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
Categorical5
Text2

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시분 has constant value ""Constant
기본키 is highly overall correlated with 측정구간High correlation
연장 is highly overall correlated with 측정구간High correlation
좌표위치위도 is highly overall correlated with 측정구간High correlation
좌표위치경도 is highly overall correlated with 측정구간High correlation
co is highly overall correlated with nox and 3 other fieldsHigh correlation
nox is highly overall correlated with co and 3 other fieldsHigh correlation
hc is highly overall correlated with co and 3 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with co and 3 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
co has unique valuesUnique
nox has unique valuesUnique
co2 has unique valuesUnique
pm has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:32:31.254284
Analysis finished2023-12-10 13:32:42.919605
Duration11.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:43.081852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T22:32:43.270572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T22:32:43.419120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:32:43.530989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:32:43.784075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
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[0122-2]
2nd row[0122-2]
3rd row[0124-0]
4th row[0124-0]
5th row[0127-2]
ValueCountFrequency (%)
0122-2 2
 
2.0%
3602-0 2
 
2.0%
4503-0 2
 
2.0%
2924-2 2
 
2.0%
3201-0 2
 
2.0%
3203-2 2
 
2.0%
3204-4 2
 
2.0%
3204-5 2
 
2.0%
3206-3 2
 
2.0%
3401-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T22:32:44.294626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 126
15.8%
[ 100
12.5%
2 100
12.5%
- 100
12.5%
] 100
12.5%
1 68
8.5%
3 64
8.0%
4 54
6.8%
9 24
 
3.0%
7 22
 
2.8%
Other values (3) 42
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 126
25.2%
2 100
20.0%
1 68
13.6%
3 64
12.8%
4 54
10.8%
9 24
 
4.8%
7 22
 
4.4%
6 20
 
4.0%
5 18
 
3.6%
8 4
 
0.8%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 126
15.8%
[ 100
12.5%
2 100
12.5%
- 100
12.5%
] 100
12.5%
1 68
8.5%
3 64
8.0%
4 54
6.8%
9 24
 
3.0%
7 22
 
2.8%
Other values (3) 42
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 126
15.8%
[ 100
12.5%
2 100
12.5%
- 100
12.5%
] 100
12.5%
1 68
8.5%
3 64
8.0%
4 54
6.8%
9 24
 
3.0%
7 22
 
2.8%
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

2023-12-10T22:32:44.509665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:32:44.645398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
공주-유성
 
4
홍성-고북
 
2
장항-화양
 
2
금남-조치원
 
2
전동-쌍전
 
2
Other values (44)
88 

Length

Max length7
Median length5
Mean length5.1
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연무-논산
2nd row연무-논산
3rd row논산-반포
4th row논산-반포
5th row금남-조치원

Common Values

ValueCountFrequency (%)
공주-유성 4
 
4.0%
홍성-고북 2
 
2.0%
장항-화양 2
 
2.0%
금남-조치원 2
 
2.0%
전동-쌍전 2
 
2.0%
둔포-평택 2
 
2.0%
장항-마서 2
 
2.0%
판교-옥산 2
 
2.0%
부여-논산 2
 
2.0%
연산-대전 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T22:32:44.793636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공주-유성 4
 
4.0%
연무-논산 2
 
2.0%
공주-어진동 2
 
2.0%
만리포-태안 2
 
2.0%
태안-서산 2
 
2.0%
당진-송악 2
 
2.0%
송악-합덕 2
 
2.0%
유구-사곡 2
 
2.0%
신평-인주 2
 
2.0%
공세-성환 2
 
2.0%
Other values (39) 78
78.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.85
Minimum1.8
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:44.941574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.2
Q15.6
median7
Q39.7
95-th percentile14.3
Maximum17.6
Range15.8
Interquartile range (IQR)4.1

Descriptive statistics

Standard deviation3.4974305
Coefficient of variation (CV)0.44553255
Kurtosis0.082202271
Mean7.85
Median Absolute Deviation (MAD)2.3
Skewness0.55760141
Sum785
Variance12.23202
MonotonicityNot monotonic
2023-12-10T22:32:45.133107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
6.4 4
 
4.0%
12.9 4
 
4.0%
9.7 4
 
4.0%
8.4 4
 
4.0%
6.6 4
 
4.0%
8.3 4
 
4.0%
9.3 4
 
4.0%
6.2 4
 
4.0%
1.8 2
 
2.0%
4.4 2
 
2.0%
Other values (32) 64
64.0%
ValueCountFrequency (%)
1.8 2
2.0%
2.0 2
2.0%
2.2 2
2.0%
2.7 2
2.0%
3.6 2
2.0%
4.0 2
2.0%
4.2 2
2.0%
4.3 2
2.0%
4.4 2
2.0%
5.2 2
2.0%
ValueCountFrequency (%)
17.6 2
2.0%
14.6 2
2.0%
14.3 2
2.0%
14.2 2
2.0%
12.9 4
4.0%
12.2 2
2.0%
11.6 2
2.0%
11.5 2
2.0%
10.6 2
2.0%
10.2 2
2.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2023-12-10T22:32:45.310583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:32:45.434851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 100
100.0%

측정시분
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

2023-12-10T22:32:45.573892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:32:45.699203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.504863
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:45.853477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.26956
median36.500575
Q336.74848
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.47892

Descriptive statistics

Standard deviation0.27125911
Coefficient of variation (CV)0.0074307664
Kurtosis-1.2112668
Mean36.504863
Median Absolute Deviation (MAD)0.23946
Skewness-0.051871256
Sum3650.4863
Variance0.073581506
MonotonicityNot monotonic
2023-12-10T22:32:46.069577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.48872 2
 
2.0%
36.75792 2
 
2.0%
36.78489 2
 
2.0%
36.90325 2
 
2.0%
36.87015 2
 
2.0%
36.51243 2
 
2.0%
36.87646 2
 
2.0%
36.9261 2
 
2.0%
36.89461 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
36.02784 2
2.0%
36.05892 2
2.0%
36.07008 2
2.0%
36.08997 2
2.0%
36.1228 2
2.0%
36.14511 2
2.0%
36.16232 2
2.0%
36.18861 2
2.0%
36.1897 2
2.0%
36.21913 2
2.0%
ValueCountFrequency (%)
36.95295 2
2.0%
36.9261 2
2.0%
36.90325 2
2.0%
36.89461 2
2.0%
36.87646 2
2.0%
36.87015 2
2.0%
36.85256 2
2.0%
36.83831 2
2.0%
36.83292 2
2.0%
36.78489 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.92549
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:46.283610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.37641
Q1126.71196
median126.89799
Q3127.15746
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.4455

Descriptive statistics

Standard deviation0.30957532
Coefficient of variation (CV)0.0024390319
Kurtosis-0.41401517
Mean126.92549
Median Absolute Deviation (MAD)0.23154
Skewness-0.13989551
Sum12692.549
Variance0.095836877
MonotonicityNot monotonic
2023-12-10T22:32:46.888419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
127.20167 2
 
2.0%
126.18913 2
 
2.0%
126.37641 2
 
2.0%
126.64887 2
 
2.0%
126.75145 2
 
2.0%
126.96572 2
 
2.0%
126.86923 2
 
2.0%
127.11 2
 
2.0%
127.15746 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.18913 2
2.0%
126.28686 2
2.0%
126.37641 2
2.0%
126.44223 2
2.0%
126.5301 2
2.0%
126.61045 2
2.0%
126.61312 2
2.0%
126.64274 2
2.0%
126.64887 2
2.0%
126.66451 2
2.0%
ValueCountFrequency (%)
127.49717 2
2.0%
127.49544 2
2.0%
127.47469 2
2.0%
127.42036 2
2.0%
127.28963 2
2.0%
127.28536 2
2.0%
127.27513 2
2.0%
127.25821 2
2.0%
127.24084 2
2.0%
127.22854 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.3504
Minimum1.38
Maximum263.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:47.118037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.38
5-th percentile4.1745
Q128.5625
median56.38
Q388.5825
95-th percentile142.593
Maximum263.9
Range262.52
Interquartile range (IQR)60.02

Descriptive statistics

Standard deviation51.364739
Coefficient of variation (CV)0.79820388
Kurtosis2.0460878
Mean64.3504
Median Absolute Deviation (MAD)31.005
Skewness1.2562679
Sum6435.04
Variance2638.3364
MonotonicityNot monotonic
2023-12-10T22:32:47.314084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.17 1
 
1.0%
12.38 1
 
1.0%
50.16 1
 
1.0%
41.88 1
 
1.0%
41.0 1
 
1.0%
76.15 1
 
1.0%
112.3 1
 
1.0%
62.08 1
 
1.0%
64.01 1
 
1.0%
91.8 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.38 1
1.0%
1.95 1
1.0%
2.62 1
1.0%
2.78 1
1.0%
3.88 1
1.0%
4.19 1
1.0%
4.63 1
1.0%
5.28 1
1.0%
6.45 1
1.0%
7.21 1
1.0%
ValueCountFrequency (%)
263.9 1
1.0%
215.93 1
1.0%
198.88 1
1.0%
195.46 1
1.0%
163.74 1
1.0%
141.48 1
1.0%
141.0 1
1.0%
138.86 1
1.0%
137.13 1
1.0%
136.88 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.6067
Minimum0.96
Maximum311.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:47.512512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.96
5-th percentile2.4195
Q116.07
median41.12
Q371.58
95-th percentile133.829
Maximum311.25
Range310.29
Interquartile range (IQR)55.51

Descriptive statistics

Standard deviation48.868945
Coefficient of variation (CV)0.94694961
Kurtosis7.5307901
Mean51.6067
Median Absolute Deviation (MAD)26.48
Skewness2.1229219
Sum5160.67
Variance2388.1737
MonotonicityNot monotonic
2023-12-10T22:32:47.705015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.13 1
 
1.0%
7.17 1
 
1.0%
26.5 1
 
1.0%
23.28 1
 
1.0%
22.93 1
 
1.0%
68.59 1
 
1.0%
153.04 1
 
1.0%
78.54 1
 
1.0%
90.19 1
 
1.0%
69.47 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.96 1
1.0%
1.09 1
1.0%
1.39 1
1.0%
1.79 1
1.0%
2.22 1
1.0%
2.43 1
1.0%
2.7 1
1.0%
3.02 1
1.0%
3.72 1
1.0%
4.24 1
1.0%
ValueCountFrequency (%)
311.25 1
1.0%
191.13 1
1.0%
179.21 1
1.0%
153.04 1
1.0%
138.37 1
1.0%
133.59 1
1.0%
128.4 1
1.0%
120.82 1
1.0%
117.55 1
1.0%
111.3 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2098
Minimum0.16
Maximum32.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:47.883587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.16
5-th percentile0.3595
Q12.69
median5.77
Q39.96
95-th percentile20.066
Maximum32.05
Range31.89
Interquartile range (IQR)7.27

Descriptive statistics

Standard deviation6.2171179
Coefficient of variation (CV)0.8623149
Kurtosis2.3816245
Mean7.2098
Median Absolute Deviation (MAD)3.735
Skewness1.3977876
Sum720.98
Variance38.652556
MonotonicityNot monotonic
2023-12-10T22:32:48.048411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.69 2
 
2.0%
1.1 1
 
1.0%
4.62 1
 
1.0%
3.63 1
 
1.0%
3.56 1
 
1.0%
9.72 1
 
1.0%
17.68 1
 
1.0%
9.94 1
 
1.0%
9.67 1
 
1.0%
10.03 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.16 1
1.0%
0.17 1
1.0%
0.22 1
1.0%
0.26 1
1.0%
0.35 1
1.0%
0.36 1
1.0%
0.44 1
1.0%
0.5 1
1.0%
0.57 1
1.0%
0.7 1
1.0%
ValueCountFrequency (%)
32.05 1
1.0%
26.41 1
1.0%
22.38 1
1.0%
21.35 1
1.0%
20.56 1
1.0%
20.04 1
1.0%
17.68 1
1.0%
17.23 1
1.0%
16.78 1
1.0%
15.06 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.727
Minimum0
Maximum20.26
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:48.208440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.645
median2.12
Q33.935
95-th percentile6.5965
Maximum20.26
Range20.26
Interquartile range (IQR)3.29

Descriptive statistics

Standard deviation2.8904379
Coefficient of variation (CV)1.0599332
Kurtosis13.307588
Mean2.727
Median Absolute Deviation (MAD)1.59
Skewness2.8140475
Sum272.7
Variance8.3546313
MonotonicityNot monotonic
2023-12-10T22:32:48.421257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14 5
 
5.0%
0.28 4
 
4.0%
0.27 3
 
3.0%
0.13 3
 
3.0%
0.0 3
 
3.0%
2.12 2
 
2.0%
0.54 2
 
2.0%
2.69 2
 
2.0%
1.07 2
 
2.0%
0.69 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 3
3.0%
0.13 3
3.0%
0.14 5
5.0%
0.27 3
3.0%
0.28 4
4.0%
0.39 1
 
1.0%
0.4 1
 
1.0%
0.42 1
 
1.0%
0.52 1
 
1.0%
0.54 2
 
2.0%
ValueCountFrequency (%)
20.26 1
1.0%
11.78 1
1.0%
9.6 1
1.0%
7.48 1
1.0%
6.91 1
1.0%
6.58 1
1.0%
6.48 1
1.0%
6.31 1
1.0%
6.16 1
1.0%
6.08 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16077.217
Minimum339.23
Maximum62717.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:48.620353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum339.23
5-th percentile1103.141
Q16455.735
median14329.615
Q322753.048
95-th percentile36188.805
Maximum62717.82
Range62378.59
Interquartile range (IQR)16297.312

Descriptive statistics

Standard deviation12539.356
Coefficient of variation (CV)0.77994569
Kurtosis1.6320325
Mean16077.217
Median Absolute Deviation (MAD)7991.32
Skewness1.1366137
Sum1607721.7
Variance1.5723546 × 108
MonotonicityNot monotonic
2023-12-10T22:32:48.798804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18109.41 1
 
1.0%
3272.22 1
 
1.0%
11964.69 1
 
1.0%
11135.48 1
 
1.0%
10843.68 1
 
1.0%
18835.01 1
 
1.0%
28728.75 1
 
1.0%
15784.89 1
 
1.0%
18491.68 1
 
1.0%
23328.85 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
339.23 1
1.0%
461.05 1
1.0%
693.42 1
1.0%
734.66 1
1.0%
1017.66 1
1.0%
1107.64 1
1.0%
1109.48 1
1.0%
1261.32 1
1.0%
1705.45 1
1.0%
1742.97 1
1.0%
ValueCountFrequency (%)
62717.82 1
1.0%
55621.44 1
1.0%
45855.15 1
1.0%
45058.03 1
1.0%
38588.42 1
1.0%
36062.51 1
1.0%
35697.4 1
1.0%
35697.3 1
1.0%
35612.03 1
1.0%
33050.92 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:32:49.163132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.94
Min length8

Characters and Unicode

Total characters1094
Distinct characters107
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 row충남 논산 은진 토양
2nd row충남 논산 은진 토양
3rd row충남 논산 연산 천호
4th row충남 논산 연산 천호
5th row충남 세종 연서 봉암
ValueCountFrequency (%)
충남 100
25.1%
청양 10
 
2.5%
금산 10
 
2.5%
예산 10
 
2.5%
아산 10
 
2.5%
공주 8
 
2.0%
서천 8
 
2.0%
천안 8
 
2.0%
세종 8
 
2.0%
서산 6
 
1.5%
Other values (97) 220
55.3%
2023-12-10T22:32:49.763794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
남 104
 
9.5%
충 100
 
9.1%
산 62
 
5.7%
천 24
 
2.2%
서 22
 
2.0%
성 16
 
1.5%
청 16
 
1.5%
안 14
 
1.3%
주 12
 
1.1%
Other values (97) 426
38.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 796
72.8%
Space Separator 298
 
27.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 104
 
13.1%
충 100
 
12.6%
산 62
 
7.8%
천 24
 
3.0%
서 22
 
2.8%
성 16
 
2.0%
청 16
 
2.0%
안 14
 
1.8%
주 12
 
1.5%
봉 12
 
1.5%
Other values (96) 414
52.0%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 796
72.8%
Common 298
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 104
 
13.1%
충 100
 
12.6%
산 62
 
7.8%
천 24
 
3.0%
서 22
 
2.8%
성 16
 
2.0%
청 16
 
2.0%
안 14
 
1.8%
주 12
 
1.5%
봉 12
 
1.5%
Other values (96) 414
52.0%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 796
72.8%
ASCII 298
 
27.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
남 104
 
13.1%
충 100
 
12.6%
산 62
 
7.8%
천 24
 
3.0%
서 22
 
2.8%
성 16
 
2.0%
청 16
 
2.0%
안 14
 
1.8%
주 12
 
1.5%
봉 12
 
1.5%
Other values (96) 414
52.0%

Interactions

2023-12-10T22:32:41.346244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:32.037700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:33.003450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:34.116351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:35.251364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:36.449207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:37.576680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:38.714323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:40.109837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:41.479618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:32.146122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:33.091358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:34.256284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:35.372438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:36.592235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:37.678028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:38.823726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:40.247835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:41.612389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:32.275536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:33.207783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:34.387376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:35.519056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:36.737010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:37.800048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:38.960027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:40.394183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:41.747079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:32.385876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:33.374658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:34.496979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:35.651433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:36.838368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:37.921087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:39.368417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:40.506513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:41.886274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:32.508768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:33.508370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:34.636817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:35.791722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:36.954051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:38.066749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:39.505326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:40.654566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:41.998721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:32.619074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:33.662539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:34.738519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:35.936451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:37.062409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:38.191180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:39.615469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:40.784120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:42.127201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:32.733370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:33.774369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:34.851495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:36.063668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:37.178762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:38.304770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:39.722895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:40.940069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:42.253785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:32.817425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:33.871995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:34.963443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:36.189141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:37.305859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:38.435912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:39.836582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:41.057304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:42.383377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:32.916179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:33.986711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:35.127396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:36.308972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:37.442851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:38.572426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:39.979735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:41.198800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:32:49.908181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.7850.8380.8350.6040.4550.4610.4000.6191.000
지점1.0001.0000.0001.0001.0001.0001.0000.8490.7930.8400.8170.9181.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0000.9991.0000.9990.8370.7940.8340.8460.8981.000
연장0.7851.0000.0000.9991.0000.7790.8240.5030.2070.2680.3770.4881.000
좌표위치위도0.8381.0000.0001.0000.7791.0000.8330.5030.3310.3070.0000.6221.000
좌표위치경도0.8351.0000.0000.9990.8240.8331.0000.7060.5750.6330.4550.7471.000
co0.6040.8490.0000.8370.5030.5030.7061.0000.8660.8950.7410.9940.849
nox0.4550.7930.0000.7940.2070.3310.5750.8661.0000.9150.9070.8950.793
hc0.4610.8400.0000.8340.2680.3070.6330.8950.9151.0000.8290.9130.840
pm0.4000.8170.0000.8460.3770.0000.4550.7410.9070.8291.0000.8120.817
co20.6190.9180.0000.8980.4880.6220.7470.9940.8950.9130.8121.0000.918
주소1.0001.0000.0001.0001.0001.0001.0000.8490.7930.8400.8170.9181.000
2023-12-10T22:32:50.056189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:32:50.164949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.0000.0310.302-0.296-0.256-0.253-0.258-0.288-0.2540.0000.753
연장0.0311.0000.094-0.1690.0900.0470.0560.0000.1000.0000.743
좌표위치위도0.3020.0941.000-0.2360.4490.4680.4670.4320.4540.0000.753
좌표위치경도-0.296-0.169-0.2361.0000.2170.2510.2440.2910.2100.0000.740
co-0.2560.0900.4490.2171.0000.9690.9810.9180.9960.0000.347
nox-0.2530.0470.4680.2510.9691.0000.9940.9740.9670.0000.325
hc-0.2580.0560.4670.2440.9810.9941.0000.9580.9740.0000.357
pm-0.2880.0000.4320.2910.9180.9740.9581.0000.9170.0000.371
co2-0.2540.1000.4540.2100.9960.9670.9740.9171.0000.0000.434
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
측정구간0.7530.7430.7530.7400.3470.3250.3570.3710.4340.0001.000

Missing values

2023-12-10T22:32:42.563380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:32:42.823529image/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

기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
01건기연[0122-2]1연무-논산11.520210501036.14511127.1050174.1788.1310.626.3118109.41충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210501036.14511127.1050157.4366.437.614.7314538.99충남 논산 은진 토양
23건기연[0124-0]1논산-반포10.220210501036.24966127.22854101.8866.269.493.3326678.2충남 논산 연산 천호
34건기연[0124-0]2논산-반포10.220210501036.24966127.2285496.0367.978.834.8227127.7충남 논산 연산 천호
45건기연[0127-2]1금남-조치원12.220210501036.56218127.28536135.4692.7214.34.4131876.56충남 세종 연서 봉암
56건기연[0127-2]2금남-조치원12.220210501036.56218127.28536137.1395.9613.744.7435697.4충남 세종 연서 봉암
67건기연[0127-7]1공주-유성5.820210501036.40916127.2582163.3871.9410.93.9113308.82충남 공주 반포 성강
78건기연[0127-7]2공주-유성5.820210501036.40916127.2582129.5127.454.381.446483.09충남 공주 반포 성강
89건기연[3209-1]1공주-유성7.620210501036.43986127.204872.9147.166.722.1419168.39충남 세종 장군 금암
910건기연[3209-1]2공주-유성7.620210501036.43986127.204883.9449.27.941.720123.39충남 세종 장군 금암
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[4001-4]1덕산-갈산12.920210501036.64566126.610451.950.960.170.0461.05충남 예산 덕산 사천
9192건기연[4001-4]2덕산-갈산12.920210501036.64566126.610455.283.020.50.141261.32충남 예산 덕산 사천
9293건기연[4004-0]1부여-공주11.620210501036.39173127.0805830.3523.543.481.567665.34충남 공주 이인 주봉
9394건기연[4004-0]2부여-공주11.620210501036.39173127.0805830.5827.553.71.697643.14충남 공주 이인 주봉
9495건기연[4502-0]1용동-예산6.420210501036.68623126.7706742.2732.84.951.8510666.52충남 예산 오가 좌방
9596건기연[4502-0]2용동-예산6.420210501036.68623126.7706743.3229.754.351.7310475.66충남 예산 오가 좌방
9697건기연[4503-0]1아산-음봉8.420210501036.83831127.0112539.0223.153.521.0710315.18충남 아산 음봉 동천
9798건기연[4503-0]2아산-음봉8.420210501036.83831127.0112553.7538.225.192.1213964.58충남 아산 음봉 동천
9899건기연[7720-1]1소원-서산14.320210501036.69795126.2868643.7825.533.890.8511529.55충남 태안 남 진산
99100건기연[7720-1]2소원-서산14.320210501036.69795126.2868634.1719.212.980.429016.44충남 태안 남 진산