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 3 (3.0%) zerosZeros
nox has 3 (3.0%) zerosZeros
hc has 3 (3.0%) zerosZeros
pm has 18 (18.0%) zerosZeros
co2 has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:38:50.272658
Analysis finished2023-12-10 13:38:59.332984
Duration9.06 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
2023-12-10T22:38:59.406835image/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
2023-12-10T22:38:59.546818image/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

2023-12-10T22:38:59.688577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:38:59.769291image/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
2023-12-10T22:38:59.953269image/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[0115-1]
2nd row[0115-1]
3rd row[0116-2]
4th row[0116-2]
5th row[0117-3]
ValueCountFrequency (%)
0115-1 2
 
2.0%
2702-2 2
 
2.0%
3006-1 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%
2607-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:39:00.316292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 136
16.9%
2 106
13.2%
0 102
12.7%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
3 48
 
6.0%
7 34
 
4.2%
9 30
 
3.7%
6 18
 
2.2%
Other values (3) 30
 
3.7%

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 (%)
1 136
27.0%
2 106
21.0%
0 102
20.2%
3 48
 
9.5%
7 34
 
6.7%
9 30
 
6.0%
6 18
 
3.6%
5 12
 
2.4%
4 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 (%)
1 136
16.9%
2 106
13.2%
0 102
12.7%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
3 48
 
6.0%
7 34
 
4.2%
9 30
 
3.7%
6 18
 
2.2%
Other values (3) 30
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 136
16.9%
2 106
13.2%
0 102
12.7%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
3 48
 
6.0%
7 34
 
4.2%
9 30
 
3.7%
6 18
 
2.2%
Other values (3) 30
 
3.7%

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

2023-12-10T22:39:00.439663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:39:00.550388image/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
남원-산동
 
2
Other values (40)
80 

Length

Max length7
Median length5
Mean length5.14
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T22:39:00.662036image/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%
옥과-순창 2
 
2.0%
신태인-태인 2
 
2.0%
정읍-태인 2
 
2.0%
부안-죽산 2
 
2.0%
군산-익산 2
 
2.0%
연장-오천 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%
Mean7.488
Minimum0.9
Maximum18.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:00.796510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q14.6
median6.45
Q39.2
95-th percentile15.7
Maximum18.9
Range18
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation4.1925205
Coefficient of variation (CV)0.55989857
Kurtosis0.10474863
Mean7.488
Median Absolute Deviation (MAD)2.35
Skewness0.79104716
Sum748.8
Variance17.577228
MonotonicityNot monotonic
2023-12-10T22:39:00.930265image/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%
6.5 4
 
4.0%
5.4 4
 
4.0%
6.0 4
 
4.0%
6.4 2
 
2.0%
2.7 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%
15.7 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%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210501
2nd row20210501
3rd row20210501
4th row20210501
5th row20210501

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2023-12-10T22:39:01.063094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:39:01.154657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 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

2023-12-10T22:39:01.256131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:39:01.371465image/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%
Mean35.664372
Minimum35.31836
Maximum36.05245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:01.470305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.31836
5-th percentile35.38211
Q135.48989
median35.68602
Q335.78758
95-th percentile35.97732
Maximum36.05245
Range0.73409
Interquartile range (IQR)0.29769

Descriptive statistics

Standard deviation0.19796987
Coefficient of variation (CV)0.0055509142
Kurtosis-1.0398863
Mean35.664372
Median Absolute Deviation (MAD)0.171365
Skewness0.12257235
Sum3566.4372
Variance0.03919207
MonotonicityNot monotonic
2023-12-10T22:39:01.616400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.62947 2
 
2.0%
35.38415 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%
35.77224 2
 
2.0%
35.74292 2
 
2.0%
35.72732 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%
Mean127.11715
Minimum126.5004
Maximum127.67801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:01.752572image/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 deviation0.30802139
Coefficient of variation (CV)0.0024231302
Kurtosis-0.93645367
Mean127.11715
Median Absolute Deviation (MAD)0.23396
Skewness0.083278539
Sum12711.715
Variance0.09487718
MonotonicityNot monotonic
2023-12-10T22:39:01.886707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.90344 2
 
2.0%
127.14097 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%
127.4985 2
 
2.0%
127.57067 2
 
2.0%
127.59682 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 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.9625
Minimum0
Maximum153.42
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:02.009660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52
Q12.55
median10.555
Q327.2325
95-th percentile65.7965
Maximum153.42
Range153.42
Interquartile range (IQR)24.6825

Descriptive statistics

Standard deviation26.03501
Coefficient of variation (CV)1.3729735
Kurtosis9.4152726
Mean18.9625
Median Absolute Deviation (MAD)8.49
Skewness2.7182777
Sum1896.25
Variance677.82177
MonotonicityNot monotonic
2023-12-10T22:39:02.138478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52 4
 
4.0%
0.65 4
 
4.0%
1.3 3
 
3.0%
0.0 3
 
3.0%
3.31 3
 
3.0%
1.05 2
 
2.0%
1.26 2
 
2.0%
2.31 2
 
2.0%
9.81 1
 
1.0%
40.61 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.52 4
4.0%
0.65 4
4.0%
1.05 2
2.0%
1.21 1
 
1.0%
1.26 2
2.0%
1.3 3
3.0%
1.78 1
 
1.0%
1.95 1
 
1.0%
1.98 1
 
1.0%
ValueCountFrequency (%)
153.42 1
1.0%
129.89 1
1.0%
82.51 1
1.0%
75.51 1
1.0%
73.14 1
1.0%
65.41 1
1.0%
65.01 1
1.0%
62.22 1
1.0%
58.35 1
1.0%
51.65 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5825
Minimum0
Maximum129.04
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:02.524553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28
Q11.63
median6.06
Q319.05
95-th percentile64.572
Maximum129.04
Range129.04
Interquartile range (IQR)17.42

Descriptive statistics

Standard deviation23.315013
Coefficient of variation (CV)1.4962306
Kurtosis7.7695273
Mean15.5825
Median Absolute Deviation (MAD)5.215
Skewness2.5687057
Sum1558.25
Variance543.58985
MonotonicityNot monotonic
2023-12-10T22:39:02.686528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 4
 
4.0%
0.32 4
 
4.0%
2.06 4
 
4.0%
0.64 3
 
3.0%
0.0 3
 
3.0%
0.55 2
 
2.0%
1.6 2
 
2.0%
0.96 2
 
2.0%
1.05 2
 
2.0%
5.91 2
 
2.0%
Other values (72) 72
72.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.28 4
4.0%
0.32 4
4.0%
0.55 2
2.0%
0.64 3
3.0%
0.96 2
2.0%
1.05 2
2.0%
1.11 1
 
1.0%
1.32 1
 
1.0%
1.33 1
 
1.0%
ValueCountFrequency (%)
129.04 1
1.0%
112.62 1
1.0%
74.19 1
1.0%
66.84 1
1.0%
65.75 1
1.0%
64.51 1
1.0%
57.07 1
1.0%
56.53 1
1.0%
49.76 1
1.0%
49.39 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2013
Minimum0
Maximum18.51
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:02.815493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.2525
median1.02
Q33.0325
95-th percentile7.959
Maximum18.51
Range18.51
Interquartile range (IQR)2.78

Descriptive statistics

Standard deviation3.2227213
Coefficient of variation (CV)1.4640082
Kurtosis8.7361349
Mean2.2013
Median Absolute Deviation (MAD)0.88
Skewness2.6636633
Sum220.13
Variance10.385933
MonotonicityNot monotonic
2023-12-10T22:39:02.952900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06 4
 
4.0%
0.04 4
 
4.0%
0.0 3
 
3.0%
1.15 3
 
3.0%
0.31 3
 
3.0%
0.12 3
 
3.0%
1.02 3
 
3.0%
0.09 2
 
2.0%
0.44 2
 
2.0%
0.14 2
 
2.0%
Other values (66) 71
71.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.04 4
4.0%
0.06 4
4.0%
0.09 2
2.0%
0.12 3
3.0%
0.13 1
 
1.0%
0.14 2
2.0%
0.17 1
 
1.0%
0.18 2
2.0%
0.2 1
 
1.0%
ValueCountFrequency (%)
18.51 1
1.0%
15.77 1
1.0%
10.18 1
1.0%
9.43 1
1.0%
8.7 1
1.0%
7.92 1
1.0%
7.59 1
1.0%
7.5 1
1.0%
6.98 1
1.0%
6.85 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.837
Minimum0
Maximum6.37
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:03.102725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.28
Q31.0875
95-th percentile3.5505
Maximum6.37
Range6.37
Interquartile range (IQR)0.9575

Descriptive statistics

Standard deviation1.2501115
Coefficient of variation (CV)1.4935621
Kurtosis5.2092165
Mean0.837
Median Absolute Deviation (MAD)0.27
Skewness2.248574
Sum83.7
Variance1.5627788
MonotonicityNot monotonic
2023-12-10T22:39:03.234740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 18
18.0%
0.13 13
13.0%
0.14 12
 
12.0%
0.28 6
 
6.0%
0.27 6
 
6.0%
0.42 4
 
4.0%
0.54 4
 
4.0%
0.41 2
 
2.0%
1.38 2
 
2.0%
0.4 2
 
2.0%
Other values (31) 31
31.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.13 13
13.0%
0.14 12
12.0%
0.27 6
 
6.0%
0.28 6
 
6.0%
0.4 2
 
2.0%
0.41 2
 
2.0%
0.42 4
 
4.0%
0.43 1
 
1.0%
0.54 4
 
4.0%
ValueCountFrequency (%)
6.37 1
1.0%
5.26 1
1.0%
4.42 1
1.0%
4.32 1
1.0%
3.56 1
1.0%
3.55 1
1.0%
3.39 1
1.0%
3.09 1
1.0%
2.99 1
1.0%
2.85 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4646.1544
Minimum0
Maximum36402.61
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:39:03.389736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138.68
Q1631.12
median2588.225
Q36505.8725
95-th percentile16668.808
Maximum36402.61
Range36402.61
Interquartile range (IQR)5874.7525

Descriptive statistics

Standard deviation6244.4376
Coefficient of variation (CV)1.3440013
Kurtosis8.8158946
Mean4646.1544
Median Absolute Deviation (MAD)2106.51
Skewness2.6330907
Sum464615.44
Variance38993001
MonotonicityNot monotonic
2023-12-10T22:39:03.587027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.68 4
 
4.0%
153.68 4
 
4.0%
307.36 3
 
3.0%
0.0 3
 
3.0%
873.34 3
 
3.0%
277.37 2
 
2.0%
324.24 2
 
2.0%
601.6 2
 
2.0%
2357.7 1
 
1.0%
10152.74 1
 
1.0%
Other values (75) 75
75.0%
ValueCountFrequency (%)
0.0 3
3.0%
138.68 4
4.0%
153.68 4
4.0%
277.37 2
2.0%
307.36 3
3.0%
318.6 1
 
1.0%
324.24 2
2.0%
461.05 1
 
1.0%
462.92 1
 
1.0%
487.28 1
 
1.0%
ValueCountFrequency (%)
36402.61 1
1.0%
30984.26 1
1.0%
19455.79 1
1.0%
17602.85 1
1.0%
17340.99 1
1.0%
16633.43 1
1.0%
15714.06 1
1.0%
15117.1 1
1.0%
14749.79 1
1.0%
14056.34 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전북 군산 개정 아동
 
4
전북 임실 덕치 회문
 
4
전북 장수 천천 용광
 
2
전북 완주 이서 이성
 
2
전북 익산 여산 제남
 
2
Other values (43)
86 

Length

Max length12
Median length11
Mean length10.96
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북 정읍 정우 우산
2nd row전북 정읍 정우 우산
3rd row전북 김제 금구 대화
4th row전북 김제 금구 대화
5th row전북 완주 이서 이성

Common Values

ValueCountFrequency (%)
전북 군산 개정 아동 4
 
4.0%
전북 임실 덕치 회문 4
 
4.0%
전북 장수 천천 용광 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 (38) 76
76.0%

Length

2023-12-10T22:39:03.764864image/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 12
 
3.0%
김제 8
 
2.0%
부안 8
 
2.0%
정읍 8
 
2.0%
익산 6
 
1.5%
군산 6
 
1.5%
무주 6
 
1.5%
Other values (93) 214
53.8%

Interactions

2023-12-10T22:38:58.062370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:50.818250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:51.741009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.656180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.647411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.413268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.420732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.190327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.251440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:58.162921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:50.889528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:51.820231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.750394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.741515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.499209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.509989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.270242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.329050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:58.270916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:50.960844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:51.905782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.860405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.821916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.586524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.602259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.353301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.409184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:58.369882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:51.043746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.009626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.963973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.903570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.682858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.690941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.449499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.499071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:58.483247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:51.113091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.093244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.067697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.975971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.783412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.772737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.537651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.578691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:58.575895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:51.185539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.258405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.194770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.058825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.929538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.854903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.623439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.666420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:58.676044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:51.257326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.363684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.294579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.138336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.055372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.928187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.710801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.749445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:58.784741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:51.332802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.473904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.390344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.233949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.178698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.014476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.800793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.856373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:58.891000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:51.668252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:52.565508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:53.507895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:54.320626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:55.321532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:56.100449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.167867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:38:57.956751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:39:03.884233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9970.7910.7490.8700.4610.3620.4210.4720.4490.998
지점1.0001.0000.0001.0001.0001.0001.0000.8610.8170.8960.8990.7551.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.1800.000
측정구간0.9971.0000.0001.0000.9950.9900.9980.8600.8160.8760.8940.8081.000
연장0.7911.0000.0000.9951.0000.6540.6590.3450.2860.2710.1980.2400.992
좌표위치위도0.7491.0000.0000.9900.6541.0000.8070.5430.5080.5650.5870.4800.997
좌표위치경도0.8701.0000.0000.9980.6590.8071.0000.3820.3720.3900.3560.3511.000
co0.4610.8610.0000.8600.3450.5430.3821.0000.9760.9800.8970.9980.868
nox0.3620.8170.0000.8160.2860.5080.3720.9761.0000.9920.9550.9760.812
hc0.4210.8960.0000.8760.2710.5650.3900.9800.9921.0000.9460.9750.887
pm0.4720.8990.0000.8940.1980.5870.3560.8970.9550.9461.0000.9020.860
co20.4490.7550.1800.8080.2400.4800.3510.9980.9760.9750.9021.0000.794
주소0.9981.0000.0001.0000.9920.9971.0000.8680.8120.8870.8600.7941.000
2023-12-10T22:39:04.047272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향측정구간
주소1.0000.0000.956
방향0.0001.0000.000
측정구간0.9560.0001.000
2023-12-10T22:39:04.155169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간주소
기본키1.0000.0630.119-0.4210.0250.0000.007-0.0340.0260.0000.7440.744
연장0.0631.000-0.060-0.050-0.151-0.162-0.154-0.115-0.1520.0000.7230.695
좌표위치위도0.119-0.0601.000-0.0900.3940.4130.4030.4100.3870.0000.7010.733
좌표위치경도-0.421-0.050-0.0901.000-0.418-0.398-0.404-0.345-0.4170.0000.7510.760
co0.025-0.1510.394-0.4181.0000.9900.9930.9340.9990.0000.4180.389
nox0.000-0.1620.413-0.3980.9901.0000.9980.9620.9880.0000.3660.327
hc0.007-0.1540.403-0.4040.9930.9981.0000.9540.9900.0000.4400.414
pm-0.034-0.1150.410-0.3450.9340.9620.9541.0000.9350.0000.4250.391
co20.026-0.1520.387-0.4170.9990.9880.9900.9351.0000.1280.3580.310
방향0.0000.0000.0000.0000.0000.0000.0000.0000.1281.0000.0000.000
측정구간0.7440.7230.7010.7510.4180.3660.4400.4250.3580.0001.0000.956
주소0.7440.6950.7330.7600.3890.3270.4140.3910.3100.0000.9561.000

Missing values

2023-12-10T22:38:59.056929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:38:59.247267image/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[0115-1]1정읍-태인6.420210501135.62947126.9034451.2946.556.62.2912543.64전북 정읍 정우 우산
12건기연[0115-1]2정읍-태인6.420210501135.62947126.9034458.3548.176.712.9914749.79전북 정읍 정우 우산
23건기연[0116-2]1금산-전주4.320210501135.78758127.035173.1474.1910.184.4217602.85전북 김제 금구 대화
34건기연[0116-2]2금산-전주4.320210501135.78758127.035175.5166.849.434.3217340.99전북 김제 금구 대화
45건기연[0117-3]1김제IC-전주5.120210501135.79995127.0582251.6557.076.643.3914056.34전북 완주 이서 이성
56건기연[0117-3]2김제IC-전주5.120210501135.79995127.0582265.4149.767.592.5515117.1전북 완주 이서 이성
67건기연[0121-4]1금마-연무4.920210501136.05245127.0806122.5322.323.331.445337.71전북 익산 여산 제남
78건기연[0121-4]2금마-연무4.920210501136.05245127.0806129.9430.924.152.067229.84전북 익산 여산 제남
89건기연[1317-0]1순창-남원2.420210501135.41493127.250872.631.640.260.13640.96전북 순창 적성 괴정
910건기연[1317-0]2순창-남원2.420210501135.41493127.250871.050.550.090.0277.37전북 순창 적성 괴정
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2912-1]1만경-백산4.620210501135.84681126.849013.312.060.310.13873.34전북 김제 만경 대동
9192건기연[2912-1]2만경-백산4.620210501135.84681126.849014.352.620.40.131150.71전북 김제 만경 대동
9293건기연[3003-0]1변산-하서3.620210501135.72216126.6459833.1717.083.020.277890.25전북 부안 하서 청호
9394건기연[3003-0]2변산-하서3.620210501135.72216126.6459812.957.541.150.273416.54전북 부안 하서 청호
9495건기연[3005-3]1부안IC-화호3.720210501135.72314126.787913.218.131.220.423469.04전북 부안 백산 덕신
9596건기연[3005-3]2부안IC-화호3.720210501135.72314126.78797.183.890.670.131716.74전북 부안 백산 덕신
9697건기연[3006-1]1신태인-태인6.520210501135.68032126.9152713.278.351.240.543499.0전북 정읍 신태인 궁사
9798건기연[3006-1]2신태인-태인6.520210501135.68032126.915279.025.910.860.422359.56전북 정읍 신태인 궁사
9899건기연[3010-0]1산내-강진15.720210501135.52124127.139821.950.960.170.0461.05전북 임실 덕치 회문
99100건기연[3010-0]2산내-강진15.720210501135.52124127.139820.650.320.060.0153.68전북 임실 덕치 회문