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 started2023-12-10 13:02:05.778484
Analysis finished2023-12-10 13:02:16.283309
Duration10.5 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
2023-12-10T22:02:16.372283image/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
2023-12-10T22:02:16.556664image/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

2023-12-10T22:02:16.702845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:16.827483image/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
2023-12-10T22:02:17.097804image/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[0141-1]
4th row[0141-1]
5th row[0142-0]
ValueCountFrequency (%)
0134-0 2
 
2.0%
4207-0 2
 
2.0%
4509-0 2
 
2.0%
3804-1 2
 
2.0%
3804-2 2
 
2.0%
3906-1 2
 
2.0%
3906-4 2
 
2.0%
3907-1 2
 
2.0%
3918-2 2
 
2.0%
4202-1 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:02:17.468529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 86
10.8%
1 70
8.8%
4 62
7.8%
2 56
7.0%
7 26
 
3.2%
8 24
 
3.0%
Other values (3) 56
7.0%

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 (%)
0 120
24.0%
3 86
17.2%
1 70
14.0%
4 62
12.4%
2 56
11.2%
7 26
 
5.2%
8 24
 
4.8%
5 22
 
4.4%
6 18
 
3.6%
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 (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 86
10.8%
1 70
8.8%
4 62
7.8%
2 56
7.0%
7 26
 
3.2%
8 24
 
3.0%
Other values (3) 56
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 86
10.8%
1 70
8.8%
4 62
7.8%
2 56
7.0%
7 26
 
3.2%
8 24
 
3.0%
Other values (3) 56
7.0%

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

2023-12-10T22:02:17.633572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:17.744124image/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
2023-12-10T22:02:18.029085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.2
Min length4

Characters and Unicode

Total characters520
Distinct characters82
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 row고양-파주
4th row고양-파주
5th row당동-파평
ValueCountFrequency (%)
송탄-오산 2
 
2.0%
부발-여주 2
 
2.0%
광주-팔당 2
 
2.0%
죽산교-일죽 2
 
2.0%
안성-죽산 2
 
2.0%
아산만-덕목 2
 
2.0%
발안ic-청북ic 2
 
2.0%
팔탄-비봉 2
 
2.0%
일영-의정부 2
 
2.0%
시흥-목감 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:02:18.555018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.2%
24
 
4.6%
22
 
4.2%
14
 
2.7%
14
 
2.7%
14
 
2.7%
14
 
2.7%
12
 
2.3%
12
 
2.3%
10
 
1.9%
Other values (72) 284
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 412
79.2%
Dash Punctuation 100
 
19.2%
Uppercase Letter 8
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
24
 
5.8%
22
 
5.3%
14
 
3.4%
14
 
3.4%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (69) 266
64.6%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 412
79.2%
Common 100
 
19.2%
Latin 8
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
24
 
5.8%
22
 
5.3%
14
 
3.4%
14
 
3.4%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (69) 266
64.6%
Latin
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 412
79.2%
ASCII 108
 
20.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
C 4
 
3.7%
I 4
 
3.7%
Hangul
ValueCountFrequency (%)
24
 
5.8%
22
 
5.3%
14
 
3.4%
14
 
3.4%
14
 
3.4%
14
 
3.4%
12
 
2.9%
12
 
2.9%
10
 
2.4%
10
 
2.4%
Other values (69) 266
64.6%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.896
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:18.752200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median8
Q311
95-th percentile18.8
Maximum27.5
Range26
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation5.0863636
Coefficient of variation (CV)0.5717585
Kurtosis2.2030702
Mean8.896
Median Absolute Deviation (MAD)2.75
Skewness1.2466205
Sum889.6
Variance25.871095
MonotonicityNot monotonic
2023-12-10T22:02:18.924510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
6.0 4
 
4.0%
4.8 4
 
4.0%
5.4 4
 
4.0%
10.6 4
 
4.0%
6.1 4
 
4.0%
6.2 2
 
2.0%
5.0 2
 
2.0%
5.6 2
 
2.0%
2.7 2
 
2.0%
18.8 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
1.5 2
2.0%
2.0 2
2.0%
2.6 2
2.0%
2.7 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 4
4.0%
5.0 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%
12.9 2
2.0%
12.6 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T22:02:19.079074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:19.178310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 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

2023-12-10T22:02:19.277943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:19.380397image/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%
Mean37.428894
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:19.492966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.380095
Q337.71834
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.55332

Descriptive statistics

Standard deviation0.3322548
Coefficient of variation (CV)0.0088769602
Kurtosis-0.97791484
Mean37.428894
Median Absolute Deviation (MAD)0.24408
Skewness0.39604729
Sum3742.8894
Variance0.11039325
MonotonicityNot monotonic
2023-12-10T22:02:19.666125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.19991 2
 
2.0%
37.02727 2
 
2.0%
36.95866 2
 
2.0%
37.05786 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%
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%
37.96019 2
2.0%
37.93198 2
2.0%
37.91419 2
2.0%
37.87708 2
2.0%
37.83225 2
2.0%
37.75866 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.21649
Minimum126.77946
Maximum127.74421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:19.824071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.83253
Q1127.0284
median127.2374
Q3127.37997
95-th percentile127.62684
Maximum127.74421
Range0.96475
Interquartile range (IQR)0.35157

Descriptive statistics

Standard deviation0.24778971
Coefficient of variation (CV)0.0019477798
Kurtosis-0.80748604
Mean127.21649
Median Absolute Deviation (MAD)0.172565
Skewness0.086706674
Sum12721.649
Variance0.061399741
MonotonicityNot monotonic
2023-12-10T22:02:19.962296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
126.9889 2
 
2.0%
127.3367 2
 
2.0%
126.92256 2
 
2.0%
126.92508 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%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.77946 2
2.0%
126.82904 2
2.0%
126.83253 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%
ValueCountFrequency (%)
127.74421 2
2.0%
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%
127.44132 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%
Mean8487.8626
Minimum940.29
Maximum36282.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:20.115494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum940.29
5-th percentile1956.8905
Q14268.215
median7484.265
Q310996.155
95-th percentile18841.392
Maximum36282.94
Range35342.65
Interquartile range (IQR)6727.94

Descriptive statistics

Standard deviation6150.2105
Coefficient of variation (CV)0.72458884
Kurtosis5.0713778
Mean8487.8626
Median Absolute Deviation (MAD)3278.73
Skewness1.8407634
Sum848786.26
Variance37825089
MonotonicityNot monotonic
2023-12-10T22:02:20.282191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11345.58 1
 
1.0%
4298.46 1
 
1.0%
4112.61 1
 
1.0%
8059.21 1
 
1.0%
8851.64 1
 
1.0%
8658.04 1
 
1.0%
8158.64 1
 
1.0%
14440.54 1
 
1.0%
15185.96 1
 
1.0%
8152.04 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
940.29 1
1.0%
1080.56 1
1.0%
1455.42 1
1.0%
1463.72 1
1.0%
1861.9 1
1.0%
1961.89 1
1.0%
1984.47 1
1.0%
1992.1 1
1.0%
1993.22 1
1.0%
2041.98 1
1.0%
ValueCountFrequency (%)
36282.94 1
1.0%
31710.6 1
1.0%
24187.01 1
1.0%
23023.92 1
1.0%
19731.96 1
1.0%
18794.52 1
1.0%
18462.91 1
1.0%
17561.67 1
1.0%
15832.38 1
1.0%
15185.96 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7990.0545
Minimum735.14
Maximum28223.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:20.425518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum735.14
5-th percentile1641.631
Q13782.165
median7576.94
Q310019.16
95-th percentile19966.14
Maximum28223.75
Range27488.61
Interquartile range (IQR)6236.995

Descriptive statistics

Standard deviation5448.2342
Coefficient of variation (CV)0.68187698
Kurtosis2.2884293
Mean7990.0545
Median Absolute Deviation (MAD)3351.45
Skewness1.3787719
Sum799005.45
Variance29683256
MonotonicityNot monotonic
2023-12-10T22:02:20.888515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10396.56 1
 
1.0%
3232.24 1
 
1.0%
3421.51 1
 
1.0%
7974.96 1
 
1.0%
9600.94 1
 
1.0%
10019.02 1
 
1.0%
9936.82 1
 
1.0%
12425.69 1
 
1.0%
12679.84 1
 
1.0%
6705.12 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
735.14 1
1.0%
845.42 1
1.0%
1154.32 1
1.0%
1158.26 1
1.0%
1587.12 1
1.0%
1644.5 1
1.0%
1917.97 1
1.0%
1923.49 1
1.0%
2028.22 1
1.0%
2331.65 1
1.0%
ValueCountFrequency (%)
28223.75 1
1.0%
24144.36 1
1.0%
23236.06 1
1.0%
22268.7 1
1.0%
21086.57 1
1.0%
19907.17 1
1.0%
18208.09 1
1.0%
17660.14 1
1.0%
16446.61 1
1.0%
15702.35 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1052.5158
Minimum96.24
Maximum4008.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:21.053647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum96.24
5-th percentile211.6385
Q1500.225
median947.23
Q31312.27
95-th percentile2711.8045
Maximum4008.21
Range3911.97
Interquartile range (IQR)812.045

Descriptive statistics

Standard deviation749.52211
Coefficient of variation (CV)0.71212433
Kurtosis2.9869923
Mean1052.5158
Median Absolute Deviation (MAD)423.57
Skewness1.5679909
Sum105251.58
Variance561783.39
MonotonicityNot monotonic
2023-12-10T22:02:21.233613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300.08 1
 
1.0%
473.66 1
 
1.0%
415.92 1
 
1.0%
991.32 1
 
1.0%
1218.63 1
 
1.0%
1268.22 1
 
1.0%
1245.84 1
 
1.0%
1736.54 1
 
1.0%
1791.52 1
 
1.0%
895.19 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
96.24 1
1.0%
111.0 1
1.0%
149.5 1
1.0%
156.75 1
1.0%
198.88 1
1.0%
212.31 1
1.0%
243.64 1
1.0%
256.22 1
1.0%
279.29 1
1.0%
314.21 1
1.0%
ValueCountFrequency (%)
4008.21 1
1.0%
3321.82 1
1.0%
3270.55 1
1.0%
2982.96 1
1.0%
2818.1 1
1.0%
2706.21 1
1.0%
2589.87 1
1.0%
2257.08 1
1.0%
2131.46 1
1.0%
2039.24 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean454.3902
Minimum43.72
Maximum1325.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:21.443097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43.72
5-th percentile76.423
Q1213.5425
median443.515
Q3595.4875
95-th percentile1133.354
Maximum1325.94
Range1282.22
Interquartile range (IQR)381.945

Descriptive statistics

Standard deviation291.66198
Coefficient of variation (CV)0.6418756
Kurtosis1.1482197
Mean454.3902
Median Absolute Deviation (MAD)183.855
Skewness1.056897
Sum45439.02
Variance85066.711
MonotonicityNot monotonic
2023-12-10T22:02:21.617078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
564.48 1
 
1.0%
195.3 1
 
1.0%
172.94 1
 
1.0%
522.86 1
 
1.0%
645.9 1
 
1.0%
594.46 1
 
1.0%
598.57 1
 
1.0%
608.29 1
 
1.0%
616.31 1
 
1.0%
405.95 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
43.72 1
1.0%
53.22 1
1.0%
54.62 1
1.0%
64.31 1
1.0%
75.91 1
1.0%
76.45 1
1.0%
111.52 1
1.0%
111.68 1
1.0%
133.3 1
1.0%
136.7 1
1.0%
ValueCountFrequency (%)
1325.94 1
1.0%
1311.51 1
1.0%
1278.65 1
1.0%
1194.83 1
1.0%
1134.57 1
1.0%
1133.29 1
1.0%
1014.05 1
1.0%
942.18 1
1.0%
864.86 1
1.0%
819.21 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2139642.9
Minimum243173.89
Maximum9321133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:21.794035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum243173.89
5-th percentile473104.54
Q11085695.7
median1901472.5
Q32787449.6
95-th percentile4614116.7
Maximum9321133
Range9077959.1
Interquartile range (IQR)1701753.9

Descriptive statistics

Standard deviation1554586
Coefficient of variation (CV)0.72656328
Kurtosis5.7010171
Mean2139642.9
Median Absolute Deviation (MAD)842332.03
Skewness1.9282697
Sum2.1396429 × 108
Variance2.4167376 × 1012
MonotonicityNot monotonic
2023-12-10T22:02:21.980736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2877507.37 1
 
1.0%
1098943.68 1
 
1.0%
1056797.02 1
 
1.0%
2039977.68 1
 
1.0%
2206433.93 1
 
1.0%
2201679.09 1
 
1.0%
2061594.18 1
 
1.0%
3662175.24 1
 
1.0%
3846136.19 1
 
1.0%
1955445.36 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
243173.89 1
1.0%
279628.47 1
1.0%
370353.86 1
1.0%
378015.09 1
1.0%
460941.85 1
1.0%
473744.68 1
1.0%
478540.42 1
1.0%
494372.5 1
1.0%
501837.25 1
1.0%
506104.49 1
1.0%
ValueCountFrequency (%)
9321133.02 1
1.0%
8214350.88 1
1.0%
6190781.62 1
1.0%
5665556.45 1
1.0%
4795931.63 1
1.0%
4604547.45 1
1.0%
4347134.49 1
1.0%
4282097.0 1
1.0%
4062487.12 1
1.0%
3846136.19 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:02:22.275581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.64
Min length8

Characters and Unicode

Total characters1064
Distinct characters107
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 조리 장곡
4th row경기 파주 조리 장곡
5th row경기 파주 문산 당동
ValueCountFrequency (%)
경기 100
25.8%
평택 14
 
3.6%
용인 12
 
3.1%
광주 10
 
2.6%
포천 8
 
2.1%
가평 8
 
2.1%
여주 8
 
2.1%
양평 6
 
1.5%
화성 6
 
1.5%
안성 6
 
1.5%
Other values (95) 210
54.1%
2023-12-10T22:02:22.760893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
288
27.1%
102
 
9.6%
100
 
9.4%
34
 
3.2%
30
 
2.8%
24
 
2.3%
18
 
1.7%
16
 
1.5%
16
 
1.5%
16
 
1.5%
Other values (97) 420
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 776
72.9%
Space Separator 288
 
27.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
102
 
13.1%
100
 
12.9%
34
 
4.4%
30
 
3.9%
24
 
3.1%
18
 
2.3%
16
 
2.1%
16
 
2.1%
16
 
2.1%
16
 
2.1%
Other values (96) 404
52.1%
Space Separator
ValueCountFrequency (%)
288
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 776
72.9%
Common 288
 
27.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
102
 
13.1%
100
 
12.9%
34
 
4.4%
30
 
3.9%
24
 
3.1%
18
 
2.3%
16
 
2.1%
16
 
2.1%
16
 
2.1%
16
 
2.1%
Other values (96) 404
52.1%
Common
ValueCountFrequency (%)
288
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 776
72.9%
ASCII 288
 
27.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
288
100.0%
Hangul
ValueCountFrequency (%)
102
 
13.1%
100
 
12.9%
34
 
4.4%
30
 
3.9%
24
 
3.1%
18
 
2.3%
16
 
2.1%
16
 
2.1%
16
 
2.1%
16
 
2.1%
Other values (96) 404
52.1%

Interactions

2023-12-10T22:02:14.983974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:06.474360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:07.692783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:08.818190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.752027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:10.840481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.778567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.616777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:13.958681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:15.071927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:06.587414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:07.855753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:08.937799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.837775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:10.941830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.862042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.721715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:14.061883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:15.148313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:06.704862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:07.969978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.040948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.938815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.051019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.970838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.825150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:14.165522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:15.239317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:06.822366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:08.088786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.156871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:10.087976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.165909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.057900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.928622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:14.277350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:15.381945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:06.922971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:08.205246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.252231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:10.220316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.262737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.141695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:13.031120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:14.365204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:15.478483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:07.300498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:08.327255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.353181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:10.343132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.358611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.251954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:13.147862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:14.461588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:15.576688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:07.391017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:08.435019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.448791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:10.460424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.450683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.333417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:13.260710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:14.572164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:15.702103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:07.496589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:08.567098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.559096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:10.590183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.567305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.443347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:13.392931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:14.679313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:15.819730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:07.600305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:08.677123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:09.665461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:10.726889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:11.672210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:12.526573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:13.517191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:14.818285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:02:22.886335image/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
기본키1.0001.0000.0001.0000.5850.8420.8290.5170.6020.6230.6700.4651.000
지점1.0001.0000.0001.0001.0001.0001.0000.9640.9450.9680.8820.9071.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9640.9450.9680.8820.9071.000
연장((km))0.5851.0000.0001.0001.0000.5610.6150.5160.5470.5650.2620.3961.000
좌표위치위도((°))0.8421.0000.0001.0000.5611.0000.8190.4320.5440.6280.5000.2901.000
좌표위치경도((°))0.8291.0000.0001.0000.6150.8191.0000.4810.4360.5040.2260.2821.000
co((g/km))0.5170.9640.0000.9640.5160.4320.4811.0000.9010.9250.7820.9940.964
nox((g/km))0.6020.9450.0000.9450.5470.5440.4360.9011.0000.9870.9640.8940.945
hc((g/km))0.6230.9680.0000.9680.5650.6280.5040.9250.9871.0000.9390.9230.968
pm((g/km))0.6700.8820.0000.8820.2620.5000.2260.7820.9640.9391.0000.8370.882
co2((g/km))0.4650.9070.0000.9070.3960.2900.2820.9940.8940.9230.8371.0000.907
주소1.0001.0000.0001.0001.0001.0001.0000.9640.9450.9680.8820.9071.000
2023-12-10T22:02:23.087224image/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
기본키1.000-0.189-0.154-0.0260.023-0.0070.0040.0210.0220.000
연장((km))-0.1891.0000.2830.0110.0250.0080.002-0.0420.0160.000
좌표위치위도((°))-0.1540.2831.0000.098-0.247-0.386-0.341-0.401-0.2590.000
좌표위치경도((°))-0.0260.0110.0981.000-0.150-0.196-0.160-0.201-0.1610.000
co((g/km))0.0230.025-0.247-0.1501.0000.9470.9750.8960.9970.000
nox((g/km))-0.0070.008-0.386-0.1960.9471.0000.9860.9740.9530.000
hc((g/km))0.0040.002-0.341-0.1600.9750.9861.0000.9490.9750.000
pm((g/km))0.021-0.042-0.401-0.2010.8960.9740.9491.0000.9020.000
co2((g/km))0.0220.016-0.259-0.1610.9970.9530.9750.9021.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T22:02:15.957215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:02:16.187761image/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-오산6.220210201037.09529127.0636411345.5810396.561300.08564.482877507.37경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210201037.09529127.0636410962.479829.761247.86493.032761208.33경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210201037.73328126.832538865.728773.31093.46602.212238581.9경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210201037.73328126.832538585.778473.411042.43511.22175700.72경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210201037.87708126.779461861.91587.12198.8875.91473744.68경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210201037.87708126.779461984.471644.5212.3176.45506104.49경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210201037.19066127.559944281.84937.5614.15355.061084945.89경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210201037.19066127.559944183.613800.26519.92261.071060105.76경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210201037.31793127.4276310105.410019.581404.5566.612497781.89경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210201037.31793127.427639764.839674.431375.75554.932388651.77경기 이천 신둔 수하
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4504-0]1안중-안성6.020210201036.96033127.066037909.136206.02873.8310.392035288.35경기 평택 팽성 남산
9192건기연[4504-0]2안중-안성6.020210201036.96033127.066038145.486411.23923.36418.842073387.68경기 평택 팽성 남산
9293건기연[4506-2]1장서-천5.420210201037.16502127.2056714196.6416446.612131.46864.863557179.56경기 용인 이동 덕성
9394건기연[4506-2]2장서-천5.420210201037.16502127.2056714282.8117660.142257.081014.053547788.82경기 용인 이동 덕성
9495건기연[4508-0]1포곡-광주3.120210201037.34342127.250536998.337428.65894.78535.861747992.94경기 용인 모현 왕산
9596건기연[4508-0]2포곡-광주3.120210201037.34342127.250537649.148144.15990.02601.581919578.24경기 용인 모현 왕산
9697건기연[4509-0]1광주-팔당19.020210201037.4815127.281015312.734239.74668.21247.221220619.45경기 광주 남종 삼성
9798건기연[4509-0]2광주-팔당19.020210201037.4815127.281014479.494211.24621.81258.251085945.64경기 광주 남종 삼성
9899건기연[4512-1]1화도-청평8.320210201037.68735127.379979804.747960.631070.61449.182506733.88경기 가평 청평 대성
99100건기연[4512-1]2화도-청평8.320210201037.68735127.379978579.146955.41955.97422.142188217.36경기 가평 청평 대성