Overview

Dataset statistics

Number of variables10
Number of observations167
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 KiB
Average record size in memory87.8 B

Variable types

Categorical2
Text1
Numeric7

Dataset

Description일자 기준으로 가뭄위기 경보수준 단계별 저수율 기준 정보 제공제공 정보-167개 시군에 대한 저수지 시설개수, 유역면적, 수혜면적, 유효저수량, 저수율, 평년저수율, 평년대비, 가뭄상황 단계
Author한국농어촌공사
URLhttps://www.data.go.kr/data/15034105/fileData.do

Alerts

시설개수 is highly overall correlated with 유역면적(ha) and 3 other fieldsHigh correlation
유역면적(ha) is highly overall correlated with 시설개수 and 2 other fieldsHigh correlation
수혜면적(ha) is highly overall correlated with 시설개수 and 2 other fieldsHigh correlation
유효저수량(천세제곱미터) is highly overall correlated with 시설개수 and 3 other fieldsHigh correlation
저수율(퍼센트) is highly overall correlated with 평년(퍼센트) and 1 other fieldsHigh correlation
평년(퍼센트) is highly overall correlated with 저수율(퍼센트)High correlation
대비(퍼센트) is highly overall correlated with 시설개수 and 2 other fieldsHigh correlation
시도 is highly overall correlated with 단계High correlation
단계 is highly overall correlated with 저수율(퍼센트) and 2 other fieldsHigh correlation
단계 is highly imbalanced (94.7%)Imbalance
시설개수 has 23 (13.8%) zerosZeros
유역면적(ha) has 23 (13.8%) zerosZeros
수혜면적(ha) has 25 (15.0%) zerosZeros
유효저수량(천세제곱미터) has 23 (13.8%) zerosZeros
저수율(퍼센트) has 23 (13.8%) zerosZeros
평년(퍼센트) has 23 (13.8%) zerosZeros

Reproduction

Analysis started2024-04-06 08:03:46.110011
Analysis finished2024-04-06 08:03:57.330961
Duration11.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
경기
31 
전남
22 
경북
22 
경남
18 
강원
18 
Other values (12)
56 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique4 ?
Unique (%)2.4%

Sample

1st row서울
2nd row부산
3rd row부산
4th row대구
5th row대구

Common Values

ValueCountFrequency (%)
경기 31
18.6%
전남 22
13.2%
경북 22
13.2%
경남 18
10.8%
강원 18
10.8%
충남 15
9.0%
전북 14
8.4%
충북 11
 
6.6%
인천 3
 
1.8%
대구 3
 
1.8%
Other values (7) 10
 
6.0%

Length

2024-04-06T17:03:57.504297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 31
18.6%
경북 22
13.2%
전남 22
13.2%
경남 18
10.8%
강원 18
10.8%
충남 15
9.0%
전북 14
8.4%
충북 11
 
6.6%
대구 3
 
1.8%
인천 3
 
1.8%
Other values (7) 10
 
6.0%

시군
Text

Distinct166
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-04-06T17:03:57.988894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.1676647
Min length3

Characters and Unicode

Total characters529
Distinct characters121
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165 ?
Unique (%)98.8%

Sample

1st row서울시
2nd row부산(군제외)
3rd row기장군
4th row대구(군제외)
5th row달성군
ValueCountFrequency (%)
고성군 2
 
1.2%
여수시 1
 
0.6%
장흥군 1
 
0.6%
나주시 1
 
0.6%
광양시 1
 
0.6%
담양군 1
 
0.6%
곡성군 1
 
0.6%
구례군 1
 
0.6%
고흥군 1
 
0.6%
보성군 1
 
0.6%
Other values (156) 156
93.4%
2024-04-06T17:03:58.819199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 91
 
17.2%
시 80
 
15.1%
주 21
 
4.0%
천 21
 
4.0%
양 16
 
3.0%
산 15
 
2.8%
성 14
 
2.6%
안 11
 
2.1%
제 11
 
2.1%
영 9
 
1.7%
Other values (111) 240
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 517
97.7%
Close Punctuation 6
 
1.1%
Open Punctuation 6
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 91
 
17.6%
시 80
 
15.5%
주 21
 
4.1%
천 21
 
4.1%
양 16
 
3.1%
산 15
 
2.9%
성 14
 
2.7%
안 11
 
2.1%
제 11
 
2.1%
영 9
 
1.7%
Other values (109) 228
44.1%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 517
97.7%
Common 12
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 91
 
17.6%
시 80
 
15.5%
주 21
 
4.1%
천 21
 
4.1%
양 16
 
3.1%
산 15
 
2.9%
성 14
 
2.7%
안 11
 
2.1%
제 11
 
2.1%
영 9
 
1.7%
Other values (109) 228
44.1%
Common
ValueCountFrequency (%)
) 6
50.0%
( 6
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 517
97.7%
ASCII 12
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 91
 
17.6%
시 80
 
15.5%
주 21
 
4.1%
천 21
 
4.1%
양 16
 
3.1%
산 15
 
2.9%
성 14
 
2.7%
안 11
 
2.1%
제 11
 
2.1%
영 9
 
1.7%
Other values (109) 228
44.1%
ASCII
ValueCountFrequency (%)
) 6
50.0%
( 6
50.0%

시설개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)33.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.580838
Minimum0
Maximum162
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:03:59.080016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q330
95-th percentile65.4
Maximum162
Range162
Interquartile range (IQR)27

Descriptive statistics

Standard deviation24.43539
Coefficient of variation (CV)1.1872884
Kurtosis9.220719
Mean20.580838
Median Absolute Deviation (MAD)12
Skewness2.4669487
Sum3437
Variance597.08831
MonotonicityNot monotonic
2024-04-06T17:03:59.858529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
13.8%
1 11
 
6.6%
6 10
 
6.0%
7 7
 
4.2%
9 6
 
3.6%
2 6
 
3.6%
15 5
 
3.0%
3 5
 
3.0%
18 5
 
3.0%
20 5
 
3.0%
Other values (46) 84
50.3%
ValueCountFrequency (%)
0 23
13.8%
1 11
6.6%
2 6
 
3.6%
3 5
 
3.0%
5 4
 
2.4%
6 10
6.0%
7 7
 
4.2%
8 3
 
1.8%
9 6
 
3.6%
10 2
 
1.2%
ValueCountFrequency (%)
162 1
0.6%
138 1
0.6%
94 1
0.6%
83 1
0.6%
79 1
0.6%
78 1
0.6%
77 1
0.6%
76 1
0.6%
69 1
0.6%
57 1
0.6%

유역면적(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct145
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9049.8309
Minimum0
Maximum85114
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:04:00.127501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11655
median6951.6
Q312351
95-th percentile25542.42
Maximum85114
Range85114
Interquartile range (IQR)10696

Descriptive statistics

Standard deviation10440.562
Coefficient of variation (CV)1.1536748
Kurtosis17.550964
Mean9049.8309
Median Absolute Deviation (MAD)5351.6
Skewness3.2117005
Sum1511321.8
Variance1.0900533 × 108
MonotonicityNot monotonic
2024-04-06T17:04:00.388147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
13.8%
9530.0 1
 
0.6%
10815.8 1
 
0.6%
12089.5 1
 
0.6%
16208.0 1
 
0.6%
14030.09 1
 
0.6%
5841.2 1
 
0.6%
11234.8 1
 
0.6%
11709.2 1
 
0.6%
25066.0 1
 
0.6%
Other values (135) 135
80.8%
ValueCountFrequency (%)
0.0 23
13.8%
86.72 1
 
0.6%
134.5 1
 
0.6%
220.0 1
 
0.6%
277.0 1
 
0.6%
410.0 1
 
0.6%
732.0 1
 
0.6%
752.0 1
 
0.6%
790.0 1
 
0.6%
802.0 1
 
0.6%
ValueCountFrequency (%)
85114.0 1
0.6%
44070.7 1
0.6%
42751.0 1
0.6%
42692.2 1
0.6%
36269.7 1
0.6%
29532.0 1
0.6%
29202.7 1
0.6%
26575.7 1
0.6%
25746.6 1
0.6%
25066.0 1
0.6%

수혜면적(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct142
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2362.3425
Minimum0
Maximum32708.6
Zeros25
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:04:00.747953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1304.45
median1382.8
Q32995.8
95-th percentile7313.55
Maximum32708.6
Range32708.6
Interquartile range (IQR)2691.35

Descriptive statistics

Standard deviation3619.8374
Coefficient of variation (CV)1.5323084
Kurtosis34.070558
Mean2362.3425
Median Absolute Deviation (MAD)1192.4
Skewness4.8897921
Sum394511.2
Variance13103223
MonotonicityNot monotonic
2024-04-06T17:04:01.105574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 25
 
15.0%
2272.4 2
 
1.2%
1109.2 1
 
0.6%
12563.2 1
 
0.6%
4212.6 1
 
0.6%
6026.7 1
 
0.6%
1745.9 1
 
0.6%
802.8 1
 
0.6%
1888.2 1
 
0.6%
5485.5 1
 
0.6%
Other values (132) 132
79.0%
ValueCountFrequency (%)
0.0 25
15.0%
4.5 1
 
0.6%
20.5 1
 
0.6%
54.0 1
 
0.6%
100.48 1
 
0.6%
106.4 1
 
0.6%
136.0 1
 
0.6%
162.0 1
 
0.6%
178.8 1
 
0.6%
188.8 1
 
0.6%
ValueCountFrequency (%)
32708.6 1
0.6%
21965.4 1
0.6%
12563.2 1
0.6%
9589.6 1
0.6%
9536.0 1
0.6%
9086.2 1
0.6%
8659.0 1
0.6%
7786.0 1
0.6%
7440.3 1
0.6%
7017.8 1
0.6%

유효저수량(천세제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct145
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17300.815
Minimum0
Maximum279405
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:04:01.370048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11857.95
median9231.38
Q321954.55
95-th percentile51876.105
Maximum279405
Range279405
Interquartile range (IQR)20096.6

Descriptive statistics

Standard deviation29166.18
Coefficient of variation (CV)1.685827
Kurtosis41.240052
Mean17300.815
Median Absolute Deviation (MAD)8494.18
Skewness5.4295094
Sum2889236
Variance8.5066605 × 108
MonotonicityNot monotonic
2024-04-06T17:04:01.649214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
13.8%
14752.5 1
 
0.6%
22932.4 1
 
0.6%
32050.1 1
 
0.6%
36263.9 1
 
0.6%
20625.1 1
 
0.6%
8207.5 1
 
0.6%
16465.0 1
 
0.6%
27332.87 1
 
0.6%
123717.5 1
 
0.6%
Other values (135) 135
80.8%
ValueCountFrequency (%)
0.0 23
13.8%
6.5 1
 
0.6%
74.0 1
 
0.6%
209.0 1
 
0.6%
299.0 1
 
0.6%
467.0 1
 
0.6%
482.8 1
 
0.6%
737.2 1
 
0.6%
828.2 1
 
0.6%
855.8 1
 
0.6%
ValueCountFrequency (%)
279405.0 1
0.6%
123981.8 1
0.6%
123717.5 1
0.6%
113519.8 1
0.6%
98414.6 1
0.6%
69336.9 1
0.6%
66569.72 1
0.6%
59873.6 1
0.6%
54057.75 1
0.6%
46785.6 1
0.6%

저수율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct102
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.946108
Minimum0
Maximum100
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:04:02.022021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180.2
median89.2
Q395.8
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation32.199975
Coefficient of variation (CV)0.41847439
Kurtosis1.7321538
Mean76.946108
Median Absolute Deviation (MAD)7.3
Skewness-1.8291613
Sum12850
Variance1036.8384
MonotonicityNot monotonic
2024-04-06T17:04:02.345928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
13.8%
100.0 11
 
6.6%
89.2 4
 
2.4%
85.5 4
 
2.4%
96.8 3
 
1.8%
90.1 3
 
1.8%
88.6 3
 
1.8%
95.8 3
 
1.8%
91.5 2
 
1.2%
91.2 2
 
1.2%
Other values (92) 109
65.3%
ValueCountFrequency (%)
0.0 23
13.8%
30.9 1
 
0.6%
43.0 1
 
0.6%
66.0 1
 
0.6%
67.4 1
 
0.6%
70.6 1
 
0.6%
71.1 1
 
0.6%
72.4 1
 
0.6%
72.6 1
 
0.6%
73.2 1
 
0.6%
ValueCountFrequency (%)
100.0 11
6.6%
99.9 1
 
0.6%
99.7 1
 
0.6%
99.6 2
 
1.2%
99.5 2
 
1.2%
99.4 1
 
0.6%
99.3 1
 
0.6%
99.2 1
 
0.6%
98.9 2
 
1.2%
98.6 2
 
1.2%

평년(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct124
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.442515
Minimum0
Maximum97.7
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:04:02.633325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q161.9
median72
Q381.5
95-th percentile92.06
Maximum97.7
Range97.7
Interquartile range (IQR)19.6

Descriptive statistics

Standard deviation27.854401
Coefficient of variation (CV)0.4322364
Kurtosis1.2480728
Mean64.442515
Median Absolute Deviation (MAD)9.6
Skewness-1.5630562
Sum10761.9
Variance775.86764
MonotonicityNot monotonic
2024-04-06T17:04:02.931955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
13.8%
77.8 3
 
1.8%
81.6 3
 
1.8%
82.0 3
 
1.8%
81.5 2
 
1.2%
69.7 2
 
1.2%
73.2 2
 
1.2%
67.4 2
 
1.2%
68.1 2
 
1.2%
58.2 2
 
1.2%
Other values (114) 123
73.7%
ValueCountFrequency (%)
0.0 23
13.8%
27.7 1
 
0.6%
52.9 1
 
0.6%
53.5 1
 
0.6%
54.0 1
 
0.6%
54.6 1
 
0.6%
55.4 1
 
0.6%
56.3 1
 
0.6%
56.5 1
 
0.6%
56.7 1
 
0.6%
ValueCountFrequency (%)
97.7 1
0.6%
97.0 1
0.6%
96.1 1
0.6%
95.9 1
0.6%
94.0 1
0.6%
93.0 1
0.6%
92.6 1
0.6%
92.5 1
0.6%
92.3 1
0.6%
91.5 1
0.6%

대비(퍼센트)
Real number (ℝ)

HIGH CORRELATION 

Distinct126
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.13473
Minimum44.3
Maximum360.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:04:03.248025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44.3
5-th percentile98.73
Q1104.2
median115.8
Q3127.35
95-th percentile149.76
Maximum360.7
Range316.4
Interquartile range (IQR)23.15

Descriptive statistics

Standard deviation26.570846
Coefficient of variation (CV)0.22303191
Kurtosis40.956481
Mean119.13473
Median Absolute Deviation (MAD)11.6
Skewness4.7073468
Sum19895.5
Variance706.00987
MonotonicityNot monotonic
2024-04-06T17:04:03.575475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 23
 
13.8%
118.5 4
 
2.4%
123.5 3
 
1.8%
98.8 2
 
1.2%
106.8 2
 
1.2%
124.2 2
 
1.2%
118.6 2
 
1.2%
107.6 2
 
1.2%
104.2 2
 
1.2%
111.7 2
 
1.2%
Other values (116) 123
73.7%
ValueCountFrequency (%)
44.3 1
0.6%
75.8 1
0.6%
82.0 1
0.6%
87.3 1
0.6%
92.2 1
0.6%
93.7 1
0.6%
95.3 1
0.6%
98.0 1
0.6%
98.7 1
0.6%
98.8 2
1.2%
ValueCountFrequency (%)
360.7 1
0.6%
181.9 1
0.6%
178.4 1
0.6%
176.3 1
0.6%
169.0 1
0.6%
167.4 1
0.6%
166.7 1
0.6%
158.3 1
0.6%
150.0 1
0.6%
149.2 1
0.6%

단계
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
정상
166 
경계
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
정상 166
99.4%
경계 1
 
0.6%

Length

2024-04-06T17:04:03.927079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:04:04.190150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 166
99.4%
경계 1
 
0.6%

Interactions

2024-04-06T17:03:54.288637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:46.858832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:48.421718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:49.477741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:50.741571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:51.922715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:53.067467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:54.493736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:47.386976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:48.587448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:49.644968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:50.919699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:52.122303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:53.243337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:54.654039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:47.540138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:48.720857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:49.799765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:51.064227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:52.275543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:53.379330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:54.832142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:47.719038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:48.839645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:49.985373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:51.232569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:52.451303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:53.554596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:54.991552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:47.889342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:48.974501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:50.228978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:51.421272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:52.617165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:53.717656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:55.160027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:48.076065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:49.123385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:50.398405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:51.580438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:52.761877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:53.871930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:55.490101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:48.238494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:49.308837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:50.580820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:51.743504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:52.918830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:03:54.030817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:04:04.319135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도시설개수유역면적(ha)수혜면적(ha)유효저수량(천세제곱미터)저수율(퍼센트)평년(퍼센트)대비(퍼센트)단계
시도1.0000.4900.0000.0000.0000.7330.5710.7330.725
시설개수0.4901.0000.5160.4050.4650.5280.4130.2780.000
유역면적(ha)0.0000.5161.0000.7900.7810.3940.4440.1350.000
수혜면적(ha)0.0000.4050.7901.0000.9610.0050.2210.0000.000
유효저수량(천세제곱미터)0.0000.4650.7810.9611.0000.2500.2030.0000.000
저수율(퍼센트)0.7330.5280.3940.0050.2501.0000.8340.7121.000
평년(퍼센트)0.5710.4130.4440.2210.2030.8341.0000.7950.000
대비(퍼센트)0.7330.2780.1350.0000.0000.7120.7951.0000.881
단계0.7250.0000.0000.0000.0001.0000.0000.8811.000
2024-04-06T17:04:04.532291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도단계
시도1.0000.635
단계0.6351.000
2024-04-06T17:04:04.774070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설개수유역면적(ha)수혜면적(ha)유효저수량(천세제곱미터)저수율(퍼센트)평년(퍼센트)대비(퍼센트)시도단계
시설개수1.0000.7680.7830.7940.1320.0230.5970.2220.000
유역면적(ha)0.7681.0000.8800.9060.1600.1440.4650.0000.000
수혜면적(ha)0.7830.8801.0000.9730.1870.1570.4560.0000.000
유효저수량(천세제곱미터)0.7940.9060.9731.0000.1930.1260.5250.0000.000
저수율(퍼센트)0.1320.1600.1870.1931.0000.6340.4470.4290.985
평년(퍼센트)0.0230.1440.1570.1260.6341.000-0.1930.2900.000
대비(퍼센트)0.5970.4650.4560.5250.447-0.1931.0000.4410.685
시도0.2220.0000.0000.0000.4290.2900.4411.0000.635
단계0.0000.0000.0000.0000.9850.0000.6850.6351.000

Missing values

2024-04-06T17:03:56.188668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:03:56.981750image/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

시도시군시설개수유역면적(ha)수혜면적(ha)유효저수량(천세제곱미터)저수율(퍼센트)평년(퍼센트)대비(퍼센트)단계
0서울서울시00.00.00.00.00.0100.0정상
1부산부산(군제외)00.00.00.00.00.0100.0정상
2부산기장군51489.0236.6828.2100.088.0113.6정상
3대구대구(군제외)1321243.0188.86340.578.876.4103.1정상
4대구달성군913107.01301.015083.995.871.9133.3정상
5대구군위군193975.3626.83610.8198.977.8127.1정상
6인천인천(군제외)00.00.00.00.00.0100.0정상
7인천강화군1713363.75838.029263.092.483.6110.5정상
8인천옹진군00.00.00.00.00.0100.0정상
9광주광주(군제외)494552.0254.86066.390.266.1136.4정상
시도시군시설개수유역면적(ha)수혜면적(ha)유효저수량(천세제곱미터)저수율(퍼센트)평년(퍼센트)대비(퍼센트)단계
157경남창녕군4715644.52139.612318.579.669.7114.2정상
158경남고성군3610576.02945.319384.586.273.3117.6정상
159경남남해군495561.61570.3910202.386.773.2118.5정상
160경남하동군2211979.14402.3341073.481.268.2119.2정상
161경남산청군3718078.12084.719851.3987.781.5107.6정상
162경남함양군139760.01886.413498.799.658.9169.0정상
163경남거창군4311986.03194.521403.590.166.6135.3정상
164경남합천군8310577.02272.417914.391.273.6123.9정상
165제주제주시84581.01778.41616.043.056.775.8정상
166제주서귀포시19937.0543.01050.030.969.744.3경계