Overview

Dataset statistics

Number of variables20
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory185.0 B

Variable types

Numeric17
Categorical2
Text1

Dataset

Description기준_년분기_코드,상권_구분_코드,상권_구분_코드_명,상권배후지_코드,상권배후지_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-15574/S/1/datasetView.do

Alerts

상권_구분_코드 has constant value ""Constant
상권_구분_코드_명 has constant value ""Constant
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 5 other fieldsHigh correlation
아파트_면적_66_제곱미터_세대_수 is highly overall correlated with 아파트_단지_수 and 5 other fieldsHigh correlation
아파트_면적_99_제곱미터_세대_수 is highly overall correlated with 아파트_면적_66_제곱미터_세대_수 and 2 other fieldsHigh correlation
아파트_면적_132_제곱미터_세대_수 is highly overall correlated with 아파트_면적_165_제곱미터_세대_수High correlation
아파트_면적_165_제곱미터_세대_수 is highly overall correlated with 아파트_면적_132_제곱미터_세대_수High correlation
아파트_가격_1_억_미만_세대_수 is highly overall correlated with 아파트_단지_수 and 5 other fieldsHigh correlation
아파트_가격_1_억_세대_수 is highly overall correlated with 아파트_단지_수 and 6 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_세대_수 and 3 other fieldsHigh correlation
아파트_가격_4_억_세대_수 is highly overall correlated with 아파트_면적_99_제곱미터_세대_수 and 2 other fieldsHigh correlation
아파트_가격_5_억_세대_수 is highly overall correlated with 아파트_가격_4_억_세대_수 and 2 other fieldsHigh correlation
아파트_가격_6_억_이상_세대_수 is highly overall correlated with 아파트_가격_1_억_미만_세대_수 and 2 other fieldsHigh correlation
아파트_평균_면적 is highly overall correlated with 아파트_가격_1_억_미만_세대_수 and 2 other fieldsHigh correlation
아파트_평균_시가 is highly overall correlated with 아파트_면적_66_제곱미터_미만_세대_수 and 5 other fieldsHigh correlation
아파트_면적_99_제곱미터_세대_수 has 223 (2.2%) zerosZeros
아파트_면적_132_제곱미터_세대_수 has 2526 (25.3%) zerosZeros
아파트_면적_165_제곱미터_세대_수 has 5419 (54.2%) zerosZeros
아파트_가격_1_억_미만_세대_수 has 555 (5.5%) zerosZeros
아파트_가격_1_억_세대_수 has 159 (1.6%) zerosZeros
아파트_가격_3_억_세대_수 has 263 (2.6%) zerosZeros
아파트_가격_4_억_세대_수 has 1200 (12.0%) zerosZeros
아파트_가격_5_억_세대_수 has 2701 (27.0%) zerosZeros
아파트_가격_6_억_이상_세대_수 has 3934 (39.3%) zerosZeros

Reproduction

Analysis started2024-05-04 06:34:50.664423
Analysis finished2024-05-04 06:36:42.335391
Duration1 minute and 51.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준_년분기_코드
Real number (ℝ)

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20216.13
Minimum20194
Maximum20234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:42.684694image/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 deviation12.214741
Coefficient of variation (CV)0.00060420767
Kurtosis-1.1955139
Mean20216.13
Median Absolute Deviation (MAD)10
Skewness-0.070756688
Sum2.021613 × 108
Variance149.19989
MonotonicityNot monotonic
2024-05-04T06:36:43.112976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20221 625
 
6.2%
20203 607
 
6.1%
20201 604
 
6.0%
20202 603
 
6.0%
20211 603
 
6.0%
20232 602
 
6.0%
20212 593
 
5.9%
20233 590
 
5.9%
20234 588
 
5.9%
20231 585
 
5.9%
Other values (7) 4000
40.0%
ValueCountFrequency (%)
20194 567
5.7%
20201 604
6.0%
20202 603
6.0%
20203 607
6.1%
20204 562
5.6%
20211 603
6.0%
20212 593
5.9%
20213 577
5.8%
20214 577
5.8%
20221 625
6.2%
ValueCountFrequency (%)
20234 588
5.9%
20233 590
5.9%
20232 602
6.0%
20231 585
5.9%
20224 582
5.8%
20223 572
5.7%
20222 563
5.6%
20221 625
6.2%
20214 577
5.8%
20213 577
5.8%

상권_구분_코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
A
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 10000
100.0%

Length

2024-05-04T06:36:43.601808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:36:43.910149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 10000
100.0%

상권_구분_코드_명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
골목상권
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row골목상권
2nd row골목상권
3rd row골목상권
4th row골목상권
5th row골목상권

Common Values

ValueCountFrequency (%)
골목상권 10000
100.0%

Length

2024-05-04T06:36:44.182393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T06:36:44.507048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
골목상권 10000
100.0%

상권배후지_코드
Real number (ℝ)

Distinct1088
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3110547
Minimum3110001
Maximum3111090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:44.879387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3110001
5-th percentile3110055
Q13110270.8
median3110551
Q33110820
95-th percentile3111037
Maximum3111090
Range1089
Interquartile range (IQR)549.25

Descriptive statistics

Standard deviation315.35848
Coefficient of variation (CV)0.00010138361
Kurtosis-1.2037531
Mean3110547
Median Absolute Deviation (MAD)274
Skewness-0.01528828
Sum3.110547 × 1010
Variance99450.969
MonotonicityNot monotonic
2024-05-04T06:36:45.378571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3110795 16
 
0.2%
3110112 15
 
0.1%
3111020 15
 
0.1%
3111028 14
 
0.1%
3110465 14
 
0.1%
3110874 14
 
0.1%
3111040 14
 
0.1%
3110458 14
 
0.1%
3110234 14
 
0.1%
3110758 14
 
0.1%
Other values (1078) 9856
98.6%
ValueCountFrequency (%)
3110001 12
0.1%
3110002 9
0.1%
3110003 7
0.1%
3110004 11
0.1%
3110005 11
0.1%
3110006 8
0.1%
3110007 8
0.1%
3110008 12
0.1%
3110009 7
0.1%
3110010 10
0.1%
ValueCountFrequency (%)
3111090 6
0.1%
3111089 10
0.1%
3111088 11
0.1%
3111087 10
0.1%
3111086 13
0.1%
3111085 9
0.1%
3111084 9
0.1%
3111083 7
0.1%
3111082 12
0.1%
3111081 10
0.1%
Distinct1088
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-04T06:36:46.058833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length24
Mean length7.4606
Min length2

Characters and Unicode

Total characters74606
Distinct characters401
Distinct categories8 ?
Distinct scripts3 ?
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 row송파역 2번
2nd row무너미어린이공원
3rd row응봉산
4th row국민대학교앞
5th row샘말어린이공원
ValueCountFrequency (%)
1번 716
 
5.2%
2번 554
 
4.1%
3번 510
 
3.7%
4번 456
 
3.3%
5번 228
 
1.7%
6번 177
 
1.3%
7번 135
 
1.0%
8번 124
 
0.9%
연신내역 52
 
0.4%
내방역 45
 
0.3%
Other values (979) 10661
78.1%
2024-05-04T06:36:47.263838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3658
 
4.9%
3432
 
4.6%
3287
 
4.4%
2569
 
3.4%
2527
 
3.4%
2119
 
2.8%
1725
 
2.3%
1 1308
 
1.8%
1268
 
1.7%
1239
 
1.7%
Other values (391) 51474
69.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64359
86.3%
Decimal Number 4459
 
6.0%
Space Separator 3658
 
4.9%
Open Punctuation 850
 
1.1%
Close Punctuation 850
 
1.1%
Uppercase Letter 364
 
0.5%
Other Punctuation 58
 
0.1%
Lowercase Letter 8
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3432
 
5.3%
3287
 
5.1%
2569
 
4.0%
2527
 
3.9%
2119
 
3.3%
1725
 
2.7%
1268
 
2.0%
1239
 
1.9%
1208
 
1.9%
1101
 
1.7%
Other values (361) 43884
68.2%
Uppercase Letter
ValueCountFrequency (%)
K 88
24.2%
T 49
13.5%
B 35
 
9.6%
G 35
 
9.6%
I 32
 
8.8%
C 27
 
7.4%
S 24
 
6.6%
N 21
 
5.8%
H 21
 
5.8%
F 9
 
2.5%
Other values (3) 23
 
6.3%
Decimal Number
ValueCountFrequency (%)
1 1308
29.3%
2 858
19.2%
3 685
15.4%
4 630
14.1%
5 323
 
7.2%
6 200
 
4.5%
7 147
 
3.3%
8 145
 
3.3%
9 114
 
2.6%
0 49
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 20
34.5%
& 20
34.5%
, 18
31.0%
Space Separator
ValueCountFrequency (%)
3658
100.0%
Open Punctuation
ValueCountFrequency (%)
( 850
100.0%
Close Punctuation
ValueCountFrequency (%)
) 850
100.0%
Lowercase Letter
ValueCountFrequency (%)
h 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64359
86.3%
Common 9875
 
13.2%
Latin 372
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3432
 
5.3%
3287
 
5.1%
2569
 
4.0%
2527
 
3.9%
2119
 
3.3%
1725
 
2.7%
1268
 
2.0%
1239
 
1.9%
1208
 
1.9%
1101
 
1.7%
Other values (361) 43884
68.2%
Common
ValueCountFrequency (%)
3658
37.0%
1 1308
 
13.2%
2 858
 
8.7%
( 850
 
8.6%
) 850
 
8.6%
3 685
 
6.9%
4 630
 
6.4%
5 323
 
3.3%
6 200
 
2.0%
7 147
 
1.5%
Other values (6) 366
 
3.7%
Latin
ValueCountFrequency (%)
K 88
23.7%
T 49
13.2%
B 35
 
9.4%
G 35
 
9.4%
I 32
 
8.6%
C 27
 
7.3%
S 24
 
6.5%
N 21
 
5.6%
H 21
 
5.6%
F 9
 
2.4%
Other values (4) 31
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64359
86.3%
ASCII 10247
 
13.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3658
35.7%
1 1308
 
12.8%
2 858
 
8.4%
( 850
 
8.3%
) 850
 
8.3%
3 685
 
6.7%
4 630
 
6.1%
5 323
 
3.2%
6 200
 
2.0%
7 147
 
1.4%
Other values (20) 738
 
7.2%
Hangul
ValueCountFrequency (%)
3432
 
5.3%
3287
 
5.1%
2569
 
4.0%
2527
 
3.9%
2119
 
3.3%
1725
 
2.7%
1268
 
2.0%
1239
 
1.9%
1208
 
1.9%
1101
 
1.7%
Other values (361) 43884
68.2%

아파트_단지_수
Real number (ℝ)

HIGH CORRELATION 

Distinct591
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean190.5444
Minimum1
Maximum1418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:47.871582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile28
Q176
median144
Q3247
95-th percentile513
Maximum1418
Range1417
Interquartile range (IQR)171

Descriptive statistics

Standard deviation165.49781
Coefficient of variation (CV)0.86855248
Kurtosis7.1289019
Mean190.5444
Median Absolute Deviation (MAD)77
Skewness2.1506054
Sum1905444
Variance27389.526
MonotonicityNot monotonic
2024-05-04T06:36:48.363078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 74
 
0.7%
62 70
 
0.7%
106 70
 
0.7%
68 69
 
0.7%
160 66
 
0.7%
204 66
 
0.7%
30 66
 
0.7%
58 65
 
0.7%
59 61
 
0.6%
98 61
 
0.6%
Other values (581) 9332
93.3%
ValueCountFrequency (%)
1 5
 
0.1%
2 9
 
0.1%
3 20
0.2%
4 4
 
< 0.1%
5 5
 
0.1%
8 12
 
0.1%
9 18
0.2%
10 44
0.4%
11 12
 
0.1%
12 9
 
0.1%
ValueCountFrequency (%)
1418 5
0.1%
1409 4
< 0.1%
1224 2
 
< 0.1%
1218 3
< 0.1%
1216 5
0.1%
1074 3
< 0.1%
1073 1
 
< 0.1%
1066 2
 
< 0.1%
961 4
< 0.1%
950 3
< 0.1%

아파트_면적_66_제곱미터_미만_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct1879
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1548.9702
Minimum0
Maximum12265
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:48.802685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile158
Q1620
median1214
Q32085
95-th percentile4075.15
Maximum12265
Range12265
Interquartile range (IQR)1465

Descriptive statistics

Standard deviation1343.4683
Coefficient of variation (CV)0.86732997
Kurtosis7.7175668
Mean1548.9702
Median Absolute Deviation (MAD)692
Skewness2.1082215
Sum15489702
Variance1804907
MonotonicityNot monotonic
2024-05-04T06:36:49.369869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292 28
 
0.3%
106 25
 
0.2%
270 23
 
0.2%
1176 23
 
0.2%
0 22
 
0.2%
1375 22
 
0.2%
410 21
 
0.2%
665 21
 
0.2%
634 20
 
0.2%
1131 20
 
0.2%
Other values (1869) 9775
97.8%
ValueCountFrequency (%)
0 22
0.2%
8 3
 
< 0.1%
11 5
 
0.1%
14 5
 
0.1%
21 5
 
0.1%
26 5
 
0.1%
27 9
0.1%
34 5
 
0.1%
35 7
 
0.1%
36 8
 
0.1%
ValueCountFrequency (%)
12265 4
< 0.1%
11182 2
 
< 0.1%
11180 3
< 0.1%
10663 5
0.1%
9916 7
0.1%
9559 2
 
< 0.1%
9514 3
< 0.1%
9370 3
< 0.1%
9084 3
< 0.1%
8729 1
 
< 0.1%

아파트_면적_66_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct1210
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean631.2173
Minimum0
Maximum5230
Zeros25
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:49.869289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75
Q1236
median432
Q3854
95-th percentile1794
Maximum5230
Range5230
Interquartile range (IQR)618

Descriptive statistics

Standard deviation587.72678
Coefficient of variation (CV)0.93110056
Kurtosis6.0818981
Mean631.2173
Median Absolute Deviation (MAD)255
Skewness2.0307215
Sum6312173
Variance345422.77
MonotonicityNot monotonic
2024-05-04T06:36:50.522465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 42
 
0.4%
131 39
 
0.4%
279 36
 
0.4%
252 35
 
0.4%
274 34
 
0.3%
354 32
 
0.3%
385 32
 
0.3%
184 32
 
0.3%
168 31
 
0.3%
316 31
 
0.3%
Other values (1200) 9656
96.6%
ValueCountFrequency (%)
0 25
0.2%
1 10
 
0.1%
2 3
 
< 0.1%
3 8
 
0.1%
4 4
 
< 0.1%
5 6
 
0.1%
7 5
 
0.1%
10 11
0.1%
11 4
 
< 0.1%
12 10
 
0.1%
ValueCountFrequency (%)
5230 5
0.1%
4195 5
0.1%
3967 4
< 0.1%
3886 4
< 0.1%
3765 5
0.1%
3719 5
0.1%
3603 4
< 0.1%
3438 5
0.1%
3368 2
 
< 0.1%
3343 4
< 0.1%

아파트_면적_99_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct488
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.6928
Minimum0
Maximum1824
Zeros223
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:51.115312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q121
median59
Q3138
95-th percentile499
Maximum1824
Range1824
Interquartile range (IQR)117

Descriptive statistics

Standard deviation173.51419
Coefficient of variation (CV)1.4258377
Kurtosis11.707245
Mean121.6928
Median Absolute Deviation (MAD)45
Skewness2.9543211
Sum1216928
Variance30107.173
MonotonicityNot monotonic
2024-05-04T06:36:51.729558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 223
 
2.2%
3 169
 
1.7%
2 162
 
1.6%
4 144
 
1.4%
5 136
 
1.4%
12 135
 
1.4%
15 132
 
1.3%
20 119
 
1.2%
6 117
 
1.2%
11 112
 
1.1%
Other values (478) 8551
85.5%
ValueCountFrequency (%)
0 223
2.2%
1 106
1.1%
2 162
1.6%
3 169
1.7%
4 144
1.4%
5 136
1.4%
6 117
1.2%
7 86
 
0.9%
8 94
0.9%
9 72
 
0.7%
ValueCountFrequency (%)
1824 2
 
< 0.1%
1380 4
< 0.1%
1365 5
0.1%
1335 4
< 0.1%
1259 5
0.1%
1233 4
< 0.1%
1229 3
< 0.1%
1208 2
 
< 0.1%
1046 3
< 0.1%
1045 6
0.1%

아파트_면적_132_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct253
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.434
Minimum0
Maximum809
Zeros2526
Zeros (%)25.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:52.398605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q333
95-th percentile182
Maximum809
Range809
Interquartile range (IQR)33

Descriptive statistics

Standard deviation75.545029
Coefficient of variation (CV)2.1319927
Kurtosis22.566734
Mean35.434
Median Absolute Deviation (MAD)6
Skewness4.1133754
Sum354340
Variance5707.0513
MonotonicityNot monotonic
2024-05-04T06:36:53.096836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2526
25.3%
1 1071
 
10.7%
2 447
 
4.5%
3 366
 
3.7%
4 279
 
2.8%
12 239
 
2.4%
5 218
 
2.2%
13 175
 
1.8%
8 172
 
1.7%
6 171
 
1.7%
Other values (243) 4336
43.4%
ValueCountFrequency (%)
0 2526
25.3%
1 1071
10.7%
2 447
 
4.5%
3 366
 
3.7%
4 279
 
2.8%
5 218
 
2.2%
6 171
 
1.7%
7 113
 
1.1%
8 172
 
1.7%
9 80
 
0.8%
ValueCountFrequency (%)
809 5
0.1%
770 1
 
< 0.1%
678 5
0.1%
668 4
 
< 0.1%
638 5
0.1%
637 2
 
< 0.1%
538 10
0.1%
530 2
 
< 0.1%
522 5
0.1%
484 4
 
< 0.1%

아파트_면적_165_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct202
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.7694
Minimum0
Maximum1829
Zeros5419
Zeros (%)54.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:53.578742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q36
95-th percentile107
Maximum1829
Range1829
Interquartile range (IQR)6

Descriptive statistics

Standard deviation83.766137
Coefficient of variation (CV)4.0331515
Kurtosis147.45667
Mean20.7694
Median Absolute Deviation (MAD)0
Skewness10.165053
Sum207694
Variance7016.7657
MonotonicityNot monotonic
2024-05-04T06:36:54.230959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5419
54.2%
1 882
 
8.8%
2 460
 
4.6%
3 331
 
3.3%
4 283
 
2.8%
6 123
 
1.2%
12 107
 
1.1%
5 98
 
1.0%
7 95
 
0.9%
24 93
 
0.9%
Other values (192) 2109
 
21.1%
ValueCountFrequency (%)
0 5419
54.2%
1 882
 
8.8%
2 460
 
4.6%
3 331
 
3.3%
4 283
 
2.8%
5 98
 
1.0%
6 123
 
1.2%
7 95
 
0.9%
8 71
 
0.7%
9 51
 
0.5%
ValueCountFrequency (%)
1829 4
< 0.1%
1174 6
0.1%
1132 3
< 0.1%
1121 3
< 0.1%
1041 1
 
< 0.1%
1026 1
 
< 0.1%
808 6
0.1%
763 4
< 0.1%
657 5
0.1%
652 4
< 0.1%

아파트_가격_1_억_미만_세대_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct838
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.2016
Minimum0
Maximum3820
Zeros555
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:55.033069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141
median158
Q3397
95-th percentile1076
Maximum3820
Range3820
Interquartile range (IQR)356

Descriptive statistics

Standard deviation404.38229
Coefficient of variation (CV)1.3425636
Kurtosis14.435382
Mean301.2016
Median Absolute Deviation (MAD)142
Skewness3.0330529
Sum3012016
Variance163525.04
MonotonicityNot monotonic
2024-05-04T06:36:55.842739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 555
 
5.5%
3 155
 
1.6%
1 115
 
1.1%
2 96
 
1.0%
6 81
 
0.8%
23 69
 
0.7%
5 68
 
0.7%
9 67
 
0.7%
4 67
 
0.7%
13 64
 
0.6%
Other values (828) 8663
86.6%
ValueCountFrequency (%)
0 555
5.5%
1 115
 
1.1%
2 96
 
1.0%
3 155
 
1.6%
4 67
 
0.7%
5 68
 
0.7%
6 81
 
0.8%
7 52
 
0.5%
8 46
 
0.5%
9 67
 
0.7%
ValueCountFrequency (%)
3820 2
 
< 0.1%
3682 4
< 0.1%
3645 3
 
< 0.1%
3448 4
< 0.1%
3334 5
0.1%
3307 4
< 0.1%
3067 5
0.1%
3008 8
0.1%
2635 5
0.1%
2629 5
0.1%

아파트_가격_1_억_세대_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1537
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean943.0923
Minimum0
Maximum9733
Zeros159
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:56.390115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile24
Q1247
median615
Q31261
95-th percentile2860
Maximum9733
Range9733
Interquartile range (IQR)1014

Descriptive statistics

Standard deviation1051.2662
Coefficient of variation (CV)1.1147013
Kurtosis12.492728
Mean943.0923
Median Absolute Deviation (MAD)437
Skewness2.7503885
Sum9430923
Variance1105160.6
MonotonicityNot monotonic
2024-05-04T06:36:56.961920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 159
 
1.6%
1 48
 
0.5%
122 36
 
0.4%
389 34
 
0.3%
14 34
 
0.3%
63 31
 
0.3%
27 28
 
0.3%
151 28
 
0.3%
219 27
 
0.3%
101 27
 
0.3%
Other values (1527) 9548
95.5%
ValueCountFrequency (%)
0 159
1.6%
1 48
 
0.5%
2 4
 
< 0.1%
3 10
 
0.1%
4 16
 
0.2%
5 12
 
0.1%
6 6
 
0.1%
7 17
 
0.2%
8 9
 
0.1%
9 11
 
0.1%
ValueCountFrequency (%)
9733 4
< 0.1%
9292 5
0.1%
9034 2
 
< 0.1%
9030 3
< 0.1%
8471 2
 
< 0.1%
8463 3
< 0.1%
8366 3
< 0.1%
8217 7
0.1%
7918 1
 
< 0.1%
7908 2
 
< 0.1%

아파트_가격_2_억_세대_수
Real number (ℝ)

HIGH CORRELATION 

Distinct1049
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.6888
Minimum0
Maximum4450
Zeros94
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:57.536125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q1125
median283
Q3600
95-th percentile1391.1
Maximum4450
Range4450
Interquartile range (IQR)475

Descriptive statistics

Standard deviation477.13909
Coefficient of variation (CV)1.0753913
Kurtosis8.7403612
Mean443.6888
Median Absolute Deviation (MAD)195
Skewness2.3852756
Sum4436888
Variance227661.71
MonotonicityNot monotonic
2024-05-04T06:36:57.962352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94
 
0.9%
110 51
 
0.5%
121 46
 
0.5%
77 46
 
0.5%
13 44
 
0.4%
41 43
 
0.4%
137 40
 
0.4%
2 39
 
0.4%
251 39
 
0.4%
149 38
 
0.4%
Other values (1039) 9520
95.2%
ValueCountFrequency (%)
0 94
0.9%
1 22
 
0.2%
2 39
0.4%
3 18
 
0.2%
4 22
 
0.2%
5 24
 
0.2%
6 18
 
0.2%
7 5
 
0.1%
8 5
 
0.1%
9 22
 
0.2%
ValueCountFrequency (%)
4450 5
0.1%
3864 5
0.1%
3733 4
< 0.1%
3278 4
< 0.1%
3176 3
< 0.1%
3164 5
0.1%
3134 3
< 0.1%
3001 2
 
< 0.1%
2934 4
< 0.1%
2639 3
< 0.1%

아파트_가격_3_억_세대_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct697
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.7594
Minimum0
Maximum4190
Zeros263
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:58.539508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q137
median111
Q3308
95-th percentile916.1
Maximum4190
Range4190
Interquartile range (IQR)271

Descriptive statistics

Standard deviation335.98156
Coefficient of variation (CV)1.4131158
Kurtosis19.311423
Mean237.7594
Median Absolute Deviation (MAD)95
Skewness3.361518
Sum2377594
Variance112883.61
MonotonicityNot monotonic
2024-05-04T06:36:59.064479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 263
 
2.6%
1 142
 
1.4%
4 117
 
1.2%
14 95
 
0.9%
12 90
 
0.9%
2 84
 
0.8%
15 81
 
0.8%
6 78
 
0.8%
13 75
 
0.8%
24 67
 
0.7%
Other values (687) 8908
89.1%
ValueCountFrequency (%)
0 263
2.6%
1 142
1.4%
2 84
 
0.8%
3 62
 
0.6%
4 117
1.2%
5 53
 
0.5%
6 78
 
0.8%
7 64
 
0.6%
8 58
 
0.6%
9 60
 
0.6%
ValueCountFrequency (%)
4190 5
0.1%
3263 4
< 0.1%
2710 5
0.1%
2392 5
0.1%
2367 4
< 0.1%
2346 4
< 0.1%
2187 4
< 0.1%
2080 3
< 0.1%
2033 4
< 0.1%
2004 2
 
< 0.1%

아파트_가격_4_억_세대_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct546
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.339
Minimum0
Maximum1888
Zeros1200
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:36:59.579703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113
median57
Q3161
95-th percentile692
Maximum1888
Range1888
Interquartile range (IQR)148

Descriptive statistics

Standard deviation254.85399
Coefficient of variation (CV)1.6951955
Kurtosis11.239442
Mean150.339
Median Absolute Deviation (MAD)53
Skewness3.1288674
Sum1503390
Variance64950.558
MonotonicityNot monotonic
2024-05-04T06:37:00.061321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1200
 
12.0%
1 238
 
2.4%
2 150
 
1.5%
3 127
 
1.3%
26 121
 
1.2%
24 118
 
1.2%
12 113
 
1.1%
4 101
 
1.0%
10 100
 
1.0%
9 92
 
0.9%
Other values (536) 7640
76.4%
ValueCountFrequency (%)
0 1200
12.0%
1 238
 
2.4%
2 150
 
1.5%
3 127
 
1.3%
4 101
 
1.0%
5 80
 
0.8%
6 76
 
0.8%
7 58
 
0.6%
8 86
 
0.9%
9 92
 
0.9%
ValueCountFrequency (%)
1888 5
0.1%
1746 2
 
< 0.1%
1660 5
0.1%
1640 5
0.1%
1632 5
0.1%
1606 3
 
< 0.1%
1601 3
 
< 0.1%
1529 8
0.1%
1523 5
0.1%
1494 4
< 0.1%

아파트_가격_5_억_세대_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct424
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.3128
Minimum0
Maximum2162
Zeros2701
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:37:00.507834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28
Q393
95-th percentile469
Maximum2162
Range2162
Interquartile range (IQR)93

Descriptive statistics

Standard deviation199.83441
Coefficient of variation (CV)2.0326388
Kurtosis21.018841
Mean98.3128
Median Absolute Deviation (MAD)28
Skewness3.9875305
Sum983128
Variance39933.791
MonotonicityNot monotonic
2024-05-04T06:37:01.054835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2701
27.0%
1 252
 
2.5%
3 132
 
1.3%
8 126
 
1.3%
12 114
 
1.1%
19 114
 
1.1%
36 112
 
1.1%
2 104
 
1.0%
18 99
 
1.0%
28 95
 
0.9%
Other values (414) 6151
61.5%
ValueCountFrequency (%)
0 2701
27.0%
1 252
 
2.5%
2 104
 
1.0%
3 132
 
1.3%
4 75
 
0.8%
5 83
 
0.8%
6 87
 
0.9%
7 88
 
0.9%
8 126
 
1.3%
9 42
 
0.4%
ValueCountFrequency (%)
2162 7
0.1%
1646 7
0.1%
1625 5
0.1%
1455 2
 
< 0.1%
1310 3
< 0.1%
1295 7
0.1%
1269 5
0.1%
1254 5
0.1%
1221 6
0.1%
1172 4
< 0.1%

아파트_가격_6_억_이상_세대_수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct603
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.6898
Minimum0
Maximum5240
Zeros3934
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:37:01.588825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q3190
95-th percentile927
Maximum5240
Range5240
Interquartile range (IQR)190

Descriptive statistics

Standard deviation386.79889
Coefficient of variation (CV)2.1057178
Kurtosis21.354663
Mean183.6898
Median Absolute Deviation (MAD)23
Skewness3.9838829
Sum1836898
Variance149613.38
MonotonicityNot monotonic
2024-05-04T06:37:02.163534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3934
39.3%
2 158
 
1.6%
1 88
 
0.9%
4 84
 
0.8%
10 67
 
0.7%
12 65
 
0.7%
24 61
 
0.6%
20 59
 
0.6%
9 53
 
0.5%
22 53
 
0.5%
Other values (593) 5378
53.8%
ValueCountFrequency (%)
0 3934
39.3%
1 88
 
0.9%
2 158
 
1.6%
3 42
 
0.4%
4 84
 
0.8%
5 21
 
0.2%
6 48
 
0.5%
7 45
 
0.4%
8 52
 
0.5%
9 53
 
0.5%
ValueCountFrequency (%)
5240 1
 
< 0.1%
3937 2
 
< 0.1%
3457 4
< 0.1%
3154 4
< 0.1%
3052 5
0.1%
2840 4
< 0.1%
2786 5
0.1%
2769 5
0.1%
2697 5
0.1%
2671 7
0.1%

아파트_평균_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.2993
Minimum20
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:37:02.604327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile47
Q151
median55
Q362
95-th percentile88
Maximum219
Range199
Interquartile range (IQR)11

Descriptive statistics

Standard deviation15.809362
Coefficient of variation (CV)0.26660284
Kurtosis17.225518
Mean59.2993
Median Absolute Deviation (MAD)5
Skewness3.3967954
Sum592993
Variance249.93591
MonotonicityNot monotonic
2024-05-04T06:37:03.020697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 712
 
7.1%
51 689
 
6.9%
53 614
 
6.1%
55 572
 
5.7%
54 549
 
5.5%
50 539
 
5.4%
49 466
 
4.7%
56 441
 
4.4%
58 376
 
3.8%
57 374
 
3.7%
Other values (105) 4668
46.7%
ValueCountFrequency (%)
20 5
 
0.1%
25 4
 
< 0.1%
26 5
 
0.1%
28 6
 
0.1%
30 4
 
< 0.1%
31 3
 
< 0.1%
34 6
 
0.1%
35 12
 
0.1%
36 18
0.2%
37 39
0.4%
ValueCountFrequency (%)
219 5
0.1%
185 1
 
< 0.1%
169 3
 
< 0.1%
168 3
 
< 0.1%
165 6
0.1%
163 5
0.1%
156 4
< 0.1%
155 5
0.1%
153 9
0.1%
152 5
0.1%

아파트_평균_시가
Real number (ℝ)

HIGH CORRELATION 

Distinct2766
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7534089 × 108
Minimum76766708
Maximum2.618619 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-04T06:37:03.425416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76766708
5-th percentile1.2610714 × 108
Q11.6080223 × 108
median2.0658759 × 108
Q32.9993808 × 108
95-th percentile6.574629 × 108
Maximum2.618619 × 109
Range2.5418523 × 109
Interquartile range (IQR)1.3913586 × 108

Descriptive statistics

Standard deviation2.1346745 × 108
Coefficient of variation (CV)0.77528425
Kurtosis23.856176
Mean2.7534089 × 108
Median Absolute Deviation (MAD)57195571
Skewness3.9509162
Sum2.7534089 × 1012
Variance4.5568354 × 1016
MonotonicityNot monotonic
2024-05-04T06:37:03.857798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151595914 9
 
0.1%
230408182 9
 
0.1%
435671435 9
 
0.1%
433678390 8
 
0.1%
224894874 8
 
0.1%
220878104 8
 
0.1%
384026596 8
 
0.1%
442346587 8
 
0.1%
205611239 8
 
0.1%
171038391 8
 
0.1%
Other values (2756) 9917
99.2%
ValueCountFrequency (%)
76766708 5
0.1%
85499572 2
 
< 0.1%
93379134 4
< 0.1%
93539720 4
< 0.1%
98026788 5
0.1%
98309564 3
< 0.1%
99797020 4
< 0.1%
100019001 4
< 0.1%
101227697 5
0.1%
101944491 5
0.1%
ValueCountFrequency (%)
2618619048 5
0.1%
2269741359 3
< 0.1%
2266772526 3
< 0.1%
2088870899 5
0.1%
1836415564 3
< 0.1%
1767888063 6
0.1%
1751403074 5
0.1%
1742137234 2
 
< 0.1%
1661362051 5
0.1%
1659977816 2
 
< 0.1%

Interactions

2024-05-04T06:36:34.253361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:04.520808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:09.326227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:14.595692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:20.473781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:26.013501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:31.977040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:38.649097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:44.691820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:51.655894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:58.390376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:03.432124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:07.979707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:11.917754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:16.442197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:21.641655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:27.971349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:34.580436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:04.794023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:09.614123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:14.914049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:20.832838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:26.338501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:32.364591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:39.040241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:45.117651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:52.147780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:58.704373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:03.752374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:08.271459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:12.127293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:16.783542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:22.000339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:28.371043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:34.922652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:04.988630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:09.882810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:15.193700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:21.125439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:26.701048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:32.737963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:39.384218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:45.676194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:52.625653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:59.034589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:04.068112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:08.541517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:12.327645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:17.062215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:22.331628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:28.714838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:35.695971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:05.218028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:10.165542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:15.483717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:21.442603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:27.037449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:33.110594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:39.711290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:46.003703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:53.043577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:59.319347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:04.344656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:08.820177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:12.522768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:17.337671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:22.721837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:29.016796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:36.106847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:05.507814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:10.524109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:15.803401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:21.738293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:27.448677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:33.467659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:40.028405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:46.577431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:53.363175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:59.696876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:04.622874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:09.071717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:12.729013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:17.613245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:23.010087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:29.377815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:36.445215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:05.805312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:10.814749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:16.197496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:22.002352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:27.734035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:33.861058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:40.327384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:46.910483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:53.753547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:59.968083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:04.885594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:09.334467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:12.957064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:17.884006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:23.296252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:29.722991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:36.892060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:06.097512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:11.117237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:16.500861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:22.360017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:28.116330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:34.302665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:40.634012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:47.213049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:54.064871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:00.271919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:05.172437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:09.615479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:13.178064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:18.144772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:23.676596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:30.143015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:37.378896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:06.396193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:11.440777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:17.058910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:22.657830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:28.482950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:34.705029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:40.962906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:47.627688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:54.435061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:00.563765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:05.368896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:09.964403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:13.432766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:18.344214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:24.168248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:30.546298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:37.804966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:06.686618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:11.793851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:17.382409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:22.939827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:28.862445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:35.014686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:41.340795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:48.053045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:54.859910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:00.847790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:05.560774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:10.219489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:13.720335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:18.618402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:24.486650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:30.870188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:38.217461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:07.002286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:12.130271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:17.691923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:23.231579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:29.291712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:35.376023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:41.817643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:48.427716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:55.194270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:01.149393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:05.752949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:10.411151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:14.020901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:18.911214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:24.945898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:31.236078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:38.504226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:07.280247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:12.436206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:18.022197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:23.503697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:29.588996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:35.791363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:42.123814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:48.717459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:55.498291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:01.430941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:06.015750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:10.583849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:14.301413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:19.192071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:25.310632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:31.642673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:38.851436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:07.546708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:12.759138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:18.334690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:23.767827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:29.851231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:36.268847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:42.415840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:49.036055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:55.792065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:01.699728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:06.279933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:10.745566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:14.775654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:19.524338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:25.687593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:31.936987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:39.122711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:07.815267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:13.066957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:18.617354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:24.085104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:30.113042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:36.603224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:42.773853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:49.399239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:56.173147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:01.955364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:06.612907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:10.909537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:15.041713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:19.796529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:26.067934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:32.356616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:39.456020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:08.109029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:13.375056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:19.145883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:24.451804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:30.426214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:36.920007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:43.205024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:49.959884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:56.621311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:02.319782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:06.898520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:11.095291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:15.346694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:20.239585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:26.474566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:32.786196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:39.726297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:08.377706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:13.631575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:19.536848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:24.787277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:30.751947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:37.290501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:43.490249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:50.431269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:57.009399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:02.583393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:07.156950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:11.315651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:15.615093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:20.516348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:26.952172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:33.156067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:40.018817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:08.741921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:13.957972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:19.850323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:25.148592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:31.202136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:37.583553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:43.796656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:50.751001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:57.344176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:02.861626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:07.417924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:11.501585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:15.889379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:20.838773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:27.284600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:33.508446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:40.490723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:09.039270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:14.321624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:20.182823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:25.548510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:31.575043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:38.316448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:44.154360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:51.190100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:35:57.767989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:03.151183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:07.708493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:11.693943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:16.187800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:21.217307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:27.632586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T06:36:33.846918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T06:37:04.238362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드상권배후지_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_코드1.0000.0000.0000.1860.3930.3650.1960.0630.1650.0980.1910.3110.3240.2790.1940.0000.098
상권배후지_코드0.0001.0000.4330.4190.2750.1550.2940.2080.4740.4430.3800.2080.2290.2070.2630.4660.451
아파트_단지_수0.0000.4331.0000.9600.4960.0980.1450.0550.8260.9280.6520.1490.1260.0710.1190.4320.332
아파트_면적_66_제곱미터_미만_세대_수0.1860.4190.9601.0000.5850.1410.1950.0760.8790.9520.6780.2860.2480.0930.1260.4400.407
아파트_면적_66_제곱미터_세대_수0.3930.2750.4960.5851.0000.6180.3600.0000.4070.5270.8200.7890.7070.4880.2700.2190.231
아파트_면적_99_제곱미터_세대_수0.3650.1550.0980.1410.6181.0000.5180.2510.0320.0570.2650.7860.5470.6220.8390.2260.149
아파트_면적_132_제곱미터_세대_수0.1960.2940.1450.1950.3600.5181.0000.5070.0820.1360.2710.2030.4110.3790.6440.4520.705
아파트_면적_165_제곱미터_세대_수0.0630.2080.0550.0760.0000.2510.5071.0000.0380.0620.0190.0000.0650.0940.5380.6720.559
아파트_가격_1_억_미만_세대_수0.1650.4740.8260.8790.4070.0320.0820.0381.0000.8190.3040.1140.1470.0640.1080.3220.302
아파트_가격_1_억_세대_수0.0980.4430.9280.9520.5270.0570.1360.0620.8191.0000.5220.1270.0950.0720.1560.4180.365
아파트_가격_2_억_세대_수0.1910.3800.6520.6780.8200.2650.2710.0190.3040.5221.0000.5700.2820.0590.1200.2810.290
아파트_가격_3_억_세대_수0.3110.2080.1490.2860.7890.7860.2030.0000.1140.1270.5701.0000.5810.3050.0880.1100.108
아파트_가격_4_억_세대_수0.3240.2290.1260.2480.7070.5470.4110.0650.1470.0950.2820.5811.0000.5250.1880.1850.101
아파트_가격_5_억_세대_수0.2790.2070.0710.0930.4880.6220.3790.0940.0640.0720.0590.3050.5251.0000.4620.1670.113
아파트_가격_6_억_이상_세대_수0.1940.2630.1190.1260.2700.8390.6440.5380.1080.1560.1200.0880.1880.4621.0000.3570.437
아파트_평균_면적0.0000.4660.4320.4400.2190.2260.4520.6720.3220.4180.2810.1100.1850.1670.3571.0000.853
아파트_평균_시가0.0980.4510.3320.4070.2310.1490.7050.5590.3020.3650.2900.1080.1010.1130.4370.8531.000
2024-05-04T06:37:04.674175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드상권배후지_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_코드1.000-0.019-0.019-0.245-0.469-0.371-0.155-0.098-0.199-0.148-0.225-0.394-0.206-0.0370.073-0.0190.186
상권배후지_코드-0.0191.0000.1020.1090.1120.0670.0750.048-0.0980.0720.1540.1570.1160.1080.136-0.0080.125
아파트_단지_수-0.0190.1021.0000.8620.5360.1510.1270.0520.6620.8500.6730.2930.059-0.067-0.188-0.409-0.488
아파트_면적_66_제곱미터_미만_세대_수-0.2450.1090.8621.0000.6960.2090.044-0.0880.7670.9110.7470.4410.138-0.106-0.324-0.474-0.592
아파트_면적_66_제곱미터_세대_수-0.4690.1120.5360.6961.0000.5980.2590.0410.4650.5390.6700.6730.4510.2330.0010.026-0.206
아파트_면적_99_제곱미터_세대_수-0.3710.0670.1510.2090.5981.0000.4800.2660.0290.0430.3210.5830.5920.4740.3700.4030.233
아파트_면적_132_제곱미터_세대_수-0.1550.0750.1270.0440.2590.4801.0000.568-0.116-0.0310.1660.2490.2800.3360.4430.4350.317
아파트_면적_165_제곱미터_세대_수-0.0980.0480.052-0.0880.0410.2660.5681.000-0.203-0.1300.0750.1270.1520.2430.4050.3800.319
아파트_가격_1_억_미만_세대_수-0.199-0.0980.6620.7670.4650.029-0.116-0.2031.0000.8200.4340.169-0.087-0.298-0.535-0.541-0.837
아파트_가격_1_억_세대_수-0.1480.0720.8500.9110.5390.043-0.031-0.1300.8201.0000.6660.241-0.061-0.260-0.457-0.535-0.725
아파트_가격_2_억_세대_수-0.2250.1540.6730.7470.6700.3210.1660.0750.4340.6661.0000.5890.202-0.021-0.174-0.248-0.290
아파트_가격_3_억_세대_수-0.3940.1570.2930.4410.6730.5830.2490.1270.1690.2410.5891.0000.6270.2540.0110.0700.007
아파트_가격_4_억_세대_수-0.2060.1160.0590.1380.4510.5920.2800.152-0.087-0.0610.2020.6271.0000.6620.3010.2540.305
아파트_가격_5_억_세대_수-0.0370.108-0.067-0.1060.2330.4740.3360.243-0.298-0.260-0.0210.2540.6621.0000.6650.3610.555
아파트_가격_6_억_이상_세대_수0.0730.136-0.188-0.3240.0010.3700.4430.405-0.535-0.457-0.1740.0110.3010.6651.0000.5000.783
아파트_평균_면적-0.019-0.008-0.409-0.4740.0260.4030.4350.380-0.541-0.535-0.2480.0700.2540.3610.5001.0000.676
아파트_평균_시가0.1860.125-0.488-0.592-0.2060.2330.3170.319-0.837-0.725-0.2900.0070.3050.5550.7830.6761.000

Missing values

2024-05-04T06:36:41.051828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T06:36:41.914226image/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_코드상권_구분_코드상권_구분_코드_명상권배후지_코드상권배후지_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
409420232A골목상권3111016송파역 2번191122240197242450509556213586531756333706477
885820222A골목상권3110343무너미어린이공원39432394869230771275015569120052138335957
586720214A골목상권3110113응봉산7933823613600547515587211731969472473035
805820204A골목상권3110282국민대학교앞6074519342192371576331911059119825422
924620224A골목상권3110391샘말어린이공원304259130376012861551333700050114138331
1744320221A골목상권3110918방배역 3번29216626181633156541166763421713046660417846815
577320223A골목상권3110131성수동카페거리56373197202003012953134774414359437776068
437420211A골목상권3110010평창동서측15141061624014426295350377434201109106123427545716
329620232A골목상권3110622달마을공원4993620767115255366299074462266627853194547094
516220222A골목상권3110061효창동주민센터21711007052283010333726243091495453265391506511
기준_년분기_코드상권_구분_코드상권_구분_코드_명상권배후지_코드상권배후지_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
1518020214A골목상권3111025경찰병원역 3번2751855400762315153947719133944627751266575094
1220920202A골목상권3110695개봉1동주민센터206226712412550033315378146794000054148554757
914120194A골목상권3110367강북구청11095941214313666782469720052122264312
919420224A골목상권3110397도봉산입구31023905465610134711599122815612053131001157
369220233A골목상권3110862난곡초등학교(난곡동벽화마을)188153623829002341049374794621051168040665
972120211A골목상권3110484역촌역 3번576446112071040415343115843248360051135977020
1142020223A골목상권3110443수색역 1번1812130002663161810035187971653
413820233A골목상권3110964싸리고개근린공원652531879356880839560861533891797499635
275620232A골목상권3110615신서초등학교9461920638360105450868015815558310470807
56520231A골목상권3110147능동우편취급국21013873546153186845622593415450187589038