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 2 (2.0%) zerosZeros
nox((g/km)) has 2 (2.0%) zerosZeros
hc((g/km)) has 2 (2.0%) zerosZeros
pm((g/km)) has 8 (8.0%) zerosZeros
co2((g/km)) has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:24:06.338242
Analysis finished2023-12-10 11:24:21.060230
Duration14.72 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
2023-12-10T20:24:21.206265image/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
2023-12-10T20:24:21.463820image/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

2023-12-10T20:24:21.681575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:24:21.843123image/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
2023-12-10T20:24:22.149757image/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%
2023-12-10T20:24:22.853264image/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

2023-12-10T20:24:23.116470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:24:23.260298image/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
2023-12-10T20:24:23.628472image/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%
2023-12-10T20:24:24.352988image/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
2023-12-10T20:24:24.600841image/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
2023-12-10T20:24:24.869524image/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

2023-12-10T20:24:25.104228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:24:25.273157image/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

2023-12-10T20:24:25.452164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:24:25.629499image/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
2023-12-10T20:24:25.811882image/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
2023-12-10T20:24:26.039916image/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
2023-12-10T20:24:26.284518image/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
2023-12-10T20:24:26.940624image/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  ZEROS 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.9107
Minimum0
Maximum372.31
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:27.203547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.524
Q18.9025
median22.08
Q365.7675
95-th percentile206.557
Maximum372.31
Range372.31
Interquartile range (IQR)56.865

Descriptive statistics

Standard deviation71.399749
Coefficient of variation (CV)1.3494387
Kurtosis4.4342578
Mean52.9107
Median Absolute Deviation (MAD)16.6
Skewness2.0585311
Sum5291.07
Variance5097.9241
MonotonicityNot monotonic
2023-12-10T20:24:27.459087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2
 
2.0%
0.65 2
 
2.0%
1.95 2
 
2.0%
3.83 2
 
2.0%
38.02 1
 
1.0%
24.71 1
 
1.0%
19.2 1
 
1.0%
22.02 1
 
1.0%
18.82 1
 
1.0%
51.0 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.44 1
1.0%
0.65 2
2.0%
1.57 1
1.0%
1.95 2
2.0%
1.98 1
1.0%
2.22 1
1.0%
2.78 1
1.0%
3.83 2
2.0%
3.93 1
1.0%
ValueCountFrequency (%)
372.31 1
1.0%
269.91 1
1.0%
256.78 1
1.0%
222.31 1
1.0%
215.24 1
1.0%
206.1 1
1.0%
203.91 1
1.0%
203.59 1
1.0%
168.32 1
1.0%
152.2 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.8061
Minimum0
Maximum283.87
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:27.701684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.8045
Q15.7575
median14.57
Q356.6025
95-th percentile149.8525
Maximum283.87
Range283.87
Interquartile range (IQR)50.845

Descriptive statistics

Standard deviation54.405266
Coefficient of variation (CV)1.3332631
Kurtosis4.0107971
Mean40.8061
Median Absolute Deviation (MAD)12.23
Skewness1.9429222
Sum4080.61
Variance2959.9329
MonotonicityNot monotonic
2023-12-10T20:24:27.948966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 2
 
2.0%
0.96 2
 
2.0%
5.97 2
 
2.0%
2.34 2
 
2.0%
14.32 2
 
2.0%
0.0 2
 
2.0%
29.34 1
 
1.0%
26.92 1
 
1.0%
13.55 1
 
1.0%
15.42 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.24 1
1.0%
0.32 2
2.0%
0.83 1
1.0%
0.96 2
2.0%
1.32 1
1.0%
1.46 1
1.0%
1.79 1
1.0%
2.28 1
1.0%
2.34 2
2.0%
ValueCountFrequency (%)
283.87 1
1.0%
190.28 1
1.0%
188.65 1
1.0%
184.82 1
1.0%
151.61 1
1.0%
149.76 1
1.0%
146.63 1
1.0%
146.33 1
1.0%
132.44 1
1.0%
130.4 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7716
Minimum0
Maximum41.54
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:28.206842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1265
Q10.7975
median2.24
Q38.1775
95-th percentile22.596
Maximum41.54
Range41.54
Interquartile range (IQR)7.38

Descriptive statistics

Standard deviation7.7224764
Coefficient of variation (CV)1.3380131
Kurtosis4.5978502
Mean5.7716
Median Absolute Deviation (MAD)1.85
Skewness2.0319369
Sum577.16
Variance59.636642
MonotonicityNot monotonic
2023-12-10T20:24:28.468174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.24 3
 
3.0%
0.06 2
 
2.0%
0.35 2
 
2.0%
0.79 2
 
2.0%
0.0 2
 
2.0%
0.17 2
 
2.0%
0.03 1
 
1.0%
4.77 1
 
1.0%
2.43 1
 
1.0%
2.02 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.03 1
1.0%
0.06 2
2.0%
0.13 1
1.0%
0.17 2
2.0%
0.2 1
1.0%
0.21 1
1.0%
0.26 1
1.0%
0.35 2
2.0%
0.38 1
1.0%
ValueCountFrequency (%)
41.54 1
1.0%
27.0 1
1.0%
26.99 1
1.0%
24.22 1
1.0%
23.28 1
1.0%
22.56 1
1.0%
20.63 1
1.0%
20.07 1
1.0%
18.28 1
1.0%
17.16 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1024
Minimum0
Maximum14.47
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:28.700486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.28
median0.855
Q33.2175
95-th percentile8.0235
Maximum14.47
Range14.47
Interquartile range (IQR)2.9375

Descriptive statistics

Standard deviation2.7418663
Coefficient of variation (CV)1.3041601
Kurtosis4.7130915
Mean2.1024
Median Absolute Deviation (MAD)0.725
Skewness2.0429468
Sum210.24
Variance7.5178305
MonotonicityNot monotonic
2023-12-10T20:24:28.967501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.0%
0.13 7
 
7.0%
0.54 7
 
7.0%
0.28 5
 
5.0%
0.27 5
 
5.0%
0.4 4
 
4.0%
0.56 3
 
3.0%
4.19 2
 
2.0%
0.39 2
 
2.0%
5.04 2
 
2.0%
Other values (55) 55
55.0%
ValueCountFrequency (%)
0.0 8
8.0%
0.13 7
7.0%
0.14 1
 
1.0%
0.27 5
5.0%
0.28 5
5.0%
0.3 1
 
1.0%
0.39 2
 
2.0%
0.4 4
4.0%
0.41 1
 
1.0%
0.42 1
 
1.0%
ValueCountFrequency (%)
14.47 1
1.0%
10.64 1
1.0%
10.37 1
1.0%
9.06 1
1.0%
8.47 1
1.0%
8.0 1
1.0%
7.7 1
1.0%
5.97 1
1.0%
5.62 1
1.0%
5.3 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13108.329
Minimum0
Maximum87097.49
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:29.206828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile402.941
Q12413.12
median5657.525
Q315498.9
95-th percentile50149.433
Maximum87097.49
Range87097.49
Interquartile range (IQR)13085.78

Descriptive statistics

Standard deviation17411.053
Coefficient of variation (CV)1.3282436
Kurtosis3.8219405
Mean13108.329
Median Absolute Deviation (MAD)4105.145
Skewness1.9674304
Sum1310832.9
Variance3.0314478 × 108
MonotonicityNot monotonic
2023-12-10T20:24:29.456728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2
 
2.0%
153.68 2
 
2.0%
461.05 2
 
2.0%
1012.03 2
 
2.0%
8889.31 1
 
1.0%
5987.41 1
 
1.0%
5637.38 1
 
1.0%
5781.72 1
 
1.0%
5104.45 1
 
1.0%
11614.65 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 2
2.0%
127.15 1
1.0%
153.68 2
2.0%
416.06 1
1.0%
461.05 2
2.0%
487.28 1
1.0%
599.58 1
1.0%
734.66 1
1.0%
948.33 1
1.0%
1012.03 2
2.0%
ValueCountFrequency (%)
87097.49 1
1.0%
69731.15 1
1.0%
60266.47 1
1.0%
57129.2 1
1.0%
53283.92 1
1.0%
49984.46 1
1.0%
48068.55 1
1.0%
47066.06 1
1.0%
40471.68 1
1.0%
38466.03 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:24:29.905593image/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%
2023-12-10T20:24:30.569347image/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

2023-12-10T20:24:19.226467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:07.226209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:08.645036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:10.369820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:11.734160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:13.199558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:14.658875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:16.477684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:17.851508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:19.362587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:07.384692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:08.794661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:10.515780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:11.895682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:13.359239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:14.806157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:16.616381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:17.992027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:19.513798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:07.566075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:08.976033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:10.700793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:12.070647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:13.524860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:14.992533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:16.757010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:18.152361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:19.665745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:07.703786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:09.128826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:10.825230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:12.226675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:13.666815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:15.126674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:16.898984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:18.295717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:19.813817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:07.860999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:09.317125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:11.008064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:12.396399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:13.846901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:15.287463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:17.060378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:18.485126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:19.957970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:08.021591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:09.483993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:11.151756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:12.543878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:14.017079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:15.441863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:17.196351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:18.652061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:20.085820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:08.172694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:09.688933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:11.296021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:12.727660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:14.187032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:16.013155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:17.335368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:18.791048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:20.208380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:08.314503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:09.890250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:11.437399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:12.880508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:14.339165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:16.179871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:17.477238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:18.938349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:20.351390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:08.490592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:10.157840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:11.586371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:13.061870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:14.510863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:16.336145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:17.688634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:19.098539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:24:30.769130image/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
기본키1.0001.0000.0001.0000.5900.8170.6380.4100.4580.3960.3760.3941.000
지점1.0001.0000.0001.0001.0001.0001.0000.8270.9010.8210.7980.8711.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.1280.1770.0530.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.8270.9010.8210.7980.8711.000
연장((km))0.5901.0000.0001.0001.0000.6050.6990.3130.3770.1440.4140.3471.000
좌표위치위도((°))0.8171.0000.0001.0000.6051.0000.6310.3040.3490.3430.2860.1831.000
좌표위치경도((°))0.6381.0000.0001.0000.6990.6311.0000.7040.5700.5300.6780.7411.000
co((g/km))0.4100.8270.0000.8270.3130.3040.7041.0000.9200.9500.9580.9930.827
nox((g/km))0.4580.9010.1280.9010.3770.3490.5700.9201.0000.9900.9030.9290.901
hc((g/km))0.3960.8210.1770.8210.1440.3430.5300.9500.9901.0000.8940.9300.821
pm((g/km))0.3760.7980.0530.7980.4140.2860.6780.9580.9030.8941.0000.9470.798
co2((g/km))0.3940.8710.0000.8710.3470.1830.7410.9930.9290.9300.9471.0000.871
주소1.0001.0000.0001.0001.0001.0001.0000.8270.9010.8210.7980.8711.000
2023-12-10T20:24:31.011071image/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
기본키1.000-0.0430.0440.061-0.328-0.281-0.287-0.253-0.3350.000
연장((km))-0.0431.0000.1860.006-0.111-0.118-0.119-0.123-0.1130.000
좌표위치위도((°))0.0440.1861.0000.223-0.235-0.247-0.240-0.242-0.2420.000
좌표위치경도((°))0.0610.0060.2231.0000.4590.4370.4490.4300.4490.000
co((g/km))-0.328-0.111-0.2350.4591.0000.9880.9920.9600.9980.000
nox((g/km))-0.281-0.118-0.2470.4370.9881.0000.9970.9800.9880.089
hc((g/km))-0.287-0.119-0.2400.4490.9920.9971.0000.9710.9880.126
pm((g/km))-0.253-0.123-0.2420.4300.9600.9800.9711.0000.9640.043
co2((g/km))-0.335-0.113-0.2420.4490.9980.9880.9880.9641.0000.000
방향0.0000.0000.0000.0000.0000.0890.1260.0430.0001.000

Missing values

2023-12-10T20:24:20.574292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:24:20.924471image/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-완사4.220210201035.12031127.9701115.088.851.350.394014.1경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210201035.12031127.9701113.238.591.170.543771.32경남 사천 곤명 작팔
23건기연[0220-2]1일반성-진북4.820210201035.10632128.44218133.798.5515.194.5630960.17경남 창원 진전 근곡
34건기연[0220-2]2일반성-진북4.820210201035.10632128.44218136.07151.6118.2810.6436542.78경남 창원 진전 근곡
45건기연[0222-1]1마산-부산9.420210201035.1839128.6395256.78188.6527.08.060266.47경남 창원 양곡
56건기연[0222-1]2마산-부산9.420210201035.1839128.6395203.59146.3322.565.0447066.06경남 창원 양곡
67건기연[0302-4]1상죽-사천10.220210201034.87506128.0095839.5522.713.781.119473.84경남 남해 창선 동대
78건기연[0302-4]2상죽-사천10.220210201034.87506128.0095830.816.393.130.916679.59경남 남해 창선 동대
89건기연[0303-1]1용현-정촌6.820210201035.01785128.0577593.4167.658.83.3326157.6경남 사천 용현 송지
910건기연[0303-1]2용현-정촌6.820210201035.01785128.05775112.2576.4810.993.7729118.32경남 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-거제8.120210201034.90831128.4093324.2816.672.370.415695.44경남 통영 광도 노산
9192건기연[7701-4]2도산-거제8.120210201034.90831128.4093322.2616.272.50.645677.67경남 통영 광도 노산
9293건기연[7702-0]1통영-고성6.420210201034.89878128.355383.832.340.350.131012.03경남 통영 도산 오륜
9394건기연[7702-0]2통영-고성6.420210201034.89878128.355383.832.340.350.131012.03경남 통영 도산 오륜
9495건기연[7702-2]1삼산-하이11.120210201034.92571128.130596.664.110.660.281600.56경남 고성 하이 덕호
9596건기연[7702-2]2삼산-하이11.120210201034.92571128.130597.555.410.730.542118.34경남 고성 하이 덕호
9697건기연[7703-0]1유포-설천11.220210201034.90043127.863412.221.460.210.13599.58경남 남해 고현 포상
9798건기연[7703-0]2유포-설천11.220210201034.90043127.863410.00.00.00.00.0경남 남해 고현 포상
9899건기연[7904-0]1마산-진영5.220210201035.28732128.61172269.91184.8226.998.4769731.15경남 창원 북 외감
99100건기연[7904-0]2마산-진영5.220210201035.28732128.61172203.91130.0220.635.348068.55경남 창원 북 외감