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 11:54:22.052529
Analysis finished2023-12-10 11:54:36.572945
Duration14.52 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
2023-12-10T20:54:36.725619image/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
2023-12-10T20:54:37.036719image/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

2023-12-10T20:54:37.294544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:37.519692image/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
2023-12-10T20:54:37.919762image/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[0201-8]
ValueCountFrequency (%)
0101-0 2
 
2.0%
2210-2 2
 
2.0%
2701-2 2
 
2.0%
1805-2 2
 
2.0%
1806-2 2
 
2.0%
1809-2 2
 
2.0%
1810-1 2
 
2.0%
1812-1 2
 
2.0%
1815-0 2
 
2.0%
2206-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:54:38.596806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 146
18.2%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 90
11.2%
3 34
 
4.2%
5 22
 
2.7%
7 20
 
2.5%
8 18
 
2.2%
Other values (3) 42
 
5.2%

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 (%)
1 146
29.1%
0 130
25.9%
2 90
17.9%
3 34
 
6.8%
5 22
 
4.4%
7 20
 
4.0%
8 18
 
3.6%
4 18
 
3.6%
6 16
 
3.2%
9 8
 
1.6%
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 (%)
1 146
18.2%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 90
11.2%
3 34
 
4.2%
5 22
 
2.7%
7 20
 
2.5%
8 18
 
2.2%
Other values (3) 42
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 146
18.2%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 90
11.2%
3 34
 
4.2%
5 22
 
2.7%
7 20
 
2.5%
8 18
 
2.2%
Other values (3) 42
 
5.2%

방향
Categorical

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T20:54:38.830362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:39.013612image/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
2023-12-10T20:54:39.373479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5
Min length3

Characters and Unicode

Total characters500
Distinct characters88
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 row목포-학산
ValueCountFrequency (%)
목포-무안 2
 
2.0%
능주-동 2
 
2.0%
도양-고흥 2
 
2.0%
황산-해남 2
 
2.0%
옥천-강진 2
 
2.0%
보성-복내 2
 
2.0%
미력-문덕 2
 
2.0%
송광-목사동 2
 
2.0%
마산-구례읍 2
 
2.0%
공음-영광 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:54:40.112292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
20.0%
동 16
 
3.2%
성 16
 
3.2%
주 16
 
3.2%
도 12
 
2.4%
산 12
 
2.4%
광 12
 
2.4%
강 10
 
2.0%
진 10
 
2.0%
천 10
 
2.0%
Other values (78) 286
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 400
80.0%
Dash Punctuation 100
 
20.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 16
 
4.0%
성 16
 
4.0%
주 16
 
4.0%
도 12
 
3.0%
산 12
 
3.0%
광 12
 
3.0%
강 10
 
2.5%
진 10
 
2.5%
천 10
 
2.5%
순 10
 
2.5%
Other values (77) 276
69.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 400
80.0%
Common 100
 
20.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 16
 
4.0%
성 16
 
4.0%
주 16
 
4.0%
도 12
 
3.0%
산 12
 
3.0%
광 12
 
3.0%
강 10
 
2.5%
진 10
 
2.5%
천 10
 
2.5%
순 10
 
2.5%
Other values (77) 276
69.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 400
80.0%
ASCII 100
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
100.0%
Hangul
ValueCountFrequency (%)
동 16
 
4.0%
성 16
 
4.0%
주 16
 
4.0%
도 12
 
3.0%
산 12
 
3.0%
광 12
 
3.0%
강 10
 
2.5%
진 10
 
2.5%
천 10
 
2.5%
순 10
 
2.5%
Other values (77) 276
69.0%

연장
Real number (ℝ)

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.59
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:40.402677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.2
Q15.4
median9.5
Q314.1
95-th percentile21.8
Maximum33.8
Range31.2
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation6.321592
Coefficient of variation (CV)0.59693975
Kurtosis2.2529062
Mean10.59
Median Absolute Deviation (MAD)4.1
Skewness1.326009
Sum1059
Variance39.962525
MonotonicityNot monotonic
2023-12-10T20:54:40.668706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5.4 6
 
6.0%
4.4 6
 
6.0%
11.4 6
 
6.0%
7.9 4
 
4.0%
10.3 4
 
4.0%
15.8 2
 
2.0%
11.7 2
 
2.0%
6.8 2
 
2.0%
14.1 2
 
2.0%
11.6 2
 
2.0%
Other values (32) 64
64.0%
ValueCountFrequency (%)
2.6 2
 
2.0%
2.7 2
 
2.0%
3.2 2
 
2.0%
4.2 2
 
2.0%
4.3 2
 
2.0%
4.4 6
6.0%
4.5 2
 
2.0%
4.9 2
 
2.0%
5.4 6
6.0%
5.6 2
 
2.0%
ValueCountFrequency (%)
33.8 2
2.0%
24.3 2
2.0%
21.8 2
2.0%
21.3 2
2.0%
19.5 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%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2023-12-10T20:54:40.928964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:41.117810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 100
100.0%

측정시간
Categorical

CONSTANT 

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

2023-12-10T20:54:41.302686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:41.474571image/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%
Mean34.936536
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:41.709933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38107
5-th percentile34.51686
Q134.73192
median34.944735
Q335.16902
95-th percentile35.32476
Maximum35.34926
Range0.96819
Interquartile range (IQR)0.4371

Descriptive statistics

Standard deviation0.26028217
Coefficient of variation (CV)0.0074501426
Kurtosis-0.78420914
Mean34.936536
Median Absolute Deviation (MAD)0.216715
Skewness-0.27006563
Sum3493.6536
Variance0.067746809
MonotonicityNot monotonic
2023-12-10T20:54:42.112375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85192 2
 
2.0%
35.05208 2
 
2.0%
34.61562 2
 
2.0%
34.80445 2
 
2.0%
34.83385 2
 
2.0%
35.05426 2
 
2.0%
35.21934 2
 
2.0%
35.34926 2
 
2.0%
35.22404 2
 
2.0%
35.16902 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.38107 2
2.0%
34.38392 2
2.0%
34.51686 2
2.0%
34.55042 2
2.0%
34.55273 2
2.0%
34.58728 2
2.0%
34.58881 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.67935 2
2.0%
ValueCountFrequency (%)
35.34926 2
2.0%
35.34316 2
2.0%
35.32476 2
2.0%
35.29816 2
2.0%
35.28858 2
2.0%
35.28584 2
2.0%
35.27304 2
2.0%
35.22404 2
2.0%
35.21934 2
2.0%
35.21885 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.92791
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:42.404845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.2341
Q1126.57464
median126.93796
Q3127.26851
95-th percentile127.55961
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.69387

Descriptive statistics

Standard deviation0.40852997
Coefficient of variation (CV)0.0032185984
Kurtosis-1.0901076
Mean126.92791
Median Absolute Deviation (MAD)0.346355
Skewness0.067138595
Sum12692.791
Variance0.16689674
MonotonicityNot monotonic
2023-12-10T20:54:42.702955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.42727 2
 
2.0%
127.2868 2
 
2.0%
126.74515 2
 
2.0%
127.10201 2
 
2.0%
127.09515 2
 
2.0%
127.26851 2
 
2.0%
127.48499 2
 
2.0%
126.46074 2
 
2.0%
126.5425 2
 
2.0%
126.66536 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.21616 2
2.0%
126.23118 2
2.0%
126.2341 2
2.0%
126.36491 2
2.0%
126.42727 2
2.0%
126.43865 2
2.0%
126.46074 2
2.0%
126.47853 2
2.0%
126.50315 2
2.0%
126.51479 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%
127.32438 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2116.8905
Minimum171.96
Maximum9376.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:43.132982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum171.96
5-th percentile222.4335
Q1906.8275
median1496.56
Q33046.0625
95-th percentile5124.0605
Maximum9376.16
Range9204.2
Interquartile range (IQR)2139.235

Descriptive statistics

Standard deviation1734.1593
Coefficient of variation (CV)0.81920121
Kurtosis3.4987794
Mean2116.8905
Median Absolute Deviation (MAD)1012.49
Skewness1.5699671
Sum211689.05
Variance3007308.3
MonotonicityNot monotonic
2023-12-10T20:54:43.449710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3495.95 1
 
1.0%
925.12 1
 
1.0%
967.12 1
 
1.0%
2723.12 1
 
1.0%
2468.49 1
 
1.0%
6040.56 1
 
1.0%
5390.56 1
 
1.0%
3575.81 1
 
1.0%
3705.05 1
 
1.0%
3767.45 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
171.96 1
1.0%
188.47 1
1.0%
190.82 1
1.0%
193.63 1
1.0%
214.52 1
1.0%
222.85 1
1.0%
244.74 1
1.0%
266.01 1
1.0%
411.5 1
1.0%
423.99 1
1.0%
ValueCountFrequency (%)
9376.16 1
1.0%
8540.12 1
1.0%
6040.56 1
1.0%
5390.56 1
1.0%
5387.6 1
1.0%
5110.19 1
1.0%
5102.87 1
1.0%
5039.4 1
1.0%
4705.73 1
1.0%
4665.89 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1848.6753
Minimum141.01
Maximum11034.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:43.684899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141.01
5-th percentile228.792
Q1742.9725
median1274.015
Q32487.21
95-th percentile4582.378
Maximum11034.11
Range10893.1
Interquartile range (IQR)1744.2375

Descriptive statistics

Standard deviation1727.6517
Coefficient of variation (CV)0.93453494
Kurtosis9.6213488
Mean1848.6753
Median Absolute Deviation (MAD)850.83
Skewness2.5440033
Sum184867.53
Variance2984780.3
MonotonicityNot monotonic
2023-12-10T20:54:44.309693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2720.64 1
 
1.0%
770.75 1
 
1.0%
762.56 1
 
1.0%
1788.52 1
 
1.0%
1699.36 1
 
1.0%
3682.27 1
 
1.0%
3514.66 1
 
1.0%
2287.5 1
 
1.0%
2402.63 1
 
1.0%
2623.77 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
141.01 1
1.0%
147.18 1
1.0%
150.52 1
1.0%
164.8 1
1.0%
175.82 1
1.0%
231.58 1
1.0%
244.2 1
1.0%
315.8 1
1.0%
336.43 1
1.0%
353.68 1
1.0%
ValueCountFrequency (%)
11034.11 1
1.0%
8911.67 1
1.0%
6408.4 1
1.0%
5416.54 1
1.0%
4643.9 1
1.0%
4579.14 1
1.0%
4049.07 1
1.0%
3743.57 1
1.0%
3682.27 1
1.0%
3658.05 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.8374
Minimum17.83
Maximum1283.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:44.576606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.83
5-th percentile25.992
Q196.315
median170.4
Q3335.025
95-th percentile605.5605
Maximum1283.17
Range1265.34
Interquartile range (IQR)238.71

Descriptive statistics

Standard deviation210.67543
Coefficient of variation (CV)0.87114496
Kurtosis6.3173316
Mean241.8374
Median Absolute Deviation (MAD)110.75
Skewness2.0167011
Sum24183.74
Variance44384.138
MonotonicityNot monotonic
2023-12-10T20:54:44.827865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
362.57 1
 
1.0%
97.22 1
 
1.0%
100.09 1
 
1.0%
265.16 1
 
1.0%
252.53 1
 
1.0%
597.29 1
 
1.0%
523.49 1
 
1.0%
338.86 1
 
1.0%
355.27 1
 
1.0%
403.4 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
17.83 1
1.0%
21.13 1
1.0%
21.94 1
1.0%
23.7 1
1.0%
24.13 1
1.0%
26.09 1
1.0%
27.42 1
1.0%
31.79 1
1.0%
42.17 1
1.0%
44.44 1
1.0%
ValueCountFrequency (%)
1283.17 1
1.0%
1003.75 1
1.0%
716.01 1
1.0%
665.97 1
1.0%
638.06 1
1.0%
603.85 1
1.0%
597.29 1
1.0%
564.43 1
1.0%
523.49 1
1.0%
521.75 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.5345
Minimum9.37
Maximum538.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:45.126804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.37
5-th percentile14.2285
Q143.4075
median79.53
Q3138.9475
95-th percentile329.4365
Maximum538.29
Range528.92
Interquartile range (IQR)95.54

Descriptive statistics

Standard deviation99.144935
Coefficient of variation (CV)0.92198258
Kurtosis5.4803258
Mean107.5345
Median Absolute Deviation (MAD)47.825
Skewness2.1747147
Sum10753.45
Variance9829.7182
MonotonicityNot monotonic
2023-12-10T20:54:45.389944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
121.88 1
 
1.0%
50.67 1
 
1.0%
41.13 1
 
1.0%
107.86 1
 
1.0%
88.92 1
 
1.0%
146.17 1
 
1.0%
138.76 1
 
1.0%
80.92 1
 
1.0%
91.41 1
 
1.0%
153.48 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
9.37 1
1.0%
12.23 1
1.0%
13.27 1
1.0%
13.35 1
1.0%
14.2 1
1.0%
14.23 1
1.0%
17.79 1
1.0%
19.71 1
1.0%
21.44 1
1.0%
21.71 1
1.0%
ValueCountFrequency (%)
538.29 1
1.0%
449.68 1
1.0%
421.92 1
1.0%
404.86 1
1.0%
379.72 1
1.0%
326.79 1
1.0%
302.97 1
1.0%
241.96 1
1.0%
240.26 1
1.0%
181.45 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540789.51
Minimum44433.93
Maximum2492701.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:45.649728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44433.93
5-th percentile61828.931
Q1223052.08
median379355.44
Q3785324.41
95-th percentile1334054.5
Maximum2492701.2
Range2448267.2
Interquartile range (IQR)562272.33

Descriptive statistics

Standard deviation450060.27
Coefficient of variation (CV)0.83222818
Kurtosis4.4290242
Mean540789.51
Median Absolute Deviation (MAD)257384.48
Skewness1.7201146
Sum54078951
Variance2.0255425 × 1011
MonotonicityNot monotonic
2023-12-10T20:54:45.941054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
897531.84 1
 
1.0%
237933.06 1
 
1.0%
249104.97 1
 
1.0%
712946.18 1
 
1.0%
638998.09 1
 
1.0%
1439928.65 1
 
1.0%
1403364.97 1
 
1.0%
933823.91 1
 
1.0%
966206.88 1
 
1.0%
892662.38 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
44433.93 1
1.0%
48075.28 1
1.0%
48422.46 1
1.0%
49095.3 1
1.0%
56572.98 1
1.0%
62105.56 1
1.0%
62379.69 1
1.0%
72402.27 1
1.0%
106007.24 1
1.0%
109659.01 1
1.0%
ValueCountFrequency (%)
2492701.17 1
1.0%
2317225.22 1
1.0%
1439928.65 1
1.0%
1403364.97 1
1.0%
1364255.62 1
1.0%
1332464.95 1
1.0%
1322265.91 1
1.0%
1294912.01 1
1.0%
1213303.27 1
1.0%
1132042.85 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:54:46.382683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.88
Min length8

Characters and Unicode

Total characters1088
Distinct characters110
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 무안 삼향 용포
ValueCountFrequency (%)
전남 100
25.1%
순천 12
 
3.0%
강진 10
 
2.5%
영광 8
 
2.0%
보성 8
 
2.0%
화순 8
 
2.0%
고흥 8
 
2.0%
구례 6
 
1.5%
무안 6
 
1.5%
해남 6
 
1.5%
Other values (99) 226
56.8%
2023-12-10T20:54:47.042899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
전 110
 
10.1%
남 110
 
10.1%
성 26
 
2.4%
순 20
 
1.8%
천 20
 
1.8%
안 16
 
1.5%
산 16
 
1.5%
광 14
 
1.3%
강 12
 
1.1%
Other values (100) 446
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 790
72.6%
Space Separator 298
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 110
 
13.9%
남 110
 
13.9%
성 26
 
3.3%
순 20
 
2.5%
천 20
 
2.5%
안 16
 
2.0%
산 16
 
2.0%
광 14
 
1.8%
강 12
 
1.5%
영 12
 
1.5%
Other values (99) 434
54.9%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 790
72.6%
Common 298
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 110
 
13.9%
남 110
 
13.9%
성 26
 
3.3%
순 20
 
2.5%
천 20
 
2.5%
안 16
 
2.0%
산 16
 
2.0%
광 14
 
1.8%
강 12
 
1.5%
영 12
 
1.5%
Other values (99) 434
54.9%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 790
72.6%
ASCII 298
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
전 110
 
13.9%
남 110
 
13.9%
성 26
 
3.3%
순 20
 
2.5%
천 20
 
2.5%
안 16
 
2.0%
산 16
 
2.0%
광 14
 
1.8%
강 12
 
1.5%
영 12
 
1.5%
Other values (99) 434
54.9%

Interactions

2023-12-10T20:54:34.563967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:22.765155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:24.305617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:25.427973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:27.037097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:28.403510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:29.543073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:30.631414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:32.898262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.717269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:22.878445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:24.432812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:25.529499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:27.184124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:28.517992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:29.666419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:30.745955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.175131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.874740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:23.407232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:24.570848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:25.653924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:27.348854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:28.651124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:29.782779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:30.883091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.401371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.002417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:23.523930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:24.693074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:25.748280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:27.503051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:28.780625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:29.884558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:30.997593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.551563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.160922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:23.672554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:24.844471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:26.073192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:27.678794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:28.938659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:30.018979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:31.159369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.743604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.289005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:23.793538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:24.943115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:26.419370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:27.825318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:29.045274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:30.132682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:31.281308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:33.881828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.471157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:23.924003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:25.063650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:26.570212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:27.981940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:29.167805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:30.256873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:31.424678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.047574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.619909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:24.058220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:25.218295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:26.720779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:28.135030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:29.298539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:30.394244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:31.628560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.236231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:35.773530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:24.172452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:25.320601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:26.879685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:28.272291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:29.427960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:30.510984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:31.779388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:34.395071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:54:47.219112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.4780.7660.8450.5010.4590.4770.6020.5081.000
지점1.0001.0000.0001.0001.0001.0001.0000.9490.8700.8790.9270.9651.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9490.8700.8790.9270.9651.000
연장0.4781.0000.0001.0001.0000.6110.7080.4620.0000.0000.0000.2841.000
좌표위치위도0.7661.0000.0001.0000.6111.0000.7390.3630.2670.4000.4890.4291.000
좌표위치경도0.8451.0000.0001.0000.7080.7391.0000.5020.5360.5590.7060.5281.000
co0.5010.9490.0000.9490.4620.3630.5021.0000.9410.9440.8460.9690.949
nox0.4590.8700.0000.8700.0000.2670.5360.9411.0000.9890.9180.8440.870
hc0.4770.8790.0000.8790.0000.4000.5590.9440.9891.0000.9160.8530.879
pm0.6020.9270.0000.9270.0000.4890.7060.8460.9180.9161.0000.8390.927
co20.5080.9650.0000.9650.2840.4290.5280.9690.8440.8530.8391.0000.965
주소1.0001.0000.0001.0001.0001.0001.0000.9490.8700.8790.9270.9651.000
2023-12-10T20:54:47.441308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.000-0.0410.362-0.028-0.360-0.380-0.362-0.340-0.3590.000
연장-0.0411.0000.026-0.221-0.068-0.104-0.102-0.114-0.0680.000
좌표위치위도0.3620.0261.0000.072-0.127-0.174-0.168-0.210-0.1190.000
좌표위치경도-0.028-0.2210.0721.000-0.043-0.002-0.0250.011-0.0410.000
co-0.360-0.068-0.127-0.0431.0000.9730.9870.9290.9990.000
nox-0.380-0.104-0.174-0.0020.9731.0000.9930.9710.9710.000
hc-0.362-0.102-0.168-0.0250.9870.9931.0000.9600.9840.000
pm-0.340-0.114-0.2100.0110.9290.9710.9601.0000.9240.000
co2-0.359-0.068-0.119-0.0410.9990.9710.9840.9241.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T20:54:36.019857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:54:36.399419image/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-무안5.420210301034.85192126.427273495.952720.64362.57121.88897531.84전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210301034.85192126.427273834.833173.2398.98145.35982456.28전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210301034.80189126.364913271.762352.51331.59102.08844781.77전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210301034.80189126.364913265.072355.83331.4103.73842951.35전남 목포 죽교
45건기연[0201-8]1목포-학산4.220210301034.82996126.478534705.733486.85490.79136.01213303.27전남 무안 삼향 용포
56건기연[0201-8]2목포-학산4.220210301034.82996126.478535039.43643.68521.75175.921294912.01전남 무안 삼향 용포
67건기연[0201-11]1암태-신안21.320210301034.86017126.23411705.781397.47196.166.57429771.37전남 신안 압해 송공
78건기연[0201-11]2암태-신안21.320210301034.86017126.23411572.651192.32165.8660.18403676.99전남 신안 압해 송공
89건기연[0202-2]1성전-강진11.420210301034.67935126.721922691.772039.82282.36108.18692319.2전남 강진 성전 도림
910건기연[0202-2]2성전-강진11.420210301034.67935126.721922842.972326.82314.26122.15727146.91전남 강진 성전 도림
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2404-1]1현경-함평13.020210301035.02245126.43865222.85175.8226.0913.2756572.98전남 무안 현경 평산
9192건기연[2404-1]2현경-함평13.020210301035.02245126.43865188.47150.5221.9413.3548075.28전남 무안 현경 평산
9293건기연[2406-3]1삼계-장성9.420210301035.28584126.74292502.932464.19335.88149.48621396.76전남 장성 동화 용정
9394건기연[2406-3]2삼계-장성9.420210301035.28584126.74292367.222079.33270.38117.97614451.73전남 장성 동화 용정
9495건기연[2408-2]1담양-순창4.920210301035.34316127.045411301.0790.49121.130.7341460.43전남 담양 금성 봉서
9596건기연[2408-2]2담양-순창4.920210301035.34316127.045411256.12743.45113.3632.33331774.5전남 담양 금성 봉서
9697건기연[2701-2]1도양-고흥4.420210301034.58881127.265983207.393545.42450.16181.45808590.26전남 고흥 고흥 등암
9798건기연[2701-2]2도양-고흥4.420210301034.58881127.265982443.932479.54330.07134.73606412.12전남 고흥 고흥 등암
9899건기연[2701-7]1소록도-도덕6.720210301034.51686127.126771166.071038.84141.4669.71292239.14전남 고흥 도양 소록
99100건기연[2701-7]2소록도-도덕6.720210301034.51686127.126771099.34962.6131.0463.87276537.59전남 고흥 도양 소록