Overview

Dataset statistics

Number of variables16
Number of observations238
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 KiB
Average record size in memory141.6 B

Variable types

Categorical5
Text1
Numeric10

Dataset

Description한국도로공사 고속도로 졸음쉼터 교통량(일별)관련 정보를 제공한다.(구분, 노선, 방향, 명칭, 차종, 합계)
URLhttps://www.data.go.kr/data/15101926/fileData.do

Alerts

총합계 is highly overall correlated with 1종 and 5 other fieldsHigh correlation
1종 is highly overall correlated with 총합계 and 3 other fieldsHigh correlation
3종 is highly overall correlated with 총합계 and 4 other fieldsHigh correlation
4종 is highly overall correlated with 총합계 and 6 other fieldsHigh correlation
5종 is highly overall correlated with 총합계 and 5 other fieldsHigh correlation
6종 is highly overall correlated with 총합계 and 4 other fieldsHigh correlation
7종 is highly overall correlated with 10종High correlation
10종 is highly overall correlated with 4종 and 4 other fieldsHigh correlation
노선 is highly overall correlated with 방향High correlation
방향 is highly overall correlated with 노선High correlation
8종 is highly overall correlated with 총합계 and 6 other fieldsHigh correlation
8종 is highly imbalanced (81.4%)Imbalance
9종 is highly imbalanced (85.5%)Imbalance
11종 is highly imbalanced (68.6%)Imbalance
2종 has 88 (37.0%) zerosZeros
6종 has 12 (5.0%) zerosZeros
7종 has 36 (15.1%) zerosZeros
10종 has 45 (18.9%) zerosZeros
12종 has 117 (49.2%) zerosZeros

Reproduction

Analysis started2023-12-12 20:13:56.857779
Analysis finished2023-12-12 20:14:09.203153
Duration12.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
경부선
30 
통영대전·중부선
29 
서해안선
22 
호남선
19 
영동선
16 
Other values (19)
122 

Length

Max length12
Median length10
Mean length4.8655462
Min length3

Unique

Unique1 ?
Unique (%)0.4%

Sample

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

Common Values

ValueCountFrequency (%)
경부선 30
12.6%
통영대전·중부선 29
12.2%
서해안선 22
 
9.2%
호남선 19
 
8.0%
영동선 16
 
6.7%
중앙선 16
 
6.7%
남해선 15
 
6.3%
중부내륙선 15
 
6.3%
당진영덕선 11
 
4.6%
평택제천선 9
 
3.8%
Other values (14) 56
23.5%

Length

2023-12-13T05:14:09.280410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경부선 30
12.6%
통영대전·중부선 29
12.2%
서해안선 22
 
9.2%
호남선 19
 
8.0%
영동선 16
 
6.7%
중앙선 16
 
6.7%
남해선 15
 
6.3%
중부내륙선 15
 
6.3%
당진영덕선 11
 
4.6%
평택제천선 9
 
3.8%
Other values (14) 56
23.5%

방향
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
서울
31 
부산
29 
하남
16 
통영
15 
순천
15 
Other values (37)
132 

Length

Max length5
Median length2
Mean length2.0882353
Min length2

Unique

Unique11 ?
Unique (%)4.6%

Sample

1st row부산
2nd row부산
3rd row서울
4th row부산
5th row서울

Common Values

ValueCountFrequency (%)
서울 31
 
13.0%
부산 29
 
12.2%
하남 16
 
6.7%
통영 15
 
6.3%
순천 15
 
6.3%
천안 12
 
5.0%
목포 11
 
4.6%
창원 10
 
4.2%
춘천 8
 
3.4%
인천 8
 
3.4%
Other values (32) 83
34.9%

Length

2023-12-13T05:14:09.410554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 31
 
13.0%
부산 29
 
12.2%
하남 16
 
6.7%
통영 15
 
6.3%
순천 15
 
6.3%
천안 12
 
5.0%
목포 11
 
4.6%
창원 10
 
4.2%
강릉 8
 
3.4%
춘천 8
 
3.4%
Other values (32) 83
34.9%

명칭
Text

Distinct151
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-13T05:14:09.749829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.1764706
Min length2

Characters and Unicode

Total characters518
Distinct characters135
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)31.1%

Sample

1st row오산
2nd row남사
3rd row청성
4th row추풍령
5th row추풍령
ValueCountFrequency (%)
오산 3
 
1.3%
금성 3
 
1.3%
삽교 3
 
1.3%
일광 3
 
1.3%
전주 3
 
1.3%
주암 3
 
1.3%
상번천 3
 
1.3%
상주 3
 
1.3%
안평 3
 
1.3%
양산 3
 
1.3%
Other values (141) 208
87.4%
2023-12-13T05:14:10.208937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27
 
5.2%
17
 
3.3%
17
 
3.3%
16
 
3.1%
15
 
2.9%
14
 
2.7%
14
 
2.7%
12
 
2.3%
11
 
2.1%
11
 
2.1%
Other values (125) 364
70.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 518
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
27
 
5.2%
17
 
3.3%
17
 
3.3%
16
 
3.1%
15
 
2.9%
14
 
2.7%
14
 
2.7%
12
 
2.3%
11
 
2.1%
11
 
2.1%
Other values (125) 364
70.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 518
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
27
 
5.2%
17
 
3.3%
17
 
3.3%
16
 
3.1%
15
 
2.9%
14
 
2.7%
14
 
2.7%
12
 
2.3%
11
 
2.1%
11
 
2.1%
Other values (125) 364
70.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 518
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
27
 
5.2%
17
 
3.3%
17
 
3.3%
16
 
3.1%
15
 
2.9%
14
 
2.7%
14
 
2.7%
12
 
2.3%
11
 
2.1%
11
 
2.1%
Other values (125) 364
70.3%

총합계
Real number (ℝ)

HIGH CORRELATION 

Distinct201
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean471.53361
Minimum37
Maximum2708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:10.353545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile159.85
Q1260
median374
Q3601.75
95-th percentile993.45
Maximum2708
Range2671
Interquartile range (IQR)341.75

Descriptive statistics

Standard deviation333.59251
Coefficient of variation (CV)0.70746284
Kurtosis12.169895
Mean471.53361
Median Absolute Deviation (MAD)141
Skewness2.8124691
Sum112225
Variance111283.96
MonotonicityNot monotonic
2023-12-13T05:14:10.518437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
334 4
 
1.7%
330 3
 
1.3%
374 3
 
1.3%
298 3
 
1.3%
457 3
 
1.3%
250 3
 
1.3%
202 3
 
1.3%
333 2
 
0.8%
582 2
 
0.8%
251 2
 
0.8%
Other values (191) 210
88.2%
ValueCountFrequency (%)
37 1
0.4%
62 1
0.4%
114 1
0.4%
118 1
0.4%
120 1
0.4%
136 1
0.4%
140 1
0.4%
142 1
0.4%
145 1
0.4%
151 1
0.4%
ValueCountFrequency (%)
2708 1
0.4%
1928 1
0.4%
1906 1
0.4%
1851 1
0.4%
1730 1
0.4%
1643 1
0.4%
1159 1
0.4%
1085 1
0.4%
1080 1
0.4%
1068 1
0.4%

1종
Real number (ℝ)

HIGH CORRELATION 

Distinct196
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.97059
Minimum16
Maximum1766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:10.663849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile106.95
Q1183.25
median254.5
Q3415.75
95-th percentile708.6
Maximum1766
Range1750
Interquartile range (IQR)232.5

Descriptive statistics

Standard deviation235.28624
Coefficient of variation (CV)0.71959451
Kurtosis10.296213
Mean326.97059
Median Absolute Deviation (MAD)96.5
Skewness2.6559191
Sum77819
Variance55359.615
MonotonicityNot monotonic
2023-12-13T05:14:10.793955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
177 4
 
1.7%
136 4
 
1.7%
235 3
 
1.3%
240 3
 
1.3%
229 3
 
1.3%
405 3
 
1.3%
212 2
 
0.8%
252 2
 
0.8%
225 2
 
0.8%
193 2
 
0.8%
Other values (186) 210
88.2%
ValueCountFrequency (%)
16 1
0.4%
35 1
0.4%
58 1
0.4%
69 1
0.4%
84 1
0.4%
87 1
0.4%
93 1
0.4%
95 1
0.4%
98 1
0.4%
99 1
0.4%
ValueCountFrequency (%)
1766 1
0.4%
1383 2
0.8%
1342 1
0.4%
1218 1
0.4%
1161 1
0.4%
834 1
0.4%
819 1
0.4%
791 1
0.4%
726 1
0.4%
715 1
0.4%

2종
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5
Minimum0
Maximum26
Zeros88
Zeros (%)37.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:10.921945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile5
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3398042
Coefficient of variation (CV)1.5598694
Kurtosis54.359284
Mean1.5
Median Absolute Deviation (MAD)1
Skewness5.9205068
Sum357
Variance5.4746835
MonotonicityNot monotonic
2023-12-13T05:14:11.020133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 88
37.0%
1 62
26.1%
2 41
17.2%
3 24
 
10.1%
5 9
 
3.8%
4 9
 
3.8%
6 2
 
0.8%
26 1
 
0.4%
7 1
 
0.4%
15 1
 
0.4%
ValueCountFrequency (%)
0 88
37.0%
1 62
26.1%
2 41
17.2%
3 24
 
10.1%
4 9
 
3.8%
5 9
 
3.8%
6 2
 
0.8%
7 1
 
0.4%
15 1
 
0.4%
26 1
 
0.4%
ValueCountFrequency (%)
26 1
 
0.4%
15 1
 
0.4%
7 1
 
0.4%
6 2
 
0.8%
5 9
 
3.8%
4 9
 
3.8%
3 24
 
10.1%
2 41
17.2%
1 62
26.1%
0 88
37.0%

3종
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.663866
Minimum9
Maximum514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:11.158607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile20.85
Q138
median55
Q395.75
95-th percentile168.4
Maximum514
Range505
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation61.998745
Coefficient of variation (CV)0.83037148
Kurtosis14.46024
Mean74.663866
Median Absolute Deviation (MAD)22.5
Skewness3.0747547
Sum17770
Variance3843.8443
MonotonicityNot monotonic
2023-12-13T05:14:11.292179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 8
 
3.4%
35 7
 
2.9%
43 6
 
2.5%
62 6
 
2.5%
49 6
 
2.5%
17 5
 
2.1%
53 5
 
2.1%
55 5
 
2.1%
40 5
 
2.1%
39 5
 
2.1%
Other values (98) 180
75.6%
ValueCountFrequency (%)
9 1
 
0.4%
17 5
2.1%
18 2
 
0.8%
19 2
 
0.8%
20 2
 
0.8%
21 1
 
0.4%
22 3
 
1.3%
24 2
 
0.8%
25 2
 
0.8%
26 8
3.4%
ValueCountFrequency (%)
514 1
0.4%
369 1
0.4%
354 1
0.4%
334 1
0.4%
260 1
0.4%
241 1
0.4%
220 1
0.4%
210 1
0.4%
193 1
0.4%
188 1
0.4%

4종
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.97479
Minimum1
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:11.425791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.85
Q112
median19
Q336.75
95-th percentile65.15
Maximum144
Range143
Interquartile range (IQR)24.75

Descriptive statistics

Standard deviation21.963899
Coefficient of variation (CV)0.814238
Kurtosis5.1818587
Mean26.97479
Median Absolute Deviation (MAD)10
Skewness1.899062
Sum6420
Variance482.41286
MonotonicityNot monotonic
2023-12-13T05:14:11.570409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 11
 
4.6%
20 10
 
4.2%
9 10
 
4.2%
11 10
 
4.2%
17 9
 
3.8%
14 9
 
3.8%
13 9
 
3.8%
12 8
 
3.4%
16 8
 
3.4%
8 8
 
3.4%
Other values (60) 146
61.3%
ValueCountFrequency (%)
1 1
 
0.4%
2 3
 
1.3%
3 3
 
1.3%
4 3
 
1.3%
5 2
 
0.8%
6 6
2.5%
7 3
 
1.3%
8 8
3.4%
9 10
4.2%
10 7
2.9%
ValueCountFrequency (%)
144 1
0.4%
120 1
0.4%
117 1
0.4%
102 1
0.4%
87 1
0.4%
81 1
0.4%
78 1
0.4%
77 1
0.4%
76 1
0.4%
72 1
0.4%

5종
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.298319
Minimum0
Maximum163
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:11.716537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median17
Q327
95-th percentile55
Maximum163
Range163
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.922192
Coefficient of variation (CV)0.93538801
Kurtosis15.191968
Mean21.298319
Median Absolute Deviation (MAD)9
Skewness3.0450959
Sum5069
Variance396.89375
MonotonicityNot monotonic
2023-12-13T05:14:11.867099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 14
 
5.9%
18 11
 
4.6%
11 11
 
4.6%
9 11
 
4.6%
12 10
 
4.2%
3 9
 
3.8%
10 9
 
3.8%
7 8
 
3.4%
19 8
 
3.4%
14 7
 
2.9%
Other values (46) 140
58.8%
ValueCountFrequency (%)
0 1
 
0.4%
1 3
 
1.3%
2 3
 
1.3%
3 9
3.8%
4 6
2.5%
5 6
2.5%
6 14
5.9%
7 8
3.4%
8 7
2.9%
9 11
4.6%
ValueCountFrequency (%)
163 1
 
0.4%
137 1
 
0.4%
91 1
 
0.4%
83 1
 
0.4%
76 1
 
0.4%
67 1
 
0.4%
66 1
 
0.4%
63 2
0.8%
60 1
 
0.4%
55 3
1.3%

6종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3319328
Minimum0
Maximum80
Zeros12
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:11.990698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.85
Q13
median5.5
Q310
95-th percentile17
Maximum80
Range80
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.3222992
Coefficient of variation (CV)1.1350758
Kurtosis30.674508
Mean7.3319328
Median Absolute Deviation (MAD)3.5
Skewness4.5533237
Sum1745
Variance69.260664
MonotonicityNot monotonic
2023-12-13T05:14:12.137853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
3 27
11.3%
2 24
10.1%
5 21
 
8.8%
6 20
 
8.4%
1 18
 
7.6%
4 17
 
7.1%
8 16
 
6.7%
9 13
 
5.5%
10 13
 
5.5%
0 12
 
5.0%
Other values (17) 57
23.9%
ValueCountFrequency (%)
0 12
5.0%
1 18
7.6%
2 24
10.1%
3 27
11.3%
4 17
7.1%
5 21
8.8%
6 20
8.4%
7 9
 
3.8%
8 16
6.7%
9 13
5.5%
ValueCountFrequency (%)
80 1
0.4%
51 1
0.4%
48 1
0.4%
47 1
0.4%
30 1
0.4%
25 1
0.4%
20 2
0.8%
19 2
0.8%
18 1
0.4%
17 2
0.8%

7종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0168067
Minimum0
Maximum40
Zeros36
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:12.289117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38
95-th percentile20.15
Maximum40
Range40
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.1334278
Coefficient of variation (CV)1.1855837
Kurtosis6.1855749
Mean6.0168067
Median Absolute Deviation (MAD)3
Skewness2.2800102
Sum1432
Variance50.885792
MonotonicityNot monotonic
2023-12-13T05:14:12.453471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 36
15.1%
1 30
12.6%
2 24
10.1%
3 24
10.1%
4 19
8.0%
8 15
 
6.3%
5 14
 
5.9%
7 13
 
5.5%
6 12
 
5.0%
10 7
 
2.9%
Other values (21) 44
18.5%
ValueCountFrequency (%)
0 36
15.1%
1 30
12.6%
2 24
10.1%
3 24
10.1%
4 19
8.0%
5 14
 
5.9%
6 12
 
5.0%
7 13
 
5.5%
8 15
6.3%
9 5
 
2.1%
ValueCountFrequency (%)
40 1
0.4%
37 1
0.4%
36 1
0.4%
34 1
0.4%
32 1
0.4%
31 1
0.4%
29 1
0.4%
26 1
0.4%
23 1
0.4%
22 1
0.4%

8종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
223 
1
 
9
2
 
4
5
 
1
6
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)0.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 223
93.7%
1 9
 
3.8%
2 4
 
1.7%
5 1
 
0.4%
6 1
 
0.4%

Length

2023-12-13T05:14:12.610996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:14:12.752993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 223
93.7%
1 9
 
3.8%
2 4
 
1.7%
5 1
 
0.4%
6 1
 
0.4%

9종
Categorical

IMBALANCE 

Distinct3
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
230 
1
 
7
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row0
2nd row0
3rd row2
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 230
96.6%
1 7
 
2.9%
2 1
 
0.4%

Length

2023-12-13T05:14:12.860090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:14:12.980831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 230
96.6%
1 7
 
2.9%
2 1
 
0.4%

10종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9579832
Minimum0
Maximum48
Zeros45
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:13.087579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile15.3
Maximum48
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.2795562
Coefficient of variation (CV)1.2665546
Kurtosis12.339027
Mean4.9579832
Median Absolute Deviation (MAD)3
Skewness2.8163906
Sum1180
Variance39.432826
MonotonicityNot monotonic
2023-12-13T05:14:13.237190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 45
18.9%
1 40
16.8%
2 31
13.0%
3 17
 
7.1%
5 16
 
6.7%
6 13
 
5.5%
4 12
 
5.0%
8 11
 
4.6%
9 7
 
2.9%
10 7
 
2.9%
Other values (15) 39
16.4%
ValueCountFrequency (%)
0 45
18.9%
1 40
16.8%
2 31
13.0%
3 17
 
7.1%
4 12
 
5.0%
5 16
 
6.7%
6 13
 
5.5%
7 6
 
2.5%
8 11
 
4.6%
9 7
 
2.9%
ValueCountFrequency (%)
48 1
 
0.4%
37 1
 
0.4%
28 1
 
0.4%
26 1
 
0.4%
25 1
 
0.4%
22 1
 
0.4%
19 3
1.3%
18 1
 
0.4%
17 2
0.8%
15 1
 
0.4%

11종
Categorical

IMBALANCE 

Distinct4
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
0
210 
1
22 
2
 
5
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 210
88.2%
1 22
 
9.2%
2 5
 
2.1%
3 1
 
0.4%

Length

2023-12-13T05:14:13.374849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:14:13.490968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 210
88.2%
1 22
 
9.2%
2 5
 
2.1%
3 1
 
0.4%

12종
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5168067
Minimum0
Maximum22
Zeros117
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-13T05:14:13.636246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6.15
Maximum22
Range22
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.839915
Coefficient of variation (CV)1.8722985
Kurtosis17.580013
Mean1.5168067
Median Absolute Deviation (MAD)1
Skewness3.6930646
Sum361
Variance8.0651172
MonotonicityNot monotonic
2023-12-13T05:14:13.814109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 117
49.2%
1 52
21.8%
2 29
 
12.2%
3 10
 
4.2%
5 9
 
3.8%
4 8
 
3.4%
7 3
 
1.3%
10 2
 
0.8%
9 2
 
0.8%
13 2
 
0.8%
Other values (4) 4
 
1.7%
ValueCountFrequency (%)
0 117
49.2%
1 52
21.8%
2 29
 
12.2%
3 10
 
4.2%
4 8
 
3.4%
5 9
 
3.8%
6 1
 
0.4%
7 3
 
1.3%
9 2
 
0.8%
10 2
 
0.8%
ValueCountFrequency (%)
22 1
 
0.4%
16 1
 
0.4%
15 1
 
0.4%
13 2
 
0.8%
10 2
 
0.8%
9 2
 
0.8%
7 3
 
1.3%
6 1
 
0.4%
5 9
3.8%
4 8
3.4%

Interactions

2023-12-13T05:14:07.296450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.011565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.062956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.978687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.962692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:02.149692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:03.616153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.761830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.642868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.441043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:07.409909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.122781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.147731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.058917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:01.055524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:02.587060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:03.726296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.836272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.715071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.510028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:07.537122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.213811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.250098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.166858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:01.161615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:02.713204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:03.874351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.928962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.799340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.581748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:07.663786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.327353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.342038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.248800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:01.272264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:02.829038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.009778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.004544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.878481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.648252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:07.776530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.428805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.430980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.329531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:01.368149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:02.942109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.116079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.102270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.947841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.722843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:07.897989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.546588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.537890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.421735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:01.467659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:03.063310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.232826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.198401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.027043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.805331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:08.021827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.653976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.635980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.518653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:01.586267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:03.178394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.358920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.318337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.114558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.902666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:08.149341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.784647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.716984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.651942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:01.751369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:03.308419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.472990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.416868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.203048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.989897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:08.290960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.891160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.819946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.754244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:01.896708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:03.433502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.574484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.500526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.295138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:07.083318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:08.715703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:58.983274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:13:59.897973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:00.874185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:02.036112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:03.531719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:04.666830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:05.571035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:06.373105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:07.174054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:14:13.920878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선방향총합계1종2종3종4종5종6종7종8종9종10종11종12종
노선1.0000.9930.5620.6130.1500.6280.3410.3470.0000.0000.3020.0000.3820.4550.000
방향0.9931.0000.4030.4350.2620.5600.2580.0000.0000.0000.5160.0000.0000.5460.000
총합계0.5620.4031.0000.9750.4920.9720.8390.9050.8120.6130.8280.0000.9080.1960.509
1종0.6130.4350.9751.0000.5310.9610.7310.8750.7210.5400.6830.0000.7970.0710.168
2종0.1500.2620.4920.5311.0000.6150.6520.3010.0560.6740.0000.0000.1630.0070.200
3종0.6280.5600.9720.9610.6151.0000.7900.8830.7650.5880.6900.0000.8260.0000.382
4종0.3410.2580.8390.7310.6520.7901.0000.8020.7590.7730.9130.0000.7760.1200.579
5종0.3470.0000.9050.8750.3010.8830.8021.0000.8490.6520.7770.0000.9130.4230.614
6종0.0000.0000.8120.7210.0560.7650.7590.8491.0000.5770.7510.0000.7790.3500.563
7종0.0000.0000.6130.5400.6740.5880.7730.6520.5771.0000.7180.0000.7210.2490.699
8종0.3020.5160.8280.6830.0000.6900.9130.7770.7510.7181.0000.0740.8290.1920.575
9종0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0741.0000.0000.1740.287
10종0.3820.0000.9080.7970.1630.8260.7760.9130.7790.7210.8290.0001.0000.3770.798
11종0.4550.5460.1960.0710.0070.0000.1200.4230.3500.2490.1920.1740.3771.0000.000
12종0.0000.0000.5090.1680.2000.3820.5790.6140.5630.6990.5750.2870.7980.0001.000
2023-12-13T05:14:14.112364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
11종방향9종8종노선
11종1.0000.2790.1650.1570.219
방향0.2791.0000.0000.2450.827
9종0.1650.0001.0000.0540.000
8종0.1570.2450.0541.0000.143
노선0.2190.8270.0000.1431.000
2023-12-13T05:14:14.618575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
총합계1종2종3종4종5종6종7종10종12종노선방향8종9종11종
총합계1.0000.9710.4650.9200.7300.6180.5080.3900.4390.2900.2240.1500.6980.0000.088
1종0.9711.0000.4420.8450.6050.4710.4060.2820.3260.1960.2540.1650.5060.0000.030
2종0.4650.4421.0000.4410.3420.2910.2690.2860.1970.2100.0660.1110.0000.0000.000
3종0.9200.8450.4411.0000.7560.6400.4830.4320.4420.2970.2530.2270.5130.0000.000
4종0.7300.6050.3420.7561.0000.7590.5700.4250.5070.3920.1260.0800.6060.0000.070
5종0.6180.4710.2910.6400.7591.0000.6870.4770.6100.4660.1230.0000.6220.0000.197
6종0.5080.4060.2690.4830.5700.6871.0000.3440.5030.3530.0000.0000.6080.0000.250
7종0.3900.2820.2860.4320.4250.4770.3441.0000.5070.3780.1200.0000.3680.0000.135
10종0.4390.3260.1970.4420.5070.6100.5030.5071.0000.4540.1380.0000.6980.0000.174
12종0.2900.1960.2100.2970.3920.4660.3530.3780.4541.0000.0000.0000.3750.1290.000
노선0.2240.2540.0660.2530.1260.1230.0000.1200.1380.0001.0000.8270.1430.0000.219
방향0.1500.1650.1110.2270.0800.0000.0000.0000.0000.0000.8271.0000.2450.0000.279
8종0.6980.5060.0000.5130.6060.6220.6080.3680.6980.3750.1430.2451.0000.0540.157
9종0.0000.0000.0000.0000.0000.0000.0000.0000.0000.1290.0000.0000.0541.0000.165
11종0.0880.0300.0000.0000.0700.1970.2500.1350.1740.0000.2190.2790.1570.1651.000

Missing values

2023-12-13T05:14:08.850816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:14:09.110381image/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

노선방향명칭총합계1종2종3종4종5종6종7종8종9종10종11종12종
0경부선부산오산990791313430214600100
1경부선부산남사749596190202415200100
2경부선서울청성26416014417183702615
3경부선부산추풍령253142036173247001302
4경부선서울추풍령16069018182651400415
5경부선부산대신20511313630108121003
6경부선서울대신44325906252351010203010
7경부선서울석적40626417430199610200
8경부선부산북안2441360491121107001000
9경부선서울북안2501320482617101100600
노선방향명칭총합계1종2종3종4종5종6종7종8종9종10종11종12종
228수도권제1순환선외측청계68341601345550213011200
229호남선의지선논산논산48030327754209900510
230호남선의지선회덕논산521343079463115000502
231호남선의지선논산서대전49737407922122600200
232호남선의지선논산북대전6254491724726144001101
233고창담양선고창장성물류242177035679600200
234고창담양선담양장성물류29822903913123200000
235고창담양선고창태령28719104823115101610
236대전남부순환선산내산내2811931601173300300
237부산외곽순환선기장일광82761411353414111300302