Overview

Dataset statistics

Number of variables15
Number of observations1218
Missing cells1213
Missing cells (%)6.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory158.3 KiB
Average record size in memory133.1 B

Variable types

Numeric13
Categorical2

Dataset

Description한국지역난방공사에서 생산하는 열생산량 정보를 지역별, 월별로 표현한 자료입니다.(2022년2월)현재 API로 서비스를 제공하고 있으며 과거 및 실시간 데이터를 확인할 수 있습니다.
Author한국지역난방공사
URLhttps://www.data.go.kr/data/15069270/fileData.do

Alerts

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
3월 has 121 (9.9%) missing valuesMissing
4월 has 121 (9.9%) missing valuesMissing
5월 has 122 (10.0%) missing valuesMissing
6월 has 121 (9.9%) missing valuesMissing
7월 has 121 (9.9%) missing valuesMissing
8월 has 123 (10.1%) missing valuesMissing
9월 has 121 (9.9%) missing valuesMissing
10월 has 121 (9.9%) missing valuesMissing
11월 has 121 (9.9%) missing valuesMissing
12월 has 121 (9.9%) missing valuesMissing
1월 has 726 (59.6%) zerosZeros
2월 has 732 (60.1%) zerosZeros
3월 has 668 (54.8%) zerosZeros
4월 has 698 (57.3%) zerosZeros
5월 has 727 (59.7%) zerosZeros
6월 has 743 (61.0%) zerosZeros
7월 has 748 (61.4%) zerosZeros
8월 has 767 (63.0%) zerosZeros
9월 has 744 (61.1%) zerosZeros
10월 has 705 (57.9%) zerosZeros
11월 has 652 (53.5%) zerosZeros
12월 has 639 (52.5%) zerosZeros

Reproduction

Analysis started2023-12-12 21:45:56.309729
Analysis finished2023-12-12 21:46:16.995447
Duration20.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

Distinct11
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.2562
Minimum2012
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:17.063268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2012
Q12015
median2017
Q32020
95-th percentile2022
Maximum2022
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1154291
Coefficient of variation (CV)0.0015443895
Kurtosis-1.1868275
Mean2017.2562
Median Absolute Deviation (MAD)3
Skewness-0.092155754
Sum2457018
Variance9.7058988
MonotonicityNot monotonic
2023-12-13T06:46:17.190392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2019 120
9.9%
2020 120
9.9%
2021 120
9.9%
2022 120
9.9%
2017 114
9.4%
2018 114
9.4%
2014 108
8.9%
2015 108
8.9%
2016 108
8.9%
2013 96
7.9%
ValueCountFrequency (%)
2012 90
7.4%
2013 96
7.9%
2014 108
8.9%
2015 108
8.9%
2016 108
8.9%
2017 114
9.4%
2018 114
9.4%
2019 120
9.9%
2020 120
9.9%
2021 120
9.9%
ValueCountFrequency (%)
2022 120
9.9%
2021 120
9.9%
2020 120
9.9%
2019 120
9.9%
2018 114
9.4%
2017 114
9.4%
2016 108
8.9%
2015 108
8.9%
2014 108
8.9%
2013 96
7.9%

사업소명
Categorical

Distinct21
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
강남
 
66
판교
 
66
고양
 
66
수원
 
66
용인
 
66
Other values (16)
888 

Length

Max length11
Median length2
Mean length2.4039409
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강남 66
 
5.4%
판교 66
 
5.4%
고양 66
 
5.4%
수원 66
 
5.4%
용인 66
 
5.4%
삼송 66
 
5.4%
파주 66
 
5.4%
화성 66
 
5.4%
대구 66
 
5.4%
광교 66
 
5.4%
Other values (11) 558
45.8%

Length

2023-12-13T06:46:17.317289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남 66
 
5.4%
대구 66
 
5.4%
세종 66
 
5.4%
청주 66
 
5.4%
중앙(상암 66
 
5.4%
판교 66
 
5.4%
광교 66
 
5.4%
분당 66
 
5.4%
화성 66
 
5.4%
파주 66
 
5.4%
Other values (11) 558
45.8%

구분
Categorical

Distinct6
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size9.6 KiB
발전회사
203 
소각로
203 
CHP
203 
PLB
203 
CES
203 

Length

Max length4
Median length3
Mean length3
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row발전회사
2nd row소각로
3rd rowCHP
4th rowPLB
5th rowCES

Common Values

ValueCountFrequency (%)
발전회사 203
16.7%
소각로 203
16.7%
CHP 203
16.7%
PLB 203
16.7%
CES 203
16.7%
기타 203
16.7%

Length

2023-12-13T06:46:17.434405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:46:17.550337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
발전회사 203
16.7%
소각로 203
16.7%
chp 203
16.7%
plb 203
16.7%
ces 203
16.7%
기타 203
16.7%

1월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct483
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23525.058
Minimum0
Maximum439820
Zeros726
Zeros (%)59.6%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:17.693277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38258
95-th percentile128404.1
Maximum439820
Range439820
Interquartile range (IQR)8258

Descriptive statistics

Standard deviation64849.456
Coefficient of variation (CV)2.7566119
Kurtosis15.595946
Mean23525.058
Median Absolute Deviation (MAD)0
Skewness3.8637928
Sum28653521
Variance4.2054519 × 109
MonotonicityNot monotonic
2023-12-13T06:46:17.861265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 726
59.6%
8 3
 
0.2%
2062 2
 
0.2%
34 2
 
0.2%
380889 2
 
0.2%
13330 2
 
0.2%
26 2
 
0.2%
2755 2
 
0.2%
2 2
 
0.2%
4070 2
 
0.2%
Other values (473) 473
38.8%
ValueCountFrequency (%)
0 726
59.6%
2 2
 
0.2%
4 1
 
0.1%
6 1
 
0.1%
8 3
 
0.2%
15 1
 
0.1%
24 1
 
0.1%
26 2
 
0.2%
32 1
 
0.1%
34 2
 
0.2%
ValueCountFrequency (%)
439820 1
0.1%
432956 1
0.1%
423248 1
0.1%
420565 1
0.1%
411091 1
0.1%
380889 2
0.2%
370945 1
0.1%
352994 1
0.1%
352959 1
0.1%
350983 1
0.1%

2월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct480
Distinct (%)39.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19463.225
Minimum0
Maximum394190
Zeros732
Zeros (%)60.1%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:18.043632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37492
95-th percentile111259
Maximum394190
Range394190
Interquartile range (IQR)7492

Descriptive statistics

Standard deviation54593.28
Coefficient of variation (CV)2.8049452
Kurtosis16.516452
Mean19463.225
Median Absolute Deviation (MAD)0
Skewness3.9679122
Sum23706208
Variance2.9804262 × 109
MonotonicityNot monotonic
2023-12-13T06:46:18.222382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 732
60.1%
43 2
 
0.2%
2875 2
 
0.2%
3133 2
 
0.2%
50 2
 
0.2%
12016 2
 
0.2%
344860 2
 
0.2%
6680 2
 
0.2%
9246 1
 
0.1%
2249 1
 
0.1%
Other values (470) 470
38.6%
ValueCountFrequency (%)
0 732
60.1%
1 1
 
0.1%
2 1
 
0.1%
5 1
 
0.1%
7 1
 
0.1%
9 1
 
0.1%
14 1
 
0.1%
16 1
 
0.1%
29 1
 
0.1%
31 1
 
0.1%
ValueCountFrequency (%)
394190 1
0.1%
369912 1
0.1%
353098 1
0.1%
344860 2
0.2%
330866 1
0.1%
326906 1
0.1%
321377 1
0.1%
316421 1
0.1%
307774 1
0.1%
302495 1
0.1%

3월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct424
Distinct (%)38.7%
Missing121
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean14446.599
Minimum0
Maximum288982
Zeros668
Zeros (%)54.8%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:18.382915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34338
95-th percentile89278.4
Maximum288982
Range288982
Interquartile range (IQR)4338

Descriptive statistics

Standard deviation42343.688
Coefficient of variation (CV)2.9310489
Kurtosis16.933925
Mean14446.599
Median Absolute Deviation (MAD)0
Skewness4.048808
Sum15847919
Variance1.7929879 × 109
MonotonicityNot monotonic
2023-12-13T06:46:18.563633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 668
54.8%
10 3
 
0.2%
591 2
 
0.2%
4 2
 
0.2%
42 2
 
0.2%
1018 2
 
0.2%
8 1
 
0.1%
31476 1
 
0.1%
21 1
 
0.1%
3328 1
 
0.1%
Other values (414) 414
34.0%
(Missing) 121
 
9.9%
ValueCountFrequency (%)
0 668
54.8%
2 1
 
0.1%
4 2
 
0.2%
6 1
 
0.1%
8 1
 
0.1%
10 3
 
0.2%
15 1
 
0.1%
18 1
 
0.1%
21 1
 
0.1%
22 1
 
0.1%
ValueCountFrequency (%)
288982 1
0.1%
278296 1
0.1%
276641 1
0.1%
263338 1
0.1%
258795 1
0.1%
240517 1
0.1%
237529 1
0.1%
235835 1
0.1%
235752 1
0.1%
232697 1
0.1%

4월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct397
Distinct (%)36.2%
Missing121
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean8653.206
Minimum0
Maximum222362
Zeros698
Zeros (%)57.3%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:18.748264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33021
95-th percentile44839
Maximum222362
Range222362
Interquartile range (IQR)3021

Descriptive statistics

Standard deviation26384.861
Coefficient of variation (CV)3.0491428
Kurtosis25.731314
Mean8653.206
Median Absolute Deviation (MAD)0
Skewness4.7825519
Sum9492567
Variance6.9616086 × 108
MonotonicityNot monotonic
2023-12-13T06:46:18.896833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 698
57.3%
134 2
 
0.2%
1 2
 
0.2%
5 2
 
0.2%
23339 1
 
0.1%
14 1
 
0.1%
1919 1
 
0.1%
970 1
 
0.1%
30835 1
 
0.1%
2509 1
 
0.1%
Other values (387) 387
31.8%
(Missing) 121
 
9.9%
ValueCountFrequency (%)
0 698
57.3%
1 2
 
0.2%
4 1
 
0.1%
5 2
 
0.2%
14 1
 
0.1%
16 1
 
0.1%
17 1
 
0.1%
25 1
 
0.1%
30 1
 
0.1%
31 1
 
0.1%
ValueCountFrequency (%)
222362 1
0.1%
214008 1
0.1%
212846 1
0.1%
187938 1
0.1%
183876 1
0.1%
180107 1
0.1%
173919 1
0.1%
171310 1
0.1%
168142 1
0.1%
157715 1
0.1%

5월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct364
Distinct (%)33.2%
Missing122
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean5025.0265
Minimum0
Maximum165320
Zeros727
Zeros (%)59.7%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:19.066477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32051.5
95-th percentile25764.75
Maximum165320
Range165320
Interquartile range (IQR)2051.5

Descriptive statistics

Standard deviation15850.879
Coefficient of variation (CV)3.1543872
Kurtosis36.363418
Mean5025.0265
Median Absolute Deviation (MAD)0
Skewness5.4397508
Sum5507429
Variance2.5125037 × 108
MonotonicityNot monotonic
2023-12-13T06:46:19.229186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 727
59.7%
46 2
 
0.2%
75 2
 
0.2%
2050 2
 
0.2%
2 2
 
0.2%
1532 2
 
0.2%
3978 2
 
0.2%
148294 1
 
0.1%
8656 1
 
0.1%
1175 1
 
0.1%
Other values (354) 354
29.1%
(Missing) 122
 
10.0%
ValueCountFrequency (%)
0 727
59.7%
2 2
 
0.2%
3 1
 
0.1%
8 1
 
0.1%
19 1
 
0.1%
22 1
 
0.1%
28 1
 
0.1%
30 1
 
0.1%
34 1
 
0.1%
46 2
 
0.2%
ValueCountFrequency (%)
165320 1
0.1%
156560 1
0.1%
148294 1
0.1%
118312 1
0.1%
115588 1
0.1%
112419 1
0.1%
102757 1
0.1%
92841 1
0.1%
90833 1
0.1%
84439 1
0.1%

6월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct347
Distinct (%)31.6%
Missing121
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean3688.3373
Minimum0
Maximum101431
Zeros743
Zeros (%)61.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:19.395572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31211
95-th percentile23334.8
Maximum101431
Range101431
Interquartile range (IQR)1211

Descriptive statistics

Standard deviation10765.208
Coefficient of variation (CV)2.9187157
Kurtosis28.015847
Mean3688.3373
Median Absolute Deviation (MAD)0
Skewness4.7492755
Sum4046106
Variance1.158897 × 108
MonotonicityNot monotonic
2023-12-13T06:46:19.882053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 743
61.0%
1605 3
 
0.2%
50 3
 
0.2%
25352 2
 
0.2%
548 2
 
0.2%
200 2
 
0.2%
48 2
 
0.2%
22685 1
 
0.1%
2667 1
 
0.1%
1029 1
 
0.1%
Other values (337) 337
27.7%
(Missing) 121
 
9.9%
ValueCountFrequency (%)
0 743
61.0%
1 1
 
0.1%
2 1
 
0.1%
4 1
 
0.1%
7 1
 
0.1%
16 1
 
0.1%
17 1
 
0.1%
20 1
 
0.1%
22 1
 
0.1%
26 1
 
0.1%
ValueCountFrequency (%)
101431 1
0.1%
99352 1
0.1%
88672 1
0.1%
81170 1
0.1%
79334 1
0.1%
78308 1
0.1%
75256 1
0.1%
65221 1
0.1%
58264 1
0.1%
55417 1
0.1%

7월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct341
Distinct (%)31.1%
Missing121
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean3679.2917
Minimum0
Maximum89886
Zeros748
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:20.018680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31198
95-th percentile27209.8
Maximum89886
Range89886
Interquartile range (IQR)1198

Descriptive statistics

Standard deviation10366.572
Coefficient of variation (CV)2.8175455
Kurtosis18.387484
Mean3679.2917
Median Absolute Deviation (MAD)0
Skewness4.0170003
Sum4036183
Variance1.0746581 × 108
MonotonicityNot monotonic
2023-12-13T06:46:20.174361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 748
61.4%
4138 2
 
0.2%
3 2
 
0.2%
865 2
 
0.2%
35848 2
 
0.2%
181 2
 
0.2%
30 2
 
0.2%
16 2
 
0.2%
1153 2
 
0.2%
1 2
 
0.2%
Other values (331) 331
27.2%
(Missing) 121
 
9.9%
ValueCountFrequency (%)
0 748
61.4%
1 2
 
0.2%
2 1
 
0.1%
3 2
 
0.2%
6 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
16 2
 
0.2%
23 1
 
0.1%
27 1
 
0.1%
ValueCountFrequency (%)
89886 1
0.1%
74844 1
0.1%
71978 1
0.1%
68798 1
0.1%
63750 1
0.1%
63025 1
0.1%
59397 1
0.1%
58539 1
0.1%
58464 1
0.1%
58377 1
0.1%

8월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct320
Distinct (%)29.2%
Missing123
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean3405.39
Minimum0
Maximum98669
Zeros767
Zeros (%)63.0%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:20.345174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3690
95-th percentile23869.2
Maximum98669
Range98669
Interquartile range (IQR)690

Descriptive statistics

Standard deviation9940.7272
Coefficient of variation (CV)2.9191157
Kurtosis20.910527
Mean3405.39
Median Absolute Deviation (MAD)0
Skewness4.1968826
Sum3728902
Variance98818057
MonotonicityNot monotonic
2023-12-13T06:46:20.553015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 767
63.0%
6 2
 
0.2%
1353 2
 
0.2%
946 2
 
0.2%
5855 2
 
0.2%
4 2
 
0.2%
2 2
 
0.2%
7922 2
 
0.2%
2850 2
 
0.2%
1148 2
 
0.2%
Other values (310) 310
25.5%
(Missing) 123
 
10.1%
ValueCountFrequency (%)
0 767
63.0%
1 1
 
0.1%
2 2
 
0.2%
4 2
 
0.2%
5 1
 
0.1%
6 2
 
0.2%
19 1
 
0.1%
27 1
 
0.1%
29 1
 
0.1%
33 1
 
0.1%
ValueCountFrequency (%)
98669 1
0.1%
70391 1
0.1%
66937 1
0.1%
63667 1
0.1%
61485 1
0.1%
56360 1
0.1%
56217 1
0.1%
56034 1
0.1%
54758 1
0.1%
54427 1
0.1%

9월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct344
Distinct (%)31.4%
Missing121
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean3345.0656
Minimum0
Maximum84533
Zeros744
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:20.717679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31121
95-th percentile24071.2
Maximum84533
Range84533
Interquartile range (IQR)1121

Descriptive statistics

Standard deviation9203.2346
Coefficient of variation (CV)2.7512867
Kurtosis20.223922
Mean3345.0656
Median Absolute Deviation (MAD)0
Skewness4.0401838
Sum3669537
Variance84699526
MonotonicityNot monotonic
2023-12-13T06:46:20.876604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 744
61.1%
2 3
 
0.2%
54 2
 
0.2%
7789 2
 
0.2%
19 2
 
0.2%
4 2
 
0.2%
217 2
 
0.2%
46 2
 
0.2%
2920 2
 
0.2%
248 2
 
0.2%
Other values (334) 334
27.4%
(Missing) 121
 
9.9%
ValueCountFrequency (%)
0 744
61.1%
1 1
 
0.1%
2 3
 
0.2%
3 1
 
0.1%
4 2
 
0.2%
5 1
 
0.1%
7 1
 
0.1%
14 1
 
0.1%
19 2
 
0.2%
21 1
 
0.1%
ValueCountFrequency (%)
84533 1
0.1%
79601 1
0.1%
74958 1
0.1%
56820 1
0.1%
55223 1
0.1%
54237 1
0.1%
49531 1
0.1%
47678 1
0.1%
46662 1
0.1%
45821 1
0.1%

10월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct386
Distinct (%)35.2%
Missing121
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean5988.7967
Minimum0
Maximum171191
Zeros705
Zeros (%)57.9%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:21.038055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32476
95-th percentile31402.6
Maximum171191
Range171191
Interquartile range (IQR)2476

Descriptive statistics

Standard deviation18193.05
Coefficient of variation (CV)3.0378473
Kurtosis25.377351
Mean5988.7967
Median Absolute Deviation (MAD)0
Skewness4.7246551
Sum6569710
Variance3.3098707 × 108
MonotonicityNot monotonic
2023-12-13T06:46:21.211871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 705
57.9%
57 3
 
0.2%
58 2
 
0.2%
2 2
 
0.2%
2269 2
 
0.2%
52 2
 
0.2%
2878 2
 
0.2%
4946 1
 
0.1%
30936 1
 
0.1%
2346 1
 
0.1%
Other values (376) 376
30.9%
(Missing) 121
 
9.9%
ValueCountFrequency (%)
0 705
57.9%
1 1
 
0.1%
2 2
 
0.2%
7 1
 
0.1%
17 1
 
0.1%
20 1
 
0.1%
21 1
 
0.1%
29 1
 
0.1%
41 1
 
0.1%
46 1
 
0.1%
ValueCountFrequency (%)
171191 1
0.1%
150285 1
0.1%
129973 1
0.1%
123365 1
0.1%
123320 1
0.1%
121303 1
0.1%
112235 1
0.1%
106956 1
0.1%
105709 1
0.1%
103984 1
0.1%

11월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct440
Distinct (%)40.1%
Missing121
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean12844.469
Minimum0
Maximum276576
Zeros652
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:21.368233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34792
95-th percentile86389.8
Maximum276576
Range276576
Interquartile range (IQR)4792

Descriptive statistics

Standard deviation37401.208
Coefficient of variation (CV)2.9118531
Kurtosis17.972574
Mean12844.469
Median Absolute Deviation (MAD)0
Skewness4.1366744
Sum14090383
Variance1.3988503 × 109
MonotonicityNot monotonic
2023-12-13T06:46:21.526231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 652
53.5%
34 3
 
0.2%
3 3
 
0.2%
202 2
 
0.2%
1505 2
 
0.2%
59733 1
 
0.1%
10203 1
 
0.1%
13356 1
 
0.1%
1283 1
 
0.1%
19326 1
 
0.1%
Other values (430) 430
35.3%
(Missing) 121
 
9.9%
ValueCountFrequency (%)
0 652
53.5%
1 1
 
0.1%
3 3
 
0.2%
6 1
 
0.1%
8 1
 
0.1%
14 1
 
0.1%
20 1
 
0.1%
23 1
 
0.1%
27 1
 
0.1%
29 1
 
0.1%
ValueCountFrequency (%)
276576 1
0.1%
249116 1
0.1%
246061 1
0.1%
241587 1
0.1%
231306 1
0.1%
212369 1
0.1%
210289 1
0.1%
210201 1
0.1%
209438 1
0.1%
207727 1
0.1%

12월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct456
Distinct (%)41.6%
Missing121
Missing (%)9.9%
Infinite0
Infinite (%)0.0%
Mean21846.221
Minimum0
Maximum405197
Zeros639
Zeros (%)52.5%
Negative0
Negative (%)0.0%
Memory size10.8 KiB
2023-12-13T06:46:21.678988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38250
95-th percentile123694.8
Maximum405197
Range405197
Interquartile range (IQR)8250

Descriptive statistics

Standard deviation59903.851
Coefficient of variation (CV)2.7420693
Kurtosis14.91733
Mean21846.221
Median Absolute Deviation (MAD)0
Skewness3.8085911
Sum23965304
Variance3.5884713 × 109
MonotonicityNot monotonic
2023-12-13T06:46:21.858802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 639
52.5%
31 2
 
0.2%
1661 2
 
0.2%
28 2
 
0.2%
3908 1
 
0.1%
3886 1
 
0.1%
2570 1
 
0.1%
339 1
 
0.1%
31710 1
 
0.1%
4123 1
 
0.1%
Other values (446) 446
36.6%
(Missing) 121
 
9.9%
ValueCountFrequency (%)
0 639
52.5%
1 1
 
0.1%
6 1
 
0.1%
15 1
 
0.1%
22 1
 
0.1%
23 1
 
0.1%
28 2
 
0.2%
29 1
 
0.1%
31 2
 
0.2%
32 1
 
0.1%
ValueCountFrequency (%)
405197 1
0.1%
392296 1
0.1%
379778 1
0.1%
364470 1
0.1%
361726 1
0.1%
332157 1
0.1%
329621 1
0.1%
328238 1
0.1%
316065 1
0.1%
315768 1
0.1%

Interactions

2023-12-13T06:46:14.926846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:57.455189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:58.972778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:00.778224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.311283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.673894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:05.104156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:06.576472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.151813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.481590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.805682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.016273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:13.182641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:15.060193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:57.569446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:59.081735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:00.909599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.407735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.780896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:05.211770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:06.667663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.229297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.575827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.893715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.113626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:13.275299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:15.176489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:57.683147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:59.187092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:01.024924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.507805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.860415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:05.310082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:06.758646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.311298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.666081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.976094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.198836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:13.355597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:15.286363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:57.804253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:59.310712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:01.139051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.625305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.973883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:05.451535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:06.897669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.418168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.772754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.081497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.292744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:13.456313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:15.395785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:57.925690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:59.419258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:01.287602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.737948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:04.071695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:05.586198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:07.017087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.529315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.872392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.184051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.376933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:13.559943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:15.489208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:58.020977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:59.515337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:01.415969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.836750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:04.172188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:05.688704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:07.401752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.630057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.964883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.267653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.459042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:13.674085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:15.586210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:58.165830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:59.951720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:01.547895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.927171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:04.280639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:05.779296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:07.495175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.715244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.071172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.364303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.548062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:13.766575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:15.710017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:58.274872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:00.069299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:01.672853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.037183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:04.380126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:05.877071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:07.590108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.825337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.158900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.460727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.632464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:13.857877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:15.831791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:58.384310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:00.190593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:01.788877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.141590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:04.507294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:05.962669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:07.672850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.927545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.250703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.551747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.720225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:14.323467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:15.954748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:58.518508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:00.302733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:01.899766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.255353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:04.648221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:06.077973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:07.771087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.033169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.401264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.639320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.800264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:14.468029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:16.051617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:58.618904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:00.414930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.001783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.355173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:04.758863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:06.214007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:07.872558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.164083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.526732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.732939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.896812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:14.580923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:16.159964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:58.730914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:00.515177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.116159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.469727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:04.886870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:06.334582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:07.969068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.280952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.615858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.832267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:12.992464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:14.702681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:16.300653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:45:58.847129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:00.642299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:02.210284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:03.566154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:04.981161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:06.435670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:08.052050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:09.386294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:10.705189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:11.930946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:13.083466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:46:14.794670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:46:21.971589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도사업소명구분1월2월3월4월5월6월7월8월9월10월11월12월
기준년도1.0000.0000.0000.0000.0000.0000.0880.0000.0000.0000.0000.0000.0000.0000.000
사업소명0.0001.0000.0000.5150.5110.4990.4020.3650.3820.3920.3700.4200.4250.4780.502
구분0.0000.0001.0000.3790.3730.3310.3190.2910.3000.3790.3720.3650.3250.3290.363
1월0.0000.5150.3791.0000.9740.9110.8530.8300.6990.6930.5390.5530.8010.8800.928
2월0.0000.5110.3730.9741.0000.9230.8910.8060.7300.6970.5350.5020.8380.8870.937
3월0.0000.4990.3310.9110.9231.0000.8770.8040.7980.7430.5980.6430.8440.9160.901
4월0.0880.4020.3190.8530.8910.8771.0000.8390.8740.8390.7160.6900.8860.8720.831
5월0.0000.3650.2910.8300.8060.8040.8391.0000.9070.8340.7270.6740.8490.8090.798
6월0.0000.3820.3000.6990.7300.7980.8740.9071.0000.9280.8020.7730.8870.8180.756
7월0.0000.3920.3790.6930.6970.7430.8390.8340.9281.0000.8750.8350.8810.8080.690
8월0.0000.3700.3720.5390.5350.5980.7160.7270.8020.8751.0000.9420.7570.6650.564
9월0.0000.4200.3650.5530.5020.6430.6900.6740.7730.8350.9421.0000.7890.6410.543
10월0.0000.4250.3250.8010.8380.8440.8860.8490.8870.8810.7570.7891.0000.9040.821
11월0.0000.4780.3290.8800.8870.9160.8720.8090.8180.8080.6650.6410.9041.0000.899
12월0.0000.5020.3630.9280.9370.9010.8310.7980.7560.6900.5640.5430.8210.8991.000
2023-12-13T06:46:22.111937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분사업소명
구분1.0000.000
사업소명0.0001.000
2023-12-13T06:46:22.222107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도1월2월3월4월5월6월7월8월9월10월11월12월사업소명구분
기준년도1.000-0.011-0.015-0.026-0.034-0.016-0.0020.0260.020-0.008-0.011-0.018-0.0190.0000.000
1월-0.0111.0000.9700.9280.8540.7760.7690.7580.7340.7460.8210.9060.9400.2160.210
2월-0.0150.9701.0000.9320.8550.7790.7680.7600.7310.7490.8290.9060.9290.2140.206
3월-0.0260.9280.9321.0000.9150.8230.8190.8050.7800.7980.8760.9270.9110.2080.181
4월-0.0340.8540.8550.9151.0000.8620.8580.8450.8230.8460.8700.8810.8510.1590.174
5월-0.0160.7760.7790.8230.8621.0000.8760.8460.8360.8310.8400.8050.7810.1420.157
6월-0.0020.7690.7680.8190.8580.8761.0000.9040.8910.8800.8670.8070.7650.1500.162
7월0.0260.7580.7600.8050.8450.8460.9041.0000.9290.8910.8590.8020.7590.1550.210
8월0.0200.7340.7310.7800.8230.8360.8910.9291.0000.9020.8370.7750.7280.1510.195
9월-0.0080.7460.7490.7980.8460.8310.8800.8910.9021.0000.8740.7960.7570.1750.190
10월-0.0110.8210.8290.8760.8700.8400.8670.8590.8370.8741.0000.8770.8340.1700.177
11월-0.0180.9060.9060.9270.8810.8050.8070.8020.7750.7960.8771.0000.9340.1960.179
12월-0.0190.9400.9290.9110.8510.7810.7650.7590.7280.7570.8340.9341.0000.2090.200
사업소명0.0000.2160.2140.2080.1590.1420.1500.1550.1510.1750.1700.1960.2091.0000.000
구분0.0000.2100.2060.1810.1740.1570.1620.2100.1950.1900.1770.1790.2000.0001.000

Missing values

2023-12-13T06:46:16.502355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:46:16.728243image/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.
2023-12-13T06:46:16.889726image/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

기준년도사업소명구분1월2월3월4월5월6월7월8월9월10월11월12월
02012강남발전회사000000000000
12012강남소각로39672390134793837660272716622289943417737507381793886738825
22012강남CHP000000000000
32012강남PLB24585120780710830923339460132291049494692926241370
42012강남CES3469232530317271649718433556259324317107003005732895
52012강남기타000000000000
62013강남발전회사000000000000
72013강남소각로38097331814032338440375603288631966680727677376933403934807
82013강남CHP000000000000
92013강남PLB2403241866788640722491733709460130897901195879
기준년도사업소명구분1월2월3월4월5월6월7월8월9월10월11월12월
12082021평택CHP000000000000
12092021평택PLB000000000004092
12102021평택CES000000000000
12112021평택기타857759264320271626672624291731082676287800
12122022평택발전회사00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12132022평택소각로78766680<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12142022평택CHP00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12152022평택PLB73576782<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12162022평택CES00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
12172022평택기타00<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>