Overview

Dataset statistics

Number of variables10
Number of observations35
Missing cells88
Missing cells (%)25.1%
Duplicate rows1
Duplicate rows (%)2.9%
Total size in memory3.0 KiB
Average record size in memory88.8 B

Variable types

Numeric3
Categorical6
Text1

Dataset

Description부산광역시 보건환경연구원에서 설치한 미세먼지신호등으로 미세먼지, 초미세먼지, 오존 상태 등 표출 및 예보한다. 부산광역시 보건환경연구원에서 설치한 미세먼지신호등으로 미세먼지, 초미세먼지, 오존 상태 등 표출 및 예보한다. 부산광역시 보건환경연구원에서 설치한 미세먼지신호등으로 미세먼지, 초미세먼지, 오존 상태 등 표출 및 예보한다.
URLhttps://www.data.go.kr/data/15114073/fileData.do

Alerts

Dataset has 1 (2.9%) duplicate rowsDuplicates
표출부면적 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
설치연도 is highly overall correlated with 관리번호 and 5 other fieldsHigh correlation
운영시간 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
표출부개수 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
표출내용 is highly overall correlated with 관리번호 and 7 other fieldsHigh correlation
관리번호 is highly overall correlated with 설치연도 and 5 other fieldsHigh correlation
위도 is highly overall correlated with 표출부면적 and 4 other fieldsHigh correlation
경도 is highly overall correlated with 표출부면적 and 4 other fieldsHigh correlation
관리번호 has 22 (62.9%) missing valuesMissing
주소 has 22 (62.9%) missing valuesMissing
위도 has 22 (62.9%) missing valuesMissing
경도 has 22 (62.9%) missing valuesMissing

Reproduction

Analysis started2023-12-12 19:04:09.338153
Analysis finished2023-12-12 19:04:10.770397
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing22
Missing (%)62.9%
Infinite0
Infinite (%)0.0%
Mean7
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T04:04:10.815532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.6
Q14
median7
Q310
95-th percentile12.4
Maximum13
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.8944405
Coefficient of variation (CV)0.55634864
Kurtosis-1.2
Mean7
Median Absolute Deviation (MAD)3
Skewness0
Sum91
Variance15.166667
MonotonicityStrictly increasing
2023-12-13T04:04:10.916490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1
 
2.9%
2 1
 
2.9%
3 1
 
2.9%
4 1
 
2.9%
5 1
 
2.9%
6 1
 
2.9%
7 1
 
2.9%
8 1
 
2.9%
9 1
 
2.9%
10 1
 
2.9%
Other values (3) 3
 
8.6%
(Missing) 22
62.9%
ValueCountFrequency (%)
1 1
2.9%
2 1
2.9%
3 1
2.9%
4 1
2.9%
5 1
2.9%
6 1
2.9%
7 1
2.9%
8 1
2.9%
9 1
2.9%
10 1
2.9%
ValueCountFrequency (%)
13 1
2.9%
12 1
2.9%
11 1
2.9%
10 1
2.9%
9 1
2.9%
8 1
2.9%
7 1
2.9%
6 1
2.9%
5 1
2.9%
4 1
2.9%

설치연도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
22 
2020
6 
2022
3 
2019
 
2
2021
 
2

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
<NA> 22
62.9%
2020 6
 
17.1%
2022 3
 
8.6%
2019 2
 
5.7%
2021 2
 
5.7%

Length

2023-12-13T04:04:11.022288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:04:11.131854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
62.9%
2020 6
 
17.1%
2022 3
 
8.6%
2019 2
 
5.7%
2021 2
 
5.7%

주소
Text

MISSING 

Distinct13
Distinct (%)100.0%
Missing22
Missing (%)62.9%
Memory size412.0 B
2023-12-13T04:04:11.285397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length15.153846
Min length9

Characters and Unicode

Total characters197
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)100.0%

Sample

1st row부산 부산진구 개금동 208-48
2nd row부산 사하구 당리동 313-6
3rd row청학동 447-5
4th row부산 북구 화명동 2280
5th row부산 해운대구 재송동 1191
ValueCountFrequency (%)
부산 12
24.0%
금정구 3
 
6.0%
부산진구 2
 
4.0%
동구 1
 
2.0%
265-10 1
 
2.0%
부곡동 1
 
2.0%
682 1
 
2.0%
전포동 1
 
2.0%
47-1 1
 
2.0%
435-6 1
 
2.0%
Other values (26) 26
52.0%
2023-12-13T04:04:11.555231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
18.8%
부 15
 
7.6%
동 14
 
7.1%
산 14
 
7.1%
구 12
 
6.1%
- 9
 
4.6%
1 8
 
4.1%
3 7
 
3.6%
2 6
 
3.0%
6 6
 
3.0%
Other values (40) 69
35.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
50.3%
Decimal Number 52
26.4%
Space Separator 37
 
18.8%
Dash Punctuation 9
 
4.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 15
15.2%
동 14
14.1%
산 14
14.1%
구 12
12.1%
금 4
 
4.0%
정 4
 
4.0%
진 2
 
2.0%
청 2
 
2.0%
명 2
 
2.0%
사 2
 
2.0%
Other values (28) 28
28.3%
Decimal Number
ValueCountFrequency (%)
1 8
15.4%
3 7
13.5%
2 6
11.5%
6 6
11.5%
4 6
11.5%
5 6
11.5%
8 5
9.6%
0 4
7.7%
7 3
 
5.8%
9 1
 
1.9%
Space Separator
ValueCountFrequency (%)
37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
50.3%
Common 98
49.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 15
15.2%
동 14
14.1%
산 14
14.1%
구 12
12.1%
금 4
 
4.0%
정 4
 
4.0%
진 2
 
2.0%
청 2
 
2.0%
명 2
 
2.0%
사 2
 
2.0%
Other values (28) 28
28.3%
Common
ValueCountFrequency (%)
37
37.8%
- 9
 
9.2%
1 8
 
8.2%
3 7
 
7.1%
2 6
 
6.1%
6 6
 
6.1%
4 6
 
6.1%
5 6
 
6.1%
8 5
 
5.1%
0 4
 
4.1%
Other values (2) 4
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
50.3%
ASCII 98
49.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
37.8%
- 9
 
9.2%
1 8
 
8.2%
3 7
 
7.1%
2 6
 
6.1%
6 6
 
6.1%
4 6
 
6.1%
5 6
 
6.1%
8 5
 
5.1%
0 4
 
4.1%
Other values (2) 4
 
4.1%
Hangul
ValueCountFrequency (%)
부 15
15.2%
동 14
14.1%
산 14
14.1%
구 12
12.1%
금 4
 
4.0%
정 4
 
4.0%
진 2
 
2.0%
청 2
 
2.0%
명 2
 
2.0%
사 2
 
2.0%
Other values (28) 28
28.3%

표출부면적
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
22 
320mm * 320mm
13 

Length

Max length13
Median length4
Mean length7.3428571
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row320mm * 320mm
2nd row320mm * 320mm
3rd row320mm * 320mm
4th row320mm * 320mm
5th row320mm * 320mm

Common Values

ValueCountFrequency (%)
<NA> 22
62.9%
320mm * 320mm 13
37.1%

Length

2023-12-13T04:04:11.674644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:04:11.768183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
320mm 26
42.6%
na 22
36.1%
13
21.3%

표출부개수
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
22 
3
13 

Length

Max length4
Median length4
Mean length2.8857143
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
<NA> 22
62.9%
3 13
37.1%

Length

2023-12-13T04:04:11.885952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:04:12.007406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
62.9%
3 13
37.1%

운영시간
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
22 
00:00~22:00
13 

Length

Max length11
Median length4
Mean length6.6
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00~22:00
2nd row00:00~22:00
3rd row00:00~22:00
4th row00:00~22:00
5th row00:00~22:00

Common Values

ValueCountFrequency (%)
<NA> 22
62.9%
00:00~22:00 13
37.1%

Length

2023-12-13T04:04:12.141571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:04:12.264422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
62.9%
00:00~22:00 13
37.1%

표출내용
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
22 
PM10, PM2.5, O3, 기타홍보사항 등
13 

Length

Max length25
Median length4
Mean length11.8
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPM10, PM2.5, O3, 기타홍보사항 등
2nd rowPM10, PM2.5, O3, 기타홍보사항 등
3rd rowPM10, PM2.5, O3, 기타홍보사항 등
4th rowPM10, PM2.5, O3, 기타홍보사항 등
5th rowPM10, PM2.5, O3, 기타홍보사항 등

Common Values

ValueCountFrequency (%)
<NA> 22
62.9%
PM10, PM2.5, O3, 기타홍보사항 등 13
37.1%

Length

2023-12-13T04:04:12.375949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:04:12.529539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
25.3%
pm10 13
14.9%
pm2.5 13
14.9%
o3 13
14.9%
기타홍보사항 13
14.9%
등 13
14.9%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing22
Missing (%)62.9%
Infinite0
Infinite (%)0.0%
Mean35.356976
Minimum35.090835
Maximum37.71555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T04:04:12.670586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.090835
5-th percentile35.099461
Q135.124864
median35.156053
Q335.229366
95-th percentile36.226498
Maximum37.71555
Range2.6247149
Interquartile range (IQR)0.1045025

Descriptive statistics

Standard deviation0.71037467
Coefficient of variation (CV)0.0200915
Kurtosis12.841037
Mean35.356976
Median Absolute Deviation (MAD)0.0504569
Skewness3.5749236
Sum459.64069
Variance0.50463217
MonotonicityNot monotonic
2023-12-13T04:04:12.814456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
35.155041 1
 
2.9%
35.1052113 1
 
2.9%
35.0908346 1
 
2.9%
35.2337978 1
 
2.9%
35.1825882 1
 
2.9%
37.7155495 1
 
2.9%
35.2293662 1
 
2.9%
35.1055958 1
 
2.9%
35.1829126 1
 
2.9%
35.1248637 1
 
2.9%
Other values (3) 3
 
8.6%
(Missing) 22
62.9%
ValueCountFrequency (%)
35.0908346 1
2.9%
35.1052113 1
2.9%
35.1055958 1
2.9%
35.1248637 1
2.9%
35.1290495 1
2.9%
35.155041 1
2.9%
35.1560527 1
2.9%
35.1825882 1
2.9%
35.1829126 1
2.9%
35.2293662 1
2.9%
ValueCountFrequency (%)
37.7155495 1
2.9%
35.2337978 1
2.9%
35.2298243 1
2.9%
35.2293662 1
2.9%
35.1829126 1
2.9%
35.1825882 1
2.9%
35.1560527 1
2.9%
35.155041 1
2.9%
35.1290495 1
2.9%
35.1248637 1
2.9%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)100.0%
Missing22
Missing (%)62.9%
Infinite0
Infinite (%)0.0%
Mean128.86688
Minimum126.74268
Maximum129.12114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-13T04:04:12.966403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.74268
5-th percentile128.05259
Q1128.97634
median129.04533
Q3129.09282
95-th percentile129.12097
Maximum129.12114
Range2.3784591
Interquartile range (IQR)0.1164769

Descriptive statistics

Standard deviation0.64115921
Coefficient of variation (CV)0.0049753609
Kurtosis12.700464
Mean128.86688
Median Absolute Deviation (MAD)0.0686033
Skewness-3.5479986
Sum1675.2694
Variance0.41108513
MonotonicityNot monotonic
2023-12-13T04:04:13.099015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
129.0229513 1
 
2.9%
128.9738129 1
 
2.9%
129.0595772 1
 
2.9%
129.0102613 1
 
2.9%
129.1211425 1
 
2.9%
126.7426834 1
 
2.9%
129.1208628 1
 
2.9%
128.925862 1
 
2.9%
128.9763409 1
 
2.9%
129.1139348 1
 
2.9%
Other values (3) 3
 
8.6%
(Missing) 22
62.9%
ValueCountFrequency (%)
126.7426834 1
2.9%
128.925862 1
2.9%
128.9738129 1
2.9%
128.9763409 1
2.9%
129.0102613 1
2.9%
129.0229513 1
2.9%
129.0453315 1
2.9%
129.0595772 1
2.9%
129.0638088 1
2.9%
129.0928178 1
2.9%
ValueCountFrequency (%)
129.1211425 1
2.9%
129.1208628 1
2.9%
129.1139348 1
2.9%
129.0928178 1
2.9%
129.0638088 1
2.9%
129.0595772 1
2.9%
129.0453315 1
2.9%
129.0229513 1
2.9%
129.0102613 1
2.9%
128.9763409 1
2.9%

데이터기준일자
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
<NA>
22 
2023-05-02
13 

Length

Max length10
Median length4
Mean length6.2285714
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-02
2nd row2023-05-02
3rd row2023-05-02
4th row2023-05-02
5th row2023-05-02

Common Values

ValueCountFrequency (%)
<NA> 22
62.9%
2023-05-02 13
37.1%

Length

2023-12-13T04:04:13.240719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T04:04:13.371082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 22
62.9%
2023-05-02 13
37.1%

Interactions

2023-12-13T04:04:10.107104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:04:09.695911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:04:09.895797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:04:10.177667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:04:09.758691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:04:09.968004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:04:10.249832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:04:09.829050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:04:10.042194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:04:13.456969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호설치연도주소위도경도
관리번호1.0001.0001.0001.0001.000
설치연도1.0001.0001.0000.0000.000
주소1.0001.0001.0001.0001.000
위도1.0000.0001.0001.0000.562
경도1.0000.0001.0000.5621.000
2023-12-13T04:04:13.580482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
표출부면적설치연도운영시간표출부개수데이터기준일자표출내용
표출부면적1.0001.0001.0001.0001.0001.000
설치연도1.0001.0001.0001.0001.0001.000
운영시간1.0001.0001.0001.0001.0001.000
표출부개수1.0001.0001.0001.0001.0001.000
데이터기준일자1.0001.0001.0001.0001.0001.000
표출내용1.0001.0001.0001.0001.0001.000
2023-12-13T04:04:13.740831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호위도경도설치연도표출부면적표출부개수운영시간표출내용데이터기준일자
관리번호1.0000.1700.2530.5771.0001.0001.0001.0001.000
위도0.1701.0000.0220.0001.0001.0001.0001.0001.000
경도0.2530.0221.0000.0001.0001.0001.0001.0001.000
설치연도0.5770.0000.0001.0001.0001.0001.0001.0001.000
표출부면적1.0001.0001.0001.0001.0001.0001.0001.0001.000
표출부개수1.0001.0001.0001.0001.0001.0001.0001.0001.000
운영시간1.0001.0001.0001.0001.0001.0001.0001.0001.000
표출내용1.0001.0001.0001.0001.0001.0001.0001.0001.000
데이터기준일자1.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T04:04:10.350204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:04:10.492922image/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.
2023-12-13T04:04:10.664299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

관리번호설치연도주소표출부면적표출부개수운영시간표출내용위도경도데이터기준일자
012019부산 부산진구 개금동 208-48320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등35.155041129.0229512023-05-02
122019부산 사하구 당리동 313-6320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등35.105211128.9738132023-05-02
232020청학동 447-5320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등35.090835129.0595772023-05-02
342020부산 북구 화명동 2280320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등35.233798129.0102612023-05-02
452020부산 해운대구 재송동 1191320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등35.182588129.1211422023-05-02
562020부산 금정구 청룡로 25320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등37.71555126.7426832023-05-02
672020부산 금정구 회동동 435-6320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등35.229366129.1208632023-05-02
782020부산 강서구 명지동 3513-3320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등35.105596128.9258622023-05-02
892021부산 사상구 삼락동 365-1320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등35.182913128.9763412023-05-02
9102021부산 남구 용호동 47-1320mm * 320mm300:00~22:00PM10, PM2.5, O3, 기타홍보사항 등35.124864129.1139352023-05-02
관리번호설치연도주소표출부면적표출부개수운영시간표출내용위도경도데이터기준일자
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Duplicate rows

Most frequently occurring

관리번호설치연도주소표출부면적표출부개수운영시간표출내용위도경도데이터기준일자# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>22