Overview

Dataset statistics

Number of variables3
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory934.0 B
Average record size in memory30.1 B

Variable types

Text1
Numeric2

Dataset

Description전북의 년 평균 대기오염에 관련된 정보입니다.(측정소(전주시, 군산시, 익산시, 정읍시 등), 오존(O3)농도, 아황산가스 농도)
Author전북특별자치도
URLhttps://www.data.go.kr/data/15059693/fileData.do

Alerts

측정소 has unique valuesUnique
오존 has 2 (6.5%) zerosZeros
아황산가스 has 1 (3.2%) zerosZeros

Reproduction

Analysis started2024-03-14 11:45:27.946302
Analysis finished2024-03-14 11:45:29.453064
Duration1.51 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정소
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size376.0 B
2024-03-14T20:45:30.142646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters217
Distinct characters61
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

Unique31 ?
Unique (%)100.0%

Sample

1st row전주시 노송동
2nd row전주시 삼천동
3rd row전주시 서신동
4th row전주시 송천동
5th row전주시 팔복동
ValueCountFrequency (%)
전주시 6
 
9.7%
익산시 6
 
9.7%
군산시 5
 
8.1%
완주군 2
 
3.2%
정읍시 2
 
3.2%
고창군 2
 
3.2%
요촌동 1
 
1.6%
고산면 1
 
1.6%
봉동읍 1
 
1.6%
진안군 1
 
1.6%
Other values (35) 35
56.5%
2024-03-14T20:45:31.398658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
31
 
14.3%
시 21
 
9.7%
동 17
 
7.8%
군 15
 
6.9%
산 13
 
6.0%
읍 11
 
5.1%
주 10
 
4.6%
전 6
 
2.8%
익 6
 
2.8%
면 5
 
2.3%
Other values (51) 82
37.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 186
85.7%
Space Separator 31
 
14.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 21
 
11.3%
동 17
 
9.1%
군 15
 
8.1%
산 13
 
7.0%
읍 11
 
5.9%
주 10
 
5.4%
전 6
 
3.2%
익 6
 
3.2%
면 5
 
2.7%
창 5
 
2.7%
Other values (50) 77
41.4%
Space Separator
ValueCountFrequency (%)
31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 186
85.7%
Common 31
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 21
 
11.3%
동 17
 
9.1%
군 15
 
8.1%
산 13
 
7.0%
읍 11
 
5.9%
주 10
 
5.4%
전 6
 
3.2%
익 6
 
3.2%
면 5
 
2.7%
창 5
 
2.7%
Other values (50) 77
41.4%
Common
ValueCountFrequency (%)
31
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 186
85.7%
ASCII 31
 
14.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
31
100.0%
Hangul
ValueCountFrequency (%)
시 21
 
11.3%
동 17
 
9.1%
군 15
 
8.1%
산 13
 
7.0%
읍 11
 
5.9%
주 10
 
5.4%
전 6
 
3.2%
익 6
 
3.2%
면 5
 
2.7%
창 5
 
2.7%
Other values (50) 77
41.4%

오존
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030483871
Minimum0
Maximum0.043
Zeros2
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-14T20:45:31.773983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0085
Q10.026
median0.033
Q30.0365
95-th percentile0.042
Maximum0.043
Range0.043
Interquartile range (IQR)0.0105

Descriptive statistics

Standard deviation0.010478139
Coefficient of variation (CV)0.34372731
Kurtosis2.9890723
Mean0.030483871
Median Absolute Deviation (MAD)0.004
Skewness-1.6383653
Sum0.945
Variance0.0001097914
MonotonicityNot monotonic
2024-03-14T20:45:32.147153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.035 4
12.9%
0.036 3
9.7%
0.03 3
9.7%
0.033 3
9.7%
0.037 2
 
6.5%
0.0 2
 
6.5%
0.023 2
 
6.5%
0.026 2
 
6.5%
0.042 2
 
6.5%
0.041 1
 
3.2%
Other values (7) 7
22.6%
ValueCountFrequency (%)
0.0 2
6.5%
0.017 1
 
3.2%
0.021 1
 
3.2%
0.022 1
 
3.2%
0.023 2
6.5%
0.026 2
6.5%
0.03 3
9.7%
0.031 1
 
3.2%
0.033 3
9.7%
0.035 4
12.9%
ValueCountFrequency (%)
0.043 1
 
3.2%
0.042 2
6.5%
0.041 1
 
3.2%
0.039 1
 
3.2%
0.038 1
 
3.2%
0.037 2
6.5%
0.036 3
9.7%
0.035 4
12.9%
0.033 3
9.7%
0.031 1
 
3.2%

아황산가스
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0032903226
Minimum0
Maximum0.006
Zeros1
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-14T20:45:32.480062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.0025
median0.003
Q30.004
95-th percentile0.005
Maximum0.006
Range0.006
Interquartile range (IQR)0.0015

Descriptive statistics

Standard deviation0.0012700013
Coefficient of variation (CV)0.38598078
Kurtosis0.35101163
Mean0.0032903226
Median Absolute Deviation (MAD)0.001
Skewness-0.067879378
Sum0.102
Variance1.6129032 × 10-6
MonotonicityNot monotonic
2024-03-14T20:45:32.773459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.003 11
35.5%
0.002 7
22.6%
0.004 6
19.4%
0.005 5
16.1%
0.0 1
 
3.2%
0.006 1
 
3.2%
ValueCountFrequency (%)
0.0 1
 
3.2%
0.002 7
22.6%
0.003 11
35.5%
0.004 6
19.4%
0.005 5
16.1%
0.006 1
 
3.2%
ValueCountFrequency (%)
0.006 1
 
3.2%
0.005 5
16.1%
0.004 6
19.4%
0.003 11
35.5%
0.002 7
22.6%
0.0 1
 
3.2%

Interactions

2024-03-14T20:45:28.550426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:45:28.070097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:45:28.792756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:45:28.302894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:45:32.948312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소오존아황산가스
측정소1.0001.0001.000
오존1.0001.0000.477
아황산가스1.0000.4771.000
2024-03-14T20:45:33.108904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
오존아황산가스
오존1.000-0.132
아황산가스-0.1321.000

Missing values

2024-03-14T20:45:29.116250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:45:29.357084image/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

측정소오존아황산가스
0전주시 노송동0.0360.003
1전주시 삼천동0.0350.003
2전주시 서신동0.00.003
3전주시 송천동0.0350.005
4전주시 팔복동0.030.005
5전주시 혁신동0.00.0
6군산시 개정동0.030.005
7군산시 비응도0.030.005
8군산시 소룡동0.0430.006
9군산시 신풍동0.0330.003
측정소오존아황산가스
21완주군 고산면0.0380.002
22완주군 봉동읍0.0230.002
23진안군 진안읍0.0330.002
24무주군 무주읍0.0360.003
25장수군 장수읍0.0420.004
26임실군 임실읍0.0260.004
27순창군 순창읍0.0350.004
28고창군 고창읍0.0390.002
29고창군 심원면0.0420.003
30부안군 부안읍0.0410.003