Overview

Dataset statistics

Number of variables11
Number of observations49
Missing cells9
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory97.7 B

Variable types

Text2
Categorical2
Numeric7

Dataset

Description용인시 저수지 현황
Author경기도 용인시
URLhttps://www.data.go.kr/data/15044265/fileData.do

Alerts

구조 is highly overall correlated with 유역면적(ha) and 7 other fieldsHigh correlation
사업기간 is highly overall correlated with 체적(㎥) and 1 other fieldsHigh correlation
유역면적(ha) is highly overall correlated with 몽리면적(ha) and 5 other fieldsHigh correlation
몽리면적(ha) is highly overall correlated with 유역면적(ha) and 6 other fieldsHigh correlation
저수량(천㎥) is highly overall correlated with 유역면적(ha) and 6 other fieldsHigh correlation
만수면적(ha) is highly overall correlated with 유역면적(ha) and 5 other fieldsHigh correlation
높이(m) is highly overall correlated with 몽리면적(ha) and 3 other fieldsHigh correlation
길이(m) is highly overall correlated with 유역면적(ha) and 5 other fieldsHigh correlation
체적(㎥) is highly overall correlated with 유역면적(ha) and 7 other fieldsHigh correlation
구조 is highly imbalanced (85.6%)Imbalance
위치 has 1 (2.0%) missing valuesMissing
저수지명 has 1 (2.0%) missing valuesMissing
유역면적(ha) has 1 (2.0%) missing valuesMissing
몽리면적(ha) has 1 (2.0%) missing valuesMissing
저수량(천㎥) has 1 (2.0%) missing valuesMissing
만수면적(ha) has 1 (2.0%) missing valuesMissing
높이(m) has 1 (2.0%) missing valuesMissing
길이(m) has 1 (2.0%) missing valuesMissing
체적(㎥) has 1 (2.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:44:15.589308
Analysis finished2023-12-12 17:44:21.966924
Duration6.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

위치
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Memory size524.0 B
2023-12-13T02:44:22.194894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length14
Mean length12.5
Min length8

Characters and Unicode

Total characters600
Distinct characters71
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

Unique48 ?
Unique (%)100.0%

Sample

1st row역북동 180-6일원
2nd row남동 613일원
3rd row남동 542일원
4th row운학동 316일원
5th row기흥 지곡동 71일원
ValueCountFrequency (%)
남사 12
 
8.6%
이동 10
 
7.1%
원삼 6
 
4.3%
포곡 5
 
3.6%
양지 4
 
2.9%
창리 3
 
2.1%
백암 3
 
2.1%
완장리 3
 
2.1%
학일리 2
 
1.4%
유운리 2
 
1.4%
Other values (79) 90
64.3%
2023-12-13T02:44:22.683967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
92
15.3%
원 55
 
9.2%
일 50
 
8.3%
리 42
 
7.0%
1 28
 
4.7%
- 28
 
4.7%
2 21
 
3.5%
4 20
 
3.3%
3 19
 
3.2%
5 18
 
3.0%
Other values (61) 227
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 322
53.7%
Decimal Number 158
26.3%
Space Separator 92
 
15.3%
Dash Punctuation 28
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 55
17.1%
일 50
15.5%
리 42
13.0%
동 17
 
5.3%
남 15
 
4.7%
사 12
 
3.7%
이 10
 
3.1%
삼 9
 
2.8%
곡 8
 
2.5%
포 5
 
1.6%
Other values (49) 99
30.7%
Decimal Number
ValueCountFrequency (%)
1 28
17.7%
2 21
13.3%
4 20
12.7%
3 19
12.0%
5 18
11.4%
0 14
8.9%
7 12
7.6%
8 11
 
7.0%
6 11
 
7.0%
9 4
 
2.5%
Space Separator
ValueCountFrequency (%)
92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 322
53.7%
Common 278
46.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 55
17.1%
일 50
15.5%
리 42
13.0%
동 17
 
5.3%
남 15
 
4.7%
사 12
 
3.7%
이 10
 
3.1%
삼 9
 
2.8%
곡 8
 
2.5%
포 5
 
1.6%
Other values (49) 99
30.7%
Common
ValueCountFrequency (%)
92
33.1%
1 28
 
10.1%
- 28
 
10.1%
2 21
 
7.6%
4 20
 
7.2%
3 19
 
6.8%
5 18
 
6.5%
0 14
 
5.0%
7 12
 
4.3%
8 11
 
4.0%
Other values (2) 15
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 322
53.7%
ASCII 278
46.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
92
33.1%
1 28
 
10.1%
- 28
 
10.1%
2 21
 
7.6%
4 20
 
7.2%
3 19
 
6.8%
5 18
 
6.5%
0 14
 
5.0%
7 12
 
4.3%
8 11
 
4.0%
Other values (2) 15
 
5.4%
Hangul
ValueCountFrequency (%)
원 55
17.1%
일 50
15.5%
리 42
13.0%
동 17
 
5.3%
남 15
 
4.7%
사 12
 
3.7%
이 10
 
3.1%
삼 9
 
2.8%
곡 8
 
2.5%
포 5
 
1.6%
Other values (49) 99
30.7%

저수지명
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing1
Missing (%)2.0%
Memory size524.0 B
2023-12-13T02:44:23.344856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.6041667
Min length2

Characters and Unicode

Total characters125
Distinct characters65
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

Unique48 ?
Unique (%)100.0%

Sample

1st row관곡
2nd row동진
3rd row신기
4th row장재미
5th row지곡
ValueCountFrequency (%)
관곡 1
 
2.1%
동진 1
 
2.1%
수역 1
 
2.1%
장율 1
 
2.1%
중리 1
 
2.1%
하리 1
 
2.1%
요산 1
 
2.1%
화산 1
 
2.1%
안악골 1
 
2.1%
시미곡 1
 
2.1%
Other values (38) 38
79.2%
2023-12-13T02:44:23.923234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
호 9
 
7.2%
곡 9
 
7.2%
리 7
 
5.6%
동 5
 
4.0%
1 5
 
4.0%
산 4
 
3.2%
아 4
 
3.2%
장 4
 
3.2%
2 4
 
3.2%
방 2
 
1.6%
Other values (55) 72
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114
91.2%
Decimal Number 9
 
7.2%
Close Punctuation 1
 
0.8%
Open Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 9
 
7.9%
곡 9
 
7.9%
리 7
 
6.1%
동 5
 
4.4%
산 4
 
3.5%
아 4
 
3.5%
장 4
 
3.5%
방 2
 
1.8%
완 2
 
1.8%
창 2
 
1.8%
Other values (51) 66
57.9%
Decimal Number
ValueCountFrequency (%)
1 5
55.6%
2 4
44.4%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 114
91.2%
Common 11
 
8.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 9
 
7.9%
곡 9
 
7.9%
리 7
 
6.1%
동 5
 
4.4%
산 4
 
3.5%
아 4
 
3.5%
장 4
 
3.5%
방 2
 
1.8%
완 2
 
1.8%
창 2
 
1.8%
Other values (51) 66
57.9%
Common
ValueCountFrequency (%)
1 5
45.5%
2 4
36.4%
) 1
 
9.1%
( 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 114
91.2%
ASCII 11
 
8.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
호 9
 
7.9%
곡 9
 
7.9%
리 7
 
6.1%
동 5
 
4.4%
산 4
 
3.5%
아 4
 
3.5%
장 4
 
3.5%
방 2
 
1.8%
완 2
 
1.8%
창 2
 
1.8%
Other values (51) 66
57.9%
ASCII
ValueCountFrequency (%)
1 5
45.5%
2 4
36.4%
) 1
 
9.1%
( 1
 
9.1%

사업기간
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Memory size524.0 B
1945-1945
11 
1944-1945
8 
1969-1970
4 
1972-1973
3 
1967-1968
3 
Other values (15)
20 

Length

Max length9
Median length9
Mean length8.8979592
Min length4

Unique

Unique11 ?
Unique (%)22.4%

Sample

1st row1944-1945
2nd row1966-1967
3rd row1967-1968
4th row1945-1945
5th row1969-1970

Common Values

ValueCountFrequency (%)
1945-1945 11
22.4%
1944-1945 8
16.3%
1969-1970 4
 
8.2%
1972-1973 3
 
6.1%
1967-1968 3
 
6.1%
1969-1969 3
 
6.1%
1970-1971 2
 
4.1%
1966-1967 2
 
4.1%
1973-1973 2
 
4.1%
1968-1968 1
 
2.0%
Other values (10) 10
20.4%

Length

2023-12-13T02:44:24.070417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1945-1945 11
22.4%
1944-1945 8
16.3%
1969-1970 4
 
8.2%
1972-1973 3
 
6.1%
1967-1968 3
 
6.1%
1969-1969 3
 
6.1%
1970-1971 2
 
4.1%
1966-1967 2
 
4.1%
1973-1973 2
 
4.1%
1970-1970 1
 
2.0%
Other values (10) 10
20.4%

유역면적(ha)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)95.8%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean97.875
Minimum7
Maximum347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T02:44:24.244160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile17.1
Q143.75
median67.5
Q3130.5
95-th percentile280.1
Maximum347
Range340
Interquartile range (IQR)86.75

Descriptive statistics

Standard deviation80.778822
Coefficient of variation (CV)0.82532641
Kurtosis1.9248262
Mean97.875
Median Absolute Deviation (MAD)34.5
Skewness1.5187759
Sum4698
Variance6525.2181
MonotonicityNot monotonic
2023-12-13T02:44:24.428079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
44 2
 
4.1%
60 2
 
4.1%
49 1
 
2.0%
148 1
 
2.0%
65 1
 
2.0%
129 1
 
2.0%
66 1
 
2.0%
36 1
 
2.0%
103 1
 
2.0%
55 1
 
2.0%
Other values (36) 36
73.5%
ValueCountFrequency (%)
7 1
2.0%
14 1
2.0%
15 1
2.0%
21 1
2.0%
22 1
2.0%
30 1
2.0%
31 1
2.0%
34 1
2.0%
36 1
2.0%
39 1
2.0%
ValueCountFrequency (%)
347 1
2.0%
313 1
2.0%
306 1
2.0%
232 1
2.0%
209 1
2.0%
203 1
2.0%
194 1
2.0%
189 1
2.0%
151 1
2.0%
150 1
2.0%

몽리면적(ha)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)72.9%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean17.458333
Minimum2
Maximum74.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T02:44:24.575089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q17.225
median12.2
Q321.4
95-th percentile48.825
Maximum74.3
Range72.3
Interquartile range (IQR)14.175

Descriptive statistics

Standard deviation15.56574
Coefficient of variation (CV)0.89159371
Kurtosis3.930306
Mean17.458333
Median Absolute Deviation (MAD)7.2
Skewness1.8990831
Sum838
Variance242.29227
MonotonicityNot monotonic
2023-12-13T02:44:24.755846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
7.0 3
 
6.1%
10.0 2
 
4.1%
2.0 2
 
4.1%
20.0 2
 
4.1%
5.0 2
 
4.1%
3.0 2
 
4.1%
13.0 2
 
4.1%
21.3 2
 
4.1%
25.0 2
 
4.1%
11.5 2
 
4.1%
Other values (25) 27
55.1%
ValueCountFrequency (%)
2.0 2
4.1%
3.0 2
4.1%
4.0 1
 
2.0%
4.7 1
 
2.0%
5.0 2
4.1%
5.3 1
 
2.0%
7.0 3
6.1%
7.3 1
 
2.0%
7.9 1
 
2.0%
8.0 2
4.1%
ValueCountFrequency (%)
74.3 1
2.0%
62.8 1
2.0%
49.0 1
2.0%
48.5 1
2.0%
37.3 1
2.0%
34.0 1
2.0%
32.0 1
2.0%
30.0 1
2.0%
25.0 2
4.1%
21.7 2
4.1%

저수량(천㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)75.0%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean44.645833
Minimum2
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T02:44:24.930188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q110.75
median25
Q350.25
95-th percentile175.4
Maximum258
Range256
Interquartile range (IQR)39.5

Descriptive statistics

Standard deviation57.111496
Coefficient of variation (CV)1.2792122
Kurtosis5.2733435
Mean44.645833
Median Absolute Deviation (MAD)15.5
Skewness2.3109345
Sum2143
Variance3261.723
MonotonicityNot monotonic
2023-12-13T02:44:25.071787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
10 3
 
6.1%
11 3
 
6.1%
7 3
 
6.1%
26 2
 
4.1%
5 2
 
4.1%
86 2
 
4.1%
25 2
 
4.1%
30 2
 
4.1%
16 2
 
4.1%
195 1
 
2.0%
Other values (26) 26
53.1%
ValueCountFrequency (%)
2 1
 
2.0%
4 1
 
2.0%
5 2
4.1%
6 1
 
2.0%
7 3
6.1%
9 1
 
2.0%
10 3
6.1%
11 3
6.1%
12 1
 
2.0%
15 1
 
2.0%
ValueCountFrequency (%)
258 1
2.0%
221 1
2.0%
195 1
2.0%
139 1
2.0%
128 1
2.0%
98 1
2.0%
86 2
4.1%
84 1
2.0%
69 1
2.0%
56 1
2.0%

만수면적(ha)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)87.5%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean1.7545833
Minimum0.25
Maximum9.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T02:44:25.252811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile0.267
Q10.715
median1.18
Q32.1525
95-th percentile4.364
Maximum9.04
Range8.79
Interquartile range (IQR)1.4375

Descriptive statistics

Standard deviation1.7524378
Coefficient of variation (CV)0.99877716
Kurtosis7.7642917
Mean1.7545833
Median Absolute Deviation (MAD)0.68
Skewness2.5476878
Sum84.22
Variance3.0710381
MonotonicityNot monotonic
2023-12-13T02:44:25.399920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1.0 3
 
6.1%
1.22 2
 
4.1%
0.98 2
 
4.1%
0.25 2
 
4.1%
3.15 2
 
4.1%
1.29 1
 
2.0%
2.0 1
 
2.0%
2.41 1
 
2.0%
2.34 1
 
2.0%
0.28 1
 
2.0%
Other values (32) 32
65.3%
ValueCountFrequency (%)
0.25 2
4.1%
0.26 1
2.0%
0.28 1
2.0%
0.34 1
2.0%
0.39 1
2.0%
0.4 1
2.0%
0.45 1
2.0%
0.55 1
2.0%
0.64 1
2.0%
0.68 1
2.0%
ValueCountFrequency (%)
9.04 1
2.0%
7.76 1
2.0%
4.56 1
2.0%
4.0 1
2.0%
3.75 1
2.0%
3.15 2
4.1%
2.91 1
2.0%
2.74 1
2.0%
2.57 1
2.0%
2.41 1
2.0%

구조
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size524.0 B
흙댐
48 
<NA>
 
1

Length

Max length4
Median length2
Mean length2.0408163
Min length2

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
흙댐 48
98.0%
<NA> 1
 
2.0%

Length

2023-12-13T02:44:25.567940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:44:25.683677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
흙댐 48
98.0%
na 1
 
2.0%

높이(m)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)18.8%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean9
Minimum5
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T02:44:25.774807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18
median9
Q310.25
95-th percentile12
Maximum15
Range10
Interquartile range (IQR)2.25

Descriptive statistics

Standard deviation2.3610311
Coefficient of variation (CV)0.26233679
Kurtosis0.27918367
Mean9
Median Absolute Deviation (MAD)1
Skewness0.33399932
Sum432
Variance5.5744681
MonotonicityNot monotonic
2023-12-13T02:44:25.872619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
8 12
24.5%
10 9
18.4%
11 6
12.2%
5 5
10.2%
7 5
10.2%
9 4
 
8.2%
12 4
 
8.2%
15 2
 
4.1%
6 1
 
2.0%
(Missing) 1
 
2.0%
ValueCountFrequency (%)
5 5
10.2%
6 1
 
2.0%
7 5
10.2%
8 12
24.5%
9 4
 
8.2%
10 9
18.4%
11 6
12.2%
12 4
 
8.2%
15 2
 
4.1%
ValueCountFrequency (%)
15 2
 
4.1%
12 4
 
8.2%
11 6
12.2%
10 9
18.4%
9 4
 
8.2%
8 12
24.5%
7 5
10.2%
6 1
 
2.0%
5 5
10.2%

길이(m)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)72.9%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean107.97917
Minimum27
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T02:44:25.999706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile50
Q179.75
median104
Q3130
95-th percentile162.6
Maximum230
Range203
Interquartile range (IQR)50.25

Descriptive statistics

Standard deviation44.34799
Coefficient of variation (CV)0.41070877
Kurtosis0.66514436
Mean107.97917
Median Absolute Deviation (MAD)26
Skewness0.62423636
Sum5183
Variance1966.7442
MonotonicityNot monotonic
2023-12-13T02:44:26.145383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
130 5
 
10.2%
80 3
 
6.1%
100 2
 
4.1%
50 2
 
4.1%
82 2
 
4.1%
121 2
 
4.1%
230 2
 
4.1%
159 2
 
4.1%
55 2
 
4.1%
127 1
 
2.0%
Other values (25) 25
51.0%
ValueCountFrequency (%)
27 1
2.0%
40 1
2.0%
50 2
4.1%
54 1
2.0%
55 2
4.1%
60 1
2.0%
62 1
2.0%
65 1
2.0%
75 1
2.0%
79 1
2.0%
ValueCountFrequency (%)
230 2
 
4.1%
164 1
 
2.0%
160 1
 
2.0%
159 2
 
4.1%
155 1
 
2.0%
152 1
 
2.0%
150 1
 
2.0%
140 1
 
2.0%
135 1
 
2.0%
130 5
10.2%

체적(㎥)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct47
Distinct (%)97.9%
Missing1
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean6761.6458
Minimum704
Maximum47895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size573.0 B
2023-12-13T02:44:26.310528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum704
5-th percentile969.9
Q13321.75
median5017
Q37124.25
95-th percentile15213.05
Maximum47895
Range47191
Interquartile range (IQR)3802.5

Descriptive statistics

Standard deviation7907.5741
Coefficient of variation (CV)1.1694748
Kurtosis17.276601
Mean6761.6458
Median Absolute Deviation (MAD)2034
Skewness3.8522375
Sum324559
Variance62529728
MonotonicityNot monotonic
2023-12-13T02:44:26.482084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
2112 2
 
4.1%
1984 1
 
2.0%
3952 1
 
2.0%
4953 1
 
2.0%
2502 1
 
2.0%
4915 1
 
2.0%
4163 1
 
2.0%
1410 1
 
2.0%
6105 1
 
2.0%
5405 1
 
2.0%
Other values (37) 37
75.5%
ValueCountFrequency (%)
704 1
2.0%
825 1
2.0%
937 1
2.0%
1031 1
2.0%
1251 1
2.0%
1410 1
2.0%
1984 1
2.0%
2112 2
4.1%
2502 1
2.0%
3016 1
2.0%
ValueCountFrequency (%)
47895 1
2.0%
32343 1
2.0%
16675 1
2.0%
12498 1
2.0%
11680 1
2.0%
9937 1
2.0%
9924 1
2.0%
8618 1
2.0%
8276 1
2.0%
8133 1
2.0%

Interactions

2023-12-13T02:44:20.749753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:16.077138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:17.172446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:17.887774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.652925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.313762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:20.039082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:20.852202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:16.503148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:17.284083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.019305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.736849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.416518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:20.164796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:20.956356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:16.614676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:17.404849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.140382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.823514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.513141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:20.274486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:21.056561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:16.720114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:17.510064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.234782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.927347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.614854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:20.368978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:21.143954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:16.819069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:17.604313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.334586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.006138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.699703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:20.452683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:21.251508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:16.908566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:17.692598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.453660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.105317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.793354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:20.546267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:21.352087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:17.036748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:17.799161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:18.569539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.213720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:19.930653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:44:20.646343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:44:26.612609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위치저수지명사업기간유역면적(ha)몽리면적(ha)저수량(천㎥)만수면적(ha)높이(m)길이(m)체적(㎥)
위치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
저수지명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사업기간1.0001.0001.0000.3580.4860.7980.8140.6840.6250.944
유역면적(ha)1.0001.0000.3581.0000.8190.9150.7990.6040.3950.648
몽리면적(ha)1.0001.0000.4860.8191.0000.8650.8080.3220.4170.696
저수량(천㎥)1.0001.0000.7980.9150.8651.0000.8680.6510.5860.906
만수면적(ha)1.0001.0000.8140.7990.8080.8681.0000.0000.5640.715
높이(m)1.0001.0000.6840.6040.3220.6510.0001.0000.5530.656
길이(m)1.0001.0000.6250.3950.4170.5860.5640.5531.0000.683
체적(㎥)1.0001.0000.9440.6480.6960.9060.7150.6560.6831.000
2023-12-13T02:44:26.754922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구조사업기간
구조1.0001.000
사업기간1.0001.000
2023-12-13T02:44:26.848019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
유역면적(ha)몽리면적(ha)저수량(천㎥)만수면적(ha)높이(m)길이(m)체적(㎥)사업기간구조
유역면적(ha)1.0000.7000.6480.6980.3900.6300.6070.1261.000
몽리면적(ha)0.7001.0000.7910.7910.5090.6520.7450.1691.000
저수량(천㎥)0.6480.7911.0000.8480.6470.7120.8150.3951.000
만수면적(ha)0.6980.7910.8481.0000.4490.8040.7240.4451.000
높이(m)0.3900.5090.6470.4491.0000.3470.8120.3841.000
길이(m)0.6300.6520.7120.8040.3471.0000.7510.2571.000
체적(㎥)0.6070.7450.8150.7240.8120.7511.0000.6661.000
사업기간0.1260.1690.3950.4450.3840.2570.6661.0001.000
구조1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T02:44:21.483716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:44:21.650148image/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.
2023-12-13T02:44:21.836020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

위치저수지명사업기간유역면적(ha)몽리면적(ha)저수량(천㎥)만수면적(ha)구조높이(m)길이(m)체적(㎥)
0역북동 180-6일원관곡1944-1945394.0181.22흙댐51271984
1남동 613일원동진1966-1967648.0191.0흙댐81303952
2남동 542일원신기1967-19688010.0563.15흙댐111006022
3운학동 316일원장재미1945-1945302.060.4흙댐560937
4기흥 지곡동 71일원지곡1969-197010734.01283.15흙댐101306825
5포곡 마성리 402일원성저1945-194511820.0261.29흙댐10875437
6포곡 마성리 578-3일원마가1971-1971715.040.34흙댐7501251
7포곡 유운리 409-2일원소운1970-1970143.0120.68흙댐5501031
8포곡 유운리 455-2일원유실1967-19682097.0251.32흙댐81306032
9포곡 신원리 590-1일원신원1969-196919413.0300.83흙댐11825840
위치저수지명사업기간유역면적(ha)몽리면적(ha)저수량(천㎥)만수면적(ha)구조높이(m)길이(m)체적(㎥)
39원삼 학일리 104일원학일1호1969-197031348.5844.0흙댐101357087
40원삼 학일리 산37-3일원학일2호1981-198115149.02583.75흙댐1523032343
41백암 고안리 산153일원아곡1945-1945729.0301.13흙댐81304992
42백암 근삼리 422일원가리산1944-194511118.0221.22흙댐101407350
43백암 가창리 374일원구백암1945-1945348.090.72흙댐10804200
44양지 남곡리 37-2일원남곡1973-197320311.5110.7흙댐8552112
45양지 평창리 537-1일원평창1944-194523225.0692.05흙댐91607236
46양지 대대리 554-1일원대대1975-197630621.31957.76흙댐1023016675
47양지 정수리 8-3일원정수리1991-19914420.0441.0흙댐1215547895
48<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>