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
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
co((g/km)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
pm((g/km)) has 6 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:15:12.995058
Analysis finished2023-12-10 12:15:20.563660
Duration7.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

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
2023-12-10T21:15:20.624735image/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
2023-12-10T21:15:20.734850image/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

2023-12-10T21:15:20.838301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:20.908886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:15:21.070020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[0216-2]
2nd row[0216-2]
3rd row[0218-1]
4th row[0218-1]
5th row[0220-2]
ValueCountFrequency (%)
0216-2 2
 
2.0%
3305-0 2
 
2.0%
7901-0 2
 
2.0%
2419-1 2
 
2.0%
2421-0 2
 
2.0%
2422-0 2
 
2.0%
2423-1 2
 
2.0%
2502-0 2
 
2.0%
3101-6 2
 
2.0%
3301-4 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:15:21.362321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 90
11.2%
2 78
9.8%
3 54
 
6.8%
4 40
 
5.0%
7 26
 
3.2%
5 24
 
3.0%
Other values (3) 38
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 150
30.0%
1 90
18.0%
2 78
15.6%
3 54
 
10.8%
4 40
 
8.0%
7 26
 
5.2%
5 24
 
4.8%
9 18
 
3.6%
6 10
 
2.0%
8 10
 
2.0%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 90
11.2%
2 78
9.8%
3 54
 
6.8%
4 40
 
5.0%
7 26
 
3.2%
5 24
 
3.0%
Other values (3) 38
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 90
11.2%
2 78
9.8%
3 54
 
6.8%
4 40
 
5.0%
7 26
 
3.2%
5 24
 
3.0%
Other values (3) 38
 
4.8%

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

2023-12-10T21:15:21.470291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:21.544205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%
Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:15:21.713992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.08
Min length5

Characters and Unicode

Total characters508
Distinct characters84
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row북천-완사
2nd row북천-완사
3rd row진주-사봉
4th row진주-사봉
5th row일반성-진북
ValueCountFrequency (%)
북천-완사 2
 
2.0%
합천-쌍림 2
 
2.0%
정곡-의령 2
 
2.0%
초계-적교 2
 
2.0%
창녕-청도 2
 
2.0%
가산-남기 2
 
2.0%
부북-금곡 2
 
2.0%
창원-대산 2
 
2.0%
서생-온산 2
 
2.0%
상리-고성 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:15:22.016423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.7%
36
 
7.1%
16
 
3.1%
14
 
2.8%
14
 
2.8%
12
 
2.4%
12
 
2.4%
12
 
2.4%
10
 
2.0%
8
 
1.6%
Other values (74) 274
53.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 404
79.5%
Dash Punctuation 100
 
19.7%
Uppercase Letter 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
36
 
8.9%
16
 
4.0%
14
 
3.5%
14
 
3.5%
12
 
3.0%
12
 
3.0%
12
 
3.0%
10
 
2.5%
8
 
2.0%
8
 
2.0%
Other values (71) 262
64.9%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 404
79.5%
Common 100
 
19.7%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
36
 
8.9%
16
 
4.0%
14
 
3.5%
14
 
3.5%
12
 
3.0%
12
 
3.0%
12
 
3.0%
10
 
2.5%
8
 
2.0%
8
 
2.0%
Other values (71) 262
64.9%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 404
79.5%
ASCII 104
 
20.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
96.2%
C 2
 
1.9%
I 2
 
1.9%
Hangul
ValueCountFrequency (%)
36
 
8.9%
16
 
4.0%
14
 
3.5%
14
 
3.5%
12
 
3.0%
12
 
3.0%
12
 
3.0%
10
 
2.5%
8
 
2.0%
8
 
2.0%
Other values (71) 262
64.9%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.598
Minimum2.7
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:22.129467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile3.3
Q15.2
median7.75
Q311
95-th percentile17.6
Maximum20.1
Range17.4
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.2070883
Coefficient of variation (CV)0.48931011
Kurtosis0.43648909
Mean8.598
Median Absolute Deviation (MAD)2.8
Skewness0.87516361
Sum859.8
Variance17.699592
MonotonicityNot monotonic
2023-12-10T21:15:22.234617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
11.2 4
 
4.0%
10.2 4
 
4.0%
6.8 4
 
4.0%
5.2 4
 
4.0%
10.5 4
 
4.0%
4.2 2
 
2.0%
9.9 2
 
2.0%
7.6 2
 
2.0%
6.1 2
 
2.0%
3.3 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
2.7 2
2.0%
3.0 2
2.0%
3.3 2
2.0%
3.5 2
2.0%
3.8 2
2.0%
3.9 2
2.0%
4.1 2
2.0%
4.2 2
2.0%
4.7 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
20.1 2
2.0%
19.3 2
2.0%
17.6 2
2.0%
17.2 2
2.0%
13.5 2
2.0%
13.3 2
2.0%
13.2 2
2.0%
12.2 2
2.0%
11.9 2
2.0%
11.2 4
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210501
2nd row20210501
3rd row20210501
4th row20210501
5th row20210501

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2023-12-10T21:15:22.338520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:22.410970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 100
100.0%

측정시간
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

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 (%)
1 100
100.0%

Length

2023-12-10T21:15:22.489988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:22.562816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

좌표위치위도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.283464
Minimum34.86496
Maximum35.72809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:22.644906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.86496
5-th percentile34.89878
Q135.10632
median35.301425
Q335.51362
95-th percentile35.62274
Maximum35.72809
Range0.86313
Interquartile range (IQR)0.4073

Descriptive statistics

Standard deviation0.24456267
Coefficient of variation (CV)0.0069313679
Kurtosis-1.0775585
Mean35.283464
Median Absolute Deviation (MAD)0.208965
Skewness-0.15959767
Sum3528.3464
Variance0.059810901
MonotonicityNot monotonic
2023-12-10T21:15:22.748142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.12031 2
 
2.0%
35.52321 2
 
2.0%
35.57918 2
 
2.0%
35.51777 2
 
2.0%
35.50716 2
 
2.0%
35.32823 2
 
2.0%
35.39903 2
 
2.0%
34.97974 2
 
2.0%
35.28102 2
 
2.0%
35.51362 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.86496 2
2.0%
34.87506 2
2.0%
34.89878 2
2.0%
34.90043 2
2.0%
34.90831 2
2.0%
34.92571 2
2.0%
34.93132 2
2.0%
34.95217 2
2.0%
34.97974 2
2.0%
34.98771 2
2.0%
ValueCountFrequency (%)
35.72809 2
2.0%
35.65095 2
2.0%
35.62274 2
2.0%
35.61557 2
2.0%
35.61463 2
2.0%
35.58349 2
2.0%
35.57918 2
2.0%
35.57341 2
2.0%
35.55894 2
2.0%
35.53342 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.38208
Minimum127.78878
Maximum129.33586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:22.850996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.78878
5-th percentile127.80269
Q1127.99393
median128.30922
Q3128.71274
95-th percentile129.24056
Maximum129.33586
Range1.54708
Interquartile range (IQR)0.71881

Descriptive statistics

Standard deviation0.45496977
Coefficient of variation (CV)0.0035438729
Kurtosis-0.72837029
Mean128.38208
Median Absolute Deviation (MAD)0.324595
Skewness0.62484388
Sum12838.208
Variance0.20699749
MonotonicityNot monotonic
2023-12-10T21:15:22.965666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.97011 2
 
2.0%
129.09913 2
 
2.0%
128.51771 2
 
2.0%
128.72421 2
 
2.0%
128.83128 2
 
2.0%
128.71274 2
 
2.0%
129.33586 2
 
2.0%
128.2752 2
 
2.0%
128.08474 2
 
2.0%
128.17297 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
127.78878 2
2.0%
127.78997 2
2.0%
127.80269 2
2.0%
127.83555 2
2.0%
127.86341 2
2.0%
127.86709 2
2.0%
127.89437 2
2.0%
127.90225 2
2.0%
127.93378 2
2.0%
127.95798 2
2.0%
ValueCountFrequency (%)
129.33586 2
2.0%
129.28105 2
2.0%
129.24056 2
2.0%
129.2158 2
2.0%
129.17553 2
2.0%
129.12842 2
2.0%
129.09913 2
2.0%
129.02747 2
2.0%
128.87085 2
2.0%
128.83128 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.7712
Minimum0
Maximum218.79
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:23.093908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.2955
Q19.8225
median19.41
Q344.74
95-th percentile138.76
Maximum218.79
Range218.79
Interquartile range (IQR)34.9175

Descriptive statistics

Standard deviation47.057997
Coefficient of variation (CV)1.2137359
Kurtosis2.7456224
Mean38.7712
Median Absolute Deviation (MAD)12.205
Skewness1.8182242
Sum3877.12
Variance2214.4551
MonotonicityNot monotonic
2023-12-10T21:15:23.404225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.11 2
 
2.0%
1.3 2
 
2.0%
11.95 2
 
2.0%
11.79 1
 
1.0%
68.07 1
 
1.0%
17.04 1
 
1.0%
18.98 1
 
1.0%
23.21 1
 
1.0%
20.4 1
 
1.0%
13.06 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.52 1
1.0%
0.65 1
1.0%
1.05 1
1.0%
1.21 1
1.0%
1.3 2
2.0%
2.78 1
1.0%
3.28 1
1.0%
3.83 1
1.0%
3.98 1
1.0%
ValueCountFrequency (%)
218.79 1
1.0%
181.26 1
1.0%
177.87 1
1.0%
159.06 1
1.0%
150.16 1
1.0%
138.16 1
1.0%
125.81 1
1.0%
125.3 1
1.0%
124.29 1
1.0%
114.44 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.8673
Minimum0
Maximum190.05
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:23.511617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.64
Q16.2975
median13.005
Q340.19
95-th percentile107.101
Maximum190.05
Range190.05
Interquartile range (IQR)33.8925

Descriptive statistics

Standard deviation38.144497
Coefficient of variation (CV)1.2771324
Kurtosis4.9565598
Mean29.8673
Median Absolute Deviation (MAD)10.2
Skewness2.1086911
Sum2986.73
Variance1455.0026
MonotonicityNot monotonic
2023-12-10T21:15:23.630695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.41 2
 
2.0%
10.47 2
 
2.0%
12.68 2
 
2.0%
0.64 2
 
2.0%
6.85 1
 
1.0%
23.59 1
 
1.0%
31.38 1
 
1.0%
12.63 1
 
1.0%
7.85 1
 
1.0%
21.02 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.28 1
1.0%
0.32 1
1.0%
0.55 1
1.0%
0.64 2
2.0%
0.96 1
1.0%
1.79 1
1.0%
1.96 1
1.0%
2.34 1
1.0%
2.38 1
1.0%
ValueCountFrequency (%)
190.05 1
1.0%
187.8 1
1.0%
118.99 1
1.0%
115.12 1
1.0%
109.4 1
1.0%
106.98 1
1.0%
99.71 1
1.0%
88.52 1
1.0%
85.42 1
1.0%
84.04 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2163
Minimum0
Maximum26.96
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:23.748698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.12
Q10.9425
median1.95
Q34.875
95-th percentile15.6465
Maximum26.96
Range26.96
Interquartile range (IQR)3.9325

Descriptive statistics

Standard deviation5.2588088
Coefficient of variation (CV)1.2472568
Kurtosis3.9930687
Mean4.2163
Median Absolute Deviation (MAD)1.47
Skewness1.9766671
Sum421.63
Variance27.65507
MonotonicityNot monotonic
2023-12-10T21:15:23.862742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.28 3
 
3.0%
1.98 2
 
2.0%
1.38 2
 
2.0%
0.12 2
 
2.0%
2.8 2
 
2.0%
0.44 2
 
2.0%
1.01 2
 
2.0%
8.87 1
 
1.0%
1.78 1
 
1.0%
2.83 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.04 1
1.0%
0.06 1
1.0%
0.09 1
1.0%
0.12 2
2.0%
0.13 1
1.0%
0.26 1
1.0%
0.32 1
1.0%
0.35 1
1.0%
0.39 1
1.0%
ValueCountFrequency (%)
26.96 1
1.0%
22.13 1
1.0%
17.07 1
1.0%
16.07 1
1.0%
15.77 1
1.0%
15.64 1
1.0%
14.62 1
1.0%
13.64 1
1.0%
12.3 1
1.0%
12.25 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6154
Minimum0
Maximum11.97
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:23.972055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.28
median0.745
Q32.0275
95-th percentile5.294
Maximum11.97
Range11.97
Interquartile range (IQR)1.7475

Descriptive statistics

Standard deviation2.1627986
Coefficient of variation (CV)1.3388626
Kurtosis7.5607952
Mean1.6154
Median Absolute Deviation (MAD)0.605
Skewness2.4955814
Sum161.54
Variance4.6776978
MonotonicityNot monotonic
2023-12-10T21:15:24.089888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27 8
 
8.0%
0.13 8
 
8.0%
0.28 7
 
7.0%
0.0 6
 
6.0%
0.4 6
 
6.0%
0.83 2
 
2.0%
0.54 2
 
2.0%
0.14 2
 
2.0%
0.55 2
 
2.0%
0.94 2
 
2.0%
Other values (51) 55
55.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.13 8
8.0%
0.14 2
 
2.0%
0.27 8
8.0%
0.28 7
7.0%
0.4 6
6.0%
0.41 1
 
1.0%
0.42 2
 
2.0%
0.54 2
 
2.0%
0.55 2
 
2.0%
ValueCountFrequency (%)
11.97 1
1.0%
10.71 1
1.0%
7.36 1
1.0%
7.19 1
1.0%
5.94 1
1.0%
5.26 1
1.0%
5.03 1
1.0%
4.95 1
1.0%
4.63 1
1.0%
4.53 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9625.8657
Minimum0
Maximum50164.18
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:24.200816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile307.36
Q12516.495
median5133.63
Q310451.235
95-th percentile33084.774
Maximum50164.18
Range50164.18
Interquartile range (IQR)7934.74

Descriptive statistics

Standard deviation11583.532
Coefficient of variation (CV)1.2033756
Kurtosis2.8059262
Mean9625.8657
Median Absolute Deviation (MAD)3040.21
Skewness1.8358953
Sum962586.57
Variance1.3417821 × 108
MonotonicityNot monotonic
2023-12-10T21:15:24.311605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3729.51 2
 
2.0%
307.36 2
 
2.0%
2844.98 1
 
1.0%
16698.85 1
 
1.0%
5760.38 1
 
1.0%
4481.06 1
 
1.0%
4541.12 1
 
1.0%
7159.89 1
 
1.0%
5360.04 1
 
1.0%
3452.14 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.0 1
1.0%
138.68 1
1.0%
153.68 1
1.0%
277.37 1
1.0%
307.36 2
2.0%
318.6 1
1.0%
734.66 1
1.0%
794.65 1
1.0%
953.96 1
1.0%
1012.03 1
1.0%
ValueCountFrequency (%)
50164.18 1
1.0%
48188.08 1
1.0%
47525.84 1
1.0%
38435.45 1
1.0%
37793.62 1
1.0%
32836.94 1
1.0%
32668.13 1
1.0%
31677.6 1
1.0%
29472.51 1
1.0%
26810.94 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:15:24.532487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.86
Min length8

Characters and Unicode

Total characters1086
Distinct characters105
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

Unique0 ?
Unique (%)0.0%

Sample

1st row경남 사천 곤명 작팔
2nd row경남 사천 곤명 작팔
3rd row경남 진주 문산 상문
4th row경남 진주 문산 상문
5th row경남 창원 진전 근곡
ValueCountFrequency (%)
경남 100
25.1%
고성 10
 
2.5%
합천 10
 
2.5%
창원 10
 
2.5%
남해 8
 
2.0%
산청 8
 
2.0%
울주 8
 
2.0%
진주 8
 
2.0%
밀양 6
 
1.5%
고현 4
 
1.0%
Other values (103) 226
56.8%
2023-12-10T21:15:24.883317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
112
 
10.3%
100
 
9.2%
32
 
2.9%
24
 
2.2%
20
 
1.8%
20
 
1.8%
16
 
1.5%
16
 
1.5%
14
 
1.3%
Other values (95) 434
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 788
72.6%
Space Separator 298
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
112
 
14.2%
100
 
12.7%
32
 
4.1%
24
 
3.0%
20
 
2.5%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (94) 420
53.3%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 788
72.6%
Common 298
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
112
 
14.2%
100
 
12.7%
32
 
4.1%
24
 
3.0%
20
 
2.5%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (94) 420
53.3%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 788
72.6%
ASCII 298
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
112
 
14.2%
100
 
12.7%
32
 
4.1%
24
 
3.0%
20
 
2.5%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (94) 420
53.3%

Interactions

2023-12-10T21:15:19.547652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:13.440608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.099408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.931670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.604306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.401483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.103169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.892565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:18.842994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.619724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:13.506264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.176180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.015125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.682407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.468179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.181304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.971896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:18.933123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.695852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:13.589843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.286891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.086605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.771583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.551010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.271484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:18.252581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.015628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.768113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:13.654773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.384192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.143083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.847735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.628149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.358372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:18.320666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.089104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.844372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:13.733083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.483960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.222086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.968728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.728668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.458985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:18.435926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.177405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.902864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:13.796970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.570206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.301435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.054585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.796524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.537367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:18.522912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.252416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.976125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:13.887653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.662769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.398221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.167838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.870912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.628001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:18.614094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.330496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:20.055052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:13.961246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.751918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.468509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.248415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.951852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.708475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:18.686907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.404227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:20.120829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.031268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:14.836967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:15.537612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:16.327218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.031464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:17.812542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:18.766552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:19.476925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:15:24.971169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
기본키1.0001.0000.0001.0000.5660.8040.6210.4390.3600.4190.2130.3771.000
지점1.0001.0000.0001.0001.0001.0001.0000.7300.8660.8140.7600.7221.000
방향0.0000.0001.0000.0000.0000.0000.0000.0480.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.7300.8660.8140.7600.7221.000
연장((km))0.5661.0000.0001.0001.0000.5600.6860.1890.3920.3460.3180.3031.000
좌표위치위도((°))0.8041.0000.0001.0000.5601.0000.5910.3760.3650.3500.3830.2771.000
좌표위치경도((°))0.6211.0000.0001.0000.6860.5911.0000.5010.4980.6630.4550.6241.000
co((g/km))0.4390.7300.0480.7300.1890.3760.5011.0000.8940.9430.8770.9430.730
nox((g/km))0.3600.8660.0000.8660.3920.3650.4980.8941.0000.9240.9730.8590.866
hc((g/km))0.4190.8140.0000.8140.3460.3500.6630.9430.9241.0000.9150.9580.814
pm((g/km))0.2130.7600.0000.7600.3180.3830.4550.8770.9730.9151.0000.8030.760
co2((g/km))0.3770.7220.0000.7220.3030.2770.6240.9430.8590.9580.8031.0000.722
주소1.0001.0000.0001.0001.0001.0001.0000.7300.8660.8140.7600.7221.000
2023-12-10T21:15:25.085325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))방향
기본키1.000-0.0500.0790.116-0.264-0.233-0.247-0.256-0.2620.000
연장((km))-0.0501.0000.2070.020-0.085-0.077-0.094-0.061-0.0750.000
좌표위치위도((°))0.0790.2071.0000.315-0.257-0.201-0.201-0.115-0.2570.000
좌표위치경도((°))0.1160.0200.3151.0000.4370.4350.4540.4330.4310.000
co((g/km))-0.264-0.085-0.2570.4371.0000.9780.9870.9390.9970.018
nox((g/km))-0.233-0.077-0.2010.4350.9781.0000.9950.9760.9770.000
hc((g/km))-0.247-0.094-0.2010.4540.9870.9951.0000.9680.9810.000
pm((g/km))-0.256-0.061-0.1150.4330.9390.9760.9681.0000.9370.000
co2((g/km))-0.262-0.075-0.2570.4310.9970.9770.9810.9371.0000.000
방향0.0000.0000.0000.0000.0180.0000.0000.0000.0001.000

Missing values

2023-12-10T21:15:20.252611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:15:20.494006image/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

기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
01건기연[0216-2]1북천-완사4.220210501135.12031127.9701111.796.851.130.42844.98경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210501135.12031127.9701114.638.861.430.563508.48경남 사천 곤명 작팔
23건기연[0218-1]1진주-사봉3.520210501135.16901128.187321.0513.331.980.945558.66경남 진주 문산 상문
34건기연[0218-1]2진주-사봉3.520210501135.16901128.187332.1329.174.081.968414.06경남 진주 문산 상문
45건기연[0220-2]1일반성-진북4.820210501135.10632128.4421888.4985.4210.544.6323523.12경남 창원 진전 근곡
56건기연[0220-2]2일반성-진북4.820210501135.10632128.44218177.87187.822.1311.9748188.08경남 창원 진전 근곡
67건기연[0222-1]1마산-부산9.420210501135.1839128.6395107.2880.8111.723.5924992.04경남 창원 양곡
78건기연[0222-1]2마산-부산9.420210501135.1839128.639584.4466.249.482.8519563.51경남 창원 양곡
89건기연[0302-4]1상죽-사천10.220210501134.87506128.0095832.017.252.970.547640.99경남 남해 창선 동대
910건기연[0302-4]2상죽-사천10.220210501134.87506128.0095828.4421.762.81.347410.14경남 남해 창선 동대
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[7702-0]1통영-고성6.420210501134.89878128.3553813.4511.61.870.582985.63경남 통영 도산 오륜
9192건기연[7702-0]2통영-고성6.420210501134.89878128.355385.232.920.490.131255.69경남 통영 도산 오륜
9293건기연[7702-2]1삼산-하이11.120210501134.92571128.1305913.77.461.280.273279.79경남 고성 하이 덕호
9394건기연[7702-2]2삼산-하이11.120210501134.92571128.1305914.118.411.280.43729.51경남 고성 하이 덕호
9495건기연[7703-0]1유포-설천11.220210501134.90043127.863411.30.640.120.0307.36경남 남해 고현 포상
9596건기연[7703-0]2유포-설천11.220210501134.90043127.863411.30.640.120.0307.36경남 남해 고현 포상
9697건기연[7901-0]1정곡-의령2.720210501135.29567128.29824.4814.482.370.835871.81경남 함안 군북 월촌
9798건기연[7901-0]2정곡-의령2.720210501135.29567128.29827.6216.622.520.917383.21경남 함안 군북 월촌
9899건기연[7904-0]1마산-진영5.220210501135.28732128.61172181.26115.1217.075.2647525.84경남 창원 북 외감
99100건기연[7904-0]2마산-진영5.220210501135.28732128.61172125.8182.0212.033.4232836.94경남 창원 북 외감