Overview

Dataset statistics

Number of variables19
Number of observations24
Missing cells227
Missing cells (%)49.8%
Duplicate rows1
Duplicate rows (%)4.2%
Total size in memory4.1 KiB
Average record size in memory175.5 B

Variable types

Numeric18
Text1

Dataset

Description경상남도 전기자동차 보급현황으로, 연도별(2011~2022) 및 시군별 친환경 전기자동차의 보급현황을 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15054823

Alerts

Dataset has 1 (4.2%) duplicate rowsDuplicates
구분 is highly overall correlated with 창원 and 16 other fieldsHigh correlation
창원 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
진주 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
통영 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
김해 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
밀양 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
거제 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
양산 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
의령 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
함안 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
창녕 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
고성 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
남해 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
하동 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
산청 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
함양 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
거창 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
합천 is highly overall correlated with 구분 and 16 other fieldsHigh correlation
구분 has 12 (50.0%) missing valuesMissing
창원 has 12 (50.0%) missing valuesMissing
진주 has 12 (50.0%) missing valuesMissing
통영 has 12 (50.0%) missing valuesMissing
사천 has 11 (45.8%) missing valuesMissing
김해 has 12 (50.0%) missing valuesMissing
밀양 has 12 (50.0%) missing valuesMissing
거제 has 12 (50.0%) missing valuesMissing
양산 has 12 (50.0%) missing valuesMissing
의령 has 12 (50.0%) missing valuesMissing
함안 has 12 (50.0%) missing valuesMissing
창녕 has 12 (50.0%) missing valuesMissing
고성 has 12 (50.0%) missing valuesMissing
남해 has 12 (50.0%) missing valuesMissing
하동 has 12 (50.0%) missing valuesMissing
산청 has 12 (50.0%) missing valuesMissing
함양 has 12 (50.0%) missing valuesMissing
거창 has 12 (50.0%) missing valuesMissing
합천 has 12 (50.0%) missing valuesMissing
진주 has 5 (20.8%) zerosZeros
통영 has 4 (16.7%) zerosZeros
김해 has 4 (16.7%) zerosZeros
밀양 has 5 (20.8%) zerosZeros
거제 has 3 (12.5%) zerosZeros
양산 has 4 (16.7%) zerosZeros
의령 has 6 (25.0%) zerosZeros
함안 has 5 (20.8%) zerosZeros
창녕 has 5 (20.8%) zerosZeros
고성 has 4 (16.7%) zerosZeros
남해 has 1 (4.2%) zerosZeros
하동 has 5 (20.8%) zerosZeros
산청 has 2 (8.3%) zerosZeros
함양 has 4 (16.7%) zerosZeros
거창 has 4 (16.7%) zerosZeros
합천 has 4 (16.7%) zerosZeros

Reproduction

Analysis started2023-12-10 23:02:28.675719
Analysis finished2023-12-10 23:03:00.917531
Duration32.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean2016.5
Minimum2011
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:00.985730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011.55
Q12013.75
median2016.5
Q32019.25
95-th percentile2021.45
Maximum2022
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.6055513
Coefficient of variation (CV)0.0017880244
Kurtosis-1.2
Mean2016.5
Median Absolute Deviation (MAD)3
Skewness0
Sum24198
Variance13
MonotonicityStrictly decreasing
2023-12-11T08:03:01.100940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2022 1
 
4.2%
2021 1
 
4.2%
2020 1
 
4.2%
2019 1
 
4.2%
2018 1
 
4.2%
2017 1
 
4.2%
2016 1
 
4.2%
2015 1
 
4.2%
2014 1
 
4.2%
2013 1
 
4.2%
Other values (2) 2
 
8.3%
(Missing) 12
50.0%
ValueCountFrequency (%)
2011 1
4.2%
2012 1
4.2%
2013 1
4.2%
2014 1
4.2%
2015 1
4.2%
2016 1
4.2%
2017 1
4.2%
2018 1
4.2%
2019 1
4.2%
2020 1
4.2%
ValueCountFrequency (%)
2022 1
4.2%
2021 1
4.2%
2020 1
4.2%
2019 1
4.2%
2018 1
4.2%
2017 1
4.2%
2016 1
4.2%
2015 1
4.2%
2014 1
4.2%
2013 1
4.2%

창원
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean530.5
Minimum21
Maximum2252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:01.204334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile35.3
Q166.5
median125
Q3680
95-th percentile1883.5
Maximum2252
Range2231
Interquartile range (IQR)613.5

Descriptive statistics

Standard deviation719.75861
Coefficient of variation (CV)1.3567552
Kurtosis2.0393376
Mean530.5
Median Absolute Deviation (MAD)91
Skewness1.6584719
Sum6366
Variance518052.45
MonotonicityNot monotonic
2023-12-11T08:03:01.296968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2252 1
 
4.2%
1582 1
 
4.2%
944 1
 
4.2%
592 1
 
4.2%
456 1
 
4.2%
139 1
 
4.2%
72 1
 
4.2%
111 1
 
4.2%
100 1
 
4.2%
50 1
 
4.2%
Other values (2) 2
 
8.3%
(Missing) 12
50.0%
ValueCountFrequency (%)
21 1
4.2%
47 1
4.2%
50 1
4.2%
72 1
4.2%
100 1
4.2%
111 1
4.2%
139 1
4.2%
456 1
4.2%
592 1
4.2%
944 1
4.2%
ValueCountFrequency (%)
2252 1
4.2%
1582 1
4.2%
944 1
4.2%
592 1
4.2%
456 1
4.2%
139 1
4.2%
111 1
4.2%
100 1
4.2%
72 1
4.2%
50 1
4.2%

진주
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)66.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean337.91667
Minimum0
Maximum2279
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:01.386684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q3164.5
95-th percentile1756.5
Maximum2279
Range2279
Interquartile range (IQR)164.5

Descriptive statistics

Standard deviation717.88483
Coefficient of variation (CV)2.1244434
Kurtosis5.0194803
Mean337.91667
Median Absolute Deviation (MAD)4
Skewness2.3560374
Sum4055
Variance515358.63
MonotonicityNot monotonic
2023-12-11T08:03:01.485702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 5
20.8%
2279 1
 
4.2%
1329 1
 
4.2%
235 1
 
4.2%
141 1
 
4.2%
63 1
 
4.2%
6 1
 
4.2%
2 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 5
20.8%
2 1
 
4.2%
6 1
 
4.2%
63 1
 
4.2%
141 1
 
4.2%
235 1
 
4.2%
1329 1
 
4.2%
2279 1
 
4.2%
ValueCountFrequency (%)
2279 1
 
4.2%
1329 1
 
4.2%
235 1
 
4.2%
141 1
 
4.2%
63 1
 
4.2%
6 1
 
4.2%
2 1
 
4.2%
0 5
20.8%

통영
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)66.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean48.416667
Minimum0
Maximum321
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:01.668326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.5
Q350
95-th percentile205.5
Maximum321
Range321
Interquartile range (IQR)50

Descriptive statistics

Standard deviation92.595364
Coefficient of variation (CV)1.9124688
Kurtosis7.9075662
Mean48.416667
Median Absolute Deviation (MAD)5.5
Skewness2.7165166
Sum581
Variance8573.9015
MonotonicityNot monotonic
2023-12-11T08:03:01.895480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4
 
16.7%
1 2
 
8.3%
321 1
 
4.2%
111 1
 
4.2%
45 1
 
4.2%
65 1
 
4.2%
27 1
 
4.2%
10 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 4
16.7%
1 2
8.3%
10 1
 
4.2%
27 1
 
4.2%
45 1
 
4.2%
65 1
 
4.2%
111 1
 
4.2%
321 1
 
4.2%
ValueCountFrequency (%)
321 1
 
4.2%
111 1
 
4.2%
65 1
 
4.2%
45 1
 
4.2%
27 1
 
4.2%
10 1
 
4.2%
1 2
8.3%
0 4
16.7%

사천
Text

MISSING 

Distinct9
Distinct (%)69.2%
Missing11
Missing (%)45.8%
Memory size324.0 B
2023-12-11T08:03:02.116164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2
Min length1

Characters and Unicode

Total characters26
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)61.5%

Sample

1st row243
2nd row173
3rd row69
4th row35
5th row40
ValueCountFrequency (%)
0 5
41.7%
243 1
 
8.3%
173 1
 
8.3%
69 1
 
8.3%
35 1
 
8.3%
40 1
 
8.3%
12 1
 
8.3%
1 1
 
8.3%
2023-12-11T08:03:02.562507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6
23.1%
6
23.1%
3 3
11.5%
1 3
11.5%
2 2
 
7.7%
4 2
 
7.7%
7 1
 
3.8%
6 1
 
3.8%
9 1
 
3.8%
5 1
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 20
76.9%
Space Separator 6
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6
30.0%
3 3
15.0%
1 3
15.0%
2 2
 
10.0%
4 2
 
10.0%
7 1
 
5.0%
6 1
 
5.0%
9 1
 
5.0%
5 1
 
5.0%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6
23.1%
6
23.1%
3 3
11.5%
1 3
11.5%
2 2
 
7.7%
4 2
 
7.7%
7 1
 
3.8%
6 1
 
3.8%
9 1
 
3.8%
5 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6
23.1%
6
23.1%
3 3
11.5%
1 3
11.5%
2 2
 
7.7%
4 2
 
7.7%
7 1
 
3.8%
6 1
 
3.8%
9 1
 
3.8%
5 1
 
3.8%

김해
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)75.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean159.83333
Minimum0
Maximum853
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:02.700173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median49.5
Q3191.25
95-th percentile590.1
Maximum853
Range853
Interquartile range (IQR)191.25

Descriptive statistics

Standard deviation251.03779
Coefficient of variation (CV)1.5706222
Kurtosis5.364137
Mean159.83333
Median Absolute Deviation (MAD)49.5
Skewness2.2121936
Sum1918
Variance63019.97
MonotonicityNot monotonic
2023-12-11T08:03:02.792872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4
 
16.7%
853 1
 
4.2%
375 1
 
4.2%
273 1
 
4.2%
164 1
 
4.2%
152 1
 
4.2%
82 1
 
4.2%
17 1
 
4.2%
2 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 4
16.7%
2 1
 
4.2%
17 1
 
4.2%
82 1
 
4.2%
152 1
 
4.2%
164 1
 
4.2%
273 1
 
4.2%
375 1
 
4.2%
853 1
 
4.2%
ValueCountFrequency (%)
853 1
 
4.2%
375 1
 
4.2%
273 1
 
4.2%
164 1
 
4.2%
152 1
 
4.2%
82 1
 
4.2%
17 1
 
4.2%
2 1
 
4.2%
0 4
16.7%

밀양
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)58.3%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean75
Minimum0
Maximum431
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:02.881016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q349.25
95-th percentile354.55
Maximum431
Range431
Interquartile range (IQR)49.25

Descriptive statistics

Standard deviation139.12911
Coefficient of variation (CV)1.8550548
Kurtosis3.7376359
Mean75
Median Absolute Deviation (MAD)10
Skewness2.1297958
Sum900
Variance19356.909
MonotonicityDecreasing
2023-12-11T08:03:02.968202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 5
20.8%
40 2
 
8.3%
431 1
 
4.2%
292 1
 
4.2%
77 1
 
4.2%
16 1
 
4.2%
4 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 5
20.8%
4 1
 
4.2%
16 1
 
4.2%
40 2
 
8.3%
77 1
 
4.2%
292 1
 
4.2%
431 1
 
4.2%
ValueCountFrequency (%)
431 1
 
4.2%
292 1
 
4.2%
77 1
 
4.2%
40 2
 
8.3%
16 1
 
4.2%
4 1
 
4.2%
0 5
20.8%

거제
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)83.3%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean78
Minimum0
Maximum529
Zeros3
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:03.097464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5
median14.5
Q352.5
95-th percentile353
Maximum529
Range529
Interquartile range (IQR)51

Descriptive statistics

Standard deviation153.8358
Coefficient of variation (CV)1.9722539
Kurtosis7.7489935
Mean78
Median Absolute Deviation (MAD)14.5
Skewness2.7305838
Sum936
Variance23665.455
MonotonicityNot monotonic
2023-12-11T08:03:03.204988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 3
 
12.5%
529 1
 
4.2%
209 1
 
4.2%
72 1
 
4.2%
45 1
 
4.2%
46 1
 
4.2%
19 1
 
4.2%
10 1
 
4.2%
4 1
 
4.2%
2 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 3
12.5%
2 1
 
4.2%
4 1
 
4.2%
10 1
 
4.2%
19 1
 
4.2%
45 1
 
4.2%
46 1
 
4.2%
72 1
 
4.2%
209 1
 
4.2%
529 1
 
4.2%
ValueCountFrequency (%)
529 1
 
4.2%
209 1
 
4.2%
72 1
 
4.2%
46 1
 
4.2%
45 1
 
4.2%
19 1
 
4.2%
10 1
 
4.2%
4 1
 
4.2%
2 1
 
4.2%
0 3
12.5%

양산
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)75.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean212.58333
Minimum0
Maximum1088
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:03.307228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q3221.5
95-th percentile885.6
Maximum1088
Range1088
Interquartile range (IQR)221.5

Descriptive statistics

Standard deviation352.84879
Coefficient of variation (CV)1.659814
Kurtosis2.7561325
Mean212.58333
Median Absolute Deviation (MAD)24
Skewness1.8547632
Sum2551
Variance124502.27
MonotonicityNot monotonic
2023-12-11T08:03:03.404614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4
 
16.7%
1088 1
 
4.2%
720 1
 
4.2%
409 1
 
4.2%
159 1
 
4.2%
125 1
 
4.2%
37 1
 
4.2%
11 1
 
4.2%
2 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 4
16.7%
2 1
 
4.2%
11 1
 
4.2%
37 1
 
4.2%
125 1
 
4.2%
159 1
 
4.2%
409 1
 
4.2%
720 1
 
4.2%
1088 1
 
4.2%
ValueCountFrequency (%)
1088 1
 
4.2%
720 1
 
4.2%
409 1
 
4.2%
159 1
 
4.2%
125 1
 
4.2%
37 1
 
4.2%
11 1
 
4.2%
2 1
 
4.2%
0 4
16.7%

의령
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct7
Distinct (%)58.3%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean18.666667
Minimum0
Maximum105
Zeros6
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:03.501507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q316.5
95-th percentile85.2
Maximum105
Range105
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation33.643406
Coefficient of variation (CV)1.8023253
Kurtosis3.6852985
Mean18.666667
Median Absolute Deviation (MAD)2.5
Skewness2.0852068
Sum224
Variance1131.8788
MonotonicityDecreasing
2023-12-11T08:03:03.596193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 6
25.0%
105 1
 
4.2%
69 1
 
4.2%
24 1
 
4.2%
14 1
 
4.2%
7 1
 
4.2%
5 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 6
25.0%
5 1
 
4.2%
7 1
 
4.2%
14 1
 
4.2%
24 1
 
4.2%
69 1
 
4.2%
105 1
 
4.2%
ValueCountFrequency (%)
105 1
 
4.2%
69 1
 
4.2%
24 1
 
4.2%
14 1
 
4.2%
7 1
 
4.2%
5 1
 
4.2%
0 6
25.0%

함안
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)66.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean40.5
Minimum0
Maximum276
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:03.681639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q326.5
95-th percentile186.9
Maximum276
Range276
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation81.509063
Coefficient of variation (CV)2.0125694
Kurtosis7.1147761
Mean40.5
Median Absolute Deviation (MAD)5
Skewness2.6197538
Sum486
Variance6643.7273
MonotonicityNot monotonic
2023-12-11T08:03:03.767561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 5
20.8%
276 1
 
4.2%
114 1
 
4.2%
55 1
 
4.2%
14 1
 
4.2%
17 1
 
4.2%
9 1
 
4.2%
1 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 5
20.8%
1 1
 
4.2%
9 1
 
4.2%
14 1
 
4.2%
17 1
 
4.2%
55 1
 
4.2%
114 1
 
4.2%
276 1
 
4.2%
ValueCountFrequency (%)
276 1
 
4.2%
114 1
 
4.2%
55 1
 
4.2%
17 1
 
4.2%
14 1
 
4.2%
9 1
 
4.2%
1 1
 
4.2%
0 5
20.8%

창녕
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)66.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean42.416667
Minimum0
Maximum221
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:03.867732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q335
95-th percentile200.1
Maximum221
Range221
Interquartile range (IQR)35

Descriptive statistics

Standard deviation76.534553
Coefficient of variation (CV)1.804351
Kurtosis2.527441
Mean42.416667
Median Absolute Deviation (MAD)5
Skewness1.9378184
Sum509
Variance5857.5379
MonotonicityNot monotonic
2023-12-11T08:03:03.963138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 5
20.8%
221 1
 
4.2%
183 1
 
4.2%
50 1
 
4.2%
30 1
 
4.2%
8 1
 
4.2%
15 1
 
4.2%
2 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 5
20.8%
2 1
 
4.2%
8 1
 
4.2%
15 1
 
4.2%
30 1
 
4.2%
50 1
 
4.2%
183 1
 
4.2%
221 1
 
4.2%
ValueCountFrequency (%)
221 1
 
4.2%
183 1
 
4.2%
50 1
 
4.2%
30 1
 
4.2%
15 1
 
4.2%
8 1
 
4.2%
2 1
 
4.2%
0 5
20.8%

고성
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)66.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean28.583333
Minimum0
Maximum194
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:04.057990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q313.75
95-th percentile142.3
Maximum194
Range194
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation59.275103
Coefficient of variation (CV)2.0737645
Kurtosis5.9133447
Mean28.583333
Median Absolute Deviation (MAD)2
Skewness2.4746698
Sum343
Variance3513.5379
MonotonicityNot monotonic
2023-12-11T08:03:04.156356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4
 
16.7%
1 2
 
8.3%
194 1
 
4.2%
100 1
 
4.2%
25 1
 
4.2%
10 1
 
4.2%
9 1
 
4.2%
3 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 4
16.7%
1 2
8.3%
3 1
 
4.2%
9 1
 
4.2%
10 1
 
4.2%
25 1
 
4.2%
100 1
 
4.2%
194 1
 
4.2%
ValueCountFrequency (%)
194 1
 
4.2%
100 1
 
4.2%
25 1
 
4.2%
10 1
 
4.2%
9 1
 
4.2%
3 1
 
4.2%
1 2
8.3%
0 4
16.7%

남해
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)75.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean27.833333
Minimum0
Maximum172
Zeros1
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:04.258965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55
Q11
median7
Q324.25
95-th percentile115.9
Maximum172
Range172
Interquartile range (IQR)23.25

Descriptive statistics

Standard deviation49.906883
Coefficient of variation (CV)1.7930617
Kurtosis7.1009587
Mean27.833333
Median Absolute Deviation (MAD)6.5
Skewness2.5892473
Sum334
Variance2490.697
MonotonicityNot monotonic
2023-12-11T08:03:04.377690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 4
 
16.7%
172 1
 
4.2%
70 1
 
4.2%
37 1
 
4.2%
17 1
 
4.2%
20 1
 
4.2%
12 1
 
4.2%
2 1
 
4.2%
0 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 1
 
4.2%
1 4
16.7%
2 1
 
4.2%
12 1
 
4.2%
17 1
 
4.2%
20 1
 
4.2%
37 1
 
4.2%
70 1
 
4.2%
172 1
 
4.2%
ValueCountFrequency (%)
172 1
 
4.2%
70 1
 
4.2%
37 1
 
4.2%
20 1
 
4.2%
17 1
 
4.2%
12 1
 
4.2%
2 1
 
4.2%
1 4
16.7%
0 1
 
4.2%

하동
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)66.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean33.666667
Minimum0
Maximum167
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:04.501227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.5
Q338.75
95-th percentile140.05
Maximum167
Range167
Interquartile range (IQR)38.75

Descriptive statistics

Standard deviation54.844132
Coefficient of variation (CV)1.6290336
Kurtosis2.4970561
Mean33.666667
Median Absolute Deviation (MAD)6.5
Skewness1.8146589
Sum404
Variance3007.8788
MonotonicityNot monotonic
2023-12-11T08:03:04.643122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 5
20.8%
167 1
 
4.2%
118 1
 
4.2%
59 1
 
4.2%
32 1
 
4.2%
12 1
 
4.2%
15 1
 
4.2%
1 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 5
20.8%
1 1
 
4.2%
12 1
 
4.2%
15 1
 
4.2%
32 1
 
4.2%
59 1
 
4.2%
118 1
 
4.2%
167 1
 
4.2%
ValueCountFrequency (%)
167 1
 
4.2%
118 1
 
4.2%
59 1
 
4.2%
32 1
 
4.2%
15 1
 
4.2%
12 1
 
4.2%
1 1
 
4.2%
0 5
20.8%

산청
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct9
Distinct (%)75.0%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean33.5
Minimum0
Maximum138
Zeros2
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:04.819646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.75
median11
Q331.75
95-th percentile138
Maximum138
Range138
Interquartile range (IQR)30

Descriptive statistics

Standard deviation51.037419
Coefficient of variation (CV)1.5235051
Kurtosis1.6585117
Mean33.5
Median Absolute Deviation (MAD)11
Skewness1.7078996
Sum402
Variance2604.8182
MonotonicityNot monotonic
2023-12-11T08:03:04.935476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
138 2
 
8.3%
2 2
 
8.3%
0 2
 
8.3%
49 1
 
4.2%
24 1
 
4.2%
26 1
 
4.2%
19 1
 
4.2%
3 1
 
4.2%
1 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 2
8.3%
1 1
4.2%
2 2
8.3%
3 1
4.2%
19 1
4.2%
24 1
4.2%
26 1
4.2%
49 1
4.2%
138 2
8.3%
ValueCountFrequency (%)
138 2
8.3%
49 1
4.2%
26 1
4.2%
24 1
4.2%
19 1
4.2%
3 1
4.2%
2 2
8.3%
1 1
4.2%
0 2
8.3%

함양
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)66.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean20.916667
Minimum0
Maximum124
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:05.090365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q320.25
95-th percentile87.7
Maximum124
Range124
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation37.814039
Coefficient of variation (CV)1.8078425
Kurtosis4.9363378
Mean20.916667
Median Absolute Deviation (MAD)1.5
Skewness2.2159156
Sum251
Variance1429.9015
MonotonicityNot monotonic
2023-12-11T08:03:05.219225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4
 
16.7%
1 2
 
8.3%
124 1
 
4.2%
58 1
 
4.2%
45 1
 
4.2%
12 1
 
4.2%
8 1
 
4.2%
2 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 4
16.7%
1 2
8.3%
2 1
 
4.2%
8 1
 
4.2%
12 1
 
4.2%
45 1
 
4.2%
58 1
 
4.2%
124 1
 
4.2%
ValueCountFrequency (%)
124 1
 
4.2%
58 1
 
4.2%
45 1
 
4.2%
12 1
 
4.2%
8 1
 
4.2%
2 1
 
4.2%
1 2
8.3%
0 4
16.7%

거창
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)66.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean31.25
Minimum0
Maximum191
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:05.355389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q327.25
95-th percentile141.5
Maximum191
Range191
Interquartile range (IQR)27.25

Descriptive statistics

Standard deviation58.331537
Coefficient of variation (CV)1.8666092
Kurtosis5.2361205
Mean31.25
Median Absolute Deviation (MAD)4
Skewness2.3163431
Sum375
Variance3402.5682
MonotonicityNot monotonic
2023-12-11T08:03:05.503004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4
 
16.7%
1 2
 
8.3%
191 1
 
4.2%
101 1
 
4.2%
43 1
 
4.2%
22 1
 
4.2%
9 1
 
4.2%
7 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 4
16.7%
1 2
8.3%
7 1
 
4.2%
9 1
 
4.2%
22 1
 
4.2%
43 1
 
4.2%
101 1
 
4.2%
191 1
 
4.2%
ValueCountFrequency (%)
191 1
 
4.2%
101 1
 
4.2%
43 1
 
4.2%
22 1
 
4.2%
9 1
 
4.2%
7 1
 
4.2%
1 2
8.3%
0 4
16.7%

합천
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct8
Distinct (%)66.7%
Missing12
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean31
Minimum0
Maximum197
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-11T08:03:05.647448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q319.75
95-th percentile148.6
Maximum197
Range197
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation61.052883
Coefficient of variation (CV)1.9694478
Kurtosis5.0700076
Mean31
Median Absolute Deviation (MAD)2
Skewness2.3184504
Sum372
Variance3727.4545
MonotonicityNot monotonic
2023-12-11T08:03:05.789157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4
 
16.7%
1 2
 
8.3%
197 1
 
4.2%
109 1
 
4.2%
40 1
 
4.2%
13 1
 
4.2%
8 1
 
4.2%
3 1
 
4.2%
(Missing) 12
50.0%
ValueCountFrequency (%)
0 4
16.7%
1 2
8.3%
3 1
 
4.2%
8 1
 
4.2%
13 1
 
4.2%
40 1
 
4.2%
109 1
 
4.2%
197 1
 
4.2%
ValueCountFrequency (%)
197 1
 
4.2%
109 1
 
4.2%
40 1
 
4.2%
13 1
 
4.2%
8 1
 
4.2%
3 1
 
4.2%
1 2
8.3%
0 4
16.7%

Interactions

2023-12-11T08:02:58.474380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:29.242988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:30.988864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:32.907285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:34.347536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:35.723776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:37.170700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:38.985158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:40.796940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:42.414547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:44.429596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:45.997261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:47.629030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:49.346020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:51.310971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:52.947499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:54.417663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:56.562174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:58.559820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:29.361383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:31.071717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:32.984854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:34.420060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:35.791050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:37.251333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:39.072733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:40.875175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:42.510842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:44.505522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:46.081572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:47.739473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:49.437490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:51.398773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:53.041816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:54.497960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:56.651073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:58.640835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:29.440361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:31.150643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:33.056562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:34.487696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:35.864248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:37.317369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:39.235889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:40.957203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:42.602925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:44.580156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:46.156886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-11T08:02:53.993649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:56.037238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:57.947339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:59.686455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:30.528454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:32.479103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:34.051870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:35.412857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:36.863619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:38.569607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:40.450742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:42.025524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:44.003375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:45.597495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:47.262081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:48.946465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:50.943953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:52.571084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:54.074201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:56.127173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:58.047020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:59.767502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:30.633235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:32.591722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:34.124645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:35.495326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:36.946565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:38.671098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:40.547628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:42.109725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:44.128819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:45.694925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:47.349198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:49.060041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:51.033251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:52.672137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:54.156741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:56.233705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:58.151936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:59.850577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:30.734488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:32.694388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:34.195687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:35.571981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:37.013942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:38.750559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:40.625432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:42.190016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:44.232695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:45.791576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:47.437515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:49.162071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:51.118777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:52.755344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:54.234540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:56.336802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:58.254507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:59.936279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:30.867381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:32.800789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:34.275715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:35.645754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:37.094401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:38.855129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:40.709279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:42.309157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:44.337932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:45.897262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:47.542996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:49.256310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:51.214877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:52.857072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:54.334158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:56.450985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:02:58.357976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:03:05.893690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분창원진주통영사천김해밀양거제양산의령함안창녕고성남해하동산청함양거창합천
구분1.0000.0000.0000.0000.7310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
창원0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9601.0001.0001.000
진주0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000
통영0.0001.0001.0001.0001.0000.9941.0001.0000.9941.0001.0001.0001.0000.9801.0000.8231.0001.0001.000
사천0.7311.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
김해0.0001.0001.0000.9941.0001.0001.0001.0001.0000.9941.0000.9941.0000.9940.9940.8891.0000.9941.000
밀양0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000
거제0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000
양산0.0001.0001.0000.9941.0001.0001.0001.0001.0000.9941.0000.9941.0000.9940.9940.8891.0000.9941.000
의령0.0001.0001.0001.0001.0000.9941.0001.0000.9941.0001.0001.0001.0000.9801.0000.8231.0001.0001.000
함안0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000
창녕0.0001.0001.0001.0001.0000.9941.0001.0000.9941.0001.0001.0001.0000.9801.0000.8231.0001.0001.000
고성0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000
남해0.0001.0001.0000.9801.0000.9941.0001.0000.9940.9801.0000.9801.0001.0000.9800.8231.0000.9801.000
하동0.0001.0001.0001.0001.0000.9941.0001.0000.9941.0001.0001.0001.0000.9801.0000.8231.0001.0001.000
산청1.0000.9600.9640.8231.0000.8890.9640.9640.8890.8230.9640.8230.9640.8230.8231.0000.9640.8230.964
함양0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000
거창0.0001.0001.0001.0001.0000.9941.0001.0000.9941.0001.0001.0001.0000.9801.0000.8231.0001.0001.000
합천0.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.9641.0001.0001.000
2023-12-11T08:03:06.096790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분창원진주통영김해밀양거제양산의령함안창녕고성남해하동산청함양거창합천
구분1.0000.9720.9210.8450.9290.9630.9440.9290.9370.8920.9140.8090.9040.9350.9560.8630.8950.959
창원0.9721.0000.9430.8240.8610.9190.9230.8610.9370.8920.9350.8090.8510.9570.9030.7880.8950.938
진주0.9210.9431.0000.8980.9040.9300.8760.9040.9710.9250.9920.9060.9190.9250.8600.8610.9060.972
통영0.8450.8240.8981.0000.9150.8930.8150.9150.9480.9500.8910.9270.8890.8910.8850.9110.9270.862
김해0.9290.8610.9040.9151.0000.9800.9281.0000.9540.8970.8970.9220.9660.8970.9410.9760.9220.944
밀양0.9630.9190.9300.8930.9801.0000.9690.9800.9690.9300.9190.9040.9800.9190.9640.9260.9040.971
거제0.9440.9230.8760.8150.9280.9691.0000.9280.9360.8840.8620.8730.9180.9270.9490.8620.8940.916
양산0.9290.8610.9040.9151.0000.9800.9281.0000.9540.8970.8970.9220.9660.8970.9410.9760.9220.944
의령0.9370.9370.9710.9480.9540.9690.9360.9541.0000.9640.9640.9550.9460.9640.9310.9150.9550.955
함안0.8920.8920.9250.9500.8970.9300.8840.8970.9641.0000.9100.8980.9260.9100.9330.8540.8980.898
창녕0.9140.9350.9920.8910.8970.9190.8620.8970.9640.9101.0000.8980.9040.9320.8460.8430.8980.965
고성0.8090.8090.9060.9270.9220.9040.8730.9220.9550.8980.8981.0000.9040.8980.8350.9180.9350.869
남해0.9040.8510.9190.8890.9660.9800.9180.9660.9460.9260.9040.9041.0000.8490.9210.9200.8490.958
하동0.9350.9570.9250.8910.8970.9190.9270.8970.9640.9100.9320.8980.8491.0000.9110.8430.9650.898
산청0.9560.9030.8600.8850.9410.9640.9490.9410.9310.9330.8460.8350.9210.9111.0000.8890.9000.900
함양0.8630.7880.8610.9110.9760.9260.8620.9760.9150.8540.8430.9180.9200.8430.8891.0000.9180.885
거창0.8950.8950.9060.9270.9220.9040.8940.9220.9550.8980.8980.9350.8490.9650.9000.9181.0000.869
합천0.9590.9380.9720.8620.9440.9710.9160.9440.9550.8980.9650.8690.9580.8980.9000.8850.8691.000

Missing values

2023-12-11T08:03:00.075639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:03:00.283972image/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.
2023-12-11T08:03:00.715275image/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

구분창원진주통영사천김해밀양거제양산의령함안창녕고성남해하동산청함양거창합천
02022225222793212438534315291088105276221194172167138124191197
1202115821329111173375292209720691141831007011813858101109
220209442354569273777240924555025375949454340
320195921416535164404515914143010173224122213
42018456632740152404612571789201226898
52017139610128216193759153121519173
620167200117410110000203101
7201511100000400000012010
8201410020000000020100001
920135001000000100102000
구분창원진주통영사천김해밀양거제양산의령함안창녕고성남해하동산청함양거창합천
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Duplicate rows

Most frequently occurring

구분창원진주통영사천김해밀양거제양산의령함안창녕고성남해하동산청함양거창합천# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>11