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 6 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 8 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 5 other fieldsHigh correlation
hc is highly overall correlated with co and 4 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 6 (6.0%) zerosZeros
nox has 6 (6.0%) zerosZeros
hc has 6 (6.0%) zerosZeros
pm has 18 (18.0%) zerosZeros
co2 has 6 (6.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:38:33.731888
Analysis finished2023-12-10 13:38:43.737263
Duration10.01 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
2023-12-10T22:38:43.826607image/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
2023-12-10T22:38:43.959585image/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

2023-12-10T22:38:44.077389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:44.167416image/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
2023-12-10T22:38:44.370037image/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%
2609-1 2
 
2.0%
3003-0 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%
2914-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:38:44.708797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 138
17.2%
2 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 98
12.2%
3 44
 
5.5%
7 34
 
4.2%
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 (%)
1 138
27.4%
2 108
21.4%
0 98
19.4%
3 44
 
8.7%
7 34
 
6.7%
9 30
 
6.0%
6 20
 
4.0%
4 14
 
2.8%
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 (%)
1 138
17.2%
2 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 98
12.2%
3 44
 
5.5%
7 34
 
4.2%
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 (%)
1 138
17.2%
2 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 98
12.2%
3 44
 
5.5%
7 34
 
4.2%
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

2023-12-10T22:38:44.863484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:44.941549image/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
군산-대야
 
4
번암-장계
 
4
순창-덕치
 
4
임실-관촌
 
2
Other values (40)
80 

Length

Max length7
Median length5
Mean length5.12
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%
군산-대야 4
 
4.0%
번암-장계 4
 
4.0%
순창-덕치 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

2023-12-10T22:38:45.041121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
순창-남원 6
 
6.0%
번암-장계 4
 
4.0%
순창-덕치 4
 
4.0%
군산-대야 4
 
4.0%
천천-서상 2
 
2.0%
변산-하서 2
 
2.0%
백여-구이 2
 
2.0%
보안-부안 2
 
2.0%
부안-죽산 2
 
2.0%
군산-익산 2
 
2.0%
Other values (35) 70
70.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.426
Minimum0.9
Maximum18.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:45.163043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q14.6
median6.45
Q39.2
95-th percentile14.6
Maximum18.9
Range18
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation4.057108
Coefficient of variation (CV)0.54633827
Kurtosis0.25378015
Mean7.426
Median Absolute Deviation (MAD)2.35
Skewness0.76364949
Sum742.6
Variance16.460125
MonotonicityNot monotonic
2023-12-10T22:38:45.283631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
4.9 4
 
4.0%
2.4 4
 
4.0%
8.5 4
 
4.0%
5.4 4
 
4.0%
6.0 4
 
4.0%
8.0 4
 
4.0%
11.1 2
 
2.0%
6.3 2
 
2.0%
7.5 2
 
2.0%
14.6 2
 
2.0%
Other values (34) 68
68.0%
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%
3.7 2
2.0%
4.1 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
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2023-12-10T22:38:45.415539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:45.777475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 100
100.0%

측정시간
Categorical

CONSTANT 

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

2023-12-10T22:38:45.867015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:45.946805image/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%
Mean35.670811
Minimum35.31836
Maximum36.05245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:46.050907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.31836
5-th percentile35.38211
Q135.48989
median35.695695
Q335.79995
95-th percentile35.97732
Maximum36.05245
Range0.73409
Interquartile range (IQR)0.31006

Descriptive statistics

Standard deviation0.19864488
Coefficient of variation (CV)0.0055688354
Kurtosis-1.0686788
Mean35.670811
Median Absolute Deviation (MAD)0.176855
Skewness0.047759858
Sum3567.0811
Variance0.039459788
MonotonicityNot monotonic
2023-12-10T22:38:46.205321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.66929 2
 
2.0%
35.71626 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%
35.77224 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%
35.9058 2
2.0%
35.90484 2
2.0%
35.88516 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.11975
Minimum126.5004
Maximum127.67801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:46.347206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5004
5-th percentile126.64598
Q1126.88133
median127.12055
Q3127.32352
95-th percentile127.59682
Maximum127.67801
Range1.17761
Interquartile range (IQR)0.44219

Descriptive statistics

Standard deviation0.30771121
Coefficient of variation (CV)0.0024206404
Kurtosis-0.93294344
Mean127.11975
Median Absolute Deviation (MAD)0.23396
Skewness0.061789914
Sum12711.975
Variance0.094686186
MonotonicityNot monotonic
2023-12-10T22:38:46.513547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.96828 2
 
2.0%
127.11512 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%
127.4985 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%
126.7879 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 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.6991
Minimum0
Maximum132.19
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:46.660658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.7
median6.58
Q319.485
95-th percentile48.219
Maximum132.19
Range132.19
Interquartile range (IQR)17.785

Descriptive statistics

Standard deviation22.074781
Coefficient of variation (CV)1.5017777
Kurtosis12.436406
Mean14.6991
Median Absolute Deviation (MAD)5.93
Skewness3.1575265
Sum1469.91
Variance487.29596
MonotonicityNot monotonic
2023-12-10T22:38:46.816844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52 7
 
7.0%
0.0 6
 
6.0%
2.26 5
 
5.0%
0.65 4
 
4.0%
3.28 2
 
2.0%
29.0 2
 
2.0%
5.56 2
 
2.0%
1.61 2
 
2.0%
1.21 2
 
2.0%
1.34 1
 
1.0%
Other values (67) 67
67.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.52 7
7.0%
0.65 4
4.0%
1.05 1
 
1.0%
1.17 1
 
1.0%
1.21 2
 
2.0%
1.26 1
 
1.0%
1.34 1
 
1.0%
1.61 2
 
2.0%
1.73 1
 
1.0%
ValueCountFrequency (%)
132.19 1
1.0%
118.88 1
1.0%
81.54 1
1.0%
67.32 1
1.0%
55.42 1
1.0%
47.84 1
1.0%
43.4 1
1.0%
39.35 1
1.0%
38.79 1
1.0%
36.41 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.9341
Minimum0
Maximum174.28
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:46.994858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.26
median4.56
Q317.9375
95-th percentile52.721
Maximum174.28
Range174.28
Interquartile range (IQR)16.6775

Descriptive statistics

Standard deviation27.337421
Coefficient of variation (CV)1.8305369
Kurtosis20.853915
Mean14.9341
Median Absolute Deviation (MAD)4.26
Skewness4.1121582
Sum1493.41
Variance747.33456
MonotonicityNot monotonic
2023-12-10T22:38:47.155037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 7
 
7.0%
0.0 6
 
6.0%
1.51 5
 
5.0%
0.32 4
 
4.0%
3.57 2
 
2.0%
1.26 2
 
2.0%
0.96 2
 
2.0%
1.96 2
 
2.0%
29.22 1
 
1.0%
25.87 1
 
1.0%
Other values (68) 68
68.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.28 7
7.0%
0.32 4
4.0%
0.55 1
 
1.0%
0.9 1
 
1.0%
0.96 2
 
2.0%
1.0 1
 
1.0%
1.05 1
 
1.0%
1.23 1
 
1.0%
1.26 2
 
2.0%
ValueCountFrequency (%)
174.28 1
1.0%
170.15 1
1.0%
61.53 1
1.0%
61.02 1
1.0%
55.78 1
1.0%
52.56 1
1.0%
52.48 1
1.0%
48.41 1
1.0%
41.71 1
1.0%
41.66 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9572
Minimum0
Maximum20.3
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:47.311399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.175
median0.68
Q32.6
95-th percentile6.1075
Maximum20.3
Range20.3
Interquartile range (IQR)2.425

Descriptive statistics

Standard deviation3.2948138
Coefficient of variation (CV)1.6834324
Kurtosis17.47357
Mean1.9572
Median Absolute Deviation (MAD)0.62
Skewness3.7176282
Sum195.72
Variance10.855798
MonotonicityNot monotonic
2023-12-10T22:38:47.472970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 7
 
7.0%
0.0 6
 
6.0%
0.22 5
 
5.0%
0.06 4
 
4.0%
0.32 3
 
3.0%
0.13 3
 
3.0%
2.56 2
 
2.0%
0.53 2
 
2.0%
0.99 2
 
2.0%
0.26 2
 
2.0%
Other values (61) 64
64.0%
ValueCountFrequency (%)
0.0 6
6.0%
0.04 7
7.0%
0.06 4
4.0%
0.09 1
 
1.0%
0.13 3
3.0%
0.14 1
 
1.0%
0.15 1
 
1.0%
0.16 2
 
2.0%
0.18 1
 
1.0%
0.2 1
 
1.0%
ValueCountFrequency (%)
20.3 1
1.0%
20.0 1
1.0%
9.24 1
1.0%
7.73 1
1.0%
7.2 1
1.0%
6.05 1
1.0%
6.04 1
1.0%
5.86 1
1.0%
5.8 1
1.0%
5.61 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9478
Minimum0
Maximum10.67
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:47.614975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.28
Q31.24
95-th percentile3.517
Maximum10.67
Range10.67
Interquartile range (IQR)1.11

Descriptive statistics

Standard deviation1.6717991
Coefficient of variation (CV)1.7638733
Kurtosis19.969407
Mean0.9478
Median Absolute Deviation (MAD)0.28
Skewness4.0100141
Sum94.78
Variance2.7949123
MonotonicityNot monotonic
2023-12-10T22:38:47.742559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0.0 18
18.0%
0.13 16
16.0%
0.14 8
 
8.0%
0.27 6
 
6.0%
0.54 5
 
5.0%
0.28 4
 
4.0%
0.41 3
 
3.0%
1.24 2
 
2.0%
3.66 1
 
1.0%
3.13 1
 
1.0%
Other values (36) 36
36.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.13 16
16.0%
0.14 8
8.0%
0.27 6
 
6.0%
0.28 4
 
4.0%
0.39 1
 
1.0%
0.4 1
 
1.0%
0.41 3
 
3.0%
0.42 1
 
1.0%
0.44 1
 
1.0%
ValueCountFrequency (%)
10.67 1
1.0%
10.26 1
1.0%
3.96 1
1.0%
3.66 1
1.0%
3.65 1
1.0%
3.51 1
1.0%
3.13 1
1.0%
3.02 1
1.0%
2.85 1
1.0%
2.59 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3633.0091
Minimum0
Maximum32833.53
Zeros6
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:38:47.862372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1452.93
median1602.41
Q34780.3375
95-th percentile11056.096
Maximum32833.53
Range32833.53
Interquartile range (IQR)4327.4075

Descriptive statistics

Standard deviation5505.21
Coefficient of variation (CV)1.5153307
Kurtosis13.401573
Mean3633.0091
Median Absolute Deviation (MAD)1448.73
Skewness3.2697145
Sum363300.91
Variance30307338
MonotonicityNot monotonic
2023-12-10T22:38:47.986397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.68 7
 
7.0%
0.0 6
 
6.0%
595.97 5
 
5.0%
153.68 4
 
4.0%
1469.32 2
 
2.0%
439.85 2
 
2.0%
318.6 2
 
2.0%
794.65 2
 
2.0%
6744.39 1
 
1.0%
7267.78 1
 
1.0%
Other values (68) 68
68.0%
ValueCountFrequency (%)
0.0 6
6.0%
138.68 7
7.0%
153.68 4
4.0%
277.37 1
 
1.0%
318.6 2
 
2.0%
322.21 1
 
1.0%
324.24 1
 
1.0%
333.6 1
 
1.0%
439.85 2
 
2.0%
457.29 1
 
1.0%
ValueCountFrequency (%)
32833.53 1
1.0%
31143.97 1
1.0%
19041.57 1
1.0%
16955.33 1
1.0%
12632.46 1
1.0%
10973.13 1
1.0%
10557.85 1
1.0%
9988.71 1
1.0%
9761.91 1
1.0%
9573.83 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

2023-12-10T22:38:48.108558image/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%
김제 8
 
2.0%
부안 8
 
2.0%
완주 8
 
2.0%
정읍 8
 
2.0%
고창 6
 
1.5%
남원 6
 
1.5%
Other values (95) 214
53.8%

Interactions

2023-12-10T22:38:42.558344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:34.592540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:35.587668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:36.735684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:37.784857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.635478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.532723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:40.662557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:41.657538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:42.636292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:34.696380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:35.684089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:36.862597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:37.888088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.733850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.634846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:40.759233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:41.767603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:42.740845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:34.824185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:35.805480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:36.978261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.001209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.872837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.735343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:40.886080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:41.861126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:42.848212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:34.940189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:35.925972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:37.108871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.104289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.000850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.839772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:40.996221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:41.964243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:42.941868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:35.043061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:36.059977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:37.229641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.189389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.103279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.928104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:41.101736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:42.062808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:43.023072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:35.124623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:36.243307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:37.349765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.274698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.195263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:40.292039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:41.204789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:42.178911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:43.104610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:35.254396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:36.367122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:37.462784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.370412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.279076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:40.392253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:41.315225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:42.279928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:43.206307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:35.380615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:36.486749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:37.588588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.465844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.373217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:40.486511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:41.423517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:42.395335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:43.302394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:35.480148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:36.604515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:37.688462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:38.552207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:39.450875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:40.568508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:41.538583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:42.475791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:38:48.193514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.6750.7900.9090.4430.6950.5420.6950.5641.000
지점1.0001.0000.0001.0001.0001.0001.0000.7500.9480.9120.9290.9581.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9980.9900.9980.8400.9530.9260.9360.9581.000
연장0.6751.0000.0000.9981.0000.5020.5030.6320.4320.5450.4390.6650.998
좌표위치위도0.7901.0000.0000.9900.5021.0000.8060.4310.6750.5080.6250.5290.997
좌표위치경도0.9091.0000.0000.9980.5030.8061.0000.1000.5050.4260.5280.3741.000
co0.4430.7500.0000.8400.6320.4310.1001.0000.9070.9930.8970.9720.759
nox0.6950.9480.0000.9530.4320.6750.5050.9071.0000.8610.9900.8490.927
hc0.5420.9120.0000.9260.5450.5080.4260.9930.8611.0000.8620.9740.913
pm0.6950.9290.0000.9360.4390.6250.5280.8970.9900.8621.0000.8600.906
co20.5640.9580.0000.9580.6650.5290.3740.9720.8490.9740.8601.0000.951
주소1.0001.0000.0001.0000.9980.9971.0000.7590.9270.9130.9060.9511.000
2023-12-10T22:38:48.322851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향측정구간
주소1.0000.0000.963
방향0.0001.0000.000
측정구간0.9630.0001.000
2023-12-10T22:38:48.421688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간주소
기본키1.000-0.0220.160-0.3850.0360.0270.0250.0040.0420.0000.7820.753
연장-0.0221.000-0.040-0.068-0.038-0.052-0.055-0.048-0.0340.0000.7110.734
좌표위치위도0.160-0.0401.000-0.0670.4510.4570.4530.4570.4480.0000.7000.725
좌표위치경도-0.385-0.068-0.0671.000-0.376-0.353-0.360-0.326-0.3770.0000.7520.753
co0.036-0.0380.451-0.3761.0000.9930.9940.9760.9990.0000.3540.285
nox0.027-0.0520.457-0.3530.9931.0000.9980.9850.9900.0000.5690.531
hc0.025-0.0550.453-0.3600.9940.9981.0000.9830.9900.0000.5380.497
pm0.004-0.0480.457-0.3260.9760.9850.9831.0000.9730.0000.5380.498
co20.042-0.0340.448-0.3770.9990.9900.9900.9731.0000.0000.6050.568
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
측정구간0.7820.7110.7000.7520.3540.5690.5380.5380.6050.0001.0000.963
주소0.7530.7340.7250.7530.2850.5310.4970.4980.5680.0000.9631.000

Missing values

2023-12-10T22:38:43.427628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:38:43.656098image/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-금구11.120210601135.66929126.9682827.9629.223.921.96744.39전북 정읍 옹동 오성
12건기연[0114-1]2태인-금구11.120210601135.66929126.9682821.0415.892.560.934866.71전북 정읍 옹동 오성
23건기연[0115-1]1정읍-태인6.420210601135.62947126.9034433.5631.864.361.768228.69전북 정읍 정우 우산
34건기연[0115-1]2정읍-태인6.420210601135.62947126.9034434.0533.074.52.178352.39전북 정읍 정우 우산
45건기연[0116-2]1금산-전주4.320210601135.78758127.035155.4248.417.23.0212632.46전북 김제 금구 대화
56건기연[0116-2]2금산-전주4.320210601135.78758127.035136.4133.914.632.129009.64전북 김제 금구 대화
67건기연[0117-3]1김제IC-전주5.120210601135.79995127.0582239.3552.486.043.5110557.85전북 완주 이서 이성
78건기연[0117-3]2김제IC-전주5.120210601135.79995127.0582243.441.666.052.599761.91전북 완주 이서 이성
89건기연[0121-4]1금마-연무4.920210601136.05245127.0806121.9325.073.731.845032.4전북 익산 여산 제남
910건기연[0121-4]2금마-연무4.920210601136.05245127.0806114.3517.482.61.293199.31전북 익산 여산 제남
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2909-1]1정읍-부안3.320210601135.60581126.778929.036.040.880.542383.89전북 정읍 고부 입석
9192건기연[2909-1]2정읍-부안3.320210601135.60581126.778925.563.570.530.271469.32전북 정읍 고부 입석
9293건기연[2911-0]1화호-김제11.720210601135.7375126.837011.981.320.20.13487.28전북 김제 부량 대평
9394건기연[2911-0]2화호-김제11.720210601135.7375126.837010.520.280.040.0138.68전북 김제 부량 대평
9495건기연[2912-1]1만경-백산4.620210601135.84681126.849013.832.340.350.131012.03전북 김제 만경 대동
9596건기연[2912-1]2만경-백산4.620210601135.84681126.849015.563.570.530.271469.32전북 김제 만경 대동
9697건기연[3003-0]1변산-하서3.620210601135.72216126.6459811.436.811.030.283006.12전북 부안 하서 청호
9798건기연[3003-0]2변산-하서3.620210601135.72216126.6459810.66.391.040.412548.88전북 부안 하서 청호
9899건기연[3005-3]1부안IC-화호3.720210601135.72314126.787910.7511.01.640.842554.51전북 부안 백산 덕신
99100건기연[3005-3]2부안IC-화호3.720210601135.72314126.78792.261.510.220.13595.97전북 부안 백산 덕신