Overview

Dataset statistics

Number of variables9
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory84.3 B

Variable types

Categorical4
Text1
Numeric4

Dataset

Description측정일시,도로변구분,측정소명,미세먼지(㎍/㎥),오존(ppm),이산화질소농도(ppm),일산화탄소농도(ppm),아황산가스농도(ppm),초미세먼지(㎍/㎥)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2223/S/1/datasetView.do

Alerts

측정일시 has constant value ""Constant
미세먼지(㎍/㎥) is highly overall correlated with 오존(ppm)High correlation
오존(ppm) is highly overall correlated with 미세먼지(㎍/㎥) and 1 other fieldsHigh correlation
이산화질소농도(ppm) is highly overall correlated with 오존(ppm)High correlation
도로변구분 is highly overall correlated with 아황산가스농도(ppm)High correlation
아황산가스농도(ppm) is highly overall correlated with 도로변구분High correlation
측정소명 has unique valuesUnique
미세먼지(㎍/㎥) has 1 (4.0%) zerosZeros

Reproduction

Analysis started2024-05-11 05:30:07.025253
Analysis finished2024-05-11 05:30:10.899617
Duration3.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일시
Categorical

CONSTANT 

Distinct1
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
202405111400
25 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202405111400
2nd row202405111400
3rd row202405111400
4th row202405111400
5th row202405111400

Common Values

ValueCountFrequency (%)
202405111400 25
100.0%

Length

2024-05-11T14:30:11.024224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:30:11.197611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202405111400 25
100.0%

도로변구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
일반도로
10 
입체
4 
경계
3 
고공
3 
중앙차로
3 

Length

Max length4
Median length4
Mean length3.2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경계
2nd row고공
3rd row고공
4th row고공
5th row경계

Common Values

ValueCountFrequency (%)
일반도로 10
40.0%
입체 4
 
16.0%
경계 3
 
12.0%
고공 3
 
12.0%
중앙차로 3
 
12.0%
전용차로 2
 
8.0%

Length

2024-05-11T14:30:11.382918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:30:11.618933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반도로 10
40.0%
입체 4
 
16.0%
경계 3
 
12.0%
고공 3
 
12.0%
중앙차로 3
 
12.0%
전용차로 2
 
8.0%

측정소명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2024-05-11T14:30:11.929529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.52
Min length2

Characters and Unicode

Total characters88
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row항동
2nd row남산
3rd row북한산
4th row관악산
5th row세곡
ValueCountFrequency (%)
항동 1
 
4.0%
정릉로 1
 
4.0%
마포아트센터 1
 
4.0%
자연사박물관 1
 
4.0%
서울숲 1
 
4.0%
신촌로 1
 
4.0%
공항대로 1
 
4.0%
영등포로 1
 
4.0%
동작대로 1
 
4.0%
시흥대로 1
 
4.0%
Other values (15) 15
60.0%
2024-05-11T14:30:12.513224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
로 15
 
17.0%
대 7
 
8.0%
산 4
 
4.5%
강 3
 
3.4%
항 2
 
2.3%
동 2
 
2.3%
릉 2
 
2.3%
포 2
 
2.3%
천 2
 
2.3%
공 2
 
2.3%
Other values (43) 47
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 88
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 15
 
17.0%
대 7
 
8.0%
산 4
 
4.5%
강 3
 
3.4%
항 2
 
2.3%
동 2
 
2.3%
릉 2
 
2.3%
포 2
 
2.3%
천 2
 
2.3%
공 2
 
2.3%
Other values (43) 47
53.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 15
 
17.0%
대 7
 
8.0%
산 4
 
4.5%
강 3
 
3.4%
항 2
 
2.3%
동 2
 
2.3%
릉 2
 
2.3%
포 2
 
2.3%
천 2
 
2.3%
공 2
 
2.3%
Other values (43) 47
53.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 88
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
로 15
 
17.0%
대 7
 
8.0%
산 4
 
4.5%
강 3
 
3.4%
항 2
 
2.3%
동 2
 
2.3%
릉 2
 
2.3%
포 2
 
2.3%
천 2
 
2.3%
공 2
 
2.3%
Other values (43) 47
53.4%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.84
Minimum0
Maximum59
Zeros1
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T14:30:12.732177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26.2
Q133
median38
Q343
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.223784
Coefficient of variation (CV)0.29661164
Kurtosis4.9167264
Mean37.84
Median Absolute Deviation (MAD)5
Skewness-1.1746325
Sum946
Variance125.97333
MonotonicityNot monotonic
2024-05-11T14:30:12.923881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
32 3
12.0%
39 3
12.0%
38 3
12.0%
46 2
 
8.0%
36 2
 
8.0%
33 2
 
8.0%
43 2
 
8.0%
25 1
 
4.0%
31 1
 
4.0%
0 1
 
4.0%
Other values (5) 5
20.0%
ValueCountFrequency (%)
0 1
 
4.0%
25 1
 
4.0%
31 1
 
4.0%
32 3
12.0%
33 2
8.0%
36 2
8.0%
37 1
 
4.0%
38 3
12.0%
39 3
12.0%
42 1
 
4.0%
ValueCountFrequency (%)
59 1
 
4.0%
57 1
 
4.0%
52 1
 
4.0%
46 2
8.0%
43 2
8.0%
42 1
 
4.0%
39 3
12.0%
38 3
12.0%
37 1
 
4.0%
36 2
8.0%

오존(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04288
Minimum0.031
Maximum0.056
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T14:30:13.203388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.031
5-th percentile0.032
Q10.039
median0.042
Q30.047
95-th percentile0.0542
Maximum0.056
Range0.025
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.0068697404
Coefficient of variation (CV)0.1602085
Kurtosis-0.47500781
Mean0.04288
Median Absolute Deviation (MAD)0.005
Skewness-0.012182272
Sum1.072
Variance4.7193333 × 10-5
MonotonicityNot monotonic
2024-05-11T14:30:13.435597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.042 3
 
12.0%
0.05 2
 
8.0%
0.039 2
 
8.0%
0.032 2
 
8.0%
0.041 2
 
8.0%
0.047 2
 
8.0%
0.055 1
 
4.0%
0.036 1
 
4.0%
0.046 1
 
4.0%
0.045 1
 
4.0%
Other values (8) 8
32.0%
ValueCountFrequency (%)
0.031 1
 
4.0%
0.032 2
8.0%
0.033 1
 
4.0%
0.036 1
 
4.0%
0.039 2
8.0%
0.04 1
 
4.0%
0.041 2
8.0%
0.042 3
12.0%
0.043 1
 
4.0%
0.044 1
 
4.0%
ValueCountFrequency (%)
0.056 1
4.0%
0.055 1
4.0%
0.051 1
4.0%
0.05 2
8.0%
0.048 1
4.0%
0.047 2
8.0%
0.046 1
4.0%
0.045 1
4.0%
0.044 1
4.0%
0.043 1
4.0%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0102
Minimum0.003
Maximum0.024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T14:30:13.646855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003
5-th percentile0.004
Q10.005
median0.01
Q30.013
95-th percentile0.0208
Maximum0.024
Range0.021
Interquartile range (IQR)0.008

Descriptive statistics

Standard deviation0.00585235
Coefficient of variation (CV)0.5737598
Kurtosis-0.089129392
Mean0.0102
Median Absolute Deviation (MAD)0.005
Skewness0.77898113
Sum0.255
Variance3.425 × 10-5
MonotonicityNot monotonic
2024-05-11T14:30:13.859142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.004 4
16.0%
0.01 4
16.0%
0.005 3
12.0%
0.012 3
12.0%
0.016 2
8.0%
0.003 1
 
4.0%
0.007 1
 
4.0%
0.008 1
 
4.0%
0.014 1
 
4.0%
0.021 1
 
4.0%
Other values (4) 4
16.0%
ValueCountFrequency (%)
0.003 1
 
4.0%
0.004 4
16.0%
0.005 3
12.0%
0.006 1
 
4.0%
0.007 1
 
4.0%
0.008 1
 
4.0%
0.01 4
16.0%
0.012 3
12.0%
0.013 1
 
4.0%
0.014 1
 
4.0%
ValueCountFrequency (%)
0.024 1
 
4.0%
0.021 1
 
4.0%
0.02 1
 
4.0%
0.016 2
8.0%
0.014 1
 
4.0%
0.013 1
 
4.0%
0.012 3
12.0%
0.01 4
16.0%
0.008 1
 
4.0%
0.007 1
 
4.0%

일산화탄소농도(ppm)
Categorical

Distinct5
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
0.4
8 
0.3
8 
0.2
5 
0.5
3 
0.7
1 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)4.0%

Sample

1st row0.4
2nd row0.3
3rd row0.2
4th row0.2
5th row0.2

Common Values

ValueCountFrequency (%)
0.4 8
32.0%
0.3 8
32.0%
0.2 5
20.0%
0.5 3
 
12.0%
0.7 1
 
4.0%

Length

2024-05-11T14:30:14.055782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:30:14.290570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.4 8
32.0%
0.3 8
32.0%
0.2 5
20.0%
0.5 3
 
12.0%
0.7 1
 
4.0%

아황산가스농도(ppm)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
0.003
17 
0.002
8 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.002
2nd row0.003
3rd row0.003
4th row0.003
5th row0.002

Common Values

ValueCountFrequency (%)
0.003 17
68.0%
0.002 8
32.0%

Length

2024-05-11T14:30:14.549788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:30:14.747347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.003 17
68.0%
0.002 8
32.0%

초미세먼지(㎍/㎥)
Real number (ℝ)

Distinct8
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.52
Minimum4
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2024-05-11T14:30:14.912677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q17
median8
Q310
95-th percentile11
Maximum12
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9174636
Coefficient of variation (CV)0.22505441
Kurtosis-0.20446158
Mean8.52
Median Absolute Deviation (MAD)2
Skewness-0.36084515
Sum213
Variance3.6766667
MonotonicityNot monotonic
2024-05-11T14:30:15.127551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 7
28.0%
8 6
24.0%
6 3
12.0%
7 3
12.0%
11 2
 
8.0%
9 2
 
8.0%
4 1
 
4.0%
12 1
 
4.0%
ValueCountFrequency (%)
4 1
 
4.0%
6 3
12.0%
7 3
12.0%
8 6
24.0%
9 2
 
8.0%
10 7
28.0%
11 2
 
8.0%
12 1
 
4.0%
ValueCountFrequency (%)
12 1
 
4.0%
11 2
 
8.0%
10 7
28.0%
9 2
 
8.0%
8 6
24.0%
7 3
12.0%
6 3
12.0%
4 1
 
4.0%

Interactions

2024-05-11T14:30:09.777484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:07.531077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.170121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.848047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:09.970902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:07.697996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.346335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:09.015810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:10.141422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:07.859107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.491911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:09.489311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:10.316206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.000901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.650838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:09.606822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:30:15.326155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로변구분측정소명미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)초미세먼지(㎍/㎥)
도로변구분1.0001.0000.3830.7850.3410.4160.8350.448
측정소명1.0001.0001.0001.0001.0001.0001.0001.000
미세먼지(㎍/㎥)0.3831.0001.0000.5900.0000.2420.0150.192
오존(ppm)0.7851.0000.5901.0000.3260.0000.3150.343
이산화질소농도(ppm)0.3411.0000.0000.3261.0000.6780.0000.290
일산화탄소농도(ppm)0.4161.0000.2420.0000.6781.0000.0000.683
아황산가스농도(ppm)0.8351.0000.0150.3150.0000.0001.0000.502
초미세먼지(㎍/㎥)0.4481.0000.1920.3430.2900.6830.5021.000
2024-05-11T14:30:15.538729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아황산가스농도(ppm)일산화탄소농도(ppm)도로변구분
아황산가스농도(ppm)1.0000.0000.578
일산화탄소농도(ppm)0.0001.0000.274
도로변구분0.5780.2741.000
2024-05-11T14:30:15.746514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)초미세먼지(㎍/㎥)도로변구분일산화탄소농도(ppm)아황산가스농도(ppm)
미세먼지(㎍/㎥)1.000-0.5010.4430.2720.2060.1020.000
오존(ppm)-0.5011.000-0.761-0.4020.3460.0000.228
이산화질소농도(ppm)0.443-0.7611.0000.3530.1000.4120.000
초미세먼지(㎍/㎥)0.272-0.4020.3531.0000.2250.4580.309
도로변구분0.2060.3460.1000.2251.0000.2740.578
일산화탄소농도(ppm)0.1020.0000.4120.4580.2741.0000.000
아황산가스농도(ppm)0.0000.2280.0000.3090.5780.0001.000

Missing values

2024-05-11T14:30:10.543850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:30:10.805310image/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

측정일시도로변구분측정소명미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)초미세먼지(㎍/㎥)
0202405111400경계항동250.050.0040.40.00211
1202405111400고공남산320.0450.0040.30.0036
2202405111400고공북한산310.0470.0050.20.0034
3202405111400고공관악산320.0550.0030.20.0037
4202405111400경계세곡00.050.0040.20.0027
5202405111400경계행주390.0420.0070.50.0029
6202405111400일반도로종로380.0320.0080.30.0029
7202405111400일반도로청계천로520.0420.010.40.00310
8202405111400일반도로한강대로590.0390.0140.40.00311
9202405111400중앙차로강남대로570.0360.0120.40.00210
측정일시도로변구분측정소명미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)초미세먼지(㎍/㎥)
15202405111400일반도로화랑로360.0410.0160.30.0038
16202405111400일반도로시흥대로330.0420.010.20.0038
17202405111400중앙차로동작대로360.0470.0120.30.00310
18202405111400일반도로영등포로380.0330.0240.40.00310
19202405111400중앙차로공항대로370.0430.010.20.0027
20202405111400일반도로신촌로390.0310.020.50.00310
21202405111400입체서울숲380.0440.0060.50.00312
22202405111400입체자연사박물관430.0480.0050.30.0028
23202405111400입체마포아트센터430.0510.0050.30.0036
24202405111400입체올림픽공원330.0560.0040.30.0028