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 5 (5.0%) zerosZeros
nox((g/km)) has 5 (5.0%) zerosZeros
hc((g/km)) has 5 (5.0%) zerosZeros
pm((g/km)) has 15 (15.0%) zerosZeros
co2((g/km)) has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:08:33.798175
Analysis finished2023-12-10 12:08:42.831002
Duration9.03 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
2023-12-10T21:08:42.912943image/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
2023-12-10T21:08:43.053354image/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

2023-12-10T21:08:43.186521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:43.287874image/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
2023-12-10T21:08:43.504888image/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%
2023-12-10T21:08:43.881000image/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

2023-12-10T21:08:44.021277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:44.114356image/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

2023-12-10T21:08:44.220723image/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
2023-12-10T21:08:44.343549image/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
2023-12-10T21:08:44.494740image/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

2023-12-10T21:08:44.692136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:44.801068image/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
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

2023-12-10T21:08:44.900426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:08:45.014744image/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%
Mean36.114899
Minimum35.65543
Maximum36.86368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:45.141479image/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
2023-12-10T21:08:45.327490image/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
2023-12-10T21:08:45.508629image/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
2023-12-10T21:08:45.713967image/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 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5235
Minimum0
Maximum125.19
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:45.924625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.494
Q14.6425
median18.85
Q335.2925
95-th percentile74.92
Maximum125.19
Range125.19
Interquartile range (IQR)30.65

Descriptive statistics

Standard deviation26.282754
Coefficient of variation (CV)1.0297472
Kurtosis3.1805955
Mean25.5235
Median Absolute Deviation (MAD)15.05
Skewness1.6393114
Sum2552.35
Variance690.78315
MonotonicityNot monotonic
2023-12-10T21:08:46.359457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52 8
 
8.0%
0.0 5
 
5.0%
3.33 2
 
2.0%
0.65 2
 
2.0%
16.99 1
 
1.0%
5.83 1
 
1.0%
3.72 1
 
1.0%
4.65 1
 
1.0%
13.67 1
 
1.0%
17.73 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.52 8
8.0%
0.65 2
 
2.0%
1.73 1
 
1.0%
1.98 1
 
1.0%
2.63 1
 
1.0%
3.31 1
 
1.0%
3.33 2
 
2.0%
3.72 1
 
1.0%
3.88 1
 
1.0%
ValueCountFrequency (%)
125.19 1
1.0%
124.13 1
1.0%
98.21 1
1.0%
90.72 1
1.0%
79.29 1
1.0%
74.69 1
1.0%
66.38 1
1.0%
65.51 1
1.0%
65.07 1
1.0%
61.5 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.1434
Minimum0
Maximum219.94
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:46.543558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.266
Q13.65
median18.325
Q346.15
95-th percentile99.8015
Maximum219.94
Range219.94
Interquartile range (IQR)42.5

Descriptive statistics

Standard deviation43.622413
Coefficient of variation (CV)1.3161719
Kurtosis6.3990792
Mean33.1434
Median Absolute Deviation (MAD)16.155
Skewness2.3273544
Sum3314.34
Variance1902.9149
MonotonicityNot monotonic
2023-12-10T21:08:46.716495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 8
 
8.0%
0.0 5
 
5.0%
2.06 3
 
3.0%
14.28 2
 
2.0%
0.32 2
 
2.0%
8.98 1
 
1.0%
4.16 1
 
1.0%
5.93 1
 
1.0%
3.32 1
 
1.0%
16.85 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.28 8
8.0%
0.32 2
 
2.0%
1.23 1
 
1.0%
1.32 1
 
1.0%
1.64 1
 
1.0%
2.06 3
 
3.0%
2.28 1
 
1.0%
2.43 1
 
1.0%
2.96 1
 
1.0%
ValueCountFrequency (%)
219.94 1
1.0%
211.94 1
1.0%
190.88 1
1.0%
147.13 1
1.0%
114.08 1
1.0%
99.05 1
1.0%
93.95 1
1.0%
91.1 1
1.0%
89.56 1
1.0%
89.04 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.139
Minimum0
Maximum23.85
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:46.898360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.038
Q10.5375
median2.785
Q35.6475
95-th percentile12.0285
Maximum23.85
Range23.85
Interquartile range (IQR)5.11

Descriptive statistics

Standard deviation4.7673671
Coefficient of variation (CV)1.1518162
Kurtosis4.6539545
Mean4.139
Median Absolute Deviation (MAD)2.415
Skewness1.932164
Sum413.9
Variance22.727789
MonotonicityNot monotonic
2023-12-10T21:08:47.057797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 8
 
8.0%
0.0 5
 
5.0%
0.06 2
 
2.0%
11.01 2
 
2.0%
0.33 2
 
2.0%
0.26 1
 
1.0%
1.2 1
 
1.0%
0.5 1
 
1.0%
2.49 1
 
1.0%
2.91 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.04 8
8.0%
0.06 2
 
2.0%
0.18 1
 
1.0%
0.2 1
 
1.0%
0.26 1
 
1.0%
0.31 1
 
1.0%
0.33 2
 
2.0%
0.36 1
 
1.0%
0.38 1
 
1.0%
ValueCountFrequency (%)
23.85 1
1.0%
21.92 1
1.0%
21.05 1
1.0%
13.84 1
1.0%
12.57 1
1.0%
12.0 1
1.0%
11.69 1
1.0%
11.21 1
1.0%
11.01 2
2.0%
9.42 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct72
Distinct (%)72.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1898
Minimum0
Maximum14.49
Zeros15
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:47.231235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2775
median1.375
Q33.2575
95-th percentile6.8785
Maximum14.49
Range14.49
Interquartile range (IQR)2.98

Descriptive statistics

Standard deviation2.7970247
Coefficient of variation (CV)1.2772969
Kurtosis7.0404105
Mean2.1898
Median Absolute Deviation (MAD)1.24
Skewness2.4078538
Sum218.98
Variance7.8233474
MonotonicityNot monotonic
2023-12-10T21:08:47.401737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 15
 
15.0%
0.13 6
 
6.0%
0.14 3
 
3.0%
0.54 3
 
3.0%
0.93 2
 
2.0%
0.4 2
 
2.0%
4.86 2
 
2.0%
1.64 2
 
2.0%
0.67 2
 
2.0%
2.82 1
 
1.0%
Other values (62) 62
62.0%
ValueCountFrequency (%)
0.0 15
15.0%
0.13 6
 
6.0%
0.14 3
 
3.0%
0.27 1
 
1.0%
0.28 1
 
1.0%
0.39 1
 
1.0%
0.4 2
 
2.0%
0.41 1
 
1.0%
0.42 1
 
1.0%
0.54 3
 
3.0%
ValueCountFrequency (%)
14.49 1
1.0%
13.66 1
1.0%
12.64 1
1.0%
9.37 1
1.0%
7.99 1
1.0%
6.82 1
1.0%
5.91 1
1.0%
5.57 1
1.0%
5.26 1
1.0%
4.86 2
2.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6246.1771
Minimum0
Maximum34263.71
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:08:47.562597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile131.746
Q11147.9225
median4622.8
Q38582.965
95-th percentile18920.33
Maximum34263.71
Range34263.71
Interquartile range (IQR)7435.0425

Descriptive statistics

Standard deviation6610.6438
Coefficient of variation (CV)1.0583504
Kurtosis3.7553436
Mean6246.1771
Median Absolute Deviation (MAD)3709.25
Skewness1.7585136
Sum624617.71
Variance43700611
MonotonicityNot monotonic
2023-12-10T21:08:47.715383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.68 8
 
8.0%
0.0 5
 
5.0%
800.28 2
 
2.0%
153.68 2
 
2.0%
4229.41 1
 
1.0%
1521.81 1
 
1.0%
730.41 1
 
1.0%
1154.48 1
 
1.0%
3035.71 1
 
1.0%
3847.27 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 5
5.0%
138.68 8
8.0%
153.68 2
 
2.0%
457.29 1
 
1.0%
487.28 1
 
1.0%
640.96 1
 
1.0%
730.41 1
 
1.0%
800.28 2
 
2.0%
873.34 1
 
1.0%
948.33 1
 
1.0%
ValueCountFrequency (%)
34263.71 1
1.0%
28734.35 1
1.0%
24423.24 1
1.0%
22505.02 1
1.0%
20001.81 1
1.0%
18863.41 1
1.0%
17650.68 1
1.0%
16986.36 1
1.0%
16670.71 1
1.0%
15967.05 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

2023-12-10T21:08:47.860522image/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

2023-12-10T21:08:41.464670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:34.377930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.204365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.059052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.882014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.698560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.540665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:39.292071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:40.493464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:41.587879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:34.456095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.290741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.133116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.969832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.790273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.635557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:39.377913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:40.615963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:41.678380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:34.541564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.375575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.207579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.052835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.881592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.716321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:39.474101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:40.717436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:41.772945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:34.643931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.480795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.316203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.139215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.969103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.796763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:39.632393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:40.846450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:41.885444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:34.755317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.574516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.418844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.225275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.057565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.877697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:39.963641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:40.967399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:41.984848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:34.843742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.678293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.502557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.322207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.151083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.951922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:40.053502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:41.085005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:42.078559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:34.931425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.764071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.593263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.410021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.251931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:39.027744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:40.161806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:41.167346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:42.176293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.022892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.838835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.673965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.519057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.353274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:39.107599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:40.249182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:41.265597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:42.296693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.113332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:35.959113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:36.774460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:37.614352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:38.441221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:39.207169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:40.370712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:41.361081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:08:47.950936image/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
기본키1.0001.0000.0001.0000.6120.7390.9100.5230.4590.4210.4640.6641.000
지점1.0001.0000.0001.0001.0001.0001.0000.8980.8640.7850.8660.8161.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.1850.0920.0000.000
측정구간1.0001.0000.0001.0000.9921.0001.0000.9010.8710.8000.8730.8081.000
연장((km))0.6121.0000.0000.9921.0000.5440.6410.0000.0000.0000.0000.0000.992
좌표위치위도((°))0.7391.0000.0001.0000.5441.0000.7810.3920.0680.2410.0000.0001.000
좌표위치경도((°))0.9101.0000.0001.0000.6410.7811.0000.4110.4580.3920.4740.2881.000
co((g/km))0.5230.8980.0000.9010.0000.3920.4111.0000.9240.8610.9310.9200.901
nox((g/km))0.4590.8640.0000.8710.0000.0680.4580.9241.0000.9240.9960.8830.871
hc((g/km))0.4210.7850.1850.8000.0000.2410.3920.8610.9241.0000.9220.9210.800
pm((g/km))0.4640.8660.0920.8730.0000.0000.4740.9310.9960.9221.0000.8920.873
co2((g/km))0.6640.8160.0000.8080.0000.0000.2880.9200.8830.9210.8921.0000.808
주소1.0001.0000.0001.0000.9921.0001.0000.9010.8710.8000.8730.8081.000
2023-12-10T21:08:48.088187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0001.000
방향0.0001.0000.000
주소1.0000.0001.000
2023-12-10T21:08:48.184753image/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
기본키1.000-0.068-0.0360.286-0.282-0.285-0.290-0.292-0.2740.0000.7530.753
연장((km))-0.0681.0000.127-0.043-0.043-0.023-0.033-0.033-0.0450.0000.6960.696
좌표위치위도((°))-0.0360.1271.000-0.1480.0300.0670.0660.0900.0130.0000.7530.753
좌표위치경도((°))0.286-0.043-0.1481.0000.2380.2190.2130.1730.2510.0000.7530.753
co((g/km))-0.282-0.0430.0300.2381.0000.9800.9870.9720.9970.0000.4480.448
nox((g/km))-0.285-0.0230.0670.2190.9801.0000.9970.9910.9810.0000.4020.402
hc((g/km))-0.290-0.0330.0660.2130.9870.9971.0000.9880.9840.1320.3310.331
pm((g/km))-0.292-0.0330.0900.1730.9720.9910.9881.0000.9740.0840.4050.405
co2((g/km))-0.274-0.0450.0130.2510.9970.9810.9840.9741.0000.0000.3160.316
방향0.0000.0000.0000.0000.0000.0000.1320.0840.0001.0000.0000.000
측정구간0.7530.6960.7530.7530.4480.4020.3310.4050.3160.0001.0001.000
주소0.7530.6960.7530.7530.4480.4020.3310.4050.3160.0001.0001.000

Missing values

2023-12-10T21:08:42.482982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:08:42.720723image/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-김천11.420210401136.07278128.086116.754.560.70.421632.42경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210401136.07278128.086115.663.760.550.281480.58경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210401136.35299128.1393523.3227.334.162.255032.31경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210401136.35299128.1393518.3419.373.01.414003.34경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210401136.50552128.1694646.4658.578.114.5110478.16경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210401136.50552128.1694630.1333.454.872.876992.72경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210401136.14659128.025449.396.810.960.692447.65경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210401136.14659128.025443.312.060.310.13873.34경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210401135.98385128.4110665.0762.548.764.7715967.05경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210401135.98385128.4110659.8557.178.34.113546.77경북 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-포항2.420210401136.06326129.227718.716.130.870.562267.74경북 포항 기계 내단
9192건기연[3107-2]2기계-포항2.420210401136.06326129.2277111.236.981.120.542723.17경북 포항 기계 내단
9293건기연[3109-1]1죽장-부남20.620210401136.2488129.040490.520.280.040.0138.68경북 청송 현동 눌인
9394건기연[3109-1]2죽장-부남20.620210401136.2488129.040490.520.280.040.0138.68경북 청송 현동 눌인
9495건기연[3113-1]1진보-석보6.020210401136.59556129.086116.77.851.20.541412.63경북 영양 입암 신구
9596건기연[3113-1]2진보-석보6.020210401136.59556129.086112.631.640.260.13640.96경북 영양 입암 신구
9697건기연[3116-1]1녹동-영양26.720210401136.86368129.012481.981.320.20.13487.28경북 봉화 소천 서천
9798건기연[3116-1]2녹동-영양26.720210401136.86368129.012480.00.00.00.00.0경북 봉화 소천 서천
9899건기연[3307-1]1고령-수륜12.920210401135.80817128.230391.731.230.180.13457.29경북 성주 수륜 계정
99100건기연[3307-1]2고령-수륜12.920210401135.80817128.230394.622.960.470.271128.25경북 성주 수륜 계정