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 측정구간High 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 co((g/km)) and 4 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
측정구간 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
기본키 has unique valuesUnique
pm((g/km)) has 4 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:41:12.259208
Analysis finished2023-12-10 13:41:22.351083
Duration10.09 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
2023-12-10T22:41:22.432195image/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
2023-12-10T22:41:22.589286image/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

2023-12-10T22:41:22.757468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:41:22.866729image/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
2023-12-10T22:41:23.148051image/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%
2023-12-10T22:41:23.497017image/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

2023-12-10T22:41:23.632029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:41:23.750974image/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

2023-12-10T22:41:23.883258image/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
2023-12-10T22:41:24.063645image/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
2023-12-10T22:41:24.245522image/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
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210301
2nd row20210301
3rd row20210301
4th row20210301
5th row20210301

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2023-12-10T22:41:24.370314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:41:24.456442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T22:41:24.550295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:41:24.642412image/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
2023-12-10T22:41:25.062596image/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
2023-12-10T22:41:25.208491image/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
2023-12-10T22:41:25.362988image/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
2023-12-10T22:41:25.491182image/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%
Mean89.8096
Minimum3.14
Maximum500.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:25.611329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.14
5-th percentile8.33
Q124.9925
median60.37
Q3115.8925
95-th percentile241.29
Maximum500.12
Range496.98
Interquartile range (IQR)90.9

Descriptive statistics

Standard deviation90.654411
Coefficient of variation (CV)1.0094067
Kurtosis6.9343692
Mean89.8096
Median Absolute Deviation (MAD)40.145
Skewness2.2246216
Sum8980.96
Variance8218.2221
MonotonicityNot monotonic
2023-12-10T22:41:25.761121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.86 2
 
2.0%
71.37 1
 
1.0%
37.56 1
 
1.0%
52.51 1
 
1.0%
115.85 1
 
1.0%
100.02 1
 
1.0%
101.64 1
 
1.0%
132.08 1
 
1.0%
183.24 1
 
1.0%
52.7 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
3.14 1
1.0%
4.18 1
1.0%
4.88 1
1.0%
6.02 1
1.0%
6.81 1
1.0%
8.41 1
1.0%
8.44 1
1.0%
8.69 1
1.0%
9.3 1
1.0%
11.21 1
1.0%
ValueCountFrequency (%)
500.12 1
1.0%
499.45 1
1.0%
282.5 1
1.0%
269.64 1
1.0%
261.81 1
1.0%
240.21 1
1.0%
238.64 1
1.0%
218.12 1
1.0%
209.17 1
1.0%
200.97 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.0418
Minimum1.66
Maximum352.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:25.933378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.66
5-th percentile4.2175
Q116.2075
median47.365
Q393.96
95-th percentile176.7645
Maximum352.14
Range350.48
Interquartile range (IQR)77.7525

Descriptive statistics

Standard deviation62.495137
Coefficient of variation (CV)0.96084576
Kurtosis5.6271963
Mean65.0418
Median Absolute Deviation (MAD)33.57
Skewness1.9547624
Sum6504.18
Variance3905.6422
MonotonicityNot monotonic
2023-12-10T22:41:26.115121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.56 2
 
2.0%
12.71 2
 
2.0%
119.76 1
 
1.0%
20.07 1
 
1.0%
40.84 1
 
1.0%
33.73 1
 
1.0%
35.43 1
 
1.0%
103.5 1
 
1.0%
69.02 1
 
1.0%
74.02 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
1.66 1
1.0%
2.64 1
1.0%
2.89 1
1.0%
3.6 1
1.0%
4.17 1
1.0%
4.22 1
1.0%
5.37 1
1.0%
6.62 1
1.0%
7.23 1
1.0%
7.64 1
1.0%
ValueCountFrequency (%)
352.14 1
1.0%
318.84 1
1.0%
182.98 1
1.0%
178.81 1
1.0%
177.42 1
1.0%
176.73 1
1.0%
172.53 1
1.0%
166.77 1
1.0%
149.88 1
1.0%
149.77 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.423
Minimum0.26
Maximum54.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:26.330756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile0.7285
Q12.3375
median6.385
Q313.09
95-th percentile26.31
Maximum54.13
Range53.87
Interquartile range (IQR)10.7525

Descriptive statistics

Standard deviation9.6083779
Coefficient of variation (CV)1.0196729
Kurtosis6.907885
Mean9.423
Median Absolute Deviation (MAD)4.405
Skewness2.2020491
Sum942.3
Variance92.320926
MonotonicityNot monotonic
2023-12-10T22:41:26.508128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.16 2
 
2.0%
10.09 2
 
2.0%
1.98 2
 
2.0%
20.91 1
 
1.0%
19.39 1
 
1.0%
4.92 1
 
1.0%
13.01 1
 
1.0%
10.94 1
 
1.0%
11.07 1
 
1.0%
13.33 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.26 1
1.0%
0.36 1
1.0%
0.44 1
1.0%
0.57 1
1.0%
0.7 1
1.0%
0.73 1
1.0%
0.75 1
1.0%
0.9 1
1.0%
0.99 1
1.0%
1.01 1
1.0%
ValueCountFrequency (%)
54.13 1
1.0%
51.59 1
1.0%
28.63 1
1.0%
27.75 1
1.0%
27.07 1
1.0%
26.27 1
1.0%
25.91 1
1.0%
22.89 1
1.0%
21.4 1
1.0%
20.91 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0122
Minimum0
Maximum15.66
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:26.656142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.7975
median2.33
Q34.1775
95-th percentile8.5225
Maximum15.66
Range15.66
Interquartile range (IQR)3.38

Descriptive statistics

Standard deviation2.9206629
Coefficient of variation (CV)0.96961122
Kurtosis3.8247389
Mean3.0122
Median Absolute Deviation (MAD)1.765
Skewness1.6790959
Sum301.22
Variance8.5302719
MonotonicityNot monotonic
2023-12-10T22:41:26.844999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27 6
 
6.0%
0.13 4
 
4.0%
0.0 4
 
4.0%
2.77 3
 
3.0%
0.54 3
 
3.0%
0.4 2
 
2.0%
1.21 2
 
2.0%
1.84 2
 
2.0%
2.53 2
 
2.0%
3.36 1
 
1.0%
Other values (71) 71
71.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.13 4
4.0%
0.27 6
6.0%
0.28 1
 
1.0%
0.4 2
 
2.0%
0.41 1
 
1.0%
0.44 1
 
1.0%
0.54 3
3.0%
0.58 1
 
1.0%
0.66 1
 
1.0%
ValueCountFrequency (%)
15.66 1
1.0%
13.52 1
1.0%
9.4 1
1.0%
9.29 1
1.0%
8.57 1
1.0%
8.52 1
1.0%
8.14 1
1.0%
7.3 1
1.0%
7.07 1
1.0%
6.98 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21850.754
Minimum832.11
Maximum118903.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:41:27.034081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum832.11
5-th percentile1911.181
Q16654.9375
median15277.67
Q328625.785
95-th percentile61125.853
Maximum118903.58
Range118071.47
Interquartile range (IQR)21970.847

Descriptive statistics

Standard deviation21531.53
Coefficient of variation (CV)0.98539069
Kurtosis6.658204
Mean21850.754
Median Absolute Deviation (MAD)10242.325
Skewness2.1677218
Sum2185075.4
Variance4.6360678 × 108
MonotonicityNot monotonic
2023-12-10T22:41:27.200263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6045.92 2
 
2.0%
19676.9 1
 
1.0%
9617.52 1
 
1.0%
13774.66 1
 
1.0%
26966.02 1
 
1.0%
23286.26 1
 
1.0%
23670.54 1
 
1.0%
34076.14 1
 
1.0%
43397.68 1
 
1.0%
12674.05 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
832.11 1
1.0%
1197.13 1
1.0%
1289.4 1
1.0%
1360.48 1
1.0%
1802.9 1
1.0%
1916.88 1
1.0%
1997.87 1
1.0%
2027.68 1
1.0%
2399.9 1
1.0%
2589.94 1
1.0%
ValueCountFrequency (%)
118903.58 1
1.0%
118033.58 1
1.0%
66976.29 1
1.0%
64272.19 1
1.0%
61401.98 1
1.0%
61111.32 1
1.0%
56627.41 1
1.0%
51346.14 1
1.0%
49788.0 1
1.0%
49787.44 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:41:27.535826image/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%
2023-12-10T22:41:28.003601image/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

2023-12-10T22:41:21.059362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:12.985993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.041659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.906644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:15.913214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.825392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.715766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.032102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.998320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:21.185023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:13.107749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.157936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.990386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.021246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.922725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.826717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.136456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:20.097489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:21.286862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:13.206296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.272147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:15.095873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.131405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.015617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.915661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.230379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:20.192089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:21.377802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:13.302986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.393831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:15.174302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.214570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.126146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:18.331939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.320335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:20.326271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:21.464432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:13.405518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.477297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:15.271219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.293760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.228754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:18.459595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.431746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:20.434538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:21.564229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:13.546053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.557534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:15.412558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.389232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.323303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:18.571702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.547066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:20.555902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:21.658942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:13.675168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.642831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:15.527224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.491311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.416539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:18.680511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.676209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:20.694053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:21.733699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:13.788292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.726828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:15.644494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.584800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.519607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:18.790178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.769367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:20.811008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:21.832087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:13.908361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:14.821114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:15.783682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:16.697072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:17.611643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:18.907951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:19.885387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:20.941840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:41:28.128123image/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
기본키1.0001.0000.0001.0000.6050.8610.8510.3680.4610.3720.3630.4171.000
지점1.0001.0000.0001.0001.0001.0001.0000.8810.9290.8260.7820.9131.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.1920.0000.0000.000
측정구간1.0001.0000.0001.0000.9991.0001.0000.9030.9460.8540.7970.9311.000
연장((km))0.6051.0000.0000.9991.0000.5650.6220.4130.2850.2720.2480.3771.000
좌표위치위도((°))0.8611.0000.0001.0000.5651.0000.8270.3560.4400.3630.2560.3681.000
좌표위치경도((°))0.8511.0000.0001.0000.6220.8271.0000.0000.2350.0000.0000.1921.000
co((g/km))0.3680.8810.0000.9030.4130.3560.0001.0000.9660.9890.8190.9920.881
nox((g/km))0.4610.9290.0000.9460.2850.4400.2350.9661.0000.9830.8800.9700.929
hc((g/km))0.3720.8260.1920.8540.2720.3630.0000.9890.9831.0000.8290.9780.826
pm((g/km))0.3630.7820.0000.7970.2480.2560.0000.8190.8800.8291.0000.8330.782
co2((g/km))0.4170.9130.0000.9310.3770.3680.1920.9920.9700.9780.8331.0000.913
주소1.0001.0000.0001.0001.0001.0001.0000.8810.9290.8260.7820.9131.000
2023-12-10T22:41:28.257763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:41:28.363076image/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
기본키1.000-0.220-0.169-0.0320.007-0.0200.003-0.0380.0030.0000.753
연장((km))-0.2201.0000.2700.067-0.050-0.045-0.046-0.042-0.0460.0000.735
좌표위치위도((°))-0.1690.2701.0000.113-0.375-0.405-0.396-0.418-0.3830.0000.753
좌표위치경도((°))-0.0320.0670.1131.000-0.266-0.275-0.250-0.244-0.2820.0000.753
co((g/km))0.007-0.050-0.375-0.2661.0000.9850.9940.9490.9960.0000.445
nox((g/km))-0.020-0.045-0.405-0.2750.9851.0000.9930.9780.9840.0000.521
hc((g/km))0.003-0.046-0.396-0.2500.9940.9931.0000.9670.9880.1990.381
pm((g/km))-0.038-0.042-0.418-0.2440.9490.9780.9671.0000.9480.0000.319
co2((g/km))0.003-0.046-0.383-0.2820.9960.9840.9880.9481.0000.0000.495
방향0.0000.0000.0000.0000.0000.0000.1990.0000.0001.0000.000
측정구간0.7530.7350.7530.7530.4450.5210.3810.3190.4950.0001.000

Missing values

2023-12-10T22:41:21.965374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:41:22.229095image/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-오산6.220210301037.09529127.06364134.11105.6513.414.3734741.01경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210301037.09529127.06364199.87166.7720.046.4251346.14경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210301037.73328126.83253133.8199.1914.115.0531506.2경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210301037.73328126.8325379.0264.248.343.8718681.21경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210301037.87708126.7794616.0513.171.670.443726.18경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210301037.87708126.7794619.7314.421.960.414619.67경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210301037.19066127.5599423.2916.512.341.546062.92경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210301037.19066127.5599439.4230.994.212.239379.07경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210301037.31793127.42763120.1789.8214.214.4127748.65경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210301037.31793127.42763146.26120.7118.216.9836343.12경기 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-안성6.020210301036.96033127.0660397.4159.758.741.925626.65경기 평택 팽성 남산
9192건기연[4504-0]2안중-안성6.020210301036.96033127.06603103.6463.9810.093.6224915.0경기 평택 팽성 남산
9293건기연[4506-2]1장서-천5.420210301037.16502127.20567114.1485.9812.515.329237.99경기 용인 이동 덕성
9394건기연[4506-2]2장서-천5.420210301037.16502127.20567184.27144.5121.49.443083.13경기 용인 이동 덕성
9495건기연[4508-0]1포곡-광주3.120210301037.34342127.2505371.057.627.433.1216780.68경기 용인 모현 왕산
9596건기연[4508-0]2포곡-광주3.120210301037.34342127.2505386.2871.169.533.8920156.23경기 용인 모현 왕산
9697건기연[4509-0]1광주-팔당19.020210301037.4815127.2810132.9923.33.531.198466.8경기 광주 남종 삼성
9798건기연[4509-0]2광주-팔당19.020210301037.4815127.2810148.4437.836.151.8411018.58경기 광주 남종 삼성
9899건기연[4512-1]1화도-청평8.320210301037.68735127.3799765.0344.566.192.516972.06경기 가평 청평 대성
99100건기연[4512-1]2화도-청평8.320210301037.68735127.3799778.854.327.892.7718742.35경기 가평 청평 대성