Overview

Dataset statistics

Number of variables6
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory56.3 B

Variable types

Categorical2
Numeric4

Dataset

Description김해시 대기환경측정망 5개소(도시대기 : 동상동, 삼방동, 장유동, 진영읍, 도로변대기 : 김해대로)에서 측정한 미세먼지, 초미세먼지, 오존에 대한 월별 농도를 제공하고 있습니다
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15105060

Alerts

연도 has constant value ""Constant
월 is highly overall correlated with 미세먼지(PM10) and 1 other fieldsHigh correlation
미세먼지(PM10) is highly overall correlated with 월 and 1 other fieldsHigh correlation
초미세먼지(PM2점5) is highly overall correlated with 월 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-11 00:54:19.190439
Analysis finished2023-12-11 00:54:20.788813
Duration1.6 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2022
40 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 40
100.0%

Length

2023-12-11T09:54:20.844964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:54:20.932642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 40
100.0%

월
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T09:54:21.013966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median4.5
Q36.25
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.3204774
Coefficient of variation (CV)0.51566165
Kurtosis-1.2416176
Mean4.5
Median Absolute Deviation (MAD)2
Skewness0
Sum180
Variance5.3846154
MonotonicityIncreasing
2023-12-11T09:54:21.118777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 5
12.5%
2 5
12.5%
3 5
12.5%
4 5
12.5%
5 5
12.5%
6 5
12.5%
7 5
12.5%
8 5
12.5%
ValueCountFrequency (%)
1 5
12.5%
2 5
12.5%
3 5
12.5%
4 5
12.5%
5 5
12.5%
6 5
12.5%
7 5
12.5%
8 5
12.5%
ValueCountFrequency (%)
8 5
12.5%
7 5
12.5%
6 5
12.5%
5 5
12.5%
4 5
12.5%
3 5
12.5%
2 5
12.5%
1 5
12.5%

측정소명
Categorical

Distinct5
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
동상동
8 
삼방동
8 
장유동
8 
진영읍
8 
김해대로
8 

Length

Max length4
Median length3
Mean length3.2
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동상동
2nd row삼방동
3rd row장유동
4th row진영읍
5th row김해대로

Common Values

ValueCountFrequency (%)
동상동 8
20.0%
삼방동 8
20.0%
장유동 8
20.0%
진영읍 8
20.0%
김해대로 8
20.0%

Length

2023-12-11T09:54:21.252190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:54:21.368468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동상동 8
20.0%
삼방동 8
20.0%
장유동 8
20.0%
진영읍 8
20.0%
김해대로 8
20.0%

오존(O3)
Real number (ℝ)

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034375
Minimum0.019
Maximum0.053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T09:54:21.493613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.019
5-th percentile0.02185
Q10.03
median0.034
Q30.038
95-th percentile0.05005
Maximum0.053
Range0.034
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.0079184546
Coefficient of variation (CV)0.23035504
Kurtosis0.47442555
Mean0.034375
Median Absolute Deviation (MAD)0.004
Skewness0.4297726
Sum1.375
Variance6.2701923 × 10-5
MonotonicityNot monotonic
2023-12-11T09:54:21.629313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.035 5
12.5%
0.03 4
 
10.0%
0.038 4
 
10.0%
0.034 4
 
10.0%
0.033 3
 
7.5%
0.019 2
 
5.0%
0.032 2
 
5.0%
0.039 2
 
5.0%
0.024 1
 
2.5%
0.046 1
 
2.5%
Other values (12) 12
30.0%
ValueCountFrequency (%)
0.019 2
5.0%
0.022 1
 
2.5%
0.024 1
 
2.5%
0.025 1
 
2.5%
0.026 1
 
2.5%
0.028 1
 
2.5%
0.029 1
 
2.5%
0.03 4
10.0%
0.031 1
 
2.5%
0.032 2
5.0%
ValueCountFrequency (%)
0.053 1
 
2.5%
0.051 1
 
2.5%
0.05 1
 
2.5%
0.049 1
 
2.5%
0.046 1
 
2.5%
0.042 1
 
2.5%
0.039 2
 
5.0%
0.038 4
10.0%
0.037 1
 
2.5%
0.035 5
12.5%

미세먼지(PM10)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.025
Minimum13
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T09:54:21.734435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q123
median29
Q333.25
95-th percentile38.1
Maximum41
Range28
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation7.0837029
Coefficient of variation (CV)0.25276371
Kurtosis-0.68032473
Mean28.025
Median Absolute Deviation (MAD)5
Skewness-0.27303803
Sum1121
Variance50.178846
MonotonicityNot monotonic
2023-12-11T09:54:21.884201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
27 4
 
10.0%
31 3
 
7.5%
35 3
 
7.5%
30 3
 
7.5%
29 3
 
7.5%
21 2
 
5.0%
24 2
 
5.0%
33 2
 
5.0%
23 2
 
5.0%
34 2
 
5.0%
Other values (13) 14
35.0%
ValueCountFrequency (%)
13 1
2.5%
16 2
5.0%
17 1
2.5%
18 1
2.5%
19 1
2.5%
20 1
2.5%
21 2
5.0%
23 2
5.0%
24 2
5.0%
25 1
2.5%
ValueCountFrequency (%)
41 1
 
2.5%
40 1
 
2.5%
38 1
 
2.5%
37 1
 
2.5%
36 1
 
2.5%
35 3
7.5%
34 2
5.0%
33 2
5.0%
32 1
 
2.5%
31 3
7.5%

초미세먼지(PM2점5)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.85
Minimum7
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T09:54:21.986590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8.95
Q113
median15.5
Q319
95-th percentile22
Maximum24
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1851385
Coefficient of variation (CV)0.2640466
Kurtosis-0.63050611
Mean15.85
Median Absolute Deviation (MAD)2.5
Skewness-0.11873304
Sum634
Variance17.515385
MonotonicityNot monotonic
2023-12-11T09:54:22.090099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
18 5
12.5%
21 5
12.5%
15 5
12.5%
16 4
10.0%
14 4
10.0%
13 3
7.5%
11 3
7.5%
22 2
 
5.0%
19 2
 
5.0%
12 1
 
2.5%
Other values (6) 6
15.0%
ValueCountFrequency (%)
7 1
 
2.5%
8 1
 
2.5%
9 1
 
2.5%
10 1
 
2.5%
11 3
7.5%
12 1
 
2.5%
13 3
7.5%
14 4
10.0%
15 5
12.5%
16 4
10.0%
ValueCountFrequency (%)
24 1
 
2.5%
22 2
 
5.0%
21 5
12.5%
20 1
 
2.5%
19 2
 
5.0%
18 5
12.5%
16 4
10.0%
15 5
12.5%
14 4
10.0%
13 3
7.5%

Interactions

2023-12-11T09:54:20.278769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:19.334792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:19.650780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:19.947618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:20.366039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:19.410574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:19.726459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:20.030981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:20.450795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:19.492234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:19.798270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:20.112029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:20.530511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:19.568086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:19.869914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:54:20.187740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:54:22.177088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월측정소명오존(O3)미세먼지(PM10)초미세먼지(PM2점5)
월1.0000.0000.7470.6740.497
측정소명0.0001.0000.0000.5330.000
오존(O3)0.7470.0001.0000.1180.386
미세먼지(PM10)0.6740.5330.1181.0000.823
초미세먼지(PM2점5)0.4970.0000.3860.8231.000
2023-12-11T09:54:22.274233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월오존(O3)미세먼지(PM10)초미세먼지(PM2점5)측정소명
월1.0000.152-0.735-0.7140.000
오존(O3)0.1521.0000.030-0.1690.000
미세먼지(PM10)-0.7350.0301.0000.9100.206
초미세먼지(PM2점5)-0.714-0.1690.9101.0000.000
측정소명0.0000.0000.2060.0001.000

Missing values

2023-12-11T09:54:20.636119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:54:20.749304image/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

연도월측정소명오존(O3)미세먼지(PM10)초미세먼지(PM2점5)
020221동상동0.0243822
120221삼방동0.0252915
220221장유동0.0223121
320221진영읍0.0193624
420221김해대로0.0193221
520222동상동0.0353520
620222삼방동0.0352714
720222장유동0.0353119
820222진영읍0.0323321
920222김해대로0.032918
연도월측정소명오존(O3)미세먼지(PM10)초미세먼지(PM2점5)
3020227동상동0.0342114
3120227삼방동0.039168
3220227장유동0.0332315
3320227진영읍0.0352716
3420227김해대로0.0292516
3520228동상동0.031911
3620228삼방동0.0331610
3720228장유동0.0282313
3820228진영읍0.032413
3920228김해대로0.0262414