Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 KiB
Average record size in memory149.3 B

Variable types

Numeric9
Categorical8

Alerts

도로종류 has constant value ""Constant
측정일 has constant value ""Constant
측정시간 has constant value ""Constant
지점 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
측정구간 is highly overall correlated with 기본키 and 7 other fieldsHigh correlation
주소 is highly overall correlated with 기본키 and 6 other fieldsHigh correlation
기본키 is highly overall correlated with 장비이정(km) and 5 other fieldsHigh correlation
장비이정(km) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
차량통과수(대) is highly overall correlated with TSP(g/km) and 1 other fieldsHigh correlation
위도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
경도(°) is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
기울기(°) is highly overall correlated with 지점 and 3 other fieldsHigh correlation
TSP(g/km) is highly overall correlated with 차량통과수(대) and 1 other fieldsHigh correlation
PM10(g/km) is highly overall correlated with 차량통과수(대) and 1 other fieldsHigh correlation
방향 is highly overall correlated with 기울기(°) and 1 other fieldsHigh correlation
기본키 has unique valuesUnique
차량통과수(대) has 16 (16.0%) zerosZeros
평균 속도(km/hr) has 16 (16.0%) zerosZeros
TSP(g/km) has 16 (16.0%) zerosZeros
PM10(g/km) has 18 (18.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:06:25.135552
Analysis finished2023-12-10 11:06:43.983948
Duration18.85 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
2023-12-10T20:06:44.171446image/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
2023-12-10T20:06:44.446621image/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
도로공사
100 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도로공사 100
100.0%

Length

2023-12-10T20:06:44.749435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:06:44.915734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로공사 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0010-1185E-8
8 
A-0010-1305E-8
8 
A-0010-1357E-8
8 
A-0010-1612E-8
8 
A-0010-3352E-9
8 
Other values (11)
60 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-0010-0083E-6
2nd rowA-0010-0083E-6
3rd rowA-0010-0083E-6
4th rowA-0010-0083E-6
5th rowA-0010-0083E-6

Common Values

ValueCountFrequency (%)
A-0010-1185E-8 8
 
8.0%
A-0010-1305E-8 8
 
8.0%
A-0010-1357E-8 8
 
8.0%
A-0010-1612E-8 8
 
8.0%
A-0010-3352E-9 8
 
8.0%
A-0010-0083E-6 6
 
6.0%
A-0010-0538E-6 6
 
6.0%
A-0010-0728S-6 6
 
6.0%
A-0010-1073E-6 6
 
6.0%
A-0010-2761E-6 6
 
6.0%
Other values (6) 30
30.0%

Length

2023-12-10T20:06:45.094872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0010-1185e-8 8
 
8.0%
a-0010-1305e-8 8
 
8.0%
a-0010-1357e-8 8
 
8.0%
a-0010-1612e-8 8
 
8.0%
a-0010-3352e-9 8
 
8.0%
a-0010-0083e-6 6
 
6.0%
a-0010-0538e-6 6
 
6.0%
a-0010-0728s-6 6
 
6.0%
a-0010-1073e-6 6
 
6.0%
a-0010-2761e-6 6
 
6.0%
Other values (6) 30
30.0%

방향
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
E
50 
S
50 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowE
2nd rowE
3rd rowE
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
E 50
50.0%
S 50
50.0%

Length

2023-12-10T20:06:45.320295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:06:45.489488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 50
50.0%
s 50
50.0%

차선
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
28 
2
28 
3
28 
4
14 
5
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 28
28.0%
2 28
28.0%
3 28
28.0%
4 14
14.0%
5 2
 
2.0%

Length

2023-12-10T20:06:45.700281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:06:45.885201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 28
28.0%
2 28
28.0%
3 28
28.0%
4 14
14.0%
5 2
 
2.0%

측정구간
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
남이JC-청주JC
 
5
청주JC-남이JC
 
5
금호JC-북대구IC
 
4
동대구JC-경산IC
 
4
경산IC-동대구JC
 
4
Other values (23)
78 

Length

Max length10
Median length9
Mean length9.4
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row노포JC-양산JC
2nd row노포JC-양산JC
3rd row노포JC-양산JC
4th row양산JC-노포JC
5th row양산JC-노포JC

Common Values

ValueCountFrequency (%)
남이JC-청주JC 5
 
5.0%
청주JC-남이JC 5
 
5.0%
금호JC-북대구IC 4
 
4.0%
동대구JC-경산IC 4
 
4.0%
경산IC-동대구JC 4
 
4.0%
도동JC-북대구IC 4
 
4.0%
북대구IC-금호JC 4
 
4.0%
천안IC-천안JC 4
 
4.0%
남구미IC-왜관IC 4
 
4.0%
청주JC-남청주IC 4
 
4.0%
Other values (18) 58
58.0%

Length

2023-12-10T20:06:46.094659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남이jc-청주jc 5
 
5.0%
청주jc-남이jc 5
 
5.0%
남구미ic-왜관ic 4
 
4.0%
왜관ic-남구미ic 4
 
4.0%
북대구ic-도동jc 4
 
4.0%
천안jc-천안ic 4
 
4.0%
청주jc-남청주ic 4
 
4.0%
남청주ic-청주jc 4
 
4.0%
천안ic-천안jc 4
 
4.0%
북대구ic-금호jc 4
 
4.0%
Other values (18) 58
58.0%

장비이정(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.5522
Minimum8.3
Maximum335.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:46.321671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.3
5-th percentile8.3
Q1118.5
median161.22
Q3298.1
95-th percentile335.2
Maximum335.2
Range326.9
Interquartile range (IQR)179.6

Descriptive statistics

Standard deviation105.84226
Coefficient of variation (CV)0.55255048
Kurtosis-1.4885677
Mean191.5522
Median Absolute Deviation (MAD)111.35
Skewness-0.062698063
Sum19155.22
Variance11202.584
MonotonicityIncreasing
2023-12-10T20:06:46.547620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
335.2 8
 
8.0%
118.5 8
 
8.0%
130.5 8
 
8.0%
135.7 8
 
8.0%
161.22 8
 
8.0%
8.3 6
 
6.0%
301.9 6
 
6.0%
306.8 6
 
6.0%
276.1 6
 
6.0%
53.4 6
 
6.0%
Other values (6) 30
30.0%
ValueCountFrequency (%)
8.3 6
6.0%
53.4 6
6.0%
72.8 6
6.0%
107.31 6
6.0%
118.5 8
8.0%
130.5 8
8.0%
135.7 8
8.0%
161.22 8
8.0%
276.1 6
6.0%
295.3 4
4.0%
ValueCountFrequency (%)
335.2 8
8.0%
306.8 6
6.0%
301.9 6
6.0%
298.7 5
5.0%
297.9 5
5.0%
295.6 4
4.0%
295.3 4
4.0%
276.1 6
6.0%
161.22 8
8.0%
135.7 8
8.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20220201
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20220201
2nd row20220201
3rd row20220201
4th row20220201
5th row20220201

Common Values

ValueCountFrequency (%)
20220201 100
100.0%

Length

2023-12-10T20:06:46.797299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:06:46.986173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20220201 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

2023-12-10T20:06:47.206262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:06:47.376539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

차량통과수(대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)51.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.91
Minimum0
Maximum180
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:47.605695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median12.5
Q338.25
95-th percentile78.4
Maximum180
Range180
Interquartile range (IQR)36.25

Descriptive statistics

Standard deviation30.272865
Coefficient of variation (CV)1.2152896
Kurtosis6.4146393
Mean24.91
Median Absolute Deviation (MAD)12
Skewness2.1043305
Sum2491
Variance916.44636
MonotonicityNot monotonic
2023-12-10T20:06:47.924744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
16.0%
2 7
 
7.0%
12 4
 
4.0%
13 4
 
4.0%
10 4
 
4.0%
21 4
 
4.0%
11 4
 
4.0%
6 3
 
3.0%
41 3
 
3.0%
1 3
 
3.0%
Other values (41) 48
48.0%
ValueCountFrequency (%)
0 16
16.0%
1 3
 
3.0%
2 7
7.0%
3 1
 
1.0%
4 1
 
1.0%
5 2
 
2.0%
6 3
 
3.0%
7 3
 
3.0%
8 2
 
2.0%
10 4
 
4.0%
ValueCountFrequency (%)
180 1
1.0%
111 1
1.0%
101 1
1.0%
88 1
1.0%
86 1
1.0%
78 1
1.0%
74 1
1.0%
72 1
1.0%
67 2
2.0%
63 1
1.0%

평균 속도(km/hr)
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.881
Minimum0
Maximum140
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:48.273639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180.8325
median91.625
Q399.7525
95-th percentile118.915
Maximum140
Range140
Interquartile range (IQR)18.92

Descriptive statistics

Standard deviation37.169196
Coefficient of variation (CV)0.46530709
Kurtosis0.82397938
Mean79.881
Median Absolute Deviation (MAD)8.855
Skewness-1.4126886
Sum7988.1
Variance1381.5491
MonotonicityNot monotonic
2023-12-10T20:06:49.003651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
98.0 4
 
4.0%
100.0 3
 
3.0%
96.0 3
 
3.0%
90.0 2
 
2.0%
93.0 2
 
2.0%
95.0 2
 
2.0%
94.5 2
 
2.0%
94.0 2
 
2.0%
101.0 2
 
2.0%
Other values (60) 62
62.0%
ValueCountFrequency (%)
0.0 16
16.0%
60.06 1
 
1.0%
66.66 1
 
1.0%
72.67 1
 
1.0%
73.0 1
 
1.0%
75.0 1
 
1.0%
79.67 2
 
2.0%
79.75 1
 
1.0%
80.33 1
 
1.0%
81.0 1
 
1.0%
ValueCountFrequency (%)
140.0 1
1.0%
128.0 1
1.0%
127.0 1
1.0%
124.44 1
1.0%
123.0 1
1.0%
118.7 1
1.0%
116.0 1
1.0%
115.0 1
1.0%
112.96 1
1.0%
111.0 1
1.0%

위도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.16489
Minimum35.306944
Maximum36.780833
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:49.281580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.306944
5-th percentile35.306944
Q135.904732
median36.0365
Q336.578819
95-th percentile36.780833
Maximum36.780833
Range1.4738889
Interquartile range (IQR)0.67408703

Descriptive statistics

Standard deviation0.42034035
Coefficient of variation (CV)0.011622885
Kurtosis-1.0370237
Mean36.16489
Median Absolute Deviation (MAD)0.35383278
Skewness-0.13939504
Sum3616.489
Variance0.17668601
MonotonicityNot monotonic
2023-12-10T20:06:49.509142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
35.90473241 16
16.0%
35.91622354 8
 
8.0%
36.0365 8
 
8.0%
36.78083333 8
 
8.0%
35.30694444 6
 
6.0%
35.68194444 6
 
6.0%
35.818274 6
 
6.0%
35.88230609 6
 
6.0%
36.38961 6
 
6.0%
36.60611111 6
 
6.0%
Other values (5) 24
24.0%
ValueCountFrequency (%)
35.30694444 6
 
6.0%
35.68194444 6
 
6.0%
35.818274 6
 
6.0%
35.88230609 6
 
6.0%
35.90473241 16
16.0%
35.91622354 8
8.0%
36.0365 8
8.0%
36.38961 6
 
6.0%
36.54 4
 
4.0%
36.55638889 4
 
4.0%
ValueCountFrequency (%)
36.78083333 8
8.0%
36.64019722 6
6.0%
36.60611111 6
6.0%
36.58444444 5
5.0%
36.57694444 5
5.0%
36.55638889 4
4.0%
36.54 4
4.0%
36.38961 6
6.0%
36.0365 8
8.0%
35.91622354 8
8.0%

경도(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.15112
Minimum127.17667
Maximum129.18111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:49.744194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.17667
5-th percentile127.17667
Q1127.42351
median128.41383
Q3128.61741
95-th percentile129.18111
Maximum129.18111
Range2.0044444
Interquartile range (IQR)1.1939049

Descriptive statistics

Standard deviation0.72677896
Coefficient of variation (CV)0.0056712652
Kurtosis-1.6741825
Mean128.15112
Median Absolute Deviation (MAD)0.74676975
Skewness-0.013756627
Sum12815.112
Variance0.52820765
MonotonicityNot monotonic
2023-12-10T20:06:49.962008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
128.5614716 16
16.0%
128.6174129 8
 
8.0%
128.4138333 8
 
8.0%
127.1766667 8
 
8.0%
129.0747222 6
 
6.0%
129.1811111 6
 
6.0%
129.140095 6
 
6.0%
128.8488161 6
 
6.0%
127.423508 6
 
6.0%
127.4083333 6
 
6.0%
Other values (5) 24
24.0%
ValueCountFrequency (%)
127.1766667 8
8.0%
127.3781278 6
 
6.0%
127.4083333 6
 
6.0%
127.423508 6
 
6.0%
127.4263889 5
 
5.0%
127.4277778 5
 
5.0%
127.4325 4
 
4.0%
127.4338889 4
 
4.0%
128.4138333 8
8.0%
128.5614716 16
16.0%
ValueCountFrequency (%)
129.1811111 6
 
6.0%
129.140095 6
 
6.0%
129.0747222 6
 
6.0%
128.8488161 6
 
6.0%
128.6174129 8
8.0%
128.5614716 16
16.0%
128.4138333 8
8.0%
127.4338889 4
 
4.0%
127.4325 4
 
4.0%
127.4277778 5
 
5.0%

기울기(°)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01314146
Minimum-3.15276
Maximum3.071416
Zeros0
Zeros (%)0.0%
Negative47
Negative (%)47.0%
Memory size1.0 KiB
2023-12-10T20:06:50.217004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.15276
5-th percentile-2.788625
Q1-0.807188
median0.166426
Q30.897883
95-th percentile2.703849
Maximum3.071416
Range6.224176
Interquartile range (IQR)1.705071

Descriptive statistics

Standard deviation1.3744688
Coefficient of variation (CV)104.59027
Kurtosis0.1525891
Mean0.01314146
Median Absolute Deviation (MAD)0.855122
Skewness-0.098643333
Sum1.314146
Variance1.8891646
MonotonicityNot monotonic
2023-12-10T20:06:50.455709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.166426 8
 
8.0%
-0.131348 8
 
8.0%
1.437416 5
 
5.0%
-1.758683 5
 
5.0%
0.897883 4
 
4.0%
-0.807188 4
 
4.0%
1.544389 4
 
4.0%
-1.359001 4
 
4.0%
0.685727 4
 
4.0%
-0.667441 4
 
4.0%
Other values (16) 50
50.0%
ValueCountFrequency (%)
-3.15276 3
3.0%
-2.788625 3
3.0%
-1.758683 5
5.0%
-1.717451 3
3.0%
-1.359001 4
4.0%
-0.868793 4
4.0%
-0.807188 4
4.0%
-0.688696 3
3.0%
-0.667441 4
4.0%
-0.550996 3
3.0%
ValueCountFrequency (%)
3.071416 3
3.0%
2.703849 3
3.0%
1.717451 3
3.0%
1.544389 4
4.0%
1.437416 5
5.0%
1.056036 4
4.0%
0.897883 4
4.0%
0.688696 3
3.0%
0.685727 4
4.0%
0.540588 3
3.0%

TSP(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8327
Minimum0
Maximum8.23
Zeros16
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:50.770248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.085
median0.395
Q31.0425
95-th percentile3.161
Maximum8.23
Range8.23
Interquartile range (IQR)0.9575

Descriptive statistics

Standard deviation1.2891645
Coefficient of variation (CV)1.548174
Kurtosis14.624922
Mean0.8327
Median Absolute Deviation (MAD)0.375
Skewness3.351175
Sum83.27
Variance1.6619452
MonotonicityNot monotonic
2023-12-10T20:06:51.110605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
16.0%
0.12 5
 
5.0%
0.02 4
 
4.0%
0.15 3
 
3.0%
2.1 2
 
2.0%
0.01 2
 
2.0%
0.39 2
 
2.0%
0.54 2
 
2.0%
0.49 2
 
2.0%
0.19 2
 
2.0%
Other values (56) 60
60.0%
ValueCountFrequency (%)
0.0 16
16.0%
0.01 2
 
2.0%
0.02 4
 
4.0%
0.03 1
 
1.0%
0.04 1
 
1.0%
0.07 1
 
1.0%
0.09 1
 
1.0%
0.11 1
 
1.0%
0.12 5
 
5.0%
0.15 3
 
3.0%
ValueCountFrequency (%)
8.23 1
1.0%
6.89 1
1.0%
3.57 1
1.0%
3.55 1
1.0%
3.18 1
1.0%
3.16 1
1.0%
2.52 1
1.0%
2.47 1
1.0%
2.27 1
1.0%
2.23 1
1.0%

PM10(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3663
Minimum0
Maximum3.62
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:06:51.495478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0375
median0.175
Q30.46
95-th percentile1.3905
Maximum3.62
Range3.62
Interquartile range (IQR)0.4225

Descriptive statistics

Standard deviation0.5671222
Coefficient of variation (CV)1.5482451
Kurtosis14.609872
Mean0.3663
Median Absolute Deviation (MAD)0.165
Skewness3.3486807
Sum36.63
Variance0.32162759
MonotonicityNot monotonic
2023-12-10T20:06:51.800971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 18
 
18.0%
0.05 6
 
6.0%
0.01 5
 
5.0%
0.07 4
 
4.0%
0.22 3
 
3.0%
0.17 3
 
3.0%
0.92 2
 
2.0%
0.1 2
 
2.0%
0.25 2
 
2.0%
0.16 2
 
2.0%
Other values (45) 53
53.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.01 5
 
5.0%
0.02 1
 
1.0%
0.03 1
 
1.0%
0.04 1
 
1.0%
0.05 6
 
6.0%
0.06 1
 
1.0%
0.07 4
 
4.0%
0.08 2
 
2.0%
0.1 2
 
2.0%
ValueCountFrequency (%)
3.62 1
1.0%
3.03 1
1.0%
1.57 1
1.0%
1.56 1
1.0%
1.4 1
1.0%
1.39 1
1.0%
1.11 1
1.0%
1.09 1
1.0%
1.0 1
1.0%
0.98 1
1.0%

주소
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
충북 청원군 남이면
13 
대구 동구 안심3동
8 
대구 북구 검단동
8 
대구 북구 관문동
8 
경북 칠곡군 석적읍 포남리
8 
Other values (9)
55 

Length

Max length15
Median length14
Mean length11.08
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경남 양산시 동면
2nd row경남 양산시 동면
3rd row경남 양산시 동면
4th row경남 양산시 동면
5th row경남 양산시 동면

Common Values

ValueCountFrequency (%)
충북 청원군 남이면 13
13.0%
대구 동구 안심3동 8
 
8.0%
대구 북구 검단동 8
 
8.0%
대구 북구 관문동 8
 
8.0%
경북 칠곡군 석적읍 포남리 8
 
8.0%
충청 천안시 구성동 8
 
8.0%
경남 양산시 동면 6
 
6.0%
울산 울주군 두서면 활천리 6
 
6.0%
경북 경주시 건천읍 모량리 6
 
6.0%
경북 경산시 진량읍 6
 
6.0%
Other values (4) 23
23.0%

Length

2023-12-10T20:06:52.096142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
충북 30
 
9.1%
대구 24
 
7.3%
청원군 24
 
7.3%
경북 20
 
6.0%
남이면 18
 
5.4%
북구 16
 
4.8%
석적읍 8
 
2.4%
구성동 8
 
2.4%
천안시 8
 
2.4%
충청 8
 
2.4%
Other values (26) 167
50.5%

Interactions

2023-12-10T20:06:41.623828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:26.730913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:28.597600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:30.556507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:32.459143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:34.244012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:36.037529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:38.172687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:40.090305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:41.807819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:26.872398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:28.772914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:30.816608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:32.630055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:34.408404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:36.208918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:38.325601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:40.273265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:41.997358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:27.035307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:29.043512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:31.125571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:32.815181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:34.587055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:36.376704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:38.537958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:40.437088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:42.176381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:27.587211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:29.236564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:31.356987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:32.998504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:34.765025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:36.563001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:38.736253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:40.597051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:42.371334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:27.750494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:29.465487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:31.534308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:33.236375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:35.013245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:36.741928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:38.974614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:40.758470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:42.575812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:27.918984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:29.664574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:31.766889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:33.413537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:35.189211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:36.972792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:39.217629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:40.937393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:42.812788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:28.102854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:29.920973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:31.966276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:33.649602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:35.391010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:37.217555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:39.598029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:41.134213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:43.011324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:28.274789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:30.147485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:32.128792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:33.879473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:35.587654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:37.416958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:39.755413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:41.289439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:43.164948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:28.414157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:30.337773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:32.276194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:34.059323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:35.789630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:37.579827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:39.907742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:06:41.438697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:06:52.294133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점방향차선측정구간장비이정(km)차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
기본키1.0000.9760.0000.0000.9890.9180.3140.3630.8900.9270.7260.0000.0000.963
지점0.9761.0000.2390.0001.0001.0000.5490.5071.0001.0000.9550.3160.3161.000
방향0.0000.2391.0000.0001.0000.0000.2730.1830.0000.0000.9190.0000.0000.000
차선0.0000.0000.0001.0000.0000.0000.2090.3930.0000.0000.0000.0700.0700.000
측정구간0.9891.0001.0000.0001.0001.0000.2940.6841.0001.0001.0000.0000.0001.000
장비이정(km)0.9181.0000.0000.0001.0001.0000.1540.3660.9270.9610.7150.0000.0001.000
차량통과수(대)0.3140.5490.2730.2090.2940.1541.0000.3720.2430.3840.2390.6460.6460.453
평균 속도(km/hr)0.3630.5070.1830.3930.6840.3660.3721.0000.5820.3470.2440.0000.0000.597
위도(°)0.8901.0000.0000.0001.0000.9270.2430.5821.0000.8630.7510.0000.0001.000
경도(°)0.9271.0000.0000.0001.0000.9610.3840.3470.8631.0000.6390.0000.0001.000
기울기(°)0.7260.9550.9190.0001.0000.7150.2390.2440.7510.6391.0000.0000.0000.862
TSP(g/km)0.0000.3160.0000.0700.0000.0000.6460.0000.0000.0000.0001.0001.0000.000
PM10(g/km)0.0000.3160.0000.0700.0000.0000.6460.0000.0000.0000.0001.0001.0000.000
주소0.9631.0000.0000.0001.0001.0000.4530.5971.0001.0000.8620.0000.0001.000
2023-12-10T20:06:52.578566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지점차선측정구간방향주소
지점1.0000.0000.9260.1700.988
차선0.0001.0000.0000.0000.000
측정구간0.9260.0001.0000.8570.915
방향0.1700.0000.8571.0000.000
주소0.9880.0000.9150.0001.000
2023-12-10T20:06:52.783754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키장비이정(km)차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)지점방향차선측정구간주소
기본키1.0000.998-0.072-0.4200.987-0.9640.031-0.039-0.0410.8540.0000.0000.8280.823
장비이정(km)0.9981.000-0.066-0.4100.989-0.9660.010-0.040-0.0420.9450.0000.0000.8750.957
차량통과수(대)-0.072-0.0661.0000.206-0.0650.083-0.1420.7920.7900.1480.1870.1460.0000.167
평균 속도(km/hr)-0.420-0.4100.2061.000-0.3930.352-0.0280.2510.2470.2770.1830.2040.3110.276
위도(°)0.9870.989-0.065-0.3931.000-0.9580.013-0.038-0.0390.9500.0000.0000.8800.962
경도(°)-0.964-0.9660.0830.352-0.9581.000-0.0060.0530.0560.9450.0000.0000.8750.957
기울기(°)0.0310.010-0.142-0.0280.013-0.0061.000-0.058-0.0640.6530.7310.0000.8850.593
TSP(g/km)-0.039-0.0400.7920.251-0.0380.053-0.0581.0000.9990.1370.0000.0360.0000.000
PM10(g/km)-0.041-0.0420.7900.247-0.0390.056-0.0640.9991.0000.1370.0000.0360.0000.000
지점0.8540.9450.1480.2770.9500.9450.6530.1370.1371.0000.1700.0000.9260.988
방향0.0000.0000.1870.1830.0000.0000.7310.0000.0000.1701.0000.0000.8570.000
차선0.0000.0000.1460.2040.0000.0000.0000.0360.0360.0000.0001.0000.0000.000
측정구간0.8280.8750.0000.3110.8800.8750.8850.0000.0000.9260.8570.0001.0000.915
주소0.8230.9570.1670.2760.9620.9570.5930.0000.0000.9880.0000.0000.9151.000

Missing values

2023-12-10T20:06:43.442899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:06:43.820780image/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

기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
01도로공사A-0010-0083E-6E1노포JC-양산JC8.320220201012106.035.306944129.074722-3.152762.10.92경남 양산시 동면
12도로공사A-0010-0083E-6E2노포JC-양산JC8.320220201034100.6735.306944129.074722-3.152761.130.5경남 양산시 동면
23도로공사A-0010-0083E-6E3노포JC-양산JC8.320220201014100.035.306944129.074722-3.152760.160.07경남 양산시 동면
34도로공사A-0010-0083E-6S1양산JC-노포JC8.320220201011127.035.306944129.0747223.0714160.120.05경남 양산시 동면
45도로공사A-0010-0083E-6S2양산JC-노포JC8.32022020103999.6735.306944129.0747223.0714160.750.33경남 양산시 동면
56도로공사A-0010-0083E-6S3양산JC-노포JC8.320220201012104.035.306944129.0747223.0714162.10.92경남 양산시 동면
67도로공사A-0010-0538E-6E1언양JC-활천IC53.420220201013109.035.681944129.1811110.225450.150.06울산 울주군 두서면 활천리
78도로공사A-0010-0538E-6E2언양JC-활천IC53.42022020102896.035.681944129.1811110.225450.40.18울산 울주군 두서면 활천리
89도로공사A-0010-0538E-6E3언양JC-활천IC53.42022020101083.035.681944129.1811110.225451.040.46울산 울주군 두서면 활천리
910도로공사A-0010-0538E-6S1활천IC-언양JC53.42022020106102.035.681944129.1811110.191580.070.03울산 울주군 두서면 활천리
기본키도로종류지점방향차선측정구간장비이정(km)측정일측정시간차량통과수(대)평균 속도(km/hr)위도(°)경도(°)기울기(°)TSP(g/km)PM10(g/km)주소
9091도로공사A-0010-3068E-6S2옥산IC-청주IC306.8202202010296.036.640197127.3781280.6886960.150.07충북 청원군 강내면
9192도로공사A-0010-3068E-6S3옥산IC-청주IC306.820220201000.036.640197127.3781280.6886960.00.0충북 청원군 강내면
9293도로공사A-0010-3352E-9E1천안JC-천안IC335.220220201027102.036.780833127.176667-1.3590011.780.78충청 천안시 구성동
9394도로공사A-0010-3352E-9E2천안JC-천안IC335.220220201000.036.780833127.176667-1.3590010.00.0충청 천안시 구성동
9495도로공사A-0010-3352E-9E3천안JC-천안IC335.220220201018079.6736.780833127.176667-1.3590013.161.39충청 천안시 구성동
9596도로공사A-0010-3352E-9E4천안JC-천안IC335.22022020107479.6736.780833127.176667-1.3590011.050.46충청 천안시 구성동
9697도로공사A-0010-3352E-9S1천안IC-천안JC335.220220201000.036.780833127.1766671.5443890.00.0충청 천안시 구성동
9798도로공사A-0010-3352E-9S2천안IC-천안JC335.2202202010184.036.780833127.1766671.5443890.010.0충청 천안시 구성동
9899도로공사A-0010-3352E-9S3천안IC-천안JC335.220220201000.036.780833127.1766671.5443890.00.0충청 천안시 구성동
99100도로공사A-0010-3352E-9S4천안IC-천안JC335.220220201000.036.780833127.1766671.5443890.00.0충청 천안시 구성동