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 12:08:15.930495
Analysis finished2023-12-10 12:08:26.133438
Duration10.2 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
2023-12-10T21:08:26.226799image/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
2023-12-10T21:08:26.379078image/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

2023-12-10T21:08:26.505089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:26.888821image/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
2023-12-10T21:08:27.092007image/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%
3103-2 2
 
2.0%
3313-0 2
 
2.0%
2613-4 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%
Other values (40) 80
80.0%
2023-12-10T21:08:27.416675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 130
16.2%
1 114
14.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 68
8.5%
2 64
8.0%
4 26
 
3.2%
5 26
 
3.2%
8 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 (%)
0 130
26.0%
1 114
22.8%
3 68
13.6%
2 64
12.8%
4 26
 
5.2%
5 26
 
5.2%
8 24
 
4.8%
6 20
 
4.0%
7 20
 
4.0%
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 (%)
0 130
16.2%
1 114
14.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 68
8.5%
2 64
8.0%
4 26
 
3.2%
5 26
 
3.2%
8 24
 
3.0%
Other values (3) 48
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
16.2%
1 114
14.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 68
8.5%
2 64
8.0%
4 26
 
3.2%
5 26
 
3.2%
8 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

2023-12-10T21:08:27.580755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:27.676601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경주-감포
 
4
상주-낙동
 
2
북후-영주
 
2
상주-함창
 
2
추풍령-김천
 
2
Other values (44)
88 

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%
상주-낙동 2
 
2.0%
북후-영주 2
 
2.0%
상주-함창 2
 
2.0%
추풍령-김천 2
 
2.0%
성주-대구 2
 
2.0%
영천-서면 2
 
2.0%
현풍-옥포 2
 
2.0%
칠곡-효령 2
 
2.0%
가산-군위 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T21:08:27.768709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경주-감포 4
 
4.0%
구성-김천 2
 
2.0%
양포-동해 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 (39) 78
78.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.642
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:27.886724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q15.9
median8.1
Q311.9
95-th percentile24.2
Maximum27.8
Range26.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.3053958
Coefficient of variation (CV)0.65395103
Kurtosis1.2040613
Mean9.642
Median Absolute Deviation (MAD)3.3
Skewness1.2036188
Sum964.2
Variance39.758016
MonotonicityNot monotonic
2023-12-10T21:08:28.015727image/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%
6.2 2
 
2.0%
8.2 2
 
2.0%
7.8 2
 
2.0%
2.2 2
 
2.0%
8.0 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%
24.2 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%
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

2023-12-10T21:08:28.119867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:28.197304image/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
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

2023-12-10T21:08:28.280165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:28.358311image/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%
Mean36.101754
Minimum35.65543
Maximum36.86368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:28.454542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.85555
median36.01706
Q336.32892
95-th percentile36.76527
Maximum36.86368
Range1.20825
Interquartile range (IQR)0.47337

Descriptive statistics

Standard deviation0.32731356
Coefficient of variation (CV)0.0090664171
Kurtosis-0.24584349
Mean36.101754
Median Absolute Deviation (MAD)0.22022
Skewness0.76567239
Sum3610.1754
Variance0.10713417
MonotonicityNot monotonic
2023-12-10T21:08:28.608082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
35.98043 2
 
2.0%
35.73646 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%
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%
36.50552 2
2.0%
36.40808 2
2.0%
36.35882 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.77776
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:28.812413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.13935
Q1128.37682
median128.69655
Q3129.25885
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.88203

Descriptive statistics

Standard deviation0.47066686
Coefficient of variation (CV)0.0036548768
Kurtosis-1.4462336
Mean128.77776
Median Absolute Deviation (MAD)0.40383
Skewness0.12963793
Sum12877.776
Variance0.22152729
MonotonicityNot monotonic
2023-12-10T21:08:28.943328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
129.49495 2
 
2.0%
128.31646 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%
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%
129.41209 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 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.1541
Minimum0.52
Maximum147.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:29.083429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile0.65
Q18.5
median21.74
Q336.1525
95-th percentile89.9465
Maximum147.37
Range146.85
Interquartile range (IQR)27.6525

Descriptive statistics

Standard deviation30.665698
Coefficient of variation (CV)1.0169661
Kurtosis3.2289617
Mean30.1541
Median Absolute Deviation (MAD)13.845
Skewness1.7424822
Sum3015.41
Variance940.38503
MonotonicityNot monotonic
2023-12-10T21:08:29.235228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.63 5
 
5.0%
0.65 3
 
3.0%
0.52 3
 
3.0%
18.36 2
 
2.0%
7.82 1
 
1.0%
12.9 1
 
1.0%
8.47 1
 
1.0%
90.64 1
 
1.0%
25.8 1
 
1.0%
35.22 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.52 3
3.0%
0.65 3
3.0%
1.05 1
 
1.0%
1.78 1
 
1.0%
2.03 1
 
1.0%
2.31 1
 
1.0%
2.63 5
5.0%
2.78 1
 
1.0%
5.23 1
 
1.0%
5.27 1
 
1.0%
ValueCountFrequency (%)
147.37 1
1.0%
141.67 1
1.0%
116.19 1
1.0%
110.02 1
1.0%
90.64 1
1.0%
89.91 1
1.0%
83.37 1
1.0%
83.25 1
1.0%
79.8 1
1.0%
76.09 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.5469
Minimum0.28
Maximum231.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:29.382745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.32
Q15.29
median14.285
Q328.0975
95-th percentile109.2175
Maximum231.46
Range231.18
Interquartile range (IQR)22.8075

Descriptive statistics

Standard deviation41.844063
Coefficient of variation (CV)1.4658006
Kurtosis9.6445164
Mean28.5469
Median Absolute Deviation (MAD)11.03
Skewness2.9324026
Sum2854.69
Variance1750.9256
MonotonicityNot monotonic
2023-12-10T21:08:29.514046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.64 5
 
5.0%
0.32 3
 
3.0%
0.28 3
 
3.0%
10.77 2
 
2.0%
5.08 1
 
1.0%
8.35 1
 
1.0%
4.53 1
 
1.0%
58.68 1
 
1.0%
14.9 1
 
1.0%
24.56 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.28 3
3.0%
0.32 3
3.0%
0.55 1
 
1.0%
1.33 1
 
1.0%
1.41 1
 
1.0%
1.6 1
 
1.0%
1.64 5
5.0%
1.79 1
 
1.0%
2.92 1
 
1.0%
3.02 1
 
1.0%
ValueCountFrequency (%)
231.46 1
1.0%
210.52 1
1.0%
176.93 1
1.0%
141.6 1
1.0%
119.81 1
1.0%
108.66 1
1.0%
106.59 1
1.0%
96.45 1
1.0%
75.04 1
1.0%
68.93 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7881
Minimum0.04
Maximum25.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:29.642585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.06
Q10.8025
median2.245
Q34.435
95-th percentile14.766
Maximum25.16
Range25.12
Interquartile range (IQR)3.6325

Descriptive statistics

Standard deviation4.6206532
Coefficient of variation (CV)1.2197812
Kurtosis5.888197
Mean3.7881
Median Absolute Deviation (MAD)1.615
Skewness2.2768605
Sum378.81
Variance21.350436
MonotonicityNot monotonic
2023-12-10T21:08:29.781167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.26 6
 
6.0%
0.04 3
 
3.0%
0.06 3
 
3.0%
0.78 2
 
2.0%
1.77 2
 
2.0%
1.5 2
 
2.0%
1.19 2
 
2.0%
9.28 1
 
1.0%
2.29 1
 
1.0%
3.72 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.04 3
3.0%
0.06 3
3.0%
0.09 1
 
1.0%
0.18 1
 
1.0%
0.21 1
 
1.0%
0.23 1
 
1.0%
0.26 6
6.0%
0.49 1
 
1.0%
0.5 1
 
1.0%
0.52 1
 
1.0%
ValueCountFrequency (%)
25.16 1
1.0%
19.33 1
1.0%
18.21 1
1.0%
16.23 1
1.0%
15.07 1
1.0%
14.75 1
1.0%
11.66 1
1.0%
10.24 1
1.0%
10.22 1
1.0%
9.54 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6738
Minimum0
Maximum15.31
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:29.936724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.27
median0.71
Q31.82
95-th percentile6.398
Maximum15.31
Range15.31
Interquartile range (IQR)1.55

Descriptive statistics

Standard deviation2.555997
Coefficient of variation (CV)1.5270624
Kurtosis11.906323
Mean1.6738
Median Absolute Deviation (MAD)0.58
Skewness3.2013339
Sum167.38
Variance6.5331208
MonotonicityNot monotonic
2023-12-10T21:08:30.081904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 8
 
8.0%
0.0 7
 
7.0%
0.54 6
 
6.0%
0.27 6
 
6.0%
0.14 6
 
6.0%
0.67 4
 
4.0%
1.61 3
 
3.0%
0.56 2
 
2.0%
1.32 2
 
2.0%
1.19 2
 
2.0%
Other values (47) 54
54.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.13 8
8.0%
0.14 6
6.0%
0.27 6
6.0%
0.28 1
 
1.0%
0.4 2
 
2.0%
0.41 1
 
1.0%
0.42 2
 
2.0%
0.54 6
6.0%
0.56 2
 
2.0%
ValueCountFrequency (%)
15.31 1
1.0%
12.65 1
1.0%
10.58 1
1.0%
8.52 1
1.0%
6.93 1
1.0%
6.37 1
1.0%
5.8 1
1.0%
4.81 1
1.0%
4.73 1
1.0%
4.58 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7458.3438
Minimum138.68
Maximum35763.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:30.252768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile153.68
Q12060.7225
median5341.35
Q39016.935
95-th percentile24601.595
Maximum35763.2
Range35624.52
Interquartile range (IQR)6956.2125

Descriptive statistics

Standard deviation7616.161
Coefficient of variation (CV)1.0211598
Kurtosis2.8746537
Mean7458.3438
Median Absolute Deviation (MAD)3427.675
Skewness1.7084839
Sum745834.38
Variance58005909
MonotonicityNot monotonic
2023-12-10T21:08:30.428596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
640.96 5
 
5.0%
153.68 3
 
3.0%
138.68 3
 
3.0%
4434.28 2
 
2.0%
2065.29 1
 
1.0%
3377.22 1
 
1.0%
2024.1 1
 
1.0%
21291.05 1
 
1.0%
6821.81 1
 
1.0%
9014.09 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
138.68 3
3.0%
153.68 3
3.0%
277.37 1
 
1.0%
462.92 1
 
1.0%
492.91 1
 
1.0%
601.6 1
 
1.0%
640.96 5
5.0%
734.66 1
 
1.0%
1255.69 1
 
1.0%
1261.32 1
 
1.0%
ValueCountFrequency (%)
35763.2 1
1.0%
33233.68 1
1.0%
28502.38 1
1.0%
28477.03 1
1.0%
25602.62 1
1.0%
24548.91 1
1.0%
21291.05 1
1.0%
21063.36 1
1.0%
18331.08 1
1.0%
18099.51 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 상주 낙동 신상
 
2
경북 영주 이산 운문
 
2
경북 상주 외서 연봉
 
2
경북 김천 봉산 태화
 
2
Other values (44)
88 

Length

Max length11
Median length11
Mean length10.98
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 상주 낙동 신상 2
 
2.0%
경북 영주 이산 운문 2
 
2.0%
경북 상주 외서 연봉 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 논공 위천 2
 
2.0%
경북 칠곡 가산 천평 2
 
2.0%
경북 군위 효령 고곡 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T21:08:30.643477image/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 8
 
2.0%
예천 6
 
1.5%
상주 6
 
1.5%
고령 6
 
1.5%
연일 6
 
1.5%
영천 6
 
1.5%
Other values (102) 222
55.5%

Interactions

2023-12-10T21:08:24.744696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:16.537420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:17.375585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:18.516834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.473620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:20.709912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.557490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:22.947688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.801348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:24.852954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:16.613954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:17.472409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:18.627300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.566722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:20.819245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.625813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.033247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.904843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:24.972191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:16.694236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:17.607320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:18.729876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.668928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:20.908729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.709399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.126172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.997104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:25.094866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:16.773320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:17.845507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:18.860224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.771256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.002366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.857156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.225564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:24.118590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:25.224927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:16.862043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:17.963249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:18.976439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.884971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.097735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:22.067265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.322990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:24.220035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:25.334008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:16.962754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:18.061278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.091610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.987826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.198633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:22.357660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.416682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:24.307688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:25.422801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:17.058113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:18.162716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.181727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:20.091574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.277786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:22.539322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.517245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:24.399252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:25.526127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:17.164437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:18.288004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.274715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:20.206332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.367562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:22.728031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.613188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:24.489936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:25.650350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:17.272166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:18.411593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:19.375503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:20.601012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:21.468423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:22.855081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:23.707245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:24.610942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:08:30.768562image/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
기본키1.0001.0000.0001.0000.7380.7530.9000.4050.3390.0940.1680.4151.000
지점1.0001.0000.0001.0001.0001.0001.0000.9070.7600.7780.7020.8491.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.1630.1150.0620.000
측정구간1.0001.0000.0001.0000.9941.0001.0000.9090.7780.7900.7290.8551.000
연장((km))0.7381.0000.0000.9941.0000.7080.8160.0000.0000.0000.0000.0000.994
좌표위치위도((°))0.7531.0000.0001.0000.7081.0000.7450.0000.0000.0000.0000.0001.000
좌표위치경도((°))0.9001.0000.0001.0000.8160.7451.0000.2820.0820.0000.0000.2591.000
co((g/km))0.4050.9070.0000.9090.0000.0000.2821.0000.9520.9450.9230.9940.909
nox((g/km))0.3390.7600.0000.7780.0000.0000.0820.9521.0000.9760.9840.9720.778
hc((g/km))0.0940.7780.1630.7900.0000.0000.0000.9450.9761.0000.9760.9620.790
pm((g/km))0.1680.7020.1150.7290.0000.0000.0000.9230.9840.9761.0000.9350.729
co2((g/km))0.4150.8490.0620.8550.0000.0000.2590.9940.9720.9620.9351.0000.855
주소1.0001.0000.0001.0000.9941.0001.0000.9090.7780.7900.7290.8551.000
2023-12-10T21:08:30.916690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0001.000
방향0.0001.0000.000
주소1.0000.0001.000
2023-12-10T21:08:31.034717image/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
기본키1.000-0.0080.0410.154-0.260-0.253-0.254-0.255-0.2550.0000.7530.753
연장((km))-0.0081.0000.041-0.064-0.201-0.192-0.192-0.150-0.1940.0000.7060.706
좌표위치위도((°))0.0410.0411.000-0.177-0.074-0.081-0.077-0.036-0.0840.0000.7530.753
좌표위치경도((°))0.154-0.064-0.1771.0000.3350.3080.3230.2210.3320.0000.7530.753
co((g/km))-0.260-0.201-0.0740.3351.0000.9860.9890.9530.9970.0000.4620.462
nox((g/km))-0.253-0.192-0.0810.3080.9861.0000.9960.9810.9870.0000.3010.301
hc((g/km))-0.254-0.192-0.0770.3230.9890.9961.0000.9730.9850.1550.3120.312
pm((g/km))-0.255-0.150-0.0360.2210.9530.9810.9731.0000.9550.1070.2620.262
co2((g/km))-0.255-0.194-0.0840.3320.9970.9870.9850.9551.0000.0590.3970.397
방향0.0000.0000.0000.0000.0000.0000.1550.1070.0591.0000.0000.000
측정구간0.7530.7060.7530.7530.4620.3010.3120.2620.3970.0001.0001.000
주소0.7530.7060.7530.7530.4620.3010.3120.2620.3970.0001.0001.000

Missing values

2023-12-10T21:08:25.814882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:08:26.045578image/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-김천11.420210501136.07278128.086117.825.080.750.42065.29경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210501136.07278128.0861112.46.811.160.272972.43경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210501136.35299128.1393520.9416.932.661.144803.6경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210501136.35299128.1393516.613.672.190.833811.7경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210501136.50552128.1694629.9122.753.371.617630.96경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210501136.50552128.1694638.6336.375.382.499322.96경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210501136.14659128.025447.214.240.70.271742.97경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210501136.14659128.0254411.246.711.090.412702.57경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210501135.98385128.4110673.5157.288.574.3117378.51경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210501135.98385128.4110671.8462.248.74.5818042.98경북 칠곡 왜관 왜관
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3307-1]1고령-수륜12.920210501135.80817128.230392.631.640.260.13640.96경북 성주 수륜 계정
9192건기연[3307-1]2고령-수륜12.920210501135.80817128.230392.031.410.210.14492.91경북 성주 수륜 계정
9293건기연[3310-0]1성주-왜관6.720210501135.97485128.3768225.0323.763.651.815622.11경북 칠곡 기산 영
9394건기연[3310-0]2성주-왜관6.720210501135.97485128.3768223.9819.32.841.476064.2경북 칠곡 기산 영
9495건기연[3311-0]1약목-구평19.420210501136.04876128.410776.583.660.620.141568.69경북 칠곡 석적 포남
9596건기연[3311-0]2약목-구평19.420210501136.04876128.4107711.766.491.10.272818.75경북 칠곡 석적 포남
9697건기연[3313-0]1구미-선산3.420210501136.23436128.3045734.0125.343.331.618879.45경북 구미 선산 동부
9798건기연[3313-0]2구미-선산3.420210501136.23436128.3045730.2524.442.91.528428.59경북 구미 선산 동부
9899건기연[3416-0]1예천-괴정13.620210501136.62861128.481618.514.890.810.272050.34경북 예천 예천 고평
99100건기연[3416-0]2예천-괴정13.620210501136.62861128.4816117.3110.61.70.74155.07경북 예천 예천 고평