Overview

Dataset statistics

Number of variables9
Number of observations29
Missing cells99
Missing cells (%)37.9%
Duplicate rows2
Duplicate rows (%)6.9%
Total size in memory2.2 KiB
Average record size in memory77.6 B

Variable types

Unsupported7
Text2

Dataset

Description대기측정망운영결과2015년11월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202598

Alerts

Dataset has 2 (6.9%) duplicate rowsDuplicates
Unnamed: 0 has 29 (100.0%) missing valuesMissing
2015년 도시 대기측정망(11월) has 14 (48.3%) missing valuesMissing
Unnamed: 2 has 11 (37.9%) missing valuesMissing
Unnamed: 3 has 6 (20.7%) missing valuesMissing
Unnamed: 4 has 6 (20.7%) missing valuesMissing
Unnamed: 5 has 6 (20.7%) missing valuesMissing
Unnamed: 6 has 7 (24.1%) missing valuesMissing
Unnamed: 7 has 7 (24.1%) missing valuesMissing
Unnamed: 8 has 13 (44.8%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 3 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-14 03:25:50.061793
Analysis finished2024-03-14 03:25:50.521794
Duration0.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing29
Missing (%)100.0%
Memory size393.0 B

2015년 도시 대기측정망(11월)
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing14
Missing (%)48.3%
Memory size364.0 B
2024-03-14T12:25:50.666736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length56
Median length44
Mean length11.266667
Min length2

Characters and Unicode

Total characters169
Distinct characters73
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)100.0%

Sample

1st row측정지역
2nd row 환경기준 지 점
3rd row전주
4th row군산
5th row익산
ValueCountFrequency (%)
측정지역 1
 
2.6%
위 1
 
2.6%
전측정소 1
 
2.6%
시간 1
 
2.6%
측정치의 1
 
2.6%
누적값÷해당지역의 1
 
2.6%
모든측정소 1
 
2.6%
시간측정치수 1
 
2.6%
2 1
 
2.6%
자료는 1
 
2.6%
Other values (28) 28
73.7%
2024-03-14T12:25:50.947290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
21.9%
정 8
 
4.7%
측 6
 
3.6%
1 5
 
3.0%
전 4
 
2.4%
지 4
 
2.4%
시 4
 
2.4%
역 3
 
1.8%
경 3
 
1.8%
료 3
 
1.8%
Other values (63) 92
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111
65.7%
Space Separator 37
 
21.9%
Decimal Number 9
 
5.3%
Other Punctuation 4
 
2.4%
Close Punctuation 2
 
1.2%
Open Punctuation 2
 
1.2%
Control 2
 
1.2%
Math Symbol 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 8
 
7.2%
측 6
 
5.4%
전 4
 
3.6%
지 4
 
3.6%
시 4
 
3.6%
역 3
 
2.7%
경 3
 
2.7%
료 3
 
2.7%
자 3
 
2.7%
의 3
 
2.7%
Other values (51) 70
63.1%
Decimal Number
ValueCountFrequency (%)
1 5
55.6%
2 2
 
22.2%
0 1
 
11.1%
5 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
, 2
50.0%
Math Symbol
ValueCountFrequency (%)
÷ 1
50.0%
= 1
50.0%
Space Separator
ValueCountFrequency (%)
37
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111
65.7%
Common 58
34.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 8
 
7.2%
측 6
 
5.4%
전 4
 
3.6%
지 4
 
3.6%
시 4
 
3.6%
역 3
 
2.7%
경 3
 
2.7%
료 3
 
2.7%
자 3
 
2.7%
의 3
 
2.7%
Other values (51) 70
63.1%
Common
ValueCountFrequency (%)
37
63.8%
1 5
 
8.6%
. 2
 
3.4%
) 2
 
3.4%
, 2
 
3.4%
2 2
 
3.4%
( 2
 
3.4%
2
 
3.4%
0 1
 
1.7%
÷ 1
 
1.7%
Other values (2) 2
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111
65.7%
ASCII 57
33.7%
None 1
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
64.9%
1 5
 
8.8%
. 2
 
3.5%
) 2
 
3.5%
, 2
 
3.5%
2 2
 
3.5%
( 2
 
3.5%
2
 
3.5%
0 1
 
1.8%
5 1
 
1.8%
Hangul
ValueCountFrequency (%)
정 8
 
7.2%
측 6
 
5.4%
전 4
 
3.6%
지 4
 
3.6%
시 4
 
3.6%
역 3
 
2.7%
경 3
 
2.7%
료 3
 
2.7%
자 3
 
2.7%
의 3
 
2.7%
Other values (51) 70
63.1%
None
ValueCountFrequency (%)
÷ 1
100.0%

Unnamed: 2
Text

MISSING 

Distinct16
Distinct (%)88.9%
Missing11
Missing (%)37.9%
Memory size364.0 B
2024-03-14T12:25:51.083834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.4444444
Min length2

Characters and Unicode

Total characters62
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)83.3%

Sample

1st row태평동
2nd row삼천동
3rd row팔복동
4th row 평 균
5th row신풍동
ValueCountFrequency (%)
평 3
14.3%
균 3
14.3%
남중동 1
 
4.8%
모현동 1
 
4.8%
지점 1
 
4.8%
부안읍 1
 
4.8%
고창읍 1
 
4.8%
죽항동 1
 
4.8%
연지동 1
 
4.8%
태평동 1
 
4.8%
Other values (7) 7
33.3%
2024-03-14T12:25:51.349396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
19.4%
동 12
19.4%
평 4
 
6.5%
균 3
 
4.8%
읍 2
 
3.2%
지 2
 
3.2%
팔 2
 
3.2%
현 1
 
1.6%
모 1
 
1.6%
점 1
 
1.6%
Other values (22) 22
35.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 50
80.6%
Space Separator 12
 
19.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12
24.0%
평 4
 
8.0%
균 3
 
6.0%
읍 2
 
4.0%
지 2
 
4.0%
팔 2
 
4.0%
현 1
 
2.0%
모 1
 
2.0%
점 1
 
2.0%
연 1
 
2.0%
Other values (21) 21
42.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 50
80.6%
Common 12
 
19.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12
24.0%
평 4
 
8.0%
균 3
 
6.0%
읍 2
 
4.0%
지 2
 
4.0%
팔 2
 
4.0%
현 1
 
2.0%
모 1
 
2.0%
점 1
 
2.0%
연 1
 
2.0%
Other values (21) 21
42.0%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 50
80.6%
ASCII 12
 
19.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
100.0%
Hangul
ValueCountFrequency (%)
동 12
24.0%
평 4
 
8.0%
균 3
 
6.0%
읍 2
 
4.0%
지 2
 
4.0%
팔 2
 
4.0%
현 1
 
2.0%
모 1
 
2.0%
점 1
 
2.0%
연 1
 
2.0%
Other values (21) 21
42.0%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6
Missing (%)20.7%
Memory size364.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6
Missing (%)20.7%
Memory size364.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6
Missing (%)20.7%
Memory size364.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7
Missing (%)24.1%
Memory size364.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7
Missing (%)24.1%
Memory size364.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing13
Missing (%)44.8%
Memory size364.0 B

Correlations

2024-03-14T12:25:51.418495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2015년 도시 대기측정망(11월)Unnamed: 2
2015년 도시 대기측정망(11월)1.0001.000
Unnamed: 21.0001.000

Missing values

2024-03-14T12:25:50.203926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T12:25:50.331809image/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.
2024-03-14T12:25:50.437718image/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

Unnamed: 02015년 도시 대기측정망(11월)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
0<NA><NA><NA>NaNNaNNaNNaNNaNNaN
1<NA>측정지역<NA>측 정 항 목NaNNaNNaNNaNNaN
2<NA>환경기준 지 점<NA>O3NO2SO2COPM-10PM-2.5
3<NA><NA><NA>8시간평균0.06ppm이하연간 평균O.O3ppm 이하연간평균O.O2ppm이하8시간평균 9ppm이하연간평균 50㎍/㎥이하연간평균 25㎍/㎥이하
4<NA><NA><NA>1시간평균 0.10ppm이하24시간평균0.06ppm 이하24시간 평균O.O5ppm이하1시간평균 25ppm이하24시간평균100㎍/㎥이하24시간평균50㎍/㎥이하
5<NA><NA><NA>NaN1시간평균 0.10ppm 이하1시간평균0.15ppm이하NaNNaNNaN
6<NA>전주태평동0.0160.0220.0040.53836
7<NA><NA>삼천동0.0160.0230.0060.646NaN
8<NA><NA>팔복동0.0160.0220.0060.544NaN
9<NA><NA>평 균0.0160.0223330.0053330.53333342.666667NaN
Unnamed: 02015년 도시 대기측정망(11월)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8
19<NA>남원죽항동0.0180.0160.0040.73327
20<NA>고창고창읍0.0270.0120.0040.64632
21<NA>부안부안읍0.0190.0180.0030.54737
22<NA>전북평균<NA>0.0173850.0192310.0039230.53846242.92307734.5
23<NA>2015년 도로변 대기측정망(11월)<NA>NaNNaNNaNNaNNaNNaN
24<NA><NA><NA>NaNNaNNaNNaNNaNNaN
25<NA>시군지점O3NO2SO2COPM-10PM-2.5
26<NA>전주시금암동-0.0310.0030.53939
27<NA>1. 일(월,년)평균 = 해당지역의 전측정소 시간 측정치의 누적값÷해당지역의 모든측정소 시간측정치수<NA>NaNNaNNaNNaNNaNNaN
28<NA>2. 위 자료는 보건환경연구원 1차 확정 자료로, 환경부 자료와 상이할수 있음<NA>NaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

2015년 도시 대기측정망(11월)Unnamed: 2# duplicates
1<NA><NA>5
0<NA>평 균3