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
co((g/km)) has 7 (7.0%) zerosZeros
nox((g/km)) has 7 (7.0%) zerosZeros
hc((g/km)) has 7 (7.0%) zerosZeros
pm((g/km)) has 13 (13.0%) zerosZeros
co2((g/km)) has 7 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:24:42.205297
Analysis finished2023-12-10 12:24:52.771066
Duration10.57 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
2023-12-10T21:24:52.865879image/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
2023-12-10T21:24:53.040188image/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

2023-12-10T21:24:53.180744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:53.301487image/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
2023-12-10T21:24:53.517588image/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%
2808-0 2
 
2.0%
3116-1 2
 
2.0%
2512-4 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%
Other values (40) 80
80.0%
2023-12-10T21:24:53.925864image/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%
2 72
9.0%
3 52
 
6.5%
4 28
 
3.5%
5 28
 
3.5%
8 26
 
3.2%
Other values (3) 50
 
6.2%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
26.0%
1 114
22.8%
2 72
14.4%
3 52
 
10.4%
4 28
 
5.6%
5 28
 
5.6%
8 26
 
5.2%
7 24
 
4.8%
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 (%)
0 130
16.2%
1 114
14.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 52
 
6.5%
4 28
 
3.5%
5 28
 
3.5%
8 26
 
3.2%
Other values (3) 50
 
6.2%

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%
2 72
9.0%
3 52
 
6.5%
4 28
 
3.5%
5 28
 
3.5%
8 26
 
3.2%
Other values (3) 50
 
6.2%

방향
Categorical

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T21:24:54.076120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:54.256234image/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
건천-대송
 
2
안정-대강
 
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%
건천-대송 2
 
2.0%
안정-대강 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

2023-12-10T21:24:54.400798image/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%
강동-흥해 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 (39) 78
78.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.584
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:54.535950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q15.9
median8.25
Q311.9
95-th percentile24.2
Maximum27.8
Range26.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.1994904
Coefficient of variation (CV)0.64685834
Kurtosis1.44989
Mean9.584
Median Absolute Deviation (MAD)3.35
Skewness1.2085788
Sum958.4
Variance38.433681
MonotonicityNot monotonic
2023-12-10T21:24:54.683788image/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%
2.2 2
 
2.0%
10.5 2
 
2.0%
11.6 2
 
2.0%
9.2 2
 
2.0%
8.2 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%
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%
14.2 2
2.0%
13.5 2
2.0%
12.9 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

2023-12-10T21:24:54.843950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:54.968378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T21:24:55.091952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:55.205885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.114899
Minimum35.65543
Maximum36.86368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:55.361293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.80916
median36.01706
Q336.35299
95-th percentile36.76527
Maximum36.86368
Range1.20825
Interquartile range (IQR)0.54383

Descriptive statistics

Standard deviation0.34536113
Coefficient of variation (CV)0.0095628435
Kurtosis-0.63930303
Mean36.114899
Median Absolute Deviation (MAD)0.24256
Skewness0.6491178
Sum3611.4899
Variance0.11927431
MonotonicityNot monotonic
2023-12-10T21:24:55.532682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
36.03299 2
 
2.0%
36.40808 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%
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)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.81652
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:55.733267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.13935
Q1128.41106
median128.76473
Q3129.26459
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.85353

Descriptive statistics

Standard deviation0.46630251
Coefficient of variation (CV)0.0036198968
Kurtosis-1.3948947
Mean128.81652
Median Absolute Deviation (MAD)0.460995
Skewness-0.025198937
Sum12881.652
Variance0.21743803
MonotonicityNot monotonic
2023-12-10T21:24:55.888223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
129.30673 2
 
2.0%
128.23413 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%
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%
129.41209 2
2.0%
129.40658 2
2.0%
129.40119 2
2.0%
129.34631 2
2.0%
129.32144 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.4836
Minimum0
Maximum180.25
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:56.074284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.4875
median20.255
Q347.25
95-th percentile96.366
Maximum180.25
Range180.25
Interquartile range (IQR)40.7625

Descriptive statistics

Standard deviation32.900155
Coefficient of variation (CV)1.0449934
Kurtosis3.9262488
Mean31.4836
Median Absolute Deviation (MAD)16.775
Skewness1.7354403
Sum3148.36
Variance1082.4202
MonotonicityNot monotonic
2023-12-10T21:24:56.273508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
2.6 2
 
2.0%
0.52 2
 
2.0%
7.86 2
 
2.0%
8.92 1
 
1.0%
26.37 1
 
1.0%
1.38 1
 
1.0%
7.78 1
 
1.0%
11.1 1
 
1.0%
4.42 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.52 2
 
2.0%
0.65 1
 
1.0%
1.38 1
 
1.0%
1.73 1
 
1.0%
1.95 1
 
1.0%
2.26 1
 
1.0%
2.31 1
 
1.0%
2.6 2
 
2.0%
2.75 1
 
1.0%
ValueCountFrequency (%)
180.25 1
1.0%
125.01 1
1.0%
117.96 1
1.0%
105.02 1
1.0%
99.14 1
1.0%
96.22 1
1.0%
92.57 1
1.0%
89.47 1
1.0%
86.19 1
1.0%
82.34 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.3602
Minimum0
Maximum272
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:56.488038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6825
median19.505
Q352.815
95-th percentile112.881
Maximum272
Range272
Interquartile range (IQR)48.1325

Descriptive statistics

Standard deviation47.821928
Coefficient of variation (CV)1.3152273
Kurtosis8.3511939
Mean36.3602
Median Absolute Deviation (MAD)17.77
Skewness2.5520891
Sum3636.02
Variance2286.9368
MonotonicityNot monotonic
2023-12-10T21:24:56.680079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
60.13 2
 
2.0%
1.28 2
 
2.0%
0.28 2
 
2.0%
18.37 2
 
2.0%
4.57 2
 
2.0%
1.09 1
 
1.0%
4.76 1
 
1.0%
10.46 1
 
1.0%
6.36 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.28 2
 
2.0%
0.32 1
 
1.0%
0.96 1
 
1.0%
1.09 1
 
1.0%
1.23 1
 
1.0%
1.28 2
 
2.0%
1.51 1
 
1.0%
1.6 1
 
1.0%
1.87 1
 
1.0%
ValueCountFrequency (%)
272.0 1
1.0%
232.18 1
1.0%
198.99 1
1.0%
131.81 1
1.0%
123.35 1
1.0%
112.33 1
1.0%
107.4 1
1.0%
104.53 1
1.0%
98.61 1
1.0%
97.33 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7801
Minimum0
Maximum30.06
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:56.833570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.76
median2.885
Q37.2425
95-th percentile15.9085
Maximum30.06
Range30.06
Interquartile range (IQR)6.4825

Descriptive statistics

Standard deviation5.7294858
Coefficient of variation (CV)1.1986121
Kurtosis5.8808593
Mean4.7801
Median Absolute Deviation (MAD)2.655
Skewness2.1659671
Sum478.01
Variance32.827007
MonotonicityNot monotonic
2023-12-10T21:24:57.012589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
0.23 3
 
3.0%
0.76 2
 
2.0%
0.35 2
 
2.0%
0.04 2
 
2.0%
2.52 2
 
2.0%
0.94 2
 
2.0%
1.63 1
 
1.0%
1.07 1
 
1.0%
2.37 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.04 2
 
2.0%
0.06 1
 
1.0%
0.16 1
 
1.0%
0.17 1
 
1.0%
0.18 1
 
1.0%
0.22 1
 
1.0%
0.23 3
3.0%
0.28 1
 
1.0%
0.35 2
 
2.0%
ValueCountFrequency (%)
30.06 1
1.0%
27.09 1
1.0%
24.79 1
1.0%
16.1 1
1.0%
16.07 1
1.0%
15.9 1
1.0%
13.42 1
1.0%
13.27 1
1.0%
12.99 1
1.0%
11.12 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4354
Minimum0
Maximum16.99
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:57.190052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.27
median1.44
Q33.5925
95-th percentile7.901
Maximum16.99
Range16.99
Interquartile range (IQR)3.3225

Descriptive statistics

Standard deviation3.1516176
Coefficient of variation (CV)1.2940862
Kurtosis7.7631687
Mean2.4354
Median Absolute Deviation (MAD)1.305
Skewness2.4828908
Sum243.54
Variance9.9326938
MonotonicityNot monotonic
2023-12-10T21:24:57.372670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
13.0%
0.13 5
 
5.0%
0.27 5
 
5.0%
0.39 3
 
3.0%
2.22 2
 
2.0%
0.26 2
 
2.0%
0.54 2
 
2.0%
0.14 2
 
2.0%
1.24 2
 
2.0%
1.72 2
 
2.0%
Other values (62) 62
62.0%
ValueCountFrequency (%)
0.0 13
13.0%
0.13 5
 
5.0%
0.14 2
 
2.0%
0.15 1
 
1.0%
0.26 2
 
2.0%
0.27 5
 
5.0%
0.39 3
 
3.0%
0.4 1
 
1.0%
0.41 1
 
1.0%
0.54 2
 
2.0%
ValueCountFrequency (%)
16.99 1
1.0%
15.96 1
1.0%
13.52 1
1.0%
8.64 1
1.0%
8.49 1
1.0%
7.87 1
1.0%
6.95 1
1.0%
6.55 1
1.0%
6.44 1
1.0%
6.06 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7702.9423
Minimum0
Maximum46081.35
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:57.536053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11552.04
median5328.7
Q311020.59
95-th percentile23121.563
Maximum46081.35
Range46081.35
Interquartile range (IQR)9468.55

Descriptive statistics

Standard deviation8089.1515
Coefficient of variation (CV)1.0501379
Kurtosis4.7737966
Mean7702.9423
Median Absolute Deviation (MAD)4341.86
Skewness1.8426259
Sum770294.23
Variance65434372
MonotonicityNot monotonic
2023-12-10T21:24:57.721516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
614.73 2
 
2.0%
138.68 2
 
2.0%
1896.66 2
 
2.0%
2348.29 1
 
1.0%
6636.41 1
 
1.0%
339.23 1
 
1.0%
1933.72 1
 
1.0%
2540.44 1
 
1.0%
961.65 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 7
7.0%
138.68 2
 
2.0%
153.68 1
 
1.0%
339.23 1
 
1.0%
457.29 1
 
1.0%
461.05 1
 
1.0%
595.97 1
 
1.0%
601.6 1
 
1.0%
614.73 2
 
2.0%
661.47 1
 
1.0%
ValueCountFrequency (%)
46081.35 1
1.0%
31879.56 1
1.0%
28139.75 1
1.0%
24010.46 1
1.0%
23893.6 1
1.0%
23080.93 1
1.0%
21793.28 1
1.0%
21678.16 1
1.0%
20182.69 1
1.0%
19863.29 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 경주 천북 오야
 
2
경북 영주 안정 신전
 
2
경북 상주 외서 연봉
 
2
경북 김천 봉산 태화
 
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 경주 천북 오야 2
 
2.0%
경북 영주 안정 신전 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

2023-12-10T21:24:57.891402image/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%
청도 6
 
1.5%
연일 6
 
1.5%
상주 6
 
1.5%
고령 6
 
1.5%
안동 6
 
1.5%
영천 6
 
1.5%
Other values (102) 224
56.0%

Interactions

2023-12-10T21:24:51.259415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:42.998069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:43.996837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:45.070476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:46.329557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:47.343003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.421043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.289316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:50.244025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.355022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:43.113876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:44.102724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:45.166211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:46.440508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:47.486106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.506244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.382385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:50.333396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.431752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:43.232970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:44.214379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:45.265543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:46.538435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:47.615454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.591108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.489023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:50.445585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.516311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:43.337267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:44.335313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:45.384128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:46.672610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:47.756653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.690685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.611635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:50.553058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.599099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:43.458463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:44.447438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:45.508454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:46.799979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:47.886727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.818120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.754043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:50.651161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.684582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:43.588820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:44.552855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:45.598431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:46.936877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.008440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.925308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.884835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:50.769944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.779552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:43.689127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:44.684720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:45.699277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:47.050218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.098691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.014325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.985381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:50.893037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.888466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:43.790879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:44.842281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:45.797959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:47.154587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.212957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.105433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:50.084775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.013960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.993409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:43.908167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:44.977094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:46.238511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:47.250995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:48.331369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:49.210871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:50.171290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:51.154992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:24:58.013906image/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
기본키1.0001.0000.0001.0000.6120.7390.9100.5530.4010.4580.4540.5571.000
지점1.0001.0000.0001.0001.0001.0001.0000.8160.7330.8750.8130.7311.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.2050.0000.000
측정구간1.0001.0000.0001.0000.9921.0001.0000.8250.7550.8820.8260.7481.000
연장((km))0.6121.0000.0000.9921.0000.5440.6410.0000.0000.0000.0000.0000.992
좌표위치위도((°))0.7391.0000.0001.0000.5441.0000.7810.0000.0000.0000.0000.0001.000
좌표위치경도((°))0.9101.0000.0001.0000.6410.7811.0000.4020.2900.2950.3860.3821.000
co((g/km))0.5530.8160.0000.8250.0000.0000.4021.0000.9360.9330.9060.9950.825
nox((g/km))0.4010.7330.0000.7550.0000.0000.2900.9361.0000.9750.9790.9480.755
hc((g/km))0.4580.8750.0000.8820.0000.0000.2950.9330.9751.0000.9770.9390.882
pm((g/km))0.4540.8130.2050.8260.0000.0000.3860.9060.9790.9771.0000.9210.826
co2((g/km))0.5570.7310.0000.7480.0000.0000.3820.9950.9480.9390.9211.0000.748
주소1.0001.0000.0001.0000.9921.0001.0000.8250.7550.8820.8260.7481.000
2023-12-10T21:24:58.467540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0001.000
방향0.0001.0000.000
주소1.0000.0001.000
2023-12-10T21:24:58.622364image/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
기본키1.000-0.068-0.0360.286-0.315-0.299-0.311-0.327-0.3060.0000.7530.753
연장((km))-0.0681.0000.127-0.043-0.058-0.060-0.056-0.047-0.0570.0000.6960.696
좌표위치위도((°))-0.0360.1271.000-0.148-0.0040.0130.0300.056-0.0280.0000.7530.753
좌표위치경도((°))0.286-0.043-0.1481.0000.1470.1210.1220.0670.1520.0000.7530.753
co((g/km))-0.315-0.058-0.0040.1471.0000.9790.9870.9640.9950.0000.3560.356
nox((g/km))-0.299-0.0600.0130.1210.9791.0000.9940.9890.9810.0000.2920.292
hc((g/km))-0.311-0.0560.0300.1220.9870.9941.0000.9840.9820.0000.4270.427
pm((g/km))-0.327-0.0470.0560.0670.9640.9890.9841.0000.9650.1470.3570.357
co2((g/km))-0.306-0.057-0.0280.1520.9950.9810.9820.9651.0000.0000.2860.286
방향0.0000.0000.0000.0000.0000.0000.0000.1470.0001.0000.0000.000
측정구간0.7530.6960.7530.7530.3560.2920.4270.3570.2860.0001.0001.000
주소0.7530.6960.7530.7530.3560.2920.4270.3570.2860.0001.0001.000

Missing values

2023-12-10T21:24:52.410569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:24:52.671120image/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-김천11.420210401036.07278128.086118.925.730.840.412348.29경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210401036.07278128.086115.273.280.520.271281.93경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210401036.35299128.1393519.5922.283.331.874606.86경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210401036.35299128.1393531.0631.014.82.226862.84경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210401036.50552128.1694662.3478.2211.126.0614077.54경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210401036.50552128.1694658.2971.719.985.9313242.21경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210401036.14659128.025443.912.640.380.261060.48경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210401036.14659128.025449.286.020.940.542262.12경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210401035.98385128.4110696.2286.3913.425.9421678.16경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210401035.98385128.4110660.0562.648.334.7114606.48경북 칠곡 왜관 왜관
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3107-2]1기계-포항2.420210401036.06326129.227717.864.570.760.271896.66경북 포항 기계 내단
9192건기연[3107-2]2기계-포항2.420210401036.06326129.2277110.87.241.050.562822.48경북 포항 기계 내단
9293건기연[3109-1]1죽장-부남20.620210401036.2488129.040490.00.00.00.00.0경북 청송 현동 눌인
9394건기연[3109-1]2죽장-부남20.620210401036.2488129.040492.311.60.230.14601.6경북 청송 현동 눌인
9495건기연[3113-1]1진보-석보6.020210401036.59556129.086116.397.411.120.41429.46경북 영양 입암 신구
9596건기연[3113-1]2진보-석보6.020210401036.59556129.086112.61.280.230.0614.73경북 영양 입암 신구
9697건기연[3116-1]1녹동-영양26.720210401036.86368129.012482.261.510.220.13595.97경북 봉화 소천 서천
9798건기연[3116-1]2녹동-영양26.720210401036.86368129.012482.751.870.280.15661.47경북 봉화 소천 서천
9899건기연[3307-1]1고령-수륜12.920210401035.80817128.230393.832.340.350.131012.03경북 성주 수륜 계정
99100건기연[3307-1]2고령-수륜12.920210401035.80817128.230393.792.290.350.131015.64경북 성주 수륜 계정