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
pm((g/km)) has 6 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:09:32.459183
Analysis finished2023-12-10 12:09:44.934455
Duration12.48 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
2023-12-10T21:09:45.040630image/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
2023-12-10T21:09:45.239216image/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

2023-12-10T21:09:45.414307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:45.544596image/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
2023-12-10T21:09:45.838790image/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%
2803-1 2
 
2.0%
3109-1 2
 
2.0%
2018-0 2
 
2.0%
2508-0 2
 
2.0%
2510-1 2
 
2.0%
2512-4 2
 
2.0%
2513-0 2
 
2.0%
2613-3 2
 
2.0%
2613-4 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:09:46.213953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 136
17.0%
1 108
13.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 78
9.8%
3 42
 
5.2%
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 (%)
0 136
27.2%
1 108
21.6%
2 78
15.6%
3 42
 
8.4%
5 30
 
6.0%
4 28
 
5.6%
8 26
 
5.2%
7 26
 
5.2%
6 16
 
3.2%
9 10
 
2.0%
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 (%)
0 136
17.0%
1 108
13.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 78
9.8%
3 42
 
5.2%
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 (%)
0 136
17.0%
1 108
13.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 78
9.8%
3 42
 
5.2%
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

2023-12-10T21:09:46.369075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:46.490326image/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

2023-12-10T21:09:46.609402image/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%
상동-본포 2
 
2.0%
구성-김천 2
 
2.0%
일반5-금성 2
 
2.0%
가산-장천 2
 
2.0%
선산-낙동 2
 
2.0%
상주-낙동 2
 
2.0%
묘산-쌍림 2
 
2.0%
쌍림-고령 2
 
2.0%
Other values (38) 76
76.0%

연장((km))
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.176
Minimum1.3
Maximum27.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:46.791360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.8
Q15.9
median8.25
Q311.4
95-th percentile22.1
Maximum27.8
Range26.5
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.7322606
Coefficient of variation (CV)0.62470147
Kurtosis1.7777737
Mean9.176
Median Absolute Deviation (MAD)3.05
Skewness1.1610831
Sum917.6
Variance32.858812
MonotonicityNot monotonic
2023-12-10T21:09:47.001150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10.6 8
 
8.0%
11.2 6
 
6.0%
6.4 4
 
4.0%
2.1 4
 
4.0%
6.2 4
 
4.0%
2.4 4
 
4.0%
8.2 2
 
2.0%
6.8 2
 
2.0%
5.9 2
 
2.0%
10.5 2
 
2.0%
Other values (31) 62
62.0%
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
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T21:09:47.151517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:47.276614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 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

2023-12-10T21:09:47.511375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:47.610049image/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%
Mean36.112147
Minimum35.65543
Maximum36.99828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:47.735888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.65543
5-th percentile35.68369
Q135.80916
median36.036925
Q336.33873
95-th percentile36.76527
Maximum36.99828
Range1.34285
Interquartile range (IQR)0.52957

Descriptive statistics

Standard deviation0.34732091
Coefficient of variation (CV)0.0096178415
Kurtosis-0.28986158
Mean36.112147
Median Absolute Deviation (MAD)0.257595
Skewness0.72722821
Sum3611.2147
Variance0.12063181
MonotonicityNot monotonic
2023-12-10T21:09:47.909487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.07278 2
 
2.0%
36.32892 2
 
2.0%
35.78841 2
 
2.0%
36.1137 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%
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%
36.59513 2
2.0%
36.58611 2
2.0%
36.50552 2
2.0%
36.49421 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%
Mean128.81122
Minimum128.02544
Maximum129.52365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:48.091210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.02544
5-th percentile128.13935
Q1128.41883
median128.71618
Q3129.26459
95-th percentile129.47131
Maximum129.52365
Range1.49821
Interquartile range (IQR)0.84576

Descriptive statistics

Standard deviation0.46493031
Coefficient of variation (CV)0.003609393
Kurtosis-1.3873763
Mean128.81122
Median Absolute Deviation (MAD)0.40509
Skewness0.046661498
Sum12881.122
Variance0.21616019
MonotonicityNot monotonic
2023-12-10T21:09:48.542736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.08611 2
 
2.0%
128.70365 2
 
2.0%
128.72871 2
 
2.0%
128.50028 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%
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 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.0561
Minimum0
Maximum293.8
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:48.723126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.7515
Q111.83
median34.01
Q377.4275
95-th percentile179.958
Maximum293.8
Range293.8
Interquartile range (IQR)65.5975

Descriptive statistics

Standard deviation57.926295
Coefficient of variation (CV)1.0917933
Kurtosis3.5862254
Mean53.0561
Median Absolute Deviation (MAD)25.29
Skewness1.8090882
Sum5305.61
Variance3355.4556
MonotonicityNot monotonic
2023-12-10T21:09:48.881659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.05 2
 
2.0%
21.12 2
 
2.0%
16.7 1
 
1.0%
50.01 1
 
1.0%
21.64 1
 
1.0%
15.0 1
 
1.0%
39.73 1
 
1.0%
11.13 1
 
1.0%
8.18 1
 
1.0%
11.85 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.65 1
1.0%
1.05 2
2.0%
1.21 1
1.0%
1.78 1
1.0%
1.98 1
1.0%
2.03 1
1.0%
2.1 1
1.0%
3.28 1
1.0%
3.57 1
1.0%
ValueCountFrequency (%)
293.8 1
1.0%
248.58 1
1.0%
207.25 1
1.0%
191.89 1
1.0%
180.3 1
1.0%
179.94 1
1.0%
177.83 1
1.0%
151.59 1
1.0%
145.82 1
1.0%
136.47 1
1.0%

nox((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.9214
Minimum0
Maximum390.63
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:49.093688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1025
Q17.08
median23.8
Q359.6075
95-th percentile198.9745
Maximum390.63
Range390.63
Interquartile range (IQR)52.5275

Descriptive statistics

Standard deviation71.0898
Coefficient of variation (CV)1.3691811
Kurtosis6.443394
Mean51.9214
Median Absolute Deviation (MAD)19.755
Skewness2.369774
Sum5192.14
Variance5053.7597
MonotonicityNot monotonic
2023-12-10T21:09:49.317670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.55 2
 
2.0%
47.62 2
 
2.0%
14.01 1
 
1.0%
3.79 1
 
1.0%
5.39 1
 
1.0%
5.53 1
 
1.0%
12.74 1
 
1.0%
8.1 1
 
1.0%
36.54 1
 
1.0%
7.14 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.32 1
1.0%
0.55 2
2.0%
0.96 1
1.0%
1.11 1
1.0%
1.32 1
1.0%
1.33 1
1.0%
1.41 1
1.0%
1.93 1
1.0%
1.96 1
1.0%
ValueCountFrequency (%)
390.63 1
1.0%
295.44 1
1.0%
278.31 1
1.0%
235.21 1
1.0%
224.9 1
1.0%
197.61 1
1.0%
171.65 1
1.0%
151.48 1
1.0%
147.95 1
1.0%
147.05 1
1.0%

hc((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.542
Minimum0
Maximum43.43
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:49.560691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1775
Q11.095
median3.56
Q38.545
95-th percentile20.8785
Maximum43.43
Range43.43
Interquartile range (IQR)7.45

Descriptive statistics

Standard deviation7.981173
Coefficient of variation (CV)1.2199898
Kurtosis5.3023931
Mean6.542
Median Absolute Deviation (MAD)2.84
Skewness2.1054993
Sum654.2
Variance63.699123
MonotonicityNot monotonic
2023-12-10T21:09:49.815464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.18 2
 
2.0%
1.23 2
 
2.0%
3.56 2
 
2.0%
1.06 2
 
2.0%
0.09 2
 
2.0%
2.63 1
 
1.0%
8.47 1
 
1.0%
0.93 1
 
1.0%
2.09 1
 
1.0%
1.39 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 1
1.0%
0.06 1
1.0%
0.09 2
2.0%
0.13 1
1.0%
0.18 2
2.0%
0.2 1
1.0%
0.21 1
1.0%
0.32 1
1.0%
0.35 1
1.0%
0.37 1
1.0%
ValueCountFrequency (%)
43.43 1
1.0%
35.15 1
1.0%
27.62 1
1.0%
25.4 1
1.0%
22.75 1
1.0%
20.78 1
1.0%
20.58 1
1.0%
19.73 1
1.0%
18.99 1
1.0%
18.36 1
1.0%

pm((g/km))
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8328
Minimum0
Maximum24.24
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:50.010736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.28
median1.125
Q32.6375
95-th percentile13.2935
Maximum24.24
Range24.24
Interquartile range (IQR)2.3575

Descriptive statistics

Standard deviation4.4450417
Coefficient of variation (CV)1.5691336
Kurtosis7.7816623
Mean2.8328
Median Absolute Deviation (MAD)0.86
Skewness2.6727688
Sum283.28
Variance19.758396
MonotonicityNot monotonic
2023-12-10T21:09:50.198813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.27 6
 
6.0%
0.13 6
 
6.0%
0.14 6
 
6.0%
0.28 6
 
6.0%
0.0 6
 
6.0%
0.4 3
 
3.0%
0.83 2
 
2.0%
1.21 2
 
2.0%
1.75 2
 
2.0%
4.78 2
 
2.0%
Other values (57) 59
59.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.13 6
6.0%
0.14 6
6.0%
0.27 6
6.0%
0.28 6
6.0%
0.4 3
3.0%
0.41 1
 
1.0%
0.42 1
 
1.0%
0.54 1
 
1.0%
0.56 1
 
1.0%
ValueCountFrequency (%)
24.24 1
1.0%
18.88 1
1.0%
18.05 1
1.0%
16.04 1
1.0%
13.36 1
1.0%
13.29 1
1.0%
9.93 1
1.0%
9.84 1
1.0%
9.22 1
1.0%
8.26 1
1.0%

co2((g/km))
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13366.598
Minimum0
Maximum68831.49
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:50.384647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile455.704
Q13080.6625
median8861.43
Q319917.865
95-th percentile45444.354
Maximum68831.49
Range68831.49
Interquartile range (IQR)16837.202

Descriptive statistics

Standard deviation14482.599
Coefficient of variation (CV)1.0834917
Kurtosis2.6638965
Mean13366.598
Median Absolute Deviation (MAD)6458.125
Skewness1.6642802
Sum1336659.8
Variance2.0974566 × 108
MonotonicityNot monotonic
2023-12-10T21:09:50.607827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
277.37 2
 
2.0%
3813.46 1
 
1.0%
10752.5 1
 
1.0%
2357.7 1
 
1.0%
5228.92 1
 
1.0%
3587.16 1
 
1.0%
9063.92 1
 
1.0%
2938.63 1
 
1.0%
2162.74 1
 
1.0%
3133.54 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.0 1
1.0%
153.68 1
1.0%
277.37 2
2.0%
318.6 1
1.0%
462.92 1
1.0%
487.28 1
1.0%
492.91 1
1.0%
554.74 1
1.0%
794.65 1
1.0%
922.09 1
1.0%
ValueCountFrequency (%)
68831.49 1
1.0%
60269.08 1
1.0%
52123.87 1
1.0%
49501.55 1
1.0%
47277.36 1
1.0%
45347.88 1
1.0%
41978.5 1
1.0%
38772.87 1
1.0%
37189.61 1
1.0%
37161.43 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

2023-12-10T21:09:50.814819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경북 100
25.0%
경주 14
 
3.5%
포항 14
 
3.5%
의성 10
 
2.5%
영천 6
 
1.5%
안동 6
 
1.5%
청도 6
 
1.5%
고령 6
 
1.5%
울진 6
 
1.5%
상주 6
 
1.5%
Other values (100) 226
56.5%

Interactions

2023-12-10T21:09:43.222700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:33.676340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:34.815935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.804924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:36.757868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:38.115043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:39.297413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:40.599552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:42.050501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:43.355274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:33.801043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:34.925210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.890047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:36.922505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:38.259513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:39.426885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:41.048638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:42.169911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:43.488754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:33.921722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.050262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.975165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:37.047000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:38.383575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:39.571512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:41.185461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:42.295323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:43.633752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:34.064558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.164186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:36.077373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:37.180740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:38.492279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:39.716488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:41.324537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:42.448259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:43.792212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:34.205042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.305308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:36.175900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:37.337960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:38.644147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:39.866958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:41.457524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:42.590181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:43.953391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:34.338782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.409230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:36.269241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:37.505844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:38.782297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:40.006417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:41.585535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:42.700636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:44.092237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:34.461482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.535838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:36.379229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:37.674813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:38.940632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:40.162090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:41.727974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:42.834109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:44.211092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:34.585642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.634025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:36.487040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:37.805064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:39.047455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:40.320840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:41.823637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:42.957345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:44.332873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:34.690839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:35.714563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:36.606513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:37.922456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:39.163532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:40.453589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:41.924550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:43.078794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:09:50.961131image/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
기본키1.0001.0000.0001.0000.5650.6530.9340.6100.4440.3030.3040.4831.000
지점1.0001.0000.0001.0001.0001.0001.0000.7420.7270.4010.7010.6861.000
방향0.0000.0001.0000.0000.0000.0000.0000.1010.0000.0860.0000.1280.000
측정구간1.0001.0000.0001.0000.9821.0001.0000.7330.7600.5280.7440.6491.000
연장((km))0.5651.0000.0000.9821.0000.7460.6250.0000.0000.0000.0000.0000.992
좌표위치위도((°))0.6531.0000.0001.0000.7461.0000.6960.2320.6570.2610.5110.3561.000
좌표위치경도((°))0.9341.0000.0001.0000.6250.6961.0000.0000.3890.2130.3300.0001.000
co((g/km))0.6100.7420.1010.7330.0000.2320.0001.0000.8920.9020.8500.9890.730
nox((g/km))0.4440.7270.0000.7600.0000.6570.3890.8921.0000.9810.9830.8950.743
hc((g/km))0.3030.4010.0860.5280.0000.2610.2130.9020.9811.0000.9760.9120.487
pm((g/km))0.3040.7010.0000.7440.0000.5110.3300.8500.9830.9761.0000.8550.728
co2((g/km))0.4830.6860.1280.6490.0000.3560.0000.9890.8950.9120.8551.0000.675
주소1.0001.0000.0001.0000.9921.0001.0000.7300.7430.4870.7280.6751.000
2023-12-10T21:09:51.215022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향주소
측정구간1.0000.0000.990
방향0.0001.0000.000
주소0.9900.0001.000
2023-12-10T21:09:51.396038image/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
기본키1.000-0.112-0.0720.302-0.185-0.185-0.171-0.156-0.1870.0000.7600.753
연장((km))-0.1121.0000.067-0.033-0.049-0.019-0.027-0.052-0.0520.0000.6500.696
좌표위치위도((°))-0.0720.0671.000-0.133-0.0110.0220.0200.058-0.0190.0000.7560.749
좌표위치경도((°))0.302-0.033-0.1331.0000.4470.3970.4050.2910.4560.0000.7600.753
co((g/km))-0.185-0.049-0.0110.4471.0000.9770.9840.9120.9980.0680.2580.251
nox((g/km))-0.185-0.0190.0220.3970.9771.0000.9970.9640.9750.0000.2870.273
hc((g/km))-0.171-0.0270.0200.4050.9840.9971.0000.9560.9790.0780.1490.129
pm((g/km))-0.156-0.0520.0580.2910.9120.9640.9561.0000.9090.0000.2750.261
co2((g/km))-0.187-0.052-0.0190.4560.9980.9750.9790.9091.0000.0890.2040.215
방향0.0000.0000.0000.0000.0680.0000.0780.0000.0891.0000.0000.000
측정구간0.7600.6500.7560.7600.2580.2870.1490.2750.2040.0001.0000.990
주소0.7530.6960.7490.7530.2510.2730.1290.2610.2150.0000.9901.000

Missing values

2023-12-10T21:09:44.533126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:09:44.839527image/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-김천11.420210101136.07278128.0861116.714.012.180.963813.46경북 김천 구성 하강
12건기연[0314-0]2구성-김천11.420210101136.07278128.0861111.227.241.140.672743.77경북 김천 구성 하강
23건기연[0317-0]1공성-상주11.920210101136.35299128.139358.024.560.70.132121.51경북 상주 청리 원장
34건기연[0317-0]2공성-상주11.920210101136.35299128.1393513.076.871.20.143105.51경북 상주 청리 원장
45건기연[0318-0]1상주-함창14.420210101136.50552128.1694656.2147.626.122.5514232.4경북 상주 외서 연봉
56건기연[0318-0]2상주-함창14.420210101136.50552128.1694645.2140.915.122.2511353.71경북 상주 외서 연봉
67건기연[0410-2]1추풍령-김천1.820210101136.14659128.0254412.148.621.230.943201.02경북 김천 봉산 태화
78건기연[0410-2]2추풍령-김천1.820210101136.14659128.0254418.916.892.481.514702.26경북 김천 봉산 태화
89건기연[0415-1]1성주-대구3.820210101135.98385128.4110673.6656.328.02.918751.44경북 칠곡 왜관 왜관
910건기연[0415-1]2성주-대구3.820210101135.98385128.4110683.2163.638.993.6121207.12경북 칠곡 왜관 왜관
기본키도로종류지점방향측정구간연장((km))측정일측정시간좌표위치위도((°))좌표위치경도((°))co((g/km))nox((g/km))hc((g/km))pm((g/km))co2((g/km))주소
9091건기연[3106-0]1양포-동해6.920210101135.98043129.49495207.25129.8420.586.7149501.55경북 포항 동해 상정
9192건기연[3106-0]2양포-동해6.920210101135.98043129.49495293.8235.2135.1518.0568831.49경북 포항 동해 상정
9293건기연[3106-2]1연일-구룡포4.720210101135.98322129.45978179.94114.7116.494.7847277.36경북 포항 동해 신정
9394건기연[3106-2]2연일-구룡포4.720210101135.98322129.4597881.8954.777.971.9221259.98경북 포항 동해 신정
9495건기연[3107-2]1기계-포항2.420210101136.06326129.2277121.1212.072.010.565045.3경북 포항 기계 내단
9596건기연[3107-2]2기계-포항2.420210101136.06326129.2277124.7221.592.631.696323.73경북 포항 기계 내단
9697건기연[3109-1]1죽장-부남20.620210101136.2488129.040492.031.410.210.14492.91경북 청송 현동 눌인
9798건기연[3109-1]2죽장-부남20.620210101136.2488129.040490.650.320.060.0153.68경북 청송 현동 눌인
9899건기연[3109-2]1현동-청운11.220210101136.28967129.036081.050.550.090.0277.37경북 청송 현동 도평
99100건기연[3109-2]2현동-청운11.220210101136.28967129.036083.572.650.370.28925.84경북 청송 현동 도평