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
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
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 unique valuesUnique
nox has unique valuesUnique
hc has unique valuesUnique
pm has unique valuesUnique
co2 has unique valuesUnique

Reproduction

Analysis started2023-12-10 11:53:00.466318
Analysis finished2023-12-10 11:53:15.338798
Duration14.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:15.474240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:53:15.751264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T20:53:16.002194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:16.185467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:53:16.525893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.02
Min length8

Characters and Unicode

Total characters802
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[0101-0]
2nd row[0101-0]
3rd row[0101-1]
4th row[0101-1]
5th row[0104-0]
ValueCountFrequency (%)
0101-0 2
 
2.0%
1812-1 2
 
2.0%
2309-0 2
 
2.0%
1704-0 2
 
2.0%
1705-1 2
 
2.0%
1706-3 2
 
2.0%
1707-1 2
 
2.0%
1801-4 2
 
2.0%
1803-0 2
 
2.0%
1806-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:53:17.151581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 156
19.5%
0 144
18.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 38
 
4.7%
5 22
 
2.7%
8 20
 
2.5%
6 14
 
1.7%
Other values (3) 36
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 502
62.6%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 156
31.1%
0 144
28.7%
2 72
14.3%
3 38
 
7.6%
5 22
 
4.4%
8 20
 
4.0%
6 14
 
2.8%
7 14
 
2.8%
9 12
 
2.4%
4 10
 
2.0%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 802
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 156
19.5%
0 144
18.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 38
 
4.7%
5 22
 
2.7%
8 20
 
2.5%
6 14
 
1.7%
Other values (3) 36
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 156
19.5%
0 144
18.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 72
9.0%
3 38
 
4.7%
5 22
 
2.7%
8 20
 
2.5%
6 14
 
1.7%
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

2023-12-10T20:53:17.406313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:17.583607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:53:17.943736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.04
Min length3

Characters and Unicode

Total characters504
Distinct characters84
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row목포-무안
2nd row목포-무안
3rd row진도-무안
4th row진도-무안
5th row학교-장산
ValueCountFrequency (%)
목포-무안 2
 
2.0%
송광-목사동 2
 
2.0%
동강-함평 2
 
2.0%
여수-순천 2
 
2.0%
순천-황전 2
 
2.0%
구례구-용방 2
 
2.0%
구례-곡성 2
 
2.0%
백동-죽림 2
 
2.0%
진도-진도대교 2
 
2.0%
옥천-강진 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:53:18.627081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.8%
동 20
 
4.0%
주 18
 
3.6%
광 14
 
2.8%
진 14
 
2.8%
성 14
 
2.8%
산 14
 
2.8%
강 12
 
2.4%
장 12
 
2.4%
학 10
 
2.0%
Other values (74) 276
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 404
80.2%
Dash Punctuation 100
 
19.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 20
 
5.0%
주 18
 
4.5%
광 14
 
3.5%
진 14
 
3.5%
성 14
 
3.5%
산 14
 
3.5%
강 12
 
3.0%
장 12
 
3.0%
학 10
 
2.5%
천 10
 
2.5%
Other values (73) 266
65.8%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 404
80.2%
Common 100
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 20
 
5.0%
주 18
 
4.5%
광 14
 
3.5%
진 14
 
3.5%
성 14
 
3.5%
산 14
 
3.5%
강 12
 
3.0%
장 12
 
3.0%
학 10
 
2.5%
천 10
 
2.5%
Other values (73) 266
65.8%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 404
80.2%
ASCII 100
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
100.0%
Hangul
ValueCountFrequency (%)
동 20
 
5.0%
주 18
 
4.5%
광 14
 
3.5%
진 14
 
3.5%
성 14
 
3.5%
산 14
 
3.5%
강 12
 
3.0%
장 12
 
3.0%
학 10
 
2.5%
천 10
 
2.5%
Other values (73) 266
65.8%

연장
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.408
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:18.864528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile2.9
Q15.4
median9.3
Q312.8
95-th percentile21.8
Maximum33.8
Range31.2
Interquartile range (IQR)7.4

Descriptive statistics

Standard deviation6.2630176
Coefficient of variation (CV)0.60175035
Kurtosis2.5691494
Mean10.408
Median Absolute Deviation (MAD)3.8
Skewness1.3665604
Sum1040.8
Variance39.22539
MonotonicityNot monotonic
2023-12-10T20:53:19.080001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5.4 6
 
6.0%
12.5 6
 
6.0%
7.4 4
 
4.0%
10.3 4
 
4.0%
7.9 4
 
4.0%
5.2 2
 
2.0%
11.4 2
 
2.0%
24.3 2
 
2.0%
6.9 2
 
2.0%
8.0 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
2.6 2
2.0%
2.7 2
2.0%
2.9 2
2.0%
3.2 2
2.0%
3.5 2
2.0%
4.2 2
2.0%
4.3 2
2.0%
4.4 2
2.0%
4.5 2
2.0%
5.2 2
2.0%
ValueCountFrequency (%)
33.8 2
2.0%
24.3 2
2.0%
21.8 2
2.0%
21.3 2
2.0%
19.0 2
2.0%
18.0 2
2.0%
17.0 2
2.0%
16.0 2
2.0%
15.8 2
2.0%
14.5 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

2023-12-10T20:53:19.309488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:19.460169image/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
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T20:53:19.609521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:19.732376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.928845
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:19.902737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38107
5-th percentile34.46151
Q134.73192
median34.935545
Q335.16902
95-th percentile35.29816
Maximum35.34926
Range0.96819
Interquartile range (IQR)0.4371

Descriptive statistics

Standard deviation0.25535571
Coefficient of variation (CV)0.0073107401
Kurtosis-0.68888305
Mean34.928845
Median Absolute Deviation (MAD)0.207525
Skewness-0.28529313
Sum3492.8845
Variance0.065206538
MonotonicityNot monotonic
2023-12-10T20:53:20.548487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85192 2
 
2.0%
35.21934 2
 
2.0%
35.01799 2
 
2.0%
35.18146 2
 
2.0%
35.17397 2
 
2.0%
34.38107 2
 
2.0%
34.54517 2
 
2.0%
34.61562 2
 
2.0%
34.80445 2
 
2.0%
34.83385 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.38107 2
2.0%
34.38392 2
2.0%
34.46151 2
2.0%
34.54517 2
2.0%
34.55042 2
2.0%
34.55273 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.70297 2
2.0%
34.71151 2
2.0%
ValueCountFrequency (%)
35.34926 2
2.0%
35.3418 2
2.0%
35.29816 2
2.0%
35.28858 2
2.0%
35.27304 2
2.0%
35.2553 2
2.0%
35.22404 2
2.0%
35.21934 2
2.0%
35.21885 2
2.0%
35.18146 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93795
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:20.802012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.29822
Q1126.64704
median126.88985
Q3127.31619
95-th percentile127.55961
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.66915

Descriptive statistics

Standard deviation0.4085643
Coefficient of variation (CV)0.0032186144
Kurtosis-1.1136284
Mean126.93795
Median Absolute Deviation (MAD)0.360085
Skewness0.057309998
Sum12693.795
Variance0.16692479
MonotonicityNot monotonic
2023-12-10T20:53:21.096325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.42727 2
 
2.0%
127.48499 2
 
2.0%
127.46028 2
 
2.0%
127.46572 2
 
2.0%
127.43777 2
 
2.0%
126.21616 2
 
2.0%
126.29822 2
 
2.0%
126.74515 2
 
2.0%
127.10201 2
 
2.0%
127.09515 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.21616 2
2.0%
126.2341 2
2.0%
126.29822 2
2.0%
126.36491 2
2.0%
126.36721 2
2.0%
126.42727 2
2.0%
126.46074 2
2.0%
126.47853 2
2.0%
126.50315 2
2.0%
126.53424 2
2.0%
ValueCountFrequency (%)
127.75881 2
2.0%
127.61069 2
2.0%
127.55961 2
2.0%
127.48499 2
2.0%
127.46572 2
2.0%
127.46028 2
2.0%
127.4424 2
2.0%
127.43777 2
2.0%
127.37931 2
2.0%
127.36361 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3609.4743
Minimum259
Maximum20424.06
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:21.358007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum259
5-th percentile435.1565
Q11347.5175
median2723.175
Q34749.0775
95-th percentile8248.652
Maximum20424.06
Range20165.06
Interquartile range (IQR)3401.56

Descriptive statistics

Standard deviation3363.2544
Coefficient of variation (CV)0.93178512
Kurtosis9.3005556
Mean3609.4743
Median Absolute Deviation (MAD)1772.21
Skewness2.4910943
Sum360947.43
Variance11311480
MonotonicityNot monotonic
2023-12-10T20:53:21.640797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6928.59 1
 
1.0%
3103.34 1
 
1.0%
513.92 1
 
1.0%
736.9 1
 
1.0%
785.13 1
 
1.0%
515.93 1
 
1.0%
521.59 1
 
1.0%
3386.35 1
 
1.0%
3534.58 1
 
1.0%
2648.67 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
259.0 1
1.0%
263.92 1
1.0%
285.81 1
1.0%
301.4 1
1.0%
417.61 1
1.0%
436.08 1
1.0%
513.92 1
1.0%
515.93 1
1.0%
521.59 1
1.0%
573.94 1
1.0%
ValueCountFrequency (%)
20424.06 1
1.0%
19079.04 1
1.0%
11173.64 1
1.0%
10408.15 1
1.0%
8353.19 1
1.0%
8243.15 1
1.0%
8233.17 1
1.0%
8153.11 1
1.0%
7952.94 1
1.0%
7203.54 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4175.773
Minimum226.86
Maximum35434.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:21.890025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum226.86
5-th percentile426.8775
Q11337.63
median3037.9
Q35026.6025
95-th percentile9864.4625
Maximum35434.64
Range35207.78
Interquartile range (IQR)3688.9725

Descriptive statistics

Standard deviation5124.9146
Coefficient of variation (CV)1.2272972
Kurtosis22.252052
Mean4175.773
Median Absolute Deviation (MAD)1857.61
Skewness4.1861477
Sum417577.3
Variance26264750
MonotonicityNot monotonic
2023-12-10T20:53:22.177468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7331.05 1
 
1.0%
3013.07 1
 
1.0%
594.11 1
 
1.0%
686.08 1
 
1.0%
737.59 1
 
1.0%
498.73 1
 
1.0%
495.51 1
 
1.0%
3239.57 1
 
1.0%
4239.18 1
 
1.0%
2864.14 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
226.86 1
1.0%
249.96 1
1.0%
271.49 1
1.0%
288.07 1
1.0%
385.22 1
1.0%
429.07 1
1.0%
495.51 1
1.0%
498.73 1
1.0%
523.33 1
1.0%
594.11 1
1.0%
ValueCountFrequency (%)
35434.64 1
1.0%
32579.18 1
1.0%
13886.35 1
1.0%
12100.67 1
1.0%
11849.82 1
1.0%
9759.97 1
1.0%
8812.19 1
1.0%
8516.74 1
1.0%
8050.28 1
1.0%
7940.49 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.0902
Minimum32.49
Maximum3556.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:22.447788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.49
5-th percentile54.749
Q1169.965
median398.99
Q3647.08
95-th percentile1252.971
Maximum3556.55
Range3524.06
Interquartile range (IQR)477.115

Descriptive statistics

Standard deviation553.4596
Coefficient of variation (CV)1.0621186
Kurtosis14.65198
Mean521.0902
Median Absolute Deviation (MAD)236.875
Skewness3.2719183
Sum52109.02
Variance306317.53
MonotonicityNot monotonic
2023-12-10T20:53:22.714778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
976.86 1
 
1.0%
392.55 1
 
1.0%
79.98 1
 
1.0%
89.67 1
 
1.0%
99.43 1
 
1.0%
63.12 1
 
1.0%
63.13 1
 
1.0%
445.22 1
 
1.0%
573.65 1
 
1.0%
375.09 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
32.49 1
1.0%
36.2 1
1.0%
36.91 1
1.0%
39.33 1
1.0%
48.08 1
1.0%
55.1 1
1.0%
63.12 1
1.0%
63.13 1
1.0%
66.5 1
1.0%
79.98 1
1.0%
ValueCountFrequency (%)
3556.55 1
1.0%
3344.92 1
1.0%
1729.08 1
1.0%
1674.75 1
1.0%
1455.34 1
1.0%
1242.32 1
1.0%
1205.88 1
1.0%
1134.35 1
1.0%
1133.38 1
1.0%
987.63 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.1889
Minimum18.34
Maximum2247.18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:23.027660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.34
5-th percentile38.224
Q197.8175
median218.6
Q3348.3
95-th percentile677.696
Maximum2247.18
Range2228.84
Interquartile range (IQR)250.4825

Descriptive statistics

Standard deviation313.89748
Coefficient of variation (CV)1.1324316
Kurtosis21.320912
Mean277.1889
Median Absolute Deviation (MAD)123.285
Skewness4.0206817
Sum27718.89
Variance98531.628
MonotonicityNot monotonic
2023-12-10T20:53:23.321309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412.71 1
 
1.0%
188.77 1
 
1.0%
44.21 1
 
1.0%
51.53 1
 
1.0%
47.48 1
 
1.0%
47.68 1
 
1.0%
50.51 1
 
1.0%
165.4 1
 
1.0%
245.89 1
 
1.0%
220.63 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
18.34 1
1.0%
19.71 1
1.0%
22.95 1
1.0%
23.24 1
1.0%
36.21 1
1.0%
38.33 1
1.0%
43.12 1
1.0%
44.21 1
1.0%
45.91 1
1.0%
47.48 1
1.0%
ValueCountFrequency (%)
2247.18 1
1.0%
1882.97 1
1.0%
892.66 1
1.0%
795.69 1
1.0%
701.56 1
1.0%
676.44 1
1.0%
553.03 1
1.0%
528.34 1
1.0%
525.37 1
1.0%
514.22 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean913204.48
Minimum64373.29
Maximum5798107.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:23.569377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64373.29
5-th percentile110257.14
Q1307774.14
median660989.98
Q31195797
95-th percentile2050567.2
Maximum5798107.9
Range5733734.6
Interquartile range (IQR)888022.86

Descriptive statistics

Standard deviation905405.78
Coefficient of variation (CV)0.99146007
Kurtosis12.735516
Mean913204.48
Median Absolute Deviation (MAD)461695.95
Skewness2.9546822
Sum91320448
Variance8.1975962 × 1011
MonotonicityNot monotonic
2023-12-10T20:53:23.854957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1695185.34 1
 
1.0%
795217.28 1
 
1.0%
120812.94 1
 
1.0%
185018.74 1
 
1.0%
194547.44 1
 
1.0%
130255.48 1
 
1.0%
132600.94 1
 
1.0%
848939.82 1
 
1.0%
854146.53 1
 
1.0%
661852.23 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
64373.29 1
1.0%
64879.25 1
1.0%
71429.31 1
1.0%
74687.31 1
1.0%
106268.63 1
1.0%
110467.06 1
1.0%
120812.94 1
1.0%
130255.48 1
1.0%
131097.96 1
1.0%
132600.94 1
1.0%
ValueCountFrequency (%)
5798107.93 1
1.0%
5329874.41 1
1.0%
2812678.22 1
1.0%
2655456.68 1
1.0%
2097976.57 1
1.0%
2048071.96 1
1.0%
2023635.19 1
1.0%
1930581.34 1
1.0%
1851258.98 1
1.0%
1802823.51 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:53:24.357980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.86
Min length8

Characters and Unicode

Total characters1086
Distinct characters108
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
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 row전남 무안 삼향 왕산
2nd row전남 무안 삼향 왕산
3rd row전남 목포 죽교
4th row전남 목포 죽교
5th row전남 나주 다시 복암
ValueCountFrequency (%)
전남 100
25.1%
순천 12
 
3.0%
강진 8
 
2.0%
보성 8
 
2.0%
화순 8
 
2.0%
나주 6
 
1.5%
영광 6
 
1.5%
장성 6
 
1.5%
구례 6
 
1.5%
곡성 6
 
1.5%
Other values (97) 232
58.3%
2023-12-10T20:53:25.111684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
전 110
 
10.1%
남 110
 
10.1%
성 28
 
2.6%
순 20
 
1.8%
천 20
 
1.8%
산 16
 
1.5%
신 14
 
1.3%
안 14
 
1.3%
곡 14
 
1.3%
Other values (98) 442
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 788
72.6%
Space Separator 298
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 110
 
14.0%
남 110
 
14.0%
성 28
 
3.6%
순 20
 
2.5%
천 20
 
2.5%
산 16
 
2.0%
신 14
 
1.8%
안 14
 
1.8%
곡 14
 
1.8%
진 14
 
1.8%
Other values (97) 428
54.3%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 788
72.6%
Common 298
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 110
 
14.0%
남 110
 
14.0%
성 28
 
3.6%
순 20
 
2.5%
천 20
 
2.5%
산 16
 
2.0%
신 14
 
1.8%
안 14
 
1.8%
곡 14
 
1.8%
진 14
 
1.8%
Other values (97) 428
54.3%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 788
72.6%
ASCII 298
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
전 110
 
14.0%
남 110
 
14.0%
성 28
 
3.6%
순 20
 
2.5%
천 20
 
2.5%
산 16
 
2.0%
신 14
 
1.8%
안 14
 
1.8%
곡 14
 
1.8%
진 14
 
1.8%
Other values (97) 428
54.3%

Interactions

2023-12-10T20:53:13.252368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.214454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.481950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:03.664161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:05.052427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:06.637097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:08.234580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.315398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.855586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.396511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.318671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.598452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:03.789883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:05.217991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:06.805184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:08.383665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.468947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.992488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.561025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.444313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.704300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:03.910081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:05.389350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:06.977589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:08.570059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.625621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.153110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.712066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.612396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.834671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:04.076363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:05.563398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:07.145301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:08.853586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.798853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.294050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.907044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.781744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.983277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:04.259915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:05.757193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:07.337926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.064990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.007357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.484468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:14.084389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:01.924877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:03.137508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:04.404741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:05.945445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:07.510334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.252353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.190508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.643545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:14.257420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.063997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:03.260108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:04.569511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:06.141117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:07.676828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.420443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.363556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.796081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:14.426643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.231959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:03.382894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:04.716879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:06.312230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:07.881223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:09.600222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.564507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:12.952072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:14.569938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:02.359851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:03.510060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:04.858147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:06.464351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:08.069128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:10.156503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:11.704439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:13.089901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:53:25.310051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.4290.7490.8160.5570.7180.5800.3180.5321.000
지점1.0001.0000.0001.0001.0001.0001.0000.9770.9920.9930.8970.9911.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9770.9920.9930.8970.9911.000
연장0.4291.0000.0001.0001.0000.5910.6500.2870.2410.3550.0000.3271.000
좌표위치위도0.7491.0000.0001.0000.5911.0000.7540.5060.6420.5480.3990.4571.000
좌표위치경도0.8161.0000.0001.0000.6500.7541.0000.6210.6990.5830.4920.6161.000
co0.5570.9770.0000.9770.2870.5060.6211.0000.9150.8980.8650.9270.977
nox0.7180.9920.0000.9920.2410.6420.6990.9151.0000.9250.8800.9090.992
hc0.5800.9930.0000.9930.3550.5480.5830.8980.9251.0000.9600.9900.993
pm0.3180.8970.0000.8970.0000.3990.4920.8650.8800.9601.0000.9660.897
co20.5320.9910.0000.9910.3270.4570.6160.9270.9090.9900.9661.0000.991
주소1.0001.0000.0001.0001.0001.0001.0000.9770.9920.9930.8970.9911.000
2023-12-10T20:53:25.576806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.0000.0850.2230.138-0.349-0.387-0.357-0.361-0.3600.000
연장0.0851.000-0.070-0.103-0.071-0.055-0.063-0.067-0.0820.000
좌표위치위도0.223-0.0701.0000.201-0.046-0.037-0.034-0.063-0.0450.000
좌표위치경도0.138-0.1030.2011.000-0.124-0.091-0.098-0.100-0.1390.000
co-0.349-0.071-0.046-0.1241.0000.9830.9880.9730.9980.000
nox-0.387-0.055-0.037-0.0910.9831.0000.9930.9870.9830.000
hc-0.357-0.063-0.034-0.0980.9880.9931.0000.9830.9850.000
pm-0.361-0.067-0.063-0.1000.9730.9870.9831.0000.9720.000
co2-0.360-0.082-0.045-0.1390.9980.9830.9850.9721.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T20:53:14.811758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:53:15.190507image/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[0101-0]1목포-무안5.420210601034.85192126.427276928.597331.05976.86412.711695185.34전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210601034.85192126.427276874.176138.6818.46360.331747787.63전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210601034.80189126.364915102.666833.84672.8394.061477467.62전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210601034.80189126.364915124.946570.91667.78386.311459011.1전남 목포 죽교
45건기연[0104-0]1학교-장산12.520210601034.99062126.654881992.692114.08308.1164.12472032.53전남 나주 다시 복암
56건기연[0104-0]2학교-장산12.520210601034.99062126.654882298.782404.59352.35184.65521836.58전남 나주 다시 복암
67건기연[0108-0]1장성-북하2.920210601035.3418126.823911584.972091.21230.58126.13427608.26전남 장성 장성 상오
78건기연[0108-0]2장성-북하2.920210601035.3418126.823911709.51983.91233.63121.99449174.06전남 장성 장성 상오
89건기연[0109-0]1광주-장성7.420210601035.2553126.81217083.377638.14987.63487.951802823.51전남 장성 진원 산정
910건기연[0109-0]2광주-장성7.420210601035.2553126.81216761.328050.28978.33525.371763236.15전남 장성 진원 산정
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2302-1]1마량-관산12.520210601034.46151126.86526417.61385.2248.0843.12106268.63전남 장흥 대덕 신
9192건기연[2302-1]2마량-관산12.520210601034.46151126.86526436.08429.0755.148.95110467.06전남 장흥 대덕 신
9293건기연[2305-3]1금정-나주5.420210601034.88845126.748242077.011905.19268.02158.38517195.6전남 영암 금정 와운
9394건기연[2305-3]2금정-나주5.420210601034.88845126.748242459.92425.6343.47189.11603119.92전남 영암 금정 와운
9495건기연[2306-0]1나주-상방9.720210601034.95043126.647043621.193741.34495.0332.39913890.67전남 나주 왕곡 신포
9596건기연[2306-0]2나주-상방9.720210601034.95043126.647043577.183646.76506.44310.7873958.87전남 나주 왕곡 신포
9697건기연[2309-0]1동강-함평5.620210601035.03781126.534241961.91844.65245.63126.73488232.52전남 함평 학교 사거
9798건기연[2309-0]2동강-함평5.620210601035.03781126.534242185.272444.28324.38170.73520280.36전남 함평 학교 사거
9899건기연[2311-1]1신광-영광8.720210601035.21885126.503151352.131691.95230.67130.43309180.81전남 영광 불갑 안맹
99100건기연[2311-1]2신광-영광8.720210601035.21885126.503151333.681694.88230.74129.01303274.41전남 영광 불갑 안맹