Overview

Dataset statistics

Number of variables5
Number of observations79
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory43.7 B

Variable types

Text1
Numeric2
Categorical2

Dataset

Description광주광역시 동구에 설치된 그늘막 현황입니다. 그늘막 관리번호, 그늘막이 설치된 위치의 위도, 경도, 설치일 등으로 구성되어 있습니다.
Author광주광역시 동구
URLhttps://www.data.go.kr/data/15103022/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
관리번호 has unique valuesUnique
위도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:04:25.968185
Analysis finished2023-12-12 17:04:26.862061
Duration0.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Text

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
2023-12-13T02:04:27.092103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.4177215
Min length4

Characters and Unicode

Total characters428
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)100.0%

Sample

1st row충장동-1
2nd row충장동-2
3rd row충장동-3
4th row충장동-4
5th row충장동-5
ValueCountFrequency (%)
충장동-1 1
 
1.3%
학동-9 1
 
1.3%
학동-6 1
 
1.3%
학동-5 1
 
1.3%
학동-4 1
 
1.3%
학동-3 1
 
1.3%
학동-2 1
 
1.3%
학동-1 1
 
1.3%
서남동-10 1
 
1.3%
서남동-8 1
 
1.3%
Other values (69) 69
87.3%
2023-12-13T02:04:27.559944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 86
20.1%
- 79
18.5%
1 34
 
7.9%
2 33
 
7.7%
지 17
 
4.0%
원 14
 
3.3%
계 14
 
3.3%
림 14
 
3.3%
학 13
 
3.0%
3 12
 
2.8%
Other values (15) 112
26.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 227
53.0%
Decimal Number 122
28.5%
Dash Punctuation 79
 
18.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 86
37.9%
지 17
 
7.5%
원 14
 
6.2%
계 14
 
6.2%
림 14
 
6.2%
학 13
 
5.7%
산 11
 
4.8%
남 10
 
4.4%
서 10
 
4.4%
충 10
 
4.4%
Other values (4) 28
 
12.3%
Decimal Number
ValueCountFrequency (%)
1 34
27.9%
2 33
27.0%
3 12
 
9.8%
4 9
 
7.4%
5 8
 
6.6%
6 7
 
5.7%
7 6
 
4.9%
8 5
 
4.1%
0 4
 
3.3%
9 4
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 227
53.0%
Common 201
47.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 86
37.9%
지 17
 
7.5%
원 14
 
6.2%
계 14
 
6.2%
림 14
 
6.2%
학 13
 
5.7%
산 11
 
4.8%
남 10
 
4.4%
서 10
 
4.4%
충 10
 
4.4%
Other values (4) 28
 
12.3%
Common
ValueCountFrequency (%)
- 79
39.3%
1 34
16.9%
2 33
16.4%
3 12
 
6.0%
4 9
 
4.5%
5 8
 
4.0%
6 7
 
3.5%
7 6
 
3.0%
8 5
 
2.5%
0 4
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 227
53.0%
ASCII 201
47.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 86
37.9%
지 17
 
7.5%
원 14
 
6.2%
계 14
 
6.2%
림 14
 
6.2%
학 13
 
5.7%
산 11
 
4.8%
남 10
 
4.4%
서 10
 
4.4%
충 10
 
4.4%
Other values (4) 28
 
12.3%
ASCII
ValueCountFrequency (%)
- 79
39.3%
1 34
16.9%
2 33
16.4%
3 12
 
6.0%
4 9
 
4.5%
5 8
 
4.0%
6 7
 
3.5%
7 6
 
3.0%
8 5
 
2.5%
0 4
 
2.0%

위도
Real number (ℝ)

UNIQUE 

Distinct79
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.144036
Minimum35.10172
Maximum35.164537
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T02:04:27.725708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.10172
5-th percentile35.117421
Q135.135685
median35.14832
Q335.154621
95-th percentile35.16152
Maximum35.164537
Range0.062817
Interquartile range (IQR)0.0189355

Descriptive statistics

Standard deviation0.014570446
Coefficient of variation (CV)0.00041459229
Kurtosis0.25722043
Mean35.144036
Median Absolute Deviation (MAD)0.00821
Skewness-0.94504867
Sum2776.3788
Variance0.00021229791
MonotonicityNot monotonic
2023-12-13T02:04:27.930008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1485 1
 
1.3%
35.15256 1
 
1.3%
35.13286 1
 
1.3%
35.13512 1
 
1.3%
35.13721 1
 
1.3%
35.14236 1
 
1.3%
35.13953 1
 
1.3%
35.13884 1
 
1.3%
35.14164 1
 
1.3%
35.1442 1
 
1.3%
Other values (69) 69
87.3%
ValueCountFrequency (%)
35.10172 1
1.3%
35.10557 1
1.3%
35.11193624 1
1.3%
35.11728 1
1.3%
35.11743678 1
1.3%
35.11806 1
1.3%
35.11904 1
1.3%
35.11969 1
1.3%
35.12054395 1
1.3%
35.12348 1
1.3%
ValueCountFrequency (%)
35.164537 1
1.3%
35.16357 1
1.3%
35.16222 1
1.3%
35.16161 1
1.3%
35.16151 1
1.3%
35.1611 1
1.3%
35.16046466 1
1.3%
35.160185 1
1.3%
35.16016363 1
1.3%
35.1592 1
1.3%

경도
Real number (ℝ)

Distinct76
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.92534
Minimum126.91252
Maximum126.93909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size843.0 B
2023-12-13T02:04:28.160801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.91252
5-th percentile126.91662
Q1126.92073
median126.92427
Q3126.93046
95-th percentile126.93735
Maximum126.93909
Range0.0265688
Interquartile range (IQR)0.009735

Descriptive statistics

Standard deviation0.0062375825
Coefficient of variation (CV)4.9143713 × 10-5
Kurtosis-0.43465791
Mean126.92534
Median Absolute Deviation (MAD)0.00433
Skewness0.30451546
Sum10027.102
Variance3.8907435 × 10-5
MonotonicityNot monotonic
2023-12-13T02:04:28.391697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.92072 2
 
2.5%
126.91903 2
 
2.5%
126.93152 2
 
2.5%
126.91336 1
 
1.3%
126.9235 1
 
1.3%
126.92828 1
 
1.3%
126.92677 1
 
1.3%
126.92486 1
 
1.3%
126.92073 1
 
1.3%
126.92173 1
 
1.3%
Other values (66) 66
83.5%
ValueCountFrequency (%)
126.9125212 1
1.3%
126.91336 1
1.3%
126.91349 1
1.3%
126.91411 1
1.3%
126.9169 1
1.3%
126.91796 1
1.3%
126.91821 1
1.3%
126.9188 1
1.3%
126.91903 2
2.5%
126.91934 1
1.3%
ValueCountFrequency (%)
126.93909 1
1.3%
126.93868 1
1.3%
126.93789 1
1.3%
126.937652 1
1.3%
126.93732 1
1.3%
126.93631 1
1.3%
126.93536 1
1.3%
126.9335768 1
1.3%
126.93249 1
1.3%
126.9320822 1
1.3%

설치일
Categorical

Distinct15
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size764.0 B
2019-07-03
11 
2019-06-17
10 
2018-07-23
10 
2018-08-29
10 
2020-08-06
7 
Other values (10)
31 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)1.3%

Sample

1st row2020-03-27
2nd row2019-07-03
3rd row2019-07-03
4th row2019-06-17
5th row2019-06-17

Common Values

ValueCountFrequency (%)
2019-07-03 11
13.9%
2019-06-17 10
12.7%
2018-07-23 10
12.7%
2018-08-29 10
12.7%
2020-08-06 7
8.9%
2019-05-05 6
7.6%
2020-03-27 4
 
5.1%
2021-07-14 4
 
5.1%
2021-07-01 4
 
5.1%
2023-08-14 4
 
5.1%
Other values (5) 9
11.4%

Length

2023-12-13T02:04:28.531563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-07-03 11
13.9%
2019-06-17 10
12.7%
2018-07-23 10
12.7%
2018-08-29 10
12.7%
2020-08-06 7
8.9%
2019-05-05 6
7.6%
2020-03-27 4
 
5.1%
2021-07-14 4
 
5.1%
2021-07-01 4
 
5.1%
2023-08-14 4
 
5.1%
Other values (5) 9
11.4%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size764.0 B
2023-08-28
79 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-28
2nd row2023-08-28
3rd row2023-08-28
4th row2023-08-28
5th row2023-08-28

Common Values

ValueCountFrequency (%)
2023-08-28 79
100.0%

Length

2023-12-13T02:04:28.657710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:04:28.748287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-28 79
100.0%

Interactions

2023-12-13T02:04:26.410521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:04:26.165382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:04:26.544158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:04:26.308881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:04:28.808225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호위도경도설치일
관리번호1.0001.0001.0001.000
위도1.0001.0000.7190.652
경도1.0000.7191.0000.000
설치일1.0000.6520.0001.000
2023-12-13T02:04:28.899806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도설치일
위도1.000-0.3160.292
경도-0.3161.0000.000
설치일0.2920.0001.000

Missing values

2023-12-13T02:04:26.692319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:04:26.817705image/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

관리번호위도경도설치일데이터기준일자
0충장동-135.1485126.913362020-03-272023-08-28
1충장동-235.15256126.918212019-07-032023-08-28
2충장동-335.14626126.919032019-07-032023-08-28
3충장동-435.14943126.922032019-06-172023-08-28
4충장동-535.14944126.922012019-06-172023-08-28
5충장동-635.14868126.921882019-06-172023-08-28
6충장동-735.14832126.913492018-07-232023-08-28
7충장동-835.14907126.922052019-05-052023-08-28
8충장동-935.151828126.9125212022-09-012023-08-28
9충장동-1035.147959126.919032022-09-012023-08-28
관리번호위도경도설치일데이터기준일자
69지원2동-135.10557126.937322018-07-232023-08-28
70지원2동-235.10172126.938682018-08-292023-08-28
71지원2동-335.12348126.932492019-07-032023-08-28
72지원2동-435.11806126.922172020-06-162023-08-28
73지원2동-535.11728126.927152020-06-162023-08-28
74지원2동-635.11969126.931732020-08-062023-08-28
75지원2동-735.11904126.930932020-08-062023-08-28
76지원2동-835.117437126.9226562019-07-072023-08-28
77지원2동-935.120544126.9335772019-07-072023-08-28
78지원2동-1035.111936126.9245852023-08-142023-08-28