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 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
측정구간 is highly overall correlated with 기본키 and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
pm((g/km)) has 4 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:41:47.782875
Analysis finished2023-12-10 13:41:58.296362
Duration10.51 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
2023-12-10T22:41:58.389781image/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
2023-12-10T22:41:58.547034image/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

2023-12-10T22:41:58.675047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:41:58.779488image/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
2023-12-10T22:41:59.039515image/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%
4206-2 2
 
2.0%
4508-0 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%
2023-12-10T22:41:59.818584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 92
11.5%
1 64
8.0%
4 64
8.0%
2 56
7.0%
7 28
 
3.5%
8 22
 
2.8%
Other values (3) 54
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120
24.0%
3 92
18.4%
1 64
12.8%
4 64
12.8%
2 56
11.2%
7 28
 
5.6%
8 22
 
4.4%
5 20
 
4.0%
6 18
 
3.6%
9 16
 
3.2%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 92
11.5%
1 64
8.0%
4 64
8.0%
2 56
7.0%
7 28
 
3.5%
8 22
 
2.8%
Other values (3) 54
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
15.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 92
11.5%
1 64
8.0%
4 64
8.0%
2 56
7.0%
7 28
 
3.5%
8 22
 
2.8%
Other values (3) 54
6.8%

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

2023-12-10T22:41:59.971098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:00.114812image/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 length5.22
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

2023-12-10T22:42:00.257331image/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%
부발-여주 2
 
2.0%
죽산교-일죽 2
 
2.0%
안성-죽산 2
 
2.0%
아산만-덕목 2
 
2.0%
발안ic-청북ic 2
 
2.0%
팔탄-비봉 2
 
2.0%
일영-의정부 2
 
2.0%
시흥-목감 2
 
2.0%
Other values (39) 78
78.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.844
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:00.423428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median7.45
Q311
95-th percentile18.8
Maximum27.5
Range26
Interquartile range (IQR)5.6

Descriptive statistics

Standard deviation5.1072205
Coefficient of variation (CV)0.57747857
Kurtosis2.1744081
Mean8.844
Median Absolute Deviation (MAD)2.8
Skewness1.2564292
Sum884.4
Variance26.083701
MonotonicityNot monotonic
2023-12-10T22:42:00.616822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
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%
4.1 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 (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%
3.1 2
2.0%
3.4 2
2.0%
4.1 2
2.0%
4.3 2
2.0%
4.8 4
4.0%
5.0 2
2.0%
ValueCountFrequency (%)
27.5 2
2.0%
19.0 2
2.0%
18.8 2
2.0%
17.6 2
2.0%
15.5 2
2.0%
15.4 2
2.0%
14.7 2
2.0%
14.6 2
2.0%
12.9 2
2.0%
12.6 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T22:42:00.778896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:00.888695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 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

2023-12-10T22:42:00.999490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:42:01.101130image/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%
Mean37.450512
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:01.229276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.95627
5-th percentile36.96033
Q137.16502
median37.3875
Q337.73328
95-th percentile38.0169
Maximum38.06264
Range1.10637
Interquartile range (IQR)0.56826

Descriptive statistics

Standard deviation0.34563853
Coefficient of variation (CV)0.0092292071
Kurtosis-1.1340527
Mean37.450512
Median Absolute Deviation (MAD)0.269725
Skewness0.32378062
Sum3745.0512
Variance0.119466
MonotonicityNot monotonic
2023-12-10T22:42:01.390468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.14026 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%
37.39258 2
 
2.0%
37.23653 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%
38.00853 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%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.21275
Minimum126.77946
Maximum127.74421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:01.536211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.83253
Q1127.0284
median127.2374
Q3127.37739
95-th percentile127.62684
Maximum127.74421
Range0.96475
Interquartile range (IQR)0.34899

Descriptive statistics

Standard deviation0.24630841
Coefficient of variation (CV)0.0019361928
Kurtosis-0.75062065
Mean127.21275
Median Absolute Deviation (MAD)0.172565
Skewness0.1268687
Sum12721.275
Variance0.060667835
MonotonicityNot monotonic
2023-12-10T22:42:01.680127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
126.91531 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%
126.85818 2
 
2.0%
127.166 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.77946 2
2.0%
126.82904 2
2.0%
126.83253 2
2.0%
126.85818 2
2.0%
126.85908 2
2.0%
126.88236 2
2.0%
126.88641 2
2.0%
126.91531 2
2.0%
126.92256 2
2.0%
126.92508 2
2.0%
ValueCountFrequency (%)
127.74421 2
2.0%
127.6367 2
2.0%
127.62684 2
2.0%
127.61201 2
2.0%
127.60231 2
2.0%
127.56566 2
2.0%
127.55994 2
2.0%
127.49045 2
2.0%
127.44132 2
2.0%
127.42763 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.6426
Minimum3.14
Maximum720.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:01.900846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.14
5-th percentile8.0085
Q130.8025
median84.21
Q3140.7375
95-th percentile263.9835
Maximum720.58
Range717.44
Interquartile range (IQR)109.935

Descriptive statistics

Standard deviation107.32103
Coefficient of variation (CV)1.0063617
Kurtosis11.729975
Mean106.6426
Median Absolute Deviation (MAD)53.6
Skewness2.7814372
Sum10664.26
Variance11517.804
MonotonicityNot monotonic
2023-12-10T22:42:02.066145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.14 2
 
2.0%
29.64 2
 
2.0%
162.56 1
 
1.0%
182.43 1
 
1.0%
137.89 1
 
1.0%
134.67 1
 
1.0%
183.12 1
 
1.0%
92.4 1
 
1.0%
60.81 1
 
1.0%
128.2 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
3.14 2
2.0%
5.23 1
1.0%
6.18 1
1.0%
7.79 1
1.0%
8.02 1
1.0%
9.74 1
1.0%
11.87 1
1.0%
13.32 1
1.0%
15.46 1
1.0%
15.59 1
1.0%
ValueCountFrequency (%)
720.58 1
1.0%
485.45 1
1.0%
455.52 1
1.0%
291.18 1
1.0%
270.13 1
1.0%
263.66 1
1.0%
242.19 1
1.0%
235.52 1
1.0%
226.26 1
1.0%
214.86 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.1208
Minimum1.66
Maximum456.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:02.266782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.66
5-th percentile4.5345
Q122.5
median67.92
Q3121.8175
95-th percentile254.774
Maximum456.02
Range454.36
Interquartile range (IQR)99.3175

Descriptive statistics

Standard deviation81.408663
Coefficient of variation (CV)0.93443429
Kurtosis3.8524544
Mean87.1208
Median Absolute Deviation (MAD)48.1
Skewness1.6586131
Sum8712.08
Variance6627.3704
MonotonicityNot monotonic
2023-12-10T22:42:02.440473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.66 2
 
2.0%
16.7 2
 
2.0%
155.45 1
 
1.0%
274.06 1
 
1.0%
168.53 1
 
1.0%
164.15 1
 
1.0%
116.37 1
 
1.0%
61.54 1
 
1.0%
53.91 1
 
1.0%
104.29 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
1.66 2
2.0%
2.92 1
1.0%
3.85 1
1.0%
4.05 1
1.0%
4.56 1
1.0%
4.82 1
1.0%
7.73 1
1.0%
9.1 1
1.0%
9.12 1
1.0%
10.63 1
1.0%
ValueCountFrequency (%)
456.02 1
1.0%
308.55 1
1.0%
306.53 1
1.0%
274.06 1
1.0%
257.13 1
1.0%
254.65 1
1.0%
230.58 1
1.0%
220.16 1
1.0%
197.9 1
1.0%
193.96 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.9722
Minimum0.26
Maximum71.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:02.620272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.26
5-th percentile0.7
Q13.395
median9.495
Q316.755
95-th percentile29.3375
Maximum71.6
Range71.34
Interquartile range (IQR)13.36

Descriptive statistics

Standard deviation11.425634
Coefficient of variation (CV)0.95434709
Kurtosis7.1804158
Mean11.9722
Median Absolute Deviation (MAD)6.465
Skewness2.1040824
Sum1197.22
Variance130.54512
MonotonicityNot monotonic
2023-12-10T22:42:02.813378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7 2
 
2.0%
0.26 2
 
2.0%
4.58 2
 
2.0%
2.8 2
 
2.0%
19.45 1
 
1.0%
6.98 1
 
1.0%
20.75 1
 
1.0%
21.51 1
 
1.0%
18.59 1
 
1.0%
7.97 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.26 2
2.0%
0.49 1
1.0%
0.55 1
1.0%
0.7 2
2.0%
0.87 1
1.0%
1.07 1
1.0%
1.25 1
1.0%
1.6 1
1.0%
1.7 1
1.0%
1.85 1
1.0%
ValueCountFrequency (%)
71.6 1
1.0%
45.66 1
1.0%
45.22 1
1.0%
33.23 1
1.0%
30.62 1
1.0%
29.27 1
1.0%
29.12 1
1.0%
28.41 1
1.0%
28.15 1
1.0%
27.6 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct84
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7778
Minimum0
Maximum18.6
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:03.001420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q11.1075
median3.185
Q37.545
95-th percentile15.2215
Maximum18.6
Range18.6
Interquartile range (IQR)6.4375

Descriptive statistics

Standard deviation4.689918
Coefficient of variation (CV)0.98160617
Kurtosis0.52796954
Mean4.7778
Median Absolute Deviation (MAD)2.485
Skewness1.1569926
Sum477.78
Variance21.99533
MonotonicityNot monotonic
2023-12-10T22:42:03.286205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 4
 
4.0%
0.0 4
 
4.0%
1.5 2
 
2.0%
2.8 2
 
2.0%
4.79 2
 
2.0%
0.14 2
 
2.0%
1.31 2
 
2.0%
1.47 2
 
2.0%
0.94 2
 
2.0%
0.7 2
 
2.0%
Other values (74) 76
76.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.13 4
4.0%
0.14 2
2.0%
0.26 1
 
1.0%
0.27 1
 
1.0%
0.41 1
 
1.0%
0.43 1
 
1.0%
0.63 1
 
1.0%
0.67 2
2.0%
0.7 2
2.0%
ValueCountFrequency (%)
18.6 1
1.0%
17.9 1
1.0%
16.05 1
1.0%
15.96 1
1.0%
15.44 1
1.0%
15.21 1
1.0%
13.34 1
1.0%
13.02 1
1.0%
12.66 1
1.0%
11.99 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26449.716
Minimum832.11
Maximum171473.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:42:03.496116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum832.11
5-th percentile2107.6435
Q18028.405
median20094.005
Q335799.195
95-th percentile62267.813
Maximum171473.05
Range170640.94
Interquartile range (IQR)27770.79

Descriptive statistics

Standard deviation26674.878
Coefficient of variation (CV)1.0085128
Kurtosis10.415704
Mean26449.716
Median Absolute Deviation (MAD)12772.14
Skewness2.6770139
Sum2644971.6
Variance7.1154913 × 108
MonotonicityNot monotonic
2023-12-10T22:42:03.689234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
832.11 2
 
2.0%
7075.03 2
 
2.0%
40448.93 1
 
1.0%
49242.94 1
 
1.0%
35567.08 1
 
1.0%
32851.67 1
 
1.0%
43079.59 1
 
1.0%
26306.98 1
 
1.0%
15635.0 1
 
1.0%
30282.62 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
832.11 2
2.0%
1255.69 1
1.0%
1758.51 1
1.0%
1844.18 1
1.0%
2121.51 1
1.0%
2305.23 1
1.0%
2683.29 1
1.0%
3870.93 1
1.0%
3981.52 1
1.0%
4181.28 1
1.0%
ValueCountFrequency (%)
171473.05 1
1.0%
126958.84 1
1.0%
118041.55 1
1.0%
76090.48 1
1.0%
63674.64 1
1.0%
62193.77 1
1.0%
61686.27 1
1.0%
56671.22 1
1.0%
55874.79 1
1.0%
54040.41 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:42:04.021123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.64
Min length8

Characters and Unicode

Total characters1064
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 조리 장곡
4th row경기 파주 조리 장곡
5th row경기 파주 문산 당동
ValueCountFrequency (%)
경기 100
25.8%
평택 14
 
3.6%
포천 12
 
3.1%
광주 10
 
2.6%
용인 10
 
2.6%
여주 8
 
2.1%
안성 6
 
1.5%
양평 6
 
1.5%
파주 6
 
1.5%
가평 6
 
1.5%
Other values (94) 210
54.1%
2023-12-10T22:42:04.616534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
288
27.1%
기 102
 
9.6%
경 100
 
9.4%
주 30
 
2.8%
평 28
 
2.6%
성 22
 
2.1%
양 20
 
1.9%
포 20
 
1.9%
천 20
 
1.9%
인 14
 
1.3%
Other values (99) 420
39.5%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.9%
주 30
 
3.9%
평 28
 
3.6%
성 22
 
2.8%
양 20
 
2.6%
포 20
 
2.6%
천 20
 
2.6%
인 14
 
1.8%
택 14
 
1.8%
Other values (98) 406
52.3%
Space Separator
ValueCountFrequency (%)
288
100.0%

Most occurring scripts

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

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.9%
주 30
 
3.9%
평 28
 
3.6%
성 22
 
2.8%
양 20
 
2.6%
포 20
 
2.6%
천 20
 
2.6%
인 14
 
1.8%
택 14
 
1.8%
Other values (98) 406
52.3%
Common
ValueCountFrequency (%)
288
100.0%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
288
100.0%
Hangul
ValueCountFrequency (%)
기 102
 
13.1%
경 100
 
12.9%
주 30
 
3.9%
평 28
 
3.6%
성 22
 
2.8%
양 20
 
2.6%
포 20
 
2.6%
천 20
 
2.6%
인 14
 
1.8%
택 14
 
1.8%
Other values (98) 406
52.3%

Interactions

2023-12-10T22:41:56.953684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:48.463089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:49.591435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:50.734904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:51.790553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:52.632008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:53.947875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:54.932197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.901407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:57.057474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:48.571643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:49.705297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:50.844536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:51.867148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:52.769209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:54.050050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.046516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.984950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:57.160591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:48.684596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:49.856455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:50.967232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:51.945290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:52.872208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:54.152674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.146733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:56.086099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:57.262904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:48.791776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:49.969254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:51.065297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:52.040284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:53.268707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:54.265013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.255365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:56.247633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:57.379656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:48.908016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:50.090351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:51.169279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:52.125262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:53.388704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:54.357336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.364579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:56.369725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:57.483691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:49.023746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:50.207319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:51.342448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:52.232801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:53.506432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:54.448867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.475184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:56.498707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:57.587313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:49.127070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:50.296569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:51.463504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:52.309404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:53.596669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:54.541234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.578333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:56.612766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:57.720049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:49.323560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:50.394597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:51.599524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:52.390236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:53.707742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:54.673675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.684669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:56.728210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:57.824897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:49.453879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:50.533140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:51.692338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:52.510871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:53.822169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:54.804122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:55.801349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:41:56.827397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:42:04.782060image/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
기본키1.0001.0000.0000.9980.6210.8500.8630.3550.3630.3420.4820.2871.000
지점1.0001.0000.0001.0001.0001.0001.0000.7230.7880.7810.7600.7251.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.4330.1520.000
측정구간0.9981.0000.0001.0000.9991.0001.0000.7640.8030.8140.7560.7691.000
연장((km))0.6211.0000.0000.9991.0000.5610.6310.2550.4290.0000.0000.3101.000
좌표위치위도((°))0.8501.0000.0001.0000.5611.0000.8210.4030.4600.4070.4640.3281.000
좌표위치경도((°))0.8631.0000.0001.0000.6310.8211.0000.1920.0000.0000.0000.0431.000
co((g/km))0.3550.7230.0000.7640.2550.4030.1921.0000.9020.9790.7100.9780.723
nox((g/km))0.3630.7880.0000.8030.4290.4600.0000.9021.0000.9340.8210.9630.788
hc((g/km))0.3420.7810.0000.8140.0000.4070.0000.9790.9341.0000.7920.8980.781
pm((g/km))0.4820.7600.4330.7560.0000.4640.0000.7100.8210.7921.0000.6820.760
co2((g/km))0.2870.7250.1520.7690.3100.3280.0430.9780.9630.8980.6821.0000.725
주소1.0001.0000.0001.0001.0001.0001.0000.7230.7880.7810.7600.7251.000
2023-12-10T22:42:04.992858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:42:05.130573image/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
기본키1.000-0.200-0.214-0.0630.0290.0580.0570.0870.0240.0000.736
연장((km))-0.2001.0000.2200.014-0.072-0.143-0.119-0.136-0.0740.0000.735
좌표위치위도((°))-0.2140.2201.0000.113-0.351-0.431-0.409-0.425-0.3580.0000.753
좌표위치경도((°))-0.0630.0140.1131.000-0.179-0.162-0.153-0.139-0.1990.0000.753
co((g/km))0.029-0.072-0.351-0.1791.0000.9550.9750.8610.9970.0000.295
nox((g/km))0.058-0.143-0.431-0.1620.9551.0000.9910.9620.9530.0000.334
hc((g/km))0.057-0.119-0.409-0.1530.9750.9911.0000.9370.9700.0000.339
pm((g/km))0.087-0.136-0.425-0.1390.8610.9620.9371.0000.8610.3120.265
co2((g/km))0.024-0.074-0.358-0.1990.9970.9530.9700.8611.0000.1060.286
방향0.0000.0000.0000.0000.0000.0000.0000.3120.1061.0000.000
측정구간0.7360.7350.7530.7530.2950.3340.3390.2650.2860.0001.000

Missing values

2023-12-10T22:41:58.002599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:41:58.205415image/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-오산6.220210101037.09529127.06364162.56155.4519.459.6840448.93경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210101037.09529127.06364235.52254.6529.1215.4462193.77경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210101037.73328126.83253119.9495.4812.24.7930996.03경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210101037.73328126.8325391.3173.429.664.4821621.78경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210101037.87708126.7794615.599.11.250.144508.72경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210101037.87708126.7794620.810.631.890.134944.06경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210101037.19066127.5599430.8522.522.981.318084.52경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210101037.19066127.5599451.8156.76.533.7113408.68경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210101037.31793127.42763114.4674.9511.983.1426861.39경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210101037.31793127.42763180.76111.1817.915.2642959.71경기 이천 신둔 수하
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4401-0]1청운-홍천8.520210101037.57336127.7442119.516.32.090.984605.17경기 양평 청운 삼성
9192건기연[4401-0]2청운-홍천8.520210101037.57336127.7442127.427.863.511.56238.69경기 양평 청운 삼성
9293건기연[4504-0]1안중-안성6.020210101036.96033127.06603112.3163.189.81.729831.04경기 평택 팽성 남산
9394건기연[4504-0]2안중-안성6.020210101036.96033127.06603129.9984.6111.434.7236912.91경기 평택 팽성 남산
9495건기연[4506-2]1장서-천5.420210101037.16502127.20567188.98230.5830.6216.0543730.06경기 용인 이동 덕성
9596건기연[4506-2]2장서-천5.420210101037.16502127.20567201.69257.1333.2317.948771.23경기 용인 이동 덕성
9697건기연[4508-0]1포곡-광주3.120210101037.34342127.2505387.1892.5511.217.6521526.61경기 용인 모현 왕산
9798건기연[4508-0]2포곡-광주3.120210101037.34342127.25053126.1121.4715.539.6929862.97경기 용인 모현 왕산
9899건기연[4509-0]1광주-팔당19.020210101037.4815127.2810129.6416.72.80.77075.03경기 광주 남종 삼성
99100건기연[4509-0]2광주-팔당19.020210101037.4815127.2810142.4823.724.011.0710199.22경기 광주 남종 삼성