Overview

Dataset statistics

Number of variables6
Number of observations2208
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory112.2 KiB
Average record size in memory52.1 B

Variable types

DateTime1
Categorical1
Numeric4

Dataset

Description광양항 중흥부두(1번 센서)의 대기정보 데이터입니다. 데이터 구성은 시간, 측정소, PM2.5수치, PM10수치, 온도, 습도로 구성되어 있습니다.데이터 갱신주기는 분기입니다.
Author여수광양항만공사
URLhttps://www.data.go.kr/data/15090374/fileData.do

Alerts

측정소 has constant value ""Constant
미세먼지(PM)2_5 is highly overall correlated with 미세먼지(PM)10High correlation
미세먼지(PM)10 is highly overall correlated with 미세먼지(PM)2_5High correlation
시간 has unique valuesUnique
미세먼지(PM)2_5 has 150 (6.8%) zerosZeros
미세먼지(PM)10 has 74 (3.4%) zerosZeros

Reproduction

Analysis started2024-03-14 21:12:22.477542
Analysis finished2024-03-14 21:12:27.752292
Duration5.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시간
Date

UNIQUE 

Distinct2208
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
Minimum2023-10-01 00:00:00
Maximum2023-12-31 23:00:00
2024-03-15T06:12:28.008081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:28.598742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

측정소
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.4 KiB
중흥부두1 실외
2208 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중흥부두1 실외
2nd row중흥부두1 실외
3rd row중흥부두1 실외
4th row중흥부두1 실외
5th row중흥부두1 실외

Common Values

ValueCountFrequency (%)
중흥부두1 실외 2208
100.0%

Length

2024-03-15T06:12:29.129111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T06:12:29.455179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중흥부두1 2208
50.0%
실외 2208
50.0%

미세먼지(PM)2_5
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.330163
Minimum0
Maximum76
Zeros150
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-03-15T06:12:29.854150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile24
Maximum76
Range76
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.2236056
Coefficient of variation (CV)1.1218858
Kurtosis9.5059738
Mean7.330163
Median Absolute Deviation (MAD)3
Skewness2.5799975
Sum16185
Variance67.62769
MonotonicityNot monotonic
2024-03-15T06:12:30.537240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 247
11.2%
1 244
11.1%
3 202
 
9.1%
4 195
 
8.8%
5 173
 
7.8%
6 168
 
7.6%
0 150
 
6.8%
7 126
 
5.7%
8 96
 
4.3%
9 90
 
4.1%
Other values (43) 517
23.4%
ValueCountFrequency (%)
0 150
6.8%
1 244
11.1%
2 247
11.2%
3 202
9.1%
4 195
8.8%
5 173
7.8%
6 168
7.6%
7 126
5.7%
8 96
 
4.3%
9 90
 
4.1%
ValueCountFrequency (%)
76 1
< 0.1%
68 1
< 0.1%
64 1
< 0.1%
63 1
< 0.1%
52 1
< 0.1%
49 2
0.1%
47 2
0.1%
46 1
< 0.1%
45 2
0.1%
44 1
< 0.1%

미세먼지(PM)10
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct63
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.709692
Minimum0
Maximum84
Zeros74
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-03-15T06:12:30.970058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median6
Q310
95-th percentile27
Maximum84
Range84
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.6181022
Coefficient of variation (CV)1.1042988
Kurtosis11.458922
Mean8.709692
Median Absolute Deviation (MAD)3
Skewness2.8577676
Sum19231
Variance92.50789
MonotonicityNot monotonic
2024-03-15T06:12:31.387436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 212
 
9.6%
2 207
 
9.4%
4 204
 
9.2%
6 179
 
8.1%
5 173
 
7.8%
1 170
 
7.7%
7 145
 
6.6%
8 111
 
5.0%
9 104
 
4.7%
10 98
 
4.4%
Other values (53) 605
27.4%
ValueCountFrequency (%)
0 74
 
3.4%
1 170
7.7%
2 207
9.4%
3 212
9.6%
4 204
9.2%
5 173
7.8%
6 179
8.1%
7 145
6.6%
8 111
5.0%
9 104
4.7%
ValueCountFrequency (%)
84 1
< 0.1%
81 2
0.1%
75 1
< 0.1%
69 1
< 0.1%
66 1
< 0.1%
64 1
< 0.1%
62 1
< 0.1%
58 2
0.1%
57 1
< 0.1%
56 2
0.1%

온도(섭씨)
Real number (ℝ)

Distinct1355
Distinct (%)61.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.599058
Minimum-1.33
Maximum42.92
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)0.2%
Memory size19.5 KiB
2024-03-15T06:12:31.799257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.33
5-th percentile6.8805
Q112.82
median18.675
Q323.175
95-th percentile32.1865
Maximum42.92
Range44.25
Interquartile range (IQR)10.355

Descriptive statistics

Standard deviation7.6883317
Coefficient of variation (CV)0.4133721
Kurtosis-0.14116785
Mean18.599058
Median Absolute Deviation (MAD)5.135
Skewness0.21856114
Sum41066.72
Variance59.110445
MonotonicityNot monotonic
2024-03-15T06:12:32.237676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.29 7
 
0.3%
21.84 6
 
0.3%
12.4 6
 
0.3%
22.94 6
 
0.3%
17.85 6
 
0.3%
18.92 6
 
0.3%
20.77 5
 
0.2%
20.14 5
 
0.2%
11.61 5
 
0.2%
22.66 5
 
0.2%
Other values (1345) 2151
97.4%
ValueCountFrequency (%)
-1.33 1
< 0.1%
-1.25 1
< 0.1%
-0.96 1
< 0.1%
-0.27 1
< 0.1%
0.2 1
< 0.1%
0.69 1
< 0.1%
1.04 1
< 0.1%
1.07 1
< 0.1%
1.82 1
< 0.1%
1.85 1
< 0.1%
ValueCountFrequency (%)
42.92 1
< 0.1%
42.55 1
< 0.1%
40.3 1
< 0.1%
40.29 1
< 0.1%
40.21 1
< 0.1%
39.93 1
< 0.1%
39.85 1
< 0.1%
39.57 1
< 0.1%
39.45 1
< 0.1%
39.19 1
< 0.1%

습도(퍼센트)
Real number (ℝ)

Distinct1635
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.962274
Minimum23.48
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.5 KiB
2024-03-15T06:12:32.536527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.48
5-th percentile30.654
Q139.01
median45.495
Q351.8625
95-th percentile62.96
Maximum85
Range61.52
Interquartile range (IQR)12.8525

Descriptive statistics

Standard deviation9.677954
Coefficient of variation (CV)0.210563
Kurtosis0.33977753
Mean45.962274
Median Absolute Deviation (MAD)6.405
Skewness0.40854893
Sum101484.7
Variance93.662794
MonotonicityNot monotonic
2024-03-15T06:12:32.972371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.3 5
 
0.2%
52.07 5
 
0.2%
43.88 5
 
0.2%
34.85 5
 
0.2%
43.28 4
 
0.2%
46.04 4
 
0.2%
43.15 4
 
0.2%
50.8 4
 
0.2%
45.18 4
 
0.2%
48.78 4
 
0.2%
Other values (1625) 2164
98.0%
ValueCountFrequency (%)
23.48 1
< 0.1%
24.24 1
< 0.1%
24.84 1
< 0.1%
24.95 1
< 0.1%
25.05 1
< 0.1%
25.27 1
< 0.1%
25.28 1
< 0.1%
25.29 1
< 0.1%
25.44 1
< 0.1%
25.49 1
< 0.1%
ValueCountFrequency (%)
85.0 1
< 0.1%
84.89 1
< 0.1%
82.54 1
< 0.1%
81.86 1
< 0.1%
81.36 1
< 0.1%
80.97 1
< 0.1%
80.62 1
< 0.1%
80.39 1
< 0.1%
80.06 1
< 0.1%
79.95 1
< 0.1%

Interactions

2024-03-15T06:12:25.906449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:22.733336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:23.834848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:24.864702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:26.185398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:23.033378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:24.106133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:25.141466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:26.547667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:23.295996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:24.353182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:25.391829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:26.811085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:23.563550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:24.607253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T06:12:25.645847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T06:12:33.209887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미세먼지(PM)2_5미세먼지(PM)10온도(섭씨)습도(퍼센트)
미세먼지(PM)2_51.0000.9330.0800.196
미세먼지(PM)100.9331.0000.0990.270
온도(섭씨)0.0800.0991.0000.500
습도(퍼센트)0.1960.2700.5001.000
2024-03-15T06:12:33.469294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미세먼지(PM)2_5미세먼지(PM)10온도(섭씨)습도(퍼센트)
미세먼지(PM)2_51.0000.9740.0600.122
미세먼지(PM)100.9741.0000.0100.125
온도(섭씨)0.0600.0101.000-0.047
습도(퍼센트)0.1220.125-0.0471.000

Missing values

2024-03-15T06:12:27.181479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T06:12:27.596350image/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

시간측정소미세먼지(PM)2_5미세먼지(PM)10온도(섭씨)습도(퍼센트)
02023-10-01 00:00중흥부두1 실외5623.0761.43
12023-10-01 01:00중흥부두1 실외4422.8861.75
22023-10-01 02:00중흥부두1 실외2322.661.84
32023-10-01 03:00중흥부두1 실외2222.5661.92
42023-10-01 04:00중흥부두1 실외2322.662.36
52023-10-01 05:00중흥부두1 실외7722.4862.65
62023-10-01 06:00중흥부두1 실외4422.462.22
72023-10-01 07:00중흥부두1 실외4422.9761.16
82023-10-01 08:00중흥부두1 실외4625.958.85
92023-10-01 09:00중흥부두1 실외3328.9754.28
시간측정소미세먼지(PM)2_5미세먼지(PM)10온도(섭씨)습도(퍼센트)
21982023-12-31 14:00중흥부두1 실외101111.7550.51
21992023-12-31 15:00중흥부두1 실외111311.5650.76
22002023-12-31 16:00중흥부두1 실외121611.7850.92
22012023-12-31 17:00중흥부두1 실외121611.650.83
22022023-12-31 18:00중흥부두1 실외121611.3550.5
22032023-12-31 19:00중흥부두1 실외131610.7750.7
22042023-12-31 20:00중흥부두1 실외131610.5351.3
22052023-12-31 21:00중흥부두1 실외131610.0951.45
22062023-12-31 22:00중흥부두1 실외131610.151.81
22072023-12-31 23:00중흥부두1 실외151710.0752.07