Overview

Dataset statistics

Number of variables5
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory46.7 B

Variable types

Numeric3
Text1
DateTime1

Dataset

Description인천광역시 서구의 지하수개발이용시설 준공 현황에 관한 데이터입니다. 연번, 소재지, 굴착깊이, 취수계획량(세제곱미터/일), 준공일자 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15105187/fileData.do

Alerts

굴착깊이(m) is highly overall correlated with 취수계획량(세제곱미터_일)High correlation
취수계획량(세제곱미터_일) is highly overall correlated with 굴착깊이(m)High correlation
연번 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-12 08:23:00.220252
Analysis finished2023-12-12 08:23:01.775343
Duration1.56 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:23:01.863751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.75
Q19.75
median18.5
Q327.25
95-th percentile34.25
Maximum36
Range35
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.535654
Coefficient of variation (CV)0.5694948
Kurtosis-1.2
Mean18.5
Median Absolute Deviation (MAD)9
Skewness0
Sum666
Variance111
MonotonicityStrictly increasing
2023-12-12T17:23:02.039362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 1
 
2.8%
20 1
 
2.8%
22 1
 
2.8%
23 1
 
2.8%
24 1
 
2.8%
25 1
 
2.8%
26 1
 
2.8%
27 1
 
2.8%
28 1
 
2.8%
29 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1 1
2.8%
2 1
2.8%
3 1
2.8%
4 1
2.8%
5 1
2.8%
6 1
2.8%
7 1
2.8%
8 1
2.8%
9 1
2.8%
10 1
2.8%
ValueCountFrequency (%)
36 1
2.8%
35 1
2.8%
34 1
2.8%
33 1
2.8%
32 1
2.8%
31 1
2.8%
30 1
2.8%
29 1
2.8%
28 1
2.8%
27 1
2.8%

소재지
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T17:23:02.315727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length8.6388889
Min length6

Characters and Unicode

Total characters311
Distinct characters37
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

Unique36 ?
Unique (%)100.0%

Sample

1st row연희동 127-1
2nd row심곡동 112
3rd row공촌동 275-1
4th row경서동 113-1
5th row공촌동 32
ValueCountFrequency (%)
경서동 8
 
10.8%
오류동 5
 
6.8%
불로동 5
 
6.8%
연희동 3
 
4.1%
검암동 3
 
4.1%
공촌동 3
 
4.1%
산 2
 
2.7%
당하동 2
 
2.7%
금곡동 2
 
2.7%
264-38 1
 
1.4%
Other values (40) 40
54.1%
2023-12-12T17:23:02.715405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
38
12.2%
동 36
11.6%
1 31
 
10.0%
- 26
 
8.4%
2 20
 
6.4%
5 18
 
5.8%
4 18
 
5.8%
3 12
 
3.9%
7 12
 
3.9%
8 11
 
3.5%
Other values (27) 89
28.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137
44.1%
Other Letter 110
35.4%
Space Separator 38
 
12.2%
Dash Punctuation 26
 
8.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 36
32.7%
경 8
 
7.3%
서 8
 
7.3%
류 5
 
4.5%
오 5
 
4.5%
불 5
 
4.5%
로 5
 
4.5%
곡 4
 
3.6%
암 3
 
2.7%
검 3
 
2.7%
Other values (15) 28
25.5%
Decimal Number
ValueCountFrequency (%)
1 31
22.6%
2 20
14.6%
5 18
13.1%
4 18
13.1%
3 12
 
8.8%
7 12
 
8.8%
8 11
 
8.0%
0 7
 
5.1%
9 6
 
4.4%
6 2
 
1.5%
Space Separator
ValueCountFrequency (%)
38
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 201
64.6%
Hangul 110
35.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 36
32.7%
경 8
 
7.3%
서 8
 
7.3%
류 5
 
4.5%
오 5
 
4.5%
불 5
 
4.5%
로 5
 
4.5%
곡 4
 
3.6%
암 3
 
2.7%
검 3
 
2.7%
Other values (15) 28
25.5%
Common
ValueCountFrequency (%)
38
18.9%
1 31
15.4%
- 26
12.9%
2 20
10.0%
5 18
9.0%
4 18
9.0%
3 12
 
6.0%
7 12
 
6.0%
8 11
 
5.5%
0 7
 
3.5%
Other values (2) 8
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201
64.6%
Hangul 110
35.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
38
18.9%
1 31
15.4%
- 26
12.9%
2 20
10.0%
5 18
9.0%
4 18
9.0%
3 12
 
6.0%
7 12
 
6.0%
8 11
 
5.5%
0 7
 
3.5%
Other values (2) 8
 
4.0%
Hangul
ValueCountFrequency (%)
동 36
32.7%
경 8
 
7.3%
서 8
 
7.3%
류 5
 
4.5%
오 5
 
4.5%
불 5
 
4.5%
로 5
 
4.5%
곡 4
 
3.6%
암 3
 
2.7%
검 3
 
2.7%
Other values (15) 28
25.5%

굴착깊이(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.5
Minimum20
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:23:02.825684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q130
median35
Q3100
95-th percentile130
Maximum300
Range280
Interquartile range (IQR)70

Descriptive statistics

Standard deviation54.25469
Coefficient of variation (CV)0.80377319
Kurtosis8.5014903
Mean67.5
Median Absolute Deviation (MAD)10
Skewness2.3441688
Sum2430
Variance2943.5714
MonotonicityNot monotonic
2023-12-12T17:23:02.942193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
30 15
41.7%
100 12
33.3%
40 2
 
5.6%
25 2
 
5.6%
130 2
 
5.6%
20 1
 
2.8%
300 1
 
2.8%
70 1
 
2.8%
ValueCountFrequency (%)
20 1
 
2.8%
25 2
 
5.6%
30 15
41.7%
40 2
 
5.6%
70 1
 
2.8%
100 12
33.3%
130 2
 
5.6%
300 1
 
2.8%
ValueCountFrequency (%)
300 1
 
2.8%
130 2
 
5.6%
100 12
33.3%
70 1
 
2.8%
40 2
 
5.6%
30 15
41.7%
25 2
 
5.6%
20 1
 
2.8%

취수계획량(세제곱미터_일)
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.25
Minimum5
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T17:23:03.080371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q110
median17.5
Q340
95-th percentile60
Maximum70
Range65
Interquartile range (IQR)30

Descriptive statistics

Standard deviation19.587715
Coefficient of variation (CV)0.77575108
Kurtosis-0.36716166
Mean25.25
Median Absolute Deviation (MAD)7.5
Skewness1.0419909
Sum909
Variance383.67857
MonotonicityNot monotonic
2023-12-12T17:23:03.215823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 14
38.9%
20 6
16.7%
60 4
 
11.1%
15 3
 
8.3%
40 3
 
8.3%
30 2
 
5.6%
50 1
 
2.8%
5 1
 
2.8%
59 1
 
2.8%
70 1
 
2.8%
ValueCountFrequency (%)
5 1
 
2.8%
10 14
38.9%
15 3
 
8.3%
20 6
16.7%
30 2
 
5.6%
40 3
 
8.3%
50 1
 
2.8%
59 1
 
2.8%
60 4
 
11.1%
70 1
 
2.8%
ValueCountFrequency (%)
70 1
 
2.8%
60 4
 
11.1%
59 1
 
2.8%
50 1
 
2.8%
40 3
 
8.3%
30 2
 
5.6%
20 6
16.7%
15 3
 
8.3%
10 14
38.9%
5 1
 
2.8%

준공일자
Date

Distinct22
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
Minimum2022-07-06 00:00:00
Maximum2023-03-31 00:00:00
2023-12-12T17:23:03.359664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:03.505657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

Interactions

2023-12-12T17:23:01.244596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:00.407987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:00.983372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:01.366830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:00.798958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:01.068244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:01.463185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:00.889805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:23:01.148165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:23:03.602831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소재지굴착깊이(m)취수계획량(세제곱미터_일)준공일자
연번1.0001.0000.7950.4970.898
소재지1.0001.0001.0001.0001.000
굴착깊이(m)0.7951.0001.0000.7740.875
취수계획량(세제곱미터_일)0.4971.0000.7741.0000.547
준공일자0.8981.0000.8750.5471.000
2023-12-12T17:23:03.709215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번굴착깊이(m)취수계획량(세제곱미터_일)
연번1.0000.4760.494
굴착깊이(m)0.4761.0000.859
취수계획량(세제곱미터_일)0.4940.8591.000

Missing values

2023-12-12T17:23:01.601512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:23:01.718481image/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

연번소재지굴착깊이(m)취수계획량(세제곱미터_일)준공일자
01연희동 127-1100502022-07-06
12심곡동 11220102022-07-06
23공촌동 275-130102022-07-08
34경서동 113-130102022-07-14
45공촌동 323052022-07-14
56경서동 58-130102022-07-19
67경서동 58-430102022-07-19
78오류동 122-2730152022-07-19
89경서동 58-530102022-07-19
910당하동 280-11100602022-07-21
연번소재지굴착깊이(m)취수계획량(세제곱미터_일)준공일자
2627원당동 1059130402023-03-02
2728불로동 274-35130602023-01-05
2829연희동 132-430102023-01-09
2930오류동 1719-4100302023-03-31
3031검암동 438-219100202023-03-31
3132오류동 1719-11100302023-03-31
3233불로동 264-38300702023-01-05
3334연희동 20740202023-03-31
3435검암동 5070602023-01-30
3536오류동 1498-15100202023-03-13