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
Categorical5
Text2

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
기본키 is highly overall correlated with 측정구간High correlation
연장((km)) is highly overall correlated with 측정구간High correlation
좌표위치위도((°)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
좌표위치경도((°)) is highly overall correlated with 측정구간High correlation
co((g/km)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with 좌표위치위도((°)) and 4 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with 좌표위치위도((°)) and 4 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with 좌표위치위도((°)) and 5 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
기본키 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 13:40:52.841889
Analysis finished2023-12-10 13:41:03.584284
Duration10.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  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
2023-12-10T22:41:03.676789image/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
2023-12-10T22:41:03.829563image/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

2023-12-10T22:41:03.970291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:41:04.090230image/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
2023-12-10T22:41:04.344506image/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%
3803-2 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%
Other values (40) 80
80.0%
2023-12-10T22:41:04.776700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 118
14.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 88
11.0%
1 68
8.5%
4 62
7.8%
2 56
7.0%
7 28
 
3.5%
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 (%)
0 118
23.6%
3 88
17.6%
1 68
13.6%
4 62
12.4%
2 56
11.2%
7 28
 
5.6%
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 (%)
0 118
14.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 88
11.0%
1 68
8.5%
4 62
7.8%
2 56
7.0%
7 28
 
3.5%
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 (%)
0 118
14.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 88
11.0%
1 68
8.5%
4 62
7.8%
2 56
7.0%
7 28
 
3.5%
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

2023-12-10T22:41:04.959543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:41:05.056461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
신팔-일동
 
4
인주-안중
 
2
와부-양평
 
2
당동-파평
 
2
이천-장호원
 
2
Other values (44)
88 

Length

Max length9
Median length5
Mean length5.2
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송탄-오산
2nd row송탄-오산
3rd row고양-파주
4th row고양-파주
5th row당동-파평

Common Values

ValueCountFrequency (%)
신팔-일동 4
 
4.0%
인주-안중 2
 
2.0%
와부-양평 2
 
2.0%
당동-파평 2
 
2.0%
이천-장호원 2
 
2.0%
이천-광주 2
 
2.0%
성남-이천 2
 
2.0%
실촌-성남 2
 
2.0%
의정부-동두천 2
 
2.0%
동두천-전곡 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T22:41:05.219376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신팔-일동 4
 
4.0%
송탄-오산 2
 
2.0%
팔탄-양감 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%
보라-용인 2
 
2.0%
Other values (39) 78
78.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.65
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:05.429349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median7.45
Q310.9
95-th percentile17.6
Maximum27.5
Range26
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.8932958
Coefficient of variation (CV)0.56569893
Kurtosis3.0037224
Mean8.65
Median Absolute Deviation (MAD)2.7
Skewness1.3807636
Sum865
Variance23.944343
MonotonicityNot monotonic
2023-12-10T22:41:05.616548image/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%
6.1 4
 
4.0%
10.6 4
 
4.0%
5.4 4
 
4.0%
6.2 2
 
2.0%
10.2 2
 
2.0%
5.0 2
 
2.0%
5.6 2
 
2.0%
2.7 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%
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%
12.1 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210401
2nd row20210401
3rd row20210401
4th row20210401
5th row20210401

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2023-12-10T22:41:05.752396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:41:05.852477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2023-12-10T22:41:05.944580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:41:06.044305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.439894
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:06.152284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.380095
Q337.72112
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.5561

Descriptive statistics

Standard deviation0.33996257
Coefficient of variation (CV)0.0090802228
Kurtosis-1.1041846
Mean37.439894
Median Absolute Deviation (MAD)0.266565
Skewness0.34176415
Sum3743.9894
Variance0.11557455
MonotonicityNot monotonic
2023-12-10T22:41:06.332996image/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%
37.08149 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%
37.23653 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%
37.94255 2
2.0%
37.93198 2
2.0%
37.91419 2
2.0%
37.87708 2
2.0%
37.83225 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.22491
Minimum126.77946
Maximum127.74421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:06.514267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.83253
Q1127.06364
median127.24601
Q3127.37997
95-th percentile127.62684
Maximum127.74421
Range0.96475
Interquartile range (IQR)0.31633

Descriptive statistics

Standard deviation0.24251604
Coefficient of variation (CV)0.0019061993
Kurtosis-0.71963311
Mean127.22491
Median Absolute Deviation (MAD)0.17499
Skewness0.052984501
Sum12722.491
Variance0.058814029
MonotonicityNot monotonic
2023-12-10T22:41:06.694424image/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%
127.44132 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%
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%
126.85908 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 (%)
127.74421 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 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.2055
Minimum1.57
Maximum831.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:06.880168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.57
5-th percentile9.7
Q133.7625
median89.23
Q3177.79
95-th percentile324.9155
Maximum831.12
Range829.55
Interquartile range (IQR)144.0275

Descriptive statistics

Standard deviation125.68808
Coefficient of variation (CV)1.0201499
Kurtosis10.391326
Mean123.2055
Median Absolute Deviation (MAD)58.285
Skewness2.5564317
Sum12320.55
Variance15797.494
MonotonicityNot monotonic
2023-12-10T22:41:07.053591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.48 2
 
2.0%
260.52 1
 
1.0%
184.98 1
 
1.0%
73.75 1
 
1.0%
106.68 1
 
1.0%
110.16 1
 
1.0%
140.05 1
 
1.0%
136.39 1
 
1.0%
268.38 1
 
1.0%
243.72 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
1.57 1
1.0%
3.24 1
1.0%
6.09 1
1.0%
7.05 1
1.0%
9.13 1
1.0%
9.73 1
1.0%
9.76 1
1.0%
10.59 1
1.0%
15.05 1
1.0%
15.21 1
1.0%
ValueCountFrequency (%)
831.12 1
1.0%
579.6 1
1.0%
368.41 1
1.0%
358.81 1
1.0%
342.12 1
1.0%
324.01 1
1.0%
299.02 1
1.0%
298.88 1
1.0%
270.97 1
1.0%
268.38 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.1696
Minimum0.83
Maximum705.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:07.224240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.83
5-th percentile6.6925
Q130.74
median104.42
Q3179.095
95-th percentile299.3925
Maximum705.47
Range704.64
Interquartile range (IQR)148.355

Descriptive statistics

Standard deviation119.88729
Coefficient of variation (CV)0.97335131
Kurtosis7.1307835
Mean123.1696
Median Absolute Deviation (MAD)74
Skewness2.1318286
Sum12316.96
Variance14372.963
MonotonicityNot monotonic
2023-12-10T22:41:07.428355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230.01 1
 
1.0%
150.93 1
 
1.0%
68.07 1
 
1.0%
125.48 1
 
1.0%
120.73 1
 
1.0%
178.35 1
 
1.0%
114.34 1
 
1.0%
200.82 1
 
1.0%
220.24 1
 
1.0%
42.48 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.83 1
1.0%
1.6 1
1.0%
3.85 1
1.0%
5.42 1
1.0%
5.79 1
1.0%
6.74 1
1.0%
8.84 1
1.0%
9.66 1
1.0%
10.44 1
1.0%
12.88 1
1.0%
ValueCountFrequency (%)
705.47 1
1.0%
628.98 1
1.0%
362.4 1
1.0%
343.14 1
1.0%
320.34 1
1.0%
298.29 1
1.0%
286.37 1
1.0%
284.24 1
1.0%
270.76 1
1.0%
267.92 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.5693
Minimum0.13
Maximum106.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:07.622962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.996
Q14.72
median13.115
Q322.38
95-th percentile44.6595
Maximum106.91
Range106.78
Interquartile range (IQR)17.66

Descriptive statistics

Standard deviation16.618825
Coefficient of variation (CV)1.002989
Kurtosis9.5070246
Mean16.5693
Median Absolute Deviation (MAD)8.8
Skewness2.4526176
Sum1656.93
Variance276.18535
MonotonicityNot monotonic
2023-12-10T22:41:07.802099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 2
 
2.0%
23.26 1
 
1.0%
15.45 1
 
1.0%
24.48 1
 
1.0%
17.23 1
 
1.0%
31.41 1
 
1.0%
30.15 1
 
1.0%
6.78 1
 
1.0%
8.05 1
 
1.0%
18.49 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.13 1
1.0%
0.29 1
1.0%
0.57 1
1.0%
0.88 1
1.0%
0.92 1
1.0%
1.0 2
2.0%
1.33 1
1.0%
1.41 1
1.0%
2.06 1
1.0%
2.08 1
1.0%
ValueCountFrequency (%)
106.91 1
1.0%
80.49 1
1.0%
52.18 1
1.0%
46.27 1
1.0%
45.6 1
1.0%
44.61 1
1.0%
41.75 1
1.0%
36.75 1
1.0%
35.27 1
1.0%
33.5 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9344
Minimum0
Maximum44.1
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:08.000939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4125
Q11.91
median6.64
Q311.8875
95-th percentile19.4865
Maximum44.1
Range44.1
Interquartile range (IQR)9.9775

Descriptive statistics

Standard deviation7.6837069
Coefficient of variation (CV)0.96840428
Kurtosis6.9516169
Mean7.9344
Median Absolute Deviation (MAD)4.855
Skewness2.1168156
Sum793.44
Variance59.039352
MonotonicityNot monotonic
2023-12-10T22:41:08.564318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.9 2
 
2.0%
1.74 2
 
2.0%
7.56 2
 
2.0%
0.72 2
 
2.0%
0.0 2
 
2.0%
0.27 2
 
2.0%
12.3 1
 
1.0%
3.08 1
 
1.0%
8.82 1
 
1.0%
8.08 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.26 1
1.0%
0.27 2
2.0%
0.42 1
1.0%
0.54 1
1.0%
0.72 2
2.0%
0.75 1
1.0%
0.8 1
1.0%
0.82 1
1.0%
0.96 1
1.0%
ValueCountFrequency (%)
44.1 1
1.0%
41.33 1
1.0%
24.86 1
1.0%
22.67 1
1.0%
21.32 1
1.0%
19.39 1
1.0%
19.16 1
1.0%
18.18 1
1.0%
17.01 1
1.0%
16.69 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29593.795
Minimum416.06
Maximum190869.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:08.747165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum416.06
5-th percentile2362.112
Q17817.38
median21566.695
Q342756.363
95-th percentile75792.563
Maximum190869.64
Range190453.58
Interquartile range (IQR)34938.983

Descriptive statistics

Standard deviation30044.294
Coefficient of variation (CV)1.0152228
Kurtosis9.4306469
Mean29593.795
Median Absolute Deviation (MAD)14443.35
Skewness2.4719431
Sum2959379.5
Variance9.0265961 × 108
MonotonicityNot monotonic
2023-12-10T22:41:08.941557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60060.03 1
 
1.0%
45084.38 1
 
1.0%
18701.83 1
 
1.0%
26757.24 1
 
1.0%
26442.08 1
 
1.0%
33410.98 1
 
1.0%
31259.23 1
 
1.0%
62217.29 1
 
1.0%
62087.12 1
 
1.0%
13619.45 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
416.06 1
1.0%
768.41 1
1.0%
1480.68 1
1.0%
1608.0 1
1.0%
2308.76 1
1.0%
2364.92 1
1.0%
2578.8 1
1.0%
2775.61 1
1.0%
3364.73 1
1.0%
3478.54 1
1.0%
ValueCountFrequency (%)
190869.64 1
1.0%
150828.91 1
1.0%
87100.41 1
1.0%
82579.23 1
1.0%
82073.45 1
1.0%
75461.99 1
1.0%
68920.42 1
1.0%
68792.52 1
1.0%
67588.6 1
1.0%
62217.29 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:41:09.326499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.7
Min length8

Characters and Unicode

Total characters1070
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
25.6%
평택 14
 
3.6%
용인 12
 
3.1%
포천 10
 
2.6%
광주 10
 
2.6%
여주 8
 
2.1%
가평 8
 
2.1%
양평 6
 
1.5%
안성 6
 
1.5%
화성 6
 
1.5%
Other values (94) 210
53.8%
2023-12-10T22:41:09.874423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
290
27.1%
기 102
 
9.5%
경 100
 
9.3%
평 34
 
3.2%
주 30
 
2.8%
성 24
 
2.2%
천 18
 
1.7%
양 18
 
1.7%
포 18
 
1.7%
용 16
 
1.5%
Other values (97) 420
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 780
72.9%
Space Separator 290
 
27.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.8%
평 34
 
4.4%
주 30
 
3.8%
성 24
 
3.1%
천 18
 
2.3%
양 18
 
2.3%
포 18
 
2.3%
용 16
 
2.1%
가 16
 
2.1%
Other values (96) 404
51.8%
Space Separator
ValueCountFrequency (%)
290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 780
72.9%
Common 290
 
27.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.8%
평 34
 
4.4%
주 30
 
3.8%
성 24
 
3.1%
천 18
 
2.3%
양 18
 
2.3%
포 18
 
2.3%
용 16
 
2.1%
가 16
 
2.1%
Other values (96) 404
51.8%
Common
ValueCountFrequency (%)
290
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 780
72.9%
ASCII 290
 
27.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
290
100.0%
Hangul
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.8%
평 34
 
4.4%
주 30
 
3.8%
성 24
 
3.1%
천 18
 
2.3%
양 18
 
2.3%
포 18
 
2.3%
용 16
 
2.1%
가 16
 
2.1%
Other values (96) 404
51.8%

Interactions

2023-12-10T22:41:01.870650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:53.465519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:54.978355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:56.145083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:57.247027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.208451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:59.155110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:00.159453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.147489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.970348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:53.588861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:55.084616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:56.272145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:57.363736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.340229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:59.263255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:00.265304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.231397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:02.428676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:53.698616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:55.207616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:56.406310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:57.469311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.443615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:59.384563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:00.375777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.318415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:02.532541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:53.900410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:55.380733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:56.538819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:57.596650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.541101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:59.494185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:00.478643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.410683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:02.623706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:54.015921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:55.503534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:56.650529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:57.730342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.638457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:59.599915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:00.564648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.489667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:02.720015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:54.128979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:55.616992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:56.748746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:57.831960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.721309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:59.696833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:00.654603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.562168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:02.828158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:54.251234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:55.745636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:56.870331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:57.931583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.848408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:59.819101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:00.800814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.639176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:02.919011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:54.381633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:55.871787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:56.988533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.016649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.952492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:59.916744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:00.909863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.706061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:03.008023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:54.508744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:55.994389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:57.100334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:58.102353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:59.043147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:00.035195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.020830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:01.773520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:41:10.024978image/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
기본키1.0001.0000.0001.0000.6050.8610.8510.1770.3810.4490.5420.2181.000
지점1.0001.0000.0001.0001.0001.0001.0000.8630.8230.8840.9230.8661.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9991.0001.0000.8870.8350.9060.9410.8901.000
연장((km))0.6051.0000.0000.9991.0000.5650.6220.2510.5060.3950.4050.3001.000
좌표위치위도((°))0.8611.0000.0001.0000.5651.0000.8270.4350.4680.5270.5300.4211.000
좌표위치경도((°))0.8511.0000.0001.0000.6220.8271.0000.1630.2490.1780.0360.1241.000
co((g/km))0.1770.8630.0000.8870.2510.4350.1631.0000.9240.9800.9290.9990.863
nox((g/km))0.3810.8230.0000.8350.5060.4680.2490.9241.0000.9430.8960.9250.823
hc((g/km))0.4490.8840.0000.9060.3950.5270.1780.9800.9431.0000.9670.9800.884
pm((g/km))0.5420.9230.0000.9410.4050.5300.0360.9290.8960.9671.0000.9300.923
co2((g/km))0.2180.8660.0000.8900.3000.4210.1240.9990.9250.9800.9301.0000.866
주소1.0001.0000.0001.0001.0001.0001.0000.8630.8230.8840.9230.8661.000
2023-12-10T22:41:10.181441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:41:10.299458image/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))방향측정구간
기본키1.000-0.220-0.169-0.032-0.028-0.051-0.051-0.019-0.0130.0000.753
연장((km))-0.2201.0000.2700.067-0.067-0.078-0.068-0.092-0.0650.0000.735
좌표위치위도((°))-0.1690.2701.0000.113-0.463-0.528-0.512-0.522-0.4640.0000.753
좌표위치경도((°))-0.0320.0670.1131.000-0.271-0.291-0.283-0.280-0.2730.0000.753
co((g/km))-0.028-0.067-0.463-0.2711.0000.9710.9800.9520.9980.0000.422
nox((g/km))-0.051-0.078-0.528-0.2910.9711.0000.9950.9890.9700.0000.367
hc((g/km))-0.051-0.068-0.512-0.2830.9800.9951.0000.9820.9760.0000.449
pm((g/km))-0.019-0.092-0.522-0.2800.9520.9890.9821.0000.9510.0000.517
co2((g/km))-0.013-0.065-0.464-0.2730.9980.9700.9760.9511.0000.0000.430
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
측정구간0.7530.7350.7530.7530.4220.3670.4490.5170.4300.0001.000

Missing values

2023-12-10T22:41:03.207989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:41:03.477757image/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-오산6.220210401037.09529127.06364260.52230.0130.612.360060.03경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210401037.09529127.06364298.88265.9235.2714.1168792.52경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210401037.73328126.83253142.82145.3119.1210.8532576.52경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210401037.73328126.83253132.45122.5516.637.9930480.69경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210401037.87708126.7794615.4810.441.410.84385.46경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210401037.87708126.7794633.7827.683.961.747883.7경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210401037.19066127.5599430.430.874.542.487303.24경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210401037.19066127.5599445.3743.656.072.5911058.62경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210401037.31793127.42763198.16211.029.8413.7542709.82경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210401037.31793127.42763178.99173.5526.4412.1239866.8경기 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-안성6.020210401036.96033127.06603139.12124.9117.676.9933138.03경기 평택 팽성 남산
9192건기연[4504-0]2안중-안성6.020210401036.96033127.06603120.55111.3715.877.5629716.97경기 평택 팽성 남산
9293건기연[4506-2]1장서-천5.420210401037.16502127.20567228.13284.2436.7519.3953688.98경기 용인 이동 덕성
9394건기연[4506-2]2장서-천5.420210401037.16502127.20567206.63263.1932.8219.1649400.28경기 용인 이동 덕성
9495건기연[4508-0]1포곡-광주3.120210401037.34342127.2505387.36100.0111.517.922573.88경기 용인 모현 왕산
9596건기연[4508-0]2포곡-광주3.120210401037.34342127.25053121.34116.7615.518.628030.19경기 용인 모현 왕산
9697건기연[4509-0]1광주-팔당19.020210401037.4815127.2810133.7121.263.161.478916.96경기 광주 남종 삼성
9798건기연[4509-0]2광주-팔당19.020210401037.4815127.2810145.5938.275.112.5411535.75경기 광주 남종 삼성
9899건기연[4512-1]1화도-청평8.320210401037.68735127.3799791.9492.3511.86.522589.48경기 가평 청평 대성
99100건기연[4512-1]2화도-청평8.320210401037.68735127.3799763.7147.447.013.3915144.98경기 가평 청평 대성