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 제공
Author한국농어촌공사
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20190710000000001095

Alerts

단계 has constant value ""Constant
시설개수 is highly overall correlated with 유역면적(ha) and 2 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(ha) is highly overall correlated with 시설개수 and 2 other fieldsHigh correlation
저수율 is highly overall correlated with 평년High correlation
평년 is highly overall correlated with 저수율High correlation
시설개수 has 23 (13.8%) zerosZeros
유역면적(ha) has 23 (13.8%) zerosZeros
수혜면적(ha) has 25 (15.0%) zerosZeros
유효저수량(ha) has 23 (13.8%) zerosZeros
저수율 has 23 (13.8%) zerosZeros
평년 has 23 (13.8%) zerosZeros

Reproduction

Analysis started2023-12-11 03:43:50.925614
Analysis finished2023-12-11 03:43:56.829988
Duration5.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
경기
31 
경북
23 
전남
22 
강원
18 
경남
18 
Other values (12)
55 

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%
경북 23
13.8%
전남 22
13.2%
강원 18
10.8%
경남 18
10.8%
충남 15
9.0%
전북 14
8.4%
충북 11
 
6.6%
인천 3
 
1.8%
제주 2
 
1.2%
Other values (7) 10
 
6.0%

Length

2023-12-11T12:43:56.920003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 31
18.6%
경북 23
13.8%
전남 22
13.2%
강원 18
10.8%
경남 18
10.8%
충남 15
9.0%
전북 14
8.4%
충북 11
 
6.6%
인천 3
 
1.8%
제주 2
 
1.2%
Other values (7) 10
 
6.0%

시군
Text

Distinct166
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2023-12-11T12:43:57.254788image/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%
순천시 1
 
0.6%
광양시 1
 
0.6%
나주시 1
 
0.6%
담양군 1
 
0.6%
곡성군 1
 
0.6%
구례군 1
 
0.6%
고흥군 1
 
0.6%
보성군 1
 
0.6%
화순군 1
 
0.6%
Other values (156) 156
93.4%
2023-12-11T12:43:57.782846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 91
 
17.2%
시 80
 
15.1%
천 21
 
4.0%
주 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%
천 21
 
4.1%
주 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%
천 21
 
4.1%
주 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%
천 21
 
4.1%
주 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 

Distinct53
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.413174
Minimum0
Maximum162
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T12:43:57.939629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median13
Q329.5
95-th percentile66.8
Maximum162
Range162
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation24.35561
Coefficient of variation (CV)1.193132
Kurtosis9.4906834
Mean20.413174
Median Absolute Deviation (MAD)12
Skewness2.5054619
Sum3409
Variance593.19573
MonotonicityNot monotonic
2023-12-11T12:43:58.094302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
13.8%
1 10
 
6.0%
6 9
 
5.4%
2 7
 
4.2%
7 6
 
3.6%
5 6
 
3.6%
9 5
 
3.0%
16 5
 
3.0%
18 5
 
3.0%
27 5
 
3.0%
Other values (43) 86
51.5%
ValueCountFrequency (%)
0 23
13.8%
1 10
6.0%
2 7
 
4.2%
3 4
 
2.4%
4 1
 
0.6%
5 6
 
3.6%
6 9
 
5.4%
7 6
 
3.6%
8 4
 
2.4%
9 5
 
3.0%
ValueCountFrequency (%)
162 1
0.6%
139 1
0.6%
92 1
0.6%
81 1
0.6%
79 1
0.6%
78 1
0.6%
77 1
0.6%
76 1
0.6%
71 1
0.6%
57 1
0.6%

유역면적(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct144
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9628.2695
Minimum0
Maximum107379
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T12:43:58.277846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11635.5
median6817
Q312588
95-th percentile26106.3
Maximum107379
Range107379
Interquartile range (IQR)10952.5

Descriptive statistics

Standard deviation12968.434
Coefficient of variation (CV)1.3469122
Kurtosis25.488952
Mean9628.2695
Median Absolute Deviation (MAD)5265
Skewness4.2217759
Sum1607921
Variance1.6818028 × 108
MonotonicityNot monotonic
2023-12-11T12:43:58.459011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
13.8%
8140 2
 
1.2%
9429 1
 
0.6%
4787 1
 
0.6%
4996 1
 
0.6%
46266 1
 
0.6%
37604 1
 
0.6%
3741 1
 
0.6%
7639 1
 
0.6%
10818 1
 
0.6%
Other values (134) 134
80.2%
ValueCountFrequency (%)
0 23
13.8%
87 1
 
0.6%
135 1
 
0.6%
220 1
 
0.6%
277 1
 
0.6%
410 1
 
0.6%
732 1
 
0.6%
752 1
 
0.6%
790 1
 
0.6%
802 1
 
0.6%
ValueCountFrequency (%)
107379 1
0.6%
84870 1
0.6%
46266 1
0.6%
42846 1
0.6%
42692 1
0.6%
37604 1
0.6%
29532 1
0.6%
29203 1
0.6%
26163 1
0.6%
25974 1
0.6%

수혜면적(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct142
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2284.4311
Minimum0
Maximum33338
Zeros25
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T12:43:58.592228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1330.5
median1329
Q32817
95-th percentile7312.2
Maximum33338
Range33338
Interquartile range (IQR)2486.5

Descriptive statistics

Standard deviation3644.85
Coefficient of variation (CV)1.5955176
Kurtosis36.122101
Mean2284.4311
Median Absolute Deviation (MAD)1123
Skewness5.0831464
Sum381500
Variance13284932
MonotonicityNot monotonic
2023-12-11T12:43:58.731657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
15.0%
2323 2
 
1.2%
21979 1
 
0.6%
5977 1
 
0.6%
2515 1
 
0.6%
803 1
 
0.6%
1891 1
 
0.6%
5310 1
 
0.6%
12563 1
 
0.6%
518 1
 
0.6%
Other values (132) 132
79.0%
ValueCountFrequency (%)
0 25
15.0%
21 1
 
0.6%
51 1
 
0.6%
54 1
 
0.6%
100 1
 
0.6%
106 1
 
0.6%
136 1
 
0.6%
162 1
 
0.6%
166 1
 
0.6%
179 1
 
0.6%
ValueCountFrequency (%)
33338 1
0.6%
21979 1
0.6%
12563 1
0.6%
10353 1
0.6%
9590 1
0.6%
9034 1
0.6%
8655 1
0.6%
7635 1
0.6%
7440 1
0.6%
7014 1
0.6%

유효저수량(ha)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct145
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15717.886
Minimum0
Maximum271569
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T12:43:58.887971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11732
median8208
Q319552
95-th percentile47728.3
Maximum271569
Range271569
Interquartile range (IQR)17820

Descriptive statistics

Standard deviation27497.447
Coefficient of variation (CV)1.7494367
Kurtosis46.511268
Mean15717.886
Median Absolute Deviation (MAD)7238
Skewness5.7556024
Sum2624887
Variance7.5610958 × 108
MonotonicityNot monotonic
2023-12-11T12:43:59.048060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
 
13.8%
18791 1
 
0.6%
31985 1
 
0.6%
33427 1
 
0.6%
20405 1
 
0.6%
8208 1
 
0.6%
13814 1
 
0.6%
25754 1
 
0.6%
103975 1
 
0.6%
3280 1
 
0.6%
Other values (135) 135
80.8%
ValueCountFrequency (%)
0 23
13.8%
7 1
 
0.6%
74 1
 
0.6%
209 1
 
0.6%
299 1
 
0.6%
467 1
 
0.6%
483 1
 
0.6%
737 1
 
0.6%
828 1
 
0.6%
970 1
 
0.6%
ValueCountFrequency (%)
271569 1
0.6%
111053 1
0.6%
104301 1
0.6%
103975 1
0.6%
85963 1
0.6%
69337 1
0.6%
59905 1
0.6%
58722 1
0.6%
48943 1
0.6%
44894 1
0.6%

저수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct101
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.565269
Minimum0
Maximum100
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T12:43:59.552378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q183.4
median93.7
Q398.05
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)14.65

Descriptive statistics

Standard deviation32.830376
Coefficient of variation (CV)0.41262195
Kurtosis1.9570167
Mean79.565269
Median Absolute Deviation (MAD)5.5
Skewness-1.9044905
Sum13287.4
Variance1077.8336
MonotonicityNot monotonic
2023-12-11T12:43:59.734006image/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%
95.9 4
 
2.4%
99.7 4
 
2.4%
97.3 3
 
1.8%
97.0 3
 
1.8%
99.6 3
 
1.8%
99.0 3
 
1.8%
95.4 2
 
1.2%
97.1 2
 
1.2%
Other values (91) 109
65.3%
ValueCountFrequency (%)
0.0 23
13.8%
60.4 1
 
0.6%
61.3 1
 
0.6%
62.2 1
 
0.6%
63.8 1
 
0.6%
68.5 1
 
0.6%
71.0 1
 
0.6%
75.9 1
 
0.6%
77.1 1
 
0.6%
77.8 1
 
0.6%
ValueCountFrequency (%)
100.0 11
6.6%
99.9 2
 
1.2%
99.8 2
 
1.2%
99.7 4
 
2.4%
99.6 3
 
1.8%
99.4 2
 
1.2%
99.3 2
 
1.2%
99.2 2
 
1.2%
99.1 1
 
0.6%
99.0 3
 
1.8%

평년
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct123
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.78503
Minimum0
Maximum98.3
Zeros23
Zeros (%)13.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T12:43:59.902002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q168.4
median78.7
Q386.85
95-th percentile95.51
Maximum98.3
Range98.3
Interquartile range (IQR)18.45

Descriptive statistics

Standard deviation29.406269
Coefficient of variation (CV)0.42750972
Kurtosis1.3769594
Mean68.78503
Median Absolute Deviation (MAD)9.1
Skewness-1.6418431
Sum11487.1
Variance864.72863
MonotonicityNot monotonic
2023-12-11T12:44:00.058192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 23
 
13.8%
85.5 3
 
1.8%
78.7 2
 
1.2%
88.7 2
 
1.2%
72.8 2
 
1.2%
74.0 2
 
1.2%
83.0 2
 
1.2%
90.9 2
 
1.2%
91.4 2
 
1.2%
70.5 2
 
1.2%
Other values (113) 125
74.9%
ValueCountFrequency (%)
0.0 23
13.8%
45.7 1
 
0.6%
48.9 1
 
0.6%
52.7 1
 
0.6%
53.6 2
 
1.2%
57.4 1
 
0.6%
57.8 1
 
0.6%
59.5 1
 
0.6%
60.0 1
 
0.6%
60.9 1
 
0.6%
ValueCountFrequency (%)
98.3 1
0.6%
98.1 1
0.6%
97.5 1
0.6%
96.4 1
0.6%
96.3 1
0.6%
96.1 1
0.6%
96.0 1
0.6%
95.8 1
0.6%
95.6 1
0.6%
95.3 1
0.6%

대비
Real number (ℝ)

Distinct122
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.09521
Minimum80.4
Maximum189.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T12:44:00.238063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80.4
5-th percentile97.36
Q1102.3
median111.1
Q3122.9
95-th percentile149.47
Maximum189.9
Range109.5
Interquartile range (IQR)20.6

Descriptive statistics

Standard deviation17.371282
Coefficient of variation (CV)0.15092967
Kurtosis3.8900001
Mean115.09521
Median Absolute Deviation (MAD)9.5
Skewness1.6368484
Sum19220.9
Variance301.76142
MonotonicityNot monotonic
2023-12-11T12:44:00.380038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 23
 
13.8%
122.9 4
 
2.4%
104.9 3
 
1.8%
106.5 3
 
1.8%
102.3 2
 
1.2%
109.1 2
 
1.2%
120.6 2
 
1.2%
130.9 2
 
1.2%
111.5 2
 
1.2%
109.8 2
 
1.2%
Other values (112) 122
73.1%
ValueCountFrequency (%)
80.4 1
0.6%
89.0 1
0.6%
91.1 1
0.6%
91.4 1
0.6%
94.7 1
0.6%
96.0 2
1.2%
96.3 1
0.6%
96.7 1
0.6%
98.9 1
0.6%
99.8 1
0.6%
ValueCountFrequency (%)
189.9 1
0.6%
189.8 1
0.6%
170.3 1
0.6%
166.4 1
0.6%
160.6 1
0.6%
154.9 1
0.6%
154.4 1
0.6%
150.9 1
0.6%
150.4 1
0.6%
147.3 1
0.6%

단계
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
정상
167 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정상 167
100.0%

Length

2023-12-11T12:44:00.556799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T12:44:00.685341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상 167
100.0%

Interactions

2023-12-11T12:43:55.885178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:51.320463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:52.095913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:52.767981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:53.902036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.568670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.229418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.995967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:51.456472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:52.202124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:52.896460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.017850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.664347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.322373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:56.087173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:51.557558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:52.300067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:53.017712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.125436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.748857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.426728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:56.179082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:51.667289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:52.405930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:53.167175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.230640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.848292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.538178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:56.274863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:51.781041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:52.501853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:53.292683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.317074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.951859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.627397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:56.363797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:51.904985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:52.598096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:53.708842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.400032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.064729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.712772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:56.466987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:51.998307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:52.680081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:53.802684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:54.481692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.152596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T12:43:55.795443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:44:00.764310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도시설개수유역면적(ha)수혜면적(ha)유효저수량(ha)저수율평년대비
시도1.0000.5100.4560.0000.0000.5330.5670.245
시설개수0.5101.0000.4790.3720.4660.4090.3930.191
유역면적(ha)0.4560.4791.0000.7850.8090.3750.6880.000
수혜면적(ha)0.0000.3720.7851.0000.9860.2800.2700.228
유효저수량(ha)0.0000.4660.8090.9861.0000.3850.3410.232
저수율0.5330.4090.3750.2800.3851.0000.7060.769
평년0.5670.3930.6880.2700.3410.7061.0000.813
대비0.2450.1910.0000.2280.2320.7690.8131.000
2023-12-11T12:44:00.898993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설개수유역면적(ha)수혜면적(ha)유효저수량(ha)저수율평년대비시도
시설개수1.0000.7470.7800.7900.1320.0310.4980.233
유역면적(ha)0.7471.0000.8700.8820.1330.1150.4290.217
수혜면적(ha)0.7800.8701.0000.9740.1790.1510.4040.000
유효저수량(ha)0.7900.8820.9741.0000.1410.1460.3830.000
저수율0.1320.1330.1790.1411.0000.6660.4130.296
평년0.0310.1150.1510.1460.6661.000-0.1800.287
대비0.4980.4290.4040.3830.413-0.1801.0000.092
시도0.2330.2170.0000.0000.2960.2870.0921.000

Missing values

2023-12-11T12:43:56.585862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:43:56.765153image/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(ha)저수율평년대비단계
0서울서울시00000.00.0100.0정상
1부산부산(군제외)00000.00.0100.0정상
2부산기장군5148923782899.889.1112.0정상
3대구대구(군제외)1321243166652277.175.4102.3정상
4대구달성군91310713291353996.173.7130.4정상
5인천인천(군제외)00000.00.0100.0정상
6인천강화군171336458392840088.384.0105.1정상
7인천옹진군00000.00.0100.0정상
8광주광주(군제외)524637255472192.171.2129.4정상
9대전대전(군제외)33514315462499.987.8113.8정상
시도시군시설개수유역면적(ha)수혜면적(ha)유효저수량(ha)저수율평년대비단계
157경남창녕군471564521401231871.075.094.7정상
158경남고성군361057629451938489.481.4109.8정상
159경남남해군4854621509985597.381.8118.9정상
160경남하동군221197934124117682.172.5113.2정상
161경남산청군351612416621795297.387.3111.5정상
162경남함양군1397601886836399.364.3154.4정상
163경남거창군431198631951750893.770.5132.9정상
164경남합천군81948319221437988.972.8122.1정상
165제주제주시6107379600160661.348.9125.4정상
166제주서귀포시19939400105080.060.0133.3정상