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
pm((g/km)) has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:15:53.930993
Analysis finished2023-12-10 12:16:00.660041
Duration6.73 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
2023-12-10T21:16:00.722352image/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
2023-12-10T21:16:00.840034image/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

2023-12-10T21:16:00.940689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:16:01.011436image/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
2023-12-10T21:16:01.170878image/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%
2023-12-10T21:16:01.467736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

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

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 150
30.0%
1 92
18.4%
2 82
16.4%
3 50
 
10.0%
4 40
 
8.0%
7 28
 
5.6%
5 24
 
4.8%
9 16
 
3.2%
6 10
 
2.0%
8 8
 
1.6%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

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

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

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

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2023-12-10T21:16:01.584596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:16:01.657908image/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
2023-12-10T21:16:01.829040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1
Min length5

Characters and Unicode

Total characters510
Distinct characters86
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row북천-완사
2nd row북천-완사
3rd row일반성-진북
4th row일반성-진북
5th row마산-부산
ValueCountFrequency (%)
북천-완사 2
 
2.0%
쌍백-합천 2
 
2.0%
유포-설천 2
 
2.0%
봉산-금양 2
 
2.0%
초계-적교 2
 
2.0%
창녕-청도 2
 
2.0%
가산-남기 2
 
2.0%
부북-금곡 2
 
2.0%
창원-대산 2
 
2.0%
서생-온산 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:16:02.168734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

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

Most occurring categories

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

Most frequent character per category

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

Most occurring scripts

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

Most frequent character per script

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

Most occurring blocks

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

Most frequent character per block

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

연장((km))
Real number (ℝ)

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.588
Minimum3
Maximum20.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:16:02.282135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation4.169521
Coefficient of variation (CV)0.48550547
Kurtosis0.49087969
Mean8.588
Median Absolute Deviation (MAD)2.85
Skewness0.96266418
Sum858.8
Variance17.384905
MonotonicityNot monotonic
2023-12-10T21:16:02.387827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
11.2 4
 
4.0%
6.8 4
 
4.0%
5.2 4
 
4.0%
11.9 4
 
4.0%
10.5 4
 
4.0%
10.2 4
 
4.0%
6.4 2
 
2.0%
20.1 2
 
2.0%
7.6 2
 
2.0%
6.1 2
 
2.0%
Other values (34) 68
68.0%
ValueCountFrequency (%)
3.0 2
2.0%
3.3 2
2.0%
3.8 2
2.0%
3.9 2
2.0%
4.1 2
2.0%
4.2 2
2.0%
4.5 2
2.0%
4.6 2
2.0%
4.7 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
20.1 2
2.0%
19.3 2
2.0%
17.6 2
2.0%
17.2 2
2.0%
13.5 2
2.0%
13.3 2
2.0%
13.2 2
2.0%
12.2 2
2.0%
11.9 4
4.0%
11.2 4
4.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T21:16:02.490185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:16:02.559669image/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
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T21:16:02.633752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:16:02.704191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

좌표위치위도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.263477
Minimum34.86496
Maximum35.72809
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:16:02.786546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.25434952
Coefficient of variation (CV)0.0072128316
Kurtosis-1.210975
Mean35.263477
Median Absolute Deviation (MAD)0.218075
Skewness-0.069016348
Sum3526.3477
Variance0.064693677
MonotonicityNot monotonic
2023-12-10T21:16:02.899815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.12031 2
 
2.0%
35.31018 2
 
2.0%
35.55894 2
 
2.0%
35.57918 2
 
2.0%
35.51777 2
 
2.0%
35.50716 2
 
2.0%
35.32823 2
 
2.0%
35.39903 2
 
2.0%
34.97974 2
 
2.0%
35.28102 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.86496 2
2.0%
34.86778 2
2.0%
34.87506 2
2.0%
34.89878 2
2.0%
34.90043 2
2.0%
34.90831 2
2.0%
34.92571 2
2.0%
34.92968 2
2.0%
34.93132 2
2.0%
34.95217 2
2.0%
ValueCountFrequency (%)
35.72809 2
2.0%
35.65095 2
2.0%
35.62274 2
2.0%
35.61557 2
2.0%
35.61463 2
2.0%
35.58349 2
2.0%
35.57918 2
2.0%
35.57341 2
2.0%
35.55894 2
2.0%
35.53342 2
2.0%

좌표위치경도((°))
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.38969
Minimum127.78878
Maximum129.33586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:16:03.203290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation0.44299345
Coefficient of variation (CV)0.0034503818
Kurtosis-0.65848451
Mean128.38969
Median Absolute Deviation (MAD)0.321945
Skewness0.56077443
Sum12838.969
Variance0.1962432
MonotonicityNot monotonic
2023-12-10T21:16:03.315658image/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 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.6408
Minimum0
Maximum335.32
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:16:03.434737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7515
Q16.685
median18.38
Q346.495
95-th percentile150.6205
Maximum335.32
Range335.32
Interquartile range (IQR)39.81

Descriptive statistics

Standard deviation59.379868
Coefficient of variation (CV)1.3606503
Kurtosis6.1346869
Mean43.6408
Median Absolute Deviation (MAD)12.955
Skewness2.2669326
Sum4364.08
Variance3525.9687
MonotonicityNot monotonic
2023-12-10T21:16:03.547029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.63 2
 
2.0%
0.52 2
 
2.0%
1.98 2
 
2.0%
1.78 2
 
2.0%
84.55 1
 
1.0%
28.62 1
 
1.0%
31.03 1
 
1.0%
23.5 1
 
1.0%
17.24 1
 
1.0%
23.86 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.52 2
2.0%
1.05 1
1.0%
1.21 1
1.0%
1.78 2
2.0%
1.98 2
2.0%
2.03 1
1.0%
2.31 1
1.0%
2.63 2
2.0%
3.31 1
1.0%
ValueCountFrequency (%)
335.32 1
1.0%
230.39 1
1.0%
220.52 1
1.0%
165.31 1
1.0%
161.84 1
1.0%
150.03 1
1.0%
144.58 1
1.0%
143.86 1
1.0%
143.55 1
1.0%
139.02 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.7275
Minimum0
Maximum211.97
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:16:03.665715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.302
Q14.265
median15.29
Q349.1275
95-th percentile122.875
Maximum211.97
Range211.97
Interquartile range (IQR)44.8625

Descriptive statistics

Standard deviation45.333341
Coefficient of variation (CV)1.2688641
Kurtosis3.3474336
Mean35.7275
Median Absolute Deviation (MAD)13.18
Skewness1.8455396
Sum3572.75
Variance2055.1118
MonotonicityNot monotonic
2023-12-10T21:16:03.769996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.64 2
 
2.0%
15.29 2
 
2.0%
0.28 2
 
2.0%
1.32 2
 
2.0%
1.33 2
 
2.0%
75.86 1
 
1.0%
34.98 1
 
1.0%
38.05 1
 
1.0%
14.7 1
 
1.0%
18.87 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.28 2
2.0%
0.55 1
1.0%
0.96 1
1.0%
1.32 2
2.0%
1.33 2
2.0%
1.41 1
1.0%
1.6 1
1.0%
1.64 2
2.0%
2.06 1
1.0%
ValueCountFrequency (%)
211.97 1
1.0%
190.55 1
1.0%
184.5 1
1.0%
139.62 1
1.0%
137.6 1
1.0%
122.1 1
1.0%
115.94 1
1.0%
108.05 1
1.0%
102.73 1
1.0%
97.74 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.941
Minimum0
Maximum34.64
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:16:03.878402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1775
Q10.6275
median2.23
Q35.66
95-th percentile16.3555
Maximum34.64
Range34.64
Interquartile range (IQR)5.0325

Descriptive statistics

Standard deviation6.4339274
Coefficient of variation (CV)1.3021509
Kurtosis5.0158067
Mean4.941
Median Absolute Deviation (MAD)1.89
Skewness2.0824551
Sum494.1
Variance41.395421
MonotonicityNot monotonic
2023-12-10T21:16:04.005830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.64 2
 
2.0%
1.79 2
 
2.0%
0.04 2
 
2.0%
0.2 2
 
2.0%
0.58 2
 
2.0%
0.18 2
 
2.0%
0.26 2
 
2.0%
0.76 1
 
1.0%
2.79 1
 
1.0%
2.64 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.04 2
2.0%
0.09 1
1.0%
0.13 1
1.0%
0.18 2
2.0%
0.2 2
2.0%
0.21 1
1.0%
0.23 1
1.0%
0.26 2
2.0%
0.31 1
1.0%
ValueCountFrequency (%)
34.64 1
1.0%
25.81 1
1.0%
25.06 1
1.0%
18.11 1
1.0%
17.98 1
1.0%
16.27 1
1.0%
14.86 1
1.0%
14.64 1
1.0%
14.49 1
1.0%
14.47 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)62.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8935
Minimum0
Maximum10.87
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:16:04.122397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1235
Q10.27
median0.93
Q32.8075
95-th percentile6.714
Maximum10.87
Range10.87
Interquartile range (IQR)2.5375

Descriptive statistics

Standard deviation2.2661824
Coefficient of variation (CV)1.196822
Kurtosis3.3226065
Mean1.8935
Median Absolute Deviation (MAD)0.79
Skewness1.8071691
Sum189.35
Variance5.1355826
MonotonicityNot monotonic
2023-12-10T21:16:04.232408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14 10
 
10.0%
0.13 7
 
7.0%
0.0 5
 
5.0%
0.27 5
 
5.0%
0.93 4
 
4.0%
0.42 3
 
3.0%
0.4 3
 
3.0%
0.83 3
 
3.0%
0.98 2
 
2.0%
0.84 2
 
2.0%
Other values (52) 56
56.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.13 7
7.0%
0.14 10
10.0%
0.27 5
5.0%
0.28 2
 
2.0%
0.4 3
 
3.0%
0.41 1
 
1.0%
0.42 3
 
3.0%
0.52 1
 
1.0%
0.56 1
 
1.0%
ValueCountFrequency (%)
10.87 1
1.0%
9.43 1
1.0%
8.75 1
1.0%
7.59 1
1.0%
7.55 1
1.0%
6.67 1
1.0%
5.88 1
1.0%
5.43 1
1.0%
5.35 1
1.0%
4.83 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10708.708
Minimum0
Maximum79039.26
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:16:04.338616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile455.704
Q11755.135
median4671.615
Q311729.032
95-th percentile37727.796
Maximum79039.26
Range79039.26
Interquartile range (IQR)9973.8975

Descriptive statistics

Standard deviation14330.747
Coefficient of variation (CV)1.338233
Kurtosis5.5052363
Mean10708.708
Median Absolute Deviation (MAD)3240.715
Skewness2.1845775
Sum1070870.8
Variance2.0537031 × 108
MonotonicityNot monotonic
2023-12-10T21:16:04.452832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
640.96 2
 
2.0%
138.68 2
 
2.0%
487.28 2
 
2.0%
462.92 2
 
2.0%
20873.0 1
 
1.0%
7554.43 1
 
1.0%
6817.66 1
 
1.0%
6650.76 1
 
1.0%
4241.26 1
 
1.0%
6004.26 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 1
1.0%
138.68 2
2.0%
277.37 1
1.0%
318.6 1
1.0%
462.92 2
2.0%
487.28 2
2.0%
492.91 1
1.0%
601.6 1
1.0%
640.96 2
2.0%
873.34 1
1.0%
ValueCountFrequency (%)
79039.26 1
1.0%
55749.55 1
1.0%
53656.6 1
1.0%
39532.11 1
1.0%
38273.02 1
1.0%
37699.1 1
1.0%
37435.23 1
1.0%
37429.54 1
1.0%
34003.89 1
1.0%
32722.46 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:16:04.675460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.8
Min length8

Characters and Unicode

Total characters1080
Distinct characters106
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경남 사천 곤명 작팔
2nd row경남 사천 곤명 작팔
3rd row경남 창원 진전 근곡
4th row경남 창원 진전 근곡
5th row경남 창원 양곡
ValueCountFrequency (%)
경남 100
25.3%
창원 12
 
3.0%
고성 10
 
2.5%
합천 10
 
2.5%
산청 8
 
2.0%
남해 8
 
2.0%
울주 6
 
1.5%
밀양 6
 
1.5%
창녕 4
 
1.0%
부북 4
 
1.0%
Other values (102) 228
57.6%
2023-12-10T21:16:05.013665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

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

Most occurring categories

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

Most frequent character per category

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

Most occurring scripts

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

Most frequent character per script

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

Most occurring blocks

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

Most frequent character per block

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

Interactions

2023-12-10T21:15:59.713185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.328515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.968066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.673366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.284978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.972192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.616402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.214343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.074782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.789481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.386222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.037986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.738159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.361656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.043460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.677573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.473652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.137723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.866682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.452129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.116963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.807015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.440752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.118242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.749438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.544760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.209919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.932679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.506758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.187715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.864472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.506964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.184546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.807847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.612338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.268416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:16:00.026304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.583290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.298461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.937322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.588947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.261847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.882655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.692722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.354261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:16:00.107489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.673078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.380843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.004038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.668563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.335411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.951329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.778629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.429445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:16:00.176078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.748064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.451968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.063070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.743072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.397902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.011342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.854703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.500075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:16:00.254290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.829431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.525289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.134093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.819134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.472356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.081362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.935438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.576950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:16:00.322773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:54.893269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:55.596565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.206223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:56.892371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:57.539508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:58.143762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.001374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:15:59.640450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:16:05.099411image/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
기본키1.0001.0000.0001.0000.5900.8170.6380.3100.4810.4850.3430.3041.000
지점1.0001.0000.0001.0001.0001.0001.0000.9250.9090.9240.7530.8541.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9250.9090.9240.7530.8541.000
연장((km))0.5901.0000.0001.0001.0000.6050.6990.0000.3150.2040.2910.0801.000
좌표위치위도((°))0.8171.0000.0001.0000.6051.0000.6310.4160.4680.5370.3210.4181.000
좌표위치경도((°))0.6381.0000.0001.0000.6990.6311.0000.5600.6660.5160.6140.6931.000
co((g/km))0.3100.9250.0000.9250.0000.4160.5601.0000.9320.9190.8250.9430.925
nox((g/km))0.4810.9090.0000.9090.3150.4680.6660.9321.0000.9300.9610.9710.909
hc((g/km))0.4850.9240.0000.9240.2040.5370.5160.9190.9301.0000.8590.9040.924
pm((g/km))0.3430.7530.0000.7530.2910.3210.6140.8250.9610.8591.0000.9500.753
co2((g/km))0.3040.8540.0000.8540.0800.4180.6930.9430.9710.9040.9501.0000.854
주소1.0001.0000.0001.0001.0001.0001.0000.9250.9090.9240.7530.8541.000
2023-12-10T21:16:05.213947image/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
기본키1.000-0.0430.0440.061-0.301-0.265-0.255-0.242-0.3130.000
연장((km))-0.0431.0000.1860.006-0.091-0.104-0.121-0.101-0.0930.000
좌표위치위도((°))0.0440.1861.0000.223-0.235-0.224-0.218-0.211-0.2350.000
좌표위치경도((°))0.0610.0060.2231.0000.4380.4210.4400.4080.4280.000
co((g/km))-0.301-0.091-0.2350.4381.0000.9850.9900.9670.9980.000
nox((g/km))-0.265-0.104-0.2240.4210.9851.0000.9960.9850.9870.000
hc((g/km))-0.255-0.121-0.2180.4400.9900.9961.0000.9810.9880.000
pm((g/km))-0.242-0.101-0.2110.4080.9670.9850.9811.0000.9680.000
co2((g/km))-0.313-0.093-0.2350.4280.9980.9870.9880.9681.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T21:16:00.437170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:16:00.595785image/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-완사4.220210201135.12031127.9701117.9911.031.660.664802.83경남 사천 곤명 작팔
12건기연[0216-2]2북천-완사4.220210201135.12031127.9701112.447.171.190.42998.67경남 사천 곤명 작팔
23건기연[0220-2]1일반성-진북4.820210201135.10632128.4421861.7248.646.892.3415639.48경남 창원 진전 근곡
34건기연[0220-2]2일반성-진북4.820210201135.10632128.44218108.41122.114.367.5527078.48경남 창원 진전 근곡
45건기연[0222-1]1마산-부산9.420210201135.1839128.6395150.03137.616.276.6739532.11경남 창원 양곡
56건기연[0222-1]2마산-부산9.420210201135.1839128.6395127.92108.0514.494.5432138.57경남 창원 양곡
67건기연[0302-4]1상죽-사천10.220210201134.87506128.0095824.0814.692.210.816355.17경남 남해 창선 동대
78건기연[0302-4]2상죽-사천10.220210201134.87506128.0095830.0921.343.031.187248.41경남 남해 창선 동대
89건기연[0303-1]1용현-정촌6.820210201135.01785128.0577574.5352.847.542.7319294.05경남 사천 용현 송지
910건기연[0303-1]2용현-정촌6.820210201135.01785128.0577592.7175.469.584.225539.4경남 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-거제8.120210201134.90831128.409336.493.210.580.01536.82경남 통영 광도 노산
9192건기연[7701-4]2도산-거제8.120210201134.90831128.4093319.1418.922.420.934703.39경남 통영 광도 노산
9293건기연[7702-0]1통영-고성6.420210201134.89878128.355385.273.280.520.271281.93경남 통영 도산 오륜
9394건기연[7702-0]2통영-고성6.420210201134.89878128.355381.210.960.130.13318.6경남 통영 도산 오륜
9495건기연[7702-2]1삼산-하이11.120210201134.92571128.130597.864.570.760.271896.66경남 고성 하이 덕호
9596건기연[7702-2]2삼산-하이11.120210201134.92571128.1305910.777.271.060.672841.18경남 고성 하이 덕호
9697건기연[7703-0]1유포-설천11.220210201134.90043127.863412.031.410.210.14492.91경남 남해 고현 포상
9798건기연[7703-0]2유포-설천11.220210201134.90043127.863410.00.00.00.00.0경남 남해 고현 포상
9899건기연[7904-0]1마산-진영5.220210201135.28732128.61172335.32211.9734.648.7579039.26경남 창원 북 외감
99100건기연[7904-0]2마산-진영5.220210201135.28732128.61172144.5897.1514.244.0237429.54경남 창원 북 외감