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 10:30:54.434755
Analysis finished2023-12-10 10:31:11.996690
Duration17.56 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
2023-12-10T19:31:12.157479image/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
2023-12-10T19:31:12.443449image/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

2023-12-10T19:31:12.720095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:12.892699image/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
2023-12-10T19:31:13.230470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.04
Min length8

Characters and Unicode

Total characters804
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[0114-1]
2nd row[0114-1]
3rd row[0115-1]
4th row[0115-1]
5th row[0116-2]
ValueCountFrequency (%)
0114-1 2
 
2.0%
2704-2 2
 
2.0%
3011-1 2
 
2.0%
2316-0 2
 
2.0%
2317-0 2
 
2.0%
2320-2 2
 
2.0%
2602-3 2
 
2.0%
2606-0 2
 
2.0%
2607-2 2
 
2.0%
2609-1 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T19:31:13.886384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 144
17.9%
0 104
12.9%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
2 96
11.9%
3 52
 
6.5%
7 34
 
4.2%
9 26
 
3.2%
6 20
 
2.5%
Other values (3) 28
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 504
62.7%
Open Punctuation 100
 
12.4%
Dash Punctuation 100
 
12.4%
Close Punctuation 100
 
12.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 144
28.6%
0 104
20.6%
2 96
19.0%
3 52
 
10.3%
7 34
 
6.7%
9 26
 
5.2%
6 20
 
4.0%
5 12
 
2.4%
4 10
 
2.0%
8 6
 
1.2%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 144
17.9%
0 104
12.9%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
2 96
11.9%
3 52
 
6.5%
7 34
 
4.2%
9 26
 
3.2%
6 20
 
2.5%
Other values (3) 28
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 144
17.9%
0 104
12.9%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
2 96
11.9%
3 52
 
6.5%
7 34
 
4.2%
9 26
 
3.2%
6 20
 
2.5%
Other values (3) 28
 
3.5%

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

2023-12-10T19:31:14.137038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:14.305178image/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
2023-12-10T19:31:14.656674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.14
Min length5

Characters and Unicode

Total characters514
Distinct characters84
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 row태인-금구
2nd row태인-금구
3rd row정읍-태인
4th row정읍-태인
5th row금산-전주
ValueCountFrequency (%)
태인-금구 2
 
2.0%
옥과-순창 2
 
2.0%
성수-남계 2
 
2.0%
보안-부안 2
 
2.0%
부안-죽산 2
 
2.0%
군산-익산 2
 
2.0%
군산-대야 2
 
2.0%
상관-소양 2
 
2.0%
연장-오천 2
 
2.0%
천천-장계 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T19:31:15.356595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.5%
22
 
4.3%
16
 
3.1%
16
 
3.1%
12
 
2.3%
12
 
2.3%
12
 
2.3%
10
 
1.9%
10
 
1.9%
10
 
1.9%
Other values (74) 294
57.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 406
79.0%
Dash Punctuation 100
 
19.5%
Uppercase Letter 8
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
5.4%
16
 
3.9%
16
 
3.9%
12
 
3.0%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (71) 276
68.0%
Uppercase Letter
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 406
79.0%
Common 100
 
19.5%
Latin 8
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22
 
5.4%
16
 
3.9%
16
 
3.9%
12
 
3.0%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (71) 276
68.0%
Latin
ValueCountFrequency (%)
C 4
50.0%
I 4
50.0%
Common
ValueCountFrequency (%)
- 100
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 406
79.0%
ASCII 108
 
21.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 100
92.6%
C 4
 
3.7%
I 4
 
3.7%
Hangul
ValueCountFrequency (%)
22
 
5.4%
16
 
3.9%
16
 
3.9%
12
 
3.0%
12
 
3.0%
12
 
3.0%
10
 
2.5%
10
 
2.5%
10
 
2.5%
10
 
2.5%
Other values (71) 276
68.0%

연장
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.51
Minimum0.9
Maximum18.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:15.604106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q14.6
median6.45
Q39.2
95-th percentile15.7
Maximum18.9
Range18
Interquartile range (IQR)4.6

Descriptive statistics

Standard deviation4.209525
Coefficient of variation (CV)0.56052264
Kurtosis0.044293781
Mean7.51
Median Absolute Deviation (MAD)2.3
Skewness0.77752431
Sum751
Variance17.720101
MonotonicityNot monotonic
2023-12-10T19:31:15.896381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
8.7 4
 
4.0%
6.5 4
 
4.0%
4.9 4
 
4.0%
2.4 4
 
4.0%
5.4 4
 
4.0%
8.0 4
 
4.0%
6.0 4
 
4.0%
5.7 2
 
2.0%
14.6 2
 
2.0%
8.6 2
 
2.0%
Other values (33) 66
66.0%
ValueCountFrequency (%)
0.9 2
2.0%
1.0 2
2.0%
2.4 4
4.0%
2.7 2
2.0%
3.1 2
2.0%
3.3 2
2.0%
3.4 2
2.0%
3.6 2
2.0%
3.7 2
2.0%
4.1 2
2.0%
ValueCountFrequency (%)
18.9 2
2.0%
17.3 2
2.0%
15.7 2
2.0%
14.6 2
2.0%
13.8 2
2.0%
13.0 2
2.0%
12.9 2
2.0%
12.8 2
2.0%
11.9 2
2.0%
11.7 2
2.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210401
2nd row20210401
3rd row20210401
4th row20210401
5th row20210401

Common Values

ValueCountFrequency (%)
20210401 100
100.0%

Length

2023-12-10T19:31:16.212566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:16.393162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210401 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

2023-12-10T19:31:16.571560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:16.761432image/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%
Mean35.678031
Minimum35.31836
Maximum36.05245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:16.958654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.31836
5-th percentile35.38415
Q135.52124
median35.689995
Q335.79995
95-th percentile35.97701
Maximum36.05245
Range0.73409
Interquartile range (IQR)0.27871

Descriptive statistics

Standard deviation0.18878614
Coefficient of variation (CV)0.0052913835
Kurtosis-0.93159077
Mean35.678031
Median Absolute Deviation (MAD)0.1586
Skewness0.028075954
Sum3567.8031
Variance0.035640208
MonotonicityNot monotonic
2023-12-10T19:31:17.254066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.66929 2
 
2.0%
35.9058 2
 
2.0%
35.75539 2
 
2.0%
35.97701 2
 
2.0%
35.9615 2
 
2.0%
35.85422 2
 
2.0%
35.77224 2
 
2.0%
35.74292 2
 
2.0%
35.72732 2
 
2.0%
35.71626 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
35.31836 2
2.0%
35.36379 2
2.0%
35.38415 2
2.0%
35.40351 2
2.0%
35.41493 2
2.0%
35.42787 2
2.0%
35.44376 2
2.0%
35.44964 2
2.0%
35.467 2
2.0%
35.48235 2
2.0%
ValueCountFrequency (%)
36.05245 2
2.0%
35.97732 2
2.0%
35.97701 2
2.0%
35.97553 2
2.0%
35.9615 2
2.0%
35.9258 2
2.0%
35.91702 2
2.0%
35.9058 2
2.0%
35.90484 2
2.0%
35.88516 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.11593
Minimum126.5004
Maximum127.67801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:17.671453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5004
5-th percentile126.64598
Q1126.89186
median127.12055
Q3127.32352
95-th percentile127.59682
Maximum127.67801
Range1.17761
Interquartile range (IQR)0.43166

Descriptive statistics

Standard deviation0.2990975
Coefficient of variation (CV)0.0023529505
Kurtosis-0.81073672
Mean127.11593
Median Absolute Deviation (MAD)0.220295
Skewness0.066779734
Sum12711.593
Variance0.089459317
MonotonicityNot monotonic
2023-12-10T19:31:18.031128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.96828 2
 
2.0%
127.00297 2
 
2.0%
126.75919 2
 
2.0%
126.91023 2
 
2.0%
126.77112 2
 
2.0%
127.21711 2
 
2.0%
127.4985 2
 
2.0%
127.57067 2
 
2.0%
127.59682 2
 
2.0%
127.11512 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.5004 2
2.0%
126.59317 2
2.0%
126.64598 2
2.0%
126.69676 2
2.0%
126.6981 2
2.0%
126.75919 2
2.0%
126.77112 2
2.0%
126.77892 2
2.0%
126.7879 2
2.0%
126.83701 2
2.0%
ValueCountFrequency (%)
127.67801 2
2.0%
127.65033 2
2.0%
127.59682 2
2.0%
127.57067 2
2.0%
127.55201 2
2.0%
127.53885 2
2.0%
127.53076 2
2.0%
127.52057 2
2.0%
127.4985 2
2.0%
127.42317 2
2.0%

co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3482.4801
Minimum107.94
Maximum18900.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:18.460422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum107.94
5-th percentile238.0925
Q1980.915
median2241.58
Q35758.565
95-th percentile10023.995
Maximum18900.04
Range18792.1
Interquartile range (IQR)4777.65

Descriptive statistics

Standard deviation3571.2231
Coefficient of variation (CV)1.0254827
Kurtosis5.246758
Mean3482.4801
Median Absolute Deviation (MAD)1631.05
Skewness1.9573967
Sum348248.01
Variance12753635
MonotonicityNot monotonic
2023-12-10T19:31:19.173371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7570.63 1
 
1.0%
999.19 1
 
1.0%
6164.97 1
 
1.0%
1092.45 1
 
1.0%
1069.87 1
 
1.0%
5542.87 1
 
1.0%
4599.98 1
 
1.0%
990.21 1
 
1.0%
1016.66 1
 
1.0%
852.48 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
107.94 1
1.0%
120.58 1
1.0%
217.74 1
1.0%
219.67 1
1.0%
223.13 1
1.0%
238.88 1
1.0%
276.32 1
1.0%
276.87 1
1.0%
281.26 1
1.0%
321.49 1
1.0%
ValueCountFrequency (%)
18900.04 1
1.0%
18709.73 1
1.0%
10428.76 1
1.0%
10203.36 1
1.0%
10141.13 1
1.0%
10017.83 1
1.0%
9676.54 1
1.0%
9272.47 1
1.0%
8868.26 1
1.0%
7570.63 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4209.8301
Minimum150.63
Maximum30240.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:19.425104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum150.63
5-th percentile274.531
Q11060.8375
median2389.505
Q36050.0175
95-th percentile14213.36
Maximum30240.34
Range30089.71
Interquartile range (IQR)4989.18

Descriptive statistics

Standard deviation5065.3664
Coefficient of variation (CV)1.2032235
Kurtosis11.397685
Mean4209.8301
Median Absolute Deviation (MAD)1857.54
Skewness2.930057
Sum420983.01
Variance25657937
MonotonicityNot monotonic
2023-12-10T19:31:19.690634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7543.45 1
 
1.0%
1086.8 1
 
1.0%
7139.23 1
 
1.0%
1703.45 1
 
1.0%
1519.42 1
 
1.0%
4285.51 1
 
1.0%
3729.36 1
 
1.0%
1080.64 1
 
1.0%
1250.92 1
 
1.0%
868.66 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
150.63 1
1.0%
151.13 1
1.0%
193.54 1
1.0%
208.79 1
1.0%
224.58 1
1.0%
277.16 1
1.0%
281.18 1
1.0%
281.34 1
1.0%
315.31 1
1.0%
338.51 1
1.0%
ValueCountFrequency (%)
30240.34 1
1.0%
28764.2 1
1.0%
15814.49 1
1.0%
15197.22 1
1.0%
14618.43 1
1.0%
14192.04 1
1.0%
10612.82 1
1.0%
10508.87 1
1.0%
9353.94 1
1.0%
8785.07 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean519.1663
Minimum18.85
Maximum3292.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:19.957411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18.85
5-th percentile35.2465
Q1139.6925
median309.975
Q3769.8
95-th percentile1599.001
Maximum3292.38
Range3273.53
Interquartile range (IQR)630.1075

Descriptive statistics

Standard deviation570.93453
Coefficient of variation (CV)1.0997142
Kurtosis9.4318988
Mean519.1663
Median Absolute Deviation (MAD)232.38
Skewness2.588757
Sum51916.63
Variance325966.24
MonotonicityNot monotonic
2023-12-10T19:31:20.219159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1089.65 1
 
1.0%
131.27 1
 
1.0%
888.13 1
 
1.0%
214.18 1
 
1.0%
205.02 1
 
1.0%
651.64 1
 
1.0%
533.73 1
 
1.0%
150.3 1
 
1.0%
156.77 1
 
1.0%
116.9 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
18.85 1
1.0%
20.37 1
1.0%
26.38 1
1.0%
27.92 1
1.0%
31.95 1
1.0%
35.42 1
1.0%
36.86 1
1.0%
41.34 1
1.0%
43.88 1
1.0%
47.87 1
1.0%
ValueCountFrequency (%)
3292.38 1
1.0%
3253.14 1
1.0%
1693.97 1
1.0%
1657.92 1
1.0%
1635.12 1
1.0%
1597.1 1
1.0%
1204.42 1
1.0%
1192.72 1
1.0%
1099.38 1
1.0%
1096.63 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273.7399
Minimum10.69
Maximum1885.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:20.505078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.69
5-th percentile20.5435
Q184.255
median163.48
Q3375.2675
95-th percentile930.8805
Maximum1885.4
Range1874.71
Interquartile range (IQR)291.0125

Descriptive statistics

Standard deviation317.00472
Coefficient of variation (CV)1.1580508
Kurtosis10.520875
Mean273.7399
Median Absolute Deviation (MAD)113.84
Skewness2.8148734
Sum27373.99
Variance100491.99
MonotonicityNot monotonic
2023-12-10T19:31:20.836912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
473.71 1
 
1.0%
87.27 1
 
1.0%
481.11 1
 
1.0%
109.46 1
 
1.0%
99.97 1
 
1.0%
235.23 1
 
1.0%
240.17 1
 
1.0%
93.33 1
 
1.0%
104.6 1
 
1.0%
76.83 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
10.69 1
1.0%
12.66 1
1.0%
17.36 1
1.0%
18.52 1
1.0%
20.23 1
1.0%
20.56 1
1.0%
20.9 1
1.0%
22.73 1
1.0%
23.22 1
1.0%
25.57 1
1.0%
ValueCountFrequency (%)
1885.4 1
1.0%
1767.06 1
1.0%
1001.21 1
1.0%
957.93 1
1.0%
942.48 1
1.0%
930.27 1
1.0%
687.25 1
1.0%
677.84 1
1.0%
619.58 1
1.0%
600.44 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean876640.63
Minimum24333.73
Maximum5011272.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:21.203664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24333.73
5-th percentile56007.704
Q1236824.05
median553441.9
Q31469642.5
95-th percentile2634460.9
Maximum5011272.9
Range4986939.2
Interquartile range (IQR)1232818.4

Descriptive statistics

Standard deviation934959.25
Coefficient of variation (CV)1.0665251
Kurtosis5.9927259
Mean876640.63
Median Absolute Deviation (MAD)404143.43
Skewness2.0969654
Sum87664063
Variance8.741488 × 1011
MonotonicityNot monotonic
2023-12-10T19:31:21.481926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1706493.41 1
 
1.0%
246491.87 1
 
1.0%
1558172.73 1
 
1.0%
264976.01 1
 
1.0%
250114.17 1
 
1.0%
1299886.68 1
 
1.0%
1171208.92 1
 
1.0%
234402.31 1
 
1.0%
253161.35 1
 
1.0%
209471.96 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
24333.73 1
1.0%
26125.21 1
1.0%
53615.78 1
1.0%
54235.32 1
1.0%
54528.82 1
1.0%
56085.54 1
1.0%
61869.15 1
1.0%
68239.17 1
1.0%
68377.18 1
1.0%
76891.29 1
1.0%
ValueCountFrequency (%)
5011272.94 1
1.0%
4956860.31 1
1.0%
2809321.6 1
1.0%
2720414.19 1
1.0%
2677247.89 1
1.0%
2632208.92 1
1.0%
2454149.9 1
1.0%
2372902.38 1
1.0%
2034196.61 1
1.0%
1880452.1 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:31:22.001899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.96
Min length8

Characters and Unicode

Total characters1096
Distinct characters103
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 row전북 정읍 옹동 오성
2nd row전북 정읍 옹동 오성
3rd row전북 정읍 정우 우산
4th row전북 정읍 정우 우산
5th row전북 김제 금구 대화
ValueCountFrequency (%)
전북 100
25.1%
임실 14
 
3.5%
순창 14
 
3.5%
장수 12
 
3.0%
정읍 10
 
2.5%
완주 10
 
2.5%
부안 8
 
2.0%
김제 8
 
2.0%
익산 6
 
1.5%
고창 6
 
1.5%
Other values (97) 210
52.8%
2023-12-10T19:31:22.768518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
104
 
9.5%
100
 
9.1%
30
 
2.7%
24
 
2.2%
20
 
1.8%
18
 
1.6%
18
 
1.6%
16
 
1.5%
16
 
1.5%
Other values (93) 452
41.2%

Most occurring categories

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

Most frequent character per category

Other Letter
ValueCountFrequency (%)
104
 
13.0%
100
 
12.5%
30
 
3.8%
24
 
3.0%
20
 
2.5%
18
 
2.3%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (92) 436
54.6%
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 (%)
104
 
13.0%
100
 
12.5%
30
 
3.8%
24
 
3.0%
20
 
2.5%
18
 
2.3%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (92) 436
54.6%
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 (%)
104
 
13.0%
100
 
12.5%
30
 
3.8%
24
 
3.0%
20
 
2.5%
18
 
2.3%
18
 
2.3%
16
 
2.0%
16
 
2.0%
16
 
2.0%
Other values (92) 436
54.6%

Interactions

2023-12-10T19:31:09.745735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:55.423885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.274592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.759897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:00.387448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:02.872886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:04.596332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:06.119380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:08.102980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:09.913121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:55.614608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.428553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.959930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:00.595215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:03.106448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:04.742812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:06.311557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:08.254110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:10.087033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:55.764690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.583541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:59.130587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:01.105717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:03.352775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:04.911095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:06.544134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:08.465639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:10.266658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:55.932482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.741740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:59.310369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:01.470110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:03.522924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:05.093189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:06.771441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:08.671401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:10.428983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:56.071984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.890125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:59.492342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:01.835039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:03.682946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:05.259965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:06.970731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:08.927421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:10.631558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:56.645156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.048590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:59.681660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:02.082123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:03.870766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:05.438489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:07.126163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:09.127992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:10.786336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:56.817523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.236706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:59.842467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:02.302057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:04.054187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:05.586066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:07.295022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:09.286159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:10.958452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:56.958582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.432954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:00.000588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:02.473057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:04.222591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:05.778680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:07.800738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:09.415258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:11.108886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:57.120390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:30:58.605875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:00.199529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:02.650679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:04.401533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:05.972989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:07.951631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:09.551398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:31:22.979820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.7650.7610.8550.5660.5660.6420.5290.5931.000
지점1.0001.0000.0001.0001.0001.0001.0000.9690.9950.9890.9470.9851.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9690.9950.9890.9470.9851.000
연장0.7651.0000.0001.0001.0000.6090.6100.3740.3770.3490.2730.5281.000
좌표위치위도0.7611.0000.0001.0000.6091.0000.7920.5540.5380.5330.4950.5031.000
좌표위치경도0.8551.0000.0001.0000.6100.7921.0000.5710.4820.4740.3540.4881.000
co0.5660.9690.0000.9690.3740.5540.5711.0000.9610.9660.9520.9890.969
nox0.5660.9950.0000.9950.3770.5380.4820.9611.0000.9940.9830.9670.995
hc0.6420.9890.0000.9890.3490.5330.4740.9660.9941.0000.9800.9740.989
pm0.5290.9470.0000.9470.2730.4950.3540.9520.9830.9801.0000.9640.947
co20.5930.9850.0000.9850.5280.5030.4880.9890.9670.9740.9641.0000.985
주소1.0001.0000.0001.0001.0001.0001.0000.9690.9950.9890.9470.9851.000
2023-12-10T19:31:23.275867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.0000.0540.012-0.308-0.214-0.198-0.201-0.217-0.2080.000
연장0.0541.0000.045-0.153-0.079-0.117-0.113-0.136-0.0790.000
좌표위치위도0.0120.0451.000-0.0020.3960.3930.3840.3770.4020.000
좌표위치경도-0.308-0.153-0.0021.000-0.311-0.299-0.314-0.289-0.3050.000
co-0.214-0.0790.396-0.3111.0000.9840.9880.9690.9980.000
nox-0.198-0.1170.393-0.2990.9841.0000.9980.9920.9830.000
hc-0.201-0.1130.384-0.3140.9880.9981.0000.9870.9860.000
pm-0.217-0.1360.377-0.2890.9690.9920.9871.0000.9680.000
co2-0.208-0.0790.402-0.3050.9980.9830.9860.9681.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T19:31:11.422304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:31:11.838186image/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[0114-1]1태인-금구11.120210401035.66929126.968287570.637543.451089.65473.711706493.41전북 정읍 옹동 오성
12건기연[0114-1]2태인-금구11.120210401035.66929126.968286355.166783.69936.01444.181509161.62전북 정읍 옹동 오성
23건기연[0115-1]1정읍-태인6.420210401035.62947126.903447534.128156.451096.63541.641846058.33전북 정읍 정우 우산
34건기연[0115-1]2정읍-태인6.420210401035.62947126.903447555.998509.021093.81600.441877276.34전북 정읍 정우 우산
45건기연[0116-2]1금산-전주4.320210401035.78758127.03519676.549353.941192.72599.152454149.9전북 김제 금구 대화
56건기연[0116-2]2금산-전주4.320210401035.78758127.03519272.478785.071099.38619.582372902.38전북 김제 금구 대화
67건기연[0117-3]1김제IC-전주5.120210401035.79995127.0582210428.7614618.431635.121001.212809321.6전북 완주 이서 이성
78건기연[0117-3]2김제IC-전주5.120210401035.79995127.0582210141.1314192.041597.1942.482720414.19전북 완주 이서 이성
89건기연[0121-4]1금마-연무4.920210401036.05245127.080614158.45830.15765.63426.86977114.78전북 익산 여산 제남
910건기연[0121-4]2금마-연무4.920210401036.05245127.080613712.415070.95659.41367.7882743.44전북 익산 여산 제남
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[3005-3]1부안IC-화호3.720210401035.72314126.78791644.352496.36308.14163.84402912.17전북 부안 백산 덕신
9192건기연[3005-3]2부안IC-화호3.720210401035.72314126.78791657.512349.34312.17159.77381037.67전북 부안 백산 덕신
9293건기연[3006-1]1신태인-태인6.520210401035.68032126.915273151.525138.4640.3360.87755371.4전북 정읍 신태인 궁사
9394건기연[3006-1]2신태인-태인6.520210401035.68032126.915273436.915188.66740.57382.77735269.19전북 정읍 신태인 궁사
9495건기연[3010-0]1산내-강진15.720210401035.52124127.13982223.13224.5831.9517.3653615.78전북 임실 덕치 회문
9596건기연[3010-0]2산내-강진15.720210401035.52124127.13982281.26281.1843.8818.5261869.15전북 임실 덕치 회문
9697건기연[3011-1]1성수-남계3.120210401035.62981127.34403862.181001.43138.565.81201546.75전북 임실 성수 오봉
9798건기연[3011-1]2성수-남계3.120210401035.62981127.34403877.82985.32137.5360.88205052.05전북 임실 성수 오봉
9899건기연[3011-3]1망월-임실12.820210401035.57439127.21392597.66532.5473.6836.73149254.88전북 임실 청웅 옥전
99100건기연[3011-3]2망월-임실12.820210401035.57439127.21392644.09560.1781.5139.3149342.06전북 임실 청웅 옥전