Overview

Dataset statistics

Number of variables11
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory101.1 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

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 unique valuesUnique
미조면 has unique valuesUnique
삼동면 has 1 (2.3%) zerosZeros
미조면 has 1 (2.3%) zerosZeros

Reproduction

Analysis started2023-12-11 00:24:52.581119
Analysis finished2023-12-11 00:25:02.080660
Duration9.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
Minimum2017-01-01 00:00:00
Maximum2020-07-01 00:00:00
2023-12-11T09:25:02.168407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:02.292432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)

남해읍
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143.76744
Minimum0.5
Maximum568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:02.436301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile13.5
Q142.25
median105
Q3197.5
95-th percentile383.5
Maximum568
Range567.5
Interquartile range (IQR)155.25

Descriptive statistics

Standard deviation132.91513
Coefficient of variation (CV)0.92451484
Kurtosis1.2086781
Mean143.76744
Median Absolute Deviation (MAD)68.5
Skewness1.2629763
Sum6182
Variance17666.433
MonotonicityNot monotonic
2023-12-11T09:25:02.580646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
43.5 2
 
4.7%
27.0 2
 
4.7%
10.0 1
 
2.3%
415.5 1
 
2.3%
13.0 1
 
2.3%
73.5 1
 
2.3%
127.5 1
 
2.3%
198.5 1
 
2.3%
89.0 1
 
2.3%
385.0 1
 
2.3%
Other values (31) 31
72.1%
ValueCountFrequency (%)
0.5 1
2.3%
10.0 1
2.3%
13.0 1
2.3%
18.0 1
2.3%
20.0 1
2.3%
26.5 1
2.3%
27.0 2
4.7%
36.5 1
2.3%
39.0 1
2.3%
41.0 1
2.3%
ValueCountFrequency (%)
568.0 1
2.3%
415.5 1
2.3%
385.0 1
2.3%
370.0 1
2.3%
322.0 1
2.3%
315.5 1
2.3%
315.0 1
2.3%
284.5 1
2.3%
268.5 1
2.3%
261.0 1
2.3%

이동면
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.27907
Minimum0.5
Maximum574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:02.714855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile13.15
Q142.25
median101.5
Q3187
95-th percentile354.25
Maximum574
Range573.5
Interquartile range (IQR)144.75

Descriptive statistics

Standard deviation128.29706
Coefficient of variation (CV)0.92115101
Kurtosis2.0709623
Mean139.27907
Median Absolute Deviation (MAD)67.5
Skewness1.4399378
Sum5989
Variance16460.135
MonotonicityNot monotonic
2023-12-11T09:25:02.843195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
24.0 2
 
4.7%
122.0 2
 
4.7%
10.5 1
 
2.3%
14.5 1
 
2.3%
51.5 1
 
2.3%
68.0 1
 
2.3%
101.5 1
 
2.3%
190.5 1
 
2.3%
79.5 1
 
2.3%
429.5 1
 
2.3%
Other values (31) 31
72.1%
ValueCountFrequency (%)
0.5 1
2.3%
10.5 1
2.3%
13.0 1
2.3%
14.5 1
2.3%
24.0 2
4.7%
30.5 1
2.3%
31.0 1
2.3%
34.0 1
2.3%
40.0 1
2.3%
42.0 1
2.3%
ValueCountFrequency (%)
574.0 1
2.3%
429.5 1
2.3%
356.0 1
2.3%
338.5 1
2.3%
334.5 1
2.3%
316.0 1
2.3%
286.5 1
2.3%
262.5 1
2.3%
220.0 1
2.3%
206.5 1
2.3%

상주면
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.19767
Minimum1
Maximum460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:02.965292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.45
Q139.5
median83.5
Q3171.75
95-th percentile329.2
Maximum460
Range459
Interquartile range (IQR)132.25

Descriptive statistics

Standard deviation103.84347
Coefficient of variation (CV)0.87118703
Kurtosis1.9005805
Mean119.19767
Median Absolute Deviation (MAD)54.5
Skewness1.3816723
Sum5125.5
Variance10783.466
MonotonicityNot monotonic
2023-12-11T09:25:03.099053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
80.0 2
 
4.7%
12.0 1
 
2.3%
237.5 1
 
2.3%
14.0 1
 
2.3%
38.0 1
 
2.3%
57.0 1
 
2.3%
63.0 1
 
2.3%
110.0 1
 
2.3%
64.0 1
 
2.3%
281.5 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
1.0 1
2.3%
12.0 1
2.3%
14.0 1
2.3%
18.5 1
2.3%
19.0 1
2.3%
27.0 1
2.3%
28.0 1
2.3%
31.0 1
2.3%
33.0 1
2.3%
38.0 1
2.3%
ValueCountFrequency (%)
460.0 1
2.3%
363.0 1
2.3%
334.5 1
2.3%
281.5 1
2.3%
253.0 1
2.3%
237.5 1
2.3%
206.0 1
2.3%
195.0 1
2.3%
194.0 1
2.3%
184.0 1
2.3%

삼동면
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.03488
Minimum0
Maximum558
Zeros1
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:03.229521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.8
Q144.75
median108
Q3178
95-th percentile324.1
Maximum558
Range558
Interquartile range (IQR)133.25

Descriptive statistics

Standard deviation117.06451
Coefficient of variation (CV)0.88661806
Kurtosis2.7731884
Mean132.03488
Median Absolute Deviation (MAD)66.5
Skewness1.494962
Sum5677.5
Variance13704.1
MonotonicityNot monotonic
2023-12-11T09:25:03.356167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
32.0 2
 
4.7%
30.0 2
 
4.7%
70.0 2
 
4.7%
17.0 1
 
2.3%
50.0 1
 
2.3%
65.0 1
 
2.3%
121.0 1
 
2.3%
200.0 1
 
2.3%
325.0 1
 
2.3%
66.0 1
 
2.3%
Other values (30) 30
69.8%
ValueCountFrequency (%)
0.0 1
2.3%
12.0 1
2.3%
17.0 1
2.3%
25.0 1
2.3%
28.0 1
2.3%
30.0 2
4.7%
32.0 2
4.7%
36.0 1
2.3%
41.5 1
2.3%
48.0 1
2.3%
ValueCountFrequency (%)
558.0 1
2.3%
341.0 1
2.3%
325.0 1
2.3%
316.0 1
2.3%
303.0 1
2.3%
267.0 1
2.3%
258.0 1
2.3%
256.0 1
2.3%
248.0 1
2.3%
200.0 1
2.3%

미조면
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.4186
Minimum0
Maximum500
Zeros1
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:03.495801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.2
Q143.25
median94
Q3166.5
95-th percentile358.1
Maximum500
Range500
Interquartile range (IQR)123.25

Descriptive statistics

Standard deviation114.9649
Coefficient of variation (CV)0.91664947
Kurtosis2.0970543
Mean125.4186
Median Absolute Deviation (MAD)56
Skewness1.5261422
Sum5393
Variance13216.928
MonotonicityNot monotonic
2023-12-11T09:25:03.649359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
14.0 1
 
2.3%
38.0 1
 
2.3%
11.5 1
 
2.3%
45.5 1
 
2.3%
61.0 1
 
2.3%
90.5 1
 
2.3%
115.0 1
 
2.3%
113.0 1
 
2.3%
312.5 1
 
2.3%
51.5 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
0.0 1
2.3%
11.5 1
2.3%
14.0 1
2.3%
16.0 1
2.3%
17.0 1
2.3%
22.0 1
2.3%
30.0 1
2.3%
34.0 1
2.3%
36.0 1
2.3%
38.0 1
2.3%
ValueCountFrequency (%)
500.0 1
2.3%
401.0 1
2.3%
363.0 1
2.3%
314.0 1
2.3%
312.5 1
2.3%
309.5 1
2.3%
210.0 1
2.3%
199.5 1
2.3%
173.0 1
2.3%
168.0 1
2.3%

남면
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.81395
Minimum1
Maximum507
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:03.782256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.1
Q147
median86.5
Q3180.75
95-th percentile338
Maximum507
Range506
Interquartile range (IQR)133.75

Descriptive statistics

Standard deviation114.22008
Coefficient of variation (CV)0.87314905
Kurtosis1.616779
Mean130.81395
Median Absolute Deviation (MAD)60.5
Skewness1.3169971
Sum5625
Variance13046.226
MonotonicityNot monotonic
2023-12-11T09:25:03.920645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
102.5 2
 
4.7%
48.5 2
 
4.7%
145.0 2
 
4.7%
11.0 1
 
2.3%
219.0 1
 
2.3%
69.0 1
 
2.3%
83.0 1
 
2.3%
146.5 1
 
2.3%
71.0 1
 
2.3%
340.0 1
 
2.3%
Other values (30) 30
69.8%
ValueCountFrequency (%)
1.0 1
2.3%
11.0 1
2.3%
19.0 1
2.3%
20.0 1
2.3%
21.5 1
2.3%
25.0 1
2.3%
26.0 1
2.3%
30.0 1
2.3%
42.5 1
2.3%
44.0 1
2.3%
ValueCountFrequency (%)
507.0 1
2.3%
368.0 1
2.3%
340.0 1
2.3%
320.0 1
2.3%
317.0 1
2.3%
270.0 1
2.3%
237.5 1
2.3%
232.0 1
2.3%
219.0 1
2.3%
204.0 1
2.3%

서면
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.34884
Minimum1
Maximum515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:04.104603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.65
Q148.75
median86
Q3179.75
95-th percentile329.6
Maximum515
Range514
Interquartile range (IQR)131

Descriptive statistics

Standard deviation115.73325
Coefficient of variation (CV)0.90878921
Kurtosis1.7581697
Mean127.34884
Median Absolute Deviation (MAD)55
Skewness1.3961608
Sum5476
Variance13394.185
MonotonicityNot monotonic
2023-12-11T09:25:04.250434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
50.0 2
 
4.7%
63.0 2
 
4.7%
73.0 2
 
4.7%
56.5 2
 
4.7%
119.0 2
 
4.7%
18.5 1
 
2.3%
48.0 1
 
2.3%
62.5 1
 
2.3%
91.0 1
 
2.3%
135.0 1
 
2.3%
Other values (28) 28
65.1%
ValueCountFrequency (%)
1.0 1
2.3%
13.0 1
2.3%
18.5 1
2.3%
20.0 1
2.3%
20.5 1
2.3%
21.0 1
2.3%
26.0 1
2.3%
31.0 1
2.3%
38.5 1
2.3%
40.0 1
2.3%
ValueCountFrequency (%)
515.0 1
2.3%
359.0 1
2.3%
330.0 1
2.3%
326.0 1
2.3%
291.5 1
2.3%
291.0 1
2.3%
278.0 1
2.3%
255.5 1
2.3%
202.0 1
2.3%
201.0 1
2.3%

고현면
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.61628
Minimum0.5
Maximum537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:04.390740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile18.55
Q140
median92
Q3190.75
95-th percentile324.85
Maximum537
Range536.5
Interquartile range (IQR)150.75

Descriptive statistics

Standard deviation119.15256
Coefficient of variation (CV)0.91927153
Kurtosis2.0467431
Mean129.61628
Median Absolute Deviation (MAD)62.5
Skewness1.4212159
Sum5573.5
Variance14197.331
MonotonicityNot monotonic
2023-12-11T09:25:04.529668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
28.0 2
 
4.7%
10.0 1
 
2.3%
386.0 1
 
2.3%
18.5 1
 
2.3%
49.0 1
 
2.3%
62.0 1
 
2.3%
96.0 1
 
2.3%
197.5 1
 
2.3%
79.5 1
 
2.3%
296.5 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
0.5 1
2.3%
10.0 1
2.3%
18.5 1
2.3%
19.0 1
2.3%
20.0 1
2.3%
27.0 1
2.3%
28.0 2
4.7%
32.0 1
2.3%
33.0 1
2.3%
39.5 1
2.3%
ValueCountFrequency (%)
537.0 1
2.3%
386.0 1
2.3%
325.0 1
2.3%
323.5 1
2.3%
296.5 1
2.3%
295.0 1
2.3%
264.0 1
2.3%
253.0 1
2.3%
201.0 1
2.3%
197.5 1
2.3%

설천면
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.86047
Minimum0.5
Maximum599
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:04.718073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile15.4
Q144.25
median92.5
Q3213.5
95-th percentile385.05
Maximum599
Range598.5
Interquartile range (IQR)169.25

Descriptive statistics

Standard deviation129.43275
Coefficient of variation (CV)0.9388678
Kurtosis2.6324051
Mean137.86047
Median Absolute Deviation (MAD)63.5
Skewness1.5295139
Sum5928
Variance16752.837
MonotonicityNot monotonic
2023-12-11T09:25:04.884491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
30.0 2
 
4.7%
10.5 1
 
2.3%
250.0 1
 
2.3%
48.0 1
 
2.3%
64.0 1
 
2.3%
103.5 1
 
2.3%
179.5 1
 
2.3%
68.0 1
 
2.3%
273.0 1
 
2.3%
81.5 1
 
2.3%
Other values (32) 32
74.4%
ValueCountFrequency (%)
0.5 1
2.3%
10.5 1
2.3%
15.0 1
2.3%
19.0 1
2.3%
24.0 1
2.3%
27.0 1
2.3%
29.0 1
2.3%
30.0 2
4.7%
40.0 1
2.3%
43.0 1
2.3%
ValueCountFrequency (%)
599.0 1
2.3%
391.0 1
2.3%
387.5 1
2.3%
363.0 1
2.3%
305.5 1
2.3%
273.0 1
2.3%
266.5 1
2.3%
250.0 1
2.3%
233.5 1
2.3%
218.5 1
2.3%

창선면
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.59302
Minimum0.5
Maximum605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T09:25:05.340307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile15
Q146
median95.5
Q3166
95-th percentile321.15
Maximum605
Range604.5
Interquartile range (IQR)120

Descriptive statistics

Standard deviation120.76597
Coefficient of variation (CV)0.93188636
Kurtosis4.4766746
Mean129.59302
Median Absolute Deviation (MAD)56.5
Skewness1.8225662
Sum5572.5
Variance14584.42
MonotonicityNot monotonic
2023-12-11T09:25:05.499421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
65.0 3
 
7.0%
152.0 2
 
4.7%
12.5 1
 
2.3%
222.0 1
 
2.3%
14.0 1
 
2.3%
51.0 1
 
2.3%
122.0 1
 
2.3%
177.5 1
 
2.3%
75.0 1
 
2.3%
300.0 1
 
2.3%
Other values (30) 30
69.8%
ValueCountFrequency (%)
0.5 1
2.3%
12.5 1
2.3%
14.0 1
2.3%
24.0 1
2.3%
24.5 1
2.3%
26.5 1
2.3%
28.0 1
2.3%
29.5 1
2.3%
34.0 1
2.3%
42.5 1
2.3%
ValueCountFrequency (%)
605.0 1
2.3%
352.0 1
2.3%
322.5 1
2.3%
309.0 1
2.3%
301.0 1
2.3%
300.0 1
2.3%
243.5 1
2.3%
225.5 1
2.3%
222.0 1
2.3%
191.0 1
2.3%

Interactions

2023-12-11T09:25:00.899891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:52.846370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.528702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:54.566510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.382294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.277595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.182869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.174371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.067060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.793007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:00.988471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:52.915913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.603705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:54.650411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.467918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.354264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.279687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.284340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.136621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.868395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:01.095826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:52.985103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.671809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:54.746666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.547299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.451247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.383244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.377188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.213760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.952215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:01.182193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.044837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.736989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:54.823775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.620262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.530669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.483677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.462002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.284807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:00.026218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:01.278691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.113379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.802346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:54.905621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.701339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.630713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.589419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.559822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.350704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:00.098020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:01.368936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.181023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.883858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:54.985262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.820277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.713310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.685107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.649149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.417746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:00.173214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:01.445250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.253235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.954980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.059037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.922022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.806452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.776285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.738158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.495562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:00.282495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:01.521199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.315850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:54.082935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.138117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.018906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.896982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.882866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.818286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.567775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:00.658162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:01.608054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.382303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:54.170885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.219172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.104095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.991466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.965789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.896200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.649971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:00.739380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:01.710735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:53.458751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:54.247341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:55.294226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:56.193756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:57.080991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.078863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:58.983836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:24:59.720111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:00.820804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:25:05.635491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분남해읍이동면상주면삼동면미조면남면서면고현면설천면창선면
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
남해읍1.0001.0000.9680.9440.9370.9480.9560.8970.9830.8900.923
이동면1.0000.9681.0000.9770.9240.9760.9860.9150.9690.8990.880
상주면1.0000.9440.9771.0000.8640.9800.9720.9090.9700.8910.885
삼동면1.0000.9370.9240.8641.0000.9100.9400.9600.9220.9660.922
미조면1.0000.9480.9760.9800.9101.0000.9790.9100.9670.8590.872
남면1.0000.9560.9860.9720.9400.9791.0000.8990.9670.9190.881
서면1.0000.8970.9150.9090.9600.9100.8991.0000.9210.9810.946
고현면1.0000.9830.9690.9700.9220.9670.9670.9211.0000.9630.924
설천면1.0000.8900.8990.8910.9660.8590.9190.9810.9631.0000.925
창선면1.0000.9230.8800.8850.9220.8720.8810.9460.9240.9251.000
2023-12-11T09:25:05.783490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남해읍이동면상주면삼동면미조면남면서면고현면설천면창선면
남해읍1.0000.9880.9530.9850.9420.9690.9850.9850.9840.983
이동면0.9881.0000.9640.9850.9570.9760.9860.9790.9720.980
상주면0.9530.9641.0000.9570.9850.9840.9730.9500.9490.952
삼동면0.9850.9850.9571.0000.9510.9720.9740.9860.9780.994
미조면0.9420.9570.9850.9511.0000.9740.9650.9420.9330.947
남면0.9690.9760.9840.9720.9741.0000.9780.9750.9710.969
서면0.9850.9860.9730.9740.9650.9781.0000.9750.9740.975
고현면0.9850.9790.9500.9860.9420.9750.9751.0000.9930.986
설천면0.9840.9720.9490.9780.9330.9710.9740.9931.0000.980
창선면0.9830.9800.9520.9940.9470.9690.9750.9860.9801.000

Missing values

2023-12-11T09:25:01.858170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:25:02.025380image/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.

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
332019-10-01261.0220.0153.0248.0168.0219.0201.0264.0250.0222.0
342019-11-0127.024.019.030.017.019.020.020.024.024.0
352019-12-0127.031.031.032.041.030.031.028.027.034.0
362020-01-01119.0122.0111.0108.094.0145.0119.0129.0121.0103.0
372020-02-0168.084.080.081.082.079.063.063.063.072.0
382020-03-0149.059.058.053.071.060.050.045.052.048.0
392020-04-0180.082.080.075.078.054.086.079.074.080.0
402020-05-01130.0120.0184.0141.0166.0204.0119.0148.0159.0152.0
412020-06-01370.0356.0363.0341.0363.0368.0330.0325.0363.0352.0
422020-07-01568.0574.0460.0558.0500.0507.0515.0537.0599.0605.0