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 11:22:40.777752
Analysis finished2023-12-10 11:22:55.913383
Duration15.14 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
2023-12-10T20:22:56.074395image/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
2023-12-10T20:22:56.358562image/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

2023-12-10T20:22:56.582278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:56.750995image/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
2023-12-10T20:22:57.067067image/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%
2023-12-10T20:22:57.604584image/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

2023-12-10T20:22:57.815033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:58.459264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:22:58.822434image/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%
2023-12-10T20:22:59.519408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.7%
산 36
 
7.1%
남 16
 
3.1%
양 14
 
2.8%
천 14
 
2.8%
창 12
 
2.4%
진 12
 
2.4%
성 12
 
2.4%
부 10
 
2.0%
정 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 (%)
산 36
 
8.9%
남 16
 
4.0%
양 14
 
3.5%
천 14
 
3.5%
창 12
 
3.0%
진 12
 
3.0%
성 12
 
3.0%
부 10
 
2.5%
정 8
 
2.0%
안 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 (%)
산 36
 
8.9%
남 16
 
4.0%
양 14
 
3.5%
천 14
 
3.5%
창 12
 
3.0%
진 12
 
3.0%
성 12
 
3.0%
부 10
 
2.5%
정 8
 
2.0%
안 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 (%)
산 36
 
8.9%
남 16
 
4.0%
양 14
 
3.5%
천 14
 
3.5%
창 12
 
3.0%
진 12
 
3.0%
성 12
 
3.0%
부 10
 
2.5%
정 8
 
2.0%
안 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
2023-12-10T20:22:59.798640image/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
2023-12-10T20:23:00.059134image/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

2023-12-10T20:23:00.292531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:23:00.458287image/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
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T20:23:00.648394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:23:00.824456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
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
2023-12-10T20:23:01.034532image/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
2023-12-10T20:23:01.313694image/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
2023-12-10T20:23:01.697804image/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
2023-12-10T20:23:01.980256image/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 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.9239
Minimum0.52
Maximum326.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:02.275278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile2.8275
Q114.75
median35.185
Q359.415
95-th percentile191.1365
Maximum326.26
Range325.74
Interquartile range (IQR)44.665

Descriptive statistics

Standard deviation67.677721
Coefficient of variation (CV)1.1485615
Kurtosis3.4062689
Mean58.9239
Median Absolute Deviation (MAD)22.95
Skewness1.8667361
Sum5892.39
Variance4580.274
MonotonicityNot monotonic
2023-12-10T20:23:02.533295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.6 2
 
2.0%
7.66 2
 
2.0%
17.63 1
 
1.0%
38.35 1
 
1.0%
47.24 1
 
1.0%
43.54 1
 
1.0%
43.04 1
 
1.0%
31.8 1
 
1.0%
41.04 1
 
1.0%
48.27 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.52 1
1.0%
0.69 1
1.0%
2.6 2
2.0%
2.78 1
1.0%
2.83 1
1.0%
3.14 1
1.0%
3.31 1
1.0%
3.79 1
1.0%
3.89 1
1.0%
3.98 1
1.0%
ValueCountFrequency (%)
326.26 1
1.0%
296.44 1
1.0%
249.87 1
1.0%
246.98 1
1.0%
195.06 1
1.0%
190.93 1
1.0%
172.31 1
1.0%
168.91 1
1.0%
163.19 1
1.0%
157.31 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.4917
Minimum0.28
Maximum296.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:02.806452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile1.7835
Q19.265
median24.295
Q354.0275
95-th percentile145.411
Maximum296.25
Range295.97
Interquartile range (IQR)44.7625

Descriptive statistics

Standard deviation54.472201
Coefficient of variation (CV)1.2243228
Kurtosis5.4832223
Mean44.4917
Median Absolute Deviation (MAD)17.685
Skewness2.1697145
Sum4449.17
Variance2967.2207
MonotonicityNot monotonic
2023-12-10T20:23:03.065487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.28 2
 
2.0%
4.68 2
 
2.0%
10.96 1
 
1.0%
34.72 1
 
1.0%
61.91 1
 
1.0%
84.18 1
 
1.0%
31.59 1
 
1.0%
40.7 1
 
1.0%
27.65 1
 
1.0%
42.16 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.28 1
1.0%
0.68 1
1.0%
1.28 2
2.0%
1.66 1
1.0%
1.79 1
1.0%
1.85 1
1.0%
1.93 1
1.0%
2.15 1
1.0%
2.25 1
1.0%
2.29 1
1.0%
ValueCountFrequency (%)
296.25 1
1.0%
227.77 1
1.0%
217.11 1
1.0%
181.08 1
1.0%
157.02 1
1.0%
144.8 1
1.0%
142.58 1
1.0%
124.93 1
1.0%
122.46 1
1.0%
118.82 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3157
Minimum0.04
Maximum38.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:03.325543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.2595
Q11.45
median3.795
Q36.7975
95-th percentile20.9075
Maximum38.62
Range38.58
Interquartile range (IQR)5.3475

Descriptive statistics

Standard deviation7.4607683
Coefficient of variation (CV)1.181305
Kurtosis4.3117247
Mean6.3157
Median Absolute Deviation (MAD)2.57
Skewness2.0010556
Sum631.57
Variance55.663063
MonotonicityNot monotonic
2023-12-10T20:23:03.627687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35 2
 
2.0%
0.7 2
 
2.0%
0.23 2
 
2.0%
0.26 2
 
2.0%
5.85 2
 
2.0%
1.45 2
 
2.0%
1.64 1
 
1.0%
2.15 1
 
1.0%
7.21 1
 
1.0%
8.09 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.04 1
1.0%
0.09 1
1.0%
0.23 2
2.0%
0.25 1
1.0%
0.26 2
2.0%
0.29 1
1.0%
0.35 2
2.0%
0.39 1
1.0%
0.4 1
1.0%
0.41 1
1.0%
ValueCountFrequency (%)
38.62 1
1.0%
31.33 1
1.0%
29.12 1
1.0%
24.43 1
1.0%
22.38 1
1.0%
20.83 1
1.0%
19.52 1
1.0%
18.47 1
1.0%
18.32 1
1.0%
16.58 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3545
Minimum0
Maximum17.58
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:03.991938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.42
median1.335
Q33.06
95-th percentile8.2895
Maximum17.58
Range17.58
Interquartile range (IQR)2.64

Descriptive statistics

Standard deviation3.1000666
Coefficient of variation (CV)1.316656
Kurtosis7.506606
Mean2.3545
Median Absolute Deviation (MAD)0.99
Skewness2.4770107
Sum235.45
Variance9.6104129
MonotonicityNot monotonic
2023-12-10T20:23:04.242248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27 7
 
7.0%
0.13 6
 
6.0%
0.0 6
 
6.0%
0.56 4
 
4.0%
0.54 3
 
3.0%
0.42 3
 
3.0%
1.47 2
 
2.0%
0.14 2
 
2.0%
3.92 2
 
2.0%
2.07 2
 
2.0%
Other values (61) 63
63.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.13 6
6.0%
0.14 2
 
2.0%
0.26 1
 
1.0%
0.27 7
7.0%
0.39 1
 
1.0%
0.41 1
 
1.0%
0.42 3
3.0%
0.52 1
 
1.0%
0.54 3
3.0%
ValueCountFrequency (%)
17.58 1
1.0%
14.64 1
1.0%
10.18 1
1.0%
10.06 1
1.0%
9.8 1
1.0%
8.21 1
1.0%
7.36 1
1.0%
7.12 1
1.0%
6.64 1
1.0%
6.3 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14714.067
Minimum138.68
Maximum84904.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:04.568377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile815.0585
Q13707.56
median9375.63
Q314286.33
95-th percentile45893.12
Maximum84904.52
Range84765.84
Interquartile range (IQR)10578.77

Descriptive statistics

Standard deviation16920.833
Coefficient of variation (CV)1.1499766
Kurtosis3.9948544
Mean14714.067
Median Absolute Deviation (MAD)5691.71
Skewness1.9596629
Sum1471406.7
Variance2.8631458 × 108
MonotonicityNot monotonic
2023-12-10T20:23:04.822862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
614.73 2
 
2.0%
2024.06 2
 
2.0%
4649.72 1
 
1.0%
9263.5 1
 
1.0%
11827.98 1
 
1.0%
12087.41 1
 
1.0%
9840.87 1
 
1.0%
9781.01 1
 
1.0%
10552.25 1
 
1.0%
12593.1 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
138.68 1
1.0%
179.92 1
1.0%
614.73 2
2.0%
734.66 1
1.0%
819.29 1
1.0%
832.11 1
1.0%
922.09 1
1.0%
942.83 1
1.0%
953.96 1
1.0%
1015.64 1
1.0%
ValueCountFrequency (%)
84904.52 1
1.0%
72171.45 1
1.0%
65538.97 1
1.0%
64506.59 1
1.0%
46951.81 1
1.0%
45837.4 1
1.0%
42447.04 1
1.0%
40332.0 1
1.0%
39966.12 1
1.0%
38789.5 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:23:05.372603image/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%
2023-12-10T20:23:06.162998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
남 112
 
10.3%
경 100
 
9.2%
산 32
 
2.9%
천 24
 
2.2%
고 20
 
1.8%
창 20
 
1.8%
주 16
 
1.5%
양 16
 
1.5%
대 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 (%)
남 112
 
14.2%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.0%
고 20
 
2.5%
창 20
 
2.5%
주 16
 
2.0%
양 16
 
2.0%
대 14
 
1.8%
동 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 (%)
남 112
 
14.2%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.0%
고 20
 
2.5%
창 20
 
2.5%
주 16
 
2.0%
양 16
 
2.0%
대 14
 
1.8%
동 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 (%)
남 112
 
14.2%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.0%
고 20
 
2.5%
창 20
 
2.5%
주 16
 
2.0%
양 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (94) 420
53.3%

Interactions

2023-12-10T20:22:53.817206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:41.553382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:43.012959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:44.479631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:46.143764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:47.563606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:49.388086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:50.747242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:52.351725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:54.002188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:41.694551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:43.159226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:44.626214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:46.293960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:47.690567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:49.538340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:50.909999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:52.496581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:54.154313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:41.837047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:43.343012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:44.897234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:46.469391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:47.837099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:49.698342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:51.117371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:52.665325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:54.302838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:41.989783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:43.480131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:45.096743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:46.626046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:47.964008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:49.831937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:51.288210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:52.805718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:54.496761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.153950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:43.688767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:45.281096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:46.793989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:48.553888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:49.998819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:51.477585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:52.984870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:54.654835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.362048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:43.855855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:45.452410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:46.953870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:48.716050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:50.138856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:51.642269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:53.136230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:54.806225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.536000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:44.019713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:45.658430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:47.109317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:48.896164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:50.296702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:51.828316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:53.296047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:54.965714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.716209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:44.187642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:45.818538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:47.276273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:49.082137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:50.456811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:52.004515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:53.477551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:55.118710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.880265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:44.338068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:46.007911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:47.434390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:49.242093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:50.595951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:52.204422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:53.652048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:23:06.362178image/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
기본키1.0001.0000.0001.0000.5660.8040.6210.4540.4820.5720.3750.4761.000
지점1.0001.0000.0001.0001.0001.0001.0000.8530.8450.8540.8380.8151.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.8530.8450.8540.8380.8151.000
연장((km))0.5661.0000.0001.0001.0000.5600.6860.3300.6030.3310.4320.0001.000
좌표위치위도((°))0.8041.0000.0001.0000.5601.0000.5910.3740.2790.5550.4260.3111.000
좌표위치경도((°))0.6211.0000.0001.0000.6860.5911.0000.5210.7010.5610.5450.7011.000
co((g/km))0.4540.8530.0000.8530.3300.3740.5211.0000.8760.9180.9070.9690.853
nox((g/km))0.4820.8450.0000.8450.6030.2790.7010.8761.0000.9550.8840.9720.845
hc((g/km))0.5720.8540.0000.8540.3310.5550.5610.9180.9551.0000.9140.9530.854
pm((g/km))0.3750.8380.0000.8380.4320.4260.5450.9070.8840.9141.0000.8610.838
co2((g/km))0.4760.8150.0000.8150.0000.3110.7010.9690.9720.9530.8611.0000.815
주소1.0001.0000.0001.0001.0001.0001.0000.8530.8450.8540.8380.8151.000
2023-12-10T20:23:06.641623image/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
기본키1.000-0.0500.0790.116-0.269-0.239-0.249-0.248-0.2690.000
연장((km))-0.0501.0000.2070.020-0.106-0.101-0.112-0.121-0.0970.000
좌표위치위도((°))0.0790.2071.0000.315-0.243-0.195-0.199-0.137-0.2400.000
좌표위치경도((°))0.1160.0200.3151.0000.3870.4000.3980.4030.3860.000
co((g/km))-0.269-0.106-0.2430.3871.0000.9800.9910.9320.9970.000
nox((g/km))-0.239-0.101-0.1950.4000.9801.0000.9940.9760.9820.000
hc((g/km))-0.249-0.112-0.1990.3980.9910.9941.0000.9610.9890.000
pm((g/km))-0.248-0.121-0.1370.4030.9320.9760.9611.0000.9360.000
co2((g/km))-0.269-0.097-0.2400.3860.9970.9820.9890.9361.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T20:22:55.339320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:22:55.765774image/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-완사4.220210501035.12031127.9701117.6310.961.640.684649.72경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210501035.12031127.9701127.621.153.141.477029.36경남 사천 곤명 작팔
23건기연[0218-1]1진주-사봉3.520210501035.16901128.187342.129.994.31.8210040.04경남 진주 문산 상문
34건기연[0218-1]2진주-사봉3.520210501035.16901128.187333.6826.393.591.99211.91경남 진주 문산 상문
45건기연[0220-2]1일반성-진북4.820210501035.10632128.44218172.31122.4619.525.8539966.12경남 창원 진전 근곡
56건기연[0220-2]2일반성-진북4.820210501035.10632128.44218249.87227.7729.1214.6465538.97경남 창원 진전 근곡
67건기연[0222-1]1마산-부산9.420210501035.1839128.6395151.35144.816.587.1242447.04경남 창원 양곡
78건기연[0222-1]2마산-부산9.420210501035.1839128.6395137.31124.9316.295.9534232.18경남 창원 양곡
89건기연[0302-4]1상죽-사천10.220210501034.87506128.0095863.1733.895.851.0615124.26경남 남해 창선 동대
910건기연[0302-4]2상죽-사천10.220210501034.87506128.0095845.6334.064.641.6310791.16경남 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-고성6.420210501034.89878128.355384.352.620.40.131150.71경남 통영 도산 오륜
9192건기연[7702-0]2통영-고성6.420210501034.89878128.355383.982.380.390.14953.96경남 통영 도산 오륜
9293건기연[7702-2]1삼산-하이11.120210501034.92571128.1305915.658.421.450.273740.84경남 고성 하이 덕호
9394건기연[7702-2]2삼산-하이11.120210501034.92571128.1305915.08.561.320.273965.65경남 고성 하이 덕호
9495건기연[7703-0]1유포-설천11.220210501034.90043127.863417.664.680.70.272024.06경남 남해 고현 포상
9596건기연[7703-0]2유포-설천11.220210501034.90043127.863413.312.150.290.13942.83경남 남해 고현 포상
9697건기연[7901-0]1정곡-의령2.720210501035.29567128.29829.6222.93.181.478128.46경남 함안 군북 월촌
9798건기연[7901-0]2정곡-의령2.720210501035.29567128.29852.9534.265.691.6812461.93경남 함안 군북 월촌
9899건기연[7904-0]1마산-진영5.220210501035.28732128.61172326.26217.1131.339.884904.52경남 창원 북 외감
99100건기연[7904-0]2마산-진영5.220210501035.28732128.61172195.06109.9218.323.9246951.81경남 창원 북 외감