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 9 (9.0%) zerosZeros
co2((g/km)) has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:09:12.568002
Analysis finished2023-12-10 12:09:24.254910
Duration11.69 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
2023-12-10T21:09:24.345329image/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
2023-12-10T21:09:24.476631image/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

2023-12-10T21:09:24.593220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:24.676927image/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
2023-12-10T21:09:24.927858image/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%
2023-12-10T21:09:25.350419image/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

2023-12-10T21:09:25.564728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:25.685435image/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

2023-12-10T21:09:25.812430image/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
2023-12-10T21:09:26.448843image/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
2023-12-10T21:09:26.642632image/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

2023-12-10T21:09:26.782926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:26.914166image/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

2023-12-10T21:09:27.034254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:27.140837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
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
2023-12-10T21:09:27.295016image/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
2023-12-10T21:09:27.471520image/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
2023-12-10T21:09:27.628193image/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
2023-12-10T21:09:27.819429image/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 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.1093
Minimum0
Maximum147.11
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:28.042035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52
Q18.7825
median25.315
Q347.015
95-th percentile84.2365
Maximum147.11
Range147.11
Interquartile range (IQR)38.2325

Descriptive statistics

Standard deviation29.830806
Coefficient of variation (CV)0.90097967
Kurtosis2.1056418
Mean33.1093
Median Absolute Deviation (MAD)18.74
Skewness1.3296578
Sum3310.93
Variance889.87699
MonotonicityNot monotonic
2023-12-10T21:09:28.248894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
0.52 3
 
3.0%
12.48 1
 
1.0%
21.12 1
 
1.0%
5.66 1
 
1.0%
4.55 1
 
1.0%
7.28 1
 
1.0%
19.36 1
 
1.0%
24.08 1
 
1.0%
44.19 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.52 3
3.0%
0.65 1
 
1.0%
1.05 1
 
1.0%
1.73 1
 
1.0%
1.78 1
 
1.0%
2.1 1
 
1.0%
2.31 1
 
1.0%
2.83 1
 
1.0%
2.94 1
 
1.0%
ValueCountFrequency (%)
147.11 1
1.0%
129.38 1
1.0%
107.54 1
1.0%
103.82 1
1.0%
89.68 1
1.0%
83.95 1
1.0%
83.14 1
1.0%
83.03 1
1.0%
81.0 1
1.0%
80.89 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.0896
Minimum0
Maximum137.84
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:28.452590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q16.615
median20.955
Q342.0125
95-th percentile104.2045
Maximum137.84
Range137.84
Interquartile range (IQR)35.3975

Descriptive statistics

Standard deviation32.06267
Coefficient of variation (CV)1.0312989
Kurtosis1.5732375
Mean31.0896
Median Absolute Deviation (MAD)16.845
Skewness1.4175064
Sum3108.96
Variance1028.0148
MonotonicityNot monotonic
2023-12-10T21:09:28.656527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
0.28 3
 
3.0%
7.53 1
 
1.0%
19.98 1
 
1.0%
3.76 1
 
1.0%
6.85 1
 
1.0%
8.54 1
 
1.0%
16.1 1
 
1.0%
18.59 1
 
1.0%
48.41 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.28 3
3.0%
0.32 1
 
1.0%
0.55 1
 
1.0%
1.11 1
 
1.0%
1.23 1
 
1.0%
1.33 1
 
1.0%
1.6 1
 
1.0%
1.88 1
 
1.0%
2.19 1
 
1.0%
ValueCountFrequency (%)
137.84 1
1.0%
123.0 1
1.0%
122.62 1
1.0%
112.65 1
1.0%
106.76 1
1.0%
104.07 1
1.0%
95.13 1
1.0%
93.88 1
1.0%
83.75 1
1.0%
81.97 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct91
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2438
Minimum0
Maximum15.45
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:28.885782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.99
median3.06
Q36.3
95-th percentile12.56
Maximum15.45
Range15.45
Interquartile range (IQR)5.31

Descriptive statistics

Standard deviation4.0078501
Coefficient of variation (CV)0.94440126
Kurtosis0.69726581
Mean4.2438
Median Absolute Deviation (MAD)2.5
Skewness1.1307974
Sum424.38
Variance16.062862
MonotonicityNot monotonic
2023-12-10T21:09:29.100870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 3
 
3.0%
0.18 3
 
3.0%
0.0 3
 
3.0%
5.24 2
 
2.0%
6.87 2
 
2.0%
1.73 2
 
2.0%
1.22 1
 
1.0%
2.39 1
 
1.0%
2.78 1
 
1.0%
2.98 1
 
1.0%
Other values (81) 81
81.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.04 3
3.0%
0.06 1
 
1.0%
0.09 1
 
1.0%
0.18 3
3.0%
0.23 1
 
1.0%
0.27 1
 
1.0%
0.31 1
 
1.0%
0.43 1
 
1.0%
0.44 1
 
1.0%
ValueCountFrequency (%)
15.45 1
1.0%
15.42 1
1.0%
15.35 1
1.0%
14.34 1
1.0%
13.7 1
1.0%
12.5 1
1.0%
12.12 1
1.0%
11.48 1
1.0%
11.18 1
1.0%
10.91 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8104
Minimum0
Maximum8.48
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:29.323193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.41
median1.285
Q32.3
95-th percentile5.149
Maximum8.48
Range8.48
Interquartile range (IQR)1.89

Descriptive statistics

Standard deviation1.8915201
Coefficient of variation (CV)1.0448078
Kurtosis2.5084715
Mean1.8104
Median Absolute Deviation (MAD)0.92
Skewness1.5987036
Sum181.04
Variance3.5778483
MonotonicityNot monotonic
2023-12-10T21:09:29.514148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
0.28 4
 
4.0%
0.14 4
 
4.0%
1.19 3
 
3.0%
1.72 2
 
2.0%
2.2 2
 
2.0%
5.13 2
 
2.0%
0.27 2
 
2.0%
1.46 2
 
2.0%
0.8 2
 
2.0%
Other values (59) 68
68.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.13 2
 
2.0%
0.14 4
4.0%
0.27 2
 
2.0%
0.28 4
4.0%
0.39 1
 
1.0%
0.4 2
 
2.0%
0.41 2
 
2.0%
0.42 2
 
2.0%
0.44 2
 
2.0%
ValueCountFrequency (%)
8.48 1
1.0%
8.37 1
1.0%
7.55 1
1.0%
6.78 1
1.0%
5.51 1
1.0%
5.13 2
2.0%
4.97 1
1.0%
4.87 1
1.0%
4.76 1
1.0%
4.67 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8036.4113
Minimum0
Maximum34492.95
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:29.698741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138.68
Q12257.885
median6361.815
Q310786.485
95-th percentile21046.889
Maximum34492.95
Range34492.95
Interquartile range (IQR)8528.6

Descriptive statistics

Standard deviation7265.9963
Coefficient of variation (CV)0.90413444
Kurtosis2.1209722
Mean8036.4113
Median Absolute Deviation (MAD)4303.565
Skewness1.3662833
Sum803641.13
Variance52794702
MonotonicityNot monotonic
2023-12-10T21:09:29.849417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
138.68 3
 
3.0%
3024.9 1
 
1.0%
4705.02 1
 
1.0%
1480.58 1
 
1.0%
910.9 1
 
1.0%
1639.93 1
 
1.0%
4860.99 1
 
1.0%
6109.15 1
 
1.0%
10306.9 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 3
3.0%
138.68 3
3.0%
153.68 1
 
1.0%
277.37 1
 
1.0%
457.29 1
 
1.0%
462.92 1
 
1.0%
554.74 1
 
1.0%
601.6 1
 
1.0%
740.29 1
 
1.0%
775.89 1
 
1.0%
ValueCountFrequency (%)
34492.95 1
1.0%
32614.25 1
1.0%
27799.19 1
1.0%
25066.8 1
1.0%
21640.62 1
1.0%
21015.64 1
1.0%
20788.56 1
1.0%
20147.92 1
1.0%
20025.83 1
1.0%
19810.14 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

2023-12-10T21:09:29.990387image/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

2023-12-10T21:09:22.672958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.378631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.417188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.599974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:16.823308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:18.096300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:19.462596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:20.573266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:21.684019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:22.800176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.461178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.548295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.728165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:16.964851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:18.220722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:19.551723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:20.691530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:21.806370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:22.930865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.581991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.674111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.853976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:17.082983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:18.340126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:19.642476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:20.794721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:21.913498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:23.074993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.707831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.809886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:16.012949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:17.218972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:18.475545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:19.771664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:20.919959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:22.029587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:23.210301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.831376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.949455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:16.161868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:17.363547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:18.589347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:19.909031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:21.042805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:22.123636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:23.335915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.916282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.081459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:16.304413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:17.487555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:18.720741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:20.056864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:21.180622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:22.219145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:23.492197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.036595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.219734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:16.447692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:17.631545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:19.133277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:20.202082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:21.308722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:22.347462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:23.622906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.171580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.355048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:16.586570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:17.810428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:19.255416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:20.349695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:21.416220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:22.464690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:23.760381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.282235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.470110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:16.700286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:17.960022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:19.359392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:20.457396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:21.551227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:22.549592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:09:30.087291image/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
기본키1.0001.0000.0001.0000.5910.7690.9190.5660.5810.5530.5350.5731.000
지점1.0001.0000.0001.0001.0001.0001.0000.8760.8270.8990.8190.9401.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9811.0001.0000.8640.8050.8650.8110.9261.000
연장((km))0.5911.0000.0000.9811.0000.6340.6420.0000.2700.3060.1490.0000.992
좌표위치위도((°))0.7691.0000.0001.0000.6341.0000.8250.0000.4770.4930.4640.0001.000
좌표위치경도((°))0.9191.0000.0001.0000.6420.8251.0000.5380.5150.5340.3390.5271.000
co((g/km))0.5660.8760.0000.8640.0000.0000.5381.0000.9480.9430.9130.9900.864
nox((g/km))0.5810.8270.0000.8050.2700.4770.5150.9481.0000.9640.9770.9590.821
hc((g/km))0.5530.8990.0000.8650.3060.4930.5340.9430.9641.0000.9310.9580.886
pm((g/km))0.5350.8190.0000.8110.1490.4640.3390.9130.9770.9311.0000.9190.813
co2((g/km))0.5730.9400.0000.9260.0000.0000.5270.9900.9590.9580.9191.0000.928
주소1.0001.0000.0001.0000.9921.0001.0000.8640.8210.8860.8130.9281.000
2023-12-10T21:09:30.249034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0000.990
방향0.0001.0000.000
주소0.9900.0001.000
2023-12-10T21:09:30.364382image/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
기본키1.000-0.169-0.0490.337-0.288-0.300-0.290-0.335-0.2870.0000.7600.753
연장((km))-0.1691.0000.028-0.0720.0270.0590.0440.0730.0270.0000.6490.696
좌표위치위도((°))-0.0490.0281.000-0.114-0.002-0.005-0.0120.016-0.0010.0000.7600.753
좌표위치경도((°))0.337-0.072-0.1141.0000.1220.0880.0920.0030.1300.0000.7600.753
co((g/km))-0.2880.027-0.0020.1221.0000.9810.9870.9590.9970.0000.3850.380
nox((g/km))-0.3000.059-0.0050.0880.9811.0000.9960.9860.9780.0000.3180.330
hc((g/km))-0.2900.044-0.0120.0920.9870.9961.0000.9810.9810.0000.3840.409
pm((g/km))-0.3350.0730.0160.0030.9590.9860.9811.0000.9560.0000.3580.358
co2((g/km))-0.2870.027-0.0010.1300.9970.9780.9810.9561.0000.0000.4890.485
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
측정구간0.7600.6490.7600.7600.3850.3180.3840.3580.4890.0001.0000.990
주소0.7530.6960.7530.7530.3800.3300.4090.3580.4850.0000.9901.000

Missing values

2023-12-10T21:09:23.963968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:09:24.170424image/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-김천11.420210201136.07278128.0861112.487.531.220.543024.9경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210201136.07278128.0861111.77.511.110.543082.95경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210201136.35299128.1393522.4922.353.341.555356.42경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210201136.35299128.1393535.3629.154.631.948104.01경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210201136.50552128.1694648.6852.116.763.511734.26경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210201136.50552128.1694641.6641.65.742.9110141.11경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210201136.14659128.025447.925.270.770.412076.55경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210201136.14659128.025448.75.520.870.422093.47경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210201135.98385128.4110683.9583.7512.125.5120147.92경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210201135.98385128.4110683.0372.0510.914.7619031.31경북 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-구룡포4.720210201135.98322129.4597825.7514.772.460.76152.94경북 포항 동해 신정
9192건기연[3106-2]2연일-구룡포4.720210201135.98322129.4597818.169.91.70.394392.63경북 포항 동해 신정
9293건기연[3106-5]1유강-광명4.920210201135.98878129.32144107.54137.8415.428.4827799.19경북 포항 연일 중단
9394건기연[3106-5]2유강-광명4.920210201135.98878129.3214480.8393.8811.486.7820788.56경북 포항 연일 중단
9495건기연[3107-2]1기계-포항2.420210201136.06326129.2277112.5911.571.730.793073.07경북 포항 기계 내단
9596건기연[3107-2]2기계-포항2.420210201136.06326129.2277113.028.781.280.83437.15경북 포항 기계 내단
9697건기연[3109-1]1죽장-부남20.620210201136.2488129.040491.050.550.090.0277.37경북 청송 현동 눌인
9798건기연[3109-1]2죽장-부남20.620210201136.2488129.040490.520.280.040.0138.68경북 청송 현동 눌인
9899건기연[3113-1]1진보-석보6.020210201136.59556129.086116.093.850.570.271608.0경북 영양 입암 신구
99100건기연[3113-1]2진보-석보6.020210201136.59556129.086116.714.310.630.281757.95경북 영양 입암 신구