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 16:22:59.906102
Analysis finished2024-04-16 16:23:06.843285
Duration6.94 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
2024-04-17T01:23:06.912001image/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
2024-04-17T01:23:07.053942image/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

2024-04-17T01:23:07.160131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:07.230559image/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
2024-04-17T01:23:07.388923image/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[0123-2]
4th row[0123-2]
5th row[0124-0]
ValueCountFrequency (%)
0122-2 2
 
2.0%
3401-2 2
 
2.0%
4001-2 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%
3203-2 2
 
2.0%
3204-4 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T01:23:07.686516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 124
15.5%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 74
9.2%
3 70
8.8%
4 48
 
6.0%
9 24
 
3.0%
6 20
 
2.5%
Other values (3) 36
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
62.5%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 124
24.8%
2 104
20.8%
1 74
14.8%
3 70
14.0%
4 48
 
9.6%
9 24
 
4.8%
6 20
 
4.0%
7 18
 
3.6%
5 14
 
2.8%
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 (%)
0 124
15.5%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 74
9.2%
3 70
8.8%
4 48
 
6.0%
9 24
 
3.0%
6 20
 
2.5%
Other values (3) 36
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 124
15.5%
2 104
13.0%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
1 74
9.2%
3 70
8.8%
4 48
 
6.0%
9 24
 
3.0%
6 20
 
2.5%
Other values (3) 36
 
4.5%

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

2024-04-17T01:23:07.801805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:07.876133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%
Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:23:08.068625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.1
Min length4

Characters and Unicode

Total characters510
Distinct characters86
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 row두마-금남
4th row두마-금남
5th row논산-반포
ValueCountFrequency (%)
연무-논산 2
 
2.0%
신평-인주 2
 
2.0%
개화-만수 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%
당진-송악 2
 
2.0%
Other values (40) 80
80.0%
2024-04-17T01:23:08.383651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.6%
40
 
7.8%
20
 
3.9%
14
 
2.7%
12
 
2.4%
12
 
2.4%
10
 
2.0%
10
 
2.0%
8
 
1.6%
8
 
1.6%
Other values (76) 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 (%)
40
 
9.9%
20
 
4.9%
14
 
3.4%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
8
 
2.0%
8
 
2.0%
8
 
2.0%
Other values (73) 264
65.0%
Uppercase Letter
ValueCountFrequency (%)
C 2
50.0%
I 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 (%)
40
 
9.9%
20
 
4.9%
14
 
3.4%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
8
 
2.0%
8
 
2.0%
8
 
2.0%
Other values (73) 264
65.0%
Latin
ValueCountFrequency (%)
C 2
50.0%
I 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%
C 2
 
1.9%
I 2
 
1.9%
Hangul
ValueCountFrequency (%)
40
 
9.9%
20
 
4.9%
14
 
3.4%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
8
 
2.0%
8
 
2.0%
8
 
2.0%
Other values (73) 264
65.0%

연장
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.98
Minimum1.8
Maximum23.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:08.498024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile2.2
Q15.3
median6.75
Q39.7
95-th percentile14.6
Maximum23.2
Range21.4
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation4.0766395
Coefficient of variation (CV)0.51085708
Kurtosis2.66153
Mean7.98
Median Absolute Deviation (MAD)2.5
Skewness1.2722984
Sum798
Variance16.61899
MonotonicityNot monotonic
2024-04-17T01:23:08.615411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4.9 4
 
4.0%
6.6 4
 
4.0%
12.9 4
 
4.0%
6.2 4
 
4.0%
9.7 4
 
4.0%
8.3 4
 
4.0%
9.3 4
 
4.0%
7.2 2
 
2.0%
4.3 2
 
2.0%
9.5 2
 
2.0%
Other values (33) 66
66.0%
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 4
4.0%
ValueCountFrequency (%)
23.2 2
2.0%
17.6 2
2.0%
14.6 2
2.0%
14.2 2
2.0%
12.9 4
4.0%
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%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210301
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210301
2nd row20210301
3rd row20210301
4th row20210301
5th row20210301

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2024-04-17T01:23:08.722937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:08.792056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 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

2024-04-17T01:23:08.869591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T01:23:08.942201image/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%
Mean36.503962
Minimum36.02784
Maximum36.95295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:09.037784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.02784
5-th percentile36.07008
Q136.25536
median36.500575
Q336.75792
95-th percentile36.90325
Maximum36.95295
Range0.92511
Interquartile range (IQR)0.50256

Descriptive statistics

Standard deviation0.2778267
Coefficient of variation (CV)0.0076108641
Kurtosis-1.2719602
Mean36.503962
Median Absolute Deviation (MAD)0.24826
Skewness0.0041346196
Sum3650.3962
Variance0.077187673
MonotonicityNot monotonic
2024-04-17T01:23:09.174838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.14511 2
 
2.0%
36.89461 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%
36.87015 2
 
2.0%
36.51243 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%
36.86711 2
2.0%
36.85256 2
2.0%
36.83292 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.94536
Minimum126.18913
Maximum127.49717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:09.299537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.18913
5-th percentile126.44223
Q1126.71493
median126.93537
Q3127.15746
95-th percentile127.47469
Maximum127.49717
Range1.30804
Interquartile range (IQR)0.44253

Descriptive statistics

Standard deviation0.29794512
Coefficient of variation (CV)0.0023470344
Kurtosis-0.38980981
Mean126.94536
Median Absolute Deviation (MAD)0.221265
Skewness-0.13207179
Sum12694.536
Variance0.088771294
MonotonicityNot monotonic
2024-04-17T01:23:09.419593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.10501 2
 
2.0%
127.15746 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%
126.75145 2
 
2.0%
126.96572 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.18913 2
2.0%
126.37641 2
2.0%
126.44223 2
2.0%
126.5301 2
2.0%
126.61045 2
2.0%
126.61312 2
2.0%
126.64274 2
2.0%
126.64887 2
2.0%
126.66451 2
2.0%
126.66796 2
2.0%
ValueCountFrequency (%)
127.49717 2
2.0%
127.49544 2
2.0%
127.47469 2
2.0%
127.42036 2
2.0%
127.28963 2
2.0%
127.28536 2
2.0%
127.27513 2
2.0%
127.25961 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%
Mean3399.1872
Minimum314.79
Maximum10289.89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:09.535720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum314.79
5-th percentile433.1635
Q11418.475
median3114.24
Q35053.685
95-th percentile6828.9285
Maximum10289.89
Range9975.1
Interquartile range (IQR)3635.21

Descriptive statistics

Standard deviation2238.8311
Coefficient of variation (CV)0.65863719
Kurtosis0.065759714
Mean3399.1872
Median Absolute Deviation (MAD)1752.72
Skewness0.67534745
Sum339918.72
Variance5012364.7
MonotonicityNot monotonic
2024-04-17T01:23:09.701844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2779.55 1
 
1.0%
6711.88 1
 
1.0%
3069.26 1
 
1.0%
9857.21 1
 
1.0%
10289.89 1
 
1.0%
1437.19 1
 
1.0%
1360.71 1
 
1.0%
2435.47 1
 
1.0%
2261.7 1
 
1.0%
6822.15 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
314.79 1
1.0%
355.8 1
1.0%
401.11 1
1.0%
416.02 1
1.0%
418.79 1
1.0%
433.92 1
1.0%
483.11 1
1.0%
534.74 1
1.0%
638.65 1
1.0%
641.35 1
1.0%
ValueCountFrequency (%)
10289.89 1
1.0%
9857.21 1
1.0%
8080.07 1
1.0%
7894.34 1
1.0%
6957.72 1
1.0%
6822.15 1
1.0%
6711.88 1
1.0%
6604.76 1
1.0%
6450.47 1
1.0%
6443.27 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2834.0021
Minimum237.84
Maximum10474.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:09.827164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum237.84
5-th percentile336.329
Q11222.2725
median2623.015
Q34024.21
95-th percentile6224.893
Maximum10474.75
Range10236.91
Interquartile range (IQR)2801.9375

Descriptive statistics

Standard deviation1979.261
Coefficient of variation (CV)0.69839787
Kurtosis1.8708289
Mean2834.0021
Median Absolute Deviation (MAD)1404.86
Skewness1.0544845
Sum283400.21
Variance3917474.2
MonotonicityNot monotonic
2024-04-17T01:23:09.945223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3379.51 1
 
1.0%
5941.89 1
 
1.0%
3691.03 1
 
1.0%
9400.11 1
 
1.0%
10474.75 1
 
1.0%
1103.31 1
 
1.0%
1078.45 1
 
1.0%
2343.46 1
 
1.0%
1772.17 1
 
1.0%
6223.57 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
237.84 1
1.0%
273.85 1
1.0%
303.15 1
1.0%
304.49 1
1.0%
319.02 1
1.0%
337.24 1
1.0%
371.93 1
1.0%
429.54 1
1.0%
446.4 1
1.0%
480.2 1
1.0%
ValueCountFrequency (%)
10474.75 1
1.0%
9400.11 1
1.0%
7162.42 1
1.0%
6644.42 1
1.0%
6250.03 1
1.0%
6223.57 1
1.0%
5941.89 1
1.0%
5200.67 1
1.0%
4988.42 1
1.0%
4981.94 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean377.0331
Minimum32.37
Maximum1306.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:10.084038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.37
5-th percentile46.106
Q1171.4125
median349.835
Q3546.445
95-th percentile762.32
Maximum1306.16
Range1273.79
Interquartile range (IQR)375.0325

Descriptive statistics

Standard deviation253.26801
Coefficient of variation (CV)0.67173946
Kurtosis1.3181168
Mean377.0331
Median Absolute Deviation (MAD)188.425
Skewness0.90181263
Sum37703.31
Variance64144.686
MonotonicityNot monotonic
2024-04-17T01:23:10.515485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
389.44 1
 
1.0%
737.11 1
 
1.0%
473.89 1
 
1.0%
1207.4 1
 
1.0%
1306.16 1
 
1.0%
146.87 1
 
1.0%
151.96 1
 
1.0%
332.87 1
 
1.0%
246.65 1
 
1.0%
758.4 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
32.37 1
1.0%
35.56 1
1.0%
41.31 1
1.0%
42.24 1
1.0%
45.27 1
1.0%
46.15 1
1.0%
51.93 1
1.0%
63.9 1
1.0%
64.07 1
1.0%
67.84 1
1.0%
ValueCountFrequency (%)
1306.16 1
1.0%
1207.4 1
1.0%
879.9 1
1.0%
845.51 1
1.0%
836.8 1
1.0%
758.4 1
1.0%
737.11 1
1.0%
683.5 1
1.0%
671.52 1
1.0%
665.66 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean147.0248
Minimum15.79
Maximum518.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:10.631743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15.79
5-th percentile22.4455
Q158.56
median139.94
Q3216.46
95-th percentile301.0145
Maximum518.37
Range502.58
Interquartile range (IQR)157.9

Descriptive statistics

Standard deviation105.65878
Coefficient of variation (CV)0.71864596
Kurtosis1.5335631
Mean147.0248
Median Absolute Deviation (MAD)80.5
Skewness1.1087051
Sum14702.48
Variance11163.778
MonotonicityNot monotonic
2024-04-17T01:23:10.747914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
253.13 1
 
1.0%
278.52 1
 
1.0%
198.25 1
 
1.0%
518.37 1
 
1.0%
486.63 1
 
1.0%
57.15 1
 
1.0%
49.41 1
 
1.0%
114.58 1
 
1.0%
92.08 1
 
1.0%
286.56 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
15.79 1
1.0%
19.5 1
1.0%
19.86 1
1.0%
20.44 1
1.0%
22.36 1
1.0%
22.45 1
1.0%
24.15 1
1.0%
26.3 1
1.0%
27.23 1
1.0%
28.68 1
1.0%
ValueCountFrequency (%)
518.37 1
1.0%
486.63 1
1.0%
454.18 1
1.0%
381.42 1
1.0%
336.06 1
1.0%
299.17 1
1.0%
292.24 1
1.0%
286.56 1
1.0%
278.52 1
1.0%
274.31 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean876670.83
Minimum81574.38
Maximum2695511.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T01:23:10.875631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum81574.38
5-th percentile112925.19
Q1364416.1
median796208.95
Q31309871.9
95-th percentile1820293.3
Maximum2695511.8
Range2613937.4
Interquartile range (IQR)945455.77

Descriptive statistics

Standard deviation582477.1
Coefficient of variation (CV)0.6644194
Kurtosis0.11221556
Mean876670.83
Median Absolute Deviation (MAD)453695.36
Skewness0.6983295
Sum87667083
Variance3.3927958 × 1011
MonotonicityNot monotonic
2024-04-17T01:23:11.003269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
718712.05 1
 
1.0%
1736008.83 1
 
1.0%
772387.98 1
 
1.0%
2556832.92 1
 
1.0%
2695511.8 1
 
1.0%
370952.55 1
 
1.0%
344806.76 1
 
1.0%
598285.44 1
 
1.0%
588039.44 1
 
1.0%
1837959.79 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
81574.38 1
1.0%
92724.55 1
1.0%
101988.89 1
1.0%
106029.66 1
1.0%
109727.65 1
1.0%
113093.48 1
1.0%
124571.38 1
1.0%
134653.8 1
1.0%
163657.69 1
1.0%
166708.93 1
1.0%
ValueCountFrequency (%)
2695511.8 1
1.0%
2556832.92 1
1.0%
2083581.14 1
1.0%
2018569.39 1
1.0%
1837959.79 1
1.0%
1819363.46 1
1.0%
1736008.83 1
1.0%
1681283.65 1
1.0%
1679139.25 1
1.0%
1666834.81 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2024-04-17T01:23:11.242292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.96
Min length8

Characters and Unicode

Total characters1096
Distinct characters106
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row충남 논산 은진 토양
2nd row충남 논산 은진 토양
3rd row충남 공주 반포 온천
4th row충남 공주 반포 온천
5th row충남 논산 연산 천호
ValueCountFrequency (%)
충남 100
25.1%
천안 12
 
3.0%
공주 10
 
2.5%
금산 10
 
2.5%
청양 10
 
2.5%
부여 8
 
2.0%
예산 8
 
2.0%
서천 8
 
2.0%
아산 8
 
2.0%
세종 6
 
1.5%
Other values (96) 218
54.8%
2024-04-17T01:23:11.584816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
102
 
9.3%
100
 
9.1%
56
 
5.1%
28
 
2.6%
22
 
2.0%
20
 
1.8%
16
 
1.5%
16
 
1.5%
14
 
1.3%
Other values (96) 424
38.7%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
102
 
12.8%
100
 
12.5%
56
 
7.0%
28
 
3.5%
22
 
2.8%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (95) 410
51.4%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

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

Most frequent character per script

Hangul
ValueCountFrequency (%)
102
 
12.8%
100
 
12.5%
56
 
7.0%
28
 
3.5%
22
 
2.8%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (95) 410
51.4%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
102
 
12.8%
100
 
12.5%
56
 
7.0%
28
 
3.5%
22
 
2.8%
20
 
2.5%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (95) 410
51.4%

Interactions

2024-04-17T01:23:05.979935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:00.586003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.227862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.850780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.443891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.121879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.856485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.461415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.070135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:06.055265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:00.650425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.297199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.919266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.521642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.189811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.921746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.525232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.133109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:06.116804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:00.708443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.361223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.976960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.589317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.253204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.981823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.588575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.191605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:06.181792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:00.777565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.420573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.041243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.656180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.353311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.043001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.657765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.257025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:06.252105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:00.864412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.486164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.108437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.733901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.474183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.113806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.732716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.327128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:06.319889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:00.936990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.549415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.170961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.814719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.553696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.175979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.800210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.631268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:06.385354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:00.998280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.612401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.239216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.901711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.623248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.237416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.866221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.696719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:06.455654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.071682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.698756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.311989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.976695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.689071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.307528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.934905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.785255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:06.519919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.143394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:01.788488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:02.379442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.052630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:03.758754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:04.379305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.001075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T01:23:05.869810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T01:23:11.700054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.5380.8120.8440.6040.4940.5140.4970.6701.000
지점1.0001.0000.0001.0001.0001.0001.0000.9650.9220.9450.9170.9731.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9650.9220.9450.9170.9731.000
연장0.5381.0000.0001.0001.0000.6000.5790.5260.4720.5780.4540.5031.000
좌표위치위도0.8121.0000.0001.0000.6001.0000.8450.4070.2220.4040.1690.4801.000
좌표위치경도0.8441.0000.0001.0000.5790.8451.0000.4490.5390.4990.4300.4931.000
co0.6040.9650.0000.9650.5260.4070.4491.0000.9560.9030.8580.9970.965
nox0.4940.9220.0000.9220.4720.2220.5390.9561.0000.9170.8940.9550.922
hc0.5140.9450.0000.9450.5780.4040.4990.9030.9171.0000.8760.8880.945
pm0.4970.9170.0000.9170.4540.1690.4300.8580.8940.8761.0000.8390.917
co20.6700.9730.0000.9730.5030.4800.4930.9970.9550.8880.8391.0000.973
주소1.0001.0000.0001.0001.0001.0001.0000.9650.9220.9450.9170.9731.000
2024-04-17T01:23:11.829084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.000-0.0120.203-0.303-0.331-0.296-0.302-0.360-0.3320.000
연장-0.0121.0000.108-0.1460.0150.0100.0080.0070.0150.000
좌표위치위도0.2030.1081.000-0.1650.4000.4480.4420.3870.3970.000
좌표위치경도-0.303-0.146-0.1651.0000.3260.3250.3380.3190.3240.000
co-0.3310.0150.4000.3261.0000.9570.9770.8810.9990.000
nox-0.2960.0100.4480.3250.9571.0000.9940.9540.9540.000
hc-0.3020.0080.4420.3380.9770.9941.0000.9340.9740.000
pm-0.3600.0070.3870.3190.8810.9540.9341.0000.8780.000
co2-0.3320.0150.3970.3240.9990.9540.9740.8781.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-04-17T01:23:06.624032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T01:23:06.783594image/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-논산11.520210301036.14511127.105012779.553379.51389.44253.13718712.05충남 논산 은진 토양
12건기연[0122-2]2연무-논산11.520210301036.14511127.105012507.253668.48393.56257.86627777.01충남 논산 은진 토양
23건기연[0123-2]1두마-금남4.920210301036.37685127.259616957.724737.72651.84189.171819363.46충남 공주 반포 온천
34건기연[0123-2]2두마-금남4.920210301036.37685127.259616418.34179.12594.4150.241681283.65충남 공주 반포 온천
45건기연[0124-0]1논산-반포10.220210301036.24966127.228546443.274456.39650.28201.321666834.81충남 논산 연산 천호
56건기연[0124-0]2논산-반포10.220210301036.24966127.228546320.174568.0629.44292.241646033.57충남 논산 연산 천호
67건기연[0127-2]1금남-조치원12.220210301036.56218127.285365721.233862.2544.51143.01491501.85충남 세종 연서 봉암
78건기연[0127-2]2금남-조치원12.220210301036.56218127.285365467.953647.22523.31153.991434407.78충남 세종 연서 봉암
89건기연[0127-7]1공주-유성5.820210301036.40916127.258215923.584956.99683.5244.811503751.55충남 공주 반포 성강
910건기연[0127-7]2공주-유성5.820210301036.40916127.258215596.534917.87654.68225.341412748.1충남 공주 반포 성강
기본키도로종류지점방향측정구간연장측정일측정시분좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3902-0]1유구-아산23.220210301036.60979126.970311012.9666.1796.3824.15264214.89충남 공주 유구 추계
9192건기연[3902-0]2유구-아산23.220210301036.60979126.97031641.35429.5464.0722.45166708.93충남 공주 유구 추계
9293건기연[3902-2]1장평-신풍12.920210301036.43536126.9549534.74446.463.930.79134653.8충남 청양 정산 해남
9394건기연[3902-2]2장평-신풍12.920210301036.43536126.9549418.79304.4942.2426.3109727.65충남 청양 정산 해남
9495건기연[3905-0]1염치-권관8.320210301036.85256126.960075337.774864.09618.84216.361386506.89충남 아산 영인 아산
9596건기연[3905-0]2염치-권관8.320210301036.85256126.960074658.413497.64483.33123.051213851.09충남 아산 영인 아산
9697건기연[4001-2]1개화-만수2.020210301036.29677126.664511328.071517.59190.1292.21317222.91충남 보령 미산 도화담
9798건기연[4001-2]2개화-만수2.020210301036.29677126.664511234.731590.44203.0492.89288940.13충남 보령 미산 도화담
9899건기연[4001-4]1덕산-갈산12.920210301036.64566126.61045719.24529.8969.4628.68187663.76충남 예산 덕산 사천
99100건기연[4001-4]2덕산-갈산12.920210301036.64566126.61045744.03588.6576.3430.52191728.4충남 예산 덕산 사천