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
co2((g/km)) has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:43:17.402183
Analysis finished2023-12-10 12:43:25.597983
Duration8.2 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
2023-12-10T21:43:25.663852image/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
2023-12-10T21:43:25.791611image/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

2023-12-10T21:43:25.927996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:43:26.026837image/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
2023-12-10T21:43:26.214845image/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%
2023-12-10T21:43:26.531710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 152
19.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 90
11.2%
2 82
10.2%
3 46
 
5.8%
4 42
 
5.2%
7 28
 
3.5%
5 26
 
3.2%
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 (%)
0 152
30.4%
1 90
18.0%
2 82
16.4%
3 46
 
9.2%
4 42
 
8.4%
7 28
 
5.6%
5 26
 
5.2%
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 (%)
0 152
19.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 90
11.2%
2 82
10.2%
3 46
 
5.8%
4 42
 
5.2%
7 28
 
3.5%
5 26
 
3.2%
Other values (3) 34
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 152
19.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 90
11.2%
2 82
10.2%
3 46
 
5.8%
4 42
 
5.2%
7 28
 
3.5%
5 26
 
3.2%
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

2023-12-10T21:43:26.639716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:43:26.723485image/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
2023-12-10T21:43:26.913669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1
Min length5

Characters and Unicode

Total characters510
Distinct characters84
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%
2023-12-10T21:43:27.262943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.6%
산 40
 
7.8%
남 14
 
2.7%
성 14
 
2.7%
양 14
 
2.7%
천 14
 
2.7%
창 14
 
2.7%
부 10
 
2.0%
마 10
 
2.0%
거 10
 
2.0%
Other values (74) 270
52.9%

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 40
 
9.9%
남 14
 
3.4%
성 14
 
3.4%
양 14
 
3.4%
천 14
 
3.4%
창 14
 
3.4%
부 10
 
2.5%
마 10
 
2.5%
거 10
 
2.5%
고 8
 
2.0%
Other values (71) 258
63.5%
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 40
 
9.9%
남 14
 
3.4%
성 14
 
3.4%
양 14
 
3.4%
천 14
 
3.4%
창 14
 
3.4%
부 10
 
2.5%
마 10
 
2.5%
거 10
 
2.5%
고 8
 
2.0%
Other values (71) 258
63.5%
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 40
 
9.9%
남 14
 
3.4%
성 14
 
3.4%
양 14
 
3.4%
천 14
 
3.4%
창 14
 
3.4%
부 10
 
2.5%
마 10
 
2.5%
거 10
 
2.5%
고 8
 
2.0%
Other values (71) 258
63.5%

연장((km))
Real number (ℝ)

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.544
Minimum3
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:27.387254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.8
Q15
median7.5
Q311.1
95-th percentile17.6
Maximum20.1
Range17.1
Interquartile range (IQR)6.1

Descriptive statistics

Standard deviation4.1999596
Coefficient of variation (CV)0.4915683
Kurtosis0.44594615
Mean8.544
Median Absolute Deviation (MAD)2.9
Skewness0.95789901
Sum854.4
Variance17.639661
MonotonicityNot monotonic
2023-12-10T21:43:27.511923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
11.2 4
 
4.0%
11.9 4
 
4.0%
5.2 4
 
4.0%
4.6 4
 
4.0%
10.5 4
 
4.0%
10.2 4
 
4.0%
8.1 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 4
4.0%
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
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T21:43:27.629101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:43:27.717496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T21:43:27.799589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:43:27.881881image/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%
Mean35.273013
Minimum34.86496
Maximum35.72809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:27.977314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.86496
5-th percentile34.87506
Q135.01568
median35.30868
Q335.50716
95-th percentile35.62274
Maximum35.72809
Range0.86313
Interquartile range (IQR)0.49148

Descriptive statistics

Standard deviation0.25389558
Coefficient of variation (CV)0.0071980121
Kurtosis-1.1870179
Mean35.273013
Median Absolute Deviation (MAD)0.206275
Skewness-0.15019382
Sum3527.3013
Variance0.064462963
MonotonicityNot monotonic
2023-12-10T21:43:28.092528image/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%
Mean128.39873
Minimum127.78878
Maximum129.33586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:28.210057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.78878
5-th percentile127.80269
Q1128.00958
median128.34945
Q3128.65347
95-th percentile129.24056
Maximum129.33586
Range1.54708
Interquartile range (IQR)0.64389

Descriptive statistics

Standard deviation0.44071182
Coefficient of variation (CV)0.0034323689
Kurtosis-0.66124608
Mean128.39873
Median Absolute Deviation (MAD)0.330315
Skewness0.51592423
Sum12839.873
Variance0.19422691
MonotonicityNot monotonic
2023-12-10T21:43:28.336180image/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%
Mean3750.5838
Minimum173.46
Maximum15312.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:28.467101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum173.46
5-th percentile465.2
Q11148.5475
median2624.06
Q34553.9
95-th percentile10326.539
Maximum15312.23
Range15138.77
Interquartile range (IQR)3405.3525

Descriptive statistics

Standard deviation3457.7652
Coefficient of variation (CV)0.92192718
Kurtosis1.5699959
Mean3750.5838
Median Absolute Deviation (MAD)1584.845
Skewness1.4254255
Sum375058.38
Variance11956140
MonotonicityNot monotonic
2023-12-10T21:43:28.593384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2179.59 1
 
1.0%
8304.56 1
 
1.0%
3835.41 1
 
1.0%
2912.56 1
 
1.0%
2986.02 1
 
1.0%
3246.32 1
 
1.0%
3809.97 1
 
1.0%
3265.28 1
 
1.0%
3305.52 1
 
1.0%
3764.69 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
173.46 1
1.0%
207.93 1
1.0%
271.67 1
1.0%
333.25 1
1.0%
448.86 1
1.0%
466.06 1
1.0%
476.44 1
1.0%
479.17 1
1.0%
490.64 1
1.0%
559.77 1
1.0%
ValueCountFrequency (%)
15312.23 1
1.0%
14993.31 1
1.0%
13316.28 1
1.0%
11737.42 1
1.0%
11343.79 1
1.0%
10273.0 1
1.0%
9654.22 1
1.0%
9452.7 1
1.0%
9286.8 1
1.0%
9147.87 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3181.4837
Minimum113.14
Maximum18264.47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:28.726727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum113.14
5-th percentile333.3855
Q1991.525
median1992.935
Q34740.255
95-th percentile8385.7035
Maximum18264.47
Range18151.33
Interquartile range (IQR)3748.73

Descriptive statistics

Standard deviation3305.2809
Coefficient of variation (CV)1.0389118
Kurtosis7.0349896
Mean3181.4837
Median Absolute Deviation (MAD)1469.62
Skewness2.2869297
Sum318148.37
Variance10924882
MonotonicityNot monotonic
2023-12-10T21:43:28.909791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1434.05 1
 
1.0%
7093.02 1
 
1.0%
7013.48 1
 
1.0%
2806.83 1
 
1.0%
3274.08 1
 
1.0%
3354.72 1
 
1.0%
3475.09 1
 
1.0%
2384.9 1
 
1.0%
2418.87 1
 
1.0%
3675.09 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
113.14 1
1.0%
128.93 1
1.0%
176.73 1
1.0%
220.0 1
1.0%
330.26 1
1.0%
333.55 1
1.0%
342.6 1
1.0%
350.14 1
1.0%
379.46 1
1.0%
406.99 1
1.0%
ValueCountFrequency (%)
18264.47 1
1.0%
17739.16 1
1.0%
11699.0 1
1.0%
11512.24 1
1.0%
9763.84 1
1.0%
8313.17 1
1.0%
7609.89 1
1.0%
7093.02 1
1.0%
7013.48 1
1.0%
6920.7 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.5835
Minimum16.63
Maximum1979.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:29.037928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.63
5-th percentile45.0815
Q1131.1625
median272.44
Q3621.3125
95-th percentile1032.272
Maximum1979.49
Range1962.86
Interquartile range (IQR)490.15

Descriptive statistics

Standard deviation390.64549
Coefficient of variation (CV)0.95609707
Kurtosis3.694114
Mean408.5835
Median Absolute Deviation (MAD)175.235
Skewness1.7667125
Sum40858.35
Variance152603.9
MonotonicityNot monotonic
2023-12-10T21:43:29.163400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
211.35 1
 
1.0%
954.01 1
 
1.0%
705.01 1
 
1.0%
338.28 1
 
1.0%
358.16 1
 
1.0%
383.65 1
 
1.0%
431.41 1
 
1.0%
337.04 1
 
1.0%
339.96 1
 
1.0%
451.04 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
16.63 1
1.0%
20.64 1
1.0%
26.0 1
1.0%
32.21 1
1.0%
44.35 1
1.0%
45.12 1
1.0%
45.62 1
1.0%
47.83 1
1.0%
50.44 1
1.0%
56.1 1
1.0%
ValueCountFrequency (%)
1979.49 1
1.0%
1828.46 1
1.0%
1535.69 1
1.0%
1519.76 1
1.0%
1110.4 1
1.0%
1028.16 1
1.0%
996.62 1
1.0%
993.95 1
1.0%
954.01 1
1.0%
916.75 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.4922
Minimum9.11
Maximum1179.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:29.289651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.11
5-th percentile21.829
Q150.3825
median90.14
Q3185.105
95-th percentile458.3155
Maximum1179.73
Range1170.62
Interquartile range (IQR)134.7225

Descriptive statistics

Standard deviation194.91023
Coefficient of variation (CV)1.2220675
Kurtosis13.263898
Mean159.4922
Median Absolute Deviation (MAD)57.49
Skewness3.1853608
Sum15949.22
Variance37989.998
MonotonicityNot monotonic
2023-12-10T21:43:29.414937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.98 2
 
2.0%
63.48 1
 
1.0%
367.72 1
 
1.0%
140.73 1
 
1.0%
171.3 1
 
1.0%
249.35 1
 
1.0%
162.86 1
 
1.0%
110.21 1
 
1.0%
112.69 1
 
1.0%
198.41 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
9.11 1
1.0%
10.32 1
1.0%
14.07 1
1.0%
18.63 1
1.0%
18.96 1
1.0%
21.98 2
2.0%
22.07 1
1.0%
24.34 1
1.0%
24.97 1
1.0%
26.36 1
1.0%
ValueCountFrequency (%)
1179.73 1
1.0%
1164.04 1
1.0%
527.49 1
1.0%
526.94 1
1.0%
507.06 1
1.0%
455.75 1
1.0%
444.12 1
1.0%
441.36 1
1.0%
409.79 1
1.0%
402.88 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean973181.28
Minimum45796.19
Maximum3959651.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:43:29.537277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45796.19
5-th percentile121907.1
Q1292345.51
median694784.56
Q31148203.5
95-th percentile2717298.2
Maximum3959651.5
Range3913855.3
Interquartile range (IQR)855857.97

Descriptive statistics

Standard deviation906873.54
Coefficient of variation (CV)0.93186496
Kurtosis1.6155739
Mean973181.28
Median Absolute Deviation (MAD)412661.16
Skewness1.4523225
Sum97318128
Variance8.2241962 × 1011
MonotonicityNot monotonic
2023-12-10T21:43:29.930401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
571013.06 1
 
1.0%
2145760.61 1
 
1.0%
1056178.28 1
 
1.0%
777871.21 1
 
1.0%
826235.76 1
 
1.0%
874111.95 1
 
1.0%
956306.31 1
 
1.0%
840964.6 1
 
1.0%
851644.89 1
 
1.0%
965288.37 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
45796.19 1
1.0%
50583.68 1
1.0%
71730.38 1
1.0%
87971.59 1
1.0%
116999.33 1
1.0%
122165.4 1
1.0%
124632.43 1
1.0%
124783.74 1
1.0%
126624.53 1
1.0%
145650.35 1
1.0%
ValueCountFrequency (%)
3959651.5 1
1.0%
3847698.81 1
1.0%
3610414.86 1
1.0%
3228517.25 1
1.0%
2973753.86 1
1.0%
2703800.56 1
1.0%
2541074.62 1
1.0%
2439562.12 1
1.0%
2430065.14 1
1.0%
2407681.08 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:43:30.191743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.8
Min length8

Characters and Unicode

Total characters1080
Distinct characters105
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%
남해 8
 
2.0%
산청 8
 
2.0%
창녕 6
 
1.5%
밀양 6
 
1.5%
울주 6
 
1.5%
기장 4
 
1.0%
Other values (102) 226
57.1%
2023-12-10T21:43:30.588063image/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%
창 24
 
2.2%
천 20
 
1.9%
고 20
 
1.9%
양 18
 
1.7%
동 14
 
1.3%
곡 14
 
1.3%
Other values (95) 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%
창 24
 
3.1%
천 20
 
2.6%
고 20
 
2.6%
양 18
 
2.3%
동 14
 
1.8%
곡 14
 
1.8%
성 14
 
1.8%
Other values (94) 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%
창 24
 
3.1%
천 20
 
2.6%
고 20
 
2.6%
양 18
 
2.3%
동 14
 
1.8%
곡 14
 
1.8%
성 14
 
1.8%
Other values (94) 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%
창 24
 
3.1%
천 20
 
2.6%
고 20
 
2.6%
양 18
 
2.3%
동 14
 
1.8%
곡 14
 
1.8%
성 14
 
1.8%
Other values (94) 420
53.6%

Interactions

2023-12-10T21:43:24.279061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:17.876647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.621363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.418458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:20.103136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.116180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.957953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.711453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.499615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.356936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:17.942639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.701769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.484228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:20.183219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.192807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.036871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.787689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.592063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.442170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.023986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.784227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.558501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:20.275862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.279276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.130423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.873528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.707439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.509930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.086595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.855965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.623479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:20.576588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.352397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.199318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.942016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.783043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.590725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.167402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.945132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.704298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:20.664891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.447233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.282076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.034989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.874226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.670343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.241857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.051606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.790455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:20.761736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.531293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.373826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.121395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.962855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.749273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.322477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.147813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.868096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:20.848172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.658122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.455367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.217232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.044082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.830295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.415075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.240334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.948802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:20.944565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.768238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.542634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.309187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.126939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:25.170110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:18.488127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:19.335348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:20.024954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.025420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:21.864697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:22.622098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:23.409378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:43:24.199253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:43:30.687502image/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
기본키1.0001.0000.0001.0000.6370.8080.6360.7380.6280.5700.5220.7461.000
지점1.0001.0000.0001.0001.0001.0001.0000.9740.9570.9710.9790.9731.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9740.9570.9710.9790.9731.000
연장((km))0.6371.0000.0001.0001.0000.5990.6870.4500.4060.4240.4530.3961.000
좌표위치위도((°))0.8081.0000.0001.0000.5991.0000.6320.7050.5700.5550.5560.6901.000
좌표위치경도((°))0.6361.0000.0001.0000.6870.6321.0000.6020.5000.5950.4900.5831.000
co((g/km))0.7380.9740.0000.9740.4500.7050.6021.0000.8630.9180.8240.9960.974
nox((g/km))0.6280.9570.0000.9570.4060.5700.5000.8631.0000.9800.8870.8320.957
hc((g/km))0.5700.9710.0000.9710.4240.5550.5950.9180.9801.0000.8720.9120.971
pm((g/km))0.5220.9790.0000.9790.4530.5560.4900.8240.8870.8721.0000.7870.979
co2((g/km))0.7460.9730.0000.9730.3960.6900.5830.9960.8320.9120.7871.0000.973
주소1.0001.0000.0001.0001.0001.0001.0000.9740.9570.9710.9790.9731.000
2023-12-10T21:43:30.810406image/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
기본키1.000-0.0300.0080.030-0.330-0.315-0.322-0.279-0.3270.000
연장((km))-0.0301.0000.170-0.005-0.164-0.115-0.151-0.104-0.1590.000
좌표위치위도((°))0.0080.1701.0000.210-0.231-0.165-0.189-0.110-0.2240.000
좌표위치경도((°))0.030-0.0050.2101.0000.4330.3880.4100.3320.4320.000
co((g/km))-0.330-0.164-0.2310.4331.0000.9740.9890.8960.9990.000
nox((g/km))-0.315-0.115-0.1650.3880.9741.0000.9930.9570.9750.000
hc((g/km))-0.322-0.151-0.1890.4100.9890.9931.0000.9380.9880.000
pm((g/km))-0.279-0.104-0.1100.3320.8960.9570.9381.0000.8970.000
co2((g/km))-0.327-0.159-0.2240.4320.9990.9750.9880.8971.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T21:43:25.296355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:43:25.523728image/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-완사4.220210101035.12031127.970112179.591434.05211.3563.48571013.06경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210101035.12031127.970111782.711187.31168.1953.9476338.46경남 사천 곤명 작팔
23건기연[0220-2]1일반성-진북4.820210101035.10632128.4421813316.2818264.471979.491164.043610414.86경남 창원 진전 근곡
34건기연[0220-2]2일반성-진북4.820210101035.10632128.4421811737.4217739.161828.461179.733228517.25경남 창원 진전 근곡
45건기연[0222-1]1마산-부산9.420210101035.1839128.63958857.949763.841110.4526.942309147.14경남 창원 양곡
56건기연[0222-1]2마산-부산9.420210101035.1839128.63958293.238313.17993.95402.882124030.55경남 창원 양곡
67건기연[0302-4]1상죽-사천10.220210101034.87506128.009584714.132947.25427.0787.911238032.86경남 남해 창선 동대
78건기연[0302-4]2상죽-사천10.220210101034.87506128.009583583.32540.14337.76108.94935939.08경남 남해 창선 동대
89건기연[0304-1]1사남-정촌6.820210101035.12732128.097759286.86083.17854.24179.652430065.14경남 진주 정촌 화개
910건기연[0304-1]2사남-정촌6.820210101035.12732128.097759452.77609.89996.62455.752439562.12경남 진주 정촌 화개
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[7701-4]1도산-거제8.120210101034.90831128.409331260.121059.49133.3868.43323463.44경남 통영 광도 노산
9192건기연[7701-4]2도산-거제8.120210101034.90831128.409331342.481003.01128.4142.51349241.99경남 통영 광도 노산
9293건기연[7702-0]1통영-고성6.420210101034.89878128.35538448.86342.644.3521.98116999.33경남 통영 도산 오륜
9394건기연[7702-0]2통영-고성6.420210101034.89878128.35538479.17350.1447.8322.07124783.74경남 통영 도산 오륜
9495건기연[7702-2]1삼산-하이11.120210101034.92571128.130591218.84877.03115.6939.66318126.43경남 고성 하이 덕호
9596건기연[7702-2]2삼산-하이11.120210101034.92571128.130591088.23830.44106.445.9283188.95경남 고성 하이 덕호
9697건기연[7703-0]1유포-설천11.220210101034.90043127.86341271.67176.7326.014.0771730.38경남 남해 고현 포상
9798건기연[7703-0]2유포-설천11.220210101034.90043127.86341333.25220.032.2118.6387971.59경남 남해 고현 포상
9899건기연[7904-0]1마산-진영5.220210101035.28732128.6117211343.796920.71028.16192.322973753.86경남 창원 북 외감
99100건기연[7904-0]2마산-진영5.220210101035.28732128.6117210273.06131.31916.75153.842703800.56경남 창원 북 외감