Overview

Dataset statistics

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

Variable types

Numeric9
Categorical4
Text3

Alerts

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

Reproduction

Analysis started2023-12-10 12:43:01.528296
Analysis finished2023-12-10 12:43:10.491076
Duration8.96 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
2023-12-10T21:43:10.561318image/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
2023-12-10T21:43:10.699553image/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

2023-12-10T21:43:10.828662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:43:10.906251image/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
2023-12-10T21:43:11.082426image/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[0216-2]
2nd row[0216-2]
3rd row[0220-2]
4th row[0220-2]
5th row[0222-1]
ValueCountFrequency (%)
0216-2 2
 
2.0%
3304-2 2
 
2.0%
7703-0 2
 
2.0%
2417-2 2
 
2.0%
2419-1 2
 
2.0%
2421-0 2
 
2.0%
2422-0 2
 
2.0%
2423-1 2
 
2.0%
2502-0 2
 
2.0%
3101-6 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:43:11.409257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 92
11.5%
2 82
10.2%
3 50
 
6.2%
4 40
 
5.0%
7 28
 
3.5%
5 24
 
3.0%
Other values (3) 34
 
4.2%

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 (%)
0 150
30.0%
1 92
18.4%
2 82
16.4%
3 50
 
10.0%
4 40
 
8.0%
7 28
 
5.6%
5 24
 
4.8%
9 16
 
3.2%
6 10
 
2.0%
8 8
 
1.6%
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 (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 92
11.5%
2 82
10.2%
3 50
 
6.2%
4 40
 
5.0%
7 28
 
3.5%
5 24
 
3.0%
Other values (3) 34
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 150
18.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 92
11.5%
2 82
10.2%
3 50
 
6.2%
4 40
 
5.0%
7 28
 
3.5%
5 24
 
3.0%
Other values (3) 34
 
4.2%

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

2023-12-10T21:43:11.530415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:43:11.614051image/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
2023-12-10T21:43:11.788080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1
Min length5

Characters and Unicode

Total characters510
Distinct characters86
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 row북천-완사
2nd row북천-완사
3rd row일반성-진북
4th row일반성-진북
5th row마산-부산
ValueCountFrequency (%)
북천-완사 2
 
2.0%
쌍백-합천 2
 
2.0%
유포-설천 2
 
2.0%
봉산-금양 2
 
2.0%
초계-적교 2
 
2.0%
창녕-청도 2
 
2.0%
가산-남기 2
 
2.0%
부북-금곡 2
 
2.0%
창원-대산 2
 
2.0%
서생-온산 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:43:12.106368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.6%
산 38
 
7.5%
성 14
 
2.7%
양 14
 
2.7%
남 14
 
2.7%
천 14
 
2.7%
창 12
 
2.4%
마 10
 
2.0%
부 10
 
2.0%
거 10
 
2.0%
Other values (76) 274
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
79.6%
Dash Punctuation 100
 
19.6%
Uppercase Letter 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 38
 
9.4%
성 14
 
3.4%
양 14
 
3.4%
남 14
 
3.4%
천 14
 
3.4%
창 12
 
3.0%
마 10
 
2.5%
부 10
 
2.5%
거 10
 
2.5%
고 8
 
2.0%
Other values (73) 262
64.5%
Uppercase Letter
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
79.6%
Common 100
 
19.6%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 38
 
9.4%
성 14
 
3.4%
양 14
 
3.4%
남 14
 
3.4%
천 14
 
3.4%
창 12
 
3.0%
마 10
 
2.5%
부 10
 
2.5%
거 10
 
2.5%
고 8
 
2.0%
Other values (73) 262
64.5%
Latin
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
79.6%
ASCII 104
 
20.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
96.2%
I 2
 
1.9%
C 2
 
1.9%
Hangul
ValueCountFrequency (%)
산 38
 
9.4%
성 14
 
3.4%
양 14
 
3.4%
남 14
 
3.4%
천 14
 
3.4%
창 12
 
3.0%
마 10
 
2.5%
부 10
 
2.5%
거 10
 
2.5%
고 8
 
2.0%
Other values (73) 262
64.5%

연장((km))
Real number (ℝ)

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.588
Minimum3
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:12.238472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.8
Q15.2
median7.5
Q311.1
95-th percentile17.6
Maximum20.1
Range17.1
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation4.169521
Coefficient of variation (CV)0.48550547
Kurtosis0.49087969
Mean8.588
Median Absolute Deviation (MAD)2.85
Skewness0.96266418
Sum858.8
Variance17.384905
MonotonicityNot monotonic
2023-12-10T21:43:12.412305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
11.2 4
 
4.0%
6.8 4
 
4.0%
5.2 4
 
4.0%
11.9 4
 
4.0%
10.5 4
 
4.0%
10.2 4
 
4.0%
6.4 2
 
2.0%
20.1 2
 
2.0%
7.6 2
 
2.0%
6.1 2
 
2.0%
Other values (34) 68
68.0%
ValueCountFrequency (%)
3.0 2
2.0%
3.3 2
2.0%
3.8 2
2.0%
3.9 2
2.0%
4.1 2
2.0%
4.2 2
2.0%
4.5 2
2.0%
4.6 2
2.0%
4.7 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
20.1 2
2.0%
19.3 2
2.0%
17.6 2
2.0%
17.2 2
2.0%
13.5 2
2.0%
13.3 2
2.0%
13.2 2
2.0%
12.2 2
2.0%
11.9 4
4.0%
11.2 4
4.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T21:43:12.550884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:43:12.629661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 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

2023-12-10T21:43:12.745857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:43:12.831045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.263477
Minimum34.86496
Maximum35.72809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:12.929728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.86496
5-th percentile34.87506
Q135.01568
median35.29762
Q335.50716
95-th percentile35.62274
Maximum35.72809
Range0.86313
Interquartile range (IQR)0.49148

Descriptive statistics

Standard deviation0.25434952
Coefficient of variation (CV)0.0072128316
Kurtosis-1.210975
Mean35.263477
Median Absolute Deviation (MAD)0.218075
Skewness-0.069016348
Sum3526.3477
Variance0.064693677
MonotonicityNot monotonic
2023-12-10T21:43:13.060871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.12031 2
 
2.0%
35.31018 2
 
2.0%
35.55894 2
 
2.0%
35.57918 2
 
2.0%
35.51777 2
 
2.0%
35.50716 2
 
2.0%
35.32823 2
 
2.0%
35.39903 2
 
2.0%
34.97974 2
 
2.0%
35.28102 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.86496 2
2.0%
34.86778 2
2.0%
34.87506 2
2.0%
34.89878 2
2.0%
34.90043 2
2.0%
34.90831 2
2.0%
34.92571 2
2.0%
34.92968 2
2.0%
34.93132 2
2.0%
34.95217 2
2.0%
ValueCountFrequency (%)
35.72809 2
2.0%
35.65095 2
2.0%
35.62274 2
2.0%
35.61557 2
2.0%
35.61463 2
2.0%
35.58349 2
2.0%
35.57918 2
2.0%
35.57341 2
2.0%
35.55894 2
2.0%
35.53342 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.38969
Minimum127.78878
Maximum129.33586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:13.179711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.78878
5-th percentile127.80269
Q1128.00958
median128.33924
Q3128.65347
95-th percentile129.24056
Maximum129.33586
Range1.54708
Interquartile range (IQR)0.64389

Descriptive statistics

Standard deviation0.44299345
Coefficient of variation (CV)0.0034503818
Kurtosis-0.65848451
Mean128.38969
Median Absolute Deviation (MAD)0.321945
Skewness0.56077443
Sum12838.969
Variance0.1962432
MonotonicityNot monotonic
2023-12-10T21:43:13.317052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.97011 2
 
2.0%
129.02747 2
 
2.0%
128.32045 2
 
2.0%
128.51771 2
 
2.0%
128.72421 2
 
2.0%
128.83128 2
 
2.0%
128.71274 2
 
2.0%
129.33586 2
 
2.0%
128.2752 2
 
2.0%
128.08474 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
127.78878 2
2.0%
127.78997 2
2.0%
127.80269 2
2.0%
127.83555 2
2.0%
127.86341 2
2.0%
127.86709 2
2.0%
127.89437 2
2.0%
127.90225 2
2.0%
127.93378 2
2.0%
127.95798 2
2.0%
ValueCountFrequency (%)
129.33586 2
2.0%
129.28105 2
2.0%
129.24056 2
2.0%
129.2158 2
2.0%
129.17553 2
2.0%
129.12842 2
2.0%
129.02747 2
2.0%
128.87085 2
2.0%
128.83128 2
2.0%
128.73303 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4154.8693
Minimum171.64
Maximum16903.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:13.453673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum171.64
5-th percentile452.372
Q11387.425
median2782.795
Q35548.2025
95-th percentile11597.984
Maximum16903.73
Range16732.09
Interquartile range (IQR)4160.7775

Descriptive statistics

Standard deviation3897.4093
Coefficient of variation (CV)0.93803415
Kurtosis1.1271055
Mean4154.8693
Median Absolute Deviation (MAD)1640.325
Skewness1.3487155
Sum415486.93
Variance15189799
MonotonicityNot monotonic
2023-12-10T21:43:13.576363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1979.26 1
 
1.0%
10686.59 1
 
1.0%
2997.29 1
 
1.0%
3330.98 1
 
1.0%
3298.57 1
 
1.0%
3959.95 1
 
1.0%
3884.81 1
 
1.0%
3253.48 1
 
1.0%
3405.21 1
 
1.0%
3693.94 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
171.64 1
1.0%
205.53 1
1.0%
258.16 1
1.0%
271.44 1
1.0%
436.45 1
1.0%
453.21 1
1.0%
468.5 1
1.0%
496.59 1
1.0%
589.34 1
1.0%
607.02 1
1.0%
ValueCountFrequency (%)
16903.73 1
1.0%
16125.79 1
1.0%
14210.2 1
1.0%
13311.26 1
1.0%
13061.44 1
1.0%
11520.96 1
1.0%
10686.59 1
1.0%
10391.8 1
1.0%
10148.91 1
1.0%
9959.79 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3928.0264
Minimum127.82
Maximum17413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:13.722130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.82
5-th percentile358.948
Q11342.03
median2821.615
Q35877.425
95-th percentile9601.5515
Maximum17413
Range17285.18
Interquartile range (IQR)4535.395

Descriptive statistics

Standard deviation3503.8372
Coefficient of variation (CV)0.89200959
Kurtosis1.9925313
Mean3928.0264
Median Absolute Deviation (MAD)2007.58
Skewness1.3492696
Sum392802.64
Variance12276875
MonotonicityNot monotonic
2023-12-10T21:43:14.117429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1347.38 1
 
1.0%
10426.05 1
 
1.0%
3322.74 1
 
1.0%
4860.07 1
 
1.0%
4845.8 1
 
1.0%
5816.27 1
 
1.0%
5756.18 1
 
1.0%
3056.49 1
 
1.0%
3248.71 1
 
1.0%
4547.64 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
127.82 1
1.0%
192.3 1
1.0%
192.79 1
1.0%
206.06 1
1.0%
316.73 1
1.0%
361.17 1
1.0%
391.04 1
1.0%
411.59 1
1.0%
433.73 1
1.0%
439.14 1
1.0%
ValueCountFrequency (%)
17413.0 1
1.0%
14774.19 1
1.0%
14175.3 1
1.0%
10426.05 1
1.0%
10021.29 1
1.0%
9579.46 1
1.0%
9325.89 1
1.0%
9293.5 1
1.0%
9108.25 1
1.0%
9004.12 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.9798
Minimum18.18
Maximum1877.21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:14.246176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.18
5-th percentile48.33
Q1182.115
median342.975
Q3762.97
95-th percentile1394.231
Maximum1877.21
Range1859.03
Interquartile range (IQR)580.855

Descriptive statistics

Standard deviation442.60555
Coefficient of variation (CV)0.88347983
Kurtosis1.1552301
Mean500.9798
Median Absolute Deviation (MAD)225.705
Skewness1.2616874
Sum50097.98
Variance195899.67
MonotonicityNot monotonic
2023-12-10T21:43:14.366390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197.73 1
 
1.0%
1392.21 1
 
1.0%
383.99 1
 
1.0%
528.81 1
 
1.0%
512.93 1
 
1.0%
663.86 1
 
1.0%
634.04 1
 
1.0%
404.38 1
 
1.0%
424.04 1
 
1.0%
572.54 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
18.18 1
1.0%
27.22 1
1.0%
27.31 1
1.0%
28.04 1
1.0%
42.82 1
1.0%
48.62 1
1.0%
52.67 1
1.0%
57.7 1
1.0%
59.86 1
1.0%
64.71 1
1.0%
ValueCountFrequency (%)
1877.21 1
1.0%
1873.3 1
1.0%
1850.04 1
1.0%
1440.21 1
1.0%
1432.63 1
1.0%
1392.21 1
1.0%
1319.25 1
1.0%
1160.11 1
1.0%
1138.81 1
1.0%
1136.6 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.0254
Minimum14.46
Maximum1117.27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:14.508536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.46
5-th percentile28.4705
Q177.25
median180.56
Q3327.575
95-th percentile528.6765
Maximum1117.27
Range1102.81
Interquartile range (IQR)250.325

Descriptive statistics

Standard deviation193.84966
Coefficient of variation (CV)0.869182
Kurtosis4.447897
Mean223.0254
Median Absolute Deviation (MAD)117.73
Skewness1.7180854
Sum22302.54
Variance37577.692
MonotonicityNot monotonic
2023-12-10T21:43:14.650959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.54 1
 
1.0%
651.03 1
 
1.0%
185.54 1
 
1.0%
296.94 1
 
1.0%
267.15 1
 
1.0%
412.72 1
 
1.0%
344.93 1
 
1.0%
184.88 1
 
1.0%
195.52 1
 
1.0%
276.09 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
14.46 1
1.0%
17.18 1
1.0%
19.43 1
1.0%
21.98 1
1.0%
22.59 1
1.0%
28.78 1
1.0%
33.62 1
1.0%
34.28 1
1.0%
34.7 1
1.0%
36.05 1
1.0%
ValueCountFrequency (%)
1117.27 1
1.0%
813.5 1
1.0%
809.52 1
1.0%
651.03 1
1.0%
541.53 1
1.0%
528.0 1
1.0%
509.12 1
1.0%
480.23 1
1.0%
466.0 1
1.0%
457.87 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1066577.2
Minimum45001.15
Maximum4329251.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:14.771513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45001.15
5-th percentile115459.78
Q1352484.4
median729465.39
Q31383176.5
95-th percentile2743012
Maximum4329251.4
Range4284250.2
Interquartile range (IQR)1030692.1

Descriptive statistics

Standard deviation1006108.9
Coefficient of variation (CV)0.94330619
Kurtosis1.1310735
Mean1066577.2
Median Absolute Deviation (MAD)446692.96
Skewness1.3542423
Sum1.0665772 × 108
Variance1.0122552 × 1012
MonotonicityNot monotonic
2023-12-10T21:43:14.908115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
515287.36 1
 
1.0%
2708872.39 1
 
1.0%
783153.26 1
 
1.0%
889859.07 1
 
1.0%
907016.56 1
 
1.0%
1022755.58 1
 
1.0%
1040562.16 1
 
1.0%
827142.01 1
 
1.0%
873558.39 1
 
1.0%
935378.48 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
45001.15 1
1.0%
50620.83 1
1.0%
67546.65 1
1.0%
71501.66 1
1.0%
114221.13 1
1.0%
115524.97 1
1.0%
121781.35 1
1.0%
126384.6 1
1.0%
152179.04 1
1.0%
154174.89 1
1.0%
ValueCountFrequency (%)
4329251.4 1
1.0%
4091269.19 1
1.0%
3703853.92 1
1.0%
3616428.82 1
1.0%
3391664.85 1
1.0%
2708872.39 1
1.0%
2702268.07 1
1.0%
2698044.3 1
1.0%
2682478.8 1
1.0%
2588061.69 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:43:15.147280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.8
Min length8

Characters and Unicode

Total characters1080
Distinct characters106
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 row경남 사천 곤명 작팔
2nd row경남 사천 곤명 작팔
3rd row경남 창원 진전 근곡
4th row경남 창원 진전 근곡
5th row경남 창원 양곡
ValueCountFrequency (%)
경남 100
25.3%
창원 12
 
3.0%
고성 10
 
2.5%
합천 10
 
2.5%
산청 8
 
2.0%
남해 8
 
2.0%
울주 6
 
1.5%
밀양 6
 
1.5%
창녕 4
 
1.0%
부북 4
 
1.0%
Other values (102) 228
57.6%
2023-12-10T21:43:15.553509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
27.4%
남 110
 
10.2%
경 100
 
9.3%
산 30
 
2.8%
천 22
 
2.0%
창 22
 
2.0%
고 20
 
1.9%
양 18
 
1.7%
동 14
 
1.3%
곡 14
 
1.3%
Other values (96) 434
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 784
72.6%
Space Separator 296
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 110
 
14.0%
경 100
 
12.8%
산 30
 
3.8%
천 22
 
2.8%
창 22
 
2.8%
고 20
 
2.6%
양 18
 
2.3%
동 14
 
1.8%
곡 14
 
1.8%
원 14
 
1.8%
Other values (95) 420
53.6%
Space Separator
ValueCountFrequency (%)
296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 784
72.6%
Common 296
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 110
 
14.0%
경 100
 
12.8%
산 30
 
3.8%
천 22
 
2.8%
창 22
 
2.8%
고 20
 
2.6%
양 18
 
2.3%
동 14
 
1.8%
곡 14
 
1.8%
원 14
 
1.8%
Other values (95) 420
53.6%
Common
ValueCountFrequency (%)
296
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 784
72.6%
ASCII 296
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296
100.0%
Hangul
ValueCountFrequency (%)
남 110
 
14.0%
경 100
 
12.8%
산 30
 
3.8%
천 22
 
2.8%
창 22
 
2.8%
고 20
 
2.6%
양 18
 
2.3%
동 14
 
1.8%
곡 14
 
1.8%
원 14
 
1.8%
Other values (95) 420
53.6%

Interactions

2023-12-10T21:43:09.505693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.061531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.831634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.038598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.831721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.674304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.603779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:07.525302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:08.392047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.581083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.139843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.934078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.133700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.910661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.790965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.695166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:07.628336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:08.464662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.658885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.228955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:03.063072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.230354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.003066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.899150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.785336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:07.718343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:08.558036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.726902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.300647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:03.133174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.312881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.077851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.978686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.877908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:07.800333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:08.646978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.808071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.415874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:03.548273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.400517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.162763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.084987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.984076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:07.923259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:08.762633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.887633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.501862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:03.653166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.506989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.248519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.185244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:07.073890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:08.014686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.161962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.965313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.585961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:03.770604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.609570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.330096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.300429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:07.204233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:08.105195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.262599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:10.046023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.681950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:03.874103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.690497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.421340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.405722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:07.343810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:08.206251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.352100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:10.120190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:02.758279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:03.955148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:04.765462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:05.521095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:06.509472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:07.433323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:08.311581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:09.427338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:43:15.672246image/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
기본키1.0001.0000.0001.0000.5900.8170.6380.7910.5530.6020.5280.7861.000
지점1.0001.0000.0001.0001.0001.0001.0000.9830.9620.9620.9270.9841.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9830.9620.9620.9270.9841.000
연장((km))0.5901.0000.0001.0001.0000.6050.6990.4470.3170.6000.3210.4681.000
좌표위치위도((°))0.8171.0000.0001.0000.6051.0000.6310.6700.5820.5630.4960.7021.000
좌표위치경도((°))0.6381.0000.0001.0000.6990.6311.0000.6250.5620.7390.4280.6321.000
co((g/km))0.7910.9830.0000.9830.4470.6700.6251.0000.8870.9210.8760.9960.983
nox((g/km))0.5530.9620.0000.9620.3170.5820.5620.8871.0000.8820.9810.8960.962
hc((g/km))0.6020.9620.0000.9620.6000.5630.7390.9210.8821.0000.8520.9040.962
pm((g/km))0.5280.9270.0000.9270.3210.4960.4280.8760.9810.8521.0000.8800.927
co2((g/km))0.7860.9840.0000.9840.4680.7020.6320.9960.8960.9040.8801.0000.984
주소1.0001.0000.0001.0001.0001.0001.0000.9830.9620.9620.9270.9841.000
2023-12-10T21:43:15.837476image/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
기본키1.000-0.0430.0440.061-0.334-0.286-0.288-0.283-0.3360.000
연장((km))-0.0431.0000.1860.006-0.181-0.193-0.207-0.185-0.1780.000
좌표위치위도((°))0.0440.1861.0000.223-0.197-0.181-0.177-0.146-0.1900.000
좌표위치경도((°))0.0610.0060.2231.0000.4440.4090.4320.3950.4420.000
co((g/km))-0.334-0.181-0.1970.4441.0000.9800.9890.9580.9980.000
nox((g/km))-0.286-0.193-0.1810.4090.9801.0000.9960.9840.9820.000
hc((g/km))-0.288-0.207-0.1770.4320.9890.9961.0000.9800.9880.000
pm((g/km))-0.283-0.185-0.1460.3950.9580.9840.9801.0000.9600.000
co2((g/km))-0.336-0.178-0.1900.4420.9980.9820.9880.9601.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T21:43:10.227859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:43:10.415646image/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[0216-2]1북천-완사4.220210201035.12031127.970111979.261347.38197.7377.54515287.36경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210201035.12031127.970112008.051777.57230.74107.67530889.4경남 사천 곤명 작팔
23건기연[0220-2]1일반성-진북4.820210201035.10632128.442189725.529293.51138.81480.232588061.69경남 창원 진전 근곡
34건기연[0220-2]2일반성-진북4.820210201035.10632128.4421813311.2617413.01877.211117.273616428.82경남 창원 진전 근곡
45건기연[0222-1]1마산-부산9.420210201035.1839128.63959959.799325.891114.16447.542584854.0경남 창원 양곡
56건기연[0222-1]2마산-부산9.420210201035.1839128.63959958.69004.121160.11389.042545894.0경남 창원 양곡
67건기연[0302-4]1상죽-사천10.220210201034.87506128.009582922.92073.29292.22114.5759170.95경남 남해 창선 동대
78건기연[0302-4]2상죽-사천10.220210201034.87506128.009583396.862391.97344.97137.27811149.53경남 남해 창선 동대
89건기연[0303-1]1용현-정촌6.820210201035.01785128.057758776.196910.03917.83358.782307189.05경남 사천 용현 송지
910건기연[0303-1]2용현-정촌6.820210201035.01785128.057759638.357780.41024.83411.122514100.93경남 사천 용현 송지
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[7701-4]1도산-거제8.120210201034.90831128.409331849.181445.91208.7148.44426277.74경남 통영 광도 노산
9192건기연[7701-4]2도산-거제8.120210201034.90831128.409331720.341491.58194.6172.39434080.72경남 통영 광도 노산
9293건기연[7702-0]1통영-고성6.420210201034.89878128.35538496.59433.7357.736.05126384.6경남 통영 도산 오륜
9394건기연[7702-0]2통영-고성6.420210201034.89878128.35538453.21391.0452.6733.62115524.97경남 통영 도산 오륜
9495건기연[7702-2]1삼산-하이11.120210201034.92571128.13059977.55755.5298.3254.2254358.28경남 고성 하이 덕호
9596건기연[7702-2]2삼산-하이11.120210201034.92571128.130591128.6861.83120.6166.12271220.43경남 고성 하이 덕호
9697건기연[7703-0]1유포-설천11.220210201034.90043127.86341271.44192.7927.3119.4371501.66경남 남해 고현 포상
9798건기연[7703-0]2유포-설천11.220210201034.90043127.86341258.16206.0627.2221.9867546.65경남 남해 고현 포상
9899건기연[7904-0]1마산-진영5.220210201035.28732128.6117214210.210021.291440.21457.873703853.92경남 창원 북 외감
99100건기연[7904-0]2마산-진영5.220210201035.28732128.6117213061.449108.251319.25388.593391664.85경남 창원 북 외감