Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Numeric9
Categorical7

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
주소 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
지점 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
기본키 is highly overall correlated with 지점 and 3 other fieldsHigh correlation
연장 is highly overall correlated with 지점 and 2 other fieldsHigh correlation
좌표위치위도 is highly overall correlated with 지점 and 2 other fieldsHigh correlation
좌표위치경도 is highly overall correlated with 지점 and 2 other fieldsHigh correlation
co is highly overall correlated with nox and 3 other fieldsHigh correlation
nox is highly overall correlated with co and 3 other fieldsHigh correlation
hc is highly overall correlated with co and 3 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with co and 3 other fieldsHigh correlation
측정시분 is highly overall correlated with 기본키High correlation
측정시분 is highly imbalanced (67.3%)Imbalance
기본키 has unique valuesUnique
pm has 8 (8.0%) zerosZeros

Reproduction

Analysis started2024-04-16 21:37:59.417703
Analysis finished2024-04-16 21:38:05.945093
Duration6.53 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
2024-04-17T06:38:06.003282image/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
2024-04-17T06:38:06.107901image/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%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2024-04-17T06:38:06.203076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T06:38:06.270361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
[0324-1]
 
4
[0325-3]
 
4
[0325-4]
 
4
[2516-3]
 
2
[1724-4]
 
2
Other values (42)
84 

Length

Max length8
Median length8
Mean length7.82
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0324-1]
2nd row[0324-1]
3rd row[0325-3]
4th row[0325-3]
5th row[0325-4]

Common Values

ValueCountFrequency (%)
[0324-1] 4
 
4.0%
[0325-3] 4
 
4.0%
[0325-4] 4
 
4.0%
[2516-3] 2
 
2.0%
[1724-4] 2
 
2.0%
[2517-0] 2
 
2.0%
[0326-2] 2
 
2.0%
[2114-0] 2
 
2.0%
[0406-1] 2
 
2.0%
[0408-1] 2
 
2.0%
Other values (37) 74
74.0%

Length

2024-04-17T06:38:06.344268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0324-1 4
 
4.0%
0325-4 4
 
4.0%
0325-3 4
 
4.0%
04008 2
 
2.0%
3716-0 2
 
2.0%
04009 2
 
2.0%
04012 2
 
2.0%
3806-0 2
 
2.0%
3409-1 2
 
2.0%
3412-1 2
 
2.0%
Other values (37) 74
74.0%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2024-04-17T06:38:06.431329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T06:38:06.502337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
수안보-충주
 
4
주덕-충주
 
4
오생-장호원
 
4
신니-신양
 
4
영동-영동IC
 
2
Other values (41)
82 

Length

Max length13
Median length5
Mean length6.3
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수안보-충주
2nd row수안보-충주
3rd row신니-신양
4th row신니-신양
5th row주덕-충주

Common Values

ValueCountFrequency (%)
수안보-충주 4
 
4.0%
주덕-충주 4
 
4.0%
오생-장호원 4
 
4.0%
신니-신양 4
 
4.0%
영동-영동IC 2
 
2.0%
삼승-보은 2
 
2.0%
대전-옥천 2
 
2.0%
약목-황간 2
 
2.0%
단양-하시 2
 
2.0%
제천-신림 2
 
2.0%
Other values (36) 72
72.0%

Length

2024-04-17T06:38:06.594245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수안보-충주 4
 
4.0%
신니-신양 4
 
4.0%
주덕-충주 4
 
4.0%
오생-장호원 4
 
4.0%
보은-장갑 2
 
2.0%
일죽-감곡ic 2
 
2.0%
목계-산척 2
 
2.0%
증평-대사 2
 
2.0%
초정-증평 2
 
2.0%
노암-중흥 2
 
2.0%
Other values (36) 72
72.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)39.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.932
Minimum1.5
Maximum22.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T06:38:06.693898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.1
Q14.6
median7.3
Q311.1
95-th percentile15.2
Maximum22.4
Range20.9
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.303179
Coefficient of variation (CV)0.5425087
Kurtosis0.9298415
Mean7.932
Median Absolute Deviation (MAD)3.55
Skewness0.84381897
Sum793.2
Variance18.517349
MonotonicityNot monotonic
2024-04-17T06:38:06.783961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
7.3 10
 
10.0%
5.0 4
 
4.0%
5.4 4
 
4.0%
3.6 4
 
4.0%
8.8 4
 
4.0%
12.3 4
 
4.0%
11.1 4
 
4.0%
3.4 4
 
4.0%
7.5 2
 
2.0%
6.4 2
 
2.0%
Other values (29) 58
58.0%
ValueCountFrequency (%)
1.5 2
2.0%
2.0 2
2.0%
2.1 2
2.0%
2.7 2
2.0%
2.8 2
2.0%
3.1 2
2.0%
3.4 4
4.0%
3.6 4
4.0%
3.7 2
2.0%
4.3 2
2.0%
ValueCountFrequency (%)
22.4 2
2.0%
15.5 2
2.0%
15.2 2
2.0%
14.4 2
2.0%
13.9 2
2.0%
13.6 2
2.0%
12.3 4
4.0%
12.1 2
2.0%
11.4 2
2.0%
11.2 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210301
2nd row20210301
3rd row20210301
4th row20210301
5th row20210301

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2024-04-17T06:38:06.875359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T06:38:06.941946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 100
100.0%

측정시분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
94 
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 94
94.0%
2 6
 
6.0%

Length

2024-04-17T06:38:07.010639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T06:38:07.080714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 94
94.0%
2 6
 
6.0%

좌표위치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.798737
Minimum36.10527
Maximum37.18469
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T06:38:07.160280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.10527
5-th percentile36.24756
Q136.62606
median36.89448
Q337.01554
95-th percentile37.11937
Maximum37.18469
Range1.07942
Interquartile range (IQR)0.38948

Descriptive statistics

Standard deviation0.28238545
Coefficient of variation (CV)0.007673781
Kurtosis-0.35548884
Mean36.798737
Median Absolute Deviation (MAD)0.14162
Skewness-0.87705345
Sum3679.8737
Variance0.079741541
MonotonicityNot monotonic
2024-04-17T06:38:07.289291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
36.88774 4
 
4.0%
36.96342 4
 
4.0%
36.9979 4
 
4.0%
37.05585 2
 
2.0%
37.03807 2
 
2.0%
36.7919 2
 
2.0%
36.80475 2
 
2.0%
36.7235 2
 
2.0%
36.83418 2
 
2.0%
36.91571 2
 
2.0%
Other values (37) 74
74.0%
ValueCountFrequency (%)
36.10527 2
2.0%
36.20366 2
2.0%
36.24756 2
2.0%
36.28264 2
2.0%
36.32083 2
2.0%
36.3326 2
2.0%
36.35443 2
2.0%
36.44592 2
2.0%
36.45034 2
2.0%
36.47999 2
2.0%
ValueCountFrequency (%)
37.18469 2
2.0%
37.15606 2
2.0%
37.11937 2
2.0%
37.1091 2
2.0%
37.07857 2
2.0%
37.06589 2
2.0%
37.05585 2
2.0%
37.04502 2
2.0%
37.03807 2
2.0%
37.03413 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.74174
Minimum127.37509
Maximum128.39227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T06:38:07.632099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.37509
5-th percentile127.44767
Q1127.57716
median127.692
Q3127.86686
95-th percentile128.27212
Maximum128.39227
Range1.01718
Interquartile range (IQR)0.2897

Descriptive statistics

Standard deviation0.23117672
Coefficient of variation (CV)0.0018097195
Kurtosis0.6709574
Mean127.74174
Median Absolute Deviation (MAD)0.139235
Skewness0.92686121
Sum12774.174
Variance0.053442674
MonotonicityNot monotonic
2024-04-17T06:38:07.743089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
127.98484 4
 
4.0%
127.86686 4
 
4.0%
127.72238 4
 
4.0%
127.84924 2
 
2.0%
127.76549 2
 
2.0%
127.6034 2
 
2.0%
127.64739 2
 
2.0%
127.55372 2
 
2.0%
127.61577 2
 
2.0%
128.10947 2
 
2.0%
Other values (37) 74
74.0%
ValueCountFrequency (%)
127.37509 2
2.0%
127.38986 2
2.0%
127.44767 2
2.0%
127.46142 2
2.0%
127.47145 2
2.0%
127.48385 2
2.0%
127.52268 2
2.0%
127.53139 2
2.0%
127.53503 2
2.0%
127.5518 2
2.0%
ValueCountFrequency (%)
128.39227 2
2.0%
128.33167 2
2.0%
128.27212 2
2.0%
128.11702 2
2.0%
128.10947 2
2.0%
127.98484 4
4.0%
127.95441 2
2.0%
127.95118 2
2.0%
127.92804 2
2.0%
127.91705 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.5328
Minimum0.52
Maximum288.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T06:38:07.852530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile1.9495
Q15.7925
median18.675
Q344.14
95-th percentile70.4375
Maximum288.58
Range288.06
Interquartile range (IQR)38.3475

Descriptive statistics

Standard deviation39.697307
Coefficient of variation (CV)1.3441769
Kurtosis22.590404
Mean29.5328
Median Absolute Deviation (MAD)15.365
Skewness4.0921752
Sum2953.28
Variance1575.8762
MonotonicityNot monotonic
2024-04-17T06:38:07.958783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.31 3
 
3.0%
3.28 2
 
2.0%
1.95 2
 
2.0%
0.52 2
 
2.0%
2.78 2
 
2.0%
12.4 2
 
2.0%
1.57 2
 
2.0%
3.24 2
 
2.0%
5.23 2
 
2.0%
3.98 2
 
2.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.52 2
2.0%
1.57 2
2.0%
1.94 1
1.0%
1.95 2
2.0%
2.03 1
1.0%
2.26 1
1.0%
2.31 1
1.0%
2.63 1
1.0%
2.78 2
2.0%
3.24 2
2.0%
ValueCountFrequency (%)
288.58 1
1.0%
222.37 1
1.0%
88.41 1
1.0%
82.24 1
1.0%
81.41 1
1.0%
69.86 1
1.0%
65.15 1
1.0%
63.99 1
1.0%
63.86 1
1.0%
60.02 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.4218
Minimum0.28
Maximum412.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T06:38:08.076734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.96
Q13.385
median12.83
Q335.44
95-th percentile95.5845
Maximum412.68
Range412.4
Interquartile range (IQR)32.055

Descriptive statistics

Standard deviation58.320626
Coefficient of variation (CV)1.8560562
Kurtosis26.695414
Mean31.4218
Median Absolute Deviation (MAD)11.115
Skewness4.7315604
Sum3142.18
Variance3401.2955
MonotonicityNot monotonic
2024-04-17T06:38:08.177832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.06 4
 
4.0%
1.6 3
 
3.0%
2.92 2
 
2.0%
1.96 2
 
2.0%
7.85 2
 
2.0%
18.59 2
 
2.0%
6.81 2
 
2.0%
0.28 2
 
2.0%
0.96 2
 
2.0%
1.79 2
 
2.0%
Other values (75) 77
77.0%
ValueCountFrequency (%)
0.28 2
2.0%
0.83 2
2.0%
0.96 2
2.0%
1.27 1
 
1.0%
1.41 1
 
1.0%
1.51 1
 
1.0%
1.6 3
3.0%
1.64 1
 
1.0%
1.79 2
2.0%
1.96 2
2.0%
ValueCountFrequency (%)
412.68 1
1.0%
352.61 1
1.0%
145.34 1
1.0%
119.07 1
1.0%
106.5 1
1.0%
95.01 1
1.0%
91.05 1
1.0%
82.51 1
1.0%
74.34 1
1.0%
71.67 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6804
Minimum0.04
Maximum39.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T06:38:08.279859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.17
Q10.5275
median2.005
Q34.87
95-th percentile9.809
Maximum39.81
Range39.77
Interquartile range (IQR)4.3425

Descriptive statistics

Standard deviation5.6587167
Coefficient of variation (CV)1.5375276
Kurtosis23.021138
Mean3.6804
Median Absolute Deviation (MAD)1.705
Skewness4.2411633
Sum368.04
Variance32.021075
MonotonicityNot monotonic
2024-04-17T06:38:08.390136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26 3
 
3.0%
0.31 3
 
3.0%
0.32 2
 
2.0%
0.29 2
 
2.0%
0.66 2
 
2.0%
8.04 2
 
2.0%
0.13 2
 
2.0%
0.49 2
 
2.0%
0.39 2
 
2.0%
0.17 2
 
2.0%
Other values (75) 78
78.0%
ValueCountFrequency (%)
0.04 2
2.0%
0.13 2
2.0%
0.17 2
2.0%
0.2 1
 
1.0%
0.21 1
 
1.0%
0.22 1
 
1.0%
0.23 1
 
1.0%
0.26 3
3.0%
0.29 2
2.0%
0.31 3
3.0%
ValueCountFrequency (%)
39.81 1
1.0%
33.25 1
1.0%
13.85 1
1.0%
11.4 1
1.0%
9.98 1
1.0%
9.8 1
1.0%
8.66 1
1.0%
8.63 1
1.0%
8.56 1
1.0%
8.33 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7429
Minimum0
Maximum22.42
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T06:38:08.504153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.14
median0.675
Q31.8325
95-th percentile5.844
Maximum22.42
Range22.42
Interquartile range (IQR)1.6925

Descriptive statistics

Standard deviation3.3527707
Coefficient of variation (CV)1.9236736
Kurtosis24.815419
Mean1.7429
Median Absolute Deviation (MAD)0.545
Skewness4.5922069
Sum174.29
Variance11.241071
MonotonicityNot monotonic
2024-04-17T06:38:08.611264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 13
 
13.0%
0.27 8
 
8.0%
0.0 8
 
8.0%
0.14 7
 
7.0%
0.67 4
 
4.0%
0.28 3
 
3.0%
1.21 2
 
2.0%
2.01 2
 
2.0%
3.66 2
 
2.0%
1.24 2
 
2.0%
Other values (46) 49
49.0%
ValueCountFrequency (%)
0.0 8
8.0%
0.13 13
13.0%
0.14 7
7.0%
0.27 8
8.0%
0.28 3
 
3.0%
0.4 1
 
1.0%
0.41 1
 
1.0%
0.42 2
 
2.0%
0.54 2
 
2.0%
0.55 1
 
1.0%
ValueCountFrequency (%)
22.42 1
1.0%
21.11 1
1.0%
8.56 1
1.0%
7.9 1
1.0%
6.68 1
1.0%
5.8 1
1.0%
4.95 1
1.0%
4.94 1
1.0%
4.16 1
1.0%
3.93 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7704.3212
Minimum138.68
Maximum80539.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T06:38:08.719391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile461.05
Q11490.44
median4434.195
Q311282.628
95-th percentile18619.505
Maximum80539.08
Range80400.4
Interquartile range (IQR)9792.1875

Descriptive statistics

Standard deviation11049.771
Coefficient of variation (CV)1.4342303
Kurtosis24.316862
Mean7704.3212
Median Absolute Deviation (MAD)3597.385
Skewness4.3286993
Sum770432.12
Variance1.2209744 × 108
MonotonicityNot monotonic
2024-04-17T06:38:08.830582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
873.34 3
 
3.0%
794.65 2
 
2.0%
461.05 2
 
2.0%
138.68 2
 
2.0%
734.66 2
 
2.0%
2972.43 2
 
2.0%
416.06 2
 
2.0%
768.41 2
 
2.0%
1255.69 2
 
2.0%
953.96 2
 
2.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
138.68 2
2.0%
416.06 2
2.0%
461.05 2
2.0%
490.89 1
1.0%
492.91 1
1.0%
595.97 1
1.0%
601.6 1
1.0%
640.96 1
1.0%
734.66 2
2.0%
768.41 2
2.0%
ValueCountFrequency (%)
80539.08 1
1.0%
63375.31 1
1.0%
24440.64 1
1.0%
23873.5 1
1.0%
20986.63 1
1.0%
18494.92 1
1.0%
17818.72 1
1.0%
17765.91 1
1.0%
16377.74 1
1.0%
15959.43 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 충주 수안보 중산
 
4
충북 충주 신니 원평
 
4
충북 충주 대소원 만정
 
4
충북 보은 탄부 상장
 
2
충북 진천 이월 중산
 
2
Other values (42)
84 

Length

Max length12
Median length11
Mean length11.1
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충북 충주 수안보 중산
2nd row충북 충주 수안보 중산
3rd row충북 충주 신니 원평
4th row충북 충주 신니 원평
5th row충북 충주 대소원 만정

Common Values

ValueCountFrequency (%)
충북 충주 수안보 중산 4
 
4.0%
충북 충주 신니 원평 4
 
4.0%
충북 충주 대소원 만정 4
 
4.0%
충북 보은 탄부 상장 2
 
2.0%
충북 진천 이월 중산 2
 
2.0%
충북 보은 수한 후평 2
 
2.0%
충북 음성 생극 송곡 2
 
2.0%
충북 음성 생극 병암 2
 
2.0%
충북 옥천 군북 이백 2
 
2.0%
충북 영동 심천 약목 2
 
2.0%
Other values (37) 74
74.0%

Length

2024-04-17T06:38:08.947233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 100
25.0%
충주 28
 
7.0%
음성 14
 
3.5%
보은 10
 
2.5%
청주 10
 
2.5%
증평 10
 
2.5%
옥천 10
 
2.5%
진천 8
 
2.0%
영동 6
 
1.5%
신니 6
 
1.5%
Other values (81) 198
49.5%

Interactions

2024-04-17T06:38:05.027400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.016786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.611220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.140194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.741258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.345813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.927142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.757453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.408947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:05.094386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.086184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.672033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.204732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.805623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.411319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.991681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.822686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.477674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:05.155385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.151446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.725924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.262061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.863326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.467454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.282207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.885048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.552944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:05.226614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.215123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.785059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.327359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.928566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.528984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.341910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.952917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.635107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:05.293488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.281124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.844243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.389778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.990346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.598708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.408310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.024754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.714441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:05.370765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.350587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.904900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.453525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.065381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.662695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.469453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.103806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.785067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:05.451259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.407311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.958480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.514614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.143354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.721945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.546592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.167546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.840027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:05.528657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.476181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.023575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.583624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.212409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.794022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.628214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.240916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.905625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:05.589811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:00.532043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.075759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:01.653937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.272666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:02.855797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:03.687203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.320798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T06:38:04.961647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T06:38:09.022522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0000.9930.0000.9940.5950.8930.8500.6700.3950.5310.4210.7110.4030.993
지점0.9931.0000.0001.0001.0000.1471.0001.0000.8120.8260.8250.8800.8481.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간0.9941.0000.0001.0001.0000.2281.0001.0000.8200.8330.8320.8870.8581.000
연장0.5951.0000.0001.0001.0000.0960.6020.5770.2290.2850.0000.0000.3841.000
측정시분0.8930.1470.0000.2280.0961.0000.0000.1060.0000.0000.0000.0000.0000.147
좌표위치위도0.8501.0000.0001.0000.6020.0001.0000.6480.5540.5720.4980.7350.5791.000
좌표위치경도0.6701.0000.0001.0000.5770.1060.6481.0000.6080.3970.3760.4080.6151.000
co0.3950.8120.0000.8200.2290.0000.5540.6081.0000.9750.9790.7800.9980.812
nox0.5310.8260.0000.8330.2850.0000.5720.3970.9751.0000.9930.9070.9760.826
hc0.4210.8250.0000.8320.0000.0000.4980.3760.9790.9931.0000.8410.9740.825
pm0.7110.8800.0000.8870.0000.0000.7350.4080.7800.9070.8411.0000.8000.880
co20.4030.8480.0000.8580.3840.0000.5790.6150.9980.9760.9740.8001.0000.848
주소0.9931.0000.0001.0001.0000.1471.0001.0000.8120.8260.8250.8800.8481.000
2024-04-17T06:38:09.130645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향측정구간지점측정시분
주소1.0000.0000.9911.0000.059
방향0.0001.0000.0000.0000.000
측정구간0.9910.0001.0000.9910.117
지점1.0000.0000.9911.0000.059
측정시분0.0590.0000.1170.0591.000
2024-04-17T06:38:09.213725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2지점방향측정구간측정시분주소
기본키1.0000.0760.143-0.0610.2480.2890.2880.3170.2570.7180.0000.7260.6990.718
연장0.0761.0000.1470.119-0.100-0.101-0.105-0.116-0.0950.7590.0000.7660.0640.759
좌표위치위도0.1430.1471.0000.3950.1430.1530.1510.1690.1380.7670.0000.7750.0000.767
좌표위치경도-0.0610.1190.3951.000-0.413-0.411-0.411-0.381-0.4240.7630.0000.7700.0980.763
co0.248-0.1000.143-0.4131.0000.9930.9940.9680.9980.3780.0000.3920.0000.378
nox0.289-0.1010.153-0.4110.9931.0000.9980.9830.9940.3930.0000.4070.0000.393
hc0.288-0.1050.151-0.4110.9940.9981.0000.9770.9940.3920.0000.4050.0000.392
pm0.317-0.1160.169-0.3810.9680.9830.9771.0000.9720.4750.0000.4860.0000.475
co20.257-0.0950.138-0.4240.9980.9940.9940.9721.0000.4190.0000.4320.0000.419
지점0.7180.7590.7670.7630.3780.3930.3920.4750.4191.0000.0000.9910.0591.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
측정구간0.7260.7660.7750.7700.3920.4070.4050.4860.4320.9910.0001.0000.1170.991
측정시분0.6990.0640.0000.0980.0000.0000.0000.0000.0000.0590.0000.1171.0000.059
주소0.7180.7590.7670.7630.3780.3930.3920.4750.4191.0000.0000.9910.0591.000

Missing values

2024-04-17T06:38:05.710340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T06:38:05.889743image/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

기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
01건기연[0324-1]1수안보-충주7.320210301136.88774127.984849.765.790.880.272578.8충북 충주 수안보 중산
12건기연[0324-1]2수안보-충주7.320210301136.88774127.984848.615.070.830.282061.6충북 충주 수안보 중산
23건기연[0325-3]1신니-신양8.820210301136.9979127.7223821.012.152.020.685054.64충북 충주 신니 원평
34건기연[0325-3]2신니-신양8.820210301136.9979127.7223831.7731.54.251.697689.48충북 충주 신니 원평
45건기연[0325-4]1주덕-충주5.020210301136.96342127.8668657.8855.967.023.5114439.23충북 충주 대소원 만정
56건기연[0325-4]2주덕-충주5.020210301136.96342127.8668669.8658.657.432.5617765.91충북 충주 대소원 만정
67건기연[0326-2]1오생-장호원5.420210301137.06589127.6041612.46.811.160.272972.43충북 음성 생극 송곡
78건기연[0326-2]2오생-장호원5.420210301137.06589127.6041611.87.71.130.553094.21충북 음성 생극 송곡
89건기연[2114-0]1오생-장호원3.620210301137.02646127.6046212.527.881.250.673051.14충북 음성 생극 병암
910건기연[2114-0]2오생-장호원3.620210301137.02646127.6046216.5814.151.780.943950.57충북 음성 생극 병암
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[04012]1충주JCT-노은JCT5.420210301137.03807127.7654959.7474.348.333.9115851.65충북 충주 노은 문성
9192건기연[04012]2충주JCT-노은JCT5.420210301137.03807127.7654956.1782.518.664.9515959.43충북 충주 노은 문성
9293건기연[04013]1노은JCT-동충주IC12.320210301137.05585127.8492448.853.57.063.6611747.43충북 충주 노은 신효
9394건기연[04013]2노은JCT-동충주IC12.320210301137.05585127.8492445.9146.856.14.1611998.03충북 충주 노은 신효
9495건기연[0324-1]1수안보-충주7.320210301236.88774127.984841.570.830.130.0416.06충북 충주 수안보 중산
9596건기연[0324-1]2수안보-충주7.320210301236.88774127.984843.332.060.330.14800.28충북 충주 수안보 중산
9697건기연[0325-3]1신니-신양8.820210301236.9979127.7223812.697.851.170.423330.35충북 충주 신니 원평
9798건기연[0325-3]2신니-신양8.820210301236.9979127.7223822.5925.123.351.365331.29충북 충주 신니 원평
9899건기연[0325-4]1주덕-충주5.020210301236.96342127.8668634.4131.683.672.018734.21충북 충주 대소원 만정
99100건기연[0325-4]2주덕-충주5.020210301236.96342127.8668644.939.334.961.8111329.27충북 충주 대소원 만정