Overview

Dataset statistics

Number of variables4
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory38.0 B

Variable types

Numeric2
Text1
DateTime1

Dataset

Description인천광역시 계양구 녹지대의 현황에 대한 데이터파일로서 연번, 녹지대위치, 면적(m2), 데이터기준일을 포함하고 있습니다.
Author인천광역시 계양구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15117145&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일 has constant value ""Constant
연번 has unique valuesUnique
녹지대위치 has unique valuesUnique

Reproduction

Analysis started2024-01-28 09:49:00.307095
Analysis finished2024-01-28 09:49:01.043682
Duration0.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-01-28T18:49:01.091934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.6
Q19
median17
Q325
95-th percentile31.4
Maximum33
Range32
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.6695398
Coefficient of variation (CV)0.56879646
Kurtosis-1.2
Mean17
Median Absolute Deviation (MAD)8
Skewness0
Sum561
Variance93.5
MonotonicityStrictly increasing
2024-01-28T18:49:01.201276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 1
 
3.0%
26 1
 
3.0%
20 1
 
3.0%
21 1
 
3.0%
22 1
 
3.0%
23 1
 
3.0%
24 1
 
3.0%
25 1
 
3.0%
27 1
 
3.0%
2 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
1 1
3.0%
2 1
3.0%
3 1
3.0%
4 1
3.0%
5 1
3.0%
6 1
3.0%
7 1
3.0%
8 1
3.0%
9 1
3.0%
10 1
3.0%
ValueCountFrequency (%)
33 1
3.0%
32 1
3.0%
31 1
3.0%
30 1
3.0%
29 1
3.0%
28 1
3.0%
27 1
3.0%
26 1
3.0%
25 1
3.0%
24 1
3.0%

녹지대위치
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2024-01-28T18:49:01.389454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length9.8181818
Min length4

Characters and Unicode

Total characters324
Distinct characters101
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row부평IC화단
2nd row부평IC녹지정비
3rd row작전2동사무소
4th row임학광장
5th row경계도로변화
ValueCountFrequency (%)
봉오대로 3
 
5.6%
효성동 2
 
3.7%
일원 2
 
3.7%
부평ic화단 1
 
1.9%
중앙녹지(1단계 1
 
1.9%
효성2동 1
 
1.9%
7-5번지외 1
 
1.9%
3개소 1
 
1.9%
서부간선수로변 1
 
1.9%
상야동법면화단 1
 
1.9%
Other values (40) 40
74.1%
2024-01-28T18:49:01.679242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21
 
6.5%
동 18
 
5.6%
지 11
 
3.4%
소 10
 
3.1%
2 10
 
3.1%
계 9
 
2.8%
대 8
 
2.5%
로 8
 
2.5%
전 7
 
2.2%
사 7
 
2.2%
Other values (91) 215
66.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 249
76.9%
Decimal Number 38
 
11.7%
Space Separator 21
 
6.5%
Close Punctuation 4
 
1.2%
Open Punctuation 4
 
1.2%
Dash Punctuation 4
 
1.2%
Uppercase Letter 4
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 18
 
7.2%
지 11
 
4.4%
소 10
 
4.0%
계 9
 
3.6%
대 8
 
3.2%
로 8
 
3.2%
전 7
 
2.8%
사 7
 
2.8%
작 6
 
2.4%
녹 6
 
2.4%
Other values (76) 159
63.9%
Decimal Number
ValueCountFrequency (%)
2 10
26.3%
1 6
15.8%
5 5
13.2%
3 5
13.2%
4 4
 
10.5%
6 3
 
7.9%
8 2
 
5.3%
0 2
 
5.3%
7 1
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Space Separator
ValueCountFrequency (%)
21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 249
76.9%
Common 71
 
21.9%
Latin 4
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 18
 
7.2%
지 11
 
4.4%
소 10
 
4.0%
계 9
 
3.6%
대 8
 
3.2%
로 8
 
3.2%
전 7
 
2.8%
사 7
 
2.8%
작 6
 
2.4%
녹 6
 
2.4%
Other values (76) 159
63.9%
Common
ValueCountFrequency (%)
21
29.6%
2 10
14.1%
1 6
 
8.5%
5 5
 
7.0%
3 5
 
7.0%
) 4
 
5.6%
( 4
 
5.6%
4 4
 
5.6%
- 4
 
5.6%
6 3
 
4.2%
Other values (3) 5
 
7.0%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 249
76.9%
ASCII 75
 
23.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21
28.0%
2 10
13.3%
1 6
 
8.0%
5 5
 
6.7%
3 5
 
6.7%
) 4
 
5.3%
( 4
 
5.3%
4 4
 
5.3%
- 4
 
5.3%
6 3
 
4.0%
Other values (5) 9
12.0%
Hangul
ValueCountFrequency (%)
동 18
 
7.2%
지 11
 
4.4%
소 10
 
4.0%
계 9
 
3.6%
대 8
 
3.2%
로 8
 
3.2%
전 7
 
2.8%
사 7
 
2.8%
작 6
 
2.4%
녹 6
 
2.4%
Other values (76) 159
63.9%

면적(m2)
Real number (ℝ)

Distinct32
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4252.1515
Minimum20
Maximum88000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2024-01-28T18:49:01.780344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21.2
Q1155
median300
Q3865
95-th percentile14450
Maximum88000
Range87980
Interquartile range (IQR)710

Descriptive statistics

Standard deviation15533.184
Coefficient of variation (CV)3.6530176
Kurtosis28.599533
Mean4252.1515
Median Absolute Deviation (MAD)234
Skewness5.2362633
Sum140321
Variance2.4127982 × 108
MonotonicityNot monotonic
2024-01-28T18:49:01.871353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
20 2
 
6.1%
230 1
 
3.0%
20000 1
 
3.0%
22 1
 
3.0%
121 1
 
3.0%
10750 1
 
3.0%
2100 1
 
3.0%
324 1
 
3.0%
88000 1
 
3.0%
2961 1
 
3.0%
Other values (22) 22
66.7%
ValueCountFrequency (%)
20 2
6.1%
22 1
3.0%
50 1
3.0%
66 1
3.0%
87 1
3.0%
121 1
3.0%
151 1
3.0%
155 1
3.0%
158 1
3.0%
200 1
3.0%
ValueCountFrequency (%)
88000 1
3.0%
20000 1
3.0%
10750 1
3.0%
5650 1
3.0%
3000 1
3.0%
2961 1
3.0%
2100 1
3.0%
870 1
3.0%
865 1
3.0%
750 1
3.0%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
Minimum2023-07-25 00:00:00
Maximum2023-07-25 00:00:00
2024-01-28T18:49:01.942690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:49:02.010074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-28T18:49:00.548056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:49:00.420105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:49:00.612619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:49:00.489379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T18:49:02.065787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번녹지대위치면적(m2)
연번1.0001.0000.371
녹지대위치1.0001.0001.000
면적(m2)0.3711.0001.000
2024-01-28T18:49:02.128371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번면적(m2)
연번1.0000.191
면적(m2)0.1911.000

Missing values

2024-01-28T18:49:00.685008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T18:49:01.018203image/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

연번녹지대위치면적(m2)데이터기준일
01부평IC화단2302023-07-25
12부평IC녹지정비56502023-07-25
23작전2동사무소202023-07-25
34임학광장7502023-07-25
45경계도로변화5602023-07-25
56계산2동사무소내1582023-07-25
67계양경찰서사거리안전지대 2개소5002023-07-25
78작전1동사무소502023-07-25
89계양2동사무소내1552023-07-25
910귤현동 군부대앞2742023-07-25
연번녹지대위치면적(m2)데이터기준일
2324계산3동사무소202023-07-25
2425계양대로 중앙녹지3242023-07-25
2526봉오대로 중앙녹지(1단계)880002023-07-25
2627봉오대로 중앙녹지(2단계)200002023-07-25
2728봉오대로 중앙녹지(3단계)29612023-07-25
2829팬더아파트 주변녹화2752023-07-25
2930장제로(병방동 3152-1번지 일원)2002023-07-25
3031효성동 623-48번지 일원5902023-07-25
3132박촌동 40-1번지872023-07-25
3233효성동 하나아파트 방음림8652023-07-25