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
co2((g/km)) has unique valuesUnique
pm((g/km)) has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:40:13.911795
Analysis finished2023-12-10 13:40:22.850613
Duration8.94 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
2023-12-10T22:40:22.914166image/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
2023-12-10T22:40:23.036246image/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

2023-12-10T22:40:23.149276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:23.236420image/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
2023-12-10T22:40:23.410850image/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[0134-0]
2nd row[0134-0]
3rd row[0142-0]
4th row[0142-0]
5th row[0328-2]
ValueCountFrequency (%)
0134-0 2
 
2.0%
4304-0 2
 
2.0%
4707-0 2
 
2.0%
3906-4 2
 
2.0%
3907-1 2
 
2.0%
3918-2 2
 
2.0%
4202-1 2
 
2.0%
4205-1 2
 
2.0%
4206-2 2
 
2.0%
4207-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:40:23.755400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 126
15.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
4 62
7.8%
1 60
7.5%
2 54
6.8%
7 30
 
3.8%
8 26
 
3.2%
Other values (3) 60
7.5%

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 (%)
0 126
25.2%
3 82
16.4%
4 62
12.4%
1 60
12.0%
2 54
10.8%
7 30
 
6.0%
8 26
 
5.2%
6 22
 
4.4%
5 22
 
4.4%
9 16
 
3.2%
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 (%)
0 126
15.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
4 62
7.8%
1 60
7.5%
2 54
6.8%
7 30
 
3.8%
8 26
 
3.2%
Other values (3) 60
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 126
15.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
4 62
7.8%
1 60
7.5%
2 54
6.8%
7 30
 
3.8%
8 26
 
3.2%
Other values (3) 60
7.5%

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

2023-12-10T22:40:23.876019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:23.960555image/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
2023-12-10T22:40:24.154665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.24
Min length4

Characters and Unicode

Total characters524
Distinct characters83
Distinct categories3 ?
Distinct scripts3 ?
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 row송탄-오산
2nd row송탄-오산
3rd row당동-파평
4th row당동-파평
5th row이천-장호원
ValueCountFrequency (%)
송탄-오산 2
 
2.0%
죽전-문형 2
 
2.0%
내각-부평 2
 
2.0%
발안ic-청북ic 2
 
2.0%
팔탄-비봉 2
 
2.0%
일영-의정부 2
 
2.0%
시흥-목감 2
 
2.0%
보라-용인 2
 
2.0%
용인-마장 2
 
2.0%
부발-여주 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:40:24.493694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.1%
28
 
5.3%
22
 
4.2%
18
 
3.4%
14
 
2.7%
12
 
2.3%
12
 
2.3%
12
 
2.3%
12
 
2.3%
12
 
2.3%
Other values (73) 282
53.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 416
79.4%
Dash Punctuation 100
 
19.1%
Uppercase Letter 8
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
28
 
6.7%
22
 
5.3%
18
 
4.3%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
Other values (70) 264
63.5%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 416
79.4%
Common 100
 
19.1%
Latin 8
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
28
 
6.7%
22
 
5.3%
18
 
4.3%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
Other values (70) 264
63.5%
Latin
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 416
79.4%
ASCII 108
 
20.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
C 4
 
3.7%
I 4
 
3.7%
Hangul
ValueCountFrequency (%)
28
 
6.7%
22
 
5.3%
18
 
4.3%
14
 
3.4%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
12
 
2.9%
10
 
2.4%
Other values (70) 264
63.5%

연장((km))
Real number (ℝ)

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.814
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:24.649440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median7.45
Q310.9
95-th percentile18.8
Maximum27.5
Range26
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.1650735
Coefficient of variation (CV)0.58600788
Kurtosis2.0186887
Mean8.814
Median Absolute Deviation (MAD)2.8
Skewness1.2326609
Sum881.4
Variance26.677984
MonotonicityNot monotonic
2023-12-10T22:40:24.781355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
6.0 4
 
4.0%
5.4 4
 
4.0%
10.6 4
 
4.0%
6.1 4
 
4.0%
9.1 2
 
2.0%
2.7 2
 
2.0%
18.8 2
 
2.0%
27.5 2
 
2.0%
7.2 2
 
2.0%
4.1 2
 
2.0%
Other values (36) 72
72.0%
ValueCountFrequency (%)
1.5 2
2.0%
2.0 2
2.0%
2.6 2
2.0%
2.7 2
2.0%
2.9 2
2.0%
3.1 2
2.0%
3.4 2
2.0%
4.1 2
2.0%
4.3 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
27.5 2
2.0%
19.0 2
2.0%
18.8 2
2.0%
17.6 2
2.0%
15.5 2
2.0%
15.4 2
2.0%
14.7 2
2.0%
14.6 2
2.0%
14.1 2
2.0%
12.9 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

2023-12-10T22:40:24.890309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:24.971064image/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

2023-12-10T22:40:25.056103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:25.141279image/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%
Mean37.431928
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:25.242159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.380095
Q337.70419
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.53917

Descriptive statistics

Standard deviation0.33503729
Coefficient of variation (CV)0.0089505753
Kurtosis-1.0057936
Mean37.431928
Median Absolute Deviation (MAD)0.266565
Skewness0.38815809
Sum3743.1928
Variance0.11224999
MonotonicityNot monotonic
2023-12-10T22:40:25.369948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.468 2
 
2.0%
37.23589 2
 
2.0%
37.71834 2
 
2.0%
37.39258 2
 
2.0%
37.23653 2
 
2.0%
37.2375 2
 
2.0%
37.29776 2
 
2.0%
37.14026 2
 
2.0%
37.19991 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
36.95627 2
2.0%
36.95866 2
2.0%
36.96033 2
2.0%
36.98521 2
2.0%
37.00613 2
2.0%
37.01644 2
2.0%
37.02727 2
2.0%
37.05786 2
2.0%
37.08149 2
2.0%
37.09529 2
2.0%
ValueCountFrequency (%)
38.06264 2
2.0%
38.06053 2
2.0%
38.0169 2
2.0%
37.99285 2
2.0%
37.98354 2
2.0%
37.96019 2
2.0%
37.91419 2
2.0%
37.87708 2
2.0%
37.83225 2
2.0%
37.76396 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.2124
Minimum126.77946
Maximum127.6367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:25.516037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.85818
Q1127.06364
median127.22912
Q3127.37739
95-th percentile127.61201
Maximum127.6367
Range0.85724
Interquartile range (IQR)0.31375

Descriptive statistics

Standard deviation0.2312289
Coefficient of variation (CV)0.00181766
Kurtosis-0.81245353
Mean127.2124
Median Absolute Deviation (MAD)0.16088
Skewness0.019330949
Sum12721.24
Variance0.053466803
MonotonicityNot monotonic
2023-12-10T22:40:25.681757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
127.2475 2
 
2.0%
126.88236 2
 
2.0%
126.92818 2
 
2.0%
126.85818 2
 
2.0%
127.166 2
 
2.0%
127.3107 2
 
2.0%
127.60231 2
 
2.0%
126.91531 2
 
2.0%
126.9889 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.77946 2
2.0%
126.82904 2
2.0%
126.85818 2
2.0%
126.85908 2
2.0%
126.88236 2
2.0%
126.88641 2
2.0%
126.91531 2
2.0%
126.92256 2
2.0%
126.92508 2
2.0%
126.92818 2
2.0%
ValueCountFrequency (%)
127.6367 2
2.0%
127.62684 2
2.0%
127.61201 2
2.0%
127.60231 2
2.0%
127.56566 2
2.0%
127.55994 2
2.0%
127.49045 2
2.0%
127.44335 2
2.0%
127.44132 2
2.0%
127.42763 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.4904
Minimum2.1
Maximum770.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:25.820259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.1
5-th percentile12.345
Q147.1175
median118.19
Q3188.4275
95-th percentile338.4025
Maximum770.85
Range768.75
Interquartile range (IQR)141.31

Descriptive statistics

Standard deviation128.68506
Coefficient of variation (CV)0.92253705
Kurtosis8.3493591
Mean139.4904
Median Absolute Deviation (MAD)71
Skewness2.3654082
Sum13949.04
Variance16559.845
MonotonicityNot monotonic
2023-12-10T22:40:25.966944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
219.95 1
 
1.0%
131.95 1
 
1.0%
207.32 1
 
1.0%
248.07 1
 
1.0%
396.62 1
 
1.0%
231.3 1
 
1.0%
184.43 1
 
1.0%
80.0 1
 
1.0%
52.94 1
 
1.0%
108.69 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
2.1 1
1.0%
3.98 1
1.0%
4.35 1
1.0%
4.54 1
1.0%
5.22 1
1.0%
12.72 1
1.0%
13.33 1
1.0%
15.68 1
1.0%
15.84 1
1.0%
16.27 1
1.0%
ValueCountFrequency (%)
770.85 1
1.0%
709.61 1
1.0%
444.8 1
1.0%
396.62 1
1.0%
369.23 1
1.0%
336.78 1
1.0%
336.49 1
1.0%
326.71 1
1.0%
313.39 1
1.0%
312.17 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.3123
Minimum1.11
Maximum635.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:26.291975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.11
5-th percentile7.507
Q141.2625
median113.805
Q3177.0325
95-th percentile321.226
Maximum635.55
Range634.44
Interquartile range (IQR)135.77

Descriptive statistics

Standard deviation111.49768
Coefficient of variation (CV)0.85561901
Kurtosis4.6729212
Mean130.3123
Median Absolute Deviation (MAD)70.795
Skewness1.7086628
Sum13031.23
Variance12431.733
MonotonicityNot monotonic
2023-12-10T22:40:26.448651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212.13 1
 
1.0%
126.46 1
 
1.0%
146.98 1
 
1.0%
232.06 1
 
1.0%
284.61 1
 
1.0%
202.67 1
 
1.0%
169.1 1
 
1.0%
63.7 1
 
1.0%
47.64 1
 
1.0%
104.98 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.11 1
1.0%
2.25 1
1.0%
2.38 1
1.0%
2.62 1
1.0%
3.46 1
1.0%
7.72 1
1.0%
8.36 1
1.0%
9.64 1
1.0%
12.18 1
1.0%
16.32 1
1.0%
ValueCountFrequency (%)
635.55 1
1.0%
541.19 1
1.0%
377.86 1
1.0%
354.25 1
1.0%
349.46 1
1.0%
319.74 1
1.0%
310.31 1
1.0%
310.28 1
1.0%
284.61 1
1.0%
259.11 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.818
Minimum0.18
Maximum92.24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:26.905211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile1.2015
Q15.385
median15.24
Q323.66
95-th percentile46.81
Maximum92.24
Range92.06
Interquartile range (IQR)18.275

Descriptive statistics

Standard deviation15.744729
Coefficient of variation (CV)0.88364177
Kurtosis6.6130705
Mean17.818
Median Absolute Deviation (MAD)9.525
Skewness2.0539692
Sum1781.8
Variance247.89649
MonotonicityNot monotonic
2023-12-10T22:40:27.086661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.85 2
 
2.0%
25.83 1
 
1.0%
18.48 1
 
1.0%
30.78 1
 
1.0%
45.78 1
 
1.0%
28.62 1
 
1.0%
23.69 1
 
1.0%
8.36 1
 
1.0%
5.41 1
 
1.0%
13.55 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.18 1
1.0%
0.39 1
1.0%
0.4 1
1.0%
0.41 1
1.0%
0.47 1
1.0%
1.24 1
1.0%
1.45 1
1.0%
1.59 1
1.0%
1.74 1
1.0%
2.71 1
1.0%
ValueCountFrequency (%)
92.24 1
1.0%
83.49 1
1.0%
47.82 1
1.0%
47.5 1
1.0%
47.38 1
1.0%
46.78 1
1.0%
45.78 1
1.0%
44.05 1
1.0%
42.69 1
1.0%
36.16 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1448
Minimum0
Maximum41.67
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:27.264627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4025
Q13.025
median7.02
Q311.55
95-th percentile20.348
Maximum41.67
Range41.67
Interquartile range (IQR)8.525

Descriptive statistics

Standard deviation6.8295071
Coefficient of variation (CV)0.83851133
Kurtosis5.0223308
Mean8.1448
Median Absolute Deviation (MAD)4.38
Skewness1.6632975
Sum814.48
Variance46.642167
MonotonicityNot monotonic
2023-12-10T22:40:27.439596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.23 2
 
2.0%
0.0 2
 
2.0%
11.49 1
 
1.0%
9.18 1
 
1.0%
8.24 1
 
1.0%
15.35 1
 
1.0%
15.87 1
 
1.0%
11.51 1
 
1.0%
10.12 1
 
1.0%
3.57 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.13 1
1.0%
0.14 1
1.0%
0.26 1
1.0%
0.41 1
1.0%
0.44 1
1.0%
0.54 1
1.0%
0.58 1
1.0%
0.83 1
1.0%
1.11 1
1.0%
ValueCountFrequency (%)
41.67 1
1.0%
25.59 1
1.0%
23.52 1
1.0%
22.92 1
1.0%
22.02 1
1.0%
20.26 1
1.0%
19.92 1
1.0%
19.34 1
1.0%
17.38 1
1.0%
15.87 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33978.907
Minimum554.74
Maximum182237.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:27.620978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum554.74
5-th percentile2968.6155
Q112098.483
median28043.275
Q345636.387
95-th percentile86841.832
Maximum182237.39
Range181682.65
Interquartile range (IQR)33537.905

Descriptive statistics

Standard deviation30689.817
Coefficient of variation (CV)0.903202
Kurtosis7.4010147
Mean33978.907
Median Absolute Deviation (MAD)16232.325
Skewness2.2099221
Sum3397890.7
Variance9.4186484 × 108
MonotonicityNot monotonic
2023-12-10T22:40:27.779401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54772.59 1
 
1.0%
29710.63 1
 
1.0%
43824.94 1
 
1.0%
68046.57 1
 
1.0%
92504.35 1
 
1.0%
57442.21 1
 
1.0%
45473.85 1
 
1.0%
18907.94 1
 
1.0%
13575.52 1
 
1.0%
26789.81 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
554.74 1
1.0%
953.96 1
1.0%
1075.77 1
1.0%
1150.71 1
1.0%
1511.42 1
1.0%
3045.31 1
1.0%
3339.21 1
1.0%
3477.74 1
1.0%
4186.8 1
1.0%
4671.77 1
1.0%
ValueCountFrequency (%)
182237.39 1
1.0%
164793.95 1
1.0%
106621.14 1
1.0%
92504.35 1
1.0%
87283.24 1
1.0%
86818.6 1
1.0%
81999.24 1
1.0%
75834.4 1
1.0%
75497.13 1
1.0%
72954.56 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:40:28.062136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

Total characters1070
Distinct characters109
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 row경기 평택 진위 신
2nd row경기 평택 진위 신
3rd row경기 파주 문산 당동
4th row경기 파주 문산 당동
5th row경기 여주 가남 심석
ValueCountFrequency (%)
경기 100
25.8%
평택 14
 
3.6%
용인 12
 
3.1%
광주 10
 
2.6%
여주 8
 
2.1%
가평 8
 
2.1%
포천 8
 
2.1%
화성 6
 
1.5%
안성 6
 
1.5%
남양주 6
 
1.5%
Other values (95) 210
54.1%
2023-12-10T22:40:28.563422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
288
26.9%
102
 
9.5%
100
 
9.3%
34
 
3.2%
32
 
3.0%
22
 
2.1%
20
 
1.9%
18
 
1.7%
16
 
1.5%
16
 
1.5%
Other values (99) 422
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 782
73.1%
Space Separator 288
 
26.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
102
 
13.0%
100
 
12.8%
34
 
4.3%
32
 
4.1%
22
 
2.8%
20
 
2.6%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (98) 406
51.9%
Space Separator
ValueCountFrequency (%)
288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 782
73.1%
Common 288
 
26.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
102
 
13.0%
100
 
12.8%
34
 
4.3%
32
 
4.1%
22
 
2.8%
20
 
2.6%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (98) 406
51.9%
Common
ValueCountFrequency (%)
288
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 782
73.1%
ASCII 288
 
26.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
288
100.0%
Hangul
ValueCountFrequency (%)
102
 
13.0%
100
 
12.8%
34
 
4.3%
32
 
4.1%
22
 
2.8%
20
 
2.6%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (98) 406
51.9%

Interactions

2023-12-10T22:40:21.504026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.342070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.997547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.054851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.877830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:17.740864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:18.988132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:19.935282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.765846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:21.878266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.408523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:15.075391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.128213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.991731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:18.005977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:19.090698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.034545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.839099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:21.963264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.479447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:15.413883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.213595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:17.093269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:18.260242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:19.190335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.121615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.916733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:22.064581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.549869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:15.497308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.310238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:17.186136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:18.383912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:19.299444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.211266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:21.006623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:22.156114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.632402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:15.592844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.407256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:17.270112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:18.500638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:19.438015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.285575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:21.089371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:22.270662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.708560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:15.681452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.518316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:17.363721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:18.608913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:19.548151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.376214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:21.197404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:22.360555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.780601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:15.776536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.596752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:17.453885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:18.700052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:19.638003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.474160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:21.283923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:22.430405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.845919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:15.861824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.666902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:17.543473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:18.788589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:19.721501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.584857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:21.359176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:22.497671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:14.909544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:15.947038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:16.761604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:17.642795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:18.888420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:19.818979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:20.659286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:21.425841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:40:28.718951image/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
기본키1.0001.0000.0001.0000.5660.8200.8530.5260.4030.4810.4090.5161.000
지점1.0001.0000.0001.0001.0001.0001.0000.9260.7780.8850.8620.9281.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9260.7780.8850.8620.9281.000
연장((km))0.5661.0000.0001.0001.0000.6660.6150.3990.2670.1470.3490.4631.000
좌표위치위도((°))0.8201.0000.0001.0000.6661.0000.8050.4520.4010.4780.4670.4631.000
좌표위치경도((°))0.8531.0000.0001.0000.6150.8051.0000.5060.1570.3210.3110.4911.000
co((g/km))0.5260.9260.0000.9260.3990.4520.5061.0000.8670.9720.8510.9980.926
nox((g/km))0.4030.7780.0000.7780.2670.4010.1570.8671.0000.9560.9840.8700.778
hc((g/km))0.4810.8850.0000.8850.1470.4780.3210.9720.9561.0000.8720.9670.885
pm((g/km))0.4090.8620.0000.8620.3490.4670.3110.8510.9840.8721.0000.8410.862
co2((g/km))0.5160.9280.0000.9280.4630.4630.4910.9980.8700.9670.8411.0000.928
주소1.0001.0000.0001.0001.0001.0001.0000.9260.7780.8850.8620.9281.000
2023-12-10T22:40:28.891363image/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
기본키1.000-0.2210.034-0.0390.021-0.058-0.026-0.0470.0090.000
연장((km))-0.2211.0000.243-0.075-0.009-0.023-0.040-0.0190.0070.000
좌표위치위도((°))0.0340.2431.0000.117-0.331-0.430-0.417-0.429-0.3280.000
좌표위치경도((°))-0.039-0.0750.1171.000-0.218-0.222-0.220-0.207-0.2170.000
co((g/km))0.021-0.009-0.331-0.2181.0000.9500.9710.9220.9970.000
nox((g/km))-0.058-0.023-0.430-0.2220.9501.0000.9910.9870.9550.000
hc((g/km))-0.026-0.040-0.417-0.2200.9710.9911.0000.9740.9700.000
pm((g/km))-0.047-0.019-0.429-0.2070.9220.9870.9741.0000.9300.000
co2((g/km))0.0090.007-0.328-0.2170.9970.9550.9700.9301.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T22:40:22.619444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:40:22.785064image/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[0134-0]1송탄-오산6.220210601037.09529127.06364219.95212.1325.8311.4954772.59경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210601037.09529127.06364326.71252.9236.1611.0775834.4경기 평택 진위 신
23건기연[0142-0]1당동-파평14.720210601037.87708126.7794628.4524.43.461.366615.01경기 파주 문산 당동
34건기연[0142-0]2당동-파평14.720210601037.87708126.7794629.7720.963.431.116926.99경기 파주 문산 당동
45건기연[0328-2]1이천-장호원9.620210601037.19066127.5599436.936.984.963.129073.97경기 여주 가남 심석
56건기연[0328-2]2이천-장호원9.620210601037.19066127.5599460.1356.397.993.9414803.14경기 여주 가남 심석
67건기연[0330-1]1이천-광주15.520210601037.31793127.42763131.51138.1219.3110.2333230.01경기 이천 신둔 수하
78건기연[0330-1]2이천-광주15.520210601037.31793127.42763173.9186.5425.4213.143701.09경기 이천 신둔 수하
89건기연[0331-0]1성남-이천7.720210601037.37777127.30168336.78377.8647.8222.9287283.24경기 광주 초월 용수
910건기연[0331-0]2성남-이천7.720210601037.37777127.30168312.17310.2842.6919.3472954.56경기 광주 초월 용수
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4509-0]1광주-팔당19.020210601037.4815127.2810140.927.04.431.559792.48경기 광주 남종 삼성
9192건기연[4509-0]2광주-팔당19.020210601037.4815127.2810146.134.795.072.6711773.49경기 광주 남종 삼성
9293건기연[4512-1]1화도-청평8.320210601037.68735127.37997121.9107.6914.546.1728086.42경기 가평 청평 대성
9394건기연[4512-1]2화도-청평8.320210601037.68735127.3799761.8155.127.73.615297.58경기 가평 청평 대성
9495건기연[4606-2]1청평-가평2.920210601037.76396127.4433588.8593.4113.06.3221269.94경기 가평 청평 상천
9596건기연[4606-2]2청평-가평2.920210601037.76396127.4433561.9558.558.243.8315148.57경기 가평 청평 상천
9697건기연[4707-0]1내각-부평6.820210601037.70419127.17103369.23349.4646.7819.9286818.6경기 남양주 진접 내각
9798건기연[4707-0]2내각-부평6.820210601037.70419127.17103200.38202.5625.7813.7651489.09경기 남양주 진접 내각
9899건기연[4708-0]1일동-이동7.920210601037.98354127.3344515.849.641.450.544186.8경기 포천 일동 사직
99100건기연[4708-0]2일동-이동7.920210601037.98354127.3344512.727.721.240.443045.31경기 포천 일동 사직