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 unique valuesUnique
nox((g/km)) has unique valuesUnique
hc((g/km)) has unique valuesUnique
pm((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:42:28.987800
Analysis finished2023-12-10 12:42:37.755156
Duration8.77 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
2023-12-10T21:42:37.831722image/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
2023-12-10T21:42:37.976402image/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

2023-12-10T21:42:38.121524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:38.231304image/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
2023-12-10T21:42:38.443497image/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%
2023-12-10T21:42:38.791334image/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

2023-12-10T21:42:38.923975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:39.021420image/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
2023-12-10T21:42:39.236392image/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%
2023-12-10T21:42:39.620387image/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
2023-12-10T21:42:39.766539image/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
2023-12-10T21:42:39.898863image/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

2023-12-10T21:42:40.033235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:40.123608image/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

2023-12-10T21:42:40.223765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:42:40.334946image/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
2023-12-10T21:42:40.454900image/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
2023-12-10T21:42:40.635006image/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
2023-12-10T21:42:40.789437image/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
2023-12-10T21:42:40.958316image/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  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4422.5188
Minimum295.75
Maximum17102.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:41.100371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum295.75
5-th percentile495.9065
Q11867.7275
median3216.155
Q36553.435
95-th percentile10927.308
Maximum17102.09
Range16806.34
Interquartile range (IQR)4685.7075

Descriptive statistics

Standard deviation3602.6805
Coefficient of variation (CV)0.81462187
Kurtosis1.6440965
Mean4422.5188
Median Absolute Deviation (MAD)1950.725
Skewness1.2815569
Sum442251.88
Variance12979307
MonotonicityNot monotonic
2023-12-10T21:42:41.240371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2631.36 1
 
1.0%
3769.29 1
 
1.0%
4045.62 1
 
1.0%
4570.14 1
 
1.0%
5280.0 1
 
1.0%
3947.71 1
 
1.0%
3518.75 1
 
1.0%
5063.77 1
 
1.0%
5269.99 1
 
1.0%
10916.9 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
295.75 1
1.0%
316.86 1
1.0%
373.95 1
1.0%
384.59 1
1.0%
480.26 1
1.0%
496.73 1
1.0%
500.99 1
1.0%
513.52 1
1.0%
687.15 1
1.0%
716.29 1
1.0%
ValueCountFrequency (%)
17102.09 1
1.0%
17084.98 1
1.0%
12553.44 1
1.0%
11311.51 1
1.0%
11125.06 1
1.0%
10916.9 1
1.0%
10485.43 1
1.0%
10249.14 1
1.0%
9870.32 1
1.0%
9462.79 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4952.7789
Minimum212.74
Maximum17125.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:41.396503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum212.74
5-th percentile409.5
Q11891.17
median3986.15
Q37899.2475
95-th percentile12136.944
Maximum17125.23
Range16912.49
Interquartile range (IQR)6008.0775

Descriptive statistics

Standard deviation3937.639
Coefficient of variation (CV)0.7950363
Kurtosis0.37427802
Mean4952.7789
Median Absolute Deviation (MAD)2612.625
Skewness0.9402946
Sum495277.89
Variance15505001
MonotonicityNot monotonic
2023-12-10T21:42:41.533239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019.21 1
 
1.0%
4437.79 1
 
1.0%
8318.64 1
 
1.0%
7764.93 1
 
1.0%
9433.37 1
 
1.0%
3340.33 1
 
1.0%
3522.85 1
 
1.0%
9538.39 1
 
1.0%
10131.22 1
 
1.0%
12051.73 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
212.74 1
1.0%
232.46 1
1.0%
273.63 1
1.0%
281.87 1
1.0%
407.98 1
1.0%
409.58 1
1.0%
480.67 1
1.0%
517.45 1
1.0%
768.3 1
1.0%
833.3 1
1.0%
ValueCountFrequency (%)
17125.23 1
1.0%
17087.52 1
1.0%
13312.35 1
1.0%
12960.2 1
1.0%
12771.43 1
1.0%
12103.55 1
1.0%
12051.73 1
1.0%
11660.41 1
1.0%
10641.48 1
1.0%
10634.21 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean605.6297
Minimum30.26
Maximum2167.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:41.703154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30.26
5-th percentile54.438
Q1268.4875
median476.9
Q3915.9575
95-th percentile1425.852
Maximum2167.92
Range2137.66
Interquartile range (IQR)647.47

Descriptive statistics

Standard deviation462.07931
Coefficient of variation (CV)0.76297333
Kurtosis1.0306964
Mean605.6297
Median Absolute Deviation (MAD)310.035
Skewness1.0354197
Sum60562.97
Variance213517.29
MonotonicityNot monotonic
2023-12-10T21:42:41.829315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
289.53 1
 
1.0%
558.37 1
 
1.0%
831.14 1
 
1.0%
910.1 1
 
1.0%
1020.4 1
 
1.0%
456.8 1
 
1.0%
433.64 1
 
1.0%
1005.24 1
 
1.0%
1041.3 1
 
1.0%
1534.19 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
30.26 1
1.0%
32.87 1
1.0%
39.07 1
1.0%
39.39 1
1.0%
53.45 1
1.0%
54.49 1
1.0%
65.83 1
1.0%
66.75 1
1.0%
103.76 1
1.0%
109.09 1
1.0%
ValueCountFrequency (%)
2167.92 1
1.0%
2152.78 1
1.0%
1617.06 1
1.0%
1534.19 1
1.0%
1476.62 1
1.0%
1423.18 1
1.0%
1382.69 1
1.0%
1259.83 1
1.0%
1202.98 1
1.0%
1138.45 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.6428
Minimum23.88
Maximum1062.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:41.954508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.88
5-th percentile36.8905
Q1118.9375
median255.29
Q3458
95-th percentile770.109
Maximum1062.8
Range1038.92
Interquartile range (IQR)339.0625

Descriptive statistics

Standard deviation236.85919
Coefficient of variation (CV)0.75760322
Kurtosis0.82969242
Mean312.6428
Median Absolute Deviation (MAD)161.4
Skewness1.0293794
Sum31264.28
Variance56102.277
MonotonicityNot monotonic
2023-12-10T21:42:42.384095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134.51 1
 
1.0%
364.07 1
 
1.0%
507.09 1
 
1.0%
442.9 1
 
1.0%
576.63 1
 
1.0%
215.58 1
 
1.0%
237.66 1
 
1.0%
594.92 1
 
1.0%
626.65 1
 
1.0%
856.74 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
23.88 1
1.0%
27.6 1
1.0%
29.88 1
1.0%
30.11 1
1.0%
35.38 1
1.0%
36.97 1
1.0%
37.98 1
1.0%
43.62 1
1.0%
61.18 1
1.0%
65.43 1
1.0%
ValueCountFrequency (%)
1062.8 1
1.0%
1047.19 1
1.0%
927.37 1
1.0%
856.74 1
1.0%
821.39 1
1.0%
767.41 1
1.0%
710.33 1
1.0%
666.99 1
1.0%
626.65 1
1.0%
594.92 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1132027
Minimum77952.67
Maximum4294132.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:42:42.530908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77952.67
5-th percentile122070.37
Q1465078.51
median843422.15
Q31644327.8
95-th percentile2763714
Maximum4294132.6
Range4216179.9
Interquartile range (IQR)1179249.3

Descriptive statistics

Standard deviation924334.46
Coefficient of variation (CV)0.81653037
Kurtosis1.2732998
Mean1132027
Median Absolute Deviation (MAD)528374.84
Skewness1.2055951
Sum1.132027 × 108
Variance8.5439419 × 1011
MonotonicityNot monotonic
2023-12-10T21:42:42.702869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
675633.32 1
 
1.0%
957535.67 1
 
1.0%
1145785.17 1
 
1.0%
1152325.33 1
 
1.0%
1367056.97 1
 
1.0%
963519.14 1
 
1.0%
931291.24 1
 
1.0%
1376537.02 1
 
1.0%
1456269.76 1
 
1.0%
2759794.43 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
77952.67 1
1.0%
83522.58 1
1.0%
96878.98 1
1.0%
101420.48 1
1.0%
117223.24 1
1.0%
122325.48 1
1.0%
128384.39 1
1.0%
133885.25 1
1.0%
163764.97 1
1.0%
166770.21 1
1.0%
ValueCountFrequency (%)
4294132.6 1
1.0%
4276635.24 1
1.0%
2991487.29 1
1.0%
2880789.68 1
1.0%
2838186.64 1
1.0%
2759794.43 1
1.0%
2725147.85 1
1.0%
2710727.99 1
1.0%
2667392.53 1
1.0%
2625727.37 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:42:42.975541image/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%
2023-12-10T21:42:43.345988image/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

2023-12-10T21:42:36.272269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:29.501872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:30.301340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.383048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.094561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.991641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.804350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.814896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.561031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:36.757160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:29.591089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:30.388060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.453073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.186092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.081223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.908207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.896912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.636071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:36.845700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:29.684702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:30.493154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.538511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.291075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.179378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.010793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.984864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.716431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:36.917639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:29.765898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:30.586374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.615774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.370848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.255684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.108434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.069302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.803575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:37.007769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:29.872510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:30.691382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.706878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.479721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.347249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.245650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.159777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.909039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:37.085471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:29.961298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.037814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.783242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.582163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.436477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.381918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.250014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.988201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:37.173534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:30.055211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.123518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.866576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.693767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.537202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.480138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.340753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:36.065817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:37.258072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:30.134801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.202139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.935008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.797966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.623563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.591878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.413951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:36.134020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:37.347857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:30.216399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:31.294101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.010931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:32.890486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:33.720762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:34.709755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:35.488922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:42:36.204293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:42:43.455854image/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
기본키1.0001.0000.0001.0000.5910.7920.6660.5850.6010.6300.7150.5841.000
지점1.0001.0000.0001.0001.0001.0001.0000.9710.9620.9730.9640.9861.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9710.9620.9730.9640.9861.000
연장((km))0.5911.0000.0001.0001.0000.5460.6500.6080.5450.5620.4120.5131.000
좌표위치위도((°))0.7921.0000.0001.0000.5461.0000.6140.5950.5690.5760.7410.6621.000
좌표위치경도((°))0.6661.0000.0001.0000.6500.6141.0000.7010.6720.6690.4560.6191.000
co((g/km))0.5850.9710.0000.9710.6080.5950.7011.0000.9480.9510.8560.9510.971
nox((g/km))0.6010.9620.0000.9620.5450.5690.6720.9481.0000.9840.9300.8450.962
hc((g/km))0.6300.9730.0000.9730.5620.5760.6690.9510.9841.0000.9170.8540.973
pm((g/km))0.7150.9640.0000.9640.4120.7410.4560.8560.9300.9171.0000.8320.964
co2((g/km))0.5840.9860.0000.9860.5130.6620.6190.9510.8450.8540.8321.0000.986
주소1.0001.0000.0001.0001.0001.0001.0000.9710.9620.9730.9640.9861.000
2023-12-10T21:42:43.668743image/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
기본키1.0000.0290.1190.086-0.402-0.301-0.338-0.280-0.3960.000
연장((km))0.0291.0000.2150.034-0.191-0.252-0.258-0.255-0.1880.000
좌표위치위도((°))0.1190.2151.0000.254-0.167-0.080-0.104-0.061-0.1730.000
좌표위치경도((°))0.0860.0340.2541.0000.4140.3440.3450.3460.4150.000
co((g/km))-0.402-0.191-0.1670.4141.0000.9430.9660.9290.9980.000
nox((g/km))-0.301-0.252-0.0800.3440.9431.0000.9890.9900.9480.000
hc((g/km))-0.338-0.258-0.1040.3450.9660.9891.0000.9800.9670.000
pm((g/km))-0.280-0.255-0.0610.3460.9290.9900.9801.0000.9340.000
co2((g/km))-0.396-0.188-0.1730.4150.9980.9480.9670.9341.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T21:42:37.494410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:42:37.683964image/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-완사4.220210401035.12031127.970112631.362019.21289.53134.51675633.32경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210401035.12031127.970112440.672011.55285.92130.02622135.17경남 사천 곤명 작팔
23건기연[0218-1]1진주-사봉3.520210401035.16901128.18737100.937184.59919.61477.871828203.88경남 진주 문산 상문
34건기연[0218-1]2진주-사봉3.520210401035.16901128.18736886.727425.98914.74463.041787964.76경남 진주 문산 상문
45건기연[0220-2]1일반성-진북4.820210401035.10632128.442189462.7913312.351382.69821.392625727.37경남 창원 진전 근곡
56건기연[0220-2]2일반성-진북4.820210401035.10632128.442189098.3811660.411259.83710.332491445.85경남 창원 진전 근곡
67건기연[0222-1]1마산-부산9.420210401035.1839128.639510249.1410641.481202.98498.052710727.99경남 창원 양곡
78건기연[0222-1]2마산-부산9.420210401035.1839128.639511125.0612103.551423.18582.362838186.64경남 창원 양곡
89건기연[0302-4]1상죽-사천10.220210401034.87506128.009582595.551871.16268.76132.61673635.12경남 남해 창선 동대
910건기연[0302-4]2상죽-사천10.220210401034.87506128.009582186.811618.67222.75126.37570292.43경남 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-거창7.920210401035.61463128.0287384.59273.6339.0729.88101420.48경남 합천 봉산 봉계
9192건기연[5904-4]2합천-거창7.920210401035.61463128.0287373.95281.8739.3930.1196878.98경남 합천 봉산 봉계
9293건기연[7701-4]1도산-거제8.120210401034.90831128.409331829.031757.58206.0196.03482029.18경남 통영 광도 노산
9394건기연[7701-4]2도산-거제8.120210401034.90831128.409331873.141483.28192.1179.52483069.2경남 통영 광도 노산
9495건기연[7702-0]1통영-고성6.420210401034.89878128.35538513.52407.9853.4536.97133885.25경남 통영 도산 오륜
9596건기연[7702-0]2통영-고성6.420210401034.89878128.35538496.73409.5854.4937.98128384.39경남 통영 도산 오륜
9697건기연[7702-2]1삼산-하이11.120210401034.92571128.130591110.77844.19114.4865.43288945.55경남 고성 하이 덕호
9798건기연[7702-2]2삼산-하이11.120210401034.92571128.130591041.2851.27109.0972.32270471.14경남 고성 하이 덕호
9899건기연[7703-0]1유포-설천11.220210401034.90043127.86341295.75212.7430.2623.8877952.67경남 남해 고현 포상
99100건기연[7703-0]2유포-설천11.220210401034.90043127.86341316.86232.4632.8727.683522.58경남 남해 고현 포상