Overview

Dataset statistics

Number of variables5
Number of observations228
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.7 KiB
Average record size in memory43.6 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description지역별 지하수 개발 가능량 대비 이용량 현황정보를 아래와 같이 제공합니다.제공정보- 시도,시군구,개발가능량(천㎥년),이용량(㎥년),이용량/개발가능량(%)
Author한국수자원공사
URLhttps://www.data.go.kr/data/15054553/fileData.do

Alerts

개발가능량(천톤_년) is highly overall correlated with 이용량(천톤_년) and 1 other fieldsHigh correlation
이용량(천톤_년) is highly overall correlated with 개발가능량(천톤_년) and 1 other fieldsHigh correlation
시도 is highly overall correlated with 개발가능량(천톤_년) and 1 other fieldsHigh correlation
개발가능량(천톤_년) has unique valuesUnique
이용량(천톤_년) has unique valuesUnique

Reproduction

Analysis started2024-04-29 22:34:22.855077
Analysis finished2024-04-29 22:34:25.576275
Duration2.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
경기도
31 
서울특별시
25 
경상북도
23 
전라남도
22 
강원특별자치도
18 
Other values (12)
109 

Length

Max length7
Median length5
Mean length4.6359649
Min length3

Unique

Unique2 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 31
13.6%
서울특별시 25
11.0%
경상북도 23
10.1%
전라남도 22
9.6%
강원특별자치도 18
7.9%
경상남도 18
7.9%
부산광역시 16
7.0%
충청남도 15
6.6%
전북특별자치도 14
 
6.1%
충청북도 11
 
4.8%
Other values (7) 35
15.4%

Length

2024-04-30T07:34:25.663451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 31
13.6%
서울특별시 25
11.0%
경상북도 23
10.1%
전라남도 22
9.6%
강원특별자치도 18
7.9%
경상남도 18
7.9%
부산광역시 16
7.0%
충청남도 15
6.6%
전북특별자치도 14
 
6.1%
충청북도 11
 
4.8%
Other values (7) 35
15.4%

시군구
Text

Distinct206
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2024-04-30T07:34:25.960591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9649123
Min length2

Characters and Unicode

Total characters676
Distinct characters136
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique199 ?
Unique (%)87.3%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
동구 6
 
2.6%
중구 6
 
2.6%
서구 5
 
2.2%
북구 4
 
1.8%
남구 4
 
1.8%
고성군 2
 
0.9%
강서구 2
 
0.9%
울주군 1
 
0.4%
남동구 1
 
0.4%
계양구 1
 
0.4%
Other values (196) 196
86.0%
2024-04-30T07:34:26.420695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 85
 
12.6%
시 77
 
11.4%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
남 12
 
1.8%
Other values (126) 317
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 676
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 85
 
12.6%
시 77
 
11.4%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
남 12
 
1.8%
Other values (126) 317
46.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 676
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 85
 
12.6%
시 77
 
11.4%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
남 12
 
1.8%
Other values (126) 317
46.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 676
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 85
 
12.6%
시 77
 
11.4%
구 74
 
10.9%
천 22
 
3.3%
주 20
 
3.0%
성 18
 
2.7%
양 18
 
2.7%
동 17
 
2.5%
산 16
 
2.4%
남 12
 
1.8%
Other values (126) 317
46.9%

개발가능량(천톤_년)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct228
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56969.496
Minimum458
Maximum730400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-30T07:34:26.573201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum458
5-th percentile1569.15
Q17076.5
median50888
Q383770.75
95-th percentile143846.9
Maximum730400
Range729942
Interquartile range (IQR)76694.25

Descriptive statistics

Standard deviation67197.394
Coefficient of variation (CV)1.1795329
Kurtosis43.784963
Mean56969.496
Median Absolute Deviation (MAD)42785.5
Skewness4.8266008
Sum12989045
Variance4.5154897 × 109
MonotonicityNot monotonic
2024-04-30T07:34:26.705735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
123015 1
 
0.4%
1866 1
 
0.4%
2009 1
 
0.4%
885 1
 
0.4%
1635 1
 
0.4%
56315 1
 
0.4%
10455 1
 
0.4%
4344 1
 
0.4%
21856 1
 
0.4%
107591 1
 
0.4%
Other values (218) 218
95.6%
ValueCountFrequency (%)
458 1
0.4%
674 1
0.4%
885 1
0.4%
898 1
0.4%
1142 1
0.4%
1279 1
0.4%
1298 1
0.4%
1362 1
0.4%
1396 1
0.4%
1451 1
0.4%
ValueCountFrequency (%)
730400 1
0.4%
256652 1
0.4%
204627 1
0.4%
201451 1
0.4%
198712 1
0.4%
196185 1
0.4%
189633 1
0.4%
188585 1
0.4%
169086 1
0.4%
159910 1
0.4%

이용량(천톤_년)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct228
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13072.245
Minimum40.6
Maximum237467.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-30T07:34:26.886183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40.6
5-th percentile321.255
Q11963.525
median8354
Q319056.2
95-th percentile35730.88
Maximum237467.9
Range237427.3
Interquartile range (IQR)17092.675

Descriptive statistics

Standard deviation19343.838
Coefficient of variation (CV)1.479764
Kurtosis79.64563
Mean13072.245
Median Absolute Deviation (MAD)7285.2
Skewness7.22389
Sum2980471.9
Variance3.7418408 × 108
MonotonicityNot monotonic
2024-04-30T07:34:27.009306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13380.6 1
 
0.4%
217.3 1
 
0.4%
703.7 1
 
0.4%
399.7 1
 
0.4%
381.8 1
 
0.4%
23194.8 1
 
0.4%
1996.8 1
 
0.4%
1069.4 1
 
0.4%
2786.0 1
 
0.4%
16998.3 1
 
0.4%
Other values (218) 218
95.6%
ValueCountFrequency (%)
40.6 1
0.4%
138.0 1
0.4%
172.9 1
0.4%
173.7 1
0.4%
217.3 1
0.4%
238.5 1
0.4%
240.5 1
0.4%
274.5 1
0.4%
275.7 1
0.4%
285.9 1
0.4%
ValueCountFrequency (%)
237467.9 1
0.4%
69574.0 1
0.4%
54209.7 1
0.4%
51453.1 1
0.4%
47769.0 1
0.4%
42957.5 1
0.4%
41539.0 1
0.4%
39007.9 1
0.4%
38527.2 1
0.4%
37883.2 1
0.4%

이용량_개발가능량(퍼센트)
Real number (ℝ)

Distinct187
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.575
Minimum0.5
Maximum115.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2024-04-30T07:34:27.134599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile5.07
Q114.375
median24.75
Q338.075
95-th percentile70.46
Maximum115.5
Range115
Interquartile range (IQR)23.7

Descriptive statistics

Standard deviation21.409565
Coefficient of variation (CV)0.72390751
Kurtosis2.9866798
Mean29.575
Median Absolute Deviation (MAD)11.05
Skewness1.5318611
Sum6743.1
Variance458.36946
MonotonicityNot monotonic
2024-04-30T07:34:27.265326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.1 3
 
1.3%
15.6 3
 
1.3%
7.5 3
 
1.3%
10.9 2
 
0.9%
15.1 2
 
0.9%
46.5 2
 
0.9%
32.5 2
 
0.9%
45.4 2
 
0.9%
54.6 2
 
0.9%
10.5 2
 
0.9%
Other values (177) 205
89.9%
ValueCountFrequency (%)
0.5 1
0.4%
1.5 1
0.4%
1.8 1
0.4%
2.0 1
0.4%
2.3 1
0.4%
2.5 1
0.4%
2.7 1
0.4%
3.7 1
0.4%
4.4 1
0.4%
4.5 1
0.4%
ValueCountFrequency (%)
115.5 1
0.4%
114.4 1
0.4%
113.2 1
0.4%
94.9 1
0.4%
94.2 1
0.4%
91.9 1
0.4%
89.6 1
0.4%
85.0 1
0.4%
82.8 1
0.4%
78.8 1
0.4%

Interactions

2024-04-30T07:34:25.134279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:34:24.390501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:34:24.733905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:34:25.238500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:34:24.533480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:34:24.858943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:34:25.337988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:34:24.610708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-30T07:34:24.985028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-30T07:34:27.350785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도개발가능량(천톤_년)이용량(천톤_년)이용량_개발가능량(퍼센트)
시도1.0000.7920.8040.417
개발가능량(천톤_년)0.7921.0000.6650.221
이용량(천톤_년)0.8040.6651.0000.380
이용량_개발가능량(퍼센트)0.4170.2210.3801.000
2024-04-30T07:34:27.449169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개발가능량(천톤_년)이용량(천톤_년)이용량_개발가능량(퍼센트)시도
개발가능량(천톤_년)1.0000.764-0.3570.548
이용량(천톤_년)0.7641.0000.2430.578
이용량_개발가능량(퍼센트)-0.3570.2431.0000.167
시도0.5480.5780.1671.000

Missing values

2024-04-30T07:34:25.444264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-30T07:34:25.535020image/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

시도시군구개발가능량(천톤_년)이용량(천톤_년)이용량_개발가능량(퍼센트)
0강원특별자치도강릉시12301513380.610.9
1강원특별자치도고성군1316847978.36.1
2강원특별자치도동해시362007123.119.7
3강원특별자치도삼척시16908624365.114.4
4강원특별자치도속초시188691863.79.9
5강원특별자치도양구군791466380.48.1
6강원특별자치도양양군1069682151.72.0
7강원특별자치도영월군14074910063.07.1
8강원특별자치도원주시13220120675.015.6
9강원특별자치도인제군2014512987.71.5
시도시군구개발가능량(천톤_년)이용량(천톤_년)이용량_개발가능량(퍼센트)
218충청북도단양군794604442.15.6
219충청북도보은군6669416566.724.8
220충청북도영동군7690226188.134.1
221충청북도옥천군5090021823.242.9
222충청북도음성군6911142957.562.2
223충청북도제천시10718128698.326.8
224충청북도증평군103476829.966.0
225충청북도진천군6911027364.639.6
226충청북도청주시13563351453.137.9
227충청북도충주시12932141539.032.1