Overview

Dataset statistics

Number of variables10
Number of observations88
Missing cells342
Missing cells (%)38.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 KiB
Average record size in memory89.5 B

Variable types

Numeric8
Categorical2

Dataset

Description한국남부발전(주)_기록물관리현황에 대한 데이터로 연도별 생산폐기(비전자, 전자) 현황 등의 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15037901/fileData.do

Alerts

1년 is highly overall correlated with 3년 and 6 other fieldsHigh correlation
3년 is highly overall correlated with 1년 and 6 other fieldsHigh correlation
5년 is highly overall correlated with 1년 and 6 other fieldsHigh correlation
10년 is highly overall correlated with 1년 and 5 other fieldsHigh correlation
30년 is highly overall correlated with 1년 and 6 other fieldsHigh correlation
준영구 is highly overall correlated with 1년 and 7 other fieldsHigh correlation
영구 is highly overall correlated with 1년 and 7 other fieldsHigh correlation
유형 is highly overall correlated with 30년 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 1년 and 6 other fieldsHigh correlation
1년 has 56 (63.6%) missing valuesMissing
3년 has 45 (51.1%) missing valuesMissing
5년 has 45 (51.1%) missing valuesMissing
10년 has 42 (47.7%) missing valuesMissing
30년 has 65 (73.9%) missing valuesMissing
준영구 has 44 (50.0%) missing valuesMissing
영구 has 45 (51.1%) missing valuesMissing

Reproduction

Analysis started2023-12-11 22:45:30.562270
Analysis finished2023-12-11 22:45:37.343104
Duration6.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도별
Real number (ℝ)

Distinct22
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.5
Minimum2001
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T07:45:37.421545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2002
Q12006
median2011.5
Q32017
95-th percentile2021
Maximum2022
Range21
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.380646
Coefficient of variation (CV)0.0031720835
Kurtosis-1.204935
Mean2011.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum177012
Variance40.712644
MonotonicityIncreasing
2023-12-12T07:45:37.551818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2001 4
 
4.5%
2013 4
 
4.5%
2022 4
 
4.5%
2021 4
 
4.5%
2020 4
 
4.5%
2019 4
 
4.5%
2018 4
 
4.5%
2017 4
 
4.5%
2016 4
 
4.5%
2015 4
 
4.5%
Other values (12) 48
54.5%
ValueCountFrequency (%)
2001 4
4.5%
2002 4
4.5%
2003 4
4.5%
2004 4
4.5%
2005 4
4.5%
2006 4
4.5%
2007 4
4.5%
2008 4
4.5%
2009 4
4.5%
2010 4
4.5%
ValueCountFrequency (%)
2022 4
4.5%
2021 4
4.5%
2020 4
4.5%
2019 4
4.5%
2018 4
4.5%
2017 4
4.5%
2016 4
4.5%
2015 4
4.5%
2014 4
4.5%
2013 4
4.5%

유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
생산
44 
폐기
44 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생산
2nd row생산
3rd row폐기
4th row폐기
5th row생산

Common Values

ValueCountFrequency (%)
생산 44
50.0%
폐기 44
50.0%

Length

2023-12-12T07:45:37.671859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:45:37.758750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생산 44
50.0%
폐기 44
50.0%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size836.0 B
비전자
44 
전자
44 

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비전자
2nd row전자
3rd row비전자
4th row전자
5th row비전자

Common Values

ValueCountFrequency (%)
비전자 44
50.0%
전자 44
50.0%

Length

2023-12-12T07:45:37.844374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:45:37.926904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비전자 44
50.0%
전자 44
50.0%

1년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)87.5%
Missing56
Missing (%)63.6%
Infinite0
Infinite (%)0.0%
Mean3098.2188
Minimum1
Maximum11965
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T07:45:38.043293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q1211.75
median2073.5
Q35055.25
95-th percentile7590.95
Maximum11965
Range11964
Interquartile range (IQR)4843.5

Descriptive statistics

Standard deviation2986.858
Coefficient of variation (CV)0.96405653
Kurtosis0.79101312
Mean3098.2188
Median Absolute Deviation (MAD)2072.5
Skewness0.92483103
Sum99143
Variance8921320.8
MonotonicityNot monotonic
2023-12-12T07:45:38.153307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 5
 
5.7%
5683 1
 
1.1%
8279 1
 
1.1%
11965 1
 
1.1%
7028 1
 
1.1%
4205 1
 
1.1%
3820 1
 
1.1%
4290 1
 
1.1%
103 1
 
1.1%
4580 1
 
1.1%
Other values (18) 18
 
20.5%
(Missing) 56
63.6%
ValueCountFrequency (%)
1 5
5.7%
2 1
 
1.1%
3 1
 
1.1%
103 1
 
1.1%
248 1
 
1.1%
356 1
 
1.1%
1322 1
 
1.1%
1393 1
 
1.1%
1402 1
 
1.1%
1698 1
 
1.1%
ValueCountFrequency (%)
11965 1
1.1%
8279 1
1.1%
7028 1
1.1%
6376 1
1.1%
6044 1
1.1%
5683 1
1.1%
5478 1
1.1%
5335 1
1.1%
4962 1
1.1%
4726 1
1.1%

3년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)95.3%
Missing45
Missing (%)51.1%
Infinite0
Infinite (%)0.0%
Mean18005.395
Minimum2
Maximum103925
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T07:45:38.279777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.5
Q150
median4909
Q325722.5
95-th percentile100672.2
Maximum103925
Range103923
Interquartile range (IQR)25672.5

Descriptive statistics

Standard deviation29360.672
Coefficient of variation (CV)1.6306597
Kurtosis3.4889209
Mean18005.395
Median Absolute Deviation (MAD)4899
Skewness2.0614157
Sum774232
Variance8.6204907 × 108
MonotonicityNot monotonic
2023-12-12T07:45:38.427587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
58 2
 
2.3%
2 2
 
2.3%
37184 1
 
1.1%
34282 1
 
1.1%
34 1
 
1.1%
35460 1
 
1.1%
2360 1
 
1.1%
34397 1
 
1.1%
3646 1
 
1.1%
892 1
 
1.1%
Other values (31) 31
35.2%
(Missing) 45
51.1%
ValueCountFrequency (%)
2 2
2.3%
4 1
1.1%
9 1
1.1%
10 1
1.1%
15 1
1.1%
17 1
1.1%
21 1
1.1%
32 1
1.1%
34 1
1.1%
42 1
1.1%
ValueCountFrequency (%)
103925 1
1.1%
102638 1
1.1%
102211 1
1.1%
86823 1
1.1%
43111 1
1.1%
40449 1
1.1%
37184 1
1.1%
35460 1
1.1%
34397 1
1.1%
34282 1
1.1%

5년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)97.7%
Missing45
Missing (%)51.1%
Infinite0
Infinite (%)0.0%
Mean7841.4186
Minimum1
Maximum49593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T07:45:38.580829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.5
Q1107.5
median3518
Q39735.5
95-th percentile46192.6
Maximum49593
Range49592
Interquartile range (IQR)9628

Descriptive statistics

Standard deviation12636.995
Coefficient of variation (CV)1.6115699
Kurtosis6.1976132
Mean7841.4186
Median Absolute Deviation (MAD)3491
Skewness2.5662257
Sum337181
Variance1.5969363 × 108
MonotonicityNot monotonic
2023-12-12T07:45:38.702552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
27 2
 
2.3%
33 1
 
1.1%
163 1
 
1.1%
136 1
 
1.1%
10428 1
 
1.1%
3151 1
 
1.1%
56 1
 
1.1%
10641 1
 
1.1%
5759 1
 
1.1%
11085 1
 
1.1%
Other values (32) 32
36.4%
(Missing) 45
51.1%
ValueCountFrequency (%)
1 1
1.1%
4 1
1.1%
18 1
1.1%
23 1
1.1%
27 2
2.3%
33 1
1.1%
38 1
1.1%
51 1
1.1%
56 1
1.1%
79 1
1.1%
ValueCountFrequency (%)
49593 1
1.1%
48835 1
1.1%
48205 1
1.1%
28081 1
1.1%
12413 1
1.1%
11540 1
1.1%
11085 1
1.1%
10641 1
1.1%
10428 1
1.1%
10293 1
1.1%

10년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)100.0%
Missing42
Missing (%)47.7%
Infinite0
Infinite (%)0.0%
Mean6486.1522
Minimum5
Maximum45456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T07:45:38.826988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile44.25
Q11075
median3752.5
Q36260.5
95-th percentile35949.75
Maximum45456
Range45451
Interquartile range (IQR)5185.5

Descriptive statistics

Standard deviation10531.409
Coefficient of variation (CV)1.6236759
Kurtosis7.4216492
Mean6486.1522
Median Absolute Deviation (MAD)2648.5
Skewness2.83103
Sum298363
Variance1.1091058 × 108
MonotonicityNot monotonic
2023-12-12T07:45:38.964768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
7956 1
 
1.1%
42 1
 
1.1%
5799 1
 
1.1%
6021 1
 
1.1%
5646 1
 
1.1%
6094 1
 
1.1%
701 1
 
1.1%
6274 1
 
1.1%
5340 1
 
1.1%
302 1
 
1.1%
Other values (36) 36
40.9%
(Missing) 42
47.7%
ValueCountFrequency (%)
5 1
1.1%
38 1
1.1%
42 1
1.1%
51 1
1.1%
54 1
1.1%
77 1
1.1%
79 1
1.1%
89 1
1.1%
302 1
1.1%
429 1
1.1%
ValueCountFrequency (%)
45456 1
1.1%
41737 1
1.1%
38348 1
1.1%
28755 1
1.1%
7956 1
1.1%
7777 1
1.1%
7700 1
1.1%
7377 1
1.1%
7214 1
1.1%
7035 1
1.1%

30년
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)82.6%
Missing65
Missing (%)73.9%
Infinite0
Infinite (%)0.0%
Mean1611.7391
Minimum8
Maximum12012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T07:45:39.089983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile11.4
Q131
median52
Q3272.5
95-th percentile10927.2
Maximum12012
Range12004
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation3626.2474
Coefficient of variation (CV)2.2498973
Kurtosis4.0954978
Mean1611.7391
Median Absolute Deviation (MAD)22
Skewness2.2770277
Sum37070
Variance13149671
MonotonicityNot monotonic
2023-12-12T07:45:39.211541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
46 2
 
2.3%
30 2
 
2.3%
70 2
 
2.3%
52 2
 
2.3%
43 1
 
1.1%
6708 1
 
1.1%
32 1
 
1.1%
11396 1
 
1.1%
12012 1
 
1.1%
28 1
 
1.1%
Other values (9) 9
 
10.2%
(Missing) 65
73.9%
ValueCountFrequency (%)
8 1
1.1%
11 1
1.1%
15 1
1.1%
28 1
1.1%
30 2
2.3%
32 1
1.1%
43 1
1.1%
46 2
2.3%
49 1
1.1%
52 2
2.3%
ValueCountFrequency (%)
12012 1
1.1%
11396 1
1.1%
6708 1
1.1%
5440 1
1.1%
334 1
1.1%
300 1
1.1%
245 1
1.1%
70 2
2.3%
53 1
1.1%
52 2
2.3%

준영구
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)95.5%
Missing44
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean471.36364
Minimum4
Maximum1071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T07:45:39.323553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q1133
median340
Q3788
95-th percentile1037.4
Maximum1071
Range1067
Interquartile range (IQR)655

Descriptive statistics

Standard deviation370.31464
Coefficient of variation (CV)0.78562412
Kurtosis-1.4690139
Mean471.36364
Median Absolute Deviation (MAD)299.5
Skewness0.28563923
Sum20740
Variance137132.93
MonotonicityNot monotonic
2023-12-12T07:45:39.466081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
47 2
 
2.3%
8 2
 
2.3%
70 1
 
1.1%
975 1
 
1.1%
118 1
 
1.1%
1034 1
 
1.1%
82 1
 
1.1%
891 1
 
1.1%
1038 1
 
1.1%
770 1
 
1.1%
Other values (32) 32
36.4%
(Missing) 44
50.0%
ValueCountFrequency (%)
4 1
1.1%
6 1
1.1%
8 2
2.3%
47 2
2.3%
54 1
1.1%
70 1
1.1%
82 1
1.1%
111 1
1.1%
118 1
1.1%
138 1
1.1%
ValueCountFrequency (%)
1071 1
1.1%
1060 1
1.1%
1038 1
1.1%
1034 1
1.1%
1017 1
1.1%
975 1
1.1%
951 1
1.1%
945 1
1.1%
919 1
1.1%
891 1
1.1%

영구
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)95.3%
Missing45
Missing (%)51.1%
Infinite0
Infinite (%)0.0%
Mean534.27907
Minimum1
Maximum1414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size924.0 B
2023-12-12T07:45:39.581557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q174.5
median411
Q3967.5
95-th percentile1300.8
Maximum1414
Range1413
Interquartile range (IQR)893

Descriptive statistics

Standard deviation499.17548
Coefficient of variation (CV)0.93429727
Kurtosis-1.5214599
Mean534.27907
Median Absolute Deviation (MAD)408
Skewness0.35006276
Sum22974
Variance249176.16
MonotonicityNot monotonic
2023-12-12T07:45:39.702390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
2 2
 
2.3%
3 2
 
2.3%
843 1
 
1.1%
897 1
 
1.1%
27 1
 
1.1%
906 1
 
1.1%
38 1
 
1.1%
1243 1
 
1.1%
1040 1
 
1.1%
1 1
 
1.1%
Other values (31) 31
35.2%
(Missing) 45
51.1%
ValueCountFrequency (%)
1 1
1.1%
2 2
2.3%
3 2
2.3%
7 1
1.1%
15 1
1.1%
27 1
1.1%
38 1
1.1%
60 1
1.1%
74 1
1.1%
75 1
1.1%
ValueCountFrequency (%)
1414 1
1.1%
1391 1
1.1%
1302 1
1.1%
1290 1
1.1%
1243 1
1.1%
1220 1
1.1%
1128 1
1.1%
1101 1
1.1%
1057 1
1.1%
1040 1
1.1%

Interactions

2023-12-12T07:45:35.810028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:30.894790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.520016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.215436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.903399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.661394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.358263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.086519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.884532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:30.968931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.586967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.324807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.975146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.744612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.447983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.188710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.977518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.064021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.666723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.423708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.064890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.844579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.545339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.266112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:36.083169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.163177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.750303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.494385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.157711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.932545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.642971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.363688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:36.181326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.236552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.837274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.564259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.253132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.011058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.724668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.463100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:36.566464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.298055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.923524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.638518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.339798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.099111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.807078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.540355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:36.657299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.367640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.016234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.714617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.480523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.184206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.888781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.623315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:36.756626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:31.440939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.121855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:32.800836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:33.570619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.258227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:34.986012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:45:35.716515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:45:39.811438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별유형구분1년3년5년10년30년준영구영구
연도별1.0000.0000.0000.6640.5040.7910.3080.0000.7140.601
유형0.0001.0000.0000.2080.0000.0000.212NaNNaNNaN
구분0.0000.0001.0001.0000.6940.6690.5121.0000.9911.000
1년0.6640.2081.0001.0000.9300.8010.7861.0000.6810.733
3년0.5040.0000.6940.9301.0000.8530.7631.0000.6650.671
5년0.7910.0000.6690.8010.8531.0000.9781.0000.8660.814
10년0.3080.2120.5120.7860.7630.9781.0000.9870.7700.775
30년0.000NaN1.0001.0001.0001.0000.9871.0000.8740.629
준영구0.714NaN0.9910.6810.6650.8660.7700.8741.0000.748
영구0.601NaN1.0000.7330.6710.8140.7750.6290.7481.000
2023-12-12T07:45:39.940363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분유형
구분1.0000.000
유형0.0001.000
2023-12-12T07:45:40.035725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도별1년3년5년10년30년준영구영구유형구분
연도별1.0000.3380.3940.4410.250-0.0330.0470.1140.0000.000
1년0.3381.0000.7770.7480.6290.7340.6110.6000.1880.913
3년0.3940.7771.0000.9880.9250.5670.9350.9470.0000.702
5년0.4410.7480.9881.0000.9360.5770.9130.9410.0000.770
10년0.2500.6290.9250.9361.0000.4730.9190.9520.2460.598
30년-0.0330.7340.5670.5770.4731.0000.6490.6961.0000.951
준영구0.0470.6110.9350.9130.9190.6491.0000.9281.0000.823
영구0.1140.6000.9470.9410.9520.6960.9281.0001.0000.911
유형0.0000.1880.0000.0000.2461.0001.0001.0001.0000.000
구분0.0000.9130.7020.7700.5980.9510.8230.9110.0001.000

Missing values

2023-12-12T07:45:36.908687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:45:37.068802image/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.
2023-12-12T07:45:37.240796image/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

연도별유형구분1년3년5년10년30년준영구영구
02001생산비전자<NA>9273824511160
12001생산전자5335490930132563<NA>323411
22001폐기비전자<NA><NA><NA><NA><NA><NA><NA>
32001폐기전자<NA><NA><NA><NA><NA><NA><NA>
42002생산비전자110337730013874
52002생산전자6376681235182955<NA>315555
62002폐기비전자<NA><NA><NA><NA><NA><NA><NA>
72002폐기전자<NA><NA><NA><NA><NA><NA><NA>
82003생산비전자<NA>15238933419199
92003생산전자6044759639943054<NA>357446
연도별유형구분1년3년5년10년30년준영구영구
782020폐기비전자<NA>14616895354<NA><NA><NA>
792020폐기전자<NA><NA><NA><NA><NA><NA><NA>
802021생산비전자<NA><NA><NA><NA>5263
812021생산전자1196510221148205383481139610171302
822021폐기비전자<NA><NA><NA><NA><NA><NA><NA>
832021폐기전자<NA><NA><NA><NA><NA><NA><NA>
842022생산비전자<NA><NA><NA>5324<NA>
852022생산전자827986823495934173767088181290
862022폐기비전자<NA><NA><NA><NA><NA><NA><NA>
872022폐기전자<NA><NA><NA><NA><NA><NA><NA>