Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory157.0 B

Variable types

Categorical2
Text2
Numeric13

Dataset

Description교통량조사 결과 데이터 제공 (노선,방향,구간,지점번호,시간,1종,2종,3종,4종,5종,6종,7종,8종,9종,10종,11종,12종)
URLhttps://www.data.go.kr/data/15062243/fileData.do

Alerts

1종 is highly overall correlated with 2종 and 4 other fieldsHigh correlation
2종 is highly overall correlated with 1종 and 4 other fieldsHigh correlation
3종 is highly overall correlated with 1종 and 6 other fieldsHigh correlation
4종 is highly overall correlated with 1종 and 6 other fieldsHigh correlation
5종 is highly overall correlated with 1종 and 7 other fieldsHigh correlation
6종 is highly overall correlated with 1종 and 7 other fieldsHigh correlation
7종 is highly overall correlated with 3종 and 5 other fieldsHigh correlation
10종 is highly overall correlated with 3종 and 5 other fieldsHigh correlation
12종 is highly overall correlated with 5종 and 3 other fieldsHigh correlation
시간 has 424 (4.2%) zerosZeros
2종 has 799 (8.0%) zerosZeros
6종 has 119 (1.2%) zerosZeros
7종 has 220 (2.2%) zerosZeros
8종 has 4249 (42.5%) zerosZeros
9종 has 8113 (81.1%) zerosZeros
10종 has 275 (2.8%) zerosZeros
11종 has 5676 (56.8%) zerosZeros
12종 has 1589 (15.9%) zerosZeros

Reproduction

Analysis started2023-12-12 05:29:35.204493
Analysis finished2023-12-12 05:29:58.273753
Duration23.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선
Categorical

Distinct32
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경부선
1039 
남해선
756 
중부·통영대전선
730 
서해안선
712 
영동선
 
603
Other values (27)
6160 

Length

Max length10
Median length8
Mean length4.8159
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중부·통영대전선
2nd row순천완주선
3rd row수도권제1순환선
4th row부산외곽순환선
5th row서해안선

Common Values

ValueCountFrequency (%)
경부선 1039
 
10.4%
남해선 756
 
7.6%
중부·통영대전선 730
 
7.3%
서해안선 712
 
7.1%
영동선 603
 
6.0%
호남선 585
 
5.9%
중앙선 543
 
5.4%
중부내륙선 535
 
5.3%
동해선 515
 
5.1%
수도권제1순환선 510
 
5.1%
Other values (22) 3472
34.7%

Length

2023-12-12T14:29:58.338471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경부선 1039
 
10.4%
남해선 756
 
7.6%
중부·통영대전선 730
 
7.3%
서해안선 712
 
7.1%
영동선 603
 
6.0%
호남선 585
 
5.9%
중앙선 543
 
5.4%
중부내륙선 535
 
5.3%
동해선 515
 
5.1%
수도권제1순환선 510
 
5.1%
Other values (22) 3472
34.7%

방향
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
종점
5021 
기점
4979 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row종점
2nd row종점
3rd row기점
4th row기점
5th row종점

Common Values

ValueCountFrequency (%)
종점 5021
50.2%
기점 4979
49.8%

Length

2023-12-12T14:29:58.460819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:29:58.774231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
종점 5021
50.2%
기점 4979
49.8%

구간
Text

Distinct536
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T14:29:58.967709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.5324
Min length5

Characters and Unicode

Total characters75324
Distinct characters209
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
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 row서청주~오창
2nd row임실~상관
3rd row상일~강일
4th row한림~광재
5th row송악~서평택
ValueCountFrequency (%)
화산jct~북영천 28
 
0.3%
동함평~문평 27
 
0.3%
강릉~강릉jct 26
 
0.3%
서김제~동군산 26
 
0.3%
황간~영동 26
 
0.3%
중동~송내 26
 
0.3%
서안동~예천 26
 
0.3%
북의성~동안동 25
 
0.2%
동순천~순천jct 25
 
0.2%
함양jct~거창 25
 
0.2%
Other values (526) 9740
97.4%
2023-12-12T14:29:59.367567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
~ 10000
 
13.3%
J 5639
 
7.5%
C 5639
 
7.5%
T 5639
 
7.5%
2293
 
3.0%
2031
 
2.7%
1979
 
2.6%
1797
 
2.4%
1768
 
2.3%
1495
 
2.0%
Other values (199) 37044
49.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48333
64.2%
Uppercase Letter 16917
 
22.5%
Math Symbol 10000
 
13.3%
Other Punctuation 36
 
< 0.1%
Close Punctuation 19
 
< 0.1%
Open Punctuation 19
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2293
 
4.7%
2031
 
4.2%
1979
 
4.1%
1797
 
3.7%
1768
 
3.7%
1495
 
3.1%
1481
 
3.1%
1086
 
2.2%
999
 
2.1%
924
 
1.9%
Other values (192) 32480
67.2%
Uppercase Letter
ValueCountFrequency (%)
J 5639
33.3%
C 5639
33.3%
T 5639
33.3%
Math Symbol
ValueCountFrequency (%)
~ 10000
100.0%
Other Punctuation
ValueCountFrequency (%)
· 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48333
64.2%
Latin 16917
 
22.5%
Common 10074
 
13.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2293
 
4.7%
2031
 
4.2%
1979
 
4.1%
1797
 
3.7%
1768
 
3.7%
1495
 
3.1%
1481
 
3.1%
1086
 
2.2%
999
 
2.1%
924
 
1.9%
Other values (192) 32480
67.2%
Common
ValueCountFrequency (%)
~ 10000
99.3%
· 36
 
0.4%
) 19
 
0.2%
( 19
 
0.2%
Latin
ValueCountFrequency (%)
J 5639
33.3%
C 5639
33.3%
T 5639
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48333
64.2%
ASCII 26955
35.8%
None 36
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
~ 10000
37.1%
J 5639
20.9%
C 5639
20.9%
T 5639
20.9%
) 19
 
0.1%
( 19
 
0.1%
Hangul
ValueCountFrequency (%)
2293
 
4.7%
2031
 
4.2%
1979
 
4.1%
1797
 
3.7%
1768
 
3.7%
1495
 
3.1%
1481
 
3.1%
1086
 
2.2%
999
 
2.1%
924
 
1.9%
Other values (192) 32480
67.2%
None
ValueCountFrequency (%)
· 36
100.0%
Distinct536
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T14:29:59.756634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.8555
Min length3

Characters and Unicode

Total characters48555
Distinct characters11
Distinct categories2 ?
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 row3523
2nd row2709
3rd row10006
4th row60003
5th row1522
ValueCountFrequency (%)
02003-1 28
 
0.3%
01200-4 27
 
0.3%
6504 26
 
0.3%
1510 26
 
0.3%
120 26
 
0.3%
10019 26
 
0.3%
5513 26
 
0.3%
3013 25
 
0.2%
2701 25
 
0.2%
1208 25
 
0.2%
Other values (526) 9740
97.4%
2023-12-12T14:30:00.285241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14214
29.3%
1 10080
20.8%
5 6461
13.3%
2 5035
 
10.4%
3 3181
 
6.6%
- 2536
 
5.2%
4 2304
 
4.7%
6 1977
 
4.1%
7 1008
 
2.1%
9 932
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46019
94.8%
Dash Punctuation 2536
 
5.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14214
30.9%
1 10080
21.9%
5 6461
14.0%
2 5035
 
10.9%
3 3181
 
6.9%
4 2304
 
5.0%
6 1977
 
4.3%
7 1008
 
2.2%
9 932
 
2.0%
8 827
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 2536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48555
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14214
29.3%
1 10080
20.8%
5 6461
13.3%
2 5035
 
10.4%
3 3181
 
6.6%
- 2536
 
5.2%
4 2304
 
4.7%
6 1977
 
4.1%
7 1008
 
2.1%
9 932
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14214
29.3%
1 10080
20.8%
5 6461
13.3%
2 5035
 
10.4%
3 3181
 
6.6%
- 2536
 
5.2%
4 2304
 
4.7%
6 1977
 
4.1%
7 1008
 
2.1%
9 932
 
1.9%

시간
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.567
Minimum0
Maximum23
Zeros424
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:00.443662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median12
Q318
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9225359
Coefficient of variation (CV)0.59847289
Kurtosis-1.198385
Mean11.567
Median Absolute Deviation (MAD)6
Skewness-0.0080141696
Sum115670
Variance47.921503
MonotonicityNot monotonic
2023-12-12T14:30:00.570701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23 440
 
4.4%
15 440
 
4.4%
8 437
 
4.4%
19 436
 
4.4%
4 430
 
4.3%
13 428
 
4.3%
20 426
 
4.3%
10 425
 
4.2%
16 425
 
4.2%
0 424
 
4.2%
Other values (14) 5689
56.9%
ValueCountFrequency (%)
0 424
4.2%
1 403
4.0%
2 391
3.9%
3 405
4.0%
4 430
4.3%
5 408
4.1%
6 406
4.1%
7 418
4.2%
8 437
4.4%
9 407
4.1%
ValueCountFrequency (%)
23 440
4.4%
22 413
4.1%
21 416
4.2%
20 426
4.3%
19 436
4.4%
18 398
4.0%
17 401
4.0%
16 425
4.2%
15 440
4.4%
14 392
3.9%

1종
Real number (ℝ)

HIGH CORRELATION 

Distinct2880
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean941.9109
Minimum0
Maximum8871
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:00.705316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile42
Q1188
median573
Q31235
95-th percentile3403.15
Maximum8871
Range8871
Interquartile range (IQR)1047

Descriptive statistics

Standard deviation1073.6686
Coefficient of variation (CV)1.1398834
Kurtosis3.6942445
Mean941.9109
Median Absolute Deviation (MAD)445
Skewness1.8926452
Sum9419109
Variance1152764.2
MonotonicityNot monotonic
2023-12-12T14:30:00.855171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 29
 
0.3%
43 28
 
0.3%
27 26
 
0.3%
56 26
 
0.3%
95 25
 
0.2%
58 25
 
0.2%
63 24
 
0.2%
76 23
 
0.2%
82 23
 
0.2%
61 23
 
0.2%
Other values (2870) 9748
97.5%
ValueCountFrequency (%)
0 5
 
0.1%
2 1
 
< 0.1%
3 5
 
0.1%
4 3
 
< 0.1%
5 5
 
0.1%
6 5
 
0.1%
7 12
0.1%
8 10
0.1%
9 13
0.1%
10 9
0.1%
ValueCountFrequency (%)
8871 1
< 0.1%
7375 1
< 0.1%
6499 1
< 0.1%
6369 1
< 0.1%
6354 1
< 0.1%
6245 1
< 0.1%
6216 1
< 0.1%
6120 1
< 0.1%
6113 1
< 0.1%
5989 1
< 0.1%

2종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct217
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.1704
Minimum0
Maximum530
Zeros799
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:01.013738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q323
95-th percentile58
Maximum530
Range530
Interquartile range (IQR)20

Descriptive statistics

Standard deviation30.393952
Coefficient of variation (CV)1.6727178
Kurtosis62.281753
Mean18.1704
Median Absolute Deviation (MAD)8
Skewness6.3023489
Sum181704
Variance923.79234
MonotonicityNot monotonic
2023-12-12T14:30:01.153239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 956
 
9.6%
0 799
 
8.0%
2 628
 
6.3%
3 463
 
4.6%
4 450
 
4.5%
8 353
 
3.5%
5 349
 
3.5%
6 334
 
3.3%
7 303
 
3.0%
15 291
 
2.9%
Other values (207) 5074
50.7%
ValueCountFrequency (%)
0 799
8.0%
1 956
9.6%
2 628
6.3%
3 463
4.6%
4 450
4.5%
5 349
 
3.5%
6 334
 
3.3%
7 303
 
3.0%
8 353
 
3.5%
9 275
 
2.8%
ValueCountFrequency (%)
530 2
< 0.1%
459 1
< 0.1%
432 1
< 0.1%
431 1
< 0.1%
428 1
< 0.1%
419 1
< 0.1%
390 1
< 0.1%
366 1
< 0.1%
347 1
< 0.1%
345 1
< 0.1%

3종
Real number (ℝ)

HIGH CORRELATION 

Distinct885
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.9361
Minimum0
Maximum1494
Zeros20
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:01.296312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q134
median89
Q3200
95-th percentile591
Maximum1494
Range1494
Interquartile range (IQR)166

Descriptive statistics

Standard deviation194.23067
Coefficient of variation (CV)1.2220677
Kurtosis6.4685621
Mean158.9361
Median Absolute Deviation (MAD)65
Skewness2.3405809
Sum1589361
Variance37725.553
MonotonicityNot monotonic
2023-12-12T14:30:01.457436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 93
 
0.9%
6 93
 
0.9%
10 92
 
0.9%
14 88
 
0.9%
12 87
 
0.9%
15 87
 
0.9%
7 83
 
0.8%
9 82
 
0.8%
21 82
 
0.8%
13 81
 
0.8%
Other values (875) 9132
91.3%
ValueCountFrequency (%)
0 20
 
0.2%
1 38
0.4%
2 58
0.6%
3 68
0.7%
4 74
0.7%
5 73
0.7%
6 93
0.9%
7 83
0.8%
8 93
0.9%
9 82
0.8%
ValueCountFrequency (%)
1494 1
< 0.1%
1386 1
< 0.1%
1370 1
< 0.1%
1334 1
< 0.1%
1327 1
< 0.1%
1290 1
< 0.1%
1284 1
< 0.1%
1283 1
< 0.1%
1281 1
< 0.1%
1275 1
< 0.1%

4종
Real number (ℝ)

HIGH CORRELATION 

Distinct375
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.9131
Minimum0
Maximum529
Zeros38
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:01.652280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q119
median40
Q379
95-th percentile192
Maximum529
Range529
Interquartile range (IQR)60

Descriptive statistics

Standard deviation63.605405
Coefficient of variation (CV)1.0441991
Kurtosis6.718485
Mean60.9131
Median Absolute Deviation (MAD)25
Skewness2.244669
Sum609131
Variance4045.6475
MonotonicityNot monotonic
2023-12-12T14:30:01.804499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 184
 
1.8%
12 169
 
1.7%
16 163
 
1.6%
20 160
 
1.6%
19 157
 
1.6%
15 154
 
1.5%
13 152
 
1.5%
11 152
 
1.5%
23 148
 
1.5%
10 147
 
1.5%
Other values (365) 8414
84.1%
ValueCountFrequency (%)
0 38
 
0.4%
1 64
0.6%
2 78
0.8%
3 112
1.1%
4 129
1.3%
5 131
1.3%
6 130
1.3%
7 140
1.4%
8 135
1.4%
9 131
1.3%
ValueCountFrequency (%)
529 1
< 0.1%
525 1
< 0.1%
513 1
< 0.1%
492 1
< 0.1%
491 1
< 0.1%
467 2
< 0.1%
454 1
< 0.1%
448 2
< 0.1%
441 1
< 0.1%
435 1
< 0.1%

5종
Real number (ℝ)

HIGH CORRELATION 

Distinct317
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.9685
Minimum0
Maximum391
Zeros54
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:01.960344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q120
median43.5
Q389
95-th percentile185.05
Maximum391
Range391
Interquartile range (IQR)69

Descriptive statistics

Standard deviation58.76027
Coefficient of variation (CV)0.93316929
Kurtosis2.4923631
Mean62.9685
Median Absolute Deviation (MAD)28.5
Skewness1.5397138
Sum629685
Variance3452.7694
MonotonicityNot monotonic
2023-12-12T14:30:02.137697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 155
 
1.6%
10 146
 
1.5%
13 144
 
1.4%
12 144
 
1.4%
17 142
 
1.4%
19 141
 
1.4%
5 140
 
1.4%
7 139
 
1.4%
9 133
 
1.3%
26 132
 
1.3%
Other values (307) 8584
85.8%
ValueCountFrequency (%)
0 54
 
0.5%
1 91
0.9%
2 108
1.1%
3 89
0.9%
4 114
1.1%
5 140
1.4%
6 130
1.3%
7 139
1.4%
8 122
1.2%
9 133
1.3%
ValueCountFrequency (%)
391 1
< 0.1%
380 1
< 0.1%
375 1
< 0.1%
370 1
< 0.1%
358 2
< 0.1%
353 1
< 0.1%
351 1
< 0.1%
350 1
< 0.1%
345 1
< 0.1%
343 1
< 0.1%

6종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct192
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.9091
Minimum0
Maximum290
Zeros119
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:02.303586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q19
median20
Q339
95-th percentile87
Maximum290
Range290
Interquartile range (IQR)30

Descriptive statistics

Standard deviation29.162374
Coefficient of variation (CV)1.0087611
Kurtosis8.1062923
Mean28.9091
Median Absolute Deviation (MAD)13
Skewness2.2413303
Sum289091
Variance850.44408
MonotonicityNot monotonic
2023-12-12T14:30:02.464540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 336
 
3.4%
4 321
 
3.2%
8 294
 
2.9%
6 287
 
2.9%
3 286
 
2.9%
9 269
 
2.7%
11 269
 
2.7%
12 259
 
2.6%
1 257
 
2.6%
5 255
 
2.5%
Other values (182) 7167
71.7%
ValueCountFrequency (%)
0 119
 
1.2%
1 257
2.6%
2 336
3.4%
3 286
2.9%
4 321
3.2%
5 255
2.5%
6 287
2.9%
7 251
2.5%
8 294
2.9%
9 269
2.7%
ValueCountFrequency (%)
290 1
< 0.1%
276 1
< 0.1%
268 1
< 0.1%
257 1
< 0.1%
254 1
< 0.1%
251 2
< 0.1%
250 1
< 0.1%
240 1
< 0.1%
233 1
< 0.1%
229 1
< 0.1%

7종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct183
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.2502
Minimum0
Maximum261
Zeros220
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:02.614616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median15
Q332
95-th percentile81
Maximum261
Range261
Interquartile range (IQR)26

Descriptive statistics

Standard deviation27.408592
Coefficient of variation (CV)1.1302419
Kurtosis7.5273422
Mean24.2502
Median Absolute Deviation (MAD)11
Skewness2.3089592
Sum242502
Variance751.23092
MonotonicityNot monotonic
2023-12-12T14:30:02.763837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 452
 
4.5%
1 451
 
4.5%
3 442
 
4.4%
4 430
 
4.3%
5 419
 
4.2%
6 338
 
3.4%
8 333
 
3.3%
7 323
 
3.2%
9 295
 
2.9%
10 267
 
2.7%
Other values (173) 6250
62.5%
ValueCountFrequency (%)
0 220
2.2%
1 451
4.5%
2 452
4.5%
3 442
4.4%
4 430
4.3%
5 419
4.2%
6 338
3.4%
7 323
3.2%
8 333
3.3%
9 295
2.9%
ValueCountFrequency (%)
261 1
< 0.1%
259 1
< 0.1%
239 1
< 0.1%
232 1
< 0.1%
221 1
< 0.1%
220 1
< 0.1%
216 1
< 0.1%
213 1
< 0.1%
194 1
< 0.1%
192 1
< 0.1%

8종
Real number (ℝ)

ZEROS 

Distinct77
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7299
Minimum0
Maximum257
Zeros4249
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:02.887625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile13
Maximum257
Range257
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.6159534
Coefficient of variation (CV)2.7898287
Kurtosis253.79297
Mean2.7299
Median Absolute Deviation (MAD)1
Skewness11.732572
Sum27299
Variance58.002746
MonotonicityNot monotonic
2023-12-12T14:30:03.012423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4249
42.5%
1 2588
25.9%
2 949
 
9.5%
3 467
 
4.7%
4 317
 
3.2%
5 203
 
2.0%
6 177
 
1.8%
8 122
 
1.2%
7 111
 
1.1%
10 98
 
1.0%
Other values (67) 719
 
7.2%
ValueCountFrequency (%)
0 4249
42.5%
1 2588
25.9%
2 949
 
9.5%
3 467
 
4.7%
4 317
 
3.2%
5 203
 
2.0%
6 177
 
1.8%
7 111
 
1.1%
8 122
 
1.2%
9 70
 
0.7%
ValueCountFrequency (%)
257 1
< 0.1%
206 1
< 0.1%
168 1
< 0.1%
141 1
< 0.1%
140 1
< 0.1%
135 1
< 0.1%
125 1
< 0.1%
115 1
< 0.1%
97 1
< 0.1%
86 1
< 0.1%

9종
Real number (ℝ)

ZEROS 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2585
Minimum0
Maximum25
Zeros8113
Zeros (%)81.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:03.116319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.85097013
Coefficient of variation (CV)3.2919541
Kurtosis239.97647
Mean0.2585
Median Absolute Deviation (MAD)0
Skewness11.937927
Sum2585
Variance0.72415017
MonotonicityNot monotonic
2023-12-12T14:30:03.221580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 8113
81.1%
1 1602
 
16.0%
2 169
 
1.7%
3 50
 
0.5%
5 15
 
0.1%
6 14
 
0.1%
4 14
 
0.1%
7 5
 
0.1%
9 4
 
< 0.1%
8 3
 
< 0.1%
Other values (9) 11
 
0.1%
ValueCountFrequency (%)
0 8113
81.1%
1 1602
 
16.0%
2 169
 
1.7%
3 50
 
0.5%
4 14
 
0.1%
5 15
 
0.1%
6 14
 
0.1%
7 5
 
0.1%
8 3
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
23 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
16 2
< 0.1%
15 2
< 0.1%
14 1
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
9 4
< 0.1%

10종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct187
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.7805
Minimum0
Maximum347
Zeros275
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:03.355772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median14
Q333
95-th percentile79
Maximum347
Range347
Interquartile range (IQR)28

Descriptive statistics

Standard deviation27.786405
Coefficient of variation (CV)1.1684534
Kurtosis10.716728
Mean23.7805
Median Absolute Deviation (MAD)11
Skewness2.5627042
Sum237805
Variance772.08433
MonotonicityNot monotonic
2023-12-12T14:30:03.480421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 523
 
5.2%
1 480
 
4.8%
3 445
 
4.5%
4 420
 
4.2%
5 416
 
4.2%
6 352
 
3.5%
7 345
 
3.5%
8 316
 
3.2%
9 314
 
3.1%
10 276
 
2.8%
Other values (177) 6113
61.1%
ValueCountFrequency (%)
0 275
2.8%
1 480
4.8%
2 523
5.2%
3 445
4.5%
4 420
4.2%
5 416
4.2%
6 352
3.5%
7 345
3.5%
8 316
3.2%
9 314
3.1%
ValueCountFrequency (%)
347 1
< 0.1%
290 1
< 0.1%
283 1
< 0.1%
270 1
< 0.1%
247 1
< 0.1%
231 1
< 0.1%
228 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
223 1
< 0.1%

11종
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7401
Minimum0
Maximum29
Zeros5676
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:03.600921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum29
Range29
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3554836
Coefficient of variation (CV)1.8314871
Kurtosis47.191745
Mean0.7401
Median Absolute Deviation (MAD)0
Skewness4.8242488
Sum7401
Variance1.8373357
MonotonicityNot monotonic
2023-12-12T14:30:03.698482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 5676
56.8%
1 2988
29.9%
2 668
 
6.7%
3 285
 
2.9%
4 146
 
1.5%
5 88
 
0.9%
6 51
 
0.5%
7 38
 
0.4%
8 21
 
0.2%
9 12
 
0.1%
Other values (7) 27
 
0.3%
ValueCountFrequency (%)
0 5676
56.8%
1 2988
29.9%
2 668
 
6.7%
3 285
 
2.9%
4 146
 
1.5%
5 88
 
0.9%
6 51
 
0.5%
7 38
 
0.4%
8 21
 
0.2%
9 12
 
0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
22 2
 
< 0.1%
15 3
 
< 0.1%
13 3
 
< 0.1%
12 5
 
0.1%
11 3
 
< 0.1%
10 10
 
0.1%
9 12
 
0.1%
8 21
0.2%
7 38
0.4%

12종
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2126
Minimum0
Maximum100
Zeros1589
Zeros (%)15.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T14:30:03.844071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile26
Maximum100
Range100
Interquartile range (IQR)6

Descriptive statistics

Standard deviation9.8986969
Coefficient of variation (CV)1.593326
Kurtosis15.797597
Mean6.2126
Median Absolute Deviation (MAD)2
Skewness3.3831664
Sum62126
Variance97.9842
MonotonicityNot monotonic
2023-12-12T14:30:04.231498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 2315
23.2%
0 1589
15.9%
2 1170
11.7%
3 781
 
7.8%
4 629
 
6.3%
5 458
 
4.6%
6 376
 
3.8%
7 301
 
3.0%
8 255
 
2.5%
10 207
 
2.1%
Other values (77) 1919
19.2%
ValueCountFrequency (%)
0 1589
15.9%
1 2315
23.2%
2 1170
11.7%
3 781
 
7.8%
4 629
 
6.3%
5 458
 
4.6%
6 376
 
3.8%
7 301
 
3.0%
8 255
 
2.5%
9 185
 
1.8%
ValueCountFrequency (%)
100 1
 
< 0.1%
97 1
 
< 0.1%
96 1
 
< 0.1%
94 1
 
< 0.1%
93 1
 
< 0.1%
91 1
 
< 0.1%
90 1
 
< 0.1%
86 3
< 0.1%
85 1
 
< 0.1%
80 1
 
< 0.1%

Interactions

2023-12-12T14:29:56.746230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:39.391235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.724632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:42.182726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.576183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:44.797344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:46.546257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:48.068853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:49.516614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.847831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:52.532593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.863507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.363268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:56.827624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:39.473721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.819056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:42.276779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.662547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:45.195980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:46.748923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:48.170981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:49.622783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.941043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:52.640436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.958685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.455920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:56.912358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:39.557192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.923169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:42.371546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.760847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:45.276950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:46.838963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:48.283694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:49.721694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:51.023180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:52.748925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:54.048990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.543382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.000594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:39.656229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:41.036685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:42.479544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.846341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:45.363236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:46.957857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:48.396821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:49.829331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:51.132021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:52.857870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:54.178988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.637352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.084876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:39.799449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:41.137502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:42.587564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.951519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:45.456888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:47.064838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:48.501630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:49.947364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:51.246862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:52.949436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:54.293221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.748019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.182823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:39.920967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:41.235247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:42.710094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:44.040594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:45.548212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:47.183979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:48.628754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.032591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:51.338922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.042547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:54.409020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.865150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.306014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.044911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:41.387541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:42.807591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:44.136912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:45.662092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:47.296465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:48.738834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.149725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:51.463934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.154603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:54.541335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.990909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.391916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.149535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:41.494758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:42.930073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:44.226478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:45.759358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:47.391669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:48.835673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.236917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:51.583781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.269105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:54.655902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:56.111656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.473876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.248040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:41.606894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.062038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:44.309041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:45.856051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:47.483644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:48.927202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.338238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:51.683913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.355932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:54.770760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:56.210890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.557080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.340294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:41.725940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.160975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:44.392791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:45.949521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:47.579776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:49.028625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.450846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:51.776489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.451739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:54.892434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:56.328057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.638970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.426234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:41.850496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.259301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:44.485808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:46.057402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:47.705128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:49.167798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.546566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:51.892173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.544335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.014481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:56.456048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.737528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.526128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:41.963142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.365749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:44.583161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:46.169759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:47.829209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:49.308179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.648567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:52.007410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.653431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.130884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:56.565692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:57.861189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:40.631570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:42.060204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:43.476550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:44.690950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:46.282014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:47.939295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:49.401264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:50.754657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:52.404788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:53.742946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:55.253582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:29:56.654102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:30:04.326466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선방향시간1종2종3종4종5종6종7종8종9종10종11종12종
노선1.0000.0000.0000.5610.3670.5570.4660.5190.3630.3990.1570.0890.4180.2650.399
방향0.0001.0000.0000.0440.0000.0350.0240.0000.0000.0000.0000.0180.0190.0000.039
시간0.0000.0001.0000.4760.1860.4780.4500.3350.4480.3610.0920.0820.2740.0890.247
1종0.5610.0440.4761.0000.5570.8540.6490.5460.5580.4240.1080.0000.3080.1370.279
2종0.3670.0000.1860.5571.0000.4320.5490.3750.2190.2060.0180.1240.1220.0510.086
3종0.5570.0350.4780.8540.4321.0000.7200.6270.6110.5020.1310.0760.3610.1540.296
4종0.4660.0240.4500.6490.5490.7201.0000.7080.6270.5020.2480.1270.3960.1370.312
5종0.5190.0000.3350.5460.3750.6270.7081.0000.7240.6610.1730.2100.5150.2160.440
6종0.3630.0000.4480.5580.2190.6110.6270.7241.0000.6140.1100.2090.5150.2850.452
7종0.3990.0000.3610.4240.2060.5020.5020.6610.6141.0000.0920.1420.7140.1700.622
8종0.1570.0000.0920.1080.0180.1310.2480.1730.1100.0921.0000.0000.1220.0000.062
9종0.0890.0180.0820.0000.1240.0760.1270.2100.2090.1420.0001.0000.2230.3290.256
10종0.4180.0190.2740.3080.1220.3610.3960.5150.5150.7140.1220.2231.0000.1500.814
11종0.2650.0000.0890.1370.0510.1540.1370.2160.2850.1700.0000.3290.1501.0000.213
12종0.3990.0390.2470.2790.0860.2960.3120.4400.4520.6220.0620.2560.8140.2131.000
2023-12-12T14:30:04.450127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선방향
노선1.0000.000
방향0.0001.000
2023-12-12T14:30:04.550565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간1종2종3종4종5종6종7종8종9종10종11종12종노선방향
시간1.0000.3940.3740.1860.0640.094-0.0140.0660.0330.0500.0060.0400.0730.0000.000
1종0.3941.0000.8830.9180.7160.5910.5940.4910.3080.2180.4090.3110.3300.2340.034
2종0.3740.8831.0000.8080.6780.5780.5300.4550.3230.1960.3820.2940.2990.1370.000
3종0.1860.9180.8081.0000.8150.6860.7140.5770.3360.2470.5060.3760.4030.2320.027
4종0.0640.7160.6780.8151.0000.8030.7390.6260.4260.2290.5720.3730.4700.1830.019
5종0.0940.5910.5780.6860.8031.0000.7590.7670.3890.2430.7090.4720.5920.2110.000
6종-0.0140.5940.5300.7140.7390.7591.0000.7150.3600.2560.6700.4010.5700.1350.000
7종0.0660.4910.4550.5770.6260.7670.7151.0000.3630.2560.7610.4120.6920.1520.000
8종0.0330.3080.3230.3360.4260.3890.3600.3631.0000.1910.3580.2240.3810.0590.000
9종0.0500.2180.1960.2470.2290.2430.2560.2560.1911.0000.2240.1900.2200.0390.026
10종0.0060.4090.3820.5060.5720.7090.6700.7610.3580.2241.0000.4050.7180.1600.014
11종0.0400.3110.2940.3760.3730.4720.4010.4120.2240.1900.4051.0000.3740.0970.000
12종0.0730.3300.2990.4030.4700.5920.5700.6920.3810.2200.7180.3741.0000.1480.028
노선0.0000.2340.1370.2320.1830.2110.1350.1520.0590.0390.1600.0970.1481.0000.000
방향0.0000.0340.0000.0270.0190.0000.0000.0000.0000.0260.0140.0000.0280.0001.000

Missing values

2023-12-12T14:29:58.015242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:29:58.197273image/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

노선방향구간지점번호시간1종2종3종4종5종6종7종8종9종10종11종12종
13139중부·통영대전선종점서청주~오창35231815395018273972319102012
10107순천완주선종점임실~상관27091048615148664330191613126
21472수도권제1순환선기점상일~강일1000623124461711001112310203911
25408부산외곽순환선기점한림~광재600032313811817236600411
7097서해안선종점송악~서평택1522036052535431426101012
16059중부내륙선종점남여주~서여주04512-11023944428297820201
24478고창담양선종점장성JCT~북광주25303519081571100300
8500호남선기점옥과~대덕JCT02505-11112461818171523471302211
8736호남선기점동광주~용봉02508-17245244518115603220102003
1410경부선기점영동~금강12119021519558162019129
노선방향구간지점번호시간1종2종3종4종5종6종7종8종9종10종11종12종
4629광주대구·무안광주선기점무안공항~북무안01200-14000000000000
18850중앙선종점제천~신림5520179272713617251227102702
941경부선종점금호JCT~칠곡물류1131220882646634724873294704117
14714평택제천선종점충주JCT~노은JCT401294842011240384632003901
12666중부·통영대전선종점서상~장수JCT350819421617174410103
9800순천완주선기점황전~구례화엄사2703156871571482494172113701
18199중앙선기점군위JCT~군위05509-1141211333001085590161111223
487경부선기점활천~경주00106-114104831200110135102822171214
4490남해선종점동김해~김해JCT01028-191608304721651074416901114
12021당진영덕선기점동청송·영양~영덕301641103403000000