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
Categorical6
Text1

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
측정구간 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
기본키 is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
연장((km)) is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
좌표위치위도((°)) is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
좌표위치경도((°)) is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
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 3 (3.0%) zerosZeros
nox((g/km)) has 3 (3.0%) zerosZeros
hc((g/km)) has 3 (3.0%) zerosZeros
pm((g/km)) has 8 (8.0%) zerosZeros
co2((g/km)) has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:24:24.242744
Analysis finished2023-12-10 12:24:34.552262
Duration10.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  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
2023-12-10T21:24:34.653571image/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
2023-12-10T21:24:34.841334image/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

2023-12-10T21:24:35.012202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:35.112672image/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
2023-12-10T21:24:35.348094image/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[0314-0]
2nd row[0314-0]
3rd row[0317-0]
4th row[0317-0]
5th row[0318-0]
ValueCountFrequency (%)
0314-0 2
 
2.0%
3103-2 2
 
2.0%
3313-0 2
 
2.0%
2613-4 2
 
2.0%
2614-3 2
 
2.0%
2803-1 2
 
2.0%
2804-2 2
 
2.0%
2805-3 2
 
2.0%
2808-0 2
 
2.0%
2809-1 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:24:35.760070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 130
16.2%
1 114
14.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 68
8.5%
2 64
8.0%
4 26
 
3.2%
5 26
 
3.2%
8 24
 
3.0%
Other values (3) 48
 
6.0%

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 (%)
0 130
26.0%
1 114
22.8%
3 68
13.6%
2 64
12.8%
4 26
 
5.2%
5 26
 
5.2%
8 24
 
4.8%
6 20
 
4.0%
7 20
 
4.0%
9 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 (%)
0 130
16.2%
1 114
14.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 68
8.5%
2 64
8.0%
4 26
 
3.2%
5 26
 
3.2%
8 24
 
3.0%
Other values (3) 48
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
16.2%
1 114
14.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
3 68
8.5%
2 64
8.0%
4 26
 
3.2%
5 26
 
3.2%
8 24
 
3.0%
Other values (3) 48
 
6.0%

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

2023-12-10T21:24:35.928786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:36.040327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경주-감포
 
4
상주-낙동
 
2
북후-영주
 
2
상주-함창
 
2
추풍령-김천
 
2
Other values (44)
88 

Length

Max length6
Median length5
Mean length5.06
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구성-김천
2nd row구성-김천
3rd row공성-상주
4th row공성-상주
5th row상주-함창

Common Values

ValueCountFrequency (%)
경주-감포 4
 
4.0%
상주-낙동 2
 
2.0%
북후-영주 2
 
2.0%
상주-함창 2
 
2.0%
추풍령-김천 2
 
2.0%
성주-대구 2
 
2.0%
영천-서면 2
 
2.0%
현풍-옥포 2
 
2.0%
칠곡-효령 2
 
2.0%
가산-군위 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T21:24:36.162269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경주-감포 4
 
4.0%
구성-김천 2
 
2.0%
양포-동해 2
 
2.0%
고령-성산 2
 
2.0%
상동-본포 2
 
2.0%
안계-봉양 2
 
2.0%
일반5-금성 2
 
2.0%
영천-의성 2
 
2.0%
안강-고경 2
 
2.0%
강동-흥해 2
 
2.0%
Other values (39) 78
78.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.642
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:36.308035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q15.9
median8.1
Q311.9
95-th percentile24.2
Maximum27.8
Range26.5
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.3053958
Coefficient of variation (CV)0.65395103
Kurtosis1.2040613
Mean9.642
Median Absolute Deviation (MAD)3.3
Skewness1.2036188
Sum964.2
Variance39.758016
MonotonicityNot monotonic
2023-12-10T21:24:36.526066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
10.6 6
 
6.0%
6.4 4
 
4.0%
2.4 4
 
4.0%
11.2 4
 
4.0%
11.4 2
 
2.0%
6.2 2
 
2.0%
8.2 2
 
2.0%
7.8 2
 
2.0%
2.2 2
 
2.0%
8.0 2
 
2.0%
Other values (35) 70
70.0%
ValueCountFrequency (%)
1.3 2
2.0%
1.4 2
2.0%
1.8 2
2.0%
2.2 2
2.0%
2.4 4
4.0%
3.4 2
2.0%
3.8 2
2.0%
4.1 2
2.0%
4.7 2
2.0%
4.8 2
2.0%
ValueCountFrequency (%)
27.8 2
2.0%
26.7 2
2.0%
24.2 2
2.0%
22.1 2
2.0%
20.6 2
2.0%
19.4 2
2.0%
15.8 2
2.0%
14.4 2
2.0%
13.6 2
2.0%
12.9 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210501
2nd row20210501
3rd row20210501
4th row20210501
5th row20210501

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2023-12-10T21:24:36.669585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:36.774838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 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

2023-12-10T21:24:36.888212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:37.025220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.101754
Minimum35.65543
Maximum36.86368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:37.150758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.85555
median36.01706
Q336.32892
95-th percentile36.76527
Maximum36.86368
Range1.20825
Interquartile range (IQR)0.47337

Descriptive statistics

Standard deviation0.32731356
Coefficient of variation (CV)0.0090664171
Kurtosis-0.24584349
Mean36.101754
Median Absolute Deviation (MAD)0.22022
Skewness0.76567239
Sum3610.1754
Variance0.10713417
MonotonicityNot monotonic
2023-12-10T21:24:37.312498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
35.98043 2
 
2.0%
35.73646 2
 
2.0%
36.59513 2
 
2.0%
36.35882 2
 
2.0%
36.32892 2
 
2.0%
36.04086 2
 
2.0%
35.99882 2
 
2.0%
36.03299 2
 
2.0%
35.94083 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
35.65543 2
2.0%
35.67783 2
2.0%
35.68369 2
2.0%
35.69757 2
2.0%
35.70376 2
2.0%
35.71373 2
2.0%
35.73646 2
2.0%
35.76368 2
2.0%
35.78841 2
2.0%
35.79392 2
2.0%
ValueCountFrequency (%)
36.86368 2
2.0%
36.84888 2
2.0%
36.76527 2
2.0%
36.75364 2
2.0%
36.62861 2
2.0%
36.59556 2
2.0%
36.59513 2
2.0%
36.50552 2
2.0%
36.40808 2
2.0%
36.35882 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.77776
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:37.478969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.13935
Q1128.37682
median128.69655
Q3129.25885
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.88203

Descriptive statistics

Standard deviation0.47066686
Coefficient of variation (CV)0.0036548768
Kurtosis-1.4462336
Mean128.77776
Median Absolute Deviation (MAD)0.40383
Skewness0.12963793
Sum12877.776
Variance0.22152729
MonotonicityNot monotonic
2023-12-10T21:24:37.714626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
129.49495 2
 
2.0%
128.31646 2
 
2.0%
128.41883 2
 
2.0%
128.46812 2
 
2.0%
128.70365 2
 
2.0%
128.80075 2
 
2.0%
129.08273 2
 
2.0%
129.30673 2
 
2.0%
128.15515 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
128.02544 2
2.0%
128.08611 2
2.0%
128.13935 2
2.0%
128.15515 2
2.0%
128.16946 2
2.0%
128.21559 2
2.0%
128.23039 2
2.0%
128.23413 2
2.0%
128.25985 2
2.0%
128.30457 2
2.0%
ValueCountFrequency (%)
129.52365 2
2.0%
129.49495 2
2.0%
129.47131 2
2.0%
129.45978 2
2.0%
129.45621 2
2.0%
129.41209 2
2.0%
129.40658 2
2.0%
129.34631 2
2.0%
129.32144 2
2.0%
129.31407 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.3319
Minimum0
Maximum225.82
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:37.893411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.041
Q115.235
median37.79
Q363.4325
95-th percentile137.3935
Maximum225.82
Range225.82
Interquartile range (IQR)48.1975

Descriptive statistics

Standard deviation45.719555
Coefficient of variation (CV)0.96593535
Kurtosis3.5785011
Mean47.3319
Median Absolute Deviation (MAD)23.43
Skewness1.7657349
Sum4733.19
Variance2090.2777
MonotonicityNot monotonic
2023-12-10T21:24:38.385410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
3.31 2
 
2.0%
1.05 2
 
2.0%
27.77 1
 
1.0%
67.71 1
 
1.0%
0.87 1
 
1.0%
2.63 1
 
1.0%
74.38 1
 
1.0%
120.7 1
 
1.0%
35.62 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.65 1
 
1.0%
0.87 1
 
1.0%
1.05 2
2.0%
1.57 1
 
1.0%
2.63 1
 
1.0%
3.28 1
 
1.0%
3.31 2
2.0%
4.58 1
 
1.0%
4.61 1
 
1.0%
ValueCountFrequency (%)
225.82 1
1.0%
201.82 1
1.0%
187.26 1
1.0%
185.27 1
1.0%
139.74 1
1.0%
137.27 1
1.0%
135.7 1
1.0%
121.43 1
1.0%
120.7 1
1.0%
110.99 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.1028
Minimum0
Maximum295.7
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:38.588365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.547
Q19.6325
median23.365
Q349.4225
95-th percentile166.0055
Maximum295.7
Range295.7
Interquartile range (IQR)39.79

Descriptive statistics

Standard deviation55.428164
Coefficient of variation (CV)1.3164959
Kurtosis7.8846506
Mean42.1028
Median Absolute Deviation (MAD)19.285
Skewness2.675536
Sum4210.28
Variance3072.2814
MonotonicityNot monotonic
2023-12-10T21:24:38.776358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
2.06 2
 
2.0%
0.55 2
 
2.0%
19.83 1
 
1.0%
44.21 1
 
1.0%
0.49 1
 
1.0%
1.64 1
 
1.0%
47.26 1
 
1.0%
81.53 1
 
1.0%
44.68 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.32 1
 
1.0%
0.49 1
 
1.0%
0.55 2
2.0%
0.83 1
 
1.0%
1.64 1
 
1.0%
1.96 1
 
1.0%
2.06 2
2.0%
2.6 1
 
1.0%
3.45 1
 
1.0%
ValueCountFrequency (%)
295.7 1
1.0%
274.81 1
1.0%
231.06 1
1.0%
181.44 1
1.0%
169.34 1
1.0%
165.83 1
1.0%
152.15 1
1.0%
150.28 1
1.0%
121.77 1
1.0%
120.49 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6627
Minimum0
Maximum32.73
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:38.960021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.089
Q11.555
median3.775
Q37.345
95-th percentile19.435
Maximum32.73
Range32.73
Interquartile range (IQR)5.79

Descriptive statistics

Standard deviation6.411146
Coefficient of variation (CV)1.1321712
Kurtosis5.6969139
Mean5.6627
Median Absolute Deviation (MAD)2.855
Skewness2.2353525
Sum566.27
Variance41.102794
MonotonicityNot monotonic
2023-12-10T21:24:39.129946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
2.46 2
 
2.0%
3.74 2
 
2.0%
0.31 2
 
2.0%
0.09 2
 
2.0%
2.26 2
 
2.0%
3.2 1
 
1.0%
1.59 1
 
1.0%
7.73 1
 
1.0%
7.01 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.06 1
 
1.0%
0.07 1
 
1.0%
0.09 2
2.0%
0.13 1
 
1.0%
0.26 1
 
1.0%
0.31 2
2.0%
0.32 1
 
1.0%
0.44 1
 
1.0%
0.5 1
 
1.0%
ValueCountFrequency (%)
32.73 1
1.0%
32.07 1
1.0%
23.76 1
1.0%
22.58 1
1.0%
20.48 1
1.0%
19.38 1
1.0%
18.23 1
1.0%
16.68 1
1.0%
15.94 1
1.0%
15.72 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4367
Minimum0
Maximum18.72
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:39.317107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.42
median1.31
Q33.03
95-th percentile9.892
Maximum18.72
Range18.72
Interquartile range (IQR)2.61

Descriptive statistics

Standard deviation3.4030385
Coefficient of variation (CV)1.3965767
Kurtosis9.3433913
Mean2.4367
Median Absolute Deviation (MAD)1.04
Skewness2.8778366
Sum243.67
Variance11.580671
MonotonicityNot monotonic
2023-12-10T21:24:39.542522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.0%
0.13 6
 
6.0%
1.47 3
 
3.0%
0.28 3
 
3.0%
0.42 3
 
3.0%
0.68 2
 
2.0%
3.12 2
 
2.0%
0.41 2
 
2.0%
0.27 2
 
2.0%
0.98 2
 
2.0%
Other values (60) 67
67.0%
ValueCountFrequency (%)
0.0 8
8.0%
0.13 6
6.0%
0.14 1
 
1.0%
0.26 1
 
1.0%
0.27 2
 
2.0%
0.28 3
 
3.0%
0.4 1
 
1.0%
0.41 2
 
2.0%
0.42 3
 
3.0%
0.54 1
 
1.0%
ValueCountFrequency (%)
18.72 1
1.0%
17.34 1
1.0%
14.22 1
1.0%
10.71 1
1.0%
10.69 1
1.0%
9.85 1
1.0%
8.74 1
1.0%
8.53 1
1.0%
5.79 1
1.0%
5.47 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11932.402
Minimum0
Maximum53091.5
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:39.733517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile276.2165
Q13616.345
median9643.555
Q314593.967
95-th percentile33950.816
Maximum53091.5
Range53091.5
Interquartile range (IQR)10977.622

Descriptive statistics

Standard deviation11533.514
Coefficient of variation (CV)0.96657108
Kurtosis3.2861072
Mean11932.402
Median Absolute Deviation (MAD)5782.96
Skewness1.7218945
Sum1193240.2
Variance1.3302195 × 108
MonotonicityNot monotonic
2023-12-10T21:24:39.907943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
873.34 2
 
2.0%
277.37 2
 
2.0%
6452.29 1
 
1.0%
15858.87 1
 
1.0%
254.3 1
 
1.0%
640.96 1
 
1.0%
19429.84 1
 
1.0%
27931.8 1
 
1.0%
9886.8 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 3
3.0%
153.68 1
 
1.0%
254.3 1
 
1.0%
277.37 2
2.0%
416.06 1
 
1.0%
640.96 1
 
1.0%
794.65 1
 
1.0%
873.34 2
2.0%
966.54 1
 
1.0%
1102.01 1
 
1.0%
ValueCountFrequency (%)
53091.5 1
1.0%
52503.86 1
1.0%
50449.41 1
1.0%
44404.23 1
1.0%
36666.61 1
1.0%
33807.88 1
1.0%
33038.99 1
1.0%
32115.93 1
1.0%
30846.58 1
1.0%
27931.8 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경북 경주 양북 안동
 
4
경북 상주 낙동 신상
 
2
경북 영주 이산 운문
 
2
경북 상주 외서 연봉
 
2
경북 김천 봉산 태화
 
2
Other values (44)
88 

Length

Max length11
Median length11
Mean length10.98
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경북 김천 구성 하강
2nd row경북 김천 구성 하강
3rd row경북 상주 청리 원장
4th row경북 상주 청리 원장
5th row경북 상주 외서 연봉

Common Values

ValueCountFrequency (%)
경북 경주 양북 안동 4
 
4.0%
경북 상주 낙동 신상 2
 
2.0%
경북 영주 이산 운문 2
 
2.0%
경북 상주 외서 연봉 2
 
2.0%
경북 김천 봉산 태화 2
 
2.0%
경북 칠곡 왜관 왜관 2
 
2.0%
경북 영천 북안 유하 2
 
2.0%
경북 달성 논공 위천 2
 
2.0%
경북 칠곡 가산 천평 2
 
2.0%
경북 군위 효령 고곡 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T21:24:40.057135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경북 100
25.0%
포항 16
 
4.0%
경주 14
 
3.5%
의성 10
 
2.5%
칠곡 8
 
2.0%
예천 6
 
1.5%
상주 6
 
1.5%
고령 6
 
1.5%
연일 6
 
1.5%
영천 6
 
1.5%
Other values (102) 222
55.5%

Interactions

2023-12-10T21:24:33.207713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:25.003675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:26.198776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:27.125640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.187186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:29.075773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:30.052970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:31.052878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:32.004411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:33.285520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:25.077122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:26.299333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:27.236243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.308165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:29.184958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:30.160216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:31.151069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:32.112653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:33.374499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:25.153062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:26.393812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:27.360171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.419260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:29.258884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:30.287024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:31.260674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:32.480748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:33.504217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:25.507747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:26.502603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:27.507892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.518467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:29.379749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:30.405181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:31.368955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:32.578472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:33.627926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:25.614862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:26.631497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:27.612284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.619603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:29.498021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:30.521946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:31.480813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:32.685901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:33.714047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:25.720094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:26.725875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:27.704555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.706836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:29.588760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:30.625856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:31.564710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:32.781838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:33.819960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:25.889551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:26.812177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:27.822933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.807299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:29.705473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:30.732112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:31.676160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:32.898987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:33.922462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:25.979221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:26.896989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:27.938924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.889867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:29.821305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:30.855121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:31.773459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:33.009401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:34.037157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:26.084051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:27.013195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.054341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:28.978157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:29.938119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:30.962641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:31.898138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:33.121064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:24:40.174749image/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
기본키1.0001.0000.0001.0000.7380.7530.9000.3540.2880.4070.2950.3371.000
지점1.0001.0000.0001.0001.0001.0001.0000.8110.8170.8560.8200.8591.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9941.0001.0000.8120.8290.8610.8310.8591.000
연장((km))0.7381.0000.0000.9941.0000.7080.8160.0000.0000.0000.0000.0000.994
좌표위치위도((°))0.7531.0000.0001.0000.7081.0000.7450.0000.0000.0000.0000.0001.000
좌표위치경도((°))0.9001.0000.0001.0000.8160.7451.0000.3550.1770.3860.2650.4481.000
co((g/km))0.3540.8110.0000.8120.0000.0000.3551.0000.8560.9520.7710.9830.812
nox((g/km))0.2880.8170.0000.8290.0000.0000.1770.8561.0000.9510.9740.8600.829
hc((g/km))0.4070.8560.0000.8610.0000.0000.3860.9520.9511.0000.9050.9510.861
pm((g/km))0.2950.8200.0000.8310.0000.0000.2650.7710.9740.9051.0000.7540.831
co2((g/km))0.3370.8590.0000.8590.0000.0000.4480.9830.8600.9510.7541.0000.859
주소1.0001.0000.0001.0000.9941.0001.0000.8120.8290.8610.8310.8591.000
2023-12-10T21:24:40.383496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0001.000
방향0.0001.0000.000
주소1.0000.0001.000
2023-12-10T21:24:40.510821image/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))방향측정구간주소
기본키1.000-0.0080.0410.154-0.207-0.224-0.215-0.222-0.2010.0000.7530.753
연장((km))-0.0081.0000.041-0.064-0.183-0.175-0.182-0.155-0.1820.0000.7060.706
좌표위치위도((°))0.0410.0411.000-0.177-0.100-0.120-0.100-0.103-0.0990.0000.7530.753
좌표위치경도((°))0.154-0.064-0.1771.0000.2550.2330.2250.1420.2600.0000.7530.753
co((g/km))-0.207-0.183-0.1000.2551.0000.9800.9880.9400.9980.0000.3330.333
nox((g/km))-0.224-0.175-0.1200.2330.9801.0000.9950.9720.9800.0000.3610.361
hc((g/km))-0.215-0.182-0.1000.2250.9880.9951.0000.9670.9840.0000.3890.389
pm((g/km))-0.222-0.155-0.1030.1420.9400.9720.9671.0000.9400.0000.3630.363
co2((g/km))-0.201-0.182-0.0990.2600.9980.9800.9840.9401.0000.0000.3870.387
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
측정구간0.7530.7060.7530.7530.3330.3610.3890.3630.3870.0001.0001.000
주소0.7530.7060.7530.7530.3330.3610.3890.3630.3870.0001.0001.000

Missing values

2023-12-10T21:24:34.219377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:24:34.435288image/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[0314-0]1구성-김천11.420210501036.07278128.0861127.7719.833.21.096452.29경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210501036.07278128.0861118.2211.371.690.684803.27경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210501036.35299128.1393525.5920.12.971.366461.55경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210501036.35299128.1393532.4323.223.741.517614.8경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210501036.50552128.1694665.3353.397.493.7716613.55경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210501036.50552128.1694654.8449.466.953.9914475.26경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210501036.14659128.0254415.489.761.450.674089.34경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210501036.14659128.0254410.076.720.90.422845.39경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210501035.98385128.4110692.3668.4410.115.7924306.43경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210501035.98385128.4110695.2178.1511.045.4723986.73경북 칠곡 왜관 왜관
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3307-1]1고령-수륜12.920210501035.80817128.2303917.018.32.811.263661.72경북 성주 수륜 계정
9192건기연[3307-1]2고령-수륜12.920210501035.80817128.230398.615.380.860.412091.45경북 성주 수륜 계정
9293건기연[3310-0]1성주-왜관6.720210501035.97485128.3768257.3551.17.484.4314238.03경북 칠곡 기산 영
9394건기연[3310-0]2성주-왜관6.720210501035.97485128.3768244.0433.64.942.4411193.04경북 칠곡 기산 영
9495건기연[3311-0]1약목-구평19.420210501036.04876128.4107722.9913.12.020.46100.0경북 칠곡 석적 포남
9596건기연[3311-0]2약목-구평19.420210501036.04876128.4107720.0315.632.360.85025.92경북 칠곡 석적 포남
9697건기연[3313-0]1구미-선산3.420210501036.23436128.3045789.9461.459.463.3921227.36경북 구미 선산 동부
9798건기연[3313-0]2구미-선산3.420210501036.23436128.3045753.4736.685.092.2514059.23경북 구미 선산 동부
9899건기연[3416-0]1예천-괴정13.620210501036.62861128.4816121.8913.312.00.685774.07경북 예천 예천 고평
99100건기연[3416-0]2예천-괴정13.620210501036.62861128.4816128.5216.952.770.986831.4경북 예천 예천 고평