Overview

Dataset statistics

Number of variables7
Number of observations53
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory64.5 B

Variable types

DateTime1
Numeric6

Dataset

Description인천광역시 부평구 대기오염측정망 공개 데이터입니다.(월별,미세먼지,초미세먼지,아황산가스,이산화질소,오존,일산화탄소)ex) 2021-01-17,19,13,0.024,0.018,0.5,0.004
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3045130&srcSe=7661IVAWM27C61E190

Alerts

미세먼지(마이크로그램당 세제곱미터) is highly overall correlated with 초미세먼지(마이크로그램당 세제곱미터) and 1 other fieldsHigh correlation
초미세먼지(마이크로그램당 세제곱미터) is highly overall correlated with 미세먼지(마이크로그램당 세제곱미터) and 3 other fieldsHigh correlation
아황산가스(10억분율) is highly overall correlated with 초미세먼지(마이크로그램당 세제곱미터) and 2 other fieldsHigh correlation
이산화질소(10억분율) is highly overall correlated with 초미세먼지(마이크로그램당 세제곱미터) and 3 other fieldsHigh correlation
오 존(10억분율) is highly overall correlated with 이산화질소(10억분율) and 1 other fieldsHigh correlation
일산화탄소(10억분율) is highly overall correlated with 미세먼지(마이크로그램당 세제곱미터) and 4 other fieldsHigh correlation
년월별 has unique valuesUnique

Reproduction

Analysis started2024-01-28 16:17:39.321988
Analysis finished2024-01-28 16:17:42.292726
Duration2.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월별
Date

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
Minimum2020-01-17 00:00:00
Maximum2022-05-19 00:00:00
2024-01-29T01:17:42.344723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:42.450876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

미세먼지(마이크로그램당 세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.150943
Minimum15
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-01-29T01:17:42.558676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile21.6
Q130
median40
Q345
95-th percentile59.8
Maximum74
Range59
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.056591
Coefficient of variation (CV)0.30795149
Kurtosis0.42986201
Mean39.150943
Median Absolute Deviation (MAD)8
Skewness0.42439448
Sum2075
Variance145.36139
MonotonicityNot monotonic
2024-01-29T01:17:42.649065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
40 7
 
13.2%
41 3
 
5.7%
42 3
 
5.7%
45 2
 
3.8%
44 2
 
3.8%
64 2
 
3.8%
22 2
 
3.8%
30 2
 
3.8%
43 2
 
3.8%
25 2
 
3.8%
Other values (23) 26
49.1%
ValueCountFrequency (%)
15 1
1.9%
20 1
1.9%
21 1
1.9%
22 2
3.8%
23 1
1.9%
25 2
3.8%
26 1
1.9%
27 1
1.9%
28 1
1.9%
29 1
1.9%
ValueCountFrequency (%)
74 1
1.9%
64 2
3.8%
57 1
1.9%
54 1
1.9%
53 2
3.8%
52 1
1.9%
50 2
3.8%
48 1
1.9%
46 1
1.9%
45 2
3.8%

초미세먼지(마이크로그램당 세제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.037736
Minimum8
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-01-29T01:17:42.734882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile10.6
Q117
median22
Q327
95-th percentile34
Maximum38
Range30
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.2932062
Coefficient of variation (CV)0.33094172
Kurtosis-0.36343713
Mean22.037736
Median Absolute Deviation (MAD)5
Skewness0.199105
Sum1168
Variance53.190856
MonotonicityNot monotonic
2024-01-29T01:17:42.817246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
22 5
 
9.4%
27 5
 
9.4%
25 4
 
7.5%
17 4
 
7.5%
13 4
 
7.5%
31 3
 
5.7%
24 3
 
5.7%
18 3
 
5.7%
23 2
 
3.8%
32 2
 
3.8%
Other values (15) 18
34.0%
ValueCountFrequency (%)
8 1
 
1.9%
9 1
 
1.9%
10 1
 
1.9%
11 1
 
1.9%
13 4
7.5%
14 1
 
1.9%
15 1
 
1.9%
16 2
3.8%
17 4
7.5%
18 3
5.7%
ValueCountFrequency (%)
38 2
 
3.8%
37 1
 
1.9%
32 2
 
3.8%
31 3
5.7%
29 1
 
1.9%
28 1
 
1.9%
27 5
9.4%
26 1
 
1.9%
25 4
7.5%
24 3
5.7%

아황산가스(10억분율)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4717736
Minimum0.004
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-01-29T01:17:42.900118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.004
5-th percentile3
Q14
median4
Q35
95-th percentile8
Maximum8
Range7.996
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5011008
Coefficient of variation (CV)0.33568355
Kurtosis1.7494478
Mean4.4717736
Median Absolute Deviation (MAD)1
Skewness0.55415839
Sum237.004
Variance2.2533036
MonotonicityNot monotonic
2024-01-29T01:17:42.980710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4.0 21
39.6%
5.0 13
24.5%
3.0 10
18.9%
8.0 4
 
7.5%
7.0 2
 
3.8%
6.0 2
 
3.8%
0.004 1
 
1.9%
ValueCountFrequency (%)
0.004 1
 
1.9%
3.0 10
18.9%
4.0 21
39.6%
5.0 13
24.5%
6.0 2
 
3.8%
7.0 2
 
3.8%
8.0 4
 
7.5%
ValueCountFrequency (%)
8.0 4
 
7.5%
7.0 2
 
3.8%
6.0 2
 
3.8%
5.0 13
24.5%
4.0 21
39.6%
3.0 10
18.9%
0.004 1
 
1.9%

이산화질소(10억분율)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.415509
Minimum0.022
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-01-29T01:17:43.069197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.022
5-th percentile17.2
Q123
median28
Q333
95-th percentile36
Maximum42
Range41.978
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.3689344
Coefficient of variation (CV)0.26878707
Kurtosis2.3039158
Mean27.415509
Median Absolute Deviation (MAD)5
Skewness-0.96300631
Sum1453.022
Variance54.301195
MonotonicityNot monotonic
2024-01-29T01:17:43.151904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
33.0 7
 
13.2%
29.0 4
 
7.5%
26.0 3
 
5.7%
24.0 3
 
5.7%
23.0 3
 
5.7%
34.0 3
 
5.7%
31.0 3
 
5.7%
36.0 3
 
5.7%
20.0 2
 
3.8%
32.0 2
 
3.8%
Other values (13) 20
37.7%
ValueCountFrequency (%)
0.022 1
 
1.9%
14.0 1
 
1.9%
16.0 1
 
1.9%
18.0 2
3.8%
19.0 2
3.8%
20.0 2
3.8%
21.0 1
 
1.9%
22.0 2
3.8%
23.0 3
5.7%
24.0 3
5.7%
ValueCountFrequency (%)
42.0 1
 
1.9%
39.0 1
 
1.9%
36.0 3
5.7%
35.0 2
 
3.8%
34.0 3
5.7%
33.0 7
13.2%
32.0 2
 
3.8%
31.0 3
5.7%
29.0 4
7.5%
28.0 2
 
3.8%

오 존(10억분율)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)50.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.906396
Minimum0.039
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-01-29T01:17:43.251887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.039
5-th percentile14
Q119
median27
Q334
95-th percentile42
Maximum51
Range50.961
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.9425573
Coefficient of variation (CV)0.36952393
Kurtosis-0.089640843
Mean26.906396
Median Absolute Deviation (MAD)7
Skewness-0.027089973
Sum1426.039
Variance98.854445
MonotonicityNot monotonic
2024-01-29T01:17:43.348819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
16.0 4
 
7.5%
34.0 4
 
7.5%
22.0 4
 
7.5%
24.0 3
 
5.7%
42.0 3
 
5.7%
37.0 2
 
3.8%
20.0 2
 
3.8%
39.0 2
 
3.8%
30.0 2
 
3.8%
33.0 2
 
3.8%
Other values (17) 25
47.2%
ValueCountFrequency (%)
0.039 1
 
1.9%
13.0 1
 
1.9%
14.0 2
3.8%
15.0 2
3.8%
16.0 4
7.5%
17.0 2
3.8%
18.0 1
 
1.9%
19.0 1
 
1.9%
20.0 2
3.8%
22.0 4
7.5%
ValueCountFrequency (%)
51.0 1
 
1.9%
43.0 1
 
1.9%
42.0 3
5.7%
39.0 2
3.8%
37.0 2
3.8%
36.0 1
 
1.9%
35.0 2
3.8%
34.0 4
7.5%
33.0 2
3.8%
31.0 2
3.8%

일산화탄소(10억분율)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53962264
Minimum0.3
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-01-29T01:17:43.427426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.4
Q10.5
median0.5
Q30.6
95-th percentile0.74
Maximum0.9
Range0.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.12456018
Coefficient of variation (CV)0.23082831
Kurtosis0.44059542
Mean0.53962264
Median Absolute Deviation (MAD)0.1
Skewness0.49431616
Sum28.6
Variance0.015515239
MonotonicityNot monotonic
2024-01-29T01:17:43.511759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.6 16
30.2%
0.5 16
30.2%
0.4 11
20.8%
0.7 5
 
9.4%
0.8 2
 
3.8%
0.3 2
 
3.8%
0.9 1
 
1.9%
ValueCountFrequency (%)
0.3 2
 
3.8%
0.4 11
20.8%
0.5 16
30.2%
0.6 16
30.2%
0.7 5
 
9.4%
0.8 2
 
3.8%
0.9 1
 
1.9%
ValueCountFrequency (%)
0.9 1
 
1.9%
0.8 2
 
3.8%
0.7 5
 
9.4%
0.6 16
30.2%
0.5 16
30.2%
0.4 11
20.8%
0.3 2
 
3.8%

Interactions

2024-01-29T01:17:41.715405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:39.500881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.140699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.526137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.949981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.342293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.779455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:39.569067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.203339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.593607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.014840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.404722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.845629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:39.636710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.267047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.662362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.076484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.465873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.925582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:39.708560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.337626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.735278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.142956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.535067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.993500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:39.779355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.397621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.798879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.204158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.591744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:42.058703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.071288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.459041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:40.863248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.278652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:17:41.652575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:17:43.583144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월별미세먼지(마이크로그램당 세제곱미터)초미세먼지(마이크로그램당 세제곱미터)아황산가스(10억분율)이산화질소(10억분율)오 존(10억분율)일산화탄소(10억분율)
년월별1.0001.0001.0001.0001.0001.0001.000
미세먼지(마이크로그램당 세제곱미터)1.0001.0000.9010.5280.6580.3270.632
초미세먼지(마이크로그램당 세제곱미터)1.0000.9011.0000.4820.2150.2840.623
아황산가스(10억분율)1.0000.5280.4821.0000.7180.5860.733
이산화질소(10억분율)1.0000.6580.2150.7181.0000.7460.548
오 존(10억분율)1.0000.3270.2840.5860.7461.0000.000
일산화탄소(10억분율)1.0000.6320.6230.7330.5480.0001.000
2024-01-29T01:17:43.676047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미세먼지(마이크로그램당 세제곱미터)초미세먼지(마이크로그램당 세제곱미터)아황산가스(10억분율)이산화질소(10억분율)오 존(10억분율)일산화탄소(10억분율)
미세먼지(마이크로그램당 세제곱미터)1.0000.8670.4480.483-0.2580.734
초미세먼지(마이크로그램당 세제곱미터)0.8671.0000.6300.531-0.2570.822
아황산가스(10억분율)0.4480.6301.0000.513-0.2130.694
이산화질소(10억분율)0.4830.5310.5131.000-0.5240.656
오 존(10억분율)-0.258-0.257-0.213-0.5241.000-0.534
일산화탄소(10억분율)0.7340.8220.6940.656-0.5341.000

Missing values

2024-01-29T01:17:42.158513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:17:42.253641image/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

년월별미세먼지(마이크로그램당 세제곱미터)초미세먼지(마이크로그램당 세제곱미터)아황산가스(10억분율)이산화질소(10억분율)오 존(10억분율)일산화탄소(10억분율)
02020-01-1745257.026.016.00.9
12020-02-1740228.028.020.00.8
22020-03-1753378.029.026.00.6
32020-04-1750328.025.035.00.6
42020-05-1753298.023.037.00.6
52020-06-1740257.018.034.00.6
62020-07-1740234.022.028.00.5
72020-08-1725154.023.029.00.4
82020-09-1731174.029.034.00.5
92020-10-1726134.029.024.00.4
년월별미세먼지(마이크로그램당 세제곱미터)초미세먼지(마이크로그램당 세제곱미터)아황산가스(10억분율)이산화질소(10억분율)오 존(10억분율)일산화탄소(10억분율)
432021-08-1922133.024.033.00.4
442021-09-191593.024.029.00.4
452021-10-1928144.029.022.00.5
462021-11-1946274.033.019.00.6
472021-12-1940275.035.014.00.7
482022-01-1944315.033.017.00.7
492022-02-1942274.027.025.00.6
502022-03-1945234.031.027.00.5
512022-04-1948243.022.042.00.5
522022-05-1933183.016.051.00.4