Overview

Dataset statistics

Number of variables8
Number of observations29
Missing cells92
Missing cells (%)39.7%
Duplicate rows2
Duplicate rows (%)6.9%
Total size in memory2.0 KiB
Average record size in memory69.6 B

Variable types

Unsupported6
Text2

Dataset

Description대기측정망운영결과2014년12월
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=201637

Alerts

Dataset has 2 (6.9%) duplicate rowsDuplicates
Unnamed: 0 has 29 (100.0%) missing valuesMissing
2014년 도시 대기측정망(12월) has 14 (48.3%) missing valuesMissing
Unnamed: 2 has 12 (41.4%) missing valuesMissing
Unnamed: 3 has 7 (24.1%) missing valuesMissing
Unnamed: 4 has 7 (24.1%) missing valuesMissing
Unnamed: 5 has 7 (24.1%) missing valuesMissing
Unnamed: 6 has 8 (27.6%) missing valuesMissing
Unnamed: 7 has 8 (27.6%) 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 01:01:48.273977
Analysis finished2024-03-14 01:01:48.689400
Duration0.42 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

2014년 도시 대기측정망(12월)
Text

MISSING 

Distinct15
Distinct (%)100.0%
Missing14
Missing (%)48.3%
Memory size364.0 B
2024-03-14T10:01:48.854071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length57
Mean length15.933333
Min length2

Characters and Unicode

Total characters239
Distinct characters89
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 (%)
3
 
5.9%
측정소 2
 
3.9%
측정지역 1
 
2.0%
상이할수 1
 
2.0%
위 1
 
2.0%
자료는 1
 
2.0%
보건환경연구원 1
 
2.0%
1차 1
 
2.0%
확정 1
 
2.0%
자료로 1
 
2.0%
Other values (38) 38
74.5%
2024-03-14T10:01:49.157012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51
 
21.3%
정 11
 
4.6%
1 9
 
3.8%
측 8
 
3.3%
2 8
 
3.3%
. 7
 
2.9%
소 5
 
2.1%
, 4
 
1.7%
전 4
 
1.7%
시 4
 
1.7%
Other values (79) 128
53.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 137
57.3%
Space Separator 51
 
21.3%
Decimal Number 27
 
11.3%
Other Punctuation 13
 
5.4%
Math Symbol 4
 
1.7%
Control 3
 
1.3%
Close Punctuation 2
 
0.8%
Open Punctuation 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 11
 
8.0%
측 8
 
5.8%
소 5
 
3.6%
전 4
 
2.9%
시 4
 
2.9%
지 4
 
2.9%
동 3
 
2.2%
료 3
 
2.2%
자 3
 
2.2%
평 3
 
2.2%
Other values (63) 89
65.0%
Decimal Number
ValueCountFrequency (%)
1 9
33.3%
2 8
29.6%
3 3
 
11.1%
4 3
 
11.1%
0 3
 
11.1%
7 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 7
53.8%
, 4
30.8%
: 2
 
15.4%
Math Symbol
ValueCountFrequency (%)
~ 2
50.0%
= 1
25.0%
÷ 1
25.0%
Space Separator
ValueCountFrequency (%)
51
100.0%
Control
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 137
57.3%
Common 102
42.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 11
 
8.0%
측 8
 
5.8%
소 5
 
3.6%
전 4
 
2.9%
시 4
 
2.9%
지 4
 
2.9%
동 3
 
2.2%
료 3
 
2.2%
자 3
 
2.2%
평 3
 
2.2%
Other values (63) 89
65.0%
Common
ValueCountFrequency (%)
51
50.0%
1 9
 
8.8%
2 8
 
7.8%
. 7
 
6.9%
, 4
 
3.9%
3 3
 
2.9%
4 3
 
2.9%
0 3
 
2.9%
3
 
2.9%
: 2
 
2.0%
Other values (6) 9
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 137
57.3%
ASCII 101
42.3%
None 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51
50.5%
1 9
 
8.9%
2 8
 
7.9%
. 7
 
6.9%
, 4
 
4.0%
3 3
 
3.0%
4 3
 
3.0%
0 3
 
3.0%
3
 
3.0%
: 2
 
2.0%
Other values (5) 8
 
7.9%
Hangul
ValueCountFrequency (%)
정 11
 
8.0%
측 8
 
5.8%
소 5
 
3.6%
전 4
 
2.9%
시 4
 
2.9%
지 4
 
2.9%
동 3
 
2.2%
료 3
 
2.2%
자 3
 
2.2%
평 3
 
2.2%
Other values (63) 89
65.0%
None
ValueCountFrequency (%)
÷ 1
100.0%

Unnamed: 2
Text

MISSING 

Distinct15
Distinct (%)88.2%
Missing12
Missing (%)41.4%
Memory size364.0 B
2024-03-14T10:01:49.298609image/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%
2024-03-14T10:01:49.558621image/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 

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

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing8
Missing (%)27.6%
Memory size364.0 B

Correlations

2024-03-14T10:01:49.630769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2014년 도시 대기측정망(12월)Unnamed: 2
2014년 도시 대기측정망(12월)1.0001.000
Unnamed: 21.0001.000

Missing values

2024-03-14T10:01:48.409881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:01:48.504922image/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.
2024-03-14T10:01:48.601946image/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(12월)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>전주태평동0.0140.0230.0040.545
7<NA><NA>삼천동교정0.0260.0060.6교정
8<NA><NA>팔복동0.0140.0210.0040.645
9<NA><NA>평 균0.0140.0233330.0046670.56666745
Unnamed: 02014년 도시 대기측정망(12월)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7
19<NA>남원죽항동0.0180.0160.0050.845
20<NA>고창고창읍0.0250.0110.0040.634
21<NA>전북평균<NA>0.0168330.0184720.0053060.64722241.833333
22<NA>2014년 도로변 대기측정망(12월)<NA>NaNNaNNaNNaNNaN
23<NA><NA><NA>NaNNaNNaNNaNNaN
24<NA>시군지점O3NO2SO2COPM-10
25<NA>전주시금암동*0.008*0.426
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
28<NA>3. 삼천동, 소룡동 측정소 : 2014.12.3~23 과학원 주관 정도관리평가, 팔봉동 측정소 교체 : 2014.12.17~<NA>NaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

2014년 도시 대기측정망(12월)Unnamed: 2# duplicates
1<NA><NA>5
0<NA>평 균3