Overview

Dataset statistics

Number of variables8
Number of observations28
Missing cells10
Missing cells (%)4.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory70.7 B

Variable types

Numeric2
Categorical3
Text3

Dataset

Description인천광역시 서구 하천점용현황에 관한 데이터입니다. 하천명, 소재지, 사용목적, 허가면적 등의 항목을 제공합니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15121232&srcSe=7661IVAWM27C61E190

Alerts

유형 has constant value ""Constant
번호 is highly overall correlated with 비고High correlation
비고 is highly overall correlated with 번호High correlation
사용목적 has 5 (17.9%) missing valuesMissing
허가기간 has 5 (17.9%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2024-01-28 05:29:59.547965
Analysis finished2024-01-28 05:30:00.476674
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T14:30:00.541816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2024-01-28T14:30:00.652033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

유형
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
지방하천
28 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방하천 28
100.0%

Length

2024-01-28T14:30:00.752869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:30:00.827474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방하천 28
100.0%

하천명
Categorical

Distinct8
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
나진포천
7 
공촌천
5 
검단천
5 
심곡천
4 
대곡천
3 
Other values (3)
4 

Length

Max length4
Median length3
Mean length3.25
Min length3

Unique

Unique2 ?
Unique (%)7.1%

Sample

1st row공촌천
2nd row공촌천
3rd row검단천
4th row검단천
5th row계양천

Common Values

ValueCountFrequency (%)
나진포천 7
25.0%
공촌천 5
17.9%
검단천 5
17.9%
심곡천 4
14.3%
대곡천 3
10.7%
대포천 2
 
7.1%
계양천 1
 
3.6%
시천천 1
 
3.6%

Length

2024-01-28T14:30:00.912685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:30:01.023097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
나진포천 7
25.0%
공촌천 5
17.9%
검단천 5
17.9%
심곡천 4
14.3%
대곡천 3
10.7%
대포천 2
 
7.1%
계양천 1
 
3.6%
시천천 1
 
3.6%

소재지
Text

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size356.0 B
2024-01-28T14:30:01.188073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length11.428571
Min length6

Characters and Unicode

Total characters320
Distinct characters36
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

Unique26 ?
Unique (%)92.9%

Sample

1st row공촌동 293-1 외 3필지
2nd row연희동 42-11
3rd row오류동 911 외 1필지
4th row오류동 1461
5th row당하동 162-5
ValueCountFrequency (%)
외 14
 
16.7%
1필지 7
 
8.3%
대곡동 5
 
6.0%
불로동 5
 
6.0%
오류동 5
 
6.0%
연희동 4
 
4.8%
2필지 3
 
3.6%
687-8 2
 
2.4%
청라동 2
 
2.4%
3필지 2
 
2.4%
Other values (34) 35
41.7%
2024-01-28T14:30:01.453692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56
17.5%
동 28
 
8.8%
1 28
 
8.8%
- 19
 
5.9%
3 15
 
4.7%
지 14
 
4.4%
외 14
 
4.4%
4 14
 
4.4%
필 14
 
4.4%
6 12
 
3.8%
Other values (26) 106
33.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 126
39.4%
Decimal Number 119
37.2%
Space Separator 56
17.5%
Dash Punctuation 19
 
5.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 28
22.2%
지 14
11.1%
외 14
11.1%
필 14
11.1%
곡 8
 
6.3%
로 5
 
4.0%
불 5
 
4.0%
대 5
 
4.0%
류 5
 
4.0%
오 5
 
4.0%
Other values (14) 23
18.3%
Decimal Number
ValueCountFrequency (%)
1 28
23.5%
3 15
12.6%
4 14
11.8%
6 12
10.1%
5 11
 
9.2%
2 10
 
8.4%
8 10
 
8.4%
0 8
 
6.7%
7 6
 
5.0%
9 5
 
4.2%
Space Separator
ValueCountFrequency (%)
56
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194
60.6%
Hangul 126
39.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 28
22.2%
지 14
11.1%
외 14
11.1%
필 14
11.1%
곡 8
 
6.3%
로 5
 
4.0%
불 5
 
4.0%
대 5
 
4.0%
류 5
 
4.0%
오 5
 
4.0%
Other values (14) 23
18.3%
Common
ValueCountFrequency (%)
56
28.9%
1 28
14.4%
- 19
 
9.8%
3 15
 
7.7%
4 14
 
7.2%
6 12
 
6.2%
5 11
 
5.7%
2 10
 
5.2%
8 10
 
5.2%
0 8
 
4.1%
Other values (2) 11
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194
60.6%
Hangul 126
39.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56
28.9%
1 28
14.4%
- 19
 
9.8%
3 15
 
7.7%
4 14
 
7.2%
6 12
 
6.2%
5 11
 
5.7%
2 10
 
5.2%
8 10
 
5.2%
0 8
 
4.1%
Other values (2) 11
 
5.7%
Hangul
ValueCountFrequency (%)
동 28
22.2%
지 14
11.1%
외 14
11.1%
필 14
11.1%
곡 8
 
6.3%
로 5
 
4.0%
불 5
 
4.0%
대 5
 
4.0%
류 5
 
4.0%
오 5
 
4.0%
Other values (14) 23
18.3%

사용목적
Text

MISSING 

Distinct15
Distinct (%)65.2%
Missing5
Missing (%)17.9%
Memory size356.0 B
2024-01-28T14:30:01.617196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length14
Mean length8.9130435
Min length4

Characters and Unicode

Total characters205
Distinct characters69
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)52.2%

Sample

1st row송유관 매설
2nd row지중 송유관시설 보호
3rd row공업용수관로매설
4th row공업용수관로매설
5th row금연안내 표지판 설치
ValueCountFrequency (%)
전주신설 7
 
15.2%
매설 3
 
6.5%
금연안내 2
 
4.3%
표지판 2
 
4.3%
설치 2
 
4.3%
공업용수관로매설 2
 
4.3%
전원공급을 1
 
2.2%
루원시티 1
 
2.2%
회선인출공사 1
 
2.2%
통신관로 1
 
2.2%
Other values (24) 24
52.2%
2024-01-28T14:30:01.927696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
 
11.2%
설 21
 
10.2%
전 13
 
6.3%
신 11
 
5.4%
주 10
 
4.9%
공 7
 
3.4%
관 7
 
3.4%
지 7
 
3.4%
매 6
 
2.9%
로 5
 
2.4%
Other values (59) 95
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 179
87.3%
Space Separator 23
 
11.2%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%
Decimal Number 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 21
 
11.7%
전 13
 
7.3%
신 11
 
6.1%
주 10
 
5.6%
공 7
 
3.9%
관 7
 
3.9%
지 7
 
3.9%
매 6
 
3.4%
로 5
 
2.8%
업 4
 
2.2%
Other values (55) 88
49.2%
Space Separator
ValueCountFrequency (%)
23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Decimal Number
ValueCountFrequency (%)
6 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 179
87.3%
Common 26
 
12.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 21
 
11.7%
전 13
 
7.3%
신 11
 
6.1%
주 10
 
5.6%
공 7
 
3.9%
관 7
 
3.9%
지 7
 
3.9%
매 6
 
3.4%
로 5
 
2.8%
업 4
 
2.2%
Other values (55) 88
49.2%
Common
ValueCountFrequency (%)
23
88.5%
( 1
 
3.8%
) 1
 
3.8%
6 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 179
87.3%
ASCII 26
 
12.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23
88.5%
( 1
 
3.8%
) 1
 
3.8%
6 1
 
3.8%
Hangul
ValueCountFrequency (%)
설 21
 
11.7%
전 13
 
7.3%
신 11
 
6.1%
주 10
 
5.6%
공 7
 
3.9%
관 7
 
3.9%
지 7
 
3.9%
매 6
 
3.4%
로 5
 
2.8%
업 4
 
2.2%
Other values (55) 88
49.2%

허가면적_제곱미터
Real number (ℝ)

Distinct21
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.10464
Minimum0.392
Maximum3323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2024-01-28T14:30:02.039773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.392
5-th percentile0.392
Q10.74
median2.3
Q326.205
95-th percentile1447.35
Maximum3323
Range3322.608
Interquartile range (IQR)25.465

Descriptive statistics

Standard deviation733.59773
Coefficient of variation (CV)3.4915827
Kurtosis13.989907
Mean210.10464
Median Absolute Deviation (MAD)1.852
Skewness3.785288
Sum5882.93
Variance538165.63
MonotonicityNot monotonic
2024-01-28T14:30:02.144190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.96 4
 
14.3%
0.392 3
 
10.7%
3.92 2
 
7.1%
0.588 2
 
7.1%
25.74 1
 
3.6%
2168.0 1
 
3.6%
1.69 1
 
3.6%
40.0 1
 
3.6%
0.608 1
 
3.6%
15.33 1
 
3.6%
Other values (11) 11
39.3%
ValueCountFrequency (%)
0.392 3
10.7%
0.504 1
 
3.6%
0.588 2
7.1%
0.608 1
 
3.6%
0.784 1
 
3.6%
1.0 1
 
3.6%
1.69 1
 
3.6%
1.96 4
14.3%
2.64 1
 
3.6%
3.92 2
7.1%
ValueCountFrequency (%)
3323.0 1
3.6%
2168.0 1
3.6%
109.0 1
3.6%
71.962 1
3.6%
58.0 1
3.6%
40.0 1
3.6%
27.6 1
3.6%
25.74 1
3.6%
15.33 1
3.6%
13.16 1
3.6%

허가기간
Text

MISSING 

Distinct19
Distinct (%)82.6%
Missing5
Missing (%)17.9%
Memory size356.0 B
2024-01-28T14:30:02.297379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters483
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)69.6%

Sample

1st row2023-01-01~2025-12-31
2nd row2023-01-01~2025-12-31
3rd row2022-01-21~2026-12-31
4th row2022-01-21~2026-12-31
5th row2021-11-23~9999-12-31
ValueCountFrequency (%)
2023-01-01~2025-12-31 3
 
13.0%
2022-01-21~2026-12-31 2
 
8.7%
2022-04-26~2025-12-31 2
 
8.7%
2022-10-07~9999-12-31 1
 
4.3%
2022-10-24~2023-06-12 1
 
4.3%
2023-05-12~9999-12-31 1
 
4.3%
2023-05-10~2024-12-31 1
 
4.3%
2023-04-28~9999-12-31 1
 
4.3%
2023-01-31~2023-12-31 1
 
4.3%
2022-11-22~9999-12-31 1
 
4.3%
Other values (9) 9
39.1%
2024-01-28T14:30:02.583961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 129
26.7%
- 92
19.0%
1 70
14.5%
0 67
13.9%
3 35
 
7.2%
9 29
 
6.0%
~ 23
 
4.8%
5 13
 
2.7%
6 9
 
1.9%
4 8
 
1.7%
Other values (2) 8
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 368
76.2%
Dash Punctuation 92
 
19.0%
Math Symbol 23
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 129
35.1%
1 70
19.0%
0 67
18.2%
3 35
 
9.5%
9 29
 
7.9%
5 13
 
3.5%
6 9
 
2.4%
4 8
 
2.2%
7 5
 
1.4%
8 3
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%
Math Symbol
ValueCountFrequency (%)
~ 23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 483
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 129
26.7%
- 92
19.0%
1 70
14.5%
0 67
13.9%
3 35
 
7.2%
9 29
 
6.0%
~ 23
 
4.8%
5 13
 
2.7%
6 9
 
1.9%
4 8
 
1.7%
Other values (2) 8
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 483
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 129
26.7%
- 92
19.0%
1 70
14.5%
0 67
13.9%
3 35
 
7.2%
9 29
 
6.0%
~ 23
 
4.8%
5 13
 
2.7%
6 9
 
1.9%
4 8
 
1.7%
Other values (2) 8
 
1.7%

비고
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size356.0 B
<NA>
19 
지장이 없는 한 영구점용
7 
연장(갱신)
2 

Length

Max length13
Median length4
Mean length6.3928571
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연장(갱신)
2nd row연장(갱신)
3rd row<NA>
4th row<NA>
5th row지장이 없는 한 영구점용

Common Values

ValueCountFrequency (%)
<NA> 19
67.9%
지장이 없는 한 영구점용 7
 
25.0%
연장(갱신) 2
 
7.1%

Length

2024-01-28T14:30:02.700528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:30:02.788832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
38.8%
지장이 7
 
14.3%
없는 7
 
14.3%
한 7
 
14.3%
영구점용 7
 
14.3%
연장(갱신 2
 
4.1%

Interactions

2024-01-28T14:30:00.030576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:29:59.874743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:00.104513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:29:59.943170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T14:30:02.849211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호하천명소재지사용목적허가면적_제곱미터허가기간비고
번호1.0000.1950.8920.3760.0000.9391.000
하천명0.1951.0001.0000.8200.0000.9790.392
소재지0.8921.0001.0001.0001.0001.0001.000
사용목적0.3760.8201.0001.0001.0000.9541.000
허가면적_제곱미터0.0000.0001.0001.0001.0001.0000.000
허가기간0.9390.9791.0000.9541.0001.0001.000
비고1.0000.3921.0001.0000.0001.0001.000
2024-01-28T14:30:02.958149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비고하천명
비고1.0000.286
하천명0.2861.000
2024-01-28T14:30:03.048055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호허가면적_제곱미터하천명비고
번호1.000-0.0970.0000.535
허가면적_제곱미터-0.0971.0000.0000.000
하천명0.0000.0001.0000.286
비고0.5350.0000.2861.000

Missing values

2024-01-28T14:30:00.210059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T14:30:00.331140image/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.
2024-01-28T14:30:00.429826image/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

번호유형하천명소재지사용목적허가면적_제곱미터허가기간비고
01지방하천공촌천공촌동 293-1 외 3필지송유관 매설25.742023-01-01~2025-12-31연장(갱신)
12지방하천공촌천연희동 42-11지중 송유관시설 보호2.642023-01-01~2025-12-31연장(갱신)
23지방하천검단천오류동 911 외 1필지공업용수관로매설13.162022-01-21~2026-12-31<NA>
34지방하천검단천오류동 1461공업용수관로매설58.02022-01-21~2026-12-31<NA>
45지방하천계양천당하동 162-5금연안내 표지판 설치1.962021-11-23~9999-12-31지장이 없는 한 영구점용
56지방하천나진포천불로동 110-5 외 3필지전주신설0.5042023-01-01~2025-12-31<NA>
67지방하천심곡천청라동 138-7 외 1필지전력공급용 송전설비 지하매설109.02022-04-26~2024-12-31<NA>
78지방하천대곡천대곡동 687-8전주신설0.7842022-04-26~2025-12-31<NA>
89지방하천대곡천대곡동 319-8 외 1필지전주신설0.3922022-04-26~2025-12-31<NA>
910지방하천나진포천불로동 54-10 외 1필지전주이설 및 관로 매설71.9622022-06-15~2025-12-31<NA>
번호유형하천명소재지사용목적허가면적_제곱미터허가기간비고
1819지방하천대곡천대곡동 687-8<NA>1.96<NA><NA>
1920지방하천검단천오류동 1460<NA>1.96<NA><NA>
2021지방하천공촌천경서동 383-2 외 1필지<NA>3.92<NA><NA>
2122지방하천심곡천청라동 1-1064 외 2필지<NA>5.88<NA><NA>
2223지방하천대포천금곡동 739<NA>1.96<NA><NA>
2324지방하천나진포천불로동 54-19통신관로 매설15.332023-01-31~2023-12-31<NA>
2425지방하천검단천오류동 1107 외 2필지학운6일반산업단지 전원공급을 위한 회선인출공사(전주신설)0.6082023-04-28~9999-12-31지장이 없는 한 영구점용
2526지방하천공촌천연희동 44-30경작토지 복토차량 진출입40.02023-05-10~2024-12-31<NA>
2627지방하천나진포천대곡동 643-5 외 2필지전주신설0.5882023-05-12~9999-12-31지장이 없는 한 영구점용
2728지방하천시천천검암동 25하수관 연결1.692023-05-15~2027-12-31<NA>