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
co has unique valuesUnique
nox has unique valuesUnique
hc has unique valuesUnique
co2 has unique valuesUnique
pm has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:32:07.271644
Analysis finished2023-12-10 13:32:21.099993
Duration13.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:21.216848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T22:32:21.467983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T22:32:21.668293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:32:21.794709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:32:22.332802image/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%
2023-12-10T22:32:22.865660image/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

2023-12-10T22:32:23.080519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:32:23.211350image/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

2023-12-10T22:32:23.383034image/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
2023-12-10T22:32:23.571633image/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
2023-12-10T22:32:23.788077image/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

2023-12-10T22:32:23.993914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:32:24.118096image/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
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T22:32:24.228280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:32:24.333166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.500377
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:24.507141image/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
2023-12-10T22:32:24.703660image/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
2023-12-10T22:32:24.912750image/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
2023-12-10T22:32:25.151056image/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  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.9294
Minimum0.52
Maximum309.62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:25.344921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.52
5-th percentile3.216
Q118.7575
median47.615
Q3101.055
95-th percentile163.518
Maximum309.62
Range309.1
Interquartile range (IQR)82.2975

Descriptive statistics

Standard deviation57.622452
Coefficient of variation (CV)0.88746319
Kurtosis2.6842845
Mean64.9294
Median Absolute Deviation (MAD)36.285
Skewness1.3916415
Sum6492.94
Variance3320.347
MonotonicityNot monotonic
2023-12-10T22:32:25.513290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.44 1
 
1.0%
125.6 1
 
1.0%
11.48 1
 
1.0%
40.33 1
 
1.0%
50.79 1
 
1.0%
162.91 1
 
1.0%
106.28 1
 
1.0%
101.64 1
 
1.0%
80.51 1
 
1.0%
17.13 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.52 1
1.0%
1.05 1
1.0%
1.57 1
1.0%
1.95 1
1.0%
3.14 1
1.0%
3.22 1
1.0%
3.31 1
1.0%
3.99 1
1.0%
5.73 1
1.0%
5.88 1
1.0%
ValueCountFrequency (%)
309.62 1
1.0%
237.84 1
1.0%
195.54 1
1.0%
191.55 1
1.0%
175.07 1
1.0%
162.91 1
1.0%
152.59 1
1.0%
152.27 1
1.0%
149.08 1
1.0%
143.43 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.3787
Minimum0.28
Maximum283.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:25.720964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile1.8215
Q116.175
median40.55
Q392.9625
95-th percentile205.8175
Maximum283.72
Range283.44
Interquartile range (IQR)76.7875

Descriptive statistics

Standard deviation65.081313
Coefficient of variation (CV)1.0109138
Kurtosis1.9087887
Mean64.3787
Median Absolute Deviation (MAD)30.945
Skewness1.4893486
Sum6437.87
Variance4235.5773
MonotonicityNot monotonic
2023-12-10T22:32:25.904703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54.72 1
 
1.0%
92.97 1
 
1.0%
10.76 1
 
1.0%
57.14 1
 
1.0%
51.17 1
 
1.0%
278.62 1
 
1.0%
159.36 1
 
1.0%
106.56 1
 
1.0%
69.45 1
 
1.0%
9.7 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.28 1
1.0%
0.55 1
1.0%
0.83 1
1.0%
0.96 1
1.0%
1.66 1
1.0%
1.83 1
1.0%
2.06 1
1.0%
2.74 1
1.0%
3.25 1
1.0%
3.97 1
1.0%
ValueCountFrequency (%)
283.72 1
1.0%
278.62 1
1.0%
252.19 1
1.0%
210.44 1
1.0%
210.14 1
1.0%
205.59 1
1.0%
201.72 1
1.0%
193.04 1
1.0%
159.36 1
1.0%
155.27 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7595
Minimum0.04
Maximum38.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:26.124555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.3075
Q12.315
median6.125
Q312.4625
95-th percentile24.784
Maximum38.73
Range38.69
Interquartile range (IQR)10.1475

Descriptive statistics

Standard deviation8.1373294
Coefficient of variation (CV)0.92897191
Kurtosis1.5428479
Mean8.7595
Median Absolute Deviation (MAD)4.555
Skewness1.2900388
Sum875.95
Variance66.21613
MonotonicityNot monotonic
2023-12-10T22:32:26.339049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.58 1
 
1.0%
13.89 1
 
1.0%
1.64 1
 
1.0%
6.76 1
 
1.0%
7.62 1
 
1.0%
27.13 1
 
1.0%
17.4 1
 
1.0%
14.25 1
 
1.0%
9.56 1
 
1.0%
1.62 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.04 1
1.0%
0.09 1
1.0%
0.13 1
1.0%
0.17 1
1.0%
0.26 1
1.0%
0.31 1
1.0%
0.33 1
1.0%
0.4 1
1.0%
0.55 1
1.0%
0.57 1
1.0%
ValueCountFrequency (%)
38.73 1
1.0%
33.85 1
1.0%
27.88 1
1.0%
27.13 1
1.0%
24.86 1
1.0%
24.78 1
1.0%
22.95 1
1.0%
22.19 1
1.0%
20.87 1
1.0%
20.79 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0499
Minimum0
Maximum17.6
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:26.576387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1235
Q11.1925
median2.555
Q35.5225
95-th percentile13.206
Maximum17.6
Range17.6
Interquartile range (IQR)4.33

Descriptive statistics

Standard deviation4.0879454
Coefficient of variation (CV)1.0093942
Kurtosis1.850477
Mean4.0499
Median Absolute Deviation (MAD)1.92
Skewness1.4967179
Sum404.99
Variance16.711298
MonotonicityNot monotonic
2023-12-10T22:32:26.794911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.27 4
 
4.0%
0.54 3
 
3.0%
0.42 3
 
3.0%
0.13 3
 
3.0%
6.23 2
 
2.0%
7.04 2
 
2.0%
2.11 2
 
2.0%
2.53 2
 
2.0%
4.12 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.13 3
3.0%
0.27 4
4.0%
0.4 1
 
1.0%
0.42 3
3.0%
0.54 3
3.0%
0.57 1
 
1.0%
0.63 1
 
1.0%
0.64 1
 
1.0%
0.72 1
 
1.0%
ValueCountFrequency (%)
17.6 1
1.0%
17.41 1
1.0%
14.81 1
1.0%
14.45 1
1.0%
13.7 1
1.0%
13.18 1
1.0%
12.9 1
1.0%
11.52 1
1.0%
10.27 1
1.0%
10.09 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15792.341
Minimum138.68
Maximum70981.63
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:32:26.995446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum138.68
5-th percentile825.6395
Q14553.14
median11771.56
Q324778.51
95-th percentile43412.035
Maximum70981.63
Range70842.95
Interquartile range (IQR)20225.37

Descriptive statistics

Standard deviation14132.27
Coefficient of variation (CV)0.89488128
Kurtosis2.1044831
Mean15792.341
Median Absolute Deviation (MAD)8993.8
Skewness1.3404102
Sum1579234.1
Variance1.9972107 × 108
MonotonicityNot monotonic
2023-12-10T22:32:27.222125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12271.47 1
 
1.0%
29113.9 1
 
1.0%
2749.41 1
 
1.0%
10069.31 1
 
1.0%
12129.22 1
 
1.0%
46356.4 1
 
1.0%
28484.66 1
 
1.0%
26386.85 1
 
1.0%
20212.34 1
 
1.0%
4091.34 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
138.68 1
1.0%
277.37 1
1.0%
416.06 1
1.0%
461.05 1
1.0%
702.7 1
1.0%
832.11 1
1.0%
873.34 1
1.0%
1053.26 1
1.0%
1409.37 1
1.0%
1510.55 1
1.0%
ValueCountFrequency (%)
70981.63 1
1.0%
59637.49 1
1.0%
52947.05 1
1.0%
46356.4 1
1.0%
44402.4 1
1.0%
43359.91 1
1.0%
38134.19 1
1.0%
37002.39 1
1.0%
36083.43 1
1.0%
34596.89 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:32:27.556307image/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%
2023-12-10T22:32:28.197928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
남 102
 
9.3%
충 100
 
9.1%
산 56
 
5.1%
서 24
 
2.2%
천 24
 
2.2%
성 20
 
1.8%
신 16
 
1.5%
청 16
 
1.5%
부 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 (%)
남 102
 
12.8%
충 100
 
12.6%
산 56
 
7.0%
서 24
 
3.0%
천 24
 
3.0%
성 20
 
2.5%
신 16
 
2.0%
청 16
 
2.0%
부 14
 
1.8%
양 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 (%)
남 102
 
12.8%
충 100
 
12.6%
산 56
 
7.0%
서 24
 
3.0%
천 24
 
3.0%
성 20
 
2.5%
신 16
 
2.0%
청 16
 
2.0%
부 14
 
1.8%
양 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 (%)
남 102
 
12.8%
충 100
 
12.6%
산 56
 
7.0%
서 24
 
3.0%
천 24
 
3.0%
성 20
 
2.5%
신 16
 
2.0%
청 16
 
2.0%
부 14
 
1.8%
양 14
 
1.8%
Other values (96) 410
51.5%

Interactions

2023-12-10T22:32:19.608720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:08.440651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:09.805900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:11.370509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:12.562354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:13.774699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:15.195337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:16.334186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:18.080903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:19.741138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:08.566170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:09.965231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:11.506200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:12.686937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:13.909202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:15.311043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:16.497621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:18.429209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:19.865185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:08.712893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:10.112053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:11.639340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:12.824563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:14.051862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:15.442021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:16.700383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:18.697302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:19.994106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:08.835309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:10.250038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:11.751228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:12.937732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:14.190165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:15.559610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:16.873851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:18.842617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:20.125024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:08.982584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:10.428875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:11.897603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:13.068925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:14.619050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:15.678626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:17.062613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:18.994654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:20.280594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:09.154221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:10.597240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:12.033978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:13.197752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:14.740374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:15.800847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:17.229584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:19.124432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:20.399603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:09.284930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:10.815639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:12.143486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:13.327186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:14.845753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:15.914908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:17.404885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:19.259725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:20.508898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:09.428435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:11.049627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:12.277632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:13.493591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:14.965568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:16.047455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:17.641958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:19.386150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:20.622715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:09.564237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:11.194043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:12.427379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:13.632111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:15.089335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:16.175157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:17.884493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:32:19.490381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:32:28.362425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.7170.8630.8480.3470.5490.5690.4700.4841.000
지점1.0001.0000.0001.0001.0001.0001.0000.8270.8570.8670.8110.8511.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0770.0000.0000.000
측정구간1.0001.0000.0001.0000.9991.0000.9990.8330.8490.8580.8060.8391.000
연장0.7171.0000.0000.9991.0000.6790.8120.4070.4920.1280.5530.5491.000
좌표위치위도0.8631.0000.0001.0000.6791.0000.8200.3930.5720.5720.5830.5131.000
좌표위치경도0.8481.0000.0000.9990.8120.8201.0000.4340.5000.4980.4530.5401.000
co0.3470.8270.0000.8330.4070.3930.4341.0000.8840.9240.8320.9780.827
nox0.5490.8570.0000.8490.4920.5720.5000.8841.0000.9790.9760.9600.857
hc0.5690.8670.0770.8580.1280.5720.4980.9240.9791.0000.9530.9680.867
pm0.4700.8110.0000.8060.5530.5830.4530.8320.9760.9531.0000.9230.811
co20.4840.8510.0000.8390.5490.5130.5400.9780.9600.9680.9231.0000.851
주소1.0001.0000.0001.0001.0001.0001.0000.8270.8570.8670.8110.8511.000
2023-12-10T22:32:28.565567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정구간방향
측정구간1.0000.000
방향0.0001.000
2023-12-10T22:32:28.699303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간
기본키1.000-0.1620.246-0.226-0.172-0.129-0.140-0.148-0.1660.0000.753
연장-0.1621.0000.095-0.0860.1690.1240.1310.1420.1660.0000.744
좌표위치위도0.2460.0951.000-0.1200.5900.6180.6160.6000.6020.0000.753
좌표위치경도-0.226-0.086-0.1201.0000.2620.2560.2620.2430.2500.0000.741
co-0.1720.1690.5900.2621.0000.9780.9860.9550.9970.0000.355
nox-0.1290.1240.6180.2560.9781.0000.9960.9890.9770.0000.361
hc-0.1400.1310.6160.2620.9860.9961.0000.9830.9820.0480.372
pm-0.1480.1420.6000.2430.9550.9890.9831.0000.9550.0000.330
co2-0.1660.1660.6020.2500.9970.9770.9820.9551.0000.0000.356
방향0.0000.0000.0000.0000.0000.0000.0480.0000.0001.0000.000
측정구간0.7530.7440.7530.7410.3550.3610.3720.3300.3560.0001.000

Missing values

2023-12-10T22:32:20.792874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:32:20.994839image/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-논산11.520210601036.14511127.1050151.4454.727.584.3212271.47충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210601036.14511127.1050148.0151.617.34.1710741.98충남 논산 은진 토양
23건기연[0124-0]1논산-반포10.220210601036.24966127.22854106.172.7511.833.7724751.33충남 논산 연산 천호
34건기연[0124-0]2논산-반포10.220210601036.24966127.22854104.7573.4211.535.5124860.05충남 논산 연산 천호
45건기연[0127-2]1금남-조치원12.220210601036.56218127.28536141.96115.215.826.6336083.43충남 세종 연서 봉암
56건기연[0127-2]2금남-조치원12.220210601036.56218127.28536175.07138.0219.277.0444402.4충남 세종 연서 봉암
67건기연[0127-7]1공주-유성5.820210601036.40916127.2582138.7345.966.512.588806.86충남 공주 반포 성강
78건기연[0127-7]2공주-유성5.820210601036.40916127.2582128.9532.334.631.86231.06충남 공주 반포 성강
89건기연[3209-1]1공주-유성7.620210601036.43986127.204871.441.426.852.117075.68충남 세종 장군 금암
910건기연[3209-1]2공주-유성7.620210601036.43986127.204849.8836.65.911.9611505.16충남 세종 장군 금암
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3706-0]1진천-음성9.320210601036.1228127.4971744.9636.834.932.3411437.13충남 금산 군북 내부
9192건기연[3706-0]2진천-음성9.320210601036.1228127.4971764.3341.156.312.2815431.72충남 금산 군북 내부
9293건기연[3707-0]1추부-군서1.820210601036.21913127.495447.144.40.660.271885.37충남 금산 추부 요광
9394건기연[3707-0]2추부-군서1.820210601036.21913127.4954411.5411.01.720.722550.82충남 금산 추부 요광
9495건기연[3901-4]1은산-청양IC6.220210601036.35819126.915855.733.970.570.41510.55충남 청양 장평 은곡
9596건기연[3901-4]2은산-청양IC6.220210601036.35819126.915850.520.280.040.0138.68충남 청양 장평 은곡
9697건기연[3902-2]1장평-신풍12.920210601036.43536126.954910.7513.932.090.912334.16충남 청양 정산 해남
9798건기연[3902-2]2장평-신풍12.920210601036.43536126.95491.050.550.090.0277.37충남 청양 정산 해남
9899건기연[3905-0]1염치-권관8.320210601036.85256126.96007115.66132.0918.228.0626335.38충남 아산 영인 아산
99100건기연[3905-0]2염치-권관8.320210601036.85256126.96007191.55252.1933.8514.8143359.91충남 아산 영인 아산