Overview

Dataset statistics

Number of variables6
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory54.6 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
URLhttps://www.data.go.kr/data/15105060/fileData.do

Alerts

미세먼지(PM10) is highly overall correlated with 초미세먼지(PM2점5) and 1 other fieldsHigh correlation
초미세먼지(PM2점5) is highly overall correlated with 미세먼지(PM10)High correlation
연도 is highly overall correlated with 미세먼지(PM10)High correlation

Reproduction

Analysis started2023-12-12 04:54:08.317493
Analysis finished2023-12-12 04:54:10.542131
Duration2.22 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
2022
60 
2023
20 

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 60
75.0%
2023 20
 
25.0%

Length

2023-12-12T13:54:10.618822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:54:10.744181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 60
75.0%
2023 20
 
25.0%

월
Real number (ℝ)

Distinct12
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:54:10.862532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median4.5
Q38.25
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation3.5220822
Coefficient of variation (CV)0.64037859
Kurtosis-1.1147561
Mean5.5
Median Absolute Deviation (MAD)2.5
Skewness0.42789017
Sum440
Variance12.405063
MonotonicityNot monotonic
2023-12-12T13:54:10.992082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 10
12.5%
2 10
12.5%
3 10
12.5%
4 10
12.5%
5 5
6.2%
6 5
6.2%
7 5
6.2%
8 5
6.2%
9 5
6.2%
10 5
6.2%
Other values (2) 10
12.5%
ValueCountFrequency (%)
1 10
12.5%
2 10
12.5%
3 10
12.5%
4 10
12.5%
5 5
6.2%
6 5
6.2%
7 5
6.2%
8 5
6.2%
9 5
6.2%
10 5
6.2%
ValueCountFrequency (%)
12 5
6.2%
11 5
6.2%
10 5
6.2%
9 5
6.2%
8 5
6.2%
7 5
6.2%
6 5
6.2%
5 5
6.2%
4 10
12.5%
3 10
12.5%

측정소명
Categorical

Distinct5
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
동상동
16 
삼방동
16 
장유동
16 
진영읍
16 
김해대로
16 

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 16
20.0%
삼방동 16
20.0%
장유동 16
20.0%
진영읍 16
20.0%
김해대로 16
20.0%

Length

2023-12-12T13:54:11.232765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:54:11.451623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동상동 16
20.0%
삼방동 16
20.0%
장유동 16
20.0%
진영읍 16
20.0%
김해대로 16
20.0%

오존(O3)
Real number (ℝ)

Distinct32
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03182875
Minimum0.017
Maximum0.053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:54:11.942268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.017
5-th percentile0.01995
Q10.026
median0.032
Q30.037
95-th percentile0.04615
Maximum0.053
Range0.036
Interquartile range (IQR)0.011

Descriptive statistics

Standard deviation0.0080737454
Coefficient of variation (CV)0.25366203
Kurtosis-0.058973448
Mean0.03182875
Median Absolute Deviation (MAD)0.006
Skewness0.42707123
Sum2.5463
Variance6.5185366 × 10-5
MonotonicityNot monotonic
2023-12-12T13:54:12.126324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.035 8
 
10.0%
0.03 6
 
7.5%
0.038 6
 
7.5%
0.024 5
 
6.2%
0.028 5
 
6.2%
0.034 4
 
5.0%
0.033 4
 
5.0%
0.02 4
 
5.0%
0.026 4
 
5.0%
0.019 3
 
3.8%
Other values (22) 31
38.8%
ValueCountFrequency (%)
0.017 1
 
1.2%
0.019 3
3.8%
0.02 4
5.0%
0.022 2
 
2.5%
0.023 2
 
2.5%
0.024 5
6.2%
0.025 2
 
2.5%
0.026 4
5.0%
0.027 2
 
2.5%
0.028 5
6.2%
ValueCountFrequency (%)
0.053 1
1.2%
0.051 1
1.2%
0.05 1
1.2%
0.049 1
1.2%
0.046 2
2.5%
0.044 1
1.2%
0.042 1
1.2%
0.0414 1
1.2%
0.041 1
1.2%
0.0405 1
1.2%

미세먼지(PM10)
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.2875
Minimum13
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:54:12.310087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16.95
Q123.75
median31.5
Q337
95-th percentile56.05
Maximum62
Range49
Interquartile range (IQR)13.25

Descriptive statistics

Standard deviation11.530031
Coefficient of variation (CV)0.3571051
Kurtosis0.44181743
Mean32.2875
Median Absolute Deviation (MAD)6.5
Skewness0.83494978
Sum2583
Variance132.94161
MonotonicityNot monotonic
2023-12-12T13:54:12.497582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
21 7
 
8.8%
34 6
 
7.5%
35 5
 
6.2%
27 5
 
6.2%
29 4
 
5.0%
38 3
 
3.8%
31 3
 
3.8%
32 3
 
3.8%
33 3
 
3.8%
30 3
 
3.8%
Other values (25) 38
47.5%
ValueCountFrequency (%)
13 1
 
1.2%
14 1
 
1.2%
16 2
 
2.5%
17 1
 
1.2%
18 2
 
2.5%
19 2
 
2.5%
20 1
 
1.2%
21 7
8.8%
23 3
3.8%
24 3
3.8%
ValueCountFrequency (%)
62 1
1.2%
61 1
1.2%
60 1
1.2%
57 1
1.2%
56 2
2.5%
55 2
2.5%
53 1
1.2%
45 1
1.2%
42 2
2.5%
41 1
1.2%

초미세먼지(PM2점5)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.025
Minimum7
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T13:54:12.667744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9
Q114
median17.5
Q321
95-th percentile24
Maximum27
Range20
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.5506294
Coefficient of variation (CV)0.26729101
Kurtosis-0.68309424
Mean17.025
Median Absolute Deviation (MAD)3.5
Skewness-0.12904859
Sum1362
Variance20.708228
MonotonicityNot monotonic
2023-12-12T13:54:12.836686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
21 8
10.0%
18 8
10.0%
16 8
10.0%
24 6
 
7.5%
14 6
 
7.5%
19 6
 
7.5%
12 6
 
7.5%
20 5
 
6.2%
15 5
 
6.2%
23 4
 
5.0%
Other values (9) 18
22.5%
ValueCountFrequency (%)
7 1
 
1.2%
8 2
 
2.5%
9 2
 
2.5%
10 1
 
1.2%
11 3
 
3.8%
12 6
7.5%
13 4
5.0%
14 6
7.5%
15 5
6.2%
16 8
10.0%
ValueCountFrequency (%)
27 1
 
1.2%
24 6
7.5%
23 4
5.0%
22 2
 
2.5%
21 8
10.0%
20 5
6.2%
19 6
7.5%
18 8
10.0%
17 2
 
2.5%
16 8
10.0%

Interactions

2023-12-12T13:54:09.828762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:08.552369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:08.926584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:09.421007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:09.955035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:08.631692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:09.053105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:09.526175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:10.096878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:08.724564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:09.227420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:09.633364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:10.205089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:08.820336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:09.323105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:54:09.724924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:54:12.960951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도월측정소명오존(O3)미세먼지(PM10)초미세먼지(PM2점5)
연도1.0000.6430.0000.1810.7200.674
월0.6431.0000.0000.7820.6740.620
측정소명0.0000.0001.0000.1850.3330.442
오존(O3)0.1810.7820.1851.0000.4670.136
미세먼지(PM10)0.7200.6740.3330.4671.0000.749
초미세먼지(PM2점5)0.6740.6200.4420.1360.7491.000
2023-12-12T13:54:13.101574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명연도
측정소명1.0000.000
연도0.0001.000
2023-12-12T13:54:13.208848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월오존(O3)미세먼지(PM10)초미세먼지(PM2점5)연도측정소명
월1.000-0.037-0.485-0.4610.4710.000
오존(O3)-0.0371.000-0.008-0.2760.1280.109
미세먼지(PM10)-0.485-0.0081.0000.8250.6990.190
초미세먼지(PM2점5)-0.461-0.2760.8251.0000.4380.153
연도0.4710.1280.6990.4381.0000.000
측정소명0.0000.1090.1900.1530.0001.000

Missing values

2023-12-12T13:54:10.367038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:54:10.499325image/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)
7020233동상동0.04145624
7120233삼방동0.04054519
7220233장유동0.0355320
7320233진영읍0.03475723
7420233김해대로0.03175627
7520234동상동0.0446118
7620234삼방동0.0465514
7720234장유동0.0415516
7820234진영읍0.0386219
7920234김해대로0.0386021