Overview

Dataset statistics

Number of variables11
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory102.4 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
이동면 has unique valuesUnique
상주면 has unique valuesUnique
미조면 has unique valuesUnique
고현면 has unique valuesUnique
삼동면 has 1 (3.3%) zerosZeros
미조면 has 1 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-11 00:25:22.652702
Analysis finished2023-12-11 00:25:33.311926
Duration10.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2017-01-01 00:00:00
Maximum2019-06-01 00:00:00
2023-12-11T09:25:33.360616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:33.695453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

남해읍
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.51667
Minimum0.5
Maximum322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:33.799040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile11.35
Q139.5
median97
Q3172
95-th percentile315.275
Maximum322
Range321.5
Interquartile range (IQR)132.5

Descriptive statistics

Standard deviation101.04442
Coefficient of variation (CV)0.8384269
Kurtosis-0.55393425
Mean120.51667
Median Absolute Deviation (MAD)64.5
Skewness0.76459635
Sum3615.5
Variance10209.974
MonotonicityNot monotonic
2023-12-11T09:25:33.896636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
43.5 2
 
6.7%
10.0 1
 
3.3%
41.0 1
 
3.3%
89.0 1
 
3.3%
198.5 1
 
3.3%
127.5 1
 
3.3%
73.5 1
 
3.3%
13.0 1
 
3.3%
39.0 1
 
3.3%
315.5 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.5 1
3.3%
10.0 1
3.3%
13.0 1
3.3%
18.0 1
3.3%
20.0 1
3.3%
26.5 1
3.3%
36.5 1
3.3%
39.0 1
3.3%
41.0 1
3.3%
43.5 2
6.7%
ValueCountFrequency (%)
322.0 1
3.3%
315.5 1
3.3%
315.0 1
3.3%
284.5 1
3.3%
268.5 1
3.3%
198.5 1
3.3%
196.5 1
3.3%
175.0 1
3.3%
163.0 1
3.3%
160.0 1
3.3%

이동면
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.73333
Minimum0.5
Maximum334.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:34.027698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile11.625
Q140.5
median90.5
Q3175.625
95-th percentile302.725
Maximum334.5
Range334
Interquartile range (IQR)135.125

Descriptive statistics

Standard deviation95.766664
Coefficient of variation (CV)0.82747694
Kurtosis-0.21494375
Mean115.73333
Median Absolute Deviation (MAD)63
Skewness0.82751775
Sum3472
Variance9171.254
MonotonicityNot monotonic
2023-12-11T09:25:34.138299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10.5 1
 
3.3%
181.0 1
 
3.3%
79.5 1
 
3.3%
190.5 1
 
3.3%
101.5 1
 
3.3%
68.0 1
 
3.3%
51.5 1
 
3.3%
14.5 1
 
3.3%
40.0 1
 
3.3%
34.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.5 1
3.3%
10.5 1
3.3%
13.0 1
3.3%
14.5 1
3.3%
24.0 1
3.3%
30.5 1
3.3%
34.0 1
3.3%
40.0 1
3.3%
42.0 1
3.3%
42.5 1
3.3%
ValueCountFrequency (%)
334.5 1
3.3%
316.0 1
3.3%
286.5 1
3.3%
262.5 1
3.3%
206.5 1
3.3%
190.5 1
3.3%
183.5 1
3.3%
181.0 1
3.3%
159.5 1
3.3%
156.5 1
3.3%

상주면
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.08333
Minimum1
Maximum334.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:34.241799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12.9
Q138.25
median73.75
Q3139.125
95-th percentile231.85
Maximum334.5
Range333.5
Interquartile range (IQR)100.875

Descriptive statistics

Standard deviation81.587385
Coefficient of variation (CV)0.81519452
Kurtosis0.7937355
Mean100.08333
Median Absolute Deviation (MAD)51
Skewness1.0288429
Sum3002.5
Variance6656.5014
MonotonicityNot monotonic
2023-12-11T09:25:34.340910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12.0 1
 
3.3%
195.0 1
 
3.3%
64.0 1
 
3.3%
110.0 1
 
3.3%
63.0 1
 
3.3%
57.0 1
 
3.3%
38.0 1
 
3.3%
14.0 1
 
3.3%
44.0 1
 
3.3%
33.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1.0 1
3.3%
12.0 1
3.3%
14.0 1
3.3%
18.5 1
3.3%
27.0 1
3.3%
28.0 1
3.3%
33.0 1
3.3%
38.0 1
3.3%
39.0 1
3.3%
40.0 1
3.3%
ValueCountFrequency (%)
334.5 1
3.3%
253.0 1
3.3%
206.0 1
3.3%
195.0 1
3.3%
194.0 1
3.3%
173.0 1
3.3%
170.5 1
3.3%
139.5 1
3.3%
138.0 1
3.3%
136.5 1
3.3%

삼동면
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.11667
Minimum0
Maximum303
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:34.442175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.25
Q137.375
median90
Q3160.5
95-th percentile262.95
Maximum303
Range303
Interquartile range (IQR)123.125

Descriptive statistics

Standard deviation85.898866
Coefficient of variation (CV)0.78007144
Kurtosis-0.49801387
Mean110.11667
Median Absolute Deviation (MAD)61
Skewness0.7124115
Sum3303.5
Variance7378.6152
MonotonicityNot monotonic
2023-12-11T09:25:34.547670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
70.0 2
 
6.7%
12.0 1
 
3.3%
124.0 1
 
3.3%
200.0 1
 
3.3%
121.0 1
 
3.3%
65.0 1
 
3.3%
50.0 1
 
3.3%
17.0 1
 
3.3%
41.5 1
 
3.3%
28.0 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 1
3.3%
12.0 1
3.3%
17.0 1
3.3%
25.0 1
3.3%
28.0 1
3.3%
30.0 1
3.3%
32.0 1
3.3%
36.0 1
3.3%
41.5 1
3.3%
48.0 1
3.3%
ValueCountFrequency (%)
303.0 1
3.3%
267.0 1
3.3%
258.0 1
3.3%
256.0 1
3.3%
200.0 1
3.3%
187.0 1
3.3%
169.0 1
3.3%
161.0 1
3.3%
159.0 1
3.3%
152.0 1
3.3%

미조면
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.5
Minimum0
Maximum401
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:34.657191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.625
Q136.5
median93.75
Q3140.125
95-th percentile264.725
Maximum401
Range401
Interquartile range (IQR)103.625

Descriptive statistics

Standard deviation90.739776
Coefficient of variation (CV)0.86832321
Kurtosis3.0750965
Mean104.5
Median Absolute Deviation (MAD)54
Skewness1.5536925
Sum3135
Variance8233.7069
MonotonicityNot monotonic
2023-12-11T09:25:34.777792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
14.0 1
 
3.3%
199.5 1
 
3.3%
113.0 1
 
3.3%
115.0 1
 
3.3%
90.5 1
 
3.3%
61.0 1
 
3.3%
45.5 1
 
3.3%
11.5 1
 
3.3%
47.0 1
 
3.3%
36.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0 1
3.3%
11.5 1
3.3%
14.0 1
3.3%
16.0 1
3.3%
22.0 1
3.3%
30.0 1
3.3%
34.0 1
3.3%
36.0 1
3.3%
38.0 1
3.3%
45.5 1
3.3%
ValueCountFrequency (%)
401.0 1
3.3%
309.5 1
3.3%
210.0 1
3.3%
199.5 1
3.3%
173.0 1
3.3%
167.0 1
3.3%
146.0 1
3.3%
144.0 1
3.3%
128.5 1
3.3%
126.0 1
3.3%

남면
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.86667
Minimum1
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:34.888173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.05
Q144.375
median84.75
Q3162.5
95-th percentile255.375
Maximum320
Range319
Interquartile range (IQR)118.125

Descriptive statistics

Standard deviation83.46886
Coefficient of variation (CV)0.78105608
Kurtosis0.066701182
Mean106.86667
Median Absolute Deviation (MAD)60
Skewness0.87524691
Sum3206
Variance6967.0506
MonotonicityNot monotonic
2023-12-11T09:25:34.998871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
102.5 2
 
6.7%
48.5 2
 
6.7%
11.0 1
 
3.3%
180.0 1
 
3.3%
71.0 1
 
3.3%
146.5 1
 
3.3%
83.0 1
 
3.3%
69.0 1
 
3.3%
49.5 1
 
3.3%
21.5 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
1.0 1
3.3%
11.0 1
3.3%
20.0 1
3.3%
21.5 1
3.3%
25.0 1
3.3%
26.0 1
3.3%
42.5 1
3.3%
44.0 1
3.3%
45.5 1
3.3%
48.5 2
6.7%
ValueCountFrequency (%)
320.0 1
3.3%
270.0 1
3.3%
237.5 1
3.3%
232.0 1
3.3%
181.5 1
3.3%
180.0 1
3.3%
167.5 1
3.3%
166.5 1
3.3%
150.5 1
3.3%
146.5 1
3.3%

서면
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.61667
Minimum1
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:35.102481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.475
Q142
median73
Q3146.625
95-th percentile285.15
Maximum326
Range325
Interquartile range (IQR)104.625

Descriptive statistics

Standard deviation90.321522
Coefficient of variation (CV)0.83928935
Kurtosis0.10852382
Mean107.61667
Median Absolute Deviation (MAD)52.25
Skewness1.0166455
Sum3228.5
Variance8157.9773
MonotonicityNot monotonic
2023-12-11T09:25:35.214458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
73.0 2
 
6.7%
56.5 2
 
6.7%
13.0 1
 
3.3%
326.0 1
 
3.3%
135.0 1
 
3.3%
91.0 1
 
3.3%
62.5 1
 
3.3%
48.0 1
 
3.3%
18.5 1
 
3.3%
50.0 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
1.0 1
3.3%
13.0 1
3.3%
18.5 1
3.3%
20.5 1
3.3%
21.0 1
3.3%
26.0 1
3.3%
38.5 1
3.3%
40.0 1
3.3%
48.0 1
3.3%
49.5 1
3.3%
ValueCountFrequency (%)
326.0 1
3.3%
291.0 1
3.3%
278.0 1
3.3%
255.5 1
3.3%
202.0 1
3.3%
192.5 1
3.3%
167.0 1
3.3%
147.0 1
3.3%
145.5 1
3.3%
136.0 1
3.3%

고현면
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.75
Minimum0.5
Maximum323.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:35.320403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile13.825
Q134.625
median85.75
Q3167.25
95-th percentile276.1
Maximum323.5
Range323
Interquartile range (IQR)132.625

Descriptive statistics

Standard deviation87.920394
Coefficient of variation (CV)0.83139853
Kurtosis0.078588051
Mean105.75
Median Absolute Deviation (MAD)55.75
Skewness0.93783252
Sum3172.5
Variance7729.9957
MonotonicityNot monotonic
2023-12-11T09:25:35.428118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
10.0 1
 
3.3%
117.5 1
 
3.3%
79.5 1
 
3.3%
197.5 1
 
3.3%
96.0 1
 
3.3%
62.0 1
 
3.3%
49.0 1
 
3.3%
18.5 1
 
3.3%
40.5 1
 
3.3%
32.0 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.5 1
3.3%
10.0 1
3.3%
18.5 1
3.3%
19.0 1
3.3%
27.0 1
3.3%
28.0 1
3.3%
32.0 1
3.3%
33.0 1
3.3%
39.5 1
3.3%
40.5 1
3.3%
ValueCountFrequency (%)
323.5 1
3.3%
295.0 1
3.3%
253.0 1
3.3%
201.0 1
3.3%
197.5 1
3.3%
192.5 1
3.3%
189.0 1
3.3%
171.5 1
3.3%
154.5 1
3.3%
131.5 1
3.3%

설천면
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.01667
Minimum0.5
Maximum387.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:35.534562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile12.525
Q140.75
median80.25
Q3176.75
95-th percentile287.95
Maximum387.5
Range387
Interquartile range (IQR)136

Descriptive statistics

Standard deviation98.871694
Coefficient of variation (CV)0.85962927
Kurtosis0.50334008
Mean115.01667
Median Absolute Deviation (MAD)56.25
Skewness1.045667
Sum3450.5
Variance9775.6118
MonotonicityNot monotonic
2023-12-11T09:25:35.641333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
30.0 2
 
6.7%
10.5 1
 
3.3%
145.0 1
 
3.3%
68.0 1
 
3.3%
179.5 1
 
3.3%
103.5 1
 
3.3%
64.0 1
 
3.3%
48.0 1
 
3.3%
15.0 1
 
3.3%
40.0 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.5 1
3.3%
10.5 1
3.3%
15.0 1
3.3%
19.0 1
3.3%
29.0 1
3.3%
30.0 2
6.7%
40.0 1
3.3%
43.0 1
3.3%
45.5 1
3.3%
48.0 1
3.3%
ValueCountFrequency (%)
387.5 1
3.3%
305.5 1
3.3%
266.5 1
3.3%
233.5 1
3.3%
218.5 1
3.3%
216.0 1
3.3%
211.0 1
3.3%
179.5 1
3.3%
168.5 1
3.3%
145.0 1
3.3%

창선면
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.88333
Minimum0.5
Maximum322.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:25:35.753646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile13.175
Q142.875
median85.25
Q3151.625
95-th percentile275.125
Maximum322.5
Range322
Interquartile range (IQR)108.75

Descriptive statistics

Standard deviation85.848572
Coefficient of variation (CV)0.80319886
Kurtosis0.35892186
Mean106.88333
Median Absolute Deviation (MAD)56.5
Skewness0.97451186
Sum3206.5
Variance7369.9773
MonotonicityNot monotonic
2023-12-11T09:25:35.873136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
65.0 2
 
6.7%
12.5 1
 
3.3%
121.0 1
 
3.3%
75.0 1
 
3.3%
177.5 1
 
3.3%
122.0 1
 
3.3%
51.0 1
 
3.3%
14.0 1
 
3.3%
42.5 1
 
3.3%
28.0 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.5 1
3.3%
12.5 1
3.3%
14.0 1
3.3%
24.5 1
3.3%
26.5 1
3.3%
28.0 1
3.3%
29.5 1
3.3%
42.5 1
3.3%
44.0 1
3.3%
48.5 1
3.3%
ValueCountFrequency (%)
322.5 1
3.3%
301.0 1
3.3%
243.5 1
3.3%
225.5 1
3.3%
191.0 1
3.3%
177.5 1
3.3%
154.5 1
3.3%
152.0 1
3.3%
150.5 1
3.3%
137.0 1
3.3%

Interactions

2023-12-11T09:25:32.315975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:22.957188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:24.197150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.195286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.222414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:27.154577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:28.244055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.558403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.425914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.301622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.399893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:23.061599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:24.291838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.277155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.306025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:27.271026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:28.356477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.650528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.516444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.422506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.492571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:23.402231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:24.393567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.376923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.400015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:27.385947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:28.449926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.742033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.592748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.522154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.577625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:23.486249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:24.482969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.511683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.489122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:27.498606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:28.867150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.818301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.677418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.598805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.659904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:23.567412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:24.587329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.603982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.573854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:27.620720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:28.976378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.898367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.753432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.691745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.760703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:23.658432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:24.697150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.704375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.665840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:27.746833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.076488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.989250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.851392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.793947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.835276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:23.766919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:24.808804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.797998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.759505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:27.846560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.163132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.068763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.949696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.901134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.904692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:23.854499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:24.918699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.895812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.862800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:27.947948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.256580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.157574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.044884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.004142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.976083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:23.979972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.021927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.001227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.949906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:28.037976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.351130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.260761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.132766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.124979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:33.049287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:24.097815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:25.112581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:26.124116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:27.054308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:28.150717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:29.460652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:30.340256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:31.218167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:25:32.225717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:25:35.966844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분남해읍이동면상주면삼동면미조면남면서면고현면설천면창선면
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
남해읍1.0001.0000.9630.8920.9680.7340.9670.8450.9600.9170.958
이동면1.0000.9631.0000.8750.8830.8760.9110.9750.8950.8730.912
상주면1.0000.8920.8751.0000.9260.9490.9570.8690.9410.9530.946
삼동면1.0000.9680.8830.9261.0000.8110.9580.8670.9350.9320.985
미조면1.0000.7340.8760.9490.8111.0000.8790.8800.8650.8440.849
남면1.0000.9670.9110.9570.9580.8791.0000.9070.9360.9570.951
서면1.0000.8450.9750.8690.8670.8800.9071.0000.8960.8860.869
고현면1.0000.9600.8950.9410.9350.8650.9360.8961.0000.9600.973
설천면1.0000.9170.8730.9530.9320.8440.9570.8860.9601.0000.931
창선면1.0000.9580.9120.9460.9850.8490.9510.8690.9730.9311.000
2023-12-11T09:25:36.088605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남해읍이동면상주면삼동면미조면남면서면고현면설천면창선면
남해읍1.0000.9790.9380.9780.9110.9680.9720.9850.9820.981
이동면0.9791.0000.9560.9750.9410.9830.9780.9760.9650.975
상주면0.9380.9561.0000.9320.9850.9790.9760.9320.9280.924
삼동면0.9780.9750.9321.0000.9190.9590.9560.9900.9750.995
미조면0.9110.9410.9850.9191.0000.9630.9550.9190.9000.910
남면0.9680.9830.9790.9590.9631.0000.9840.9680.9620.953
서면0.9720.9780.9760.9560.9550.9841.0000.9620.9600.960
고현면0.9850.9760.9320.9900.9190.9680.9621.0000.9860.984
설천면0.9820.9650.9280.9750.9000.9620.9600.9861.0000.971
창선면0.9810.9750.9240.9950.9100.9530.9600.9840.9711.000

Missing values

2023-12-11T09:25:33.147562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:25:33.266676image/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
202018-09-01322.0286.5253.0267.0309.5270.0278.0295.0305.5301.0
212018-10-01315.5316.0170.5187.0128.5181.5202.0192.5218.5191.0
222018-11-0139.034.033.028.036.042.538.532.030.028.0
232018-12-0143.540.044.041.547.048.550.040.540.042.5
242019-01-0113.014.514.017.011.521.518.518.515.014.0
252019-02-0143.551.538.050.045.549.548.049.048.051.0
262019-03-0173.568.057.065.061.069.062.562.064.065.0
272019-04-01127.5101.563.0121.090.583.091.096.0103.5122.0
282019-05-01198.5190.5110.0200.0115.0146.5135.0197.5179.5177.5
292019-06-0189.079.564.070.0113.071.073.079.568.075.0