.265593image/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

기준_년분기_코드자치구_코드자치구_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
02023111110종로구23361099941859857758802886624238151516722761188271271719514
12023111140중구108162612179740157160114335521975710226257163462292261797
22023111170용산구304213605550818641210176231438265790317517491159793671578405236
32023111200성동구192810647554216522749014123591220013098361392746562459388770
42023111215광진구4692304799238965507396320018381132172087711544344553240830723
52023111230동대문구2408162386582175014939231776376833142716572136275159276363203
62023111260중랑구405931132782411361443438123030720518952292112830852188352147
72023111290성북구4334274711078525115192444976189456380160730423654292661240570167
82023111305강북구5209378296874108414831122722644134591068134170168453151355915
92023111320도봉구352027339739488046986983717399271926831837127142256169896516
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4162021411680강남구522728946118835157310130122249665104865828318720542065573737092638
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4182021211680강남구560668953454992243913274850981519541177998494724572659751572557171127
4192021111680강남구560668953454992243913274850981519541177998494724572659751572557171127
4202020411680강남구560668953454992243913274850981519541177998494724572659751572557171127
4212020311680강남구560668953454992243913274850981519541177998494724572659751572557171127
4222020211680강남구560668953454992243913274850981519541177998494724572659751572557171127
4232020111680강남구560668953454992243913274850981519541177998494724572659751572557171127
4242019411680강남구560668953454992243913274850981519541177998494724572659751572557171127