Overview

Dataset statistics

Number of variables7
Number of observations937
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory59.1 B

Variable types

Categorical2
Numeric3
Text1
DateTime1

Dataset

Description나주시 16개 저수지의 수위에 따른 저수량에 관한 정보를 제공하며 측정당시의 수위를 기준으로 산출한 결과입니다. *2022년 공공데이터 기업매칭 지원사업 결과
Author전라남도 나주시
URLhttps://www.data.go.kr/data/15110610/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
소재지 is highly overall correlated with 수위 and 1 other fieldsHigh correlation
저수지 is highly overall correlated with 수위 and 1 other fieldsHigh correlation
수위 is highly overall correlated with 저수지 and 1 other fieldsHigh correlation
수심 is highly overall correlated with 저수량High correlation
저수량 is highly overall correlated with 수심High correlation
수심 has 16 (1.7%) zerosZeros
저수량 has 17 (1.8%) zerosZeros

Reproduction

Analysis started2023-12-12 00:17:53.814856
Analysis finished2023-12-12 00:17:55.584120
Duration1.77 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

저수지
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
한수
115 
봉산
88 
금계
83 
덕림
82 
석교
79 
Other values (11)
490 

Length

Max length3
Median length2
Mean length2.1152615
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한수 115
12.3%
봉산 88
9.4%
금계 83
8.9%
덕림 82
8.8%
석교 79
8.4%
남석 72
 
7.7%
옥당 70
 
7.5%
나주호 64
 
6.8%
상남 51
 
5.4%
노안1 44
 
4.7%
Other values (6) 189
20.2%

Length

2023-12-12T09:17:55.645172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한수 115
12.3%
봉산 88
9.4%
금계 83
8.9%
덕림 82
8.8%
석교 79
8.4%
남석 72
 
7.7%
옥당 70
 
7.5%
나주호 64
 
6.8%
상남 51
 
5.4%
노안1 44
 
4.7%
Other values (6) 189
20.2%

수위
Real number (ℝ)

HIGH CORRELATION 

Distinct682
Distinct (%)72.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.0608
Minimum9.75
Maximum150.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2023-12-12T09:17:55.754218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.75
5-th percentile12.648
Q129.2
median42.7
Q365.77
95-th percentile145.42
Maximum150.1
Range140.35
Interquartile range (IQR)36.57

Descriptive statistics

Standard deviation35.26743
Coefficient of variation (CV)0.67742773
Kurtosis1.7587512
Mean52.0608
Median Absolute Deviation (MAD)16.3
Skewness1.5096412
Sum48780.97
Variance1243.7916
MonotonicityNot monotonic
2023-12-12T09:17:55.884127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.0 4
 
0.4%
38.0 4
 
0.4%
46.0 4
 
0.4%
45.5 4
 
0.4%
36.0 4
 
0.4%
36.5 4
 
0.4%
37.5 4
 
0.4%
37.0 4
 
0.4%
44.5 4
 
0.4%
44.0 4
 
0.4%
Other values (672) 897
95.7%
ValueCountFrequency (%)
9.75 1
0.1%
9.8 1
0.1%
9.9 1
0.1%
10.0 1
0.1%
10.1 1
0.1%
10.2 1
0.1%
10.3 1
0.1%
10.4 1
0.1%
10.5 1
0.1%
10.6 1
0.1%
ValueCountFrequency (%)
150.1 1
0.1%
150.0 1
0.1%
149.9 1
0.1%
149.8 1
0.1%
149.7 1
0.1%
149.6 1
0.1%
149.5 1
0.1%
149.4 1
0.1%
149.3 1
0.1%
149.2 1
0.1%

수심
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct530
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2806083
Minimum0
Maximum31.09
Zeros16
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2023-12-12T09:17:56.033331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21
Q11.45
median3.11
Q35.71
95-th percentile10.85
Maximum31.09
Range31.09
Interquartile range (IQR)4.26

Descriptive statistics

Standard deviation4.6349556
Coefficient of variation (CV)1.0827796
Kurtosis11.678971
Mean4.2806083
Median Absolute Deviation (MAD)1.98
Skewness2.9966661
Sum4010.93
Variance21.482813
MonotonicityNot monotonic
2023-12-12T09:17:56.154668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
1.7%
1.13 4
 
0.4%
3.13 4
 
0.4%
2.03 4
 
0.4%
1.93 4
 
0.4%
1.83 4
 
0.4%
1.73 4
 
0.4%
1.63 4
 
0.4%
1.53 4
 
0.4%
1.43 4
 
0.4%
Other values (520) 885
94.5%
ValueCountFrequency (%)
0.0 16
1.7%
0.01 2
 
0.2%
0.03 4
 
0.4%
0.05 1
 
0.1%
0.07 2
 
0.2%
0.08 3
 
0.3%
0.1 3
 
0.3%
0.11 2
 
0.2%
0.13 4
 
0.4%
0.15 1
 
0.1%
ValueCountFrequency (%)
31.09 1
0.1%
30.99 1
0.1%
30.49 1
0.1%
29.99 1
0.1%
29.49 1
0.1%
28.99 1
0.1%
28.49 1
0.1%
27.99 1
0.1%
27.49 1
0.1%
26.99 1
0.1%

저수량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct921
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2177767
Minimum0
Maximum99152805
Zeros17
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2023-12-12T09:17:56.340204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100.77
Q12668.14
median13229.76
Q355517.3
95-th percentile4006978.9
Maximum99152805
Range99152805
Interquartile range (IQR)52849.16

Descriptive statistics

Standard deviation11350499
Coefficient of variation (CV)5.2119893
Kurtosis40.518749
Mean2177767
Median Absolute Deviation (MAD)12675.407
Skewness6.2175652
Sum2.0405677 × 109
Variance1.2883382 × 1014
MonotonicityNot monotonic
2023-12-12T09:17:56.500281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
1.8%
4833.76 1
 
0.1%
6482.41 1
 
0.1%
7306.73 1
 
0.1%
8131.06 1
 
0.1%
8955.38 1
 
0.1%
9779.71 1
 
0.1%
10604.03 1
 
0.1%
11428.36 1
 
0.1%
12707.51 1
 
0.1%
Other values (911) 911
97.2%
ValueCountFrequency (%)
0.0 17
1.8%
0.02 1
 
0.1%
0.04 1
 
0.1%
2.66 1
 
0.1%
5.32 1
 
0.1%
7.2 1
 
0.1%
7.98 1
 
0.1%
14.3 1
 
0.1%
17.17 1
 
0.1%
20.33 1
 
0.1%
ValueCountFrequency (%)
99152805.0 1
0.1%
98503359.0 1
0.1%
95336382.8 1
0.1%
92169406.6 1
0.1%
89002430.4 1
0.1%
85835454.2 1
0.1%
82668478.0 1
0.1%
79501501.8 1
0.1%
76334525.6 1
0.1%
73167549.4 1
0.1%

면적
Text

Distinct919
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2023-12-12T09:17:56.901370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.1419424
Min length1

Characters and Unicode

Total characters4818
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique916 ?
Unique (%)97.8%

Sample

1st row0
2nd row266.82
3rd row533.64
4th row800.45
5th row1067.27
ValueCountFrequency (%)
0 17
 
1.8%
8630 2
 
0.2%
5,975 2
 
0.2%
9,818 1
 
0.1%
6033 1
 
0.1%
4742.6 1
 
0.1%
7602 1
 
0.1%
13008 1
 
0.1%
8125 1
 
0.1%
8648 1
 
0.1%
Other values (909) 909
97.0%
2023-12-12T09:17:57.427643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 637
13.2%
1 601
12.5%
2 551
11.4%
4 434
9.0%
6 415
8.6%
3 411
8.5%
8 398
8.3%
5 368
7.6%
7 345
7.2%
9 291
6.0%
Other values (4) 367
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4451
92.4%
Other Punctuation 365
 
7.6%
Uppercase Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 637
14.3%
1 601
13.5%
2 551
12.4%
4 434
9.8%
6 415
9.3%
3 411
9.2%
8 398
8.9%
5 368
8.3%
7 345
7.8%
9 291
6.5%
Other Punctuation
ValueCountFrequency (%)
, 185
50.7%
. 180
49.3%
Uppercase Letter
ValueCountFrequency (%)
E 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4817
> 99.9%
Latin 1
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 637
13.2%
1 601
12.5%
2 551
11.4%
4 434
9.0%
6 415
8.6%
3 411
8.5%
8 398
8.3%
5 368
7.6%
7 345
7.2%
9 291
6.0%
Other values (3) 366
7.6%
Latin
ValueCountFrequency (%)
E 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 637
13.2%
1 601
12.5%
2 551
11.4%
4 434
9.0%
6 415
8.6%
3 411
8.5%
8 398
8.3%
5 368
7.6%
7 345
7.2%
9 291
6.0%
Other values (4) 367
7.6%

소재지
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
전라남도 나주시 경현동 123
115 
전라남도 나주시 남평읍 서산리 804-1
88 
전라남도 나주시 문평면 북동리 42
83 
전라남도 나주시 다도면 덕림리 740
82 
전라남도 나주시 문평면 학교리 260
79 
Other values (11)
490 

Length

Max length22
Median length20
Mean length19.667022
Min length16

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도 나주시 문평면 북동리 42
2nd row전라남도 나주시 문평면 북동리 42
3rd row전라남도 나주시 문평면 북동리 42
4th row전라남도 나주시 문평면 북동리 42
5th row전라남도 나주시 문평면 북동리 42

Common Values

ValueCountFrequency (%)
전라남도 나주시 경현동 123 115
12.3%
전라남도 나주시 남평읍 서산리 804-1 88
9.4%
전라남도 나주시 문평면 북동리 42 83
8.9%
전라남도 나주시 다도면 덕림리 740 82
8.8%
전라남도 나주시 문평면 학교리 260 79
8.4%
전라남도 나주시 남평읍 남석리 87 72
 
7.7%
전라남도 나주시 문평면 오룡리 183-1 70
 
7.5%
전라남도 나주시 다도면 판촌리 354 64
 
6.8%
전라남도 나주시 남평읍 남석리 59 51
 
5.4%
전라남도 나주시 노안면 안산리 477 44
 
4.7%
Other values (6) 189
20.2%

Length

2023-12-12T09:17:57.580929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라남도 937
20.5%
나주시 937
20.5%
문평면 232
 
5.1%
남평읍 211
 
4.6%
다도면 182
 
4.0%
남석리 123
 
2.7%
123 115
 
2.5%
경현동 115
 
2.5%
서산리 88
 
1.9%
804-1 88
 
1.9%
Other values (32) 1542
33.7%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
Minimum2022-12-16 00:00:00
Maximum2022-12-16 00:00:00
2023-12-12T09:17:57.724139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:57.833496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T09:17:55.044412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:54.389416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:54.738489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:55.139856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:54.496902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:54.850279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:55.252437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:54.614858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:17:54.948637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:17:57.910735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
저수지수위수심저수량소재지
저수지1.0000.9740.6310.5131.000
수위0.9741.0000.5190.3830.974
수심0.6310.5191.0000.9820.631
저수량0.5130.3830.9821.0000.513
소재지1.0000.9740.6310.5131.000
2023-12-12T09:17:58.009891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지저수지
소재지1.0001.000
저수지1.0001.000
2023-12-12T09:17:58.106934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수위수심저수량저수지소재지
수위1.0000.3290.1710.9090.909
수심0.3291.0000.9050.3060.306
저수량0.1710.9051.0000.2280.228
저수지0.9090.3060.2281.0001.000
소재지0.9090.3060.2281.0001.000

Missing values

2023-12-12T09:17:55.383778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:17:55.522401image/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

저수지수위수심저수량면적소재지데이터기준일자
0금계23.570.00.00전라남도 나주시 문평면 북동리 422022-12-16
1금계23.60.0399.53266.82전라남도 나주시 문평면 북동리 422022-12-16
2금계23.70.13199.06533.64전라남도 나주시 문평면 북동리 422022-12-16
3금계23.80.23298.58800.45전라남도 나주시 문평면 북동리 422022-12-16
4금계23.90.33398.111067.27전라남도 나주시 문평면 북동리 422022-12-16
5금계24.00.43497.641334.09전라남도 나주시 문평면 북동리 422022-12-16
6금계24.10.53597.171600.91전라남도 나주시 문평면 북동리 422022-12-16
7금계24.20.63696.691867.73전라남도 나주시 문평면 북동리 422022-12-16
8금계24.30.73796.222134.55전라남도 나주시 문평면 북동리 422022-12-16
9금계24.40.83895.752401.36전라남도 나주시 문평면 북동리 422022-12-16
저수지수위수심저수량면적소재지데이터기준일자
927후동12.561.587382.2577,934전라남도 나주시 공산면 상방리 4032022-12-16
928후동12.661.688276.8298,248전라남도 나주시 공산면 상방리 4032022-12-16
929후동12.761.789171.48,562전라남도 나주시 공산면 상방리 4032022-12-16
930후동12.861.8810065.9718,876전라남도 나주시 공산면 상방리 4032022-12-16
931후동12.961.9810960.5429,190전라남도 나주시 공산면 상방리 4032022-12-16
932후동13.062.0811855.1149,504전라남도 나주시 공산면 상방리 4032022-12-16
933후동13.162.1812749.6859,818전라남도 나주시 공산면 상방리 4032022-12-16
934후동13.262.2813644.25610,132전라남도 나주시 공산면 상방리 4032022-12-16
935후동13.362.3814538.82810,446전라남도 나주시 공산면 상방리 4032022-12-16
936후동13.462.4815433.39910,760전라남도 나주시 공산면 상방리 4032022-12-16