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 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:09:03.753804
Analysis finished2023-12-10 12:09:16.512011
Duration12.76 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
2023-12-10T21:09:16.607367image/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
2023-12-10T21:09:16.775688image/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

2023-12-10T21:09:16.927243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:17.062554image/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
2023-12-10T21:09:17.309827image/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%
3204-5 2
 
2.0%
3902-2 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%
3201-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:09:17.839492image/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 76
9.5%
3 68
8.5%
4 44
 
5.5%
9 26
 
3.2%
6 26
 
3.2%
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%
2 104
20.8%
1 76
15.2%
3 68
13.6%
4 44
 
8.8%
9 26
 
5.2%
6 26
 
5.2%
7 18
 
3.6%
5 16
 
3.2%
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 (%)
0 118
14.8%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 76
9.5%
3 68
8.5%
4 44
 
5.5%
9 26
 
3.2%
6 26
 
3.2%
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%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 76
9.5%
3 68
8.5%
4 44
 
5.5%
9 26
 
3.2%
6 26
 
3.2%
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

2023-12-10T21:09:18.049683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:18.202833image/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
정안-공주
 
2
연산-대전
 
2
금남-조치원
 
2
전동-쌍전
 
2
Other values (44)
88 

Length

Max length8
Median length5
Mean length5.16
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%
정안-공주 2
 
2.0%
연산-대전 2
 
2.0%
금남-조치원 2
 
2.0%
전동-쌍전 2
 
2.0%
전의-천안 2
 
2.0%
천안-성환 2
 
2.0%
둔포-평택 2
 
2.0%
장항-마서 2
 
2.0%
판교-옥산 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T21:09:18.371689image/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%
유구-예산 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%
만리포-태안 2
 
2.0%
태안-서산 2
 
2.0%
Other values (39) 78
78.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.708
Minimum1.8
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:18.550401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.7
Q15.6
median7
Q39.5
95-th percentile14.2
Maximum17.6
Range15.8
Interquartile range (IQR)3.9

Descriptive statistics

Standard deviation3.2493567
Coefficient of variation (CV)0.4215564
Kurtosis0.6652635
Mean7.708
Median Absolute Deviation (MAD)1.95
Skewness0.72056914
Sum770.8
Variance10.558319
MonotonicityNot monotonic
2023-12-10T21:09:18.743642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
6.6 4
 
4.0%
6.2 4
 
4.0%
9.3 4
 
4.0%
8.3 4
 
4.0%
6.0 4
 
4.0%
9.7 4
 
4.0%
14.2 2
 
2.0%
6.4 2
 
2.0%
2.2 2
 
2.0%
7.2 2
 
2.0%
Other values (34) 68
68.0%
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 2
2.0%
5.2 2
2.0%
ValueCountFrequency (%)
17.6 2
2.0%
14.6 2
2.0%
14.2 2
2.0%
12.9 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
20210601
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210601
2nd row20210601
3rd row20210601
4th row20210601
5th row20210601

Common Values

ValueCountFrequency (%)
20210601 100
100.0%

Length

2023-12-10T21:09:18.922014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:19.019214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210601 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

2023-12-10T21:09:19.109518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:19.236107image/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%
Mean36.500377
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:19.368732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.25196
median36.500575
Q336.75627
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.50431

Descriptive statistics

Standard deviation0.27814759
Coefficient of variation (CV)0.0076204032
Kurtosis-1.2928401
Mean36.500377
Median Absolute Deviation (MAD)0.249765
Skewness-0.034534636
Sum3650.0377
Variance0.077366081
MonotonicityNot monotonic
2023-12-10T21:09:19.527834image/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%
36.89461 2
2.0%
36.87646 2
2.0%
36.87015 2
2.0%
36.86711 2
2.0%
36.85256 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%
Mean126.94872
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:19.700017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.75145
median126.93537
Q3127.15746
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.40601

Descriptive statistics

Standard deviation0.29489484
Coefficient of variation (CV)0.0023229447
Kurtosis-0.29518431
Mean126.94872
Median Absolute Deviation (MAD)0.21971
Skewness-0.16447943
Sum12694.872
Variance0.086962968
MonotonicityNot monotonic
2023-12-10T21:09:19.914155image/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%
127.25821 2
2.0%
127.24084 2
2.0%
127.22854 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.9705
Minimum0.52
Maximum155.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:20.110331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile1.3335
Q111.6125
median35.705
Q363.91
95-th percentile95.9645
Maximum155.17
Range154.65
Interquartile range (IQR)52.2975

Descriptive statistics

Standard deviation32.333249
Coefficient of variation (CV)0.80892781
Kurtosis1.0423078
Mean39.9705
Median Absolute Deviation (MAD)25.02
Skewness0.98069275
Sum3997.05
Variance1045.439
MonotonicityNot monotonic
2023-12-10T21:09:20.328590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.57 2
 
2.0%
0.65 2
 
2.0%
33.23 1
 
1.0%
10.75 1
 
1.0%
5.88 1
 
1.0%
37.78 1
 
1.0%
63.62 1
 
1.0%
74.66 1
 
1.0%
97.95 1
 
1.0%
76.15 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
0.52 1
1.0%
0.65 2
2.0%
1.17 1
1.0%
1.21 1
1.0%
1.34 1
1.0%
1.78 1
1.0%
1.94 1
1.0%
1.98 1
1.0%
2.78 1
1.0%
3.31 1
1.0%
ValueCountFrequency (%)
155.17 1
1.0%
139.05 1
1.0%
117.98 1
1.0%
102.24 1
1.0%
97.95 1
1.0%
95.86 1
1.0%
85.84 1
1.0%
82.81 1
1.0%
82.42 1
1.0%
81.91 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.8189
Minimum0.28
Maximum154.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:20.540759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.998
Q19.365
median32.29
Q359.6075
95-th percentile135.8815
Maximum154.17
Range153.89
Interquartile range (IQR)50.2425

Descriptive statistics

Standard deviation40.794668
Coefficient of variation (CV)0.95272573
Kurtosis0.61889962
Mean42.8189
Median Absolute Deviation (MAD)23.64
Skewness1.1752558
Sum4281.89
Variance1664.2049
MonotonicityNot monotonic
2023-12-10T21:09:21.050622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.32 2
 
2.0%
35.67 1
 
1.0%
28.06 1
 
1.0%
3.25 1
 
1.0%
53.44 1
 
1.0%
54.7 1
 
1.0%
91.71 1
 
1.0%
114.18 1
 
1.0%
97.5 1
 
1.0%
84.65 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
0.28 1
1.0%
0.32 2
2.0%
0.9 1
1.0%
0.96 1
1.0%
1.0 1
1.0%
1.27 1
1.0%
1.32 1
1.0%
1.33 1
1.0%
1.79 1
1.0%
2.06 1
1.0%
ValueCountFrequency (%)
154.17 1
1.0%
154.0 1
1.0%
146.35 1
1.0%
137.72 1
1.0%
136.48 1
1.0%
135.85 1
1.0%
132.39 1
1.0%
124.09 1
1.0%
116.67 1
1.0%
114.18 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6637
Minimum0.04
Maximum19.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:21.245191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.149
Q11.4575
median4.525
Q38.495
95-th percentile16.311
Maximum19.73
Range19.69
Interquartile range (IQR)7.0375

Descriptive statistics

Standard deviation4.9606121
Coefficient of variation (CV)0.87586067
Kurtosis0.34236669
Mean5.6637
Median Absolute Deviation (MAD)3.33
Skewness0.98845556
Sum566.37
Variance24.607672
MonotonicityNot monotonic
2023-12-10T21:09:21.419838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 2
 
2.0%
4.0 2
 
2.0%
0.06 2
 
2.0%
0.2 2
 
2.0%
2.24 2
 
2.0%
6.4 2
 
2.0%
4.96 1
 
1.0%
1.49 1
 
1.0%
6.38 1
 
1.0%
9.68 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.04 1
1.0%
0.06 2
2.0%
0.13 2
2.0%
0.15 1
1.0%
0.18 1
1.0%
0.2 2
2.0%
0.26 1
1.0%
0.31 1
1.0%
0.46 1
1.0%
0.52 1
1.0%
ValueCountFrequency (%)
19.73 1
1.0%
18.58 1
1.0%
18.5 1
1.0%
17.26 1
1.0%
16.33 1
1.0%
16.31 1
1.0%
15.71 1
1.0%
14.9 1
1.0%
13.4 1
1.0%
13.36 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct79
Distinct (%)79.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7393
Minimum0
Maximum9.77
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:21.626666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.6775
median2.15
Q33.7
95-th percentile8.325
Maximum9.77
Range9.77
Interquartile range (IQR)3.0225

Descriptive statistics

Standard deviation2.6084634
Coefficient of variation (CV)0.95223721
Kurtosis0.62264326
Mean2.7393
Median Absolute Deviation (MAD)1.485
Skewness1.2049007
Sum273.93
Variance6.8040813
MonotonicityNot monotonic
2023-12-10T21:09:21.834797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 8
 
8.0%
0.0 3
 
3.0%
2.69 2
 
2.0%
3.05 2
 
2.0%
0.54 2
 
2.0%
2.36 2
 
2.0%
0.14 2
 
2.0%
0.66 2
 
2.0%
0.67 2
 
2.0%
0.27 2
 
2.0%
Other values (69) 73
73.0%
ValueCountFrequency (%)
0.0 3
 
3.0%
0.13 8
8.0%
0.14 2
 
2.0%
0.27 2
 
2.0%
0.28 1
 
1.0%
0.4 1
 
1.0%
0.54 2
 
2.0%
0.55 2
 
2.0%
0.66 2
 
2.0%
0.67 2
 
2.0%
ValueCountFrequency (%)
9.77 1
1.0%
9.49 1
1.0%
9.46 1
1.0%
9.44 1
1.0%
8.8 1
1.0%
8.3 1
1.0%
8.22 1
1.0%
7.99 1
1.0%
7.46 1
1.0%
7.26 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9684.8634
Minimum138.68
Maximum38147.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:22.025558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile333.0305
Q12912.405
median8398.745
Q315184.895
95-th percentile23026.128
Maximum38147.12
Range38008.44
Interquartile range (IQR)12272.49

Descriptive statistics

Standard deviation7918.9566
Coefficient of variation (CV)0.81766322
Kurtosis1.1332592
Mean9684.8634
Median Absolute Deviation (MAD)6007.57
Skewness1.0179971
Sum968486.34
Variance62709873
MonotonicityNot monotonic
2023-12-10T21:09:22.191923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
153.68 2
 
2.0%
6996.51 1
 
1.0%
8358.56 1
 
1.0%
1409.37 1
 
1.0%
9117.53 1
 
1.0%
9945.78 1
 
1.0%
17501.39 1
 
1.0%
19567.78 1
 
1.0%
23846.9 1
 
1.0%
18416.91 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
138.68 1
1.0%
153.68 2
2.0%
318.6 1
1.0%
322.21 1
1.0%
333.6 1
1.0%
462.92 1
1.0%
487.28 1
1.0%
490.89 1
1.0%
734.66 1
1.0%
873.34 1
1.0%
ValueCountFrequency (%)
38147.12 1
1.0%
34316.17 1
1.0%
28459.24 1
1.0%
25224.29 1
1.0%
23846.9 1
1.0%
22982.93 1
1.0%
21661.51 1
1.0%
20431.78 1
1.0%
20120.66 1
1.0%
19567.78 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:09:22.585626image/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%
천안 10
 
2.5%
청양 10
 
2.5%
금산 10
 
2.5%
서천 10
 
2.5%
세종 10
 
2.5%
아산 10
 
2.5%
예산 8
 
2.0%
부여 8
 
2.0%
서산 6
 
1.5%
Other values (97) 216
54.3%
2023-12-10T21:09:23.169872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
102
 
9.3%
100
 
9.1%
56
 
5.1%
24
 
2.2%
24
 
2.2%
20
 
1.8%
16
 
1.5%
16
 
1.5%
14
 
1.3%
Other values (97) 424
38.8%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
102
 
12.8%
100
 
12.6%
56
 
7.0%
24
 
3.0%
24
 
3.0%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (96) 410
51.5%
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 (%)
102
 
12.8%
100
 
12.6%
56
 
7.0%
24
 
3.0%
24
 
3.0%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (96) 410
51.5%
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 (%)
102
 
12.8%
100
 
12.6%
56
 
7.0%
24
 
3.0%
24
 
3.0%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (96) 410
51.5%

Interactions

2023-12-10T21:09:14.861142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:04.471094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:05.605762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:07.267208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:08.416864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:09.640513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:11.026517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:12.270068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.389979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.980232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:04.612285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:05.719592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:07.378715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:08.581345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:09.738447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:11.157893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:12.383982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.491466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.117054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:04.759497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:05.843093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:07.503158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:08.717083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:09.866276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:11.298441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:12.511045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.979291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.243159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:04.879992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:06.334375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:07.609972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:08.832232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:09.987322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:11.422386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:12.629871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.097565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.370708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:05.001644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:06.492362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:07.733812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:08.951107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:10.162572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:11.589395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:12.743463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.220572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.519154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:05.137162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:06.679031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:07.865802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:09.079413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:10.327374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:11.738776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:12.937490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.338090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.661498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:05.273309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:06.857370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:07.998451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:09.234657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:10.509848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:11.889858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.098420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.477150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.795907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:05.402141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:07.024634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:08.132106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:09.384306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:10.697689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:12.017962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.203859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.613718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:15.915664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:05.487942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:07.145281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:08.261209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:09.513708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:10.840611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:12.142179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:13.296056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:14.735980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:09:23.349508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.7170.8630.8480.5770.6330.6390.5630.5031.000
지점1.0001.0000.0001.0001.0001.0001.0000.8770.8110.9230.8210.8281.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.1500.0000.1130.1300.000
측정구간1.0001.0000.0001.0000.9991.0000.9990.8570.8020.9100.8090.8071.000
연장0.7171.0000.0000.9991.0000.6790.8120.4890.0000.4680.0000.3981.000
좌표위치위도0.8631.0000.0001.0000.6791.0000.8200.2140.5910.6730.5850.3701.000
좌표위치경도0.8481.0000.0000.9990.8120.8201.0000.4240.4900.4900.5150.3141.000
co0.5770.8770.0000.8570.4890.2140.4241.0000.9290.9390.8950.9950.877
nox0.6330.8110.1500.8020.0000.5910.4900.9291.0000.9750.9660.9210.811
hc0.6390.9230.0000.9100.4680.6730.4900.9390.9751.0000.9520.9240.923
pm0.5630.8210.1130.8090.0000.5850.5150.8950.9660.9521.0000.8980.821
co20.5030.8280.1300.8070.3980.3700.3140.9950.9210.9240.8981.0000.828
주소1.0001.0000.0001.0001.0001.0001.0000.8770.8110.9230.8210.8281.000
2023-12-10T21:09:23.537076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T21:09:23.666531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.1620.246-0.226-0.174-0.135-0.150-0.141-0.1570.0000.753
연장-0.1621.0000.095-0.0860.1530.1050.1140.1260.1550.0000.744
좌표위치위도0.2460.0951.000-0.1200.5880.6140.6100.6080.6070.0000.753
좌표위치경도-0.226-0.086-0.1201.0000.2140.1870.2060.1770.1900.0000.741
co-0.1740.1530.5880.2141.0000.9740.9840.9600.9970.0000.370
nox-0.1350.1050.6140.1870.9741.0000.9970.9930.9750.1060.311
hc-0.1500.1140.6100.2060.9840.9971.0000.9880.9820.0000.450
pm-0.1410.1260.6080.1770.9600.9930.9881.0000.9620.0770.317
co2-0.1570.1550.6070.1900.9970.9750.9820.9621.0000.0960.322
방향0.0000.0000.0000.0000.0000.1060.0000.0770.0961.0000.000
측정구간0.7530.7440.7530.7410.3700.3110.4500.3170.3220.0001.000

Missing values

2023-12-10T21:09:16.127618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:09:16.407483image/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-논산11.520210601136.14511127.1050131.6435.674.962.696996.51충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210601136.14511127.1050128.3830.374.132.326401.49충남 논산 은진 토양
23건기연[0124-0]1논산-반포10.220210601136.24966127.2285448.2235.575.752.0411178.97충남 논산 연산 천호
34건기연[0124-0]2논산-반포10.220210601136.24966127.2285466.459.418.244.516526.46충남 논산 연산 천호
45건기연[0127-2]1금남-조치원12.220210601136.56218127.2853677.7656.678.733.2418244.4충남 세종 연서 봉암
56건기연[0127-2]2금남-조치원12.220210601136.56218127.2853676.1166.238.983.6619077.77충남 세종 연서 봉암
67건기연[0127-7]1공주-유성5.820210601136.40916127.2582115.8820.33.071.173439.39충남 공주 반포 성강
78건기연[0127-7]2공주-유성5.820210601136.40916127.2582111.214.82.240.882376.55충남 공주 반포 성강
89건기연[3209-1]1공주-유성7.620210601136.43986127.204853.6343.066.42.4412410.06충남 세종 장군 금암
910건기연[3209-1]2공주-유성7.620210601136.43986127.204826.2916.12.420.946945.51충남 세종 장군 금암
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3706-0]1진천-음성9.320210601136.1228127.4971723.6714.722.20.946252.09충남 금산 군북 내부
9192건기연[3706-0]2진천-음성9.320210601136.1228127.4971733.5824.763.461.437950.62충남 금산 군북 내부
9293건기연[3707-0]1추부-군서1.820210601136.21913127.495449.299.41.450.662047.96충남 금산 추부 요광
9394건기연[3707-0]2추부-군서1.820210601136.21913127.495449.349.491.460.672053.59충남 금산 추부 요광
9495건기연[3901-4]1은산-청양IC6.220210601136.35819126.915851.941.270.20.13490.89충남 청양 장평 은곡
9596건기연[3901-4]2은산-청양IC6.220210601136.35819126.915851.210.960.130.13318.6충남 청양 장평 은곡
9697건기연[3902-2]1장평-신풍12.920210601136.43536126.95490.520.280.040.0138.68충남 청양 정산 해남
9798건기연[3902-2]2장평-신풍12.920210601136.43536126.95491.981.320.20.13487.28충남 청양 정산 해남
9899건기연[3905-0]1염치-권관8.320210601136.85256126.9600785.84135.8515.718.321661.51충남 아산 영인 아산
99100건기연[3905-0]2염치-권관8.320210601136.85256126.9600779.72124.0914.97.4618973.44충남 아산 영인 아산