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 7 (7.0%) zerosZeros
co2((g/km)) has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:24:06.193164
Analysis finished2023-12-10 12:24:16.564888
Duration10.37 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
2023-12-10T21:24:16.659042image/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
2023-12-10T21:24:16.816187image/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

2023-12-10T21:24:17.011194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:17.490654image/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
2023-12-10T21:24:17.757933image/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%
2809-1 2
 
2.0%
3307-1 2
 
2.0%
2513-0 2
 
2.0%
2613-3 2
 
2.0%
2613-4 2
 
2.0%
2614-3 2
 
2.0%
2802-1 2
 
2.0%
2803-1 2
 
2.0%
2804-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:24:18.162411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 60
7.5%
5 28
 
3.5%
8 26
 
3.2%
4 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 (%)
0 132
26.4%
1 110
22.0%
2 72
14.4%
3 60
12.0%
5 28
 
5.6%
8 26
 
5.2%
4 24
 
4.8%
7 22
 
4.4%
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 (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 60
7.5%
5 28
 
3.5%
8 26
 
3.2%
4 24
 
3.0%
Other values (3) 48
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 132
16.5%
1 110
13.8%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 60
7.5%
5 28
 
3.5%
8 26
 
3.2%
4 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

2023-12-10T21:24:18.344902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:18.462338image/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
청도-경산
 
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%
청도-경산 2
 
2.0%
상주-함창 2
 
2.0%
문경-연풍 2
 
2.0%
추풍령-김천 2
 
2.0%
성주-대구 2
 
2.0%
영천-서면 2
 
2.0%
칠곡-효령 2
 
2.0%
Other values (38) 76
76.0%

Length

2023-12-10T21:24:18.602314image/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%
안강-고경 2
 
2.0%
구성-김천 2
 
2.0%
신성-금수 2
 
2.0%
쌍림-고령 2
 
2.0%
고령-성산 2
 
2.0%
장수-예천 2
 
2.0%
상동-본포 2
 
2.0%
안계-봉양 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%
Mean9.508
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:18.749877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q16
median8.25
Q311.4
95-th percentile24.2
Maximum27.8
Range26.5
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation6.0630639
Coefficient of variation (CV)0.63768026
Kurtosis1.8827396
Mean9.508
Median Absolute Deviation (MAD)2.95
Skewness1.3523195
Sum950.8
Variance36.760743
MonotonicityNot monotonic
2023-12-10T21:24:18.883053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
10.6 8
 
8.0%
8.7 4
 
4.0%
6.4 4
 
4.0%
2.4 4
 
4.0%
11.2 4
 
4.0%
11.4 2
 
2.0%
8.3 2
 
2.0%
8.2 2
 
2.0%
7.8 2
 
2.0%
10.4 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%
2.2 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%
4.9 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%
15.8 2
2.0%
14.4 2
2.0%
12.9 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
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2023-12-10T21:24:19.015994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:19.131209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 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

2023-12-10T21:24:19.242092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:24:19.362199image/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%
Mean36.10728
Minimum35.65543
Maximum36.86368
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:19.493716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.80916
median35.999975
Q336.33873
95-th percentile36.76527
Maximum36.86368
Range1.20825
Interquartile range (IQR)0.52957

Descriptive statistics

Standard deviation0.34685079
Coefficient of variation (CV)0.0096061179
Kurtosis-0.48975979
Mean36.10728
Median Absolute Deviation (MAD)0.22393
Skewness0.74474781
Sum3610.728
Variance0.12030547
MonotonicityNot monotonic
2023-12-10T21:24:19.702207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
35.94083 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%
36.35882 2
 
2.0%
36.32892 2
 
2.0%
36.04086 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%
35.73646 2
2.0%
35.76368 2
2.0%
35.78841 2
2.0%
ValueCountFrequency (%)
36.86368 2
2.0%
36.84888 2
2.0%
36.76527 2
2.0%
36.75364 2
2.0%
36.73871 2
2.0%
36.73238 2
2.0%
36.59556 2
2.0%
36.59513 2
2.0%
36.50552 2
2.0%
36.40808 2
2.0%

좌표위치경도((°))
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.77497
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:19.875094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.08611
Q1128.37682
median128.69655
Q3129.25885
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.88203

Descriptive statistics

Standard deviation0.47509731
Coefficient of variation (CV)0.0036893607
Kurtosis-1.4327779
Mean128.77497
Median Absolute Deviation (MAD)0.42619
Skewness0.10486581
Sum12877.497
Variance0.22571745
MonotonicityNot monotonic
2023-12-10T21:24:20.020491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
128.15515 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%
128.46812 2
 
2.0%
128.70365 2
 
2.0%
128.80075 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
128.02544 2
2.0%
128.08594 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%
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%
Mean40.0497
Minimum0
Maximum170.68
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:20.195764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.6695
Q113.1425
median26.67
Q355.3175
95-th percentile113.938
Maximum170.68
Range170.68
Interquartile range (IQR)42.175

Descriptive statistics

Standard deviation37.351591
Coefficient of variation (CV)0.93263098
Kurtosis1.6801961
Mean40.0497
Median Absolute Deviation (MAD)22.545
Skewness1.3414176
Sum4004.97
Variance1395.1413
MonotonicityNot monotonic
2023-12-10T21:24:20.384736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
1.78 2
 
2.0%
0.52 2
 
2.0%
9.85 1
 
1.0%
19.8 1
 
1.0%
15.56 1
 
1.0%
1.95 1
 
1.0%
1.94 1
 
1.0%
15.74 1
 
1.0%
14.75 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.52 2
2.0%
1.73 1
 
1.0%
1.78 2
2.0%
1.94 1
 
1.0%
1.95 1
 
1.0%
2.43 1
 
1.0%
2.68 1
 
1.0%
2.87 1
 
1.0%
3.42 1
 
1.0%
ValueCountFrequency (%)
170.68 1
1.0%
159.22 1
1.0%
140.97 1
1.0%
130.35 1
1.0%
114.85 1
1.0%
113.89 1
1.0%
108.66 1
1.0%
107.64 1
1.0%
103.12 1
1.0%
95.8 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.9187
Minimum0
Maximum295.82
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:20.553351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.926
Q18.6825
median22.975
Q365.7075
95-th percentile142.3105
Maximum295.82
Range295.82
Interquartile range (IQR)57.025

Descriptive statistics

Standard deviation52.704535
Coefficient of variation (CV)1.2000477
Kurtosis5.9145454
Mean43.9187
Median Absolute Deviation (MAD)20.72
Skewness2.1594195
Sum4391.87
Variance2777.768
MonotonicityNot monotonic
2023-12-10T21:24:20.700010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
1.33 2
 
2.0%
0.28 2
 
2.0%
5.89 1
 
1.0%
13.3 1
 
1.0%
12.81 1
 
1.0%
0.96 1
 
1.0%
1.27 1
 
1.0%
14.37 1
 
1.0%
13.79 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.28 2
2.0%
0.96 1
 
1.0%
1.23 1
 
1.0%
1.27 1
 
1.0%
1.33 2
2.0%
1.66 1
 
1.0%
1.73 1
 
1.0%
1.9 1
 
1.0%
2.22 1
 
1.0%
ValueCountFrequency (%)
295.82 1
1.0%
224.81 1
1.0%
196.21 1
1.0%
173.36 1
1.0%
149.54 1
1.0%
141.93 1
1.0%
139.99 1
1.0%
134.44 1
1.0%
124.43 1
1.0%
108.35 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7331
Minimum0
Maximum28.96
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:20.845416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1635
Q11.295
median3.49
Q38.23
95-th percentile16.6365
Maximum28.96
Range28.96
Interquartile range (IQR)6.935

Descriptive statistics

Standard deviation6.0548389
Coefficient of variation (CV)1.0561195
Kurtosis2.5212348
Mean5.7331
Median Absolute Deviation (MAD)3.065
Skewness1.5677308
Sum573.31
Variance36.661074
MonotonicityNot monotonic
2023-12-10T21:24:20.997298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.01 3
 
3.0%
0.18 3
 
3.0%
0.0 3
 
3.0%
2.2 3
 
3.0%
0.99 2
 
2.0%
3.7 2
 
2.0%
0.04 2
 
2.0%
7.15 1
 
1.0%
0.2 1
 
1.0%
0.27 1
 
1.0%
Other values (79) 79
79.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.04 2
2.0%
0.17 1
 
1.0%
0.18 3
3.0%
0.2 1
 
1.0%
0.22 1
 
1.0%
0.26 1
 
1.0%
0.27 1
 
1.0%
0.33 1
 
1.0%
0.35 1
 
1.0%
ValueCountFrequency (%)
28.96 1
1.0%
25.15 1
1.0%
23.28 1
1.0%
21.41 1
1.0%
17.14 1
1.0%
16.61 1
1.0%
16.51 1
1.0%
16.46 1
1.0%
16.11 1
1.0%
13.77 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8862
Minimum0
Maximum17.69
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:21.204599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6875
median1.54
Q34.1675
95-th percentile8.064
Maximum17.69
Range17.69
Interquartile range (IQR)3.48

Descriptive statistics

Standard deviation3.2918696
Coefficient of variation (CV)1.1405549
Kurtosis5.5290188
Mean2.8862
Median Absolute Deviation (MAD)1.4
Skewness2.0866814
Sum288.62
Variance10.836406
MonotonicityNot monotonic
2023-12-10T21:24:21.390085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
0.13 4
 
4.0%
0.14 3
 
3.0%
0.94 3
 
3.0%
1.06 2
 
2.0%
1.21 2
 
2.0%
0.54 2
 
2.0%
0.27 2
 
2.0%
2.75 2
 
2.0%
0.4 2
 
2.0%
Other values (71) 71
71.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.13 4
4.0%
0.14 3
3.0%
0.15 1
 
1.0%
0.27 2
 
2.0%
0.39 1
 
1.0%
0.4 2
 
2.0%
0.41 1
 
1.0%
0.42 1
 
1.0%
0.54 2
 
2.0%
ValueCountFrequency (%)
17.69 1
1.0%
15.12 1
1.0%
13.54 1
1.0%
11.41 1
1.0%
8.71 1
1.0%
8.03 1
1.0%
7.4 1
1.0%
7.38 1
1.0%
7.03 1
1.0%
6.84 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9932.6614
Minimum0
Maximum49157.8
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:24:21.573031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile441.3595
Q13483.225
median6193.325
Q313800.103
95-th percentile28898.161
Maximum49157.8
Range49157.8
Interquartile range (IQR)10316.878

Descriptive statistics

Standard deviation9588.579
Coefficient of variation (CV)0.96535849
Kurtosis3.1635165
Mean9932.6614
Median Absolute Deviation (MAD)5338.56
Skewness1.6221652
Sum993266.14
Variance91940847
MonotonicityNot monotonic
2023-12-10T21:24:21.781316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3
 
3.0%
462.92 2
 
2.0%
138.68 2
 
2.0%
2383.94 1
 
1.0%
5225.07 1
 
1.0%
3619.63 1
 
1.0%
461.05 1
 
1.0%
490.89 1
 
1.0%
3574.15 1
 
1.0%
3624.08 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 3
3.0%
138.68 2
2.0%
457.29 1
 
1.0%
461.05 1
 
1.0%
462.92 2
2.0%
490.89 1
 
1.0%
646.6 1
 
1.0%
688.53 1
 
1.0%
815.68 1
 
1.0%
893.85 1
 
1.0%
ValueCountFrequency (%)
49157.8 1
1.0%
42050.16 1
1.0%
36413.56 1
1.0%
32418.52 1
1.0%
29331.2 1
1.0%
28875.37 1
1.0%
26705.41 1
1.0%
25619.56 1
1.0%
25130.37 1
1.0%
22246.78 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 경산 남천 협석
 
2
경북 의성 봉양 화전
 
2
경북 상주 외서 연봉
 
2
경북 문경 문경 진안
 
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 경산 남천 협석 2
 
2.0%
경북 의성 봉양 화전 2
 
2.0%
경북 상주 외서 연봉 2
 
2.0%
경북 문경 문경 진안 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%
Other values (39) 78
78.0%

Length

2023-12-10T21:24:21.955511image/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 6
 
1.5%
고령 6
 
1.5%
연일 6
 
1.5%
상주 6
 
1.5%
영천 6
 
1.5%
청도 4
 
1.0%
Other values (102) 226
56.5%

Interactions

2023-12-10T21:24:15.201951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:06.964459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:07.918949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:08.900684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.888216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:10.946953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.244202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:13.132746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:14.182566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:15.296395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:07.071989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:08.012037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.009201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.983665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:11.071966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.320254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:13.239117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:14.304491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:15.388281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:07.183654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:08.100627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.189085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:10.093233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:11.435753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.403321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:13.347664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:14.423769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:15.507359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:07.289456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:08.190085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.279212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:10.204518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:11.533224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.493689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:13.459970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:14.549364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:15.618574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:07.402399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:08.331329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.364568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:10.332340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:11.653725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.591013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:13.577153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:14.679990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:15.719620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:07.500837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:08.473007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.464121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:10.459943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:11.799436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.685971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:13.705396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:14.788111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:15.810586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:07.593115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:08.556278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.559222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:10.594746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:11.914762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.791435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:13.819621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:14.895336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:15.905973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:07.700834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:08.641038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.668533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:10.747595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.019284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.926893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:13.956767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:14.998588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:16.046232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:07.822542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:08.779137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:09.779027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:10.844104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:12.136511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:13.016925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:14.062054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:24:15.108702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:24:22.069149image/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
기본키1.0001.0000.0000.9980.5190.7300.9270.6050.4600.5450.4420.6161.000
지점1.0001.0000.0001.0001.0001.0001.0000.8780.8240.9060.8570.8711.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간0.9981.0000.0001.0000.9871.0001.0000.8460.8360.8950.8510.8391.000
연장((km))0.5191.0000.0000.9871.0000.5370.6620.0000.0000.0000.0000.0000.992
좌표위치위도((°))0.7301.0000.0001.0000.5371.0000.7800.2550.0000.0750.0000.0001.000
좌표위치경도((°))0.9271.0000.0001.0000.6620.7801.0000.4450.0990.2800.1600.2181.000
co((g/km))0.6050.8780.0000.8460.0000.2550.4451.0000.8510.8930.8690.9950.859
nox((g/km))0.4600.8240.0000.8360.0000.0000.0990.8511.0000.9730.9890.8780.834
hc((g/km))0.5450.9060.0000.8950.0000.0750.2800.8930.9731.0000.9790.9130.908
pm((g/km))0.4420.8570.0000.8510.0000.0000.1600.8690.9890.9791.0000.8910.864
co2((g/km))0.6160.8710.0000.8390.0000.0000.2180.9950.8780.9130.8911.0000.853
주소1.0001.0000.0001.0000.9921.0001.0000.8590.8340.9080.8640.8531.000
2023-12-10T21:24:22.240525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0000.990
방향0.0001.0000.000
주소0.9900.0001.000
2023-12-10T21:24:22.388369image/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
기본키1.000-0.088-0.0390.310-0.290-0.290-0.296-0.284-0.2870.0000.7440.753
연장((km))-0.0881.0000.024-0.076-0.095-0.079-0.081-0.050-0.0900.0000.6730.695
좌표위치위도((°))-0.0390.0241.000-0.175-0.143-0.131-0.114-0.107-0.1570.0000.7600.753
좌표위치경도((°))0.310-0.076-0.1751.0000.1980.1900.1700.1320.2160.0000.7600.753
co((g/km))-0.290-0.095-0.1430.1981.0000.9840.9880.9750.9960.0000.3630.373
nox((g/km))-0.290-0.079-0.1310.1900.9841.0000.9970.9890.9820.0000.3610.357
hc((g/km))-0.296-0.081-0.1140.1700.9880.9971.0000.9870.9820.0000.4410.461
pm((g/km))-0.284-0.050-0.1070.1320.9750.9890.9871.0000.9750.0000.3780.393
co2((g/km))-0.287-0.090-0.1570.2160.9960.9820.9820.9751.0000.0000.3540.365
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
측정구간0.7440.6730.7600.7600.3630.3610.4410.3780.3540.0001.0000.990
주소0.7530.6950.7530.7530.3730.3570.4610.3930.3650.0000.9901.000

Missing values

2023-12-10T21:24:16.189364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:24:16.462202image/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-김천11.420210601036.07278128.086119.855.890.960.42383.94경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210601036.07278128.0861110.465.850.990.272511.38경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210601036.35299128.1393543.5839.786.132.969842.83경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210601036.35299128.1393528.522.323.281.747256.14경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210601036.50552128.1694675.7383.6212.36.1317489.43경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210601036.50552128.1694681.2691.7913.57.418786.18경북 상주 외서 연봉
67건기연[0323-2]1문경-연풍8.720210601036.73871128.0859419.9119.633.071.354439.03경북 문경 문경 진안
78건기연[0323-2]2문경-연풍8.720210601036.73871128.0859420.3824.983.751.724515.0경북 문경 문경 진안
89건기연[0410-2]1추풍령-김천1.820210601036.14659128.025448.335.740.770.422336.78경북 김천 봉산 태화
910건기연[0410-2]2추풍령-김천1.820210601036.14659128.0254410.856.580.990.392908.22경북 김천 봉산 태화
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3109-1]1죽장-부남20.620210601036.2488129.040490.520.280.040.0138.68경북 청송 현동 눌인
9192건기연[3109-1]2죽장-부남20.620210601036.2488129.040492.431.660.220.13688.53경북 청송 현동 눌인
9293건기연[3113-1]1진보-석보6.020210601036.59556129.0861113.228.31.320.683210.45경북 영양 입암 신구
9394건기연[3113-1]2진보-석보6.020210601036.59556129.0861111.196.621.080.412696.93경북 영양 입암 신구
9495건기연[3116-1]1녹동-영양26.720210601036.86368129.012481.731.230.180.13457.29경북 봉화 소천 서천
9596건기연[3116-1]2녹동-영양26.720210601036.86368129.012480.00.00.00.00.0경북 봉화 소천 서천
9697건기연[3307-1]1고령-수륜12.920210601035.80817128.230394.683.50.470.41302.67경북 성주 수륜 계정
9798건기연[3307-1]2고령-수륜12.920210601035.80817128.230391.781.330.180.14462.92경북 성주 수륜 계정
9899건기연[3310-0]1성주-왜관6.720210601035.97485128.3768243.831.895.02.7110463.51경북 칠곡 기산 영
99100건기연[3310-0]2성주-왜관6.720210601035.97485128.3768231.9526.413.812.388062.68경북 칠곡 기산 영