Overview

Dataset statistics

Number of variables18
Number of observations368
Missing cells12
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory56.2 KiB
Average record size in memory156.4 B

Variable types

Categorical3
Text1
Boolean2
Numeric12

Dataset

Description부산광역시_공공기관온실가스배출량현황_20231231
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
계측가능여부 is highly imbalanced (95.1%)Imbalance
임차여부 is highly imbalanced (86.4%)Imbalance
1월 has 57 (15.5%) zerosZeros
2월 has 56 (15.2%) zerosZeros
3월 has 53 (14.4%) zerosZeros
4월 has 69 (18.8%) zerosZeros
5월 has 80 (21.7%) zerosZeros
6월 has 71 (19.3%) zerosZeros
7월 has 73 (19.8%) zerosZeros
8월 has 70 (19.0%) zerosZeros
9월 has 79 (21.5%) zerosZeros
10월 has 76 (20.7%) zerosZeros
11월 has 57 (15.5%) zerosZeros
12월 has 62 (16.8%) zerosZeros

Reproduction

Analysis started2024-03-13 13:16:42.724059
Analysis finished2024-03-13 13:16:59.841694
Duration17.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소속기관명
Categorical

Distinct41
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
부산광역시
68 
상수도사업본부
44 
북부소방서
 
20
체육시설관리사업소
 
16
부산진소방서
 
13
Other values (36)
207 

Length

Max length14
Median length5
Mean length6.3179348
Min length4

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 68
18.5%
상수도사업본부 44
 
12.0%
북부소방서 20
 
5.4%
체육시설관리사업소 16
 
4.3%
부산진소방서 13
 
3.5%
시립박물관 13
 
3.5%
기장소방서 11
 
3.0%
해운대소방서 10
 
2.7%
금정소방서 10
 
2.7%
동래소방서 10
 
2.7%
Other values (31) 153
41.6%

Length

2024-03-13T22:16:59.908937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 68
18.4%
상수도사업본부 44
 
11.9%
북부소방서 20
 
5.4%
체육시설관리사업소 16
 
4.3%
부산진소방서 13
 
3.5%
시립박물관 13
 
3.5%
기장소방서 11
 
3.0%
동래소방서 10
 
2.7%
남부소방서 10
 
2.7%
사하소방서 10
 
2.7%
Other values (32) 155
41.9%
Distinct253
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
2024-03-13T22:17:00.157482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length24
Mean length11.095109
Min length2

Characters and Unicode

Total characters4083
Distinct characters215
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

Unique149 ?
Unique (%)40.5%

Sample

1st row시청사
2nd row시청사
3rd row교통문화연수원
4th row교통문화연수원
5th row부산민속예술관
ValueCountFrequency (%)
경유차량 45
 
6.9%
휘발유차량 38
 
5.8%
청사 36
 
5.5%
14
 
2.2%
구조대 14
 
2.2%
본서 10
 
1.5%
남부소방서 10
 
1.5%
금정소방서 9
 
1.4%
보건환경연구원 9
 
1.4%
부산광역시 8
 
1.2%
Other values (190) 457
70.3%
2024-03-13T22:17:00.549527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
288
 
7.1%
1 214
 
5.2%
147
 
3.6%
146
 
3.6%
140
 
3.4%
123
 
3.0%
123
 
3.0%
117
 
2.9%
111
 
2.7%
9 106
 
2.6%
Other values (205) 2568
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3384
82.9%
Decimal Number 322
 
7.9%
Space Separator 288
 
7.1%
Other Punctuation 33
 
0.8%
Uppercase Letter 29
 
0.7%
Open Punctuation 13
 
0.3%
Close Punctuation 13
 
0.3%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
147
 
4.3%
146
 
4.3%
140
 
4.1%
123
 
3.6%
123
 
3.6%
117
 
3.5%
111
 
3.3%
104
 
3.1%
102
 
3.0%
92
 
2.7%
Other values (190) 2179
64.4%
Uppercase Letter
ValueCountFrequency (%)
P 8
27.6%
G 7
24.1%
L 6
20.7%
C 3
 
10.3%
E 2
 
6.9%
A 2
 
6.9%
N 1
 
3.4%
Decimal Number
ValueCountFrequency (%)
1 214
66.5%
9 106
32.9%
2 2
 
0.6%
Space Separator
ValueCountFrequency (%)
288
100.0%
Other Punctuation
ValueCountFrequency (%)
, 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3384
82.9%
Common 670
 
16.4%
Latin 29
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
147
 
4.3%
146
 
4.3%
140
 
4.1%
123
 
3.6%
123
 
3.6%
117
 
3.5%
111
 
3.3%
104
 
3.1%
102
 
3.0%
92
 
2.7%
Other values (190) 2179
64.4%
Common
ValueCountFrequency (%)
288
43.0%
1 214
31.9%
9 106
 
15.8%
, 33
 
4.9%
( 13
 
1.9%
) 13
 
1.9%
2 2
 
0.3%
- 1
 
0.1%
Latin
ValueCountFrequency (%)
P 8
27.6%
G 7
24.1%
L 6
20.7%
C 3
 
10.3%
E 2
 
6.9%
A 2
 
6.9%
N 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3384
82.9%
ASCII 699
 
17.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
288
41.2%
1 214
30.6%
9 106
 
15.2%
, 33
 
4.7%
( 13
 
1.9%
) 13
 
1.9%
P 8
 
1.1%
G 7
 
1.0%
L 6
 
0.9%
C 3
 
0.4%
Other values (5) 8
 
1.1%
Hangul
ValueCountFrequency (%)
147
 
4.3%
146
 
4.3%
140
 
4.1%
123
 
3.6%
123
 
3.6%
117
 
3.5%
111
 
3.3%
104
 
3.1%
102
 
3.0%
92
 
2.7%
Other values (190) 2179
64.4%

계측가능여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size500.0 B
True
366 
False
 
2
ValueCountFrequency (%)
True 366
99.5%
False 2
 
0.5%
2024-03-13T22:17:00.665983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

임차여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size500.0 B
False
361 
True
 
7
ValueCountFrequency (%)
False 361
98.1%
True 7
 
1.9%
2024-03-13T22:17:00.789178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

연료명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
전력
151 
도시가스(LNG)
65 
가스/디젤 오일(경유)
59 
휘발유
41 
보일러 등유
24 
Other values (5)
28 

Length

Max length12
Median length9
Mean length5.4728261
Min length2

Unique

Unique2 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
전력 151
41.0%
도시가스(LNG) 65
17.7%
가스/디젤 오일(경유) 59
 
16.0%
휘발유 41
 
11.1%
보일러 등유 24
 
6.5%
실내 등유 15
 
4.1%
LPG(차량) 7
 
1.9%
프로판 4
 
1.1%
CNG(차량) 1
 
0.3%
도시가스(LPG) 1
 
0.3%

Length

2024-03-13T22:17:00.943707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:17:01.074754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전력 151
32.4%
도시가스(lng 65
13.9%
가스/디젤 59
 
12.7%
오일(경유 59
 
12.7%
휘발유 41
 
8.8%
등유 39
 
8.4%
보일러 24
 
5.2%
실내 15
 
3.2%
lpg(차량 7
 
1.5%
프로판 4
 
0.9%
Other values (2) 2
 
0.4%

단위
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
kWh
151 
146 
67 
kg
 
4

Length

Max length3
Median length1
Mean length1.8315217
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
kWh 151
41.0%
146
39.7%
67
18.2%
kg 4
 
1.1%

Length

2024-03-13T22:17:01.228242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T22:17:01.644642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kwh 151
41.0%
146
39.7%
67
18.2%
kg 4
 
1.1%

1월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct290
Distinct (%)79.2%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean18738.997
Minimum0
Maximum1027955
Zeros57
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:01.786154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q153.5
median1519.5
Q310647.5
95-th percentile88144
Maximum1027955
Range1027955
Interquartile range (IQR)10594

Descriptive statistics

Standard deviation71005.605
Coefficient of variation (CV)3.7891891
Kurtosis121.38752
Mean18738.997
Median Absolute Deviation (MAD)1519.5
Skewness9.7650756
Sum6858473
Variance5.041796 × 109
MonotonicityNot monotonic
2024-03-13T22:17:01.946584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
 
15.5%
100 4
 
1.1%
40 4
 
1.1%
56 3
 
0.8%
27 2
 
0.5%
130 2
 
0.5%
300 2
 
0.5%
15 2
 
0.5%
87 2
 
0.5%
25 2
 
0.5%
Other values (280) 286
77.7%
ValueCountFrequency (%)
0 57
15.5%
1 1
 
0.3%
3 1
 
0.3%
7 1
 
0.3%
10 1
 
0.3%
12 1
 
0.3%
13 1
 
0.3%
15 2
 
0.5%
19 1
 
0.3%
20 2
 
0.5%
ValueCountFrequency (%)
1027955 1
0.3%
525522 1
0.3%
390499 1
0.3%
313479 1
0.3%
233010 1
0.3%
189684 1
0.3%
178473 1
0.3%
169366 1
0.3%
145152 1
0.3%
141328 1
0.3%

2월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct286
Distinct (%)78.1%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean18020.883
Minimum0
Maximum915421
Zeros56
Zeros (%)15.2%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:02.101402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q145
median1399.5
Q310758.75
95-th percentile91745.75
Maximum915421
Range915421
Interquartile range (IQR)10713.75

Descriptive statistics

Standard deviation67690.455
Coefficient of variation (CV)3.7562231
Kurtosis102.48179
Mean18020.883
Median Absolute Deviation (MAD)1399.5
Skewness9.1167208
Sum6595643
Variance4.5819977 × 109
MonotonicityNot monotonic
2024-03-13T22:17:02.251154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 56
 
15.2%
20 5
 
1.4%
40 4
 
1.1%
80 3
 
0.8%
94 3
 
0.8%
84 2
 
0.5%
27 2
 
0.5%
300 2
 
0.5%
150 2
 
0.5%
120 2
 
0.5%
Other values (276) 285
77.4%
ValueCountFrequency (%)
0 56
15.2%
2 1
 
0.3%
3 2
 
0.5%
4 2
 
0.5%
6 1
 
0.3%
9 1
 
0.3%
10 1
 
0.3%
12 1
 
0.3%
14 1
 
0.3%
17 1
 
0.3%
ValueCountFrequency (%)
915421 1
0.3%
600539 1
0.3%
365877 1
0.3%
360281 1
0.3%
183749 1
0.3%
172591 1
0.3%
165960 1
0.3%
163440 1
0.3%
131760 1
0.3%
122506 1
0.3%

3월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct289
Distinct (%)79.0%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean15264.036
Minimum0
Maximum932355
Zeros53
Zeros (%)14.4%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:02.404529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q154
median1008.5
Q38343.75
95-th percentile73807.75
Maximum932355
Range932355
Interquartile range (IQR)8289.75

Descriptive statistics

Standard deviation63112.46
Coefficient of variation (CV)4.1347165
Kurtosis135.24845
Mean15264.036
Median Absolute Deviation (MAD)1008.5
Skewness10.485774
Sum5586637
Variance3.9831826 × 109
MonotonicityNot monotonic
2024-03-13T22:17:02.570994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 53
 
14.4%
20 5
 
1.4%
40 4
 
1.1%
64 4
 
1.1%
74 3
 
0.8%
80 2
 
0.5%
37 2
 
0.5%
53 2
 
0.5%
98 2
 
0.5%
22 2
 
0.5%
Other values (279) 287
78.0%
ValueCountFrequency (%)
0 53
14.4%
2 1
 
0.3%
4 2
 
0.5%
5 2
 
0.5%
10 1
 
0.3%
16 1
 
0.3%
17 1
 
0.3%
20 5
 
1.4%
22 2
 
0.5%
25 2
 
0.5%
ValueCountFrequency (%)
932355 1
0.3%
528619 1
0.3%
299389 1
0.3%
228577 1
0.3%
184497 1
0.3%
147438 1
0.3%
145297 1
0.3%
142678 1
0.3%
120012 1
0.3%
111456 1
0.3%

4월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct276
Distinct (%)75.4%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13746.484
Minimum0
Maximum864233
Zeros69
Zeros (%)18.8%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:02.726081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.25
median648.5
Q36277.75
95-th percentile63945.25
Maximum864233
Range864233
Interquartile range (IQR)6238.5

Descriptive statistics

Standard deviation59492.552
Coefficient of variation (CV)4.3278379
Kurtosis130.64655
Mean13746.484
Median Absolute Deviation (MAD)648.5
Skewness10.367996
Sum5031213
Variance3.5393638 × 109
MonotonicityNot monotonic
2024-03-13T22:17:02.876073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 69
 
18.8%
40 6
 
1.6%
46 3
 
0.8%
41 3
 
0.8%
215 2
 
0.5%
10 2
 
0.5%
11 2
 
0.5%
80 2
 
0.5%
27001 2
 
0.5%
1 2
 
0.5%
Other values (266) 273
74.2%
ValueCountFrequency (%)
0 69
18.8%
1 2
 
0.5%
3 1
 
0.3%
10 2
 
0.5%
11 2
 
0.5%
13 1
 
0.3%
14 1
 
0.3%
19 2
 
0.5%
20 2
 
0.5%
25 2
 
0.5%
ValueCountFrequency (%)
864233 1
0.3%
527490 1
0.3%
275892 1
0.3%
219721 1
0.3%
173449 1
0.3%
145489 1
0.3%
133596 1
0.3%
130973 1
0.3%
124317 1
0.3%
107964 1
0.3%

5월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct266
Distinct (%)72.7%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean14243.434
Minimum0
Maximum947019
Zeros80
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:03.051886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q117.5
median538.5
Q35927.5
95-th percentile73231.5
Maximum947019
Range947019
Interquartile range (IQR)5910

Descriptive statistics

Standard deviation65904.946
Coefficient of variation (CV)4.6270404
Kurtosis125.74013
Mean14243.434
Median Absolute Deviation (MAD)538.5
Skewness10.252946
Sum5213097
Variance4.3434619 × 109
MonotonicityNot monotonic
2024-03-13T22:17:03.246083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 80
 
21.7%
6380 3
 
0.8%
40 3
 
0.8%
20 3
 
0.8%
221 3
 
0.8%
6 3
 
0.8%
120 2
 
0.5%
156 2
 
0.5%
66 2
 
0.5%
35 2
 
0.5%
Other values (256) 263
71.5%
ValueCountFrequency (%)
0 80
21.7%
2 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
6 3
 
0.8%
7 1
 
0.3%
8 1
 
0.3%
9 1
 
0.3%
10 2
 
0.5%
17 1
 
0.3%
ValueCountFrequency (%)
947019 1
0.3%
528618 1
0.3%
461909 1
0.3%
248813 1
0.3%
189340 1
0.3%
138618 1
0.3%
136073 1
0.3%
134528 1
0.3%
120366 1
0.3%
99052 1
0.3%

6월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct268
Distinct (%)73.2%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean16600.855
Minimum0
Maximum1137970
Zeros71
Zeros (%)19.3%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:03.385657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130.5
median487.5
Q35910.25
95-th percentile74446.5
Maximum1137970
Range1137970
Interquartile range (IQR)5879.75

Descriptive statistics

Standard deviation79188.095
Coefficient of variation (CV)4.7701214
Kurtosis125.44271
Mean16600.855
Median Absolute Deviation (MAD)487.5
Skewness10.249556
Sum6075913
Variance6.2707544 × 109
MonotonicityNot monotonic
2024-03-13T22:17:03.537249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71
 
19.3%
40 5
 
1.4%
50 4
 
1.1%
100 3
 
0.8%
10 3
 
0.8%
80 3
 
0.8%
44 2
 
0.5%
65 2
 
0.5%
8781 2
 
0.5%
39 2
 
0.5%
Other values (258) 269
73.1%
ValueCountFrequency (%)
0 71
19.3%
1 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
6 2
 
0.5%
7 1
 
0.3%
10 3
 
0.8%
11 1
 
0.3%
13 1
 
0.3%
14 1
 
0.3%
ValueCountFrequency (%)
1137970 1
0.3%
633150 1
0.3%
529146 1
0.3%
355833 1
0.3%
272231 1
0.3%
160668 1
0.3%
139896 1
0.3%
138294 1
0.3%
137464 1
0.3%
116504 1
0.3%

7월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct270
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19014.372
Minimum0
Maximum1340699
Zeros73
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:03.681056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126
median514
Q36834.25
95-th percentile86509.3
Maximum1340699
Range1340699
Interquartile range (IQR)6808.25

Descriptive statistics

Standard deviation90166.116
Coefficient of variation (CV)4.741998
Kurtosis136.77043
Mean19014.372
Median Absolute Deviation (MAD)514
Skewness10.622412
Sum6997289
Variance8.1299285 × 109
MonotonicityNot monotonic
2024-03-13T22:17:03.859916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 73
 
19.8%
20 6
 
1.6%
80 3
 
0.8%
71 3
 
0.8%
90 3
 
0.8%
40 3
 
0.8%
12 2
 
0.5%
50 2
 
0.5%
392 2
 
0.5%
87 2
 
0.5%
Other values (260) 269
73.1%
ValueCountFrequency (%)
0 73
19.8%
2 1
 
0.3%
4 1
 
0.3%
6 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
12 2
 
0.5%
14 1
 
0.3%
15 2
 
0.5%
17 1
 
0.3%
ValueCountFrequency (%)
1340699 1
0.3%
620055 1
0.3%
577956 1
0.3%
487523 1
0.3%
296590 1
0.3%
183528 1
0.3%
162594 1
0.3%
147198 1
0.3%
133443 1
0.3%
124459 1
0.3%

8월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct270
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21277.359
Minimum0
Maximum1421764
Zeros70
Zeros (%)19.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:04.020925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q127
median739
Q37687.5
95-th percentile105756.9
Maximum1421764
Range1421764
Interquartile range (IQR)7660.5

Descriptive statistics

Standard deviation97310.854
Coefficient of variation (CV)4.5734461
Kurtosis129.133
Mean21277.359
Median Absolute Deviation (MAD)739
Skewness10.323902
Sum7830068
Variance9.4694022 × 109
MonotonicityNot monotonic
2024-03-13T22:17:04.188606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70
 
19.0%
40 4
 
1.1%
67 4
 
1.1%
24 4
 
1.1%
57 3
 
0.8%
17 3
 
0.8%
47 3
 
0.8%
49 2
 
0.5%
58 2
 
0.5%
50 2
 
0.5%
Other values (260) 271
73.6%
ValueCountFrequency (%)
0 70
19.0%
1 1
 
0.3%
2 1
 
0.3%
6 1
 
0.3%
7 1
 
0.3%
9 2
 
0.5%
10 2
 
0.5%
12 1
 
0.3%
14 1
 
0.3%
15 1
 
0.3%
ValueCountFrequency (%)
1421764 1
0.3%
673235 1
0.3%
644088 1
0.3%
575801 1
0.3%
233265 1
0.3%
203940 1
0.3%
178453 1
0.3%
167940 1
0.3%
152700 1
0.3%
151382 1
0.3%

9월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct264
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19378.356
Minimum0
Maximum1155096
Zeros79
Zeros (%)21.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:04.342786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.75
median587.5
Q37387
95-th percentile90929.75
Maximum1155096
Range1155096
Interquartile range (IQR)7364.25

Descriptive statistics

Standard deviation86250.212
Coefficient of variation (CV)4.4508529
Kurtosis105.76315
Mean19378.356
Median Absolute Deviation (MAD)587.5
Skewness9.5290203
Sum7131235
Variance7.439099 × 109
MonotonicityNot monotonic
2024-03-13T22:17:04.493702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 79
 
21.5%
40 6
 
1.6%
88 4
 
1.1%
100 4
 
1.1%
80 3
 
0.8%
17 3
 
0.8%
43 3
 
0.8%
219 2
 
0.5%
20 2
 
0.5%
53 2
 
0.5%
Other values (254) 260
70.7%
ValueCountFrequency (%)
0 79
21.5%
2 1
 
0.3%
4 1
 
0.3%
10 1
 
0.3%
15 1
 
0.3%
17 3
 
0.8%
18 2
 
0.5%
19 1
 
0.3%
20 2
 
0.5%
22 1
 
0.3%
ValueCountFrequency (%)
1155096 1
0.3%
778895 1
0.3%
637915 1
0.3%
425523 1
0.3%
183597 1
0.3%
176434 1
0.3%
168138 1
0.3%
164286 1
0.3%
151720 1
0.3%
147258 1
0.3%

10월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct274
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14850.769
Minimum0
Maximum900794
Zeros76
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:04.641894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.75
median543
Q36212.75
95-th percentile67585.3
Maximum900794
Range900794
Interquartile range (IQR)6190

Descriptive statistics

Standard deviation67352.327
Coefficient of variation (CV)4.5352753
Kurtosis105.77384
Mean14850.769
Median Absolute Deviation (MAD)543
Skewness9.5321
Sum5465083
Variance4.5363359 × 109
MonotonicityNot monotonic
2024-03-13T22:17:04.772797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 76
 
20.7%
40 4
 
1.1%
18 3
 
0.8%
1 3
 
0.8%
50 3
 
0.8%
102 2
 
0.5%
62 2
 
0.5%
84 2
 
0.5%
8 2
 
0.5%
20 2
 
0.5%
Other values (264) 269
73.1%
ValueCountFrequency (%)
0 76
20.7%
1 3
 
0.8%
5 1
 
0.3%
8 2
 
0.5%
10 1
 
0.3%
15 1
 
0.3%
16 1
 
0.3%
18 3
 
0.8%
20 2
 
0.5%
21 1
 
0.3%
ValueCountFrequency (%)
900794 1
0.3%
616679 1
0.3%
486896 1
0.3%
330922 1
0.3%
149990 1
0.3%
136674 1
0.3%
131078 1
0.3%
130901 1
0.3%
123672 1
0.3%
114372 1
0.3%

11월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct288
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15711.565
Minimum0
Maximum907384
Zeros57
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:04.901250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q147.5
median670.5
Q35955.25
95-th percentile74111
Maximum907384
Range907384
Interquartile range (IQR)5907.75

Descriptive statistics

Standard deviation67989.425
Coefficient of variation (CV)4.3273489
Kurtosis98.220141
Mean15711.565
Median Absolute Deviation (MAD)670.5
Skewness9.0340498
Sum5781856
Variance4.6225619 × 109
MonotonicityNot monotonic
2024-03-13T22:17:05.044436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57
 
15.5%
40 4
 
1.1%
58 3
 
0.8%
80 3
 
0.8%
20 3
 
0.8%
30 3
 
0.8%
35 2
 
0.5%
50 2
 
0.5%
300 2
 
0.5%
399 2
 
0.5%
Other values (278) 287
78.0%
ValueCountFrequency (%)
0 57
15.5%
1 2
 
0.5%
4 1
 
0.3%
7 2
 
0.5%
9 1
 
0.3%
10 1
 
0.3%
13 2
 
0.5%
18 2
 
0.5%
19 2
 
0.5%
20 3
 
0.8%
ValueCountFrequency (%)
907384 1
0.3%
538109 1
0.3%
500328 1
0.3%
338714 1
0.3%
303556 1
0.3%
149058 1
0.3%
145940 1
0.3%
138160 1
0.3%
133767 1
0.3%
117528 1
0.3%

12월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct288
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16643.226
Minimum0
Maximum963655
Zeros62
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2024-03-13T22:17:05.176675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q144
median982
Q37651.25
95-th percentile68225.15
Maximum963655
Range963655
Interquartile range (IQR)7607.25

Descriptive statistics

Standard deviation68758.413
Coefficient of variation (CV)4.1313153
Kurtosis113.81837
Mean16643.226
Median Absolute Deviation (MAD)982
Skewness9.660427
Sum6124707
Variance4.7277194 × 109
MonotonicityNot monotonic
2024-03-13T22:17:05.330320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 62
 
16.8%
40 5
 
1.4%
10 3
 
0.8%
126 3
 
0.8%
164 2
 
0.5%
90 2
 
0.5%
46 2
 
0.5%
25 2
 
0.5%
400 2
 
0.5%
54 2
 
0.5%
Other values (278) 283
76.9%
ValueCountFrequency (%)
0 62
16.8%
1 1
 
0.3%
2 1
 
0.3%
8 1
 
0.3%
9 2
 
0.5%
10 3
 
0.8%
12 1
 
0.3%
14 1
 
0.3%
15 1
 
0.3%
19 2
 
0.5%
ValueCountFrequency (%)
963655 1
0.3%
576484 1
0.3%
402864 1
0.3%
369343 1
0.3%
183942 1
0.3%
154686 1
0.3%
150152 1
0.3%
138129 1
0.3%
137225 1
0.3%
136512 1
0.3%

Interactions

2024-03-13T22:16:58.046580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:43.965111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.254695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.428589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.000776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.233422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:50.523167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:51.818034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.955560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:54.226069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.707989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:56.903857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:58.135366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:44.091869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.345485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.533672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.128112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.331963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:50.629572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:51.945931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.052871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:54.358183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.807552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.027166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:58.220828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:44.181116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.422383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.662065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.213778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.411155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:50.740139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.034744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.149040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:54.458636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.884646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.115107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:58.321054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:44.290018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.511291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.751836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.305611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.497382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:50.847970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.124676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.257852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:54.863364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.966488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.202929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:58.423022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:44.393328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.611277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.850161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.406377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.623433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:50.964046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.213300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.354157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:54.946078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:56.053833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.292663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:58.516424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:44.495418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.698486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.949454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.500585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.741499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:51.065687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.305014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.440725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.026879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:56.137827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.406120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:58.669032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:44.600338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.786365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:47.069920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.601177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.877956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:51.186963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.404604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.538942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.116018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:56.235585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.510813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:58.795466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:44.702489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.885895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:47.177233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.705746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.990246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:51.308444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.499684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.623116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.214183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:56.406857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.617218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:58.882646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:44.812047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.035664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:47.305739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.847858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:50.093212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:51.424973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.590871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.734957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.319651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:56.553503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.706685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:58.984865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:44.912989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.160309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:47.716809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:48.968776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:50.206926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:51.520271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.684646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.821595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.423433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:56.649407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.796318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:59.081266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.004047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.241537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:47.807023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.051461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:50.299614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:51.619286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.770100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:53.916346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.517181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:56.728565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.872647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:59.200928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:45.136998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:46.316166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:47.896869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:49.134092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:50.410316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:51.704637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:52.855365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:54.042588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:55.609791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:56.813419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T22:16:57.951893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T22:17:05.740781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소속기관명계측가능여부임차여부연료명단위1월2월3월4월5월6월7월8월9월10월11월12월
소속기관명1.0000.2540.4900.2790.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
계측가능여부0.2541.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
임차여부0.4900.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
연료명0.2790.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
단위0.0000.0000.0001.0001.0000.1530.1430.0840.0990.0000.0000.0000.2040.0790.1140.1130.098
1월0.0000.0000.0000.0000.1531.0001.0000.9910.9940.9880.9580.9880.9660.9880.9880.8660.991
2월0.0000.0000.0000.0000.1431.0001.0000.9920.9930.9870.9570.9870.9620.9870.9870.8640.990
3월0.0000.0000.0000.0000.0840.9910.9921.0000.9970.9990.9640.9850.9500.9970.9980.9210.999
4월0.0000.0000.0000.0000.0990.9940.9930.9971.0000.9960.9650.9860.9310.9930.9930.8900.996
5월0.0000.0000.0000.0000.0000.9880.9870.9990.9961.0000.9780.9910.9710.9990.9980.9190.999
6월0.0000.0000.0000.0000.0000.9580.9570.9640.9650.9781.0000.9740.9190.9700.9740.9170.968
7월0.0000.0000.0000.0000.0000.9880.9870.9850.9860.9910.9741.0000.9870.9880.9860.9030.987
8월0.0000.0000.0000.0000.2040.9660.9620.9500.9310.9710.9190.9871.0000.9770.9680.8470.956
9월0.0000.0000.0000.0000.0790.9880.9870.9970.9930.9990.9700.9880.9771.0001.0000.9070.999
10월0.0000.0000.0000.0000.1140.9880.9870.9980.9930.9980.9740.9860.9681.0001.0000.9220.999
11월0.0000.0000.0000.0000.1130.8660.8640.9210.8900.9190.9170.9030.8470.9070.9221.0000.928
12월0.0000.0000.0000.0000.0980.9910.9900.9990.9960.9990.9680.9870.9560.9990.9990.9281.000
2024-03-13T22:17:05.861537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소속기관명연료명단위임차여부계측가능여부
소속기관명1.0000.0930.0000.3900.201
연료명0.0931.0000.9920.0000.000
단위0.0000.9921.0000.0000.000
임차여부0.3900.0000.0001.0000.000
계측가능여부0.2010.0000.0000.0001.000
2024-03-13T22:17:05.960626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월7월8월9월10월11월12월소속기관명계측가능여부임차여부연료명단위
1월1.0000.9690.9500.9150.9060.9090.9060.9030.9020.9210.9360.9460.0000.0000.0000.0000.099
2월0.9691.0000.9620.9300.9240.9120.9230.9120.9230.9290.9300.9380.0000.0000.0000.0000.092
3월0.9500.9621.0000.9500.9250.9230.9220.9160.9210.9350.9150.9200.0000.0000.0000.0000.054
4월0.9150.9300.9501.0000.9560.9420.9370.9290.9250.9490.9170.9170.0000.0000.0000.0000.063
5월0.9060.9240.9250.9561.0000.9480.9440.9300.9340.9430.9310.9110.0000.0000.0000.0000.000
6월0.9090.9120.9230.9420.9481.0000.9610.9580.9440.9360.9350.9240.0000.0000.0000.0000.000
7월0.9060.9230.9220.9370.9440.9611.0000.9790.9650.9440.9220.9170.0000.0000.0000.0000.000
8월0.9030.9120.9160.9290.9300.9580.9791.0000.9700.9480.9190.9190.0000.0000.0000.0000.081
9월0.9020.9230.9210.9250.9340.9440.9650.9701.0000.9620.9210.9130.0000.0000.0000.0000.050
10월0.9210.9290.9350.9490.9430.9360.9440.9480.9621.0000.9290.9270.0000.0000.0000.0000.073
11월0.9360.9300.9150.9170.9310.9350.9220.9190.9210.9291.0000.9520.0000.0000.0000.0000.092
12월0.9460.9380.9200.9170.9110.9240.9170.9190.9130.9270.9521.0000.0000.0000.0000.0000.063
소속기관명0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.2010.3900.0930.000
계측가능여부0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2011.0000.0000.0000.000
임차여부0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3900.0001.0000.0000.000
연료명0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0930.0000.0001.0000.992
단위0.0990.0920.0540.0630.0000.0000.0000.0810.0500.0730.0920.0630.0000.0000.0000.9921.000

Missing values

2024-03-13T22:16:59.373725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T22:16:59.606925image/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.
2024-03-13T22:16:59.753914image/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

소속기관명시설명계측가능여부임차여부연료명단위1월2월3월4월5월6월7월8월9월10월11월12월
0부산광역시시청사YN도시가스(LNG)33925216911216581307965150283399141438169824307553122908
1부산광역시시청사YN전력kWh10279559154219323558642339470191137970134069914217641155096900794907384963655
2부산광역시교통문화연수원YN도시가스(LNG)000000000000
3부산광역시교통문화연수원YN전력kWh120081094610347669263807567101249941875960081087310913
4부산광역시부산민속예술관YN전력kWh319027852875194618862262291737824077284221312610
5부산광역시수영사적원YN전력kWh72676436503694941621789377
6부산광역시부산글로벌빌리지YN도시가스(LNG)399532191252236015791897449529556908983478
7부산광역시부산글로벌빌리지YN전력kWh446533628628623234362158930850437474771136858246152865141903
8부산광역시벡스코YN도시가스(LNG)2978934685100521949173273944668748870451595122979339986
9부산광역시벡스코YN전력kWh313479365877299389275892461909633150620055673235637915486896500328402864
소속기관명시설명계측가능여부임차여부연료명단위1월2월3월4월5월6월7월8월9월10월11월12월
358현대미술관현대미술관 청사YN가스/디젤 오일(경유)334366624119
359현대미술관현대미술관 청사YN전력kWh741155775644465383808450610670013344315138295491502695639768509
360현대미술관현대미술관 경유차량YN가스/디젤 오일(경유)43105950085440100668625
361부산근현대역사관부산근현대역사관YN도시가스(LNG)293737323180205323092139108221571
362부산근현대역사관부산근현대역사관YN전력kWh17258178421480699378746970612890172461736215000963626007
363부산근현대역사관임시수도기념관YN전력kWh10419100127755520741384169714191479500644036587608
364부산도서관부산도서관YN도시가스(LNG)75518310498066846866819102396266144619346886
365부산도서관부산도서관YN전력kWh117506105957662175558453082681661043461324081326069068484528107154
366부산도서관부산도서관 휘발유차량YN휘발유064074490140
367부산도서관부산도서관 경유차량YN가스/디젤 오일(경유)466648639589570571597678548585639597