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 11:53:54.271643
Analysis finished2023-12-10 11:54:09.842538
Duration15.57 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
2023-12-10T20:54:10.043962image/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
2023-12-10T20:54:10.372176image/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

2023-12-10T20:54:10.588939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:10.737840image/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
2023-12-10T20:54:10.984982image/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%
1801-4 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%
2023-12-10T20:54:11.602829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 148
18.5%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 88
11.0%
3 36
 
4.5%
5 22
 
2.7%
7 20
 
2.5%
4 18
 
2.2%
Other values (3) 40
 
5.0%

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 (%)
1 148
29.5%
0 130
25.9%
2 88
17.5%
3 36
 
7.2%
5 22
 
4.4%
7 20
 
4.0%
4 18
 
3.6%
8 16
 
3.2%
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 (%)
1 148
18.5%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 88
11.0%
3 36
 
4.5%
5 22
 
2.7%
7 20
 
2.5%
4 18
 
2.2%
Other values (3) 40
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 148
18.5%
0 130
16.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 88
11.0%
3 36
 
4.5%
5 22
 
2.7%
7 20
 
2.5%
4 18
 
2.2%
Other values (3) 40
 
5.0%

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

2023-12-10T20:54:11.858780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:12.062608image/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
2023-12-10T20:54:12.494120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5
Min length3

Characters and Unicode

Total characters500
Distinct characters89
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%
백동-죽림 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%
2023-12-10T20:54:13.161229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
20.0%
동 18
 
3.6%
주 16
 
3.2%
성 16
 
3.2%
도 12
 
2.4%
광 12
 
2.4%
강 12
 
2.4%
순 10
 
2.0%
산 10
 
2.0%
진 10
 
2.0%
Other values (79) 284
56.8%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 18
 
4.5%
주 16
 
4.0%
성 16
 
4.0%
도 12
 
3.0%
광 12
 
3.0%
강 12
 
3.0%
순 10
 
2.5%
산 10
 
2.5%
진 10
 
2.5%
천 10
 
2.5%
Other values (78) 274
68.5%
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 18
 
4.5%
주 16
 
4.0%
성 16
 
4.0%
도 12
 
3.0%
광 12
 
3.0%
강 12
 
3.0%
순 10
 
2.5%
산 10
 
2.5%
진 10
 
2.5%
천 10
 
2.5%
Other values (78) 274
68.5%
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 18
 
4.5%
주 16
 
4.0%
성 16
 
4.0%
도 12
 
3.0%
광 12
 
3.0%
강 12
 
3.0%
순 10
 
2.5%
산 10
 
2.5%
진 10
 
2.5%
천 10
 
2.5%
Other values (78) 274
68.5%

연장
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.612
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:13.422389image/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 deviation6.3263323
Coefficient of variation (CV)0.59614892
Kurtosis2.2179807
Mean10.612
Median Absolute Deviation (MAD)4.1
Skewness1.312909
Sum1061.2
Variance40.022481
MonotonicityNot monotonic
2023-12-10T20:54:14.079831image/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%
11.4 4
 
4.0%
7.9 4
 
4.0%
10.3 4
 
4.0%
4.5 2
 
2.0%
15.8 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%
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
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210401
2nd row20210401
3rd row20210401
4th row20210401
5th row20210401

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2023-12-10T20:54:14.338314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:14.566286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2023-12-10T20:54:14.764019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:54:14.945820image/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%
Mean34.93902
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:15.145570image/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 deviation0.25748651
Coefficient of variation (CV)0.0073695972
Kurtosis-0.73213445
Mean34.93902
Median Absolute Deviation (MAD)0.216715
Skewness-0.27081356
Sum3493.902
Variance0.066299301
MonotonicityNot monotonic
2023-12-10T20:54:15.395610image/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%
34.58881 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.67935 2
2.0%
34.71151 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%
Mean126.94458
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:15.629757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.2341
Q1126.59409
median126.97424
Q3127.2868
95-th percentile127.55961
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.69271

Descriptive statistics

Standard deviation0.40833892
Coefficient of variation (CV)0.0032166707
Kurtosis-1.0929558
Mean126.94458
Median Absolute Deviation (MAD)0.334575
Skewness-0.013967779
Sum12694.458
Variance0.16674068
MonotonicityNot monotonic
2023-12-10T20:54:15.869100image/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%
126.53424 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%
127.34844 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3300.3396
Minimum209.84
Maximum18883.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:16.115981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum209.84
5-th percentile331.151
Q11332.7575
median2454.52
Q34620.9575
95-th percentile7704.669
Maximum18883.96
Range18674.12
Interquartile range (IQR)3288.2

Descriptive statistics

Standard deviation2959.5094
Coefficient of variation (CV)0.89672875
Kurtosis9.335367
Mean3300.3396
Median Absolute Deviation (MAD)1506.745
Skewness2.461154
Sum330033.96
Variance8758696
MonotonicityNot monotonic
2023-12-10T20:54:16.346745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6070.27 1
 
1.0%
1189.08 1
 
1.0%
1627.46 1
 
1.0%
4914.85 1
 
1.0%
4973.27 1
 
1.0%
8208.53 1
 
1.0%
7678.15 1
 
1.0%
4316.06 1
 
1.0%
4278.22 1
 
1.0%
4120.93 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
209.84 1
1.0%
221.9 1
1.0%
270.16 1
1.0%
314.69 1
1.0%
322.24 1
1.0%
331.62 1
1.0%
547.02 1
1.0%
551.11 1
1.0%
699.39 1
1.0%
702.22 1
1.0%
ValueCountFrequency (%)
18883.96 1
1.0%
15582.74 1
1.0%
10340.83 1
1.0%
9539.07 1
1.0%
8208.53 1
1.0%
7678.15 1
1.0%
7137.38 1
1.0%
7030.88 1
1.0%
6534.67 1
1.0%
6233.03 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3964.6083
Minimum144.04
Maximum36067.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:16.571463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum144.04
5-th percentile339.9595
Q11256.0625
median2845.48
Q34958.2925
95-th percentile8231.5535
Maximum36067.36
Range35923.32
Interquartile range (IQR)3702.23

Descriptive statistics

Standard deviation4909.1398
Coefficient of variation (CV)1.2382408
Kurtosis24.819823
Mean3964.6083
Median Absolute Deviation (MAD)1736.53
Skewness4.423687
Sum396460.83
Variance24099654
MonotonicityNot monotonic
2023-12-10T20:54:16.817715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5930.88 1
 
1.0%
920.58 1
 
1.0%
1880.26 1
 
1.0%
5036.18 1
 
1.0%
5305.14 1
 
1.0%
7647.74 1
 
1.0%
8258.6 1
 
1.0%
3829.36 1
 
1.0%
3676.2 1
 
1.0%
3871.48 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
144.04 1
1.0%
154.52 1
1.0%
237.73 1
1.0%
314.12 1
1.0%
335.01 1
1.0%
340.22 1
1.0%
506.91 1
1.0%
539.96 1
1.0%
642.87 1
1.0%
756.66 1
1.0%
ValueCountFrequency (%)
36067.36 1
1.0%
29572.1 1
1.0%
13993.9 1
1.0%
11162.51 1
1.0%
8258.6 1
1.0%
8230.13 1
1.0%
8229.11 1
1.0%
8164.05 1
1.0%
8041.43 1
1.0%
7647.74 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean485.039
Minimum20.81
Maximum3539.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:17.059934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20.81
5-th percentile48.889
Q1164.915
median380.24
Q3621.4075
95-th percentile1121.924
Maximum3539.84
Range3519.03
Interquartile range (IQR)456.4925

Descriptive statistics

Standard deviation500.22123
Coefficient of variation (CV)1.031301
Kurtosis17.356047
Mean485.039
Median Absolute Deviation (MAD)230.155
Skewness3.4950558
Sum48503.9
Variance250221.27
MonotonicityNot monotonic
2023-12-10T20:54:17.290561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
787.05 1
 
1.0%
120.62 1
 
1.0%
227.48 1
 
1.0%
716.12 1
 
1.0%
739.03 1
 
1.0%
1126.37 1
 
1.0%
1121.69 1
 
1.0%
526.59 1
 
1.0%
508.93 1
 
1.0%
533.88 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
20.81 1
1.0%
22.22 1
1.0%
34.18 1
1.0%
42.89 1
1.0%
44.88 1
1.0%
49.1 1
1.0%
66.45 1
1.0%
70.09 1
1.0%
78.74 1
1.0%
91.29 1
1.0%
ValueCountFrequency (%)
3539.84 1
1.0%
2801.27 1
1.0%
1594.34 1
1.0%
1346.17 1
1.0%
1126.37 1
1.0%
1121.69 1
1.0%
1038.6 1
1.0%
995.01 1
1.0%
960.13 1
1.0%
956.99 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266.9994
Minimum14.07
Maximum2111.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:17.533527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.07
5-th percentile28.833
Q196.99
median208.075
Q3340.76
95-th percentile556.0205
Maximum2111.17
Range2097.1
Interquartile range (IQR)243.77

Descriptive statistics

Standard deviation298.02431
Coefficient of variation (CV)1.1161984
Kurtosis21.102903
Mean266.9994
Median Absolute Deviation (MAD)129.215
Skewness4.0008965
Sum26699.94
Variance88818.491
MonotonicityNot monotonic
2023-12-10T20:54:17.740397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
348.51 1
 
1.0%
65.75 1
 
1.0%
124.6 1
 
1.0%
337.96 1
 
1.0%
332.3 1
 
1.0%
464.52 1
 
1.0%
496.86 1
 
1.0%
229.32 1
 
1.0%
233.7 1
 
1.0%
304.49 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
14.07 1
1.0%
15.81 1
1.0%
24.78 1
1.0%
25.26 1
1.0%
25.85 1
1.0%
28.99 1
1.0%
41.52 1
1.0%
46.57 1
1.0%
47.94 1
1.0%
48.7 1
1.0%
ValueCountFrequency (%)
2111.17 1
1.0%
1825.81 1
1.0%
907.49 1
1.0%
724.56 1
1.0%
564.96 1
1.0%
555.55 1
1.0%
552.29 1
1.0%
496.86 1
1.0%
486.66 1
1.0%
483.09 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean839385.98
Minimum55365.55
Maximum5431996.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:54:17.980730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum55365.55
5-th percentile79800.591
Q1322162.91
median577574.51
Q31157097.9
95-th percentile1892325
Maximum5431996.1
Range5376630.5
Interquartile range (IQR)834935

Descriptive statistics

Standard deviation816801.21
Coefficient of variation (CV)0.9730937
Kurtosis13.075558
Mean839385.98
Median Absolute Deviation (MAD)384558.14
Skewness2.9826018
Sum83938598
Variance6.6716422 × 1011
MonotonicityNot monotonic
2023-12-10T20:54:18.234900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1521032.96 1
 
1.0%
309339.63 1
 
1.0%
418793.12 1
 
1.0%
1192761.84 1
 
1.0%
1214060.79 1
 
1.0%
1893021.15 1
 
1.0%
1892288.38 1
 
1.0%
1096381.82 1
 
1.0%
1089165.58 1
 
1.0%
1022694.0 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
55365.55 1
1.0%
58536.59 1
1.0%
68204.89 1
1.0%
76012.45 1
1.0%
77311.42 1
1.0%
79931.6 1
1.0%
136634.83 1
1.0%
139668.37 1
1.0%
164208.63 1
1.0%
166227.77 1
1.0%
ValueCountFrequency (%)
5431996.06 1
1.0%
4576645.9 1
1.0%
2687319.9 1
1.0%
2465439.25 1
1.0%
1893021.15 1
1.0%
1892288.38 1
1.0%
1809091.14 1
1.0%
1762241.74 1
1.0%
1712748.12 1
1.0%
1579971.12 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:54:18.655241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.88
Min length8

Characters and Unicode

Total characters1088
Distinct characters109
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%
고흥 10
 
2.5%
강진 10
 
2.5%
영광 8
 
2.0%
보성 8
 
2.0%
화순 8
 
2.0%
주암 6
 
1.5%
구례 6
 
1.5%
미력 6
 
1.5%
Other values (99) 224
56.3%
2023-12-10T20:54:19.291425image/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%
천 20
 
1.8%
순 20
 
1.8%
안 16
 
1.5%
양 14
 
1.3%
광 14
 
1.3%
산 14
 
1.3%
Other values (99) 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%
천 20
 
2.5%
순 20
 
2.5%
안 16
 
2.0%
양 14
 
1.8%
광 14
 
1.8%
산 14
 
1.8%
흥 12
 
1.5%
Other values (98) 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%
천 20
 
2.5%
순 20
 
2.5%
안 16
 
2.0%
양 14
 
1.8%
광 14
 
1.8%
산 14
 
1.8%
흥 12
 
1.5%
Other values (98) 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%
천 20
 
2.5%
순 20
 
2.5%
안 16
 
2.0%
양 14
 
1.8%
광 14
 
1.8%
산 14
 
1.8%
흥 12
 
1.5%
Other values (98) 434
54.9%

Interactions

2023-12-10T20:54:07.335254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:55.178085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:56.900662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:58.077024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:59.377174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:00.729043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:02.771740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:04.329792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:05.899028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:07.514339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:55.472450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:57.040351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:58.202033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:59.502124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:00.869047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:02.929262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:04.509331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:06.040607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:07.703142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:55.697276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:57.157824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:58.321394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:59.630768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:01.018045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:03.116908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:04.697695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:06.190636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:07.883613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:55.911347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:57.267387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:58.427500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:59.760067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:01.143474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:03.284921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:04.856874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:06.327369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:08.101754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:56.149312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:57.399324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:58.583087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:59.907006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:01.283116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:03.471935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:05.052120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:06.506767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:08.341229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:56.283942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:57.542259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:58.755466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:00.059834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:01.500913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:03.650932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:05.221926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:06.702663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:08.607384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:56.484090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:57.693668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:58.978801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:00.202499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:01.647303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:03.847693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:05.389285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:06.867522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:08.777433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:56.641888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:57.821257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:59.126754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:00.401981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:02.408218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:04.018218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:05.573545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:07.028227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:08.920469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:56.772290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:57.942863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:53:59.240500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:00.579443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:02.617409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:04.170976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:05.731486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:07.177407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:54:19.470662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.4690.7710.8270.4500.4330.4900.4250.4091.000
지점1.0001.0000.0001.0001.0001.0001.0000.8900.9360.8870.8490.9591.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.8900.9360.8870.8490.9591.000
연장0.4691.0000.0001.0001.0000.5990.7110.3690.2780.1550.0000.2141.000
좌표위치위도0.7711.0000.0001.0000.5991.0000.7290.5080.2860.3270.2250.3741.000
좌표위치경도0.8271.0000.0001.0000.7110.7291.0000.6030.5660.5090.5380.5601.000
co0.4500.8900.0000.8900.3690.5080.6031.0000.9280.9570.9400.9420.890
nox0.4330.9360.0000.9360.2780.2860.5660.9281.0000.9260.9540.9430.936
hc0.4900.8870.0000.8870.1550.3270.5090.9570.9261.0000.9940.9880.887
pm0.4250.8490.0000.8490.0000.2250.5380.9400.9540.9941.0000.9860.849
co20.4090.9590.0000.9590.2140.3740.5600.9420.9430.9880.9861.0000.959
주소1.0001.0000.0001.0001.0001.0001.0000.8900.9360.8870.8490.9591.000
2023-12-10T20:54:19.694545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.000-0.0470.377-0.029-0.316-0.333-0.312-0.326-0.3350.000
연장-0.0471.0000.024-0.189-0.069-0.078-0.082-0.093-0.0720.000
좌표위치위도0.3770.0241.0000.018-0.105-0.145-0.144-0.161-0.1060.000
좌표위치경도-0.029-0.1890.0181.000-0.006-0.003-0.019-0.020-0.0040.000
co-0.316-0.069-0.105-0.0061.0000.9800.9880.9690.9980.000
nox-0.333-0.078-0.145-0.0030.9801.0000.9940.9890.9820.000
hc-0.312-0.082-0.144-0.0190.9880.9941.0000.9850.9860.000
pm-0.326-0.093-0.161-0.0200.9690.9890.9851.0000.9700.000
co2-0.335-0.072-0.106-0.0040.9980.9820.9860.9701.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T20:54:09.160679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:54:09.651471image/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-무안5.420210401034.85192126.427276070.275930.88787.05348.511521032.96전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210401034.85192126.427276233.035899.95778.35342.741579971.12전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210401034.80189126.364915116.648041.43755.02474.291511013.27전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210401034.80189126.364915042.68229.11767.32486.661490029.7전남 목포 죽교
45건기연[0201-8]1목포-학산4.220210401034.82996126.478537030.887063.38960.13411.091762241.74전남 무안 삼향 용포
56건기연[0201-8]2목포-학산4.220210401034.82996126.478537137.387621.5995.01471.481809091.14전남 무안 삼향 용포
67건기연[0201-11]1암태-신안21.320210401034.86017126.23411529.211419.26204.0102.93351583.44전남 신안 압해 송공
78건기연[0201-11]2암태-신안21.320210401034.86017126.23411272.781256.25168.1696.81313356.29전남 신안 압해 송공
89건기연[0202-2]1성전-강진11.420210401034.67935126.721923813.944108.41551.2302.35936274.6전남 강진 성전 도림
910건기연[0202-2]2성전-강진11.420210401034.67935126.721923464.013573.68493.63259.42852668.62전남 강진 성전 도림
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2404-1]1현경-함평13.020210401035.02245126.43865331.62335.0149.128.9979931.6전남 무안 현경 평산
9192건기연[2404-1]2현경-함평13.020210401035.02245126.43865270.16237.7334.1824.7868204.89전남 무안 현경 평산
9293건기연[2406-3]1삼계-장성9.420210401035.28584126.74295728.958230.131038.6564.961355481.18전남 장성 동화 용정
9394건기연[2406-3]2삼계-장성9.420210401035.28584126.74295623.518164.05956.99555.551425252.06전남 장성 동화 용정
9495건기연[2408-2]1담양-순창4.920210401035.34316127.045411650.131732.54235.26105.78415120.3전남 담양 금성 봉서
9596건기연[2408-2]2담양-순창4.920210401035.34316127.045411551.261521.33208.54103.48392720.6전남 담양 금성 봉서
9697건기연[2701-2]1도양-고흥4.420210401034.58881127.265984130.094905.04644.38318.621016249.19전남 고흥 고흥 등암
9798건기연[2701-2]2도양-고흥4.420210401034.58881127.265983834.054932.33613.75328.36959323.93전남 고흥 고흥 등암
9899건기연[2701-7]1소록도-도덕6.720210401034.51686127.126771680.171911.11255.26136.53405967.2전남 고흥 도양 소록
99100건기연[2701-7]2소록도-도덕6.720210401034.51686127.126771590.631816.14239.97132.0389801.89전남 고흥 도양 소록