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
co((g/km)) has 5 (5.0%) zerosZeros
nox((g/km)) has 5 (5.0%) zerosZeros
hc((g/km)) has 5 (5.0%) zerosZeros
pm((g/km)) has 11 (11.0%) zerosZeros
co2((g/km)) has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:23:09.513375
Analysis finished2023-12-10 11:23:25.367384
Duration15.85 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
2023-12-10T20:23:25.487921image/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
2023-12-10T20:23:25.744579image/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

2023-12-10T20:23:26.030435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:23:26.199278image/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
2023-12-10T20:23:26.540756image/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%
3302-2 2
 
2.0%
7702-2 2
 
2.0%
2416-1 2
 
2.0%
2417-2 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%
Other values (40) 80
80.0%
2023-12-10T20:23:27.088762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 148
18.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 94
11.8%
2 82
10.2%
3 54
 
6.8%
4 40
 
5.0%
5 26
 
3.2%
7 22
 
2.8%
Other values (3) 34
 
4.2%

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 (%)
0 148
29.6%
1 94
18.8%
2 82
16.4%
3 54
 
10.8%
4 40
 
8.0%
5 26
 
5.2%
7 22
 
4.4%
9 14
 
2.8%
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 (%)
0 148
18.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 94
11.8%
2 82
10.2%
3 54
 
6.8%
4 40
 
5.0%
5 26
 
3.2%
7 22
 
2.8%
Other values (3) 34
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 148
18.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 94
11.8%
2 82
10.2%
3 54
 
6.8%
4 40
 
5.0%
5 26
 
3.2%
7 22
 
2.8%
Other values (3) 34
 
4.2%

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

2023-12-10T20:23:27.308220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:23:27.465136image/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
2023-12-10T20:23:27.864182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1
Min length5

Characters and Unicode

Total characters510
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%
대암-송계 2
 
2.0%
삼산-하이 2
 
2.0%
언양-울산 2
 
2.0%
봉산-금양 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%
Other values (40) 80
80.0%
2023-12-10T20:23:28.684408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.6%
산 34
 
6.7%
남 16
 
3.1%
양 14
 
2.7%
천 14
 
2.7%
성 12
 
2.4%
창 12
 
2.4%
부 10
 
2.0%
거 10
 
2.0%
진 10
 
2.0%
Other values (74) 278
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
79.6%
Dash Punctuation 100
 
19.6%
Uppercase Letter 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 34
 
8.4%
남 16
 
3.9%
양 14
 
3.4%
천 14
 
3.4%
성 12
 
3.0%
창 12
 
3.0%
부 10
 
2.5%
거 10
 
2.5%
진 10
 
2.5%
해 8
 
2.0%
Other values (71) 266
65.5%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
79.6%
Common 100
 
19.6%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 34
 
8.4%
남 16
 
3.9%
양 14
 
3.4%
천 14
 
3.4%
성 12
 
3.0%
창 12
 
3.0%
부 10
 
2.5%
거 10
 
2.5%
진 10
 
2.5%
해 8
 
2.0%
Other values (71) 266
65.5%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 2
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
79.6%
ASCII 104
 
20.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
96.2%
C 2
 
1.9%
I 2
 
1.9%
Hangul
ValueCountFrequency (%)
산 34
 
8.4%
남 16
 
3.9%
양 14
 
3.4%
천 14
 
3.4%
성 12
 
3.0%
창 12
 
3.0%
부 10
 
2.5%
거 10
 
2.5%
진 10
 
2.5%
해 8
 
2.0%
Other values (71) 266
65.5%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.77
Minimum3
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:28.923516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.5
Q15.2
median8
Q311.1
95-th percentile17.6
Maximum20.1
Range17.1
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation4.1565453
Coefficient of variation (CV)0.47395043
Kurtosis0.38545154
Mean8.77
Median Absolute Deviation (MAD)3
Skewness0.84288011
Sum877
Variance17.276869
MonotonicityNot monotonic
2023-12-10T20:23:29.162628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
11.2 4
 
4.0%
10.5 4
 
4.0%
10.2 4
 
4.0%
6.8 4
 
4.0%
11.9 4
 
4.0%
19.3 2
 
2.0%
7.6 2
 
2.0%
6.1 2
 
2.0%
5.2 2
 
2.0%
3.3 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
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%
4.6 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 4
4.0%
11.2 4
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210401
2nd row20210401
3rd row20210401
4th row20210401
5th row20210401

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2023-12-10T20:23:29.424705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:23:29.583531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2023-12-10T20:23:29.760563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:23:29.913957image/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%
Mean35.267754
Minimum34.86496
Maximum35.72809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:30.099678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.86496
5-th percentile34.87506
Q135.01568
median35.29762
Q335.51362
95-th percentile35.62274
Maximum35.72809
Range0.86313
Interquartile range (IQR)0.49794

Descriptive statistics

Standard deviation0.25608125
Coefficient of variation (CV)0.0072610593
Kurtosis-1.237137
Mean35.267754
Median Absolute Deviation (MAD)0.22287
Skewness-0.093501301
Sum3526.7754
Variance0.065577607
MonotonicityNot monotonic
2023-12-10T20:23:30.356647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.12031 2
 
2.0%
35.62274 2
 
2.0%
35.65095 2
 
2.0%
35.55894 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%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.86496 2
2.0%
34.86778 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%
34.92968 2
2.0%
34.93132 2
2.0%
34.95217 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%
Mean128.38996
Minimum127.78878
Maximum129.33586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:30.596053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.78878
5-th percentile127.80269
Q1127.99393
median128.3277
Q3128.71274
95-th percentile129.24056
Maximum129.33586
Range1.54708
Interquartile range (IQR)0.71881

Descriptive statistics

Standard deviation0.45680578
Coefficient of variation (CV)0.0035579555
Kurtosis-0.80524587
Mean128.38996
Median Absolute Deviation (MAD)0.33558
Skewness0.57049697
Sum12838.996
Variance0.20867152
MonotonicityNot monotonic
2023-12-10T20:23:30.851300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.97011 2
 
2.0%
128.19963 2
 
2.0%
128.12366 2
 
2.0%
128.32045 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%
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  ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.3671
Minimum0
Maximum228.51
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:31.123182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6175
Q18.2325
median17.65
Q344.075
95-th percentile160.046
Maximum228.51
Range228.51
Interquartile range (IQR)35.8425

Descriptive statistics

Standard deviation52.946486
Coefficient of variation (CV)1.2799178
Kurtosis2.4175347
Mean41.3671
Median Absolute Deviation (MAD)14.32
Skewness1.7677509
Sum4136.71
Variance2803.3304
MonotonicityNot monotonic
2023-12-10T20:23:31.376024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
1.05 2
 
2.0%
10.46 1
 
1.0%
41.39 1
 
1.0%
24.94 1
 
1.0%
16.01 1
 
1.0%
17.52 1
 
1.0%
64.37 1
 
1.0%
24.93 1
 
1.0%
122.59 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.65 1
 
1.0%
0.74 1
 
1.0%
1.05 2
 
2.0%
1.3 1
 
1.0%
1.34 1
 
1.0%
1.57 1
 
1.0%
1.61 1
 
1.0%
1.95 1
 
1.0%
2.78 1
 
1.0%
ValueCountFrequency (%)
228.51 1
1.0%
201.98 1
1.0%
196.72 1
1.0%
184.98 1
1.0%
183.15 1
1.0%
158.83 1
1.0%
140.93 1
1.0%
139.32 1
1.0%
128.5 1
1.0%
122.59 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.4643
Minimum0
Maximum220.41
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:31.625676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.304
Q15.5475
median14.455
Q346.53
95-th percentile129.702
Maximum220.41
Range220.41
Interquartile range (IQR)40.9825

Descriptive statistics

Standard deviation46.777587
Coefficient of variation (CV)1.2828324
Kurtosis3.0877891
Mean36.4643
Median Absolute Deviation (MAD)13.56
Skewness1.7965176
Sum3646.43
Variance2188.1426
MonotonicityNot monotonic
2023-12-10T20:23:32.218033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.55 2
 
2.0%
5.85 1
 
1.0%
45.41 1
 
1.0%
42.42 1
 
1.0%
10.04 1
 
1.0%
11.55 1
 
1.0%
108.18 1
 
1.0%
38.95 1
 
1.0%
113.32 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.32 1
 
1.0%
0.55 2
 
2.0%
0.64 1
 
1.0%
0.77 1
 
1.0%
0.83 1
 
1.0%
0.96 1
 
1.0%
1.0 1
 
1.0%
1.26 1
 
1.0%
1.79 1
 
1.0%
ValueCountFrequency (%)
220.41 1
1.0%
190.5 1
1.0%
183.52 1
1.0%
142.79 1
1.0%
138.1 1
1.0%
129.26 1
1.0%
119.28 1
1.0%
113.32 1
1.0%
108.18 1
1.0%
103.25 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9519
Minimum0
Maximum28.81
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:32.452543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.057
Q10.8225
median2.17
Q36.115
95-th percentile16.711
Maximum28.81
Range28.81
Interquartile range (IQR)5.2925

Descriptive statistics

Standard deviation6.1850559
Coefficient of variation (CV)1.2490268
Kurtosis2.5672711
Mean4.9519
Median Absolute Deviation (MAD)2.005
Skewness1.7140352
Sum495.19
Variance38.254917
MonotonicityNot monotonic
2023-12-10T20:23:32.793152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.84 3
 
3.0%
1.55 2
 
2.0%
2.17 2
 
2.0%
1.66 2
 
2.0%
0.77 2
 
2.0%
2.54 2
 
2.0%
0.09 2
 
2.0%
0.86 2
 
2.0%
2.63 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.06 1
 
1.0%
0.09 2
 
2.0%
0.1 1
 
1.0%
0.12 1
 
1.0%
0.13 1
 
1.0%
0.15 1
 
1.0%
0.16 1
 
1.0%
0.17 1
 
1.0%
0.26 1
 
1.0%
ValueCountFrequency (%)
28.81 1
1.0%
23.97 1
1.0%
22.92 1
1.0%
19.64 1
1.0%
19.2 1
1.0%
16.58 1
1.0%
16.49 1
1.0%
16.21 1
1.0%
15.47 1
1.0%
15.21 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1485
Minimum0
Maximum12.93
Zeros11
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:33.086574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.385
median0.965
Q32.935
95-th percentile7.172
Maximum12.93
Range12.93
Interquartile range (IQR)2.55

Descriptive statistics

Standard deviation2.6187337
Coefficient of variation (CV)1.2188661
Kurtosis3.0637294
Mean2.1485
Median Absolute Deviation (MAD)0.83
Skewness1.7286201
Sum214.85
Variance6.8577664
MonotonicityNot monotonic
2023-12-10T20:23:33.343730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 11
 
11.0%
0.14 4
 
4.0%
0.4 4
 
4.0%
0.27 3
 
3.0%
0.42 3
 
3.0%
0.28 3
 
3.0%
0.13 3
 
3.0%
0.84 3
 
3.0%
0.39 2
 
2.0%
4.9 2
 
2.0%
Other values (57) 62
62.0%
ValueCountFrequency (%)
0.0 11
11.0%
0.13 3
 
3.0%
0.14 4
 
4.0%
0.27 3
 
3.0%
0.28 3
 
3.0%
0.37 1
 
1.0%
0.39 2
 
2.0%
0.4 4
 
4.0%
0.41 1
 
1.0%
0.42 3
 
3.0%
ValueCountFrequency (%)
12.93 1
1.0%
10.9 1
1.0%
8.72 1
1.0%
8.03 1
1.0%
7.97 1
1.0%
7.13 1
1.0%
6.9 1
1.0%
6.54 1
1.0%
6.04 1
1.0%
5.76 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10167.185
Minimum0
Maximum56102.83
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:23:33.748959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile145.996
Q12122.005
median4521.36
Q310641.903
95-th percentile43151.789
Maximum56102.83
Range56102.83
Interquartile range (IQR)8519.8975

Descriptive statistics

Standard deviation13001.433
Coefficient of variation (CV)1.2787643
Kurtosis2.7870539
Mean10167.185
Median Absolute Deviation (MAD)3538.365
Skewness1.8374771
Sum1016718.5
Variance1.6903725 × 108
MonotonicityNot monotonic
2023-12-10T20:23:34.059698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
277.37 2
 
2.0%
2511.38 1
 
1.0%
10450.4 1
 
1.0%
6455.65 1
 
1.0%
4228.03 1
 
1.0%
4580.42 1
 
1.0%
16637.81 1
 
1.0%
6654.87 1
 
1.0%
27859.91 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 5
5.0%
153.68 1
 
1.0%
185.55 1
 
1.0%
277.37 2
 
2.0%
307.36 1
 
1.0%
333.6 1
 
1.0%
416.06 1
 
1.0%
439.85 1
 
1.0%
461.05 1
 
1.0%
734.66 1
 
1.0%
ValueCountFrequency (%)
56102.83 1
1.0%
52084.5 1
1.0%
49029.18 1
1.0%
46973.96 1
1.0%
43424.62 1
1.0%
43137.43 1
1.0%
33465.67 1
1.0%
31932.25 1
1.0%
29669.19 1
1.0%
28332.98 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:23:34.680080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.82
Min length8

Characters and Unicode

Total characters1082
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 100
25.3%
합천 10
 
2.5%
고성 10
 
2.5%
산청 8
 
2.0%
울주 8
 
2.0%
남해 8
 
2.0%
진주 8
 
2.0%
창원 8
 
2.0%
밀양 6
 
1.5%
범서 4
 
1.0%
Other values (103) 226
57.1%
2023-12-10T20:23:35.417078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
27.4%
남 112
 
10.4%
경 100
 
9.2%
산 32
 
3.0%
천 24
 
2.2%
고 20
 
1.8%
창 18
 
1.7%
양 18
 
1.7%
주 16
 
1.5%
대 14
 
1.3%
Other values (97) 432
39.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 786
72.6%
Space Separator 296
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 112
 
14.2%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.1%
고 20
 
2.5%
창 18
 
2.3%
양 18
 
2.3%
주 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (96) 418
53.2%
Space Separator
ValueCountFrequency (%)
296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 786
72.6%
Common 296
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 112
 
14.2%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.1%
고 20
 
2.5%
창 18
 
2.3%
양 18
 
2.3%
주 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (96) 418
53.2%
Common
ValueCountFrequency (%)
296
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 786
72.6%
ASCII 296
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296
100.0%
Hangul
ValueCountFrequency (%)
남 112
 
14.2%
경 100
 
12.7%
산 32
 
4.1%
천 24
 
3.1%
고 20
 
2.5%
창 18
 
2.3%
양 18
 
2.3%
주 16
 
2.0%
대 14
 
1.8%
동 14
 
1.8%
Other values (96) 418
53.2%

Interactions

2023-12-10T20:23:23.521274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:10.514844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:12.369621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:13.935013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:15.604729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:17.516870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:18.954196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:20.522973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:21.881150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:23.653677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:10.653926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:12.566988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:14.108312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:15.820838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:17.671756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:19.100477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:20.689409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:22.026013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:23.804534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:10.810271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:12.721298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:14.266765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:16.008865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:17.846531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:19.276917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:20.858784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:22.184221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:23.933168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:10.950179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:12.842863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:14.395805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:16.193900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:17.997098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:19.449644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:20.995005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:22.666897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:24.098262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:11.117644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:12.997895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:14.626279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:16.469302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:18.187941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:19.687473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:21.168473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:22.833683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:24.223340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:11.697314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:13.131810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:14.777920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:16.642908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:18.322269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:19.897458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:21.306547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:22.979592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:24.367522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:11.823334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:13.290124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:14.952624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:16.914023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:18.455172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:20.074715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:21.441349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:23.105204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:24.520844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:11.990297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:13.477875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:15.195858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:17.157619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:18.608092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:20.235923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:21.591100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:23.246447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:24.662949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:12.147809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:13.711758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:15.397467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:17.341168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:18.789624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:20.393231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:21.736108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:23:23.375025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:23:35.620178image/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
기본키1.0001.0000.0001.0000.5910.7920.6660.3260.2880.3770.3600.3731.000
지점1.0001.0000.0001.0001.0001.0001.0000.8400.8290.8300.8020.9031.000
방향0.0000.0001.0000.0000.0000.0000.0000.0910.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.8400.8290.8300.8020.9031.000
연장((km))0.5911.0000.0001.0001.0000.5460.6500.2700.4130.1530.4200.4661.000
좌표위치위도((°))0.7921.0000.0001.0000.5461.0000.6140.4310.3220.4720.3500.5381.000
좌표위치경도((°))0.6661.0000.0001.0000.6500.6141.0000.6670.6470.5390.5800.6751.000
co((g/km))0.3260.8400.0910.8400.2700.4310.6671.0000.9690.9330.9550.9770.840
nox((g/km))0.2880.8290.0000.8290.4130.3220.6470.9691.0000.9250.9770.9540.829
hc((g/km))0.3770.8300.0000.8300.1530.4720.5390.9330.9251.0000.9120.9120.830
pm((g/km))0.3600.8020.0000.8020.4200.3500.5800.9550.9770.9121.0000.9380.802
co2((g/km))0.3730.9030.0000.9030.4660.5380.6750.9770.9540.9120.9381.0000.903
주소1.0001.0000.0001.0001.0001.0001.0000.8400.8290.8300.8020.9031.000
2023-12-10T20:23:35.926335image/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
기본키1.0000.0290.1190.086-0.286-0.228-0.250-0.187-0.2870.000
연장((km))0.0291.0000.2150.034-0.142-0.151-0.161-0.133-0.1270.000
좌표위치위도((°))0.1190.2151.0000.254-0.261-0.231-0.237-0.222-0.2590.000
좌표위치경도((°))0.0860.0340.2541.0000.4950.4520.4680.4310.4870.000
co((g/km))-0.286-0.142-0.2610.4951.0000.9800.9900.9650.9970.082
nox((g/km))-0.228-0.151-0.2310.4520.9801.0000.9960.9870.9820.000
hc((g/km))-0.250-0.161-0.2370.4680.9900.9961.0000.9810.9870.000
pm((g/km))-0.187-0.133-0.2220.4310.9650.9870.9811.0000.9690.000
co2((g/km))-0.287-0.127-0.2590.4870.9970.9820.9870.9691.0000.000
방향0.0000.0000.0000.0000.0820.0000.0000.0000.0001.000

Missing values

2023-12-10T20:23:24.883971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:23:25.223360image/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-완사4.220210401035.12031127.9701110.465.850.990.272511.38경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210401035.12031127.970118.285.140.770.282174.0경남 사천 곤명 작팔
23건기연[0218-1]1진주-사봉3.520210401035.16901128.187317.0414.332.170.844218.73경남 진주 문산 상문
34건기연[0218-1]2진주-사봉3.520210401035.16901128.187317.7811.241.660.694666.61경남 진주 문산 상문
45건기연[0220-2]1일반성-진북4.820210401035.10632128.44218107.7881.3513.434.4722415.95경남 창원 진전 근곡
56건기연[0220-2]2일반성-진북4.820210401035.10632128.4421871.3249.027.562.616839.32경남 창원 진전 근곡
67건기연[0222-1]1마산-부산9.420210401035.1839128.6395201.98190.522.928.7252084.5경남 창원 양곡
78건기연[0222-1]2마산-부산9.420210401035.1839128.6395139.32119.2816.215.1531932.25경남 창원 양곡
89건기연[0302-4]1상죽-사천10.220210401034.87506128.0095811.766.491.10.272818.75경남 남해 창선 동대
910건기연[0302-4]2상죽-사천10.220210401034.87506128.0095818.5312.131.780.954876.5경남 남해 창선 동대
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[5904-4]1합천-거창7.920210401035.61463128.02870.00.00.00.00.0경남 합천 봉산 봉계
9192건기연[5904-4]2합천-거창7.920210401035.61463128.02870.740.770.10.14185.55경남 합천 봉산 봉계
9293건기연[7701-4]1도산-거제8.120210401034.90831128.4093313.598.131.230.43590.82경남 통영 광도 노산
9394건기연[7701-4]2도산-거제8.120210401034.90831128.4093321.4113.131.970.695641.02경남 통영 광도 노산
9495건기연[7702-0]1통영-고성6.420210401034.89878128.355386.64.280.670.41615.53경남 통영 도산 오륜
9596건기연[7702-0]2통영-고성6.420210401034.89878128.355387.134.440.710.391780.03경남 통영 도산 오륜
9697건기연[7702-2]1삼산-하이11.120210401034.92571128.130599.025.910.860.422359.56경남 고성 하이 덕호
9798건기연[7702-2]2삼산-하이11.120210401034.92571128.130599.726.030.970.522424.61경남 고성 하이 덕호
9899건기연[7703-0]1유포-설천11.220210401034.90043127.863410.00.00.00.00.0경남 남해 고현 포상
99100건기연[7703-0]2유포-설천11.220210401034.90043127.863411.341.00.150.13333.6경남 남해 고현 포상