Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells21415
Missing cells (%)13.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory151.0 B

Variable types

Numeric13
Categorical3

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 부동산 거래 통계를 조회할 수 있는 서비스로 충남의 거래원인별 거래 건수 정보를 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/bigdata/collect/view.chungnam?menuCd=DOM_000000201001001000&apiIdx=2562

Alerts

지역명 is highly overall correlated with 번호 and 2 other fieldsHigh correlation
지역구분 레벨 is highly overall correlated with 번호 and 5 other fieldsHigh correlation
번호 is highly overall correlated with 지역코드 and 4 other fieldsHigh correlation
지역코드 is highly overall correlated with 번호 and 4 other fieldsHigh correlation
합계_건수 is highly overall correlated with 번호 and 8 other fieldsHigh correlation
매매_건수 is highly overall correlated with 번호 and 8 other fieldsHigh correlation
판결_건수 is highly overall correlated with 합계_건수 and 4 other fieldsHigh correlation
교환_건수 is highly overall correlated with 합계_건수 and 4 other fieldsHigh correlation
증여_건수 is highly overall correlated with 합계_건수 and 5 other fieldsHigh correlation
분양권_건수 is highly overall correlated with 합계_건수 and 1 other fieldsHigh correlation
분양권전매_건수 is highly overall correlated with 기타_건수.1High correlation
기타_건수 is highly overall correlated with 합계_건수 and 4 other fieldsHigh correlation
기타_건수.1 is highly overall correlated with 분양권전매_건수High correlation
지역구분 레벨 is highly imbalanced (50.8%)Imbalance
분양권_건수 has 1632 (16.3%) missing valuesMissing
분양권전매_건수 has 5930 (59.3%) missing valuesMissing
분양권검인_건수 has 5930 (59.3%) missing valuesMissing
기타_건수 has 1632 (16.3%) missing valuesMissing
기타_건수.1 has 6291 (62.9%) missing valuesMissing
번호 has unique valuesUnique
판결_건수 has 4484 (44.8%) zerosZeros
교환_건수 has 6151 (61.5%) zerosZeros
증여_건수 has 402 (4.0%) zerosZeros
분양권_건수 has 3080 (30.8%) zerosZeros
분양권전매_건수 has 2965 (29.6%) zerosZeros
분양권검인_건수 has 3893 (38.9%) zerosZeros
기타_건수 has 2234 (22.3%) zerosZeros
기타_건수.1 has 2321 (23.2%) zerosZeros

Reproduction

Analysis started2024-01-09 22:21:02.918830
Analysis finished2024-01-09 22:21:19.996796
Duration17.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8609.6454
Minimum1
Maximum17170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:20.057423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile844.9
Q14278.75
median8610.5
Q312924.5
95-th percentile16366.05
Maximum17170
Range17169
Interquartile range (IQR)8645.75

Descriptive statistics

Standard deviation4978.4777
Coefficient of variation (CV)0.57824422
Kurtosis-1.2069772
Mean8609.6454
Median Absolute Deviation (MAD)4324
Skewness-0.0024048544
Sum86096454
Variance24785240
MonotonicityNot monotonic
2024-01-10T07:21:20.167594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15413 1
 
< 0.1%
10907 1
 
< 0.1%
16416 1
 
< 0.1%
3250 1
 
< 0.1%
15429 1
 
< 0.1%
14172 1
 
< 0.1%
9770 1
 
< 0.1%
3504 1
 
< 0.1%
5984 1
 
< 0.1%
622 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
17170 1
< 0.1%
17168 1
< 0.1%
17167 1
< 0.1%
17166 1
< 0.1%
17165 1
< 0.1%
17164 1
< 0.1%
17162 1
< 0.1%
17160 1
< 0.1%
17158 1
< 0.1%
17157 1
< 0.1%

지역코드
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44416.575
Minimum44000
Maximum44825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:20.265394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44000
5-th percentile44000
Q144150
median44230
Q344770
95-th percentile44825
Maximum44825
Range825
Interquartile range (IQR)620

Descriptive statistics

Standard deviation306.53042
Coefficient of variation (CV)0.006901262
Kurtosis-1.7349551
Mean44416.575
Median Absolute Deviation (MAD)100
Skewness0.30109012
Sum4.4416575 × 108
Variance93960.898
MonotonicityNot monotonic
2024-01-10T07:21:20.354374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
44825 617
 
6.2%
44150 584
 
5.8%
44230 583
 
5.8%
44790 582
 
5.8%
44770 582
 
5.8%
44000 581
 
5.8%
44180 579
 
5.8%
44800 577
 
5.8%
44760 574
 
5.7%
44200 565
 
5.7%
Other values (8) 4176
41.8%
ValueCountFrequency (%)
44000 581
5.8%
44130 559
5.6%
44131 511
5.1%
44133 488
4.9%
44150 584
5.8%
44180 579
5.8%
44200 565
5.7%
44210 557
5.6%
44230 583
5.8%
44250 559
5.6%
ValueCountFrequency (%)
44825 617
6.2%
44810 560
5.6%
44800 577
5.8%
44790 582
5.8%
44770 582
5.8%
44760 574
5.7%
44710 552
5.5%
44270 390
3.9%
44250 559
5.6%
44230 583
5.8%

지역명
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
태안군
 
617
공주시
 
584
논산시
 
583
서천군
 
582
청양군
 
582
Other values (13)
7052 

Length

Max length3
Median length3
Mean length2.9419
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row예산군
2nd row서천군
3rd row논산시
4th row서북구
5th row부여군

Common Values

ValueCountFrequency (%)
태안군 617
 
6.2%
공주시 584
 
5.8%
논산시 583
 
5.8%
서천군 582
 
5.8%
청양군 582
 
5.8%
충남 581
 
5.8%
보령시 579
 
5.8%
홍성군 577
 
5.8%
부여군 574
 
5.7%
아산시 565
 
5.7%
Other values (8) 4176
41.8%

Length

2024-01-10T07:21:20.444048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태안군 617
 
6.2%
공주시 584
 
5.8%
논산시 583
 
5.8%
서천군 582
 
5.8%
청양군 582
 
5.8%
충남 581
 
5.8%
보령시 579
 
5.8%
홍성군 577
 
5.8%
부여군 574
 
5.7%
아산시 565
 
5.7%
Other values (8) 4176
41.8%

조사분기
Real number (ℝ)

Distinct198
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201405.85
Minimum200601
Maximum202206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:20.539685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum200601
5-th percentile200612
Q1201007
median201407
Q3201807
95-th percentile202110
Maximum202206
Range1605
Interquartile range (IQR)800

Descriptive statistics

Standard deviation470.22923
Coefficient of variation (CV)0.0023347347
Kurtosis-1.1592278
Mean201405.85
Median Absolute Deviation (MAD)400
Skewness-0.03340863
Sum2.0140585 × 109
Variance221115.53
MonotonicityNot monotonic
2024-01-10T07:21:20.648546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202205 63
 
0.6%
201202 63
 
0.6%
202110 61
 
0.6%
201706 61
 
0.6%
201301 60
 
0.6%
201703 59
 
0.6%
202009 59
 
0.6%
201206 59
 
0.6%
202202 59
 
0.6%
201711 59
 
0.6%
Other values (188) 9397
94.0%
ValueCountFrequency (%)
200601 37
0.4%
200602 45
0.4%
200603 38
0.4%
200604 46
0.5%
200605 46
0.5%
200606 38
0.4%
200607 45
0.4%
200608 48
0.5%
200609 45
0.4%
200610 39
0.4%
ValueCountFrequency (%)
202206 55
0.5%
202205 63
0.6%
202204 49
0.5%
202203 58
0.6%
202202 59
0.6%
202201 58
0.6%
202112 51
0.5%
202111 57
0.6%
202110 61
0.6%
202109 52
0.5%

거래유형
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
5
2042 
2
2031 
3
2025 
1
1993 
4
1909 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row2
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 2042
20.4%
2 2031
20.3%
3 2025
20.2%
1 1993
19.9%
4 1909
19.1%

Length

2024-01-10T07:21:20.752735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:21:20.830489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 2042
20.4%
2 2031
20.3%
3 2025
20.2%
1 1993
19.9%
4 1909
19.1%

합계_건수
Real number (ℝ)

HIGH CORRELATION 

Distinct2522
Distinct (%)25.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean951.7302
Minimum0
Maximum25927
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:20.924479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25.95
Q1110
median379
Q3851.25
95-th percentile4172.05
Maximum25927
Range25927
Interquartile range (IQR)741.25

Descriptive statistics

Standard deviation2004.8274
Coefficient of variation (CV)2.1065081
Kurtosis31.848717
Mean951.7302
Median Absolute Deviation (MAD)295
Skewness5.0160303
Sum9517302
Variance4019332.8
MonotonicityNot monotonic
2024-01-10T07:21:21.033930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 39
 
0.4%
72 38
 
0.4%
66 36
 
0.4%
78 33
 
0.3%
93 32
 
0.3%
21 32
 
0.3%
36 31
 
0.3%
82 31
 
0.3%
91 30
 
0.3%
59 30
 
0.3%
Other values (2512) 9668
96.7%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 8
 
0.1%
2 7
 
0.1%
3 10
0.1%
4 17
0.2%
5 18
0.2%
6 23
0.2%
7 14
0.1%
8 15
0.1%
9 13
0.1%
ValueCountFrequency (%)
25927 1
< 0.1%
23450 1
< 0.1%
23070 1
< 0.1%
23024 1
< 0.1%
21064 1
< 0.1%
20701 1
< 0.1%
19845 1
< 0.1%
19701 1
< 0.1%
19259 1
< 0.1%
19191 1
< 0.1%

매매_건수
Real number (ℝ)

HIGH CORRELATION 

Distinct2031
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631.0989
Minimum0
Maximum17621
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:21.139997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18
Q180
median246.5
Q3569
95-th percentile2539.2
Maximum17621
Range17621
Interquartile range (IQR)489

Descriptive statistics

Standard deviation1350.4152
Coefficient of variation (CV)2.1397838
Kurtosis34.489289
Mean631.0989
Median Absolute Deviation (MAD)188.5
Skewness5.2446178
Sum6310989
Variance1823621.2
MonotonicityNot monotonic
2024-01-10T07:21:21.247958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 46
 
0.5%
61 46
 
0.5%
50 46
 
0.5%
66 43
 
0.4%
16 42
 
0.4%
21 42
 
0.4%
17 42
 
0.4%
70 41
 
0.4%
68 41
 
0.4%
51 40
 
0.4%
Other values (2021) 9571
95.7%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 10
 
0.1%
2 13
 
0.1%
3 16
0.2%
4 24
0.2%
5 27
0.3%
6 33
0.3%
7 23
0.2%
8 28
0.3%
9 27
0.3%
ValueCountFrequency (%)
17621 1
< 0.1%
17179 1
< 0.1%
15871 1
< 0.1%
14700 1
< 0.1%
14615 1
< 0.1%
14123 1
< 0.1%
13417 1
< 0.1%
13011 1
< 0.1%
12593 1
< 0.1%
12506 1
< 0.1%

판결_건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct154
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2391
Minimum0
Maximum244
Zeros4484
Zeros (%)44.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:21.361128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile23
Maximum244
Range244
Interquartile range (IQR)4

Descriptive statistics

Standard deviation18.731279
Coefficient of variation (CV)3.0022406
Kurtosis38.726008
Mean6.2391
Median Absolute Deviation (MAD)1
Skewness5.7277049
Sum62391
Variance350.86082
MonotonicityNot monotonic
2024-01-10T07:21:21.470525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4484
44.8%
1 1424
 
14.2%
2 743
 
7.4%
3 482
 
4.8%
4 381
 
3.8%
5 290
 
2.9%
6 281
 
2.8%
7 212
 
2.1%
8 199
 
2.0%
9 148
 
1.5%
Other values (144) 1356
 
13.6%
ValueCountFrequency (%)
0 4484
44.8%
1 1424
 
14.2%
2 743
 
7.4%
3 482
 
4.8%
4 381
 
3.8%
5 290
 
2.9%
6 281
 
2.8%
7 212
 
2.1%
8 199
 
2.0%
9 148
 
1.5%
ValueCountFrequency (%)
244 1
< 0.1%
243 1
< 0.1%
228 1
< 0.1%
221 1
< 0.1%
213 1
< 0.1%
203 1
< 0.1%
197 2
< 0.1%
186 1
< 0.1%
184 1
< 0.1%
167 1
< 0.1%

교환_건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9846
Minimum0
Maximum200
Zeros6151
Zeros (%)61.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:21.581049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile13
Maximum200
Range200
Interquartile range (IQR)3

Descriptive statistics

Standard deviation13.651388
Coefficient of variation (CV)3.4260373
Kurtosis53.713026
Mean3.9846
Median Absolute Deviation (MAD)0
Skewness6.7165863
Sum39846
Variance186.3604
MonotonicityNot monotonic
2024-01-10T07:21:21.693834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6151
61.5%
2 871
 
8.7%
4 464
 
4.6%
1 430
 
4.3%
6 299
 
3.0%
3 256
 
2.6%
7 196
 
2.0%
5 191
 
1.9%
8 188
 
1.9%
9 127
 
1.3%
Other values (118) 827
 
8.3%
ValueCountFrequency (%)
0 6151
61.5%
1 430
 
4.3%
2 871
 
8.7%
3 256
 
2.6%
4 464
 
4.6%
5 191
 
1.9%
6 299
 
3.0%
7 196
 
2.0%
8 188
 
1.9%
9 127
 
1.3%
ValueCountFrequency (%)
200 1
< 0.1%
189 1
< 0.1%
182 1
< 0.1%
175 2
< 0.1%
174 1
< 0.1%
171 1
< 0.1%
153 1
< 0.1%
149 1
< 0.1%
143 1
< 0.1%
141 1
< 0.1%

증여_건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct596
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.124
Minimum0
Maximum3118
Zeros402
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:21.818961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median29
Q392
95-th percentile223.05
Maximum3118
Range3118
Interquartile range (IQR)82

Descriptive statistics

Standard deviation234.78262
Coefficient of variation (CV)2.6051065
Kurtosis41.783667
Mean90.124
Median Absolute Deviation (MAD)27
Skewness6.1080791
Sum901240
Variance55122.878
MonotonicityNot monotonic
2024-01-10T07:21:21.923920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 402
 
4.0%
1 366
 
3.7%
2 318
 
3.2%
3 244
 
2.4%
4 242
 
2.4%
6 210
 
2.1%
5 209
 
2.1%
11 193
 
1.9%
13 188
 
1.9%
10 180
 
1.8%
Other values (586) 7448
74.5%
ValueCountFrequency (%)
0 402
4.0%
1 366
3.7%
2 318
3.2%
3 244
2.4%
4 242
2.4%
5 209
2.1%
6 210
2.1%
7 168
1.7%
8 176
1.8%
9 159
 
1.6%
ValueCountFrequency (%)
3118 1
< 0.1%
2952 1
< 0.1%
2938 1
< 0.1%
2816 1
< 0.1%
2370 1
< 0.1%
2330 1
< 0.1%
2327 1
< 0.1%
2298 1
< 0.1%
2292 1
< 0.1%
2240 1
< 0.1%

분양권_건수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct969
Distinct (%)11.6%
Missing1632
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean150.13719
Minimum0
Maximum5089
Zeros3080
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:22.029103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q364
95-th percentile874.6
Maximum5089
Range5089
Interquartile range (IQR)64

Descriptive statistics

Standard deviation446.99508
Coefficient of variation (CV)2.9772442
Kurtosis35.349337
Mean150.13719
Median Absolute Deviation (MAD)4
Skewness5.3016768
Sum1256348
Variance199804.6
MonotonicityNot monotonic
2024-01-10T07:21:22.136659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3080
30.8%
1 466
 
4.7%
2 301
 
3.0%
3 221
 
2.2%
4 192
 
1.9%
5 146
 
1.5%
6 127
 
1.3%
7 110
 
1.1%
8 93
 
0.9%
9 80
 
0.8%
Other values (959) 3552
35.5%
(Missing) 1632
16.3%
ValueCountFrequency (%)
0 3080
30.8%
1 466
 
4.7%
2 301
 
3.0%
3 221
 
2.2%
4 192
 
1.9%
5 146
 
1.5%
6 127
 
1.3%
7 110
 
1.1%
8 93
 
0.9%
9 80
 
0.8%
ValueCountFrequency (%)
5089 1
< 0.1%
5038 1
< 0.1%
4994 1
< 0.1%
4972 1
< 0.1%
4753 1
< 0.1%
4663 1
< 0.1%
4299 1
< 0.1%
4294 1
< 0.1%
4269 1
< 0.1%
4221 1
< 0.1%

분양권전매_건수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct298
Distinct (%)7.3%
Missing5930
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean25.852826
Minimum0
Maximum1336
Zeros2965
Zeros (%)29.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:22.475642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile160
Maximum1336
Range1336
Interquartile range (IQR)2

Descriptive statistics

Standard deviation93.887619
Coefficient of variation (CV)3.6316192
Kurtosis43.499393
Mean25.852826
Median Absolute Deviation (MAD)0
Skewness5.797448
Sum105221
Variance8814.8849
MonotonicityNot monotonic
2024-01-10T07:21:22.587640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2965
29.6%
1 76
 
0.8%
2 66
 
0.7%
4 43
 
0.4%
5 38
 
0.4%
7 38
 
0.4%
6 36
 
0.4%
3 32
 
0.3%
11 29
 
0.3%
8 28
 
0.3%
Other values (288) 719
 
7.2%
(Missing) 5930
59.3%
ValueCountFrequency (%)
0 2965
29.6%
1 76
 
0.8%
2 66
 
0.7%
3 32
 
0.3%
4 43
 
0.4%
5 38
 
0.4%
6 36
 
0.4%
7 38
 
0.4%
8 28
 
0.3%
9 14
 
0.1%
ValueCountFrequency (%)
1336 1
< 0.1%
1325 1
< 0.1%
926 1
< 0.1%
906 1
< 0.1%
905 1
< 0.1%
854 1
< 0.1%
823 1
< 0.1%
776 1
< 0.1%
721 1
< 0.1%
689 1
< 0.1%

분양권검인_건수
Real number (ℝ)

MISSING  ZEROS 

Distinct102
Distinct (%)2.5%
Missing5930
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean12.260197
Minimum0
Maximum2361
Zeros3893
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:22.697420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2361
Range2361
Interquartile range (IQR)0

Descriptive statistics

Standard deviation109.03444
Coefficient of variation (CV)8.8933676
Kurtosis157.32263
Mean12.260197
Median Absolute Deviation (MAD)0
Skewness11.689231
Sum49899
Variance11888.508
MonotonicityNot monotonic
2024-01-10T07:21:22.801297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3893
38.9%
1 14
 
0.1%
3 12
 
0.1%
2 12
 
0.1%
4 9
 
0.1%
9 5
 
0.1%
7 4
 
< 0.1%
304 3
 
< 0.1%
36 3
 
< 0.1%
91 3
 
< 0.1%
Other values (92) 112
 
1.1%
(Missing) 5930
59.3%
ValueCountFrequency (%)
0 3893
38.9%
1 14
 
0.1%
2 12
 
0.1%
3 12
 
0.1%
4 9
 
0.1%
5 1
 
< 0.1%
7 4
 
< 0.1%
8 2
 
< 0.1%
9 5
 
0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
2361 1
< 0.1%
1543 1
< 0.1%
1534 1
< 0.1%
1492 2
< 0.1%
1487 1
< 0.1%
1437 1
< 0.1%
1423 2
< 0.1%
1401 1
< 0.1%
1383 1
< 0.1%
1313 1
< 0.1%

기타_건수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct513
Distinct (%)6.1%
Missing1632
Missing (%)16.3%
Infinite0
Infinite (%)0.0%
Mean52.977892
Minimum0
Maximum5362
Zeros2234
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:22.901951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q336
95-th percentile191
Maximum5362
Range5362
Interquartile range (IQR)36

Descriptive statistics

Standard deviation186.67574
Coefficient of variation (CV)3.5236536
Kurtosis202.15995
Mean52.977892
Median Absolute Deviation (MAD)6
Skewness10.95288
Sum443319
Variance34847.832
MonotonicityNot monotonic
2024-01-10T07:21:23.008721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2234
22.3%
1 709
 
7.1%
2 395
 
4.0%
3 264
 
2.6%
4 262
 
2.6%
6 170
 
1.7%
5 165
 
1.7%
7 143
 
1.4%
8 136
 
1.4%
9 124
 
1.2%
Other values (503) 3766
37.7%
(Missing) 1632
16.3%
ValueCountFrequency (%)
0 2234
22.3%
1 709
 
7.1%
2 395
 
4.0%
3 264
 
2.6%
4 262
 
2.6%
5 165
 
1.7%
6 170
 
1.7%
7 143
 
1.4%
8 136
 
1.4%
9 124
 
1.2%
ValueCountFrequency (%)
5362 1
< 0.1%
4566 1
< 0.1%
4334 1
< 0.1%
3411 1
< 0.1%
3099 1
< 0.1%
3014 1
< 0.1%
2832 1
< 0.1%
2224 1
< 0.1%
2221 1
< 0.1%
1766 1
< 0.1%

기타_건수.1
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct487
Distinct (%)13.1%
Missing6291
Missing (%)62.9%
Infinite0
Infinite (%)0.0%
Mean107.29253
Minimum0
Maximum4518
Zeros2321
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-10T07:21:23.136160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314
95-th percentile696.6
Maximum4518
Range4518
Interquartile range (IQR)14

Descriptive statistics

Standard deviation372.63926
Coefficient of variation (CV)3.4731146
Kurtosis34.773004
Mean107.29253
Median Absolute Deviation (MAD)0
Skewness5.3478262
Sum397948
Variance138860.02
MonotonicityNot monotonic
2024-01-10T07:21:23.277193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2321
 
23.2%
1 88
 
0.9%
2 49
 
0.5%
4 48
 
0.5%
5 40
 
0.4%
7 35
 
0.4%
8 34
 
0.3%
3 30
 
0.3%
6 27
 
0.3%
9 24
 
0.2%
Other values (477) 1013
 
10.1%
(Missing) 6291
62.9%
ValueCountFrequency (%)
0 2321
23.2%
1 88
 
0.9%
2 49
 
0.5%
3 30
 
0.3%
4 48
 
0.5%
5 40
 
0.4%
6 27
 
0.3%
7 35
 
0.4%
8 34
 
0.3%
9 24
 
0.2%
ValueCountFrequency (%)
4518 1
< 0.1%
3773 1
< 0.1%
3745 1
< 0.1%
3646 1
< 0.1%
3501 1
< 0.1%
3253 1
< 0.1%
3238 1
< 0.1%
3228 1
< 0.1%
3153 1
< 0.1%
2914 1
< 0.1%

지역구분 레벨
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8420 
2
999 
0
 
581

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 8420
84.2%
2 999
 
10.0%
0 581
 
5.8%

Length

2024-01-10T07:21:23.402387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:21:23.499306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8420
84.2%
2 999
 
10.0%
0 581
 
5.8%

Interactions

2024-01-10T07:21:18.457870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:05.552416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.542716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.555750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.825187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.817442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.992719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.151574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:13.158498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.361967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.335294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.342644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.419088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:18.532954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:05.630236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.618711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.648041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.901150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.896993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.094839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.231189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:13.240831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.440052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.414918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.434825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.495985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:18.817813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:05.709296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.685557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.726529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.973050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.974116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.184896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.305183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:13.311490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.507038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.484163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.521838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.567531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:18.887165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:05.794073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.763071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.812404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.063139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.057827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.291290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.387994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:13.397265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.588762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.578228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.621212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.669161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:18.956737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:05.869009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.837240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.889789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.138165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.132898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.388960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.463180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:13.479744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.657987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.652658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.711297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.744834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:19.029702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:05.945719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.913811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.985312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.217097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.213879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.491472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.544558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:13.558775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.735427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.734371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.809063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.824895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:19.100302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.019952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.990794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.074560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.294951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.312656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.582422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.618870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:13.850912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.807578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.807226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.897122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.898755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:19.175262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.100720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.093400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.159875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.384515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.418151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.682453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.697922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:13.931453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.887434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.890609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.977634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.975233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:19.247819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.178797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.181325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.237586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.460759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.518254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.778218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.775059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.002894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.960027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.968002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.058769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:18.048831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:19.325126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.249957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.264603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.313212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.530244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.611608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.867849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.846619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.073157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.028776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.045600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.138283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:18.116881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:19.401749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.330026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.340733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.389158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.608108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.714605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:11.946479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.925477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.148042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.107541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.120424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.211520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:18.218961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:19.468357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.399690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.412734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.681555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.676987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.805352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.014345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.998173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.223336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.189847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.193081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.276025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:18.307757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:19.548848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:06.472408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:07.485337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:08.753882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:09.748257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:10.900616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:12.082050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:13.085581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:14.294308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:15.257863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:16.272411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:17.353814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:21:18.377342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:21:23.582225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드지역명조사분기거래유형합계_건수매매_건수판결_건수교환_건수증여_건수분양권_건수분양권전매_건수분양권검인_건수기타_건수기타_건수.1지역구분 레벨
번호1.0000.9940.9810.2160.0680.6260.6270.4470.4180.3940.5060.3710.1750.3370.5100.872
지역코드0.9941.0001.0000.1240.0000.1590.1310.0560.0590.0220.1920.3440.0670.0000.1920.524
지역명0.9811.0001.0000.1320.0000.6410.6340.4730.4480.6260.5000.4630.2210.5550.4611.000
조사분기0.2160.1240.1321.0000.0000.0960.0880.0790.0950.0730.2080.0770.2080.0390.0990.128
거래유형0.0680.0000.0000.0001.0000.2730.2620.2350.2580.1970.2150.2390.1070.1330.3560.000
합계_건수0.6260.1590.6410.0960.2731.0000.9690.7980.7490.8090.7570.4120.1730.5920.5900.764
매매_건수0.6270.1310.6340.0880.2620.9691.0000.7860.7750.8020.6630.4140.1730.5950.5090.759
판결_건수0.4470.0560.4730.0790.2350.7980.7861.0000.7740.6710.5100.1260.0000.6200.2120.584
교환_건수0.4180.0590.4480.0950.2580.7490.7750.7741.0000.6410.3630.0000.0000.6220.0000.561
증여_건수0.3940.0220.6260.0730.1970.8090.8020.6710.6411.0000.4900.2940.0000.7730.2390.812
분양권_건수0.5060.1920.5000.2080.2150.7570.6630.5100.3630.4901.000NaNNaN0.251NaN0.574
분양권전매_건수0.3710.3440.4630.0770.2390.4120.4140.1260.0000.294NaN1.0000.651NaN0.6780.476
분양권검인_건수0.1750.0670.2210.2080.1070.1730.1730.0000.0000.000NaN0.6511.000NaN0.5200.238
기타_건수0.3370.0000.5550.0390.1330.5920.5950.6200.6220.7730.251NaNNaN1.000NaN0.733
기타_건수.10.5100.1920.4610.0990.3560.5900.5090.2120.0000.239NaN0.6780.520NaN1.0000.530
지역구분 레벨0.8720.5241.0000.1280.0000.7640.7590.5840.5610.8120.5740.4760.2380.7330.5301.000
2024-01-10T07:21:23.711735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역명지역구분 레벨거래유형
지역명1.0000.9990.000
지역구분 레벨0.9991.0000.000
거래유형0.0000.0001.000
2024-01-10T07:21:23.793559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호지역코드조사분기합계_건수매매_건수판결_건수교환_건수증여_건수분양권_건수분양권전매_건수분양권검인_건수기타_건수기타_건수.1지역명거래유형지역구분 레벨
번호1.0000.9980.025-0.585-0.598-0.346-0.195-0.323-0.461-0.233-0.064-0.280-0.1700.9010.0280.803
지역코드0.9981.000-0.014-0.586-0.598-0.344-0.194-0.324-0.467-0.229-0.051-0.285-0.1650.9990.0000.837
조사분기0.025-0.0141.0000.1300.1010.0730.0890.1510.148-0.059-0.3200.223-0.1150.0510.0000.078
합계_건수-0.585-0.5860.1301.0000.9730.6720.5370.7980.6000.066-0.0230.692-0.0140.3080.1170.640
매매_건수-0.598-0.5980.1010.9731.0000.6770.5540.7910.534-0.022-0.0550.662-0.1140.3020.1110.633
판결_건수-0.346-0.3440.0730.6720.6771.0000.6040.7370.242-0.156-0.0800.670-0.2250.2020.1000.428
교환_건수-0.195-0.1940.0890.5370.5540.6041.0000.6850.082-0.320-0.1120.665-0.3960.1890.1070.405
증여_건수-0.323-0.3240.1510.7980.7910.7370.6851.0000.265-0.208-0.1350.775-0.2940.2570.1150.520
분양권_건수-0.461-0.4670.1480.6000.5340.2420.0820.2651.000NaNNaN0.216NaN0.2160.0910.418
분양권전매_건수-0.233-0.229-0.0590.066-0.022-0.156-0.320-0.208NaN1.0000.324NaN0.8560.2140.1480.344
분양권검인_건수-0.064-0.051-0.320-0.023-0.055-0.080-0.112-0.135NaN0.3241.000NaN0.2890.0940.0660.154
기타_건수-0.280-0.2850.2230.6920.6620.6700.6650.7750.216NaNNaN1.000NaN0.2160.0770.438
기타_건수.1-0.170-0.165-0.115-0.014-0.114-0.225-0.396-0.294NaN0.8560.289NaN1.0000.1950.1560.374
지역명0.9010.9990.0510.3080.3020.2020.1890.2570.2160.2140.0940.2160.1951.0000.0000.999
거래유형0.0280.0000.0000.1170.1110.1000.1070.1150.0910.1480.0660.0770.1560.0001.0000.000
지역구분 레벨0.8030.8370.0780.6400.6330.4280.4050.5200.4180.3440.1540.4380.3740.9990.0001.000

Missing values

2024-01-10T07:21:19.652883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:21:19.823347image/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.
2024-01-10T07:21:19.938801image/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

번호지역코드지역명조사분기거래유형합계_건수매매_건수판결_건수교환_건수증여_건수분양권_건수분양권전매_건수분양권검인_건수기타_건수기타_건수.1지역구분 레벨
154121541344810예산군20070415323007374133<NA><NA>15<NA>1
126951269644770서천군20111241019100100<NA><NA>0<NA>1
8541854244230논산시20210225333411131271005001
3284328544133서북구20120155052401056203<NA><NA>5<NA>2
116991170044760부여군2012084665300130<NA><NA>0<NA>1
3360336144133서북구201802329045201035234700102
129311293244770서천군201701463440019<NA>00<NA>01
5636563744180보령시202205324214720235006501
3614361544133서북구202201264152126692004102
2729273044131동남구202010415116191059<NA>1630<NA>6692
번호지역코드지역명조사분기거래유형합계_건수매매_건수판결_건수교환_건수증여_건수분양권_건수분양권전매_건수분양권검인_건수기타_건수기타_건수.1지역구분 레벨
3384338544133서북구201812238224814690006002
3454345544133서북구201704413785091021<NA>1470<NA>7002
40140244000충남2012023602545503173241091<NA><NA>22<NA>0
106911069244710금산군2009094685700100<NA><NA>1<NA>1
11511644000충남20070734072312882229675<NA><NA>30<NA>0
2128212944131동남구201204113329934410222<NA><NA>207<NA>2
135571355844790청양군201305324170043<NA><NA>0<NA>1
8583858444230논산시202107269757936610004801
166221662344825태안군200812370610090<NA><NA>0<NA>1
7384738544210서산시2018012102273621614300012501