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
co is highly overall correlated with nox and 3 other fieldsHigh correlation
nox is highly overall correlated with co and 3 other fieldsHigh correlation
hc is highly overall correlated with co and 3 other fieldsHigh correlation
pm is highly overall correlated with co and 3 other fieldsHigh correlation
co2 is highly overall correlated with co and 3 other fieldsHigh correlation
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 16:22:32.713245
Analysis finished2024-04-16 16:22:40.161977
Duration7.45 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
2024-04-17T01:22:40.246657image/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
2024-04-17T01:22:40.370842image/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

2024-04-17T01:22:40.472523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:40.548441image/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
2024-04-17T01:22:40.724538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.98
Min length7

Characters and Unicode

Total characters798
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%
3604-3 2
 
2.0%
7720-1 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%
3404-1 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T01:22:41.016957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 128
16.0%
[ 100
12.5%
2 100
12.5%
] 100
12.5%
- 98
12.3%
1 68
8.5%
3 62
7.8%
4 56
7.0%
7 22
 
2.8%
9 22
 
2.8%
Other values (3) 42
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.7%
Open Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%
Dash Punctuation 98
 
12.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 128
25.6%
2 100
20.0%
1 68
13.6%
3 62
12.4%
4 56
11.2%
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%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 798
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 128
16.0%
[ 100
12.5%
2 100
12.5%
] 100
12.5%
- 98
12.3%
1 68
8.5%
3 62
7.8%
4 56
7.0%
7 22
 
2.8%
9 22
 
2.8%
Other values (3) 42
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 798
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 128
16.0%
[ 100
12.5%
2 100
12.5%
] 100
12.5%
- 98
12.3%
1 68
8.5%
3 62
7.8%
4 56
7.0%
7 22
 
2.8%
9 22
 
2.8%
Other values (3) 42
 
5.3%

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

2024-04-17T01:22:41.128355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:41.204883image/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
2024-04-17T01:22:41.387264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length5.2
Min length4

Characters and Unicode

Total characters520
Distinct characters91
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%
청양-정산 2
 
2.0%
소원-서산 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%
공세-성환 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T01:22:41.704844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.2%
산 42
 
8.1%
성 16
 
3.1%
양 14
 
2.7%
서 12
 
2.3%
공 10
 
1.9%
주 10
 
1.9%
전 8
 
1.5%
안 8
 
1.5%
부 8
 
1.5%
Other values (81) 292
56.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
78.1%
Dash Punctuation 100
 
19.2%
Uppercase Letter 14
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 42
 
10.3%
성 16
 
3.9%
양 14
 
3.4%
서 12
 
3.0%
공 10
 
2.5%
주 10
 
2.5%
전 8
 
2.0%
안 8
 
2.0%
부 8
 
2.0%
장 8
 
2.0%
Other values (76) 270
66.5%
Uppercase Letter
ValueCountFrequency (%)
C 6
42.9%
I 4
28.6%
J 2
 
14.3%
T 2
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
78.1%
Common 100
 
19.2%
Latin 14
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 42
 
10.3%
성 16
 
3.9%
양 14
 
3.4%
서 12
 
3.0%
공 10
 
2.5%
주 10
 
2.5%
전 8
 
2.0%
안 8
 
2.0%
부 8
 
2.0%
장 8
 
2.0%
Other values (76) 270
66.5%
Latin
ValueCountFrequency (%)
C 6
42.9%
I 4
28.6%
J 2
 
14.3%
T 2
 
14.3%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
78.1%
ASCII 114
 
21.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
87.7%
C 6
 
5.3%
I 4
 
3.5%
J 2
 
1.8%
T 2
 
1.8%
Hangul
ValueCountFrequency (%)
산 42
 
10.3%
성 16
 
3.9%
양 14
 
3.4%
서 12
 
3.0%
공 10
 
2.5%
주 10
 
2.5%
전 8
 
2.0%
안 8
 
2.0%
부 8
 
2.0%
장 8
 
2.0%
Other values (76) 270
66.5%

연장
Real number (ℝ)

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.77
Minimum1.8
Maximum17.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:41.814423image/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 deviation3.5481251
Coefficient of variation (CV)0.45664416
Kurtosis-0.0011659238
Mean7.77
Median Absolute Deviation (MAD)2.5
Skewness0.56765417
Sum777
Variance12.589192
MonotonicityNot monotonic
2024-04-17T01:22:41.924498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
9.7 4
 
4.0%
6.4 4
 
4.0%
6.2 4
 
4.0%
8.3 4
 
4.0%
12.9 4
 
4.0%
8.4 4
 
4.0%
3.6 4
 
4.0%
6.6 4
 
4.0%
9.3 4
 
4.0%
11.5 2
 
2.0%
Other values (31) 62
62.0%
ValueCountFrequency (%)
1.8 2
2.0%
2.0 2
2.0%
2.2 2
2.0%
2.7 2
2.0%
3.6 4
4.0%
4.0 2
2.0%
4.2 2
2.0%
4.3 2
2.0%
4.4 2
2.0%
5.2 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
20210501
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210501
2nd row20210501
3rd row20210501
4th row20210501
5th row20210501

Common Values

ValueCountFrequency (%)
20210501 100
100.0%

Length

2024-04-17T01:22:42.032186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:42.102236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210501 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

2024-04-17T01:22:42.177895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:22:42.259379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.503017
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:42.369006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.26956
median36.500575
Q336.74848
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.47892

Descriptive statistics

Standard deviation0.27201625
Coefficient of variation (CV)0.0074518841
Kurtosis-1.2310639
Mean36.503017
Median Absolute Deviation (MAD)0.23946
Skewness-0.034363717
Sum3650.3017
Variance0.073992839
MonotonicityNot monotonic
2024-04-17T01:22:42.503471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.1228 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%
36.39222 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%
36.89461 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%
36.78489 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.93079
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:42.636736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.37641
Q1126.71196
median126.89799
Q3127.15746
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.4455

Descriptive statistics

Standard deviation0.31656506
Coefficient of variation (CV)0.0024939975
Kurtosis-0.46399024
Mean126.93079
Median Absolute Deviation (MAD)0.23154
Skewness-0.091534384
Sum12693.079
Variance0.10021344
MonotonicityNot monotonic
2024-04-17T01:22:42.783195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
127.49717 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%
126.66796 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%
127.4696 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%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6961.8407
Minimum576.17
Maximum34525.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:42.926774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum576.17
5-th percentile1117.8165
Q13404.6225
median6380.475
Q39848.9825
95-th percentile12699.889
Maximum34525.68
Range33949.51
Interquartile range (IQR)6444.36

Descriptive statistics

Standard deviation5152.5909
Coefficient of variation (CV)0.74011905
Kurtosis11.449708
Mean6961.8407
Median Absolute Deviation (MAD)3399.295
Skewness2.5693315
Sum696184.07
Variance26549193
MonotonicityNot monotonic
2024-04-17T01:22:43.037849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5299.63 1
 
1.0%
11683.19 1
 
1.0%
10211.65 1
 
1.0%
5208.23 1
 
1.0%
4360.48 1
 
1.0%
4333.57 1
 
1.0%
4317.07 1
 
1.0%
8090.0 1
 
1.0%
9835.97 1
 
1.0%
6631.05 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
576.17 1
1.0%
715.38 1
1.0%
917.9 1
1.0%
1047.14 1
1.0%
1067.4 1
1.0%
1120.47 1
1.0%
1127.27 1
1.0%
1155.11 1
1.0%
1217.38 1
1.0%
1219.61 1
1.0%
ValueCountFrequency (%)
34525.68 1
1.0%
31202.31 1
1.0%
14425.77 1
1.0%
12987.64 1
1.0%
12739.39 1
1.0%
12697.81 1
1.0%
12589.65 1
1.0%
12418.38 1
1.0%
12298.15 1
1.0%
12189.66 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7732.8989
Minimum386.15
Maximum96814.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:43.154591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum386.15
5-th percentile860.496
Q12816.6775
median5307.5
Q38862.75
95-th percentile13977.189
Maximum96814.76
Range96428.61
Interquartile range (IQR)6046.0725

Descriptive statistics

Standard deviation13106.353
Coefficient of variation (CV)1.6948822
Kurtosis38.254702
Mean7732.8989
Median Absolute Deviation (MAD)2732.145
Skewness5.9756073
Sum773289.89
Variance1.7177649 × 108
MonotonicityNot monotonic
2024-04-17T01:22:43.266871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7509.27 1
 
1.0%
10521.31 1
 
1.0%
6894.04 1
 
1.0%
4119.13 1
 
1.0%
3154.25 1
 
1.0%
2866.69 1
 
1.0%
2993.08 1
 
1.0%
9264.89 1
 
1.0%
14413.42 1
 
1.0%
8791.77 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
386.15 1
1.0%
483.54 1
1.0%
781.42 1
1.0%
782.03 1
1.0%
792.21 1
1.0%
864.09 1
1.0%
900.76 1
1.0%
961.44 1
1.0%
964.13 1
1.0%
1117.9 1
1.0%
ValueCountFrequency (%)
96814.76 1
1.0%
92071.35 1
1.0%
21133.4 1
1.0%
21133.17 1
1.0%
14413.42 1
1.0%
13954.23 1
1.0%
12201.1 1
1.0%
12036.73 1
1.0%
11644.9 1
1.0%
11580.39 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean919.4308
Minimum56.09
Maximum7756.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:43.378860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56.09
5-th percentile121.1265
Q1385.3725
median735.695
Q31143.5025
95-th percentile1657.8565
Maximum7756.76
Range7700.67
Interquartile range (IQR)758.13

Descriptive statistics

Standard deviation1090.2263
Coefficient of variation (CV)1.1857622
Kurtosis29.900856
Mean919.4308
Median Absolute Deviation (MAD)372.675
Skewness5.0226314
Sum91943.08
Variance1188593.3
MonotonicityNot monotonic
2024-04-17T01:22:43.500227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
841.23 1
 
1.0%
1429.01 1
 
1.0%
1020.12 1
 
1.0%
579.84 1
 
1.0%
443.46 1
 
1.0%
418.75 1
 
1.0%
424.38 1
 
1.0%
1203.74 1
 
1.0%
1651.43 1
 
1.0%
1078.02 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
56.09 1
1.0%
71.36 1
1.0%
112.83 1
1.0%
114.27 1
1.0%
115.36 1
1.0%
121.43 1
1.0%
128.18 1
1.0%
135.35 1
1.0%
136.63 1
1.0%
146.82 1
1.0%
ValueCountFrequency (%)
7756.76 1
1.0%
7724.13 1
1.0%
2162.69 1
1.0%
2137.07 1
1.0%
1779.96 1
1.0%
1651.43 1
1.0%
1612.69 1
1.0%
1530.14 1
1.0%
1516.7 1
1.0%
1507.41 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean438.0327
Minimum23.4
Maximum5573.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:43.613873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.4
5-th percentile62.53
Q1145.415
median282
Q3484.83
95-th percentile880.55
Maximum5573.28
Range5549.88
Interquartile range (IQR)339.415

Descriptive statistics

Standard deviation767.72213
Coefficient of variation (CV)1.7526594
Kurtosis37.173436
Mean438.0327
Median Absolute Deviation (MAD)163.49
Skewness5.8738685
Sum43803.27
Variance589397.28
MonotonicityNot monotonic
2024-04-17T01:22:43.970241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
538.88 1
 
1.0%
483.46 1
 
1.0%
232.39 1
 
1.0%
189.45 1
 
1.0%
137.08 1
 
1.0%
112.1 1
 
1.0%
119.76 1
 
1.0%
559.98 1
 
1.0%
917.79 1
 
1.0%
501.81 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
23.4 1
1.0%
36.51 1
1.0%
47.97 1
1.0%
50.49 1
1.0%
55.69 1
1.0%
62.89 1
1.0%
64.02 1
1.0%
64.26 1
1.0%
65.31 1
1.0%
72.81 1
1.0%
ValueCountFrequency (%)
5573.28 1
1.0%
5399.82 1
1.0%
1325.2 1
1.0%
1319.33 1
1.0%
917.79 1
1.0%
878.59 1
1.0%
812.82 1
1.0%
771.69 1
1.0%
708.2 1
1.0%
700.86 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1845460.1
Minimum150800.8
Maximum11773702
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:22:44.075555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150800.8
5-th percentile291356.78
Q1883962.82
median1639158.1
Q32533049.8
95-th percentile3283034.3
Maximum11773702
Range11622901
Interquartile range (IQR)1649086.9

Descriptive statistics

Standard deviation1626392.8
Coefficient of variation (CV)0.88129394
Kurtosis20.707604
Mean1845460.1
Median Absolute Deviation (MAD)876279.74
Skewness3.8326789
Sum1.8454601 × 108
Variance2.6451536 × 1012
MonotonicityNot monotonic
2024-04-17T01:22:44.204751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1359675.6 1
 
1.0%
2991286.89 1
 
1.0%
2664371.28 1
 
1.0%
1347861.19 1
 
1.0%
1142737.74 1
 
1.0%
1135898.18 1
 
1.0%
1139308.28 1
 
1.0%
2025928.33 1
 
1.0%
2573415.78 1
 
1.0%
1688375.44 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
150800.8 1
1.0%
187226.3 1
1.0%
228559.39 1
1.0%
259142.21 1
1.0%
265918.36 1
1.0%
292695.64 1
1.0%
293262.74 1
1.0%
296801.94 1
1.0%
309998.08 1
1.0%
315156.35 1
1.0%
ValueCountFrequency (%)
11773702.13 1
1.0%
10512584.59 1
1.0%
3757833.47 1
1.0%
3526607.57 1
1.0%
3512689.54 1
1.0%
3270947.19 1
1.0%
3268747.05 1
1.0%
3197582.3 1
1.0%
3196334.04 1
1.0%
3152673.87 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:22:44.443917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.86
Min length7

Characters and Unicode

Total characters1086
Distinct characters109
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충남 논산 은진 토양
2nd row충남 논산 은진 토양
3rd row충남 논산 연산 천호
4th row충남 논산 연산 천호
5th row충남 세종 연서 봉암
ValueCountFrequency (%)
충남 98
24.7%
금산 10
 
2.5%
아산 10
 
2.5%
청양 10
 
2.5%
예산 10
 
2.5%
서천 8
 
2.0%
천안 8
 
2.0%
공주 8
 
2.0%
부여 6
 
1.5%
세종 6
 
1.5%
Other values (98) 222
56.1%
2024-04-17T01:22:44.770639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
296
27.3%
남 102
 
9.4%
충 98
 
9.0%
산 62
 
5.7%
천 24
 
2.2%
서 22
 
2.0%
성 16
 
1.5%
청 16
 
1.5%
안 14
 
1.3%
오 12
 
1.1%
Other values (99) 424
39.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 790
72.7%
Space Separator 296
 
27.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 102
 
12.9%
충 98
 
12.4%
산 62
 
7.8%
천 24
 
3.0%
서 22
 
2.8%
성 16
 
2.0%
청 16
 
2.0%
안 14
 
1.8%
오 12
 
1.5%
양 12
 
1.5%
Other values (98) 412
52.2%
Space Separator
ValueCountFrequency (%)
296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 790
72.7%
Common 296
 
27.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 102
 
12.9%
충 98
 
12.4%
산 62
 
7.8%
천 24
 
3.0%
서 22
 
2.8%
성 16
 
2.0%
청 16
 
2.0%
안 14
 
1.8%
오 12
 
1.5%
양 12
 
1.5%
Other values (98) 412
52.2%
Common
ValueCountFrequency (%)
296
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 790
72.7%
ASCII 296
 
27.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
296
100.0%
Hangul
ValueCountFrequency (%)
남 102
 
12.9%
충 98
 
12.4%
산 62
 
7.8%
천 24
 
3.0%
서 22
 
2.8%
성 16
 
2.0%
청 16
 
2.0%
안 14
 
1.8%
오 12
 
1.5%
양 12
 
1.5%
Other values (98) 412
52.2%

Interactions

2024-04-17T01:22:39.181083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.128117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.797560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:34.823188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.461680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.207721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.948337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.587435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.272137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:39.252588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.188416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.904058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:34.894512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.538810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.279065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.022808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.655441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.334148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:39.343082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.264356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.995754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:34.967996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.623017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.359956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.094088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.734724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.423471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:39.415498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.340935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:34.063761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.029890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.696747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.447230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.156796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.802642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.493759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:39.493026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.423852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:34.429483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.112824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.767241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.560930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.229953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.883444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.578953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:39.566442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.506056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:34.510693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.190068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.843926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.635630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.302602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.963126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.665692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:39.642798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.566171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:34.582702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.256314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.933200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.706410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.378373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.042745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.729757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:39.717988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.656056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:34.660379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.328570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.017736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.792684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.453756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.120427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.801110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:39.786937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:33.722749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:34.731976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:35.394086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.125330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:36.865645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:37.516919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.195911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:22:38.865147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T01:22:44.880271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.7440.8420.8630.5850.4960.5790.5140.4171.000
지점1.0001.0000.0001.0001.0001.0001.0000.9690.9890.9670.9920.8641.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9690.9890.9670.9920.8641.000
연장0.7441.0000.0001.0001.0000.8000.8270.4000.3810.3970.3480.2221.000
좌표위치위도0.8421.0000.0001.0000.8001.0000.8220.4580.4220.3600.4210.4151.000
좌표위치경도0.8631.0000.0001.0000.8270.8221.0000.5780.4130.5700.4400.5461.000
co0.5850.9690.0000.9690.4000.4580.5781.0000.8470.8960.8160.9830.969
nox0.4960.9890.0000.9890.3810.4220.4130.8471.0000.9640.9990.8090.989
hc0.5790.9670.0000.9670.3970.3600.5700.8960.9641.0000.9610.8660.967
pm0.5140.9920.0000.9920.3480.4210.4400.8160.9990.9611.0000.7770.992
co20.4170.8640.0000.8640.2220.4150.5460.9830.8090.8660.7771.0000.864
주소1.0001.0000.0001.0001.0001.0001.0000.9690.9890.9670.9920.8641.000
2024-04-17T01:22:45.015156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.000-0.0130.275-0.198-0.151-0.127-0.133-0.173-0.1530.000
연장-0.0131.0000.121-0.212-0.026-0.081-0.061-0.109-0.0160.000
좌표위치위도0.2750.1211.000-0.2450.3800.3990.4000.3430.3840.000
좌표위치경도-0.198-0.212-0.2451.0000.2720.3190.3180.3670.2690.000
co-0.151-0.0260.3800.2721.0000.9450.9700.8960.9980.000
nox-0.127-0.0810.3990.3190.9451.0000.9940.9790.9460.000
hc-0.133-0.0610.4000.3180.9700.9941.0000.9630.9690.000
pm-0.173-0.1090.3430.3670.8960.9790.9631.0000.8970.000
co2-0.153-0.0160.3840.2690.9980.9460.9690.8971.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T01:22:39.896136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T01:22:40.077674image/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-논산11.520210501036.14511127.105015299.637509.27841.23538.881359675.6충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210501036.14511127.105015627.867657.53842.54541.11469011.42충남 논산 은진 토양
23건기연[0124-0]1논산-반포10.220210501036.24966127.228549888.026656.02970.68313.722573836.81충남 논산 연산 천호
34건기연[0124-0]2논산-반포10.220210501036.24966127.2285410264.447301.271040.2503.482673255.78충남 논산 연산 천호
45건기연[0127-2]1금남-조치원12.220210501036.56218127.2853610467.218736.921169.64463.362686768.12충남 세종 연서 봉암
56건기연[0127-2]2금남-조치원12.220210501036.56218127.2853610408.28329.921134.79402.682673660.49충남 세종 연서 봉암
67건기연[0127-7]1공주-유성5.820210501036.40916127.2582110252.069989.111362.75535.712555889.62충남 공주 반포 성강
78건기연[0127-7]2공주-유성5.820210501036.40916127.258219352.829055.11191.43465.682390611.8충남 공주 반포 성강
89건기연[0127-8]1전동-쌍전6.220210501036.62718127.289638801.4210311.751273.71684.542246825.96충남 세종 조치원 신안
910건기연[0127-8]2전동-쌍전6.220210501036.62718127.289639133.6710697.111350.25708.22300494.47충남 세종 조치원 신안
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[4004-0]1부여-공주11.620210501036.39173127.080583657.963197.93428.06212.51923610.27충남 공주 이인 주봉
9192건기연[4004-0]2부여-공주11.620210501036.39173127.080583535.883023.4410.78194.18893755.25충남 공주 이인 주봉
9293건기연[4502-0]1용동-예산6.420210501036.68623126.770676687.595508.72787.51311.01589962.9충남 예산 오가 좌방
9394건기연[4502-0]2용동-예산6.420210501036.68623126.770675971.215342.15700.2321.831542037.91충남 예산 오가 좌방
9495건기연[4503-0]1아산-음봉8.420210501036.83831127.011255239.923467.37536.12152.611238848.58충남 아산 음봉 동천
9596건기연[4503-0]2아산-음봉8.420210501036.83831127.011255091.463862.36543.97210.131306026.13충남 아산 음봉 동천
9697건기연[7720-1]1소원-서산14.320210501036.69795126.286865459.23769.45541.92156.911414964.02충남 태안 남 진산
9798건기연[7720-1]2소원-서산14.320210501036.69795126.286867148.974690.38685.65170.721862805.41충남 태안 남 진산
9899건기연[00124]1비룡JCT-대전IC3.620210501036.34752127.469634525.6896814.767756.765573.2811773702.13대전 동 비룡
99100건기연[00124]2비룡JCT-대전IC3.620210501036.34752127.469631202.3192071.357724.135399.8210512584.59대전 동 비룡