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 4 (4.0%) zerosZeros
nox has 4 (4.0%) zerosZeros
hc has 4 (4.0%) zerosZeros
pm has 14 (14.0%) zerosZeros
co2 has 4 (4.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:39:55.578074
Analysis finished2023-12-10 13:40:04.657076
Duration9.08 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
2023-12-10T22:40:04.745717image/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
2023-12-10T22:40:05.163054image/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

2023-12-10T22:40:05.297755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:05.383059image/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
2023-12-10T22:40:05.588258image/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%
2602-4 2
 
2.0%
2907-3 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%
2023-12-10T22:40:05.932150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 140
17.4%
2 110
13.7%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 96
11.9%
3 36
 
4.5%
7 36
 
4.5%
9 26
 
3.2%
6 24
 
3.0%
Other values (3) 36
 
4.5%

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 (%)
1 140
27.8%
2 110
21.8%
0 96
19.0%
3 36
 
7.1%
7 36
 
7.1%
9 26
 
5.2%
6 24
 
4.8%
4 18
 
3.6%
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 (%)
1 140
17.4%
2 110
13.7%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 96
11.9%
3 36
 
4.5%
7 36
 
4.5%
9 26
 
3.2%
6 24
 
3.0%
Other values (3) 36
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 140
17.4%
2 110
13.7%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 96
11.9%
3 36
 
4.5%
7 36
 
4.5%
9 26
 
3.2%
6 24
 
3.0%
Other values (3) 36
 
4.5%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2023-12-10T22:40:06.071974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:06.162553image/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

2023-12-10T22:40:06.269492image/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%
군산-전주 2
 
2.0%
답동-부무 2
 
2.0%
태인-금구 2
 
2.0%
상관-소양 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 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.372
Minimum0.9
Maximum18.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:06.418751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q14.7
median6.45
Q39
95-th percentile14.6
Maximum18.9
Range18
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.0197512
Coefficient of variation (CV)0.54527282
Kurtosis0.41032216
Mean7.372
Median Absolute Deviation (MAD)2.3
Skewness0.7942268
Sum737.2
Variance16.1584
MonotonicityNot monotonic
2023-12-10T22:40:06.537854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
6.0 4
 
4.0%
3.2 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%
8.0 4
 
4.0%
11.1 2
 
2.0%
8.6 2
 
2.0%
Other values (32) 64
64.0%
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 4
4.0%
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%
11.5 2
2.0%
11.1 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T22:40:06.662973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:06.750055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 100
100.0%

측정시간
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T22:40:06.843832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:40:06.924738image/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%
Mean35.683149
Minimum35.31836
Maximum36.05245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:07.022866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.31836
5-th percentile35.38211
Q135.48989
median35.695695
Q335.88516
95-th percentile35.97732
Maximum36.05245
Range0.73409
Interquartile range (IQR)0.39527

Descriptive statistics

Standard deviation0.20785553
Coefficient of variation (CV)0.0058250332
Kurtosis-1.270877
Mean35.683149
Median Absolute Deviation (MAD)0.197635
Skewness-0.022904578
Sum3568.3149
Variance0.043203921
MonotonicityNot monotonic
2023-12-10T22:40:07.180938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.66929 2
 
2.0%
35.85422 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%
35.9389 2
2.0%
35.9258 2
2.0%
35.91702 2
2.0%
35.91078 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.13827
Minimum126.5004
Maximum127.67801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:07.323643image/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 deviation0.29416512
Coefficient of variation (CV)0.0023137417
Kurtosis-0.72954965
Mean127.13827
Median Absolute Deviation (MAD)0.206645
Skewness0.0086568734
Sum12713.827
Variance0.086533115
MonotonicityNot monotonic
2023-12-10T22:40:07.468540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.96828 2
 
2.0%
127.21711 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%
126.84434 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 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.3462
Minimum0
Maximum196.96
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:07.606961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52
Q14.9175
median16.985
Q337.23
95-th percentile104.664
Maximum196.96
Range196.96
Interquartile range (IQR)32.3125

Descriptive statistics

Standard deviation34.405269
Coefficient of variation (CV)1.2137524
Kurtosis5.8427084
Mean28.3462
Median Absolute Deviation (MAD)13.885
Skewness2.1413633
Sum2834.62
Variance1183.7225
MonotonicityNot monotonic
2023-12-10T22:40:07.744537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.0%
1.3 3
 
3.0%
0.52 3
 
3.0%
3.83 3
 
3.0%
9.64 2
 
2.0%
4.93 2
 
2.0%
1.05 2
 
2.0%
27.79 1
 
1.0%
10.13 1
 
1.0%
32.94 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.52 3
3.0%
0.65 1
 
1.0%
1.05 2
2.0%
1.3 3
3.0%
2.03 1
 
1.0%
2.26 1
 
1.0%
2.62 1
 
1.0%
2.63 1
 
1.0%
2.68 1
 
1.0%
ValueCountFrequency (%)
196.96 1
1.0%
129.76 1
1.0%
116.98 1
1.0%
105.91 1
1.0%
105.31 1
1.0%
104.63 1
1.0%
101.24 1
1.0%
85.3 1
1.0%
84.12 1
1.0%
82.62 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.3567
Minimum0
Maximum168.95
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:07.892970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q12.9125
median13.99
Q333.785
95-th percentile82.751
Maximum168.95
Range168.95
Interquartile range (IQR)30.8725

Descriptive statistics

Standard deviation31.426346
Coefficient of variation (CV)1.2902547
Kurtosis5.2286505
Mean24.3567
Median Absolute Deviation (MAD)11.985
Skewness2.1211233
Sum2435.67
Variance987.61521
MonotonicityNot monotonic
2023-12-10T22:40:08.031388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.0%
0.64 3
 
3.0%
0.28 3
 
3.0%
2.34 3
 
3.0%
5.48 2
 
2.0%
2.99 2
 
2.0%
0.55 2
 
2.0%
23.95 1
 
1.0%
11.02 1
 
1.0%
33.35 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.28 3
3.0%
0.32 1
 
1.0%
0.55 2
2.0%
0.64 3
3.0%
1.39 1
 
1.0%
1.41 1
 
1.0%
1.51 1
 
1.0%
1.64 1
 
1.0%
1.73 1
 
1.0%
ValueCountFrequency (%)
168.95 1
1.0%
122.61 1
1.0%
121.61 1
1.0%
108.16 1
1.0%
97.02 1
1.0%
82.0 1
1.0%
78.07 1
1.0%
76.58 1
1.0%
74.11 1
1.0%
73.77 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2009
Minimum0
Maximum22.73
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:08.201000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.4475
median1.845
Q34.6525
95-th percentile11.4195
Maximum22.73
Range22.73
Interquartile range (IQR)4.205

Descriptive statistics

Standard deviation4.0604262
Coefficient of variation (CV)1.2685264
Kurtosis5.8226616
Mean3.2009
Median Absolute Deviation (MAD)1.5
Skewness2.1860304
Sum320.09
Variance16.487061
MonotonicityNot monotonic
2023-12-10T22:40:08.356351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.0%
0.49 3
 
3.0%
0.12 3
 
3.0%
0.04 3
 
3.0%
0.35 3
 
3.0%
2.57 2
 
2.0%
0.53 2
 
2.0%
0.44 2
 
2.0%
0.84 2
 
2.0%
0.57 2
 
2.0%
Other values (71) 74
74.0%
ValueCountFrequency (%)
0.0 4
4.0%
0.04 3
3.0%
0.06 1
 
1.0%
0.09 2
2.0%
0.12 3
3.0%
0.21 1
 
1.0%
0.22 2
2.0%
0.26 1
 
1.0%
0.27 1
 
1.0%
0.35 3
3.0%
ValueCountFrequency (%)
22.73 1
1.0%
15.2 1
1.0%
15.06 1
1.0%
14.35 1
1.0%
11.6 1
1.0%
11.41 1
1.0%
10.84 1
1.0%
10.53 1
1.0%
9.98 1
1.0%
9.73 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.277
Minimum0
Maximum7.64
Zeros14
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:08.500417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.765
Q31.765
95-th percentile4.7025
Maximum7.64
Range7.64
Interquartile range (IQR)1.635

Descriptive statistics

Standard deviation1.6697465
Coefficient of variation (CV)1.3075541
Kurtosis3.5731847
Mean1.277
Median Absolute Deviation (MAD)0.635
Skewness1.9375071
Sum127.7
Variance2.7880535
MonotonicityNot monotonic
2023-12-10T22:40:08.661883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 14
 
14.0%
0.13 12
 
12.0%
0.14 8
 
8.0%
0.27 5
 
5.0%
0.8 3
 
3.0%
0.82 3
 
3.0%
0.26 2
 
2.0%
0.28 2
 
2.0%
1.1 2
 
2.0%
0.97 2
 
2.0%
Other values (46) 47
47.0%
ValueCountFrequency (%)
0.0 14
14.0%
0.13 12
12.0%
0.14 8
8.0%
0.26 2
 
2.0%
0.27 5
 
5.0%
0.28 2
 
2.0%
0.41 1
 
1.0%
0.42 1
 
1.0%
0.53 1
 
1.0%
0.54 1
 
1.0%
ValueCountFrequency (%)
7.64 1
1.0%
7.02 1
1.0%
6.67 1
1.0%
5.68 1
1.0%
4.75 1
1.0%
4.7 1
1.0%
4.69 1
1.0%
4.58 1
1.0%
4.41 1
1.0%
4.16 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7054.8065
Minimum0
Maximum47729.82
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:40:08.834546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138.68
Q11280.9725
median4108.105
Q39642.8475
95-th percentile26901.62
Maximum47729.82
Range47729.82
Interquartile range (IQR)8361.875

Descriptive statistics

Standard deviation8584.3199
Coefficient of variation (CV)1.2168044
Kurtosis5.3995864
Mean7054.8065
Median Absolute Deviation (MAD)3438.095
Skewness2.1100704
Sum705480.65
Variance73690548
MonotonicityNot monotonic
2023-12-10T22:40:09.029938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4
 
4.0%
307.36 3
 
3.0%
138.68 3
 
3.0%
1012.03 3
 
3.0%
2543.2 2
 
2.0%
1295.03 2
 
2.0%
277.37 2
 
2.0%
6540.51 1
 
1.0%
2546.81 1
 
1.0%
7597.04 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 4
4.0%
138.68 3
3.0%
153.68 1
 
1.0%
277.37 2
2.0%
307.36 3
3.0%
492.91 1
 
1.0%
595.97 1
 
1.0%
640.96 1
 
1.0%
646.6 1
 
1.0%
693.42 1
 
1.0%
ValueCountFrequency (%)
47729.82 1
1.0%
33744.61 1
1.0%
28333.38 1
1.0%
27790.4 1
1.0%
27433.04 1
1.0%
26873.65 1
1.0%
26721.22 1
1.0%
20493.22 1
1.0%
19288.83 1
1.0%
19286.76 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

2023-12-10T22:40:09.530321image/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%
군산 8
 
2.0%
정읍 8
 
2.0%
익산 6
 
1.5%
고창 6
 
1.5%
진안 6
 
1.5%
Other values (94) 212
53.3%

Interactions

2023-12-10T22:40:03.507661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.123157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.883891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:57.826912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.750550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:59.636207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:00.814770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.709814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.540427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:03.588591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.192687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.964498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:57.921361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.844877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:59.722919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:00.905862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.788353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.646532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:03.685044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.269943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:57.052232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.017269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.948594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:59.819547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.010252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.869507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.745041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:03.790751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.350437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:57.133877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.121783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:59.046471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:00.238831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.108708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.962095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.846665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:03.883352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.426962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:57.257698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.220952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:59.125310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:00.343230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.199061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.058332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.946228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:03.977403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.527562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:57.432115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.333962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:59.226705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:00.438920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.294242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.159311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:03.066224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:04.064261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.633573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:57.539715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.452934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:59.348282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:00.530167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.400702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.256166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:03.188287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:04.143866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.711761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:57.622300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.537522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:59.443263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:00.615922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.492767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.342580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:03.305466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:04.237900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:56.801559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:57.733689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:58.659776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:39:59.557626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:00.721242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:01.615543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:02.448184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:40:03.419689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:40:09.867146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9980.6090.8100.8940.4120.4760.4430.4530.3530.998
지점1.0001.0000.0001.0001.0001.0001.0000.7990.8110.8050.8400.7751.000
방향0.0000.0001.0000.0000.0000.0000.0000.1050.0000.2110.0000.1340.000
측정구간0.9981.0000.0001.0000.9980.9900.9980.8380.8510.8360.8860.8131.000
연장0.6091.0000.0000.9981.0000.5280.5590.3840.3620.3170.6060.3360.998
좌표위치위도0.8101.0000.0000.9900.5281.0000.7980.4070.4610.4630.4880.3960.997
좌표위치경도0.8941.0000.0000.9980.5590.7981.0000.3100.3290.4400.3890.3011.000
co0.4120.7990.1050.8380.3840.4070.3101.0000.8940.9790.9131.0000.813
nox0.4760.8110.0000.8510.3620.4610.3290.8941.0000.9480.9780.8960.795
hc0.4430.8050.2110.8360.3170.4630.4400.9790.9481.0000.9080.9790.812
pm0.4530.8400.0000.8860.6060.4880.3890.9130.9780.9081.0000.9150.844
co20.3530.7750.1340.8130.3360.3960.3011.0000.8960.9790.9151.0000.782
주소0.9981.0000.0001.0000.9980.9971.0000.8130.7950.8120.8440.7821.000
2023-12-10T22:40:10.212108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향측정구간
주소1.0000.0000.963
방향0.0001.0000.000
측정구간0.9630.0001.000
2023-12-10T22:40:10.352335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간주소
기본키1.0000.0570.090-0.2610.0540.0570.0410.0330.0590.0000.7500.736
연장0.0571.000-0.082-0.091-0.005-0.0050.006-0.019-0.0020.0000.7120.734
좌표위치위도0.090-0.0821.000-0.1060.4660.4980.4830.4940.4730.0000.7000.728
좌표위치경도-0.261-0.091-0.1061.000-0.446-0.442-0.444-0.406-0.4450.0000.7520.753
co0.054-0.0050.466-0.4461.0000.9840.9920.9460.9980.0720.3910.344
nox0.057-0.0050.498-0.4420.9841.0000.9950.9770.9840.0000.3660.316
hc0.0410.0060.483-0.4440.9920.9951.0000.9670.9890.1510.3880.343
pm0.033-0.0190.494-0.4060.9460.9770.9671.0000.9440.0000.4130.368
co20.059-0.0020.473-0.4450.9980.9840.9890.9441.0000.0940.3630.315
방향0.0000.0000.0000.0000.0720.0000.1510.0000.0941.0000.0000.000
측정구간0.7500.7120.7000.7520.3910.3660.3880.4130.3630.0001.0000.963
주소0.7360.7340.7280.7530.3440.3160.3430.3680.3150.0000.9631.000

Missing values

2023-12-10T22:40:04.374596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:40:04.581542image/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-금구11.120210101135.66929126.9682850.4735.455.41.3411832.62전북 정읍 옹동 오성
12건기연[0114-1]2태인-금구11.120210101135.66929126.9682832.1522.783.020.948395.92전북 정읍 옹동 오성
23건기연[0115-1]1정읍-태인6.420210101135.62947126.9034465.244.396.841.9115311.79전북 정읍 정우 우산
34건기연[0115-1]2정읍-태인6.420210101135.62947126.9034450.0238.345.312.2612889.3전북 정읍 정우 우산
45건기연[0116-2]1금산-전주4.320210101135.78758127.035184.1274.1110.844.6919288.83전북 김제 금구 대화
56건기연[0116-2]2금산-전주4.320210101135.78758127.0351116.98108.1615.067.0226721.22전북 김제 금구 대화
67건기연[0117-3]1김제IC-전주5.120210101135.79995127.0582273.2264.438.172.8518231.63전북 완주 이서 이성
78건기연[0117-3]2김제IC-전주5.120210101135.79995127.0582254.4242.86.081.8513781.15전북 완주 이서 이성
89건기연[0120-1]1전주-삼례3.220210101135.91078127.054716.8217.612.670.973901.51전북 완주 삼례 후정
910건기연[0120-1]2전주-삼례3.220210101135.91078127.054743.1332.045.211.549872.73전북 완주 삼례 후정
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2706-2]1순창-덕치12.820210101135.50807127.152399.775.170.90.132331.47전북 임실 덕치 회문
9192건기연[2706-2]2순창-덕치12.820210101135.50807127.1523916.8810.071.520.424439.83전북 임실 덕치 회문
9293건기연[2710-0]1삼례-익산18.920210101135.9058127.00297105.9173.7710.534.1627433.04전북 익산 춘포 덕실
9394건기연[2710-0]2삼례-익산18.920210101135.9058127.00297101.2476.589.734.4128333.38전북 익산 춘포 덕실
9495건기연[2711-0]1익산-임피5.420210101135.97553126.8918670.5259.367.952.9617755.79전북 군산 임피 영창
9596건기연[2711-0]2익산-임피5.420210101135.97553126.8918645.2635.664.91.8111539.4전북 군산 임피 영창
9697건기연[2907-3]1답동-부무5.720210101135.48989126.988441.30.640.120.0307.36전북 순창 쌍치 금평
9798건기연[2907-3]2답동-부무5.720210101135.48989126.988441.050.550.090.0277.37전북 순창 쌍치 금평
9899건기연[2909-1]1정읍-부안3.320210101135.60581126.7789217.9410.751.630.544741.54전북 정읍 고부 입석
99100건기연[2909-1]2정읍-부안3.320210101135.60581126.7789218.259.71.680.274355.57전북 정읍 고부 입석