Overview

Dataset statistics

Number of variables8
Number of observations24
Missing cells1
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory75.5 B

Variable types

Text2
Numeric5
Categorical1

Dataset

Description전북특별자치도 대기오염 측정망 운영 결과 데이터입니다. 측정항목, 측정위치(전주시, 군산시, 익산시 등) 대기정보를 제공합니다.
Author전라북도
URLhttps://www.bigdatahub.go.kr/index.jeonbuk?startPage=12&menuCd=DOM_000000103007001000&pListTypeStr=&pId=3081364

Alerts

이산화질소 is highly overall correlated with 미세먼지(10㎛이하)High correlation
이산화 황 is highly overall correlated with 미세먼지(2.5㎛이하)High correlation
미세먼지(10㎛이하) is highly overall correlated with 이산화질소 and 1 other fieldsHigh correlation
미세먼지(2.5㎛이하) is highly overall correlated with 이산화 황 and 1 other fieldsHigh correlation
오존 has 1 (4.2%) missing valuesMissing
측정지역 has unique valuesUnique

Reproduction

Analysis started2024-03-14 00:17:28.182328
Analysis finished2024-03-14 00:17:30.443819
Duration2.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Text

Distinct14
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-14T09:17:30.514814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0833333
Min length2

Characters and Unicode

Total characters50
Distinct characters24
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

Unique9 ?
Unique (%)37.5%

Sample

1st row전주
2nd row전주
3rd row전주
4th row전주
5th row전주
ValueCountFrequency (%)
전주 5
20.8%
군산 3
12.5%
익산 3
12.5%
정읍 2
 
8.3%
고창 2
 
8.3%
남원 1
 
4.2%
김제 1
 
4.2%
완주 1
 
4.2%
진안 1
 
4.2%
임실 1
 
4.2%
Other values (4) 4
16.7%
2024-03-14T09:17:30.766495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 7
14.0%
산 6
 
12.0%
전 5
 
10.0%
군 3
 
6.0%
익 3
 
6.0%
창 3
 
6.0%
2
 
4.0%
고 2
 
4.0%
읍 2
 
4.0%
정 2
 
4.0%
Other values (14) 15
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48
96.0%
Space Separator 2
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 7
14.6%
산 6
12.5%
전 5
 
10.4%
군 3
 
6.2%
익 3
 
6.2%
창 3
 
6.2%
고 2
 
4.2%
읍 2
 
4.2%
정 2
 
4.2%
안 2
 
4.2%
Other values (13) 13
27.1%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48
96.0%
Common 2
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 7
14.6%
산 6
12.5%
전 5
 
10.4%
군 3
 
6.2%
익 3
 
6.2%
창 3
 
6.2%
고 2
 
4.2%
읍 2
 
4.2%
정 2
 
4.2%
안 2
 
4.2%
Other values (13) 13
27.1%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48
96.0%
ASCII 2
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 7
14.6%
산 6
12.5%
전 5
 
10.4%
군 3
 
6.2%
익 3
 
6.2%
창 3
 
6.2%
고 2
 
4.2%
읍 2
 
4.2%
정 2
 
4.2%
안 2
 
4.2%
Other values (13) 13
27.1%
ASCII
ValueCountFrequency (%)
2
100.0%

측정지역
Text

UNIQUE 

Distinct24
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size324.0 B
2024-03-14T09:17:30.941328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters72
Distinct characters45
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

Unique24 ?
Unique (%)100.0%

Sample

1st row중앙동
2nd row삼천동
3rd row팔복동
4th row송천동
5th row금암동
ValueCountFrequency (%)
중앙동 1
 
4.2%
삼천동 1
 
4.2%
심원면 1
 
4.2%
고창읍 1
 
4.2%
순창읍 1
 
4.2%
장수읍 1
 
4.2%
무주읍 1
 
4.2%
임실읍 1
 
4.2%
진안읍 1
 
4.2%
고산면 1
 
4.2%
Other values (14) 14
58.3%
2024-03-14T09:17:31.241996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 14
 
19.4%
읍 7
 
9.7%
중 2
 
2.8%
천 2
 
2.8%
팔 2
 
2.8%
고 2
 
2.8%
창 2
 
2.8%
면 2
 
2.8%
신 2
 
2.8%
안 2
 
2.8%
Other values (35) 35
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 14
 
19.4%
읍 7
 
9.7%
중 2
 
2.8%
천 2
 
2.8%
팔 2
 
2.8%
고 2
 
2.8%
창 2
 
2.8%
면 2
 
2.8%
신 2
 
2.8%
안 2
 
2.8%
Other values (35) 35
48.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 14
 
19.4%
읍 7
 
9.7%
중 2
 
2.8%
천 2
 
2.8%
팔 2
 
2.8%
고 2
 
2.8%
창 2
 
2.8%
면 2
 
2.8%
신 2
 
2.8%
안 2
 
2.8%
Other values (35) 35
48.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 14
 
19.4%
읍 7
 
9.7%
중 2
 
2.8%
천 2
 
2.8%
팔 2
 
2.8%
고 2
 
2.8%
창 2
 
2.8%
면 2
 
2.8%
신 2
 
2.8%
안 2
 
2.8%
Other values (35) 35
48.6%

오존
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)65.2%
Missing1
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean0.02173913
Minimum0.014
Maximum0.034
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-14T09:17:31.346731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.014
5-th percentile0.0151
Q10.017
median0.021
Q30.0245
95-th percentile0.0316
Maximum0.034
Range0.02
Interquartile range (IQR)0.0075

Descriptive statistics

Standard deviation0.005378555
Coefficient of variation (CV)0.24741353
Kurtosis-0.10857628
Mean0.02173913
Median Absolute Deviation (MAD)0.004
Skewness0.62022901
Sum0.5
Variance2.8928854 × 10-5
MonotonicityNot monotonic
2024-03-14T09:17:31.460133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.016 3
12.5%
0.02 2
 
8.3%
0.017 2
 
8.3%
0.022 2
 
8.3%
0.021 2
 
8.3%
0.024 2
 
8.3%
0.027 2
 
8.3%
0.019 1
 
4.2%
0.028 1
 
4.2%
0.025 1
 
4.2%
Other values (5) 5
20.8%
ValueCountFrequency (%)
0.014 1
 
4.2%
0.015 1
 
4.2%
0.016 3
12.5%
0.017 2
8.3%
0.019 1
 
4.2%
0.02 2
8.3%
0.021 2
8.3%
0.022 2
8.3%
0.023 1
 
4.2%
0.024 2
8.3%
ValueCountFrequency (%)
0.034 1
4.2%
0.032 1
4.2%
0.028 1
4.2%
0.027 2
8.3%
0.025 1
4.2%
0.024 2
8.3%
0.023 1
4.2%
0.022 2
8.3%
0.021 2
8.3%
0.02 2
8.3%

이산화질소
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.018291667
Minimum0.009
Maximum0.037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-14T09:17:31.580962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.01015
Q10.01375
median0.016
Q30.02125
95-th percentile0.03195
Maximum0.037
Range0.028
Interquartile range (IQR)0.0075

Descriptive statistics

Standard deviation0.0068618616
Coefficient of variation (CV)0.37513594
Kurtosis1.5114958
Mean0.018291667
Median Absolute Deviation (MAD)0.003
Skewness1.2253374
Sum0.439
Variance4.7085145 × 10-5
MonotonicityNot monotonic
2024-03-14T09:17:31.676353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.016 4
16.7%
0.013 3
 
12.5%
0.015 2
 
8.3%
0.025 1
 
4.2%
0.021 1
 
4.2%
0.009 1
 
4.2%
0.01 1
 
4.2%
0.017 1
 
4.2%
0.018 1
 
4.2%
0.019 1
 
4.2%
Other values (8) 8
33.3%
ValueCountFrequency (%)
0.009 1
 
4.2%
0.01 1
 
4.2%
0.011 1
 
4.2%
0.013 3
12.5%
0.014 1
 
4.2%
0.015 2
8.3%
0.016 4
16.7%
0.017 1
 
4.2%
0.018 1
 
4.2%
0.019 1
 
4.2%
ValueCountFrequency (%)
0.037 1
4.2%
0.033 1
4.2%
0.026 1
4.2%
0.025 1
4.2%
0.024 1
4.2%
0.022 1
4.2%
0.021 1
4.2%
0.02 1
4.2%
0.019 1
4.2%
0.018 1
4.2%

이산화 황
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004125
Minimum0.002
Maximum0.007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-14T09:17:31.775297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.002
5-th percentile0.003
Q10.003
median0.004
Q30.005
95-th percentile0.006
Maximum0.007
Range0.005
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0011539158
Coefficient of variation (CV)0.27973717
Kurtosis0.55833879
Mean0.004125
Median Absolute Deviation (MAD)0.001
Skewness0.66274291
Sum0.099
Variance1.3315217 × 10-6
MonotonicityNot monotonic
2024-03-14T09:17:32.086483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.004 10
41.7%
0.003 6
25.0%
0.005 4
 
16.7%
0.006 2
 
8.3%
0.007 1
 
4.2%
0.002 1
 
4.2%
ValueCountFrequency (%)
0.002 1
 
4.2%
0.003 6
25.0%
0.004 10
41.7%
0.005 4
 
16.7%
0.006 2
 
8.3%
0.007 1
 
4.2%
ValueCountFrequency (%)
0.007 1
 
4.2%
0.006 2
 
8.3%
0.005 4
 
16.7%
0.004 10
41.7%
0.003 6
25.0%
0.002 1
 
4.2%

일산화탄소
Categorical

Distinct5
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
0.6
11 
0.5
7 
0.8
3 
0.4
2 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)4.2%

Sample

1st row0.6
2nd row0.6
3rd row0.8
4th row0.6
5th row0.8

Common Values

ValueCountFrequency (%)
0.6 11
45.8%
0.5 7
29.2%
0.8 3
 
12.5%
0.4 2
 
8.3%
1.0 1
 
4.2%

Length

2024-03-14T09:17:32.177545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T09:17:32.273409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.6 11
45.8%
0.5 7
29.2%
0.8 3
 
12.5%
0.4 2
 
8.3%
1.0 1
 
4.2%

미세먼지(10㎛이하)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.375
Minimum49
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-14T09:17:32.372953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile49.15
Q155.75
median61
Q366.5
95-th percentile74.85
Maximum75
Range26
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation8.0098445
Coefficient of variation (CV)0.13050663
Kurtosis-0.75313533
Mean61.375
Median Absolute Deviation (MAD)5.5
Skewness0.17588671
Sum1473
Variance64.157609
MonotonicityNot monotonic
2024-03-14T09:17:32.461671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
61 4
16.7%
62 2
 
8.3%
71 2
 
8.3%
75 2
 
8.3%
60 2
 
8.3%
52 2
 
8.3%
49 2
 
8.3%
68 1
 
4.2%
63 1
 
4.2%
59 1
 
4.2%
Other values (5) 5
20.8%
ValueCountFrequency (%)
49 2
8.3%
50 1
 
4.2%
52 2
8.3%
55 1
 
4.2%
56 1
 
4.2%
59 1
 
4.2%
60 2
8.3%
61 4
16.7%
62 2
8.3%
63 1
 
4.2%
ValueCountFrequency (%)
75 2
8.3%
74 1
 
4.2%
71 2
8.3%
68 1
 
4.2%
66 1
 
4.2%
63 1
 
4.2%
62 2
8.3%
61 4
16.7%
60 2
8.3%
59 1
 
4.2%

미세먼지(2.5㎛이하)
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.875
Minimum26
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2024-03-14T09:17:32.549915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile28.6
Q135.75
median40
Q344.5
95-th percentile49.85
Maximum51
Range25
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation6.7005029
Coefficient of variation (CV)0.16803769
Kurtosis-0.48735665
Mean39.875
Median Absolute Deviation (MAD)4.5
Skewness-0.20050703
Sum957
Variance44.896739
MonotonicityNot monotonic
2024-03-14T09:17:32.639904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
36 3
12.5%
42 3
12.5%
46 2
 
8.3%
39 2
 
8.3%
35 2
 
8.3%
50 1
 
4.2%
38 1
 
4.2%
44 1
 
4.2%
26 1
 
4.2%
41 1
 
4.2%
Other values (7) 7
29.2%
ValueCountFrequency (%)
26 1
 
4.2%
28 1
 
4.2%
32 1
 
4.2%
33 1
 
4.2%
35 2
8.3%
36 3
12.5%
38 1
 
4.2%
39 2
8.3%
41 1
 
4.2%
42 3
12.5%
ValueCountFrequency (%)
51 1
 
4.2%
50 1
 
4.2%
49 1
 
4.2%
48 1
 
4.2%
46 2
8.3%
44 1
 
4.2%
43 1
 
4.2%
42 3
12.5%
41 1
 
4.2%
39 2
8.3%

Interactions

2024-03-14T09:17:29.920722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:28.409427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:28.825538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.177202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.575157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.995139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:28.511308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:28.901964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.268255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.657700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:30.067380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:28.588234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:28.978078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.356109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.724735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:30.136132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:28.662112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.051138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.419822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.789072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:30.220587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:28.739288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.112146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.485516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T09:17:29.853099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T09:17:32.722865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군측정지역오존이산화질소이산화 황일산화탄소미세먼지(10㎛이하)미세먼지(2.5㎛이하)
시군1.0001.0000.6040.0000.5930.0000.0000.428
측정지역1.0001.0001.0001.0001.0001.0001.0001.000
오존0.6041.0001.0000.2460.7160.4890.0000.621
이산화질소0.0001.0000.2461.0000.1540.0000.6780.000
이산화 황0.5931.0000.7160.1541.0000.0000.0000.595
일산화탄소0.0001.0000.4890.0000.0001.0000.7860.342
미세먼지(10㎛이하)0.0001.0000.0000.6780.0000.7861.0000.000
미세먼지(2.5㎛이하)0.4281.0000.6210.0000.5950.3420.0001.000
2024-03-14T09:17:32.838495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
오존이산화질소이산화 황미세먼지(10㎛이하)미세먼지(2.5㎛이하)일산화탄소
오존1.000-0.262-0.093-0.434-0.4730.000
이산화질소-0.2621.0000.3140.5010.3750.000
이산화 황-0.0930.3141.0000.4070.6020.000
미세먼지(10㎛이하)-0.4340.5010.4071.0000.6260.358
미세먼지(2.5㎛이하)-0.4730.3750.6020.6261.0000.000
일산화탄소0.0000.0000.0000.3580.0001.000

Missing values

2024-03-14T09:17:30.310611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T09:17:30.406064image/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

시군측정지역오존이산화질소이산화 황일산화탄소미세먼지(10㎛이하)미세먼지(2.5㎛이하)
0전주중앙동0.0160.0250.0040.66850
1전주삼천동0.020.0220.0050.66242
2전주팔복동0.0170.0240.0060.86349
3전주송천동0.0160.0330.0050.67146
4전주금암동<NA>0.0370.0050.87543
5군산신풍동0.0190.0140.0030.86039
6군산소룡동0.0280.020.0070.66142
7군산개정동0.0170.0160.0041.05936
8익산팔봉동0.0250.0260.0040.66646
9익산모현동0.0140.0160.0060.57448
시군측정지역오존이산화질소이산화 황일산화탄소미세먼지(10㎛이하)미세먼지(2.5㎛이하)
14김제요촌동0.0210.0180.0030.46139
15완주고산면0.0240.0130.0020.56035
16진안진안읍0.0210.0170.0030.65536
17임실임실읍0.0160.0130.0040.65642
18무주무주읍0.0230.0150.0040.64933
19장수장수읍0.0320.010.0040.55241
20순창순창읍0.0240.0150.0040.54935
21고창고창읍0.0270.0130.0040.47126
22고창심원면0.0340.0090.0040.55036
23부안부안읍0.0270.0160.0050.66144