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
Categorical5
Numeric7
DateTime1

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 21:07:47.874567
Analysis finished2024-01-09 21:07:52.682640
Duration4.81 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
2024-01-10T06:07:52.829136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3387097
Min length2

Characters and Unicode

Total characters145
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%
무술 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%
봉암 1
 
1.6%
수단 1
 
1.6%
Other values (51) 51
82.3%
2024-01-10T06:07:53.244424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8
 
5.5%
골 5
 
3.4%
실 4
 
2.8%
장 4
 
2.8%
소 4
 
2.8%
원 3
 
2.1%
농 3
 
2.1%
지 3
 
2.1%
상 3
 
2.1%
산 3
 
2.1%
Other values (74) 105
72.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 138
95.2%
Decimal Number 7
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8
 
5.8%
골 5
 
3.6%
실 4
 
2.9%
장 4
 
2.9%
소 4
 
2.9%
원 3
 
2.2%
농 3
 
2.2%
지 3
 
2.2%
상 3
 
2.2%
산 3
 
2.2%
Other values (71) 98
71.0%
Decimal Number
ValueCountFrequency (%)
1 3
42.9%
2 3
42.9%
3 1
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 138
95.2%
Common 7
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8
 
5.8%
골 5
 
3.6%
실 4
 
2.9%
장 4
 
2.9%
소 4
 
2.9%
원 3
 
2.2%
농 3
 
2.2%
지 3
 
2.2%
상 3
 
2.2%
산 3
 
2.2%
Other values (71) 98
71.0%
Common
ValueCountFrequency (%)
1 3
42.9%
2 3
42.9%
3 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 138
95.2%
ASCII 7
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8
 
5.8%
골 5
 
3.6%
실 4
 
2.9%
장 4
 
2.9%
소 4
 
2.9%
원 3
 
2.2%
농 3
 
2.2%
지 3
 
2.2%
상 3
 
2.2%
산 3
 
2.2%
Other values (71) 98
71.0%
ASCII
ValueCountFrequency (%)
1 3
42.9%
2 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

2024-01-10T06:07:53.501094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:53.641413image/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

2024-01-10T06:07:53.765153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:53.871568image/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
남양
11 
운곡
10 
대치
10 
화성
9 
청양
7 
Other values (5)
15 

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 (%)
남양 11
17.7%
운곡 10
16.1%
대치 10
16.1%
화성 9
14.5%
청양 7
11.3%
비봉 5
8.1%
정산 3
 
4.8%
목면 3
 
4.8%
청남 3
 
4.8%
장평 1
 
1.6%

Length

2024-01-10T06:07:53.958934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:54.083539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남양 11
17.7%
운곡 10
16.1%
대치 10
16.1%
화성 9
14.5%
청양 7
11.3%
비봉 5
8.1%
정산 3
 
4.8%
목면 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
2024-01-10T06:07:54.290010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.5483871
Min length2

Characters and Unicode

Total characters158
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%
용마2 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%
상갑 2
 
3.2%
Other values (40) 40
64.5%
2024-01-10T06:07:54.621346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 17
 
10.8%
2 17
 
10.8%
정 7
 
4.4%
산 5
 
3.2%
신 5
 
3.2%
용 4
 
2.5%
직 4
 
2.5%
양 4
 
2.5%
장 4
 
2.5%
농 3
 
1.9%
Other values (51) 88
55.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 124
78.5%
Decimal Number 34
 
21.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 7
 
5.6%
산 5
 
4.0%
신 5
 
4.0%
용 4
 
3.2%
직 4
 
3.2%
양 4
 
3.2%
장 4
 
3.2%
농 3
 
2.4%
왕 3
 
2.4%
백 3
 
2.4%
Other values (49) 82
66.1%
Decimal Number
ValueCountFrequency (%)
1 17
50.0%
2 17
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 124
78.5%
Common 34
 
21.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 7
 
5.6%
산 5
 
4.0%
신 5
 
4.0%
용 4
 
3.2%
직 4
 
3.2%
양 4
 
3.2%
장 4
 
3.2%
농 3
 
2.4%
왕 3
 
2.4%
백 3
 
2.4%
Other values (49) 82
66.1%
Common
ValueCountFrequency (%)
1 17
50.0%
2 17
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 124
78.5%
ASCII 34
 
21.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 17
50.0%
2 17
50.0%
Hangul
ValueCountFrequency (%)
정 7
 
5.6%
산 5
 
4.0%
신 5
 
4.0%
용 4
 
3.2%
직 4
 
3.2%
양 4
 
3.2%
장 4
 
3.2%
농 3
 
2.4%
왕 3
 
2.4%
백 3
 
2.4%
Other values (49) 82
66.1%

준공년도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1962.629
Minimum1945
Maximum2016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:54.765955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1945
5-th percentile1945
Q11945
median1958.5
Q31971.75
95-th percentile2001.75
Maximum2016
Range71
Interquartile range (IQR)26.75

Descriptive statistics

Standard deviation18.716377
Coefficient of variation (CV)0.0095363801
Kurtosis0.67324923
Mean1962.629
Median Absolute Deviation (MAD)13.5
Skewness1.111085
Sum121683
Variance350.30275
MonotonicityNot monotonic
2024-01-10T06:07:54.902734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1945 21
33.9%
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 (18) 19
30.6%
ValueCountFrequency (%)
1945 21
33.9%
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 (%)
2016 1
 
1.6%
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%

수혜면적(ha)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5032258
Minimum0.5
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:55.044500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11
median1.5
Q34.2
95-th percentile21.9
Maximum32
Range31.5
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation6.8985379
Coefficient of variation (CV)1.5319103
Kurtosis7.0029877
Mean4.5032258
Median Absolute Deviation (MAD)0.5
Skewness2.7220256
Sum279.2
Variance47.589825
MonotonicityNot monotonic
2024-01-10T06:07:55.180747image/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%
1.1 2
 
3.2%
4.2 2
 
3.2%
6.0 2
 
3.2%
10.0 2
 
3.2%
0.5 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%
22.0 1
1.6%
20.0 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%
Mean15.342581
Minimum0.58
Maximum147.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:55.327099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.58
5-th percentile0.997
Q12.955
median4.32
Q310.3225
95-th percentile86.726
Maximum147.5
Range146.92
Interquartile range (IQR)7.3675

Descriptive statistics

Standard deviation29.036977
Coefficient of variation (CV)1.8925745
Kurtosis10.573948
Mean15.342581
Median Absolute Deviation (MAD)2.86
Skewness3.248876
Sum951.24
Variance843.14604
MonotonicityNot monotonic
2024-01-10T06:07:55.478066image/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%
7.0 1
 
1.6%
2.06 1
 
1.6%
10.09 1
 
1.6%
25.44 1
 
1.6%
21.63 1
 
1.6%
7.73 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%
1.6 1
1.6%
1.98 1
1.6%
ValueCountFrequency (%)
147.5 1
1.6%
123.47 1
1.6%
105.37 1
1.6%
88.58 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%
21.63 1
1.6%

유효저수량(ton)
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)91.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.893387
Minimum0.56
Maximum143.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:55.630886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.56
5-th percentile0.967
Q12.8725
median4.19
Q310.025
95-th percentile84.2
Maximum143.2
Range142.64
Interquartile range (IQR)7.1525

Descriptive statistics

Standard deviation28.191853
Coefficient of variation (CV)1.8929108
Kurtosis10.57243
Mean14.893387
Median Absolute Deviation (MAD)2.85
Skewness3.2486058
Sum923.39
Variance794.78058
MonotonicityNot monotonic
2024-01-10T06:07:55.786181image/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%
6.8 1
 
1.6%
2.0 1
 
1.6%
9.8 1
 
1.6%
24.7 1
 
1.6%
21.0 1
 
1.6%
7.5 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%
1.4 1
1.6%
1.92 1
1.6%
ValueCountFrequency (%)
143.2 1
1.6%
119.87 1
1.6%
102.3 1
1.6%
86.0 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%
21.0 1
1.6%

장(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.741935
Minimum18
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:55.915081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile35.05
Q159.25
median73.5
Q391.75
95-th percentile123.9
Maximum270
Range252
Interquartile range (IQR)32.5

Descriptive statistics

Standard deviation37.601964
Coefficient of variation (CV)0.48997936
Kurtosis10.612592
Mean76.741935
Median Absolute Deviation (MAD)16.5
Skewness2.4069279
Sum4758
Variance1413.9077
MonotonicityNot monotonic
2024-01-10T06:07:56.031396image/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%
78.0 2
 
3.2%
38.0 2
 
3.2%
104.0 2
 
3.2%
73.0 2
 
3.2%
37.0 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%
124.0 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%

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
2024-01-10T06:07:56.384998image/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
2024-01-10T06:07:56.476480image/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 

Distinct43
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3983871
Minimum2.6
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-01-10T06:07:56.621079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.01
Q14.225
median5.6
Q38.225
95-th percentile18.185
Maximum23.8
Range21.2
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.9081228
Coefficient of variation (CV)0.66340443
Kurtosis3.1650405
Mean7.3983871
Median Absolute Deviation (MAD)1.5
Skewness1.8521991
Sum458.7
Variance24.089669
MonotonicityNot monotonic
2024-01-10T06:07:56.743985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
7.0 5
 
8.1%
4.2 4
 
6.5%
5.0 3
 
4.8%
5.6 3
 
4.8%
3.7 2
 
3.2%
3.2 2
 
3.2%
23.8 2
 
3.2%
16.0 2
 
3.2%
3.0 2
 
3.2%
8.0 2
 
3.2%
Other values (33) 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 2
3.2%
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

2024-01-10T06:07:56.885104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:56.993494image/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

2024-01-10T06:07:57.100637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T06:07:57.185903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
청양군청 62
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
Minimum2023-06-09 00:00:00
Maximum2023-06-09 00:00:00
2024-01-10T06:07:57.259923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:57.341166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T06:07:51.897825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.269694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.817240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.403545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.975049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:50.769826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.288175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.968992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.348325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.891798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.483758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:50.052936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:50.847654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.382032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:52.047980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.422718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.969490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.579331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:50.361055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:50.925884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.477005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:52.126517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.504769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.061495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.655997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:50.441590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.011345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.580157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:52.200118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.593177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.160986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.744284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:50.537082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.084545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.673677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:52.262519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.669368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.248152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.819874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:50.621991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.151911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.746690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:52.343387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:48.748432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.335879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:49.907451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:50.702723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.225996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T06:07:51.828090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T06:07:57.413372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
저수지명읍면리준공년도수혜면적(ha)총저수량(천ton)유효저수량(ton)장(m)폭(m)고(m)
저수지명1.0000.9740.9931.0001.0001.0001.0000.9521.0000.987
읍면0.9741.0001.0000.4400.0000.0000.0000.0000.4850.000
리0.9931.0001.0000.8760.0000.0000.0000.0000.7730.000
준공년도1.0000.4400.8761.0000.4450.6470.6470.4530.5030.870
수혜면적(ha)1.0000.0000.0000.4451.0000.8970.8970.8660.7570.668
총저수량(천ton)1.0000.0000.0000.6470.8971.0001.0000.6480.6740.685
유효저수량(ton)1.0000.0000.0000.6470.8971.0001.0000.6480.6740.685
장(m)0.9520.0000.0000.4530.8660.6480.6481.0000.3570.414
폭(m)1.0000.4850.7730.5030.7570.6740.6740.3571.0000.486
고(m)0.9870.0000.0000.8700.6680.6850.6850.4140.4861.000
2024-01-10T06:07:57.524574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
준공년도수혜면적(ha)총저수량(천ton)유효저수량(ton)장(m)폭(m)고(m)읍면
준공년도1.0000.3560.2670.2670.3150.2820.6650.000
수혜면적(ha)0.3561.0000.6600.6600.5290.4670.6090.000
총저수량(천ton)0.2670.6601.0001.0000.6080.1830.5960.000
유효저수량(ton)0.2670.6601.0001.0000.6080.1830.5960.000
장(m)0.3150.5290.6080.6081.0000.1840.4480.000
폭(m)0.2820.4670.1830.1830.1841.0000.3440.259
고(m)0.6650.6090.5960.5960.4480.3441.0000.000
읍면0.0000.0000.0000.0000.0000.2590.0001.000

Missing values

2024-01-10T06:07:52.441027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T06:07:52.605927image/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
0방축충청남도청양청양송방19453.010.410.1122.03.03.7041-940-2373청양군청2023-06-09
1사기점충청남도청양청양백천19451.53.713.674.02.54.2041-940-2373청양군청2023-06-09
2안부동충청남도청양청양학당219583.32.882.896.02.03.3041-940-2373청양군청2023-06-09
3여우실충청남도청양청양학당219451.03.463.3665.02.05.3041-940-2373청양군청2023-06-09
4장승1충청남도청양청양장승119611.00.680.6635.03.04.2041-940-2373청양군청2023-06-09
5적누1충청남도청양청양적누119593.08.037.862.04.08.7041-940-2373청양군청2023-06-09
6지푸실충청남도청양청양장승219601.01.271.2338.02.05.4041-940-2373청양군청2023-06-09
7미동충청남도청양운곡미량119561.02.832.7568.01.54.4041-940-2373청양군청2023-06-09
8사라동충청남도청양운곡위라1196610.051.550.064.02.58.0041-940-2373청양군청2023-06-09
9소주골충청남도청양운곡효제119878.07.367.15142.02.510.0041-940-2373청양군청2023-06-09
저수지명시도명시군명읍면리준공년도수혜면적(ha)총저수량(천ton)유효저수량(ton)장(m)폭(m)고(m)전화번호관리부서데이터기준일자
52턱골충청남도청양화성수정19571.01.191.1632.02.03.4041-940-2373청양군청2023-06-09
53용당충청남도청양화성용당19571.41.131.138.02.03.0041-940-2373청양군청2023-06-09
54장계충청남도청양화성장계19451.53.443.3446.01.54.9041-940-2373청양군청2023-06-09
55정자충청남도청양화성산정196425.0105.37102.3270.04.016.0041-940-2373청양군청2023-06-09
56합천충청남도청양화성산정19851.03.193.185.02.014.0041-940-2373청양군청2023-06-09
57느랭이못충청남도청양비봉양사119701.04.314.1860.02.04.3041-940-2373청양군청2023-06-09
58여술충청남도청양비봉양사219451.00.990.9625.02.52.6041-940-2373청양군청2023-06-09
59원동충청남도청양비봉신원119581.56.596.461.02.07.0041-940-2373청양군청2023-06-09
60작은원골충청남도청양비봉신원119552.09.899.692.02.018.3041-940-2373청양군청2023-06-09
61중묵충청남도청양비봉중묵19621.45.835.6678.02.04.3041-940-2373청양군청2023-06-09