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
Categorical6
Text1

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
주소 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
기본키 is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
연장 is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
좌표위치위도 is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
좌표위치경도 is highly overall correlated with 측정구간 and 1 other fieldsHigh correlation
co is highly overall correlated with nox and 4 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 5 other fieldsHigh correlation
기본키 has unique valuesUnique
co has 10 (10.0%) zerosZeros
nox has 10 (10.0%) zerosZeros
hc has 10 (10.0%) zerosZeros
pm has 17 (17.0%) zerosZeros
co2 has 10 (10.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:07:08.850375
Analysis finished2023-12-10 11:07:26.387228
Duration17.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  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
2023-12-10T20:07:26.515523image/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
2023-12-10T20:07:26.765899image/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

2023-12-10T20:07:26.982465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:27.111961image/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
2023-12-10T20:07:27.407429image/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%
2605-0 2
 
2.0%
2909-1 2
 
2.0%
2115-1 2
 
2.0%
2204-0 2
 
2.0%
2205-3 2
 
2.0%
2313-2 2
 
2.0%
2316-0 2
 
2.0%
2317-0 2
 
2.0%
2320-2 2
 
2.0%
Other values (40) 80
80.0%
2023-12-10T20:07:28.107423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 144
17.9%
2 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 96
11.9%
3 36
 
4.5%
7 36
 
4.5%
9 28
 
3.5%
6 22
 
2.7%
Other values (3) 34
 
4.2%

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%
2 108
21.4%
0 96
19.0%
3 36
 
7.1%
7 36
 
7.1%
9 28
 
5.6%
6 22
 
4.4%
4 16
 
3.2%
5 12
 
2.4%
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%
2 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 96
11.9%
3 36
 
4.5%
7 36
 
4.5%
9 28
 
3.5%
6 22
 
2.7%
Other values (3) 34
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 144
17.9%
2 108
13.4%
[ 100
12.4%
- 100
12.4%
] 100
12.4%
0 96
11.9%
3 36
 
4.5%
7 36
 
4.5%
9 28
 
3.5%
6 22
 
2.7%
Other values (3) 34
 
4.2%

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

2023-12-10T20:07:28.334426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:28.486741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 50
50.0%
2 50
50.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
순창-남원
 
6
번암-장계
 
4
군산-대야
 
4
순창-덕치
 
4
오수-임실
 
2
Other values (40)
80 

Length

Max length7
Median length5
Mean length5.08
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row태인-금구
2nd row태인-금구
3rd row정읍-태인
4th row정읍-태인
5th row금산-전주

Common Values

ValueCountFrequency (%)
순창-남원 6
 
6.0%
번암-장계 4
 
4.0%
군산-대야 4
 
4.0%
순창-덕치 4
 
4.0%
오수-임실 2
 
2.0%
금산-전주 2
 
2.0%
김제IC-전주 2
 
2.0%
전주-삼례 2
 
2.0%
금마-연무 2
 
2.0%
고원-삼계 2
 
2.0%
Other values (35) 70
70.0%

Length

2023-12-10T20:07:29.027835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
순창-남원 6
 
6.0%
군산-대야 4
 
4.0%
순창-덕치 4
 
4.0%
번암-장계 4
 
4.0%
공덕-완주 2
 
2.0%
정읍-부안 2
 
2.0%
태인-금구 2
 
2.0%
연장-오천 2
 
2.0%
팔형치-공음 2
 
2.0%
고창-흥덕 2
 
2.0%
Other values (35) 70
70.0%

연장
Real number (ℝ)

HIGH CORRELATION 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.542
Minimum0.9
Maximum18.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:29.254694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile2.4
Q14.9
median7
Q39.2
95-th percentile14.6
Maximum18.9
Range18
Interquartile range (IQR)4.3

Descriptive statistics

Standard deviation4.0194522
Coefficient of variation (CV)0.53294248
Kurtosis0.27818798
Mean7.542
Median Absolute Deviation (MAD)2.15
Skewness0.71364062
Sum754.2
Variance16.155996
MonotonicityNot monotonic
2023-12-10T20:07:29.494688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
8.0 4
 
4.0%
6.0 4
 
4.0%
4.9 4
 
4.0%
2.4 4
 
4.0%
8.5 4
 
4.0%
8.7 4
 
4.0%
5.4 4
 
4.0%
11.1 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%
3.0 2
2.0%
3.2 2
2.0%
3.3 2
2.0%
3.4 2
2.0%
4.1 2
2.0%
4.3 2
2.0%
ValueCountFrequency (%)
18.9 2
2.0%
17.3 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%
11.5 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

2023-12-10T20:07:29.713176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:29.866822image/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

2023-12-10T20:07:30.036893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:07:30.193410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

좌표위치위도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.679121
Minimum35.31836
Maximum36.05245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:30.358566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.31836
5-th percentile35.38211
Q135.48989
median35.695695
Q335.87978
95-th percentile35.97732
Maximum36.05245
Range0.73409
Interquartile range (IQR)0.38989

Descriptive statistics

Standard deviation0.20475796
Coefficient of variation (CV)0.0057388735
Kurtosis-1.212975
Mean35.679121
Median Absolute Deviation (MAD)0.18911
Skewness-0.0035158343
Sum3567.9121
Variance0.041925822
MonotonicityNot monotonic
2023-12-10T20:07:30.607132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.66929 2
 
2.0%
35.77224 2
 
2.0%
35.52961 2
 
2.0%
35.44964 2
 
2.0%
35.467 2
 
2.0%
35.69967 2
 
2.0%
35.75539 2
 
2.0%
35.97701 2
 
2.0%
35.9615 2
 
2.0%
35.98108 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
35.31836 2
2.0%
35.36379 2
2.0%
35.38211 2
2.0%
35.38415 2
2.0%
35.39881 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%
ValueCountFrequency (%)
36.05245 2
2.0%
35.98108 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%

좌표위치경도
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.13812
Minimum126.5004
Maximum127.67801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:30.925520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5004
5-th percentile126.69676
Q1126.91023
median127.13348
Q3127.32352
95-th percentile127.59682
Maximum127.67801
Range1.17761
Interquartile range (IQR)0.41329

Descriptive statistics

Standard deviation0.29431485
Coefficient of variation (CV)0.0023149221
Kurtosis-0.73212861
Mean127.13812
Median Absolute Deviation (MAD)0.206645
Skewness0.0085936673
Sum12713.812
Variance0.08662123
MonotonicityNot monotonic
2023-12-10T20:07:31.217113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.96828 2
 
2.0%
127.4985 2
 
2.0%
126.59317 2
 
2.0%
126.5004 2
 
2.0%
126.6981 2
 
2.0%
126.69676 2
 
2.0%
126.75919 2
 
2.0%
126.91023 2
 
2.0%
126.77112 2
 
2.0%
126.7716 2
 
2.0%
Other values (40) 80
80.0%
ValueCountFrequency (%)
126.5004 2
2.0%
126.59317 2
2.0%
126.69676 2
2.0%
126.6981 2
2.0%
126.75919 2
2.0%
126.77112 2
2.0%
126.7716 2
2.0%
126.77892 2
2.0%
126.83701 2
2.0%
126.88133 2
2.0%
ValueCountFrequency (%)
127.67801 2
2.0%
127.65033 2
2.0%
127.59682 2
2.0%
127.57067 2
2.0%
127.56884 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%

co
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.2982
Minimum0
Maximum225.38
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:31.482456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.1475
median14.33
Q335.19
95-th percentile96.624
Maximum225.38
Range225.38
Interquartile range (IQR)32.0425

Descriptive statistics

Standard deviation37.144845
Coefficient of variation (CV)1.4124482
Kurtosis10.431395
Mean26.2982
Median Absolute Deviation (MAD)12.55
Skewness2.8473304
Sum2629.82
Variance1379.7395
MonotonicityNot monotonic
2023-12-10T20:07:31.725598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
3.31 2
 
2.0%
1.78 2
 
2.0%
5.88 2
 
2.0%
2.1 2
 
2.0%
41.93 1
 
1.0%
18.08 1
 
1.0%
25.82 1
 
1.0%
34.77 1
 
1.0%
31.33 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.44 1
 
1.0%
0.52 1
 
1.0%
0.74 1
 
1.0%
1.05 1
 
1.0%
1.57 1
 
1.0%
1.73 1
 
1.0%
1.78 2
 
2.0%
1.95 1
 
1.0%
2.05 1
 
1.0%
ValueCountFrequency (%)
225.38 1
1.0%
179.03 1
1.0%
127.74 1
1.0%
115.32 1
1.0%
107.34 1
1.0%
96.06 1
1.0%
86.89 1
1.0%
79.96 1
1.0%
76.41 1
1.0%
63.5 1
1.0%

nox
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.5298
Minimum0
Maximum248.25
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:31.958081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.0475
median9.295
Q331.2775
95-th percentile77.774
Maximum248.25
Range248.25
Interquartile range (IQR)29.23

Descriptive statistics

Standard deviation35.012234
Coefficient of variation (CV)1.626222
Kurtosis21.168127
Mean21.5298
Median Absolute Deviation (MAD)8.845
Skewness4.0071026
Sum2152.98
Variance1225.8565
MonotonicityNot monotonic
2023-12-10T20:07:32.214967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
2.06 2
 
2.0%
1.33 2
 
2.0%
4.6 2
 
2.0%
1.11 2
 
2.0%
30.9 1
 
1.0%
0.55 1
 
1.0%
34.77 1
 
1.0%
22.31 1
 
1.0%
40.88 1
 
1.0%
Other values (77) 77
77.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.24 1
 
1.0%
0.28 1
 
1.0%
0.55 1
 
1.0%
0.77 1
 
1.0%
0.83 1
 
1.0%
0.96 1
 
1.0%
1.11 2
 
2.0%
1.23 1
 
1.0%
1.33 2
 
2.0%
ValueCountFrequency (%)
248.25 1
1.0%
183.32 1
1.0%
84.29 1
1.0%
83.12 1
1.0%
80.32 1
1.0%
77.64 1
1.0%
69.87 1
1.0%
61.51 1
1.0%
49.91 1
1.0%
49.21 1
1.0%

hc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.007
Minimum0
Maximum31.63
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:32.457227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3075
median1.43
Q34.2525
95-th percentile11.0255
Maximum31.63
Range31.63
Interquartile range (IQR)3.945

Descriptive statistics

Standard deviation4.6789007
Coefficient of variation (CV)1.5560029
Kurtosis17.056618
Mean3.007
Median Absolute Deviation (MAD)1.295
Skewness3.5885971
Sum300.7
Variance21.892112
MonotonicityNot monotonic
2023-12-10T20:07:32.749648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
0.18 5
 
5.0%
0.31 2
 
2.0%
0.55 2
 
2.0%
1.94 2
 
2.0%
1.38 2
 
2.0%
4.57 1
 
1.0%
0.09 1
 
1.0%
4.7 1
 
1.0%
3.13 1
 
1.0%
Other values (73) 73
73.0%
ValueCountFrequency (%)
0.0 10
10.0%
0.03 1
 
1.0%
0.04 1
 
1.0%
0.09 1
 
1.0%
0.1 1
 
1.0%
0.13 1
 
1.0%
0.17 1
 
1.0%
0.18 5
5.0%
0.2 1
 
1.0%
0.22 1
 
1.0%
ValueCountFrequency (%)
31.63 1
1.0%
23.96 1
1.0%
12.96 1
1.0%
12.85 1
1.0%
11.51 1
1.0%
11.0 1
1.0%
9.49 1
1.0%
9.18 1
1.0%
7.72 1
1.0%
6.99 1
1.0%

pm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2289
Minimum0
Maximum13.72
Zeros17
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:32.978960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.13
median0.54
Q31.8
95-th percentile4.2875
Maximum13.72
Range13.72
Interquartile range (IQR)1.67

Descriptive statistics

Standard deviation2.0032372
Coefficient of variation (CV)1.630106
Kurtosis19.205587
Mean1.2289
Median Absolute Deviation (MAD)0.54
Skewness3.8488063
Sum122.89
Variance4.0129594
MonotonicityNot monotonic
2023-12-10T20:07:33.228945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 17
 
17.0%
0.13 12
 
12.0%
0.14 8
 
8.0%
0.28 3
 
3.0%
0.68 2
 
2.0%
1.94 2
 
2.0%
1.08 2
 
2.0%
0.42 2
 
2.0%
0.54 2
 
2.0%
0.44 2
 
2.0%
Other values (45) 48
48.0%
ValueCountFrequency (%)
0.0 17
17.0%
0.13 12
12.0%
0.14 8
8.0%
0.23 1
 
1.0%
0.27 2
 
2.0%
0.28 3
 
3.0%
0.39 1
 
1.0%
0.4 1
 
1.0%
0.42 2
 
2.0%
0.44 2
 
2.0%
ValueCountFrequency (%)
13.72 1
1.0%
10.74 1
1.0%
5.18 1
1.0%
4.98 1
1.0%
4.62 1
1.0%
4.27 1
1.0%
4.16 1
1.0%
4.07 1
1.0%
3.79 1
1.0%
2.56 1
1.0%

co2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6483.7786
Minimum0
Maximum53802.9
Zeros10
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:07:33.495106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1838.67
median3780.17
Q38683.7225
95-th percentile22998.844
Maximum53802.9
Range53802.9
Interquartile range (IQR)7845.0525

Descriptive statistics

Standard deviation8984.538
Coefficient of variation (CV)1.3856947
Kurtosis9.7554687
Mean6483.7786
Median Absolute Deviation (MAD)3317.25
Skewness2.7523482
Sum648377.86
Variance80721922
MonotonicityNot monotonic
2023-12-10T20:07:33.733106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 10
 
10.0%
873.34 2
 
2.0%
462.92 2
 
2.0%
554.74 2
 
2.0%
10759.11 1
 
1.0%
7279.71 1
 
1.0%
5904.86 1
 
1.0%
8622.34 1
 
1.0%
8217.65 1
 
1.0%
1844.14 1
 
1.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
0.0 10
10.0%
127.15 1
 
1.0%
138.68 1
 
1.0%
185.55 1
 
1.0%
277.37 1
 
1.0%
396.2 1
 
1.0%
416.06 1
 
1.0%
457.29 1
 
1.0%
461.05 1
 
1.0%
462.92 2
 
2.0%
ValueCountFrequency (%)
53802.9 1
1.0%
43029.76 1
1.0%
30489.87 1
1.0%
29941.21 1
1.0%
24756.62 1
1.0%
22906.33 1
1.0%
22235.59 1
1.0%
20331.47 1
1.0%
16007.88 1
1.0%
15026.67 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전북 군산 개정 아동
 
4
전북 정읍 정우 우산
 
2
전북 김제 금구 대화
 
2
전북 완주 이서 이성
 
2
전북 완주 삼례 후정
 
2
Other values (44)
88 

Length

Max length11
Median length11
Mean length10.94
Min length8

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

Common Values

ValueCountFrequency (%)
전북 군산 개정 아동 4
 
4.0%
전북 정읍 정우 우산 2
 
2.0%
전북 김제 금구 대화 2
 
2.0%
전북 완주 이서 이성 2
 
2.0%
전북 완주 삼례 후정 2
 
2.0%
전북 익산 여산 제남 2
 
2.0%
전북 순창 적성 괴정 2
 
2.0%
전북 순창 유등 건곡 2
 
2.0%
전북 남원 대산 풍촌 2
 
2.0%
전북 임실 삼계 후천 2
 
2.0%
Other values (39) 78
78.0%

Length

2023-12-10T20:07:34.189147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전북 100
25.1%
순창 16
 
4.0%
장수 14
 
3.5%
완주 12
 
3.0%
임실 10
 
2.5%
정읍 8
 
2.0%
김제 8
 
2.0%
익산 6
 
1.5%
고창 6
 
1.5%
군산 6
 
1.5%
Other values (94) 212
53.3%

Interactions

2023-12-10T20:07:24.316772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:10.090459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.900010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:13.812861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:15.642720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:17.194123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:19.485945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:21.091301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:22.668649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:24.470355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:10.270297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.097705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:13.981266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:15.791822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:17.408413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:19.679846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:21.227839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:22.866160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:24.667518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:10.523903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.335979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:14.174305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:15.965659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:17.673718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:19.862945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:21.381104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:23.121326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:24.859073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:10.771103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.590353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:14.359519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:16.178318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:18.444335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:20.077199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:21.606715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:23.325554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:25.024676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:10.986045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.792245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:14.559556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:16.367651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:18.671388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:20.274110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:21.792252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:23.534485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:25.163935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.165910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:12.962983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:14.744200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:16.543571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:18.847233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:20.421785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:21.942891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:23.678829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:25.307833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.340680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:13.208472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:14.964340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:16.696221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:18.987048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:20.588543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:22.109509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:23.828331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:25.462576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.525065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:13.433836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:15.205051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:16.861857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:19.138735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:20.785258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:22.328915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:24.013748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:25.637416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:11.727104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:13.637002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:15.450121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:17.028591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:19.306059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:20.950603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:22.490824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:07:24.173055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:07:34.479619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향측정구간연장좌표위치위도좌표위치경도conoxhcpmco2주소
기본키1.0001.0000.0000.9990.5920.7790.8980.4900.5070.3790.4950.5100.998
지점1.0001.0000.0001.0001.0001.0001.0000.8990.7970.8500.8390.9251.000
방향0.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
측정구간0.9991.0000.0001.0000.9980.9900.9980.9160.8420.8760.8710.9351.000
연장0.5921.0000.0000.9981.0000.5010.5230.5280.4350.3810.3630.5350.998
좌표위치위도0.7791.0000.0000.9900.5011.0000.7960.5160.5540.4760.5270.5380.997
좌표위치경도0.8981.0000.0000.9980.5230.7961.0000.3570.3310.2210.3710.3951.000
co0.4900.8990.0000.9160.5280.5160.3571.0000.9570.9190.9320.9960.904
nox0.5070.7970.0000.8420.4350.5540.3310.9571.0000.9480.9970.9490.805
hc0.3790.8500.0000.8760.3810.4760.2210.9190.9481.0000.9320.9220.872
pm0.4950.8390.0000.8710.3630.5270.3710.9320.9970.9321.0000.9270.844
co20.5100.9250.0000.9350.5350.5380.3950.9960.9490.9220.9271.0000.929
주소0.9981.0000.0001.0000.9980.9971.0000.9040.8050.8720.8440.9291.000
2023-12-10T20:07:34.789714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주소방향측정구간
주소1.0000.0000.963
방향0.0001.0000.000
측정구간0.9630.0001.000
2023-12-10T20:07:34.948734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키연장좌표위치위도좌표위치경도conoxhcpmco2방향측정구간주소
기본키1.0000.1200.077-0.283-0.034-0.056-0.059-0.046-0.0310.0000.7660.736
연장0.1201.000-0.043-0.147-0.047-0.057-0.051-0.047-0.0380.0000.7110.733
좌표위치위도0.077-0.0431.000-0.0780.4300.4470.4390.4630.4290.0000.7000.727
좌표위치경도-0.283-0.147-0.0781.000-0.292-0.285-0.289-0.308-0.2980.0000.7520.753
co-0.034-0.0470.430-0.2921.0000.9900.9930.9580.9980.0000.5060.461
nox-0.056-0.0570.447-0.2850.9901.0000.9970.9750.9860.0000.4170.363
hc-0.059-0.0510.439-0.2890.9930.9971.0000.9670.9880.0000.4520.402
pm-0.046-0.0470.463-0.3080.9580.9750.9671.0000.9560.0000.4540.404
co2-0.031-0.0380.429-0.2980.9980.9860.9880.9561.0000.0000.5460.507
방향0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
측정구간0.7660.7110.7000.7520.5060.4170.4520.4540.5460.0001.0000.963
주소0.7360.7330.7270.7530.4610.3630.4020.4040.5070.0000.9631.000

Missing values

2023-12-10T20:07:25.885217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:07:26.256770image/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-금구11.120210201035.66929126.9682841.9330.94.572.0910759.11전북 정읍 옹동 오성
12건기연[0114-1]2태인-금구11.120210201035.66929126.9682840.8437.374.812.1310931.68전북 정읍 옹동 오성
23건기연[0115-1]1정읍-태인6.420210201035.62947126.9034463.541.036.672.3415026.67전북 정읍 정우 우산
34건기연[0115-1]2정읍-태인6.420210201035.62947126.9034454.4349.916.493.7914440.88전북 정읍 정우 우산
45건기연[0116-2]1금산-전주4.320210201035.78758127.035186.8969.879.494.0722235.59전북 김제 금구 대화
56건기연[0116-2]2금산-전주4.320210201035.78758127.035196.0677.6411.05.1822906.33전북 김제 금구 대화
67건기연[0117-3]1김제IC-전주5.120210201035.79995127.0582279.9661.519.184.1620331.47전북 완주 이서 이성
78건기연[0117-3]2김제IC-전주5.120210201035.79995127.05822107.3484.2912.964.9824756.62전북 완주 이서 이성
89건기연[0120-1]1전주-삼례3.220210201035.91078127.054733.3727.754.381.797608.71전북 완주 삼례 후정
910건기연[0120-1]2전주-삼례3.220210201035.91078127.054738.2339.926.082.258867.87전북 완주 삼례 후정
기본키도로종류지점방향측정구간연장측정일측정시간좌표위치위도좌표위치경도conoxhcpmco2주소
9091건기연[2710-0]1삼례-익산18.920210201035.9058127.00297115.3283.1211.514.6229941.21전북 익산 춘포 덕실
9192건기연[2710-0]2삼례-익산18.920210201035.9058127.00297127.7480.3212.854.2730489.87전북 익산 춘포 덕실
9293건기연[2711-0]1익산-임피5.420210201035.97553126.8918651.2138.775.41.8313081.39전북 군산 임피 영창
9394건기연[2711-0]2익산-임피5.420210201035.97553126.8918653.839.756.022.0413667.58전북 군산 임피 영창
9495건기연[2907-3]1답동-부무5.720210201035.48989126.988440.440.240.030.0127.15전북 순창 쌍치 금평
9596건기연[2907-3]2답동-부무5.720210201035.48989126.988440.00.00.00.00.0전북 순창 쌍치 금평
9697건기연[2909-1]1정읍-부안3.320210201035.60581126.7789219.911.881.940.74769.8전북 정읍 고부 입석
9798건기연[2909-1]2정읍-부안3.320210201035.60581126.7789215.849.051.510.423783.97전북 정읍 고부 입석
9899건기연[2911-0]1화호-김제11.720210201035.7375126.837011.781.330.180.14462.92전북 김제 부량 대평
99100건기연[2911-0]2화호-김제11.720210201035.7375126.837010.00.00.00.00.0전북 김제 부량 대평