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 co and 5 other fieldsHigh correlation
좌표위치경도 is highly overall correlated with 측정구간High correlation
co is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
nox is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
hc is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
pm is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
co2 is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 3 other fieldsHigh correlation
기본키 has unique valuesUnique
pm has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:10:21.816860
Analysis finished2023-12-10 12:10:28.456691
Duration6.64 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
2023-12-10T21:10:28.519320image/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
2023-12-10T21:10:28.635095image/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

2023-12-10T21:10:28.737165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:28.807326image/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
2023-12-10T21:10:28.965762image/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[0123-2]
4th row[0123-2]
5th row[0124-0]
ValueCountFrequency (%)
0122-2 2
 
2.0%
3204-5 2
 
2.0%
3901-4 2
 
2.0%
2915-4 2
 
2.0%
2918-0 2
 
2.0%
2921-3 2
 
2.0%
2922-0 2
 
2.0%
2923-0 2
 
2.0%
2924-2 2
 
2.0%
3201-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:10:29.263837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 118
14.8%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 78
9.8%
3 72
9.0%
4 44
 
5.5%
6 24
 
3.0%
9 22
 
2.8%
Other values (3) 38
 
4.8%

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 (%)
0 118
23.6%
2 104
20.8%
1 78
15.6%
3 72
14.4%
4 44
 
8.8%
6 24
 
4.8%
9 22
 
4.4%
7 18
 
3.6%
5 14
 
2.8%
8 6
 
1.2%
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 (%)
0 118
14.8%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 78
9.8%
3 72
9.0%
4 44
 
5.5%
6 24
 
3.0%
9 22
 
2.8%
Other values (3) 38
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 118
14.8%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 78
9.8%
3 72
9.0%
4 44
 
5.5%
6 24
 
3.0%
9 22
 
2.8%
Other values (3) 38
 
4.8%

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

2023-12-10T21:10:29.378477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:29.470118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
공주-유성
 
4
신평-인주
 
4
부여-논산
 
2
논산-반포
 
2
금남-조치원
 
2
Other values (43)
86 

Length

Max length8
Median length5
Mean length5.16
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row연무-논산
2nd row연무-논산
3rd row두마-금남
4th row두마-금남
5th row논산-반포

Common Values

ValueCountFrequency (%)
공주-유성 4
 
4.0%
신평-인주 4
 
4.0%
부여-논산 2
 
2.0%
논산-반포 2
 
2.0%
금남-조치원 2
 
2.0%
전동-쌍전 2
 
2.0%
천안-성환 2
 
2.0%
성환-천안 2
 
2.0%
둔포-평택 2
 
2.0%
장항-마서 2
 
2.0%
Other values (38) 76
76.0%

Length

2023-12-10T21:10:29.572006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공주-유성 4
 
4.0%
신평-인주 4
 
4.0%
당진-송악 2
 
2.0%
연무-논산 2
 
2.0%
유구-사곡 2
 
2.0%
구룡-부여 2
 
2.0%
청양-홍성 2
 
2.0%
홍성-고북 2
 
2.0%
고북-서산 2
 
2.0%
서산-지곡 2
 
2.0%
Other values (38) 76
76.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.76
Minimum1.8
Maximum23.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:29.683712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.7
Q15.3
median6.6
Q39.5
95-th percentile14.6
Maximum23.2
Range21.4
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.896126
Coefficient of variation (CV)0.5020781
Kurtosis3.9441891
Mean7.76
Median Absolute Deviation (MAD)2
Skewness1.6081813
Sum776
Variance15.179798
MonotonicityNot monotonic
2023-12-10T21:10:29.807954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
4.9 6
 
6.0%
6.6 6
 
6.0%
6.2 4
 
4.0%
9.3 4
 
4.0%
6.0 4
 
4.0%
9.7 4
 
4.0%
11.5 2
 
2.0%
6.3 2
 
2.0%
6.4 2
 
2.0%
2.2 2
 
2.0%
Other values (32) 64
64.0%
ValueCountFrequency (%)
1.8 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%
4.9 6
6.0%
5.2 2
 
2.0%
ValueCountFrequency (%)
23.2 2
2.0%
17.6 2
2.0%
14.6 2
2.0%
14.2 2
2.0%
12.4 2
2.0%
12.2 2
2.0%
11.5 2
2.0%
10.6 2
2.0%
10.2 2
2.0%
10.0 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T21:10:30.120036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:30.186596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 100
100.0%

측정시분
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 100
100.0%

Length

2023-12-10T21:10:30.259082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:30.332590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 100
100.0%

좌표위치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.501134
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:30.417003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.25196
median36.500575
Q336.75792
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.50596

Descriptive statistics

Standard deviation0.28202987
Coefficient of variation (CV)0.0077266056
Kurtosis-1.2950152
Mean36.501134
Median Absolute Deviation (MAD)0.249765
Skewness0.009421949
Sum3650.1134
Variance0.079540847
MonotonicityNot monotonic
2023-12-10T21:10:30.530716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.62485 2
 
2.0%
36.27491 2
 
2.0%
36.47481 2
 
2.0%
36.58635 2
 
2.0%
36.72496 2
 
2.0%
36.83292 2
 
2.0%
36.75792 2
 
2.0%
36.78489 2
 
2.0%
36.90325 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%
36.13314 2
2.0%
36.14511 2
2.0%
36.16232 2
2.0%
36.18861 2
2.0%
36.1897 2
2.0%
ValueCountFrequency (%)
36.95295 2
2.0%
36.9261 2
2.0%
36.90325 2
2.0%
36.89991 2
2.0%
36.89461 2
2.0%
36.89111 2
2.0%
36.87646 2
2.0%
36.87015 2
2.0%
36.86711 2
2.0%
36.83292 2
2.0%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.95143
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:30.644700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.75145
median126.89799
Q3127.20167
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.45022

Descriptive statistics

Standard deviation0.29846876
Coefficient of variation (CV)0.0023510468
Kurtosis-0.39684785
Mean126.95143
Median Absolute Deviation (MAD)0.23154
Skewness-0.16762123
Sum12695.143
Variance0.089083599
MonotonicityNot monotonic
2023-12-10T21:10:30.782267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
126.8775 2
 
2.0%
126.85936 2
 
2.0%
126.79526 2
 
2.0%
126.61312 2
 
2.0%
126.5301 2
 
2.0%
126.44223 2
 
2.0%
126.18913 2
 
2.0%
126.37641 2
 
2.0%
126.64887 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.18913 2
2.0%
126.37641 2
2.0%
126.44223 2
2.0%
126.5301 2
2.0%
126.60118 2
2.0%
126.61312 2
2.0%
126.64274 2
2.0%
126.64887 2
2.0%
126.66796 2
2.0%
126.70896 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%
127.25961 2
2.0%
127.25821 2
2.0%
127.24084 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.9362
Minimum0.52
Maximum279.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:30.899881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile3.2175
Q112.9875
median38.875
Q378.115
95-th percentile121.852
Maximum279.4
Range278.88
Interquartile range (IQR)65.1275

Descriptive statistics

Standard deviation50.379221
Coefficient of variation (CV)0.97002131
Kurtosis6.4068632
Mean51.9362
Median Absolute Deviation (MAD)28.765
Skewness2.0481171
Sum5193.62
Variance2538.0659
MonotonicityNot monotonic
2023-12-10T21:10:31.005896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.28 2
 
2.0%
5.23 2
 
2.0%
33.11 1
 
1.0%
80.2 1
 
1.0%
16.6 1
 
1.0%
32.01 1
 
1.0%
35.49 1
 
1.0%
151.14 1
 
1.0%
175.93 1
 
1.0%
86.36 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.52 1
1.0%
1.21 1
1.0%
1.3 1
1.0%
1.98 1
1.0%
2.03 1
1.0%
3.28 2
2.0%
3.31 1
1.0%
3.88 1
1.0%
4.35 1
1.0%
5.23 2
2.0%
ValueCountFrequency (%)
279.4 1
1.0%
273.14 1
1.0%
175.93 1
1.0%
151.14 1
1.0%
130.25 1
1.0%
121.41 1
1.0%
120.33 1
1.0%
111.34 1
1.0%
107.87 1
1.0%
106.92 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.1759
Minimum0.28
Maximum255.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:31.119797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile1.9325
Q19.1425
median33.42
Q372.67
95-th percentile127.0365
Maximum255.95
Range255.67
Interquartile range (IQR)63.5275

Descriptive statistics

Standard deviation50.690328
Coefficient of variation (CV)1.0521927
Kurtosis5.0829117
Mean48.1759
Median Absolute Deviation (MAD)26.505
Skewness2.001893
Sum4817.59
Variance2569.5094
MonotonicityNot monotonic
2023-12-10T21:10:31.237989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.96 2
 
2.0%
2.92 2
 
2.0%
31.8 1
 
1.0%
73.15 1
 
1.0%
18.02 1
 
1.0%
43.96 1
 
1.0%
32.95 1
 
1.0%
203.32 1
 
1.0%
220.15 1
 
1.0%
72.51 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.28 1
1.0%
0.64 1
1.0%
0.96 1
1.0%
1.32 1
1.0%
1.41 1
1.0%
1.96 2
2.0%
2.06 1
1.0%
2.43 1
1.0%
2.62 1
1.0%
2.92 2
2.0%
ValueCountFrequency (%)
255.95 1
1.0%
241.08 1
1.0%
220.15 1
1.0%
203.32 1
1.0%
145.78 1
1.0%
126.05 1
1.0%
114.59 1
1.0%
109.65 1
1.0%
106.49 1
1.0%
99.2 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5191
Minimum0.04
Maximum32.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:31.346366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.305
Q11.385
median4.935
Q39.745
95-th percentile15.408
Maximum32.59
Range32.55
Interquartile range (IQR)8.36

Descriptive statistics

Standard deviation6.3988067
Coefficient of variation (CV)0.98154756
Kurtosis4.3913927
Mean6.5191
Median Absolute Deviation (MAD)3.91
Skewness1.7682217
Sum651.91
Variance40.944727
MonotonicityNot monotonic
2023-12-10T21:10:31.457406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.49 3
 
3.0%
0.32 2
 
2.0%
3.78 2
 
2.0%
4.49 1
 
1.0%
11.91 1
 
1.0%
4.95 1
 
1.0%
4.92 1
 
1.0%
21.66 1
 
1.0%
24.62 1
 
1.0%
9.53 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.04 1
 
1.0%
0.12 1
 
1.0%
0.13 1
 
1.0%
0.2 1
 
1.0%
0.21 1
 
1.0%
0.31 1
 
1.0%
0.32 2
2.0%
0.36 1
 
1.0%
0.4 1
 
1.0%
0.49 3
3.0%
ValueCountFrequency (%)
32.59 1
1.0%
32.54 1
1.0%
24.62 1
1.0%
21.66 1
1.0%
17.65 1
1.0%
15.29 1
1.0%
15.05 1
1.0%
14.74 1
1.0%
14.12 1
1.0%
13.73 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7505
Minimum0
Maximum15.24
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:31.570411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.55
median2.06
Q33.7875
95-th percentile7.3645
Maximum15.24
Range15.24
Interquartile range (IQR)3.2375

Descriptive statistics

Standard deviation2.9363395
Coefficient of variation (CV)1.0675657
Kurtosis5.7566601
Mean2.7505
Median Absolute Deviation (MAD)1.51
Skewness2.1356481
Sum275.05
Variance8.6220896
MonotonicityNot monotonic
2023-12-10T21:10:31.686122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 8
 
8.0%
0.55 4
 
4.0%
0.14 4
 
4.0%
0.54 3
 
3.0%
0.27 3
 
3.0%
1.82 2
 
2.0%
0.0 2
 
2.0%
0.56 2
 
2.0%
12.23 1
 
1.0%
4.26 1
 
1.0%
Other values (70) 70
70.0%
ValueCountFrequency (%)
0.0 2
 
2.0%
0.13 8
8.0%
0.14 4
4.0%
0.27 3
 
3.0%
0.4 1
 
1.0%
0.41 1
 
1.0%
0.54 3
 
3.0%
0.55 4
4.0%
0.56 2
 
2.0%
0.66 1
 
1.0%
ValueCountFrequency (%)
15.24 1
1.0%
14.54 1
1.0%
12.23 1
1.0%
11.18 1
1.0%
9.35 1
1.0%
7.26 1
1.0%
7.05 1
1.0%
6.62 1
1.0%
5.68 1
1.0%
5.66 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12691.895
Minimum138.68
Maximum67929.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:31.797887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile779.563
Q13193.1175
median9779.64
Q319467.16
95-th percentile31062.963
Maximum67929.34
Range67790.66
Interquartile range (IQR)16274.042

Descriptive statistics

Standard deviation12396.237
Coefficient of variation (CV)0.97670494
Kurtosis6.236603
Mean12691.895
Median Absolute Deviation (MAD)7065.88
Skewness2.0602399
Sum1269189.5
Variance1.5366668 × 108
MonotonicityNot monotonic
2023-12-10T21:10:31.915061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
794.65 2
 
2.0%
1255.69 2
 
2.0%
7574.19 1
 
1.0%
19863.28 1
 
1.0%
3820.95 1
 
1.0%
8316.52 1
 
1.0%
8574.55 1
 
1.0%
40043.3 1
 
1.0%
45804.84 1
 
1.0%
22000.76 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
138.68 1
1.0%
307.36 1
1.0%
318.6 1
1.0%
487.28 1
1.0%
492.91 1
1.0%
794.65 2
2.0%
873.34 1
1.0%
1017.66 1
1.0%
1150.71 1
1.0%
1255.69 2
2.0%
ValueCountFrequency (%)
67929.34 1
1.0%
66497.91 1
1.0%
45804.84 1
1.0%
40043.3 1
1.0%
32693.98 1
1.0%
30977.12 1
1.0%
28687.28 1
1.0%
27927.46 1
1.0%
25771.0 1
1.0%
25558.83 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:10:32.144034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.94
Min length8

Characters and Unicode

Total characters1094
Distinct characters105
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 공주 반포 온천
4th row충남 공주 반포 온천
5th row충남 논산 연산 천호
ValueCountFrequency (%)
충남 100
25.1%
천안 12
 
3.0%
금산 10
 
2.5%
서천 10
 
2.5%
공주 10
 
2.5%
예산 8
 
2.0%
세종 8
 
2.0%
청양 8
 
2.0%
부여 8
 
2.0%
성환 6
 
1.5%
Other values (95) 218
54.8%
2023-12-10T21:10:32.472569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
충 100
 
9.1%
남 100
 
9.1%
산 46
 
4.2%
천 28
 
2.6%
성 24
 
2.2%
서 24
 
2.2%
신 16
 
1.5%
안 16
 
1.5%
주 14
 
1.3%
Other values (95) 428
39.1%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
충 100
 
12.6%
남 100
 
12.6%
산 46
 
5.8%
천 28
 
3.5%
성 24
 
3.0%
서 24
 
3.0%
신 16
 
2.0%
안 16
 
2.0%
주 14
 
1.8%
청 14
 
1.8%
Other values (94) 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 (%)
충 100
 
12.6%
남 100
 
12.6%
산 46
 
5.8%
천 28
 
3.5%
성 24
 
3.0%
서 24
 
3.0%
신 16
 
2.0%
안 16
 
2.0%
주 14
 
1.8%
청 14
 
1.8%
Other values (94) 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 (%)
충 100
 
12.6%
남 100
 
12.6%
산 46
 
5.8%
천 28
 
3.5%
성 24
 
3.0%
서 24
 
3.0%
신 16
 
2.0%
안 16
 
2.0%
주 14
 
1.8%
청 14
 
1.8%
Other values (94) 414
52.0%

Interactions

2023-12-10T21:10:27.490442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.284655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.843261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.454540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.047116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.705184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.340061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.135058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.827986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.563686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.342618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.904176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.528870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.132594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.781113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.601807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.207488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.896789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.628033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.395690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.959158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.591955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.206047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.849873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.654524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.272209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.960762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.700303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.452668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.016856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.651298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.277859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.927381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.715573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.339205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.025899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.776254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.517029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.087354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.716935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.345181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.008838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.796633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.427464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.102679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.844495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.576546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.148564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.780177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.412792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.071509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.865848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.509722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.168972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.913828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.636867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.216901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.841919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.479178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.134468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.925223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.589530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.235665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.997964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.699636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.288008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.908301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.548759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.202672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.992332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.671182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.307254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:28.095700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:22.771554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.367245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:23.977724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:24.622553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:25.270892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.064310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:26.749452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:27.399519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:10:32.561144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9960.6150.8660.8630.4220.4170.4120.4900.4141.000
지점1.0001.0000.0001.0001.0001.0001.0000.9060.9090.8880.9180.8801.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간0.9961.0000.0001.0001.0001.0000.9980.9110.9130.8940.9220.8961.000
연장0.6151.0000.0001.0001.0000.5320.6500.4470.3320.2590.3300.4481.000
좌표위치위도0.8661.0000.0001.0000.5321.0000.8710.1630.2150.2600.3530.1651.000
좌표위치경도0.8631.0000.0000.9980.6500.8711.0000.4750.3500.4170.3910.4171.000
co0.4220.9060.0000.9110.4470.1630.4751.0000.9280.9480.9210.9990.906
nox0.4170.9090.0000.9130.3320.2150.3500.9281.0000.9970.9960.9370.909
hc0.4120.8880.0000.8940.2590.2600.4170.9480.9971.0000.9920.9510.888
pm0.4900.9180.0000.9220.3300.3530.3910.9210.9960.9921.0000.9260.918
co20.4140.8800.0000.8960.4480.1650.4170.9990.9370.9510.9261.0000.880
주소1.0001.0000.0001.0001.0001.0001.0000.9060.9090.8880.9180.8801.000
2023-12-10T21:10:32.667527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T21:10:32.756932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.0980.238-0.249-0.284-0.232-0.253-0.252-0.2740.0000.727
연장-0.0981.0000.081-0.1280.1060.0470.0550.0450.1050.0000.736
좌표위치위도0.2380.0811.000-0.1400.5520.6150.5980.6100.5650.0000.760
좌표위치경도-0.249-0.128-0.1401.0000.2750.2480.2690.2500.2660.0000.740
co-0.2840.1060.5520.2751.0000.9760.9860.9520.9980.0000.451
nox-0.2320.0470.6150.2480.9761.0000.9960.9870.9760.0000.472
hc-0.2530.0550.5980.2690.9860.9961.0000.9780.9840.0000.439
pm-0.2520.0450.6100.2500.9520.9870.9781.0000.9520.0000.490
co2-0.2740.1050.5650.2660.9980.9760.9840.9521.0000.0000.432
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
측정구간0.7270.7360.7600.7400.4510.4720.4390.4900.4320.0001.000

Missing values

2023-12-10T21:10:28.226135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:10:28.397538image/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-논산11.520210201136.14511127.1050133.1131.84.492.257574.19충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210201136.14511127.1050143.545.755.983.429883.36충남 논산 은진 토양
23건기연[0123-2]1두마-금남4.920210201136.37685127.25961130.25109.6515.295.6632693.98충남 공주 반포 온천
34건기연[0123-2]2두마-금남4.920210201136.37685127.2596186.8168.5110.183.0419953.99충남 공주 반포 온천
45건기연[0124-0]1논산-반포10.220210201136.24966127.2285462.7747.996.652.8316061.8충남 논산 연산 천호
56건기연[0124-0]2논산-반포10.220210201136.24966127.22854106.9283.512.95.3624874.0충남 논산 연산 천호
67건기연[0127-2]1금남-조치원12.220210201136.56218127.28536101.1781.311.324.225558.83충남 세종 연서 봉암
78건기연[0127-2]2금남-조치원12.220210201136.56218127.28536121.4190.8813.324.6430977.12충남 세종 연서 봉암
89건기연[0127-7]1공주-유성5.820210201136.40916127.2582138.1542.586.212.578203.34충남 공주 반포 성강
910건기연[0127-7]2공주-유성5.820210201136.40916127.2582115.4616.422.480.913582.32충남 공주 반포 성강
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3606-0]1공주-어진동4.420210201136.48872127.20167105.7887.2213.733.7223764.55충남 세종 장군 은용
9192건기연[3606-0]2공주-어진동4.420210201136.48872127.2016789.9759.289.132.1723350.34충남 세종 장군 은용
9293건기연[3706-0]1진천-음성9.320210201136.1228127.4971738.1125.823.781.349100.79충남 금산 군북 내부
9394건기연[3706-0]2진천-음성9.320210201136.1228127.4971739.328.23.781.6110271.93충남 금산 군북 내부
9495건기연[3707-0]1추부-군서1.820210201136.21913127.495449.949.721.510.662201.64충남 금산 추부 요광
9596건기연[3707-0]2추부-군서1.820210201136.21913127.4954413.288.131.310.553195.48충남 금산 추부 요광
9697건기연[3901-4]1은산-청양IC6.220210201136.35819126.915851.210.960.130.13318.6충남 청양 장평 은곡
9798건기연[3901-4]2은산-청양IC6.220210201136.35819126.915850.520.280.040.0138.68충남 청양 장평 은곡
9899건기연[3902-0]1유구-아산23.220210201136.60979126.970313.882.430.360.141017.66충남 공주 유구 추계
99100건기연[3902-0]2유구-아산23.220210201136.60979126.9703112.117.481.120.413186.03충남 공주 유구 추계