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
Categorical6
Text1

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
측정구간 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
기본키 is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
연장((km)) is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
좌표위치위도((°)) is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
좌표위치경도((°)) is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
co((g/km)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
pm((g/km)) has 7 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:07:58.219574
Analysis finished2023-12-10 12:08:08.290554
Duration10.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  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
2023-12-10T21:08:08.373358image/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
2023-12-10T21:08:08.526218image/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

2023-12-10T21:08:08.695005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:08.819822image/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
2023-12-10T21:08:09.031992image/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%
2023-12-10T21:08:09.463136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 60
7.5%
5 28
 
3.5%
8 26
 
3.2%
4 24
 
3.0%
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 (%)
0 132
26.4%
1 110
22.0%
2 72
14.4%
3 60
12.0%
5 28
 
5.6%
8 26
 
5.2%
4 24
 
4.8%
7 22
 
4.4%
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 (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 60
7.5%
5 28
 
3.5%
8 26
 
3.2%
4 24
 
3.0%
Other values (3) 48
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 60
7.5%
5 28
 
3.5%
8 26
 
3.2%
4 24
 
3.0%
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

2023-12-10T21:08:09.615888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:09.722066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경주-감포
 
4
현풍-옥포
 
4
공성-상주
 
2
청도-경산
 
2
상주-함창
 
2
Other values (43)
86 

Length

Max length6
Median length5
Mean length5.06
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구성-김천
2nd row구성-김천
3rd row공성-상주
4th row공성-상주
5th row상주-함창

Common Values

ValueCountFrequency (%)
경주-감포 4
 
4.0%
현풍-옥포 4
 
4.0%
공성-상주 2
 
2.0%
청도-경산 2
 
2.0%
상주-함창 2
 
2.0%
문경-연풍 2
 
2.0%
추풍령-김천 2
 
2.0%
성주-대구 2
 
2.0%
영천-서면 2
 
2.0%
칠곡-효령 2
 
2.0%
Other values (38) 76
76.0%

Length

2023-12-10T21:08:09.835661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경주-감포 4
 
4.0%
현풍-옥포 4
 
4.0%
안강-고경 2
 
2.0%
구성-김천 2
 
2.0%
신성-금수 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 (38) 76
76.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.508
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:09.985630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q16
median8.25
Q311.4
95-th percentile24.2
Maximum27.8
Range26.5
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation6.0630639
Coefficient of variation (CV)0.63768026
Kurtosis1.8827396
Mean9.508
Median Absolute Deviation (MAD)2.95
Skewness1.3523195
Sum950.8
Variance36.760743
MonotonicityNot monotonic
2023-12-10T21:08:10.127141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
10.6 8
 
8.0%
8.7 4
 
4.0%
6.4 4
 
4.0%
2.4 4
 
4.0%
11.2 4
 
4.0%
11.4 2
 
2.0%
8.3 2
 
2.0%
8.2 2
 
2.0%
7.8 2
 
2.0%
10.4 2
 
2.0%
Other values (33) 66
66.0%
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%
3.8 2
2.0%
4.1 2
2.0%
4.7 2
2.0%
4.8 2
2.0%
4.9 2
2.0%
ValueCountFrequency (%)
27.8 2
2.0%
26.7 2
2.0%
24.2 2
2.0%
22.1 2
2.0%
20.6 2
2.0%
15.8 2
2.0%
14.4 2
2.0%
12.9 2
2.0%
12.3 2
2.0%
12.0 2
2.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2023-12-10T21:08:10.246634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:10.327687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 100
100.0%

측정시간
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T21:08:10.415736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:10.497099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

좌표위치위도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.10728
Minimum35.65543
Maximum36.86368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:10.600232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.80916
median35.999975
Q336.33873
95-th percentile36.76527
Maximum36.86368
Range1.20825
Interquartile range (IQR)0.52957

Descriptive statistics

Standard deviation0.34685079
Coefficient of variation (CV)0.0096061179
Kurtosis-0.48975979
Mean36.10728
Median Absolute Deviation (MAD)0.22393
Skewness0.74474781
Sum3610.728
Variance0.12030547
MonotonicityNot monotonic
2023-12-10T21:08:10.848067image/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%
35.71695 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%
36.73871 2
2.0%
36.73238 2
2.0%
36.59556 2
2.0%
36.59513 2
2.0%
36.50552 2
2.0%
36.40808 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.77497
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:11.006543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.08611
Q1128.37682
median128.69655
Q3129.25885
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.88203

Descriptive statistics

Standard deviation0.47509731
Coefficient of variation (CV)0.0036893607
Kurtosis-1.4327779
Mean128.77497
Median Absolute Deviation (MAD)0.42619
Skewness0.10486581
Sum12877.497
Variance0.22571745
MonotonicityNot monotonic
2023-12-10T21:08:11.158290image/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%
128.08594 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%
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%
129.41209 2
2.0%
129.40658 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 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.3559
Minimum0.52
Maximum98.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:11.338223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile1.182
Q17.61
median17.875
Q337.39
95-th percentile73.0995
Maximum98.84
Range98.32
Interquartile range (IQR)29.78

Descriptive statistics

Standard deviation23.592438
Coefficient of variation (CV)0.9304516
Kurtosis0.78924267
Mean25.3559
Median Absolute Deviation (MAD)13.085
Skewness1.1859513
Sum2535.59
Variance556.60312
MonotonicityNot monotonic
2023-12-10T21:08:11.528988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.65 3
 
3.0%
1.73 2
 
2.0%
17.82 2
 
2.0%
0.52 2
 
2.0%
7.9 2
 
2.0%
1.34 2
 
2.0%
1.3 2
 
2.0%
14.79 1
 
1.0%
1.21 1
 
1.0%
8.92 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.52 2
2.0%
0.65 3
3.0%
1.21 1
 
1.0%
1.3 2
2.0%
1.34 2
2.0%
1.73 2
2.0%
1.78 1
 
1.0%
2.03 1
 
1.0%
2.94 1
 
1.0%
3.04 1
 
1.0%
ValueCountFrequency (%)
98.84 1
1.0%
94.89 1
1.0%
82.16 1
1.0%
76.75 1
1.0%
76.32 1
1.0%
72.93 1
1.0%
72.6 1
1.0%
72.29 1
1.0%
71.92 1
1.0%
63.8 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.4986
Minimum0.28
Maximum187.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:11.706170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.624
Q14.86
median15.74
Q348.49
95-th percentile99.1045
Maximum187.62
Range187.34
Interquartile range (IQR)43.63

Descriptive statistics

Standard deviation35.637398
Coefficient of variation (CV)1.1684929
Kurtosis3.1859096
Mean30.4986
Median Absolute Deviation (MAD)13.06
Skewness1.6922258
Sum3049.86
Variance1270.0241
MonotonicityNot monotonic
2023-12-10T21:08:11.998662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 3
 
3.0%
1.23 2
 
2.0%
6.04 2
 
2.0%
0.28 2
 
2.0%
4.92 2
 
2.0%
1.0 2
 
2.0%
0.64 2
 
2.0%
6.02 1
 
1.0%
13.84 1
 
1.0%
0.96 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
0.28 2
2.0%
0.32 3
3.0%
0.64 2
2.0%
0.96 1
 
1.0%
1.0 2
2.0%
1.23 2
2.0%
1.33 1
 
1.0%
1.41 1
 
1.0%
2.19 1
 
1.0%
2.28 1
 
1.0%
ValueCountFrequency (%)
187.62 1
1.0%
124.36 1
1.0%
115.72 1
1.0%
106.33 1
1.0%
105.65 1
1.0%
98.76 1
1.0%
95.83 1
1.0%
93.2 1
1.0%
90.01 1
1.0%
86.84 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8756
Minimum0.04
Maximum21.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:12.164664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.117
Q10.7375
median2.365
Q36.2125
95-th percentile11.633
Maximum21.98
Range21.94
Interquartile range (IQR)5.475

Descriptive statistics

Standard deviation4.0839126
Coefficient of variation (CV)1.0537498
Kurtosis2.8026496
Mean3.8756
Median Absolute Deviation (MAD)1.965
Skewness1.5052444
Sum387.56
Variance16.678342
MonotonicityNot monotonic
2023-12-10T21:08:12.352294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.18 3
 
3.0%
0.06 3
 
3.0%
0.4 2
 
2.0%
2.39 2
 
2.0%
0.04 2
 
2.0%
3.6 2
 
2.0%
0.79 2
 
2.0%
0.15 2
 
2.0%
0.12 2
 
2.0%
2.04 2
 
2.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.04 2
2.0%
0.06 3
3.0%
0.12 2
2.0%
0.13 1
 
1.0%
0.15 2
2.0%
0.18 3
3.0%
0.21 1
 
1.0%
0.31 1
 
1.0%
0.33 1
 
1.0%
0.35 1
 
1.0%
ValueCountFrequency (%)
21.98 1
1.0%
12.82 1
1.0%
12.5 1
1.0%
11.86 1
1.0%
11.69 1
1.0%
11.63 1
1.0%
11.61 1
1.0%
11.24 1
1.0%
10.86 1
1.0%
9.66 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9974
Minimum0
Maximum12.61
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:12.502165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4
median1.105
Q33.0475
95-th percentile6.3355
Maximum12.61
Range12.61
Interquartile range (IQR)2.6475

Descriptive statistics

Standard deviation2.2262287
Coefficient of variation (CV)1.1145633
Kurtosis4.4960777
Mean1.9974
Median Absolute Deviation (MAD)0.95
Skewness1.8303048
Sum199.74
Variance4.9560942
MonotonicityNot monotonic
2023-12-10T21:08:12.965891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 9
 
9.0%
0.0 7
 
7.0%
0.27 4
 
4.0%
0.55 3
 
3.0%
0.93 3
 
3.0%
0.4 3
 
3.0%
0.54 3
 
3.0%
1.09 2
 
2.0%
1.07 2
 
2.0%
0.84 2
 
2.0%
Other values (61) 62
62.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.13 9
9.0%
0.14 2
 
2.0%
0.27 4
4.0%
0.28 1
 
1.0%
0.4 3
 
3.0%
0.41 1
 
1.0%
0.42 1
 
1.0%
0.54 3
 
3.0%
0.55 3
 
3.0%
ValueCountFrequency (%)
12.61 1
1.0%
7.71 1
1.0%
7.09 1
1.0%
7.07 1
1.0%
6.82 1
1.0%
6.31 1
1.0%
5.97 1
1.0%
5.65 1
1.0%
5.37 1
1.0%
4.88 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6237.6079
Minimum138.68
Maximum24182.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:13.163739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile299.676
Q11922.89
median4371.48
Q39204.255
95-th percentile18830.925
Maximum24182.1
Range24043.42
Interquartile range (IQR)7281.365

Descriptive statistics

Standard deviation5847.1789
Coefficient of variation (CV)0.93740726
Kurtosis0.77199292
Mean6237.6079
Median Absolute Deviation (MAD)3168.28
Skewness1.1979952
Sum623760.79
Variance34189502
MonotonicityNot monotonic
2023-12-10T21:08:13.324517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
153.68 3
 
3.0%
457.29 2
 
2.0%
138.68 2
 
2.0%
1922.89 2
 
2.0%
333.6 2
 
2.0%
307.36 2
 
2.0%
2262.12 1
 
1.0%
3613.4 1
 
1.0%
318.6 1
 
1.0%
2314.6 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
138.68 2
2.0%
153.68 3
3.0%
307.36 2
2.0%
318.6 1
 
1.0%
333.6 2
2.0%
457.29 2
2.0%
462.92 1
 
1.0%
492.91 1
 
1.0%
775.89 1
 
1.0%
787.16 1
 
1.0%
ValueCountFrequency (%)
24182.1 1
1.0%
23074.72 1
1.0%
20808.92 1
1.0%
20258.09 1
1.0%
19582.48 1
1.0%
18791.37 1
1.0%
17264.54 1
1.0%
16910.67 1
1.0%
16452.0 1
1.0%
15335.09 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경북 경주 양북 안동
 
4
경북 경산 남천 협석
 
2
경북 의성 봉양 화전
 
2
경북 상주 외서 연봉
 
2
경북 문경 문경 진안
 
2
Other values (44)
88 

Length

Max length11
Median length11
Mean length10.96
Min length10

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

Common Values

ValueCountFrequency (%)
경북 경주 양북 안동 4
 
4.0%
경북 경산 남천 협석 2
 
2.0%
경북 의성 봉양 화전 2
 
2.0%
경북 상주 외서 연봉 2
 
2.0%
경북 문경 문경 진안 2
 
2.0%
경북 김천 봉산 태화 2
 
2.0%
경북 칠곡 왜관 왜관 2
 
2.0%
경북 영천 북안 유하 2
 
2.0%
경북 달성 현풍 성하 2
 
2.0%
경북 달성 논공 위천 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T21:08:13.488929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경북 100
25.0%
포항 16
 
4.0%
경주 14
 
3.5%
의성 10
 
2.5%
칠곡 6
 
1.5%
고령 6
 
1.5%
연일 6
 
1.5%
상주 6
 
1.5%
영천 6
 
1.5%
청도 4
 
1.0%
Other values (102) 226
56.5%

Interactions

2023-12-10T21:08:07.025278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:58.976008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:00.169541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.129961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:02.095847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.067033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.902061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:04.906841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.762648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:07.125251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:59.070894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:00.283622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.247427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:02.207962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.155224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.996691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:04.998129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.856352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:07.210579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:59.155398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:00.379730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.361235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:02.314874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.257425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:04.089196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.114605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.969309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:07.307066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:59.247989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:00.480695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.468075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:02.420305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.350161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:04.180279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.212902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:06.061101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:07.407404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:59.365645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:00.591188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.570622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:02.535317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.465359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:04.372154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.329051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:06.160448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:07.491523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:59.454242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:00.708132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.663286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:02.637898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.561433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:04.483423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.426830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:06.257259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:07.603487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:59.605608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:00.825629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.764844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:02.744403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.644235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:04.600261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.525296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:06.370291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:07.697131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:07:59.701673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:00.920052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.857278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:02.834951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.722275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:04.688526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.597174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:06.831114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:07.786478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:00.060044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.019573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:01.992272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:02.954149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:03.814813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:04.781905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:05.677474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:06.931356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:08:13.637895image/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
기본키1.0001.0000.0000.9980.5190.7300.9270.4890.5100.4950.4620.6241.000
지점1.0001.0000.0001.0001.0001.0001.0000.8480.7860.8080.7450.9341.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간0.9981.0000.0001.0000.9871.0001.0000.8090.7760.7810.7520.9101.000
연장((km))0.5191.0000.0000.9871.0000.5370.6620.0000.0000.0000.0000.0000.992
좌표위치위도((°))0.7301.0000.0001.0000.5371.0000.7800.0000.0000.0000.0000.3041.000
좌표위치경도((°))0.9271.0000.0001.0000.6620.7801.0000.3620.4040.4620.2390.2781.000
co((g/km))0.4890.8480.0000.8090.0000.0000.3621.0000.8520.8330.8220.9870.831
nox((g/km))0.5100.7860.0000.7760.0000.0000.4040.8521.0000.8980.9850.8200.801
hc((g/km))0.4950.8080.0000.7810.0000.0000.4620.8330.8981.0000.8720.8230.838
pm((g/km))0.4620.7450.0000.7520.0000.0000.2390.8220.9850.8721.0000.8430.766
co2((g/km))0.6240.9340.0000.9100.0000.3040.2780.9870.8200.8230.8431.0000.919
주소1.0001.0000.0001.0000.9921.0001.0000.8310.8010.8380.7660.9191.000
2023-12-10T21:08:13.808272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0000.990
방향0.0001.0000.000
주소0.9900.0001.000
2023-12-10T21:08:13.919363image/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))방향측정구간주소
기본키1.000-0.088-0.0390.310-0.305-0.291-0.297-0.280-0.2960.0000.7440.753
연장((km))-0.0881.0000.024-0.076-0.142-0.134-0.146-0.131-0.1530.0000.6730.695
좌표위치위도((°))-0.0390.0241.000-0.175-0.029-0.030-0.010-0.000-0.0430.0000.7600.753
좌표위치경도((°))0.310-0.076-0.1751.0000.1920.1740.1620.1260.2130.0000.7600.753
co((g/km))-0.305-0.142-0.0290.1921.0000.9790.9850.9720.9960.0000.3220.340
nox((g/km))-0.291-0.134-0.0300.1740.9791.0000.9970.9920.9820.0000.2950.332
hc((g/km))-0.297-0.146-0.0100.1620.9850.9971.0000.9890.9830.0000.3300.363
pm((g/km))-0.280-0.131-0.0000.1260.9720.9920.9891.0000.9730.0000.2750.300
co2((g/km))-0.296-0.153-0.0430.2130.9960.9820.9830.9731.0000.0000.4640.475
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
측정구간0.7440.6730.7600.7600.3220.2950.3300.2750.4640.0001.0000.990
주소0.7530.6950.7530.7530.3400.3320.3630.3000.4750.0000.9901.000

Missing values

2023-12-10T21:08:07.965581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:08:08.189221image/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-김천11.420210601136.07278128.086119.286.020.940.542262.12경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210601136.07278128.086119.036.040.880.542383.89경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210601136.35299128.1393512.2811.791.740.932981.25경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210601136.35299128.1393511.0210.741.590.792657.01경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210601136.50552128.1694655.5462.428.984.512091.44경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210601136.50552128.1694646.1153.167.613.9810063.22경북 상주 외서 연봉
67건기연[0323-2]1문경-연풍8.720210601136.73871128.0859423.7225.964.041.685061.76경북 문경 문경 진안
78건기연[0323-2]2문경-연풍8.720210601136.73871128.0859428.6932.875.042.416155.16경북 문경 문경 진안
89건기연[0410-2]1추풍령-김천1.820210601136.14659128.025448.135.760.820.552123.42경북 김천 봉산 태화
910건기연[0410-2]2추풍령-김천1.820210601136.14659128.025447.94.920.790.41922.89경북 김천 봉산 태화
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3109-1]1죽장-부남20.620210601136.2488129.040490.650.320.060.0153.68경북 청송 현동 눌인
9192건기연[3109-1]2죽장-부남20.620210601136.2488129.040491.731.230.180.13457.29경북 청송 현동 눌인
9293건기연[3113-1]1진보-석보6.020210601136.59556129.0861111.5911.091.730.722556.45경북 영양 입암 신구
9394건기연[3113-1]2진보-석보6.020210601136.59556129.086113.932.280.380.13948.33경북 영양 입암 신구
9495건기연[3116-1]1녹동-영양26.720210601136.86368129.012480.650.320.060.0153.68경북 봉화 소천 서천
9596건기연[3116-1]2녹동-영양26.720210601136.86368129.012482.942.190.310.27775.89경북 봉화 소천 서천
9697건기연[3307-1]1고령-수륜12.920210601135.80817128.230392.031.410.210.14492.91경북 성주 수륜 계정
9798건기연[3307-1]2고령-수륜12.920210601135.80817128.230391.781.330.180.14462.92경북 성주 수륜 계정
9899건기연[3310-0]1성주-왜관6.720210601135.97485128.3768217.6915.932.341.374383.66경북 칠곡 기산 영
99100건기연[3310-0]2성주-왜관6.720210601135.97485128.3768225.9525.633.732.16229.28경북 칠곡 기산 영