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 4 (4.0%) zerosZeros
nox((g/km)) has 4 (4.0%) zerosZeros
hc((g/km)) has 4 (4.0%) zerosZeros
pm((g/km)) has 17 (17.0%) zerosZeros
co2((g/km)) has 4 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:08:50.086521
Analysis finished2023-12-10 12:09:03.045772
Duration12.96 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
2023-12-10T21:09:03.174451image/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
2023-12-10T21:09:03.351027image/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

2023-12-10T21:09:03.495285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:03.607923image/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
2023-12-10T21:09:03.864061image/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%
2805-3 2
 
2.0%
3113-1 2
 
2.0%
2508-0 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%
Other values (40) 80
80.0%
2023-12-10T21:09:04.469651image/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 74
9.2%
3 48
 
6.0%
4 28
 
3.5%
5 28
 
3.5%
8 26
 
3.2%
Other values (3) 52
 
6.5%

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 74
14.8%
3 48
 
9.6%
4 28
 
5.6%
5 28
 
5.6%
8 26
 
5.2%
7 24
 
4.8%
6 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%
2 74
9.2%
3 48
 
6.0%
4 28
 
3.5%
5 28
 
3.5%
8 26
 
3.2%
Other values (3) 52
 
6.5%

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 74
9.2%
3 48
 
6.0%
4 28
 
3.5%
5 28
 
3.5%
8 26
 
3.2%
Other values (3) 52
 
6.5%

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

2023-12-10T21:09:04.637973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:04.779970image/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
산내-현곡
 
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%
산내-현곡 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

2023-12-10T21:09:04.932623image/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%
안강-고경 2
 
2.0%
선산-낙동 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%
Other values (39) 78
78.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.368
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:05.101544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q14.9
median8.1
Q311.6
95-th percentile24.2
Maximum27.8
Range26.5
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation6.268647
Coefficient of variation (CV)0.66915532
Kurtosis1.464504
Mean9.368
Median Absolute Deviation (MAD)3.3
Skewness1.2364118
Sum936.8
Variance39.295935
MonotonicityNot monotonic
2023-12-10T21:09:05.281919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
10.6 6
 
6.0%
6.4 4
 
4.0%
2.1 4
 
4.0%
2.4 4
 
4.0%
11.2 4
 
4.0%
6.8 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 (34) 68
68.0%
ValueCountFrequency (%)
1.3 2
2.0%
1.4 2
2.0%
1.8 2
2.0%
2.1 4
4.0%
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%
12.3 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210301
2nd row20210301
3rd row20210301
4th row20210301
5th row20210301

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2023-12-10T21:09:05.456976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:05.569667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 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

2023-12-10T21:09:05.674173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:05.798488image/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%
Mean36.138701
Minimum35.65543
Maximum36.99828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:05.954489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.85555
median36.036925
Q336.35882
95-th percentile36.84888
Maximum36.99828
Range1.34285
Interquartile range (IQR)0.50327

Descriptive statistics

Standard deviation0.3640965
Coefficient of variation (CV)0.010074975
Kurtosis-0.5807842
Mean36.138701
Median Absolute Deviation (MAD)0.267845
Skewness0.66031204
Sum3613.8701
Variance0.13256626
MonotonicityNot monotonic
2023-12-10T21:09:06.217116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
35.99882 2
 
2.0%
36.33873 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%
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 (%)
36.99828 2
2.0%
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%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.84016
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:06.514353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.13935
Q1128.41883
median128.81361
Q3129.30673
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.8879

Descriptive statistics

Standard deviation0.46594798
Coefficient of variation (CV)0.0036164809
Kurtosis-1.3820494
Mean128.84016
Median Absolute Deviation (MAD)0.43981
Skewness-0.096551312
Sum12884.016
Variance0.21710752
MonotonicityNot monotonic
2023-12-10T21:09:06.823716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
129.08273 2
 
2.0%
128.30572 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%
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%
128.23413 2
2.0%
128.25985 2
2.0%
128.30572 2
2.0%
128.31646 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%
129.41245 2
2.0%
129.41209 2
2.0%
129.40658 2
2.0%
129.40119 2
2.0%
129.34631 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.0219
Minimum0
Maximum79.75
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:07.090427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52
Q13.9475
median12.37
Q326.115
95-th percentile53.5465
Maximum79.75
Range79.75
Interquartile range (IQR)22.1675

Descriptive statistics

Standard deviation16.593449
Coefficient of variation (CV)0.97482942
Kurtosis2.3060137
Mean17.0219
Median Absolute Deviation (MAD)9.84
Skewness1.4802113
Sum1702.19
Variance275.34255
MonotonicityNot monotonic
2023-12-10T21:09:07.288400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52 5
 
5.0%
0.0 4
 
4.0%
3.31 4
 
4.0%
1.3 2
 
2.0%
5.98 2
 
2.0%
5.56 2
 
2.0%
0.65 2
 
2.0%
1.95 2
 
2.0%
11.07 1
 
1.0%
15.92 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.52 5
5.0%
0.65 2
 
2.0%
1.05 1
 
1.0%
1.3 2
 
2.0%
1.57 1
 
1.0%
1.95 2
 
2.0%
1.98 1
 
1.0%
2.26 1
 
1.0%
2.78 1
 
1.0%
ValueCountFrequency (%)
79.75 1
1.0%
66.82 1
1.0%
63.09 1
1.0%
60.09 1
1.0%
54.81 1
1.0%
53.48 1
1.0%
50.34 1
1.0%
46.01 1
1.0%
40.23 1
1.0%
39.46 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.2395
Minimum0
Maximum62.73
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:07.508073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q12.5875
median8.1
Q320.455
95-th percentile43.7935
Maximum62.73
Range62.73
Interquartile range (IQR)17.8675

Descriptive statistics

Standard deviation13.959874
Coefficient of variation (CV)1.054411
Kurtosis1.1477125
Mean13.2395
Median Absolute Deviation (MAD)7.14
Skewness1.3339766
Sum1323.95
Variance194.87809
MonotonicityNot monotonic
2023-12-10T21:09:07.689864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 5
 
5.0%
0.0 4
 
4.0%
2.06 4
 
4.0%
4.53 2
 
2.0%
0.96 2
 
2.0%
3.54 2
 
2.0%
3.57 2
 
2.0%
0.32 2
 
2.0%
8.13 2
 
2.0%
8.1 2
 
2.0%
Other values (71) 73
73.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.28 5
5.0%
0.32 2
 
2.0%
0.55 1
 
1.0%
0.64 2
 
2.0%
0.83 1
 
1.0%
0.96 2
 
2.0%
1.32 1
 
1.0%
1.51 1
 
1.0%
1.79 1
 
1.0%
ValueCountFrequency (%)
62.73 1
1.0%
46.02 1
1.0%
45.35 1
1.0%
44.75 1
1.0%
43.86 1
1.0%
43.79 1
1.0%
40.82 1
1.0%
40.2 1
1.0%
40.04 1
1.0%
39.64 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8443
Minimum0
Maximum7.73
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:07.882026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.3975
median1.2
Q32.675
95-th percentile5.3635
Maximum7.73
Range7.73
Interquartile range (IQR)2.2775

Descriptive statistics

Standard deviation1.8121756
Coefficient of variation (CV)0.98258178
Kurtosis0.93003788
Mean1.8443
Median Absolute Deviation (MAD)1.035
Skewness1.2066766
Sum184.43
Variance3.2839803
MonotonicityNot monotonic
2023-12-10T21:09:08.114708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 5
 
5.0%
0.0 4
 
4.0%
0.31 4
 
4.0%
1.16 2
 
2.0%
2.24 2
 
2.0%
2.57 2
 
2.0%
0.53 2
 
2.0%
0.06 2
 
2.0%
0.17 2
 
2.0%
0.12 2
 
2.0%
Other values (71) 73
73.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.04 5
5.0%
0.06 2
 
2.0%
0.09 1
 
1.0%
0.12 2
 
2.0%
0.13 1
 
1.0%
0.17 2
 
2.0%
0.2 1
 
1.0%
0.22 1
 
1.0%
0.26 1
 
1.0%
ValueCountFrequency (%)
7.73 1
1.0%
6.97 1
1.0%
6.75 1
1.0%
6.05 1
1.0%
6.0 1
1.0%
5.33 1
1.0%
5.19 1
1.0%
4.95 1
1.0%
4.82 1
1.0%
4.63 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6995
Minimum0
Maximum2.8
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:08.332496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.42
Q30.9975
95-th percentile2.3835
Maximum2.8
Range2.8
Interquartile range (IQR)0.8675

Descriptive statistics

Standard deviation0.74227112
Coefficient of variation (CV)1.0611453
Kurtosis0.67840533
Mean0.6995
Median Absolute Deviation (MAD)0.34
Skewness1.26479
Sum69.95
Variance0.55096641
MonotonicityNot monotonic
2023-12-10T21:09:08.596563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 17
17.0%
0.27 10
 
10.0%
0.13 10
 
10.0%
0.56 4
 
4.0%
0.42 4
 
4.0%
0.14 4
 
4.0%
0.4 3
 
3.0%
0.28 3
 
3.0%
1.89 2
 
2.0%
0.69 2
 
2.0%
Other values (34) 41
41.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.13 10
10.0%
0.14 4
 
4.0%
0.27 10
10.0%
0.28 3
 
3.0%
0.39 1
 
1.0%
0.4 3
 
3.0%
0.41 1
 
1.0%
0.42 4
 
4.0%
0.52 1
 
1.0%
ValueCountFrequency (%)
2.8 1
1.0%
2.72 1
1.0%
2.56 1
1.0%
2.47 1
1.0%
2.45 1
1.0%
2.38 1
1.0%
2.3 1
1.0%
2.22 1
1.0%
2.0 1
1.0%
1.89 2
2.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4257.976
Minimum0
Maximum20601.69
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:09.162746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138.68
Q11006.885
median3208.51
Q36350.235
95-th percentile13966.321
Maximum20601.69
Range20601.69
Interquartile range (IQR)5343.35

Descriptive statistics

Standard deviation4156.9058
Coefficient of variation (CV)0.97626333
Kurtosis2.5937024
Mean4257.976
Median Absolute Deviation (MAD)2599.585
Skewness1.5269235
Sum425797.6
Variance17279866
MonotonicityNot monotonic
2023-12-10T21:09:09.376554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.68 5
 
5.0%
0.0 4
 
4.0%
873.34 4
 
4.0%
307.36 2
 
2.0%
1572.4 2
 
2.0%
1469.32 2
 
2.0%
153.68 2
 
2.0%
461.05 2
 
2.0%
2908.66 1
 
1.0%
3815.84 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 4
4.0%
138.68 5
5.0%
153.68 2
 
2.0%
277.37 1
 
1.0%
307.36 2
 
2.0%
416.06 1
 
1.0%
461.05 2
 
2.0%
487.28 1
 
1.0%
595.97 1
 
1.0%
734.66 1
 
1.0%
ValueCountFrequency (%)
20601.69 1
1.0%
16597.3 1
1.0%
15753.58 1
1.0%
14416.42 1
1.0%
14179.14 1
1.0%
13955.12 1
1.0%
12029.88 1
1.0%
11836.12 1
1.0%
9412.13 1
1.0%
8687.95 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 건천 대곡
 
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 건천 대곡 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

2023-12-10T21:09:09.566205image/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%
고령 6
 
1.5%
청도 6
 
1.5%
연일 6
 
1.5%
상주 6
 
1.5%
울진 6
 
1.5%
안동 6
 
1.5%
Other values (101) 224
56.0%

Interactions

2023-12-10T21:09:01.123556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:50.835933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:51.946953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:53.028212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:54.497707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:56.112612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:57.126088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:58.264490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:59.999791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:01.249703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:50.940750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:52.064735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:53.150288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:54.611409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:56.217152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:57.256550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:58.668493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:00.084473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:01.373014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:51.033623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:52.166169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:53.253626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:54.725980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:56.308773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:57.371850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:58.853140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:00.203932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:01.500060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:51.163133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:52.289838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:53.375960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:54.859126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:56.417127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:57.489221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:59.058976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:00.338444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:01.625418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:51.325449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:52.412193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:53.508443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:55.109648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:56.537461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:57.587014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:59.258617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:00.460748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:01.750499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:51.473303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:52.535572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:53.983902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:55.296972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:56.684291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:57.676046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:59.452786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:00.577421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:01.851514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:51.585491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:52.663004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:54.131690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:55.458290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:56.804293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:57.771648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:59.604805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:00.702574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:02.000961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:51.710544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:52.787407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:54.278021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:55.665766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:56.916461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:57.894513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:59.733543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:00.888537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:02.420935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:51.812240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:52.898581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:54.388504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:55.873717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:57.014026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:58.025866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:08:59.881498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:01.019599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:09:09.692708image/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
기본키1.0001.0000.0001.0000.6300.7970.9110.4030.4350.5270.6290.4191.000
지점1.0001.0000.0001.0001.0001.0001.0000.8410.8620.8630.9380.7281.000
방향0.0000.0001.0000.0000.0000.0000.0000.1150.0000.2070.0000.4020.000
측정구간1.0001.0000.0001.0000.9921.0001.0000.8250.8660.8470.9270.7251.000
연장((km))0.6301.0000.0000.9921.0000.6380.6490.0000.0960.0000.0490.0000.992
좌표위치위도((°))0.7971.0000.0001.0000.6381.0000.8150.1140.2200.4710.4940.2421.000
좌표위치경도((°))0.9111.0000.0001.0000.6490.8151.0000.2490.4190.3700.6750.2791.000
co((g/km))0.4030.8410.1150.8250.0000.1140.2491.0000.8890.9830.9230.9910.825
nox((g/km))0.4350.8620.0000.8660.0960.2200.4190.8891.0000.8920.8730.8790.866
hc((g/km))0.5270.8630.2070.8470.0000.4710.3700.9830.8921.0000.9350.9600.847
pm((g/km))0.6290.9380.0000.9270.0490.4940.6750.9230.8730.9351.0000.9040.927
co2((g/km))0.4190.7280.4020.7250.0000.2420.2790.9910.8790.9600.9041.0000.725
주소1.0001.0000.0001.0000.9921.0001.0000.8250.8660.8470.9270.7251.000
2023-12-10T21:09:09.907816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0001.000
방향0.0001.0000.000
주소1.0000.0001.000
2023-12-10T21:09:10.101547image/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
기본키1.000-0.091-0.0210.325-0.195-0.225-0.202-0.233-0.1910.0000.7530.753
연장((km))-0.0911.0000.092-0.054-0.049-0.008-0.0210.014-0.0500.0000.6960.696
좌표위치위도((°))-0.0210.0921.000-0.129-0.054-0.058-0.0770.005-0.0620.0000.7530.753
좌표위치경도((°))0.325-0.054-0.1291.0000.3180.2500.2680.1610.3230.0000.7530.753
co((g/km))-0.195-0.049-0.0540.3181.0000.9800.9880.9410.9980.0790.3340.334
nox((g/km))-0.225-0.008-0.0580.2500.9801.0000.9960.9740.9780.0000.4060.406
hc((g/km))-0.202-0.021-0.0770.2680.9880.9961.0000.9640.9850.1490.3580.358
pm((g/km))-0.2330.0140.0050.1610.9410.9740.9641.0000.9400.0000.4820.482
co2((g/km))-0.191-0.050-0.0620.3230.9980.9780.9850.9401.0000.2940.2480.248
방향0.0000.0000.0000.0000.0790.0000.1490.0000.2941.0000.0000.000
측정구간0.7530.6960.7530.7530.3340.4060.3580.4820.2480.0001.0001.000
주소0.7530.6960.7530.7530.3340.4060.3580.4820.2480.0001.0001.000

Missing values

2023-12-10T21:09:02.572255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:09:02.917197image/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-김천11.420210301136.07278128.086116.774.530.660.41787.92경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210301136.07278128.086113.312.060.310.13873.34경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210301136.35299128.1393511.988.221.190.83159.78경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210301136.35299128.1393510.286.060.920.272717.48경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210301136.50552128.1694626.324.343.31.536029.06경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210301136.50552128.1694626.7324.553.341.636180.82경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210301136.14659128.025448.185.850.830.562129.05경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210301136.14659128.025443.312.060.310.13873.34경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210301135.98385128.4110633.8424.383.661.468687.95경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210301135.98385128.4110627.920.713.311.386518.42경북 칠곡 왜관 왜관
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3106-5]1유강-광명4.920210301135.98878129.3214446.0132.514.421.8912029.88경북 포항 연일 중단
9192건기연[3106-5]2유강-광명4.920210301135.98878129.3214431.9721.03.081.748441.07경북 포항 연일 중단
9293건기연[3107-2]1기계-포항2.420210301136.06326129.227713.882.430.360.141017.66경북 포항 기계 내단
9394건기연[3107-2]2기계-포항2.420210301136.06326129.2277112.348.11.190.673257.24경북 포항 기계 내단
9495건기연[3109-1]1죽장-부남20.620210301136.2488129.040490.650.320.060.0153.68경북 청송 현동 눌인
9596건기연[3109-1]2죽장-부남20.620210301136.2488129.040491.30.640.120.0307.36경북 청송 현동 눌인
9697건기연[3113-1]1진보-석보6.020210301136.59556129.086111.981.320.20.13487.28경북 영양 입암 신구
9798건기연[3113-1]2진보-석보6.020210301136.59556129.086111.30.640.120.0307.36경북 영양 입암 신구
9899건기연[3116-1]1녹동-영양26.720210301136.86368129.012486.614.130.620.271746.69경북 봉화 소천 서천
99100건기연[3116-1]2녹동-영양26.720210301136.86368129.012480.00.00.00.00.0경북 봉화 소천 서천