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
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2024-04-17 01:51:45.097384
Analysis finished2024-04-17 01:51:52.167232
Duration7.07 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
2024-04-17T10:51:52.236188image/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
2024-04-17T10:51:52.351647image/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

2024-04-17T10:51:52.465063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:52.541873image/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
2024-04-17T10:51:52.713567image/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%
2809-1 2
 
2.0%
3307-1 2
 
2.0%
2513-0 2
 
2.0%
2613-3 2
 
2.0%
2613-4 2
 
2.0%
2614-3 2
 
2.0%
2802-1 2
 
2.0%
2803-1 2
 
2.0%
2804-2 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T10:51:53.016090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 134
16.8%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 56
7.0%
5 28
 
3.5%
4 26
 
3.2%
8 26
 
3.2%
Other values (3) 48
 
6.0%

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%
1 110
22.0%
2 72
14.4%
3 56
11.2%
5 28
 
5.6%
4 26
 
5.2%
8 26
 
5.2%
7 24
 
4.8%
6 16
 
3.2%
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%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 56
7.0%
5 28
 
3.5%
4 26
 
3.2%
8 26
 
3.2%
Other values (3) 48
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134
16.8%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 56
7.0%
5 28
 
3.5%
4 26
 
3.2%
8 26
 
3.2%
Other values (3) 48
 
6.0%

방향
Categorical

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2024-04-17T10:51:53.122784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:53.195401image/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
2024-04-17T10:51:53.380192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.06
Min length5

Characters and Unicode

Total characters506
Distinct characters86
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%
안강-고경 2
 
2.0%
고령-수륜 2
 
2.0%
상주-낙동 2
 
2.0%
묘산-쌍림 2
 
2.0%
쌍림-고령 2
 
2.0%
고령-성산 2
 
2.0%
장수-예천 2
 
2.0%
상동-본포 2
 
2.0%
안계-봉양 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T10:51:53.681066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.8%
22
 
4.3%
22
 
4.3%
18
 
3.6%
18
 
3.6%
16
 
3.2%
14
 
2.8%
10
 
2.0%
10
 
2.0%
10
 
2.0%
Other values (76) 266
52.6%

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%
22
 
5.4%
18
 
4.5%
18
 
4.5%
16
 
4.0%
14
 
3.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
8
 
2.0%
Other values (74) 256
63.4%
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%
22
 
5.4%
18
 
4.5%
18
 
4.5%
16
 
4.0%
14
 
3.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
8
 
2.0%
Other values (74) 256
63.4%
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%
22
 
5.4%
18
 
4.5%
18
 
4.5%
16
 
4.0%
14
 
3.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
8
 
2.0%
Other values (74) 256
63.4%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.234
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:53.809665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q15.9
median8.1
Q311.6
95-th percentile22.1
Maximum27.8
Range26.5
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation5.8448496
Coefficient of variation (CV)0.6329705
Kurtosis2.1204653
Mean9.234
Median Absolute Deviation (MAD)3.25
Skewness1.300893
Sum923.4
Variance34.162267
MonotonicityNot monotonic
2024-04-17T10:51:53.938687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
10.6 6
 
6.0%
6.4 4
 
4.0%
2.4 4
 
4.0%
11.2 4
 
4.0%
11.4 2
 
2.0%
8.0 2
 
2.0%
11.6 2
 
2.0%
9.2 2
 
2.0%
8.2 2
 
2.0%
7.8 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%
2.1 2
2.0%
2.2 2
2.0%
2.4 4
4.0%
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%
15.8 2
2.0%
14.4 2
2.0%
14.2 2
2.0%
13.5 2
2.0%
12.9 2
2.0%
12.3 2
2.0%

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

2024-04-17T10:51:54.054098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:54.128618image/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

2024-04-17T10:51:54.204859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:54.287970image/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%
Mean36.118213
Minimum35.65543
Maximum36.86368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:54.388660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.85555
median36.01706
Q336.35299
95-th percentile36.76527
Maximum36.86368
Range1.20825
Interquartile range (IQR)0.49744

Descriptive statistics

Standard deviation0.34317786
Coefficient of variation (CV)0.0095015182
Kurtosis-0.61573085
Mean36.118213
Median Absolute Deviation (MAD)0.24256
Skewness0.64520075
Sum3611.8213
Variance0.11777104
MonotonicityNot monotonic
2024-04-17T10:51:54.506789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
35.94083 2
 
2.0%
35.67783 2
 
2.0%
35.70376 2
 
2.0%
35.73646 2
 
2.0%
36.73238 2
 
2.0%
36.59513 2
 
2.0%
36.35882 2
 
2.0%
36.32892 2
 
2.0%
36.04086 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%
35.73116 2
2.0%
35.73646 2
2.0%
35.76368 2
2.0%
35.78841 2
2.0%
ValueCountFrequency (%)
36.86368 2
2.0%
36.84888 2
2.0%
36.76527 2
2.0%
36.75364 2
2.0%
36.73238 2
2.0%
36.59556 2
2.0%
36.59513 2
2.0%
36.58611 2
2.0%
36.50552 2
2.0%
36.49421 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.79581
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:54.620169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.13935
Q1128.40296
median128.71618
Q3129.25885
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.85589

Descriptive statistics

Standard deviation0.46232566
Coefficient of variation (CV)0.0035896016
Kurtosis-1.3808056
Mean128.79581
Median Absolute Deviation (MAD)0.40509
Skewness0.04953416
Sum12879.581
Variance0.21374502
MonotonicityNot monotonic
2024-04-17T10:51:54.738410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
128.15515 2
 
2.0%
128.21559 2
 
2.0%
128.25985 2
 
2.0%
128.31646 2
 
2.0%
128.53474 2
 
2.0%
128.41883 2
 
2.0%
128.46812 2
 
2.0%
128.70365 2
 
2.0%
128.80075 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%
128.30572 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%
129.40119 2
2.0%
129.34631 2
2.0%
129.32144 2
2.0%
129.31407 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4238.0469
Minimum197.63
Maximum12920.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:54.855454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum197.63
5-th percentile491.1615
Q11601.935
median3323.315
Q36421.94
95-th percentile10087.351
Maximum12920.38
Range12722.75
Interquartile range (IQR)4820.005

Descriptive statistics

Standard deviation3193.9333
Coefficient of variation (CV)0.75363331
Kurtosis0.030513521
Mean4238.0469
Median Absolute Deviation (MAD)2229.56
Skewness0.85824371
Sum423804.69
Variance10201210
MonotonicityNot monotonic
2024-04-17T10:51:54.995858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2260.0 1
 
1.0%
1102.72 1
 
1.0%
7082.69 1
 
1.0%
4255.99 1
 
1.0%
4110.47 1
 
1.0%
1622.42 1
 
1.0%
1488.26 1
 
1.0%
853.5 1
 
1.0%
698.32 1
 
1.0%
1444.49 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
197.63 1
1.0%
198.46 1
1.0%
232.7 1
1.0%
248.0 1
1.0%
473.33 1
1.0%
492.1 1
1.0%
535.09 1
1.0%
582.37 1
1.0%
698.32 1
1.0%
770.25 1
1.0%
ValueCountFrequency (%)
12920.38 1
1.0%
12450.41 1
1.0%
12345.69 1
1.0%
11528.48 1
1.0%
10422.73 1
1.0%
10069.7 1
1.0%
10019.37 1
1.0%
9871.67 1
1.0%
9778.16 1
1.0%
9614.32 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5818.5061
Minimum167.26
Maximum22824.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:55.132201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum167.26
5-th percentile411.601
Q11649.935
median3963.52
Q37542.18
95-th percentile19376.648
Maximum22824.97
Range22657.71
Interquartile range (IQR)5892.245

Descriptive statistics

Standard deviation5545.5136
Coefficient of variation (CV)0.95308203
Kurtosis1.5417189
Mean5818.5061
Median Absolute Deviation (MAD)2687.875
Skewness1.4713798
Sum581850.61
Variance30752721
MonotonicityNot monotonic
2024-04-17T10:51:55.256183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2219.68 1
 
1.0%
1466.6 1
 
1.0%
7527.94 1
 
1.0%
7584.9 1
 
1.0%
5718.09 1
 
1.0%
2140.2 1
 
1.0%
1839.88 1
 
1.0%
1362.23 1
 
1.0%
994.97 1
 
1.0%
1674.81 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
167.26 1
1.0%
209.42 1
1.0%
216.65 1
1.0%
224.24 1
1.0%
346.83 1
1.0%
415.01 1
1.0%
614.71 1
1.0%
646.49 1
1.0%
680.29 1
1.0%
764.37 1
1.0%
ValueCountFrequency (%)
22824.97 1
1.0%
20817.87 1
1.0%
20639.65 1
1.0%
20570.14 1
1.0%
20568.48 1
1.0%
19313.92 1
1.0%
18317.12 1
1.0%
17142.38 1
1.0%
16905.6 1
1.0%
16349.0 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean696.8416
Minimum23.57
Maximum2536.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:55.378051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.57
5-th percentile57.6015
Q1225.9475
median488.5
Q3944.4075
95-th percentile2098.0635
Maximum2536.22
Range2512.65
Interquartile range (IQR)718.46

Descriptive statistics

Standard deviation593.10894
Coefficient of variation (CV)0.85113883
Kurtosis1.0134062
Mean696.8416
Median Absolute Deviation (MAD)338.575
Skewness1.2250295
Sum69684.16
Variance351778.22
MonotonicityNot monotonic
2024-04-17T10:51:55.495733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301.36 1
 
1.0%
184.42 1
 
1.0%
973.68 1
 
1.0%
822.26 1
 
1.0%
735.21 1
 
1.0%
272.01 1
 
1.0%
240.43 1
 
1.0%
201.6 1
 
1.0%
145.11 1
 
1.0%
222.12 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
23.57 1
1.0%
27.88 1
1.0%
30.27 1
1.0%
32.33 1
1.0%
51.93 1
1.0%
57.9 1
1.0%
81.82 1
1.0%
85.77 1
1.0%
92.62 1
1.0%
102.02 1
1.0%
ValueCountFrequency (%)
2536.22 1
1.0%
2278.54 1
1.0%
2247.9 1
1.0%
2182.41 1
1.0%
2117.89 1
1.0%
2097.02 1
1.0%
1974.82 1
1.0%
1919.52 1
1.0%
1651.34 1
1.0%
1558.51 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean391.7063
Minimum17.29
Maximum1470.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:55.606579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.29
5-th percentile30.071
Q1133.85
median276.565
Q3508.255
95-th percentile1240.3885
Maximum1470.49
Range1453.2
Interquartile range (IQR)374.405

Descriptive statistics

Standard deviation357.81817
Coefficient of variation (CV)0.91348587
Kurtosis1.5877153
Mean391.7063
Median Absolute Deviation (MAD)178.955
Skewness1.4594835
Sum39170.63
Variance128033.84
MonotonicityNot monotonic
2024-04-17T10:51:55.724820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.29 2
 
2.0%
256.76 1
 
1.0%
482.27 1
 
1.0%
383.02 1
 
1.0%
155.45 1
 
1.0%
135.53 1
 
1.0%
94.3 1
 
1.0%
76.88 1
 
1.0%
141.7 1
 
1.0%
149.02 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
17.29 2
2.0%
18.42 1
1.0%
19.93 1
1.0%
24.96 1
1.0%
30.34 1
1.0%
33.6 1
1.0%
45.9 1
1.0%
47.63 1
1.0%
51.12 1
1.0%
53.46 1
1.0%
ValueCountFrequency (%)
1470.49 1
1.0%
1446.86 1
1.0%
1364.56 1
1.0%
1310.05 1
1.0%
1307.62 1
1.0%
1236.85 1
1.0%
1181.53 1
1.0%
1172.03 1
1.0%
1138.9 1
1.0%
1025.42 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1076236.2
Minimum45967.71
Maximum3525723.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:55.838612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45967.71
5-th percentile123978.41
Q1392179.71
median850859.56
Q31578248.7
95-th percentile2667458
Maximum3525723.9
Range3479756.1
Interquartile range (IQR)1186069

Descriptive statistics

Standard deviation833098.92
Coefficient of variation (CV)0.77408559
Kurtosis0.22563297
Mean1076236.2
Median Absolute Deviation (MAD)558043.77
Skewness0.92644183
Sum1.0762362 × 108
Variance6.9405381 × 1011
MonotonicityNot monotonic
2024-04-17T10:51:55.955912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
566005.89 1
 
1.0%
269332.18 1
 
1.0%
1824251.48 1
 
1.0%
1134838.93 1
 
1.0%
988726.38 1
 
1.0%
402190.43 1
 
1.0%
370531.78 1
 
1.0%
168761.21 1
 
1.0%
149819.2 1
 
1.0%
358131.89 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
45967.71 1
1.0%
47428.33 1
1.0%
61272.67 1
1.0%
63736.23 1
1.0%
112800.72 1
1.0%
124566.71 1
1.0%
129786.23 1
1.0%
145217.14 1
1.0%
149819.2 1
1.0%
168761.21 1
1.0%
ValueCountFrequency (%)
3525723.85 1
1.0%
3391958.44 1
1.0%
3092005.36 1
1.0%
2902913.42 1
1.0%
2671774.8 1
1.0%
2667230.8 1
1.0%
2480756.76 1
1.0%
2473965.11 1
1.0%
2394753.24 1
1.0%
2374044.54 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T10:51:56.411360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.94
Min length10

Characters and Unicode

Total characters1094
Distinct characters103
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 6
 
1.5%
고령 6
 
1.5%
청도 6
 
1.5%
영천 6
 
1.5%
연일 6
 
1.5%
상주 6
 
1.5%
Other values (103) 226
56.5%
2024-04-17T10:51:56.722003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
300
27.4%
116
 
10.6%
106
 
9.7%
34
 
3.1%
26
 
2.4%
26
 
2.4%
20
 
1.8%
18
 
1.6%
16
 
1.5%
14
 
1.3%
Other values (93) 418
38.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 794
72.6%
Space Separator 300
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
116
 
14.6%
106
 
13.4%
34
 
4.3%
26
 
3.3%
26
 
3.3%
20
 
2.5%
18
 
2.3%
16
 
2.0%
14
 
1.8%
12
 
1.5%
Other values (92) 406
51.1%
Space Separator
ValueCountFrequency (%)
300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 794
72.6%
Common 300
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
116
 
14.6%
106
 
13.4%
34
 
4.3%
26
 
3.3%
26
 
3.3%
20
 
2.5%
18
 
2.3%
16
 
2.0%
14
 
1.8%
12
 
1.5%
Other values (92) 406
51.1%
Common
ValueCountFrequency (%)
300
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 794
72.6%
ASCII 300
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300
100.0%
Hangul
ValueCountFrequency (%)
116
 
14.6%
106
 
13.4%
34
 
4.3%
26
 
3.3%
26
 
3.3%
20
 
2.5%
18
 
2.3%
16
 
2.0%
14
 
1.8%
12
 
1.5%
Other values (92) 406
51.1%

Interactions

2024-04-17T10:51:50.978406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:45.529179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.182256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.818746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.731245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.390961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.047211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.692459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.334449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:51.058139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:45.587458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.244941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.885340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.800964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.464437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.107070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.756622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.401944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:51.126262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:45.655072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.308321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.955799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.876637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.532817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.166430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.819679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.469004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:51.202319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:45.738394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.377470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.269120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.950592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.618617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.250581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.898526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.541451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:51.278768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:45.830392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.456527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.345813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.027601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.692307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.335761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.970044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.614965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:51.351750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:45.912484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.527067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.423525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.099591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.761538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.403163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.040575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.687002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:51.415899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:45.977757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.594398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.495184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.171410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.823417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.472703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.105878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.757778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:51.490175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.042243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.671165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.566333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.241442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.889589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.543988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.177456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.827540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:51.570998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.109739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:46.742746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:47.641344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.316706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:48.971390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:49.621518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.261647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:50.903440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T10:51:56.807365image/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
기본키1.0001.0000.0001.0000.4820.7510.8980.7030.6620.7030.6460.7651.000
지점1.0001.0000.0001.0001.0001.0001.0000.9800.9540.9840.9530.9961.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9800.9540.9840.9530.9961.000
연장((km))0.4821.0000.0001.0001.0000.5360.6400.1780.0000.2470.1450.3321.000
좌표위치위도((°))0.7511.0000.0001.0000.5361.0000.7720.4800.5080.6380.3560.5781.000
좌표위치경도((°))0.8981.0000.0001.0000.6400.7721.0000.7310.6390.7050.6350.7611.000
co((g/km))0.7030.9800.0000.9800.1780.4800.7311.0000.9370.9340.9230.9700.980
nox((g/km))0.6620.9540.0000.9540.0000.5080.6390.9371.0000.9620.9600.9230.954
hc((g/km))0.7030.9840.0000.9840.2470.6380.7050.9340.9621.0000.9800.9490.984
pm((g/km))0.6460.9530.0000.9530.1450.3560.6350.9230.9600.9801.0000.9380.953
co2((g/km))0.7650.9960.0000.9960.3320.5780.7610.9700.9230.9490.9381.0000.996
주소1.0001.0000.0001.0001.0001.0001.0000.9800.9540.9840.9530.9961.000
2024-04-17T10:51:56.923323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))방향
기본키1.000-0.052-0.0720.276-0.367-0.355-0.380-0.364-0.3410.000
연장((km))-0.0521.0000.162-0.083-0.0410.019-0.0040.019-0.0480.000
좌표위치위도((°))-0.0720.1621.000-0.115-0.071-0.015-0.0140.012-0.0820.000
좌표위치경도((°))0.276-0.083-0.1151.0000.1850.1660.1450.0990.2020.000
co((g/km))-0.367-0.041-0.0710.1851.0000.9680.9770.9580.9980.000
nox((g/km))-0.3550.019-0.0150.1660.9681.0000.9950.9910.9710.000
hc((g/km))-0.380-0.004-0.0140.1450.9770.9951.0000.9900.9760.000
pm((g/km))-0.3640.0190.0120.0990.9580.9910.9901.0000.9590.000
co2((g/km))-0.341-0.048-0.0820.2020.9980.9710.9760.9591.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T10:51:51.944525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T10:51:52.098048image/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-김천11.420210401036.07278128.086112260.02219.68301.36183.46566005.89경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210401036.07278128.086112230.942098.02300.09150.23548708.23경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210401036.35299128.139353147.274105.48575.35331.22725380.89경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210401036.35299128.139352847.283728.14469.15275.34713374.82경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210401036.50552128.169466598.759053.581168.54681.731588169.82경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210401036.50552128.169466560.278939.681143.73693.991574941.71경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210401036.14659128.025441787.81953.13253.12163.18449106.37경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210401036.14659128.025441648.721553.57222.43128.81406276.98경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210401035.98385128.411069529.1510822.461385.02780.942394753.24경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210401035.98385128.411069778.1611016.041385.89771.02347996.68경북 칠곡 왜관 왜관
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3109-1]1죽장-부남20.620210401036.2488129.04049582.371068.02121.4469.61145217.14경북 청송 현동 눌인
9192건기연[3109-1]2죽장-부남20.620210401036.2488129.04049535.09865.58102.0260.03129786.23경북 청송 현동 눌인
9293건기연[3113-1]1진보-석보6.020210401036.59556129.086111329.271573.1223.09110.9309454.11경북 영양 입암 신구
9394건기연[3113-1]2진보-석보6.020210401036.59556129.086111288.611441.99209.88101.98299247.22경북 영양 입암 신구
9495건기연[3116-1]1녹동-영양26.720210401036.86368129.01248197.63224.2432.3317.2945967.71경북 봉화 소천 서천
9596건기연[3116-1]2녹동-영양26.720210401036.86368129.01248198.46209.4230.2718.4247428.33경북 봉화 소천 서천
9697건기연[3307-1]1고령-수륜12.920210401035.80817128.23039835.93886.94118.3271.65202900.58경북 성주 수륜 계정
9798건기연[3307-1]2고령-수륜12.920210401035.80817128.23039842.481175.7131.1584.76220011.72경북 성주 수륜 계정
9899건기연[3310-0]1성주-왜관6.720210401035.97485128.376825708.435850.08772.04479.81450986.91경북 칠곡 기산 영
99100건기연[3310-0]2성주-왜관6.720210401035.97485128.376825561.846299.04779.38500.181453633.01경북 칠곡 기산 영