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 nox and 2 other fieldsHigh 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 좌표위치위도 and 4 other fieldsHigh correlation
hc is highly overall correlated with 좌표위치위도 and 4 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
nox has unique valuesUnique
co2 has unique valuesUnique

Reproduction

Analysis started2023-12-10 12:10:33.999325
Analysis finished2023-12-10 12:10:41.068339
Duration7.07 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
2023-12-10T21:10:41.132063image/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
2023-12-10T21:10:41.248145image/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

2023-12-10T21:10:41.365677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:41.441328image/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
2023-12-10T21:10:41.601679image/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%
3204-0 2
 
2.0%
3706-0 2
 
2.0%
2915-2 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%
Other values (40) 80
80.0%
2023-12-10T21:10:41.885716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 118
14.8%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 78
9.8%
3 70
8.8%
4 44
 
5.5%
6 28
 
3.5%
7 18
 
2.2%
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%
2 106
21.2%
1 78
15.6%
3 70
14.0%
4 44
 
8.8%
6 28
 
5.6%
7 18
 
3.6%
9 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%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 78
9.8%
3 70
8.8%
4 44
 
5.5%
6 28
 
3.5%
7 18
 
2.2%
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%
2 106
13.2%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 78
9.8%
3 70
8.8%
4 44
 
5.5%
6 28
 
3.5%
7 18
 
2.2%
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

2023-12-10T21:10:41.990659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:42.066538image/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 length5.12
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

2023-12-10T21:10:42.154269image/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%
태안-서산 2
 
2.0%
연무-논산 2
 
2.0%
당진-송악 2
 
2.0%
화양-한산 2
 
2.0%
구룡-부여 2
 
2.0%
청양-홍성 2
 
2.0%
홍성-고북 2
 
2.0%
고북-서산 2
 
2.0%
Other values (38) 76
76.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.57
Minimum1.8
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:42.258582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.7
Q15.3
median6.65
Q39.5
95-th percentile14.2
Maximum17.6
Range15.8
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.2491335
Coefficient of variation (CV)0.42921183
Kurtosis0.71031104
Mean7.57
Median Absolute Deviation (MAD)2
Skewness0.77149306
Sum757
Variance10.556869
MonotonicityNot monotonic
2023-12-10T21:10:42.392652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4.9 6
 
6.0%
6.6 6
 
6.0%
9.7 4
 
4.0%
9.3 4
 
4.0%
6.0 4
 
4.0%
4.2 2
 
2.0%
4.0 2
 
2.0%
6.4 2
 
2.0%
2.2 2
 
2.0%
7.2 2
 
2.0%
Other values (33) 66
66.0%
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 (%)
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%
11.4 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
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T21:10:42.501006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:42.575483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 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

2023-12-10T21:10:42.648422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:10:42.718548image/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%
Mean36.512798
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:42.802582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.25196
median36.51428
Q336.76405
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.51209

Descriptive statistics

Standard deviation0.28602506
Coefficient of variation (CV)0.0078335565
Kurtosis-1.355159
Mean36.512798
Median Absolute Deviation (MAD)0.26062
Skewness-0.07746355
Sum3651.2798
Variance0.081810337
MonotonicityNot monotonic
2023-12-10T21:10:42.919309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.87015 2
 
2.0%
36.07008 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%
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%
Mean126.94368
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:43.036193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.71493
median126.87882
Q3127.20167
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.48674

Descriptive statistics

Standard deviation0.30407213
Coefficient of variation (CV)0.002395331
Kurtosis-0.53441001
Mean126.94368
Median Absolute Deviation (MAD)0.246255
Skewness-0.13261952
Sum12694.368
Variance0.092459863
MonotonicityNot monotonic
2023-12-10T21:10:43.154126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
126.75145 2
 
2.0%
126.78607 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%
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%
126.53713 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%
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 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.6383
Minimum1.73
Maximum339.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:43.271215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile7.628
Q128.915
median57.675
Q3102.815
95-th percentile154.8735
Maximum339.4
Range337.67
Interquartile range (IQR)73.9

Descriptive statistics

Standard deviation55.681777
Coefficient of variation (CV)0.79958552
Kurtosis5.4564925
Mean69.6383
Median Absolute Deviation (MAD)34.245
Skewness1.7775535
Sum6963.83
Variance3100.4603
MonotonicityNot monotonic
2023-12-10T21:10:43.580602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.52 2
 
2.0%
57.16 1
 
1.0%
59.79 1
 
1.0%
151.66 1
 
1.0%
159.55 1
 
1.0%
107.58 1
 
1.0%
128.86 1
 
1.0%
75.82 1
 
1.0%
12.26 1
 
1.0%
16.04 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
1.73 1
1.0%
3.33 1
1.0%
4.58 1
1.0%
6.5 1
1.0%
7.02 1
1.0%
7.66 1
1.0%
8.09 1
1.0%
9.17 1
1.0%
9.76 1
1.0%
11.33 1
1.0%
ValueCountFrequency (%)
339.4 1
1.0%
258.89 1
1.0%
212.67 1
1.0%
159.55 1
1.0%
159.31 1
1.0%
154.64 1
1.0%
151.66 1
1.0%
145.72 1
1.0%
142.94 1
1.0%
134.08 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.2908
Minimum1.23
Maximum320.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:43.688761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.23
5-th percentile4.651
Q121.4975
median49.985
Q370.065
95-th percentile156.4575
Maximum320.91
Range319.68
Interquartile range (IQR)48.5675

Descriptive statistics

Standard deviation56.408337
Coefficient of variation (CV)0.98459677
Kurtosis9.1479667
Mean57.2908
Median Absolute Deviation (MAD)23.655
Skewness2.6531246
Sum5729.08
Variance3181.9005
MonotonicityNot monotonic
2023-12-10T21:10:43.795344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.01 1
 
1.0%
45.71 1
 
1.0%
241.8 1
 
1.0%
177.88 1
 
1.0%
133.04 1
 
1.0%
88.68 1
 
1.0%
61.99 1
 
1.0%
6.86 1
 
1.0%
9.11 1
 
1.0%
85.56 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.23 1
1.0%
2.06 1
1.0%
2.6 1
1.0%
3.82 1
1.0%
4.1 1
1.0%
4.68 1
1.0%
4.94 1
1.0%
5.56 1
1.0%
5.79 1
1.0%
6.62 1
1.0%
ValueCountFrequency (%)
320.91 1
1.0%
317.61 1
1.0%
241.8 1
1.0%
202.39 1
1.0%
177.88 1
1.0%
155.33 1
1.0%
133.04 1
1.0%
119.7 1
1.0%
115.15 1
1.0%
104.28 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9007
Minimum0.18
Maximum40.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:43.913347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.18
5-th percentile0.696
Q12.96
median6.845
Q310.445
95-th percentile18.6525
Maximum40.63
Range40.45
Interquartile range (IQR)7.485

Descriptive statistics

Standard deviation6.9651507
Coefficient of variation (CV)0.88158653
Kurtosis6.4318892
Mean7.9007
Median Absolute Deviation (MAD)3.805
Skewness2.1093285
Sum790.07
Variance48.513324
MonotonicityNot monotonic
2023-12-10T21:10:44.040152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.7 2
 
2.0%
4.79 2
 
2.0%
7.91 2
 
2.0%
8.18 1
 
1.0%
6.04 1
 
1.0%
22.5 1
 
1.0%
15.17 1
 
1.0%
12.8 1
 
1.0%
1.06 1
 
1.0%
1.41 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0.18 1
1.0%
0.33 1
1.0%
0.44 1
1.0%
0.58 1
1.0%
0.62 1
1.0%
0.7 1
1.0%
0.84 1
1.0%
0.85 1
1.0%
0.88 1
1.0%
1.01 1
1.0%
ValueCountFrequency (%)
40.63 1
1.0%
34.9 1
1.0%
28.42 1
1.0%
25.51 1
1.0%
22.5 1
1.0%
18.45 1
1.0%
17.83 1
1.0%
16.2 1
1.0%
15.17 1
1.0%
14.76 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION 

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9305
Minimum0.13
Maximum19.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:44.159394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile0.14
Q10.96
median1.935
Q33.56
95-th percentile9.1265
Maximum19.41
Range19.28
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation3.3782708
Coefficient of variation (CV)1.1527967
Kurtosis10.357975
Mean2.9305
Median Absolute Deviation (MAD)1.13
Skewness2.9040482
Sum293.05
Variance11.412714
MonotonicityNot monotonic
2023-12-10T21:10:44.281641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.14 7
 
7.0%
1.8 4
 
4.0%
0.27 4
 
4.0%
0.98 3
 
3.0%
1.61 3
 
3.0%
0.94 2
 
2.0%
2.82 2
 
2.0%
0.96 2
 
2.0%
2.42 2
 
2.0%
0.13 2
 
2.0%
Other values (68) 69
69.0%
ValueCountFrequency (%)
0.13 2
 
2.0%
0.14 7
7.0%
0.27 4
4.0%
0.41 1
 
1.0%
0.53 1
 
1.0%
0.55 2
 
2.0%
0.8 1
 
1.0%
0.81 1
 
1.0%
0.82 1
 
1.0%
0.83 1
 
1.0%
ValueCountFrequency (%)
19.41 1
1.0%
18.7 1
1.0%
14.34 1
1.0%
10.62 1
1.0%
10.39 1
1.0%
9.06 1
1.0%
8.3 1
1.0%
6.9 1
1.0%
6.41 1
1.0%
6.17 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17173.826
Minimum457.29
Maximum84264.55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:44.408689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum457.29
5-th percentile1960.2075
Q16594.7275
median14042.715
Q324190.95
95-th percentile38354.196
Maximum84264.55
Range83807.26
Interquartile range (IQR)17596.222

Descriptive statistics

Standard deviation13670.538
Coefficient of variation (CV)0.79601002
Kurtosis5.5861352
Mean17173.826
Median Absolute Deviation (MAD)8286.65
Skewness1.796943
Sum1717382.6
Variance1.868836 × 108
MonotonicityNot monotonic
2023-12-10T21:10:44.515236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14239.15 1
 
1.0%
16662.6 1
 
1.0%
41238.79 1
 
1.0%
38268.49 1
 
1.0%
28633.15 1
 
1.0%
30645.74 1
 
1.0%
19305.96 1
 
1.0%
3236.62 1
 
1.0%
4243.02 1
 
1.0%
30030.15 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
457.29 1
1.0%
800.28 1
1.0%
1102.01 1
1.0%
1711.08 1
1.0%
1849.77 1
1.0%
1966.02 1
1.0%
2024.06 1
1.0%
2183.42 1
1.0%
2578.8 1
1.0%
2994.85 1
1.0%
ValueCountFrequency (%)
84264.55 1
1.0%
60488.13 1
1.0%
54638.25 1
1.0%
41238.79 1
1.0%
39982.62 1
1.0%
38268.49 1
1.0%
37259.73 1
1.0%
34244.44 1
1.0%
34228.78 1
1.0%
33498.22 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:10:44.733404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.94
Min length8

Characters and Unicode

Total characters1094
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 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%
서천 10
 
2.5%
금산 10
 
2.5%
공주 8
 
2.0%
세종 8
 
2.0%
서산 8
 
2.0%
부여 8
 
2.0%
예산 8
 
2.0%
아산 6
 
1.5%
Other values (95) 220
55.3%
2023-12-10T21:10:45.084476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
충 100
 
9.1%
남 100
 
9.1%
산 52
 
4.8%
천 28
 
2.6%
서 26
 
2.4%
성 24
 
2.2%
신 18
 
1.6%
안 16
 
1.5%
부 14
 
1.3%
Other values (99) 418
38.2%

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 52
 
6.5%
천 28
 
3.5%
서 26
 
3.3%
성 24
 
3.0%
신 18
 
2.3%
안 16
 
2.0%
부 14
 
1.8%
금 12
 
1.5%
Other values (98) 406
51.0%
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 52
 
6.5%
천 28
 
3.5%
서 26
 
3.3%
성 24
 
3.0%
신 18
 
2.3%
안 16
 
2.0%
부 14
 
1.8%
금 12
 
1.5%
Other values (98) 406
51.0%
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 52
 
6.5%
천 28
 
3.5%
서 26
 
3.3%
성 24
 
3.0%
신 18
 
2.3%
안 16
 
2.0%
부 14
 
1.8%
금 12
 
1.5%
Other values (98) 406
51.0%

Interactions

2023-12-10T21:10:40.170002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:34.489141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.278581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.949242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.555900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.214565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.907905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.551288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.471015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.246771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:34.554545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.346361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.012002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.624362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.296608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.977649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.623771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.540739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.324377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:34.627097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.420321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.081146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.697584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.377024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.059541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.717542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.631067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.381352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:34.686636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.486716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.140388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.762004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.444504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.122367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.798166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.704558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.452493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:34.750248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.559241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.210868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.830804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.514686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.192321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.886546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.784724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.540523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:34.815664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.632947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.277578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.901839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.593426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.267072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.974856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.863035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.606855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:34.879865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.704556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.344549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.982318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.670582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.330952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.061027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.932494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.676945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:34.951655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.796382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.419545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.060529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.751289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.408640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.140717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.009382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.755437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.217346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:35.882292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:36.496229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.144654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:37.841611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:38.487238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:39.411977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:10:40.088259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:10:45.172525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9980.6930.8840.8220.3340.3590.3620.3990.3101.000
지점1.0001.0000.0001.0001.0001.0001.0000.7640.7630.7520.6340.7621.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간0.9981.0000.0001.0000.9981.0000.9980.7890.7900.7760.7110.7881.000
연장0.6931.0000.0000.9981.0000.7220.8640.3410.2930.3300.3230.3381.000
좌표위치위도0.8841.0000.0001.0000.7221.0000.8410.0760.4660.3050.4240.1441.000
좌표위치경도0.8221.0000.0000.9980.8640.8411.0000.3290.5440.5440.4060.2431.000
co0.3340.7640.0000.7890.3410.0760.3291.0000.8710.9270.9081.0000.764
nox0.3590.7630.0000.7900.2930.4660.5440.8711.0000.9780.9540.8740.763
hc0.3620.7520.0000.7760.3300.3050.5440.9270.9781.0000.8700.9230.752
pm0.3990.6340.0000.7110.3230.4240.4060.9080.9540.8701.0000.9070.634
co20.3100.7620.0000.7880.3380.1440.2431.0000.8740.9230.9071.0000.762
주소1.0001.0000.0001.0001.0001.0001.0000.7640.7630.7520.6340.7621.000
2023-12-10T21:10:45.277878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T21:10:45.347505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.1380.273-0.279-0.164-0.202-0.188-0.274-0.1510.0000.744
연장-0.1381.0000.098-0.1650.2010.1960.1830.1850.2160.0000.741
좌표위치위도0.2730.0981.000-0.1680.4940.5190.5220.4400.4940.0000.760
좌표위치경도-0.279-0.165-0.1681.0000.1650.1480.1760.1150.1530.0000.739
co-0.1640.2010.4940.1651.0000.9630.9820.8220.9960.0000.306
nox-0.2020.1960.5190.1480.9631.0000.9890.9260.9590.0000.314
hc-0.1880.1830.5220.1760.9820.9891.0000.8870.9740.0000.302
pm-0.2740.1850.4400.1150.8220.9260.8871.0000.8160.0000.246
co2-0.1510.2160.4940.1530.9960.9590.9740.8161.0000.0000.312
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
측정구간0.7440.7410.7600.7390.3060.3140.3020.2460.3120.0001.000

Missing values

2023-12-10T21:10:40.857208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:10:41.010559image/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-논산11.520210101136.14511127.1050157.1671.018.185.0414239.15충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210101136.14511127.1050142.854.496.534.2810084.78충남 논산 은진 토양
23건기연[0123-2]1두마-금남4.920210101136.37685127.25961134.08119.716.26.933498.22충남 공주 반포 온천
34건기연[0123-2]2두마-금남4.920210101136.37685127.2596173.6863.868.763.4118444.46충남 공주 반포 온천
45건기연[0124-0]1논산-반포10.220210101136.24966127.2285491.0268.510.163.9121374.09충남 논산 연산 천호
56건기연[0124-0]2논산-반포10.220210101136.24966127.22854103.5969.7510.13.4926999.89충남 논산 연산 천호
67건기연[0127-2]1금남-조치원12.220210101136.56218127.28536132.496.7714.234.6330957.66충남 세종 연서 봉암
78건기연[0127-2]2금남-조치원12.220210101136.56218127.28536127.45104.2814.615.9832088.09충남 세종 연서 봉암
89건기연[0127-7]1공주-유성5.820210101136.40916127.2582156.064.939.194.1312094.76충남 공주 반포 성강
910건기연[0127-7]2공주-유성5.820210101136.40916127.2582130.9533.614.791.86695.8충남 공주 반포 성강
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3602-0]1보령-청양9.320210101136.39222126.6679629.9316.92.610.417901.32충남 보령 청라 나원
9192건기연[3602-0]2보령-청양9.320210101136.39222126.6679639.7527.433.70.9810371.74충남 보령 청라 나원
9293건기연[3604-3]1청양-정산14.620210101136.46306126.8459322.1511.362.010.145257.06충남 청양 대치 시전
9394건기연[3604-3]2청양-정산14.620210101136.46306126.8459341.6421.923.830.539944.2충남 청양 대치 시전
9495건기연[3606-0]1공주-어진동4.420210101136.48872127.2016782.8550.537.821.2821578.79충남 세종 장군 은용
9596건기연[3606-0]2공주-어진동4.420210101136.48872127.20167117.3360.7410.70.9727874.57충남 세종 장군 은용
9697건기연[3706-0]1진천-음성9.320210101136.1228127.49717101.965.429.922.4224098.83충남 금산 군북 내부
9798건기연[3706-0]2진천-음성9.320210101136.1228127.4971754.2435.685.01.4714250.54충남 금산 군북 내부
9899건기연[3707-0]1추부-군서1.820210101136.21913127.4954417.7415.791.860.984546.97충남 금산 추부 요광
99100건기연[3707-0]2추부-군서1.820210101136.21913127.495449.174.940.850.142183.42충남 금산 추부 요광