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 11:54:49.489258
Analysis finished2023-12-10 11:55:03.135532
Duration13.65 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
2023-12-10T20:55:03.258612image/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
2023-12-10T20:55:03.493035image/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

2023-12-10T20:55:03.712210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:55:04.229012image/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
2023-12-10T20:55:04.544794image/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%
1813-0 2
 
2.0%
2314-0 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%
1809-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:55:05.083651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 152
19.0%
0 142
17.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 80
10.0%
3 34
 
4.2%
5 22
 
2.7%
8 18
 
2.2%
6 14
 
1.7%
Other values (3) 40
 
5.0%

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%
0 142
28.3%
2 80
15.9%
3 34
 
6.8%
5 22
 
4.4%
8 18
 
3.6%
6 14
 
2.8%
4 14
 
2.8%
7 14
 
2.8%
9 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%
0 142
17.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 80
10.0%
3 34
 
4.2%
5 22
 
2.7%
8 18
 
2.2%
6 14
 
1.7%
Other values (3) 40
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 152
19.0%
0 142
17.7%
[ 100
12.5%
- 100
12.5%
] 100
12.5%
2 80
10.0%
3 34
 
4.2%
5 22
 
2.7%
8 18
 
2.2%
6 14
 
1.7%
Other values (3) 40
 
5.0%

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

2023-12-10T20:55:05.305909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:55:05.464833image/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
2023-12-10T20:55:05.788483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5
Min length3

Characters and Unicode

Total characters500
Distinct characters84
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%
목사동-압록 2
 
2.0%
마전-성송 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%
보성-복내 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:55:06.357468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
20.0%
18
 
3.6%
18
 
3.6%
16
 
3.2%
14
 
2.8%
14
 
2.8%
12
 
2.4%
12
 
2.4%
10
 
2.0%
10
 
2.0%
Other values (74) 276
55.2%

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%
14
 
3.5%
14
 
3.5%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (73) 266
66.5%
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%
14
 
3.5%
14
 
3.5%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (73) 266
66.5%
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%
14
 
3.5%
14
 
3.5%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (73) 266
66.5%

연장
Real number (ℝ)

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.646
Minimum2.6
Maximum33.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:06.536838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile3.2
Q15.4
median9.65
Q314.1
95-th percentile21.8
Maximum33.8
Range31.2
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation6.327947
Coefficient of variation (CV)0.59439667
Kurtosis2.1931012
Mean10.646
Median Absolute Deviation (MAD)4.25
Skewness1.2898487
Sum1064.6
Variance40.042913
MonotonicityNot monotonic
2023-12-10T20:55:06.698230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
5.4 6
 
6.0%
11.4 6
 
6.0%
7.4 4
 
4.0%
10.3 4
 
4.0%
7.9 4
 
4.0%
4.4 4
 
4.0%
6.8 2
 
2.0%
24.3 2
 
2.0%
8.0 2
 
2.0%
4.5 2
 
2.0%
Other values (32) 64
64.0%
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 4
4.0%
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%
19.5 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%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210201
2nd row20210201
3rd row20210201
4th row20210201
5th row20210201

Common Values

ValueCountFrequency (%)
20210201 100
100.0%

Length

2023-12-10T20:55:06.940096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:55:07.088508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210201 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

2023-12-10T20:55:07.229160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:55:07.393015image/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%
Mean34.940673
Minimum34.38107
Maximum35.34926
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:07.571581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.38107
5-th percentile34.55042
Q134.80189
median34.944735
Q335.16902
95-th percentile35.29816
Maximum35.34926
Range0.96819
Interquartile range (IQR)0.36713

Descriptive statistics

Standard deviation0.24578151
Coefficient of variation (CV)0.0070342522
Kurtosis-0.58584911
Mean34.940673
Median Absolute Deviation (MAD)0.18919
Skewness-0.31837261
Sum3494.0673
Variance0.06040855
MonotonicityNot monotonic
2023-12-10T20:55:07.816066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.85192 2
 
2.0%
35.34926 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%
35.05426 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
34.38107 2
2.0%
34.38392 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%
34.67935 2
2.0%
34.70297 2
2.0%
34.7172 2
2.0%
ValueCountFrequency (%)
35.34926 2
2.0%
35.32476 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%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.91247
Minimum126.21616
Maximum127.75881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:08.039476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.21616
5-th percentile126.2341
Q1126.57464
median126.82531
Q3127.2868
95-th percentile127.55961
Maximum127.75881
Range1.54265
Interquartile range (IQR)0.71216

Descriptive statistics

Standard deviation0.41384533
Coefficient of variation (CV)0.0032608721
Kurtosis-1.1212689
Mean126.91247
Median Absolute Deviation (MAD)0.323765
Skewness0.16093528
Sum12691.247
Variance0.17126796
MonotonicityNot monotonic
2023-12-10T20:55:08.295718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.42727 2
 
2.0%
126.46074 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%
127.26851 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.21616 2
2.0%
126.23118 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%
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%
Mean2861.0153
Minimum194.96
Maximum15966.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:08.511016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum194.96
5-th percentile346.263
Q11042.2925
median1967.62
Q33924.43
95-th percentile7203.8165
Maximum15966.26
Range15771.3
Interquartile range (IQR)2882.1375

Descriptive statistics

Standard deviation2709.7765
Coefficient of variation (CV)0.94713808
Kurtosis7.8701911
Mean2861.0153
Median Absolute Deviation (MAD)1358.87
Skewness2.3028944
Sum286101.53
Variance7342888.9
MonotonicityNot monotonic
2023-12-10T20:55:08.789604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5461.86 1
 
1.0%
1744.63 1
 
1.0%
1131.31 1
 
1.0%
348.65 1
 
1.0%
327.32 1
 
1.0%
741.52 1
 
1.0%
797.83 1
 
1.0%
347.26 1
 
1.0%
354.77 1
 
1.0%
2789.18 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
194.96 1
1.0%
199.58 1
1.0%
238.59 1
1.0%
247.07 1
1.0%
327.32 1
1.0%
347.26 1
1.0%
348.65 1
1.0%
354.77 1
1.0%
465.29 1
1.0%
491.45 1
1.0%
ValueCountFrequency (%)
15966.26 1
1.0%
14943.08 1
1.0%
7994.88 1
1.0%
7693.44 1
1.0%
7538.53 1
1.0%
7186.2 1
1.0%
6976.75 1
1.0%
6901.76 1
1.0%
6471.26 1
1.0%
6358.33 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3016.5887
Minimum145.92
Maximum25447.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:09.025160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum145.92
5-th percentile339.5275
Q11100.3975
median2063
Q33856.7625
95-th percentile7231.499
Maximum25447.05
Range25301.13
Interquartile range (IQR)2756.365

Descriptive statistics

Standard deviation3605.6033
Coefficient of variation (CV)1.1952585
Kurtosis20.72477
Mean3016.5887
Median Absolute Deviation (MAD)1245.275
Skewness3.9632593
Sum301658.87
Variance13000375
MonotonicityNot monotonic
2023-12-10T20:55:09.240350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5234.26 1
 
1.0%
1651.49 1
 
1.0%
1081.98 1
 
1.0%
387.05 1
 
1.0%
311.55 1
 
1.0%
802.5 1
 
1.0%
940.92 1
 
1.0%
341.0 1
 
1.0%
357.46 1
 
1.0%
2556.63 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
145.92 1
1.0%
162.64 1
1.0%
163.5 1
1.0%
169.94 1
1.0%
311.55 1
1.0%
341.0 1
1.0%
357.46 1
1.0%
387.05 1
1.0%
391.52 1
1.0%
400.88 1
1.0%
ValueCountFrequency (%)
25447.05 1
1.0%
21488.93 1
1.0%
9590.01 1
1.0%
7661.41 1
1.0%
7551.82 1
1.0%
7214.64 1
1.0%
7182.24 1
1.0%
7159.64 1
1.0%
7003.3 1
1.0%
6270.36 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean379.5769
Minimum21.15
Maximum2566.05
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:09.428258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.15
5-th percentile43.3945
Q1142.9975
median259.33
Q3478.8925
95-th percentile1030.38
Maximum2566.05
Range2544.9
Interquartile range (IQR)335.895

Descriptive statistics

Standard deviation402.82726
Coefficient of variation (CV)1.0612534
Kurtosis13.024027
Mean379.5769
Median Absolute Deviation (MAD)166.295
Skewness3.0538509
Sum37957.69
Variance162269.8
MonotonicityNot monotonic
2023-12-10T20:55:09.624539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
679.11 1
 
1.0%
229.4 1
 
1.0%
141.19 1
 
1.0%
50.32 1
 
1.0%
39.3 1
 
1.0%
90.54 1
 
1.0%
111.89 1
 
1.0%
43.61 1
 
1.0%
44.3 1
 
1.0%
352.33 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
21.15 1
1.0%
21.48 1
1.0%
25.07 1
1.0%
26.31 1
1.0%
39.3 1
1.0%
43.61 1
1.0%
44.3 1
1.0%
49.49 1
1.0%
50.32 1
1.0%
53.41 1
1.0%
ValueCountFrequency (%)
2566.05 1
1.0%
2349.92 1
1.0%
1124.95 1
1.0%
1063.99 1
1.0%
1055.84 1
1.0%
1029.04 1
1.0%
933.65 1
1.0%
908.14 1
1.0%
880.95 1
1.0%
866.02 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.7187
Minimum9.64
Maximum1531.52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:09.883573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.64
5-th percentile24.024
Q170.73
median125.14
Q3242.95
95-th percentile467.9215
Maximum1531.52
Range1521.88
Interquartile range (IQR)172.22

Descriptive statistics

Standard deviation211.4172
Coefficient of variation (CV)1.1202769
Kurtosis19.60352
Mean188.7187
Median Absolute Deviation (MAD)76.98
Skewness3.7793283
Sum18871.87
Variance44697.234
MonotonicityNot monotonic
2023-12-10T20:55:10.201727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
297.99 1
 
1.0%
124.98 1
 
1.0%
67.39 1
 
1.0%
24.05 1
 
1.0%
23.53 1
 
1.0%
47.94 1
 
1.0%
58.63 1
 
1.0%
28.79 1
 
1.0%
29.94 1
 
1.0%
140.23 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
9.64 1
1.0%
11.19 1
1.0%
11.48 1
1.0%
11.73 1
1.0%
23.53 1
1.0%
24.05 1
1.0%
26.24 1
1.0%
27.47 1
1.0%
28.79 1
1.0%
29.21 1
1.0%
ValueCountFrequency (%)
1531.52 1
1.0%
1155.88 1
1.0%
620.54 1
1.0%
543.52 1
1.0%
535.21 1
1.0%
464.38 1
1.0%
443.55 1
1.0%
404.15 1
1.0%
394.1 1
1.0%
374.49 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean724209.43
Minimum50209.14
Maximum4484630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:55:10.523087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50209.14
5-th percentile83219.982
Q1247422.6
median484130.36
Q3996273.21
95-th percentile1771442.3
Maximum4484630
Range4434420.9
Interquartile range (IQR)748850.61

Descriptive statistics

Standard deviation713957.5
Coefficient of variation (CV)0.98584397
Kurtosis10.728842
Mean724209.43
Median Absolute Deviation (MAD)351751.03
Skewness2.674735
Sum72420943
Variance5.0973531 × 1011
MonotonicityNot monotonic
2023-12-10T20:55:10.754173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1360011.57 1
 
1.0%
430917.12 1
 
1.0%
280614.35 1
 
1.0%
83265.67 1
 
1.0%
82351.91 1
 
1.0%
183799.78 1
 
1.0%
191451.7 1
 
1.0%
86887.14 1
 
1.0%
88593.79 1
 
1.0%
700115.44 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
50209.14 1
1.0%
51363.37 1
1.0%
56354.12 1
1.0%
59079.14 1
1.0%
82351.91 1
1.0%
83265.67 1
1.0%
86887.14 1
1.0%
88593.79 1
1.0%
119324.0 1
1.0%
125311.96 1
1.0%
ValueCountFrequency (%)
4484629.99 1
1.0%
4030914.67 1
1.0%
2050108.69 1
1.0%
1793105.31 1
1.0%
1779696.91 1
1.0%
1771007.82 1
1.0%
1675100.21 1
1.0%
1621367.3 1
1.0%
1619592.78 1
1.0%
1614919.0 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:55:11.141881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.88
Min length8

Characters and Unicode

Total characters1088
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 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%
보성 8
 
2.0%
영광 8
 
2.0%
화순 8
 
2.0%
곡성 6
 
1.5%
나주 6
 
1.5%
해남 6
 
1.5%
미력 6
 
1.5%
Other values (98) 228
57.3%
2023-12-10T20:55:11.688004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.4%
110
 
10.1%
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 (99) 440
40.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 790
72.6%
Space Separator 298
 
27.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
110
 
13.9%
110
 
13.9%
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 (98) 426
53.9%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 790
72.6%
Common 298
 
27.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
110
 
13.9%
110
 
13.9%
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 (98) 426
53.9%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 790
72.6%
ASCII 298
 
27.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
298
100.0%
Hangul
ValueCountFrequency (%)
110
 
13.9%
110
 
13.9%
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 (98) 426
53.9%

Interactions

2023-12-10T20:55:01.417699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:50.427253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:51.944962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:53.300810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:54.651184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:56.346967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:57.685075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:58.846872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:00.080691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:01.550139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:50.600381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:52.092816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:53.450008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:54.804454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:56.468997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:57.823584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:58.980155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:00.220195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:01.689773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:50.760125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:52.244804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:53.603689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:54.967325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:56.616450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:57.955522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:59.151465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:00.359837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:01.805031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:50.918012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:52.383496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:53.755666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:55.116886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:56.749126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:58.084575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:59.263614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:00.505010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:01.924455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:51.121950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:52.544369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:53.917780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:55.285437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:56.944230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:58.216354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:59.406466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:00.662735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:02.070605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:51.306916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:52.712914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:54.090985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:55.438252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:57.125137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:58.356471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:59.546617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:00.835181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:02.185652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:51.459643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:52.857183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:54.229714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:55.939900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:57.262705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:58.501806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:59.676422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:00.973216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:02.289308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:51.605912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:52.996491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:54.362823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:56.064502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:57.403321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:58.613026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:59.800062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:01.154961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:02.452171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:51.801858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:53.162868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:54.522512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:56.210998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:57.555326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:58.746461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:54:59.946585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:55:01.293391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:55:11.870202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.4570.7800.8190.5600.3930.5280.4640.4111.000
지점1.0001.0000.0001.0001.0001.0001.0000.9870.8690.9860.9080.9151.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9870.8690.9860.9080.9151.000
연장0.4571.0000.0001.0001.0000.5650.6870.4180.2690.4860.0000.2941.000
좌표위치위도0.7801.0000.0001.0000.5651.0000.7440.4620.2760.5070.3630.3261.000
좌표위치경도0.8191.0000.0001.0000.6870.7441.0000.5220.4910.4790.5190.4471.000
co0.5600.9870.0000.9870.4180.4620.5221.0000.9710.9830.8460.9090.987
nox0.3930.8690.0000.8690.2690.2760.4910.9711.0000.9690.9580.9720.869
hc0.5280.9860.0000.9860.4860.5070.4790.9830.9691.0000.8570.9200.986
pm0.4640.9080.0000.9080.0000.3630.5190.8460.9580.8571.0000.9900.908
co20.4110.9150.0000.9150.2940.3260.4470.9090.9720.9200.9901.0000.915
주소1.0001.0000.0001.0001.0001.0001.0000.9870.8690.9860.9080.9151.000
2023-12-10T20:55:12.079059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.0000.0370.4130.068-0.418-0.443-0.431-0.416-0.4240.000
연장0.0371.000-0.037-0.173-0.031-0.038-0.036-0.058-0.0330.000
좌표위치위도0.413-0.0371.0000.168-0.152-0.127-0.146-0.147-0.1540.000
좌표위치경도0.068-0.1730.1681.000-0.109-0.070-0.096-0.088-0.0940.000
co-0.418-0.031-0.152-0.1091.0000.9830.9890.9610.9990.000
nox-0.443-0.038-0.127-0.0700.9831.0000.9950.9850.9850.000
hc-0.431-0.036-0.146-0.0960.9890.9951.0000.9820.9880.000
pm-0.416-0.058-0.147-0.0880.9610.9850.9821.0000.9620.000
co2-0.424-0.033-0.154-0.0940.9990.9850.9880.9621.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T20:55:02.656841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:55:02.982348image/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-무안5.420210201034.85192126.427275461.865234.26679.11297.991360011.57전남 무안 삼향 왕산
12건기연[0101-0]2목포-무안5.420210201034.85192126.427275919.426270.36820.56338.61457088.76전남 무안 삼향 왕산
23건기연[0101-1]1진도-무안21.820210201034.80189126.364914726.725083.96573.32263.311288343.83전남 목포 죽교
34건기연[0101-1]2진도-무안21.820210201034.80189126.364914664.275089.76564.36278.121279979.07전남 목포 죽교
45건기연[0104-0]1학교-장산12.520210201034.99062126.654881335.351296.64192.3393.24321586.09전남 나주 다시 복암
56건기연[0104-0]2학교-장산12.520210201034.99062126.654881350.081273.92189.0394.21328486.46전남 나주 다시 복암
67건기연[0109-0]1광주-장성7.420210201035.2553126.81216246.96207.19821.43394.11569065.28전남 장성 진원 산정
78건기연[0109-0]2광주-장성7.420210201035.2553126.81217538.537661.411029.04464.381779696.91전남 장성 진원 산정
89건기연[0201-4]1금계-강진12.620210201034.70297126.649762407.462909.01375.58194.32579016.05전남 영암 학산 묵동
910건기연[0201-4]2금계-강진12.620210201034.70297126.649762179.682394.49330.91169.39516488.56전남 영암 학산 묵동
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2306-0]1나주-상방9.720210201034.95043126.647042754.922827.63377.93244.63692598.89전남 나주 왕곡 신포
9192건기연[2306-0]2나주-상방9.720210201034.95043126.647042799.82541.56341.61224.46709745.31전남 나주 왕곡 신포
9293건기연[2309-0]1동강-함평5.620210201035.03781126.534241499.281420.31183.3687.7373651.82전남 함평 학교 사거
9394건기연[2309-0]2동강-함평5.620210201035.03781126.534241570.051606.55204.53105.28385855.4전남 함평 학교 사거
9495건기연[2311-1]1신광-영광8.720210201035.21885126.50315957.931102.15139.8483.7229604.51전남 영광 불갑 안맹
9596건기연[2311-1]2신광-영광8.720210201035.21885126.50315984.281168.74150.4986.86232651.45전남 영광 불갑 안맹
9697건기연[2314-0]1마전-성송4.420210201035.32476126.59409491.45400.8854.2227.47125768.99전남 영광 대마 홍교
9798건기연[2314-0]2마전-성송4.420210201035.32476126.59409500.01391.5254.8326.24128062.95전남 영광 대마 홍교
9899건기연[2401-0]1지도-해제19.520210201035.06002126.231181203.721155.56151.1889.15300297.83전남 신안 지도 광정
99100건기연[2401-0]2지도-해제19.520210201035.06002126.231181249.551153.54153.2284.72312659.35전남 신안 지도 광정