Overview

Dataset statistics

Number of variables5
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory47.3 B

Variable types

Numeric2
Text2
Categorical1

Dataset

Description인천광역시 계양구 가로수벽현황에 대한 데이터파일로서 도로명, 주요수종, 식재본수, 데이터기준일을 포함하는 파일입니다.
Author인천광역시 계양구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15117144&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일 has constant value ""Constant
연번 has unique valuesUnique
도로명 has unique valuesUnique
식재본수 has unique valuesUnique

Reproduction

Analysis started2024-01-28 11:30:41.147133
Analysis finished2024-01-28 11:30:41.678087
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-28T20:30:41.735768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q17
median13
Q319
95-th percentile23.8
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.56613852
Kurtosis-1.2
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum325
Variance54.166667
MonotonicityStrictly increasing
2024-01-28T20:30:41.825318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1
 
4.0%
2 1
 
4.0%
25 1
 
4.0%
24 1
 
4.0%
23 1
 
4.0%
22 1
 
4.0%
21 1
 
4.0%
20 1
 
4.0%
19 1
 
4.0%
18 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
5 1
4.0%
6 1
4.0%
7 1
4.0%
8 1
4.0%
9 1
4.0%
10 1
4.0%
ValueCountFrequency (%)
25 1
4.0%
24 1
4.0%
23 1
4.0%
22 1
4.0%
21 1
4.0%
20 1
4.0%
19 1
4.0%
18 1
4.0%
17 1
4.0%
16 1
4.0%

도로명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-01-28T20:30:41.977745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.28
Min length3

Characters and Unicode

Total characters107
Distinct characters54
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

Unique25 ?
Unique (%)100.0%

Sample

1st row마장로
2nd row안남로
3rd row게양대로
4th row주부토로
5th row장제로
ValueCountFrequency (%)
마장로 1
 
4.0%
도두리로 1
 
4.0%
봉오대로894번길 1
 
4.0%
봉오대로(서운산단 1
 
4.0%
서운산단로 1
 
4.0%
계산천 1
 
4.0%
정서진로 1
 
4.0%
아라로 1
 
4.0%
방축로~다남로 1
 
4.0%
만봉길 1
 
4.0%
Other values (15) 15
60.0%
2024-01-28T20:30:42.271588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 22
20.6%
산 6
 
5.6%
대 5
 
4.7%
오 4
 
3.7%
봉 4
 
3.7%
서 4
 
3.7%
계 3
 
2.8%
길 3
 
2.8%
양 2
 
1.9%
지 2
 
1.9%
Other values (44) 52
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 101
94.4%
Decimal Number 3
 
2.8%
Math Symbol 1
 
0.9%
Open Punctuation 1
 
0.9%
Close Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 22
21.8%
산 6
 
5.9%
대 5
 
5.0%
오 4
 
4.0%
봉 4
 
4.0%
서 4
 
4.0%
계 3
 
3.0%
길 3
 
3.0%
양 2
 
2.0%
지 2
 
2.0%
Other values (38) 46
45.5%
Decimal Number
ValueCountFrequency (%)
8 1
33.3%
9 1
33.3%
4 1
33.3%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 101
94.4%
Common 6
 
5.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 22
21.8%
산 6
 
5.9%
대 5
 
5.0%
오 4
 
4.0%
봉 4
 
4.0%
서 4
 
4.0%
계 3
 
3.0%
길 3
 
3.0%
양 2
 
2.0%
지 2
 
2.0%
Other values (38) 46
45.5%
Common
ValueCountFrequency (%)
~ 1
16.7%
( 1
16.7%
) 1
16.7%
8 1
16.7%
9 1
16.7%
4 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 101
94.4%
ASCII 6
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 22
21.8%
산 6
 
5.9%
대 5
 
5.0%
오 4
 
4.0%
봉 4
 
4.0%
서 4
 
4.0%
계 3
 
3.0%
길 3
 
3.0%
양 2
 
2.0%
지 2
 
2.0%
Other values (38) 46
45.5%
ASCII
ValueCountFrequency (%)
~ 1
16.7%
( 1
16.7%
) 1
16.7%
8 1
16.7%
9 1
16.7%
4 1
16.7%

주요수종
Text

Distinct13
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-01-28T20:30:42.407045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length4
Mean length5.08
Min length2

Characters and Unicode

Total characters127
Distinct characters35
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)32.0%

Sample

1st row사철나무
2nd row사철나무
3rd row쥐똥나무
4th row쥐똥나무
5th row사철나무
ValueCountFrequency (%)
사철나무 9
36.0%
쥐똥나무 2
 
8.0%
화살나무 2
 
8.0%
황매화 2
 
8.0%
개나리 2
 
8.0%
쥐똥+사철나무 1
 
4.0%
화양목 1
 
4.0%
남천 1
 
4.0%
산철쭉 1
 
4.0%
꽃댕강+화살나무+피라칸사스 1
 
4.0%
Other values (3) 3
 
12.0%
2024-01-28T20:30:42.844619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
나 18
14.2%
무 16
12.6%
철 13
 
10.2%
사 11
 
8.7%
+ 9
 
7.1%
화 8
 
6.3%
살 5
 
3.9%
쥐 3
 
2.4%
똥 3
 
2.4%
매 3
 
2.4%
Other values (25) 38
29.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118
92.9%
Math Symbol 9
 
7.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
나 18
15.3%
무 16
13.6%
철 13
 
11.0%
사 11
 
9.3%
화 8
 
6.8%
살 5
 
4.2%
쥐 3
 
2.5%
똥 3
 
2.5%
매 3
 
2.5%
강 3
 
2.5%
Other values (24) 35
29.7%
Math Symbol
ValueCountFrequency (%)
+ 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118
92.9%
Common 9
 
7.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
나 18
15.3%
무 16
13.6%
철 13
 
11.0%
사 11
 
9.3%
화 8
 
6.8%
살 5
 
4.2%
쥐 3
 
2.5%
똥 3
 
2.5%
매 3
 
2.5%
강 3
 
2.5%
Other values (24) 35
29.7%
Common
ValueCountFrequency (%)
+ 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118
92.9%
ASCII 9
 
7.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
나 18
15.3%
무 16
13.6%
철 13
 
11.0%
사 11
 
9.3%
화 8
 
6.8%
살 5
 
4.2%
쥐 3
 
2.5%
똥 3
 
2.5%
매 3
 
2.5%
강 3
 
2.5%
Other values (24) 35
29.7%
ASCII
ValueCountFrequency (%)
+ 9
100.0%

식재본수
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13412.8
Minimum320
Maximum84467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-01-28T20:30:42.942236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum320
5-th percentile796
Q12651
median7316
Q317515
95-th percentile36392.6
Maximum84467
Range84147
Interquartile range (IQR)14864

Descriptive statistics

Standard deviation18070.309
Coefficient of variation (CV)1.3472436
Kurtosis9.7339979
Mean13412.8
Median Absolute Deviation (MAD)5082
Skewness2.8228673
Sum335320
Variance3.2653607 × 108
MonotonicityNot monotonic
2024-01-28T20:30:43.041589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3179 1
 
4.0%
320 1
 
4.0%
11990 1
 
4.0%
2760 1
 
4.0%
6610 1
 
4.0%
8636 1
 
4.0%
12270 1
 
4.0%
33115 1
 
4.0%
1200 1
 
4.0%
23858 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
320 1
4.0%
720 1
4.0%
1100 1
4.0%
1200 1
4.0%
1892 1
4.0%
2234 1
4.0%
2651 1
4.0%
2760 1
4.0%
3179 1
4.0%
5334 1
4.0%
ValueCountFrequency (%)
84467 1
4.0%
37212 1
4.0%
33115 1
4.0%
27337 1
4.0%
23858 1
4.0%
18684 1
4.0%
17515 1
4.0%
12270 1
4.0%
11990 1
4.0%
10190 1
4.0%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-07-25
25 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-25
2nd row2023-07-25
3rd row2023-07-25
4th row2023-07-25
5th row2023-07-25

Common Values

ValueCountFrequency (%)
2023-07-25 25
100.0%

Length

2024-01-28T20:30:43.137166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:30:43.204927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-25 25
100.0%

Interactions

2024-01-28T20:30:41.438862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:41.292567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:41.495710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:30:41.366359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T20:30:43.247676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번도로명주요수종식재본수
연번1.0001.0000.6420.000
도로명1.0001.0001.0001.000
주요수종0.6421.0001.0000.000
식재본수0.0001.0000.0001.000
2024-01-28T20:30:43.315371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번식재본수
연번1.0000.126
식재본수0.1261.000

Missing values

2024-01-28T20:30:41.567962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T20:30:41.643078image/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

연번도로명주요수종식재본수데이터기준일
01마장로사철나무31792023-07-25
12안남로사철나무3202023-07-25
23게양대로쥐똥나무22342023-07-25
34주부토로쥐똥나무11002023-07-25
45장제로사철나무273372023-07-25
56서부간선로화살나무372122023-07-25
67경명대로쥐똥+사철나무186842023-07-25
78봉오대로사철나무844672023-07-25
89아나지로사철나무85002023-07-25
910계산로화양목62302023-07-25
연번도로명주요수종식재본수데이터기준일
1516양지로황매화53342023-07-25
1617만봉길황매화26512023-07-25
1718방축로~다남로남천238582023-07-25
1819아라로산철쭉12002023-07-25
1920정서진로개나리331152023-07-25
2021계산천개나리122702023-07-25
2122서운산단로꽃댕강+화살나무+피라칸사스86362023-07-25
2223봉오대로(서운산단)꽃댕강+낙상홍+옥매화+좀작살+홍가시66102023-07-25
2324봉오대로894번길꽃댕강+화살나무27602023-07-25
2425굴포천백철쭉+산철쭉119902023-07-25