Overview

Dataset statistics

Number of variables11
Number of observations33
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory96.0 B

Variable types

Numeric4
Categorical5
Text2

Dataset

Description부산광역시_대기질 진단평가시스템의 대기측정소정보(환경공단) 데이터로 대기질지점코드, 지역명,대기질지점명,설치날짜, 측정소종류,측정항목,위도,경도,주소,광화학측정소코드,권역 등 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15120977/fileData.do

Alerts

지역명 has constant value ""Constant
광화학측정소코드 is highly overall correlated with 측정소종류 and 1 other fieldsHigh correlation
권역 is highly overall correlated with 경도 and 2 other fieldsHigh correlation
측정소종류 is highly overall correlated with 대기질지점코드 and 2 other fieldsHigh correlation
대기질지점코드 is highly overall correlated with 측정소종류High correlation
경도 is highly overall correlated with 권역High correlation
측정항목 is highly imbalanced (60.2%)Imbalance
대기질지점코드 has unique valuesUnique
대기질지점명 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
주소 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:13:13.579947
Analysis finished2023-12-12 20:13:16.265525
Duration2.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대기질지점코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221246.27
Minimum221112
Maximum221902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T05:13:16.366914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum221112
5-th percentile221137
Q1221174
median221202
Q3221251
95-th percentile221530.8
Maximum221902
Range790
Interquartile range (IQR)77

Descriptive statistics

Standard deviation175.28446
Coefficient of variation (CV)0.0007922595
Kurtosis11.462786
Mean221246.27
Median Absolute Deviation (MAD)39
Skewness3.4097406
Sum7301127
Variance30724.642
MonotonicityNot monotonic
2023-12-13T05:13:16.557190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
221271 1
 
3.0%
221253 1
 
3.0%
221183 1
 
3.0%
221282 1
 
3.0%
221152 1
 
3.0%
221163 1
 
3.0%
221283 1
 
3.0%
221191 1
 
3.0%
221221 1
 
3.0%
221281 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
221112 1
3.0%
221131 1
3.0%
221141 1
3.0%
221142 1
3.0%
221152 1
3.0%
221162 1
3.0%
221163 1
3.0%
221172 1
3.0%
221174 1
3.0%
221181 1
3.0%
ValueCountFrequency (%)
221902 1
3.0%
221901 1
3.0%
221284 1
3.0%
221283 1
3.0%
221282 1
3.0%
221281 1
3.0%
221271 1
3.0%
221253 1
3.0%
221251 1
3.0%
221241 1
3.0%

지역명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
부산
33 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산 33
100.0%

Length

2023-12-13T05:13:16.760022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:13:16.905219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산 33
100.0%

대기질지점명
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-13T05:13:17.121718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.030303
Min length2

Characters and Unicode

Total characters100
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row광안동
2nd row대신동
3rd row수정동
4th row용호동
5th row광복동
ValueCountFrequency (%)
광안동 1
 
3.0%
당리동 1
 
3.0%
부산북항 1
 
3.0%
심락동 1
 
3.0%
초량동 1
 
3.0%
온천동 1
 
3.0%
부곡동 1
 
3.0%
연산동 1
 
3.0%
회동동 1
 
3.0%
청룡동 1
 
3.0%
Other values (23) 23
69.7%
2023-12-13T05:13:17.549688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 29
29.0%
산 4
 
4.0%
대 4
 
4.0%
장 4
 
4.0%
부 3
 
3.0%
명 3
 
3.0%
학 2
 
2.0%
포 2
 
2.0%
천 2
 
2.0%
덕 2
 
2.0%
Other values (37) 45
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 29
29.0%
산 4
 
4.0%
대 4
 
4.0%
장 4
 
4.0%
부 3
 
3.0%
명 3
 
3.0%
학 2
 
2.0%
포 2
 
2.0%
천 2
 
2.0%
덕 2
 
2.0%
Other values (37) 45
45.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 29
29.0%
산 4
 
4.0%
대 4
 
4.0%
장 4
 
4.0%
부 3
 
3.0%
명 3
 
3.0%
학 2
 
2.0%
포 2
 
2.0%
천 2
 
2.0%
덕 2
 
2.0%
Other values (37) 45
45.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 29
29.0%
산 4
 
4.0%
대 4
 
4.0%
장 4
 
4.0%
부 3
 
3.0%
명 3
 
3.0%
학 2
 
2.0%
포 2
 
2.0%
천 2
 
2.0%
덕 2
 
2.0%
Other values (37) 45
45.0%

설치날짜
Real number (ℝ)

Distinct17
Distinct (%)51.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.7879
Minimum1979
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T05:13:17.716705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1979
5-th percentile1984.2
Q11999
median2017
Q32019
95-th percentile2020.4
Maximum2021
Range42
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.805657
Coefficient of variation (CV)0.0063748179
Kurtosis-0.50243848
Mean2008.7879
Median Absolute Deviation (MAD)4
Skewness-0.84741576
Sum66290
Variance163.98485
MonotonicityNot monotonic
2023-12-13T05:13:17.868347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2019 5
15.2%
2020 5
15.2%
2017 4
12.1%
1999 3
9.1%
2021 2
 
6.1%
2002 2
 
6.1%
2018 2
 
6.1%
1992 1
 
3.0%
1997 1
 
3.0%
2000 1
 
3.0%
Other values (7) 7
21.2%
ValueCountFrequency (%)
1979 1
 
3.0%
1983 1
 
3.0%
1985 1
 
3.0%
1991 1
 
3.0%
1992 1
 
3.0%
1997 1
 
3.0%
1999 3
9.1%
2000 1
 
3.0%
2002 2
6.1%
2004 1
 
3.0%
ValueCountFrequency (%)
2021 2
 
6.1%
2020 5
15.2%
2019 5
15.2%
2018 2
 
6.1%
2017 4
12.1%
2012 1
 
3.0%
2005 1
 
3.0%
2004 1
 
3.0%
2002 2
 
6.1%
2000 1
 
3.0%

측정소종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
도시대기
28 
도로변대기
5 

Length

Max length5
Median length4
Mean length4.1515152
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도시대기 28
84.8%
도로변대기 5
 
15.2%

Length

2023-12-13T05:13:18.018613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:13:18.134003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도시대기 28
84.8%
도로변대기 5
 
15.2%

측정항목
Categorical

IMBALANCE 

Distinct3
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size396.0 B
SO2, CO, O3, NO2, PM10, PM2.5
29 
O3, NO2, PM10, PM2.5
3 
SO2, CO, NO2, PM10, PM2.5
 
1

Length

Max length29
Median length29
Mean length28.060606
Min length20

Unique

Unique1 ?
Unique (%)3.0%

Sample

1st rowO3, NO2, PM10, PM2.5
2nd rowSO2, CO, O3, NO2, PM10, PM2.5
3rd rowSO2, CO, O3, NO2, PM10, PM2.5
4th rowSO2, CO, O3, NO2, PM10, PM2.5
5th rowSO2, CO, O3, NO2, PM10, PM2.5

Common Values

ValueCountFrequency (%)
SO2, CO, O3, NO2, PM10, PM2.5 29
87.9%
O3, NO2, PM10, PM2.5 3
 
9.1%
SO2, CO, NO2, PM10, PM2.5 1
 
3.0%

Length

2023-12-13T05:13:18.268871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:13:18.402571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
no2 33
17.3%
pm10 33
17.3%
pm2.5 33
17.3%
o3 32
16.8%
so2 30
15.7%
co 30
15.7%

위도
Real number (ℝ)

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.162134
Minimum35.063038
Maximum35.325306
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T05:13:18.553916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.063038
5-th percentile35.079746
Q135.114404
median35.155263
Q335.207146
95-th percentile35.257229
Maximum35.325306
Range0.262268
Interquartile range (IQR)0.092742

Descriptive statistics

Standard deviation0.061996203
Coefficient of variation (CV)0.0017631525
Kurtosis0.013437174
Mean35.162134
Median Absolute Deviation (MAD)0.049523
Skewness0.542536
Sum1160.3504
Variance0.0038435292
MonotonicityNot monotonic
2023-12-13T05:13:18.725433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
35.152309 1
 
3.0%
35.22947 1
 
3.0%
35.233532 1
 
3.0%
35.173784 1
 
3.0%
35.155626 1
 
3.0%
35.204728 1
 
3.0%
35.155263 1
 
3.0%
35.274045 1
 
3.0%
35.184734 1
 
3.0%
35.114404 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
35.063038 1
3.0%
35.074896 1
3.0%
35.08298 1
3.0%
35.086632 1
3.0%
35.090782 1
3.0%
35.099849 1
3.0%
35.105548 1
3.0%
35.10574 1
3.0%
35.114404 1
3.0%
35.122568 1
3.0%
ValueCountFrequency (%)
35.325306 1
3.0%
35.274045 1
3.0%
35.246018 1
3.0%
35.233532 1
3.0%
35.229749 1
3.0%
35.22947 1
3.0%
35.20958 1
3.0%
35.207825 1
3.0%
35.207146 1
3.0%
35.204728 1
3.0%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.04496
Minimum128.83417
Maximum129.21168
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size429.0 B
2023-12-13T05:13:18.906847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.83417
5-th percentile128.90098
Q1128.98605
median129.05692
Q3129.09266
95-th percentile129.17587
Maximum129.21168
Range0.377511
Interquartile range (IQR)0.106608

Descriptive statistics

Standard deviation0.083182084
Coefficient of variation (CV)0.00064459769
Kurtosis0.56115969
Mean129.04496
Median Absolute Deviation (MAD)0.050842
Skewness-0.45867947
Sum4258.4838
Variance0.0069192591
MonotonicityNot monotonic
2023-12-13T05:13:19.079484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
129.108101 1
 
3.0%
129.1209 1
 
3.0%
129.010245 1
 
3.0%
128.986052 1
 
3.0%
129.063259 1
 
3.0%
129.1042 1
 
3.0%
129.022568 1
 
3.0%
129.090793 1
 
3.0%
129.078112 1
 
3.0%
129.017251 1
 
3.0%
Other values (23) 23
69.7%
ValueCountFrequency (%)
128.834169 1
3.0%
128.8639 1
3.0%
128.9257 1
3.0%
128.966762 1
3.0%
128.971367 1
3.0%
128.972983 1
3.0%
128.976359 1
3.0%
128.979342 1
3.0%
128.986052 1
3.0%
129.006081 1
3.0%
ValueCountFrequency (%)
129.21168 1
3.0%
129.178434 1
3.0%
129.174166 1
3.0%
129.120934 1
3.0%
129.1209 1
3.0%
129.115711 1
3.0%
129.108101 1
3.0%
129.1042 1
3.0%
129.09266 1
3.0%
129.090793 1
3.0%

주소
Text

UNIQUE 

Distinct33
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size396.0 B
2023-12-13T05:13:19.406992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length29
Mean length27.727273
Min length9

Characters and Unicode

Total characters915
Distinct characters144
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)100.0%

Sample

1st row부산 수영구 광안로21번가길 57한바다중학교 옥상
2nd row부산 서구 대신로 150부산국민체육센터 옥상
3rd row부산 동구 구청로 1동구청 지상
4th row부산 남구 용호동
5th row부산 중구 광복로 55번길 10광복동주민센터 옥상
ValueCountFrequency (%)
부산 32
 
16.8%
옥상 13
 
6.8%
운동장 4
 
2.1%
동구 3
 
1.6%
금정구 3
 
1.6%
사상구 3
 
1.6%
강서구 3
 
1.6%
화단 3
 
1.6%
북구 2
 
1.1%
기장군 2
 
1.1%
Other values (115) 122
64.2%
2023-12-13T05:13:19.898758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
157
 
17.2%
부 46
 
5.0%
산 44
 
4.8%
동 38
 
4.2%
구 34
 
3.7%
1 30
 
3.3%
로 28
 
3.1%
상 19
 
2.1%
장 18
 
2.0%
3 16
 
1.7%
Other values (134) 485
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 621
67.9%
Space Separator 157
 
17.2%
Decimal Number 112
 
12.2%
Close Punctuation 12
 
1.3%
Open Punctuation 12
 
1.3%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 46
 
7.4%
산 44
 
7.1%
동 38
 
6.1%
구 34
 
5.5%
로 28
 
4.5%
상 19
 
3.1%
장 18
 
2.9%
옥 13
 
2.1%
대 12
 
1.9%
길 12
 
1.9%
Other values (120) 357
57.5%
Decimal Number
ValueCountFrequency (%)
1 30
26.8%
3 16
14.3%
2 14
12.5%
5 10
 
8.9%
4 9
 
8.0%
0 9
 
8.0%
9 8
 
7.1%
8 6
 
5.4%
6 6
 
5.4%
7 4
 
3.6%
Space Separator
ValueCountFrequency (%)
157
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 621
67.9%
Common 294
32.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 46
 
7.4%
산 44
 
7.1%
동 38
 
6.1%
구 34
 
5.5%
로 28
 
4.5%
상 19
 
3.1%
장 18
 
2.9%
옥 13
 
2.1%
대 12
 
1.9%
길 12
 
1.9%
Other values (120) 357
57.5%
Common
ValueCountFrequency (%)
157
53.4%
1 30
 
10.2%
3 16
 
5.4%
2 14
 
4.8%
) 12
 
4.1%
( 12
 
4.1%
5 10
 
3.4%
4 9
 
3.1%
0 9
 
3.1%
9 8
 
2.7%
Other values (4) 17
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 621
67.9%
ASCII 294
32.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
157
53.4%
1 30
 
10.2%
3 16
 
5.4%
2 14
 
4.8%
) 12
 
4.1%
( 12
 
4.1%
5 10
 
3.4%
4 9
 
3.1%
0 9
 
3.1%
9 8
 
2.7%
Other values (4) 17
 
5.8%
Hangul
ValueCountFrequency (%)
부 46
 
7.4%
산 44
 
7.1%
동 38
 
6.1%
구 34
 
5.5%
로 28
 
4.5%
상 19
 
3.1%
장 18
 
2.9%
옥 13
 
2.1%
대 12
 
1.9%
길 12
 
1.9%
Other values (120) 357
57.5%

광화학측정소코드
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Memory size396.0 B
953,973,941,952,932
5 
<NA>
5 
973,953,941,952,932
4 
973,941,953,952,932
3 
941,973,953,952,932
3 
Other values (9)
13 

Length

Max length19
Median length19
Mean length16.727273
Min length4

Unique

Unique6 ?
Unique (%)18.2%

Sample

1st row973,953,941,952,932
2nd row953,973,941,952,932
3rd row953,973,941,952,932
4th row973,941,953,952,932
5th row941,973,953,952,932

Common Values

ValueCountFrequency (%)
953,973,941,952,932 5
15.2%
<NA> 5
15.2%
973,953,941,952,932 4
12.1%
973,941,953,952,932 3
9.1%
941,973,953,952,932 3
9.1%
952,953,973,941,932 3
9.1%
941,953,973,952,932 2
 
6.1%
952,932,973,953,941 2
 
6.1%
973,952,941,953,932 1
 
3.0%
932,952,973,941,953 1
 
3.0%
Other values (4) 4
12.1%

Length

2023-12-13T05:13:20.424437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
953,973,941,952,932 5
15.2%
na 5
15.2%
973,953,941,952,932 4
12.1%
973,941,953,952,932 3
9.1%
941,973,953,952,932 3
9.1%
952,953,973,941,932 3
9.1%
941,953,973,952,932 2
 
6.1%
952,932,973,953,941 2
 
6.1%
973,952,941,953,932 1
 
3.0%
932,952,973,941,953 1
 
3.0%
Other values (4) 4
12.1%

권역
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)15.2%
Missing0
Missing (%)0.0%
Memory size396.0 B
남부권역
10 
서부권역
9 
중부권역
7 
<NA>
5 
동부권역
2 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남부권역 10
30.3%
서부권역 9
27.3%
중부권역 7
21.2%
<NA> 5
15.2%
동부권역 2
 
6.1%

Length

2023-12-13T05:13:20.623817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:13:20.768207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남부권역 10
30.3%
서부권역 9
27.3%
중부권역 7
21.2%
na 5
15.2%
동부권역 2
 
6.1%

Interactions

2023-12-13T05:13:15.444843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:14.070102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:14.527474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:15.015220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:15.573782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:14.191989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:14.705400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:15.125195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:15.675709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:14.290126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:14.790736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:15.219512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:15.780216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:14.407636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:14.900551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:15.336472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:13:20.903045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대기질지점코드대기질지점명설치날짜측정소종류측정항목위도경도주소광화학측정소코드권역
대기질지점코드1.0001.0000.0000.8340.0000.1310.5451.0000.0000.000
대기질지점명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
설치날짜0.0001.0001.0000.0000.0000.5200.0001.0000.4430.000
측정소종류0.8341.0000.0001.0000.2240.0000.0001.000NaNNaN
측정항목0.0001.0000.0000.2241.0000.5650.0001.0000.4590.244
위도0.1311.0000.5200.0000.5651.0000.0001.0000.8150.594
경도0.5451.0000.0000.0000.0000.0001.0001.0000.6400.919
주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
광화학측정소코드0.0001.0000.443NaN0.4590.8150.6401.0001.0000.900
권역0.0001.0000.000NaN0.2440.5940.9191.0000.9001.000
2023-12-13T05:13:21.066912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
광화학측정소코드측정항목권역측정소종류
광화학측정소코드1.0000.3040.6101.000
측정항목0.3041.0000.1430.359
권역0.6100.1431.0001.000
측정소종류1.0000.3591.0001.000
2023-12-13T05:13:21.201886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대기질지점코드설치날짜위도경도측정소종류측정항목광화학측정소코드권역
대기질지점코드1.0000.1850.045-0.1450.6020.0000.0000.000
설치날짜0.1851.000-0.125-0.0100.0000.0000.0880.000
위도0.045-0.1251.0000.4820.0000.2540.4530.365
경도-0.145-0.0100.4821.0000.0000.0000.2760.574
측정소종류0.6020.0000.0000.0001.0000.3591.0001.000
측정항목0.0000.0000.2540.0000.3591.0000.3040.143
광화학측정소코드0.0000.0880.4530.2761.0000.3041.0000.610
권역0.0000.0000.3650.5741.0000.1430.6101.000

Missing values

2023-12-13T05:13:15.953555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:13:16.180181image/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

대기질지점코드지역명대기질지점명설치날짜측정소종류측정항목위도경도주소광화학측정소코드권역
0221271부산광안동2017도시대기O3, NO2, PM10, PM2.535.152309129.108101부산 수영구 광안로21번가길 57한바다중학교 옥상973,953,941,952,932남부권역
1221281부산대신동2012도시대기SO2, CO, O3, NO2, PM10, PM2.535.114404129.017251부산 서구 대신로 150부산국민체육센터 옥상953,973,941,952,932남부권역
2221241부산수정동2017도시대기SO2, CO, O3, NO2, PM10, PM2.535.139059129.056923부산 동구 구청로 1동구청 지상953,973,941,952,932남부권역
3221174부산용호동2021도시대기SO2, CO, O3, NO2, PM10, PM2.535.127969129.115711부산 남구 용호동973,941,953,952,932남부권역
4221112부산광복동1985도시대기SO2, CO, O3, NO2, PM10, PM2.535.099849129.030344부산 중구 광복로 55번길 10광복동주민센터 옥상941,973,953,952,932남부권역
5221141부산태종대2019도시대기SO2, CO, O3, NO2, PM10, PM2.535.063038129.080909부산 영도구 전망로 24태종대유원지관리사무소 3층941,973,953,952,932남부권역
6221142부산청학동2020도시대기SO2, CO, O3, NO2, PM10, PM2.535.090782129.059531부산 영도구 청학남로 13번길 18 청학동주민어울림마당941,973,953,952,932남부권역
7221192부산좌동2005도시대기SO2, CO, O3, NO2, PM10, PM2.535.170893129.174166부산 해운대구 양운로 91좌1동주민센터 옥상973,952,941,953,932남부권역
8221193부산재송동2020도시대기SO2, CO, O3, NO2, PM10, PM2.535.183181129.120934부산 해운대구 재송동 1191 동부하수처리장옆 공원973,941,953,952,932남부권역
9221172부산대연동1983도시대기SO2, CO, O3, NO2, PM10, PM2.535.129493129.086128부산 남구 수영로 196번길 80부산공업고등학교 공동실습관 옥상973,941,953,952,932남부권역
대기질지점코드지역명대기질지점명설치날짜측정소종류측정항목위도경도주소광화학측정소코드권역
23221283부산개금동2019도시대기SO2, CO, O3, NO2, PM10, PM2.535.155263129.022568부산 부산진구 개금온정로17번길 51개금3동 어린이놀이터 지상 (개금동)953,973,941,952,932중부권역
24221191부산청룡동2002도시대기O3, NO2, PM10, PM2.535.274045129.090793부산 금정구 청룡로 25청룡노포동주민센터 옥상952,932,973,953,941중부권역
25221253부산회동동2020도시대기SO2, CO, O3, NO2, PM10, PM2.535.22947129.1209부산 금정구 금사로 217(회동마루)953,973,941,952,932중부권역
26221221부산연산동2018도시대기SO2, CO, O3, NO2, PM10, PM2.535.184734129.078112부산 연제구 중앙대로 1001부산광역시청 녹음광장 창고973,953,952,941,932중부권역
27221251부산부곡동2000도시대기SO2, CO, O3, NO2, PM10, PM2.535.229749129.09266부산 금정구 부곡로156번길 7부곡2동 주민센터 옥상 (부곡동)952,932,973,953,941중부권역
28221162부산온천동1997도로변대기SO2, CO, O3, NO2, PM10, PM2.535.207146129.076217부산 동래구 중앙대로1335번길 38내산초등학교 동쪽 대로변 (온천동)<NA><NA>
29221131부산초량동1999도로변대기SO2, CO, O3, NO2, PM10, PM2.535.127142129.046701부산 동구 중앙대로349번길 14부산진역 1번 출구(윤흥신장군 동상옆)<NA><NA>
30221184부산심락동2021도로변대기SO2, CO, NO2, PM10, PM2.535.182831128.976359부산 사상구 삼락동<NA><NA>
31221901부산부산북항2017도로변대기SO2, CO, O3, NO2, PM10, PM2.535.122568129.055276부산 동구 충장대로 314자성대부두(관공선부두) 내 (좌천동)<NA><NA>
32221902부산부산신항2017도로변대기SO2, CO, O3, NO2, PM10, PM2.535.074896128.834169부산 강서구 신항남로 416부산신항다목적터미널(주) 본관 옥상 (성북동)<NA><NA>