Overview

Dataset statistics

Number of variables8
Number of observations264
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.9 KiB
Average record size in memory65.5 B

Variable types

Categorical6
Numeric1
Text1

Dataset

Description용인도시공사에서 운영하는 장사시설(평온의 숲)의 대기배출가스 측정 현황을 제공합니다. 2023년 기록부터 제공하며 분기별로 업데이트 됩니다.(단위 : ppm, 먼지 : mg/Sm3)
Author용인도시공사
URLhttps://www.data.go.kr/data/15114760/fileData.do

Alerts

일산화탄소 is highly overall correlated with 황산화물 and 2 other fieldsHigh correlation
먼지 is highly overall correlated with 일산화탄소 and 2 other fieldsHigh correlation
질소산화물 is highly overall correlated with 일산화탄소 and 2 other fieldsHigh correlation
황산화물 is highly overall correlated with 일산화탄소 and 2 other fieldsHigh correlation
일산화탄소 is highly imbalanced (82.4%)Imbalance
황산화물 is highly imbalanced (80.9%)Imbalance
질소산화물 is highly imbalanced (82.4%)Imbalance
먼지 is highly imbalanced (81.6%)Imbalance

Reproduction

Analysis started2024-04-06 08:19:03.844029
Analysis finished2024-04-06 08:19:05.331299
Duration1.49 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일
Categorical

Distinct24
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-01-10
 
11
2023-01-26
 
11
2023-02-09
 
11
2023-02-23
 
11
2023-03-09
 
11
Other values (19)
209 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-10
2nd row2023-01-10
3rd row2023-01-10
4th row2023-01-10
5th row2023-01-10

Common Values

ValueCountFrequency (%)
2023-01-10 11
 
4.2%
2023-01-26 11
 
4.2%
2023-02-09 11
 
4.2%
2023-02-23 11
 
4.2%
2023-03-09 11
 
4.2%
2023-03-23 11
 
4.2%
2023-04-14 11
 
4.2%
2023-04-27 11
 
4.2%
2023-05-03 11
 
4.2%
2023-05-17 11
 
4.2%
Other values (14) 154
58.3%

Length

2024-04-06T17:19:05.450894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-01-10 11
 
4.2%
2023-01-26 11
 
4.2%
2023-12-01 11
 
4.2%
2023-11-08 11
 
4.2%
2023-11-03 11
 
4.2%
2023-10-19 11
 
4.2%
2023-10-05 11
 
4.2%
2023-09-18 11
 
4.2%
2023-09-08 11
 
4.2%
2023-08-24 11
 
4.2%
Other values (14) 154
58.3%

측정시간
Categorical

Distinct18
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
07:00
46 
10:00
44 
08:00
43 
09:00
38 
11:00
31 
Other values (13)
62 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique6 ?
Unique (%)2.3%

Sample

1st row12:05
2nd row08:15
3rd row07:00
4th row08:15
5th row07:00

Common Values

ValueCountFrequency (%)
07:00 46
17.4%
10:00 44
16.7%
08:00 43
16.3%
09:00 38
14.4%
11:00 31
11.7%
12:00 31
11.7%
10:15 7
 
2.7%
08:15 6
 
2.3%
11:15 4
 
1.5%
12:05 4
 
1.5%
Other values (8) 10
 
3.8%

Length

2024-04-06T17:19:05.707715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07:00 46
17.4%
10:00 44
16.7%
08:00 43
16.3%
09:00 38
14.4%
11:00 31
11.7%
12:00 31
11.7%
10:15 7
 
2.7%
08:15 6
 
2.3%
12:05 4
 
1.5%
11:15 4
 
1.5%
Other values (8) 10
 
3.8%

측정호기
Real number (ℝ)

Distinct11
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2024-04-06T17:19:05.954351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile11
Maximum11
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1682839
Coefficient of variation (CV)0.52804732
Kurtosis-1.2203489
Mean6
Median Absolute Deviation (MAD)3
Skewness0
Sum1584
Variance10.038023
MonotonicityNot monotonic
2024-04-06T17:19:06.178752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
11 24
9.1%
1 24
9.1%
2 24
9.1%
3 24
9.1%
4 24
9.1%
5 24
9.1%
6 24
9.1%
7 24
9.1%
8 24
9.1%
9 24
9.1%
ValueCountFrequency (%)
1 24
9.1%
2 24
9.1%
3 24
9.1%
4 24
9.1%
5 24
9.1%
6 24
9.1%
7 24
9.1%
8 24
9.1%
9 24
9.1%
10 24
9.1%
ValueCountFrequency (%)
11 24
9.1%
10 24
9.1%
9 24
9.1%
8 24
9.1%
7 24
9.1%
6 24
9.1%
5 24
9.1%
4 24
9.1%
3 24
9.1%
2 24
9.1%

일산화탄소
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
미측정
242 
20.1
 
2
1.7
 
2
1
 
2
3
 
1
Other values (15)
 
15

Length

Max length4
Median length3
Mean length3.0075758
Min length1

Unique

Unique16 ?
Unique (%)6.1%

Sample

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

Common Values

ValueCountFrequency (%)
미측정 242
91.7%
20.1 2
 
0.8%
1.7 2
 
0.8%
1 2
 
0.8%
3 1
 
0.4%
2.4 1
 
0.4%
10.3 1
 
0.4%
12.3 1
 
0.4%
25.7 1
 
0.4%
66 1
 
0.4%
Other values (10) 10
 
3.8%

Length

2024-04-06T17:19:06.423224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미측정 242
91.7%
1.7 2
 
0.8%
1 2
 
0.8%
20.1 2
 
0.8%
5.3 1
 
0.4%
14.7 1
 
0.4%
53.3 1
 
0.4%
2.7 1
 
0.4%
71.3 1
 
0.4%
6.3 1
 
0.4%
Other values (10) 10
 
3.8%

염화수소
Text

Distinct111
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-04-06T17:19:06.920955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6325758
Min length3

Characters and Unicode

Total characters959
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)23.5%

Sample

1st row불검출
2nd row0.16
3rd row불검출
4th row불검출
5th row불검출
ValueCountFrequency (%)
불검출 83
31.4%
0.29 6
 
2.3%
0.22 5
 
1.9%
0.31 4
 
1.5%
0.58 4
 
1.5%
1.12 3
 
1.1%
0.06 3
 
1.1%
0.3 3
 
1.1%
0.07 3
 
1.1%
0.61 3
 
1.1%
Other values (101) 147
55.7%
2024-04-06T17:19:07.687409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 181
18.9%
0 160
16.7%
불 83
8.7%
검 83
8.7%
출 83
8.7%
1 83
8.7%
2 53
 
5.5%
3 39
 
4.1%
6 39
 
4.1%
8 36
 
3.8%
Other values (4) 119
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 529
55.2%
Other Letter 249
26.0%
Other Punctuation 181
 
18.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160
30.2%
1 83
15.7%
2 53
 
10.0%
3 39
 
7.4%
6 39
 
7.4%
8 36
 
6.8%
9 31
 
5.9%
4 31
 
5.9%
7 29
 
5.5%
5 28
 
5.3%
Other Letter
ValueCountFrequency (%)
불 83
33.3%
검 83
33.3%
출 83
33.3%
Other Punctuation
ValueCountFrequency (%)
. 181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 710
74.0%
Hangul 249
 
26.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 181
25.5%
0 160
22.5%
1 83
11.7%
2 53
 
7.5%
3 39
 
5.5%
6 39
 
5.5%
8 36
 
5.1%
9 31
 
4.4%
4 31
 
4.4%
7 29
 
4.1%
Hangul
ValueCountFrequency (%)
불 83
33.3%
검 83
33.3%
출 83
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 710
74.0%
Hangul 249
 
26.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 181
25.5%
0 160
22.5%
1 83
11.7%
2 53
 
7.5%
3 39
 
5.5%
6 39
 
5.5%
8 36
 
5.1%
9 31
 
4.4%
4 31
 
4.4%
7 29
 
4.1%
Hangul
ValueCountFrequency (%)
불 83
33.3%
검 83
33.3%
출 83
33.3%

황산화물
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
미측정
242 
불검출
 
16
1
 
2
1.3
 
1
1.4
 
1
Other values (2)
 
2

Length

Max length3
Median length3
Mean length2.9848485
Min length1

Unique

Unique4 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
미측정 242
91.7%
불검출 16
 
6.1%
1 2
 
0.8%
1.3 1
 
0.4%
1.4 1
 
0.4%
0.3 1
 
0.4%
4.3 1
 
0.4%

Length

2024-04-06T17:19:08.002487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:19:08.275399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미측정 242
91.7%
불검출 16
 
6.1%
1 2
 
0.8%
1.3 1
 
0.4%
1.4 1
 
0.4%
0.3 1
 
0.4%
4.3 1
 
0.4%

질소산화물
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct20
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
미측정
242 
25.5
 
2
9.3
 
2
9.7
 
2
10.3
 
1
Other values (15)
 
15

Length

Max length4
Median length3
Mean length3.0037879
Min length1

Unique

Unique16 ?
Unique (%)6.1%

Sample

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

Common Values

ValueCountFrequency (%)
미측정 242
91.7%
25.5 2
 
0.8%
9.3 2
 
0.8%
9.7 2
 
0.8%
10.3 1
 
0.4%
19 1
 
0.4%
4 1
 
0.4%
33 1
 
0.4%
36.8 1
 
0.4%
4.7 1
 
0.4%
Other values (10) 10
 
3.8%

Length

2024-04-06T17:19:08.530298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미측정 242
91.7%
9.3 2
 
0.8%
9.7 2
 
0.8%
25.5 2
 
0.8%
59.7 1
 
0.4%
31.3 1
 
0.4%
47.7 1
 
0.4%
25 1
 
0.4%
38 1
 
0.4%
17.7 1
 
0.4%
Other values (10) 10
 
3.8%

먼지
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct15
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
미측정
242 
3.1
 
3
1.6
 
3
1.9
 
2
1.5
 
2
Other values (10)
 
12

Length

Max length4
Median length3
Mean length2.9886364
Min length1

Unique

Unique8 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
미측정 242
91.7%
3.1 3
 
1.1%
1.6 3
 
1.1%
1.9 2
 
0.8%
1.5 2
 
0.8%
2.5 2
 
0.8%
1 2
 
0.8%
6.2 1
 
0.4%
4.2 1
 
0.4%
10.3 1
 
0.4%
Other values (5) 5
 
1.9%

Length

2024-04-06T17:19:08.852318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미측정 242
91.7%
3.1 3
 
1.1%
1.6 3
 
1.1%
1.9 2
 
0.8%
1.5 2
 
0.8%
2.5 2
 
0.8%
1 2
 
0.8%
6.2 1
 
0.4%
4.2 1
 
0.4%
10.3 1
 
0.4%
Other values (5) 5
 
1.9%

Interactions

2024-04-06T17:19:04.753261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:19:09.052072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일측정시간측정호기일산화탄소황산화물질소산화물먼지
측정일1.0000.1420.0000.2880.4640.3720.233
측정시간0.1421.0000.8160.0000.0000.0000.000
측정호기0.0000.8161.0000.1490.0000.1840.000
일산화탄소0.2880.0000.1491.0000.9851.0000.996
황산화물0.4640.0000.0000.9851.0000.9900.946
질소산화물0.3720.0000.1841.0000.9901.0000.993
먼지0.2330.0000.0000.9960.9460.9931.000
2024-04-06T17:19:09.289228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일산화탄소먼지측정일질소산화물황산화물측정시간
일산화탄소1.0000.9340.0800.9420.9060.000
먼지0.9341.0000.0690.9110.7980.000
측정일0.0800.0691.0000.1070.2100.035
질소산화물0.9420.9110.1071.0000.9270.000
황산화물0.9060.7980.2100.9271.0000.000
측정시간0.0000.0000.0350.0000.0001.000
2024-04-06T17:19:09.506765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정호기측정일측정시간일산화탄소황산화물질소산화물먼지
측정호기1.0000.0000.4490.0440.0000.0560.000
측정일0.0001.0000.0350.0800.2100.1070.069
측정시간0.4490.0351.0000.0000.0000.0000.000
일산화탄소0.0440.0800.0001.0000.9060.9420.934
황산화물0.0000.2100.0000.9061.0000.9270.798
질소산화물0.0560.1070.0000.9420.9271.0000.911
먼지0.0000.0690.0000.9340.7980.9111.000

Missing values

2024-04-06T17:19:04.983669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:19:05.237881image/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

측정일측정시간측정호기일산화탄소염화수소황산화물질소산화물먼지
02023-01-1012:0511미측정불검출미측정미측정미측정
12023-01-1008:151미측정0.16미측정미측정미측정
22023-01-1007:002미측정불검출미측정미측정미측정
32023-01-1008:153미측정불검출미측정미측정미측정
42023-01-1007:004미측정불검출미측정미측정미측정
52023-01-1010:155미측정불검출미측정미측정미측정
62023-01-1011:156미측정0.16미측정미측정미측정
72023-01-1011:157미측정0.94미측정미측정미측정
82023-01-1011:158미측정0.1미측정미측정미측정
92023-01-1012:059미측정불검출미측정미측정미측정
측정일측정시간측정호기일산화탄소염화수소황산화물질소산화물먼지
2542023-12-0407:002미측정0.35미측정미측정미측정
2552023-12-0408:003미측정1.18미측정미측정미측정
2562023-12-0407:004미측정1.29미측정미측정미측정
2572023-12-0410:005미측정0.97미측정미측정미측정
2582023-12-0409:006미측정1.39미측정미측정미측정
2592023-12-0410:007미측정0.63미측정미측정미측정
2602023-12-0409:008미측정1.14미측정미측정미측정
2612023-12-0412:009미측정0.58미측정미측정미측정
2622023-12-0411:0010미측정불검출미측정미측정미측정
2632023-12-0412:0011미측정0.61미측정미측정미측정