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 started2024-04-17 01:51:32.489485
Analysis finished2024-04-17 01:51:39.224174
Duration6.73 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
2024-04-17T10:51:39.286912image/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
2024-04-17T10:51:39.398586image/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

2024-04-17T10:51:39.501659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:39.573506image/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
2024-04-17T10:51:39.734967image/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[0314-0]
2nd row[0314-0]
3rd row[0317-0]
4th row[0317-0]
5th row[0318-0]
ValueCountFrequency (%)
0314-0 2
 
2.0%
3104-1 2
 
2.0%
3416-0 2
 
2.0%
2614-3 2
 
2.0%
2803-1 2
 
2.0%
2804-2 2
 
2.0%
2805-3 2
 
2.0%
2808-0 2
 
2.0%
2809-1 2
 
2.0%
2814-2 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T10:51:40.022384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 134
16.8%
1 108
13.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 70
8.8%
2 66
8.2%
4 26
 
3.2%
5 26
 
3.2%
8 24
 
3.0%
Other values (3) 46
 
5.8%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134
26.8%
1 108
21.6%
3 70
14.0%
2 66
13.2%
4 26
 
5.2%
5 26
 
5.2%
8 24
 
4.8%
7 20
 
4.0%
6 18
 
3.6%
9 8
 
1.6%
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 (%)
0 134
16.8%
1 108
13.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 70
8.8%
2 66
8.2%
4 26
 
3.2%
5 26
 
3.2%
8 24
 
3.0%
Other values (3) 46
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134
16.8%
1 108
13.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 70
8.8%
2 66
8.2%
4 26
 
3.2%
5 26
 
3.2%
8 24
 
3.0%
Other values (3) 46
 
5.8%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2024-04-17T10:51:40.129224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:40.209649image/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
2024-04-17T10:51:40.390374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.06
Min length5

Characters and Unicode

Total characters506
Distinct characters92
Distinct categories3 ?
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 row구성-김천
2nd row구성-김천
3rd row공성-상주
4th row공성-상주
5th row상주-함창
ValueCountFrequency (%)
구성-김천 2
 
2.0%
감포-병포 2
 
2.0%
예천-괴정 2
 
2.0%
고령-성산 2
 
2.0%
상동-본포 2
 
2.0%
안계-봉양 2
 
2.0%
일반5-금성 2
 
2.0%
영천-의성 2
 
2.0%
안강-고경 2
 
2.0%
강동-흥해 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T10:51:40.687650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.8%
22
 
4.3%
20
 
4.0%
18
 
3.6%
16
 
3.2%
16
 
3.2%
14
 
2.8%
10
 
2.0%
10
 
2.0%
10
 
2.0%
Other values (82) 270
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 404
79.8%
Dash Punctuation 100
 
19.8%
Decimal Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
5.4%
20
 
5.0%
18
 
4.5%
16
 
4.0%
16
 
4.0%
14
 
3.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (80) 258
63.9%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Decimal Number
ValueCountFrequency (%)
5 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 404
79.8%
Common 102
 
20.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22
 
5.4%
20
 
5.0%
18
 
4.5%
16
 
4.0%
16
 
4.0%
14
 
3.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (80) 258
63.9%
Common
ValueCountFrequency (%)
- 100
98.0%
5 2
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 404
79.8%
ASCII 102
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
98.0%
5 2
 
2.0%
Hangul
ValueCountFrequency (%)
22
 
5.4%
20
 
5.0%
18
 
4.5%
16
 
4.0%
16
 
4.0%
14
 
3.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (80) 258
63.9%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.426
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:40.801613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q15.9
median8.1
Q311.9
95-th percentile22.1
Maximum27.8
Range26.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.9761753
Coefficient of variation (CV)0.63400968
Kurtosis1.6566936
Mean9.426
Median Absolute Deviation (MAD)3.3
Skewness1.2315226
Sum942.6
Variance35.714671
MonotonicityNot monotonic
2024-04-17T10:51:40.911498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
10.6 6
 
6.0%
11.2 4
 
4.0%
6.4 4
 
4.0%
2.4 4
 
4.0%
11.4 2
 
2.0%
7.8 2
 
2.0%
2.2 2
 
2.0%
8.0 2
 
2.0%
4.1 2
 
2.0%
8.3 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
1.3 2
2.0%
1.4 2
2.0%
1.8 2
2.0%
2.2 2
2.0%
2.4 4
4.0%
3.4 2
2.0%
3.8 2
2.0%
4.1 2
2.0%
4.7 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
27.8 2
2.0%
26.7 2
2.0%
22.1 2
2.0%
20.6 2
2.0%
19.4 2
2.0%
15.8 2
2.0%
14.4 2
2.0%
13.6 2
2.0%
13.4 2
2.0%
12.9 2
2.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2024-04-17T10:51:41.012195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:41.083295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2024-04-17T10:51:41.160727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:41.243883image/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%
Mean36.116278
Minimum35.65543
Maximum36.86368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:41.328201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.86035
median36.036925
Q336.33873
95-th percentile36.76527
Maximum36.86368
Range1.20825
Interquartile range (IQR)0.47838

Descriptive statistics

Standard deviation0.3301361
Coefficient of variation (CV)0.0091409227
Kurtosis-0.42690831
Mean36.116278
Median Absolute Deviation (MAD)0.23588
Skewness0.67249545
Sum3611.6278
Variance0.10898985
MonotonicityNot monotonic
2024-04-17T10:51:41.443617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
35.98322 2
 
2.0%
36.59513 2
 
2.0%
36.35882 2
 
2.0%
36.32892 2
 
2.0%
36.04086 2
 
2.0%
35.99882 2
 
2.0%
36.03299 2
 
2.0%
35.94083 2
 
2.0%
35.68369 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
35.65543 2
2.0%
35.67783 2
2.0%
35.68369 2
2.0%
35.69757 2
2.0%
35.70376 2
2.0%
35.71373 2
2.0%
35.73646 2
2.0%
35.76368 2
2.0%
35.78841 2
2.0%
35.79392 2
2.0%
ValueCountFrequency (%)
36.86368 2
2.0%
36.84888 2
2.0%
36.76527 2
2.0%
36.75364 2
2.0%
36.62861 2
2.0%
36.59556 2
2.0%
36.59513 2
2.0%
36.53536 2
2.0%
36.50552 2
2.0%
36.40808 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.77003
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:41.850072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.13935
Q1128.37682
median128.69655
Q3129.248
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.87118

Descriptive statistics

Standard deviation0.4632236
Coefficient of variation (CV)0.0035972937
Kurtosis-1.4037572
Mean128.77003
Median Absolute Deviation (MAD)0.391405
Skewness0.13732556
Sum12877.003
Variance0.21457611
MonotonicityNot monotonic
2024-04-17T10:51:41.962829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
129.45978 2
 
2.0%
128.41883 2
 
2.0%
128.46812 2
 
2.0%
128.70365 2
 
2.0%
128.80075 2
 
2.0%
129.08273 2
 
2.0%
129.30673 2
 
2.0%
128.15515 2
 
2.0%
129.47131 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
128.02544 2
2.0%
128.08611 2
2.0%
128.13935 2
2.0%
128.15515 2
2.0%
128.16946 2
2.0%
128.21559 2
2.0%
128.23039 2
2.0%
128.23413 2
2.0%
128.25985 2
2.0%
128.30457 2
2.0%
ValueCountFrequency (%)
129.52365 2
2.0%
129.49495 2
2.0%
129.47131 2
2.0%
129.45978 2
2.0%
129.45621 2
2.0%
129.40658 2
2.0%
129.34631 2
2.0%
129.32144 2
2.0%
129.31407 2
2.0%
129.30673 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4541.7533
Minimum285.73
Maximum15136.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:42.085488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum285.73
5-th percentile809.278
Q11902.665
median4130.785
Q36050.1325
95-th percentile10598.717
Maximum15136.67
Range14850.94
Interquartile range (IQR)4147.4675

Descriptive statistics

Standard deviation3137.3718
Coefficient of variation (CV)0.69078428
Kurtosis1.2643353
Mean4541.7533
Median Absolute Deviation (MAD)2156.47
Skewness1.0443471
Sum454175.33
Variance9843101.8
MonotonicityNot monotonic
2024-04-17T10:51:42.199942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3603.06 1
 
1.0%
4645.86 1
 
1.0%
7058.19 1
 
1.0%
1615.37 1
 
1.0%
2077.1 1
 
1.0%
4883.74 1
 
1.0%
4868.17 1
 
1.0%
502.36 1
 
1.0%
469.0 1
 
1.0%
6929.42 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
285.73 1
1.0%
290.68 1
1.0%
469.0 1
1.0%
502.36 1
1.0%
697.33 1
1.0%
815.17 1
1.0%
896.54 1
1.0%
929.75 1
1.0%
989.96 1
1.0%
1001.81 1
1.0%
ValueCountFrequency (%)
15136.67 1
1.0%
14121.8 1
1.0%
13095.7 1
1.0%
11924.62 1
1.0%
10795.87 1
1.0%
10588.34 1
1.0%
10379.6 1
1.0%
9497.83 1
1.0%
8350.97 1
1.0%
8279.89 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4600.8329
Minimum191.59
Maximum21396.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:42.318284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191.59
5-th percentile735.1005
Q11527.3025
median3449.48
Q35401.655
95-th percentile15991.002
Maximum21396.71
Range21205.12
Interquartile range (IQR)3874.3525

Descriptive statistics

Standard deviation4381.8992
Coefficient of variation (CV)0.95241432
Kurtosis3.850313
Mean4600.8329
Median Absolute Deviation (MAD)1945.585
Skewness1.9717832
Sum460083.29
Variance19201040
MonotonicityNot monotonic
2024-04-17T10:51:42.426560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2832.76 1
 
1.0%
4205.37 1
 
1.0%
5052.36 1
 
1.0%
1300.03 1
 
1.0%
1332.48 1
 
1.0%
3561.82 1
 
1.0%
3479.62 1
 
1.0%
461.36 1
 
1.0%
319.62 1
 
1.0%
5061.69 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
191.59 1
1.0%
196.24 1
1.0%
319.62 1
1.0%
461.36 1
1.0%
689.32 1
1.0%
737.51 1
1.0%
769.47 1
1.0%
770.58 1
1.0%
847.24 1
1.0%
861.15 1
1.0%
ValueCountFrequency (%)
21396.71 1
1.0%
19216.8 1
1.0%
17791.74 1
1.0%
16592.98 1
1.0%
16588.6 1
1.0%
15959.55 1
1.0%
13445.92 1
1.0%
13411.12 1
1.0%
12447.44 1
1.0%
11924.05 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean579.5092
Minimum28.11
Maximum2190.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:42.535939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28.11
5-th percentile103.9225
Q1224.4725
median485.515
Q3749.44
95-th percentile1629.815
Maximum2190.85
Range2162.74
Interquartile range (IQR)524.9675

Descriptive statistics

Standard deviation460.36007
Coefficient of variation (CV)0.79439648
Kurtosis1.7145031
Mean579.5092
Median Absolute Deviation (MAD)265.025
Skewness1.3976766
Sum57950.92
Variance211931.39
MonotonicityNot monotonic
2024-04-17T10:51:42.655935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415.84 1
 
1.0%
564.58 1
 
1.0%
714.12 1
 
1.0%
176.11 1
 
1.0%
193.2 1
 
1.0%
487.09 1
 
1.0%
487.54 1
 
1.0%
63.45 1
 
1.0%
45.36 1
 
1.0%
739.47 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
28.11 1
1.0%
28.51 1
1.0%
45.36 1
1.0%
63.45 1
1.0%
97.7 1
1.0%
104.25 1
1.0%
108.82 1
1.0%
109.14 1
1.0%
112.01 1
1.0%
118.23 1
1.0%
ValueCountFrequency (%)
2190.85 1
1.0%
1840.12 1
1.0%
1727.76 1
1.0%
1671.08 1
1.0%
1641.12 1
1.0%
1629.22 1
1.0%
1612.64 1
1.0%
1563.36 1
1.0%
1548.05 1
1.0%
1522.85 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean279.549
Minimum15.63
Maximum1286.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:42.759637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.63
5-th percentile43.058
Q189.4825
median202.385
Q3329.6075
95-th percentile960.531
Maximum1286.35
Range1270.72
Interquartile range (IQR)240.125

Descriptive statistics

Standard deviation272.91365
Coefficient of variation (CV)0.9762641
Kurtosis3.8423159
Mean279.549
Median Absolute Deviation (MAD)119.72
Skewness2.0045544
Sum27954.9
Variance74481.862
MonotonicityNot monotonic
2024-04-17T10:51:42.873858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184.21 1
 
1.0%
236.54 1
 
1.0%
343.14 1
 
1.0%
78.26 1
 
1.0%
41.38 1
 
1.0%
151.25 1
 
1.0%
146.83 1
 
1.0%
42.07 1
 
1.0%
24.39 1
 
1.0%
235.33 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
15.63 1
1.0%
18.09 1
1.0%
24.39 1
1.0%
41.38 1
1.0%
42.07 1
1.0%
43.11 1
1.0%
51.09 1
1.0%
56.96 1
1.0%
59.17 1
1.0%
59.22 1
1.0%
ValueCountFrequency (%)
1286.35 1
1.0%
1158.87 1
1.0%
1148.27 1
1.0%
1102.77 1
1.0%
1013.18 1
1.0%
957.76 1
1.0%
948.37 1
1.0%
862.29 1
1.0%
628.08 1
1.0%
598.94 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1178903
Minimum75090.84
Maximum3944135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:42.988180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75090.84
5-th percentile191209.09
Q1481796.26
median1072841.3
Q31560147
95-th percentile2817138.4
Maximum3944135
Range3869044.1
Interquartile range (IQR)1078350.8

Descriptive statistics

Standard deviation835440.91
Coefficient of variation (CV)0.7086596
Kurtosis1.2722078
Mean1178903
Median Absolute Deviation (MAD)562303.73
Skewness1.0856708
Sum1.178903 × 108
Variance6.9796152 × 1011
MonotonicityNot monotonic
2024-04-17T10:51:43.109962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864364.76 1
 
1.0%
1198046.28 1
 
1.0%
1837844.08 1
 
1.0%
411118.84 1
 
1.0%
541995.6 1
 
1.0%
1289755.15 1
 
1.0%
1273805.05 1
 
1.0%
125983.8 1
 
1.0%
123421.92 1
 
1.0%
1792721.9 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
75090.84 1
1.0%
76711.86 1
1.0%
123421.92 1
1.0%
125983.8 1
1.0%
164659.82 1
1.0%
192606.42 1
1.0%
227685.94 1
1.0%
239752.68 1
1.0%
246697.25 1
1.0%
254230.85 1
1.0%
ValueCountFrequency (%)
3944134.96 1
1.0%
3669113.9 1
1.0%
3605583.79 1
1.0%
3154638.35 1
1.0%
2819103.56 1
1.0%
2817034.94 1
1.0%
2751867.49 1
1.0%
2630878.99 1
1.0%
2291106.29 1
1.0%
2173713.75 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T10:51:43.350102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.98
Min length10

Characters and Unicode

Total characters1098
Distinct characters102
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 row경북 김천 구성 하강
2nd row경북 김천 구성 하강
3rd row경북 상주 청리 원장
4th row경북 상주 청리 원장
5th row경북 상주 외서 연봉
ValueCountFrequency (%)
경북 100
25.0%
포항 16
 
4.0%
경주 12
 
3.0%
의성 10
 
2.5%
칠곡 8
 
2.0%
연일 6
 
1.5%
고령 6
 
1.5%
예천 6
 
1.5%
상주 6
 
1.5%
영천 6
 
1.5%
Other values (103) 224
56.0%
2024-04-17T10:51:43.675801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
300
27.3%
116
 
10.6%
106
 
9.7%
32
 
2.9%
26
 
2.4%
26
 
2.4%
20
 
1.8%
16
 
1.5%
14
 
1.3%
14
 
1.3%
Other values (92) 428
39.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 798
72.7%
Space Separator 300
 
27.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
116
 
14.5%
106
 
13.3%
32
 
4.0%
26
 
3.3%
26
 
3.3%
20
 
2.5%
16
 
2.0%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (91) 414
51.9%
Space Separator
ValueCountFrequency (%)
300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 798
72.7%
Common 300
 
27.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
116
 
14.5%
106
 
13.3%
32
 
4.0%
26
 
3.3%
26
 
3.3%
20
 
2.5%
16
 
2.0%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (91) 414
51.9%
Common
ValueCountFrequency (%)
300
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 798
72.7%
ASCII 300
 
27.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300
100.0%
Hangul
ValueCountFrequency (%)
116
 
14.5%
106
 
13.3%
32
 
4.0%
26
 
3.3%
26
 
3.3%
20
 
2.5%
16
 
2.0%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (91) 414
51.9%

Interactions

2024-04-17T10:51:38.273404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:32.899930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.513779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.132394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.796468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.461654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.150537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.977126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.640237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.342579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:32.965781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.580076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.205195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.865809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.530969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.212105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.046394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.705895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.422785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.030092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.641477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.275731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.934395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.614008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.275849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.118643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.781959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.496915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.099582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.716439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.351325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.025390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.701177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.343045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.193498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.854753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.579114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.171726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.798087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.427686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.099619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.798469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.412114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.284467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.930159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.654541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.247971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.870129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.518515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.175009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.873242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.480757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.356691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.000751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.726545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.310139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.930247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.581956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.242278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.936428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.544481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.418843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.066405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.791438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.369866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.991276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.649432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.309235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.003531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.842894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.491437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.127869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.866017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:33.438649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.059884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:34.719806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:35.379030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.072283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:36.905675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:37.560448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:38.193547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T10:51:43.764967image/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
기본키1.0001.0000.0001.0000.6100.7450.8760.6200.5790.5590.3860.5021.000
지점1.0001.0000.0001.0001.0001.0001.0000.9610.9610.9200.9020.9751.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9610.9610.9200.9020.9751.000
연장((km))0.6101.0000.0001.0001.0000.5650.6690.1660.0000.0000.2800.4111.000
좌표위치위도((°))0.7451.0000.0001.0000.5651.0000.7160.4570.3470.1720.3230.3841.000
좌표위치경도((°))0.8761.0000.0001.0000.6690.7161.0000.6080.6080.5180.4130.5181.000
co((g/km))0.6200.9610.0000.9610.1660.4570.6081.0000.9690.9630.8600.9730.961
nox((g/km))0.5790.9610.0000.9610.0000.3470.6080.9691.0000.9780.9150.8870.961
hc((g/km))0.5590.9200.0000.9200.0000.1720.5180.9630.9781.0000.9060.8640.920
pm((g/km))0.3860.9020.0000.9020.2800.3230.4130.8600.9150.9061.0000.9330.902
co2((g/km))0.5020.9750.0000.9750.4110.3840.5180.9730.8870.8640.9331.0000.975
주소1.0001.0000.0001.0001.0001.0001.0000.9610.9610.9200.9020.9751.000
2024-04-17T10:51:43.886826image/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
기본키1.0000.0710.0620.198-0.337-0.343-0.337-0.342-0.3320.000
연장((km))0.0711.0000.112-0.103-0.195-0.178-0.191-0.185-0.1930.000
좌표위치위도((°))0.0620.1121.000-0.143-0.156-0.169-0.153-0.136-0.1670.000
좌표위치경도((°))0.198-0.103-0.1431.0000.3170.2640.2760.1470.3220.000
co((g/km))-0.337-0.195-0.1560.3171.0000.9740.9840.9330.9980.000
nox((g/km))-0.343-0.178-0.1690.2640.9741.0000.9960.9750.9750.000
hc((g/km))-0.337-0.191-0.1530.2760.9840.9961.0000.9660.9820.000
pm((g/km))-0.342-0.185-0.1360.1470.9330.9750.9661.0000.9320.000
co2((g/km))-0.332-0.193-0.1670.3220.9980.9750.9820.9321.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T10:51:38.984381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T10:51:39.159277image/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[0314-0]1구성-김천11.420210501036.07278128.086113603.062832.76415.84184.21864364.76경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210501036.07278128.086113235.072510.52371.91142.53818506.45경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210501036.35299128.139352902.573055.31421.24203.26712844.87경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210501036.35299128.139352944.963263.99420.39214.03746067.08경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210501036.50552128.169466238.246149.97811.29426.351587678.18경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210501036.50552128.169466513.676436.58867.73452.011639430.6경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210501036.14659128.025441834.761457.08207.53105.59468403.32경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210501036.14659128.025441699.481286.48186.2684.26435716.31경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210501035.98385128.411068265.767548.731001.38598.942125889.9경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210501035.98385128.411067666.277112.8935.6515.121959148.49경북 칠곡 왜관 왜관
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3310-0]1성주-왜관6.720210501035.97485128.376825379.795893.47769.63479.11365592.5경북 칠곡 기산 영
9192건기연[3310-0]2성주-왜관6.720210501035.97485128.376825043.694796.14613.97344.91333615.81경북 칠곡 기산 영
9293건기연[3311-0]1약목-구평19.420210501036.04876128.410771714.231256.66190.6163.39436461.86경북 칠곡 석적 포남
9394건기연[3311-0]2약목-구평19.420210501036.04876128.410771632.621273.4192.8566.81410582.94경북 칠곡 석적 포남
9495건기연[3313-0]1구미-선산3.420210501036.23436128.304575074.664216.32540.47260.591317825.6경북 구미 선산 동부
9596건기연[3313-0]2구미-선산3.420210501036.23436128.304575197.854244.11555.96247.481344598.1경북 구미 선산 동부
9697건기연[3416-0]1예천-괴정13.620210501036.62861128.481613358.142325.99368.54133.72797068.03경북 예천 예천 고평
9798건기연[3416-0]2예천-괴정13.620210501036.62861128.481612715.642276.54318.87148.22702105.66경북 예천 예천 고평
9899건기연[3420-0]1임동-월전13.420210501036.53536129.025311181.25861.15120.3959.22307186.93경북 청송 진보 후평
99100건기연[3420-0]2임동-월전13.420210501036.53536129.025311155.48847.24118.2361.91300684.45경북 청송 진보 후평