Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory88.3 B

Variable types

Numeric6
Categorical4

Alerts

측정일 has constant value ""Constant
시군구명 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
지점 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
시도명 is highly overall correlated with 시가지 CO 량(g/km) and 3 other fieldsHigh correlation
기본키 is highly overall correlated with 지점 and 1 other fieldsHigh correlation
도로인접 CO 량(g/km) is highly overall correlated with 도로인접 PM 량(g/km)High correlation
도로인접 PM 량(g/km) is highly overall correlated with 도로인접 CO 량(g/km)High correlation
시가지 CO 량(g/km) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
시가지 PM 2.5 량(g/km) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
시가지 PM 10 량(g/km) is highly overall correlated with 지점 and 1 other fieldsHigh correlation
시도명 is highly imbalanced (61.9%)Imbalance
기본키 has unique valuesUnique
도로인접 CO 량(g/km) has 7 (7.0%) zerosZeros
도로인접 PM 량(g/km) has 8 (8.0%) zerosZeros
시가지 CO 량(g/km) has 4 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:37:05.516227
Analysis finished2023-12-10 11:37:12.231926
Duration6.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:12.349132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:37:12.560565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200501
2nd row20200501
3rd row20200501
4th row20200501
5th row20200501

Common Values

ValueCountFrequency (%)
20200501 100
100.0%

Length

2023-12-10T20:37:12.845132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:12.970301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200501 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0100-1312S-8
8 
A-0100-1592S-6
8 
A-0550-0096E-8
7 
A-5510-0116E-6
 
6
A-0010-0083E-6
 
6
Other values (16)
65 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowA-0550-0096E-8
2nd rowA-0550-0096E-8
3rd rowA-0550-0096E-8
4th rowA-0550-0096E-8
5th rowA-0550-0096E-8

Common Values

ValueCountFrequency (%)
A-0100-1312S-8 8
 
8.0%
A-0100-1592S-6 8
 
8.0%
A-0550-0096E-8 7
 
7.0%
A-5510-0116E-6 6
 
6.0%
A-0010-0083E-6 6
 
6.0%
A-0100-1501S-6 6
 
6.0%
A-0550-0058E-6 6
 
6.0%
A-0351-1078E-4 4
 
4.0%
A-0351-0390S-4 4
 
4.0%
A-0351-0642S-4 4
 
4.0%
Other values (11) 41
41.0%

Length

2023-12-10T20:37:13.155005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0100-1312s-8 8
 
8.0%
a-0100-1592s-6 8
 
8.0%
a-0550-0096e-8 7
 
7.0%
a-5510-0116e-6 6
 
6.0%
a-0010-0083e-6 6
 
6.0%
a-0100-1501s-6 6
 
6.0%
a-0550-0058e-6 6
 
6.0%
a-5510-0085e-4 4
 
4.0%
a-0160-0058e-4 4
 
4.0%
a-0100-0407s-4 4
 
4.0%
Other values (11) 41
41.0%

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경남
87 
대구
 
5
부산
 
4
울산
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경남 87
87.0%
대구 5
 
5.0%
부산 4
 
4.0%
울산 4
 
4.0%

Length

2023-12-10T20:37:13.328376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:37:13.449472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경남 87
87.0%
대구 5
 
5.0%
부산 4
 
4.0%
울산 4
 
4.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
김해시
39 
진주시
12 
양산시
12 
창원시
8 
달성군
5 
Other values (6)
24 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
김해시 39
39.0%
진주시 12
 
12.0%
양산시 12
 
12.0%
창원시 8
 
8.0%
달성군 5
 
5.0%
함양군 4
 
4.0%
함안군 4
 
4.0%
고성군 4
 
4.0%
금정구 4
 
4.0%
하동군 4
 
4.0%

Length

2023-12-10T20:37:13.632359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
김해시 39
39.0%
진주시 12
 
12.0%
양산시 12
 
12.0%
창원시 8
 
8.0%
달성군 5
 
5.0%
함양군 4
 
4.0%
함안군 4
 
4.0%
고성군 4
 
4.0%
금정구 4
 
4.0%
하동군 4
 
4.0%

도로인접 CO 량(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4972.6392
Minimum0
Maximum10942.65
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:13.822946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13567.5
median4992.21
Q36557.8475
95-th percentile8618.7855
Maximum10942.65
Range10942.65
Interquartile range (IQR)2990.3475

Descriptive statistics

Standard deviation2383.9066
Coefficient of variation (CV)0.47940469
Kurtosis-0.0082223408
Mean4972.6392
Median Absolute Deviation (MAD)1486.595
Skewness-0.27787144
Sum497263.92
Variance5683010.5
MonotonicityNot monotonic
2023-12-10T20:37:14.008041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
4302.08 1
 
1.0%
3524.27 1
 
1.0%
5456.7 1
 
1.0%
3614.54 1
 
1.0%
7569.74 1
 
1.0%
7985.71 1
 
1.0%
6653.06 1
 
1.0%
8116.96 1
 
1.0%
8931.26 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.32 1
 
1.0%
67.91 1
 
1.0%
1907.66 1
 
1.0%
2403.48 1
 
1.0%
2591.93 1
 
1.0%
2673.05 1
 
1.0%
2922.16 1
 
1.0%
2962.57 1
 
1.0%
2967.58 1
 
1.0%
ValueCountFrequency (%)
10942.65 1
1.0%
8931.26 1
1.0%
8731.64 1
1.0%
8702.08 1
1.0%
8661.83 1
1.0%
8616.52 1
1.0%
8558.89 1
1.0%
8502.11 1
1.0%
8446.67 1
1.0%
8377.86 1
1.0%

도로인접 PM 량(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean354.5029
Minimum0
Maximum1570.29
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:14.172052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q163.1875
median235.43
Q3549.7375
95-th percentile1009.4295
Maximum1570.29
Range1570.29
Interquartile range (IQR)486.55

Descriptive statistics

Standard deviation354.64393
Coefficient of variation (CV)1.0003978
Kurtosis1.4895329
Mean354.5029
Median Absolute Deviation (MAD)186.135
Skewness1.3084014
Sum35450.29
Variance125772.32
MonotonicityNot monotonic
2023-12-10T20:37:14.336714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.0%
421.1 1
 
1.0%
83.41 1
 
1.0%
599.8 1
 
1.0%
44.11 1
 
1.0%
678.81 1
 
1.0%
401.38 1
 
1.0%
77.32 1
 
1.0%
829.05 1
 
1.0%
384.91 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 8
8.0%
5.14 1
 
1.0%
19.61 1
 
1.0%
21.16 1
 
1.0%
26.84 1
 
1.0%
30.94 1
 
1.0%
31.39 1
 
1.0%
35.9 1
 
1.0%
36.4 1
 
1.0%
36.83 1
 
1.0%
ValueCountFrequency (%)
1570.29 1
1.0%
1541.1 1
1.0%
1224.41 1
1.0%
1183.84 1
1.0%
1101.0 1
1.0%
1004.61 1
1.0%
980.84 1
1.0%
945.23 1
1.0%
876.51 1
1.0%
848.97 1
1.0%

시가지 CO 량(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.321
Minimum0
Maximum0.6
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:14.521382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.3
median0.3
Q30.4
95-th percentile0.5
Maximum0.6
Range0.6
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.12736252
Coefficient of variation (CV)0.39676798
Kurtosis0.92962301
Mean0.321
Median Absolute Deviation (MAD)0
Skewness-0.2848611
Sum32.1
Variance0.016221212
MonotonicityNot monotonic
2023-12-10T20:37:14.676252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.3 59
59.0%
0.4 13
 
13.0%
0.5 12
 
12.0%
0.1 8
 
8.0%
0.6 4
 
4.0%
0.0 4
 
4.0%
ValueCountFrequency (%)
0.0 4
 
4.0%
0.1 8
 
8.0%
0.3 59
59.0%
0.4 13
 
13.0%
0.5 12
 
12.0%
0.6 4
 
4.0%
ValueCountFrequency (%)
0.6 4
 
4.0%
0.5 12
 
12.0%
0.4 13
 
13.0%
0.3 59
59.0%
0.1 8
 
8.0%
0.0 4
 
4.0%

시가지 PM 2.5 량(g/km)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.89
Minimum30
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:14.838899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q130
median33
Q335
95-th percentile38.05
Maximum39
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9640779
Coefficient of variation (CV)0.090120944
Kurtosis-0.98007387
Mean32.89
Median Absolute Deviation (MAD)3
Skewness0.56288481
Sum3289
Variance8.7857576
MonotonicityNot monotonic
2023-12-10T20:37:14.989201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
30 39
39.0%
35 20
20.0%
33 16
16.0%
31 8
 
8.0%
37 8
 
8.0%
39 5
 
5.0%
38 4
 
4.0%
ValueCountFrequency (%)
30 39
39.0%
31 8
 
8.0%
33 16
16.0%
35 20
20.0%
37 8
 
8.0%
38 4
 
4.0%
39 5
 
5.0%
ValueCountFrequency (%)
39 5
 
5.0%
38 4
 
4.0%
37 8
 
8.0%
35 20
20.0%
33 16
16.0%
31 8
 
8.0%
30 39
39.0%

시가지 PM 10 량(g/km)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.27
Minimum11
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:37:15.102435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile13
Q116
median18
Q318
95-th percentile22
Maximum23
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.7037385
Coefficient of variation (CV)0.15655695
Kurtosis0.34495494
Mean17.27
Median Absolute Deviation (MAD)1
Skewness-0.41780264
Sum1727
Variance7.310202
MonotonicityNot monotonic
2023-12-10T20:37:15.558607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
18 43
43.0%
19 16
 
16.0%
13 12
 
12.0%
16 8
 
8.0%
17 5
 
5.0%
23 4
 
4.0%
11 4
 
4.0%
14 4
 
4.0%
22 4
 
4.0%
ValueCountFrequency (%)
11 4
 
4.0%
13 12
 
12.0%
14 4
 
4.0%
16 8
 
8.0%
17 5
 
5.0%
18 43
43.0%
19 16
 
16.0%
22 4
 
4.0%
23 4
 
4.0%
ValueCountFrequency (%)
23 4
 
4.0%
22 4
 
4.0%
19 16
 
16.0%
18 43
43.0%
17 5
 
5.0%
16 8
 
8.0%
14 4
 
4.0%
13 12
 
12.0%
11 4
 
4.0%

Interactions

2023-12-10T20:37:11.001047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:06.037911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:07.391274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:08.274765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:09.194262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:10.128595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:11.140757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:06.581486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:07.537538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:08.410803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:09.345004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:10.270573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:11.262558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:06.737558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:07.688687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:08.570743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:09.492243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:10.409774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:11.412583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:07.012389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:07.859416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:08.732981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:09.668243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:10.570243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:11.553994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:07.168566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:08.004613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:08.878397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:09.837590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:10.728794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:11.689696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:07.282828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:08.142592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:09.027745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:09.978306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:37:10.862717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:37:15.698649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점시도명시군구명도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)
기본키1.0000.9740.6900.8240.2620.0000.7430.8120.753
지점0.9741.0001.0001.0000.5620.3941.0001.0001.000
시도명0.6901.0001.0001.0000.1380.1200.8050.8620.511
시군구명0.8241.0001.0001.0000.3160.1481.0001.0001.000
도로인접 CO 량(g/km)0.2620.5620.1380.3161.0000.6010.3610.2990.327
도로인접 PM 량(g/km)0.0000.3940.1200.1480.6011.0000.0910.0000.000
시가지 CO 량(g/km)0.7431.0000.8051.0000.3610.0911.0000.8840.844
시가지 PM 2.5 량(g/km)0.8121.0000.8621.0000.2990.0000.8841.0000.964
시가지 PM 10 량(g/km)0.7531.0000.5111.0000.3270.0000.8440.9641.000
2023-12-10T20:37:15.870171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명지점시도명
시군구명1.0000.9420.963
지점0.9421.0000.907
시도명0.9630.9071.000
2023-12-10T20:37:16.001650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)지점시도명시군구명
기본키1.0000.1470.120-0.2660.024-0.2740.7970.4750.534
도로인접 CO 량(g/km)0.1471.0000.664-0.1720.346-0.1050.2250.0740.135
도로인접 PM 량(g/km)0.1200.6641.000-0.0270.1330.0520.1450.0690.058
시가지 CO 량(g/km)-0.266-0.172-0.0271.0000.3690.4430.9170.6480.973
시가지 PM 2.5 량(g/km)0.0240.3460.1330.3691.0000.0030.9220.7810.978
시가지 PM 10 량(g/km)-0.274-0.1050.0520.4430.0031.0000.9220.4890.978
지점0.7970.2250.1450.9170.9220.9221.0000.9070.942
시도명0.4750.0740.0690.6480.7810.4890.9071.0000.963
시군구명0.5340.1350.0580.9730.9780.9780.9420.9631.000

Missing values

2023-12-10T20:37:11.896767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:37:12.133222image/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

기본키측정일지점시도명시군구명도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)
0120200501A-0550-0096E-8경남김해시0.00.00.33018
1220200501A-0550-0096E-8경남김해시0.00.00.33018
2320200501A-0550-0096E-8경남김해시0.00.00.33018
3420200501A-0550-0096E-8경남김해시3324.6557.780.33018
4520200501A-0550-0096E-8경남김해시3398.45357.730.33018
5620200501A-0550-0096E-8경남김해시8377.86706.060.33018
6720200501A-0550-0096E-8경남김해시8702.08379.370.33018
7820200501A-0351-1078E-4경남함양군3873.1892.370.43119
8920200501A-0351-1078E-4경남함양군5981.85411.90.43119
91020200501A-0351-1078E-4경남함양군4010.94101.20.43119
기본키측정일지점시도명시군구명도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)
909120200501A-0010-0083E-6경남양산시4991.22516.40.33313
919220200501A-0100-0407S-4경남하동군5810.17100.740.33714
929320200501A-0100-0407S-4경남하동군7736.64807.590.33714
939420200501A-0100-0407S-4경남하동군4830.590.810.33714
949520200501A-0100-0407S-4경남하동군6378.1876.510.33714
959620200501A-0160-0058E-4울산울주군6845.07134.660.33722
969720200501A-0160-0058E-4울산울주군6791.321224.410.33722
979820200501A-0160-0058E-4울산울주군5656.37172.080.33722
989920200501A-0160-0058E-4울산울주군6526.11751.890.33722
9910020200501A-0450-0480S-4대구달성군5813.46221.780.43917