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
Categorical4
Text3

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시분 has constant value ""Constant
좌표위치위도 is highly overall correlated with nox and 2 other fieldsHigh correlation
co is highly overall correlated with nox and 3 other fieldsHigh correlation
nox is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
hc is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with 좌표위치위도 and 4 other fieldsHigh correlation
기본키 has unique valuesUnique
co has unique valuesUnique
nox has unique valuesUnique
hc has unique valuesUnique
pm has unique valuesUnique
co2 has unique valuesUnique

Reproduction

Analysis started2024-04-16 16:22:46.168921
Analysis finished2024-04-16 16:22:53.265248
Duration7.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

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
2024-04-17T01:22:53.334733image/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
2024-04-17T01:22:53.449942image/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

2024-04-17T01:22:53.554134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:53.641604image/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
2024-04-17T01:22:53.794940image/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%
3602-0 2
 
2.0%
4503-0 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%
3401-2 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T01:22:54.335355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 126
15.8%
[ 100
12.5%
2 100
12.5%
- 100
12.5%
] 100
12.5%
1 70
8.8%
3 64
8.0%
4 54
6.8%
7 22
 
2.8%
9 22
 
2.8%
Other values (3) 42
 
5.2%

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 (%)
0 126
25.2%
2 100
20.0%
1 70
14.0%
3 64
12.8%
4 54
10.8%
7 22
 
4.4%
9 22
 
4.4%
6 20
 
4.0%
5 18
 
3.6%
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 (%)
0 126
15.8%
[ 100
12.5%
2 100
12.5%
- 100
12.5%
] 100
12.5%
1 70
8.8%
3 64
8.0%
4 54
6.8%
7 22
 
2.8%
9 22
 
2.8%
Other values (3) 42
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 126
15.8%
[ 100
12.5%
2 100
12.5%
- 100
12.5%
] 100
12.5%
1 70
8.8%
3 64
8.0%
4 54
6.8%
7 22
 
2.8%
9 22
 
2.8%
Other values (3) 42
 
5.2%

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

2024-04-17T01:22:54.441703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:54.517948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:22:54.686838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1
Min length4

Characters and Unicode

Total characters510
Distinct characters88
Distinct categories3 ?
Distinct scripts3 ?
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 row연무-논산
2nd row연무-논산
3rd row논산-반포
4th row논산-반포
5th row금남-조치원
ValueCountFrequency (%)
연무-논산 2
 
2.0%
보령-청양 2
 
2.0%
아산-음봉 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%
유구-사곡 2
 
2.0%
신평-인주 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T01:22:54.990977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.6%
산 42
 
8.2%
성 18
 
3.5%
양 14
 
2.7%
서 12
 
2.4%
주 10
 
2.0%
공 10
 
2.0%
안 10
 
2.0%
천 10
 
2.0%
청 8
 
1.6%
Other values (78) 276
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
79.6%
Dash Punctuation 100
 
19.6%
Uppercase Letter 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 42
 
10.3%
성 18
 
4.4%
양 14
 
3.4%
서 12
 
3.0%
주 10
 
2.5%
공 10
 
2.5%
안 10
 
2.5%
천 10
 
2.5%
청 8
 
2.0%
장 8
 
2.0%
Other values (75) 264
65.0%
Uppercase Letter
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
79.6%
Common 100
 
19.6%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 42
 
10.3%
성 18
 
4.4%
양 14
 
3.4%
서 12
 
3.0%
주 10
 
2.5%
공 10
 
2.5%
안 10
 
2.5%
천 10
 
2.5%
청 8
 
2.0%
장 8
 
2.0%
Other values (75) 264
65.0%
Latin
ValueCountFrequency (%)
I 2
50.0%
C 2
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
79.6%
ASCII 104
 
20.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
96.2%
I 2
 
1.9%
C 2
 
1.9%
Hangul
ValueCountFrequency (%)
산 42
 
10.3%
성 18
 
4.4%
양 14
 
3.4%
서 12
 
3.0%
주 10
 
2.5%
공 10
 
2.5%
안 10
 
2.5%
천 10
 
2.5%
청 8
 
2.0%
장 8
 
2.0%
Other values (75) 264
65.0%

연장
Real number (ℝ)

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.796
Minimum1.8
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:55.107223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.2
Q15.4
median6.75
Q39.7
95-th percentile14.3
Maximum17.6
Range15.8
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation3.5218774
Coefficient of variation (CV)0.45175441
Kurtosis0.041114712
Mean7.796
Median Absolute Deviation (MAD)2.4
Skewness0.58090351
Sum779.6
Variance12.40362
MonotonicityNot monotonic
2024-04-17T01:22:55.219750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
6.4 4
 
4.0%
12.9 4
 
4.0%
9.7 4
 
4.0%
8.4 4
 
4.0%
6.6 4
 
4.0%
8.3 4
 
4.0%
9.3 4
 
4.0%
6.2 4
 
4.0%
2.0 2
 
2.0%
1.8 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%
14.3 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%

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

2024-04-17T01:22:55.366709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:55.468221image/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
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

2024-04-17T01:22:55.543720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:55.616274image/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%
Mean36.514064
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:55.698586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.26956
median36.51428
Q336.75792
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.48836

Descriptive statistics

Standard deviation0.27670091
Coefficient of variation (CV)0.0075779269
Kurtosis-1.2604037
Mean36.514064
Median Absolute Deviation (MAD)0.24418
Skewness-0.090444243
Sum3651.4064
Variance0.076563394
MonotonicityNot monotonic
2024-04-17T01:22:55.809695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.48872 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%
36.9261 2
 
2.0%
36.89461 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%
36.87646 2
2.0%
36.87015 2
2.0%
36.85256 2
2.0%
36.83831 2
2.0%
36.83292 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.92393
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:55.952006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.37641
Q1126.71196
median126.89799
Q3127.13389
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.42193

Descriptive statistics

Standard deviation0.30834305
Coefficient of variation (CV)0.0024293533
Kurtosis-0.38452844
Mean126.92393
Median Absolute Deviation (MAD)0.22932
Skewness-0.13565476
Sum12692.393
Variance0.095075437
MonotonicityNot monotonic
2024-04-17T01:22:56.083811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
127.20167 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%
127.11 2
 
2.0%
127.15746 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.18913 2
2.0%
126.28686 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%
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%
Mean6096.2396
Minimum504.65
Maximum18378.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:56.195757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum504.65
5-th percentile737.0985
Q12496.8025
median5548.02
Q39129.265
95-th percentile13300.701
Maximum18378.51
Range17873.86
Interquartile range (IQR)6632.4625

Descriptive statistics

Standard deviation4147.6607
Coefficient of variation (CV)0.6803638
Kurtosis-0.0059192253
Mean6096.2396
Median Absolute Deviation (MAD)3296.185
Skewness0.67720549
Sum609623.96
Variance17203090
MonotonicityNot monotonic
2024-04-17T01:22:56.309433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5279.95 1
 
1.0%
2486.37 1
 
1.0%
2701.65 1
 
1.0%
2361.3 1
 
1.0%
2264.43 1
 
1.0%
12432.13 1
 
1.0%
14893.2 1
 
1.0%
10728.85 1
 
1.0%
10943.06 1
 
1.0%
18220.73 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
504.65 1
1.0%
534.5 1
1.0%
719.42 1
1.0%
725.58 1
1.0%
726.43 1
1.0%
737.66 1
1.0%
833.02 1
1.0%
858.15 1
1.0%
1086.43 1
1.0%
1124.94 1
1.0%
ValueCountFrequency (%)
18378.51 1
1.0%
18220.73 1
1.0%
14893.2 1
1.0%
14336.68 1
1.0%
13949.76 1
1.0%
13266.54 1
1.0%
12622.19 1
1.0%
12432.13 1
1.0%
11244.23 1
1.0%
11013.04 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7537.157
Minimum456.7
Maximum34582.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:56.427257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum456.7
5-th percentile774.661
Q12693.925
median6037.3
Q39090.0725
95-th percentile19750.896
Maximum34582.25
Range34125.55
Interquartile range (IQR)6396.1475

Descriptive statistics

Standard deviation6657.5927
Coefficient of variation (CV)0.88330291
Kurtosis4.1649239
Mean7537.157
Median Absolute Deviation (MAD)3365.77
Skewness1.8334937
Sum753715.7
Variance44323541
MonotonicityNot monotonic
2024-04-17T01:22:56.549590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7624.18 1
 
1.0%
2541.22 1
 
1.0%
2890.17 1
 
1.0%
2316.38 1
 
1.0%
2142.61 1
 
1.0%
21322.41 1
 
1.0%
29220.28 1
 
1.0%
19008.74 1
 
1.0%
19848.85 1
 
1.0%
32410.99 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
456.7 1
1.0%
577.57 1
1.0%
734.96 1
1.0%
742.37 1
1.0%
770.69 1
1.0%
774.87 1
1.0%
818.7 1
1.0%
836.54 1
1.0%
957.82 1
1.0%
1447.03 1
1.0%
ValueCountFrequency (%)
34582.25 1
1.0%
32410.99 1
1.0%
29220.28 1
1.0%
21322.41 1
1.0%
19848.85 1
1.0%
19745.74 1
1.0%
19008.74 1
1.0%
18276.63 1
1.0%
17541.39 1
1.0%
17527.74 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean929.49
Minimum66.15
Maximum3799.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:56.661735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum66.15
5-th percentile110.2875
Q1368.2875
median796.565
Q31166.2075
95-th percentile2188.1645
Maximum3799.37
Range3733.22
Interquartile range (IQR)797.92

Descriptive statistics

Standard deviation746.35993
Coefficient of variation (CV)0.8029779
Kurtosis3.3466085
Mean929.49
Median Absolute Deviation (MAD)414.21
Skewness1.6097925
Sum92949
Variance557053.14
MonotonicityNot monotonic
2024-04-17T01:22:56.773209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
874.33 1
 
1.0%
365.64 1
 
1.0%
389.9 1
 
1.0%
312.57 1
 
1.0%
288.1 1
 
1.0%
2427.27 1
 
1.0%
3218.44 1
 
1.0%
2107.62 1
 
1.0%
2163.95 1
 
1.0%
3708.08 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
66.15 1
1.0%
77.17 1
1.0%
95.5 1
1.0%
98.5 1
1.0%
110.24 1
1.0%
110.29 1
1.0%
117.18 1
1.0%
117.71 1
1.0%
137.54 1
1.0%
201.16 1
1.0%
ValueCountFrequency (%)
3799.37 1
1.0%
3708.08 1
1.0%
3218.44 1
1.0%
2481.16 1
1.0%
2427.27 1
1.0%
2175.58 1
1.0%
2163.95 1
1.0%
2107.62 1
1.0%
2017.58 1
1.0%
1999.59 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean497.0446
Minimum44.42
Maximum2102.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:56.889245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum44.42
5-th percentile62.2615
Q1187.5275
median390.03
Q3609.46
95-th percentile1278.0465
Maximum2102.77
Range2058.35
Interquartile range (IQR)421.9325

Descriptive statistics

Standard deviation432.75535
Coefficient of variation (CV)0.87065697
Kurtosis3.74051
Mean497.0446
Median Absolute Deviation (MAD)206.21
Skewness1.8013242
Sum49704.46
Variance187277.19
MonotonicityNot monotonic
2024-04-17T01:22:57.013186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
577.37 1
 
1.0%
133.95 1
 
1.0%
195.59 1
 
1.0%
154.72 1
 
1.0%
145.41 1
 
1.0%
1500.49 1
 
1.0%
2102.77 1
 
1.0%
1276.41 1
 
1.0%
1309.14 1
 
1.0%
2039.85 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
44.42 1
1.0%
50.02 1
1.0%
50.58 1
1.0%
59.09 1
1.0%
60.01 1
1.0%
62.38 1
1.0%
67.21 1
1.0%
67.52 1
1.0%
70.14 1
1.0%
93.32 1
1.0%
ValueCountFrequency (%)
2102.77 1
1.0%
2073.49 1
1.0%
2039.85 1
1.0%
1500.49 1
1.0%
1309.14 1
1.0%
1276.41 1
1.0%
1211.45 1
1.0%
1200.41 1
1.0%
1197.58 1
1.0%
1174.17 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1543357
Minimum126036.27
Maximum4819400.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:57.161457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126036.27
5-th percentile179391.26
Q1617197.34
median1385414.8
Q32291211.4
95-th percentile3216156.8
Maximum4819400.2
Range4693364
Interquartile range (IQR)1674014.1

Descriptive statistics

Standard deviation1073894.8
Coefficient of variation (CV)0.69581748
Kurtosis0.025729784
Mean1543357
Median Absolute Deviation (MAD)857601.16
Skewness0.6984709
Sum1.543357 × 108
Variance1.1532499 × 1012
MonotonicityNot monotonic
2024-04-17T01:22:57.278802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1337611.57 1
 
1.0%
594132.38 1
 
1.0%
671583.0 1
 
1.0%
595871.55 1
 
1.0%
580530.78 1
 
1.0%
3194974.97 1
 
1.0%
3849866.49 1
 
1.0%
2795935.7 1
 
1.0%
2882862.54 1
 
1.0%
4635346.55 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126036.27 1
1.0%
135306.99 1
1.0%
172811.65 1
1.0%
173539.25 1
1.0%
176014.3 1
1.0%
179568.99 1
1.0%
197140.61 1
1.0%
202861.18 1
1.0%
250756.92 1
1.0%
255382.51 1
1.0%
ValueCountFrequency (%)
4819400.22 1
1.0%
4635346.55 1
1.0%
3849866.49 1
1.0%
3638926.93 1
1.0%
3577658.75 1
1.0%
3197130.34 1
1.0%
3194974.97 1
1.0%
3174257.1 1
1.0%
2893532.68 1
1.0%
2882862.54 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:22:57.506931image/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%
아산 10
 
2.5%
청양 10
 
2.5%
예산 10
 
2.5%
천안 10
 
2.5%
금산 10
 
2.5%
공주 8
 
2.0%
서천 8
 
2.0%
성환 6
 
1.5%
부여 6
 
1.5%
Other values (97) 220
55.3%
2024-04-17T01:22:57.878831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
남 104
 
9.5%
충 100
 
9.1%
산 62
 
5.7%
천 26
 
2.4%
서 22
 
2.0%
성 18
 
1.6%
청 16
 
1.5%
안 16
 
1.5%
주 14
 
1.3%
Other values (97) 418
38.2%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 104
 
13.1%
충 100
 
12.6%
산 62
 
7.8%
천 26
 
3.3%
서 22
 
2.8%
성 18
 
2.3%
청 16
 
2.0%
안 16
 
2.0%
주 14
 
1.8%
양 12
 
1.5%
Other values (96) 406
51.0%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 796
72.8%
Common 298
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 104
 
13.1%
충 100
 
12.6%
산 62
 
7.8%
천 26
 
3.3%
서 22
 
2.8%
성 18
 
2.3%
청 16
 
2.0%
안 16
 
2.0%
주 14
 
1.8%
양 12
 
1.5%
Other values (96) 406
51.0%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 796
72.8%
ASCII 298
 
27.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
남 104
 
13.1%
충 100
 
12.6%
산 62
 
7.8%
천 26
 
3.3%
서 22
 
2.8%
성 18
 
2.3%
청 16
 
2.0%
안 16
 
2.0%
주 14
 
1.8%
양 12
 
1.5%
Other values (96) 406
51.0%

Interactions

2024-04-17T01:22:52.243202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:46.652357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.322639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.991159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.641703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.322237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.204291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.916767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.630857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.312356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:46.715451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.389249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.058126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.720947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.633816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.273218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.999188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.694809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.389442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:46.791051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.464983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.130218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.797188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.704583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.352948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.075393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.767135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.455764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:46.852337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.530532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.192738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.865428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.770439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.419696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.162331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.836186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.524171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:46.928659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.605617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.261842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.936557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.844942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.493906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.276476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.911243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.598518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.019696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.678788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.339899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.011945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.919105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.568664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.352781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.984371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.673233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.106495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.766477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.417120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.114762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.993138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.667164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.427742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.054451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.755467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.188301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.852622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.496777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.188467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.068169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.761333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.500074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.121363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.871146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.254057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:47.919574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:48.565713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:49.253561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.133621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:50.847106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:51.562133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:52.181024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T01:22:57.965210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.7910.8320.8490.7370.5380.5020.4230.5621.000
지점1.0001.0000.0001.0001.0001.0001.0000.9780.9690.9380.9310.9871.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9780.9690.9380.9310.9871.000
연장0.7911.0000.0001.0001.0000.7860.8320.5430.4190.2330.3500.4221.000
좌표위치위도0.8321.0000.0001.0000.7861.0000.8400.5780.4330.4150.3870.5281.000
좌표위치경도0.8491.0000.0001.0000.8320.8401.0000.7220.5280.5270.3970.5911.000
co0.7370.9780.0000.9780.5430.5780.7221.0000.9040.8950.8290.9590.978
nox0.5380.9690.0000.9690.4190.4330.5280.9041.0000.9850.9830.9550.969
hc0.5020.9380.0000.9380.2330.4150.5270.8950.9851.0000.9830.9560.938
pm0.4230.9310.0000.9310.3500.3870.3970.8290.9830.9831.0000.9200.931
co20.5620.9870.0000.9870.4220.5280.5910.9590.9550.9560.9201.0000.987
주소1.0001.0000.0001.0001.0001.0001.0000.9780.9690.9380.9310.9871.000
2024-04-17T01:22:58.094462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.0000.0620.246-0.289-0.237-0.236-0.241-0.247-0.2350.000
연장0.0621.0000.069-0.189-0.046-0.110-0.101-0.083-0.0410.000
좌표위치위도0.2460.0691.000-0.1960.4980.5160.5030.4870.5010.000
좌표위치경도-0.289-0.189-0.1961.0000.3860.3380.3750.3350.3690.000
co-0.237-0.0460.4980.3861.0000.9660.9810.9390.9980.000
nox-0.236-0.1100.5160.3380.9661.0000.9940.9830.9680.000
hc-0.241-0.1010.5030.3750.9810.9941.0000.9720.9800.000
pm-0.247-0.0830.4870.3350.9390.9830.9721.0000.9420.000
co2-0.235-0.0410.5010.3690.9980.9680.9800.9421.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T01:22:53.017647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T01:22:53.198615image/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-논산11.520210401036.14511127.105015279.957624.18874.33577.371337611.57충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210401036.14511127.105014912.97569.81825.94559.981268277.03충남 논산 은진 토양
23건기연[0124-0]1논산-반포10.220210401036.24966127.228549040.287076.831034.76358.632288349.94충남 논산 연산 천호
34건기연[0124-0]2논산-반포10.220210401036.24966127.228549688.568833.741222.03690.112432540.35충남 논산 연산 천호
45건기연[0127-2]1금남-조치원12.220210401036.56218127.2853611013.0410298.471328.08612.112820761.32충남 세종 연서 봉암
56건기연[0127-2]2금남-조치원12.220210401036.56218127.2853610126.928930.841172.41508.352624090.19충남 세종 연서 봉암
67건기연[0127-7]1공주-유성5.820210401036.40916127.258219939.2310757.391441.74648.562444814.56충남 공주 반포 성강
78건기연[0127-7]2공주-유성5.820210401036.40916127.258219396.4710118.91331.53589.442331435.49충남 공주 반포 성강
89건기연[0127-8]1전동-쌍전6.220210401036.62718127.2896310064.1413964.551642.43957.02540565.0충남 세종 조치원 신안
910건기연[0127-8]2전동-쌍전6.220210401036.62718127.2896310103.2513389.191614.64938.712544437.96충남 세종 조치원 신안
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[4001-4]1덕산-갈산12.920210401036.64566126.610451155.821491.59210.1997.14255382.51충남 예산 덕산 사천
9192건기연[4001-4]2덕산-갈산12.920210401036.64566126.610451124.941447.03202.0693.32250756.92충남 예산 덕산 사천
9293건기연[4004-0]1부여-공주11.620210401036.39173127.080582986.923156.53425.15235.7721742.46충남 공주 이인 주봉
9394건기연[4004-0]2부여-공주11.620210401036.39173127.080582981.093284.68427.82235.28726477.24충남 공주 이인 주봉
9495건기연[4502-0]1용동-예산6.420210401036.68623126.770674739.615071.92647.37367.631204058.88충남 예산 오가 좌방
9596건기연[4502-0]2용동-예산6.420210401036.68623126.770674722.354908.94632.74360.921197893.82충남 예산 오가 좌방
9697건기연[4503-0]1아산-음봉8.420210401036.83831127.011254959.964553.21591.52291.561265361.18충남 아산 음봉 동천
9798건기연[4503-0]2아산-음봉8.420210401036.83831127.011255298.775032.94674.39336.581323718.07충남 아산 음봉 동천
9899건기연[7720-1]1소원-서산14.320210401036.69795126.286862868.672598.46354.36193.45718372.6충남 태안 남 진산
99100건기연[7720-1]2소원-서산14.320210401036.69795126.286862983.622716.32369.17207.75747464.53충남 태안 남 진산