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
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
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 unique valuesUnique
nox((g/km)) has unique valuesUnique
hc((g/km)) has unique valuesUnique
pm((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2024-04-17 01:51:19.611509
Analysis finished2024-04-17 01:51:26.573392
Duration6.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

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
2024-04-17T10:51:26.636178image/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
2024-04-17T10:51:26.775087image/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

2024-04-17T10:51:26.889729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:26.976377image/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
2024-04-17T10:51:27.147393image/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%
3020-1 2
 
2.0%
3311-0 2
 
2.0%
2613-4 2
 
2.0%
2614-3 2
 
2.0%
2802-1 2
 
2.0%
2803-1 2
 
2.0%
2804-2 2
 
2.0%
2805-3 2
 
2.0%
2808-0 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T10:51:27.441617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 70
8.8%
2 70
8.8%
5 26
 
3.2%
8 26
 
3.2%
4 22
 
2.8%
Other values (3) 44
 
5.5%

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 (%)
0 132
26.4%
1 110
22.0%
3 70
14.0%
2 70
14.0%
5 26
 
5.2%
8 26
 
5.2%
4 22
 
4.4%
7 20
 
4.0%
6 16
 
3.2%
9 8
 
1.6%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 70
8.8%
2 70
8.8%
5 26
 
3.2%
8 26
 
3.2%
4 22
 
2.8%
Other values (3) 44
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 70
8.8%
2 70
8.8%
5 26
 
3.2%
8 26
 
3.2%
4 22
 
2.8%
Other values (3) 44
 
5.5%

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

2024-04-17T10:51:27.551804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:27.624717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%
Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T10:51:27.798691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.06
Min length5

Characters and Unicode

Total characters506
Distinct characters89
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row구성-김천
2nd row구성-김천
3rd row공성-상주
4th row공성-상주
5th row상주-함창
ValueCountFrequency (%)
구성-김천 2
 
2.0%
신성-금수 2
 
2.0%
약목-구평 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%
일반5-금성 2
 
2.0%
영천-의성 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T10:51:28.090022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.8%
22
 
4.3%
20
 
4.0%
18
 
3.6%
16
 
3.2%
14
 
2.8%
14
 
2.8%
12
 
2.4%
10
 
2.0%
10
 
2.0%
Other values (79) 270
53.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 404
79.8%
Dash Punctuation 100
 
19.8%
Decimal Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
5.4%
20
 
5.0%
18
 
4.5%
16
 
4.0%
14
 
3.5%
14
 
3.5%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (77) 258
63.9%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Decimal Number
ValueCountFrequency (%)
5 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 404
79.8%
Common 102
 
20.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22
 
5.4%
20
 
5.0%
18
 
4.5%
16
 
4.0%
14
 
3.5%
14
 
3.5%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (77) 258
63.9%
Common
ValueCountFrequency (%)
- 100
98.0%
5 2
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 404
79.8%
ASCII 102
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
98.0%
5 2
 
2.0%
Hangul
ValueCountFrequency (%)
22
 
5.4%
20
 
5.0%
18
 
4.5%
16
 
4.0%
14
 
3.5%
14
 
3.5%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (77) 258
63.9%

연장((km))
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.924
Minimum1.3
Maximum26.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:28.203315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q15.9
median8.1
Q311.2
95-th percentile20.6
Maximum26.7
Range25.4
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation5.294005
Coefficient of variation (CV)0.59323229
Kurtosis1.9000042
Mean8.924
Median Absolute Deviation (MAD)3.1
Skewness1.1823676
Sum892.4
Variance28.026489
MonotonicityNot monotonic
2024-04-17T10:51:28.310815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
10.6 8
 
8.0%
8.7 4
 
4.0%
6.4 4
 
4.0%
2.4 4
 
4.0%
11.2 4
 
4.0%
11.4 2
 
2.0%
6.9 2
 
2.0%
10.4 2
 
2.0%
2.2 2
 
2.0%
8.0 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
1.3 2
2.0%
1.4 2
2.0%
1.8 2
2.0%
2.2 2
2.0%
2.4 4
4.0%
3.4 2
2.0%
3.8 2
2.0%
4.1 2
2.0%
4.7 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
26.7 2
2.0%
22.1 2
2.0%
20.6 2
2.0%
19.4 2
2.0%
15.8 2
2.0%
14.4 2
2.0%
12.9 2
2.0%
12.3 2
2.0%
12.0 2
2.0%
11.9 2
2.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2024-04-17T10:51:28.408222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:28.483567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 100
100.0%

측정시간
Categorical

CONSTANT 

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

2024-04-17T10:51:28.559528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:51:28.629362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.121485
Minimum35.65543
Maximum36.86368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:28.713775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.86035
median36.036925
Q336.33873
95-th percentile36.76527
Maximum36.86368
Range1.20825
Interquartile range (IQR)0.47838

Descriptive statistics

Standard deviation0.3410521
Coefficient of variation (CV)0.0094418072
Kurtosis-0.47339739
Mean36.121485
Median Absolute Deviation (MAD)0.23588
Skewness0.69161357
Sum3612.1485
Variance0.11631653
MonotonicityNot monotonic
2024-04-17T10:51:28.844299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
35.85555 2
 
2.0%
35.73646 2
 
2.0%
36.73238 2
 
2.0%
36.59513 2
 
2.0%
36.35882 2
 
2.0%
36.32892 2
 
2.0%
36.04086 2
 
2.0%
35.99882 2
 
2.0%
36.03299 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
35.65543 2
2.0%
35.67783 2
2.0%
35.68369 2
2.0%
35.69757 2
2.0%
35.70376 2
2.0%
35.71373 2
2.0%
35.71695 2
2.0%
35.73646 2
2.0%
35.78841 2
2.0%
35.79392 2
2.0%
ValueCountFrequency (%)
36.86368 2
2.0%
36.84888 2
2.0%
36.76527 2
2.0%
36.75364 2
2.0%
36.73871 2
2.0%
36.73238 2
2.0%
36.59556 2
2.0%
36.59513 2
2.0%
36.50552 2
2.0%
36.40808 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.75297
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:28.958013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.08611
Q1128.31646
median128.685
Q3129.248
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.93154

Descriptive statistics

Standard deviation0.47051287
Coefficient of variation (CV)0.0036543845
Kurtosis-1.3974087
Mean128.75297
Median Absolute Deviation (MAD)0.41313
Skewness0.18001032
Sum12875.297
Variance0.22138236
MonotonicityNot monotonic
2024-04-17T10:51:29.082810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
129.52365 2
 
2.0%
128.31646 2
 
2.0%
128.53474 2
 
2.0%
128.41883 2
 
2.0%
128.46812 2
 
2.0%
128.70365 2
 
2.0%
128.80075 2
 
2.0%
129.08273 2
 
2.0%
129.30673 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
128.02544 2
2.0%
128.08594 2
2.0%
128.08611 2
2.0%
128.13935 2
2.0%
128.15515 2
2.0%
128.16946 2
2.0%
128.21559 2
2.0%
128.23039 2
2.0%
128.23413 2
2.0%
128.25985 2
2.0%
ValueCountFrequency (%)
129.52365 2
2.0%
129.49495 2
2.0%
129.47131 2
2.0%
129.45978 2
2.0%
129.45621 2
2.0%
129.40658 2
2.0%
129.34631 2
2.0%
129.32144 2
2.0%
129.31407 2
2.0%
129.30673 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4617.0158
Minimum206.4
Maximum14012.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:29.195210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum206.4
5-th percentile539.17
Q11767.92
median3726.69
Q36444.9125
95-th percentile11231.365
Maximum14012.21
Range13805.81
Interquartile range (IQR)4676.9925

Descriptive statistics

Standard deviation3452.2246
Coefficient of variation (CV)0.74771773
Kurtosis0.26374329
Mean4617.0158
Median Absolute Deviation (MAD)2200.105
Skewness0.94927778
Sum461701.58
Variance11917854
MonotonicityNot monotonic
2024-04-17T10:51:29.306338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2637.25 1
 
1.0%
736.75 1
 
1.0%
3838.65 1
 
1.0%
323.7 1
 
1.0%
331.03 1
 
1.0%
6843.89 1
 
1.0%
9002.56 1
 
1.0%
4520.73 1
 
1.0%
4879.02 1
 
1.0%
1895.8 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
206.4 1
1.0%
228.29 1
1.0%
323.7 1
1.0%
331.03 1
1.0%
516.18 1
1.0%
540.38 1
1.0%
736.75 1
1.0%
874.0 1
1.0%
928.36 1
1.0%
933.65 1
1.0%
ValueCountFrequency (%)
14012.21 1
1.0%
13958.69 1
1.0%
13286.8 1
1.0%
12815.22 1
1.0%
12736.45 1
1.0%
11152.15 1
1.0%
11070.81 1
1.0%
10750.16 1
1.0%
10018.6 1
1.0%
9944.82 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5913.9403
Minimum203.31
Maximum23374.08
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:29.423987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum203.31
5-th percentile698.2
Q11766.96
median3749
Q37871.22
95-th percentile17041.866
Maximum23374.08
Range23170.77
Interquartile range (IQR)6104.26

Descriptive statistics

Standard deviation5265.623
Coefficient of variation (CV)0.89037474
Kurtosis0.70204317
Mean5913.9403
Median Absolute Deviation (MAD)2447.925
Skewness1.214073
Sum591394.03
Variance27726786
MonotonicityNot monotonic
2024-04-17T10:51:29.549155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2600.39 1
 
1.0%
922.41 1
 
1.0%
4662.03 1
 
1.0%
304.41 1
 
1.0%
289.19 1
 
1.0%
6961.02 1
 
1.0%
12447.04 1
 
1.0%
7359.96 1
 
1.0%
5720.85 1
 
1.0%
2234.55 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
203.31 1
1.0%
205.5 1
1.0%
289.19 1
1.0%
304.41 1
1.0%
604.72 1
1.0%
703.12 1
1.0%
785.11 1
1.0%
792.38 1
1.0%
812.12 1
1.0%
880.66 1
1.0%
ValueCountFrequency (%)
23374.08 1
1.0%
18409.08 1
1.0%
18398.08 1
1.0%
17419.04 1
1.0%
17267.32 1
1.0%
17030.0 1
1.0%
16890.59 1
1.0%
15611.29 1
1.0%
15360.32 1
1.0%
15300.91 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean718.8611
Minimum26.12
Maximum2272.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:29.654468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26.12
5-th percentile88.0465
Q1255.43
median506.395
Q31034.55
95-th percentile1934.8735
Maximum2272.56
Range2246.44
Interquartile range (IQR)779.12

Descriptive statistics

Standard deviation579.00591
Coefficient of variation (CV)0.80544894
Kurtosis-0.0058422328
Mean718.8611
Median Absolute Deviation (MAD)312.36
Skewness0.96756884
Sum71886.11
Variance335247.85
MonotonicityNot monotonic
2024-04-17T10:51:29.763073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
343.11 1
 
1.0%
136.07 1
 
1.0%
516.53 1
 
1.0%
41.25 1
 
1.0%
41.8 1
 
1.0%
894.02 1
 
1.0%
1628.61 1
 
1.0%
872.42 1
 
1.0%
786.29 1
 
1.0%
306.57 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
26.12 1
1.0%
29.69 1
1.0%
41.25 1
1.0%
41.8 1
1.0%
79.05 1
1.0%
88.52 1
1.0%
104.6 1
1.0%
107.16 1
1.0%
107.23 1
1.0%
120.78 1
1.0%
ValueCountFrequency (%)
2272.56 1
1.0%
2130.72 1
1.0%
2102.15 1
1.0%
1979.55 1
1.0%
1937.22 1
1.0%
1934.75 1
1.0%
1913.92 1
1.0%
1852.09 1
1.0%
1733.67 1
1.0%
1703.14 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean391.8312
Minimum18.32
Maximum1440.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:29.867998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.32
5-th percentile43.1605
Q1132.7275
median274.72
Q3551.81
95-th percentile1092.9155
Maximum1440.83
Range1422.51
Interquartile range (IQR)419.0825

Descriptive statistics

Standard deviation331.79851
Coefficient of variation (CV)0.84678942
Kurtosis0.60456829
Mean391.8312
Median Absolute Deviation (MAD)175.115
Skewness1.173227
Sum39183.12
Variance110090.25
MonotonicityNot monotonic
2024-04-17T10:51:29.975501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213.72 1
 
1.0%
74.84 1
 
1.0%
269.4 1
 
1.0%
27.42 1
 
1.0%
28.44 1
 
1.0%
409.28 1
 
1.0%
691.82 1
 
1.0%
459.63 1
 
1.0%
369.79 1
 
1.0%
158.38 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
18.32 1
1.0%
20.49 1
1.0%
27.42 1
1.0%
28.44 1
1.0%
39.56 1
1.0%
43.35 1
1.0%
43.99 1
1.0%
48.28 1
1.0%
65.51 1
1.0%
71.33 1
1.0%
ValueCountFrequency (%)
1440.83 1
1.0%
1212.03 1
1.0%
1162.7 1
1.0%
1155.1 1
1.0%
1093.59 1
1.0%
1092.88 1
1.0%
1072.88 1
1.0%
1032.1 1
1.0%
1030.28 1
1.0%
1015.94 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1173164.2
Minimum54415.7
Maximum3682463.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:51:30.345390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum54415.7
5-th percentile132970.91
Q1447371.92
median930070.34
Q31657298.3
95-th percentile3010793.6
Maximum3682463.5
Range3628047.8
Interquartile range (IQR)1209926.4

Descriptive statistics

Standard deviation894413.85
Coefficient of variation (CV)0.76239444
Kurtosis0.25418783
Mean1173164.2
Median Absolute Deviation (MAD)561145.59
Skewness0.9670498
Sum1.1731642 × 108
Variance7.9997613 × 1011
MonotonicityNot monotonic
2024-04-17T10:51:30.453862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
674787.73 1
 
1.0%
164898.51 1
 
1.0%
1045947.98 1
 
1.0%
80840.14 1
 
1.0%
83338.79 1
 
1.0%
1775929.01 1
 
1.0%
2165664.01 1
 
1.0%
1130119.49 1
 
1.0%
1117235.6 1
 
1.0%
457553.74 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
54415.7 1
1.0%
57177.48 1
1.0%
80840.14 1
1.0%
83338.79 1
1.0%
124214.84 1
1.0%
133431.76 1
1.0%
164898.51 1
1.0%
208612.78 1
1.0%
220317.54 1
1.0%
238280.27 1
1.0%
ValueCountFrequency (%)
3682463.55 1
1.0%
3474220.17 1
1.0%
3444817.85 1
1.0%
3311990.91 1
1.0%
3192254.84 1
1.0%
3001243.0 1
1.0%
2891558.48 1
1.0%
2798001.85 1
1.0%
2489711.23 1
1.0%
2471199.89 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T10:51:30.681907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.96
Min length10

Characters and Unicode

Total characters1096
Distinct characters102
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row경북 김천 구성 하강
2nd row경북 김천 구성 하강
3rd row경북 상주 청리 원장
4th row경북 상주 청리 원장
5th row경북 상주 외서 연봉
ValueCountFrequency (%)
경북 100
25.0%
포항 16
 
4.0%
경주 12
 
3.0%
의성 10
 
2.5%
칠곡 8
 
2.0%
상주 6
 
1.5%
연일 6
 
1.5%
영천 6
 
1.5%
고령 6
 
1.5%
김천 4
 
1.0%
Other values (105) 226
56.5%
2024-04-17T10:51:31.011768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
300
27.4%
120
 
10.9%
106
 
9.7%
30
 
2.7%
28
 
2.6%
26
 
2.4%
22
 
2.0%
16
 
1.5%
14
 
1.3%
14
 
1.3%
Other values (92) 420
38.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 796
72.6%
Space Separator 300
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
120
 
15.1%
106
 
13.3%
30
 
3.8%
28
 
3.5%
26
 
3.3%
22
 
2.8%
16
 
2.0%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (91) 406
51.0%
Space Separator
ValueCountFrequency (%)
300
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 796
72.6%
Common 300
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
120
 
15.1%
106
 
13.3%
30
 
3.8%
28
 
3.5%
26
 
3.3%
22
 
2.8%
16
 
2.0%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (91) 406
51.0%
Common
ValueCountFrequency (%)
300
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 796
72.6%
ASCII 300
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
300
100.0%
Hangul
ValueCountFrequency (%)
120
 
15.1%
106
 
13.3%
30
 
3.8%
28
 
3.5%
26
 
3.3%
22
 
2.8%
16
 
2.0%
14
 
1.8%
14
 
1.8%
14
 
1.8%
Other values (91) 406
51.0%

Interactions

2024-04-17T10:51:25.395123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.032292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.636670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.489200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.192135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.869220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.516298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.122263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.750388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:25.467899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.093455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.698659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.555885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.271224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.938372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.580066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.192108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.819821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:25.533234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.154149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.758008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.630736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.338675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.003854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.641901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.250736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.883574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:25.607960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.219555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.822198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.699339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.413024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.079238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.711085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.322128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.955842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:25.692807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.289546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.891548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.781406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.490222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.160032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.785048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.391551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:25.030186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:26.028811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.358783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.956885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.870660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.569990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.233121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.856513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.467698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:25.103558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:26.089386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.420079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.014476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.943608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.648420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.297041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.919666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.531078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:25.168560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:26.151684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.485532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.322780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.021737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.716620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.362996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.980221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.593151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:25.233356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:26.229963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:20.559313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:21.400123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.110588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:22.792926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:23.439193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.046646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:24.675949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:25.318872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T10:51:31.115594image/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
기본키1.0001.0000.0001.0000.5500.7270.9010.6220.5290.6440.5420.7341.000
지점1.0001.0000.0001.0001.0001.0001.0000.9750.9640.9690.9180.9891.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9750.9640.9690.9180.9891.000
연장((km))0.5501.0000.0001.0001.0000.6380.6000.5140.3280.4190.3220.2541.000
좌표위치위도((°))0.7271.0000.0001.0000.6381.0000.7250.5190.3780.6400.4420.5391.000
좌표위치경도((°))0.9011.0000.0001.0000.6000.7251.0000.5330.5310.6350.5890.7481.000
co((g/km))0.6220.9750.0000.9750.5140.5190.5331.0000.9170.8390.8150.9150.975
nox((g/km))0.5290.9640.0000.9640.3280.3780.5310.9171.0000.8900.9350.8280.964
hc((g/km))0.6440.9690.0000.9690.4190.6400.6350.8390.8901.0000.9430.9230.969
pm((g/km))0.5420.9180.0000.9180.3220.4420.5890.8150.9350.9431.0000.9280.918
co2((g/km))0.7340.9890.0000.9890.2540.5390.7480.9150.8280.9230.9281.0000.989
주소1.0001.0000.0001.0001.0001.0001.0000.9750.9640.9690.9180.9891.000
2024-04-17T10:51:31.257661image/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))방향
기본키1.000-0.008-0.0640.247-0.319-0.335-0.338-0.342-0.3130.000
연장((km))-0.0081.0000.098-0.094-0.168-0.110-0.131-0.105-0.1740.000
좌표위치위도((°))-0.0640.0981.000-0.203-0.160-0.129-0.134-0.118-0.1700.000
좌표위치경도((°))0.247-0.094-0.2031.0000.2160.1880.1820.1450.2280.000
co((g/km))-0.319-0.168-0.1600.2161.0000.9680.9810.9630.9980.000
nox((g/km))-0.335-0.110-0.1290.1880.9681.0000.9940.9910.9700.000
hc((g/km))-0.338-0.131-0.1340.1820.9810.9941.0000.9890.9790.000
pm((g/km))-0.342-0.105-0.1180.1450.9630.9910.9891.0000.9650.000
co2((g/km))-0.313-0.174-0.1700.2280.9980.9700.9790.9651.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T10:51:26.343366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T10:51:26.510132image/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-김천11.420210601036.07278128.086112637.252600.39343.11213.72674787.73경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210601036.07278128.086112566.532317.22330.58166.89636515.67경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210601036.35299128.139353177.723631.79506.56281.51761382.63경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210601036.35299128.139352986.323717.34463.92263.39763287.2경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210601036.50552128.169467362.369557.871252.91706.031762628.81경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210601036.50552128.169467178.739504.311234.3715.571720401.43경북 상주 외서 연봉
67건기연[0323-2]1문경-연풍8.720210601036.73871128.085941711.02328.55297.37150.92416742.42경북 문경 문경 진안
78건기연[0323-2]2문경-연풍8.720210601036.73871128.085943132.865116.17611.44356.8776496.67경북 문경 문경 진안
89건기연[0410-2]1추풍령-김천1.820210601036.14659128.025441754.991619.8213.73134.53448678.36경북 김천 봉산 태화
910건기연[0410-2]2추풍령-김천1.820210601036.14659128.025441686.381638.24213.79118.42435012.63경북 김천 봉산 태화
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3116-1]1녹동-영양26.720210601036.86368129.01248206.4203.3126.1218.3254415.7경북 봉화 소천 서천
9192건기연[3116-1]2녹동-영양26.720210601036.86368129.01248228.29205.529.6920.4957177.48경북 봉화 소천 서천
9293건기연[3307-1]1고령-수륜12.920210601035.80817128.23039980.681244.06152.296.26247135.32경북 성주 수륜 계정
9394건기연[3307-1]2고령-수륜12.920210601035.80817128.23039960.471307.29153.7792.92241616.29경북 성주 수륜 계정
9495건기연[3310-0]1성주-왜관6.720210601035.97485128.376825942.576432.3823.02519.471529253.93경북 칠곡 기산 영
9596건기연[3310-0]2성주-왜관6.720210601035.97485128.376825884.776090.56793.31486.021511018.74경북 칠곡 기산 영
9697건기연[3311-0]1약목-구평19.420210601036.04876128.410772925.622554.23381.19127.32677479.73경북 칠곡 석적 포남
9798건기연[3311-0]2약목-구평19.420210601036.04876128.410772219.282144.36293.59125.9559304.16경북 칠곡 석적 포남
9899건기연[3313-0]1구미-선산3.420210601036.23436128.304575911.025933.49785.21389.651447431.19경북 구미 선산 동부
99100건기연[3313-0]2구미-선산3.420210601036.23436128.304576032.026162.66810.34377.831480411.2경북 구미 선산 동부