Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

Numeric9
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
co((g/km)) is highly overall correlated with nox((g/km)) and 3 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
pm((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
co2((g/km)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
co((g/km)) has unique valuesUnique
nox((g/km)) has unique valuesUnique
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:22:30.031819
Analysis finished2023-12-10 11:22:43.979635
Duration13.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:44.106496image/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
2023-12-10T20:22:44.345536image/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

2023-12-10T20:22:44.566787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:44.733816image/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
2023-12-10T20:22:45.215755image/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%
4302-3 2
 
2.0%
4606-2 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%
4205-1 2
 
2.0%
4206-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:22:46.039662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 122
15.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
1 66
8.2%
4 64
8.0%
2 54
6.8%
7 28
 
3.5%
8 24
 
3.0%
Other values (3) 60
7.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 122
24.4%
3 82
16.4%
1 66
13.2%
4 64
12.8%
2 54
10.8%
7 28
 
5.6%
8 24
 
4.8%
6 22
 
4.4%
5 22
 
4.4%
9 16
 
3.2%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 122
15.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
1 66
8.2%
4 64
8.0%
2 54
6.8%
7 28
 
3.5%
8 24
 
3.0%
Other values (3) 60
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 122
15.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 82
10.2%
1 66
8.2%
4 64
8.0%
2 54
6.8%
7 28
 
3.5%
8 24
 
3.0%
Other values (3) 60
7.5%

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

2023-12-10T20:22:46.291791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:46.848166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:22:47.191143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.22
Min length4

Characters and Unicode

Total characters522
Distinct characters81
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송탄-오산
2nd row송탄-오산
3rd row고양-파주
4th row고양-파주
5th row당동-파평
ValueCountFrequency (%)
송탄-오산 2
 
2.0%
봉담-향남 2
 
2.0%
청평-가평 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%
용인-마장 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:22:47.767689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.2%
평 28
 
5.4%
천 24
 
4.6%
부 16
 
3.1%
주 14
 
2.7%
양 14
 
2.7%
안 14
 
2.7%
성 12
 
2.3%
중 12
 
2.3%
동 10
 
1.9%
Other values (71) 278
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 414
79.3%
Dash Punctuation 100
 
19.2%
Uppercase Letter 8
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
평 28
 
6.8%
천 24
 
5.8%
부 16
 
3.9%
주 14
 
3.4%
양 14
 
3.4%
안 14
 
3.4%
성 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
청 10
 
2.4%
Other values (68) 260
62.8%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 414
79.3%
Common 100
 
19.2%
Latin 8
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
평 28
 
6.8%
천 24
 
5.8%
부 16
 
3.9%
주 14
 
3.4%
양 14
 
3.4%
안 14
 
3.4%
성 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
청 10
 
2.4%
Other values (68) 260
62.8%
Latin
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 414
79.3%
ASCII 108
 
20.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
C 4
 
3.7%
I 4
 
3.7%
Hangul
ValueCountFrequency (%)
평 28
 
6.8%
천 24
 
5.8%
부 16
 
3.9%
주 14
 
3.4%
양 14
 
3.4%
안 14
 
3.4%
성 12
 
2.9%
중 12
 
2.9%
동 10
 
2.4%
청 10
 
2.4%
Other values (68) 260
62.8%

연장((km))
Real number (ℝ)

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.394
Minimum1.5
Maximum27.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:48.015607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile2.6
Q15.4
median7.05
Q310.6
95-th percentile17.6
Maximum27.5
Range26
Interquartile range (IQR)5.2

Descriptive statistics

Standard deviation4.9335139
Coefficient of variation (CV)0.5877429
Kurtosis3.1436001
Mean8.394
Median Absolute Deviation (MAD)2.65
Skewness1.4663356
Sum839.4
Variance24.33956
MonotonicityNot monotonic
2023-12-10T20:22:48.297560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
6.0 4
 
4.0%
4.8 4
 
4.0%
5.4 4
 
4.0%
10.6 4
 
4.0%
6.1 4
 
4.0%
6.2 2
 
2.0%
5.6 2
 
2.0%
2.7 2
 
2.0%
27.5 2
 
2.0%
7.2 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%
2.9 2
2.0%
3.1 2
2.0%
3.4 2
2.0%
4.1 2
2.0%
4.3 2
2.0%
4.8 4
4.0%
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
20210501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210501
2nd row20210501
3rd row20210501
4th row20210501
5th row20210501

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2023-12-10T20:22:48.517824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:48.685453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 100
100.0%

측정시간
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T20:22:48.857564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:22:49.027844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

좌표위치위도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.435444
Minimum36.95627
Maximum38.06264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:49.229055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.33487706
Coefficient of variation (CV)0.0089454544
Kurtosis-1.0532316
Mean37.435444
Median Absolute Deviation (MAD)0.266565
Skewness0.34387861
Sum3743.5444
Variance0.11214264
MonotonicityNot monotonic
2023-12-10T20:22:49.511478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.09529 2
 
2.0%
37.43583 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%
37.29776 2
 
2.0%
37.14026 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%
37.91419 2
2.0%
37.87708 2
2.0%
37.83225 2
2.0%
37.76396 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.22314
Minimum126.77946
Maximum127.74421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:49.789435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.77946
5-th percentile126.83253
Q1127.06364
median127.2374
Q3127.37997
95-th percentile127.62684
Maximum127.74421
Range0.96475
Interquartile range (IQR)0.31633

Descriptive statistics

Standard deviation0.24306995
Coefficient of variation (CV)0.0019105796
Kurtosis-0.73130917
Mean127.22314
Median Absolute Deviation (MAD)0.172565
Skewness0.080976282
Sum12722.314
Variance0.059083001
MonotonicityNot monotonic
2023-12-10T20:22:50.049622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.06364 2
 
2.0%
127.26262 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%
127.60231 2
 
2.0%
126.91531 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%
127.44335 2
2.0%
127.44132 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.6683
Minimum0.52
Maximum475.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:50.296465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile11.1285
Q134.865
median76.685
Q3141.95
95-th percentile236.466
Maximum475.34
Range474.82
Interquartile range (IQR)107.085

Descriptive statistics

Standard deviation87.228689
Coefficient of variation (CV)0.88405991
Kurtosis4.2794847
Mean98.6683
Median Absolute Deviation (MAD)48.16
Skewness1.7342904
Sum9866.83
Variance7608.8441
MonotonicityNot monotonic
2023-12-10T20:22:50.526499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212.16 1
 
1.0%
141.17 1
 
1.0%
192.9 1
 
1.0%
112.89 1
 
1.0%
122.61 1
 
1.0%
48.69 1
 
1.0%
52.9 1
 
1.0%
92.96 1
 
1.0%
92.51 1
 
1.0%
126.98 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.52 1
1.0%
3.36 1
1.0%
4.04 1
1.0%
5.28 1
1.0%
5.4 1
1.0%
11.43 1
1.0%
12.21 1
1.0%
13.15 1
1.0%
13.26 1
1.0%
13.42 1
1.0%
ValueCountFrequency (%)
475.34 1
1.0%
431.3 1
1.0%
331.93 1
1.0%
252.12 1
1.0%
248.55 1
1.0%
235.83 1
1.0%
235.58 1
1.0%
235.2 1
1.0%
212.16 1
1.0%
209.34 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.1116
Minimum0.28
Maximum474.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:50.776656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile6.59
Q121.8475
median70.76
Q3136.9975
95-th percentile208.891
Maximum474.86
Range474.58
Interquartile range (IQR)115.15

Descriptive statistics

Standard deviation83.959714
Coefficient of variation (CV)0.94218614
Kurtosis5.3467945
Mean89.1116
Median Absolute Deviation (MAD)50.885
Skewness1.911115
Sum8911.16
Variance7049.2336
MonotonicityNot monotonic
2023-12-10T20:22:51.134298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179.67 1
 
1.0%
139.86 1
 
1.0%
132.44 1
 
1.0%
91.52 1
 
1.0%
113.17 1
 
1.0%
38.7 1
 
1.0%
43.29 1
 
1.0%
111.62 1
 
1.0%
88.34 1
 
1.0%
119.57 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.28 1
1.0%
2.16 1
1.0%
2.83 1
1.0%
3.02 1
1.0%
3.17 1
1.0%
6.77 1
1.0%
6.81 1
1.0%
7.32 1
1.0%
7.73 1
1.0%
8.17 1
1.0%
ValueCountFrequency (%)
474.86 1
1.0%
388.19 1
1.0%
352.65 1
1.0%
293.21 1
1.0%
234.37 1
1.0%
207.55 1
1.0%
183.74 1
1.0%
182.77 1
1.0%
179.67 1
1.0%
177.9 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.0721
Minimum0.04
Maximum62.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:51.446478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile1.0035
Q13.355
median9.935
Q316.995
95-th percentile28.5795
Maximum62.26
Range62.22
Interquartile range (IQR)13.64

Descriptive statistics

Standard deviation10.970545
Coefficient of variation (CV)0.90875198
Kurtosis4.682295
Mean12.0721
Median Absolute Deviation (MAD)6.74
Skewness1.7720219
Sum1207.21
Variance120.35285
MonotonicityNot monotonic
2023-12-10T20:22:51.712160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.92 2
 
2.0%
24.74 1
 
1.0%
5.83 1
 
1.0%
15.46 1
 
1.0%
4.83 1
 
1.0%
5.54 1
 
1.0%
13.47 1
 
1.0%
12.37 1
 
1.0%
16.52 1
 
1.0%
14.05 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.04 1
1.0%
0.32 1
1.0%
0.4 1
1.0%
0.48 1
1.0%
0.5 1
1.0%
1.03 1
1.0%
1.06 1
1.0%
1.19 1
1.0%
1.24 1
1.0%
1.33 1
1.0%
ValueCountFrequency (%)
62.26 1
1.0%
47.39 1
1.0%
47.02 1
1.0%
34.27 1
1.0%
31.61 1
1.0%
28.42 1
1.0%
27.22 1
1.0%
25.92 1
1.0%
25.7 1
1.0%
25.68 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2608
Minimum0
Maximum29.36
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:51.951176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2635
Q11.6075
median4.07
Q37.675
95-th percentile12.7195
Maximum29.36
Range29.36
Interquartile range (IQR)6.0675

Descriptive statistics

Standard deviation5.1834411
Coefficient of variation (CV)0.98529523
Kurtosis5.8390162
Mean5.2608
Median Absolute Deviation (MAD)2.845
Skewness2.02017
Sum526.08
Variance26.868062
MonotonicityNot monotonic
2023-12-10T20:22:52.241265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.81 3
 
3.0%
0.54 3
 
3.0%
0.28 2
 
2.0%
0.27 2
 
2.0%
2.66 2
 
2.0%
0.14 2
 
2.0%
0.13 2
 
2.0%
1.61 2
 
2.0%
1.88 2
 
2.0%
9.26 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.13 2
2.0%
0.14 2
2.0%
0.27 2
2.0%
0.28 2
2.0%
0.54 3
3.0%
0.55 1
 
1.0%
0.81 3
3.0%
0.82 1
 
1.0%
0.83 1
 
1.0%
ValueCountFrequency (%)
29.36 1
1.0%
24.97 1
1.0%
20.03 1
1.0%
18.55 1
1.0%
13.85 1
1.0%
12.66 1
1.0%
12.37 1
1.0%
11.96 1
1.0%
11.77 1
1.0%
10.77 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24187.56
Minimum138.68
Maximum120194.56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:22:52.493976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile2927.218
Q18597.71
median18327.56
Q334917.78
95-th percentile58655.719
Maximum120194.56
Range120055.88
Interquartile range (IQR)26320.07

Descriptive statistics

Standard deviation21732.196
Coefficient of variation (CV)0.89848648
Kurtosis5.2554125
Mean24187.56
Median Absolute Deviation (MAD)11631.26
Skewness1.9089515
Sum2418756
Variance4.7228834 × 108
MonotonicityNot monotonic
2023-12-10T20:22:52.769550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49122.43 1
 
1.0%
35893.59 1
 
1.0%
49885.98 1
 
1.0%
28611.83 1
 
1.0%
30220.71 1
 
1.0%
12572.33 1
 
1.0%
12364.89 1
 
1.0%
23511.6 1
 
1.0%
21031.48 1
 
1.0%
30269.3 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
138.68 1
1.0%
878.97 1
1.0%
1058.89 1
1.0%
1261.32 1
1.0%
1428.08 1
1.0%
3006.12 1
1.0%
3137.38 1
1.0%
3228.65 1
1.0%
3230.99 1
1.0%
3332.27 1
1.0%
ValueCountFrequency (%)
120194.56 1
1.0%
112258.94 1
1.0%
84887.27 1
1.0%
59494.72 1
1.0%
58712.32 1
1.0%
58652.74 1
1.0%
55653.0 1
1.0%
55647.13 1
1.0%
53339.08 1
1.0%
51667.77 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:22:53.225657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.74
Min length8

Characters and Unicode

Total characters1074
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
25.6%
평택 14
 
3.6%
용인 12
 
3.1%
광주 10
 
2.6%
포천 8
 
2.1%
가평 8
 
2.1%
여주 8
 
2.1%
안성 6
 
1.5%
양평 6
 
1.5%
화성 6
 
1.5%
Other values (94) 212
54.4%
2023-12-10T20:22:54.077118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
290
27.0%
기 102
 
9.5%
경 100
 
9.3%
평 36
 
3.4%
주 32
 
3.0%
성 24
 
2.2%
양 20
 
1.9%
천 18
 
1.7%
인 16
 
1.5%
용 16
 
1.5%
Other values (99) 420
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 784
73.0%
Space Separator 290
 
27.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 102
 
13.0%
경 100
 
12.8%
평 36
 
4.6%
주 32
 
4.1%
성 24
 
3.1%
양 20
 
2.6%
천 18
 
2.3%
인 16
 
2.0%
용 16
 
2.0%
포 16
 
2.0%
Other values (98) 404
51.5%
Space Separator
ValueCountFrequency (%)
290
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 784
73.0%
Common 290
 
27.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 102
 
13.0%
경 100
 
12.8%
평 36
 
4.6%
주 32
 
4.1%
성 24
 
3.1%
양 20
 
2.6%
천 18
 
2.3%
인 16
 
2.0%
용 16
 
2.0%
포 16
 
2.0%
Other values (98) 404
51.5%
Common
ValueCountFrequency (%)
290
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 784
73.0%
ASCII 290
 
27.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
290
100.0%
Hangul
ValueCountFrequency (%)
기 102
 
13.0%
경 100
 
12.8%
평 36
 
4.6%
주 32
 
4.1%
성 24
 
3.1%
양 20
 
2.6%
천 18
 
2.3%
인 16
 
2.0%
용 16
 
2.0%
포 16
 
2.0%
Other values (98) 404
51.5%

Interactions

2023-12-10T20:22:41.964237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:30.792399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:31.994035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:33.172755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:34.626746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:36.382401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:37.783480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:39.057502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:40.486506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.109539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:30.931190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:32.101719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:33.324829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:34.775212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:36.542095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:37.965464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:39.219715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:40.633293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.250067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:31.049779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:32.232901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:33.462087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:34.937307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:36.696124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:38.094625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:39.375441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:40.784400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.414669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:31.183953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:32.359927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:33.611608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:35.092026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:36.858782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:38.235604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:39.538577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:40.939782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.570009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:31.331555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:32.485322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:33.754468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:35.609986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:37.005389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:38.372701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:39.678243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:41.062099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.712948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:31.456301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:32.595337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:33.906727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:35.743275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:37.177344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:38.498644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:39.823869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:41.281878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:42.854007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:31.582222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:32.737060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:34.051357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:35.887496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:37.321658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:38.603087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:39.974715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:41.488146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:43.010054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:31.721999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:32.892088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:34.249536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:36.038874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:37.474705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:38.763165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:40.131465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:41.652403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:43.174119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:31.857232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:33.038514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:34.469993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:36.204690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:37.634818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:38.906427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:40.297010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:22:41.806761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:22:54.358201image/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
기본키1.0001.0000.0001.0000.5690.8460.8220.3980.2710.4400.3210.4061.000
지점1.0001.0000.0001.0001.0001.0001.0000.9370.8520.8900.8750.9531.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9370.8520.8900.8750.9531.000
연장((km))0.5691.0000.0001.0001.0000.5780.6170.5160.1730.4270.2470.3481.000
좌표위치위도((°))0.8461.0000.0001.0000.5781.0000.8180.5000.3040.4970.3640.4691.000
좌표위치경도((°))0.8221.0000.0001.0000.6170.8181.0000.3720.0000.1190.0000.4071.000
co((g/km))0.3980.9370.0000.9370.5160.5000.3721.0000.8760.9620.9460.9820.937
nox((g/km))0.2710.8520.0000.8520.1730.3040.0000.8761.0000.9360.9690.8830.852
hc((g/km))0.4400.8900.0000.8900.4270.4970.1190.9620.9361.0000.9710.8670.890
pm((g/km))0.3210.8750.0000.8750.2470.3640.0000.9460.9690.9711.0000.8560.875
co2((g/km))0.4060.9530.0000.9530.3480.4690.4070.9820.8830.8670.8561.0000.953
주소1.0001.0000.0001.0001.0001.0001.0000.9370.8520.8900.8750.9531.000
2023-12-10T20:22:54.734601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장((km))좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))방향
기본키1.000-0.236-0.0770.0320.0110.0180.0250.0310.0200.000
연장((km))-0.2361.0000.1910.020-0.049-0.051-0.054-0.055-0.0430.000
좌표위치위도((°))-0.0770.1911.0000.099-0.299-0.367-0.347-0.366-0.2960.000
좌표위치경도((°))0.0320.0200.0991.000-0.203-0.214-0.202-0.197-0.2070.000
co((g/km))0.011-0.049-0.299-0.2031.0000.9740.9890.9460.9980.000
nox((g/km))0.018-0.051-0.367-0.2140.9741.0000.9920.9860.9720.000
hc((g/km))0.025-0.054-0.347-0.2020.9890.9921.0000.9750.9850.000
pm((g/km))0.031-0.055-0.366-0.1970.9460.9860.9751.0000.9450.000
co2((g/km))0.020-0.043-0.296-0.2070.9980.9720.9850.9451.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T20:22:43.417766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:22:43.841730image/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-오산6.220210501137.09529127.06364212.16179.6724.749.2649122.43경기 평택 진위 신
12건기연[0134-0]2송탄-오산6.220210501137.09529127.06364205.32183.7423.079.4251667.77경기 평택 진위 신
23건기연[0141-1]1고양-파주4.820210501137.73328126.83253149.85142.6119.4710.134592.51경기 파주 조리 장곡
34건기연[0141-1]2고양-파주4.820210501137.73328126.8325371.3664.988.933.8516411.92경기 파주 조리 장곡
45건기연[0142-0]1당동-파평14.720210501137.87708126.7794613.157.321.240.283137.38경기 파주 문산 당동
56건기연[0142-0]2당동-파평14.720210501137.87708126.7794613.427.731.190.273549.59경기 파주 문산 당동
67건기연[0328-2]1이천-장호원9.620210501137.19066127.5599440.4529.844.662.269515.33경기 여주 가남 심석
78건기연[0328-2]2이천-장호원9.620210501137.19066127.5599426.2217.682.551.46848.16경기 여주 가남 심석
89건기연[0330-1]1이천-광주15.520210501137.31793127.4276398.2575.9610.924.2724934.32경기 이천 신둔 수하
910건기연[0330-1]2이천-광주15.520210501137.31793127.4276391.8572.0110.344.223341.64경기 이천 신둔 수하
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[4508-0]1포곡-광주3.120210501137.34342127.2505362.4566.098.924.2914943.44경기 용인 모현 왕산
9192건기연[4508-0]2포곡-광주3.120210501137.34342127.25053111.76126.3415.97.7224874.91경기 용인 모현 왕산
9293건기연[4509-0]1광주-팔당19.020210501137.4815127.2810124.1219.462.851.386056.76경기 광주 남종 삼성
9394건기연[4509-0]2광주-팔당19.020210501137.4815127.2810128.8122.13.281.67347.96경기 광주 남종 삼성
9495건기연[4512-1]1화도-청평8.320210501137.68735127.3799780.9466.028.923.6920573.87경기 가평 청평 대성
9596건기연[4512-1]2화도-청평8.320210501137.68735127.3799779.6561.258.993.3418640.95경기 가평 청평 대성
9697건기연[4606-2]1청평-가평2.920210501137.76396127.4433560.0644.796.242.4515465.14경기 가평 청평 상천
9798건기연[4606-2]2청평-가평2.920210501137.76396127.4433563.5144.26.752.214919.67경기 가평 청평 상천
9899건기연[4707-0]1내각-부평6.820210501137.70419127.17103202.17182.7724.9610.250090.33경기 남양주 진접 내각
99100건기연[4707-0]2내각-부평6.820210501137.70419127.17103149.58143.9218.719.0239452.32경기 남양주 진접 내각