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 11:53:28.139135
Analysis finished2023-12-10 11:53:42.485793
Duration14.35 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
2023-12-10T20:53:42.610650image/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
2023-12-10T20:53:42.848623image/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

2023-12-10T20:53:43.182129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:43.387155image/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
2023-12-10T20:53:43.735699image/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[0201-4]
ValueCountFrequency (%)
0101-0 2
 
2.0%
2213-1 2
 
2.0%
2901-0 2
 
2.0%
1806-2 2
 
2.0%
1809-2 2
 
2.0%
1812-1 2
 
2.0%
1815-0 2
 
2.0%
2206-0 2
 
2.0%
2207-1 2
 
2.0%
2209-1 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:53:44.393668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 146
18.2%
0 134
16.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 92
11.5%
3 34
 
4.2%
5 22
 
2.7%
7 20
 
2.5%
4 16
 
2.0%
Other values (3) 38
 
4.7%

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%
0 134
26.7%
2 92
18.3%
3 34
 
6.8%
5 22
 
4.4%
7 20
 
4.0%
4 16
 
3.2%
9 14
 
2.8%
8 12
 
2.4%
6 12
 
2.4%
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%
0 134
16.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 92
11.5%
3 34
 
4.2%
5 22
 
2.7%
7 20
 
2.5%
4 16
 
2.0%
Other values (3) 38
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 146
18.2%
0 134
16.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 92
11.5%
3 34
 
4.2%
5 22
 
2.7%
7 20
 
2.5%
4 16
 
2.0%
Other values (3) 38
 
4.7%

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

2023-12-10T20:53:44.632032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:44.798195image/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
2023-12-10T20:53:45.155242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.04
Min length3

Characters and Unicode

Total characters504
Distinct characters89
Distinct categories4 ?
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 row금계-강진
ValueCountFrequency (%)
목포-무안 2
 
2.0%
동복-승주 2
 
2.0%
18-용정 2
 
2.0%
옥천-강진 2
 
2.0%
보성-복내 2
 
2.0%
송광-목사동 2
 
2.0%
마산-구례읍 2
 
2.0%
공음-영광 2
 
2.0%
법성-해보 2
 
2.0%
문장-광주 2
 
2.0%
Other values (41) 82
80.4%
2023-12-10T20:53:45.744351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.8%
주 20
 
4.0%
동 16
 
3.2%
광 12
 
2.4%
성 12
 
2.4%
도 12
 
2.4%
강 12
 
2.4%
옥 10
 
2.0%
순 10
 
2.0%
천 10
 
2.0%
Other values (79) 290
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 398
79.0%
Dash Punctuation 100
 
19.8%
Decimal Number 4
 
0.8%
Space Separator 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 20
 
5.0%
동 16
 
4.0%
광 12
 
3.0%
성 12
 
3.0%
도 12
 
3.0%
강 12
 
3.0%
옥 10
 
2.5%
순 10
 
2.5%
천 10
 
2.5%
진 10
 
2.5%
Other values (75) 274
68.8%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
8 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 398
79.0%
Common 106
 
21.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 20
 
5.0%
동 16
 
4.0%
광 12
 
3.0%
성 12
 
3.0%
도 12
 
3.0%
강 12
 
3.0%
옥 10
 
2.5%
순 10
 
2.5%
천 10
 
2.5%
진 10
 
2.5%
Other values (75) 274
68.8%
Common
ValueCountFrequency (%)
- 100
94.3%
2
 
1.9%
1 2
 
1.9%
8 2
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 398
79.0%
ASCII 106
 
21.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
94.3%
2
 
1.9%
1 2
 
1.9%
8 2
 
1.9%
Hangul
ValueCountFrequency (%)
주 20
 
5.0%
동 16
 
4.0%
광 12
 
3.0%
성 12
 
3.0%
도 12
 
3.0%
강 12
 
3.0%
옥 10
 
2.5%
순 10
 
2.5%
천 10
 
2.5%
진 10
 
2.5%
Other values (75) 274
68.8%

연장
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.986
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:45.989962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.2
Q15.4
median8.35
Q312.8
95-th percentile21.3
Maximum33.8
Range31.2
Interquartile range (IQR)7.4

Descriptive statistics

Standard deviation6.094608
Coefficient of variation (CV)0.61031524
Kurtosis3.1398309
Mean9.986
Median Absolute Deviation (MAD)3.65
Skewness1.4886353
Sum998.6
Variance37.144246
MonotonicityNot monotonic
2023-12-10T20:53:46.240663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5.4 6
 
6.0%
4.4 6
 
6.0%
7.9 4
 
4.0%
10.3 4
 
4.0%
7.5 4
 
4.0%
21.3 2
 
2.0%
9.7 2
 
2.0%
11.7 2
 
2.0%
6.8 2
 
2.0%
14.1 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
2.6 2
 
2.0%
2.7 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 6
6.0%
4.5 2
 
2.0%
4.9 2
 
2.0%
5.2 2
 
2.0%
ValueCountFrequency (%)
33.8 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%
14.5 2
2.0%
14.3 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

2023-12-10T20:53:46.441975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:46.592306image/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
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

2023-12-10T20:53:46.762935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:53:46.915598image/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%
Mean34.939802
Minimum34.38392
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:47.110870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38392
5-th percentile34.55042
Q134.80189
median34.943685
Q335.09485
95-th percentile35.32476
Maximum35.34926
Range0.96534
Interquartile range (IQR)0.29296

Descriptive statistics

Standard deviation0.23860242
Coefficient of variation (CV)0.0068289575
Kurtosis-0.65389501
Mean34.939802
Median Absolute Deviation (MAD)0.14648
Skewness-0.1618726
Sum3493.9802
Variance0.056931116
MonotonicityNot monotonic
2023-12-10T20:53:47.358443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85192 2
 
2.0%
34.95043 2
 
2.0%
34.80445 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%
35.04504 2
 
2.0%
35.06978 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.38392 2
2.0%
34.51686 2
2.0%
34.55042 2
2.0%
34.55273 2
2.0%
34.58881 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.70297 2
2.0%
34.71151 2
2.0%
34.7172 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%
35.27304 2
2.0%
35.22404 2
2.0%
35.21934 2
2.0%
35.21885 2
2.0%
35.17397 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.94629
Minimum126.23118
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:47.570513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.23118
5-th percentile126.36491
Q1126.59409
median126.97955
Q3127.26851
95-th percentile127.55961
Maximum127.75881
Range1.52763
Interquartile range (IQR)0.67442

Descriptive statistics

Standard deviation0.39877236
Coefficient of variation (CV)0.0031412683
Kurtosis-1.0915614
Mean126.94629
Median Absolute Deviation (MAD)0.331145
Skewness-0.0043922599
Sum12694.629
Variance0.1590194
MonotonicityNot monotonic
2023-12-10T20:53:47.819074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.42727 2
 
2.0%
126.64704 2
 
2.0%
127.10201 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%
126.987 2
 
2.0%
127.25441 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.23118 2
2.0%
126.2341 2
2.0%
126.36491 2
2.0%
126.36721 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%
126.53424 2
2.0%
ValueCountFrequency (%)
127.75881 2
2.0%
127.61069 2
2.0%
127.55961 2
2.0%
127.48499 2
2.0%
127.46028 2
2.0%
127.4424 2
2.0%
127.43777 2
2.0%
127.37931 2
2.0%
127.34844 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%
Mean4400.4721
Minimum353.48
Maximum21660.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:48.048360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum353.48
5-th percentile770.755
Q11931.145
median3871.55
Q36047.9275
95-th percentile9136.8385
Maximum21660.3
Range21306.82
Interquartile range (IQR)4116.7825

Descriptive statistics

Standard deviation3379.2285
Coefficient of variation (CV)0.76792408
Kurtosis7.6147327
Mean4400.4721
Median Absolute Deviation (MAD)2015.92
Skewness2.0621546
Sum440047.21
Variance11419185
MonotonicityNot monotonic
2023-12-10T20:53:48.286847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6202.78 1
 
1.0%
5536.17 1
 
1.0%
2222.22 1
 
1.0%
756.03 1
 
1.0%
771.53 1
 
1.0%
2052.01 1
 
1.0%
1955.86 1
 
1.0%
4153.45 1
 
1.0%
4118.82 1
 
1.0%
7597.89 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
353.48 1
1.0%
374.69 1
1.0%
398.62 1
1.0%
413.38 1
1.0%
756.03 1
1.0%
771.53 1
1.0%
785.5 1
1.0%
847.98 1
1.0%
952.36 1
1.0%
1021.29 1
1.0%
ValueCountFrequency (%)
21660.3 1
1.0%
17801.62 1
1.0%
11120.78 1
1.0%
9435.26 1
1.0%
9269.05 1
1.0%
9129.88 1
1.0%
8728.38 1
1.0%
8312.65 1
1.0%
8092.99 1
1.0%
8070.8 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3823.0119
Minimum272.63
Maximum33057.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:48.523058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum272.63
5-th percentile624.7925
Q11550.685
median2976.01
Q34734.2525
95-th percentile8059.844
Maximum33057.73
Range32785.1
Interquartile range (IQR)3183.5675

Descriptive statistics

Standard deviation4016.7728
Coefficient of variation (CV)1.0506828
Kurtosis29.507248
Mean3823.0119
Median Absolute Deviation (MAD)1622.515
Skewness4.5523726
Sum382301.19
Variance16134463
MonotonicityNot monotonic
2023-12-10T20:53:48.770844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5747.73 1
 
1.0%
3779.13 1
 
1.0%
1910.79 1
 
1.0%
626.98 1
 
1.0%
642.61 1
 
1.0%
2024.61 1
 
1.0%
1935.27 1
 
1.0%
2800.73 1
 
1.0%
3022.83 1
 
1.0%
5296.93 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
272.63 1
1.0%
276.97 1
1.0%
302.36 1
1.0%
326.29 1
1.0%
583.23 1
1.0%
626.98 1
1.0%
630.24 1
1.0%
642.61 1
1.0%
728.53 1
1.0%
851.08 1
1.0%
ValueCountFrequency (%)
33057.73 1
1.0%
19110.36 1
1.0%
9237.11 1
1.0%
8745.33 1
1.0%
8264.36 1
1.0%
8049.08 1
1.0%
7994.86 1
1.0%
7967.11 1
1.0%
7488.69 1
1.0%
7454.38 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean505.3868
Minimum38.06
Maximum3273.23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:48.987980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.06
5-th percentile82.816
Q1209.49
median424.93
Q3655.56
95-th percentile1100.781
Maximum3273.23
Range3235.17
Interquartile range (IQR)446.07

Descriptive statistics

Standard deviation442.75464
Coefficient of variation (CV)0.87607084
Kurtosis16.323598
Mean505.3868
Median Absolute Deviation (MAD)221.08
Skewness3.1741269
Sum50538.68
Variance196031.67
MonotonicityNot monotonic
2023-12-10T20:53:49.198131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
775.64 1
 
1.0%
552.15 1
 
1.0%
265.7 1
 
1.0%
79.7 1
 
1.0%
83.62 1
 
1.0%
273.29 1
 
1.0%
257.2 1
 
1.0%
411.21 1
 
1.0%
430.44 1
 
1.0%
781.14 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
38.06 1
1.0%
39.99 1
1.0%
43.44 1
1.0%
44.8 1
1.0%
79.7 1
1.0%
82.98 1
1.0%
83.62 1
1.0%
88.33 1
1.0%
108.48 1
1.0%
116.08 1
1.0%
ValueCountFrequency (%)
3273.23 1
1.0%
2245.28 1
1.0%
1186.77 1
1.0%
1168.0 1
1.0%
1132.72 1
1.0%
1099.1 1
1.0%
1042.06 1
1.0%
1012.04 1
1.0%
955.27 1
1.0%
919.95 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.662
Minimum15.73
Maximum1959.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:49.416246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.73
5-th percentile36.1805
Q196.7125
median169.735
Q3272.605
95-th percentile516.622
Maximum1959.97
Range1944.24
Interquartile range (IQR)175.8925

Descriptive statistics

Standard deviation236.3839
Coefficient of variation (CV)1.0712488
Kurtosis29.694166
Mean220.662
Median Absolute Deviation (MAD)99.645
Skewness4.5254184
Sum22066.2
Variance55877.348
MonotonicityNot monotonic
2023-12-10T20:53:50.071623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
318.96 1
 
1.0%
172.08 1
 
1.0%
143.13 1
 
1.0%
36.34 1
 
1.0%
39.09 1
 
1.0%
152.81 1
 
1.0%
105.68 1
 
1.0%
168.43 1
 
1.0%
169.81 1
 
1.0%
243.78 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
15.73 1
1.0%
19.7 1
1.0%
22.26 1
1.0%
22.82 1
1.0%
33.15 1
1.0%
36.34 1
1.0%
38.28 1
1.0%
39.09 1
1.0%
45.74 1
1.0%
46.37 1
1.0%
ValueCountFrequency (%)
1959.97 1
1.0%
902.92 1
1.0%
752.81 1
1.0%
589.99 1
1.0%
578.79 1
1.0%
513.35 1
1.0%
512.44 1
1.0%
510.61 1
1.0%
479.47 1
1.0%
410.16 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1131270.6
Minimum90537.49
Maximum6184806.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:53:50.353282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum90537.49
5-th percentile197213.77
Q1490932.21
median1000313.9
Q31545613.2
95-th percentile2230564.2
Maximum6184806.1
Range6094268.6
Interquartile range (IQR)1054681

Descriptive statistics

Standard deviation907243.61
Coefficient of variation (CV)0.8019687
Kurtosis10.700315
Mean1131270.6
Median Absolute Deviation (MAD)523181.63
Skewness2.4689067
Sum1.1312706 × 108
Variance8.2309097 × 1011
MonotonicityNot monotonic
2023-12-10T20:53:50.609232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1557626.27 1
 
1.0%
1436236.16 1
 
1.0%
566411.46 1
 
1.0%
194146.07 1
 
1.0%
197375.23 1
 
1.0%
500907.06 1
 
1.0%
490055.34 1
 
1.0%
1083953.81 1
 
1.0%
1067080.9 1
 
1.0%
1963576.78 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
90537.49 1
1.0%
97027.77 1
1.0%
101353.61 1
1.0%
106602.89 1
1.0%
194146.07 1
1.0%
197375.23 1
1.0%
202767.8 1
1.0%
220000.32 1
1.0%
235576.79 1
1.0%
239864.45 1
1.0%
ValueCountFrequency (%)
6184806.11 1
1.0%
4811658.78 1
1.0%
2617890.18 1
1.0%
2437046.73 1
1.0%
2385065.56 1
1.0%
2222432.58 1
1.0%
2161766.95 1
1.0%
2148881.87 1
1.0%
2126865.19 1
1.0%
2085207.51 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:53:51.031890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.88
Min length8

Characters and Unicode

Total characters1088
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 영암 학산 묵동
ValueCountFrequency (%)
전남 100
25.1%
순천 14
 
3.5%
고흥 10
 
2.5%
화순 10
 
2.5%
주암 8
 
2.0%
보성 8
 
2.0%
영광 8
 
2.0%
강진 8
 
2.0%
무안 6
 
1.5%
신안 6
 
1.5%
Other values (97) 220
55.3%
2023-12-10T20:53:51.613027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
남 110
 
10.1%
전 108
 
9.9%
순 24
 
2.2%
천 22
 
2.0%
성 22
 
2.0%
안 18
 
1.7%
산 16
 
1.5%
양 16
 
1.5%
신 14
 
1.3%
Other values (98) 440
40.4%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 110
 
13.9%
전 108
 
13.7%
순 24
 
3.0%
천 22
 
2.8%
성 22
 
2.8%
안 18
 
2.3%
산 16
 
2.0%
양 16
 
2.0%
신 14
 
1.8%
광 14
 
1.8%
Other values (97) 426
53.9%
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 (%)
남 110
 
13.9%
전 108
 
13.7%
순 24
 
3.0%
천 22
 
2.8%
성 22
 
2.8%
안 18
 
2.3%
산 16
 
2.0%
양 16
 
2.0%
신 14
 
1.8%
광 14
 
1.8%
Other values (97) 426
53.9%
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 (%)
남 110
 
13.9%
전 108
 
13.7%
순 24
 
3.0%
천 22
 
2.8%
성 22
 
2.8%
안 18
 
2.3%
산 16
 
2.0%
양 16
 
2.0%
신 14
 
1.8%
광 14
 
1.8%
Other values (97) 426
53.9%

Interactions

2023-12-10T20:53:40.370655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:28.963311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:30.347391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:31.989125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:33.429488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:34.933795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:36.314502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:37.635027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:39.001739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:40.824000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:29.111315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:30.485603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:32.111288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:33.575286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:35.070666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:36.447075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:37.775572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:39.157071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:40.960705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:29.265268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:30.617679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:32.241693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:33.734462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:35.213912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:36.599143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:37.977214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:39.312184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:41.147600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:29.416612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:30.746690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:32.438178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:33.877869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:35.366149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:36.727794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:38.136679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:39.444742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:41.301236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:29.605716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:30.887585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:32.749467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:34.054280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:35.531435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:36.892385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:38.300658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:39.612686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:41.404774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:29.737664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:31.018847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:32.875408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:34.210218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:35.657699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:37.028086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:38.434802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:39.747995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:41.536252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:29.871642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:31.146852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:33.001286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:34.419557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:35.799412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:37.175346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:38.573233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:39.913822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:41.670585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:30.024994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:31.283202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:33.134760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:34.592949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:35.948540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:37.319976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:38.704232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:40.064695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:41.807879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:30.199498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:31.825503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:33.297263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:34.775129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:36.122206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:37.479827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:38.858488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:40.223659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:53:51.812406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.5310.8250.7710.4870.5070.4270.2860.4741.000
지점1.0001.0000.0001.0001.0001.0001.0000.9090.8550.8590.7510.8971.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9090.8550.8590.7510.8971.000
연장0.5311.0000.0001.0001.0000.5350.6550.3420.0000.0000.0000.0001.000
좌표위치위도0.8251.0000.0001.0000.5351.0000.7760.5060.4880.3890.2910.4491.000
좌표위치경도0.7711.0000.0001.0000.6550.7761.0000.5640.5350.4570.4610.5391.000
co0.4870.9090.0000.9090.3420.5060.5641.0000.9230.9220.8770.9770.909
nox0.5070.8550.0000.8550.0000.4880.5350.9231.0000.9320.9250.9240.855
hc0.4270.8590.0000.8590.0000.3890.4570.9220.9321.0000.9770.9120.859
pm0.2860.7510.0000.7510.0000.2910.4610.8770.9250.9771.0000.8620.751
co20.4740.8970.0000.8970.0000.4490.5390.9770.9240.9120.8621.0000.897
주소1.0001.0000.0001.0001.0001.0001.0000.9090.8550.8590.7510.8971.000
2023-12-10T20:53:52.067255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.000-0.0520.3610.090-0.306-0.309-0.293-0.290-0.2990.000
연장-0.0521.0000.093-0.120-0.056-0.066-0.067-0.083-0.0530.000
좌표위치위도0.3610.0931.000-0.025-0.372-0.375-0.368-0.363-0.3660.000
좌표위치경도0.090-0.120-0.0251.000-0.0200.007-0.0100.003-0.0220.000
co-0.306-0.056-0.372-0.0201.0000.9830.9900.9070.9980.000
nox-0.309-0.066-0.3750.0070.9831.0000.9970.9550.9780.000
hc-0.293-0.067-0.368-0.0100.9900.9971.0000.9440.9850.000
pm-0.290-0.083-0.3630.0030.9070.9550.9441.0000.8980.000
co2-0.299-0.053-0.366-0.0220.9980.9780.9850.8981.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T20:53:42.030717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:53:42.362922image/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-무안5.420210501034.85192126.427276202.785747.73775.64318.961557626.27전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210501034.85192126.427276305.694559.27631.86215.061632916.39전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210501034.80189126.364915559.973933.04541.43169.661460845.31전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210501034.80189126.364915711.953986.32555.98166.71499942.5전남 목포 죽교
45건기연[0201-4]1금계-강진12.620210501034.70297126.649763000.862499.4345.0174.38764932.33전남 영암 학산 묵동
56건기연[0201-4]2금계-강진12.620210501034.70297126.649762664.752399.1324.69209.9672950.26전남 영암 학산 묵동
67건기연[0201-8]1목포-학산4.220210501034.82996126.478539435.266573.93955.27269.822437046.73전남 무안 삼향 용포
78건기연[0201-8]2목포-학산4.220210501034.82996126.4785311120.787454.381186.77293.082617890.18전남 무안 삼향 용포
89건기연[0201-9]1목포-청계3.520210501034.83404126.367216170.754252.92612.78212.381603754.27전남 신안 압해 신장
910건기연[0201-9]2목포-청계3.520210501034.83404126.367216039.544082.97590.11197.971573702.03전남 신안 압해 신장
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2701-2]1도양-고흥4.420210501034.58881127.265985592.464294.86613.05209.811423869.49전남 고흥 고흥 등암
9192건기연[2701-2]2도양-고흥4.420210501034.58881127.265985718.084428.35623.73219.421466437.55전남 고흥 고흥 등암
9293건기연[2701-7]1소록도-도덕6.720210501034.51686127.126773048.262470.14341.27140.63780266.46전남 고흥 도양 소록
9394건기연[2701-7]2소록도-도덕6.720210501034.51686127.126773075.262330.13324.92129.63790708.97전남 고흥 도양 소록
9495건기연[2702-1]1옥과-주암7.520210501035.09485127.244031881.921555.23210.69121.44481941.97전남 순천 주암 한곡
9596건기연[2702-1]2옥과-주암7.520210501035.09485127.244031927.861625.97221.83128.66491224.5전남 순천 주암 한곡
9697건기연[2901-0]1일반 18-용정11.020210501034.82047127.094075832.614700.05626.56217.571541608.9전남 보성 미력 용정
9798건기연[2901-0]2일반 18-용정11.020210501034.82047127.094076290.324649.0654.89189.531643625.3전남 보성 미력 용정
9899건기연[2903-1]1금릉-능주5.220210501034.94833126.972098045.417488.69919.95410.162148881.87전남 화순 춘양 우봉
99100건기연[2903-1]2금릉-능주5.220210501034.94833126.972097989.956277.12858.08359.712065456.12전남 화순 춘양 우봉