Overview

Dataset statistics

Number of variables5
Number of observations144
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.3 KiB
Average record size in memory44.9 B

Variable types

Numeric4
Categorical1

Dataset

Description대전교통공사 2년 주기로 측정하는 역사 공기질 권고기준의 역사 측정결과(22개역 역사 공기질 권고기준 측정결과)이산화질소(0.1ppm이하), 라돈(148Bq_세제곱미터 이하), 휘발성유기화합물(500mcg_세제곱미터 이하),
Author대전교통공사
URLhttps://www.data.go.kr/data/15053143/fileData.do

Alerts

측정연도 is highly overall correlated with 이산화질소High correlation
이산화질소 is highly overall correlated with 측정연도High correlation
이산화질소 has 88 (61.1%) zerosZeros

Reproduction

Analysis started2023-12-12 17:13:51.095017
Analysis finished2023-12-12 17:13:53.366774
Duration2.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정연도
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017
Minimum2011
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:13:53.432727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12014
median2017
Q32020
95-th percentile2023
Maximum2023
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.7807018
Coefficient of variation (CV)0.0018744184
Kurtosis-1.216021
Mean2017
Median Absolute Deviation (MAD)3
Skewness0
Sum290448
Variance14.293706
MonotonicityIncreasing
2023-12-13T02:13:53.596145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2011 12
 
8.3%
2013 12
 
8.3%
2015 12
 
8.3%
2017 12
 
8.3%
2019 12
 
8.3%
2021 12
 
8.3%
2023 12
 
8.3%
2012 10
 
6.9%
2014 10
 
6.9%
2016 10
 
6.9%
Other values (3) 30
20.8%
ValueCountFrequency (%)
2011 12
8.3%
2012 10
6.9%
2013 12
8.3%
2014 10
6.9%
2015 12
8.3%
2016 10
6.9%
2017 12
8.3%
2018 10
6.9%
2019 12
8.3%
2020 10
6.9%
ValueCountFrequency (%)
2023 12
8.3%
2022 10
6.9%
2021 12
8.3%
2020 10
6.9%
2019 12
8.3%
2018 10
6.9%
2017 12
8.3%
2016 10
6.9%
2015 12
8.3%
2014 10
6.9%

역사명
Categorical

Distinct22
Distinct (%)15.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
판암
 
7
서대전네거리
 
7
오룡
 
7
대전
 
7
중앙로
 
7
Other values (17)
109 

Length

Max length6
Median length2
Mean length2.6805556
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row판암
2nd row신흥
3rd row대동
4th row대전
5th row중앙로

Common Values

ValueCountFrequency (%)
판암 7
 
4.9%
서대전네거리 7
 
4.9%
오룡 7
 
4.9%
대전 7
 
4.9%
중앙로 7
 
4.9%
중구청 7
 
4.9%
대동 7
 
4.9%
정부청사 7
 
4.9%
용문 7
 
4.9%
탄방 7
 
4.9%
Other values (12) 74
51.4%

Length

2023-12-13T02:13:53.765113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
판암 7
 
4.9%
정부청사 7
 
4.9%
서대전네거리 7
 
4.9%
시청 7
 
4.9%
탄방 7
 
4.9%
용문 7
 
4.9%
신흥 7
 
4.9%
대동 7
 
4.9%
중구청 7
 
4.9%
중앙로 7
 
4.9%
Other values (12) 74
51.4%

이산화질소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0088263889
Minimum0
Maximum0.053
Zeros88
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:13:53.890557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.018
95-th percentile0.03285
Maximum0.053
Range0.053
Interquartile range (IQR)0.018

Descriptive statistics

Standard deviation0.012671939
Coefficient of variation (CV)1.4356878
Kurtosis0.50827265
Mean0.0088263889
Median Absolute Deviation (MAD)0
Skewness1.2156041
Sum1.271
Variance0.00016057804
MonotonicityNot monotonic
2023-12-13T02:13:54.022731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.0 88
61.1%
0.022 6
 
4.2%
0.013 4
 
2.8%
0.017 3
 
2.1%
0.015 3
 
2.1%
0.018 3
 
2.1%
0.024 3
 
2.1%
0.025 3
 
2.1%
0.026 3
 
2.1%
0.02 2
 
1.4%
Other values (22) 26
 
18.1%
ValueCountFrequency (%)
0.0 88
61.1%
0.005 1
 
0.7%
0.007 1
 
0.7%
0.009 1
 
0.7%
0.01 1
 
0.7%
0.011 2
 
1.4%
0.012 1
 
0.7%
0.013 4
 
2.8%
0.014 1
 
0.7%
0.015 3
 
2.1%
ValueCountFrequency (%)
0.053 1
0.7%
0.042 2
1.4%
0.041 1
0.7%
0.04 1
0.7%
0.038 1
0.7%
0.035 1
0.7%
0.033 1
0.7%
0.032 1
0.7%
0.031 2
1.4%
0.03 1
0.7%

라돈
Real number (ℝ)

Distinct64
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.809028
Minimum11
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:13:54.174819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile14.03
Q121.75
median29
Q337
95-th percentile64.7
Maximum80
Range69
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation14.961741
Coefficient of variation (CV)0.47036147
Kurtosis0.89825506
Mean31.809028
Median Absolute Deviation (MAD)8
Skewness1.1038251
Sum4580.5
Variance223.85369
MonotonicityNot monotonic
2023-12-13T02:13:54.345146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.0 8
 
5.6%
33.0 6
 
4.2%
36.0 6
 
4.2%
25.0 5
 
3.5%
23.0 5
 
3.5%
18.0 5
 
3.5%
24.0 5
 
3.5%
20.0 5
 
3.5%
12.0 4
 
2.8%
31.0 4
 
2.8%
Other values (54) 91
63.2%
ValueCountFrequency (%)
11.0 3
2.1%
12.0 4
2.8%
14.0 1
 
0.7%
14.2 2
 
1.4%
15.0 3
2.1%
16.0 3
2.1%
16.3 1
 
0.7%
17.0 2
 
1.4%
17.5 1
 
0.7%
18.0 5
3.5%
ValueCountFrequency (%)
80.0 1
0.7%
74.0 2
1.4%
70.0 1
0.7%
69.0 1
0.7%
66.0 2
1.4%
65.0 1
0.7%
63.0 1
0.7%
61.3 1
0.7%
59.0 1
0.7%
57.3 1
0.7%

휘발성유기화합물
Real number (ℝ)

Distinct110
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.30625
Minimum4
Maximum344
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T02:13:54.519608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile12
Q125
median58.5
Q395.5
95-th percentile207.22
Maximum344
Range340
Interquartile range (IQR)70.5

Descriptive statistics

Standard deviation68.44652
Coefficient of variation (CV)0.92114081
Kurtosis3.9909145
Mean74.30625
Median Absolute Deviation (MAD)34.5
Skewness1.8963074
Sum10700.1
Variance4684.926
MonotonicityNot monotonic
2023-12-13T02:13:54.661445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.0 4
 
2.8%
78.0 4
 
2.8%
81.0 3
 
2.1%
31.0 3
 
2.1%
15.0 3
 
2.1%
72.0 2
 
1.4%
47.0 2
 
1.4%
5.0 2
 
1.4%
105.0 2
 
1.4%
80.0 2
 
1.4%
Other values (100) 117
81.2%
ValueCountFrequency (%)
4.0 1
0.7%
5.0 2
1.4%
7.0 1
0.7%
9.0 1
0.7%
10.0 1
0.7%
10.8 1
0.7%
12.0 2
1.4%
12.5 1
0.7%
12.6 1
0.7%
13.0 2
1.4%
ValueCountFrequency (%)
344.0 1
0.7%
332.0 1
0.7%
294.4 1
0.7%
294.0 1
0.7%
292.3 1
0.7%
268.0 1
0.7%
264.0 1
0.7%
209.2 1
0.7%
196.0 1
0.7%
195.0 1
0.7%

Interactions

2023-12-13T02:13:52.698228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:51.297858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:51.699061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:52.139034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:52.824298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:51.380640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:51.798992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:52.263677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:52.928640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:51.477248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:51.900672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:52.392056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:53.045454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:51.575970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:52.013876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:13:52.549533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:13:54.753424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정연도역사명이산화질소라돈휘발성유기화합물
측정연도1.0000.0000.6510.4620.751
역사명0.0001.0000.0000.3800.000
이산화질소0.6510.0001.0000.0000.246
라돈0.4620.3800.0001.0000.268
휘발성유기화합물0.7510.0000.2460.2681.000
2023-12-13T02:13:54.874167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정연도이산화질소라돈휘발성유기화합물역사명
측정연도1.0000.824-0.041-0.3750.000
이산화질소0.8241.0000.084-0.3270.000
라돈-0.0410.0841.000-0.1190.140
휘발성유기화합물-0.375-0.327-0.1191.0000.000
역사명0.0000.0000.1400.0001.000

Missing values

2023-12-13T02:13:53.203197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:13:53.329614image/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

측정연도역사명이산화질소라돈휘발성유기화합물
02011판암0.022.048.0
12011신흥0.033.072.0
22011대동0.037.076.0
32011대전0.048.099.0
42011중앙로0.056.061.0
52011중구청0.063.045.0
62011서대전네거리0.044.0115.0
72011오룡0.033.0102.0
82011용문0.026.0142.0
92011탄방0.019.0130.0
측정연도역사명이산화질소라돈휘발성유기화합물
1342023대동0.02561.313.0
1352023대전0.02237.387.7
1362023중앙로0.01734.513.5
1372023중구청0.01366.012.5
1382023서대전네거리0.01857.312.6
1392023오룡0.03517.510.8
1402023용문0.03329.018.5
1412023탄방0.01826.515.5
1422023시청0.03216.014.0
1432023정부청사0.02534.523.8