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 12:25:21.122296
Analysis finished2023-12-10 12:25:32.973845
Duration11.85 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
2023-12-10T21:25:33.074932image/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
2023-12-10T21:25:33.278566image/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

2023-12-10T21:25:33.452992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:25:33.565759image/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
2023-12-10T21:25:33.839529image/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%
2804-2 2
 
2.0%
3109-1 2
 
2.0%
2018-0 2
 
2.0%
2508-0 2
 
2.0%
2512-4 2
 
2.0%
2513-0 2
 
2.0%
2613-3 2
 
2.0%
2613-4 2
 
2.0%
2614-3 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:25:34.299374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 134
16.8%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 74
9.2%
3 46
 
5.8%
5 30
 
3.8%
4 28
 
3.5%
8 26
 
3.2%
Other values (3) 52
 
6.5%

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 (%)
0 134
26.8%
1 110
22.0%
2 74
14.8%
3 46
 
9.2%
5 30
 
6.0%
4 28
 
5.6%
8 26
 
5.2%
7 26
 
5.2%
6 18
 
3.6%
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 (%)
0 134
16.8%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 74
9.2%
3 46
 
5.8%
5 30
 
3.8%
4 28
 
3.5%
8 26
 
3.2%
Other values (3) 52
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134
16.8%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 74
9.2%
3 46
 
5.8%
5 30
 
3.8%
4 28
 
3.5%
8 26
 
3.2%
Other values (3) 52
 
6.5%

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

2023-12-10T21:25:34.509103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:25:34.627295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경주-감포
 
4
현풍-옥포
 
4
공성-상주
 
2
금천-산내
 
2
상주-함창
 
2
Other values (43)
86 

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%
현풍-옥포 4
 
4.0%
공성-상주 2
 
2.0%
금천-산내 2
 
2.0%
상주-함창 2
 
2.0%
추풍령-김천 2
 
2.0%
성주-대구 2
 
2.0%
영천-서면 2
 
2.0%
칠곡-효령 2
 
2.0%
가산-군위 2
 
2.0%
Other values (38) 76
76.0%

Length

2023-12-10T21:25:34.770749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경주-감포 4
 
4.0%
현풍-옥포 4
 
4.0%
안계-봉양 2
 
2.0%
구성-김천 2
 
2.0%
영천-의성 2
 
2.0%
선산-낙동 2
 
2.0%
상주-낙동 2
 
2.0%
묘산-쌍림 2
 
2.0%
쌍림-고령 2
 
2.0%
고령-성산 2
 
2.0%
Other values (38) 76
76.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.046
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:25:34.941129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q14.9
median8.1
Q311.4
95-th percentile22.1
Maximum27.8
Range26.5
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation5.757883
Coefficient of variation (CV)0.63651149
Kurtosis1.8083523
Mean9.046
Median Absolute Deviation (MAD)3.25
Skewness1.2048275
Sum904.6
Variance33.153216
MonotonicityNot monotonic
2023-12-10T21:25:35.148442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
10.6 8
 
8.0%
6.4 4
 
4.0%
2.1 4
 
4.0%
2.4 4
 
4.0%
11.2 4
 
4.0%
10.4 2
 
2.0%
5.9 2
 
2.0%
10.5 2
 
2.0%
11.6 2
 
2.0%
9.2 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
1.3 2
2.0%
1.4 2
2.0%
1.8 2
2.0%
2.1 4
4.0%
2.2 2
2.0%
2.4 4
4.0%
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%
24.2 2
2.0%
22.1 2
2.0%
20.6 2
2.0%
15.8 2
2.0%
14.4 2
2.0%
14.2 2
2.0%
13.5 2
2.0%
12.3 2
2.0%
12.0 2
2.0%

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

2023-12-10T21:25:35.323544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:25:35.424615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T21:25:35.514555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:25:35.648883image/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%
Mean36.115767
Minimum35.65543
Maximum36.99828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:25:35.805575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.80916
median36.01706
Q336.35299
95-th percentile36.76527
Maximum36.99828
Range1.34285
Interquartile range (IQR)0.54383

Descriptive statistics

Standard deviation0.35356797
Coefficient of variation (CV)0.0097898509
Kurtosis-0.43965038
Mean36.115767
Median Absolute Deviation (MAD)0.26699
Skewness0.70722182
Sum3611.5767
Variance0.12501031
MonotonicityNot monotonic
2023-12-10T21:25:36.030505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
36.04086 2
 
2.0%
35.78841 2
 
2.0%
36.33873 2
 
2.0%
36.40808 2
 
2.0%
35.67783 2
 
2.0%
35.70376 2
 
2.0%
35.73646 2
 
2.0%
36.73238 2
 
2.0%
36.59513 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%
35.71695 2
2.0%
35.73116 2
2.0%
35.73646 2
2.0%
35.76368 2
2.0%
ValueCountFrequency (%)
36.99828 2
2.0%
36.84888 2
2.0%
36.76527 2
2.0%
36.75364 2
2.0%
36.73238 2
2.0%
36.59556 2
2.0%
36.59513 2
2.0%
36.58611 2
2.0%
36.50552 2
2.0%
36.49421 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.82864
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:25:36.233273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.13935
Q1128.41883
median128.76473
Q3129.30673
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.8879

Descriptive statistics

Standard deviation0.46868387
Coefficient of variation (CV)0.0036380409
Kurtosis-1.4175525
Mean128.82864
Median Absolute Deviation (MAD)0.460995
Skewness-0.033810343
Sum12882.864
Variance0.21966457
MonotonicityNot monotonic
2023-12-10T21:25:36.453447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
128.80075 2
 
2.0%
128.72871 2
 
2.0%
128.30572 2
 
2.0%
128.23413 2
 
2.0%
128.21559 2
 
2.0%
128.25985 2
 
2.0%
128.31646 2
 
2.0%
128.53474 2
 
2.0%
128.41883 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%
128.23413 2
2.0%
128.25985 2
2.0%
128.30572 2
2.0%
128.31646 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%
129.41245 2
2.0%
129.41209 2
2.0%
129.40658 2
2.0%
129.40119 2
2.0%
129.34631 2
2.0%

co((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.9626
Minimum0
Maximum190.94
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:25:36.667445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.3095
Q114.3075
median34.39
Q361.365
95-th percentile114.2295
Maximum190.94
Range190.94
Interquartile range (IQR)47.0575

Descriptive statistics

Standard deviation38.995869
Coefficient of variation (CV)0.90767015
Kurtosis2.2056114
Mean42.9626
Median Absolute Deviation (MAD)21.815
Skewness1.3871542
Sum4296.26
Variance1520.6778
MonotonicityNot monotonic
2023-12-10T21:25:36.854812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
4.58 2
 
2.0%
14.58 1
 
1.0%
2.26 1
 
1.0%
13.49 1
 
1.0%
2.1 1
 
1.0%
14.6 1
 
1.0%
26.68 1
 
1.0%
37.4 1
 
1.0%
51.49 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.52 1
 
1.0%
1.3 1
 
1.0%
1.31 1
 
1.0%
2.0 1
 
1.0%
2.1 1
 
1.0%
2.18 1
 
1.0%
2.26 1
 
1.0%
2.31 1
 
1.0%
2.83 1
 
1.0%
ValueCountFrequency (%)
190.94 1
1.0%
169.47 1
1.0%
154.25 1
1.0%
123.77 1
1.0%
119.92 1
1.0%
113.93 1
1.0%
108.55 1
1.0%
102.91 1
1.0%
100.8 1
1.0%
100.3 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.3577
Minimum0
Maximum230.69
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:25:37.046981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.735
Q18.9075
median24.82
Q350.61
95-th percentile120.461
Maximum230.69
Range230.69
Interquartile range (IQR)41.7025

Descriptive statistics

Standard deviation40.311123
Coefficient of variation (CV)1.0790579
Kurtosis5.2931095
Mean37.3577
Median Absolute Deviation (MAD)19.525
Skewness1.9837554
Sum3735.77
Variance1624.9866
MonotonicityNot monotonic
2023-12-10T21:25:37.250839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
14.83 2
 
2.0%
9.07 1
 
1.0%
1.51 1
 
1.0%
6.56 1
 
1.0%
11.24 1
 
1.0%
5.99 1
 
1.0%
1.11 1
 
1.0%
9.61 1
 
1.0%
34.86 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.28 1
 
1.0%
0.64 1
 
1.0%
0.74 1
 
1.0%
1.11 1
 
1.0%
1.23 1
 
1.0%
1.41 1
 
1.0%
1.51 1
 
1.0%
1.6 1
 
1.0%
1.88 1
 
1.0%
ValueCountFrequency (%)
230.69 1
1.0%
158.74 1
1.0%
142.71 1
1.0%
137.6 1
1.0%
124.09 1
1.0%
120.27 1
1.0%
99.86 1
1.0%
98.36 1
1.0%
95.51 1
1.0%
91.17 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1926
Minimum0
Maximum24.01
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:25:37.790060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.119
Q11.44
median3.745
Q37.37
95-th percentile15.0815
Maximum24.01
Range24.01
Interquartile range (IQR)5.93

Descriptive statistics

Standard deviation5.0116482
Coefficient of variation (CV)0.96515198
Kurtosis1.8494563
Mean5.1926
Median Absolute Deviation (MAD)2.78
Skewness1.3772912
Sum519.26
Variance25.116617
MonotonicityNot monotonic
2023-12-10T21:25:37.989015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
1.45 2
 
2.0%
0.44 2
 
2.0%
1.67 2
 
2.0%
4.78 2
 
2.0%
5.06 1
 
1.0%
2.49 1
 
1.0%
0.98 1
 
1.0%
1.91 1
 
1.0%
0.95 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.04 1
 
1.0%
0.1 1
 
1.0%
0.12 1
 
1.0%
0.17 1
 
1.0%
0.18 1
 
1.0%
0.19 1
 
1.0%
0.22 1
 
1.0%
0.23 1
 
1.0%
0.27 1
 
1.0%
ValueCountFrequency (%)
24.01 1
1.0%
20.54 1
1.0%
17.4 1
1.0%
17.1 1
1.0%
15.3 1
1.0%
15.07 1
1.0%
14.43 1
1.0%
14.34 1
1.0%
13.01 1
1.0%
12.87 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1112
Minimum0
Maximum14.23
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:25:38.183988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.4175
median1.475
Q32.95
95-th percentile6.6805
Maximum14.23
Range14.23
Interquartile range (IQR)2.5325

Descriptive statistics

Standard deviation2.3285589
Coefficient of variation (CV)1.1029551
Kurtosis6.9172189
Mean2.1112
Median Absolute Deviation (MAD)1.075
Skewness2.1543532
Sum211.12
Variance5.4221864
MonotonicityNot monotonic
2023-12-10T21:25:38.396394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 8
 
8.0%
0.14 5
 
5.0%
0.13 4
 
4.0%
1.81 3
 
3.0%
0.4 3
 
3.0%
0.68 2
 
2.0%
0.27 2
 
2.0%
0.8 2
 
2.0%
0.84 2
 
2.0%
2.18 2
 
2.0%
Other values (67) 67
67.0%
ValueCountFrequency (%)
0.0 8
8.0%
0.13 4
4.0%
0.14 5
5.0%
0.26 1
 
1.0%
0.27 2
 
2.0%
0.28 1
 
1.0%
0.4 3
 
3.0%
0.41 1
 
1.0%
0.42 1
 
1.0%
0.55 1
 
1.0%
ValueCountFrequency (%)
14.23 1
1.0%
8.96 1
1.0%
7.08 1
1.0%
6.85 1
1.0%
6.69 1
1.0%
6.68 1
1.0%
6.55 1
1.0%
6.02 1
1.0%
5.56 1
1.0%
5.48 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10613.328
Minimum0
Maximum48661.11
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:25:38.615736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile377.755
Q13424.4775
median8328.24
Q315215.59
95-th percentile27093.185
Maximum48661.11
Range48661.11
Interquartile range (IQR)11791.112

Descriptive statistics

Standard deviation9840.4759
Coefficient of variation (CV)0.92718101
Kurtosis3.3411926
Mean10613.328
Median Absolute Deviation (MAD)5515.125
Skewness1.6126977
Sum1061332.8
Variance96834965
MonotonicityNot monotonic
2023-12-10T21:25:38.820079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
3532.69 1
 
1.0%
595.97 1
 
1.0%
2746.65 1
 
1.0%
887.85 1
 
1.0%
554.74 1
 
1.0%
3853.21 1
 
1.0%
6452.21 1
 
1.0%
9734.95 1
 
1.0%
11462.65 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.0 3
3.0%
138.68 1
 
1.0%
307.36 1
 
1.0%
381.46 1
 
1.0%
554.74 1
 
1.0%
561.37 1
 
1.0%
595.97 1
 
1.0%
601.6 1
 
1.0%
635.76 1
 
1.0%
740.29 1
 
1.0%
ValueCountFrequency (%)
48661.11 1
1.0%
47369.94 1
1.0%
40342.02 1
1.0%
32553.78 1
1.0%
28561.79 1
1.0%
27015.89 1
1.0%
25896.03 1
1.0%
25442.53 1
1.0%
25134.75 1
1.0%
24915.36 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 청도 운문 대천
 
2
경북 안동 일직 원호
 
2
경북 상주 외서 연봉
 
2
경북 김천 봉산 태화
 
2
Other values (44)
88 

Length

Max length11
Median length11
Mean length10.96
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 청도 운문 대천 2
 
2.0%
경북 안동 일직 원호 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 현풍 성하 2
 
2.0%
경북 달성 논공 위천 2
 
2.0%
경북 칠곡 가산 천평 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T21:25:38.988136image/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%
고령 6
 
1.5%
안동 6
 
1.5%
청도 6
 
1.5%
연일 6
 
1.5%
상주 6
 
1.5%
영천 6
 
1.5%
Other values (100) 224
56.0%

Interactions

2023-12-10T21:25:31.398335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:21.926765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:22.930208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:24.230001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:25.342675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:26.464014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:27.593876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:28.757560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:29.913690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:31.493110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:22.045660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:23.029607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:24.373443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:25.463228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:26.578085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:27.683922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:28.883857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:30.020629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:31.611805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:22.126525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:23.421763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:24.479859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:25.588034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:26.681238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:27.809375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:29.016437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:30.128880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:31.744457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:22.238900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:23.543599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:24.608791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:25.733173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:26.829276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:27.962108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:29.146712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:30.261566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:31.884901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:22.353507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:23.668210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:24.734562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:25.862089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:26.964747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:28.174295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:29.281730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:30.753556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:32.012451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:22.486813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:23.782678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:24.849600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:25.981351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:27.082067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:28.301749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:29.427077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:30.864792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:32.116941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:22.603391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:23.870761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:24.938467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:26.097332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:27.201952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:28.398609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:29.546262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:30.992761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:32.223395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:22.706113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:23.981086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:25.031867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:26.208832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:27.332345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:28.512635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:29.662410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:31.110090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:32.363373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:22.822685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:24.111784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:25.196377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:26.330954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:27.472823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:28.647785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:29.795208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:25:31.280475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:25:39.099777image/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
기본키1.0001.0000.0001.0000.5910.7690.9190.4250.5430.6840.5050.4501.000
지점1.0001.0000.0001.0001.0001.0001.0000.8580.8040.8480.8960.9021.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.2130.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9811.0001.0000.8540.8230.8330.9050.8881.000
연장((km))0.5911.0000.0000.9811.0000.6340.6420.0000.3000.2470.3150.2290.992
좌표위치위도((°))0.7691.0000.0001.0000.6341.0000.8250.0000.0000.0000.1700.1191.000
좌표위치경도((°))0.9191.0000.0001.0000.6420.8251.0000.3740.4700.4210.3780.4051.000
co((g/km))0.4250.8580.0000.8540.0000.0000.3741.0000.8820.9100.7820.9850.855
nox((g/km))0.5430.8040.2130.8230.3000.0000.4700.8821.0000.9270.9310.8550.818
hc((g/km))0.6840.8480.0000.8330.2470.0000.4210.9100.9271.0000.9070.8940.840
pm((g/km))0.5050.8960.0000.9050.3150.1700.3780.7820.9310.9071.0000.7970.917
co2((g/km))0.4500.9020.0000.8880.2290.1190.4050.9850.8550.8940.7971.0000.899
주소1.0001.0000.0001.0000.9921.0001.0000.8550.8180.8400.9170.8991.000
2023-12-10T21:25:39.283360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0000.990
방향0.0001.0000.000
주소0.9900.0001.000
2023-12-10T21:25:39.405586image/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
기본키1.000-0.169-0.0490.337-0.251-0.289-0.278-0.283-0.2580.0000.7600.753
연장((km))-0.1691.0000.028-0.0720.0050.0200.0200.0310.0010.0000.6490.696
좌표위치위도((°))-0.0490.0281.000-0.1140.0150.0030.014-0.0000.0020.0000.7600.753
좌표위치경도((°))0.337-0.072-0.1141.0000.1880.1400.1490.0690.1820.0000.7600.753
co((g/km))-0.2510.0050.0150.1881.0000.9780.9860.9500.9960.0000.3830.381
nox((g/km))-0.2890.0200.0030.1400.9781.0000.9950.9830.9780.1520.3380.349
hc((g/km))-0.2780.0200.0140.1490.9860.9951.0000.9730.9820.0000.3480.350
pm((g/km))-0.2830.031-0.0000.0690.9500.9830.9731.0000.9510.0000.4780.467
co2((g/km))-0.2580.0010.0020.1820.9960.9780.9820.9511.0000.0000.4300.445
방향0.0000.0000.0000.0000.0000.1520.0000.0000.0001.0000.0000.000
측정구간0.7600.6490.7600.7600.3830.3380.3480.4780.4300.0001.0000.990
주소0.7530.6960.7530.7530.3810.3490.3500.4670.4450.0000.9901.000

Missing values

2023-12-10T21:25:32.567303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:25:32.863588image/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-김천11.420210201036.07278128.0861114.589.071.450.683532.69경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210201036.07278128.0861115.658.421.450.273740.84경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210201036.35299128.1393526.4825.523.61.816818.58경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210201036.35299128.1393524.2714.382.360.945869.89경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210201036.50552128.1694650.3849.476.383.3612456.27경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210201036.50552128.1694641.8242.445.392.9410308.28경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210201036.14659128.0254411.767.651.120.553097.82경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210201036.14659128.0254417.4311.41.670.834578.4경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210201035.98385128.41106113.9398.3615.076.0225896.03경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210201035.98385128.41106102.9199.8614.347.0825134.75경북 칠곡 왜관 왜관
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3106-2]1연일-구룡포4.720210201035.98322129.4597832.0419.922.971.118413.02경북 포항 동해 신정
9192건기연[3106-2]2연일-구룡포4.720210201035.98322129.4597834.2719.393.250.848182.67경북 포항 동해 신정
9293건기연[3106-5]1유강-광명4.920210201035.98878129.32144169.47230.6924.0114.2347369.94경북 포항 연일 중단
9394건기연[3106-5]2유강-광명4.920210201035.98878129.32144100.887.7212.846.6924915.36경북 포항 연일 중단
9495건기연[3107-2]1기계-포항2.420210201036.06326129.2277127.7324.893.271.817037.65경북 포항 기계 내단
9596건기연[3107-2]2기계-포항2.420210201036.06326129.2277119.1112.631.851.075045.15경북 포항 기계 내단
9697건기연[3109-1]1죽장-부남20.620210201036.2488129.040494.632.70.440.141107.64경북 청송 현동 눌인
9798건기연[3109-1]2죽장-부남20.620210201036.2488129.040492.831.880.270.14740.29경북 청송 현동 눌인
9899건기연[3113-1]1진보-석보6.020210201036.59556129.086118.355.360.790.42203.97경북 영양 입암 신구
99100건기연[3113-1]2진보-석보6.020210201036.59556129.086114.582.60.440.131102.01경북 영양 입암 신구