Overview

Dataset statistics

Number of variables15
Number of observations2050
Missing cells2213
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory266.4 KiB
Average record size in memory133.1 B

Variable types

Numeric13
Categorical2

Dataset

Description한국지역난방공사 월별, 지사별, 설비별 전기 생산량 정보 입니다. 현재 API를 통하여 실시간으로 데이터를 제공하고 있습니다.
Author한국지역난방공사
URLhttps://www.data.go.kr/data/15083008/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 221 (10.8%) missing valuesMissing
4월 has 221 (10.8%) missing valuesMissing
5월 has 222 (10.8%) missing valuesMissing
6월 has 222 (10.8%) missing valuesMissing
7월 has 221 (10.8%) missing valuesMissing
8월 has 221 (10.8%) missing valuesMissing
9월 has 221 (10.8%) missing valuesMissing
10월 has 222 (10.8%) missing valuesMissing
11월 has 221 (10.8%) missing valuesMissing
12월 has 221 (10.8%) missing valuesMissing
1월 has 1828 (89.2%) zerosZeros
2월 has 1826 (89.1%) zerosZeros
3월 has 1637 (79.9%) zerosZeros
4월 has 1648 (80.4%) zerosZeros
5월 has 1664 (81.2%) zerosZeros
6월 has 1649 (80.4%) zerosZeros
7월 has 1649 (80.4%) zerosZeros
8월 has 1654 (80.7%) zerosZeros
9월 has 1662 (81.1%) zerosZeros
10월 has 1647 (80.3%) zerosZeros
11월 has 1633 (79.7%) zerosZeros
12월 has 1622 (79.1%) zerosZeros

Reproduction

Analysis started2023-12-12 17:07:37.850948
Analysis finished2023-12-12 17:07:57.706809
Duration19.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년도
Real number (ℝ)

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.6415
Minimum2012
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:07:57.775614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2012
5-th percentile2013
Q12015
median2018
Q32020
95-th percentile2022
Maximum2022
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9433794
Coefficient of variation (CV)0.0014588218
Kurtosis-1.0541355
Mean2017.6415
Median Absolute Deviation (MAD)2
Skewness-0.19017566
Sum4136165
Variance8.6634825
MonotonicityNot monotonic
2023-12-13T02:07:57.897054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2019 220
10.7%
2020 220
10.7%
2021 220
10.7%
2022 220
10.7%
2017 209
10.2%
2018 209
10.2%
2015 190
9.3%
2016 190
9.3%
2014 180
8.8%
2012 96
4.7%
ValueCountFrequency (%)
2012 96
4.7%
2013 96
4.7%
2014 180
8.8%
2015 190
9.3%
2016 190
9.3%
2017 209
10.2%
2018 209
10.2%
2019 220
10.7%
2020 220
10.7%
2021 220
10.7%
ValueCountFrequency (%)
2022 220
10.7%
2021 220
10.7%
2020 220
10.7%
2019 220
10.7%
2018 209
10.2%
2017 209
10.2%
2016 190
9.3%
2015 190
9.3%
2014 180
8.8%
2013 96
4.7%

사업소명
Categorical

Distinct21
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
강남
 
108
대구
 
108
고양
 
108
수원
 
108
용인
 
108
Other values (16)
1510 

Length

Max length13
Median length2
Mean length2.3804878
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강남 108
 
5.3%
대구 108
 
5.3%
고양 108
 
5.3%
수원 108
 
5.3%
용인 108
 
5.3%
화성 108
 
5.3%
삼송 108
 
5.3%
판교 108
 
5.3%
파주 108
 
5.3%
광교 108
 
5.3%
Other values (11) 970
47.3%

Length

2023-12-13T02:07:58.048292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남 108
 
5.2%
광교 108
 
5.2%
양산 108
 
5.2%
세종 108
 
5.2%
광주전남 108
 
5.2%
청주 108
 
5.2%
대구 108
 
5.2%
분당 108
 
5.2%
중앙(상암 108
 
5.2%
파주 108
 
5.2%
Other values (12) 994
47.9%

구분
Categorical

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size16.1 KiB
소각로_CHP
206 
우드칩_CHP
206 
태양광
206 
LSWR
174 
B_C
174 
Other values (9)
1084 

Length

Max length7
Median length6
Mean length4.6839024
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row복합열병합
2nd rowCES
3rd row소각로_CHP
4th row우드칩_CHP
5th row태양광

Common Values

ValueCountFrequency (%)
소각로_CHP 206
10.0%
우드칩_CHP 206
10.0%
태양광 206
10.0%
LSWR 174
8.5%
B_C 174
8.5%
LNG_복합 174
8.5%
LNG_CES 174
8.5%
바이오가스 174
8.5%
연료전지 174
8.5%
풍력 174
8.5%
Other values (4) 214
10.4%

Length

2023-12-13T02:07:58.182123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소각로_chp 206
10.0%
우드칩_chp 206
10.0%
태양광 206
10.0%
lswr 174
8.5%
b_c 174
8.5%
lng_복합 174
8.5%
lng_ces 174
8.5%
바이오가스 174
8.5%
연료전지 174
8.5%
풍력 174
8.5%
Other values (4) 214
10.4%

1월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct221
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6982663.5
Minimum0
Maximum5.4688654 × 108
Zeros1828
Zeros (%)89.2%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:07:58.344701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8295646.5
Maximum5.4688654 × 108
Range5.4688654 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation45832804
Coefficient of variation (CV)6.5637996
Kurtosis72.088807
Mean6982663.5
Median Absolute Deviation (MAD)0
Skewness8.2037853
Sum1.431446 × 1010
Variance2.1006459 × 1015
MonotonicityNot monotonic
2023-12-13T02:07:58.460715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1828
89.2%
39144.0 2
 
0.1%
345856017.3 2
 
0.1%
119238055.0 1
 
< 0.1%
22567000.0 1
 
< 0.1%
1851171.0 1
 
< 0.1%
8477.0 1
 
< 0.1%
24705780.0 1
 
< 0.1%
1754354.0 1
 
< 0.1%
5426.0 1
 
< 0.1%
Other values (211) 211
 
10.3%
ValueCountFrequency (%)
0.0 1828
89.2%
1547.0 1
 
< 0.1%
1694.0 1
 
< 0.1%
2582.0 1
 
< 0.1%
2719.0 1
 
< 0.1%
3332.0 1
 
< 0.1%
3528.0 1
 
< 0.1%
3564.0 1
 
< 0.1%
3773.0 1
 
< 0.1%
3884.0 1
 
< 0.1%
ValueCountFrequency (%)
546886539.0 1
< 0.1%
542967210.0 1
< 0.1%
525361480.0 1
< 0.1%
523905800.0 1
< 0.1%
522916360.0 1
< 0.1%
377625820.0 1
< 0.1%
367632974.0 1
< 0.1%
360899718.0 1
< 0.1%
358852800.0 1
< 0.1%
357646860.0 1
< 0.1%

2월
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct223
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6050895.7
Minimum0
Maximum4.8421777 × 108
Zeros1826
Zeros (%)89.1%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:07:58.580445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6893515
Maximum4.8421777 × 108
Range4.8421777 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation39932607
Coefficient of variation (CV)6.5994539
Kurtosis72.07267
Mean6050895.7
Median Absolute Deviation (MAD)0
Skewness8.2145305
Sum1.2404336 × 1010
Variance1.5946131 × 1015
MonotonicityNot monotonic
2023-12-13T02:07:58.700540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1826
89.1%
47680.0 2
 
0.1%
308890043.2 2
 
0.1%
609340.0 1
 
< 0.1%
1761311.0 1
 
< 0.1%
8825.0 1
 
< 0.1%
21483500.0 1
 
< 0.1%
1653120.0 1
 
< 0.1%
6955.0 1
 
< 0.1%
21798800.0 1
 
< 0.1%
Other values (213) 213
 
10.4%
ValueCountFrequency (%)
0.0 1826
89.1%
232.0 1
 
< 0.1%
4056.0 1
 
< 0.1%
4102.0 1
 
< 0.1%
4112.0 1
 
< 0.1%
4128.0 1
 
< 0.1%
4356.0 1
 
< 0.1%
4365.0 1
 
< 0.1%
4535.0 1
 
< 0.1%
4595.0 1
 
< 0.1%
ValueCountFrequency (%)
484217770.0 1
< 0.1%
479748650.0 1
< 0.1%
453676370.0 1
< 0.1%
449901080.0 1
< 0.1%
419470960.0 1
< 0.1%
357540847.0 1
< 0.1%
335732100.0 1
< 0.1%
328237346.0 1
< 0.1%
321791000.0 1
< 0.1%
318832380.0 1
< 0.1%

3월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct193
Distinct (%)10.6%
Missing221
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean5872237.6
Minimum0
Maximum5.2109675 × 108
Zeros1637
Zeros (%)79.9%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:07:58.813531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3111935.6
Maximum5.2109675 × 108
Range5.2109675 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40768869
Coefficient of variation (CV)6.9426463
Kurtosis79.305611
Mean5872237.6
Median Absolute Deviation (MAD)0
Skewness8.6059798
Sum1.0740323 × 1010
Variance1.6621006 × 1015
MonotonicityNot monotonic
2023-12-13T02:07:58.932417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1637
79.9%
643192.0 1
 
< 0.1%
20259582.0 1
 
< 0.1%
1957789.0 1
 
< 0.1%
10900.0 1
 
< 0.1%
9352100.0 1
 
< 0.1%
1864257.7 1
 
< 0.1%
9018.0 1
 
< 0.1%
21003300.0 1
 
< 0.1%
1794401.0 1
 
< 0.1%
Other values (183) 183
 
8.9%
(Missing) 221
 
10.8%
ValueCountFrequency (%)
0.0 1637
79.9%
2057.0 1
 
< 0.1%
3997.0 1
 
< 0.1%
5103.0 1
 
< 0.1%
5241.0 1
 
< 0.1%
5292.0 1
 
< 0.1%
5594.0 1
 
< 0.1%
6043.0 1
 
< 0.1%
6195.0 1
 
< 0.1%
6250.0 1
 
< 0.1%
ValueCountFrequency (%)
521096750.0 1
< 0.1%
494409083.0 1
< 0.1%
477846190.0 1
< 0.1%
459384130.0 1
< 0.1%
373418119.0 1
< 0.1%
366141760.0 1
< 0.1%
365622312.0 1
< 0.1%
356940513.0 1
< 0.1%
356688020.0 1
< 0.1%
350103240.0 1
< 0.1%

4월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct182
Distinct (%)10.0%
Missing221
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean4503386.9
Minimum0
Maximum4.9491328 × 108
Zeros1648
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:07:59.045680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1744180.4
Maximum4.9491328 × 108
Range4.9491328 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33340610
Coefficient of variation (CV)7.4034523
Kurtosis94.987095
Mean4503386.9
Median Absolute Deviation (MAD)0
Skewness9.3209249
Sum8.2366946 × 109
Variance1.1115963 × 1015
MonotonicityNot monotonic
2023-12-13T02:07:59.165785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1648
80.4%
7245417.0 1
 
< 0.1%
1899535.0 1
 
< 0.1%
14858400.0 1
 
< 0.1%
1898390.0 1
 
< 0.1%
9090.0 1
 
< 0.1%
8744500.0 1
 
< 0.1%
1844114.0 1
 
< 0.1%
10862.0 1
 
< 0.1%
1547.0 1
 
< 0.1%
Other values (172) 172
 
8.4%
(Missing) 221
 
10.8%
ValueCountFrequency (%)
0.0 1648
80.4%
1547.0 1
 
< 0.1%
5284.0 1
 
< 0.1%
5544.0 1
 
< 0.1%
5698.0 1
 
< 0.1%
5903.0 1
 
< 0.1%
6074.0 1
 
< 0.1%
6209.0 1
 
< 0.1%
6400.0 1
 
< 0.1%
6564.0 1
 
< 0.1%
ValueCountFrequency (%)
494913280.0 1
< 0.1%
418897650.0 1
< 0.1%
356420759.0 1
< 0.1%
353990869.0 1
< 0.1%
351823069.0 1
< 0.1%
348250326.5 1
< 0.1%
327301230.0 1
< 0.1%
325240963.7 1
< 0.1%
321519000.0 1
< 0.1%
299586000.0 1
< 0.1%

5월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct165
Distinct (%)9.0%
Missing222
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean2699953.1
Minimum0
Maximum4.8465706 × 108
Zeros1664
Zeros (%)81.2%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:07:59.286786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile194270.55
Maximum4.8465706 × 108
Range4.8465706 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23298166
Coefficient of variation (CV)8.6291002
Kurtosis191.94714
Mean2699953.1
Median Absolute Deviation (MAD)0
Skewness12.56263
Sum4.9355142 × 109
Variance5.4280453 × 1014
MonotonicityNot monotonic
2023-12-13T02:07:59.430960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1664
81.2%
5409379.0 1
 
< 0.1%
13472.0 1
 
< 0.1%
11104.0 1
 
< 0.1%
1568596.0 1
 
< 0.1%
12424.0 1
 
< 0.1%
1843067.0 1
 
< 0.1%
7306.0 1
 
< 0.1%
877800.0 1
 
< 0.1%
1712982.0 1
 
< 0.1%
Other values (155) 155
 
7.6%
(Missing) 222
 
10.8%
ValueCountFrequency (%)
0.0 1664
81.2%
5569.0 1
 
< 0.1%
6109.0 1
 
< 0.1%
6741.0 1
 
< 0.1%
6995.0 1
 
< 0.1%
7306.0 1
 
< 0.1%
7537.0 1
 
< 0.1%
7647.0 1
 
< 0.1%
7686.0 1
 
< 0.1%
7711.0 1
 
< 0.1%
ValueCountFrequency (%)
484657060.0 1
< 0.1%
376136510.0 1
< 0.1%
357381408.0 1
< 0.1%
257176420.0 1
< 0.1%
217914190.0 1
< 0.1%
215684743.1 1
< 0.1%
195095283.0 1
< 0.1%
180986000.0 1
< 0.1%
179202000.0 1
< 0.1%
168827430.0 1
< 0.1%

6월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct180
Distinct (%)9.8%
Missing222
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean2662445.6
Minimum0
Maximum4.5618945 × 108
Zeros1649
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:07:59.555580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile224778.4
Maximum4.5618945 × 108
Range4.5618945 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24240714
Coefficient of variation (CV)9.1046795
Kurtosis174.27306
Mean2662445.6
Median Absolute Deviation (MAD)0
Skewness12.355812
Sum4.8669506 × 109
Variance5.8761224 × 1014
MonotonicityNot monotonic
2023-12-13T02:07:59.678692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1649
80.4%
757091.0 1
 
< 0.1%
964128.0 1
 
< 0.1%
291163.0 1
 
< 0.1%
1071944.0 1
 
< 0.1%
9730.0 1
 
< 0.1%
560900.0 1
 
< 0.1%
1109187.0 1
 
< 0.1%
6273.6 1
 
< 0.1%
175000.0 1
 
< 0.1%
Other values (170) 170
 
8.3%
(Missing) 222
 
10.8%
ValueCountFrequency (%)
0.0 1649
80.4%
950.0 1
 
< 0.1%
1174.0 1
 
< 0.1%
2721.0 1
 
< 0.1%
5456.0 1
 
< 0.1%
6124.0 1
 
< 0.1%
6273.6 1
 
< 0.1%
6511.0 1
 
< 0.1%
6562.0 1
 
< 0.1%
6569.0 1
 
< 0.1%
ValueCountFrequency (%)
456189450.0 1
< 0.1%
405841130.0 1
< 0.1%
333061200.0 1
< 0.1%
323651116.0 1
< 0.1%
294809890.0 1
< 0.1%
237082900.0 1
< 0.1%
229609691.0 1
< 0.1%
217608000.0 1
< 0.1%
188887200.0 1
< 0.1%
170043000.0 1
< 0.1%

7월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct181
Distinct (%)9.9%
Missing221
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean3207505.8
Minimum0
Maximum4.2227898 × 108
Zeros1649
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:07:59.853377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile452752.8
Maximum4.2227898 × 108
Range4.2227898 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation25905562
Coefficient of variation (CV)8.0765442
Kurtosis124.29499
Mean3207505.8
Median Absolute Deviation (MAD)0
Skewness10.501212
Sum5.8665281 × 109
Variance6.7109815 × 1014
MonotonicityNot monotonic
2023-12-13T02:08:00.216920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1649
80.4%
3075669.0 1
 
< 0.1%
494400.0 1
 
< 0.1%
11231.0 1
 
< 0.1%
7187.0 1
 
< 0.1%
340518.0 1
 
< 0.1%
4017.6 1
 
< 0.1%
1258400.0 1
 
< 0.1%
580019.0 1
 
< 0.1%
7178.0 1
 
< 0.1%
Other values (171) 171
 
8.3%
(Missing) 221
 
10.8%
ValueCountFrequency (%)
0.0 1649
80.4%
3805.0 1
 
< 0.1%
4017.0 1
 
< 0.1%
4017.6 1
 
< 0.1%
4774.0 1
 
< 0.1%
4841.0 1
 
< 0.1%
4848.0 1
 
< 0.1%
5134.0 1
 
< 0.1%
5248.0 1
 
< 0.1%
5421.0 1
 
< 0.1%
ValueCountFrequency (%)
422278983.0 1
< 0.1%
387036310.0 1
< 0.1%
346187440.0 1
< 0.1%
294269879.0 1
< 0.1%
290951628.0 1
< 0.1%
271667410.0 1
< 0.1%
265039570.0 1
< 0.1%
248652758.0 1
< 0.1%
210114430.0 1
< 0.1%
196281620.0 1
< 0.1%

8월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct176
Distinct (%)9.6%
Missing221
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean3243415.9
Minimum0
Maximum4.3253766 × 108
Zeros1654
Zeros (%)80.7%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:08:00.335612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile268744
Maximum4.3253766 × 108
Range4.3253766 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26495555
Coefficient of variation (CV)8.1690282
Kurtosis123.2003
Mean3243415.9
Median Absolute Deviation (MAD)0
Skewness10.506456
Sum5.9322076 × 109
Variance7.0201446 × 1014
MonotonicityNot monotonic
2023-12-13T02:08:00.447268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1654
80.7%
6512168.0 1
 
< 0.1%
231634.0 1
 
< 0.1%
9770.0 1
 
< 0.1%
290000.0 1
 
< 0.1%
1291224.0 1
 
< 0.1%
8232.0 1
 
< 0.1%
205200.0 1
 
< 0.1%
835085.0 1
 
< 0.1%
4790.0 1
 
< 0.1%
Other values (166) 166
 
8.1%
(Missing) 221
 
10.8%
ValueCountFrequency (%)
0.0 1654
80.7%
499.0 1
 
< 0.1%
1521.0 1
 
< 0.1%
2614.0 1
 
< 0.1%
3682.0 1
 
< 0.1%
4173.0 1
 
< 0.1%
4790.0 1
 
< 0.1%
4943.0 1
 
< 0.1%
5178.0 1
 
< 0.1%
5253.0 1
 
< 0.1%
ValueCountFrequency (%)
432537660.0 1
< 0.1%
378507260.0 1
< 0.1%
325737350.0 1
< 0.1%
325542770.0 1
< 0.1%
306618646.0 1
< 0.1%
295619003.0 1
< 0.1%
294489862.0 1
< 0.1%
246090200.0 1
< 0.1%
218460000.0 1
< 0.1%
207298750.0 1
< 0.1%

9월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct168
Distinct (%)9.2%
Missing221
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean2448958.7
Minimum0
Maximum3.9152766 × 108
Zeros1662
Zeros (%)81.1%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:08:00.560465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile140191
Maximum3.9152766 × 108
Range3.9152766 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation20891326
Coefficient of variation (CV)8.5306976
Kurtosis152.32988
Mean2448958.7
Median Absolute Deviation (MAD)0
Skewness11.44086
Sum4.4791455 × 109
Variance4.3644751 × 1014
MonotonicityNot monotonic
2023-12-13T02:08:00.694385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1662
81.1%
7633342.0 1
 
< 0.1%
9172.0 1
 
< 0.1%
7798.0 1
 
< 0.1%
790233.0 1
 
< 0.1%
8006.0 1
 
< 0.1%
1308576.0 1
 
< 0.1%
7702.0 1
 
< 0.1%
732500.0 1
 
< 0.1%
290847.0 1
 
< 0.1%
Other values (158) 158
 
7.7%
(Missing) 221
 
10.8%
ValueCountFrequency (%)
0.0 1662
81.1%
1213.0 1
 
< 0.1%
2720.0 1
 
< 0.1%
3136.0 1
 
< 0.1%
4704.0 1
 
< 0.1%
4906.0 1
 
< 0.1%
5029.0 1
 
< 0.1%
5358.0 1
 
< 0.1%
5372.0 1
 
< 0.1%
5536.0 1
 
< 0.1%
ValueCountFrequency (%)
391527660.0 1
< 0.1%
330784590.0 1
< 0.1%
276441600.0 1
< 0.1%
242364013.0 1
< 0.1%
212624633.0 1
< 0.1%
207543861.0 1
< 0.1%
179549430.0 1
< 0.1%
173920486.2 1
< 0.1%
169541000.0 1
< 0.1%
166298467.0 1
< 0.1%

10월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct181
Distinct (%)9.9%
Missing222
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean3859090.7
Minimum0
Maximum4.7276047 × 108
Zeros1647
Zeros (%)80.3%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:08:00.828401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile970953.75
Maximum4.7276047 × 108
Range4.7276047 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30481042
Coefficient of variation (CV)7.8985037
Kurtosis97.963361
Mean3859090.7
Median Absolute Deviation (MAD)0
Skewness9.4721684
Sum7.0544178 × 109
Variance9.2909394 × 1014
MonotonicityNot monotonic
2023-12-13T02:08:00.966553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1647
80.3%
7051.0 2
 
0.1%
7579921.0 1
 
< 0.1%
2185977.0 1
 
< 0.1%
287800.0 1
 
< 0.1%
1158936.0 1
 
< 0.1%
7892.0 1
 
< 0.1%
1466871.0 1
 
< 0.1%
7754.0 1
 
< 0.1%
3132400.0 1
 
< 0.1%
Other values (171) 171
 
8.3%
(Missing) 222
 
10.8%
ValueCountFrequency (%)
0.0 1647
80.3%
595.0 1
 
< 0.1%
916.0 1
 
< 0.1%
1567.0 1
 
< 0.1%
4124.0 1
 
< 0.1%
4757.0 1
 
< 0.1%
5067.0 1
 
< 0.1%
5427.0 1
 
< 0.1%
5464.0 1
 
< 0.1%
5470.0 1
 
< 0.1%
ValueCountFrequency (%)
472760470.0 1
< 0.1%
368950880.0 1
< 0.1%
347686634.0 1
< 0.1%
344402890.0 1
< 0.1%
330693112.0 1
< 0.1%
316606710.0 1
< 0.1%
293874240.0 1
< 0.1%
253855741.8 1
< 0.1%
252556133.0 1
< 0.1%
248880038.9 1
< 0.1%

11월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct197
Distinct (%)10.8%
Missing221
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean5699162.4
Minimum0
Maximum5.3902939 × 108
Zeros1633
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:08:01.096455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4113494.8
Maximum5.3902939 × 108
Range5.3902939 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation39781690
Coefficient of variation (CV)6.9802695
Kurtosis76.657079
Mean5699162.4
Median Absolute Deviation (MAD)0
Skewness8.4909583
Sum1.0423768 × 1010
Variance1.5825828 × 1015
MonotonicityNot monotonic
2023-12-13T02:08:01.233387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1633
79.7%
7177514.0 1
 
< 0.1%
1924620.5 1
 
< 0.1%
16598800.0 1
 
< 0.1%
1773089.0 1
 
< 0.1%
4901.0 1
 
< 0.1%
5809005.0 1
 
< 0.1%
1176894.0 1
 
< 0.1%
5891.0 1
 
< 0.1%
1445760.0 1
 
< 0.1%
Other values (187) 187
 
9.1%
(Missing) 221
 
10.8%
ValueCountFrequency (%)
0.0 1633
79.7%
408.0 1
 
< 0.1%
2533.0 1
 
< 0.1%
2893.0 1
 
< 0.1%
2963.0 1
 
< 0.1%
3223.0 1
 
< 0.1%
3235.0 1
 
< 0.1%
3528.0 1
 
< 0.1%
3926.0 1
 
< 0.1%
4006.0 1
 
< 0.1%
ValueCountFrequency (%)
539029390.0 1
< 0.1%
460731130.0 1
< 0.1%
447041480.0 1
< 0.1%
358532560.0 1
< 0.1%
353637794.0 1
< 0.1%
350891000.0 1
< 0.1%
349718969.0 1
< 0.1%
346840630.0 1
< 0.1%
346216253.0 1
< 0.1%
339567700.0 1
< 0.1%

12월
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct208
Distinct (%)11.4%
Missing221
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean7016959.7
Minimum0
Maximum5.5750897 × 108
Zeros1622
Zeros (%)79.1%
Negative0
Negative (%)0.0%
Memory size18.1 KiB
2023-12-13T02:08:01.364931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8943953.9
Maximum5.5750897 × 108
Range5.5750897 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation45767605
Coefficient of variation (CV)6.5224266
Kurtosis71.030333
Mean7016959.7
Median Absolute Deviation (MAD)0
Skewness8.1462599
Sum1.2834019 × 1010
Variance2.0946736 × 1015
MonotonicityNot monotonic
2023-12-13T02:08:01.491023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1622
79.1%
6779408.0 1
 
< 0.1%
3943261.0 1
 
< 0.1%
1371825.0 1
 
< 0.1%
6631.0 1
 
< 0.1%
19395700.0 1
 
< 0.1%
1861581.0 1
 
< 0.1%
5944.0 1
 
< 0.1%
21028954.0 1
 
< 0.1%
1197471.0 1
 
< 0.1%
Other values (198) 198
 
9.7%
(Missing) 221
 
10.8%
ValueCountFrequency (%)
0.0 1622
79.1%
173.0 1
 
< 0.1%
1109.0 1
 
< 0.1%
1396.0 1
 
< 0.1%
2277.0 1
 
< 0.1%
2412.0 1
 
< 0.1%
2618.0 1
 
< 0.1%
3117.0 1
 
< 0.1%
3147.0 1
 
< 0.1%
3340.0 1
 
< 0.1%
ValueCountFrequency (%)
557508970.0 1
< 0.1%
535208030.0 1
< 0.1%
522562240.0 1
< 0.1%
489020310.0 1
< 0.1%
475603130.0 1
< 0.1%
369877122.0 1
< 0.1%
368120186.0 1
< 0.1%
367880568.0 1
< 0.1%
360265020.0 1
< 0.1%
360189000.0 1
< 0.1%

Interactions

2023-12-13T02:07:55.551963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:39.368824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.573704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:41.972376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:43.097941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:44.620404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.140194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.442832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.644478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.684480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.852725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:52.281745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:53.833203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:55.667420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:39.472381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.653700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.058897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:43.194985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:44.724840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.253763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.516958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.714491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.769888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.955771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:52.387354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:53.934858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:55.769720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:39.580140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.732498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.149089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:43.324116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:44.831180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.352542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.592017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.788998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.863634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:51.066013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:52.498406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:54.036788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:55.897557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:39.670210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.833906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.229736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:43.446357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:44.951746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.446142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.670950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.875319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.949354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:51.168982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:52.588453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:54.167016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:56.001913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:39.780530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.920939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.309995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:43.563616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:45.071790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.570477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.746642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.951703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.037356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:51.265755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:52.683685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:54.292609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:56.157340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:39.877534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:41.009468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.395169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:43.671249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:45.190272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.691796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.821266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.031649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.122015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:51.367167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:52.784318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:54.407209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:56.299796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:39.973454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:41.112094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.487325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:43.791465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:45.327342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.804916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.906749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.142390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.211815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:51.471605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:52.883955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:54.522941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:56.411343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.066997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:41.194242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.562360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:43.903189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:45.446793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.898963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.983583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.227328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.294183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:51.611555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:53.010553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:54.608950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:56.533391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.143326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:41.275103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.647246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:44.014620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:45.557496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.982604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.058436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.295486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.386676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:51.729685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:53.191453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:54.716894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:56.662309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.219316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:41.355475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.728873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:44.150069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:45.645646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.077946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.127784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.380619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.467066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:51.833434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:53.325947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:54.812271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:56.818155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.325858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:41.453305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.822258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:44.273616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:45.755312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.174878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.212502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.466185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.569962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:51.942962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:53.475248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:54.935134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:56.931954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.410278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:41.794692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.903217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:44.391726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:45.886540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.260998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.493782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.537848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.648291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:52.052369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:53.602244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:55.048266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:57.039387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:40.498210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:41.889757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:42.994781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:44.518487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:46.015496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:47.366117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:48.568840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:49.610357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:50.729245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:52.157516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:53.724947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:07:55.162820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:08:01.589550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도사업소명구분1월2월3월4월5월6월7월8월9월10월11월12월
기준년도1.0000.1010.5100.0000.0590.0000.0190.0000.0650.0450.0770.0000.0510.0250.000
사업소명0.1011.0000.0000.3990.3410.3410.3260.2560.2140.2820.2750.2540.2820.3280.431
구분0.5100.0001.0000.3980.3610.3600.3270.2890.3050.3120.3230.2760.3620.3910.447
1월0.0000.3990.3981.0000.9700.9290.8870.8120.8350.8820.9040.8460.8950.8800.873
2월0.0590.3410.3610.9701.0000.9010.9030.8580.8630.9440.9150.8870.9650.8820.911
3월0.0000.3410.3600.9290.9011.0000.9590.8730.9500.8860.9490.9370.8910.8910.897
4월0.0190.3260.3270.8870.9030.9591.0000.9010.9670.8780.9790.9630.9120.9180.899
5월0.0000.2560.2890.8120.8580.8730.9011.0000.9070.8420.9060.8840.8310.9520.790
6월0.0650.2140.3050.8350.8630.9500.9670.9071.0000.8810.9730.9720.8830.8830.823
7월0.0450.2820.3120.8820.9440.8860.8780.8420.8811.0000.8940.9080.9490.8280.814
8월0.0770.2750.3230.9040.9150.9490.9790.9060.9730.8941.0000.9740.9020.8910.852
9월0.0000.2540.2760.8460.8870.9370.9630.8840.9720.9080.9741.0000.8970.8680.838
10월0.0510.2820.3620.8950.9650.8910.9120.8310.8830.9490.9020.8971.0000.8680.860
11월0.0250.3280.3910.8800.8820.8910.9180.9520.8830.8280.8910.8680.8681.0000.863
12월0.0000.4310.4470.8730.9110.8970.8990.7900.8230.8140.8520.8380.8600.8631.000
2023-12-13T02:08:01.725489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업소명구분
사업소명1.0000.000
구분0.0001.000
2023-12-13T02:08:01.816164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준년도1월2월3월4월5월6월7월8월9월10월11월12월사업소명구분
기준년도1.000-0.035-0.041-0.057-0.064-0.066-0.053-0.039-0.042-0.046-0.044-0.055-0.0420.0530.238
1월-0.0351.0000.9850.9760.9440.8860.9340.9310.9150.8760.9170.9380.9490.1910.208
2월-0.0410.9851.0000.9650.9320.8800.9230.9200.9090.8710.9120.9320.9480.1380.161
3월-0.0570.9760.9651.0000.9620.8970.9510.9240.9200.8870.9230.9380.9310.1320.154
4월-0.0640.9440.9320.9621.0000.9020.9410.9050.9200.8920.8980.9150.9000.1260.138
5월-0.0660.8860.8800.8970.9021.0000.8980.8790.8920.8610.8460.8660.8540.1080.131
6월-0.0530.9340.9230.9510.9410.8981.0000.9150.9290.8880.9060.9060.8960.0800.128
7월-0.0390.9310.9200.9240.9050.8790.9151.0000.9420.8770.8960.9130.8920.1120.137
8월-0.0420.9150.9090.9200.9200.8920.9290.9421.0000.8910.8850.8960.8880.1040.136
9월-0.0460.8760.8710.8870.8920.8610.8880.8770.8911.0000.9090.8900.8670.0960.114
10월-0.0440.9170.9120.9230.8980.8460.9060.8960.8850.9091.0000.9250.8980.1120.161
11월-0.0550.9380.9320.9380.9150.8660.9060.9130.8960.8900.9251.0000.9490.1410.183
12월-0.0420.9490.9480.9310.9000.8540.8960.8920.8880.8670.8980.9491.0000.1750.180
사업소명0.0530.1910.1380.1320.1260.1080.0800.1120.1040.0960.1120.1410.1751.0000.000
구분0.2380.2080.1610.1540.1380.1310.1280.1370.1360.1140.1610.1830.1800.0001.000

Missing values

2023-12-13T02:07:57.181547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:07:57.383374image/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.
2023-12-13T02:07:57.564276image/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
02012강남복합열병합0.00.00.00.00.00.00.00.00.00.00.00.0
12012강남CES23299212.021672145.020589398.010505366.01074205.02082465.0165762.0182439.0165103.05705507.019200536.023123316.0
22012강남소각로_CHP6619941.05221334.0643192.07245417.05409379.0757091.03075669.06512168.07633342.07579921.07177514.06779408.0
32012강남우드칩_CHP0.00.00.00.00.00.00.00.00.00.00.00.0
42012강남태양광0.00.00.00.00.00.00.00.00.00.00.00.0
52012강남ETC0.00.00.00.00.00.00.00.00.00.00.00.0
62013강남복합열병합0.00.00.00.00.00.00.00.00.00.00.00.0
72013강남CES22725105.019888196.020259582.011445842.00.0223091.091276.0944549.0152535.01922904.018977456.022380793.0
82013강남소각로_CHP6397481.05547367.06519110.04669607.07691532.08477679.07032386.00.06260774.010302728.09119954.09326537.0
92013강남우드칩_CHP0.00.00.00.00.00.00.00.00.00.00.00.0
기준년도사업소명구분1월2월3월4월5월6월7월8월9월10월11월12월
20402022평택B_C0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20412022평택LNG_복합0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20422022평택LNG_CES0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20432022평택소각로_CHP0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20442022평택우드칩_CHP0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20452022평택태양광0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20462022평택바이오가스0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20472022평택SRF0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20482022평택연료전지0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
20492022평택풍력0.00.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>