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
좌표위치경도((°)) is highly overall correlated with co((g/km)) and 3 other fieldsHigh correlation
co((g/km)) is highly overall correlated with 좌표위치경도((°)) and 4 other fieldsHigh correlation
nox((g/km)) is highly overall correlated with 좌표위치경도((°)) and 4 other fieldsHigh correlation
hc((g/km)) is highly overall correlated with 좌표위치경도((°)) and 4 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 좌표위치경도((°)) and 4 other fieldsHigh correlation
기본키 has unique valuesUnique
co((g/km)) has 5 (5.0%) zerosZeros
nox((g/km)) has 5 (5.0%) zerosZeros
hc((g/km)) has 5 (5.0%) zerosZeros
pm((g/km)) has 13 (13.0%) zerosZeros
co2((g/km)) has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:24:33.212748
Analysis finished2023-12-10 11:24:47.007678
Duration13.79 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
2023-12-10T20:24:47.114383image/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
2023-12-10T20:24:47.348690image/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

2023-12-10T20:24:47.532550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:24:47.665828image/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
2023-12-10T20:24:47.961425image/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%
2023-12-10T20:24:48.496688image/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

2023-12-10T20:24:48.696328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:24:48.831396image/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
2023-12-10T20:24:49.075923image/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%
2023-12-10T20:24:49.647064image/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
2023-12-10T20:24:49.875749image/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
2023-12-10T20:24:50.112828image/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

2023-12-10T20:24:50.309505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:24:50.454804image/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

2023-12-10T20:24:50.601138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:24:50.735847image/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
2023-12-10T20:24:50.896609image/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
2023-12-10T20:24:51.106623image/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)

HIGH CORRELATION 

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
2023-12-10T20:24:51.308225image/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
2023-12-10T20:24:51.544810image/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 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.2894
Minimum0
Maximum265.1
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:51.752507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6175
Q17.025
median17.015
Q338.0025
95-th percentile134.641
Maximum265.1
Range265.1
Interquartile range (IQR)30.9775

Descriptive statistics

Standard deviation50.834406
Coefficient of variation (CV)1.3276365
Kurtosis5.6990073
Mean38.2894
Median Absolute Deviation (MAD)12.745
Skewness2.2625457
Sum3828.94
Variance2584.1368
MonotonicityNot monotonic
2023-12-10T20:24:51.945566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
3.89 2
 
2.0%
6.49 1
 
1.0%
29.39 1
 
1.0%
27.78 1
 
1.0%
22.83 1
 
1.0%
13.34 1
 
1.0%
13.07 1
 
1.0%
30.52 1
 
1.0%
23.54 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.65 1
 
1.0%
1.98 1
 
1.0%
2.26 1
 
1.0%
2.68 1
 
1.0%
2.92 1
 
1.0%
3.31 1
 
1.0%
3.36 1
 
1.0%
3.89 2
 
2.0%
3.93 1
 
1.0%
ValueCountFrequency (%)
265.1 1
1.0%
240.08 1
1.0%
175.85 1
1.0%
170.78 1
1.0%
141.5 1
1.0%
134.28 1
1.0%
123.91 1
1.0%
117.83 1
1.0%
112.72 1
1.0%
107.28 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.7272
Minimum0
Maximum198.74
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:52.158762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.304
Q13.88
median10.25
Q333.5125
95-th percentile98.3485
Maximum198.74
Range198.74
Interquartile range (IQR)29.6325

Descriptive statistics

Standard deviation38.803251
Coefficient of variation (CV)1.3994652
Kurtosis6.194355
Mean27.7272
Median Absolute Deviation (MAD)8.265
Skewness2.3561853
Sum2772.72
Variance1505.6923
MonotonicityNot monotonic
2023-12-10T20:24:52.446234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
1.93 2
 
2.0%
3.21 1
 
1.0%
32.31 1
 
1.0%
18.79 1
 
1.0%
12.31 1
 
1.0%
7.62 1
 
1.0%
6.87 1
 
1.0%
22.63 1
 
1.0%
18.81 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.32 1
 
1.0%
1.32 1
 
1.0%
1.51 1
 
1.0%
1.73 1
 
1.0%
1.93 2
 
2.0%
2.0 1
 
1.0%
2.06 1
 
1.0%
2.22 1
 
1.0%
2.24 1
 
1.0%
ValueCountFrequency (%)
198.74 1
1.0%
186.42 1
1.0%
144.74 1
1.0%
134.69 1
1.0%
108.39 1
1.0%
97.82 1
1.0%
93.87 1
1.0%
93.1 1
1.0%
82.44 1
1.0%
81.25 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8702
Minimum0
Maximum27.03
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:52.678368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.057
Q10.6675
median1.68
Q34.375
95-th percentile15.082
Maximum27.03
Range27.03
Interquartile range (IQR)3.7075

Descriptive statistics

Standard deviation5.1925339
Coefficient of variation (CV)1.3416707
Kurtosis5.3737164
Mean3.8702
Median Absolute Deviation (MAD)1.275
Skewness2.2203385
Sum387.02
Variance26.962408
MonotonicityNot monotonic
2023-12-10T20:24:52.862808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.35 3
 
3.0%
0.58 2
 
2.0%
1.37 2
 
2.0%
2.41 2
 
2.0%
0.7 2
 
2.0%
0.4 2
 
2.0%
0.27 2
 
2.0%
0.2 1
 
1.0%
2.33 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.06 1
 
1.0%
0.2 1
 
1.0%
0.22 1
 
1.0%
0.27 2
 
2.0%
0.3 1
 
1.0%
0.31 1
 
1.0%
0.35 3
3.0%
0.38 1
 
1.0%
0.4 2
 
2.0%
ValueCountFrequency (%)
27.03 1
1.0%
23.87 1
1.0%
16.71 1
1.0%
16.08 1
1.0%
15.88 1
1.0%
15.04 1
1.0%
13.42 1
1.0%
13.23 1
1.0%
11.9 1
1.0%
11.51 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2397
Minimum0
Maximum10.02
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:53.048107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1375
median0.415
Q31.405
95-th percentile4.825
Maximum10.02
Range10.02
Interquartile range (IQR)1.2675

Descriptive statistics

Standard deviation1.9838711
Coefficient of variation (CV)1.6002832
Kurtosis8.0329397
Mean1.2397
Median Absolute Deviation (MAD)0.39
Skewness2.7530899
Sum123.97
Variance3.9357444
MonotonicityNot monotonic
2023-12-10T20:24:53.256835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 13
 
13.0%
0.13 12
 
12.0%
0.27 7
 
7.0%
0.28 6
 
6.0%
0.14 5
 
5.0%
0.4 4
 
4.0%
0.56 2
 
2.0%
0.26 2
 
2.0%
0.54 2
 
2.0%
1.07 2
 
2.0%
Other values (43) 45
45.0%
ValueCountFrequency (%)
0.0 13
13.0%
0.13 12
12.0%
0.14 5
 
5.0%
0.26 2
 
2.0%
0.27 7
7.0%
0.28 6
6.0%
0.4 4
 
4.0%
0.41 1
 
1.0%
0.42 2
 
2.0%
0.43 1
 
1.0%
ValueCountFrequency (%)
10.02 1
1.0%
9.62 1
1.0%
8.37 1
1.0%
6.99 1
1.0%
6.82 1
1.0%
4.72 1
1.0%
4.69 1
1.0%
3.82 1
1.0%
3.68 1
1.0%
3.41 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9686.2549
Minimum0
Maximum68338.98
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:24:53.771160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile145.996
Q11715.325
median4255.235
Q39371.2875
95-th percentile30285.266
Maximum68338.98
Range68338.98
Interquartile range (IQR)7655.9625

Descriptive statistics

Standard deviation12902.674
Coefficient of variation (CV)1.3320601
Kurtosis6.1975627
Mean9686.2549
Median Absolute Deviation (MAD)3149.49
Skewness2.3178048
Sum968625.49
Variance1.66479 × 108
MonotonicityNot monotonic
2023-12-10T20:24:53.980286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
922.09 2
 
2.0%
1536.82 1
 
1.0%
7278.1 1
 
1.0%
7331.59 1
 
1.0%
5457.58 1
 
1.0%
3556.81 1
 
1.0%
3105.51 1
 
1.0%
7916.2 1
 
1.0%
6053.8 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 5
5.0%
153.68 1
 
1.0%
487.28 1
 
1.0%
595.97 1
 
1.0%
646.6 1
 
1.0%
821.31 1
 
1.0%
873.34 1
 
1.0%
922.09 2
 
2.0%
948.33 1
 
1.0%
948.46 1
 
1.0%
ValueCountFrequency (%)
68338.98 1
1.0%
62439.91 1
1.0%
45037.11 1
1.0%
41666.74 1
1.0%
35374.73 1
1.0%
30017.4 1
1.0%
29596.77 1
1.0%
29444.91 1
1.0%
28277.8 1
1.0%
28026.05 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:24:54.403999image/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%
2023-12-10T20:24:54.926729image/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

2023-12-10T20:24:44.841311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:34.132329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:35.475577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:37.136742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:38.389919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:39.743785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:40.989452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:42.347823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:43.679748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:45.010836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:34.280986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:35.624261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:37.262768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:38.519221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:39.880038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:41.130721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:42.478845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:43.804390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:45.174840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:34.443472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:35.760993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:37.413456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:38.647922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:40.041843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:41.283056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:42.637865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:43.948983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:45.620998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:34.576931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:35.875408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:37.537640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:38.766534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:40.178607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:41.422163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:42.778079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:44.055778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:45.778327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:34.733246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:36.027153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:37.695421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:38.936456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:40.351104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:41.589111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:42.964721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:44.177284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:45.911140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:34.874417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:36.161019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:37.827241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:39.088224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:40.485778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:41.733106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:43.105819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:44.296221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:46.062224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:35.027418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:36.305824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:37.984523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:39.256301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:40.615634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:41.882536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:43.255674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:44.441453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:46.190485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:35.179518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:36.830871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:38.115938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:39.405896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:40.733001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:42.024460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:43.396738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:44.579499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:46.329316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:35.317004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:36.977114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:38.238216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:39.575785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:40.862151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:42.175323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:43.539324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:24:44.709176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:24:55.061330image/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
기본키1.0001.0000.0001.0000.6370.8080.6360.5410.4560.4450.4890.5411.000
지점1.0001.0000.0001.0001.0001.0001.0000.8930.8770.7460.7410.8941.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.8930.8770.7460.7410.8941.000
연장((km))0.6371.0000.0001.0001.0000.5990.6870.3380.5350.5100.3290.3281.000
좌표위치위도((°))0.8081.0000.0001.0000.5991.0000.6320.4010.4890.3440.4390.4271.000
좌표위치경도((°))0.6361.0000.0001.0000.6870.6321.0000.6090.6770.7220.3460.5561.000
co((g/km))0.5410.8930.0000.8930.3380.4010.6091.0000.9040.9270.8740.9880.893
nox((g/km))0.4560.8770.0000.8770.5350.4890.6770.9041.0000.9810.8650.9090.877
hc((g/km))0.4450.7460.0000.7460.5100.3440.7220.9270.9811.0000.8500.9280.746
pm((g/km))0.4890.7410.0000.7410.3290.4390.3460.8740.8650.8501.0000.9020.741
co2((g/km))0.5410.8940.0000.8940.3280.4270.5560.9880.9090.9280.9021.0000.894
주소1.0001.0000.0001.0001.0001.0001.0000.8930.8770.7460.7410.8941.000
2023-12-10T20:24:55.232565image/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
기본키1.000-0.0300.0080.030-0.256-0.255-0.246-0.172-0.2690.000
연장((km))-0.0301.0000.170-0.005-0.096-0.100-0.098-0.046-0.1030.000
좌표위치위도((°))0.0080.1701.0000.210-0.179-0.148-0.160-0.089-0.1780.000
좌표위치경도((°))0.030-0.0050.2101.0000.5060.5040.5030.4670.5050.000
co((g/km))-0.256-0.096-0.1790.5061.0000.9900.9930.9100.9980.000
nox((g/km))-0.255-0.100-0.1480.5040.9901.0000.9970.9450.9890.000
hc((g/km))-0.246-0.098-0.1600.5030.9930.9971.0000.9340.9900.000
pm((g/km))-0.172-0.046-0.0890.4670.9100.9450.9341.0000.9080.000
co2((g/km))-0.269-0.103-0.1780.5050.9980.9890.9900.9081.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T20:24:46.583487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:24:46.887323image/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-완사4.220210101035.12031127.970116.493.210.580.01536.82경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210101035.12031127.9701113.958.01.230.273688.28경남 사천 곤명 작팔
23건기연[0220-2]1일반성-진북4.820210101035.10632128.4421895.65108.3913.236.8224562.24경남 창원 진전 근곡
34건기연[0220-2]2일반성-진북4.820210101035.10632128.44218112.72144.7415.8810.0229596.77경남 창원 진전 근곡
45건기연[0222-1]1마산-부산9.420210101035.1839128.6395141.5134.6916.716.9935374.73경남 창원 양곡
56건기연[0222-1]2마산-부산9.420210101035.1839128.639593.4493.110.824.6924894.66경남 창원 양곡
67건기연[0302-4]1상죽-사천10.220210101034.87506128.0095821.4911.311.970.275123.98경남 남해 창선 동대
78건기연[0302-4]2상죽-사천10.220210101034.87506128.0095815.578.931.370.274109.96경남 남해 창선 동대
89건기연[0304-1]1사남-정촌6.820210101035.12732128.09775123.9168.9911.511.6129444.91경남 진주 정촌 화개
910건기연[0304-1]2사남-정촌6.820210101035.12732128.09775117.8382.4411.94.7228026.05경남 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-거제8.120210101034.90831128.4093310.465.850.990.272511.38경남 통영 광도 노산
9192건기연[7701-4]2도산-거제8.120210101034.90831128.4093326.0713.912.410.46225.99경남 통영 광도 노산
9293건기연[7702-0]1통영-고성6.420210101034.89878128.355383.312.060.310.13873.34경남 통영 도산 오륜
9394건기연[7702-0]2통영-고성6.420210101034.89878128.355380.00.00.00.00.0경남 통영 도산 오륜
9495건기연[7702-2]1삼산-하이11.120210101034.92571128.130596.453.720.570.131705.45경남 고성 하이 덕호
9596건기연[7702-2]2삼산-하이11.120210101034.92571128.1305914.398.131.370.43459.71경남 고성 하이 덕호
9697건기연[7703-0]1유포-설천11.220210101034.90043127.863410.00.00.00.00.0경남 남해 고현 포상
9798건기연[7703-0]2유포-설천11.220210101034.90043127.863412.261.510.220.13595.97경남 남해 고현 포상
9899건기연[7904-0]1마산-진영5.220210101035.28732128.61172170.7897.8215.042.7945037.11경남 창원 북 외감
99100건기연[7904-0]2마산-진영5.220210101035.28732128.61172102.4460.999.051.5827028.14경남 창원 북 외감