Overview

Dataset statistics

Number of variables8
Number of observations28
Missing cells85
Missing cells (%)37.9%
Duplicate rows2
Duplicate rows (%)7.1%
Total size in memory1.9 KiB
Average record size in memory69.7 B

Variable types

Unsupported6
Text2

Dataset

Description대기측정망운영결과2014년10월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=201482

Alerts

Dataset has 2 (7.1%) duplicate rowsDuplicates
Unnamed: 0 has 28 (100.0%) missing valuesMissing
2014년 도시 대기측정망(10월) has 14 (50.0%) missing valuesMissing
Unnamed: 2 has 11 (39.3%) missing valuesMissing
Unnamed: 3 has 6 (21.4%) missing valuesMissing
Unnamed: 4 has 6 (21.4%) missing valuesMissing
Unnamed: 5 has 6 (21.4%) missing valuesMissing
Unnamed: 6 has 7 (25.0%) missing valuesMissing
Unnamed: 7 has 7 (25.0%) 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

Reproduction

Analysis started2024-03-14 01:07:18.822652
Analysis finished2024-03-14 01:07:19.209962
Duration0.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing28
Missing (%)100.0%
Memory size384.0 B

2014년 도시 대기측정망(10월)
Text

MISSING 

Distinct14
Distinct (%)100.0%
Missing14
Missing (%)50.0%
Memory size356.0 B
2024-03-14T10:07:19.323747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length44
Mean length12
Min length2

Characters and Unicode

Total characters168
Distinct characters72
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

Unique14 ?
Unique (%)100.0%

Sample

1st row측정지역
2nd row 환경기준 지 점
3rd row전주
4th row군산
5th row익산
ValueCountFrequency (%)
측정지역 1
 
2.7%
해당지역의 1
 
2.7%
시간 1
 
2.7%
측정치의 1
 
2.7%
누적값÷해당지역의 1
 
2.7%
모든측정소 1
 
2.7%
시간측정치수 1
 
2.7%
2 1
 
2.7%
위 1
 
2.7%
자료는 1
 
2.7%
Other values (27) 27
73.0%
2024-03-14T10:07:19.596282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
37
22.0%
정 8
 
4.8%
측 6
 
3.6%
전 4
 
2.4%
1 4
 
2.4%
시 4
 
2.4%
지 4
 
2.4%
경 3
 
1.8%
료 3
 
1.8%
자 3
 
1.8%
Other values (62) 92
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109
64.9%
Space Separator 37
 
22.0%
Decimal Number 9
 
5.4%
Other Punctuation 4
 
2.4%
Control 3
 
1.8%
Open Punctuation 2
 
1.2%
Close Punctuation 2
 
1.2%
Math Symbol 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 8
 
7.3%
측 6
 
5.5%
전 4
 
3.7%
시 4
 
3.7%
지 4
 
3.7%
경 3
 
2.8%
료 3
 
2.8%
자 3
 
2.8%
환 3
 
2.8%
역 3
 
2.8%
Other values (50) 68
62.4%
Decimal Number
ValueCountFrequency (%)
1 4
44.4%
2 2
22.2%
0 2
22.2%
4 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%
Control
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109
64.9%
Common 59
35.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 8
 
7.3%
측 6
 
5.5%
전 4
 
3.7%
시 4
 
3.7%
지 4
 
3.7%
경 3
 
2.8%
료 3
 
2.8%
자 3
 
2.8%
환 3
 
2.8%
역 3
 
2.8%
Other values (50) 68
62.4%
Common
ValueCountFrequency (%)
37
62.7%
1 4
 
6.8%
3
 
5.1%
. 2
 
3.4%
( 2
 
3.4%
, 2
 
3.4%
) 2
 
3.4%
2 2
 
3.4%
0 2
 
3.4%
4 1
 
1.7%
Other values (2) 2
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109
64.9%
ASCII 58
34.5%
None 1
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
37
63.8%
1 4
 
6.9%
3
 
5.2%
. 2
 
3.4%
( 2
 
3.4%
, 2
 
3.4%
) 2
 
3.4%
2 2
 
3.4%
0 2
 
3.4%
4 1
 
1.7%
Hangul
ValueCountFrequency (%)
정 8
 
7.3%
측 6
 
5.5%
전 4
 
3.7%
시 4
 
3.7%
지 4
 
3.7%
경 3
 
2.8%
료 3
 
2.8%
자 3
 
2.8%
환 3
 
2.8%
역 3
 
2.8%
Other values (50) 68
62.4%
None
ValueCountFrequency (%)
÷ 1
100.0%

Unnamed: 2
Text

MISSING 

Distinct15
Distinct (%)88.2%
Missing11
Missing (%)39.3%
Memory size356.0 B
2024-03-14T10:07:19.735402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.4705882
Min length2

Characters and Unicode

Total characters59
Distinct characters30
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

Unique14 ?
Unique (%)82.4%

Sample

1st row태평동
2nd row삼천동
3rd row팔복동
4th row 평 균
5th row신풍동
ValueCountFrequency (%)
평 3
15.0%
균 3
15.0%
태평동 1
 
5.0%
삼천동 1
 
5.0%
팔복동 1
 
5.0%
신풍동 1
 
5.0%
소룡동 1
 
5.0%
개정동 1
 
5.0%
팔봉동 1
 
5.0%
모현동 1
 
5.0%
Other values (6) 6
30.0%
2024-03-14T10:07:20.243309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
20.3%
동 12
20.3%
평 4
 
6.8%
균 3
 
5.1%
팔 2
 
3.4%
지 2
 
3.4%
항 1
 
1.7%
중 1
 
1.7%
연 1
 
1.7%
죽 1
 
1.7%
Other values (20) 20
33.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47
79.7%
Space Separator 12
 
20.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12
25.5%
평 4
 
8.5%
균 3
 
6.4%
팔 2
 
4.3%
지 2
 
4.3%
항 1
 
2.1%
중 1
 
2.1%
연 1
 
2.1%
죽 1
 
2.1%
창 1
 
2.1%
Other values (19) 19
40.4%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 47
79.7%
Common 12
 
20.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12
25.5%
평 4
 
8.5%
균 3
 
6.4%
팔 2
 
4.3%
지 2
 
4.3%
항 1
 
2.1%
중 1
 
2.1%
연 1
 
2.1%
죽 1
 
2.1%
창 1
 
2.1%
Other values (19) 19
40.4%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47
79.7%
ASCII 12
 
20.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
100.0%
Hangul
ValueCountFrequency (%)
동 12
25.5%
평 4
 
8.5%
균 3
 
6.4%
팔 2
 
4.3%
지 2
 
4.3%
항 1
 
2.1%
중 1
 
2.1%
연 1
 
2.1%
죽 1
 
2.1%
창 1
 
2.1%
Other values (19) 19
40.4%

Unnamed: 3
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6
Missing (%)21.4%
Memory size356.0 B

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6
Missing (%)21.4%
Memory size356.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6
Missing (%)21.4%
Memory size356.0 B

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7
Missing (%)25.0%
Memory size356.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing7
Missing (%)25.0%
Memory size356.0 B

Correlations

2024-03-14T10:07:20.319118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2014년 도시 대기측정망(10월)Unnamed: 2
2014년 도시 대기측정망(10월)1.0001.000
Unnamed: 21.0001.000

Missing values

2024-03-14T10:07:18.955678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:07:19.050517image/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.
2024-03-14T10:07:19.142883image/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: 02014년 도시 대기측정망(10월)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
0<NA><NA><NA>NaNNaNNaNNaNNaN
1<NA>측정지역<NA>측 정 항 목NaNNaNNaNNaN
2<NA>환경기준 지 점<NA>O3NO2SO2COPM-10
3<NA><NA><NA>8시간평균0.06ppm이하연간 평균O.O3ppm 이하연간평균O.O2ppm이하8시간평균 9ppm이하연간평균 50㎍/㎥이하
4<NA><NA><NA>1시간평균 0.10ppm이하24시간평균0.06ppm 이하24시간 평균O.O5ppm이하1시간평균 25ppm이하24시간평균100㎍/㎥이하
5<NA><NA><NA>NaN1시간평균 0.10ppm 이하1시간평균0.15ppm이하NaNNaN
6<NA>전주태평동0.0240.0150.0030.334
7<NA><NA>삼천동0.0250.0210.0030.440
8<NA><NA>팔복동0.0190.0180.0040.337
9<NA><NA>평 균0.0226670.0180.0033330.33333337
Unnamed: 02014년 도시 대기측정망(10월)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
18<NA>정읍연지동0.020.0140.0020.237
19<NA>남원죽항동0.0260.010.0040.535
20<NA>고창고창읍0.0340.0090.0040.6동불
21<NA>전북평균<NA>0.0258890.0143890.0038890.44444439.333333
22<NA>2014년 도로변 대기측정망(10월)<NA>NaNNaNNaNNaNNaN
23<NA><NA><NA>NaNNaNNaNNaNNaN
24<NA>시군지점O3NO2SO2COPM-10
25<NA>전주시금암동*0.007*0.319
26<NA>1. 일(월,년)평균 = 해당지역의 전측정소 시간 측정치의 누적값÷해당지역의 모든측정소 시간측정치수<NA>NaNNaNNaNNaNNaN
27<NA>2. 위 자료는 보건환경연구원 1차 확정 자료로, 환경부 자료와 상이할수 있음<NA>NaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

2014년 도시 대기측정망(10월)Unnamed: 2# duplicates
1<NA><NA>5
0<NA>평 균3