Overview

Dataset statistics

Number of variables17
Number of observations30
Missing cells10
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 KiB
Average record size in memory153.3 B

Variable types

Categorical2
Text3
Numeric12

Dataset

Description경기도 가평군의 공동주택 지번별 상수도 사용량(시군명, 사용연도, 공동주택명, 도로명주소, 지번주소, 월별사용량) 데이터 현황입니다.
Author경기도 가평군
URLhttps://www.data.go.kr/data/15025318/fileData.do

Alerts

시군명 has constant value ""Constant
사용연도 has constant value ""Constant
1월 is highly overall correlated with 2월 and 10 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
3월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
4월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
5월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
6월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
7월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
8월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
9월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
10월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
11월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
12월 is highly overall correlated with 1월 and 10 other fieldsHigh correlation
소재지도로명주소 has 1 (3.3%) missing valuesMissing
1월 has 1 (3.3%) missing valuesMissing
2월 has 1 (3.3%) missing valuesMissing
3월 has 1 (3.3%) missing valuesMissing
4월 has 1 (3.3%) missing valuesMissing
5월 has 1 (3.3%) missing valuesMissing
6월 has 1 (3.3%) missing valuesMissing
7월 has 1 (3.3%) missing valuesMissing
8월 has 1 (3.3%) missing valuesMissing
9월 has 1 (3.3%) missing valuesMissing
공동주택명 has unique valuesUnique
10월 has unique valuesUnique
11월 has unique valuesUnique
12월 has unique valuesUnique

Reproduction

Analysis started2024-03-14 20:16:49.328594
Analysis finished2024-03-14 20:17:25.595871
Duration36.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size368.0 B
가평군
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가평군 30
100.0%

Length

2024-03-15T05:17:25.724136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:17:25.890806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가평군 30
100.0%

사용연도
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size368.0 B
2023
30 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 30
100.0%

Length

2024-03-15T05:17:26.060794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:17:26.385257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 30
100.0%

공동주택명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size368.0 B
2024-03-15T05:17:27.122698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.1
Min length3

Characters and Unicode

Total characters213
Distinct characters79
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row경남아너스빌
2nd row로얄아파트
3rd row북한강코아루아파트
4th row선힐아파트
5th row세양청마루
ValueCountFrequency (%)
맹호아파트 3
 
7.1%
아산빌리지 3
 
7.1%
어젤리아아파트 2
 
4.8%
가평 2
 
4.8%
블루핀아파트 1
 
2.4%
3동 1
 
2.4%
4,5동 1
 
2.4%
a동 1
 
2.4%
b동 1
 
2.4%
c동 1
 
2.4%
Other values (26) 26
61.9%
2024-03-15T05:17:28.357930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
 
12.2%
18
 
8.5%
17
 
8.0%
12
 
5.6%
8
 
3.8%
7
 
3.3%
2 6
 
2.8%
5
 
2.3%
5
 
2.3%
5
 
2.3%
Other values (69) 104
48.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 184
86.4%
Space Separator 12
 
5.6%
Decimal Number 12
 
5.6%
Uppercase Letter 3
 
1.4%
Other Punctuation 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
 
14.1%
18
 
9.8%
17
 
9.2%
8
 
4.3%
7
 
3.8%
5
 
2.7%
5
 
2.7%
5
 
2.7%
4
 
2.2%
4
 
2.2%
Other values (59) 85
46.2%
Decimal Number
ValueCountFrequency (%)
2 6
50.0%
1 3
25.0%
4 1
 
8.3%
3 1
 
8.3%
5 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
B 1
33.3%
A 1
33.3%
C 1
33.3%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 184
86.4%
Common 26
 
12.2%
Latin 3
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
 
14.1%
18
 
9.8%
17
 
9.2%
8
 
4.3%
7
 
3.8%
5
 
2.7%
5
 
2.7%
5
 
2.7%
4
 
2.2%
4
 
2.2%
Other values (59) 85
46.2%
Common
ValueCountFrequency (%)
12
46.2%
2 6
23.1%
1 3
 
11.5%
, 2
 
7.7%
4 1
 
3.8%
3 1
 
3.8%
5 1
 
3.8%
Latin
ValueCountFrequency (%)
B 1
33.3%
A 1
33.3%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 184
86.4%
ASCII 29
 
13.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
26
 
14.1%
18
 
9.8%
17
 
9.2%
8
 
4.3%
7
 
3.8%
5
 
2.7%
5
 
2.7%
5
 
2.7%
4
 
2.2%
4
 
2.2%
Other values (59) 85
46.2%
ASCII
ValueCountFrequency (%)
12
41.4%
2 6
20.7%
1 3
 
10.3%
, 2
 
6.9%
B 1
 
3.4%
A 1
 
3.4%
4 1
 
3.4%
3 1
 
3.4%
5 1
 
3.4%
C 1
 
3.4%
Distinct28
Distinct (%)96.6%
Missing1
Missing (%)3.3%
Memory size368.0 B
2024-03-15T05:17:29.209358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length20.724138
Min length18

Characters and Unicode

Total characters601
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)93.1%

Sample

1st row경기도 가평군 청평면 골안길 7-28
2nd row경기도 가평군 청평면 구청평로 56
3rd row경기도 가평군 설악면 자잠로 86
4th row경기도 가평군 가평읍 문화로 63
5th row경기도 가평군 청평면 경춘로 807-45
ValueCountFrequency (%)
가평군 29
20.0%
경기도 28
19.3%
가평읍 12
 
8.3%
청평면 10
 
6.9%
문화로 5
 
3.4%
설악면 4
 
2.8%
가화로 4
 
2.8%
경춘로 3
 
2.1%
청평중앙로82번길 3
 
2.1%
조종면 3
 
2.1%
Other values (40) 44
30.3%
2024-03-15T05:17:31.133003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
116
19.3%
56
 
9.3%
45
 
7.5%
33
 
5.5%
31
 
5.2%
29
 
4.8%
28
 
4.7%
28
 
4.7%
1 23
 
3.8%
18
 
3.0%
Other values (44) 194
32.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 377
62.7%
Space Separator 116
 
19.3%
Decimal Number 99
 
16.5%
Dash Punctuation 9
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
56
14.9%
45
11.9%
33
8.8%
31
 
8.2%
29
 
7.7%
28
 
7.4%
28
 
7.4%
18
 
4.8%
17
 
4.5%
12
 
3.2%
Other values (32) 80
21.2%
Decimal Number
ValueCountFrequency (%)
1 23
23.2%
2 17
17.2%
8 13
13.1%
5 9
 
9.1%
9 8
 
8.1%
6 7
 
7.1%
4 7
 
7.1%
7 6
 
6.1%
3 5
 
5.1%
0 4
 
4.0%
Space Separator
ValueCountFrequency (%)
116
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 377
62.7%
Common 224
37.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
56
14.9%
45
11.9%
33
8.8%
31
 
8.2%
29
 
7.7%
28
 
7.4%
28
 
7.4%
18
 
4.8%
17
 
4.5%
12
 
3.2%
Other values (32) 80
21.2%
Common
ValueCountFrequency (%)
116
51.8%
1 23
 
10.3%
2 17
 
7.6%
8 13
 
5.8%
- 9
 
4.0%
5 9
 
4.0%
9 8
 
3.6%
6 7
 
3.1%
4 7
 
3.1%
7 6
 
2.7%
Other values (2) 9
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 377
62.7%
ASCII 224
37.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
116
51.8%
1 23
 
10.3%
2 17
 
7.6%
8 13
 
5.8%
- 9
 
4.0%
5 9
 
4.0%
9 8
 
3.6%
6 7
 
3.1%
4 7
 
3.1%
7 6
 
2.7%
Other values (2) 9
 
4.0%
Hangul
ValueCountFrequency (%)
56
14.9%
45
11.9%
33
8.8%
31
 
8.2%
29
 
7.7%
28
 
7.4%
28
 
7.4%
18
 
4.8%
17
 
4.5%
12
 
3.2%
Other values (32) 80
21.2%
Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size368.0 B
2024-03-15T05:17:32.177230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length20.166667
Min length18

Characters and Unicode

Total characters605
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st row경기도 가평군 청평면 청평리 306
2nd row경기도 가평군 청평면 청평리 626-8
3rd row경기도 가평군 설악면 선촌리 33-19
4th row경기도 가평군 가평읍 대곡리 402-1
5th row경기도 가평군 청평면 청평리 472
ValueCountFrequency (%)
가평군 31
22.6%
경기도 30
21.9%
가평읍 11
 
8.0%
청평면 10
 
7.3%
청평리 6
 
4.4%
설악면 4
 
2.9%
조종면 4
 
2.9%
현리 4
 
2.9%
477-21 2
 
1.5%
대곡리 2
 
1.5%
Other values (31) 33
24.1%
2024-03-15T05:17:33.583158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
107
17.7%
62
 
10.2%
42
 
6.9%
31
 
5.1%
31
 
5.1%
30
 
5.0%
30
 
5.0%
30
 
5.0%
1 23
 
3.8%
- 23
 
3.8%
Other values (25) 196
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 356
58.8%
Decimal Number 119
 
19.7%
Space Separator 107
 
17.7%
Dash Punctuation 23
 
3.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
62
17.4%
42
11.8%
31
8.7%
31
8.7%
30
8.4%
30
8.4%
30
8.4%
20
 
5.6%
18
 
5.1%
17
 
4.8%
Other values (13) 45
12.6%
Decimal Number
ValueCountFrequency (%)
1 23
19.3%
7 18
15.1%
2 16
13.4%
3 14
11.8%
6 12
10.1%
4 11
9.2%
0 8
 
6.7%
9 6
 
5.0%
8 6
 
5.0%
5 5
 
4.2%
Space Separator
ValueCountFrequency (%)
107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 356
58.8%
Common 249
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
62
17.4%
42
11.8%
31
8.7%
31
8.7%
30
8.4%
30
8.4%
30
8.4%
20
 
5.6%
18
 
5.1%
17
 
4.8%
Other values (13) 45
12.6%
Common
ValueCountFrequency (%)
107
43.0%
1 23
 
9.2%
- 23
 
9.2%
7 18
 
7.2%
2 16
 
6.4%
3 14
 
5.6%
6 12
 
4.8%
4 11
 
4.4%
0 8
 
3.2%
9 6
 
2.4%
Other values (2) 11
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 356
58.8%
ASCII 249
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
107
43.0%
1 23
 
9.2%
- 23
 
9.2%
7 18
 
7.2%
2 16
 
6.4%
3 14
 
5.6%
6 12
 
4.8%
4 11
 
4.4%
0 8
 
3.2%
9 6
 
2.4%
Other values (2) 11
 
4.4%
Hangul
ValueCountFrequency (%)
62
17.4%
42
11.8%
31
8.7%
31
8.7%
30
8.4%
30
8.4%
30
8.4%
20
 
5.6%
18
 
5.1%
17
 
4.8%
Other values (13) 45
12.6%

1월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2163.5172
Minimum40
Maximum5508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:33.888752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile269.2
Q1575
median1796
Q33350
95-th percentile4721.4
Maximum5508
Range5468
Interquartile range (IQR)2775

Descriptive statistics

Standard deviation1674.8653
Coefficient of variation (CV)0.77414002
Kurtosis-1.2472196
Mean2163.5172
Median Absolute Deviation (MAD)1407
Skewness0.38308286
Sum62742
Variance2805173.7
MonotonicityNot monotonic
2024-03-15T05:17:34.089897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4022 1
 
3.3%
810 1
 
3.3%
40 1
 
3.3%
2023 1
 
3.3%
3331 1
 
3.3%
961 1
 
3.3%
1472 1
 
3.3%
1516 1
 
3.3%
676 1
 
3.3%
389 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
40 1
3.3%
220 1
3.3%
343 1
3.3%
389 1
3.3%
456 1
3.3%
490 1
3.3%
572 1
3.3%
575 1
3.3%
657 1
3.3%
676 1
3.3%
ValueCountFrequency (%)
5508 1
3.3%
4799 1
3.3%
4605 1
3.3%
4506 1
3.3%
4062 1
3.3%
4022 1
3.3%
3402 1
3.3%
3350 1
3.3%
3331 1
3.3%
3272 1
3.3%

2월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2340.0345
Minimum208
Maximum6497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:34.345375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum208
5-th percentile298.2
Q1670
median1778
Q33924
95-th percentile5250.2
Maximum6497
Range6289
Interquartile range (IQR)3254

Descriptive statistics

Standard deviation1842.3639
Coefficient of variation (CV)0.7873234
Kurtosis-0.85636331
Mean2340.0345
Median Absolute Deviation (MAD)1367
Skewness0.52420826
Sum67861
Variance3394304.7
MonotonicityNot monotonic
2024-03-15T05:17:34.736425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4094 1
 
3.3%
881 1
 
3.3%
251 1
 
3.3%
1778 1
 
3.3%
4067 1
 
3.3%
971 1
 
3.3%
1632 1
 
3.3%
1731 1
 
3.3%
746 1
 
3.3%
411 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
208 1
3.3%
251 1
3.3%
369 1
3.3%
411 1
3.3%
463 1
3.3%
506 1
3.3%
543 1
3.3%
670 1
3.3%
680 1
3.3%
746 1
3.3%
ValueCountFrequency (%)
6497 1
3.3%
5703 1
3.3%
4571 1
3.3%
4491 1
3.3%
4202 1
3.3%
4094 1
3.3%
4067 1
3.3%
3924 1
3.3%
3755 1
3.3%
3493 1
3.3%

3월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)96.6%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2180.1034
Minimum180
Maximum6357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:35.372454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum180
5-th percentile241
Q1699
median1794
Q33542
95-th percentile4641
Maximum6357
Range6177
Interquartile range (IQR)2843

Descriptive statistics

Standard deviation1714.7551
Coefficient of variation (CV)0.7865476
Kurtosis-0.65089982
Mean2180.1034
Median Absolute Deviation (MAD)1409
Skewness0.56005652
Sum63223
Variance2940385.2
MonotonicityNot monotonic
2024-03-15T05:17:35.724401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
385 2
 
6.7%
4116 1
 
3.3%
233 1
 
3.3%
180 1
 
3.3%
4605 1
 
3.3%
3388 1
 
3.3%
783 1
 
3.3%
1447 1
 
3.3%
1474 1
 
3.3%
634 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
180 1
3.3%
233 1
3.3%
253 1
3.3%
384 1
3.3%
385 2
6.7%
634 1
3.3%
699 1
3.3%
755 1
3.3%
767 1
3.3%
783 1
3.3%
ValueCountFrequency (%)
6357 1
3.3%
4665 1
3.3%
4605 1
3.3%
4196 1
3.3%
4116 1
3.3%
3756 1
3.3%
3659 1
3.3%
3542 1
3.3%
3388 1
3.3%
3254 1
3.3%

4월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2121.069
Minimum132
Maximum5783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:36.013997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum132
5-th percentile274.6
Q1586
median1884
Q33551
95-th percentile4606
Maximum5783
Range5651
Interquartile range (IQR)2965

Descriptive statistics

Standard deviation1654.8227
Coefficient of variation (CV)0.78018335
Kurtosis-1.0341395
Mean2121.069
Median Absolute Deviation (MAD)1409
Skewness0.44317882
Sum61511
Variance2738438.1
MonotonicityNot monotonic
2024-03-15T05:17:36.405201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3551 1
 
3.3%
805 1
 
3.3%
132 1
 
3.3%
1884 1
 
3.3%
3716 1
 
3.3%
793 1
 
3.3%
1396 1
 
3.3%
1613 1
 
3.3%
586 1
 
3.3%
380 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
132 1
3.3%
217 1
3.3%
361 1
3.3%
380 1
3.3%
430 1
3.3%
475 1
3.3%
521 1
3.3%
586 1
3.3%
587 1
3.3%
624 1
3.3%
ValueCountFrequency (%)
5783 1
3.3%
4788 1
3.3%
4333 1
3.3%
4109 1
3.3%
3886 1
3.3%
3716 1
3.3%
3581 1
3.3%
3551 1
3.3%
3459 1
3.3%
3249 1
3.3%

5월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2107.4138
Minimum235
Maximum6275
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:36.776628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum235
5-th percentile270
Q1627
median1776
Q33373
95-th percentile4698.2
Maximum6275
Range6040
Interquartile range (IQR)2746

Descriptive statistics

Standard deviation1668.7092
Coefficient of variation (CV)0.79182799
Kurtosis-0.39991848
Mean2107.4138
Median Absolute Deviation (MAD)1351
Skewness0.63484354
Sum61115
Variance2784590.5
MonotonicityNot monotonic
2024-03-15T05:17:37.029429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3735 1
 
3.3%
987 1
 
3.3%
306 1
 
3.3%
1776 1
 
3.3%
3535 1
 
3.3%
744 1
 
3.3%
1299 1
 
3.3%
1510 1
 
3.3%
488 1
 
3.3%
380 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
235 1
3.3%
246 1
3.3%
306 1
3.3%
380 1
3.3%
407 1
3.3%
425 1
3.3%
488 1
3.3%
627 1
3.3%
643 1
3.3%
681 1
3.3%
ValueCountFrequency (%)
6275 1
3.3%
5121 1
3.3%
4064 1
3.3%
3951 1
3.3%
3735 1
3.3%
3535 1
3.3%
3438 1
3.3%
3373 1
3.3%
3163 1
3.3%
3079 1
3.3%

6월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2315.1379
Minimum272
Maximum5842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:37.375333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum272
5-th percentile343.6
Q1664
median2000
Q33758
95-th percentile5086.2
Maximum5842
Range5570
Interquartile range (IQR)3094

Descriptive statistics

Standard deviation1747.796
Coefficient of variation (CV)0.75494248
Kurtosis-1.217202
Mean2315.1379
Median Absolute Deviation (MAD)1475
Skewness0.38682088
Sum67139
Variance3054790.8
MonotonicityNot monotonic
2024-03-15T05:17:37.772255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3847 1
 
3.3%
909 1
 
3.3%
525 1
 
3.3%
1978 1
 
3.3%
3758 1
 
3.3%
892 1
 
3.3%
1469 1
 
3.3%
2000 1
 
3.3%
664 1
 
3.3%
382 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
272 1
3.3%
318 1
3.3%
382 1
3.3%
406 1
3.3%
524 1
3.3%
525 1
3.3%
644 1
3.3%
664 1
3.3%
712 1
3.3%
729 1
3.3%
ValueCountFrequency (%)
5842 1
3.3%
5307 1
3.3%
4755 1
3.3%
4517 1
3.3%
4326 1
3.3%
3917 1
3.3%
3847 1
3.3%
3758 1
3.3%
3610 1
3.3%
3315 1
3.3%

7월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)96.6%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2409.1034
Minimum296
Maximum7296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:38.112750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum296
5-th percentile373.4
Q1668
median1927
Q33791
95-th percentile5241.8
Maximum7296
Range7000
Interquartile range (IQR)3123

Descriptive statistics

Standard deviation1899.7733
Coefficient of variation (CV)0.78858105
Kurtosis-0.29777852
Mean2409.1034
Median Absolute Deviation (MAD)1437
Skewness0.68438277
Sum69864
Variance3609138.7
MonotonicityNot monotonic
2024-03-15T05:17:38.386329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
668 2
 
6.7%
4311 1
 
3.3%
355 1
 
3.3%
686 1
 
3.3%
1927 1
 
3.3%
3718 1
 
3.3%
929 1
 
3.3%
1658 1
 
3.3%
1670 1
 
3.3%
655 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
296 1
3.3%
355 1
3.3%
401 1
3.3%
415 1
3.3%
490 1
3.3%
655 1
3.3%
668 2
6.7%
686 1
3.3%
740 1
3.3%
929 1
3.3%
ValueCountFrequency (%)
7296 1
3.3%
5533 1
3.3%
4805 1
3.3%
4679 1
3.3%
4311 1
3.3%
4110 1
3.3%
3860 1
3.3%
3791 1
3.3%
3718 1
3.3%
3717 1
3.3%

8월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2187.7931
Minimum131
Maximum6103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:38.763294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum131
5-th percentile274.6
Q1605
median1781
Q33594
95-th percentile4617.2
Maximum6103
Range5972
Interquartile range (IQR)2989

Descriptive statistics

Standard deviation1721.6147
Coefficient of variation (CV)0.78691844
Kurtosis-0.94314297
Mean2187.7931
Median Absolute Deviation (MAD)1355
Skewness0.48784346
Sum63446
Variance2963957.3
MonotonicityNot monotonic
2024-03-15T05:17:39.146821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4231 1
 
3.3%
887 1
 
3.3%
131 1
 
3.3%
1506 1
 
3.3%
3594 1
 
3.3%
845 1
 
3.3%
1578 1
 
3.3%
1781 1
 
3.3%
679 1
 
3.3%
416 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
131 1
3.3%
243 1
3.3%
322 1
3.3%
351 1
3.3%
416 1
3.3%
426 1
3.3%
562 1
3.3%
605 1
3.3%
679 1
3.3%
709 1
3.3%
ValueCountFrequency (%)
6103 1
3.3%
4686 1
3.3%
4514 1
3.3%
4477 1
3.3%
4231 1
3.3%
4073 1
3.3%
3717 1
3.3%
3594 1
3.3%
3210 1
3.3%
3099 1
3.3%

9월
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean2498.5862
Minimum297
Maximum7189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:39.416800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum297
5-th percentile350.6
Q1690
median2069
Q33937
95-th percentile5536.4
Maximum7189
Range6892
Interquartile range (IQR)3247

Descriptive statistics

Standard deviation1981.072
Coefficient of variation (CV)0.79287718
Kurtosis-0.74593447
Mean2498.5862
Median Absolute Deviation (MAD)1648
Skewness0.54241175
Sum72459
Variance3924646.3
MonotonicityNot monotonic
2024-03-15T05:17:39.747263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4141 1
 
3.3%
891 1
 
3.3%
392 1
 
3.3%
2688 1
 
3.3%
3928 1
 
3.3%
885 1
 
3.3%
1508 1
 
3.3%
1767 1
 
3.3%
690 1
 
3.3%
421 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
297 1
3.3%
349 1
3.3%
353 1
3.3%
392 1
3.3%
421 1
3.3%
477 1
3.3%
666 1
3.3%
690 1
3.3%
757 1
3.3%
759 1
3.3%
ValueCountFrequency (%)
7189 1
3.3%
5778 1
3.3%
5174 1
3.3%
4890 1
3.3%
4404 1
3.3%
4400 1
3.3%
4141 1
3.3%
3937 1
3.3%
3928 1
3.3%
3847 1
3.3%

10월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2288.2
Minimum252
Maximum5804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:40.194709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum252
5-th percentile321.8
Q1652
median2002.5
Q33560.25
95-th percentile4872.3
Maximum5804
Range5552
Interquartile range (IQR)2908.25

Descriptive statistics

Standard deviation1687.7353
Coefficient of variation (CV)0.73758208
Kurtosis-1.0814496
Mean2288.2
Median Absolute Deviation (MAD)1416.5
Skewness0.39601515
Sum68646
Variance2848450.5
MonotonicityNot monotonic
2024-03-15T05:17:40.639895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4338 1
 
3.3%
5253 1
 
3.3%
4289 1
 
3.3%
1504 1
 
3.3%
2013 1
 
3.3%
3125 1
 
3.3%
759 1
 
3.3%
1608 1
 
3.3%
1651 1
 
3.3%
603 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
252 1
3.3%
320 1
3.3%
324 1
3.3%
358 1
3.3%
424 1
3.3%
569 1
3.3%
603 1
3.3%
648 1
3.3%
664 1
3.3%
759 1
3.3%
ValueCountFrequency (%)
5804 1
3.3%
5253 1
3.3%
4407 1
3.3%
4338 1
3.3%
4324 1
3.3%
4289 1
3.3%
4065 1
3.3%
3600 1
3.3%
3441 1
3.3%
3125 1
3.3%

11월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3210.7333
Minimum249
Maximum24564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:41.066915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum249
5-th percentile360.4
Q1683.25
median2600.5
Q34088.5
95-th percentile6213.55
Maximum24564
Range24315
Interquartile range (IQR)3405.25

Descriptive statistics

Standard deviation4441.5185
Coefficient of variation (CV)1.3833346
Kurtosis19.412943
Mean3210.7333
Median Absolute Deviation (MAD)1812
Skewness4.0403401
Sum96322
Variance19727087
MonotonicityNot monotonic
2024-03-15T05:17:41.564157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3985 1
 
3.3%
5509 1
 
3.3%
4719 1
 
3.3%
24564 1
 
3.3%
1993 1
 
3.3%
3747 1
 
3.3%
773 1
 
3.3%
1377 1
 
3.3%
1344 1
 
3.3%
681 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
249 1
3.3%
355 1
3.3%
367 1
3.3%
386 1
3.3%
446 1
3.3%
597 1
3.3%
657 1
3.3%
681 1
3.3%
690 1
3.3%
773 1
3.3%
ValueCountFrequency (%)
24564 1
3.3%
6790 1
3.3%
5509 1
3.3%
5112 1
3.3%
4719 1
3.3%
4358 1
3.3%
4210 1
3.3%
4123 1
3.3%
3985 1
3.3%
3747 1
3.3%

12월
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2497.7
Minimum283
Maximum6434
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size398.0 B
2024-03-15T05:17:42.060720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum283
5-th percentile342.85
Q1673.25
median2451.5
Q33800.5
95-th percentile5284.75
Maximum6434
Range6151
Interquartile range (IQR)3127.25

Descriptive statistics

Standard deviation1851.2305
Coefficient of variation (CV)0.74117408
Kurtosis-1.0761348
Mean2497.7
Median Absolute Deviation (MAD)1733.5
Skewness0.36354797
Sum74931
Variance3427054.4
MonotonicityNot monotonic
2024-03-15T05:17:42.564946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4286 1
 
3.3%
5063 1
 
3.3%
5249 1
 
3.3%
5314 1
 
3.3%
1895 1
 
3.3%
3543 1
 
3.3%
732 1
 
3.3%
1537 1
 
3.3%
1799 1
 
3.3%
663 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
283 1
3.3%
328 1
3.3%
361 1
3.3%
402 1
3.3%
457 1
3.3%
494 1
3.3%
612 1
3.3%
663 1
3.3%
704 1
3.3%
732 1
3.3%
ValueCountFrequency (%)
6434 1
3.3%
5314 1
3.3%
5249 1
3.3%
5063 1
3.3%
4753 1
3.3%
4286 1
3.3%
4145 1
3.3%
3873 1
3.3%
3583 1
3.3%
3543 1
3.3%

Interactions

2024-03-15T05:17:22.206684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:50.193148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:53.231459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:56.883878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:00.206442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:02.518312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:05.166608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:08.673331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:11.796326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:14.069623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:16.405067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:18.997473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:22.449342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:50.433730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:53.472304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:57.145708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:00.370548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:02.740985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:05.387070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:08.931250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:12.052365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:14.323401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:16.554230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:19.436786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:22.706363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:50.679924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:53.730859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:57.423988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:00.516888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:02.944478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:05.637215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:09.204280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:12.219370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:14.567005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:16.730632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:19.691427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:22.852411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:50.933054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:53.982937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:57.693792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:00.665404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:03.197262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:05.888617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:09.423126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:12.469086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:14.721111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:16.978067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:19.940600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:22.999847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:51.177437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:54.238392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:57.971363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:00.819485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:03.358969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:06.146319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:09.743321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:12.716400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:14.978045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:17.199904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:20.200006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:23.145657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:51.423777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:54.492071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:58.230426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:01.012820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:03.509107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:06.454704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:10.006704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:12.864358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:15.167186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:17.446622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:20.450489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:23.437029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:51.676729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:54.811258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:58.503131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:01.259747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:03.669851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:06.741809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:10.256910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:13.008212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:15.368225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:17.644088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:20.700199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:23.589202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:51.933042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:55.065862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:58.775184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:01.500368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:03.929247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:07.009907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:10.514937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:13.304316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:15.539623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:17.814917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:20.953724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:23.807259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:52.179847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:55.319118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:59.053837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:01.646092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:04.174921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:07.282465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:10.765343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:13.448865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:15.776986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:18.011203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:21.211756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:23.992401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:52.437155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:55.577329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:59.403124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:01.800276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:04.429658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:07.580867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:11.037800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:13.620756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:15.935364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:18.265440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:21.467659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:24.142823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:52.694355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:56.214884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:59.666541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:02.001812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:04.718671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:07.861557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:11.288446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:13.766580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:16.085793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:18.509887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:21.720105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:24.300035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:52.975403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:56.504377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:16:59.942100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:02.258845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:05.017016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:08.164442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:11.547113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:13.922639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:16.252275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:18.751314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:17:21.956613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:17:42.964354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공동주택명소재지도로명주소소재지지번주소1월2월3월4월5월6월7월8월9월10월11월12월
공동주택명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지도로명주소1.0001.0001.0000.9851.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지지번주소1.0001.0001.0000.9660.9790.9540.9740.9660.9740.9920.9880.9550.9210.9660.971
1월1.0000.9850.9661.0000.9670.9380.9000.8740.8580.9760.9530.9560.9570.9710.898
2월1.0001.0000.9790.9671.0000.9650.9570.9160.8910.9620.9900.9510.9490.9980.872
3월1.0001.0000.9540.9380.9651.0000.8800.8970.8370.9400.9350.9360.9550.9920.870
4월1.0001.0000.9740.9000.9570.8801.0000.9080.9590.9110.9330.8560.9061.0000.912
5월1.0001.0000.9660.8740.9160.8970.9081.0000.9500.9310.8930.8870.8600.9420.964
6월1.0001.0000.9740.8580.8910.8370.9590.9501.0000.9130.9030.8910.8740.9530.866
7월1.0001.0000.9920.9760.9620.9400.9110.9310.9131.0000.9590.9540.9710.9980.874
8월1.0001.0000.9880.9530.9900.9350.9330.8930.9030.9591.0000.9210.9480.9840.874
9월1.0001.0000.9550.9560.9510.9360.8560.8870.8910.9540.9211.0000.9020.9730.896
10월1.0001.0000.9210.9570.9490.9550.9060.8600.8740.9710.9480.9021.0000.8980.806
11월1.0001.0000.9660.9710.9980.9921.0000.9420.9530.9980.9840.9730.8981.0000.977
12월1.0001.0000.9710.8980.8720.8700.9120.9640.8660.8740.8740.8960.8060.9771.000
2024-03-15T05:17:43.473639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월
1월1.0000.9920.9560.9800.9710.9820.9700.9810.9870.8470.8280.799
2월0.9921.0000.9480.9880.9830.9910.9780.9900.9840.8670.8470.817
3월0.9560.9481.0000.9350.9370.9350.9340.9270.9560.8180.7860.765
4월0.9800.9880.9351.0000.9820.9800.9670.9820.9680.8430.8240.793
5월0.9710.9830.9370.9821.0000.9820.9750.9750.9670.8650.8430.824
6월0.9820.9910.9350.9800.9821.0000.9930.9830.9860.9070.8930.865
7월0.9700.9780.9340.9670.9750.9931.0000.9680.9790.9330.9180.898
8월0.9810.9900.9270.9820.9750.9830.9681.0000.9730.8520.8280.795
9월0.9870.9840.9560.9680.9670.9860.9790.9731.0000.8790.8620.835
10월0.8470.8670.8180.8430.8650.9070.9330.8520.8791.0000.9150.918
11월0.8280.8470.7860.8240.8430.8930.9180.8280.8620.9151.0000.990
12월0.7990.8170.7650.7930.8240.8650.8980.7950.8350.9180.9901.000

Missing values

2024-03-15T05:17:24.533067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:17:24.953733image/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.
2024-03-15T05:17:25.403737image/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

시군명사용연도공동주택명소재지도로명주소소재지지번주소1월2월3월4월5월6월7월8월9월10월11월12월
0가평군2023경남아너스빌경기도 가평군 청평면 골안길 7-28경기도 가평군 청평면 청평리 306402240944116355137353847431142314141433839854286
1가평군2023로얄아파트경기도 가평군 청평면 구청평로 56경기도 가평군 청평면 청평리 626-88108819198059879091048887891851804881
2가평군2023북한강코아루아파트경기도 가평군 설악면 자잠로 86경기도 가평군 설악면 선촌리 33-19335039243254345934383917386037173937360041233583
3가평군2023선힐아파트경기도 가평군 가평읍 문화로 63경기도 가평군 가평읍 대곡리 402-1327232262325278628113261318930993363304333462944
4가평군2023세양청마루경기도 가평군 청평면 경춘로 807-45경기도 가평군 청평면 청평리 472460545714196433333734755480546865174440751124753
5가평군2023파란채경기도 가평군 가평읍 가화로 223경기도 가평군 가평읍 읍내리 771-1406242023659410939514326411045144400432442103873
6가평군2023꿈에그린경기도 가평군 조종면 청군로 1149경기도 가평군 조종면 현리 477-21324430052289358130793130333629313029265930363063
7가평군2023이안지안스경기도 가평군 청평면 구청평로 20경기도 가평군 청평면 청평리 657-1278428812885274230623040292826873410272230703219
8가평군2023청아2차경기도 가평군 설악면 미사리로 52-14경기도 가평군 설악면 선촌리 25-1179621121794194918912090198520042069199221651959
9가평군2023어젤리아아파트 1동경기도 가평군 설악면 신천중앙로88번길 19-31경기도 가평군 설악면 신천리426-1490506385475407524490426477424446457
시군명사용연도공동주택명소재지도로명주소소재지지번주소1월2월3월4월5월6월7월8월9월10월11월12월
20가평군2023맹호아파트 A동경기도 가평군 조종면 청군로 1354-18경기도 가평군 조종면 현리 175-1572680699624681712668709757664657494
21가평군2023맹호아파트 B동<NA>경기도 가평군 조종면 현리 477-21389411385380380382401416421358386361
22가평군2023맹호아파트 C동경기도 가평군 조종면 운악청계로 155-5경기도 가평군 조종면 현리 78-1676746634586488664655679690603681663
23가평군2023블루핀아파트경기도 가평군 가평읍 가화로 219경기도 가평군 가평읍 읍내리766-3151617311474161315102000167017811767165113441799
24가평군2023청수아파트경기도 가평군 청평면 경춘로 722경기도 가평군 청평면 청평리634-2147216321447139612991469165815781508160813771537
25가평군2023청운2차아파트경기 가평군 가평읍 경반안로 12경기도 가평군 가평읍 경반리 21-7961971783793744892929845885759773732
26가평군2023가평읍 코아루아파트경기도 가평군 가평읍 석봉로191번길 37경기도 가평군 가평읍 읍내리870-2333140673388371635353758371835943928312537473543
27가평군2023센트럴파크더스카이경기도 가평군 가평읍 보납로 11경기도 가평군 가평읍 읍내리 457-5202317784605188417761978192715062688201319931895
28가평군2023가평 자이아파트경기도 가평군 가평읍 문화로 91경기도 가평군 가평읍 대곡리390-2<NA><NA><NA><NA><NA><NA><NA><NA><NA>1504245645314
29가평군2023가평 이편한세상아파트경기도 가평군 가평읍 문화로 167경기도 가평군 가평군 대곡리 69440251180132306525686131392428947195249