Overview

Dataset statistics

Number of variables11
Number of observations90
Missing cells121
Missing cells (%)12.2%
Duplicate rows1
Duplicate rows (%)1.1%
Total size in memory8.7 KiB
Average record size in memory99.5 B

Variable types

DateTime1
Numeric10

Dataset

Description농작물 경작, 재난재해 사전 예방을 위한 남해군내 월별, 지역별 강우량 현황입니다. 남해군의 10개 읍면의 강우량 데이터를 제공합니다.
Author경상남도 남해군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3075966

Alerts

Dataset has 1 (1.1%) duplicate rowsDuplicates
남해읍 is highly overall correlated with 이동면 and 8 other fieldsHigh correlation
이동면 is highly overall correlated with 남해읍 and 8 other fieldsHigh correlation
상주면 is highly overall correlated with 남해읍 and 8 other fieldsHigh correlation
삼동면 is highly overall correlated with 남해읍 and 8 other fieldsHigh correlation
미조면 is highly overall correlated with 남해읍 and 8 other fieldsHigh correlation
남면 is highly overall correlated with 남해읍 and 8 other fieldsHigh correlation
서면 is highly overall correlated with 남해읍 and 8 other fieldsHigh correlation
고현면 is highly overall correlated with 남해읍 and 8 other fieldsHigh correlation
설천면 is highly overall correlated with 남해읍 and 8 other fieldsHigh correlation
창선면 is highly overall correlated with 남해읍 and 8 other fieldsHigh correlation
구분 has 11 (12.2%) missing valuesMissing
남해읍 has 11 (12.2%) missing valuesMissing
이동면 has 11 (12.2%) missing valuesMissing
상주면 has 11 (12.2%) missing valuesMissing
삼동면 has 11 (12.2%) missing valuesMissing
미조면 has 11 (12.2%) missing valuesMissing
남면 has 11 (12.2%) missing valuesMissing
서면 has 11 (12.2%) missing valuesMissing
고현면 has 11 (12.2%) missing valuesMissing
설천면 has 11 (12.2%) missing valuesMissing
창선면 has 11 (12.2%) missing valuesMissing
남해읍 has 2 (2.2%) zerosZeros
이동면 has 2 (2.2%) zerosZeros
상주면 has 2 (2.2%) zerosZeros
삼동면 has 4 (4.4%) zerosZeros
미조면 has 4 (4.4%) zerosZeros
남면 has 2 (2.2%) zerosZeros
서면 has 2 (2.2%) zerosZeros
고현면 has 2 (2.2%) zerosZeros
설천면 has 2 (2.2%) zerosZeros
창선면 has 3 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-11 00:25:07.482438
Analysis finished2023-12-11 00:25:17.461360
Duration9.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

MISSING 

Distinct79
Distinct (%)100.0%
Missing11
Missing (%)12.2%
Memory size852.0 B
Minimum2017-01-01 00:00:00
Maximum2023-07-01 00:00:00
2023-12-11T09:25:17.528674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:17.667500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

남해읍
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct74
Distinct (%)93.7%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean142.89873
Minimum0
Maximum624
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:18.029938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.7
Q137.75
median102.5
Q3194.5
95-th percentile410.5
Maximum624
Range624
Interquartile range (IQR)156.75

Descriptive statistics

Standard deviation141.14789
Coefficient of variation (CV)0.98774766
Kurtosis1.6550639
Mean142.89873
Median Absolute Deviation (MAD)73.5
Skewness1.373708
Sum11289
Variance19922.727
MonotonicityNot monotonic
2023-12-11T09:25:18.142108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.0 2
 
2.2%
27.0 2
 
2.2%
43.5 2
 
2.2%
0.0 2
 
2.2%
20.0 2
 
2.2%
342.0 1
 
1.1%
98.0 1
 
1.1%
35.5 1
 
1.1%
192.5 1
 
1.1%
435.5 1
 
1.1%
Other values (64) 64
71.1%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 2
2.2%
0.5 1
1.1%
2.0 1
1.1%
5.0 1
1.1%
8.0 1
1.1%
10.0 1
1.1%
12.0 1
1.1%
13.0 1
1.1%
16.0 1
1.1%
18.0 1
1.1%
ValueCountFrequency (%)
624.0 1
1.1%
602.0 1
1.1%
435.5 1
1.1%
415.0 1
1.1%
410.0 1
1.1%
385.0 1
1.1%
370.0 1
1.1%
342.0 1
1.1%
328.0 1
1.1%
322.0 1
1.1%

이동면
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct75
Distinct (%)94.9%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean140.43038
Minimum0
Maximum624
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:18.253240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.75
Q141
median101.5
Q3188
95-th percentile430.5
Maximum624
Range624
Interquartile range (IQR)147

Descriptive statistics

Standard deviation137.34104
Coefficient of variation (CV)0.97800095
Kurtosis2.2156312
Mean140.43038
Median Absolute Deviation (MAD)71
Skewness1.5170116
Sum11094
Variance18862.562
MonotonicityNot monotonic
2023-12-11T09:25:18.376229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2
 
2.2%
24.0 2
 
2.2%
0.5 2
 
2.2%
122.0 2
 
2.2%
29.0 1
 
1.1%
151.0 1
 
1.1%
51.0 1
 
1.1%
62.5 1
 
1.1%
139.5 1
 
1.1%
444.0 1
 
1.1%
Other values (65) 65
72.2%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 2
2.2%
0.5 2
2.2%
3.0 1
1.1%
7.5 1
1.1%
10.5 1
1.1%
13.0 1
1.1%
14.5 1
1.1%
15.0 1
1.1%
16.5 1
1.1%
22.0 1
1.1%
ValueCountFrequency (%)
624.0 1
1.1%
561.0 1
1.1%
520.0 1
1.1%
444.0 1
1.1%
429.0 1
1.1%
356.0 1
1.1%
338.0 1
1.1%
334.5 1
1.1%
318.0 1
1.1%
316.0 1
1.1%

상주면
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct75
Distinct (%)94.9%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean116.42405
Minimum0
Maximum476
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:18.496414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.8
Q134.25
median83.5
Q3158.5
95-th percentile367.45
Maximum476
Range476
Interquartile range (IQR)124.25

Descriptive statistics

Standard deviation112.00804
Coefficient of variation (CV)0.96206963
Kurtosis2.0677861
Mean116.42405
Median Absolute Deviation (MAD)56
Skewness1.5075351
Sum9197.5
Variance12545.802
MonotonicityNot monotonic
2023-12-11T09:25:18.620570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.0 2
 
2.2%
0.0 2
 
2.2%
12.0 2
 
2.2%
58.0 2
 
2.2%
97.0 1
 
1.1%
3.0 1
 
1.1%
44.5 1
 
1.1%
8.0 1
 
1.1%
116.5 1
 
1.1%
407.5 1
 
1.1%
Other values (65) 65
72.2%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 2
2.2%
0.5 1
1.1%
1.0 1
1.1%
3.0 1
1.1%
6.5 1
1.1%
8.0 1
1.1%
12.0 2
2.2%
14.0 1
1.1%
18.0 1
1.1%
18.5 1
1.1%
ValueCountFrequency (%)
476.0 1
1.1%
473.0 1
1.1%
412.0 1
1.1%
407.5 1
1.1%
363.0 1
1.1%
334.5 1
1.1%
295.0 1
1.1%
281.0 1
1.1%
253.0 1
1.1%
237.0 1
1.1%

삼동면
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct66
Distinct (%)83.5%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean135.37975
Minimum0
Maximum637
Zeros4
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:18.731617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7
Q135.75
median108
Q3178
95-th percentile351.2
Maximum637
Range637
Interquartile range (IQR)142.25

Descriptive statistics

Standard deviation133.17983
Coefficient of variation (CV)0.98375002
Kurtosis3.4613305
Mean135.37975
Median Absolute Deviation (MAD)72.5
Skewness1.7197289
Sum10695
Variance17736.867
MonotonicityNot monotonic
2023-12-11T09:25:18.859539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.4%
32.0 2
 
2.2%
200.0 2
 
2.2%
19.0 2
 
2.2%
70.0 2
 
2.2%
167.0 2
 
2.2%
66.0 2
 
2.2%
131.0 2
 
2.2%
127.0 2
 
2.2%
30.0 2
 
2.2%
Other values (56) 57
63.3%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 4
4.4%
3.0 1
 
1.1%
7.0 1
 
1.1%
12.0 1
 
1.1%
14.0 1
 
1.1%
17.0 1
 
1.1%
19.0 2
2.2%
25.0 1
 
1.1%
26.0 1
 
1.1%
28.0 1
 
1.1%
ValueCountFrequency (%)
637.0 1
1.1%
606.0 1
1.1%
486.0 1
1.1%
443.0 1
1.1%
341.0 1
1.1%
339.0 1
1.1%
325.0 1
1.1%
316.0 1
1.1%
303.0 1
1.1%
279.0 1
1.1%

미조면
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct71
Distinct (%)89.9%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean122.08228
Minimum0
Maximum503
Zeros4
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:18.980986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.7
Q137
median94
Q3162.75
95-th percentile363.1
Maximum503
Range503
Interquartile range (IQR)125.75

Descriptive statistics

Standard deviation117.64419
Coefficient of variation (CV)0.96364678
Kurtosis1.6625615
Mean122.08228
Median Absolute Deviation (MAD)60.5
Skewness1.4474093
Sum9644.5
Variance13840.157
MonotonicityNot monotonic
2023-12-11T09:25:19.104001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.4%
50.0 2
 
2.2%
17.0 2
 
2.2%
167.0 2
 
2.2%
126.0 2
 
2.2%
113.0 2
 
2.2%
10.0 1
 
1.1%
338.5 1
 
1.1%
364.0 1
 
1.1%
86.0 1
 
1.1%
Other values (61) 61
67.8%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 4
4.4%
3.0 1
 
1.1%
8.0 1
 
1.1%
10.0 1
 
1.1%
11.5 1
 
1.1%
13.5 1
 
1.1%
14.0 1
 
1.1%
16.0 1
 
1.1%
17.0 2
2.2%
22.0 1
 
1.1%
ValueCountFrequency (%)
503.0 1
1.1%
488.0 1
1.1%
401.0 1
1.1%
364.0 1
1.1%
363.0 1
1.1%
359.5 1
1.1%
338.5 1
1.1%
324.0 1
1.1%
314.0 1
1.1%
312.0 1
1.1%

남면
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct70
Distinct (%)88.6%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean132.3481
Minimum0
Maximum554
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:19.220812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.85
Q143.25
median93
Q3176.25
95-th percentile370.7
Maximum554
Range554
Interquartile range (IQR)133

Descriptive statistics

Standard deviation126.73219
Coefficient of variation (CV)0.95756709
Kurtosis1.930017
Mean132.3481
Median Absolute Deviation (MAD)67
Skewness1.4537655
Sum10455.5
Variance16061.047
MonotonicityNot monotonic
2023-12-11T09:25:19.334664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
145.0 2
 
2.2%
55.0 2
 
2.2%
41.0 2
 
2.2%
0.0 2
 
2.2%
232.0 2
 
2.2%
26.0 2
 
2.2%
129.5 2
 
2.2%
102.5 2
 
2.2%
48.5 2
 
2.2%
106.5 1
 
1.1%
Other values (60) 60
66.7%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 2
2.2%
1.0 1
1.1%
2.5 1
1.1%
4.0 1
1.1%
6.5 1
1.1%
11.0 1
1.1%
12.0 1
1.1%
15.5 1
1.1%
18.0 1
1.1%
19.0 1
1.1%
ValueCountFrequency (%)
554.0 1
1.1%
547.0 1
1.1%
460.0 1
1.1%
395.0 1
1.1%
368.0 1
1.1%
348.0 1
1.1%
340.0 1
1.1%
320.0 1
1.1%
319.0 1
1.1%
317.0 1
1.1%

서면
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct71
Distinct (%)89.9%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean131.37975
Minimum0
Maximum620
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:19.492899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9
Q141
median86
Q3179.75
95-th percentile362.05
Maximum620
Range620
Interquartile range (IQR)138.75

Descriptive statistics

Standard deviation131.86194
Coefficient of variation (CV)1.0036702
Kurtosis2.5308285
Mean131.37975
Median Absolute Deviation (MAD)60
Skewness1.5831323
Sum10379
Variance17387.572
MonotonicityNot monotonic
2023-12-11T09:25:19.665843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291.0 2
 
2.2%
63.0 2
 
2.2%
50.0 2
 
2.2%
1.0 2
 
2.2%
119.0 2
 
2.2%
56.5 2
 
2.2%
73.0 2
 
2.2%
0.0 2
 
2.2%
47.0 1
 
1.1%
130.0 1
 
1.1%
Other values (61) 61
67.8%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 2
2.2%
1.0 2
2.2%
2.0 1
1.1%
5.0 1
1.1%
13.0 1
1.1%
14.0 1
1.1%
15.0 1
1.1%
18.5 1
1.1%
19.0 1
1.1%
20.0 1
1.1%
ValueCountFrequency (%)
620.0 1
1.1%
558.5 1
1.1%
460.0 1
1.1%
389.5 1
1.1%
359.0 1
1.1%
339.5 1
1.1%
338.0 1
1.1%
330.0 1
1.1%
326.0 1
1.1%
301.5 1
1.1%

고현면
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct75
Distinct (%)94.9%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean136.60127
Minimum0
Maximum629
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:19.837035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.15
Q134.5
median93
Q3195
95-th percentile381.95
Maximum629
Range629
Interquartile range (IQR)160.5

Descriptive statistics

Standard deviation134.66322
Coefficient of variation (CV)0.98581236
Kurtosis2.1325845
Mean136.60127
Median Absolute Deviation (MAD)65
Skewness1.4716018
Sum10791.5
Variance18134.182
MonotonicityNot monotonic
2023-12-11T09:25:20.035552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.0 2
 
2.2%
32.0 2
 
2.2%
20.0 2
 
2.2%
0.0 2
 
2.2%
108.0 1
 
1.1%
58.0 1
 
1.1%
39.0 1
 
1.1%
248.0 1
 
1.1%
357.5 1
 
1.1%
493.0 1
 
1.1%
Other values (65) 65
72.2%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 2
2.2%
0.5 1
1.1%
2.0 1
1.1%
5.5 1
1.1%
6.5 1
1.1%
10.0 1
1.1%
14.5 1
1.1%
16.0 1
1.1%
18.5 1
1.1%
19.0 1
1.1%
ValueCountFrequency (%)
629.0 1
1.1%
542.5 1
1.1%
493.0 1
1.1%
386.0 1
1.1%
381.5 1
1.1%
357.5 1
1.1%
345.0 1
1.1%
325.0 1
1.1%
323.5 1
1.1%
296.0 1
1.1%

설천면
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct73
Distinct (%)92.4%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean140.84177
Minimum0
Maximum655
Zeros2
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:20.207589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.85
Q134
median99
Q3213.5
95-th percentile400.65
Maximum655
Range655
Interquartile range (IQR)179.5

Descriptive statistics

Standard deviation141.83666
Coefficient of variation (CV)1.0070639
Kurtosis1.9016882
Mean140.84177
Median Absolute Deviation (MAD)70
Skewness1.4496786
Sum11126.5
Variance20117.638
MonotonicityNot monotonic
2023-12-11T09:25:20.347891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.0 2
 
2.2%
0.0 2
 
2.2%
48.0 2
 
2.2%
115.0 2
 
2.2%
0.5 2
 
2.2%
30.0 2
 
2.2%
32.0 1
 
1.1%
26.0 1
 
1.1%
13.0 1
 
1.1%
18.0 1
 
1.1%
Other values (63) 63
70.0%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 2
2.2%
0.5 2
2.2%
2.0 1
1.1%
5.5 1
1.1%
10.5 1
1.1%
13.0 1
1.1%
15.0 1
1.1%
16.5 1
1.1%
18.0 1
1.1%
19.0 1
1.1%
ValueCountFrequency (%)
655.0 1
1.1%
525.0 1
1.1%
505.0 1
1.1%
487.5 1
1.1%
391.0 1
1.1%
387.5 1
1.1%
363.0 1
1.1%
344.0 1
1.1%
333.5 1
1.1%
310.0 1
1.1%

창선면
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct71
Distinct (%)89.9%
Missing11
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean130.71519
Minimum0
Maximum655
Zeros3
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size942.0 B
2023-12-11T09:25:20.503216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.85
Q137.25
median99
Q3184.25
95-th percentile363.5
Maximum655
Range655
Interquartile range (IQR)147

Descriptive statistics

Standard deviation132.57924
Coefficient of variation (CV)1.0142604
Kurtosis3.9924473
Mean130.71519
Median Absolute Deviation (MAD)68.5
Skewness1.8529357
Sum10326.5
Variance17577.254
MonotonicityNot monotonic
2023-12-11T09:25:20.682859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.3%
65.0 3
 
3.3%
197.0 2
 
2.2%
72.0 2
 
2.2%
14.0 2
 
2.2%
152.0 2
 
2.2%
131.0 1
 
1.1%
540.0 1
 
1.1%
467.0 1
 
1.1%
125.0 1
 
1.1%
Other values (61) 61
67.8%
(Missing) 11
 
12.2%
ValueCountFrequency (%)
0.0 3
3.3%
0.5 1
 
1.1%
2.0 1
 
1.1%
6.0 1
 
1.1%
12.5 1
 
1.1%
14.0 2
2.2%
16.5 1
 
1.1%
18.0 1
 
1.1%
23.0 1
 
1.1%
24.0 1
 
1.1%
ValueCountFrequency (%)
655.0 1
1.1%
549.0 1
1.1%
540.0 1
1.1%
467.0 1
1.1%
352.0 1
1.1%
322.5 1
1.1%
309.0 1
1.1%
301.0 1
1.1%
300.0 1
1.1%
272.0 1
1.1%

Interactions

2023-12-11T09:25:16.169402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:07.796160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.661233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.399148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.081439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.885896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:11.910570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.353575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.230500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.178496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.266541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:07.879224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.743140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.462213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.153490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.998136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:12.040764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.443282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.338413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.275896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.359155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:07.969647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.832473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.523304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.228526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:11.093971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:12.178005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.531207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.441536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.378168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.432571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.049726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.911247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.579729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.292833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:11.183674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:12.304468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.619208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.523481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.460776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.526363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.137332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.984181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.647171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.366960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:11.258031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:12.430032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.710368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.612782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.562405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.607133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.232791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.048734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.711024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.440275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:11.345075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:12.846997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.795522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.703111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.656835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.703286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.320857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.124570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.773823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.516078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:11.447625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:12.950378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.876378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.801584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.761372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.782677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.415410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.193360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.838401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.591914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:11.534991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.055572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.964621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.884624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.878587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.867779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.514496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.263252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.921593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.686181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:11.627798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.149734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.046384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.976361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.981861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.952348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:08.589882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.329993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:09.998514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:10.800051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:11.731079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:13.252364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:14.129933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:15.059126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:16.073727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:25:20.813109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분남해읍이동면상주면삼동면미조면남면서면고현면설천면창선면
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
남해읍1.0001.0000.8980.8790.8960.9560.8850.9100.8850.8490.870
이동면1.0000.8981.0000.9430.9440.8720.9510.9220.9550.8750.911
상주면1.0000.8790.9431.0000.8800.9180.9590.9080.9440.8440.864
삼동면1.0000.8960.9440.8801.0000.8840.9120.9870.9000.9530.978
미조면1.0000.9560.8720.9180.8841.0000.8840.8630.8500.8290.842
남면1.0000.8850.9510.9590.9120.8841.0000.9290.9600.8710.882
서면1.0000.9100.9220.9080.9870.8630.9291.0000.9240.9740.967
고현면1.0000.8850.9550.9440.9000.8500.9600.9241.0000.9440.924
설천면1.0000.8490.8750.8440.9530.8290.8710.9740.9441.0000.970
창선면1.0000.8700.9110.8640.9780.8420.8820.9670.9240.9701.000
2023-12-11T09:25:20.945021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남해읍이동면상주면삼동면미조면남면서면고현면설천면창선면
남해읍1.0000.9830.9490.9880.9570.9760.9850.9840.9780.985
이동면0.9831.0000.9470.9850.9560.9740.9740.9680.9570.975
상주면0.9490.9471.0000.9430.9650.9630.9560.9390.9370.947
삼동면0.9880.9850.9431.0000.9530.9710.9760.9800.9750.992
미조면0.9570.9560.9650.9531.0000.9680.9630.9430.9390.957
남면0.9760.9740.9630.9710.9681.0000.9800.9700.9610.966
서면0.9850.9740.9560.9760.9630.9801.0000.9830.9740.976
고현면0.9840.9680.9390.9800.9430.9700.9831.0000.9880.975
설천면0.9780.9570.9370.9750.9390.9610.9740.9881.0000.973
창선면0.9850.9750.9470.9920.9570.9660.9760.9750.9731.000

Missing values

2023-12-11T09:25:17.073512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:25:17.217710image/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.
2023-12-11T09:25:17.343760image/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

구분남해읍이동면상주면삼동면미조면남면서면고현면설천면창선면
02017-01-0110.010.512.012.014.011.013.010.010.512.5
12017-02-0141.043.039.049.038.045.549.541.543.049.5
22017-03-0120.013.018.525.016.020.020.528.030.024.5
32017-04-01105.0122.0117.0110.0117.0102.573.092.092.595.5
42017-05-0150.542.040.536.030.048.556.539.556.044.0
52017-06-0161.067.097.070.097.086.556.558.563.565.0
62017-07-01133.0138.0138.0132.0146.0150.5125.0154.5168.5113.0
72017-08-01284.5206.5194.0258.0173.0237.5291.0253.0266.5243.5
82017-09-01138.0156.5173.0152.0167.0145.0147.097.5100.0137.0
92017-10-01148.0159.5136.0131.0126.0166.5145.5122.0116.5124.0
구분남해읍이동면상주면삼동면미조면남면서면고현면설천면창선면
80<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
81<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
82<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
83<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
84<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
85<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
86<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
87<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
88<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
89<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구분남해읍이동면상주면삼동면미조면남면서면고현면설천면창선면# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>11