Overview

Dataset statistics

Number of variables5
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory43.0 B

Variable types

Numeric1
Categorical2
Text2

Dataset

Description인천광역시 계양구 관내 자동차 공회전 제한지역 지정에 대한 데이터로 구분, 명칭, 주소, 변경내역 등에 대한 내용입니다.
Author인천광역시 계양구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15041870&srcSe=7661IVAWM27C61E190

Alerts

구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
변경내역 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 구분 and 1 other fieldsHigh correlation
변경내역 is highly imbalanced (56.5%)Imbalance
연번 has unique valuesUnique
명칭 has unique valuesUnique

Reproduction

Analysis started2024-03-18 05:34:27.120074
Analysis finished2024-03-18 05:34:29.126815
Duration2.01 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2024-03-18T14:34:29.201214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q117.5
median34
Q350.5
95-th percentile63.7
Maximum67
Range66
Interquartile range (IQR)33

Descriptive statistics

Standard deviation19.485037
Coefficient of variation (CV)0.57308932
Kurtosis-1.2
Mean34
Median Absolute Deviation (MAD)17
Skewness0
Sum2278
Variance379.66667
MonotonicityStrictly increasing
2024-03-18T14:34:29.329251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
44 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
45 1
 
1.5%
43 1
 
1.5%
2 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size668.0 B
주차장
51 
차고지
11 
다중이용시설
 
5

Length

Max length6
Median length3
Mean length3.2238806
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
주차장 51
76.1%
차고지 11
 
16.4%
다중이용시설 5
 
7.5%

Length

2024-03-18T14:34:29.474001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:34:29.599863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주차장 51
76.1%
차고지 11
 
16.4%
다중이용시설 5
 
7.5%

명칭
Text

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-03-18T14:34:29.819178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length10
Mean length6.641791
Min length2

Characters and Unicode

Total characters445
Distinct characters137
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

Unique67 ?
Unique (%)100.0%

Sample

1st row(유)삼삼운수
2nd row공성교통
3rd row동원택시(합)
4th row동화운수
5th row영부교통(합)
ValueCountFrequency (%)
부설 5
 
5.6%
작전역 4
 
4.5%
계양점 3
 
3.4%
계양역 2
 
2.2%
양지말공원 1
 
1.1%
81 1
 
1.1%
효성동 1
 
1.1%
작전동94-1 1
 
1.1%
명현쌈지 1
 
1.1%
마장로 1
 
1.1%
Other values (69) 69
77.5%
2024-03-18T14:34:30.165098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
계 24
 
5.4%
22
 
4.9%
양 18
 
4.0%
( 17
 
3.8%
) 17
 
3.8%
산 12
 
2.7%
공 12
 
2.7%
원 11
 
2.5%
지 10
 
2.2%
주 9
 
2.0%
Other values (127) 293
65.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 357
80.2%
Decimal Number 24
 
5.4%
Space Separator 22
 
4.9%
Open Punctuation 17
 
3.8%
Close Punctuation 17
 
3.8%
Uppercase Letter 6
 
1.3%
Dash Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 24
 
6.7%
양 18
 
5.0%
산 12
 
3.4%
공 12
 
3.4%
원 11
 
3.1%
지 10
 
2.8%
주 9
 
2.5%
동 9
 
2.5%
전 8
 
2.2%
설 7
 
2.0%
Other values (109) 237
66.4%
Decimal Number
ValueCountFrequency (%)
1 7
29.2%
9 3
12.5%
2 3
12.5%
4 3
12.5%
0 2
 
8.3%
5 2
 
8.3%
3 2
 
8.3%
8 1
 
4.2%
7 1
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
C 2
33.3%
B 1
16.7%
G 1
16.7%
I 1
16.7%
R 1
16.7%
Space Separator
ValueCountFrequency (%)
22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 357
80.2%
Common 82
 
18.4%
Latin 6
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 24
 
6.7%
양 18
 
5.0%
산 12
 
3.4%
공 12
 
3.4%
원 11
 
3.1%
지 10
 
2.8%
주 9
 
2.5%
동 9
 
2.5%
전 8
 
2.2%
설 7
 
2.0%
Other values (109) 237
66.4%
Common
ValueCountFrequency (%)
22
26.8%
( 17
20.7%
) 17
20.7%
1 7
 
8.5%
9 3
 
3.7%
2 3
 
3.7%
4 3
 
3.7%
- 2
 
2.4%
0 2
 
2.4%
5 2
 
2.4%
Other values (3) 4
 
4.9%
Latin
ValueCountFrequency (%)
C 2
33.3%
B 1
16.7%
G 1
16.7%
I 1
16.7%
R 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 357
80.2%
ASCII 88
 
19.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
계 24
 
6.7%
양 18
 
5.0%
산 12
 
3.4%
공 12
 
3.4%
원 11
 
3.1%
지 10
 
2.8%
주 9
 
2.5%
동 9
 
2.5%
전 8
 
2.2%
설 7
 
2.0%
Other values (109) 237
66.4%
ASCII
ValueCountFrequency (%)
22
25.0%
( 17
19.3%
) 17
19.3%
1 7
 
8.0%
9 3
 
3.4%
2 3
 
3.4%
4 3
 
3.4%
- 2
 
2.3%
0 2
 
2.3%
5 2
 
2.3%
Other values (8) 10
11.4%

주소
Text

Distinct65
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2024-03-18T14:34:30.387179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length20.656716
Min length16

Characters and Unicode

Total characters1384
Distinct characters64
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)95.5%

Sample

1st row인천광역시 계양구 아나지로 510(서운동)
2nd row인천광역시 계양구 봉오대로543번길 3(효성동)
3rd row인천광역시 계양구 봉오대로636(작전동)
4th row인천광역시 계양구 효성동 580
5th row인천광역시 계양구 아나지로148번길 7(효성동)
ValueCountFrequency (%)
인천광역시 67
25.2%
계양구 67
25.2%
계산동 9
 
3.4%
작전동 7
 
2.6%
효성동 7
 
2.6%
아나지로 6
 
2.3%
귤현동 4
 
1.5%
448(작전동 3
 
1.1%
동양동 3
 
1.1%
장제로 2
 
0.8%
Other values (84) 91
34.2%
2024-03-18T14:34:30.732357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
199
 
14.4%
계 94
 
6.8%
양 76
 
5.5%
동 70
 
5.1%
인 67
 
4.8%
광 67
 
4.8%
역 67
 
4.8%
시 67
 
4.8%
구 67
 
4.8%
천 67
 
4.8%
Other values (54) 543
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 862
62.3%
Decimal Number 242
 
17.5%
Space Separator 199
 
14.4%
Open Punctuation 27
 
2.0%
Close Punctuation 27
 
2.0%
Dash Punctuation 25
 
1.8%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
계 94
10.9%
양 76
 
8.8%
동 70
 
8.1%
인 67
 
7.8%
광 67
 
7.8%
역 67
 
7.8%
시 67
 
7.8%
구 67
 
7.8%
천 67
 
7.8%
산 28
 
3.2%
Other values (39) 192
22.3%
Decimal Number
ValueCountFrequency (%)
1 43
17.8%
4 30
12.4%
8 29
12.0%
5 28
11.6%
3 26
10.7%
2 19
7.9%
0 19
7.9%
9 16
 
6.6%
6 16
 
6.6%
7 16
 
6.6%
Space Separator
ValueCountFrequency (%)
199
100.0%
Open Punctuation
ValueCountFrequency (%)
( 27
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 862
62.3%
Common 522
37.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
계 94
10.9%
양 76
 
8.8%
동 70
 
8.1%
인 67
 
7.8%
광 67
 
7.8%
역 67
 
7.8%
시 67
 
7.8%
구 67
 
7.8%
천 67
 
7.8%
산 28
 
3.2%
Other values (39) 192
22.3%
Common
ValueCountFrequency (%)
199
38.1%
1 43
 
8.2%
4 30
 
5.7%
8 29
 
5.6%
5 28
 
5.4%
( 27
 
5.2%
) 27
 
5.2%
3 26
 
5.0%
- 25
 
4.8%
2 19
 
3.6%
Other values (5) 69
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 862
62.3%
ASCII 522
37.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
199
38.1%
1 43
 
8.2%
4 30
 
5.7%
8 29
 
5.6%
5 28
 
5.4%
( 27
 
5.2%
) 27
 
5.2%
3 26
 
5.0%
- 25
 
4.8%
2 19
 
3.6%
Other values (5) 69
 
13.2%
Hangul
ValueCountFrequency (%)
계 94
10.9%
양 76
 
8.8%
동 70
 
8.1%
인 67
 
7.8%
광 67
 
7.8%
역 67
 
7.8%
시 67
 
7.8%
구 67
 
7.8%
천 67
 
7.8%
산 28
 
3.2%
Other values (39) 192
22.3%

변경내역
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
<NA>
61 
명칭변경
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 61
91.0%
명칭변경 6
 
9.0%

Length

2024-03-18T14:34:30.850444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T14:34:30.951401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 61
91.0%
명칭변경 6
 
9.0%

Interactions

2024-03-18T14:34:28.791029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T14:34:31.014152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분명칭주소
연번1.0000.8251.0001.000
구분0.8251.0001.0001.000
명칭1.0001.0001.0001.000
주소1.0001.0001.0001.000
2024-03-18T14:34:31.094726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분변경내역
구분1.0001.000
변경내역1.0001.000
2024-03-18T14:34:31.172360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분변경내역
연번1.0000.6871.000
구분0.6871.0001.000
변경내역1.0001.0001.000

Missing values

2024-03-18T14:34:28.993468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T14:34:29.077028image/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

연번구분명칭주소변경내역
01차고지(유)삼삼운수인천광역시 계양구 아나지로 510(서운동)<NA>
12차고지공성교통인천광역시 계양구 봉오대로543번길 3(효성동)<NA>
23차고지동원택시(합)인천광역시 계양구 봉오대로636(작전동)<NA>
34차고지동화운수인천광역시 계양구 효성동 580<NA>
45차고지영부교통(합)인천광역시 계양구 아나지로148번길 7(효성동)<NA>
56차고지한일운수(주)인천광역시 계양구 계산로 134(계산동)<NA>
67차고지RC운수인천광역시 계양구 아나지로 111(효성동)<NA>
78주차장계산복개천인천광역시 계양구 계산동 1035<NA>
89주차장계산택지1인천광역시 계양구 계산동 106<NA>
910주차장계산택지2인천광역시 계양구 계산동 1079-2<NA>
연번구분명칭주소변경내역
5758주차장인천어린이과학관 부설인천광역시 계양구 방축로21(방축동)<NA>
5859다중이용시설홈플러스테스코(주) 계산점인천광역시 계양구 오조산공원로 14(계산동)<NA>
5960다중이용시설롯데쇼핑(주) 롯데마트 계양점인천광역시 계양구 장제로 822(계산동)명칭변경
6061다중이용시설홈플러스(주) 작전점인천광역시 계양구 게양대로 27(작전동)<NA>
6162다중이용시설(주)이마트 계양점인천광역시 계양구 봉오대로 785(작전동)<NA>
6263다중이용시설메트로몰 계양점인천광역시 계양구 장제로 738(작전동)<NA>
6364차고지(주)서인천관광인천광역시 계양구 아나지로 448(작전동)<NA>
6465차고지(주)화이트투어인천광역시 계양구 아나지로 448(작전동)<NA>
6566차고지(주)청송관광인천광역시 계양구 아나지로 448(작전동)<NA>
6667차고지주식회사GB플랜인천광역시 계양구 아나지로 454(서운동)<NA>