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
주소 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 9 other fieldsHigh correlation
기본키 is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
연장 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 is highly overall correlated with nox and 5 other fieldsHigh correlation
nox is highly overall correlated with co and 4 other fieldsHigh correlation
hc is highly overall correlated with co and 5 other fieldsHigh correlation
pm is highly overall correlated with co and 4 other fieldsHigh correlation
co2 is highly overall correlated with co and 5 other fieldsHigh correlation
기본키 has unique valuesUnique
co has 9 (9.0%) zerosZeros
nox has 9 (9.0%) zerosZeros
hc has 9 (9.0%) zerosZeros
pm has 17 (17.0%) zerosZeros
co2 has 9 (9.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:06:09.225951
Analysis finished2023-12-10 11:06:25.492588
Duration16.27 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
2023-12-10T20:06:25.666718image/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
2023-12-10T20:06:26.025656image/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

2023-12-10T20:06:26.287090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:06:26.459973image/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
2023-12-10T20:06:26.812520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.04
Min length8

Characters and Unicode

Total characters804
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[0114-1]
2nd row[0114-1]
3rd row[0115-1]
4th row[0115-1]
5th row[0116-2]
ValueCountFrequency (%)
0114-1 2
 
2.0%
2607-2 2
 
2.0%
2912-1 2
 
2.0%
2204-0 2
 
2.0%
2205-3 2
 
2.0%
2313-2 2
 
2.0%
2316-0 2
 
2.0%
2317-0 2
 
2.0%
2320-2 2
 
2.0%
2602-3 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:06:27.436498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 144
17.9%
2 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 94
11.7%
3 40
 
5.0%
7 36
 
4.5%
9 30
 
3.7%
6 20
 
2.5%
Other values (3) 32
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 504
62.7%
Open Punctuation 100
 
12.4%
Dash Punctuation 100
 
12.4%
Close Punctuation 100
 
12.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 144
28.6%
2 108
21.4%
0 94
18.7%
3 40
 
7.9%
7 36
 
7.1%
9 30
 
6.0%
6 20
 
4.0%
4 16
 
3.2%
5 10
 
2.0%
8 6
 
1.2%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 144
17.9%
2 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 94
11.7%
3 40
 
5.0%
7 36
 
4.5%
9 30
 
3.7%
6 20
 
2.5%
Other values (3) 32
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 144
17.9%
2 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 94
11.7%
3 40
 
5.0%
7 36
 
4.5%
9 30
 
3.7%
6 20
 
2.5%
Other values (3) 32
 
4.0%

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

2023-12-10T20:06:27.712782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:06:27.879270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
순창-남원
 
6
번암-장계
 
4
순창-덕치
 
4
군산-대야
 
4
임실-관촌
 
2
Other values (40)
80 

Length

Max length7
Median length5
Mean length5.08
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row태인-금구
2nd row태인-금구
3rd row정읍-태인
4th row정읍-태인
5th row금산-전주

Common Values

ValueCountFrequency (%)
순창-남원 6
 
6.0%
번암-장계 4
 
4.0%
순창-덕치 4
 
4.0%
군산-대야 4
 
4.0%
임실-관촌 2
 
2.0%
금산-전주 2
 
2.0%
김제IC-전주 2
 
2.0%
금마-연무 2
 
2.0%
고원-삼계 2
 
2.0%
임실-남원 2
 
2.0%
Other values (35) 70
70.0%

Length

2023-12-10T20:06:28.108432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
순창-남원 6
 
6.0%
순창-덕치 4
 
4.0%
군산-대야 4
 
4.0%
번암-장계 4
 
4.0%
연장-오천 2
 
2.0%
만경-백산 2
 
2.0%
태인-금구 2
 
2.0%
천천-서상 2
 
2.0%
고창-흥덕 2
 
2.0%
보안-부안 2
 
2.0%
Other values (35) 70
70.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.526
Minimum0.9
Maximum18.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:28.345180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q14.7
median7
Q39.2
95-th percentile14.6
Maximum18.9
Range18
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation4.025214
Coefficient of variation (CV)0.53484108
Kurtosis0.26684782
Mean7.526
Median Absolute Deviation (MAD)2.25
Skewness0.72288321
Sum752.6
Variance16.202347
MonotonicityNot monotonic
2023-12-10T20:06:28.632434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
6.0 4
 
4.0%
4.9 4
 
4.0%
2.4 4
 
4.0%
8.5 4
 
4.0%
5.4 4
 
4.0%
8.7 4
 
4.0%
8.0 4
 
4.0%
6.3 2
 
2.0%
7.5 2
 
2.0%
14.6 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
0.9 2
2.0%
1.0 2
2.0%
2.4 4
4.0%
2.7 2
2.0%
3.0 2
2.0%
3.3 2
2.0%
3.4 2
2.0%
3.6 2
2.0%
4.1 2
2.0%
4.3 2
2.0%
ValueCountFrequency (%)
18.9 2
2.0%
17.3 2
2.0%
14.6 2
2.0%
13.8 2
2.0%
13.0 2
2.0%
12.9 2
2.0%
12.8 2
2.0%
11.9 2
2.0%
11.7 2
2.0%
11.5 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210401
2nd row20210401
3rd row20210401
4th row20210401
5th row20210401

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2023-12-10T20:06:29.067761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:06:29.396347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2023-12-10T20:06:29.598741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:06:29.805409image/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%
Mean35.674689
Minimum35.31836
Maximum36.05245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:30.094552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.31836
5-th percentile35.38211
Q135.48989
median35.695695
Q335.84681
95-th percentile35.97732
Maximum36.05245
Range0.73409
Interquartile range (IQR)0.35692

Descriptive statistics

Standard deviation0.20152891
Coefficient of variation (CV)0.0056490726
Kurtosis-1.1386676
Mean35.674689
Median Absolute Deviation (MAD)0.18819
Skewness0.024059121
Sum3567.4689
Variance0.040613901
MonotonicityNot monotonic
2023-12-10T20:06:30.407076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.66929 2
 
2.0%
35.72732 2
 
2.0%
35.44964 2
 
2.0%
35.467 2
 
2.0%
35.69967 2
 
2.0%
35.75539 2
 
2.0%
35.97701 2
 
2.0%
35.9615 2
 
2.0%
35.98108 2
 
2.0%
35.85422 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
35.31836 2
2.0%
35.36379 2
2.0%
35.38211 2
2.0%
35.38415 2
2.0%
35.39881 2
2.0%
35.40351 2
2.0%
35.41493 2
2.0%
35.42787 2
2.0%
35.44376 2
2.0%
35.44964 2
2.0%
ValueCountFrequency (%)
36.05245 2
2.0%
35.98108 2
2.0%
35.97732 2
2.0%
35.97701 2
2.0%
35.97553 2
2.0%
35.9615 2
2.0%
35.9258 2
2.0%
35.91702 2
2.0%
35.9058 2
2.0%
35.90484 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.12731
Minimum126.5004
Maximum127.67801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:30.751800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5004
5-th percentile126.64598
Q1126.89186
median127.13348
Q3127.32352
95-th percentile127.59682
Maximum127.67801
Range1.17761
Interquartile range (IQR)0.43166

Descriptive statistics

Standard deviation0.30404953
Coefficient of variation (CV)0.0023916933
Kurtosis-0.86290988
Mean127.12731
Median Absolute Deviation (MAD)0.233045
Skewness0.014865509
Sum12712.731
Variance0.092446114
MonotonicityNot monotonic
2023-12-10T20:06:31.040020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.96828 2
 
2.0%
127.59682 2
 
2.0%
126.5004 2
 
2.0%
126.6981 2
 
2.0%
126.69676 2
 
2.0%
126.75919 2
 
2.0%
126.91023 2
 
2.0%
126.77112 2
 
2.0%
126.7716 2
 
2.0%
127.21711 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.5004 2
2.0%
126.59317 2
2.0%
126.64598 2
2.0%
126.69676 2
2.0%
126.6981 2
2.0%
126.75919 2
2.0%
126.77112 2
2.0%
126.7716 2
2.0%
126.77892 2
2.0%
126.83701 2
2.0%
ValueCountFrequency (%)
127.67801 2
2.0%
127.65033 2
2.0%
127.59682 2
2.0%
127.57067 2
2.0%
127.56884 2
2.0%
127.55201 2
2.0%
127.53885 2
2.0%
127.53076 2
2.0%
127.52057 2
2.0%
127.4985 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.1416
Minimum0
Maximum212.48
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:31.758737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.6675
median12.175
Q329.4475
95-th percentile80.6415
Maximum212.48
Range212.48
Interquartile range (IQR)26.78

Descriptive statistics

Standard deviation35.558218
Coefficient of variation (CV)1.4729023
Kurtosis12.696108
Mean24.1416
Median Absolute Deviation (MAD)11
Skewness3.1241269
Sum2414.16
Variance1264.3869
MonotonicityNot monotonic
2023-12-10T20:06:32.111215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
0.52 3
 
3.0%
4.52 2
 
2.0%
1.05 2
 
2.0%
5.23 2
 
2.0%
2.68 2
 
2.0%
5.27 1
 
1.0%
2.1 1
 
1.0%
5.84 1
 
1.0%
43.04 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.52 3
 
3.0%
0.65 1
 
1.0%
0.74 1
 
1.0%
1.05 2
 
2.0%
1.3 1
 
1.0%
1.38 1
 
1.0%
1.94 1
 
1.0%
1.98 1
 
1.0%
2.1 1
 
1.0%
ValueCountFrequency (%)
212.48 1
1.0%
200.53 1
1.0%
101.69 1
1.0%
91.18 1
1.0%
87.32 1
1.0%
80.29 1
1.0%
79.85 1
1.0%
75.6 1
1.0%
70.95 1
1.0%
66.71 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.486
Minimum0
Maximum244.95
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:32.609272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.73
median8.745
Q328.29
95-th percentile68.2865
Maximum244.95
Range244.95
Interquartile range (IQR)26.56

Descriptive statistics

Standard deviation38.620853
Coefficient of variation (CV)1.7175511
Kurtosis20.121315
Mean22.486
Median Absolute Deviation (MAD)8.195
Skewness4.0008072
Sum2248.6
Variance1491.5703
MonotonicityNot monotonic
2023-12-10T20:06:32.889469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
0.28 3
 
3.0%
3.02 2
 
2.0%
0.55 2
 
2.0%
2.92 2
 
2.0%
1.73 2
 
2.0%
3.28 1
 
1.0%
1.11 1
 
1.0%
3.5 1
 
1.0%
45.77 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.28 3
 
3.0%
0.32 1
 
1.0%
0.55 2
 
2.0%
0.64 1
 
1.0%
0.77 1
 
1.0%
1.09 1
 
1.0%
1.11 1
 
1.0%
1.27 1
 
1.0%
1.32 1
 
1.0%
ValueCountFrequency (%)
244.95 1
1.0%
241.02 1
1.0%
99.76 1
1.0%
75.23 1
1.0%
71.64 1
1.0%
68.11 1
1.0%
66.16 1
1.0%
64.88 1
1.0%
64.67 1
1.0%
61.13 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0794
Minimum0
Maximum32.86
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:33.269153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2675
median1.295
Q33.835
95-th percentile9.771
Maximum32.86
Range32.86
Interquartile range (IQR)3.5675

Descriptive statistics

Standard deviation5.1545972
Coefficient of variation (CV)1.6738966
Kurtosis19.733438
Mean3.0794
Median Absolute Deviation (MAD)1.2
Skewness3.9455332
Sum307.94
Variance26.569872
MonotonicityNot monotonic
2023-12-10T20:06:33.573711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
0.04 3
 
3.0%
0.27 3
 
3.0%
0.44 3
 
3.0%
0.2 2
 
2.0%
2.32 2
 
2.0%
0.09 2
 
2.0%
0.57 2
 
2.0%
0.49 2
 
2.0%
0.26 2
 
2.0%
Other values (68) 70
70.0%
ValueCountFrequency (%)
0.0 9
9.0%
0.04 3
 
3.0%
0.06 1
 
1.0%
0.09 2
 
2.0%
0.1 1
 
1.0%
0.12 1
 
1.0%
0.16 1
 
1.0%
0.18 1
 
1.0%
0.2 2
 
2.0%
0.21 1
 
1.0%
ValueCountFrequency (%)
32.86 1
1.0%
31.91 1
1.0%
12.2 1
1.0%
10.03 1
1.0%
9.79 1
1.0%
9.77 1
1.0%
9.49 1
1.0%
9.45 1
1.0%
9.22 1
1.0%
8.54 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3948
Minimum0
Maximum14.6
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:33.896795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.605
Q31.7625
95-th percentile4.453
Maximum14.6
Range14.6
Interquartile range (IQR)1.6325

Descriptive statistics

Standard deviation2.3395452
Coefficient of variation (CV)1.6773338
Kurtosis18.58027
Mean1.3948
Median Absolute Deviation (MAD)0.605
Skewness3.8412672
Sum139.48
Variance5.4734717
MonotonicityNot monotonic
2023-12-10T20:06:34.183227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
17.0%
0.13 9
 
9.0%
0.14 9
 
9.0%
0.27 6
 
6.0%
0.28 3
 
3.0%
1.35 2
 
2.0%
0.8 2
 
2.0%
1.37 2
 
2.0%
0.67 2
 
2.0%
2.32 1
 
1.0%
Other values (47) 47
47.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.13 9
9.0%
0.14 9
9.0%
0.26 1
 
1.0%
0.27 6
 
6.0%
0.28 3
 
3.0%
0.36 1
 
1.0%
0.4 1
 
1.0%
0.41 1
 
1.0%
0.53 1
 
1.0%
ValueCountFrequency (%)
14.6 1
1.0%
14.38 1
1.0%
6.96 1
1.0%
4.83 1
1.0%
4.51 1
1.0%
4.45 1
1.0%
4.06 1
1.0%
4.0 1
1.0%
3.97 1
1.0%
3.82 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5937.8077
Minimum0
Maximum48951.45
Zeros9
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:34.534363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1645.19
median2946.21
Q36974.2275
95-th percentile21629.248
Maximum48951.45
Range48951.45
Interquartile range (IQR)6329.0375

Descriptive statistics

Standard deviation8543.9569
Coefficient of variation (CV)1.4389076
Kurtosis10.834739
Mean5937.8077
Median Absolute Deviation (MAD)2653.845
Skewness2.9027415
Sum593780.77
Variance72999200
MonotonicityNot monotonic
2023-12-10T20:06:34.793979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9
 
9.0%
138.68 3
 
3.0%
1191.95 2
 
2.0%
277.37 2
 
2.0%
1255.69 2
 
2.0%
646.6 2
 
2.0%
1281.93 1
 
1.0%
554.74 1
 
1.0%
1442.83 1
 
1.0%
10928.54 1
 
1.0%
Other values (76) 76
76.0%
ValueCountFrequency (%)
0.0 9
9.0%
138.68 3
 
3.0%
153.68 1
 
1.0%
185.55 1
 
1.0%
277.37 2
 
2.0%
307.36 1
 
1.0%
339.23 1
 
1.0%
487.28 1
 
1.0%
490.89 1
 
1.0%
554.74 1
 
1.0%
ValueCountFrequency (%)
48951.45 1
1.0%
47374.93 1
1.0%
26617.95 1
1.0%
23668.75 1
1.0%
22144.69 1
1.0%
21602.12 1
1.0%
18025.02 1
1.0%
17498.92 1
1.0%
16846.74 1
1.0%
16462.34 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전북 군산 개정 아동
 
4
전북 정읍 정우 우산
 
2
전북 김제 금구 대화
 
2
전북 완주 이서 이성
 
2
전북 익산 여산 제남
 
2
Other values (44)
88 

Length

Max length11
Median length11
Mean length10.94
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북 정읍 옹동 오성
2nd row전북 정읍 옹동 오성
3rd row전북 정읍 정우 우산
4th row전북 정읍 정우 우산
5th row전북 김제 금구 대화

Common Values

ValueCountFrequency (%)
전북 군산 개정 아동 4
 
4.0%
전북 정읍 정우 우산 2
 
2.0%
전북 김제 금구 대화 2
 
2.0%
전북 완주 이서 이성 2
 
2.0%
전북 익산 여산 제남 2
 
2.0%
전북 순창 적성 괴정 2
 
2.0%
전북 순창 유등 건곡 2
 
2.0%
전북 남원 대산 풍촌 2
 
2.0%
전북 임실 삼계 후천 2
 
2.0%
전북 임실 지사 영천 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T20:06:35.042471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북 100
25.1%
순창 16
 
4.0%
장수 14
 
3.5%
임실 10
 
2.5%
완주 10
 
2.5%
김제 8
 
2.0%
정읍 8
 
2.0%
무주 6
 
1.5%
부안 6
 
1.5%
남원 6
 
1.5%
Other values (95) 214
53.8%

Interactions

2023-12-10T20:06:23.287078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:10.756867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:12.139809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:13.677155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:15.379214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:17.026576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:18.508778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:19.935848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:21.808168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:23.437113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:10.891911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:12.298478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:13.868397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:15.569074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:17.223894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:18.627188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:20.102080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:21.940331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:23.602024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:11.086363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:12.460681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:14.076764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:15.733733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:17.377306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:18.766416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:20.270421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:22.096420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:23.763947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:11.256845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:12.627142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:14.256186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:15.891618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:17.568756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:18.935695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:20.812634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:22.264125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:23.939634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:11.407697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:12.791285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:14.425885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:16.051401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:17.750703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:19.080094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:20.979995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:22.436866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:24.088964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:11.536703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:12.990314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:14.577193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:16.199341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:17.930246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:19.235963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:21.166496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:22.599969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:24.257470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:11.670269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:13.130840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:14.741103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:16.432135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:18.067852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:19.372870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:21.364054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:22.764025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:24.424278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:11.836863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:13.299215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:14.960701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:16.673764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:18.228272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:19.541596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:21.525017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:22.962898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:24.624640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:11.999979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:13.470659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:15.214750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:16.881144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:18.385988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:19.780998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:21.674359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:23.130093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:06:35.226040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.6130.7990.8890.6110.4910.7020.5150.5261.000
지점1.0001.0000.0001.0001.0001.0001.0000.9800.8900.9500.8840.9211.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9980.9900.9980.9830.9060.9580.9030.9321.000
연장0.6131.0000.0000.9981.0000.4850.4920.5360.3430.3440.3770.4280.998
좌표위치위도0.7991.0000.0000.9900.4851.0000.7950.5380.5010.6320.4530.4550.997
좌표위치경도0.8891.0000.0000.9980.4920.7951.0000.5830.4450.6540.4590.5301.000
co0.6110.9800.0000.9830.5360.5380.5831.0000.9630.8520.9540.9420.981
nox0.4910.8900.0000.9060.3430.5010.4450.9631.0000.9060.9910.8580.888
hc0.7020.9500.0000.9580.3440.6320.6540.8520.9061.0000.8700.8480.934
pm0.5150.8840.0000.9030.3770.4530.4590.9540.9910.8701.0000.8480.885
co20.5260.9210.0000.9320.4280.4550.5300.9420.8580.8480.8481.0000.939
주소1.0001.0000.0001.0000.9980.9971.0000.9810.8880.9340.8850.9391.000
2023-12-10T20:06:35.517345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향측정구간
주소1.0000.0000.963
방향0.0001.0000.000
측정구간0.9630.0001.000
2023-12-10T20:06:35.689946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간주소
기본키1.0000.0120.138-0.3680.0490.0290.0320.0110.0460.0000.7820.753
연장0.0121.000-0.023-0.097-0.012-0.005-0.012-0.006-0.0100.0000.7110.733
좌표위치위도0.138-0.0231.000-0.0630.4670.4890.4840.4660.4690.0000.7000.726
좌표위치경도-0.368-0.097-0.0631.000-0.389-0.387-0.387-0.419-0.3900.0000.7520.753
co0.049-0.0120.467-0.3891.0000.9890.9930.9670.9990.0000.6760.647
nox0.029-0.0050.489-0.3870.9891.0000.9970.9830.9870.0000.5040.460
hc0.032-0.0120.484-0.3870.9930.9971.0000.9780.9900.0000.5800.542
pm0.011-0.0060.466-0.4190.9670.9830.9781.0000.9660.0000.5000.456
co20.046-0.0100.469-0.3900.9990.9870.9900.9661.0000.0000.5470.506
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
측정구간0.7820.7110.7000.7520.6760.5040.5800.5000.5470.0001.0000.963
주소0.7530.7330.7260.7530.6470.4600.5420.4560.5060.0000.9631.000

Missing values

2023-12-10T20:06:24.881590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:06:25.354759image/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

기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
01건기연[0114-1]1태인-금구11.120210401035.66929126.9682856.4754.468.063.3212557.59전북 정읍 옹동 오성
12건기연[0114-1]2태인-금구11.120210401035.66929126.9682844.9543.346.033.0711608.49전북 정읍 옹동 오성
23건기연[0115-1]1정읍-태인6.420210401035.62947126.9034464.5460.98.544.015762.32전북 정읍 정우 우산
34건기연[0115-1]2정읍-태인6.420210401035.62947126.9034466.7166.169.454.8316462.34전북 정읍 정우 우산
45건기연[0116-2]1금산-전주4.320210401035.78758127.035187.3275.239.774.5122144.69전북 김제 금구 대화
56건기연[0116-2]2금산-전주4.320210401035.78758127.035170.9551.167.673.416846.74전북 김제 금구 대화
67건기연[0117-3]1김제IC-전주5.120210401035.79995127.0582275.659.729.493.9717498.92전북 완주 이서 이성
78건기연[0117-3]2김제IC-전주5.120210401035.79995127.0582280.2999.7612.26.9621602.12전북 완주 이서 이성
89건기연[0121-4]1금마-연무4.920210401036.05245127.0806119.3616.282.51.354501.27전북 익산 여산 제남
910건기연[0121-4]2금마-연무4.920210401036.05245127.0806118.6115.612.441.234345.83전북 익산 여산 제남
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2907-3]1답동-부무5.720210401035.48989126.988441.981.320.20.13487.28전북 순창 쌍치 금평
9192건기연[2907-3]2답동-부무5.720210401035.48989126.988442.681.730.270.14646.6전북 순창 쌍치 금평
9293건기연[2909-1]1정읍-부안3.320210401035.60581126.7789214.468.851.430.673512.18전북 정읍 고부 입석
9394건기연[2909-1]2정읍-부안3.320210401035.60581126.7789212.2411.411.790.722710.13전북 정읍 고부 입석
9495건기연[2911-0]1화호-김제11.720210401035.7375126.837010.00.00.00.00.0전북 김제 부량 대평
9596건기연[2911-0]2화호-김제11.720210401035.7375126.837010.00.00.00.00.0전북 김제 부량 대평
9697건기연[2912-1]1만경-백산4.620210401035.84681126.8490129.3431.594.772.26821.23전북 김제 만경 대동
9798건기연[2912-1]2만경-백산4.620210401035.84681126.8490111.766.491.10.272818.75전북 김제 만경 대동
9899건기연[3003-0]1변산-하서3.620210401035.72216126.6459818.4414.022.280.674214.38전북 부안 하서 청호
99100건기연[3003-0]2변산-하서3.620210401035.72216126.645986.714.310.630.281757.95전북 부안 하서 청호