Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.0 KiB
Average record size in memory71.3 B

Variable types

Categorical2
Numeric6

Alerts

관측소명 is highly overall correlated with 당년 누적 강우량(mm) and 2 other fieldsHigh correlation
댐명 is highly overall correlated with 당년 누적 강우량(mm) and 2 other fieldsHigh correlation
당년 누적 강우량(mm) is highly overall correlated with 전년 누적 강우량(mm) and 3 other fieldsHigh correlation
전년 누적 강우량(mm) is highly overall correlated with 당년 누적 강우량(mm) and 3 other fieldsHigh correlation
예년 평균 누적 강우량(mm) is highly overall correlated with 당년 누적 강우량(mm) and 1 other fieldsHigh correlation
강우량(mm) has 89 (89.0%) zerosZeros
전년 강우량(mm) has 71 (71.0%) zerosZeros
전년 누적 강우량(mm) has 16 (16.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:59:28.582031
Analysis finished2023-12-10 13:59:36.588437
Duration8.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
군남
18 
군위
18 
괴산
16 
감포
14 
광동
14 
Other values (2)
20 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row감포
2nd row감포
3rd row감포
4th row감포
5th row감포

Common Values

ValueCountFrequency (%)
군남 18
18.0%
군위 18
18.0%
괴산 16
16.0%
감포 14
14.0%
광동 14
14.0%
구천 14
14.0%
남강 6
 
6.0%

Length

2023-12-10T22:59:36.717167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:59:36.995045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군남 18
18.0%
군위 18
18.0%
괴산 16
16.0%
감포 14
14.0%
광동 14
14.0%
구천 14
14.0%
남강 6
 
6.0%

관측소명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경주시(감포댐)
14 
거제시(구천댐)
14 
군위군(양지리)
11 
연천군(횡산리)
10 
삼척시(광동댐)
9 
Other values (10)
42 

Length

Max length9
Median length8
Mean length7.91
Min length6

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row경주시(감포댐)
2nd row경주시(감포댐)
3rd row경주시(감포댐)
4th row경주시(감포댐)
5th row경주시(감포댐)

Common Values

ValueCountFrequency (%)
경주시(감포댐) 14
14.0%
거제시(구천댐) 14
14.0%
군위군(양지리) 11
11.0%
연천군(횡산리) 10
10.0%
삼척시(광동댐) 9
9.0%
괴산댐(FTP) 9
9.0%
연천군(군남댐) 8
8.0%
내속리(전) 7
7.0%
군위군(군위댐) 7
7.0%
태백시(상사미동) 5
 
5.0%
Other values (5) 6
6.0%

Length

2023-12-10T22:59:37.354208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경주시(감포댐 14
14.0%
거제시(구천댐 14
14.0%
군위군(양지리 11
11.0%
연천군(횡산리 10
10.0%
삼척시(광동댐 9
9.0%
괴산댐(ftp 9
9.0%
연천군(군남댐 8
8.0%
내속리(전 7
7.0%
군위군(군위댐 7
7.0%
태백시(상사미동 5
 
5.0%
Other values (5) 6
6.0%

관측일
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190516
Minimum20190501
Maximum20190531
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:37.617088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190501
5-th percentile20190503
Q120190509
median20190515
Q320190522
95-th percentile20190530
Maximum20190531
Range30
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.5904516
Coefficient of variation (CV)4.2546964 × 10-7
Kurtosis-1.1224793
Mean20190516
Median Absolute Deviation (MAD)7
Skewness0.1057485
Sum2.0190516 × 109
Variance73.795859
MonotonicityNot monotonic
2023-12-10T22:59:37.893678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190507 6
 
6.0%
20190505 6
 
6.0%
20190520 5
 
5.0%
20190522 5
 
5.0%
20190530 5
 
5.0%
20190515 5
 
5.0%
20190513 5
 
5.0%
20190510 4
 
4.0%
20190517 4
 
4.0%
20190511 4
 
4.0%
Other values (20) 51
51.0%
ValueCountFrequency (%)
20190501 1
 
1.0%
20190502 3
3.0%
20190503 4
4.0%
20190504 1
 
1.0%
20190505 6
6.0%
20190507 6
6.0%
20190508 4
4.0%
20190509 3
3.0%
20190510 4
4.0%
20190511 4
4.0%
ValueCountFrequency (%)
20190531 2
 
2.0%
20190530 5
5.0%
20190529 2
 
2.0%
20190528 4
4.0%
20190527 2
 
2.0%
20190526 4
4.0%
20190525 1
 
1.0%
20190524 4
4.0%
20190523 1
 
1.0%
20190522 5
5.0%

강우량(mm)
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.775
Minimum0
Maximum78
Zeros89
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:38.109451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile11.1
Maximum78
Range78
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.139837
Coefficient of variation (CV)4.3747159
Kurtosis27.410082
Mean2.775
Median Absolute Deviation (MAD)0
Skewness5.2333646
Sum277.5
Variance147.37563
MonotonicityNot monotonic
2023-12-10T22:59:38.338670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0.0 89
89.0%
8.0 2
 
2.0%
11.0 1
 
1.0%
63.5 1
 
1.0%
3.0 1
 
1.0%
78.0 1
 
1.0%
66.0 1
 
1.0%
2.0 1
 
1.0%
13.0 1
 
1.0%
4.0 1
 
1.0%
ValueCountFrequency (%)
0.0 89
89.0%
2.0 1
 
1.0%
3.0 1
 
1.0%
4.0 1
 
1.0%
8.0 2
 
2.0%
11.0 1
 
1.0%
13.0 1
 
1.0%
21.0 1
 
1.0%
63.5 1
 
1.0%
66.0 1
 
1.0%
ValueCountFrequency (%)
78.0 1
1.0%
66.0 1
1.0%
63.5 1
1.0%
21.0 1
1.0%
13.0 1
1.0%
11.0 1
1.0%
8.0 2
2.0%
4.0 1
1.0%
3.0 1
1.0%
2.0 1
1.0%

전년 강우량(mm)
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7
Minimum0
Maximum65
Zeros71
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:38.495377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.125
95-th percentile20.05
Maximum65
Range65
Interquartile range (IQR)1.125

Descriptive statistics

Standard deviation9.3778699
Coefficient of variation (CV)2.5345594
Kurtosis19.512544
Mean3.7
Median Absolute Deviation (MAD)0
Skewness3.9204713
Sum370
Variance87.944444
MonotonicityNot monotonic
2023-12-10T22:59:38.630185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 71
71.0%
3.0 4
 
4.0%
1.0 4
 
4.0%
12.0 2
 
2.0%
1.5 1
 
1.0%
8.0 1
 
1.0%
9.0 1
 
1.0%
14.0 1
 
1.0%
10.0 1
 
1.0%
7.0 1
 
1.0%
Other values (13) 13
 
13.0%
ValueCountFrequency (%)
0.0 71
71.0%
1.0 4
 
4.0%
1.5 1
 
1.0%
2.0 1
 
1.0%
3.0 4
 
4.0%
5.0 1
 
1.0%
6.0 1
 
1.0%
7.0 1
 
1.0%
8.0 1
 
1.0%
9.0 1
 
1.0%
ValueCountFrequency (%)
65.0 1
1.0%
34.0 1
1.0%
32.0 1
1.0%
28.0 1
1.0%
21.0 1
1.0%
20.0 1
1.0%
19.0 1
1.0%
18.0 1
1.0%
17.5 1
1.0%
14.0 1
1.0%

당년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)38.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.325
Minimum52
Maximum564
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:38.876001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum52
5-th percentile52
Q186
median171
Q3202.5
95-th percentile483
Maximum564
Range512
Interquartile range (IQR)116.5

Descriptive statistics

Standard deviation128.05977
Coefficient of variation (CV)0.66933109
Kurtosis1.1636549
Mean191.325
Median Absolute Deviation (MAD)63
Skewness1.3151104
Sum19132.5
Variance16399.305
MonotonicityNot monotonic
2023-12-10T22:59:39.068828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
137.0 10
 
10.0%
182.5 9
 
9.0%
52.0 7
 
7.0%
403.0 7
 
7.0%
171.0 5
 
5.0%
66.0 5
 
5.0%
62.0 5
 
5.0%
67.5 4
 
4.0%
86.0 3
 
3.0%
174.0 3
 
3.0%
Other values (28) 42
42.0%
ValueCountFrequency (%)
52.0 7
7.0%
62.0 5
5.0%
66.0 5
5.0%
67.5 4
 
4.0%
85.0 2
 
2.0%
86.0 3
 
3.0%
101.0 1
 
1.0%
104.0 2
 
2.0%
137.0 10
10.0%
158.0 1
 
1.0%
ValueCountFrequency (%)
564.0 1
 
1.0%
561.0 2
 
2.0%
483.0 3
3.0%
469.0 1
 
1.0%
403.0 7
7.0%
325.0 2
 
2.0%
314.0 2
 
2.0%
277.0 1
 
1.0%
275.0 1
 
1.0%
246.0 1
 
1.0%

전년 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333.175
Minimum0
Maximum774
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:39.738153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1222
median345.5
Q3416.875
95-th percentile730.25
Maximum774
Range774
Interquartile range (IQR)194.875

Descriptive statistics

Standard deviation206.23844
Coefficient of variation (CV)0.61900935
Kurtosis-0.077879614
Mean333.175
Median Absolute Deviation (MAD)97
Skewness0.19546791
Sum33317.5
Variance42534.295
MonotonicityNot monotonic
2023-12-10T22:59:40.087327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
442.5 3
 
3.0%
410.5 3
 
3.0%
383.0 3
 
3.0%
416.5 3
 
3.0%
212.0 2
 
2.0%
730.0 2
 
2.0%
690.0 2
 
2.0%
656.0 2
 
2.0%
396.0 2
 
2.0%
Other values (48) 62
62.0%
ValueCountFrequency (%)
0.0 16
16.0%
166.0 1
 
1.0%
179.0 1
 
1.0%
188.0 2
 
2.0%
202.0 2
 
2.0%
212.0 2
 
2.0%
222.0 2
 
2.0%
234.0 1
 
1.0%
235.0 2
 
2.0%
261.0 2
 
2.0%
ValueCountFrequency (%)
774.0 1
1.0%
772.0 1
1.0%
769.0 2
2.0%
735.0 1
1.0%
730.0 2
2.0%
718.0 1
1.0%
690.0 2
2.0%
656.0 2
2.0%
567.0 2
2.0%
495.0 1
1.0%

예년 평균 누적 강우량(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221.694
Minimum85.6
Maximum418.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:59:40.352954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum85.6
5-th percentile99.055
Q1139.45
median206.9
Q3297.425
95-th percentile371.945
Maximum418.3
Range332.7
Interquartile range (IQR)157.975

Descriptive statistics

Standard deviation93.711647
Coefficient of variation (CV)0.42270718
Kurtosis-1.1007536
Mean221.694
Median Absolute Deviation (MAD)77.45
Skewness0.31167712
Sum22169.4
Variance8781.8727
MonotonicityNot monotonic
2023-12-10T22:59:40.651252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 3
 
3.0%
275.4 2
 
2.0%
117.0 2
 
2.0%
337.2 1
 
1.0%
187.1 1
 
1.0%
178.0 1
 
1.0%
116.0 1
 
1.0%
172.0 1
 
1.0%
136.4 1
 
1.0%
134.6 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
85.6 1
 
1.0%
86.9 1
 
1.0%
87.5 1
 
1.0%
93.5 1
 
1.0%
94.4 1
 
1.0%
99.3 1
 
1.0%
99.5 1
 
1.0%
100.0 3
3.0%
100.4 1
 
1.0%
106.1 1
 
1.0%
ValueCountFrequency (%)
418.3 1
1.0%
417.9 1
1.0%
404.3 1
1.0%
384.7 1
1.0%
380.4 1
1.0%
371.5 1
1.0%
368.4 1
1.0%
362.7 1
1.0%
356.9 1
1.0%
355.7 1
1.0%

Interactions

2023-12-10T22:59:34.891502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:29.487236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:30.719877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:31.995317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:32.964416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:33.859623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:35.058551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:29.725365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:30.866124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:32.148643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:33.103726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:34.149962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:35.235430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:29.911362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:31.013936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:32.306883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:33.250350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:34.307653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:35.578220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:30.071662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:31.310379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:32.443910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:33.421472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:34.464417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:35.791220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:30.287529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:31.547937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:32.592458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:33.547780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:34.600816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:35.959101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:30.547247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:31.847414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:32.791185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:33.712386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:59:34.754147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:59:40.828521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
댐명1.0001.0000.0000.0000.2060.8600.8850.719
관측소명1.0001.0000.0000.0000.0000.9180.9230.839
관측일0.0000.0001.0000.4450.2520.1960.0000.436
강우량(mm)0.0000.0000.4451.0000.0290.4970.3670.505
전년 강우량(mm)0.2060.0000.2520.0291.0000.3190.4350.000
당년 누적 강우량(mm)0.8600.9180.1960.4970.3191.0000.9370.805
전년 누적 강우량(mm)0.8850.9230.0000.3670.4350.9371.0000.777
예년 평균 누적 강우량(mm)0.7190.8390.4360.5050.0000.8050.7771.000
2023-12-10T22:59:41.033252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측소명댐명
관측소명1.0000.956
댐명0.9561.000
2023-12-10T22:59:41.207952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)댐명관측소명
관측일1.0000.236-0.0920.3030.2760.2980.0000.000
강우량(mm)0.2361.000-0.0730.2750.1100.2490.0000.000
전년 강우량(mm)-0.092-0.0731.0000.0510.1960.0280.0880.000
당년 누적 강우량(mm)0.3030.2750.0511.0000.7020.7560.6840.676
전년 누적 강우량(mm)0.2760.1100.1960.7021.0000.5850.7310.687
예년 평균 누적 강우량(mm)0.2980.2490.0280.7560.5851.0000.4640.489
댐명0.0000.0000.0880.6840.7310.4641.0000.956
관측소명0.0000.0000.0000.6760.6870.4890.9561.000

Missing values

2023-12-10T22:59:36.223659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:59:36.503504image/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

댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
0감포경주시(감포댐)201905280.00.0202.5442.5337.2
1감포경주시(감포댐)201905300.017.5202.5460.0339.0
2감포경주시(감포댐)201905260.00.0197.0442.5325.4
3감포경주시(감포댐)201905080.00.0182.5410.5275.4
4감포경주시(감포댐)201905070.018.0182.5410.5275.4
5감포경주시(감포댐)201905020.08.0182.5383.0275.8
6감포경주시(감포댐)201905090.00.0182.5410.5276.4
7감포경주시(감포댐)201905140.00.0182.5416.5296.8
8감포경주시(감포댐)201905130.01.5182.5416.5294.9
9감포경주시(감포댐)201905160.00.0182.5416.5302.3
댐명관측소명관측일강우량(mm)전년 강우량(mm)당년 누적 강우량(mm)전년 누적 강우량(mm)예년 평균 누적 강우량(mm)
90군위군위군(군위댐)201905150.00.0137.0289.0204.2
91군위군위군(군위댐)201905220.01.0178.0295.0220.8
92군위군위군(양지리)201905070.014.0137.0261.086.9
93군위군위군(양지리)201905120.09.0137.0270.0106.1
94남강남원시(덕동리)201905178.00.0246.0495.0245.9
95남강산청군(하정리)201905200.00.0277.0418.0194.8
96남강산청군(향양리)201905100.00.0201.0342.0242.3
97남강남원시(의지리)201905090.00.0214.0376.0153.5
98남강함양군(운곡리)201905210.00.0275.0419.0186.7
99남강남원시(의지리)201905110.00.0214.0376.0162.0