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
측정시간 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 started2023-12-10 11:55:14.540921
Analysis finished2023-12-10 11:55:27.288809
Duration12.75 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
2023-12-10T20:55:27.420540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T20:55:27.662698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

도로종류
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건기연 100
100.0%

Length

2023-12-10T20:55:27.884329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:55:28.047773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건기연 100
100.0%

지점
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:55:28.344041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.02
Min length8

Characters and Unicode

Total characters802
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[0101-0]
2nd row[0101-0]
3rd row[0101-1]
4th row[0101-1]
5th row[0104-0]
ValueCountFrequency (%)
0101-0 2
 
2.0%
1812-1 2
 
2.0%
2309-0 2
 
2.0%
1704-0 2
 
2.0%
1705-1 2
 
2.0%
1706-3 2
 
2.0%
1707-1 2
 
2.0%
1801-4 2
 
2.0%
1805-2 2
 
2.0%
1806-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:55:28.891339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 152
19.0%
0 140
17.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 80
10.0%
3 38
 
4.7%
5 22
 
2.7%
8 18
 
2.2%
6 14
 
1.7%
Other values (3) 38
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 502
62.6%
Open Punctuation 100
 
12.5%
Dash Punctuation 100
 
12.5%
Close Punctuation 100
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 152
30.3%
0 140
27.9%
2 80
15.9%
3 38
 
7.6%
5 22
 
4.4%
8 18
 
3.6%
6 14
 
2.8%
7 14
 
2.8%
9 12
 
2.4%
4 12
 
2.4%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 802
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 152
19.0%
0 140
17.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 80
10.0%
3 38
 
4.7%
5 22
 
2.7%
8 18
 
2.2%
6 14
 
1.7%
Other values (3) 38
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 152
19.0%
0 140
17.5%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 80
10.0%
3 38
 
4.7%
5 22
 
2.7%
8 18
 
2.2%
6 14
 
1.7%
Other values (3) 38
 
4.7%

방향
Categorical

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-10T20:55:29.098896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:55:29.234298image/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
2023-12-10T20:55:29.538812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5
Min length3

Characters and Unicode

Total characters500
Distinct characters83
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 row목포-무안
2nd row목포-무안
3rd row진도-무안
4th row진도-무안
5th row학교-장산
ValueCountFrequency (%)
목포-무안 2
 
2.0%
송광-목사동 2
 
2.0%
동강-함평 2
 
2.0%
여수-순천 2
 
2.0%
순천-황전 2
 
2.0%
구례구-용방 2
 
2.0%
구례-곡성 2
 
2.0%
백동-죽림 2
 
2.0%
황산-해남 2
 
2.0%
옥천-강진 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:55:29.991188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
20.0%
18
 
3.6%
18
 
3.6%
16
 
3.2%
16
 
3.2%
14
 
2.8%
12
 
2.4%
12
 
2.4%
10
 
2.0%
10
 
2.0%
Other values (73) 274
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 400
80.0%
Dash Punctuation 100
 
20.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
18
 
4.5%
18
 
4.5%
16
 
4.0%
16
 
4.0%
14
 
3.5%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (72) 264
66.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 400
80.0%
Common 100
 
20.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
18
 
4.5%
18
 
4.5%
16
 
4.0%
16
 
4.0%
14
 
3.5%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (72) 264
66.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 400
80.0%
ASCII 100
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
100.0%
Hangul
ValueCountFrequency (%)
18
 
4.5%
18
 
4.5%
16
 
4.0%
16
 
4.0%
14
 
3.5%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (72) 264
66.0%

연장
Real number (ℝ)

Distinct41
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.622
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:30.138539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.2
Q15.6
median9.95
Q312.8
95-th percentile21.8
Maximum33.8
Range31.2
Interquartile range (IQR)7.2

Descriptive statistics

Standard deviation6.143611
Coefficient of variation (CV)0.57838552
Kurtosis2.763711
Mean10.622
Median Absolute Deviation (MAD)4.05
Skewness1.3819463
Sum1062.2
Variance37.743956
MonotonicityNot monotonic
2023-12-10T20:55:30.339585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
5.4 6
 
6.0%
11.4 6
 
6.0%
10.2 4
 
4.0%
12.5 4
 
4.0%
7.4 4
 
4.0%
10.3 4
 
4.0%
7.9 4
 
4.0%
24.3 2
 
2.0%
8.0 2
 
2.0%
4.5 2
 
2.0%
Other values (31) 62
62.0%
ValueCountFrequency (%)
2.6 2
 
2.0%
2.7 2
 
2.0%
3.2 2
 
2.0%
3.5 2
 
2.0%
4.2 2
 
2.0%
4.3 2
 
2.0%
4.4 2
 
2.0%
4.5 2
 
2.0%
5.2 2
 
2.0%
5.4 6
6.0%
ValueCountFrequency (%)
33.8 2
2.0%
24.3 2
2.0%
21.8 2
2.0%
21.3 2
2.0%
19.0 2
2.0%
18.0 2
2.0%
17.0 2
2.0%
16.0 2
2.0%
15.8 2
2.0%
14.5 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210101
2nd row20210101
3rd row20210101
4th row20210101
5th row20210101

Common Values

ValueCountFrequency (%)
20210101 100
100.0%

Length

2023-12-10T20:55:30.572176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:55:30.695617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210101 100
100.0%

측정시간
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T20:55:30.829376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:55:30.972795image/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%
Mean34.915181
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:31.148239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38107
5-th percentile34.46151
Q134.72412
median34.91025
Q335.1103
95-th percentile35.28858
Maximum35.34926
Range0.96819
Interquartile range (IQR)0.38618

Descriptive statistics

Standard deviation0.25068995
Coefficient of variation (CV)0.00717997
Kurtosis-0.73780007
Mean34.915181
Median Absolute Deviation (MAD)0.18959
Skewness-0.24055724
Sum3491.5181
Variance0.062845451
MonotonicityNot monotonic
2023-12-10T20:55:31.458294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85192 2
 
2.0%
35.21934 2
 
2.0%
35.01799 2
 
2.0%
35.18146 2
 
2.0%
35.17397 2
 
2.0%
34.38107 2
 
2.0%
34.58728 2
 
2.0%
34.61562 2
 
2.0%
34.80445 2
 
2.0%
34.83385 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.38107 2
2.0%
34.38392 2
2.0%
34.46151 2
2.0%
34.55042 2
2.0%
34.55273 2
2.0%
34.58728 2
2.0%
34.61562 2
2.0%
34.64175 2
2.0%
34.64863 2
2.0%
34.67935 2
2.0%
ValueCountFrequency (%)
35.34926 2
2.0%
35.29816 2
2.0%
35.28858 2
2.0%
35.27304 2
2.0%
35.2553 2
2.0%
35.22404 2
2.0%
35.21934 2
2.0%
35.21885 2
2.0%
35.18146 2
2.0%
35.18107 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.92664
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:31.664834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.36491
Q1126.64704
median126.85189
Q3127.2868
95-th percentile127.55961
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.63976

Descriptive statistics

Standard deviation0.40110752
Coefficient of variation (CV)0.0031601523
Kurtosis-1.0765807
Mean126.92664
Median Absolute Deviation (MAD)0.32267
Skewness0.17521501
Sum12692.664
Variance0.16088724
MonotonicityNot monotonic
2023-12-10T20:55:31.877166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.42727 2
 
2.0%
127.48499 2
 
2.0%
127.46028 2
 
2.0%
127.46572 2
 
2.0%
127.43777 2
 
2.0%
126.21616 2
 
2.0%
126.51479 2
 
2.0%
126.74515 2
 
2.0%
127.10201 2
 
2.0%
127.09515 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.21616 2
2.0%
126.2341 2
2.0%
126.36491 2
2.0%
126.36721 2
2.0%
126.42727 2
2.0%
126.46074 2
2.0%
126.47853 2
2.0%
126.50315 2
2.0%
126.51479 2
2.0%
126.53424 2
2.0%
ValueCountFrequency (%)
127.75881 2
2.0%
127.61069 2
2.0%
127.55961 2
2.0%
127.48499 2
2.0%
127.46572 2
2.0%
127.46028 2
2.0%
127.4424 2
2.0%
127.43777 2
2.0%
127.37931 2
2.0%
127.36361 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2926.8023
Minimum227.79
Maximum15355.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:32.435102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum227.79
5-th percentile329.4835
Q1964.835
median2474.65
Q34446.825
95-th percentile6449.4235
Maximum15355.11
Range15127.32
Interquartile range (IQR)3481.99

Descriptive statistics

Standard deviation2645.9418
Coefficient of variation (CV)0.90403845
Kurtosis6.7026364
Mean2926.8023
Median Absolute Deviation (MAD)1658.66
Skewness2.0184601
Sum292680.23
Variance7001008.1
MonotonicityNot monotonic
2023-12-10T20:55:32.660066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4066.52 1
 
1.0%
1463.88 1
 
1.0%
379.97 1
 
1.0%
712.08 1
 
1.0%
831.09 1
 
1.0%
330.05 1
 
1.0%
348.53 1
 
1.0%
5772.55 1
 
1.0%
8540.26 1
 
1.0%
1688.36 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
227.79 1
1.0%
231.84 1
1.0%
235.42 1
1.0%
293.7 1
1.0%
318.72 1
1.0%
330.05 1
1.0%
342.85 1
1.0%
348.53 1
1.0%
350.02 1
1.0%
368.96 1
1.0%
ValueCountFrequency (%)
15355.11 1
1.0%
14397.79 1
1.0%
8540.26 1
1.0%
7518.05 1
1.0%
6477.23 1
1.0%
6447.96 1
1.0%
6267.26 1
1.0%
6172.22 1
1.0%
5799.51 1
1.0%
5772.55 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2693.0156
Minimum151.26
Maximum24081.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:32.913276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum151.26
5-th percentile277.5645
Q1754.49
median1727.445
Q33688.6125
95-th percentile6121.776
Maximum24081.44
Range23930.18
Interquartile range (IQR)2934.1225

Descriptive statistics

Standard deviation3115.9034
Coefficient of variation (CV)1.1570313
Kurtosis23.009305
Mean2693.0156
Median Absolute Deviation (MAD)1178.025
Skewness3.9081227
Sum269301.56
Variance9708854.3
MonotonicityNot monotonic
2023-12-10T20:55:33.197267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3388.2 1
 
1.0%
1254.56 1
 
1.0%
272.71 1
 
1.0%
598.7 1
 
1.0%
666.2 1
 
1.0%
290.2 1
 
1.0%
320.89 1
 
1.0%
7029.52 1
 
1.0%
5450.92 1
 
1.0%
1187.06 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
151.26 1
1.0%
171.31 1
1.0%
187.25 1
1.0%
220.85 1
1.0%
272.71 1
1.0%
277.82 1
1.0%
290.2 1
1.0%
293.08 1
1.0%
305.01 1
1.0%
319.17 1
1.0%
ValueCountFrequency (%)
24081.44 1
1.0%
14121.83 1
1.0%
7174.89 1
1.0%
7133.16 1
1.0%
7029.52 1
1.0%
6074.0 1
1.0%
5973.86 1
1.0%
5872.08 1
1.0%
5788.18 1
1.0%
5643.34 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean341.955
Minimum22.35
Maximum2424.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:33.488546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22.35
5-th percentile36.2285
Q198.61
median245.05
Q3478.82
95-th percentile803.1835
Maximum2424.49
Range2402.14
Interquartile range (IQR)380.21

Descriptive statistics

Standard deviation348.60315
Coefficient of variation (CV)1.0194416
Kurtosis13.377782
Mean341.955
Median Absolute Deviation (MAD)174.795
Skewness2.8558707
Sum34195.5
Variance121524.16
MonotonicityNot monotonic
2023-12-10T20:55:33.718026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
419.94 1
 
1.0%
172.07 1
 
1.0%
38.42 1
 
1.0%
74.82 1
 
1.0%
84.74 1
 
1.0%
34.68 1
 
1.0%
37.31 1
 
1.0%
907.75 1
 
1.0%
809.71 1
 
1.0%
165.55 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
22.35 1
1.0%
22.79 1
1.0%
22.86 1
1.0%
28.89 1
1.0%
34.68 1
1.0%
36.31 1
1.0%
36.58 1
1.0%
37.31 1
1.0%
37.41 1
1.0%
38.42 1
1.0%
ValueCountFrequency (%)
2424.49 1
1.0%
1673.96 1
1.0%
911.6 1
1.0%
907.75 1
1.0%
809.71 1
1.0%
802.84 1
1.0%
767.56 1
1.0%
732.19 1
1.0%
706.04 1
1.0%
695.04 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.9594
Minimum8.92
Maximum1458.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:33.948615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.92
5-th percentile15.093
Q139.1925
median105.53
Q3181.1025
95-th percentile467.5005
Maximum1458.67
Range1449.75
Interquartile range (IQR)141.91

Descriptive statistics

Standard deviation192.81537
Coefficient of variation (CV)1.228441
Kurtosis20.328561
Mean156.9594
Median Absolute Deviation (MAD)70.365
Skewness3.6364466
Sum15695.94
Variance37177.766
MonotonicityNot monotonic
2023-12-10T20:55:34.186475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154.17 1
 
1.0%
106.99 1
 
1.0%
15.14 1
 
1.0%
29.64 1
 
1.0%
29.21 1
 
1.0%
21.84 1
 
1.0%
24.66 1
 
1.0%
457.13 1
 
1.0%
137.74 1
 
1.0%
66.87 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
8.92 1
1.0%
10.58 1
1.0%
11.39 1
1.0%
12.89 1
1.0%
14.2 1
1.0%
15.14 1
1.0%
16.55 1
1.0%
18.28 1
1.0%
19.23 1
1.0%
21.84 1
1.0%
ValueCountFrequency (%)
1458.67 1
1.0%
574.21 1
1.0%
563.79 1
1.0%
526.65 1
1.0%
518.05 1
1.0%
464.84 1
1.0%
457.13 1
1.0%
411.57 1
1.0%
404.57 1
1.0%
396.68 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean756125.35
Minimum60179.7
Maximum4325651.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:34.456295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum60179.7
5-th percentile84981.788
Q1250504.81
median632707.99
Q31144744.8
95-th percentile1663294.5
Maximum4325651.2
Range4265471.5
Interquartile range (IQR)894239.97

Descriptive statistics

Standard deviation706548.98
Coefficient of variation (CV)0.93443366
Kurtosis8.6015207
Mean756125.35
Median Absolute Deviation (MAD)427418.02
Skewness2.2980887
Sum75612535
Variance4.9921147 × 1011
MonotonicityNot monotonic
2023-12-10T20:55:34.705846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1037389.27 1
 
1.0%
372004.18 1
 
1.0%
90662.5 1
 
1.0%
182289.77 1
 
1.0%
213682.46 1
 
1.0%
85251.22 1
 
1.0%
89699.77 1
 
1.0%
1425379.03 1
 
1.0%
2223732.8 1
 
1.0%
440334.45 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
60179.7 1
1.0%
61729.15 1
1.0%
62168.56 1
1.0%
76642.85 1
1.0%
79862.59 1
1.0%
85251.22 1
1.0%
88992.7 1
1.0%
89497.24 1
1.0%
89699.77 1
1.0%
90662.5 1
1.0%
ValueCountFrequency (%)
4325651.24 1
1.0%
3865034.06 1
1.0%
2223732.8 1
1.0%
1920774.49 1
1.0%
1699569.68 1
1.0%
1661385.26 1
1.0%
1643640.75 1
1.0%
1539470.78 1
1.0%
1498693.17 1
1.0%
1464432.58 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:55:35.120518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.86
Min length8

Characters and Unicode

Total characters1086
Distinct characters108
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 row전남 무안 삼향 왕산
2nd row전남 무안 삼향 왕산
3rd row전남 목포 죽교
4th row전남 목포 죽교
5th row전남 나주 다시 복암
ValueCountFrequency (%)
전남 100
25.1%
순천 12
 
3.0%
강진 10
 
2.5%
해남 8
 
2.0%
보성 8
 
2.0%
화순 8
 
2.0%
구례 6
 
1.5%
주암 6
 
1.5%
곡성 6
 
1.5%
영광 6
 
1.5%
Other values (97) 228
57.3%
2023-12-10T20:55:35.925373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
112
 
10.3%
110
 
10.1%
26
 
2.4%
20
 
1.8%
20
 
1.8%
18
 
1.7%
16
 
1.5%
16
 
1.5%
14
 
1.3%
Other values (98) 436
40.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 788
72.6%
Space Separator 298
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
112
 
14.2%
110
 
14.0%
26
 
3.3%
20
 
2.5%
20
 
2.5%
18
 
2.3%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (97) 422
53.6%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 788
72.6%
Common 298
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
112
 
14.2%
110
 
14.0%
26
 
3.3%
20
 
2.5%
20
 
2.5%
18
 
2.3%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (97) 422
53.6%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 788
72.6%
ASCII 298
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
112
 
14.2%
110
 
14.0%
26
 
3.3%
20
 
2.5%
20
 
2.5%
18
 
2.3%
16
 
2.0%
16
 
2.0%
14
 
1.8%
14
 
1.8%
Other values (97) 422
53.6%

Interactions

2023-12-10T20:55:25.577343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:15.236115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:16.333687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:17.640623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:18.839585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:20.132871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:21.434757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:23.036286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:24.202328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:25.713609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:15.348785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:16.537108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:17.760462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:18.974998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:20.251669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:21.567273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:23.141769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:24.329315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:25.851260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:15.473005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:16.676889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:17.889559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:19.138284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:20.403962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:21.716390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:23.269846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:24.477038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:25.976338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:15.599880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:16.814004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:18.018309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:19.270227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:20.561019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:21.845735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:23.391336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:24.623503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:26.099775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:15.726384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:16.999572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:18.173017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:19.431421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:20.722467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:22.001194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:23.523922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:24.796589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:26.248981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:15.849049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:17.142505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:18.320085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:19.570298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:20.875750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:22.132870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:23.658387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:24.992406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:26.369698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:15.969969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:17.253776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:18.445988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:19.712886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:21.014999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:22.256184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:23.783183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:25.163438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:26.485833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:16.084454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:17.374973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:18.560979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:19.851763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:21.147042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:22.414105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:23.917885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:25.306234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:26.617344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:16.214789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:17.511274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:18.698638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:20.008313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:21.296010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:22.571587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:24.062442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:25.448297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:55:36.124918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.4560.7590.8090.5690.6940.5090.6090.5451.000
지점1.0001.0000.0001.0001.0001.0001.0000.9390.9030.8950.8900.8991.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9390.9030.8950.8900.8991.000
연장0.4561.0000.0001.0001.0000.5630.6790.3500.0000.2210.1510.6831.000
좌표위치위도0.7591.0000.0001.0000.5631.0000.7590.3720.3290.1510.3110.3231.000
좌표위치경도0.8091.0000.0001.0000.6790.7591.0000.5390.5510.3950.5440.5371.000
co0.5690.9390.0000.9390.3500.3720.5391.0000.8210.8340.7070.9420.939
nox0.6940.9030.0000.9030.0000.3290.5510.8211.0000.9390.9690.9130.903
hc0.5090.8950.0000.8950.2210.1510.3950.8340.9391.0000.8180.9300.895
pm0.6090.8900.0000.8900.1510.3110.5440.7070.9690.8181.0000.8040.890
co20.5450.8990.0000.8990.6830.3230.5370.9420.9130.9300.8041.0000.899
주소1.0001.0000.0001.0001.0001.0001.0000.9390.9030.8950.8900.8991.000
2023-12-10T20:55:36.390288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.0000.0440.3120.173-0.376-0.418-0.409-0.383-0.3770.000
연장0.0441.000-0.039-0.157-0.123-0.166-0.167-0.194-0.1220.000
좌표위치위도0.312-0.0391.0000.242-0.120-0.160-0.156-0.195-0.1140.000
좌표위치경도0.173-0.1570.2421.000-0.137-0.100-0.111-0.078-0.1290.000
co-0.376-0.123-0.120-0.1371.0000.9740.9840.8960.9990.000
nox-0.418-0.166-0.160-0.1000.9741.0000.9970.9520.9750.000
hc-0.409-0.167-0.156-0.1110.9840.9971.0000.9430.9840.000
pm-0.383-0.194-0.195-0.0780.8960.9520.9431.0000.8940.000
co2-0.377-0.122-0.114-0.1290.9990.9750.9840.8941.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T20:55:26.821563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:55:27.160823image/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

기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
01건기연[0101-0]1목포-무안5.420210101034.85192126.427274066.523388.2419.94154.171037389.27전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210101034.85192126.427274394.73334.99434.75135.331136181.77전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210101034.80189126.364914103.172867.57394.17104.381068376.28전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210101034.80189126.364913961.052741.24378.33109.881032354.63전남 목포 죽교
45건기연[0104-0]1학교-장산12.520210101034.99062126.654881042.63758.12111.8550.23268605.25전남 나주 다시 복암
56건기연[0104-0]2학교-장산12.520210101034.99062126.654881001.58669.0100.2342.22261197.84전남 나주 다시 복암
67건기연[0109-0]1광주-장성7.420210101035.2553126.81214959.075225.48651.4372.81277103.9전남 장성 진원 산정
78건기연[0109-0]2광주-장성7.420210101035.2553126.81214970.755152.35644.94375.621284289.87전남 장성 진원 산정
89건기연[0201-4]1금계-강진12.620210101034.70297126.649762109.781648.41229.8570.93536162.95전남 영암 학산 묵동
910건기연[0201-4]2금계-강진12.620210101034.70297126.649762828.864032.69476.09270.28701606.05전남 영암 학산 묵동
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2302-1]1마량-관산12.520210101034.46151126.86526342.85293.0836.3126.2688992.7전남 장흥 대덕 신
9192건기연[2302-1]2마량-관산12.520210101034.46151126.86526368.96305.0138.8227.4895805.51전남 장흥 대덕 신
9293건기연[2305-3]1금정-나주5.420210101034.88845126.748241408.521380.73188.24112.94352803.68전남 영암 금정 와운
9394건기연[2305-3]2금정-나주5.420210101034.88845126.748241430.491371.22192.01109.81353778.38전남 영암 금정 와운
9495건기연[2306-0]1나주-상방9.720210101034.95043126.647041735.191877.97250.22155.25429611.16전남 나주 왕곡 신포
9596건기연[2306-0]2나주-상방9.720210101034.95043126.647041600.761421.99195.65117.69403309.59전남 나주 왕곡 신포
9697건기연[2309-0]1동강-함평5.620210101035.03781126.534241164.811044.84125.0854.34295905.94전남 함평 학교 사거
9798건기연[2309-0]2동강-함평5.620210101035.03781126.534241426.71230.9170.2577.01332260.64전남 함평 학교 사거
9899건기연[2311-1]1신광-영광8.720210101035.21885126.50315843.25743.690.250.45216481.48전남 영광 불갑 안맹
99100건기연[2311-1]2신광-영광8.720210101035.21885126.50315854.6771.0193.7554.9218425.71전남 영광 불갑 안맹