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 started2024-04-21 09:44:26.728354
Analysis finished2024-04-21 09:44:46.066186
Duration19.34 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
2024-04-21T18:44:46.266015image/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
2024-04-21T18:44:46.701525image/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 size928.0 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

2024-04-21T18:44:47.104997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:44:47.391239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
2024-04-21T18:44:48.174067image/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%
2024-04-21T18:44:49.122276image/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 size928.0 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

2024-04-21T18:44:49.341781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:44:49.500801image/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 size928.0 B
2024-04-21T18:44:50.218249image/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%
2024-04-21T18:44:51.182566image/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
2024-04-21T18:44:51.419919image/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
2024-04-21T18:44:51.653974image/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 size928.0 B
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2024-04-21T18:44:51.866509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:44:52.023913image/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 size928.0 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

2024-04-21T18:44:52.182282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:44:52.349667image/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
2024-04-21T18:44:52.533904image/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
2024-04-21T18:44:52.784365image/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
2024-04-21T18:44:53.250209image/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
2024-04-21T18:44:53.500025image/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%
Mean11352.477
Minimum1451.71
Maximum41068.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-21T18:44:53.745682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1451.71
5-th percentile2279.2495
Q15985.3025
median10786.97
Q315379.78
95-th percentile22998.34
Maximum41068.82
Range39617.11
Interquartile range (IQR)9394.4775

Descriptive statistics

Standard deviation7160.6306
Coefficient of variation (CV)0.63075494
Kurtosis3.083974
Mean11352.477
Median Absolute Deviation (MAD)4771.82
Skewness1.3174838
Sum1135247.7
Variance51274631
MonotonicityNot monotonic
2024-04-21T18:44:54.011458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13932.35 1
 
1.0%
12146.01 1
 
1.0%
16826.17 1
 
1.0%
21434.14 1
 
1.0%
21501.05 1
 
1.0%
18014.19 1
 
1.0%
17718.23 1
 
1.0%
5644.94 1
 
1.0%
5307.18 1
 
1.0%
10807.28 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1451.71 1
1.0%
1460.67 1
1.0%
1474.08 1
1.0%
1483.76 1
1.0%
2265.37 1
1.0%
2279.98 1
1.0%
2461.97 1
1.0%
2607.17 1
1.0%
2680.6 1
1.0%
2749.12 1
1.0%
ValueCountFrequency (%)
41068.82 1
1.0%
36077.21 1
1.0%
28883.83 1
1.0%
26469.89 1
1.0%
23892.49 1
1.0%
22951.28 1
1.0%
21897.71 1
1.0%
21501.05 1
1.0%
21434.14 1
1.0%
19494.58 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12059.859
Minimum1278.95
Maximum43765.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-21T18:44:54.276150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1278.95
5-th percentile2376.795
Q15410.115
median11651.11
Q315909.677
95-th percentile26527.055
Maximum43765.6
Range42486.65
Interquartile range (IQR)10499.562

Descriptive statistics

Standard deviation7537.815
Coefficient of variation (CV)0.62503341
Kurtosis2.3173738
Mean12059.859
Median Absolute Deviation (MAD)4809.11
Skewness1.1463275
Sum1205985.9
Variance56818656
MonotonicityNot monotonic
2024-04-21T18:44:54.518221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13439.77 1
 
1.0%
15830.43 1
 
1.0%
14743.75 1
 
1.0%
18340.52 1
 
1.0%
18523.03 1
 
1.0%
16416.9 1
 
1.0%
16153.34 1
 
1.0%
5152.97 1
 
1.0%
4928.37 1
 
1.0%
11251.21 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1278.95 1
1.0%
1303.27 1
1.0%
2237.27 1
1.0%
2303.58 1
1.0%
2319.13 1
1.0%
2379.83 1
1.0%
2472.93 1
1.0%
2600.52 1
1.0%
3514.27 1
1.0%
3561.88 1
1.0%
ValueCountFrequency (%)
43765.6 1
1.0%
29574.54 1
1.0%
29394.18 1
1.0%
28937.69 1
1.0%
28001.94 1
1.0%
26449.43 1
1.0%
24627.75 1
1.0%
23971.45 1
1.0%
22726.83 1
1.0%
22450.72 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1546.3977
Minimum172.98
Maximum5643.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-21T18:44:54.750596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum172.98
5-th percentile327.4195
Q1773.4975
median1464.065
Q32019.4925
95-th percentile3495.28
Maximum5643.5
Range5470.52
Interquartile range (IQR)1245.995

Descriptive statistics

Standard deviation959.10922
Coefficient of variation (CV)0.62022158
Kurtosis2.7398803
Mean1546.3977
Median Absolute Deviation (MAD)656.16
Skewness1.2394957
Sum154639.77
Variance919890.5
MonotonicityNot monotonic
2024-04-21T18:44:54.982860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1643.42 1
 
1.0%
1870.8 1
 
1.0%
1930.67 1
 
1.0%
2580.22 1
 
1.0%
2628.26 1
 
1.0%
2260.29 1
 
1.0%
2219.26 1
 
1.0%
632.28 1
 
1.0%
565.7 1
 
1.0%
1418.57 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
172.98 1
1.0%
178.7 1
1.0%
263.66 1
1.0%
276.87 1
1.0%
301.95 1
1.0%
328.76 1
1.0%
343.6 1
1.0%
347.71 1
1.0%
461.26 1
1.0%
489.11 1
1.0%
ValueCountFrequency (%)
5643.5 1
1.0%
3931.21 1
1.0%
3896.01 1
1.0%
3801.6 1
1.0%
3538.6 1
1.0%
3493.0 1
1.0%
3323.2 1
1.0%
2815.65 1
1.0%
2686.65 1
1.0%
2680.56 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean774.0606
Minimum97.19
Maximum2997.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-21T18:44:55.284538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97.19
5-th percentile146.91
Q1385.53
median710.09
Q3987.8275
95-th percentile1693.814
Maximum2997.86
Range2900.67
Interquartile range (IQR)602.2975

Descriptive statistics

Standard deviation498.79971
Coefficient of variation (CV)0.64439362
Kurtosis3.1968571
Mean774.0606
Median Absolute Deviation (MAD)303.345
Skewness1.340723
Sum77406.06
Variance248801.16
MonotonicityNot monotonic
2024-04-21T18:44:55.712923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
718.81 1
 
1.0%
1122.52 1
 
1.0%
984.17 1
 
1.0%
1070.5 1
 
1.0%
1150.34 1
 
1.0%
962.53 1
 
1.0%
962.86 1
 
1.0%
302.81 1
 
1.0%
243.51 1
 
1.0%
706.87 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
97.19 1
1.0%
107.59 1
1.0%
127.42 1
1.0%
138.88 1
1.0%
146.34 1
1.0%
146.94 1
1.0%
150.82 1
1.0%
151.65 1
1.0%
213.91 1
1.0%
223.21 1
1.0%
ValueCountFrequency (%)
2997.86 1
1.0%
2095.89 1
1.0%
1871.01 1
1.0%
1804.84 1
1.0%
1705.67 1
1.0%
1693.19 1
1.0%
1641.4 1
1.0%
1599.11 1
1.0%
1527.23 1
1.0%
1514.48 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2849083.9
Minimum347266.68
Maximum10449496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-21T18:44:56.070203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum347266.68
5-th percentile539953.44
Q11481862.3
median2727176.1
Q33879020.7
95-th percentile5657818.4
Maximum10449496
Range10102230
Interquartile range (IQR)2397158.4

Descriptive statistics

Standard deviation1812576.7
Coefficient of variation (CV)0.63619631
Kurtosis3.2778232
Mean2849083.9
Median Absolute Deviation (MAD)1232060.1
Skewness1.3383832
Sum2.8490839 × 108
Variance3.2854341 × 1012
MonotonicityNot monotonic
2024-04-21T18:44:56.331120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3534744.2 1
 
1.0%
3166278.13 1
 
1.0%
4292024.61 1
 
1.0%
5459869.56 1
 
1.0%
5423111.4 1
 
1.0%
4488617.74 1
 
1.0%
4420469.05 1
 
1.0%
1424767.67 1
 
1.0%
1346236.01 1
 
1.0%
2726070.29 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
347266.68 1
1.0%
356806.63 1
1.0%
367226.16 1
1.0%
367805.35 1
1.0%
536531.18 1
1.0%
540133.56 1
1.0%
612825.5 1
1.0%
643920.38 1
1.0%
660002.35 1
1.0%
682301.51 1
1.0%
ValueCountFrequency (%)
10449496.48 1
1.0%
9290917.06 1
1.0%
6926768.72 1
1.0%
6680583.41 1
1.0%
5851739.84 1
1.0%
5647611.98 1
1.0%
5584208.47 1
1.0%
5459869.56 1
1.0%
5423111.4 1
1.0%
5024528.44 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
2024-04-21T18:44:57.199359image/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%
2024-04-21T18:44:58.299003image/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

2024-04-21T18:44:42.844126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:27.722233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:30.092665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:32.256633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:34.355924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:36.090567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:37.939951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:39.194361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:40.681206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:43.087567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:27.956172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:30.333260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:32.502580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:34.492995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:36.344164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:38.077224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:39.336517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:40.869929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:43.322487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:28.190793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:30.561730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:32.746006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:34.627041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:36.590989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:38.207094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:39.470196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:41.109928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:43.570562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:28.432390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:30.804357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:32.926089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:34.767744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:36.849257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:38.345021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:39.615970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:41.357328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:43.808943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:28.670292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:31.038373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:33.162184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:34.903601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:37.100527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:38.482195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:39.752228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:41.600155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:44.076437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:28.928465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:31.301093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:33.430609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:35.126995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:37.328950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:38.635817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:39.911559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:41.863266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:44.310352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:29.163057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:31.535600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:33.665312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:35.362509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:37.471760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:38.765614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:40.044685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:42.102230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:44.558648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:29.606006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:31.775475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:33.910562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:35.600672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:37.626721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:38.905203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:40.395752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:42.347919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:44.806026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:29.851528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:32.017351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:34.158037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:35.848573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:37.786675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:39.055955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:40.538270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:44:42.597931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T18:44:58.475205image/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
기본키1.0001.0000.0001.0000.5660.8200.8530.5290.4440.4680.4420.5511.000
지점1.0001.0000.0001.0001.0001.0001.0000.9590.9370.9350.9100.9591.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9590.9370.9350.9100.9591.000
연장((km))0.5661.0000.0001.0001.0000.6660.6150.4690.4750.3970.3640.4821.000
좌표위치위도((°))0.8201.0000.0001.0000.6661.0000.8050.4530.5180.5180.4560.5291.000
좌표위치경도((°))0.8531.0000.0001.0000.6150.8051.0000.5000.4320.3340.3620.4521.000
co((g/km))0.5290.9590.0000.9590.4690.4530.5001.0000.8570.8700.8270.9990.959
nox((g/km))0.4440.9370.0000.9370.4750.5180.4320.8571.0000.9850.9710.8490.937
hc((g/km))0.4680.9350.0000.9350.3970.5180.3340.8700.9851.0000.9520.8600.935
pm((g/km))0.4420.9100.0000.9100.3640.4560.3620.8270.9710.9521.0000.8180.910
co2((g/km))0.5510.9590.0000.9590.4820.5290.4520.9990.8490.8600.8181.0000.959
주소1.0001.0000.0001.0001.0001.0001.0000.9590.9370.9350.9100.9591.000
2024-04-21T18:44:58.725086image/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
기본키1.000-0.2210.034-0.0390.001-0.041-0.022-0.0100.0040.000
연장((km))-0.2211.0000.243-0.0750.015-0.0080.006-0.0080.0350.000
좌표위치위도((°))0.0340.2431.0000.117-0.316-0.450-0.418-0.450-0.3130.000
좌표위치경도((°))-0.039-0.0750.1171.000-0.156-0.191-0.181-0.157-0.1780.000
co((g/km))0.0010.015-0.316-0.1561.0000.9210.9470.8840.9950.000
nox((g/km))-0.041-0.008-0.450-0.1910.9211.0000.9900.9830.9230.000
hc((g/km))-0.0220.006-0.418-0.1810.9470.9901.0000.9720.9430.000
pm((g/km))-0.010-0.008-0.450-0.1570.8840.9830.9721.0000.8860.000
co2((g/km))0.0040.035-0.313-0.1780.9950.9230.9430.8861.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-21T18:44:45.181390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T18:44:45.814747image/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-오산6.220210601037.09529127.0636413932.3513439.771643.42718.813534744.2경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210601037.09529127.0636414036.4112034.481547.08561.223574998.4경기 평택 진위 신
23건기연[0142-0]1당동-파평14.720210601037.87708126.779462461.972319.13301.95138.88612825.5경기 파주 문산 당동
34건기연[0142-0]2당동-파평14.720210601037.87708126.779462680.62600.52343.6151.65660002.35경기 파주 문산 당동
45건기연[0328-2]1이천-장호원9.620210601037.19066127.559946087.447417.43971.87577.181491515.94경기 여주 가남 심석
56건기연[0328-2]2이천-장호원9.620210601037.19066127.559946164.756584.56909.64473.381497212.67경기 여주 가남 심석
67건기연[0330-1]1이천-광주15.520210601037.31793127.4276312624.5615369.681962.691056.83136969.41경기 이천 신둔 수하
78건기연[0330-1]2이천-광주15.520210601037.31793127.4276313725.3715770.022118.411081.043333642.22경기 이천 신둔 수하
89건기연[0331-0]1성남-이천7.720210601037.37777127.3016828883.8329394.183896.011693.196926768.72경기 광주 초월 용수
910건기연[0331-0]2성남-이천7.720210601037.37777127.3016826469.8929574.543801.61871.016680583.41경기 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-팔당19.020210601037.4815127.281015381.424296.07612.81299.241370563.66경기 광주 남종 삼성
9192건기연[4509-0]2광주-팔당19.020210601037.4815127.281015032.094115.82578.24286.711278711.9경기 광주 남종 삼성
9293건기연[4512-1]1화도-청평8.320210601037.68735127.3799710384.0810323.151291.68643.852609107.47경기 가평 청평 대성
9394건기연[4512-1]2화도-청평8.320210601037.68735127.3799711150.6710462.731399.49658.392800856.2경기 가평 청평 대성
9495건기연[4606-2]1청평-가평2.920210601037.76396127.443358981.4510045.81338.24664.02168885.17경기 가평 청평 상천
9596건기연[4606-2]2청평-가평2.920210601037.76396127.4433510637.4810565.131531.55680.42393398.81경기 가평 청평 상천
9697건기연[4707-0]1내각-부평6.820210601037.70419127.1710312507.4713255.091683.19828.293133843.84경기 남양주 진접 내각
9798건기연[4707-0]2내각-부평6.820210601037.70419127.1710311640.511753.81490.92767.732942214.67경기 남양주 진접 내각
9899건기연[4708-0]1일동-이동7.920210601037.98354127.334453003.73561.88461.26213.91728123.59경기 포천 일동 사직
99100건기연[4708-0]2일동-이동7.920210601037.98354127.334453188.824071.86527.38242.42756463.8경기 포천 일동 사직