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 12:14:58.917651
Analysis finished2023-12-10 12:15:05.773507
Duration6.86 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
2023-12-10T21:15:05.837053image/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
2023-12-10T21:15:05.947608image/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

2023-12-10T21:15:06.306064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:06.390896image/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
2023-12-10T21:15:06.588627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8
Min length7

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%
5802-0 2
 
2.0%
00101-2 2
 
2.0%
2422-0 2
 
2.0%
2502-0 2
 
2.0%
3101-6 2
 
2.0%
3301-4 2
 
2.0%
3302-2 2
 
2.0%
3304-2 2
 
2.0%
3305-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:15:06.959365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 158
19.8%
[ 100
12.5%
] 100
12.5%
- 98
12.2%
1 92
11.5%
2 70
8.8%
3 50
 
6.2%
4 40
 
5.0%
7 28
 
3.5%
5 24
 
3.0%
Other values (3) 40
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 502
62.7%
Open Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%
Dash Punctuation 98
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 158
31.5%
1 92
18.3%
2 70
13.9%
3 50
 
10.0%
4 40
 
8.0%
7 28
 
5.6%
5 24
 
4.8%
9 20
 
4.0%
6 10
 
2.0%
8 10
 
2.0%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 158
19.8%
[ 100
12.5%
] 100
12.5%
- 98
12.2%
1 92
11.5%
2 70
8.8%
3 50
 
6.2%
4 40
 
5.0%
7 28
 
3.5%
5 24
 
3.0%
Other values (3) 40
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 158
19.8%
[ 100
12.5%
] 100
12.5%
- 98
12.2%
1 92
11.5%
2 70
8.8%
3 50
 
6.2%
4 40
 
5.0%
7 28
 
3.5%
5 24
 
3.0%
Other values (3) 40
 
5.0%

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

2023-12-10T21:15:07.091033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:07.189645image/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
2023-12-10T21:15:07.402168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.3
Min length5

Characters and Unicode

Total characters530
Distinct characters88
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%
진영-김해 2
 
2.0%
노포jct-양산jct 2
 
2.0%
가산-남기 2
 
2.0%
창원-대산 2
 
2.0%
서생-온산 2
 
2.0%
상리-고성 2
 
2.0%
대암-송계 2
 
2.0%
쌍백-합천 2
 
2.0%
합천-쌍림 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:15:07.776179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
18.9%
32
 
6.0%
18
 
3.4%
18
 
3.4%
16
 
3.0%
14
 
2.6%
12
 
2.3%
12
 
2.3%
C 10
 
1.9%
8
 
1.5%
Other values (78) 290
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 404
76.2%
Dash Punctuation 100
 
18.9%
Uppercase Letter 26
 
4.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
32
 
7.9%
18
 
4.5%
18
 
4.5%
16
 
4.0%
14
 
3.5%
12
 
3.0%
12
 
3.0%
8
 
2.0%
8
 
2.0%
8
 
2.0%
Other values (73) 258
63.9%
Uppercase Letter
ValueCountFrequency (%)
C 10
38.5%
J 6
23.1%
T 6
23.1%
I 4
 
15.4%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 404
76.2%
Common 100
 
18.9%
Latin 26
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
32
 
7.9%
18
 
4.5%
18
 
4.5%
16
 
4.0%
14
 
3.5%
12
 
3.0%
12
 
3.0%
8
 
2.0%
8
 
2.0%
8
 
2.0%
Other values (73) 258
63.9%
Latin
ValueCountFrequency (%)
C 10
38.5%
J 6
23.1%
T 6
23.1%
I 4
 
15.4%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 404
76.2%
ASCII 126
 
23.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
79.4%
C 10
 
7.9%
J 6
 
4.8%
T 6
 
4.8%
I 4
 
3.2%
Hangul
ValueCountFrequency (%)
32
 
7.9%
18
 
4.5%
18
 
4.5%
16
 
4.0%
14
 
3.5%
12
 
3.0%
12
 
3.0%
8
 
2.0%
8
 
2.0%
8
 
2.0%
Other values (73) 258
63.9%

연장((km))
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.67
Minimum2.7
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:07.910696image/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 deviation4.3182605
Coefficient of variation (CV)0.49806926
Kurtosis0.32825521
Mean8.67
Median Absolute Deviation (MAD)2.8
Skewness0.89779419
Sum867
Variance18.647374
MonotonicityNot monotonic
2023-12-10T21:15:08.039462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5.2 4
 
4.0%
10.2 4
 
4.0%
6.8 4
 
4.0%
17.2 4
 
4.0%
11.2 4
 
4.0%
7.9 4
 
4.0%
10.5 4
 
4.0%
4.7 2
 
2.0%
3.3 2
 
2.0%
5.7 2
 
2.0%
Other values (33) 66
66.0%
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 4
4.0%
13.5 2
2.0%
13.2 2
2.0%
12.2 2
2.0%
11.9 2
2.0%
11.2 4
4.0%
11.1 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2023-12-10T21:15:08.156080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:08.231077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 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

2023-12-10T21:15:08.312818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:15:08.411549image/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%
Mean35.289759
Minimum34.86496
Maximum35.72809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:08.511789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.86496
5-th percentile34.89878
Q135.10632
median35.306605
Q335.51777
95-th percentile35.62274
Maximum35.72809
Range0.86313
Interquartile range (IQR)0.41145

Descriptive statistics

Standard deviation0.2471748
Coefficient of variation (CV)0.007004151
Kurtosis-1.0941769
Mean35.289759
Median Absolute Deviation (MAD)0.20909
Skewness-0.19565486
Sum3528.9759
Variance0.061095382
MonotonicityNot monotonic
2023-12-10T21:15:08.691873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.12031 2
 
2.0%
34.98771 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%
35.62274 2
 
2.0%
35.31018 2
 
2.0%
35.52321 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%
35.60563 2
2.0%
35.58349 2
2.0%
35.57918 2
2.0%
35.57341 2
2.0%
35.55894 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.40958
Minimum127.78878
Maximum129.33586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:08.806238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.78878
5-th percentile127.80269
Q1128.00958
median128.3277
Q3128.72421
95-th percentile129.24056
Maximum129.33586
Range1.54708
Interquartile range (IQR)0.71463

Descriptive statistics

Standard deviation0.46780703
Coefficient of variation (CV)0.0036430851
Kurtosis-0.91693938
Mean128.40958
Median Absolute Deviation (MAD)0.33558
Skewness0.54843134
Sum12840.958
Variance0.21884342
MonotonicityNot monotonic
2023-12-10T21:15:08.951872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.97011 2
 
2.0%
127.80269 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%
128.19963 2
 
2.0%
129.02747 2
 
2.0%
129.09913 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%
127.95798 2
2.0%
127.97011 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%
129.14403 2
2.0%
129.12842 2
2.0%
129.09913 2
2.0%
129.07556 2
2.0%
129.02747 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.7595
Minimum0
Maximum252.74
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:09.102698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.65
Q16.1225
median18.22
Q338.745
95-th percentile143.255
Maximum252.74
Range252.74
Interquartile range (IQR)32.6225

Descriptive statistics

Standard deviation52.54087
Coefficient of variation (CV)1.4293141
Kurtosis6.9612103
Mean36.7595
Median Absolute Deviation (MAD)13.085
Skewness2.5962978
Sum3675.95
Variance2760.543
MonotonicityNot monotonic
2023-12-10T21:15:09.258182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52 3
 
3.0%
4.09 2
 
2.0%
2.83 2
 
2.0%
3.28 2
 
2.0%
0.65 2
 
2.0%
7.14 1
 
1.0%
18.31 1
 
1.0%
24.18 1
 
1.0%
78.23 1
 
1.0%
58.17 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.52 3
3.0%
0.65 2
2.0%
1.34 1
 
1.0%
1.78 1
 
1.0%
2.26 1
 
1.0%
2.68 1
 
1.0%
2.83 2
2.0%
2.94 1
 
1.0%
3.28 2
2.0%
ValueCountFrequency (%)
252.74 1
1.0%
241.18 1
1.0%
218.64 1
1.0%
218.58 1
1.0%
203.77 1
1.0%
140.07 1
1.0%
121.1 1
1.0%
85.45 1
1.0%
85.38 1
1.0%
84.03 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.3359
Minimum0
Maximum616
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:09.408509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32
Q15.12
median15.19
Q346.575
95-th percentile116.5955
Maximum616
Range616
Interquartile range (IQR)41.455

Descriptive statistics

Standard deviation90.78895
Coefficient of variation (CV)2.0950055
Kurtosis23.366598
Mean43.3359
Median Absolute Deviation (MAD)13.13
Skewness4.5887132
Sum4333.59
Variance8242.6334
MonotonicityNot monotonic
2023-12-10T21:15:09.553848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 3
 
3.0%
2.93 2
 
2.0%
1.88 2
 
2.0%
1.96 2
 
2.0%
0.32 2
 
2.0%
4.4 1
 
1.0%
11.64 1
 
1.0%
18.77 1
 
1.0%
70.91 1
 
1.0%
57.34 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.28 3
3.0%
0.32 2
2.0%
1.0 1
 
1.0%
1.33 1
 
1.0%
1.51 1
 
1.0%
1.73 1
 
1.0%
1.88 2
2.0%
1.96 2
2.0%
2.16 1
 
1.0%
ValueCountFrequency (%)
616.0 1
1.0%
509.68 1
1.0%
327.27 1
1.0%
317.5 1
1.0%
181.3 1
1.0%
113.19 1
1.0%
90.42 1
1.0%
86.42 1
1.0%
84.74 1
1.0%
74.63 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3272
Minimum0
Maximum61.35
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:09.677945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.75
median2.165
Q35.85
95-th percentile16.4195
Maximum61.35
Range61.35
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation9.5490347
Coefficient of variation (CV)1.7925054
Kurtosis18.538501
Mean5.3272
Median Absolute Deviation (MAD)1.845
Skewness4.030517
Sum532.72
Variance91.184063
MonotonicityNot monotonic
2023-12-10T21:15:09.809682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 3
 
3.0%
0.27 3
 
3.0%
0.32 3
 
3.0%
0.06 2
 
2.0%
1.51 2
 
2.0%
2.4 2
 
2.0%
3.1 2
 
2.0%
0.41 2
 
2.0%
1.66 2
 
2.0%
5.91 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.04 3
3.0%
0.06 2
2.0%
0.15 1
 
1.0%
0.18 1
 
1.0%
0.22 1
 
1.0%
0.27 3
3.0%
0.31 1
 
1.0%
0.32 3
3.0%
0.37 1
 
1.0%
ValueCountFrequency (%)
61.35 1
1.0%
53.32 1
1.0%
35.32 1
1.0%
33.03 1
1.0%
25.34 1
1.0%
15.95 1
1.0%
13.08 1
1.0%
11.23 1
1.0%
10.39 1
1.0%
10.35 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6604
Minimum0
Maximum37.3
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:09.955728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3625
median0.95
Q32.8275
95-th percentile6.379
Maximum37.3
Range37.3
Interquartile range (IQR)2.465

Descriptive statistics

Standard deviation5.4794405
Coefficient of variation (CV)2.0596303
Kurtosis23.540832
Mean2.6604
Median Absolute Deviation (MAD)0.815
Skewness4.6106348
Sum266.04
Variance30.024269
MonotonicityNot monotonic
2023-12-10T21:15:10.093321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 6
 
6.0%
0.13 6
 
6.0%
0.14 5
 
5.0%
0.27 4
 
4.0%
0.28 4
 
4.0%
0.4 3
 
3.0%
0.79 3
 
3.0%
1.21 3
 
3.0%
0.54 2
 
2.0%
0.81 2
 
2.0%
Other values (59) 62
62.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.13 6
6.0%
0.14 5
5.0%
0.27 4
4.0%
0.28 4
4.0%
0.39 1
 
1.0%
0.4 3
3.0%
0.42 1
 
1.0%
0.44 1
 
1.0%
0.54 2
 
2.0%
ValueCountFrequency (%)
37.3 1
1.0%
30.77 1
1.0%
19.77 1
1.0%
19.58 1
1.0%
10.35 1
1.0%
6.17 1
1.0%
5.58 1
1.0%
5.32 1
1.0%
5.29 1
1.0%
5.07 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9222.4466
Minimum0
Maximum71262.41
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:15:10.216846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile153.68
Q11484.84
median4548.37
Q38785.195
95-th percentile33357.359
Maximum71262.41
Range71262.41
Interquartile range (IQR)7300.355

Descriptive statistics

Standard deviation13885.087
Coefficient of variation (CV)1.5055752
Kurtosis8.8469513
Mean9222.4466
Median Absolute Deviation (MAD)3421.32
Skewness2.8850332
Sum922244.66
Variance1.9279565 × 108
MonotonicityNot monotonic
2023-12-10T21:15:10.347741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.68 3
 
3.0%
1064.53 2
 
2.0%
740.29 2
 
2.0%
794.65 2
 
2.0%
153.68 2
 
2.0%
1885.37 1
 
1.0%
4829.64 1
 
1.0%
6120.42 1
 
1.0%
17946.52 1
 
1.0%
14431.32 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
138.68 3
3.0%
153.68 2
2.0%
333.6 1
 
1.0%
462.92 1
 
1.0%
595.97 1
 
1.0%
646.6 1
 
1.0%
740.29 2
2.0%
775.89 1
 
1.0%
794.65 2
2.0%
ValueCountFrequency (%)
71262.41 1
1.0%
65436.9 1
1.0%
62273.29 1
1.0%
58331.55 1
1.0%
46739.8 1
1.0%
32653.02 1
1.0%
30896.54 1
1.0%
21468.4 1
1.0%
21395.8 1
1.0%
19883.13 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:15:10.587591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.86
Min length8

Characters and Unicode

Total characters1086
Distinct characters107
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 96
24.1%
울주 10
 
2.5%
합천 10
 
2.5%
고성 10
 
2.5%
진주 8
 
2.0%
창원 8
 
2.0%
남해 8
 
2.0%
창녕 6
 
1.5%
산청 6
 
1.5%
부산 4
 
1.0%
Other values (106) 232
58.3%
2023-12-10T21:15:10.979350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
108
 
9.9%
96
 
8.8%
32
 
2.9%
26
 
2.4%
20
 
1.8%
20
 
1.8%
18
 
1.7%
14
 
1.3%
14
 
1.3%
Other values (97) 440
40.5%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
108
 
13.7%
96
 
12.2%
32
 
4.1%
26
 
3.3%
20
 
2.5%
20
 
2.5%
18
 
2.3%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (96) 426
54.1%
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 (%)
108
 
13.7%
96
 
12.2%
32
 
4.1%
26
 
3.3%
20
 
2.5%
20
 
2.5%
18
 
2.3%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (96) 426
54.1%
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 (%)
108
 
13.7%
96
 
12.2%
32
 
4.1%
26
 
3.3%
20
 
2.5%
20
 
2.5%
18
 
2.3%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (96) 426
54.1%

Interactions

2023-12-10T21:15:04.811650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:14:59.353155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.016697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.679645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.312678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.206331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.855179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.493737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.170045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.879467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:14:59.419883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.097891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.745575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.382330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.272874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.923460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.558778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.241998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.953635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:14:59.499679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.175605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.821858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.459796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.344285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.997580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.633569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.318053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:05.015021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:14:59.570880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.239274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.884396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.525793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.409030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.060107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.701166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.380584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:05.095166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:14:59.664684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.319808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.967896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.606880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.496678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.140211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.781607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.461341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:05.171537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:14:59.729931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.386945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.034036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.693329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.566901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.208678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.846224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.526762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:05.242855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:14:59.797364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.461848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.109095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.776449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.640069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.276752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.931280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.598305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:05.312718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:14:59.862108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.531604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.178113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.852024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.707754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.346900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.014373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.667845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:05.396456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:14:59.934811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:00.604627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:01.243731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.129282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:02.776308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:03.423064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.087885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:04.740080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:15:11.075531image/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
기본키1.0001.0000.0001.0000.4420.8260.6220.5060.3550.3520.3510.4551.000
지점1.0001.0000.0001.0001.0001.0001.0000.8260.8540.8530.8680.7451.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.8260.8540.8530.8680.7451.000
연장((km))0.4421.0000.0001.0001.0000.6110.6870.3100.4070.3410.4020.3551.000
좌표위치위도((°))0.8261.0000.0001.0000.6111.0000.5670.3460.0000.1200.0000.2541.000
좌표위치경도((°))0.6221.0000.0001.0000.6870.5671.0000.5420.4610.4750.4610.5031.000
co((g/km))0.5060.8260.0000.8260.3100.3460.5421.0000.8140.8970.8150.9860.826
nox((g/km))0.3550.8540.0000.8540.4070.0000.4610.8141.0000.9711.0000.8990.854
hc((g/km))0.3520.8530.0000.8530.3410.1200.4750.8970.9711.0000.9730.9400.853
pm((g/km))0.3510.8680.0000.8680.4020.0000.4610.8151.0000.9731.0000.9010.868
co2((g/km))0.4550.7450.0000.7450.3550.2540.5030.9860.8990.9400.9011.0000.745
주소1.0001.0000.0001.0001.0001.0001.0000.8260.8540.8530.8680.7451.000
2023-12-10T21:15:11.221864image/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
기본키1.0000.0430.1210.206-0.035-0.002-0.0110.018-0.0330.000
연장((km))0.0431.0000.2510.105-0.046-0.080-0.092-0.053-0.0430.000
좌표위치위도((°))0.1210.2511.0000.356-0.097-0.061-0.064-0.052-0.1010.000
좌표위치경도((°))0.2060.1050.3561.0000.4910.4660.4800.4600.4890.000
co((g/km))-0.035-0.046-0.0970.4911.0000.9790.9870.9700.9970.000
nox((g/km))-0.002-0.080-0.0610.4660.9791.0000.9950.9920.9810.000
hc((g/km))-0.011-0.092-0.0640.4800.9870.9951.0000.9860.9850.000
pm((g/km))0.018-0.053-0.0520.4600.9700.9920.9861.0000.9720.000
co2((g/km))-0.033-0.043-0.1010.4890.9970.9810.9850.9721.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T21:15:05.529797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:15:05.711483image/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-완사4.220210601135.12031127.970116.614.130.620.271746.69경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210601135.12031127.970116.194.040.590.281619.26경남 사천 곤명 작팔
23건기연[0218-1]1진주-사봉3.520210601135.16901128.187320.7617.262.561.145161.46경남 진주 문산 상문
34건기연[0218-1]2진주-사봉3.520210601135.16901128.187320.1415.942.40.935061.52경남 진주 문산 상문
45건기연[0220-2]1일반성-진북4.820210601135.10632128.4421882.3786.4211.235.5819883.13경남 창원 진전 근곡
56건기연[0220-2]2일반성-진북4.820210601135.10632128.4421881.3584.7410.235.3221468.4경남 창원 진전 근곡
67건기연[0302-4]1상죽-사천10.220210601134.87506128.0095819.5212.61.860.945148.24경남 남해 창선 동대
78건기연[0302-4]2상죽-사천10.220210601134.87506128.0095816.110.41.630.843879.58경남 남해 창선 동대
89건기연[0303-1]1용현-정촌6.820210601135.01785128.0577585.3866.010.123.7719745.82경남 사천 용현 송지
910건기연[0303-1]2용현-정촌6.820210601135.01785128.0577585.4572.2810.354.4621395.8경남 사천 용현 송지
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[7901-0]1정곡-의령2.720210601135.29567128.29811.857.791.140.563099.84경남 함안 군북 월촌
9192건기연[7901-0]2정곡-의령2.720210601135.29567128.29823.0620.953.31.325166.07경남 함안 군북 월촌
9293건기연[7904-0]1마산-진영5.220210601135.28732128.61172121.190.4213.085.0230896.54경남 창원 북 외감
9394건기연[7904-0]2마산-진영5.220210601135.28732128.6117276.9155.718.62.7717899.67경남 창원 북 외감
9495건기연[7904-3]1창녕-남지7.920210601135.44323128.560236.635.14.952.518318.91경남 창녕 도천 덕곡
9596건기연[7904-3]2창녕-남지7.920210601135.44323128.560224.6223.653.21.455583.59경남 창녕 도천 덕곡
9697건기연[00101-2]1노포JCT-양산JCT7.320210601135.30603129.07556241.18327.2735.3219.7765436.9부산 금정 노포
9798건기연[00101-2]2노포JCT-양산JCT7.320210601135.30603129.07556218.58317.533.0319.5862273.29부산 금정 노포
9899건기연[00106]1언양JCT-활천IC17.220210601135.60563129.14403218.64509.6853.3230.7758331.55울산 울주 언양 반곡
99100건기연[00106]2언양JCT-활천IC17.220210601135.60563129.14403252.74616.061.3537.371262.41울산 울주 언양 반곡