Overview

Dataset statistics

Number of variables18
Number of observations382
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.3 KiB
Average record size in memory156.3 B

Variable types

Categorical3
Text1
Boolean2
Numeric12

Dataset

Description부산광역시_공공기관온실가스배출량현황_20221231
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3067482

Alerts

연료명 is highly overall correlated with 단위High correlation
단위 is highly overall correlated with 연료명High correlation
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
소속기관명 is highly overall correlated with 계측가능여부High correlation
계측가능여부 is highly overall correlated with 소속기관명High correlation
계측가능여부 is highly imbalanced (95.3%)Imbalance
임차여부 is highly imbalanced (86.8%)Imbalance
1월 has 75 (19.6%) zerosZeros
2월 has 71 (18.6%) zerosZeros
3월 has 75 (19.6%) zerosZeros
4월 has 86 (22.5%) zerosZeros
5월 has 92 (24.1%) zerosZeros
6월 has 94 (24.6%) zerosZeros
7월 has 93 (24.3%) zerosZeros
8월 has 92 (24.1%) zerosZeros
9월 has 97 (25.4%) zerosZeros
10월 has 90 (23.6%) zerosZeros
11월 has 79 (20.7%) zerosZeros
12월 has 76 (19.9%) zerosZeros

Reproduction

Analysis started2024-03-13 13:17:08.637568
Analysis finished2024-03-13 13:17:25.172178
Duration16.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소속기관명
Categorical

HIGH CORRELATION 

Distinct40
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
부산광역시
75 
상수도사업본부
49 
북부소방서
 
18
시립박물관
 
18
체육시설관리사업소
 
16
Other values (35)
206 

Length

Max length14
Median length5
Mean length6.2879581
Min length4

Unique

Unique1 ?
Unique (%)0.3%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 75
19.6%
상수도사업본부 49
 
12.8%
북부소방서 18
 
4.7%
시립박물관 18
 
4.7%
체육시설관리사업소 16
 
4.2%
부산진소방서 13
 
3.4%
해운대소방서 12
 
3.1%
동래소방서 12
 
3.1%
금정소방서 12
 
3.1%
사하소방서 11
 
2.9%
Other values (30) 146
38.2%

Length

2024-03-13T22:17:25.245394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 75
19.5%
상수도사업본부 49
 
12.8%
북부소방서 18
 
4.7%
시립박물관 18
 
4.7%
체육시설관리사업소 16
 
4.2%
부산진소방서 13
 
3.4%
해운대소방서 12
 
3.1%
동래소방서 12
 
3.1%
금정소방서 12
 
3.1%
사하소방서 11
 
2.9%
Other values (31) 148
38.5%

시설명
Text

Distinct264
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2024-03-13T22:17:25.600673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length24
Mean length11.133508
Min length2

Characters and Unicode

Total characters4253
Distinct characters218
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)41.9%

Sample

1st row시청사
2nd row시청사
3rd row교통문화연수원
4th row교통문화연수원
5th row센텀벤처타운
ValueCountFrequency (%)
경유차량 48
 
7.0%
휘발유차량 43
 
6.3%
청사 39
 
5.7%
구조대 15
 
2.2%
금정소방서 12
 
1.8%
및 12
 
1.8%
본서 11
 
1.6%
남부소방서 10
 
1.5%
차량 10
 
1.5%
부산광역시 9
 
1.3%
Other values (197) 474
69.4%
2024-03-13T22:17:26.091991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
307
 
7.2%
1 222
 
5.2%
센 160
 
3.8%
터 159
 
3.7%
소 152
 
3.6%
사 129
 
3.0%
전 127
 
3.0%
안 120
 
2.8%
량 113
 
2.7%
차 112
 
2.6%
Other values (208) 2652
62.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3525
82.9%
Decimal Number 334
 
7.9%
Space Separator 307
 
7.2%
Other Punctuation 31
 
0.7%
Uppercase Letter 23
 
0.5%
Close Punctuation 16
 
0.4%
Open Punctuation 16
 
0.4%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
센 160
 
4.5%
터 159
 
4.5%
소 152
 
4.3%
사 129
 
3.7%
전 127
 
3.6%
안 120
 
3.4%
량 113
 
3.2%
차 112
 
3.2%
유 104
 
3.0%
관 103
 
2.9%
Other values (192) 2246
63.7%
Uppercase Letter
ValueCountFrequency (%)
P 5
21.7%
G 5
21.7%
C 4
17.4%
L 3
13.0%
N 2
 
8.7%
A 2
 
8.7%
E 2
 
8.7%
Decimal Number
ValueCountFrequency (%)
1 222
66.5%
9 110
32.9%
2 2
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 28
90.3%
. 3
 
9.7%
Space Separator
ValueCountFrequency (%)
307
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3525
82.9%
Common 705
 
16.6%
Latin 23
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
센 160
 
4.5%
터 159
 
4.5%
소 152
 
4.3%
사 129
 
3.7%
전 127
 
3.6%
안 120
 
3.4%
량 113
 
3.2%
차 112
 
3.2%
유 104
 
3.0%
관 103
 
2.9%
Other values (192) 2246
63.7%
Common
ValueCountFrequency (%)
307
43.5%
1 222
31.5%
9 110
 
15.6%
, 28
 
4.0%
) 16
 
2.3%
( 16
 
2.3%
. 3
 
0.4%
2 2
 
0.3%
- 1
 
0.1%
Latin
ValueCountFrequency (%)
P 5
21.7%
G 5
21.7%
C 4
17.4%
L 3
13.0%
N 2
 
8.7%
A 2
 
8.7%
E 2
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3525
82.9%
ASCII 728
 
17.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
307
42.2%
1 222
30.5%
9 110
 
15.1%
, 28
 
3.8%
) 16
 
2.2%
( 16
 
2.2%
P 5
 
0.7%
G 5
 
0.7%
C 4
 
0.5%
L 3
 
0.4%
Other values (6) 12
 
1.6%
Hangul
ValueCountFrequency (%)
센 160
 
4.5%
터 159
 
4.5%
소 152
 
4.3%
사 129
 
3.7%
전 127
 
3.6%
안 120
 
3.4%
량 113
 
3.2%
차 112
 
3.2%
유 104
 
3.0%
관 103
 
2.9%
Other values (192) 2246
63.7%

계측가능여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size514.0 B
True
380 
False
 
2
ValueCountFrequency (%)
True 380
99.5%
False 2
 
0.5%
2024-03-13T22:17:26.225579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

임차여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size514.0 B
False
375 
True
 
7
ValueCountFrequency (%)
False 375
98.2%
True 7
 
1.8%
2024-03-13T22:17:26.310220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

연료명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
전력
153 
도시가스(LNG)
65 
가스/디젤 오일(경유)
62 
휘발유
47 
보일러 등유
32 
Other values (5)
23 

Length

Max length12
Median length9
Mean length5.4842932
Min length2

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row도시가스(LNG)
2nd row전력
3rd row도시가스(LNG)
4th row전력
5th row도시가스(LNG)

Common Values

ValueCountFrequency (%)
전력 153
40.1%
도시가스(LNG) 65
17.0%
가스/디젤 오일(경유) 62
16.2%
휘발유 47
 
12.3%
보일러 등유 32
 
8.4%
실내 등유 14
 
3.7%
LPG(차량) 4
 
1.0%
프로판 2
 
0.5%
CNG(차량) 2
 
0.5%
도시가스(LPG) 1
 
0.3%

Length

2024-03-13T22:17:26.412440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:17:26.540165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전력 153
31.2%
도시가스(lng 65
13.3%
가스/디젤 62
12.7%
오일(경유 62
12.7%
휘발유 47
 
9.6%
등유 46
 
9.4%
보일러 32
 
6.5%
실내 14
 
2.9%
lpg(차량 4
 
0.8%
프로판 2
 
0.4%
Other values (2) 3
 
0.6%

단위
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
ℓ
159 
kWh
153 
㎥
68 
kg
 
2

Length

Max length3
Median length1
Mean length1.8062827
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row㎥
2nd rowkWh
3rd row㎥
4th rowkWh
5th row㎥

Common Values

ValueCountFrequency (%)
ℓ 159
41.6%
kWh 153
40.1%
㎥ 68
17.8%
kg 2
 
0.5%

Length

2024-03-13T22:17:26.715788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:17:26.858903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ℓ 159
41.6%
kwh 153
40.1%
㎥ 68
17.8%
kg 2
 
0.5%

1월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct288
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16538.764
Minimum0
Maximum998360
Zeros75
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:27.011239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.25
median1042.5
Q38674.25
95-th percentile68777.8
Maximum998360
Range998360
Interquartile range (IQR)8641

Descriptive statistics

Standard deviation66137.619
Coefficient of variation (CV)3.9989457
Kurtosis137.72679
Mean16538.764
Median Absolute Deviation (MAD)1042.5
Skewness10.440283
Sum6317807.8
Variance4.3741847 × 109
MonotonicityNot monotonic
2024-03-13T22:17:27.157325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 75
 
19.6%
50.0 3
 
0.8%
40.0 3
 
0.8%
25.0 3
 
0.8%
20.0 3
 
0.8%
1528.0 2
 
0.5%
45.0 2
 
0.5%
76.0 2
 
0.5%
66.0 2
 
0.5%
35.0 2
 
0.5%
Other values (278) 285
74.6%
ValueCountFrequency (%)
0.0 75
19.6%
1.0 1
 
0.3%
3.0 1
 
0.3%
5.0 1
 
0.3%
8.0 1
 
0.3%
9.0 1
 
0.3%
18.0 1
 
0.3%
20.0 3
 
0.8%
22.0 1
 
0.3%
24.0 1
 
0.3%
ValueCountFrequency (%)
998360.0 1
0.3%
522094.0 1
0.3%
296425.0 1
0.3%
289343.0 1
0.3%
190764.0 1
0.3%
169663.0 1
0.3%
166427.0 1
0.3%
157608.0 1
0.3%
150672.0 1
0.3%
140362.0 1
0.3%

2월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct289
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16278.258
Minimum0
Maximum885148
Zeros71
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:27.309890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q133.25
median1029.5
Q39609.25
95-th percentile74522.35
Maximum885148
Range885148
Interquartile range (IQR)9576

Descriptive statistics

Standard deviation61880.737
Coefficient of variation (CV)3.8014348
Kurtosis117.53854
Mean16278.258
Median Absolute Deviation (MAD)1029.5
Skewness9.6810334
Sum6218294.6
Variance3.8292256 × 109
MonotonicityNot monotonic
2024-03-13T22:17:27.461821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 71
 
18.6%
20.0 4
 
1.0%
30.0 3
 
0.8%
120.0 3
 
0.8%
39.0 3
 
0.8%
94.0 2
 
0.5%
15.0 2
 
0.5%
38.0 2
 
0.5%
74.0 2
 
0.5%
35.0 2
 
0.5%
Other values (279) 288
75.4%
ValueCountFrequency (%)
0.0 71
18.6%
1.0 1
 
0.3%
4.0 1
 
0.3%
6.0 1
 
0.3%
7.0 1
 
0.3%
10.0 1
 
0.3%
11.5 1
 
0.3%
15.0 2
 
0.5%
17.0 1
 
0.3%
20.0 4
 
1.0%
ValueCountFrequency (%)
885148.0 1
0.3%
544282.0 1
0.3%
312121.0 1
0.3%
279081.0 1
0.3%
171972.0 1
0.3%
168153.0 1
0.3%
161497.0 1
0.3%
149867.0 1
0.3%
141523.0 1
0.3%
134940.0 1
0.3%

3월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct285
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14193.367
Minimum0
Maximum894632
Zeros75
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:27.631838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128.5
median762
Q38239.75
95-th percentile61470.15
Maximum894632
Range894632
Interquartile range (IQR)8211.25

Descriptive statistics

Standard deviation57738.244
Coefficient of variation (CV)4.0679736
Kurtosis150.90255
Mean14193.367
Median Absolute Deviation (MAD)762
Skewness10.962136
Sum5421866.3
Variance3.3337048 × 109
MonotonicityNot monotonic
2024-03-13T22:17:27.777035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 75
 
19.6%
40.0 4
 
1.0%
120.0 4
 
1.0%
50.0 4
 
1.0%
34.0 3
 
0.8%
28.0 3
 
0.8%
10.0 2
 
0.5%
32.0 2
 
0.5%
10633.0 2
 
0.5%
30.0 2
 
0.5%
Other values (275) 281
73.6%
ValueCountFrequency (%)
0.0 75
19.6%
2.0 1
 
0.3%
5.0 2
 
0.5%
6.0 1
 
0.3%
7.0 1
 
0.3%
8.0 1
 
0.3%
10.0 2
 
0.5%
10.262 1
 
0.3%
15.0 1
 
0.3%
18.0 1
 
0.3%
ValueCountFrequency (%)
894632.0 1
0.3%
442053.0 1
0.3%
259720.0 1
0.3%
218863.0 1
0.3%
155646.0 1
0.3%
143092.0 1
0.3%
140811.0 1
0.3%
126396.0 1
0.3%
120561.0 1
0.3%
119053.0 1
0.3%

4월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct277
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12989.822
Minimum0
Maximum855853
Zeros86
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:28.035085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.25
median386.1005
Q36230
95-th percentile54316.35
Maximum855853
Range855853
Interquartile range (IQR)6211.75

Descriptive statistics

Standard deviation57335.082
Coefficient of variation (CV)4.4138466
Kurtosis138.56088
Mean12989.822
Median Absolute Deviation (MAD)386.1005
Skewness10.693955
Sum4962112
Variance3.2873116 × 109
MonotonicityNot monotonic
2024-03-13T22:17:28.173618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 86
 
22.5%
20.0 4
 
1.0%
30.0 3
 
0.8%
53.0 2
 
0.5%
80.0 2
 
0.5%
800.0 2
 
0.5%
50.0 2
 
0.5%
43.0 2
 
0.5%
357.0 2
 
0.5%
5821.0 2
 
0.5%
Other values (267) 275
72.0%
ValueCountFrequency (%)
0.0 86
22.5%
2.0 1
 
0.3%
2.4 1
 
0.3%
3.0 2
 
0.5%
5.0 1
 
0.3%
8.5 1
 
0.3%
10.0 2
 
0.5%
12.7 1
 
0.3%
18.0 1
 
0.3%
19.0 1
 
0.3%
ValueCountFrequency (%)
855853.0 1
0.3%
502114.0 1
0.3%
325632.0 1
0.3%
196899.0 1
0.3%
142373.0 1
0.3%
132615.0 1
0.3%
127008.0 1
0.3%
112926.0 1
0.3%
107197.0 1
0.3%
106045.0 1
0.3%

5월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct267
Distinct (%)69.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12736.102
Minimum0
Maximum931552
Zeros92
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:28.689802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.75
median341
Q35313.75
95-th percentile57376.5
Maximum931552
Range931552
Interquartile range (IQR)5308

Descriptive statistics

Standard deviation61331.019
Coefficient of variation (CV)4.8155249
Kurtosis145.65154
Mean12736.102
Median Absolute Deviation (MAD)341
Skewness11.006921
Sum4865191.1
Variance3.7614938 × 109
MonotonicityNot monotonic
2024-03-13T22:17:28.844283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 92
 
24.1%
30.0 5
 
1.3%
40.0 3
 
0.8%
5.0 3
 
0.8%
50.0 3
 
0.8%
20.0 3
 
0.8%
179.0 2
 
0.5%
49.0 2
 
0.5%
520.0 2
 
0.5%
89.0 2
 
0.5%
Other values (257) 265
69.4%
ValueCountFrequency (%)
0.0 92
24.1%
3.0 1
 
0.3%
5.0 3
 
0.8%
8.0 1
 
0.3%
10.0 2
 
0.5%
12.0 1
 
0.3%
15.0 1
 
0.3%
16.0 1
 
0.3%
19.0 1
 
0.3%
20.0 3
 
0.8%
ValueCountFrequency (%)
931552.0 1
0.3%
452512.0 1
0.3%
448614.0 1
0.3%
209220.0 1
0.3%
151773.0 1
0.3%
133416.0 1
0.3%
130903.0 1
0.3%
113814.0 1
0.3%
103309.0 1
0.3%
102326.0 1
0.3%

6월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct264
Distinct (%)69.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14396.828
Minimum0
Maximum1075614
Zeros94
Zeros (%)24.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:29.016175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.25
median320.5
Q34784
95-th percentile71090.95
Maximum1075614
Range1075614
Interquartile range (IQR)4779.75

Descriptive statistics

Standard deviation69855.713
Coefficient of variation (CV)4.85216
Kurtosis149.66462
Mean14396.828
Median Absolute Deviation (MAD)320.5
Skewness11.056059
Sum5499588.3
Variance4.8798206 × 109
MonotonicityNot monotonic
2024-03-13T22:17:29.178789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 94
 
24.6%
30.0 4
 
1.0%
80.0 3
 
0.8%
100.0 3
 
0.8%
40.0 3
 
0.8%
34.0 3
 
0.8%
89.0 3
 
0.8%
20.0 2
 
0.5%
459.0 2
 
0.5%
96.0 2
 
0.5%
Other values (254) 263
68.8%
ValueCountFrequency (%)
0.0 94
24.6%
4.0 2
 
0.5%
5.0 1
 
0.3%
8.0 1
 
0.3%
10.0 2
 
0.5%
12.0 1
 
0.3%
13.5 1
 
0.3%
15.0 1
 
0.3%
19.0 1
 
0.3%
20.0 2
 
0.5%
ValueCountFrequency (%)
1075614.0 1
0.3%
479430.0 1
0.3%
455827.0 1
0.3%
309232.0 1
0.3%
210703.0 1
0.3%
152010.0 1
0.3%
146124.0 1
0.3%
139089.0 1
0.3%
122886.0 1
0.3%
112834.0 1
0.3%

7월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct265
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18338.096
Minimum0
Maximum1302656
Zeros93
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:29.352266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median333.85
Q36013
95-th percentile74114
Maximum1302656
Range1302656
Interquartile range (IQR)6006

Descriptive statistics

Standard deviation89752.848
Coefficient of variation (CV)4.8943385
Kurtosis126.43393
Mean18338.096
Median Absolute Deviation (MAD)333.85
Skewness10.310364
Sum7005152.6
Variance8.0555738 × 109
MonotonicityNot monotonic
2024-03-13T22:17:29.528272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 93
 
24.3%
20.0 7
 
1.8%
40.0 4
 
1.0%
35.0 3
 
0.8%
100.0 3
 
0.8%
60.0 3
 
0.8%
55.0 2
 
0.5%
5021.0 2
 
0.5%
80.0 2
 
0.5%
28.0 2
 
0.5%
Other values (255) 261
68.3%
ValueCountFrequency (%)
0.0 93
24.3%
4.0 1
 
0.3%
5.0 1
 
0.3%
7.0 2
 
0.5%
10.0 1
 
0.3%
12.0 1
 
0.3%
14.0 1
 
0.3%
16.0 1
 
0.3%
19.37 1
 
0.3%
20.0 7
 
1.8%
ValueCountFrequency (%)
1302656.0 1
0.3%
726360.0 1
0.3%
573950.0 1
0.3%
544819.0 1
0.3%
266972.0 1
0.3%
192006.0 1
0.3%
166154.0 1
0.3%
159840.0 1
0.3%
134979.0 1
0.3%
128549.0 1
0.3%

8월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct262
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19451.024
Minimum0
Maximum1378206
Zeros92
Zeros (%)24.1%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:29.677075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.25
median327.36
Q37230.75
95-th percentile75576.05
Maximum1378206
Range1378206
Interquartile range (IQR)7224.5

Descriptive statistics

Standard deviation92799.717
Coefficient of variation (CV)4.7709424
Kurtosis133.99841
Mean19451.024
Median Absolute Deviation (MAD)327.36
Skewness10.537318
Sum7430291.2
Variance8.6117874 × 109
MonotonicityNot monotonic
2024-03-13T22:17:29.831161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 92
 
24.1%
20.0 8
 
2.1%
40.0 4
 
1.0%
67.0 3
 
0.8%
125.0 2
 
0.5%
82.0 2
 
0.5%
42.0 2
 
0.5%
5144.0 2
 
0.5%
80.0 2
 
0.5%
27.0 2
 
0.5%
Other values (252) 263
68.8%
ValueCountFrequency (%)
0.0 92
24.1%
1.0 1
 
0.3%
4.0 2
 
0.5%
6.0 1
 
0.3%
7.0 1
 
0.3%
10.0 2
 
0.5%
12.0 1
 
0.3%
14.0 1
 
0.3%
16.7 1
 
0.3%
18.0 1
 
0.3%
ValueCountFrequency (%)
1378206.0 1
0.3%
676125.0 1
0.3%
645656.0 1
0.3%
504937.0 1
0.3%
267705.0 1
0.3%
186606.0 1
0.3%
186385.0 1
0.3%
163945.0 1
0.3%
159354.0 1
0.3%
151995.0 1
0.3%

9월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct262
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16479.845
Minimum0
Maximum1154542
Zeros97
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:29.982767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median341
Q36604.25
95-th percentile72424.2
Maximum1154542
Range1154542
Interquartile range (IQR)6604.25

Descriptive statistics

Standard deviation78976.456
Coefficient of variation (CV)4.7923058
Kurtosis132.64802
Mean16479.845
Median Absolute Deviation (MAD)341
Skewness10.581157
Sum6295300.7
Variance6.2372805 × 109
MonotonicityNot monotonic
2024-03-13T22:17:30.171552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 97
 
25.4%
30.0 4
 
1.0%
36.0 3
 
0.8%
5.0 3
 
0.8%
20.0 3
 
0.8%
40.0 3
 
0.8%
4347.0 2
 
0.5%
56.0 2
 
0.5%
17.0 2
 
0.5%
35.0 2
 
0.5%
Other values (252) 261
68.3%
ValueCountFrequency (%)
0.0 97
25.4%
4.0 1
 
0.3%
5.0 3
 
0.8%
10.0 2
 
0.5%
13.81 1
 
0.3%
17.0 2
 
0.5%
18.0 1
 
0.3%
20.0 3
 
0.8%
22.359 1
 
0.3%
25.0 2
 
0.5%
ValueCountFrequency (%)
1154542.0 1
0.3%
720661.0 1
0.3%
461734.0 1
0.3%
345934.0 1
0.3%
253045.0 1
0.3%
172964.0 1
0.3%
147780.0 1
0.3%
145536.0 1
0.3%
134550.0 1
0.3%
132154.0 1
0.3%

10월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct269
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13534.643
Minimum0
Maximum935138
Zeros90
Zeros (%)23.6%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:30.356281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.25
median251.5
Q35421.25
95-th percentile57369.85
Maximum935138
Range935138
Interquartile range (IQR)5412

Descriptive statistics

Standard deviation63875.435
Coefficient of variation (CV)4.7194031
Kurtosis129.24022
Mean13534.643
Median Absolute Deviation (MAD)251.5
Skewness10.362453
Sum5170233.6
Variance4.0800712 × 109
MonotonicityNot monotonic
2024-03-13T22:17:30.514262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 90
 
23.6%
40.0 6
 
1.6%
30.0 4
 
1.0%
90.0 3
 
0.8%
187.0 2
 
0.5%
20.0 2
 
0.5%
26.0 2
 
0.5%
135.0 2
 
0.5%
41.0 2
 
0.5%
100.0 2
 
0.5%
Other values (259) 267
69.9%
ValueCountFrequency (%)
0.0 90
23.6%
1.0 1
 
0.3%
3.0 1
 
0.3%
5.0 1
 
0.3%
6.0 1
 
0.3%
7.0 1
 
0.3%
9.0 1
 
0.3%
10.0 2
 
0.5%
11.0 1
 
0.3%
11.5 1
 
0.3%
ValueCountFrequency (%)
935138.0 1
0.3%
520266.0 1
0.3%
425548.0 1
0.3%
310010.0 1
0.3%
188745.0 1
0.3%
132174.0 1
0.3%
123670.0 1
0.3%
117414.0 1
0.3%
114016.0 1
0.3%
111586.0 1
0.3%

11월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct286
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13082.735
Minimum0
Maximum901306
Zeros79
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:30.653081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q120
median483
Q35004
95-th percentile59138.9
Maximum901306
Range901306
Interquartile range (IQR)4984

Descriptive statistics

Standard deviation60868.817
Coefficient of variation (CV)4.6526065
Kurtosis132.42649
Mean13082.735
Median Absolute Deviation (MAD)483
Skewness10.436633
Sum4997604.7
Variance3.7050128 × 109
MonotonicityNot monotonic
2024-03-13T22:17:30.788806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 79
 
20.7%
20.0 3
 
0.8%
40.0 3
 
0.8%
19.0 2
 
0.5%
125.0 2
 
0.5%
7.0 2
 
0.5%
140.0 2
 
0.5%
378.0 2
 
0.5%
75.0 2
 
0.5%
1480.0 2
 
0.5%
Other values (276) 283
74.1%
ValueCountFrequency (%)
0.0 79
20.7%
1.0 1
 
0.3%
2.0 1
 
0.3%
4.0 1
 
0.3%
6.0 2
 
0.5%
7.0 2
 
0.5%
8.0 1
 
0.3%
12.0 1
 
0.3%
15.0 1
 
0.3%
16.0 1
 
0.3%
ValueCountFrequency (%)
901306.0 1
0.3%
443717.0 1
0.3%
441560.0 1
0.3%
282986.0 1
0.3%
156399.0 1
0.3%
135445.0 1
0.3%
134316.0 1
0.3%
124704.0 1
0.3%
118818.0 1
0.3%
118584.0 1
0.3%

12월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct285
Distinct (%)74.8%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean15430.167
Minimum0
Maximum1020203
Zeros76
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2024-03-13T22:17:30.915040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median653
Q36970
95-th percentile69197
Maximum1020203
Range1020203
Interquartile range (IQR)6940

Descriptive statistics

Standard deviation67017.913
Coefficient of variation (CV)4.3433045
Kurtosis140.89952
Mean15430.167
Median Absolute Deviation (MAD)653
Skewness10.586098
Sum5878893.5
Variance4.4914007 × 109
MonotonicityNot monotonic
2024-03-13T22:17:31.061162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 76
 
19.9%
45.0 6
 
1.6%
50.0 4
 
1.0%
40.0 4
 
1.0%
25.0 3
 
0.8%
20.0 2
 
0.5%
500.0 2
 
0.5%
35.0 2
 
0.5%
300.0 2
 
0.5%
115.0 2
 
0.5%
Other values (275) 278
72.8%
ValueCountFrequency (%)
0.0 76
19.9%
2.0 1
 
0.3%
7.0 1
 
0.3%
8.0 1
 
0.3%
9.5 1
 
0.3%
12.0 1
 
0.3%
16.0 1
 
0.3%
17.0 2
 
0.5%
18.7 1
 
0.3%
19.0 1
 
0.3%
ValueCountFrequency (%)
1020203.0 1
0.3%
419454.0 1
0.3%
393923.0 1
0.3%
368406.0 1
0.3%
222082.0 1
0.3%
184338.0 1
0.3%
144136.0 1
0.3%
143489.0 1
0.3%
136836.0 1
0.3%
126037.0 1
0.3%

Interactions

2024-03-13T22:17:23.571344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:09.687981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.834919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:12.077552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:13.339970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:14.846790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.279965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.471514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.591975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.803358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.896934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.363763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:23.695193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:09.805073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.949750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:12.175613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:13.470454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:14.988961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.356751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.564225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.698744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.905694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.994673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.481473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:23.779143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:09.911517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.059505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:12.278223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:13.586205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:15.085788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.438503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.675501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.806750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.992057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:21.114771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.576050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:23.866042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.009587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.144504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:12.377011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:13.696742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:15.202321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.563514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.770223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.948000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.067127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:21.244506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.652459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:23.962248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.095747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.228903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:12.466161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:13.783943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:15.320207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.704777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.873986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.040334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.141993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:21.331474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.739320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:24.072690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.177053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.331312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:12.549501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:13.897763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:15.696353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.793347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.956894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.118440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.218903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:21.413424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.824831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:24.166221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.261817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.430501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:12.649417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:14.003066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:15.773747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.878514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.039361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.214094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.306660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:21.518890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.915456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:24.267506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.343984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.539169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:12.741694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:14.122234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:15.860401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.974378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.142803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.316632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.403211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:21.604122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:23.017477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:24.397126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.438259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.628513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:12.893245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:14.363481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:15.955268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.064948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.247436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.417398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.499322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:21.694528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:23.125835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:24.492220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.549109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.739152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:13.020044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:14.554959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.045492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.165823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.334244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.514253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.618592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.104697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:23.221831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:24.572139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.640563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.830103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:13.133876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:14.661911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.127017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.263498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.412702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.596839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.708728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.184095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:23.328227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:24.670104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:10.730799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:11.950607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:13.222747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:14.741471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:16.198488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:17.382752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:18.501829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:19.682333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:20.813630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:22.270792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:17:23.457381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:17:31.182364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소속기관명계측가능여부임차여부연료명단위1월2월3월4월5월6월7월8월9월10월11월12월
소속기관명1.0000.7040.4680.3610.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
계측가능여부0.7041.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
임차여부0.4680.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
연료명0.3610.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
단위0.0000.0000.0001.0001.0000.1310.1240.1220.1040.0810.1040.0000.0810.0640.0630.0810.104
1월0.0000.0000.0000.0000.1311.0001.0001.0000.9670.9880.9840.8680.9790.9120.9350.9880.898
2월0.0000.0000.0000.0000.1241.0001.0001.0000.9590.9850.9820.8640.9820.9180.9390.9850.891
3월0.0000.0000.0000.0000.1221.0001.0001.0000.9720.9890.9820.8710.9760.9140.9380.9890.890
4월0.0000.0000.0000.0000.1040.9670.9590.9721.0000.9690.9450.9850.9310.9930.9670.9690.990
5월0.0000.0000.0000.0000.0810.9880.9850.9890.9691.0000.9980.8870.9960.9080.9541.0000.898
6월0.0000.0000.0000.0000.1040.9840.9820.9820.9450.9981.0000.8950.9970.9130.9550.9980.886
7월0.0000.0000.0000.0000.0000.8680.8640.8710.9850.8870.8951.0000.8870.9940.9690.8870.998
8월0.0000.0000.0000.0000.0810.9790.9820.9760.9310.9960.9970.8871.0000.9080.9540.9960.898
9월0.0000.0000.0000.0000.0640.9120.9180.9140.9930.9080.9130.9940.9081.0000.9800.9080.989
10월0.0000.0000.0000.0000.0630.9350.9390.9380.9670.9540.9550.9690.9540.9801.0000.9540.964
11월0.0000.0000.0000.0000.0810.9880.9850.9890.9691.0000.9980.8870.9960.9080.9541.0000.898
12월0.0000.0000.0000.0000.1040.8980.8910.8900.9900.8980.8860.9980.8980.9890.9640.8981.000
2024-03-13T22:17:31.359359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소속기관명연료명단위임차여부계측가능여부
소속기관명1.0000.1170.0000.3540.543
연료명0.1171.0000.9920.0000.000
단위0.0000.9921.0000.0000.000
임차여부0.3540.0000.0001.0000.000
계측가능여부0.5430.0000.0000.0001.000
2024-03-13T22:17:31.523454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월소속기관명계측가능여부임차여부연료명단위
1월1.0000.9650.9530.9220.9050.8950.8880.8830.8890.8830.9200.9190.0000.0000.0000.0000.107
2월0.9651.0000.9580.9190.9150.8980.9010.9010.8930.8870.9260.9230.0000.0000.0000.0000.101
3월0.9530.9581.0000.9410.9290.9120.9120.9020.9070.8970.9220.9260.0000.0000.0000.0000.100
4월0.9220.9190.9411.0000.9600.9430.9280.9170.9390.9330.9330.9020.0000.0000.0000.0000.067
5월0.9050.9150.9290.9601.0000.9680.9560.9460.9580.9500.9290.9020.0000.0000.0000.0000.066
6월0.8950.8980.9120.9430.9681.0000.9700.9530.9640.9540.9130.9040.0000.0000.0000.0000.084
7월0.8880.9010.9120.9280.9560.9701.0000.9800.9590.9440.9050.9120.0000.0000.0000.0000.000
8월0.8830.9010.9020.9170.9460.9530.9801.0000.9640.9490.9110.9140.0000.0000.0000.0000.066
9월0.8890.8930.9070.9390.9580.9640.9590.9641.0000.9580.9250.9010.0000.0000.0000.0000.041
10월0.8830.8870.8970.9330.9500.9540.9440.9490.9581.0000.9220.9030.0000.0000.0000.0000.043
11월0.9200.9260.9220.9330.9290.9130.9050.9110.9250.9221.0000.9300.0000.0000.0000.0000.066
12월0.9190.9230.9260.9020.9020.9040.9120.9140.9010.9030.9301.0000.0000.0000.0000.0000.067
소속기관명0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.5430.3540.1170.000
계측가능여부0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.5431.0000.0000.0000.000
임차여부0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3540.0001.0000.0000.000
연료명0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1170.0000.0001.0000.992
단위0.1070.1010.1000.0670.0660.0840.0000.0660.0410.0430.0660.0670.0000.0000.0000.9921.000

Missing values

2024-03-13T22:17:24.842033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:17:25.077070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

소속기관명시설명계측가능여부임차여부연료명단위1월2월3월4월5월6월7월8월9월10월11월12월
0부산광역시시청사YN도시가스(LNG)㎥43649.037740.014614.07094.05584.019537.038631.041888.08996.06964.07997.026463.0
1부산광역시시청사YN전력kWh998360.0885148.0894632.0855853.0931552.01075614.01302656.01378206.01154542.0935138.0901306.01020203.0
2부산광역시교통문화연수원YN도시가스(LNG)㎥0.00.00.00.00.00.00.00.00.00.00.00.0
3부산광역시교통문화연수원YN전력kWh11422.010250.011568.07323.06164.07243.09411.08698.06826.06276.07496.011351.0
4부산광역시센텀벤처타운YN도시가스(LNG)㎥704.3673.3584.42.483.9144.9334.7964.0877.19.0748.3689.0
5부산광역시센텀벤처타운YN전력kWh4685.04390.03295.02781.02317.03042.03157.03522.03479.02449.04321.06343.0
6부산광역시부산민속예술관YN전력kWh3970.02494.026662.02124.01303.01644.03190.02854.03036.02464.01098.03044.0
7부산광역시수영사적원YN전력kWh69.051.079.061.075.071.067.0141.0164.090.048.045.0
8부산광역시부산글로벌빌리지YN도시가스(LNG)㎥3008.02628.02055.0199.0436.02205.03516.03547.01598.01086.0195.03996.0
9부산광역시부산글로벌빌리지YN전력kWh39317.033137.028033.022178.022461.033196.041752.044964.032920.027186.025126.045112.0
소속기관명시설명계측가능여부임차여부연료명단위1월2월3월4월5월6월7월8월9월10월11월12월
372시립미술관부산시립미술관YN도시가스(LNG)㎥20329.021378.09796.02530.06817.012903.016338.028872.019806.010040.01863.031229.0
373시립미술관부산시립미술관YN전력kWh169663.0149867.0104968.0106045.0151773.0210703.0266972.0267705.0253045.0188745.0156399.0222082.0
374시립미술관부산시립미술관 휘발유차량YN휘발유ℓ62.039.068.082.096.00.00.00.00.00.00.00.0
375시립미술관부산시립미술관 경유차량YN가스/디젤 오일(경유)ℓ0.04.08.00.05.00.00.00.00.03.06.00.0
376부산예술회관부산예술회관YN전력kWh21240.019507.015116.010368.06926.09696.015706.016123.018648.013050.08895.012576.0
377부산예술회관부산예술회관 휘발유차량YN휘발유ℓ0.00.00.00.00.00.00.00.00.00.00.00.0
378현대미술관현대미술관 청사YN도시가스(LPG)㎥5912.05301.02351.01076.01294.03574.04333.03120.02242.01413.02233.08093.0
379현대미술관현대미술관 청사YN가스/디젤 오일(경유)ℓ1.01.02.03.03.04.05.04.05.011.02.02.0
380현대미술관현대미술관 청사YN전력kWh72207.058920.050021.049666.057643.0109070.0112249.087461.073099.051192.043752.077268.0
381현대미술관현대미술관 경유차량YN가스/디젤 오일(경유)ℓ0.094.0120.053.046.053.043.044.056.036.0105.0<NA>