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 01:51:58.759010
Analysis finished2024-04-17 01:52:05.628909
Duration6.87 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
2024-04-17T10:52:05.692639image/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
2024-04-17T10:52:05.812888image/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

2024-04-17T10:52:05.941976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:52:06.058014image/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
2024-04-17T10:52:06.232227image/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%
2024-04-17T10:52:06.535160image/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%
2 74
9.2%
3 52
 
6.5%
5 28
 
3.5%
4 26
 
3.2%
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 (%)
0 132
26.4%
1 110
22.0%
2 74
14.8%
3 52
 
10.4%
5 28
 
5.6%
4 26
 
5.2%
8 26
 
5.2%
7 26
 
5.2%
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 (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 74
9.2%
3 52
 
6.5%
5 28
 
3.5%
4 26
 
3.2%
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 (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 74
9.2%
3 52
 
6.5%
5 28
 
3.5%
4 26
 
3.2%
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

2024-04-17T10:52:06.654313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:52:06.750873image/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
2024-04-17T10:52:06.935953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.06
Min length5

Characters and Unicode

Total characters506
Distinct characters87
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%
영천-의성 2
 
2.0%
녹동-영양 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 (40) 80
80.0%
2024-04-17T10:52:07.235086image/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%
16
 
3.2%
14
 
2.8%
10
 
2.0%
10
 
2.0%
10
 
2.0%
Other values (77) 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%
16
 
4.0%
14
 
3.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
8
 
2.0%
Other values (75) 260
64.4%
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%
16
 
4.0%
14
 
3.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
8
 
2.0%
Other values (75) 260
64.4%
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%
16
 
4.0%
14
 
3.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
8
 
2.0%
Other values (75) 260
64.4%

연장((km))
Real number (ℝ)

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.142
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:52:07.354266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q14.9
median8.1
Q311.6
95-th percentile22.1
Maximum27.8
Range26.5
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation5.9204863
Coefficient of variation (CV)0.6476139
Kurtosis1.9839538
Mean9.142
Median Absolute Deviation (MAD)3.3
Skewness1.2647924
Sum914.2
Variance35.052158
MonotonicityNot monotonic
2024-04-17T10:52:07.463585image/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%
11.4 2
 
2.0%
2.2 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%
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%
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

2024-04-17T10:52:07.563177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:52:07.636168image/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
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

2024-04-17T10:52:07.960486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T10:52:08.028254image/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%
Mean36.138682
Minimum35.65543
Maximum36.99828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:52:08.115677image/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 deviation0.36411463
Coefficient of variation (CV)0.010075482
Kurtosis-0.58096113
Mean36.138682
Median Absolute Deviation (MAD)0.267845
Skewness0.66024174
Sum3613.8682
Variance0.13257946
MonotonicityNot monotonic
2024-04-17T10:52:08.263222image/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 (%)
36.99828 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)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.81653
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:52:08.385463image/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 deviation0.4663118
Coefficient of variation (CV)0.0036199687
Kurtosis-1.3948903
Mean128.81653
Median Absolute Deviation (MAD)0.460995
Skewness-0.025166769
Sum12881.653
Variance0.21744669
MonotonicityNot monotonic
2024-04-17T10:52:08.499686image/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%
129.41245 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  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2234.3013
Minimum109.32
Maximum8491.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:52:08.618246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum109.32
5-th percentile295.5245
Q1891.785
median2227.57
Q33079.525
95-th percentile5373.3415
Maximum8491.41
Range8382.09
Interquartile range (IQR)2187.74

Descriptive statistics

Standard deviation1600.6343
Coefficient of variation (CV)0.71639142
Kurtosis2.3718898
Mean2234.3013
Median Absolute Deviation (MAD)1180.22
Skewness1.1639609
Sum223430.13
Variance2562030.1
MonotonicityNot monotonic
2024-04-17T10:52:08.726400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1787.27 1
 
1.0%
1060.16 1
 
1.0%
2212.55 1
 
1.0%
813.65 1
 
1.0%
807.49 1
 
1.0%
284.97 1
 
1.0%
239.18 1
 
1.0%
547.54 1
 
1.0%
608.68 1
 
1.0%
374.96 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
109.32 1
1.0%
119.02 1
1.0%
239.18 1
1.0%
242.6 1
1.0%
284.97 1
1.0%
296.08 1
1.0%
308.81 1
1.0%
347.87 1
1.0%
374.96 1
1.0%
385.49 1
1.0%
ValueCountFrequency (%)
8491.41 1
1.0%
7743.31 1
1.0%
5769.65 1
1.0%
5596.72 1
1.0%
5521.76 1
1.0%
5365.53 1
1.0%
4315.23 1
1.0%
4233.0 1
1.0%
4011.07 1
1.0%
3927.26 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1969.1788
Minimum74.27
Maximum10484.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:52:08.857594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum74.27
5-th percentile239.6525
Q1639.94
median1886.08
Q32697.445
95-th percentile4638.756
Maximum10484.06
Range10409.79
Interquartile range (IQR)2057.505

Descriptive statistics

Standard deviation1610.1762
Coefficient of variation (CV)0.8176892
Kurtosis6.9147315
Mean1969.1788
Median Absolute Deviation (MAD)1084.095
Skewness1.8591236
Sum196917.88
Variance2592667.5
MonotonicityNot monotonic
2024-04-17T10:52:08.971495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1408.29 1
 
1.0%
856.09 1
 
1.0%
1746.37 1
 
1.0%
590.8 1
 
1.0%
561.53 1
 
1.0%
322.47 1
 
1.0%
199.23 1
 
1.0%
435.44 1
 
1.0%
471.71 1
 
1.0%
315.0 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
74.27 1
1.0%
77.27 1
1.0%
167.76 1
1.0%
199.06 1
1.0%
199.23 1
1.0%
241.78 1
1.0%
243.17 1
1.0%
272.91 1
1.0%
280.08 1
1.0%
308.8 1
1.0%
ValueCountFrequency (%)
10484.06 1
1.0%
6340.3 1
1.0%
5000.0 1
1.0%
4885.88 1
1.0%
4762.37 1
1.0%
4632.25 1
1.0%
4520.97 1
1.0%
4307.5 1
1.0%
4234.56 1
1.0%
4198.03 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.9278
Minimum10.98
Maximum1458.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:52:09.083905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.98
5-th percentile33.6175
Q192.65
median260.585
Q3368.25
95-th percentile592.293
Maximum1458.14
Range1447.16
Interquartile range (IQR)275.6

Descriptive statistics

Standard deviation209.72838
Coefficient of variation (CV)0.80071064
Kurtosis9.7528003
Mean261.9278
Median Absolute Deviation (MAD)141.29
Skewness2.1536602
Sum26192.78
Variance43985.992
MonotonicityNot monotonic
2024-04-17T10:52:09.196939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190.66 1
 
1.0%
122.3 1
 
1.0%
233.01 1
 
1.0%
88.69 1
 
1.0%
84.49 1
 
1.0%
47.57 1
 
1.0%
29.05 1
 
1.0%
62.01 1
 
1.0%
65.65 1
 
1.0%
38.32 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
10.98 1
1.0%
11.09 1
1.0%
23.67 1
1.0%
29.05 1
1.0%
29.58 1
1.0%
33.83 1
1.0%
37.14 1
1.0%
37.96 1
1.0%
38.32 1
1.0%
39.87 1
1.0%
ValueCountFrequency (%)
1458.14 1
1.0%
848.08 1
1.0%
651.76 1
1.0%
610.03 1
1.0%
602.04 1
1.0%
591.78 1
1.0%
579.08 1
1.0%
529.99 1
1.0%
465.2 1
1.0%
455.91 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.0312
Minimum4.82
Maximum535.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:52:09.314663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.82
5-th percentile13.2025
Q137.875
median95.05
Q3148.2575
95-th percentile269.7195
Maximum535.97
Range531.15
Interquartile range (IQR)110.3825

Descriptive statistics

Standard deviation87.839402
Coefficient of variation (CV)0.79831358
Kurtosis4.7863004
Mean110.0312
Median Absolute Deviation (MAD)55.575
Skewness1.6508544
Sum11003.12
Variance7715.7605
MonotonicityNot monotonic
2024-04-17T10:52:09.430396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
113.77 1
 
1.0%
54.06 1
 
1.0%
94.39 1
 
1.0%
35.88 1
 
1.0%
32.6 1
 
1.0%
23.13 1
 
1.0%
17.97 1
 
1.0%
33.71 1
 
1.0%
38.61 1
 
1.0%
20.77 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
4.82 1
1.0%
7.14 1
1.0%
10.26 1
1.0%
11.37 1
1.0%
12.87 1
1.0%
13.22 1
1.0%
13.92 1
1.0%
17.35 1
1.0%
17.97 1
1.0%
19.56 1
1.0%
ValueCountFrequency (%)
535.97 1
1.0%
348.19 1
1.0%
316.88 1
1.0%
279.66 1
1.0%
271.99 1
1.0%
269.6 1
1.0%
260.14 1
1.0%
243.74 1
1.0%
236.24 1
1.0%
225.13 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean572157.47
Minimum28462.9
Maximum1999285.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T10:52:09.549541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28462.9
5-th percentile74370.162
Q1229446.64
median580604.28
Q3797275.96
95-th percentile1421829
Maximum1999285.4
Range1970822.5
Interquartile range (IQR)567829.33

Descriptive statistics

Standard deviation404631.99
Coefficient of variation (CV)0.70720391
Kurtosis1.7022603
Mean572157.47
Median Absolute Deviation (MAD)293620.66
Skewness1.0383282
Sum57215747
Variance1.6372705 × 1011
MonotonicityNot monotonic
2024-04-17T10:52:09.666684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
462480.66 1
 
1.0%
268626.39 1
 
1.0%
580594.16 1
 
1.0%
208671.41 1
 
1.0%
208576.94 1
 
1.0%
65256.28 1
 
1.0%
60589.35 1
 
1.0%
140003.54 1
 
1.0%
157315.56 1
 
1.0%
97050.5 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
28462.9 1
1.0%
31440.35 1
1.0%
60589.35 1
1.0%
63749.18 1
1.0%
65256.28 1
1.0%
74849.84 1
1.0%
81310.38 1
1.0%
86172.6 1
1.0%
97050.5 1
1.0%
100652.97 1
1.0%
ValueCountFrequency (%)
1999285.37 1
1.0%
1960498.38 1
1.0%
1484789.47 1
1.0%
1471967.73 1
1.0%
1455580.16 1
1.0%
1420052.59 1
1.0%
1125879.07 1
1.0%
1005769.31 1
1.0%
1004745.39 1
1.0%
991690.87 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T10:52:09.893763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.96
Min length10

Characters and Unicode

Total characters1096
Distinct characters104
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%
연일 6
 
1.5%
청도 6
 
1.5%
영천 6
 
1.5%
상주 6
 
1.5%
울진 6
 
1.5%
고령 6
 
1.5%
Other values (103) 226
56.5%
2024-04-17T10:52:10.234217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
300
27.4%
116
 
10.6%
106
 
9.7%
34
 
3.1%
26
 
2.4%
26
 
2.4%
20
 
1.8%
16
 
1.5%
16
 
1.5%
14
 
1.3%
Other values (94) 422
38.5%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
116
 
14.6%
106
 
13.3%
34
 
4.3%
26
 
3.3%
26
 
3.3%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
10
 
1.3%
Other values (93) 412
51.8%
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 (%)
116
 
14.6%
106
 
13.3%
34
 
4.3%
26
 
3.3%
26
 
3.3%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
10
 
1.3%
Other values (93) 412
51.8%
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 (%)
116
 
14.6%
106
 
13.3%
34
 
4.3%
26
 
3.3%
26
 
3.3%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
10
 
1.3%
Other values (93) 412
51.8%

Interactions

2024-04-17T10:52:04.635893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.176759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.779926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.461768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.142456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.835213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.459477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.321401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.000404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.706470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.237581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.868066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.530548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.223323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.916253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.523762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.391655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.067946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.783413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.298179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.948700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.600268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.296183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.984354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.826023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.461514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.135233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.868035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.369934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.035421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.679631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.373281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.053275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.895089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.552994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.212648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.949986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.437993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.113017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.773184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.447112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.124809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.975892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.630551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.285804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:05.028368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.498061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.175533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.838326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.515362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.184401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.040153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.698403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.353626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:05.101234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.557323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.238333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.907715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.587088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.248031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.108975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.765593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.420105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:05.191105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.625987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.321632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.983988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.661148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.314138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.178234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.837344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.492674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:05.267128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:51:59.690450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:00.385637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.060788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:01.733730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:02.376476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.247271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:03.912465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T10:52:04.557249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T10:52:10.328193image/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
기본키1.0001.0000.0001.0000.5530.7870.8960.5810.2990.3130.4280.5841.000
지점1.0001.0000.0001.0001.0001.0001.0000.9690.9150.8790.8560.9751.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9690.9150.8790.8560.9751.000
연장((km))0.5531.0000.0001.0001.0000.5990.6450.3600.2140.2940.0000.4111.000
좌표위치위도((°))0.7871.0000.0001.0000.5991.0000.7970.3940.3570.3400.2360.4611.000
좌표위치경도((°))0.8961.0000.0001.0000.6450.7971.0000.5510.4990.3690.4980.5321.000
co((g/km))0.5810.9690.0000.9690.3600.3940.5511.0000.8480.8450.8930.9980.969
nox((g/km))0.2990.9150.0000.9150.2140.3570.4990.8481.0000.9920.8630.8480.915
hc((g/km))0.3130.8790.0000.8790.2940.3400.3690.8450.9921.0000.8420.8400.879
pm((g/km))0.4280.8560.0000.8560.0000.2360.4980.8930.8630.8421.0000.8900.856
co2((g/km))0.5840.9750.0000.9750.4110.4610.5320.9980.8480.8400.8901.0000.975
주소1.0001.0000.0001.0001.0001.0001.0000.9690.9150.8790.8560.9751.000
2024-04-17T10:52:10.446744image/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
기본키1.000-0.017-0.0760.306-0.285-0.312-0.298-0.324-0.2750.000
연장((km))-0.0171.0000.105-0.125-0.0720.003-0.0170.006-0.0870.000
좌표위치위도((°))-0.0760.1051.000-0.080-0.205-0.150-0.180-0.071-0.2120.000
좌표위치경도((°))0.306-0.125-0.0801.0000.3670.3030.2940.1920.3910.000
co((g/km))-0.285-0.072-0.2050.3671.0000.9430.9640.8880.9970.000
nox((g/km))-0.3120.003-0.1500.3030.9431.0000.9920.9660.9430.000
hc((g/km))-0.298-0.017-0.1800.2940.9640.9921.0000.9540.9590.000
pm((g/km))-0.3240.006-0.0710.1920.8880.9660.9541.0000.8880.000
co2((g/km))-0.275-0.087-0.2120.3910.9970.9430.9590.8881.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T10:52:05.397470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T10:52:05.564949image/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-김천11.420210301036.07278128.086111787.271408.29190.66113.77462480.66경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210301036.07278128.086111580.311078.41158.2455.46410468.37경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210301036.35299128.139351237.11969.7150.8975.53290541.35경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210301036.35299128.139351034.54776.94112.0838.54263507.35경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210301036.50552128.169462608.192761.89353.48180.28644121.61경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210301036.50552128.169462564.372518.6331.62172.17632899.22경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210301036.14659128.025441014.12763.82104.4370.12265012.35경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210301036.14659128.02544917.83672.1797.350.95236371.71경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210301035.98385128.411063872.753314.98465.2201.81978081.69경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210301035.98385128.411063496.863068.48424.2208.31877782.67경북 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-포항2.420210301036.06326129.227712233.911686.23226.91117.62581009.1경북 포항 기계 내단
9192건기연[3107-2]2기계-포항2.420210301036.06326129.227712404.181889.44259.91144.62619780.11경북 포항 기계 내단
9293건기연[3109-1]1죽장-부남20.620210301036.2488129.04049296.08241.7833.8312.8774849.84경북 청송 현동 눌인
9394건기연[3109-1]2죽장-부남20.620210301036.2488129.04049347.87315.0443.4617.3586172.6경북 청송 현동 눌인
9495건기연[3113-1]1진보-석보6.020210301036.59556129.08611479.15308.845.5823.58126526.11경북 영양 입암 신구
9596건기연[3113-1]2진보-석보6.020210301036.59556129.08611524.56383.1556.9626.57134926.5경북 영양 입암 신구
9697건기연[3116-1]1녹동-영양26.720210301036.86368129.01248119.0274.2711.094.8231440.35경북 봉화 소천 서천
9798건기연[3116-1]2녹동-영양26.720210301036.86368129.01248109.3277.2710.987.1428462.9경북 봉화 소천 서천
9899건기연[3307-1]1고령-수륜12.920210301035.80817128.23039563.4417.2955.9526.91146857.07경북 성주 수륜 계정
99100건기연[3307-1]2고령-수륜12.920210301035.80817128.23039611.94484.9461.3632.16159120.79경북 성주 수륜 계정