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 10:31:25.893100
Analysis finished2023-12-10 10:31:41.401676
Duration15.51 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
2023-12-10T19:31:41.591156image/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
2023-12-10T19:31:41.964661image/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

2023-12-10T19:31:42.319171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:42.470931image/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
2023-12-10T19:31:42.813436image/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%
2609-2 2
 
2.0%
3006-1 2
 
2.0%
2313-2 2
 
2.0%
2316-0 2
 
2.0%
2317-0 2
 
2.0%
2320-2 2
 
2.0%
2602-3 2
 
2.0%
2605-0 2
 
2.0%
2606-0 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T19:31:43.461329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 138
17.2%
0 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
2 100
12.4%
3 46
 
5.7%
7 34
 
4.2%
9 26
 
3.2%
6 22
 
2.7%
Other values (3) 30
 
3.7%

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 (%)
1 138
27.4%
0 108
21.4%
2 100
19.8%
3 46
 
9.1%
7 34
 
6.7%
9 26
 
5.2%
6 22
 
4.4%
5 14
 
2.8%
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 (%)
1 138
17.2%
0 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
2 100
12.4%
3 46
 
5.7%
7 34
 
4.2%
9 26
 
3.2%
6 22
 
2.7%
Other values (3) 30
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 138
17.2%
0 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
2 100
12.4%
3 46
 
5.7%
7 34
 
4.2%
9 26
 
3.2%
6 22
 
2.7%
Other values (3) 30
 
3.7%

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

2023-12-10T19:31:43.845679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:44.040316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:31:44.398936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.14
Min length5

Characters and Unicode

Total characters514
Distinct characters83
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%
천천-서상 2
 
2.0%
신태인-태인 2
 
2.0%
고창-흥덕 2
 
2.0%
보안-부안 2
 
2.0%
부안-죽산 2
 
2.0%
군산-익산 2
 
2.0%
군산-대야 2
 
2.0%
공덕-완주 2
 
2.0%
상관-소양 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T19:31:45.082738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 100
 
19.5%
산 22
 
4.3%
주 20
 
3.9%
안 16
 
3.1%
부 12
 
2.3%
전 12
 
2.3%
무 12
 
2.3%
원 10
 
1.9%
계 10
 
1.9%
천 10
 
1.9%
Other values (73) 290
56.4%

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 (%)
산 22
 
5.4%
주 20
 
4.9%
안 16
 
3.9%
부 12
 
3.0%
전 12
 
3.0%
무 12
 
3.0%
원 10
 
2.5%
계 10
 
2.5%
천 10
 
2.5%
창 10
 
2.5%
Other values (70) 272
67.0%
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 (%)
산 22
 
5.4%
주 20
 
4.9%
안 16
 
3.9%
부 12
 
3.0%
전 12
 
3.0%
무 12
 
3.0%
원 10
 
2.5%
계 10
 
2.5%
천 10
 
2.5%
창 10
 
2.5%
Other values (70) 272
67.0%
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 (%)
산 22
 
5.4%
주 20
 
4.9%
안 16
 
3.9%
부 12
 
3.0%
전 12
 
3.0%
무 12
 
3.0%
원 10
 
2.5%
계 10
 
2.5%
천 10
 
2.5%
창 10
 
2.5%
Other values (70) 272
67.0%

연장
Real number (ℝ)

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.372
Minimum0.9
Maximum18.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:45.313199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q14.6
median6.35
Q39
95-th percentile15.7
Maximum18.9
Range18
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation4.1445127
Coefficient of variation (CV)0.56219652
Kurtosis0.30120815
Mean7.372
Median Absolute Deviation (MAD)2.3
Skewness0.8739959
Sum737.2
Variance17.176986
MonotonicityNot monotonic
2023-12-10T19:31:45.550225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
6.0 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%
8.7 4
 
4.0%
2.7 2
 
2.0%
7.5 2
 
2.0%
14.6 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%
3.2 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%
11.9 2
2.0%
11.7 2
2.0%
11.5 2
2.0%

측정일
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20210301 100
100.0%

Length

2023-12-10T19:31:45.830968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:46.007512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210301 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

2023-12-10T19:31:46.187327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:31:46.354035image/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%
Mean35.689758
Minimum35.31836
Maximum36.05245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:46.615023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.31836
5-th percentile35.38415
Q135.52124
median35.71921
Q335.85422
95-th percentile35.97701
Maximum36.05245
Range0.73409
Interquartile range (IQR)0.33298

Descriptive statistics

Standard deviation0.19275366
Coefficient of variation (CV)0.0054008117
Kurtosis-1.0337454
Mean35.689758
Median Absolute Deviation (MAD)0.16868
Skewness-0.096643046
Sum3568.9758
Variance0.037153975
MonotonicityNot monotonic
2023-12-10T19:31:46.889710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.66929 2
 
2.0%
35.31836 2
 
2.0%
35.69967 2
 
2.0%
35.75539 2
 
2.0%
35.97701 2
 
2.0%
35.9615 2
 
2.0%
35.87978 2
 
2.0%
35.85422 2
 
2.0%
35.77224 2
 
2.0%
35.74292 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%
35.91078 2
2.0%
35.9058 2
2.0%
35.90484 2
2.0%

좌표위치경도
Real number (ℝ)

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.10549
Minimum126.5004
Maximum127.67801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:47.182220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5004
5-th percentile126.64598
Q1126.89186
median127.09554
Q3127.31509
95-th percentile127.59682
Maximum127.67801
Range1.17761
Interquartile range (IQR)0.42323

Descriptive statistics

Standard deviation0.29756854
Coefficient of variation (CV)0.0023411148
Kurtosis-0.75418874
Mean127.10549
Median Absolute Deviation (MAD)0.208945
Skewness0.16316954
Sum12710.549
Variance0.088547038
MonotonicityNot monotonic
2023-12-10T19:31:47.471943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.96828 2
 
2.0%
127.14275 2
 
2.0%
126.69676 2
 
2.0%
126.75919 2
 
2.0%
126.91023 2
 
2.0%
126.77112 2
 
2.0%
126.98112 2
 
2.0%
127.21711 2
 
2.0%
127.4985 2
 
2.0%
127.57067 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%
Mean1975.7985
Minimum45.22
Maximum9229.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:47.773809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum45.22
5-th percentile127.351
Q1542.0025
median1318.845
Q33137.0725
95-th percentile5617.6465
Maximum9229.83
Range9184.61
Interquartile range (IQR)2595.07

Descriptive statistics

Standard deviation1871.7403
Coefficient of variation (CV)0.94733358
Kurtosis2.5619906
Mean1975.7985
Median Absolute Deviation (MAD)994.45
Skewness1.4890608
Sum197579.85
Variance3503411.6
MonotonicityNot monotonic
2023-12-10T19:31:48.040190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3554.83 1
 
1.0%
425.43 1
 
1.0%
3813.96 1
 
1.0%
466.14 1
 
1.0%
459.21 1
 
1.0%
398.93 1
 
1.0%
425.67 1
 
1.0%
492.32 1
 
1.0%
572.0 1
 
1.0%
4829.79 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
45.22 1
1.0%
58.91 1
1.0%
99.44 1
1.0%
103.38 1
1.0%
121.67 1
1.0%
127.65 1
1.0%
150.93 1
1.0%
151.03 1
1.0%
158.58 1
1.0%
199.53 1
1.0%
ValueCountFrequency (%)
9229.83 1
1.0%
8721.07 1
1.0%
5980.74 1
1.0%
5958.51 1
1.0%
5680.28 1
1.0%
5614.35 1
1.0%
5330.63 1
1.0%
4969.01 1
1.0%
4829.79 1
1.0%
4733.2 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1688.0117
Minimum42.51
Maximum9835.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:48.307354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42.51
5-th percentile111.4105
Q1469.5275
median1082.43
Q32382.0675
95-th percentile5138.7655
Maximum9835.96
Range9793.45
Interquartile range (IQR)1912.54

Descriptive statistics

Standard deviation1796.2145
Coefficient of variation (CV)1.0641007
Kurtosis6.754208
Mean1688.0117
Median Absolute Deviation (MAD)744.365
Skewness2.2646997
Sum168801.17
Variance3226386.5
MonotonicityNot monotonic
2023-12-10T19:31:48.720323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2875.38 1
 
1.0%
387.29 1
 
1.0%
2511.25 1
 
1.0%
416.49 1
 
1.0%
406.3 1
 
1.0%
369.39 1
 
1.0%
395.4 1
 
1.0%
380.16 1
 
1.0%
585.49 1
 
1.0%
3699.0 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
42.51 1
1.0%
58.88 1
1.0%
84.21 1
1.0%
97.95 1
1.0%
105.91 1
1.0%
111.7 1
1.0%
116.08 1
1.0%
146.1 1
1.0%
149.99 1
1.0%
160.45 1
1.0%
ValueCountFrequency (%)
9835.96 1
1.0%
9503.07 1
1.0%
6020.29 1
1.0%
5925.39 1
1.0%
5400.12 1
1.0%
5125.01 1
1.0%
5122.15 1
1.0%
4267.27 1
1.0%
3699.0 1
1.0%
3653.97 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217.4955
Minimum4.89
Maximum1134.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:49.099263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.89
5-th percentile14.693
Q156.145
median141.43
Q3315.8275
95-th percentile671.8125
Maximum1134.38
Range1129.49
Interquartile range (IQR)259.6825

Descriptive statistics

Standard deviation217.52689
Coefficient of variation (CV)1.0001443
Kurtosis4.6143731
Mean217.4955
Median Absolute Deviation (MAD)99.385
Skewness1.883137
Sum21749.55
Variance47317.946
MonotonicityNot monotonic
2023-12-10T19:31:49.377099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
379.01 1
 
1.0%
44.39 1
 
1.0%
363.06 1
 
1.0%
54.21 1
 
1.0%
52.39 1
 
1.0%
42.72 1
 
1.0%
45.64 1
 
1.0%
48.43 1
 
1.0%
67.49 1
 
1.0%
487.18 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
4.89 1
1.0%
6.75 1
1.0%
10.47 1
1.0%
11.22 1
1.0%
14.37 1
1.0%
14.71 1
1.0%
14.81 1
1.0%
15.44 1
1.0%
17.54 1
1.0%
19.72 1
1.0%
ValueCountFrequency (%)
1134.38 1
1.0%
1097.06 1
1.0%
705.83 1
1.0%
683.01 1
1.0%
677.75 1
1.0%
671.5 1
1.0%
638.53 1
1.0%
549.14 1
1.0%
487.18 1
1.0%
483.21 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89.8205
Minimum3.35
Maximum474.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:49.699102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.35
5-th percentile7.75
Q129.6725
median60.91
Q3119.2275
95-th percentile291.3325
Maximum474.87
Range471.52
Interquartile range (IQR)89.555

Descriptive statistics

Standard deviation90.034221
Coefficient of variation (CV)1.0023794
Kurtosis4.5987301
Mean89.8205
Median Absolute Deviation (MAD)39.205
Skewness1.9811904
Sum8982.05
Variance8106.1609
MonotonicityNot monotonic
2023-12-10T19:31:50.040371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
147.12 1
 
1.0%
23.45 1
 
1.0%
96.62 1
 
1.0%
36.33 1
 
1.0%
36.35 1
 
1.0%
27.87 1
 
1.0%
29.88 1
 
1.0%
19.28 1
 
1.0%
43.56 1
 
1.0%
160.29 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
3.35 1
1.0%
4.69 1
1.0%
6.35 1
1.0%
7.28 1
1.0%
7.37 1
1.0%
7.77 1
1.0%
8.17 1
1.0%
9.25 1
1.0%
9.38 1
1.0%
10.09 1
1.0%
ValueCountFrequency (%)
474.87 1
1.0%
419.8 1
1.0%
328.74 1
1.0%
326.22 1
1.0%
299.36 1
1.0%
290.91 1
1.0%
285.74 1
1.0%
221.68 1
1.0%
193.99 1
1.0%
184.24 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean509857.49
Minimum11622.87
Maximum2467608.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:31:50.297245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11622.87
5-th percentile31865.132
Q1138298.59
median338049.79
Q3810536.5
95-th percentile1412666.7
Maximum2467608.8
Range2455985.9
Interquartile range (IQR)672237.91

Descriptive statistics

Standard deviation488784.71
Coefficient of variation (CV)0.95866928
Kurtosis2.9893994
Mean509857.49
Median Absolute Deviation (MAD)249973.43
Skewness1.565196
Sum50985749
Variance2.3891049 × 1011
MonotonicityNot monotonic
2023-12-10T19:31:50.590008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
913546.11 1
 
1.0%
109253.02 1
 
1.0%
999560.79 1
 
1.0%
118575.69 1
 
1.0%
117569.01 1
 
1.0%
102593.13 1
 
1.0%
109440.88 1
 
1.0%
127777.91 1
 
1.0%
144082.52 1
 
1.0%
1245635.12 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
11622.87 1
1.0%
14066.63 1
1.0%
25821.12 1
1.0%
26551.54 1
1.0%
30444.54 1
1.0%
31939.9 1
1.0%
38123.7 1
1.0%
39416.07 1
1.0%
41856.8 1
1.0%
51690.52 1
1.0%
ValueCountFrequency (%)
2467608.82 1
1.0%
2307902.9 1
1.0%
1585613.69 1
1.0%
1518637.04 1
1.0%
1456104.71 1
1.0%
1410380.5 1
1.0%
1355761.83 1
1.0%
1314797.9 1
1.0%
1245635.12 1
1.0%
1220480.94 1
1.0%

주소
Text

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:31:51.056677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length10.96
Min length8

Characters and Unicode

Total characters1096
Distinct characters104
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%
순창 14
 
3.5%
장수 12
 
3.0%
완주 12
 
3.0%
정읍 10
 
2.5%
임실 10
 
2.5%
김제 10
 
2.5%
부안 8
 
2.0%
무주 6
 
1.5%
익산 6
 
1.5%
Other values (96) 210
52.8%
2023-12-10T19:31:51.768914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
298
27.2%
전 102
 
9.3%
북 100
 
9.1%
산 30
 
2.7%
창 24
 
2.2%
주 22
 
2.0%
천 18
 
1.6%
정 18
 
1.6%
안 16
 
1.5%
동 16
 
1.5%
Other values (94) 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 (%)
전 102
 
12.8%
북 100
 
12.5%
산 30
 
3.8%
창 24
 
3.0%
주 22
 
2.8%
천 18
 
2.3%
정 18
 
2.3%
안 16
 
2.0%
동 16
 
2.0%
구 16
 
2.0%
Other values (93) 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 (%)
전 102
 
12.8%
북 100
 
12.5%
산 30
 
3.8%
창 24
 
3.0%
주 22
 
2.8%
천 18
 
2.3%
정 18
 
2.3%
안 16
 
2.0%
동 16
 
2.0%
구 16
 
2.0%
Other values (93) 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 (%)
전 102
 
12.8%
북 100
 
12.5%
산 30
 
3.8%
창 24
 
3.0%
주 22
 
2.8%
천 18
 
2.3%
정 18
 
2.3%
안 16
 
2.0%
동 16
 
2.0%
구 16
 
2.0%
Other values (93) 436
54.6%

Interactions

2023-12-10T19:31:38.863903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:26.800554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:28.337521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:29.710766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:31.718676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:33.164192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:34.645169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:35.988318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:37.436116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:39.008787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:27.006620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:28.481818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:29.871076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:31.946528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:33.315640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:34.769338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:36.126085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:37.575796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:39.179869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:27.180862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:28.634368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:30.052248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:32.122451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:33.470272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:34.931673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:36.320234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:37.756617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:39.391792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:27.338027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:28.805545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:30.265362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:32.294599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:33.641815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:35.139137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:36.488184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:37.937424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:39.567528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:27.490946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:28.960039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:30.827393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:32.437877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:33.787267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:35.296854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:36.634659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:38.088831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:39.714079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:27.663360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:29.115700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:31.014596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:32.571620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:33.927464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:35.423449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:36.775368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:38.232987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:39.862853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:27.838142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:29.262727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:31.243111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:32.711548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:34.089815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:35.550672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:36.983920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:38.404741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:40.010553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:27.993665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:29.425232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:31.392146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:32.850559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:34.234235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:35.690914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:37.121749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:38.555128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:40.184951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:28.160871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:29.560062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:31.546281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:32.996837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:34.399266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:35.832896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:37.275674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:31:38.709788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:31:52.337534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0001.0000.7290.8180.8800.5280.5130.5660.3900.5411.000
지점1.0001.0000.0001.0001.0001.0001.0000.9770.9760.9750.9010.9561.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간1.0001.0000.0001.0001.0001.0001.0000.9770.9760.9750.9010.9561.000
연장0.7291.0000.0001.0001.0000.6500.6470.4240.4300.4030.2600.4571.000
좌표위치위도0.8181.0000.0001.0000.6501.0000.7940.5650.5710.5450.4270.5661.000
좌표위치경도0.8801.0000.0001.0000.6470.7941.0000.4700.4310.4240.0000.4001.000
co0.5280.9770.0000.9770.4240.5650.4701.0000.9720.9800.8620.9900.977
nox0.5130.9760.0000.9760.4300.5710.4310.9721.0000.9910.9080.9730.976
hc0.5660.9750.0000.9750.4030.5450.4240.9800.9911.0000.8770.9770.975
pm0.3900.9010.0000.9010.2600.4270.0000.8620.9080.8771.0000.8660.901
co20.5410.9560.0000.9560.4570.5660.4000.9900.9730.9770.8661.0000.956
주소1.0001.0000.0001.0001.0001.0001.0000.9770.9760.9750.9010.9561.000
2023-12-10T19:31:52.597184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향
기본키1.0000.0920.009-0.362-0.161-0.199-0.169-0.232-0.1620.000
연장0.0921.0000.012-0.123-0.108-0.111-0.091-0.120-0.1130.000
좌표위치위도0.0090.0121.000-0.0110.3080.2800.2970.2270.3080.000
좌표위치경도-0.362-0.123-0.0111.000-0.267-0.235-0.267-0.205-0.2620.000
co-0.161-0.1080.308-0.2671.0000.9890.9960.9490.9990.000
nox-0.199-0.1110.280-0.2350.9891.0000.9950.9760.9880.000
hc-0.169-0.0910.297-0.2670.9960.9951.0000.9620.9940.000
pm-0.232-0.1200.227-0.2050.9490.9760.9621.0000.9440.000
co2-0.162-0.1130.308-0.2620.9990.9880.9940.9441.0000.000
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T19:31:40.451570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:31:41.232355image/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-금구11.120210301035.66929126.968283554.832875.38379.01147.12913546.11전북 정읍 옹동 오성
12건기연[0114-1]2태인-금구11.120210301035.66929126.968283115.992368.75316.99118.95804641.09전북 정읍 옹동 오성
23건기연[0115-1]1정읍-태인6.420210301035.62947126.903444131.773325.61458.81175.11049265.31전북 정읍 정우 우산
34건기연[0115-1]2정읍-태인6.420210301035.62947126.903443865.113085.52412.41193.99993270.99전북 정읍 정우 우산
45건기연[0116-2]1금산-전주4.320210301035.78758127.03515980.745122.15671.5290.911518637.04전북 김제 금구 대화
56건기연[0116-2]2금산-전주4.320210301035.78758127.03515680.285125.01638.53328.741456104.71전북 김제 금구 대화
67건기연[0117-3]1김제IC-전주5.120210301035.79995127.058225958.515400.12677.75285.741585613.69전북 완주 이서 이성
78건기연[0117-3]2김제IC-전주5.120210301035.79995127.058224969.014267.27549.14221.681314797.9전북 완주 이서 이성
89건기연[0120-1]1전주-삼례3.220210301035.91078127.05471916.631069.32167.9912.54504842.03전북 완주 삼례 후정
910건기연[0120-1]2전주-삼례3.220210301035.91078127.05472395.871838.07267.1997.46609424.49전북 완주 삼례 후정
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2912-1]1만경-백산4.620210301035.84681126.849011375.391156.81170.9464.42341763.14전북 김제 만경 대동
9192건기연[2912-1]2만경-백산4.620210301035.84681126.849011256.08940.54139.0242.75318321.42전북 김제 만경 대동
9293건기연[3003-0]1변산-하서3.620210301035.72216126.645982320.251546.07225.1250.27602540.0전북 부안 하서 청호
9394건기연[3003-0]2변산-하서3.620210301035.72216126.645982022.61416.04194.7664.23526662.28전북 부안 하서 청호
9495건기연[3005-3]1부안IC-화호3.720210301035.72314126.7879894.43643.8189.1533.66232234.22전북 부안 백산 덕신
9596건기연[3005-3]2부안IC-화호3.720210301035.72314126.7879851.5593.9784.9932.98221447.58전북 부안 백산 덕신
9697건기연[3006-1]1신태인-태인6.520210301035.68032126.915271265.19933.1134.4759.81326016.67전북 정읍 신태인 궁사
9798건기연[3006-1]2신태인-태인6.520210301035.68032126.915271164.24902.01130.2259.29297255.32전북 정읍 신태인 궁사
9899건기연[3010-0]1산내-강진15.720210301035.52124127.13982151.03111.714.817.2839416.07전북 임실 덕치 회문
99100건기연[3010-0]2산내-강진15.720210301035.52124127.13982150.93116.0817.546.3538123.7전북 임실 덕치 회문