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 4 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 4 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 3 other fieldsHigh correlation
nox is highly overall correlated with co and 3 other fieldsHigh correlation
hc is highly overall correlated with co and 3 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with co and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
co has 10 (10.0%) zerosZeros
nox has 10 (10.0%) zerosZeros
hc has 10 (10.0%) zerosZeros
pm has 30 (30.0%) zerosZeros
co2 has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:39:22.208512
Analysis finished2023-12-10 13:39:31.927408
Duration9.72 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
2023-12-10T22:39:32.016030image/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
2023-12-10T22:39:32.201545image/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

2023-12-10T22:39:32.348209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:39:32.466450image/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
2023-12-10T22:39:32.720572image/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%
2605-0 2
 
2.0%
2909-1 2
 
2.0%
2115-1 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%
Other values (40) 80
80.0%
2023-12-10T22:39:33.222594image/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 96
11.9%
3 36
 
4.5%
7 36
 
4.5%
9 28
 
3.5%
6 22
 
2.7%
Other values (3) 34
 
4.2%

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 96
19.0%
3 36
 
7.1%
7 36
 
7.1%
9 28
 
5.6%
6 22
 
4.4%
4 16
 
3.2%
5 12
 
2.4%
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 96
11.9%
3 36
 
4.5%
7 36
 
4.5%
9 28
 
3.5%
6 22
 
2.7%
Other values (3) 34
 
4.2%

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 96
11.9%
3 36
 
4.5%
7 36
 
4.5%
9 28
 
3.5%
6 22
 
2.7%
Other values (3) 34
 
4.2%

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

2023-12-10T22:39:33.407979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:39:33.532547image/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
군산-대야
 
4
순창-덕치
 
4
오수-임실
 
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%
군산-대야 4
 
4.0%
순창-덕치 4
 
4.0%
오수-임실 2
 
2.0%
금산-전주 2
 
2.0%
김제IC-전주 2
 
2.0%
전주-삼례 2
 
2.0%
금마-연무 2
 
2.0%
고원-삼계 2
 
2.0%
Other values (35) 70
70.0%

Length

2023-12-10T22:39:33.691489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
순창-남원 6
 
6.0%
군산-대야 4
 
4.0%
순창-덕치 4
 
4.0%
번암-장계 4
 
4.0%
공덕-완주 2
 
2.0%
정읍-부안 2
 
2.0%
태인-금구 2
 
2.0%
연장-오천 2
 
2.0%
팔형치-공음 2
 
2.0%
고창-흥덕 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%
Mean7.542
Minimum0.9
Maximum18.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:33.889596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q14.9
median7
Q39.2
95-th percentile14.6
Maximum18.9
Range18
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.0194522
Coefficient of variation (CV)0.53294248
Kurtosis0.27818798
Mean7.542
Median Absolute Deviation (MAD)2.15
Skewness0.71364062
Sum754.2
Variance16.155996
MonotonicityNot monotonic
2023-12-10T22:39:34.061146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
8.0 4
 
4.0%
6.0 4
 
4.0%
4.9 4
 
4.0%
2.4 4
 
4.0%
8.5 4
 
4.0%
8.7 4
 
4.0%
5.4 4
 
4.0%
11.1 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%
3.2 2
2.0%
3.3 2
2.0%
3.4 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
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210301
2nd row20210301
3rd row20210301
4th row20210301
5th row20210301

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2023-12-10T22:39:34.231082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:39:34.341955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 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

2023-12-10T22:39:34.454554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:39:34.565893image/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%
Mean35.679121
Minimum35.31836
Maximum36.05245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:34.701321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.31836
5-th percentile35.38211
Q135.48989
median35.695695
Q335.87978
95-th percentile35.97732
Maximum36.05245
Range0.73409
Interquartile range (IQR)0.38989

Descriptive statistics

Standard deviation0.20475796
Coefficient of variation (CV)0.0057388735
Kurtosis-1.212975
Mean35.679121
Median Absolute Deviation (MAD)0.18911
Skewness-0.0035158343
Sum3567.9121
Variance0.041925822
MonotonicityNot monotonic
2023-12-10T22:39:34.874449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.66929 2
 
2.0%
35.77224 2
 
2.0%
35.52961 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%
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%
35.91078 2
2.0%
35.9058 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.13812
Minimum126.5004
Maximum127.67801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:35.064839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5004
5-th percentile126.69676
Q1126.91023
median127.13348
Q3127.32352
95-th percentile127.59682
Maximum127.67801
Range1.17761
Interquartile range (IQR)0.41329

Descriptive statistics

Standard deviation0.29431485
Coefficient of variation (CV)0.0023149221
Kurtosis-0.73212861
Mean127.13812
Median Absolute Deviation (MAD)0.206645
Skewness0.0085936673
Sum12713.812
Variance0.08662123
MonotonicityNot monotonic
2023-12-10T22:39:35.292243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.96828 2
 
2.0%
127.4985 2
 
2.0%
126.59317 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%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.5004 2
2.0%
126.59317 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%
126.88133 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 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.3582
Minimum0
Maximum102.83
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:35.854494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.5225
median6.27
Q320.42
95-th percentile49.6455
Maximum102.83
Range102.83
Interquartile range (IQR)18.8975

Descriptive statistics

Standard deviation19.127024
Coefficient of variation (CV)1.3321325
Kurtosis6.2680404
Mean14.3582
Median Absolute Deviation (MAD)5.75
Skewness2.2796098
Sum1435.82
Variance365.84306
MonotonicityNot monotonic
2023-12-10T22:39:36.052145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
1.57 4
 
4.0%
0.65 4
 
4.0%
1.3 3
 
3.0%
0.52 3
 
3.0%
1.05 3
 
3.0%
3.31 2
 
2.0%
4.4 2
 
2.0%
1.38 2
 
2.0%
23.93 1
 
1.0%
Other values (66) 66
66.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.52 3
 
3.0%
0.65 4
 
4.0%
1.05 3
 
3.0%
1.3 3
 
3.0%
1.38 2
 
2.0%
1.57 4
 
4.0%
1.73 1
 
1.0%
2.1 1
 
1.0%
2.26 1
 
1.0%
ValueCountFrequency (%)
102.83 1
1.0%
89.13 1
1.0%
72.01 1
1.0%
58.28 1
1.0%
50.89 1
1.0%
49.58 1
1.0%
47.89 1
1.0%
47.41 1
1.0%
46.9 1
1.0%
39.98 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.8483
Minimum0
Maximum121.49
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:36.223403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.83
median3.805
Q315.25
95-th percentile35.375
Maximum121.49
Range121.49
Interquartile range (IQR)14.42

Descriptive statistics

Standard deviation17.688589
Coefficient of variation (CV)1.6305401
Kurtosis19.037779
Mean10.8483
Median Absolute Deviation (MAD)3.55
Skewness3.7874215
Sum1084.83
Variance312.88617
MonotonicityNot monotonic
2023-12-10T22:39:36.382037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
0.32 4
 
4.0%
0.83 4
 
4.0%
2.06 3
 
3.0%
0.64 3
 
3.0%
0.28 3
 
3.0%
0.55 3
 
3.0%
1.09 2
 
2.0%
2.71 2
 
2.0%
1.6 2
 
2.0%
Other values (64) 64
64.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.28 3
 
3.0%
0.32 4
 
4.0%
0.55 3
 
3.0%
0.64 3
 
3.0%
0.83 4
 
4.0%
1.09 2
 
2.0%
1.11 1
 
1.0%
1.23 1
 
1.0%
1.51 1
 
1.0%
ValueCountFrequency (%)
121.49 1
1.0%
93.92 1
1.0%
43.94 1
1.0%
38.48 1
1.0%
37.56 1
1.0%
35.26 1
1.0%
34.47 1
1.0%
34.37 1
1.0%
34.08 1
1.0%
30.36 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4938
Minimum0
Maximum13.26
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:36.557345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.57
Q32.1975
95-th percentile5.2005
Maximum13.26
Range13.26
Interquartile range (IQR)2.0675

Descriptive statistics

Standard deviation2.1749254
Coefficient of variation (CV)1.4559683
Kurtosis11.40708
Mean1.4938
Median Absolute Deviation (MAD)0.53
Skewness2.9397626
Sum149.38
Variance4.7303006
MonotonicityNot monotonic
2023-12-10T22:39:36.751132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
0.4 4
 
4.0%
0.13 4
 
4.0%
0.06 4
 
4.0%
0.12 3
 
3.0%
0.09 3
 
3.0%
0.04 3
 
3.0%
0.57 2
 
2.0%
0.16 2
 
2.0%
2.47 2
 
2.0%
Other values (59) 63
63.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.04 3
 
3.0%
0.06 4
 
4.0%
0.09 3
 
3.0%
0.12 3
 
3.0%
0.13 4
 
4.0%
0.16 2
 
2.0%
0.18 2
 
2.0%
0.22 1
 
1.0%
0.23 1
 
1.0%
ValueCountFrequency (%)
13.26 1
1.0%
11.18 1
1.0%
6.92 1
1.0%
5.55 1
1.0%
5.4 1
1.0%
5.19 1
1.0%
5.1 1
1.0%
5.02 1
1.0%
4.7 1
1.0%
4.2 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5265
Minimum0
Maximum6.04
Zeros30
Zeros (%)30.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:36.929245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.14
Q30.7
95-th percentile1.886
Maximum6.04
Range6.04
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.86327351
Coefficient of variation (CV)1.6396458
Kurtosis18.717945
Mean0.5265
Median Absolute Deviation (MAD)0.14
Skewness3.7005546
Sum52.65
Variance0.74524116
MonotonicityNot monotonic
2023-12-10T22:39:37.127298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0.0 30
30.0%
0.14 11
 
11.0%
0.13 11
 
11.0%
0.27 6
 
6.0%
0.54 4
 
4.0%
0.28 4
 
4.0%
0.58 2
 
2.0%
0.94 2
 
2.0%
1.37 2
 
2.0%
0.84 2
 
2.0%
Other values (26) 26
26.0%
ValueCountFrequency (%)
0.0 30
30.0%
0.13 11
 
11.0%
0.14 11
 
11.0%
0.27 6
 
6.0%
0.28 4
 
4.0%
0.4 1
 
1.0%
0.42 1
 
1.0%
0.44 1
 
1.0%
0.52 1
 
1.0%
0.54 4
 
4.0%
ValueCountFrequency (%)
6.04 1
1.0%
4.15 1
1.0%
2.28 1
1.0%
2.05 1
1.0%
2.0 1
1.0%
1.88 1
1.0%
1.74 1
1.0%
1.6 1
1.0%
1.37 2
2.0%
1.36 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3577.7936
Minimum0
Maximum27604.84
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:37.309409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1396.8525
median1598.645
Q34733.97
95-th percentile11577.39
Maximum27604.84
Range27604.84
Interquartile range (IQR)4337.1175

Descriptive statistics

Standard deviation4841.5258
Coefficient of variation (CV)1.3532155
Kurtosis7.9227263
Mean3577.7936
Median Absolute Deviation (MAD)1459.965
Skewness2.4913466
Sum357779.36
Variance23440372
MonotonicityNot monotonic
2023-12-10T22:39:37.474730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
416.06 4
 
4.0%
153.68 4
 
4.0%
307.36 3
 
3.0%
138.68 3
 
3.0%
277.37 3
 
3.0%
873.34 2
 
2.0%
1156.34 2
 
2.0%
339.23 2
 
2.0%
6187.44 1
 
1.0%
Other values (66) 66
66.0%
ValueCountFrequency (%)
0.0 10
10.0%
138.68 3
 
3.0%
153.68 4
 
4.0%
277.37 3
 
3.0%
307.36 3
 
3.0%
339.23 2
 
2.0%
416.06 4
 
4.0%
457.29 1
 
1.0%
554.74 1
 
1.0%
595.97 1
 
1.0%
ValueCountFrequency (%)
27604.84 1
1.0%
23194.03 1
1.0%
17197.2 1
1.0%
15300.2 1
1.0%
12186.16 1
1.0%
11545.35 1
1.0%
11218.09 1
1.0%
11126.49 1
1.0%
11051.68 1
1.0%
10203.52 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 완주 삼례 후정
 
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 완주 삼례 후정 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%
Other values (39) 78
78.0%

Length

2023-12-10T22:39:37.662227image/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%
완주 12
 
3.0%
임실 10
 
2.5%
정읍 8
 
2.0%
김제 8
 
2.0%
익산 6
 
1.5%
고창 6
 
1.5%
군산 6
 
1.5%
Other values (94) 212
53.3%

Interactions

2023-12-10T22:39:30.365877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:22.749995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.384795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:24.276047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.181896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.984099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.881873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:27.917119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:29.273449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:30.486308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:22.811970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.459920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:24.372646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.267437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.063628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.976376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:28.095943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:29.397100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:30.620854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:22.885983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.613405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:24.488669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.367563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.156713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:27.081265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:28.470145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:29.501913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:30.774191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:22.961193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.702592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:24.620464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.468701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.265265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:27.206215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:28.582588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:29.635959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:30.911272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.027094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.801385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:24.729343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.548685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.351460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:27.345707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:28.708402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:29.744869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:31.030173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.095500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.892567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:24.825430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.636319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.434396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:27.467359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:28.807882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:29.864715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:31.150871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.163913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.985184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:24.920269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.721308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.529411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:27.586565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:28.917785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:29.986744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:31.272079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.234975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:24.074462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.010030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.820242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.663870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:27.680283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:29.034605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:30.102399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:31.391238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:23.309547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:24.179651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.098316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:25.903969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:26.796315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:27.796269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:29.162370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:30.230655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:39:37.791816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9990.5920.7790.8980.4590.4500.4720.4680.4720.998
지점1.0001.0000.0001.0001.0001.0001.0000.8600.8510.8000.7860.8851.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간0.9991.0000.0001.0000.9980.9900.9980.9020.8750.8400.8290.9171.000
연장0.5921.0000.0000.9981.0000.5010.5230.6250.5160.4050.3060.6720.998
좌표위치위도0.7791.0000.0000.9900.5011.0000.7960.4530.3690.3460.3580.4630.997
좌표위치경도0.8981.0000.0000.9980.5230.7961.0000.1950.3250.2020.0720.1951.000
co0.4590.8600.0000.9020.6250.4530.1951.0000.9830.9540.9471.0000.867
nox0.4500.8510.0000.8750.5160.3690.3250.9831.0000.9830.9840.9810.849
hc0.4720.8000.0000.8400.4050.3460.2020.9540.9831.0000.9210.9570.803
pm0.4680.7860.0000.8290.3060.3580.0720.9470.9840.9211.0000.9470.786
co20.4720.8850.0000.9170.6720.4630.1951.0000.9810.9570.9471.0000.887
주소0.9981.0000.0001.0000.9980.9971.0000.8670.8490.8030.7860.8871.000
2023-12-10T22:39:37.956631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향측정구간
주소1.0000.0000.963
방향0.0001.0000.000
측정구간0.9630.0001.000
2023-12-10T22:39:38.091858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간주소
기본키1.0000.1200.077-0.2830.0130.0070.009-0.0470.0110.0000.7660.736
연장0.1201.000-0.043-0.147-0.025-0.032-0.029-0.019-0.0210.0000.7110.733
좌표위치위도0.077-0.0431.000-0.0780.4120.4080.4140.3730.4110.0000.7000.727
좌표위치경도-0.283-0.147-0.0781.000-0.407-0.406-0.413-0.339-0.4060.0000.7520.753
co0.013-0.0250.412-0.4071.0000.9940.9970.9310.9990.0000.4380.398
nox0.007-0.0320.408-0.4060.9941.0000.9970.9530.9930.0000.4590.410
hc0.009-0.0290.414-0.4130.9970.9971.0000.9400.9960.0000.3940.334
pm-0.047-0.0190.373-0.3390.9310.9530.9401.0000.9330.0000.4020.345
co20.011-0.0210.411-0.4060.9990.9930.9960.9331.0000.0000.4640.425
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
측정구간0.7660.7110.7000.7520.4380.4590.3940.4020.4640.0001.0000.963
주소0.7360.7330.7270.7530.3980.4100.3340.3450.4250.0000.9631.000

Missing values

2023-12-10T22:39:31.572774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:39:31.831485image/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-금구11.120210301135.66929126.9682828.6421.242.761.077466.36전북 정읍 옹동 오성
12건기연[0114-1]2태인-금구11.120210301135.66929126.9682826.3919.732.540.946870.38전북 정읍 옹동 오성
23건기연[0115-1]1정읍-태인6.420210301135.62947126.9034447.8934.375.191.611218.09전북 정읍 정우 우산
34건기연[0115-1]2정읍-태인6.420210301135.62947126.9034450.8934.475.12.0512186.16전북 정읍 정우 우산
45건기연[0116-2]1금산-전주4.320210301135.78758127.035149.5837.565.552.011545.35전북 김제 금구 대화
56건기연[0116-2]2금산-전주4.320210301135.78758127.035139.9835.264.22.2810203.52전북 김제 금구 대화
67건기연[0117-3]1김제IC-전주5.120210301135.79995127.0582247.4130.365.021.3711126.49전북 완주 이서 이성
78건기연[0117-3]2김제IC-전주5.120210301135.79995127.0582229.2717.362.640.87736.39전북 완주 이서 이성
89건기연[0120-1]1전주-삼례3.220210301135.91078127.054714.937.381.330.03534.69전북 완주 삼례 후정
910건기연[0120-1]2전주-삼례3.220210301135.91078127.054715.849.641.450.544186.8전북 완주 삼례 후정
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2710-0]1삼례-익산18.920210301135.9058127.0029758.2838.485.41.7415300.2전북 익산 춘포 덕실
9192건기연[2710-0]2삼례-익산18.920210301135.9058127.0029772.0143.946.921.8817197.2전북 익산 춘포 덕실
9293건기연[2711-0]1익산-임피5.420210301135.97553126.8918627.9516.12.470.547382.18전북 군산 임피 영창
9394건기연[2711-0]2익산-임피5.420210301135.97553126.8918630.716.612.860.547333.63전북 군산 임피 영창
9495건기연[2907-3]1답동-부무5.720210301135.48989126.988440.650.320.060.0153.68전북 순창 쌍치 금평
9596건기연[2907-3]2답동-부무5.720210301135.48989126.988441.30.640.120.0307.36전북 순창 쌍치 금평
9697건기연[2909-1]1정읍-부안3.320210301135.60581126.778928.715.230.790.272301.43전북 정읍 고부 입석
9798건기연[2909-1]2정읍-부안3.320210301135.60581126.778925.933.450.530.131566.77전북 정읍 고부 입석
9899건기연[2911-0]1화호-김제11.720210301135.7375126.837011.30.640.120.0307.36전북 김제 부량 대평
99100건기연[2911-0]2화호-김제11.720210301135.7375126.837010.520.280.040.0138.68전북 김제 부량 대평