Overview

Dataset statistics

Number of variables15
Number of observations62
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.8 KiB
Average record size in memory129.1 B

Variable types

Text2
Categorical6
Numeric7

Dataset

Description충청남도 청양군의 저수지명, 위치, 준공년도, 수혜면적, 총저수량, 유효저수량, 면적, 설치년도 등에 대한 현황을 나타낸 자료입니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=415&beforeMenuCd=DOM_000000201001001000&publicdatapk=15040017

Alerts

시도명 has constant value ""Constant
시군명 has constant value ""Constant
전화번호 has constant value ""Constant
관리부서 has constant value ""Constant
데이터기준일자 has constant value ""Constant
준공년도 is highly overall correlated with 고(m)High correlation
수혜면적(ha) is highly overall correlated with 총저수량(천ton) and 3 other fieldsHigh correlation
총저수량(천ton) is highly overall correlated with 수혜면적(ha) and 3 other fieldsHigh correlation
유효저수량(ton) is highly overall correlated with 수혜면적(ha) and 3 other fieldsHigh correlation
장(m) is highly overall correlated with 수혜면적(ha) and 2 other fieldsHigh correlation
고(m) is highly overall correlated with 준공년도 and 3 other fieldsHigh correlation

Reproduction

Analysis started2024-01-09 21:07:37.854970
Analysis finished2024-01-09 21:07:42.685631
Duration4.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

저수지명
Text

Distinct61
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-01-10T06:07:42.840981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3548387
Min length2

Characters and Unicode

Total characters146
Distinct characters84
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)96.8%

Sample

1st row방축
2nd row사기점
3rd row안부동
4th row여우실
5th row장승1
ValueCountFrequency (%)
턱골 2
 
3.2%
방축 1
 
1.6%
용지골 1
 
1.6%
중절 1
 
1.6%
내직 1
 
1.6%
상장 1
 
1.6%
외직 1
 
1.6%
중추 1
 
1.6%
고실 1
 
1.6%
백금 1
 
1.6%
Other values (51) 51
82.3%
2024-01-10T06:07:43.174850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8
 
5.5%
골 5
 
3.4%
실 5
 
3.4%
소 4
 
2.7%
장 4
 
2.7%
중 3
 
2.1%
상 3
 
2.1%
추 3
 
2.1%
광 3
 
2.1%
산 3
 
2.1%
Other values (74) 105
71.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139
95.2%
Decimal Number 7
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8
 
5.8%
골 5
 
3.6%
실 5
 
3.6%
소 4
 
2.9%
장 4
 
2.9%
중 3
 
2.2%
상 3
 
2.2%
추 3
 
2.2%
광 3
 
2.2%
산 3
 
2.2%
Other values (71) 98
70.5%
Decimal Number
ValueCountFrequency (%)
2 3
42.9%
1 3
42.9%
3 1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 139
95.2%
Common 7
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8
 
5.8%
골 5
 
3.6%
실 5
 
3.6%
소 4
 
2.9%
장 4
 
2.9%
중 3
 
2.2%
상 3
 
2.2%
추 3
 
2.2%
광 3
 
2.2%
산 3
 
2.2%
Other values (71) 98
70.5%
Common
ValueCountFrequency (%)
2 3
42.9%
1 3
42.9%
3 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 139
95.2%
ASCII 7
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8
 
5.8%
골 5
 
3.6%
실 5
 
3.6%
소 4
 
2.9%
장 4
 
2.9%
중 3
 
2.2%
상 3
 
2.2%
추 3
 
2.2%
광 3
 
2.2%
산 3
 
2.2%
Other values (71) 98
70.5%
ASCII
ValueCountFrequency (%)
2 3
42.9%
1 3
42.9%
3 1
 
14.3%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
충청남도
62 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 62
100.0%

Length

2024-01-10T06:07:43.301894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:43.388453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 62
100.0%

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
청양
62 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청양 62
100.0%

Length

2024-01-10T06:07:43.472847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:43.555755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청양 62
100.0%

읍면
Categorical

Distinct10
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size628.0 B
운곡
10 
대치
10 
남양
10 
화성
9 
청양
7 
Other values (5)
16 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.6%

Sample

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

Common Values

ValueCountFrequency (%)
운곡 10
16.1%
대치 10
16.1%
남양 10
16.1%
화성 9
14.5%
청양 7
11.3%
비봉 5
8.1%
목면 4
 
6.5%
정산 3
 
4.8%
청남 3
 
4.8%
장평 1
 
1.6%

Length

2024-01-10T06:07:43.641443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:43.752272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운곡 10
16.1%
대치 10
16.1%
남양 10
16.1%
화성 9
14.5%
청양 7
11.3%
비봉 5
8.1%
목면 4
 
6.5%
정산 3
 
4.8%
청남 3
 
4.8%
장평 1
 
1.6%

리
Text

Distinct50
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-01-10T06:07:43.963629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.5645161
Min length2

Characters and Unicode

Total characters159
Distinct characters61
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)64.5%

Sample

1st row송방
2nd row백천
3rd row학당2
4th row학당2
5th row장승1
ValueCountFrequency (%)
농소 3
 
4.8%
산정 3
 
4.8%
상갑 2
 
3.2%
수정 2
 
3.2%
탄정 2
 
3.2%
본의1 2
 
3.2%
학당2 2
 
3.2%
추광 2
 
3.2%
신원1 2
 
3.2%
용마2 2
 
3.2%
Other values (40) 40
64.5%
2024-01-10T06:07:44.277576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 18
 
11.3%
1 17
 
10.7%
정 7
 
4.4%
산 5
 
3.1%
용 4
 
2.5%
직 4
 
2.5%
신 4
 
2.5%
양 4
 
2.5%
장 4
 
2.5%
농 3
 
1.9%
Other values (51) 89
56.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124
78.0%
Decimal Number 35
 
22.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 7
 
5.6%
산 5
 
4.0%
용 4
 
3.2%
직 4
 
3.2%
신 4
 
3.2%
양 4
 
3.2%
장 4
 
3.2%
농 3
 
2.4%
수 3
 
2.4%
백 3
 
2.4%
Other values (49) 83
66.9%
Decimal Number
ValueCountFrequency (%)
2 18
51.4%
1 17
48.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 124
78.0%
Common 35
 
22.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 7
 
5.6%
산 5
 
4.0%
용 4
 
3.2%
직 4
 
3.2%
신 4
 
3.2%
양 4
 
3.2%
장 4
 
3.2%
농 3
 
2.4%
수 3
 
2.4%
백 3
 
2.4%
Other values (49) 83
66.9%
Common
ValueCountFrequency (%)
2 18
51.4%
1 17
48.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 124
78.0%
ASCII 35
 
22.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 18
51.4%
1 17
48.6%
Hangul
ValueCountFrequency (%)
정 7
 
5.6%
산 5
 
4.0%
용 4
 
3.2%
직 4
 
3.2%
신 4
 
3.2%
양 4
 
3.2%
장 4
 
3.2%
농 3
 
2.4%
수 3
 
2.4%
백 3
 
2.4%
Other values (49) 83
66.9%

준공년도
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)43.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1961.4839
Minimum1945
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:44.703799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1945
5-th percentile1945
Q11945
median1958
Q31970.75
95-th percentile1996.5
Maximum2012
Range67
Interquartile range (IQR)25.75

Descriptive statistics

Standard deviation17.531931
Coefficient of variation (CV)0.0089380957
Kurtosis0.54929501
Mean1961.4839
Median Absolute Deviation (MAD)13
Skewness1.0594919
Sum121612
Variance307.36859
MonotonicityNot monotonic
2024-01-10T06:07:44.814821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1945 22
35.5%
1962 3
 
4.8%
1958 3
 
4.8%
1984 3
 
4.8%
1961 3
 
4.8%
1966 2
 
3.2%
1987 2
 
3.2%
1975 2
 
3.2%
1957 2
 
3.2%
1956 2
 
3.2%
Other values (17) 18
29.0%
ValueCountFrequency (%)
1945 22
35.5%
1950 2
 
3.2%
1955 1
 
1.6%
1956 2
 
3.2%
1957 2
 
3.2%
1958 3
 
4.8%
1959 1
 
1.6%
1960 1
 
1.6%
1961 3
 
4.8%
1962 3
 
4.8%
ValueCountFrequency (%)
2012 1
 
1.6%
2008 1
 
1.6%
2002 1
 
1.6%
1997 1
 
1.6%
1987 2
3.2%
1985 1
 
1.6%
1984 3
4.8%
1980 1
 
1.6%
1977 1
 
1.6%
1975 2
3.2%

수혜면적(ha)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4064516
Minimum0.5
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:44.925991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11
median1.5
Q34.2
95-th percentile19.8
Maximum32
Range31.5
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation6.6879356
Coefficient of variation (CV)1.5177599
Kurtosis7.8271803
Mean4.4064516
Median Absolute Deviation (MAD)0.5
Skewness2.8149443
Sum273.2
Variance44.728482
MonotonicityNot monotonic
2024-01-10T06:07:45.036816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1.0 22
35.5%
1.5 4
 
6.5%
3.0 3
 
4.8%
1.4 3
 
4.8%
2.0 3
 
4.8%
4.2 2
 
3.2%
1.1 2
 
3.2%
6.0 2
 
3.2%
10.0 2
 
3.2%
1.2 1
 
1.6%
Other values (18) 18
29.0%
ValueCountFrequency (%)
0.5 1
 
1.6%
1.0 22
35.5%
1.1 2
 
3.2%
1.2 1
 
1.6%
1.4 3
 
4.8%
1.5 4
 
6.5%
1.6 1
 
1.6%
2.0 3
 
4.8%
2.1 1
 
1.6%
3.0 3
 
4.8%
ValueCountFrequency (%)
32.0 1
1.6%
29.0 1
1.6%
25.0 1
1.6%
20.0 1
1.6%
16.0 1
1.6%
13.0 1
1.6%
10.0 2
3.2%
8.0 1
1.6%
6.0 2
3.2%
5.6 1
1.6%

총저수량(천ton)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.492903
Minimum0.58
Maximum147.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:45.171536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.58
5-th percentile0.997
Q13.09
median4.87
Q313.955
95-th percentile87.797
Maximum147.5
Range146.92
Interquartile range (IQR)10.865

Descriptive statistics

Standard deviation29.883975
Coefficient of variation (CV)1.8119293
Kurtosis8.8306544
Mean16.492903
Median Absolute Deviation (MAD)2.835
Skewness2.9838017
Sum1022.56
Variance893.05194
MonotonicityNot monotonic
2024-01-10T06:07:45.297473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.71 3
 
4.8%
3.19 2
 
3.2%
3.44 2
 
3.2%
3.09 2
 
3.2%
4.12 1
 
1.6%
2.06 1
 
1.6%
72.92 1
 
1.6%
10.09 1
 
1.6%
25.44 1
 
1.6%
21.63 1
 
1.6%
Other values (47) 47
75.8%
ValueCountFrequency (%)
0.58 1
1.6%
0.68 1
1.6%
0.95 1
1.6%
0.99 1
1.6%
1.13 1
1.6%
1.19 1
1.6%
1.27 1
1.6%
1.32 1
1.6%
1.98 1
1.6%
2.06 1
1.6%
ValueCountFrequency (%)
147.5 1
1.6%
123.47 1
1.6%
105.37 1
1.6%
88.58 1
1.6%
72.92 1
1.6%
51.5 1
1.6%
42.33 1
1.6%
32.96 1
1.6%
27.09 1
1.6%
25.44 1
1.6%

유효저수량(ton)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.012742
Minimum0.56
Maximum143.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:45.435628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.56
5-th percentile0.967
Q13
median4.725
Q313.55
95-th percentile85.24
Maximum143.2
Range142.64
Interquartile range (IQR)10.55

Descriptive statistics

Standard deviation29.013115
Coefficient of variation (CV)1.8118767
Kurtosis8.8300656
Mean16.012742
Median Absolute Deviation (MAD)2.75
Skewness2.9837172
Sum992.79
Variance841.76082
MonotonicityNot monotonic
2024-01-10T06:07:45.558627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.6 3
 
4.8%
3.1 2
 
3.2%
3.34 2
 
3.2%
3.0 2
 
3.2%
4.0 1
 
1.6%
2.0 1
 
1.6%
70.8 1
 
1.6%
9.8 1
 
1.6%
24.7 1
 
1.6%
21.0 1
 
1.6%
Other values (47) 47
75.8%
ValueCountFrequency (%)
0.56 1
1.6%
0.66 1
1.6%
0.92 1
1.6%
0.96 1
1.6%
1.1 1
1.6%
1.16 1
1.6%
1.23 1
1.6%
1.28 1
1.6%
1.92 1
1.6%
2.0 1
1.6%
ValueCountFrequency (%)
143.2 1
1.6%
119.87 1
1.6%
102.3 1
1.6%
86.0 1
1.6%
70.8 1
1.6%
50.0 1
1.6%
41.1 1
1.6%
32.0 1
1.6%
26.3 1
1.6%
24.7 1
1.6%

장(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.080645
Minimum18
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:45.703855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile35.05
Q159.25
median73.5
Q390.25
95-th percentile121.85
Maximum270
Range252
Interquartile range (IQR)31

Descriptive statistics

Standard deviation37.114598
Coefficient of variation (CV)0.48783233
Kurtosis11.492423
Mean76.080645
Median Absolute Deviation (MAD)15
Skewness2.5233482
Sum4717
Variance1377.4934
MonotonicityNot monotonic
2024-01-10T06:07:45.846160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
65.0 4
 
6.5%
74.0 4
 
6.5%
60.0 3
 
4.8%
92.0 2
 
3.2%
46.0 2
 
3.2%
38.0 2
 
3.2%
78.0 2
 
3.2%
104.0 2
 
3.2%
73.0 2
 
3.2%
95.0 1
 
1.6%
Other values (38) 38
61.3%
ValueCountFrequency (%)
18.0 1
1.6%
25.0 1
1.6%
32.0 1
1.6%
35.0 1
1.6%
36.0 1
1.6%
37.0 1
1.6%
38.0 2
3.2%
40.0 1
1.6%
44.0 1
1.6%
46.0 2
3.2%
ValueCountFrequency (%)
270.0 1
1.6%
160.0 1
1.6%
142.0 1
1.6%
122.0 1
1.6%
119.0 1
1.6%
112.5 1
1.6%
111.0 1
1.6%
107.5 1
1.6%
104.0 2
3.2%
96.0 1
1.6%

폭(m)
Real number (ℝ)

Distinct9
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2112903
Minimum1.2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:45.961989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.2
5-th percentile1.5
Q12
median2
Q32.5
95-th percentile4
Maximum5
Range3.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.72226984
Coefficient of variation (CV)0.32662823
Kurtosis3.9500574
Mean2.2112903
Median Absolute Deviation (MAD)0.1
Skewness1.8673558
Sum137.1
Variance0.52167372
MonotonicityNot monotonic
2024-01-10T06:07:46.080281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2.0 31
50.0%
1.5 10
 
16.1%
2.5 9
 
14.5%
3.0 4
 
6.5%
4.0 4
 
6.5%
5.0 1
 
1.6%
1.8 1
 
1.6%
1.6 1
 
1.6%
1.2 1
 
1.6%
ValueCountFrequency (%)
1.2 1
 
1.6%
1.5 10
 
16.1%
1.6 1
 
1.6%
1.8 1
 
1.6%
2.0 31
50.0%
2.5 9
 
14.5%
3.0 4
 
6.5%
4.0 4
 
6.5%
5.0 1
 
1.6%
ValueCountFrequency (%)
5.0 1
 
1.6%
4.0 4
 
6.5%
3.0 4
 
6.5%
2.5 9
 
14.5%
2.0 31
50.0%
1.8 1
 
1.6%
1.6 1
 
1.6%
1.5 10
 
16.1%
1.2 1
 
1.6%

고(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1564516
Minimum2.6
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:46.207220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.01
Q14.225
median5.6
Q38.225
95-th percentile16
Maximum23.8
Range21.2
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.4330954
Coefficient of variation (CV)0.61945439
Kurtosis3.3571225
Mean7.1564516
Median Absolute Deviation (MAD)1.5
Skewness1.8256594
Sum443.7
Variance19.652335
MonotonicityNot monotonic
2024-01-10T06:07:46.327408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
7.0 5
 
8.1%
4.2 4
 
6.5%
5.6 3
 
4.8%
5.0 3
 
4.8%
3.7 2
 
3.2%
4.3 2
 
3.2%
4.4 2
 
3.2%
8.0 2
 
3.2%
3.2 2
 
3.2%
16.0 2
 
3.2%
Other values (34) 35
56.5%
ValueCountFrequency (%)
2.6 1
 
1.6%
2.7 1
 
1.6%
3.0 2
3.2%
3.2 2
3.2%
3.3 1
 
1.6%
3.4 1
 
1.6%
3.7 2
3.2%
3.8 1
 
1.6%
4.1 1
 
1.6%
4.2 4
6.5%
ValueCountFrequency (%)
23.8 1
1.6%
19.4 1
1.6%
18.3 1
1.6%
16.0 2
3.2%
15.8 1
1.6%
14.0 1
1.6%
11.3 1
1.6%
11.0 1
1.6%
10.2 1
1.6%
10.0 1
1.6%

전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
041-940-2373
62 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row041-940-2373
2nd row041-940-2373
3rd row041-940-2373
4th row041-940-2373
5th row041-940-2373

Common Values

ValueCountFrequency (%)
041-940-2373 62
100.0%

Length

2024-01-10T06:07:46.435437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:46.516493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
041-940-2373 62
100.0%

관리부서
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
청양군청
62 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
청양군청 62
100.0%

Length

2024-01-10T06:07:46.597428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:46.671779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청양군청 62
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
2020-05-27
62 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-05-27
2nd row2020-05-27
3rd row2020-05-27
4th row2020-05-27
5th row2020-05-27

Common Values

ValueCountFrequency (%)
2020-05-27 62
100.0%

Length

2024-01-10T06:07:46.754157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:46.829845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-05-27 62
100.0%

Interactions

2024-01-10T06:07:41.915660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:38.259285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.031044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.605733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.246526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.792767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.326297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.992474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:38.332254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.123688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.685308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.325683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.865446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.412662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:42.060523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:38.404920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.206472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.762462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.402375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.947627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.515036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:42.135145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:38.729604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.292532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.850027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.480858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.023411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.595516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:42.211935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:38.810371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.380698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.013654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.567865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.102858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.683548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:42.273515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:38.879633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.454966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.095344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.642000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.176563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.762860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:42.352493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:38.961094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:39.540897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.176864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:40.725715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.264159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:41.848145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:07:46.890120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
저수지명읍면리준공년도수혜면적(ha)총저수량(천ton)유효저수량(ton)장(m)폭(m)고(m)
저수지명1.0000.9720.9931.0001.0001.0001.0000.9521.0000.987
읍면0.9721.0001.0000.4590.0000.0000.0000.0000.4750.000
리0.9931.0001.0000.8930.4330.0000.0000.0000.7730.000
준공년도1.0000.4590.8931.0000.8280.7420.7420.6870.4470.602
수혜면적(ha)1.0000.0000.4330.8281.0000.9470.9470.7060.5930.682
총저수량(천ton)1.0000.0000.0000.7420.9471.0001.0000.6380.7120.875
유효저수량(ton)1.0000.0000.0000.7420.9471.0001.0000.6380.7120.875
장(m)0.9520.0000.0000.6870.7060.6380.6381.0000.2780.403
폭(m)1.0000.4750.7730.4470.5930.7120.7120.2781.0000.631
고(m)0.9870.0000.0000.6020.6820.8750.8750.4030.6311.000
2024-01-10T06:07:47.005920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공년도수혜면적(ha)총저수량(천ton)유효저수량(ton)장(m)폭(m)고(m)읍면
준공년도1.0000.2880.2800.2800.2640.2060.6190.000
수혜면적(ha)0.2881.0000.7300.7300.5160.4650.5980.000
총저수량(천ton)0.2800.7301.0001.0000.6610.2650.6610.000
유효저수량(ton)0.2800.7301.0001.0000.6610.2650.6610.000
장(m)0.2640.5160.6610.6611.0000.1630.4330.000
폭(m)0.2060.4650.2650.2650.1631.0000.3310.252
고(m)0.6190.5980.6610.6610.4330.3311.0000.000
읍면0.0000.0000.0000.0000.0000.2520.0001.000

Missing values

2024-01-10T06:07:42.448528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:07:42.616218image/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

저수지명시도명시군명읍면리준공년도수혜면적(ha)총저수량(천ton)유효저수량(ton)장(m)폭(m)고(m)전화번호관리부서데이터기준일자
0방축충청남도청양청양송방19453.010.410.1122.03.03.7041-940-2373청양군청2020-05-27
1사기점충청남도청양청양백천19451.53.713.674.02.54.2041-940-2373청양군청2020-05-27
2안부동충청남도청양청양학당219583.32.882.896.02.03.3041-940-2373청양군청2020-05-27
3여우실충청남도청양청양학당219451.03.463.3665.02.05.3041-940-2373청양군청2020-05-27
4장승1충청남도청양청양장승119611.00.680.6635.03.04.2041-940-2373청양군청2020-05-27
5적누1충청남도청양청양적누119593.08.037.862.04.08.7041-940-2373청양군청2020-05-27
6지푸실충청남도청양청양장승219601.01.271.2338.02.05.4041-940-2373청양군청2020-05-27
7미동충청남도청양운곡미량119561.02.832.7568.01.54.4041-940-2373청양군청2020-05-27
8사라동충청남도청양운곡위라1196610.051.550.064.02.58.0041-940-2373청양군청2020-05-27
9소주골충청남도청양운곡효제119878.07.367.15142.02.510.0041-940-2373청양군청2020-05-27
저수지명시도명시군명읍면리준공년도수혜면적(ha)총저수량(천ton)유효저수량(ton)장(m)폭(m)고(m)전화번호관리부서데이터기준일자
52턱골충청남도청양화성수정19571.01.191.1632.02.03.4041-940-2373청양군청2020-05-27
53용당충청남도청양화성용당19571.41.131.138.02.03.0041-940-2373청양군청2020-05-27
54장계충청남도청양화성장계19451.53.443.3446.01.54.9041-940-2373청양군청2020-05-27
55정자충청남도청양화성산정196425.0105.37102.3270.04.016.0041-940-2373청양군청2020-05-27
56합천충청남도청양화성산정19851.03.193.185.02.014.0041-940-2373청양군청2020-05-27
57느랭이못충청남도청양비봉양사119701.04.314.1860.02.04.3041-940-2373청양군청2020-05-27
58여술충청남도청양비봉양사219451.00.990.9625.02.52.6041-940-2373청양군청2020-05-27
59원동충청남도청양비봉신원119581.56.596.461.02.07.0041-940-2373청양군청2020-05-27
60작은원골충청남도청양비봉신원119552.09.899.692.02.018.3041-940-2373청양군청2020-05-27
61중묵충청남도청양비봉중묵19621.45.835.6678.02.04.3041-940-2373청양군청2020-05-27