Overview

Dataset statistics

Number of variables12
Number of observations3438
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory345.9 KiB
Average record size in memory103.0 B

Variable types

Text2
Numeric7
Categorical2
Boolean1

Dataset

Description공사관리 저수지의 수원공타입과 시설명 유효저수량 및 유효면적 수혜면적 및 위치정보를 포함하는 자료임 수원공타입(주-M)(보조-S)(부속-I)
URLhttps://www.data.go.kr/data/15117184/fileData.do

Alerts

관리구분 (공사-시도) has constant value ""Constant
사용여부 has constant value ""Constant
유역면적(ha) is highly overall correlated with 수혜면적(ha) and 1 other fieldsHigh correlation
수혜면적(ha) is highly overall correlated with 유역면적(ha) and 1 other fieldsHigh correlation
유효저수량(천m3) is highly overall correlated with 유역면적(ha) and 1 other fieldsHigh correlation
지사코드 is highly overall correlated with 주소코드 and 1 other fieldsHigh correlation
주소코드 is highly overall correlated with 지사코드 and 1 other fieldsHigh correlation
시군코드 is highly overall correlated with 지사코드 and 1 other fieldsHigh correlation
유역면적(ha) is highly skewed (γ1 = 26.43367175)Skewed
수혜면적(ha) is highly skewed (γ1 = 30.13258614)Skewed
유효저수량(천m3) is highly skewed (γ1 = 31.64986601)Skewed
표준코드 has unique valuesUnique
통계코드 has 120 (3.5%) zerosZeros
수혜면적(ha) has 1537 (44.7%) zerosZeros

Reproduction

Analysis started2023-12-12 06:12:36.968606
Analysis finished2023-12-12 06:12:44.509249
Duration7.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

표준코드
Text

UNIQUE 

Distinct3438
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
2023-12-12T15:12:44.756634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters34380
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3438 ?
Unique (%)100.0%

Sample

1st row2671010056
2nd row2671010067
3rd row2671010085
4th row2671010097
5th row2671010098
ValueCountFrequency (%)
2671010056 1
 
< 0.1%
4713010167 1
 
< 0.1%
4715010143 1
 
< 0.1%
4713010183 1
 
< 0.1%
4713010184 1
 
< 0.1%
4713010186 1
 
< 0.1%
4713010188 1
 
< 0.1%
4713010198 1
 
< 0.1%
4713010200 1
 
< 0.1%
4713010202 1
 
< 0.1%
Other values (3428) 3428
99.7%
2023-12-12T15:12:45.216204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9843
28.6%
1 6593
19.2%
4 4598
13.4%
7 2885
 
8.4%
8 2309
 
6.7%
2 2044
 
5.9%
3 1800
 
5.2%
6 1794
 
5.2%
5 1448
 
4.2%
9 1064
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34378
> 99.9%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9843
28.6%
1 6593
19.2%
4 4598
13.4%
7 2885
 
8.4%
8 2309
 
6.7%
2 2044
 
5.9%
3 1800
 
5.2%
6 1794
 
5.2%
5 1448
 
4.2%
9 1064
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
Z 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34378
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9843
28.6%
1 6593
19.2%
4 4598
13.4%
7 2885
 
8.4%
8 2309
 
6.7%
2 2044
 
5.9%
3 1800
 
5.2%
6 1794
 
5.2%
5 1448
 
4.2%
9 1064
 
3.1%
Latin
ValueCountFrequency (%)
Z 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9843
28.6%
1 6593
19.2%
4 4598
13.4%
7 2885
 
8.4%
8 2309
 
6.7%
2 2044
 
5.9%
3 1800
 
5.2%
6 1794
 
5.2%
5 1448
 
4.2%
9 1064
 
3.1%

통계코드
Real number (ℝ)

ZEROS 

Distinct3319
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9030143 × 109
Minimum0
Maximum9.999 × 109
Zeros120
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size30.3 KiB
2023-12-12T15:12:45.397212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9430001 × 109
Q11.9450082 × 109
median1.954 × 109
Q31.968002 × 109
95-th percentile1.9980007 × 109
Maximum9.999 × 109
Range9.999 × 109
Interquartile range (IQR)22993844

Descriptive statistics

Standard deviation4.4873783 × 108
Coefficient of variation (CV)0.23580371
Kurtosis103.77272
Mean1.9030143 × 109
Median Absolute Deviation (MAD)8992970.5
Skewness2.9135412
Sum6.5425631 × 1012
Variance2.0136564 × 1017
MonotonicityNot monotonic
2023-12-12T15:12:45.568828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120
 
3.5%
1958000002 1
 
< 0.1%
1945008865 1
 
< 0.1%
1971000421 1
 
< 0.1%
1965000416 1
 
< 0.1%
1965000417 1
 
< 0.1%
1956000135 1
 
< 0.1%
1957000146 1
 
< 0.1%
1957000147 1
 
< 0.1%
1957000148 1
 
< 0.1%
Other values (3309) 3309
96.2%
ValueCountFrequency (%)
0 120
3.5%
1900000155 1
 
< 0.1%
1900000230 1
 
< 0.1%
1900000741 1
 
< 0.1%
1915000007 1
 
< 0.1%
1920000001 1
 
< 0.1%
1924000011 1
 
< 0.1%
1924000012 1
 
< 0.1%
1924000013 1
 
< 0.1%
1924000014 1
 
< 0.1%
ValueCountFrequency (%)
9999000041 1
< 0.1%
9999000011 1
< 0.1%
9999000009 1
< 0.1%
4886010225 1
< 0.1%
4784010174 1
< 0.1%
4713012000 1
< 0.1%
4691010215 1
< 0.1%
4689010050 1
< 0.1%
4689010049 1
< 0.1%
2915510001 1
< 0.1%

수원공타입(주-M)(보조-S)(부속-I)
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
M
1907 
S
1531 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
M 1907
55.5%
S 1531
44.5%

Length

2023-12-12T15:12:45.736632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:12:45.848603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 1907
55.5%
s 1531
44.5%

시설명
Text

Distinct2542
Distinct (%)73.9%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
2023-12-12T15:12:46.312307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.1948807
Min length1

Characters and Unicode

Total characters7546
Distinct characters387
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2070 ?
Unique (%)60.2%

Sample

1st row용천
2nd row병산
3rd row송정
4th row안평
5th row임기
ValueCountFrequency (%)
대곡 12
 
0.3%
연화 12
 
0.3%
학동 12
 
0.3%
백운 9
 
0.3%
가곡 8
 
0.2%
신흥 8
 
0.2%
용암 8
 
0.2%
방축 8
 
0.2%
대동 8
 
0.2%
용산 8
 
0.2%
Other values (2531) 3345
97.3%
2023-12-12T15:12:46.905913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
곡 273
 
3.6%
산 261
 
3.5%
동 251
 
3.3%
신 161
 
2.1%
대 158
 
2.1%
정 139
 
1.8%
천 137
 
1.8%
지 116
 
1.5%
용 113
 
1.5%
성 112
 
1.5%
Other values (377) 5825
77.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7261
96.2%
Decimal Number 175
 
2.3%
Close Punctuation 52
 
0.7%
Open Punctuation 52
 
0.7%
Space Separator 5
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
곡 273
 
3.8%
산 261
 
3.6%
동 251
 
3.5%
신 161
 
2.2%
대 158
 
2.2%
정 139
 
1.9%
천 137
 
1.9%
지 116
 
1.6%
용 113
 
1.6%
성 112
 
1.5%
Other values (367) 5540
76.3%
Decimal Number
ValueCountFrequency (%)
1 82
46.9%
2 79
45.1%
3 9
 
5.1%
4 3
 
1.7%
5 1
 
0.6%
6 1
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7261
96.2%
Common 285
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
곡 273
 
3.8%
산 261
 
3.6%
동 251
 
3.5%
신 161
 
2.2%
대 158
 
2.2%
정 139
 
1.9%
천 137
 
1.9%
지 116
 
1.6%
용 113
 
1.6%
성 112
 
1.5%
Other values (367) 5540
76.3%
Common
ValueCountFrequency (%)
1 82
28.8%
2 79
27.7%
) 52
18.2%
( 52
18.2%
3 9
 
3.2%
5
 
1.8%
4 3
 
1.1%
5 1
 
0.4%
6 1
 
0.4%
- 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7261
96.2%
ASCII 285
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
곡 273
 
3.8%
산 261
 
3.6%
동 251
 
3.5%
신 161
 
2.2%
대 158
 
2.2%
정 139
 
1.9%
천 137
 
1.9%
지 116
 
1.6%
용 113
 
1.6%
성 112
 
1.5%
Other values (367) 5540
76.3%
ASCII
ValueCountFrequency (%)
1 82
28.8%
2 79
27.7%
) 52
18.2%
( 52
18.2%
3 9
 
3.2%
5
 
1.8%
4 3
 
1.1%
5 1
 
0.4%
6 1
 
0.4%
- 1
 
0.4%

유역면적(ha)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct819
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean439.33944
Minimum0
Maximum76200
Zeros4
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size30.3 KiB
2023-12-12T15:12:47.065585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q135
median110
Q3365
95-th percentile1601.2
Maximum76200
Range76200
Interquartile range (IQR)330

Descriptive statistics

Standard deviation1789.1999
Coefficient of variation (CV)4.0724773
Kurtosis1000.2136
Mean439.33944
Median Absolute Deviation (MAD)92
Skewness26.433672
Sum1510449
Variance3201236.3
MonotonicityNot monotonic
2023-12-12T15:12:47.271069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 70
 
2.0%
20 65
 
1.9%
15 57
 
1.7%
30 55
 
1.6%
25 49
 
1.4%
40 48
 
1.4%
10 47
 
1.4%
45 45
 
1.3%
60 41
 
1.2%
35 40
 
1.2%
Other values (809) 2921
85.0%
ValueCountFrequency (%)
0 4
 
0.1%
1 3
 
0.1%
2 6
 
0.2%
3 12
 
0.3%
4 23
0.7%
5 38
1.1%
6 27
0.8%
7 23
0.7%
8 32
0.9%
9 23
0.7%
ValueCountFrequency (%)
76200 1
< 0.1%
37360 1
< 0.1%
21880 1
< 0.1%
15040 1
< 0.1%
14960 1
< 0.1%
13330 1
< 0.1%
12280 1
< 0.1%
12000 1
< 0.1%
10900 1
< 0.1%
10625 1
< 0.1%

수혜면적(ha)
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct461
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.61257
Minimum0
Maximum30266
Zeros1537
Zeros (%)44.7%
Negative0
Negative (%)0.0%
Memory size30.3 KiB
2023-12-12T15:12:47.407058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q391.75
95-th percentile379.15
Maximum30266
Range30266
Interquartile range (IQR)91.75

Descriptive statistics

Standard deviation673.04066
Coefficient of variation (CV)5.8723113
Kurtosis1220.1761
Mean114.61257
Median Absolute Deviation (MAD)13
Skewness30.132586
Sum394038
Variance452983.73
MonotonicityNot monotonic
2023-12-12T15:12:47.916291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1537
44.7%
12 28
 
0.8%
15 28
 
0.8%
13 23
 
0.7%
18 21
 
0.6%
25 21
 
0.6%
10 18
 
0.5%
20 18
 
0.5%
16 18
 
0.5%
11 18
 
0.5%
Other values (451) 1708
49.7%
ValueCountFrequency (%)
0 1537
44.7%
1 3
 
0.1%
2 12
 
0.3%
3 10
 
0.3%
4 16
 
0.5%
5 13
 
0.4%
6 14
 
0.4%
7 17
 
0.5%
8 11
 
0.3%
9 14
 
0.4%
ValueCountFrequency (%)
30266 1
< 0.1%
11139 1
< 0.1%
9054 1
< 0.1%
8382 1
< 0.1%
7738 1
< 0.1%
6918 1
< 0.1%
6245 1
< 0.1%
5714 1
< 0.1%
3218 1
< 0.1%
3217 1
< 0.1%

유효저수량(천m3)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1190
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean836.99564
Minimum1
Maximum258562
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.3 KiB
2023-12-12T15:12:48.071552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q118
median95
Q3566.25
95-th percentile2735.95
Maximum258562
Range258561
Interquartile range (IQR)548.25

Descriptive statistics

Standard deviation5707.7265
Coefficient of variation (CV)6.8193026
Kurtosis1283.832
Mean836.99564
Median Absolute Deviation (MAD)89
Skewness31.649866
Sum2877591
Variance32578142
MonotonicityNot monotonic
2023-12-12T15:12:48.239344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 73
 
2.1%
9 67
 
1.9%
6 66
 
1.9%
7 66
 
1.9%
10 65
 
1.9%
8 60
 
1.7%
4 56
 
1.6%
3 49
 
1.4%
12 48
 
1.4%
15 45
 
1.3%
Other values (1180) 2843
82.7%
ValueCountFrequency (%)
1 20
 
0.6%
2 29
 
0.8%
3 49
1.4%
4 56
1.6%
5 73
2.1%
6 66
1.9%
7 66
1.9%
8 60
1.7%
9 67
1.9%
10 65
1.9%
ValueCountFrequency (%)
258562 1
< 0.1%
106545 1
< 0.1%
99708 1
< 0.1%
76670 1
< 0.1%
57688 1
< 0.1%
46071 1
< 0.1%
34940 1
< 0.1%
31348 1
< 0.1%
30337 1
< 0.1%
28150 1
< 0.1%

지사코드
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31620.39
Minimum31121
Maximum31911
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.3 KiB
2023-12-12T15:12:48.373800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31121
5-th percentile31223.85
Q131526
median31635
Q331733
95-th percentile31832
Maximum31911
Range790
Interquartile range (IQR)207

Descriptive statistics

Standard deviation175.3387
Coefficient of variation (CV)0.0055451149
Kurtosis0.62193562
Mean31620.39
Median Absolute Deviation (MAD)102
Skewness-0.98348223
Sum1.087109 × 108
Variance30743.658
MonotonicityNot monotonic
2023-12-12T15:12:48.524870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31636 160
 
4.7%
31633 139
 
4.0%
31733 94
 
2.7%
31823 90
 
2.6%
31632 89
 
2.6%
31526 87
 
2.5%
31833 83
 
2.4%
31623 80
 
2.3%
31521 77
 
2.2%
31732 74
 
2.2%
Other values (83) 2465
71.7%
ValueCountFrequency (%)
31121 9
 
0.3%
31122 9
 
0.3%
31123 21
0.6%
31124 14
0.4%
31126 12
 
0.3%
31128 17
0.5%
31129 1
 
< 0.1%
31131 9
 
0.3%
31132 19
0.6%
31221 31
0.9%
ValueCountFrequency (%)
31911 9
 
0.3%
31834 71
2.1%
31833 83
2.4%
31832 56
1.6%
31831 70
2.0%
31829 15
 
0.4%
31828 39
1.1%
31827 47
1.4%
31826 16
 
0.5%
31825 39
1.1%

주소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct2552
Distinct (%)74.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5573716 × 109
Minimum2.671031 × 109
Maximum5.013032 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.3 KiB
2023-12-12T15:12:48.669881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.671031 × 109
5-th percentile3.171037 × 109
Q14.5190114 × 109
median4.681037 × 109
Q34.773034 × 109
95-th percentile4.886032 × 109
Maximum5.013032 × 109
Range2.342001 × 109
Interquartile range (IQR)2.5402264 × 108

Descriptive statistics

Standard deviation4.1001808 × 108
Coefficient of variation (CV)0.089968104
Kurtosis8.9640387
Mean4.5573716 × 109
Median Absolute Deviation (MAD)1.0599701 × 108
Skewness-2.9659483
Sum1.5668244 × 1013
Variance1.6811483 × 1017
MonotonicityNot monotonic
2023-12-12T15:12:48.833137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4713025623 7
 
0.2%
4775035042 7
 
0.2%
3171034026 6
 
0.2%
4824037029 6
 
0.2%
3171025327 6
 
0.2%
4571031028 6
 
0.2%
4824037025 6
 
0.2%
4888038024 6
 
0.2%
4889040022 6
 
0.2%
4723035024 6
 
0.2%
Other values (2542) 3376
98.2%
ValueCountFrequency (%)
2671031033 1
< 0.1%
2671032026 1
< 0.1%
2671033024 1
< 0.1%
2671033030 1
< 0.1%
2671033031 1
< 0.1%
2714010500 1
< 0.1%
2714012300 1
< 0.1%
2723012100 2
0.1%
2723012200 1
< 0.1%
2723013000 2
0.1%
ValueCountFrequency (%)
5013032023 1
 
< 0.1%
5011031022 1
 
< 0.1%
5011025325 1
 
< 0.1%
5011025321 1
 
< 0.1%
5011025028 5
0.1%
4889045027 1
 
< 0.1%
4889044027 1
 
< 0.1%
4889044026 1
 
< 0.1%
4889044024 1
 
< 0.1%
4889044023 2
 
0.1%

관리구분 (공사-시도)
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.0 KiB
S
3438 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
S 3438
100.0%

Length

2023-12-12T15:12:48.993639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:12:49.090196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
s 3438
100.0%

시군코드
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45567.797
Minimum26710
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.3 KiB
2023-12-12T15:12:49.189679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26710
5-th percentile31710
Q145190
median46810
Q347730
95-th percentile48860
Maximum50130
Range23420
Interquartile range (IQR)2540

Descriptive statistics

Standard deviation4117.6865
Coefficient of variation (CV)0.090363959
Kurtosis9.0265227
Mean45567.797
Median Absolute Deviation (MAD)1060
Skewness-2.976152
Sum1.5666208 × 108
Variance16955342
MonotonicityNot monotonic
2023-12-12T15:12:49.319116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46170 162
 
4.7%
46830 139
 
4.0%
47230 94
 
2.7%
48890 83
 
2.4%
46820 79
 
2.3%
31710 78
 
2.3%
45190 77
 
2.2%
47130 76
 
2.2%
48240 69
 
2.0%
47110 58
 
1.7%
Other values (134) 2523
73.4%
ValueCountFrequency (%)
26710 5
 
0.1%
27000 13
 
0.4%
27710 9
 
0.3%
28710 17
 
0.5%
29000 52
1.5%
30000 3
 
0.1%
31000 7
 
0.2%
31710 78
2.3%
36110 1
 
< 0.1%
41110 1
 
< 0.1%
ValueCountFrequency (%)
50130 1
 
< 0.1%
50110 8
 
0.2%
48890 83
2.4%
48880 43
1.3%
48870 13
 
0.4%
48860 35
1.0%
48850 22
 
0.6%
48840 49
1.4%
48820 36
1.0%
48740 47
1.4%

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
True
3438 
ValueCountFrequency (%)
True 3438
100.0%
2023-12-12T15:12:49.431658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-12T15:12:43.421474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:38.189487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:38.983729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.032568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.778145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:41.577137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:42.584225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:43.535663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:38.304145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:39.088165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.140074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.880165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:41.707135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:42.708028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:43.667779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:38.438900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:39.205676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.262678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.992518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:41.857776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:42.827607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:43.763164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:38.565315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:39.306609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.368244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:41.128448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:42.037061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:42.939548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:43.863585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:38.670918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:39.401577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.468856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:41.243228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:42.193213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:43.045774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:43.976637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:38.780668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:39.809010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.588054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:41.369725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:42.364810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:43.178162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:44.086442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:38.882998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:39.922693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:40.691389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:41.466203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:42.474383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:12:43.318118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:12:49.495136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계코드수원공타입(주-M)(보조-S)(부속-I)유역면적(ha)수혜면적(ha)유효저수량(천m3)지사코드주소코드시군코드
통계코드1.0000.1570.0000.0000.0000.1410.0000.034
수원공타입(주-M)(보조-S)(부속-I)0.1571.0000.0470.0350.0510.2640.3060.273
유역면적(ha)0.0000.0471.0000.9290.7440.0530.0000.000
수혜면적(ha)0.0000.0350.9291.0000.9140.0000.0000.000
유효저수량(천m3)0.0000.0510.7440.9141.0000.0000.0000.000
지사코드0.1410.2640.0530.0000.0001.0000.8330.830
주소코드0.0000.3060.0000.0000.0000.8331.0000.998
시군코드0.0340.2730.0000.0000.0000.8300.9981.000
2023-12-12T15:12:49.623748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계코드유역면적(ha)수혜면적(ha)유효저수량(천m3)지사코드주소코드시군코드수원공타입(주-M)(보조-S)(부속-I)
통계코드1.0000.3380.3480.367-0.0130.0800.0800.192
유역면적(ha)0.3381.0000.7740.864-0.207-0.153-0.1540.057
수혜면적(ha)0.3480.7741.0000.828-0.255-0.151-0.1510.043
유효저수량(천m3)0.3670.8640.8281.000-0.235-0.157-0.1570.037
지사코드-0.013-0.207-0.255-0.2351.0000.8070.8070.264
주소코드0.080-0.153-0.151-0.1570.8071.0001.0000.230
시군코드0.080-0.154-0.151-0.1570.8071.0001.0000.202
수원공타입(주-M)(보조-S)(부속-I)0.1920.0570.0430.0370.2640.2300.2021.000

Missing values

2023-12-12T15:12:44.241374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:12:44.444906image/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

표준코드통계코드수원공타입(주-M)(보조-S)(부속-I)시설명유역면적(ha)수혜면적(ha)유효저수량(천m3)지사코드주소코드관리구분 (공사-시도)시군코드사용여부
026710100561958000002M용천21049116318232671031033S26710Y
126710100671945000102M병산66056164318232671032026S26710Y
226710100851958000004M송정23049343318232671033024S26710Y
326710100971945000119M안평11247141318232671033030S26710Y
426710100981958000003M임기2773667318232671033031S26710Y
527140100051945000123M단산6088592072317272714010500S27000Y
627140100231945000126M신9502474317342714012300S27000Y
727230100061945000180M도남45050385317232723012100S27000Y
827230100071945000182S광대3506317232723012100S27000Y
927230100121945000183S신45012317232723012200S27000Y
표준코드통계코드수원공타입(주-M)(보조-S)(부속-I)시설명유역면적(ha)수혜면적(ha)유효저수량(천m3)지사코드주소코드관리구분 (공사-시도)시군코드사용여부
342848890106220M양리414150831318334889045027S48890Y
342949710100011956000250S광령525051319115011025321S50110Y
343049710100021960000787S귀엄15500681319115011025325S50110Y
343149710100031957000235S용수328127253319115011031022S50110Y
343250110100040M상대0488483319115011025028S50110Y
343350110100050M지향0670133319115011025028S50110Y
343450110100060M동명005319115011025028S50110Y
343550110100070M함덕11818565319115011025028S50110Y
343650110100080M송당1860615784319115011025028S50110Y
343750130100010M성읍99375431050319115013032023S50130Y