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 7 (7.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:09:47.028122
Analysis finished2023-12-10 12:09:57.573671
Duration10.55 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
2023-12-10T21:09:57.661061image/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
2023-12-10T21:09:57.817185image/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

2023-12-10T21:09:57.964998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:58.067321image/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
2023-12-10T21:09:58.304366image/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%
3404-1 2
 
2.0%
4004-0 2
 
2.0%
2923-0 2
 
2.0%
2924-2 2
 
2.0%
3201-0 2
 
2.0%
3203-2 2
 
2.0%
3204-4 2
 
2.0%
3204-5 2
 
2.0%
3206-3 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T21:09:58.677952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 124
15.5%
[ 100
12.5%
2 100
12.5%
- 100
12.5%
] 100
12.5%
1 72
9.0%
3 66
8.2%
4 52
6.5%
9 24
 
3.0%
6 22
 
2.8%
Other values (3) 40
 
5.0%

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 (%)
0 124
24.8%
2 100
20.0%
1 72
14.4%
3 66
13.2%
4 52
10.4%
9 24
 
4.8%
6 22
 
4.4%
7 18
 
3.6%
5 16
 
3.2%
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 (%)
0 124
15.5%
[ 100
12.5%
2 100
12.5%
- 100
12.5%
] 100
12.5%
1 72
9.0%
3 66
8.2%
4 52
6.5%
9 24
 
3.0%
6 22
 
2.8%
Other values (3) 40
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124
15.5%
[ 100
12.5%
2 100
12.5%
- 100
12.5%
] 100
12.5%
1 72
9.0%
3 66
8.2%
4 52
6.5%
9 24
 
3.0%
6 22
 
2.8%
Other values (3) 40
 
5.0%

방향
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
50 
2
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

Length

2023-12-10T21:09:58.826548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:58.910053image/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
진산-옥천
 
2
금남-조치원
 
2
전동-쌍전
 
2
Other values (43)
86 

Length

Max length7
Median length5
Mean length5.1
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T21:09:59.026249image/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%
성환-입장 2
 
2.0%
연무-논산 2
 
2.0%
보령-청양 2
 
2.0%
서산-지곡 2
 
2.0%
만리포-태안 2
 
2.0%
태안-서산 2
 
2.0%
당진-송악 2
 
2.0%
송악-합덕 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%
Mean7.626
Minimum1.8
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:59.159189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.2
Q15.4
median6.65
Q39.7
95-th percentile14.2
Maximum17.6
Range15.8
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.3973733
Coefficient of variation (CV)0.44549873
Kurtosis0.35892162
Mean7.626
Median Absolute Deviation (MAD)2.2
Skewness0.65479014
Sum762.6
Variance11.542145
MonotonicityNot monotonic
2023-12-10T21:09:59.295918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
6.6 6
 
6.0%
6.4 4
 
4.0%
12.9 4
 
4.0%
6.2 4
 
4.0%
9.7 4
 
4.0%
8.3 4
 
4.0%
9.3 4
 
4.0%
7.2 2
 
2.0%
4.3 2
 
2.0%
9.5 2
 
2.0%
Other values (32) 64
64.0%
ValueCountFrequency (%)
1.8 2
2.0%
2.0 2
2.0%
2.2 2
2.0%
2.7 2
2.0%
3.6 2
2.0%
4.0 2
2.0%
4.2 2
2.0%
4.3 2
2.0%
4.4 2
2.0%
4.9 2
2.0%
ValueCountFrequency (%)
17.6 2
2.0%
14.6 2
2.0%
14.2 2
2.0%
12.9 4
4.0%
12.2 2
2.0%
11.6 2
2.0%
11.5 2
2.0%
10.6 2
2.0%
10.2 2
2.0%
10.0 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2023-12-10T21:09:59.452725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:59.556058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2023-12-10T21:09:59.658666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:09:59.816631image/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%
Mean36.509959
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:09:59.938432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.26956
median36.500575
Q336.75792
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.48836

Descriptive statistics

Standard deviation0.27698834
Coefficient of variation (CV)0.0075866517
Kurtosis-1.2420607
Mean36.509959
Median Absolute Deviation (MAD)0.24826
Skewness-0.032283321
Sum3650.9959
Variance0.076722539
MonotonicityNot monotonic
2023-12-10T21:10:00.404212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.39222 2
 
2.0%
36.83292 2
 
2.0%
36.75792 2
 
2.0%
36.78489 2
 
2.0%
36.90325 2
 
2.0%
36.87015 2
 
2.0%
36.51243 2
 
2.0%
36.87646 2
 
2.0%
36.89111 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
36.02784 2
2.0%
36.05892 2
2.0%
36.07008 2
2.0%
36.08997 2
2.0%
36.1228 2
2.0%
36.14511 2
2.0%
36.16232 2
2.0%
36.18861 2
2.0%
36.1897 2
2.0%
36.21913 2
2.0%
ValueCountFrequency (%)
36.95295 2
2.0%
36.9261 2
2.0%
36.90325 2
2.0%
36.89991 2
2.0%
36.89461 2
2.0%
36.89111 2
2.0%
36.87646 2
2.0%
36.87015 2
2.0%
36.85256 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%
Mean126.93826
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:00.563616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.71493
median126.89799
Q3127.15746
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.44253

Descriptive statistics

Standard deviation0.29727074
Coefficient of variation (CV)0.0023418529
Kurtosis-0.39186337
Mean126.93826
Median Absolute Deviation (MAD)0.22932
Skewness-0.080539248
Sum12693.826
Variance0.08836989
MonotonicityNot monotonic
2023-12-10T21:10:00.760194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
126.66796 2
 
2.0%
126.44223 2
 
2.0%
126.18913 2
 
2.0%
126.37641 2
 
2.0%
126.64887 2
 
2.0%
126.75145 2
 
2.0%
126.96572 2
 
2.0%
126.86923 2
 
2.0%
126.81017 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%
126.61045 2
2.0%
126.61312 2
2.0%
126.64274 2
2.0%
126.64887 2
2.0%
126.66451 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%
127.25821 2
2.0%
127.24084 2
2.0%
127.22854 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.143
Minimum0
Maximum317.79
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:00.921628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.65
Q110.73
median30.985
Q353.365
95-th percentile95.487
Maximum317.79
Range317.79
Interquartile range (IQR)42.635

Descriptive statistics

Standard deviation39.873734
Coefficient of variation (CV)1.0453749
Kurtosis23.459689
Mean38.143
Median Absolute Deviation (MAD)21.12
Skewness3.6740782
Sum3814.3
Variance1589.9147
MonotonicityNot monotonic
2023-12-10T21:10:01.114484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.52 3
 
3.0%
1.98 2
 
2.0%
0.65 2
 
2.0%
11.22 2
 
2.0%
33.51 1
 
1.0%
23.13 1
 
1.0%
36.91 1
 
1.0%
64.77 1
 
1.0%
40.22 1
 
1.0%
50.66 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.52 3
3.0%
0.65 2
2.0%
1.05 1
 
1.0%
1.98 2
2.0%
2.26 1
 
1.0%
2.63 1
 
1.0%
3.33 1
 
1.0%
3.56 1
 
1.0%
4.09 1
 
1.0%
ValueCountFrequency (%)
317.79 1
1.0%
103.82 1
1.0%
103.24 1
1.0%
97.21 1
1.0%
96.19 1
1.0%
95.45 1
1.0%
81.37 1
1.0%
79.82 1
1.0%
79.73 1
1.0%
79.26 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION 

Distinct94
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.3585
Minimum0
Maximum428.77
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:01.275875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32
Q19.2275
median31.68
Q359.65
95-th percentile125.372
Maximum428.77
Range428.77
Interquartile range (IQR)50.4225

Descriptive statistics

Standard deviation55.255091
Coefficient of variation (CV)1.2456483
Kurtosis23.065991
Mean44.3585
Median Absolute Deviation (MAD)23.45
Skewness3.8308848
Sum4435.85
Variance3053.1251
MonotonicityNot monotonic
2023-12-10T21:10:01.441535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 3
 
3.0%
0.32 2
 
2.0%
7.36 2
 
2.0%
1.32 2
 
2.0%
100.62 2
 
2.0%
89.11 1
 
1.0%
112.02 1
 
1.0%
84.68 1
 
1.0%
125.79 1
 
1.0%
115.93 1
 
1.0%
Other values (84) 84
84.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.28 3
3.0%
0.32 2
2.0%
0.55 1
 
1.0%
1.32 2
2.0%
1.51 1
 
1.0%
1.64 1
 
1.0%
2.06 1
 
1.0%
2.58 1
 
1.0%
2.62 1
 
1.0%
ValueCountFrequency (%)
428.77 1
1.0%
169.74 1
1.0%
165.4 1
1.0%
148.54 1
1.0%
125.79 1
1.0%
125.35 1
1.0%
117.55 1
1.0%
115.93 1
1.0%
112.02 1
1.0%
102.13 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7159
Minimum0
Maximum52.96
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:01.604350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q11.3775
median4.525
Q38.635
95-th percentile14.2515
Maximum52.96
Range52.96
Interquartile range (IQR)7.2575

Descriptive statistics

Standard deviation6.6184913
Coefficient of variation (CV)1.1579089
Kurtosis25.505758
Mean5.7159
Median Absolute Deviation (MAD)3.25
Skewness3.966743
Sum571.59
Variance43.804426
MonotonicityNot monotonic
2023-12-10T21:10:01.781801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 3
 
3.0%
0.2 2
 
2.0%
4.14 2
 
2.0%
3.54 2
 
2.0%
0.06 2
 
2.0%
5.73 2
 
2.0%
0.64 1
 
1.0%
7.48 1
 
1.0%
16.08 1
 
1.0%
4.58 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.04 3
3.0%
0.06 2
2.0%
0.09 1
 
1.0%
0.2 2
2.0%
0.22 1
 
1.0%
0.26 1
 
1.0%
0.33 1
 
1.0%
0.35 1
 
1.0%
0.4 1
 
1.0%
ValueCountFrequency (%)
52.96 1
1.0%
19.48 1
1.0%
18.76 1
1.0%
16.08 1
1.0%
15.04 1
1.0%
14.21 1
1.0%
13.3 1
1.0%
13.28 1
1.0%
12.67 1
1.0%
12.63 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8692
Minimum0
Maximum25.86
Zeros7
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:01.926212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.6975
median1.99
Q33.945
95-th percentile7.8625
Maximum25.86
Range25.86
Interquartile range (IQR)3.2475

Descriptive statistics

Standard deviation3.4740326
Coefficient of variation (CV)1.2108018
Kurtosis18.95871
Mean2.8692
Median Absolute Deviation (MAD)1.325
Skewness3.4905469
Sum286.92
Variance12.068902
MonotonicityNot monotonic
2023-12-10T21:10:02.126636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 7
 
7.0%
0.13 5
 
5.0%
1.94 3
 
3.0%
0.7 3
 
3.0%
0.27 2
 
2.0%
0.28 2
 
2.0%
1.95 2
 
2.0%
3.53 1
 
1.0%
7.84 1
 
1.0%
7.39 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 7
7.0%
0.13 5
5.0%
0.14 1
 
1.0%
0.27 2
 
2.0%
0.28 2
 
2.0%
0.39 1
 
1.0%
0.4 1
 
1.0%
0.54 1
 
1.0%
0.55 1
 
1.0%
0.66 1
 
1.0%
ValueCountFrequency (%)
25.86 1
1.0%
11.54 1
1.0%
11.34 1
1.0%
10.7 1
1.0%
8.29 1
1.0%
7.84 1
1.0%
7.6 1
1.0%
7.44 1
1.0%
7.39 1
1.0%
7.26 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9285.1666
Minimum0
Maximum75485.31
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:10:02.323512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile153.68
Q12676.1675
median7262.815
Q313633.173
95-th percentile22217.757
Maximum75485.31
Range75485.31
Interquartile range (IQR)10957.005

Descriptive statistics

Standard deviation9682.426
Coefficient of variation (CV)1.0427843
Kurtosis21.009898
Mean9285.1666
Median Absolute Deviation (MAD)5193.04
Skewness3.4458896
Sum928516.66
Variance93749373
MonotonicityNot monotonic
2023-12-10T21:10:02.506146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138.68 3
 
3.0%
153.68 2
 
2.0%
487.28 2
 
2.0%
17424.5 1
 
1.0%
16517.33 1
 
1.0%
10761.43 1
 
1.0%
12741.44 1
 
1.0%
19538.61 1
 
1.0%
17265.62 1
 
1.0%
1619.26 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
138.68 3
3.0%
153.68 2
2.0%
277.37 1
 
1.0%
487.28 2
2.0%
595.97 1
 
1.0%
640.96 1
 
1.0%
800.28 1
 
1.0%
995.6 1
 
1.0%
1064.53 1
 
1.0%
ValueCountFrequency (%)
75485.31 1
1.0%
26612.93 1
1.0%
24825.92 1
1.0%
23839.36 1
1.0%
23605.65 1
1.0%
22144.71 1
1.0%
20919.81 1
1.0%
20853.6 1
1.0%
20844.09 1
1.0%
19538.61 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T21:10:02.809853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.96
Min length8

Characters and Unicode

Total characters1096
Distinct characters106
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%
금산 10
 
2.5%
예산 10
 
2.5%
청양 10
 
2.5%
아산 8
 
2.0%
서천 8
 
2.0%
세종 8
 
2.0%
공주 8
 
2.0%
논산 6
 
1.5%
Other values (95) 220
55.3%
2023-12-10T21:10:03.228684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
남 102
 
9.3%
충 100
 
9.1%
산 58
 
5.3%
천 24
 
2.2%
서 22
 
2.0%
성 20
 
1.8%
청 16
 
1.5%
안 14
 
1.3%
주 14
 
1.3%
Other values (96) 428
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 798
72.8%
Space Separator 298
 
27.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 102
 
12.8%
충 100
 
12.5%
산 58
 
7.3%
천 24
 
3.0%
서 22
 
2.8%
성 20
 
2.5%
청 16
 
2.0%
안 14
 
1.8%
주 14
 
1.8%
금 12
 
1.5%
Other values (95) 416
52.1%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 798
72.8%
Common 298
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 102
 
12.8%
충 100
 
12.5%
산 58
 
7.3%
천 24
 
3.0%
서 22
 
2.8%
성 20
 
2.5%
청 16
 
2.0%
안 14
 
1.8%
주 14
 
1.8%
금 12
 
1.5%
Other values (95) 416
52.1%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 798
72.8%
ASCII 298
 
27.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
남 102
 
12.8%
충 100
 
12.5%
산 58
 
7.3%
천 24
 
3.0%
서 22
 
2.8%
성 20
 
2.5%
청 16
 
2.0%
안 14
 
1.8%
주 14
 
1.8%
금 12
 
1.5%
Other values (95) 416
52.1%

Interactions

2023-12-10T21:09:56.266871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:47.686677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:48.526511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:49.752571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.762490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:52.035187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:53.245006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.572772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.478561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:56.362662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:47.779642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:48.632655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:49.871491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.864009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:52.174072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:53.363414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.654781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.561314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:56.495760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:47.882323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:48.760629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.007040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.993265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:52.340665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:53.507734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.770280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.644882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:56.588302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:47.966642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:48.869154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.109026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:51.139192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:52.466339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:53.921839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.870309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.718928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:56.691614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:48.051252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:49.022454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.217582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:51.338221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:52.633609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.054134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.983847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.799720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:56.789812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:48.156026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:49.208083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.335075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:51.492576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:52.760639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.176652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.085823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.887207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:56.887418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:48.256214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:49.317111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.418716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:51.618551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:52.867328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.288499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.170180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.971533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:57.000780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:48.341012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:49.440076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.535386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:51.743900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:52.980532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.376761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.276813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:56.063902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:57.106762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:48.419299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:49.581013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:50.649773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:51.854224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:53.093727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:54.468798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:55.384191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:09:56.152885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:10:03.345948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9980.7130.8150.7840.5760.5620.5830.4440.5271.000
지점1.0001.0000.0001.0001.0001.0001.0000.8700.9010.8650.8660.8921.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간0.9981.0000.0001.0000.9981.0000.9970.8290.8780.8480.8680.8561.000
연장0.7131.0000.0000.9981.0000.7590.8180.1800.3660.1720.3380.2171.000
좌표위치위도0.8151.0000.0001.0000.7591.0000.8430.6370.7280.6780.5770.6411.000
좌표위치경도0.7841.0000.0000.9970.8180.8431.0000.5400.5080.5120.2200.5451.000
co0.5760.8700.0000.8290.1800.6370.5401.0000.9500.9560.7730.9990.870
nox0.5620.9010.0000.8780.3660.7280.5080.9501.0000.9960.9230.9560.901
hc0.5830.8650.0000.8480.1720.6780.5120.9560.9961.0000.9090.9580.865
pm0.4440.8660.0000.8680.3380.5770.2200.7730.9230.9091.0000.7810.866
co20.5270.8920.0000.8560.2170.6410.5450.9990.9560.9580.7811.0000.892
주소1.0001.0000.0001.0001.0001.0001.0000.8700.9010.8650.8660.8921.000
2023-12-10T21:10:03.467158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T21:10:03.565936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.0180.215-0.286-0.285-0.220-0.241-0.238-0.2700.0000.744
연장-0.0181.0000.037-0.1390.028-0.031-0.020-0.0120.0340.0000.742
좌표위치위도0.2150.0371.000-0.2090.5870.6380.6230.6320.6010.0000.760
좌표위치경도-0.286-0.139-0.2091.0000.2280.1970.2150.1850.2140.0000.738
co-0.2850.0280.5870.2281.0000.9640.9770.9440.9970.0000.410
nox-0.220-0.0310.6380.1970.9641.0000.9950.9890.9660.0000.467
hc-0.241-0.0200.6230.2150.9770.9951.0000.9790.9740.0000.431
pm-0.238-0.0120.6320.1850.9440.9890.9791.0000.9450.0000.415
co2-0.2700.0340.6010.2140.9970.9660.9740.9451.0000.0000.439
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000
측정구간0.7440.7420.7600.7380.4100.4670.4310.4150.4390.0001.000

Missing values

2023-12-10T21:09:57.269353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:09:57.485495image/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-논산11.520210401136.14511127.1050137.8141.285.733.228414.51충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210401136.14511127.1050123.9727.863.742.095303.8충남 논산 은진 토양
23건기연[0124-0]1논산-반포10.220210401136.24966127.2285448.734.655.671.6211240.3충남 논산 연산 천호
34건기연[0124-0]2논산-반포10.220210401136.24966127.2285479.7368.7710.264.9918446.26충남 논산 연산 천호
45건기연[0127-2]1금남-조치원12.220210401136.56218127.2853677.6570.618.974.4620853.6충남 세종 연서 봉암
56건기연[0127-2]2금남-조치원12.220210401136.56218127.2853677.6169.58.854.0820844.09충남 세종 연서 봉암
67건기연[0127-7]1공주-유성5.820210401136.40916127.2582136.3243.196.282.497673.88충남 공주 반포 성강
78건기연[0127-7]2공주-유성5.820210401136.40916127.2582113.4116.222.451.012966.89충남 공주 반포 성강
89건기연[3209-1]1공주-유성7.620210401136.43986127.204848.4241.215.732.5212184.41충남 세종 장군 금암
910건기연[3209-1]2공주-유성7.620210401136.43986127.204846.5435.445.231.9510879.93충남 세종 장군 금암
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3905-0]1염치-권관8.320210401136.85256126.9600759.5377.0111.14.7413057.54충남 아산 영인 아산
9192건기연[3905-0]2염치-권관8.320210401136.85256126.96007317.79428.7752.9625.8675485.31충남 아산 영인 아산
9293건기연[4001-2]1개화-만수2.020210401136.29677126.664519.029.771.460.72097.22충남 보령 미산 도화담
9394건기연[4001-2]2개화-만수2.020210401136.29677126.664510.650.320.060.0153.68충남 보령 미산 도화담
9495건기연[4001-4]1덕산-갈산12.920210401136.64566126.610450.520.280.040.0138.68충남 예산 덕산 사천
9596건기연[4001-4]2덕산-갈산12.920210401136.64566126.610451.050.550.090.0277.37충남 예산 덕산 사천
9697건기연[4004-0]1부여-공주11.620210401136.39173127.0805810.447.361.050.692725.02충남 공주 이인 주봉
9798건기연[4004-0]2부여-공주11.620210401136.39173127.0805813.7117.522.021.233398.93충남 공주 이인 주봉
9899건기연[4502-0]1용동-예산6.420210401136.68623126.7706713.89.521.360.833604.0충남 예산 오가 좌방
99100건기연[4502-0]2용동-예산6.420210401136.68623126.7706730.1726.584.171.946874.56충남 예산 오가 좌방