Overview

Dataset statistics

Number of variables20
Number of observations10000
Missing cells46734
Missing cells (%)23.4%
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,상권_코드,상권_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-15566/S/1/datasetView.do

Alerts

상권_구분_코드_명 is highly overall correlated with 상권_코드 and 1 other fieldsHigh correlation
상권_구분_코드 is highly overall correlated with 상권_코드 and 1 other fieldsHigh correlation
상권_코드 is highly overall correlated with 상권_구분_코드 and 1 other fieldsHigh correlation
아파트_단지_수 is highly overall correlated with 아파트_면적_66_제곱미터_미만_세대_수 and 4 other fieldsHigh correlation
아파트_면적_66_제곱미터_미만_세대_수 is highly overall correlated with 아파트_단지_수 and 4 other fieldsHigh correlation
아파트_면적_66_제곱미터_세대_수 is highly overall correlated with 아파트_단지_수 and 4 other fieldsHigh correlation
아파트_면적_99_제곱미터_세대_수 is highly overall correlated with 아파트_면적_132_제곱미터_세대_수 and 2 other fieldsHigh correlation
아파트_면적_132_제곱미터_세대_수 is highly overall correlated with 아파트_면적_99_제곱미터_세대_수 and 4 other fieldsHigh correlation
아파트_면적_165_제곱미터_세대_수 is highly overall correlated with 아파트_면적_132_제곱미터_세대_수 and 3 other fieldsHigh correlation
아파트_가격_1_억_미만_세대_수 is highly overall correlated with 아파트_단지_수 and 3 other fieldsHigh correlation
아파트_가격_1_억_세대_수 is highly overall correlated with 아파트_단지_수 and 4 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_수High correlation
아파트_가격_6_억_이상_세대_수 is highly overall correlated with 아파트_면적_132_제곱미터_세대_수 and 2 other fieldsHigh correlation
아파트_평균_면적 is highly overall correlated with 아파트_면적_132_제곱미터_세대_수 and 2 other fieldsHigh correlation
아파트_평균_시가 is highly overall correlated with 아파트_면적_132_제곱미터_세대_수 and 4 other fieldsHigh correlation
아파트_면적_66_제곱미터_미만_세대_수 has 248 (2.5%) missing valuesMissing
아파트_면적_66_제곱미터_세대_수 has 813 (8.1%) missing valuesMissing
아파트_면적_99_제곱미터_세대_수 has 3628 (36.3%) missing valuesMissing
아파트_면적_132_제곱미터_세대_수 has 6531 (65.3%) missing valuesMissing
아파트_면적_165_제곱미터_세대_수 has 7889 (78.9%) missing valuesMissing
아파트_가격_1_억_미만_세대_수 has 1943 (19.4%) missing valuesMissing
아파트_가격_1_억_세대_수 has 681 (6.8%) missing valuesMissing
아파트_가격_2_억_세대_수 has 1049 (10.5%) missing valuesMissing
아파트_가격_3_억_세대_수 has 3628 (36.3%) missing valuesMissing
아파트_가격_4_억_세대_수 has 5828 (58.3%) missing valuesMissing
아파트_가격_5_억_세대_수 has 7082 (70.8%) missing valuesMissing
아파트_가격_6_억_이상_세대_수 has 7414 (74.1%) missing valuesMissing

Reproduction

Analysis started2024-03-13 08:38:17.474092
Analysis finished2024-03-13 08:38:48.044800
Duration30.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준_년분기_코드
Real number (ℝ)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20214.962
Minimum20194
Maximum20233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:48.088192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20194
5-th percentile20194
Q120203
median20214
Q320223.25
95-th percentile20233
Maximum20233
Range39
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation11.72629
Coefficient of variation (CV)0.00058007975
Kurtosis-1.1329628
Mean20214.962
Median Absolute Deviation (MAD)10
Skewness-0.016033158
Sum2.0214962 × 108
Variance137.50588
MonotonicityNot monotonic
2024-03-13T17:38:48.187083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
20203 669
 
6.7%
20214 660
 
6.6%
20233 660
 
6.6%
20201 641
 
6.4%
20224 632
 
6.3%
20221 630
 
6.3%
20212 621
 
6.2%
20213 620
 
6.2%
20223 619
 
6.2%
20202 619
 
6.2%
Other values (6) 3629
36.3%
ValueCountFrequency (%)
20194 614
6.1%
20201 641
6.4%
20202 619
6.2%
20203 669
6.7%
20204 606
6.1%
20211 605
6.0%
20212 621
6.2%
20213 620
6.2%
20214 660
6.6%
20221 630
6.3%
ValueCountFrequency (%)
20233 660
6.6%
20232 608
6.1%
20231 600
6.0%
20224 632
6.3%
20223 619
6.2%
20222 596
6.0%
20221 630
6.3%
20214 660
6.6%
20213 620
6.2%
20212 621
6.2%

상권_구분_코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
A
7060 
D
1485 
R
1424 
U
 
31

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 7060
70.6%
D 1485
 
14.8%
R 1424
 
14.2%
U 31
 
0.3%

Length

2024-03-13T17:38:48.283651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T17:38:48.364018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 7060
70.6%
d 1485
 
14.8%
r 1424
 
14.2%
u 31
 
0.3%

상권_구분_코드_명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
골목상권
7060 
발달상권
1485 
전통시장
1424 
관광특구
 
31

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
골목상권 7060
70.6%
발달상권 1485
 
14.8%
전통시장 1424
 
14.2%
관광특구 31
 
0.3%

Length

2024-03-13T17:38:48.467631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T17:38:48.556173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
골목상권 7060
70.6%
발달상권 1485
 
14.8%
전통시장 1424
 
14.2%
관광특구 31
 
0.3%

상권_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct1493
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3114431.4
Minimum3001491
Maximum3130326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:48.665042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3001491
5-th percentile3110077
Q13110392
median3110771
Q33120076
95-th percentile3130219
Maximum3130326
Range128835
Interquartile range (IQR)9684

Descriptive statistics

Standard deviation9507.2094
Coefficient of variation (CV)0.0030526308
Kurtosis59.887423
Mean3114431.4
Median Absolute Deviation (MAD)442
Skewness-4.5676915
Sum3.1144314 × 1010
Variance90387031
MonotonicityNot monotonic
2024-03-13T17:38:48.828108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3130273 14
 
0.1%
3110919 14
 
0.1%
3120012 13
 
0.1%
3110659 13
 
0.1%
3110529 13
 
0.1%
3111056 12
 
0.1%
3110455 12
 
0.1%
3110660 12
 
0.1%
3110953 12
 
0.1%
3110696 12
 
0.1%
Other values (1483) 9873
98.7%
ValueCountFrequency (%)
3001491 8
0.1%
3001493 9
0.1%
3001494 7
0.1%
3001495 7
0.1%
3110001 8
0.1%
3110002 5
0.1%
3110003 6
0.1%
3110004 8
0.1%
3110005 8
0.1%
3110006 5
0.1%
ValueCountFrequency (%)
3130326 7
0.1%
3130325 6
0.1%
3130324 9
0.1%
3130323 9
0.1%
3130322 6
0.1%
3130321 6
0.1%
3130320 11
0.1%
3130319 8
0.1%
3130318 3
 
< 0.1%
3130317 8
0.1%
Distinct1493
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-13T17:38:49.082056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length26
Mean length7.4815
Min length2

Characters and Unicode

Total characters74815
Distinct characters431
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row선정릉역 4번
2nd row상봉역 3번
3rd row마천중앙시장
4th row삼선동주민센터
5th row구파발역
ValueCountFrequency (%)
1번 518
 
4.0%
2번 393
 
3.0%
3번 340
 
2.6%
4번 325
 
2.5%
5번 177
 
1.4%
6번 136
 
1.0%
8번 98
 
0.8%
7번 96
 
0.7%
골목형상점가 83
 
0.6%
내방역 50
 
0.4%
Other values (1347) 10840
83.0%
2024-03-13T17:38:49.461265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3496
 
4.7%
3056
 
4.1%
2370
 
3.2%
2165
 
2.9%
2006
 
2.7%
1917
 
2.6%
1841
 
2.5%
1684
 
2.3%
) 1344
 
1.8%
( 1344
 
1.8%
Other values (421) 53592
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 65264
87.2%
Decimal Number 3341
 
4.5%
Space Separator 3056
 
4.1%
Close Punctuation 1344
 
1.8%
Open Punctuation 1344
 
1.8%
Uppercase Letter 285
 
0.4%
Other Punctuation 164
 
0.2%
Lowercase Letter 12
 
< 0.1%
Connector Punctuation 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3496
 
5.4%
2370
 
3.6%
2165
 
3.3%
2006
 
3.1%
1917
 
2.9%
1841
 
2.8%
1684
 
2.6%
1322
 
2.0%
1240
 
1.9%
1141
 
1.7%
Other values (386) 46082
70.6%
Uppercase Letter
ValueCountFrequency (%)
K 65
22.8%
T 34
11.9%
B 32
11.2%
G 31
10.9%
C 25
 
8.8%
I 24
 
8.4%
S 23
 
8.1%
A 11
 
3.9%
D 9
 
3.2%
H 8
 
2.8%
Other values (4) 23
 
8.1%
Decimal Number
ValueCountFrequency (%)
1 961
28.8%
2 636
19.0%
3 514
15.4%
4 469
14.0%
5 260
 
7.8%
6 166
 
5.0%
8 125
 
3.7%
7 117
 
3.5%
9 67
 
2.0%
0 26
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 117
71.3%
. 16
 
9.8%
& 14
 
8.5%
! 10
 
6.1%
? 7
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
h 7
58.3%
a 5
41.7%
Space Separator
ValueCountFrequency (%)
3056
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1344
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1344
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 65264
87.2%
Common 9254
 
12.4%
Latin 297
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3496
 
5.4%
2370
 
3.6%
2165
 
3.3%
2006
 
3.1%
1917
 
2.9%
1841
 
2.8%
1684
 
2.6%
1322
 
2.0%
1240
 
1.9%
1141
 
1.7%
Other values (386) 46082
70.6%
Common
ValueCountFrequency (%)
3056
33.0%
) 1344
14.5%
( 1344
14.5%
1 961
 
10.4%
2 636
 
6.9%
3 514
 
5.6%
4 469
 
5.1%
5 260
 
2.8%
6 166
 
1.8%
8 125
 
1.4%
Other values (9) 379
 
4.1%
Latin
ValueCountFrequency (%)
K 65
21.9%
T 34
11.4%
B 32
10.8%
G 31
10.4%
C 25
 
8.4%
I 24
 
8.1%
S 23
 
7.7%
A 11
 
3.7%
D 9
 
3.0%
H 8
 
2.7%
Other values (6) 35
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 65264
87.2%
ASCII 9551
 
12.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3496
 
5.4%
2370
 
3.6%
2165
 
3.3%
2006
 
3.1%
1917
 
2.9%
1841
 
2.8%
1684
 
2.6%
1322
 
2.0%
1240
 
1.9%
1141
 
1.7%
Other values (386) 46082
70.6%
ASCII
ValueCountFrequency (%)
3056
32.0%
) 1344
14.1%
( 1344
14.1%
1 961
 
10.1%
2 636
 
6.7%
3 514
 
5.4%
4 469
 
4.9%
5 260
 
2.7%
6 166
 
1.7%
8 125
 
1.3%
Other values (25) 676
 
7.1%

아파트_단지_수
Real number (ℝ)

HIGH CORRELATION 

Distinct260
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.3383
Minimum1
Maximum725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:49.580565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median27
Q362
95-th percentile154
Maximum725
Range724
Interquartile range (IQR)54

Descriptive statistics

Standard deviation60.887517
Coefficient of variation (CV)1.3139782
Kurtosis25.10869
Mean46.3383
Median Absolute Deviation (MAD)22
Skewness3.7199955
Sum463383
Variance3707.2898
MonotonicityNot monotonic
2024-03-13T17:38:49.706981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 615
 
6.2%
2 395
 
4.0%
3 363
 
3.6%
5 276
 
2.8%
4 258
 
2.6%
7 255
 
2.5%
6 234
 
2.3%
8 205
 
2.1%
9 194
 
1.9%
16 180
 
1.8%
Other values (250) 7025
70.2%
ValueCountFrequency (%)
1 615
6.2%
2 395
4.0%
3 363
3.6%
4 258
2.6%
5 276
2.8%
6 234
 
2.3%
7 255
2.5%
8 205
 
2.1%
9 194
 
1.9%
10 145
 
1.5%
ValueCountFrequency (%)
725 3
< 0.1%
724 4
< 0.1%
717 6
0.1%
427 2
 
< 0.1%
426 2
 
< 0.1%
425 2
 
< 0.1%
420 6
0.1%
397 1
 
< 0.1%
396 2
 
< 0.1%
389 4
< 0.1%

아파트_면적_66_제곱미터_미만_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1003
Distinct (%)10.3%
Missing248
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean362.72149
Minimum1
Maximum5784
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:49.829174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q168
median208
Q3488
95-th percentile1223
Maximum5784
Range5783
Interquartile range (IQR)420

Descriptive statistics

Standard deviation467.94591
Coefficient of variation (CV)1.290097
Kurtosis27.276238
Mean362.72149
Median Absolute Deviation (MAD)168
Skewness3.7758069
Sum3537260
Variance218973.37
MonotonicityNot monotonic
2024-03-13T17:38:49.951985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 86
 
0.9%
20 83
 
0.8%
24 76
 
0.8%
30 75
 
0.8%
12 62
 
0.6%
37 62
 
0.6%
11 62
 
0.6%
10 60
 
0.6%
25 55
 
0.5%
2 54
 
0.5%
Other values (993) 9077
90.8%
(Missing) 248
 
2.5%
ValueCountFrequency (%)
1 41
0.4%
2 54
0.5%
3 16
 
0.2%
4 51
0.5%
5 19
 
0.2%
6 40
0.4%
7 44
0.4%
8 86
0.9%
9 25
 
0.2%
10 60
0.6%
ValueCountFrequency (%)
5784 6
0.1%
5660 3
< 0.1%
5658 4
< 0.1%
3555 2
 
< 0.1%
3494 1
 
< 0.1%
3406 2
 
< 0.1%
3392 2
 
< 0.1%
3047 1
 
< 0.1%
2982 4
< 0.1%
2881 2
 
< 0.1%

아파트_면적_66_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct437
Distinct (%)4.8%
Missing813
Missing (%)8.1%
Infinite0
Infinite (%)0.0%
Mean100.26189
Minimum1
Maximum1242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:50.064671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q119
median58
Q3128
95-th percentile364
Maximum1242
Range1241
Interquartile range (IQR)109

Descriptive statistics

Standard deviation129.7886
Coefficient of variation (CV)1.2944958
Kurtosis12.476055
Mean100.26189
Median Absolute Deviation (MAD)46
Skewness2.9380573
Sum921106
Variance16845.081
MonotonicityNot monotonic
2024-03-13T17:38:50.180641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 230
 
2.3%
2 206
 
2.1%
12 187
 
1.9%
11 170
 
1.7%
3 146
 
1.5%
5 137
 
1.4%
6 136
 
1.4%
4 123
 
1.2%
13 122
 
1.2%
8 121
 
1.2%
Other values (427) 7609
76.1%
(Missing) 813
 
8.1%
ValueCountFrequency (%)
1 230
2.3%
2 206
2.1%
3 146
1.5%
4 123
1.2%
5 137
1.4%
6 136
1.4%
7 100
1.0%
8 121
1.2%
9 93
0.9%
10 92
 
0.9%
ValueCountFrequency (%)
1242 4
< 0.1%
1203 3
< 0.1%
1091 4
< 0.1%
1052 3
< 0.1%
906 1
 
< 0.1%
886 2
 
< 0.1%
870 5
0.1%
845 5
0.1%
827 2
 
< 0.1%
787 4
< 0.1%

아파트_면적_99_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct132
Distinct (%)2.1%
Missing3628
Missing (%)36.3%
Infinite0
Infinite (%)0.0%
Mean19.370684
Minimum1
Maximum518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:50.325215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q321
95-th percentile77
Maximum518
Range517
Interquartile range (IQR)19

Descriptive statistics

Standard deviation35.998907
Coefficient of variation (CV)1.858422
Kurtosis43.941842
Mean19.370684
Median Absolute Deviation (MAD)6
Skewness5.3278141
Sum123430
Variance1295.9213
MonotonicityNot monotonic
2024-03-13T17:38:50.461537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1171
 
11.7%
2 733
 
7.3%
3 482
 
4.8%
4 285
 
2.9%
5 227
 
2.3%
8 179
 
1.8%
12 170
 
1.7%
6 165
 
1.7%
7 162
 
1.6%
9 153
 
1.5%
Other values (122) 2645
26.5%
(Missing) 3628
36.3%
ValueCountFrequency (%)
1 1171
11.7%
2 733
7.3%
3 482
4.8%
4 285
 
2.9%
5 227
 
2.3%
6 165
 
1.7%
7 162
 
1.6%
8 179
 
1.8%
9 153
 
1.5%
10 132
 
1.3%
ValueCountFrequency (%)
518 4
< 0.1%
348 3
< 0.1%
342 4
< 0.1%
315 4
< 0.1%
301 6
0.1%
277 2
 
< 0.1%
273 6
0.1%
256 3
< 0.1%
239 3
< 0.1%
238 3
< 0.1%

아파트_면적_132_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct108
Distinct (%)3.1%
Missing6531
Missing (%)65.3%
Infinite0
Infinite (%)0.0%
Mean19.966561
Minimum1
Maximum837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:50.583926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q318
95-th percentile76
Maximum837
Range836
Interquartile range (IQR)17

Descriptive statistics

Standard deviation55.876342
Coefficient of variation (CV)2.798496
Kurtosis112.77341
Mean19.966561
Median Absolute Deviation (MAD)3
Skewness9.1847195
Sum69264
Variance3122.1655
MonotonicityNot monotonic
2024-03-13T17:38:50.716490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1048
 
10.5%
2 348
 
3.5%
3 237
 
2.4%
4 137
 
1.4%
5 99
 
1.0%
8 97
 
1.0%
6 96
 
1.0%
7 92
 
0.9%
10 85
 
0.9%
12 79
 
0.8%
Other values (98) 1151
 
11.5%
(Missing) 6531
65.3%
ValueCountFrequency (%)
1 1048
10.5%
2 348
 
3.5%
3 237
 
2.4%
4 137
 
1.4%
5 99
 
1.0%
6 96
 
1.0%
7 92
 
0.9%
8 97
 
1.0%
9 33
 
0.3%
10 85
 
0.9%
ValueCountFrequency (%)
837 7
0.1%
644 3
< 0.1%
497 4
< 0.1%
334 4
< 0.1%
277 3
< 0.1%
270 6
0.1%
259 3
< 0.1%
225 4
< 0.1%
205 5
0.1%
202 5
0.1%

아파트_면적_165_제곱미터_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct105
Distinct (%)5.0%
Missing7889
Missing (%)78.9%
Infinite0
Infinite (%)0.0%
Mean25.635244
Minimum1
Maximum720
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:50.846717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median7
Q326
95-th percentile107
Maximum720
Range719
Interquartile range (IQR)25

Descriptive statistics

Standard deviation53.618112
Coefficient of variation (CV)2.091578
Kurtosis49.619033
Mean25.635244
Median Absolute Deviation (MAD)6
Skewness5.6292919
Sum54116
Variance2874.902
MonotonicityNot monotonic
2024-03-13T17:38:51.323120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 533
 
5.3%
2 182
 
1.8%
3 152
 
1.5%
4 91
 
0.9%
12 83
 
0.8%
8 65
 
0.7%
7 63
 
0.6%
9 43
 
0.4%
10 42
 
0.4%
6 41
 
0.4%
Other values (95) 816
 
8.2%
(Missing) 7889
78.9%
ValueCountFrequency (%)
1 533
5.3%
2 182
 
1.8%
3 152
 
1.5%
4 91
 
0.9%
5 33
 
0.3%
6 41
 
0.4%
7 63
 
0.6%
8 65
 
0.7%
9 43
 
0.4%
10 42
 
0.4%
ValueCountFrequency (%)
720 3
< 0.1%
349 3
< 0.1%
333 7
0.1%
323 5
0.1%
319 5
0.1%
287 3
< 0.1%
242 2
 
< 0.1%
223 5
0.1%
201 5
0.1%
192 3
< 0.1%

아파트_가격_1_억_미만_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct401
Distinct (%)5.0%
Missing1943
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean90.932357
Minimum1
Maximum1766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:51.437780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median39
Q3107
95-th percentile344
Maximum1766
Range1765
Interquartile range (IQR)95

Descriptive statistics

Standard deviation157.0728
Coefficient of variation (CV)1.7273588
Kurtosis39.2841
Mean90.932357
Median Absolute Deviation (MAD)33
Skewness5.1620746
Sum732642
Variance24671.865
MonotonicityNot monotonic
2024-03-13T17:38:51.550385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 338
 
3.4%
2 316
 
3.2%
4 237
 
2.4%
3 182
 
1.8%
5 145
 
1.5%
12 138
 
1.4%
7 133
 
1.3%
18 133
 
1.3%
9 129
 
1.3%
6 127
 
1.3%
Other values (391) 6179
61.8%
(Missing) 1943
 
19.4%
ValueCountFrequency (%)
1 338
3.4%
2 316
3.2%
3 182
1.8%
4 237
2.4%
5 145
1.5%
6 127
 
1.3%
7 133
 
1.3%
8 108
 
1.1%
9 129
 
1.3%
10 114
 
1.1%
ValueCountFrequency (%)
1766 6
0.1%
1732 3
< 0.1%
1682 6
0.1%
1567 7
0.1%
1500 2
 
< 0.1%
1373 4
< 0.1%
1350 4
< 0.1%
1316 3
< 0.1%
1228 1
 
< 0.1%
1218 2
 
< 0.1%

아파트_가격_1_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct789
Distinct (%)8.5%
Missing681
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean247.56594
Minimum1
Maximum4572
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:51.680811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q141
median129
Q3326
95-th percentile859
Maximum4572
Range4571
Interquartile range (IQR)285

Descriptive statistics

Standard deviation351.59715
Coefficient of variation (CV)1.4202161
Kurtosis37.869902
Mean247.56594
Median Absolute Deviation (MAD)104
Skewness4.4906667
Sum2307067
Variance123620.55
MonotonicityNot monotonic
2024-03-13T17:38:51.800661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 125
 
1.2%
12 109
 
1.1%
8 102
 
1.0%
17 89
 
0.9%
2 87
 
0.9%
1 81
 
0.8%
28 75
 
0.8%
10 75
 
0.8%
22 74
 
0.7%
6 74
 
0.7%
Other values (779) 8428
84.3%
(Missing) 681
 
6.8%
ValueCountFrequency (%)
1 81
0.8%
2 87
0.9%
3 58
0.6%
4 59
0.6%
5 71
0.7%
6 74
0.7%
7 54
0.5%
8 102
1.0%
9 45
0.4%
10 75
0.8%
ValueCountFrequency (%)
4572 3
< 0.1%
4568 4
< 0.1%
4566 6
0.1%
3232 2
 
< 0.1%
3153 2
 
< 0.1%
3150 2
 
< 0.1%
3092 1
 
< 0.1%
2875 1
 
< 0.1%
2179 3
< 0.1%
2073 2
 
< 0.1%

아파트_가격_2_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct470
Distinct (%)5.3%
Missing1049
Missing (%)10.5%
Infinite0
Infinite (%)0.0%
Mean106.62049
Minimum1
Maximum1227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:51.911214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q116
median52
Q3133
95-th percentile408
Maximum1227
Range1226
Interquartile range (IQR)117

Descriptive statistics

Standard deviation150.90915
Coefficient of variation (CV)1.415386
Kurtosis11.711137
Mean106.62049
Median Absolute Deviation (MAD)43
Skewness3.0005677
Sum954360
Variance22773.572
MonotonicityNot monotonic
2024-03-13T17:38:52.037511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 341
 
3.4%
2 185
 
1.8%
6 178
 
1.8%
11 175
 
1.8%
10 171
 
1.7%
3 168
 
1.7%
4 150
 
1.5%
5 141
 
1.4%
7 133
 
1.3%
12 126
 
1.3%
Other values (460) 7183
71.8%
(Missing) 1049
 
10.5%
ValueCountFrequency (%)
1 341
3.4%
2 185
1.8%
3 168
1.7%
4 150
1.5%
5 141
1.4%
6 178
1.8%
7 133
 
1.3%
8 96
 
1.0%
9 90
 
0.9%
10 171
1.7%
ValueCountFrequency (%)
1227 1
 
< 0.1%
1223 4
< 0.1%
1173 4
< 0.1%
1116 2
 
< 0.1%
1114 4
< 0.1%
1060 2
 
< 0.1%
1028 6
0.1%
1011 1
 
< 0.1%
1009 1
 
< 0.1%
1006 1
 
< 0.1%

아파트_가격_3_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct266
Distinct (%)4.2%
Missing3628
Missing (%)36.3%
Infinite0
Infinite (%)0.0%
Mean49.875078
Minimum1
Maximum824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:52.160130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median19
Q361
95-th percentile213
Maximum824
Range823
Interquartile range (IQR)56

Descriptive statistics

Standard deviation77.788789
Coefficient of variation (CV)1.5596725
Kurtosis12.614876
Mean49.875078
Median Absolute Deviation (MAD)17
Skewness3.1012107
Sum317804
Variance6051.0957
MonotonicityNot monotonic
2024-03-13T17:38:52.272348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 722
 
7.2%
2 354
 
3.5%
3 249
 
2.5%
4 193
 
1.9%
5 192
 
1.9%
8 171
 
1.7%
12 165
 
1.7%
6 163
 
1.6%
11 127
 
1.3%
13 117
 
1.2%
Other values (256) 3919
39.2%
(Missing) 3628
36.3%
ValueCountFrequency (%)
1 722
7.2%
2 354
3.5%
3 249
 
2.5%
4 193
 
1.9%
5 192
 
1.9%
6 163
 
1.6%
7 75
 
0.8%
8 171
 
1.7%
9 99
 
1.0%
10 111
 
1.1%
ValueCountFrequency (%)
824 1
 
< 0.1%
582 2
 
< 0.1%
563 5
0.1%
560 5
0.1%
533 1
 
< 0.1%
528 3
< 0.1%
527 1
 
< 0.1%
519 4
< 0.1%
502 3
< 0.1%
500 3
< 0.1%

아파트_가격_4_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct162
Distinct (%)3.9%
Missing5828
Missing (%)58.3%
Infinite0
Infinite (%)0.0%
Mean33.112895
Minimum1
Maximum634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:52.388191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median14
Q343
95-th percentile127
Maximum634
Range633
Interquartile range (IQR)39

Descriptive statistics

Standard deviation50.883562
Coefficient of variation (CV)1.5366691
Kurtosis23.442194
Mean33.112895
Median Absolute Deviation (MAD)12
Skewness3.861601
Sum138147
Variance2589.1369
MonotonicityNot monotonic
2024-03-13T17:38:52.505035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 516
 
5.2%
2 246
 
2.5%
3 190
 
1.9%
8 164
 
1.6%
4 142
 
1.4%
5 121
 
1.2%
12 121
 
1.2%
9 116
 
1.2%
11 115
 
1.1%
7 110
 
1.1%
Other values (152) 2331
 
23.3%
(Missing) 5828
58.3%
ValueCountFrequency (%)
1 516
5.2%
2 246
2.5%
3 190
 
1.9%
4 142
 
1.4%
5 121
 
1.2%
6 102
 
1.0%
7 110
 
1.1%
8 164
 
1.6%
9 116
 
1.2%
10 73
 
0.7%
ValueCountFrequency (%)
634 1
 
< 0.1%
485 5
0.1%
484 4
< 0.1%
353 5
0.1%
319 5
0.1%
302 2
 
< 0.1%
286 7
0.1%
274 6
0.1%
241 4
< 0.1%
229 1
 
< 0.1%

아파트_가격_5_억_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct111
Distinct (%)3.8%
Missing7082
Missing (%)70.8%
Infinite0
Infinite (%)0.0%
Mean23.700822
Minimum1
Maximum408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:52.620237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median11.5
Q328
95-th percentile91
Maximum408
Range407
Interquartile range (IQR)25

Descriptive statistics

Standard deviation36.965892
Coefficient of variation (CV)1.5596881
Kurtosis28.909362
Mean23.700822
Median Absolute Deviation (MAD)9.5
Skewness4.3125918
Sum69159
Variance1366.4771
MonotonicityNot monotonic
2024-03-13T17:38:52.757419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 401
 
4.0%
2 196
 
2.0%
3 161
 
1.6%
5 136
 
1.4%
12 111
 
1.1%
4 107
 
1.1%
6 97
 
1.0%
7 90
 
0.9%
8 78
 
0.8%
11 76
 
0.8%
Other values (101) 1465
 
14.6%
(Missing) 7082
70.8%
ValueCountFrequency (%)
1 401
4.0%
2 196
2.0%
3 161
1.6%
4 107
 
1.1%
5 136
 
1.4%
6 97
 
1.0%
7 90
 
0.9%
8 78
 
0.8%
9 66
 
0.7%
10 51
 
0.5%
ValueCountFrequency (%)
408 4
< 0.1%
364 2
 
< 0.1%
285 2
 
< 0.1%
264 5
0.1%
246 4
< 0.1%
232 3
 
< 0.1%
210 1
 
< 0.1%
183 2
 
< 0.1%
177 3
 
< 0.1%
148 9
0.1%

아파트_가격_6_억_이상_세대_수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct206
Distinct (%)8.0%
Missing7414
Missing (%)74.1%
Infinite0
Infinite (%)0.0%
Mean71.924594
Minimum1
Maximum1234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:52.880679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median24
Q376
95-th percentile298.75
Maximum1234
Range1233
Interquartile range (IQR)70

Descriptive statistics

Standard deviation136.60833
Coefficient of variation (CV)1.8993271
Kurtosis29.216654
Mean71.924594
Median Absolute Deviation (MAD)22
Skewness4.6529953
Sum185997
Variance18661.837
MonotonicityNot monotonic
2024-03-13T17:38:52.996940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 236
 
2.4%
2 143
 
1.4%
12 110
 
1.1%
3 93
 
0.9%
4 93
 
0.9%
11 58
 
0.6%
9 56
 
0.6%
6 52
 
0.5%
8 48
 
0.5%
5 48
 
0.5%
Other values (196) 1649
 
16.5%
(Missing) 7414
74.1%
ValueCountFrequency (%)
1 236
2.4%
2 143
1.4%
3 93
 
0.9%
4 93
 
0.9%
5 48
 
0.5%
6 52
 
0.5%
7 40
 
0.4%
8 48
 
0.5%
9 56
 
0.6%
10 18
 
0.2%
ValueCountFrequency (%)
1234 3
< 0.1%
1212 7
0.1%
1156 3
< 0.1%
1014 4
< 0.1%
851 4
< 0.1%
703 3
< 0.1%
644 2
 
< 0.1%
617 5
0.1%
589 3
< 0.1%
557 5
0.1%

아파트_평균_면적
Real number (ℝ)

HIGH CORRELATION 

Distinct147
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.1791
Minimum13
Maximum234
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:53.124416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile36.95
Q148
median52
Q359
95-th percentile88
Maximum234
Range221
Interquartile range (IQR)11

Descriptive statistics

Standard deviation20.766062
Coefficient of variation (CV)0.36964035
Kurtosis18.341009
Mean56.1791
Median Absolute Deviation (MAD)5
Skewness3.4954166
Sum561791
Variance431.22935
MonotonicityNot monotonic
2024-03-13T17:38:53.262187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 621
 
6.2%
51 544
 
5.4%
49 530
 
5.3%
52 505
 
5.1%
53 488
 
4.9%
48 456
 
4.6%
54 456
 
4.6%
55 426
 
4.3%
47 364
 
3.6%
56 358
 
3.6%
Other values (137) 5252
52.5%
ValueCountFrequency (%)
13 7
 
0.1%
14 11
0.1%
15 19
0.2%
16 8
0.1%
17 4
 
< 0.1%
18 15
0.1%
19 14
0.1%
20 13
0.1%
21 6
 
0.1%
22 12
0.1%
ValueCountFrequency (%)
234 2
 
< 0.1%
219 15
0.1%
210 3
 
< 0.1%
209 5
 
0.1%
206 4
 
< 0.1%
195 5
 
0.1%
187 7
0.1%
183 3
 
< 0.1%
182 3
 
< 0.1%
176 4
 
< 0.1%

아파트_평균_시가
Real number (ℝ)

HIGH CORRELATION 

Distinct3167
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3441449 × 108
Minimum22819512
Maximum2.5701647 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-13T17:38:53.385942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22819512
5-th percentile1.0977222 × 108
Q11.4173814 × 108
median1.7526741 × 108
Q32.3982516 × 108
95-th percentile5.431317 × 108
Maximum2.5701647 × 109
Range2.5473452 × 109
Interquartile range (IQR)98087022

Descriptive statistics

Standard deviation2.0548753 × 108
Coefficient of variation (CV)0.8765991
Kurtosis35.407071
Mean2.3441449 × 108
Median Absolute Deviation (MAD)40527198
Skewness5.028366
Sum2.3441449 × 1012
Variance4.2225125 × 1016
MonotonicityNot monotonic
2024-03-13T17:38:53.502232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132500000 8
 
0.1%
176000000 8
 
0.1%
247669402 8
 
0.1%
124512220 8
 
0.1%
295428571 8
 
0.1%
139204545 8
 
0.1%
512306645 8
 
0.1%
62375000 7
 
0.1%
142373977 7
 
0.1%
309843975 7
 
0.1%
Other values (3157) 9923
99.2%
ValueCountFrequency (%)
22819512 3
< 0.1%
52500000 4
< 0.1%
62375000 7
0.1%
67500000 4
< 0.1%
68454545 4
< 0.1%
69579220 3
< 0.1%
71085714 4
< 0.1%
71466667 3
< 0.1%
71711538 3
< 0.1%
72877527 1
 
< 0.1%
ValueCountFrequency (%)
2570164683 4
< 0.1%
2356767764 3
< 0.1%
2319833333 2
 
< 0.1%
2268491240 5
0.1%
2246509886 4
< 0.1%
2091562810 2
 
< 0.1%
1960000000 4
< 0.1%
1868677755 4
< 0.1%
1765885979 5
0.1%
1669967665 5
0.1%

Interactions

2024-03-13T17:38:45.681516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:21.298316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.805896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.252094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:25.996045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.559315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.993955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.356535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.019291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.478159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.971868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:36.639491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.099712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.514075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.994235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.778962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.194859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.767937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:21.384076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.890543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.374964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.080807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.650622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.080863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.438613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.113497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.590211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.052889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:36.739617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.204601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.602300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:41.091559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.865709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.313883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.848357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:21.469555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.972927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.477573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.163591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.732156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.160380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.524036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.204292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.677185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.131387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:36.821714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.287242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.681524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:41.180881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.961795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.408134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.938079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:21.556688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.062571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.563369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.249205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.816593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.241886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.600567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.287862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.760620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.216919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:36.902589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.366477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.769558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:41.263138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.054692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.485467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.020900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:21.641597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.154868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.642081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.336312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.895367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.316386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.688396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.368239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.841919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.290759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:36.983703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.446126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.889615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:41.348439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.143662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.577829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.120018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:21.722532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.241119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.723475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.415447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.971891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.415966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.762725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.445579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.921607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.378112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.066901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.535245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.990762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:41.420403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.221910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.658181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.191258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:21.806589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.333792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.800027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.503892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.047045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.491058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.830475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.544885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.003163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.482437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.143785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.610068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.065144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:41.499834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.298037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.749928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.265301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:21.884945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.426373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.888635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.659180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.120246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.566740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.216072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.621138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.081341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.571946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.221357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.703077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.143474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:41.576674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.383042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.837893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.352455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:21.977229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.517555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.978248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.753700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.201068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.650173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.290283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.710526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.163812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.660631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.303798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.811294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.223176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:41.928012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.466084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.921088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.442775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.074590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.607536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:25.074990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.846578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.283770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.731246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.372611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.789074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.248592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.741260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.384920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.894656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.302410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.042766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.548500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.014248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.542117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.203657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.683487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:25.155300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:26.922765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.361777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.813900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.462132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.864095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.329073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.817467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.459880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.969176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.375500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.161913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.634105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.085989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.639445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.294129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.764500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:25.251667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.002757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.448080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.892022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.541462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:32.950676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.422046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.892643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.537580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.045441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.451941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.267148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.710266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.167361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.719844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.368453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.839079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:25.580111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.093784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.523317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:29.963603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.613716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.029425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.520159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:35.962913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.635737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.119195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.538731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.349317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.794623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.240683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.817457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.457220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:23.916629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:25.658005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.188680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.604205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.038339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.695386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.104654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.611532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:36.040716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.762289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.192075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.617151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.426671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.868092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.332705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.911462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.546716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.003364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:25.742884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.288355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.682882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.122148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.776558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.210646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.728352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:36.127805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.848076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.279626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.701720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.512362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:43.954573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.428419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:46.989154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.629941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.082280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:25.824625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.371373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.812218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.196752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.853715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.308740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.806720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:36.211270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:37.926496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.356685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.789361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.595246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.032984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.530686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:47.339730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:22.714677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:24.159217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:25.896813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:27.455456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:28.921530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:30.279490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:31.935246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:33.397120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:34.882360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:36.558608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:38.009090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:39.436006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:40.889842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:42.682629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:44.117224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T17:38:45.606331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T17:38:53.591589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드상권_구분_코드상권_구분_코드_명상권_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_코드1.0000.0000.0000.0000.0000.0380.1310.1470.1760.0000.1110.0390.0000.0970.1600.1880.1500.0000.070
상권_구분_코드0.0001.0001.0001.0000.2090.2640.1850.3170.3870.1160.1600.2570.2040.1090.2660.1530.2030.2610.235
상권_구분_코드_명0.0001.0001.0001.0000.2090.2640.1850.3170.3870.1160.1600.2570.2040.1090.2660.1530.2030.2610.235
상권_코드0.0001.0001.0001.0000.2520.2090.1700.2540.5370.2930.1860.2180.1870.0550.0960.1540.3790.2780.156
아파트_단지_수0.0000.2090.2090.2521.0000.8830.5940.1800.0450.1300.6770.8130.6590.4920.2580.1630.0370.1730.106
아파트_면적_66_제곱미터_미만_세대_수0.0380.2640.2640.2090.8831.0000.5560.2230.0600.0800.7090.9660.6210.4900.3090.1820.0000.1630.100
아파트_면적_66_제곱미터_세대_수0.1310.1850.1850.1700.5940.5561.0000.3490.2240.1350.5390.5450.7390.6460.5190.4080.2770.0910.065
아파트_면적_99_제곱미터_세대_수0.1470.3170.3170.2540.1800.2230.3491.0000.6530.4530.1360.1680.2790.3660.7420.7390.7220.2550.260
아파트_면적_132_제곱미터_세대_수0.1760.3870.3870.5370.0450.0600.2240.6531.0000.5820.0000.0000.2160.2340.3600.4600.8420.3300.326
아파트_면적_165_제곱미터_세대_수0.0000.1160.1160.2930.1300.0800.1350.4530.5821.0000.0000.0410.2160.1240.6400.6170.7270.5690.645
아파트_가격_1_억_미만_세대_수0.1110.1600.1600.1860.6770.7090.5390.1360.0000.0001.0000.6570.1310.1650.2000.1120.0000.1150.137
아파트_가격_1_억_세대_수0.0390.2570.2570.2180.8130.9660.5450.1680.0000.0410.6571.0000.4080.2740.2340.0000.0000.2390.158
아파트_가격_2_억_세대_수0.0000.2040.2040.1870.6590.6210.7390.2790.2160.2160.1310.4081.0000.6170.5190.2120.1750.1270.081
아파트_가격_3_억_세대_수0.0970.1090.1090.0550.4920.4900.6460.3660.2340.1240.1650.2740.6171.0000.7250.2910.2180.1000.157
아파트_가격_4_억_세대_수0.1600.2660.2660.0960.2580.3090.5190.7420.3600.6400.2000.2340.5190.7251.0000.6610.2860.1840.126
아파트_가격_5_억_세대_수0.1880.1530.1530.1540.1630.1820.4080.7390.4600.6170.1120.0000.2120.2910.6611.0000.4210.2220.242
아파트_가격_6_억_이상_세대_수0.1500.2030.2030.3790.0370.0000.2770.7220.8420.7270.0000.0000.1750.2180.2860.4211.0000.4270.437
아파트_평균_면적0.0000.2610.2610.2780.1730.1630.0910.2550.3300.5690.1150.2390.1270.1000.1840.2220.4271.0000.806
아파트_평균_시가0.0700.2350.2350.1560.1060.1000.0650.2600.3260.6450.1370.1580.0810.1570.1260.2420.4370.8061.000
2024-03-13T17:38:53.745715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권_구분_코드_명상권_구분_코드
상권_구분_코드_명1.0001.000
상권_구분_코드1.0001.000
2024-03-13T17:38:53.829479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준_년분기_코드상권_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_시가상권_구분_코드상권_구분_코드_명
기준_년분기_코드1.000-0.0150.008-0.075-0.129-0.093-0.0270.035-0.137-0.069-0.011-0.075-0.085-0.077-0.102-0.0300.1550.0000.000
상권_코드-0.0151.000-0.259-0.202-0.1350.0880.1810.129-0.242-0.198-0.0880.1230.1320.0650.159-0.0200.1531.0001.000
아파트_단지_수0.008-0.2591.0000.9310.7430.263-0.042-0.1470.6260.8670.7210.3530.2560.145-0.011-0.046-0.1300.1450.145
아파트_면적_66_제곱미터_미만_세대_수-0.075-0.2020.9311.0000.7110.216-0.076-0.2370.7040.9280.7160.3400.2610.139-0.067-0.133-0.1890.1210.121
아파트_면적_66_제곱미터_세대_수-0.129-0.1350.7430.7111.0000.4330.135-0.0350.4260.6560.7170.5210.4630.3710.2000.2140.0700.1120.112
아파트_면적_99_제곱미터_세대_수-0.0930.0880.2630.2160.4331.0000.5400.3820.0210.1360.3840.5030.5200.4630.4710.3600.3580.1460.146
아파트_면적_132_제곱미터_세대_수-0.0270.181-0.042-0.0760.1350.5401.0000.526-0.222-0.1550.0820.3580.4130.4610.5850.5420.5390.2760.276
아파트_면적_165_제곱미터_세대_수0.0350.129-0.147-0.237-0.0350.3820.5261.000-0.313-0.273-0.1250.1540.3270.3430.6840.6420.6190.0750.075
아파트_가격_1_억_미만_세대_수-0.137-0.2420.6260.7040.4260.021-0.222-0.3131.0000.6930.223-0.070-0.085-0.140-0.264-0.190-0.5130.0960.096
아파트_가격_1_억_세대_수-0.069-0.1980.8670.9280.6560.136-0.155-0.2730.6931.0000.6030.1450.080-0.016-0.178-0.107-0.2640.1170.117
아파트_가격_2_억_세대_수-0.011-0.0880.7210.7160.7170.3840.082-0.1250.2230.6031.0000.5140.2720.096-0.1150.0770.2200.1230.123
아파트_가격_3_억_세대_수-0.0750.1230.3530.3400.5210.5030.3580.154-0.0700.1450.5141.0000.6300.3250.0790.1850.4000.0700.070
아파트_가격_4_억_세대_수-0.0850.1320.2560.2610.4630.5200.4130.327-0.0850.0800.2720.6301.0000.5540.2970.1600.3220.1220.122
아파트_가격_5_억_세대_수-0.0770.0650.1450.1390.3710.4630.4610.343-0.140-0.0160.0960.3250.5541.0000.4460.2000.3470.0980.098
아파트_가격_6_억_이상_세대_수-0.1020.159-0.011-0.0670.2000.4710.5850.684-0.264-0.178-0.1150.0790.2970.4461.0000.4650.5480.1310.131
아파트_평균_면적-0.030-0.020-0.046-0.1330.2140.3600.5420.642-0.190-0.1070.0770.1850.1600.2000.4651.0000.5530.1580.158
아파트_평균_시가0.1550.153-0.130-0.1890.0700.3580.5390.619-0.513-0.2640.2200.4000.3220.3470.5480.5531.0000.1430.143
상권_구분_코드0.0001.0000.1450.1210.1120.1460.2760.0750.0960.1170.1230.0700.1220.0980.1310.1580.1431.0001.000
상권_구분_코드_명0.0001.0000.1450.1210.1120.1460.2760.0750.0960.1170.1230.0700.1220.0980.1310.1580.1431.0001.000

Missing values

2024-03-13T17:38:47.482393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T17:38:47.718461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-13T17:38:47.916232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

기준_년분기_코드상권_구분_코드상권_구분_코드_명상권_코드상권_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
1488820223A골목상권3110971선정릉역 4번1311185117336171849356914468204644267919793
759520213A골목상권3110262상봉역 3번70620933<NA><NA>132500831<NA><NA><NA>45141865678
2097220203R전통시장3130317마천중앙시장105341<NA><NA><NA>3254224<NA><NA><NA>59233830264
798020224A골목상권3110300삼선동주민센터39308233<NA><NA>180127243<NA><NA><NA>37114912922
1949920223D발달상권3120089구파발역56939<NA><NA><NA><NA><NA>61158<NA>3367491601010
1779020213A골목상권3110925서초중학교723091882777715221611231131258723579342704360
383520233R전통시장3130326고덕 골목형상점가250<NA><NA><NA><NA><NA>24<NA>7<NA>3753619557576
775820194A골목상권3110276신내IC북측5521<NA><NA><NA>21311<NA><NA><NA><NA>37123821238
990720221A골목상권3110447구산초등학교504384013<NA><NA>121356131<NA><NA><NA>48128503435
907320223A골목상권3110374송중초등학교835541562<NA><NA>445021642<NA><NA><NA>53168111067
기준_년분기_코드상권_구분_코드상권_구분_코드_명상권_코드상권_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
112020232A골목상권3110394방학중학교12111742331<NA><NA>41298610<NA><NA><NA><NA>51122902818
1499820213A골목상권3110848양지어린이공원107807885<NA><NA>8846034210<NA><NA><NA>47183717501
2339620223R전통시장3130213방신전통시장31024<NA><NA><NA>91<NA>1212<NA><NA>63287294444
1140520202A골목상권3110511연희초등학교957742118414324403792003251<NA>1365179028427
2123720212R전통시장3130227고척골목시장상점가(그라운드 고척)7441<NA><NA><NA>405<NA><NA><NA><NA><NA>4785795918
549020223A골목상권3110095한강진역 3번3773101534511<NA>324313841146100655605993
1041120224A골목상권3110400도봉구보건소815801691<NA><NA>17953041<NA><NA><NA><NA>54130816765
2112320214R전통시장3130325명일전통시장52619<NA><NA><NA>3191<NA>115661291791515
594920212A골목상권3110126서울숲역 1번9114<NA><NA><NA><NA><NA>11671818<NA><NA>42261677083
2091720222R전통시장3130307도곡시장1<NA>8<NA><NA><NA><NA><NA><NA><NA>8<NA><NA>75469000000