Overview

Dataset statistics

Number of variables12
Number of observations82
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.2 KiB
Average record size in memory102.6 B

Variable types

Categorical5
Text2
Numeric5

Dataset

Description전라남도 곡성군 저수지(저수지명, 준공년도, 수혜면적, 높이, 길이, 관리기관부서 등) 현황을 제공합니다. 업데이트 주기 : 연간
URLhttps://www.data.go.kr/data/3074911/fileData.do

Alerts

시도명 has constant value ""Constant
전화번호 has constant value ""Constant
관리부서 has constant value ""Constant
데이터기준일자 has constant value ""Constant
수혜면적(ha) is highly overall correlated with 높이(m) and 1 other fieldsHigh correlation
높이(m) is highly overall correlated with 수혜면적(ha) and 1 other fieldsHigh correlation
길이(m) is highly overall correlated with 계획저수량(천세제곱미터)High correlation
계획저수량(천세제곱미터) is highly overall correlated with 수혜면적(ha) and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 23:01:08.368417
Analysis finished2023-12-12 23:01:11.479544
Duration3.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
전라남도
82 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 82
100.0%

Length

2023-12-13T08:01:11.552592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:11.646996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 82
100.0%

읍면
Categorical

Distinct11
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Memory size788.0 B
목사동면
13 
곡성읍
12 
고달면
11 
입면
10 
옥과면
8 
Other values (6)
28 

Length

Max length4
Median length3
Mean length2.9634146
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
목사동면 13
15.9%
곡성읍 12
14.6%
고달면 11
13.4%
입면 10
12.2%
옥과면 8
9.8%
오산면 8
9.8%
겸면 6
7.3%
삼기면 4
 
4.9%
죽곡면 4
 
4.9%
오곡면 3
 
3.7%

Length

2023-12-13T08:01:11.764450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목사동면 13
15.9%
곡성읍 12
14.6%
고달면 11
13.4%
입면 10
12.2%
옥과면 8
9.8%
오산면 8
9.8%
겸면 6
7.3%
삼기면 4
 
4.9%
죽곡면 4
 
4.9%
오곡면 3
 
3.7%

리
Text

Distinct55
Distinct (%)67.1%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-13T08:01:12.003575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters164
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)42.7%

Sample

1st row구원
2nd row구원
3rd row구원
4th row월봉
5th row신월
ValueCountFrequency (%)
백곡 6
 
7.3%
구원 4
 
4.9%
죽동 3
 
3.7%
신기 2
 
2.4%
대사 2
 
2.4%
마전 2
 
2.4%
죽림 2
 
2.4%
설옥 2
 
2.4%
목동 2
 
2.4%
약천 2
 
2.4%
Other values (45) 55
67.1%
2023-12-13T08:01:12.299298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
곡 11
 
6.7%
동 8
 
4.9%
백 6
 
3.7%
구 6
 
3.7%
봉 6
 
3.7%
죽 6
 
3.7%
대 6
 
3.7%
전 6
 
3.7%
신 5
 
3.0%
월 5
 
3.0%
Other values (54) 99
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 164
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
곡 11
 
6.7%
동 8
 
4.9%
백 6
 
3.7%
구 6
 
3.7%
봉 6
 
3.7%
죽 6
 
3.7%
대 6
 
3.7%
전 6
 
3.7%
신 5
 
3.0%
월 5
 
3.0%
Other values (54) 99
60.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 164
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
곡 11
 
6.7%
동 8
 
4.9%
백 6
 
3.7%
구 6
 
3.7%
봉 6
 
3.7%
죽 6
 
3.7%
대 6
 
3.7%
전 6
 
3.7%
신 5
 
3.0%
월 5
 
3.0%
Other values (54) 99
60.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 164
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
곡 11
 
6.7%
동 8
 
4.9%
백 6
 
3.7%
구 6
 
3.7%
봉 6
 
3.7%
죽 6
 
3.7%
대 6
 
3.7%
전 6
 
3.7%
신 5
 
3.0%
월 5
 
3.0%
Other values (54) 99
60.4%

저수지명
Text

Distinct81
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-12-13T08:01:12.570468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4.5
Mean length2.5365854
Min length2

Characters and Unicode

Total characters208
Distinct characters79
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)97.6%

Sample

1st row구원2
2nd row구중
3rd row구원3
4th row월봉
5th row신월
ValueCountFrequency (%)
연화 2
 
2.4%
구원2 1
 
1.2%
설옥1 1
 
1.2%
송전 1
 
1.2%
죽림1 1
 
1.2%
죽림2 1
 
1.2%
수리 1
 
1.2%
배감 1
 
1.2%
소룡 1
 
1.2%
율사 1
 
1.2%
Other values (71) 71
86.6%
2023-12-13T08:01:12.953239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 19
 
9.1%
1 18
 
8.7%
곡 9
 
4.3%
구 7
 
3.4%
대 6
 
2.9%
월 6
 
2.9%
수 6
 
2.9%
전 6
 
2.9%
백 6
 
2.9%
신 5
 
2.4%
Other values (69) 120
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 166
79.8%
Decimal Number 40
 
19.2%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
곡 9
 
5.4%
구 7
 
4.2%
대 6
 
3.6%
월 6
 
3.6%
수 6
 
3.6%
전 6
 
3.6%
백 6
 
3.6%
신 5
 
3.0%
사 5
 
3.0%
죽 5
 
3.0%
Other values (64) 105
63.3%
Decimal Number
ValueCountFrequency (%)
2 19
47.5%
1 18
45.0%
3 3
 
7.5%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 166
79.8%
Common 42
 
20.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
곡 9
 
5.4%
구 7
 
4.2%
대 6
 
3.6%
월 6
 
3.6%
수 6
 
3.6%
전 6
 
3.6%
백 6
 
3.6%
신 5
 
3.0%
사 5
 
3.0%
죽 5
 
3.0%
Other values (64) 105
63.3%
Common
ValueCountFrequency (%)
2 19
45.2%
1 18
42.9%
3 3
 
7.1%
( 1
 
2.4%
) 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 166
79.8%
ASCII 42
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 19
45.2%
1 18
42.9%
3 3
 
7.1%
( 1
 
2.4%
) 1
 
2.4%
Hangul
ValueCountFrequency (%)
곡 9
 
5.4%
구 7
 
4.2%
대 6
 
3.6%
월 6
 
3.6%
수 6
 
3.6%
전 6
 
3.6%
백 6
 
3.6%
신 5
 
3.0%
사 5
 
3.0%
죽 5
 
3.0%
Other values (64) 105
63.3%

준공년도
Real number (ℝ)

Distinct26
Distinct (%)31.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1960.6707
Minimum1945
Maximum2005
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-13T08:01:13.081390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1945
5-th percentile1945
Q11945
median1961
Q31969
95-th percentile1994.7
Maximum2005
Range60
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.436893
Coefficient of variation (CV)0.0078732713
Kurtosis0.34626637
Mean1960.6707
Median Absolute Deviation (MAD)15
Skewness0.81645325
Sum160775
Variance238.29765
MonotonicityNot monotonic
2023-12-13T08:01:13.255352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1945 31
37.8%
1970 8
 
9.8%
1968 6
 
7.3%
1960 4
 
4.9%
1961 4
 
4.9%
1967 3
 
3.7%
1969 3
 
3.7%
1963 3
 
3.7%
1999 2
 
2.4%
1962 2
 
2.4%
Other values (16) 16
19.5%
ValueCountFrequency (%)
1945 31
37.8%
1947 1
 
1.2%
1957 1
 
1.2%
1958 1
 
1.2%
1960 4
 
4.9%
1961 4
 
4.9%
1962 2
 
2.4%
1963 3
 
3.7%
1964 1
 
1.2%
1965 1
 
1.2%
ValueCountFrequency (%)
2005 1
1.2%
1999 2
2.4%
1998 1
1.2%
1995 1
1.2%
1989 1
1.2%
1984 1
1.2%
1979 1
1.2%
1978 1
1.2%
1977 1
1.2%
1974 1
1.2%

수혜면적(ha)
Real number (ℝ)

HIGH CORRELATION 

Distinct64
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.723171
Minimum0.1
Maximum48.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-13T08:01:13.387486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.93
Q14.325
median7.7
Q313.45
95-th percentile28.95
Maximum48.7
Range48.6
Interquartile range (IQR)9.125

Descriptive statistics

Standard deviation10.45736
Coefficient of variation (CV)0.97521152
Kurtosis4.5423468
Mean10.723171
Median Absolute Deviation (MAD)4.1
Skewness2.0703627
Sum879.3
Variance109.35637
MonotonicityNot monotonic
2023-12-13T08:01:13.536957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 3
 
3.7%
5.0 3
 
3.7%
1.5 3
 
3.7%
7.0 3
 
3.7%
45.0 2
 
2.4%
18.6 2
 
2.4%
4.5 2
 
2.4%
5.1 2
 
2.4%
11.3 2
 
2.4%
2.7 2
 
2.4%
Other values (54) 58
70.7%
ValueCountFrequency (%)
0.1 1
 
1.2%
0.5 1
 
1.2%
0.7 1
 
1.2%
0.8 1
 
1.2%
0.9 1
 
1.2%
1.5 3
3.7%
2.0 1
 
1.2%
2.7 2
2.4%
2.8 1
 
1.2%
2.9 1
 
1.2%
ValueCountFrequency (%)
48.7 1
1.2%
46.0 1
1.2%
45.0 2
2.4%
29.0 1
1.2%
28.0 1
1.2%
26.3 1
1.2%
25.0 1
1.2%
24.0 1
1.2%
20.6 1
1.2%
19.9 1
1.2%

높이(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7182927
Minimum2.5
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-13T08:01:13.660708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.025
Q15
median6.5
Q38.5
95-th percentile18.63
Maximum27
Range24.5
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation4.8809311
Coefficient of variation (CV)0.63238482
Kurtosis5.4122625
Mean7.7182927
Median Absolute Deviation (MAD)1.5
Skewness2.2510364
Sum632.9
Variance23.823488
MonotonicityNot monotonic
2023-12-13T08:01:13.779516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5.0 15
18.3%
6.5 8
 
9.8%
8.5 6
 
7.3%
8.0 6
 
7.3%
4.5 5
 
6.1%
2.5 4
 
4.9%
7.0 4
 
4.9%
7.5 3
 
3.7%
6.0 3
 
3.7%
3.5 3
 
3.7%
Other values (21) 25
30.5%
ValueCountFrequency (%)
2.5 4
 
4.9%
3.0 1
 
1.2%
3.5 3
 
3.7%
4.0 2
 
2.4%
4.2 1
 
1.2%
4.5 5
 
6.1%
5.0 15
18.3%
5.5 2
 
2.4%
6.0 3
 
3.7%
6.2 1
 
1.2%
ValueCountFrequency (%)
27.0 1
1.2%
24.3 1
1.2%
24.0 1
1.2%
22.3 1
1.2%
18.7 1
1.2%
17.3 1
1.2%
14.5 1
1.2%
14.0 1
1.2%
13.5 1
1.2%
12.0 1
1.2%

길이(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)53.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.02439
Minimum25
Maximum335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-13T08:01:13.925867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile40.5
Q164.25
median95
Q3120
95-th percentile174
Maximum335
Range310
Interquartile range (IQR)55.75

Descriptive statistics

Standard deviation46.964651
Coefficient of variation (CV)0.46953199
Kurtosis6.96004
Mean100.02439
Median Absolute Deviation (MAD)30
Skewness1.8211533
Sum8202
Variance2205.6784
MonotonicityNot monotonic
2023-12-13T08:01:14.068601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
95 6
 
7.3%
100 6
 
7.3%
57 4
 
4.9%
120 4
 
4.9%
90 4
 
4.9%
70 3
 
3.7%
55 3
 
3.7%
140 3
 
3.7%
60 3
 
3.7%
40 2
 
2.4%
Other values (34) 44
53.7%
ValueCountFrequency (%)
25 2
2.4%
36 1
 
1.2%
40 2
2.4%
50 1
 
1.2%
55 3
3.7%
56 2
2.4%
57 4
4.9%
60 3
3.7%
62 1
 
1.2%
64 2
2.4%
ValueCountFrequency (%)
335 1
 
1.2%
230 1
 
1.2%
190 1
 
1.2%
182 1
 
1.2%
175 1
 
1.2%
155 1
 
1.2%
151 1
 
1.2%
150 1
 
1.2%
145 1
 
1.2%
140 3
3.7%

계획저수량(천세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.72561
Minimum1.2
Maximum250.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size870.0 B
2023-12-13T08:01:14.199229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.505
Q17.325
median13.1
Q319.525
95-th percentile166.01
Maximum250.4
Range249.2
Interquartile range (IQR)12.2

Descriptive statistics

Standard deviation51.609856
Coefficient of variation (CV)1.7362085
Kurtosis8.9787949
Mean29.72561
Median Absolute Deviation (MAD)5.95
Skewness3.0800373
Sum2437.5
Variance2663.5772
MonotonicityNot monotonic
2023-12-13T08:01:14.335582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.0 3
 
3.7%
17.7 2
 
2.4%
1.2 2
 
2.4%
11.7 2
 
2.4%
12.4 2
 
2.4%
13.5 2
 
2.4%
1.7 2
 
2.4%
1.4 2
 
2.4%
98.5 1
 
1.2%
16.5 1
 
1.2%
Other values (63) 63
76.8%
ValueCountFrequency (%)
1.2 2
2.4%
1.4 2
2.4%
1.5 1
1.2%
1.6 1
1.2%
1.7 2
2.4%
1.9 1
1.2%
2.4 1
1.2%
3.3 1
1.2%
3.7 1
1.2%
4.3 1
1.2%
ValueCountFrequency (%)
250.4 1
1.2%
228.7 1
1.2%
211.3 1
1.2%
204.0 1
1.2%
168.0 1
1.2%
128.2 1
1.2%
98.5 1
1.2%
82.9 1
1.2%
59.2 1
1.2%
57.8 1
1.2%

전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
061-360-8331
82 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row061-360-8331
2nd row061-360-8331
3rd row061-360-8331
4th row061-360-8331
5th row061-360-8331

Common Values

ValueCountFrequency (%)
061-360-8331 82
100.0%

Length

2023-12-13T08:01:14.472626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:14.587603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
061-360-8331 82
100.0%

관리부서
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
전라남도 곡성군 농정과
82 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도 곡성군 농정과
2nd row전라남도 곡성군 농정과
3rd row전라남도 곡성군 농정과
4th row전라남도 곡성군 농정과
5th row전라남도 곡성군 농정과

Common Values

ValueCountFrequency (%)
전라남도 곡성군 농정과 82
100.0%

Length

2023-12-13T08:01:14.734249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:14.841875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 82
33.3%
곡성군 82
33.3%
농정과 82
33.3%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size788.0 B
2023-08-23
82 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-23
2nd row2023-08-23
3rd row2023-08-23
4th row2023-08-23
5th row2023-08-23

Common Values

ValueCountFrequency (%)
2023-08-23 82
100.0%

Length

2023-12-13T08:01:15.039468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:01:15.127396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-23 82
100.0%

Interactions

2023-12-13T08:01:10.723294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.737998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.178602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.851168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.302243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.812110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.817856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.254620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.941190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.380160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.907946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:08.914898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.330389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.033579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.482790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.002752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.005928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.403851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.111362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.566511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:11.099278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.082816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:09.485862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.218652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:01:10.646014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:01:15.193669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면리저수지명준공년도수혜면적(ha)높이(m)길이(m)계획저수량(천세제곱미터)
읍면1.0001.0000.8610.4310.3870.2390.1480.434
리1.0001.0001.0000.0000.6710.5890.0000.856
저수지명0.8611.0001.0001.0000.9560.9861.0001.000
준공년도0.4310.0001.0001.0000.6790.6870.0000.757
수혜면적(ha)0.3870.6710.9560.6791.0000.5850.4810.797
높이(m)0.2390.5890.9860.6870.5851.0000.3930.918
길이(m)0.1480.0001.0000.0000.4810.3931.0000.473
계획저수량(천세제곱미터)0.4340.8561.0000.7570.7970.9180.4731.000
2023-12-13T08:01:15.317906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공년도수혜면적(ha)높이(m)길이(m)계획저수량(천세제곱미터)읍면
준공년도1.0000.2780.497-0.0560.1750.186
수혜면적(ha)0.2781.0000.6000.4610.7110.193
높이(m)0.4970.6001.0000.0800.6070.105
길이(m)-0.0560.4610.0801.0000.5760.094
계획저수량(천세제곱미터)0.1750.7110.6070.5761.0000.213
읍면0.1860.1930.1050.0940.2131.000

Missing values

2023-12-13T08:01:11.234672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:01:11.410127image/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

시도명읍면리저수지명준공년도수혜면적(ha)높이(m)길이(m)계획저수량(천세제곱미터)전화번호관리부서데이터기준일자
0전라남도곡성읍구원구원2196114.38.5828.8061-360-8331전라남도 곡성군 농정과2023-08-23
1전라남도곡성읍구원구중19457.55.514016.4061-360-8331전라남도 곡성군 농정과2023-08-23
2전라남도곡성읍구원구원319656.010.06014.8061-360-8331전라남도 곡성군 농정과2023-08-23
3전라남도곡성읍월봉월봉194512.75.58518.4061-360-8331전라남도 곡성군 농정과2023-08-23
4전라남도곡성읍신월신월19708.08.3979.6061-360-8331전라남도 곡성군 농정과2023-08-23
5전라남도곡성읍죽동은곡19459.84.512015.9061-360-8331전라남도 곡성군 농정과2023-08-23
6전라남도곡성읍죽동죽동219708.89.36514.4061-360-8331전라남도 곡성군 농정과2023-08-23
7전라남도곡성읍죽동죽동119459.34.033525.6061-360-8331전라남도 곡성군 농정과2023-08-23
8전라남도곡성읍학정영운19451.53.510013.7061-360-8331전라남도 곡성군 농정과2023-08-23
9전라남도곡성읍신기신기119457.04.210018.8061-360-8331전라남도 곡성군 농정과2023-08-23
시도명읍면리저수지명준공년도수혜면적(ha)높이(m)길이(m)계획저수량(천세제곱미터)전화번호관리부서데이터기준일자
72전라남도겸면칠봉칠봉19684.37.5554.3061-360-8331전라남도 곡성군 농정과2023-08-23
73전라남도겸면대흥대흥19455.08.0757.1061-360-8331전라남도 곡성군 농정과2023-08-23
74전라남도오산면연화연화19684.58.09518.7061-360-8331전라남도 곡성군 농정과2023-08-23
75전라남도오산면가곡가곡19647.48.011317.7061-360-8331전라남도 곡성군 농정과2023-08-23
76전라남도오산면단사단사196915.49.55728.8061-360-8331전라남도 곡성군 농정과2023-08-23
77전라남도오산면선세선세19704.08.55510.9061-360-8331전라남도 곡성군 농정과2023-08-23
78전라남도오산면봉동봉동19572.87.05511.6061-360-8331전라남도 곡성군 농정과2023-08-23
79전라남도오산면조양조양119455.05.0564.6061-360-8331전라남도 곡성군 농정과2023-08-23
80전라남도오산면조양조양219677.06.5403.7061-360-8331전라남도 곡성군 농정과2023-08-23
81전라남도오산면성덕성덕119781.54.5401.7061-360-8331전라남도 곡성군 농정과2023-08-23