Overview

Dataset statistics

Number of variables14
Number of observations205
Missing cells9
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.4 KiB
Average record size in memory126.6 B

Variable types

Numeric12
Categorical2

Dataset

Description경상북도 구미시 버스정보시스템의 버스제공정보이력 테이블 데이터로 버스단말기번호, 이격거리, 이격시간등의 정보를 제공합니다.
Author경상북도 구미시
URLhttps://www.data.go.kr/data/15049486/fileData.do

Alerts

앞차통과시각 is highly overall correlated with 버스단말기번호 and 12 other fieldsHigh correlation
뒤차통과시각 is highly overall correlated with 버스단말기번호 and 12 other fieldsHigh correlation
버스단말기번호 is highly overall correlated with 앞차통과시각 and 1 other fieldsHigh correlation
앞차이격거리 is highly overall correlated with 앞차이격시간 and 6 other fieldsHigh correlation
앞차이격시간 is highly overall correlated with 앞차이격거리 and 6 other fieldsHigh correlation
앞차통과정류소(ID) is highly overall correlated with 앞차이격거리 and 6 other fieldsHigh correlation
앞차차량번호 is highly overall correlated with 앞차이격거리 and 5 other fieldsHigh correlation
뒤차이격거리 is highly overall correlated with 뒤차이격시간 and 4 other fieldsHigh correlation
뒤차이격시간 is highly overall correlated with 뒤차이격거리 and 4 other fieldsHigh correlation
뒤차통과정류소식별자 is highly overall correlated with 뒤차이격거리 and 4 other fieldsHigh correlation
뒤차차량번호 is highly overall correlated with 뒤차이격거리 and 4 other fieldsHigh correlation
소통정보 is highly overall correlated with 잔여거리 and 3 other fieldsHigh correlation
잔여거리 is highly overall correlated with 앞차이격거리 and 6 other fieldsHigh correlation
남은시간 is highly overall correlated with 앞차이격거리 and 7 other fieldsHigh correlation
앞차통과시각 is highly imbalanced (89.0%)Imbalance
뒤차통과시각 is highly imbalanced (89.0%)Imbalance
앞차차량번호 has 3 (1.5%) missing valuesMissing
뒤차차량번호 has 3 (1.5%) missing valuesMissing
소통정보 has 3 (1.5%) missing valuesMissing
버스단말기번호 has unique valuesUnique
앞차이격거리 has 166 (81.0%) zerosZeros
앞차이격시간 has 168 (82.0%) zerosZeros
앞차통과정류소(ID) has 166 (81.0%) zerosZeros
앞차차량번호 has 163 (79.5%) zerosZeros
뒤차이격거리 has 138 (67.3%) zerosZeros
뒤차이격시간 has 140 (68.3%) zerosZeros
뒤차통과정류소식별자 has 139 (67.8%) zerosZeros
뒤차차량번호 has 135 (65.9%) zerosZeros
소통정보 has 24 (11.7%) zerosZeros
잔여거리 has 3 (1.5%) zerosZeros
남은시간 has 27 (13.2%) zerosZeros

Reproduction

Analysis started2023-12-12 14:34:07.992807
Analysis finished2023-12-12 14:34:23.089377
Duration15.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

버스단말기번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5300.7024
Minimum1111
Maximum9135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:23.164918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1111
5-th percentile5009.2
Q15050
median5107
Q35551
95-th percentile5591.8
Maximum9135
Range8024
Interquartile range (IQR)501

Descriptive statistics

Standard deviation519.11056
Coefficient of variation (CV)0.097932408
Kurtosis37.117598
Mean5300.7024
Median Absolute Deviation (MAD)395
Skewness-0.33992935
Sum1086644
Variance269475.77
MonotonicityStrictly increasing
2023-12-12T23:34:23.307637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111 1
 
0.5%
5539 1
 
0.5%
5529 1
 
0.5%
5530 1
 
0.5%
5531 1
 
0.5%
5532 1
 
0.5%
5533 1
 
0.5%
5534 1
 
0.5%
5535 1
 
0.5%
5536 1
 
0.5%
Other values (195) 195
95.1%
ValueCountFrequency (%)
1111 1
0.5%
3333 1
0.5%
5001 1
0.5%
5002 1
0.5%
5003 1
0.5%
5004 1
0.5%
5005 1
0.5%
5006 1
0.5%
5007 1
0.5%
5008 1
0.5%
ValueCountFrequency (%)
9135 1
0.5%
7777 1
0.5%
5607 1
0.5%
5606 1
0.5%
5605 1
0.5%
5604 1
0.5%
5602 1
0.5%
5600 1
0.5%
5597 1
0.5%
5593 1
0.5%

앞차이격거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5268293
Minimum0
Maximum56
Zeros166
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:23.463121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile23.8
Maximum56
Range56
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.7635336
Coefficient of variation (CV)2.4848194
Kurtosis9.5884243
Mean3.5268293
Median Absolute Deviation (MAD)0
Skewness2.9456849
Sum723
Variance76.799522
MonotonicityNot monotonic
2023-12-12T23:34:23.615617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 166
81.0%
16 3
 
1.5%
13 3
 
1.5%
22 3
 
1.5%
10 2
 
1.0%
6 2
 
1.0%
14 2
 
1.0%
8 2
 
1.0%
26 2
 
1.0%
15 2
 
1.0%
Other values (17) 18
 
8.8%
ValueCountFrequency (%)
0 166
81.0%
3 1
 
0.5%
4 2
 
1.0%
6 2
 
1.0%
7 1
 
0.5%
8 2
 
1.0%
10 2
 
1.0%
11 1
 
0.5%
13 3
 
1.5%
14 2
 
1.0%
ValueCountFrequency (%)
56 1
0.5%
40 1
0.5%
38 1
0.5%
34 1
0.5%
31 1
0.5%
30 1
0.5%
28 1
0.5%
26 2
1.0%
25 1
0.5%
24 1
0.5%

앞차이격시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)14.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8731707
Minimum0
Maximum413
Zeros168
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:23.741647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile36.8
Maximum413
Range413
Interquartile range (IQR)0

Descriptive statistics

Standard deviation31.191432
Coefficient of variation (CV)4.538143
Kurtosis142.40355
Mean6.8731707
Median Absolute Deviation (MAD)0
Skewness11.106599
Sum1409
Variance972.9054
MonotonicityNot monotonic
2023-12-12T23:34:23.880693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0 168
82.0%
44 3
 
1.5%
33 3
 
1.5%
39 2
 
1.0%
27 2
 
1.0%
5 2
 
1.0%
21 2
 
1.0%
16 2
 
1.0%
10 1
 
0.5%
52 1
 
0.5%
Other values (19) 19
 
9.3%
ValueCountFrequency (%)
0 168
82.0%
5 2
 
1.0%
6 1
 
0.5%
8 1
 
0.5%
9 1
 
0.5%
10 1
 
0.5%
12 1
 
0.5%
13 1
 
0.5%
15 1
 
0.5%
16 2
 
1.0%
ValueCountFrequency (%)
413 1
 
0.5%
89 1
 
0.5%
52 1
 
0.5%
46 1
 
0.5%
44 3
1.5%
43 1
 
0.5%
39 2
1.0%
37 1
 
0.5%
36 1
 
0.5%
34 1
 
0.5%

앞차통과정류소(ID)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct39
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.83415
Minimum0
Maximum2644
Zeros166
Zeros (%)81.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:24.044448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile756.2
Maximum2644
Range2644
Interquartile range (IQR)0

Descriptive statistics

Standard deviation345.78744
Coefficient of variation (CV)2.9596436
Kurtosis24.248148
Mean116.83415
Median Absolute Deviation (MAD)0
Skewness4.4548346
Sum23951
Variance119568.95
MonotonicityNot monotonic
2023-12-12T23:34:24.218494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 166
81.0%
795 2
 
1.0%
559 1
 
0.5%
259 1
 
0.5%
134 1
 
0.5%
366 1
 
0.5%
741 1
 
0.5%
361 1
 
0.5%
316 1
 
0.5%
79 1
 
0.5%
Other values (29) 29
 
14.1%
ValueCountFrequency (%)
0 166
81.0%
5 1
 
0.5%
37 1
 
0.5%
46 1
 
0.5%
79 1
 
0.5%
94 1
 
0.5%
134 1
 
0.5%
137 1
 
0.5%
141 1
 
0.5%
245 1
 
0.5%
ValueCountFrequency (%)
2644 1
0.5%
2213 1
0.5%
2033 1
0.5%
1066 1
0.5%
1042 1
0.5%
863 1
0.5%
857 1
0.5%
813 1
0.5%
795 2
1.0%
760 1
0.5%

앞차통과시각
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
202 
<NA>
 
3

Length

Max length4
Median length1
Mean length1.0439024
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 202
98.5%
<NA> 3
 
1.5%

Length

2023-12-12T23:34:24.351506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:34:24.459680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 202
98.5%
na 3
 
1.5%

앞차차량번호
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct39
Distinct (%)19.3%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1032.4505
Minimum0
Maximum5607
Zeros163
Zeros (%)79.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:24.570572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5567.75
Maximum5607
Range5607
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2118.9421
Coefficient of variation (CV)2.0523425
Kurtosis0.51100268
Mean1032.4505
Median Absolute Deviation (MAD)0
Skewness1.5775065
Sum208555
Variance4489915.4
MonotonicityNot monotonic
2023-12-12T23:34:24.719628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
0 163
79.5%
5037 2
 
1.0%
5051 1
 
0.5%
5550 1
 
0.5%
5524 1
 
0.5%
5506 1
 
0.5%
5575 1
 
0.5%
5591 1
 
0.5%
5593 1
 
0.5%
5607 1
 
0.5%
Other values (29) 29
 
14.1%
(Missing) 3
 
1.5%
ValueCountFrequency (%)
0 163
79.5%
5002 1
 
0.5%
5014 1
 
0.5%
5020 1
 
0.5%
5021 1
 
0.5%
5036 1
 
0.5%
5037 2
 
1.0%
5038 1
 
0.5%
5047 1
 
0.5%
5048 1
 
0.5%
ValueCountFrequency (%)
5607 1
0.5%
5600 1
0.5%
5597 1
0.5%
5593 1
0.5%
5591 1
0.5%
5589 1
0.5%
5581 1
0.5%
5575 1
0.5%
5571 1
0.5%
5569 1
0.5%

뒤차이격거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8146341
Minimum0
Maximum56
Zeros138
Zeros (%)67.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:24.866711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile27.8
Maximum56
Range56
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.52936
Coefficient of variation (CV)1.810838
Kurtosis4.4141726
Mean5.8146341
Median Absolute Deviation (MAD)0
Skewness2.072204
Sum1192
Variance110.86743
MonotonicityNot monotonic
2023-12-12T23:34:25.034293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 138
67.3%
15 5
 
2.4%
7 5
 
2.4%
22 4
 
2.0%
19 4
 
2.0%
8 4
 
2.0%
13 3
 
1.5%
18 3
 
1.5%
26 3
 
1.5%
10 3
 
1.5%
Other values (22) 33
 
16.1%
ValueCountFrequency (%)
0 138
67.3%
1 1
 
0.5%
2 2
 
1.0%
4 1
 
0.5%
5 2
 
1.0%
6 2
 
1.0%
7 5
 
2.4%
8 4
 
2.0%
9 1
 
0.5%
10 3
 
1.5%
ValueCountFrequency (%)
56 1
 
0.5%
51 1
 
0.5%
41 1
 
0.5%
39 1
 
0.5%
37 1
 
0.5%
33 1
 
0.5%
31 1
 
0.5%
30 1
 
0.5%
28 3
1.5%
27 2
1.0%

뒤차이격시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8536585
Minimum0
Maximum162
Zeros140
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:25.193035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q315
95-th percentile46.4
Maximum162
Range162
Interquartile range (IQR)15

Descriptive statistics

Standard deviation19.601872
Coefficient of variation (CV)1.9892989
Kurtosis18.714441
Mean9.8536585
Median Absolute Deviation (MAD)0
Skewness3.4271552
Sum2020
Variance384.23338
MonotonicityNot monotonic
2023-12-12T23:34:25.356704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 140
68.3%
16 4
 
2.0%
17 4
 
2.0%
33 4
 
2.0%
13 3
 
1.5%
37 3
 
1.5%
34 3
 
1.5%
15 2
 
1.0%
39 2
 
1.0%
5 2
 
1.0%
Other values (27) 38
 
18.5%
ValueCountFrequency (%)
0 140
68.3%
5 2
 
1.0%
6 1
 
0.5%
8 2
 
1.0%
9 1
 
0.5%
10 2
 
1.0%
12 1
 
0.5%
13 3
 
1.5%
15 2
 
1.0%
16 4
 
2.0%
ValueCountFrequency (%)
162 1
0.5%
89 1
0.5%
82 1
0.5%
53 2
1.0%
52 1
0.5%
50 2
1.0%
48 1
0.5%
47 2
1.0%
44 2
1.0%
42 1
0.5%

뒤차통과정류소식별자
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)28.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.9122
Minimum0
Maximum5380
Zeros139
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:25.540374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3259
95-th percentile1101.8
Maximum5380
Range5380
Interquartile range (IQR)259

Descriptive statistics

Standard deviation748.92261
Coefficient of variation (CV)2.647191
Kurtosis21.274997
Mean282.9122
Median Absolute Deviation (MAD)0
Skewness4.310714
Sum57997
Variance560885.07
MonotonicityNot monotonic
2023-12-12T23:34:25.707225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139
67.8%
46 3
 
1.5%
669 2
 
1.0%
861 2
 
1.0%
751 2
 
1.0%
366 2
 
1.0%
259 2
 
1.0%
401 2
 
1.0%
5380 1
 
0.5%
79 1
 
0.5%
Other values (49) 49
 
23.9%
ValueCountFrequency (%)
0 139
67.8%
38 1
 
0.5%
40 1
 
0.5%
46 3
 
1.5%
75 1
 
0.5%
79 1
 
0.5%
80 1
 
0.5%
91 1
 
0.5%
92 1
 
0.5%
94 1
 
0.5%
ValueCountFrequency (%)
5380 1
0.5%
4369 1
0.5%
4208 1
0.5%
4197 1
0.5%
2431 1
0.5%
2216 1
0.5%
2214 1
0.5%
2212 1
0.5%
2170 1
0.5%
2095 1
0.5%

뒤차통과시각
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
0
202 
<NA>
 
3

Length

Max length4
Median length1
Mean length1.0439024
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 202
98.5%
<NA> 3
 
1.5%

Length

2023-12-12T23:34:25.854426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:34:25.974634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 202
98.5%
na 3
 
1.5%

뒤차차량번호
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct65
Distinct (%)32.2%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1767.7426
Minimum0
Maximum5600
Zeros135
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:26.081581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35047.75
95-th percentile5562.9
Maximum5600
Range5600
Interquartile range (IQR)5047.75

Descriptive statistics

Standard deviation2519.5607
Coefficient of variation (CV)1.4252984
Kurtosis-1.4666996
Mean1767.7426
Median Absolute Deviation (MAD)0
Skewness0.73056491
Sum357084
Variance6348186.4
MonotonicityNot monotonic
2023-12-12T23:34:26.263971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 135
65.9%
5553 2
 
1.0%
5017 2
 
1.0%
5030 2
 
1.0%
5508 1
 
0.5%
5502 1
 
0.5%
5537 1
 
0.5%
5564 1
 
0.5%
5587 1
 
0.5%
5518 1
 
0.5%
Other values (55) 55
26.8%
(Missing) 3
 
1.5%
ValueCountFrequency (%)
0 135
65.9%
5001 1
 
0.5%
5002 1
 
0.5%
5008 1
 
0.5%
5013 1
 
0.5%
5017 2
 
1.0%
5020 1
 
0.5%
5022 1
 
0.5%
5030 2
 
1.0%
5039 1
 
0.5%
ValueCountFrequency (%)
5600 1
0.5%
5593 1
0.5%
5591 1
0.5%
5587 1
0.5%
5585 1
0.5%
5579 1
0.5%
5575 1
0.5%
5569 1
0.5%
5568 1
0.5%
5564 1
0.5%

소통정보
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct11
Distinct (%)5.4%
Missing3
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean1936.005
Minimum0
Maximum3222
Zeros24
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:26.414109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12000
median2222
Q32222
95-th percentile2222
Maximum3222
Range3222
Interquartile range (IQR)222

Descriptive statistics

Standard deviation729.48256
Coefficient of variation (CV)0.37679788
Kurtosis3.2123122
Mean1936.005
Median Absolute Deviation (MAD)0
Skewness-2.1369062
Sum391073
Variance532144.81
MonotonicityNot monotonic
2023-12-12T23:34:26.848680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2222 114
55.6%
2000 28
 
13.7%
0 24
 
11.7%
2220 19
 
9.3%
2200 9
 
4.4%
2223 2
 
1.0%
3222 2
 
1.0%
1331 1
 
0.5%
2210 1
 
0.5%
2232 1
 
0.5%
(Missing) 3
 
1.5%
ValueCountFrequency (%)
0 24
 
11.7%
1331 1
 
0.5%
2000 28
 
13.7%
2200 9
 
4.4%
2210 1
 
0.5%
2220 19
 
9.3%
2222 114
55.6%
2223 2
 
1.0%
2232 1
 
0.5%
3122 1
 
0.5%
ValueCountFrequency (%)
3222 2
 
1.0%
3122 1
 
0.5%
2232 1
 
0.5%
2223 2
 
1.0%
2222 114
55.6%
2220 19
 
9.3%
2210 1
 
0.5%
2200 9
 
4.4%
2000 28
 
13.7%
1331 1
 
0.5%

잔여거리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct134
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean779.06829
Minimum0
Maximum4170
Zeros3
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:27.003750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.4
Q1157
median311
Q31173
95-th percentile2571.4
Maximum4170
Range4170
Interquartile range (IQR)1016

Descriptive statistics

Standard deviation853.9781
Coefficient of variation (CV)1.0961531
Kurtosis1.3207463
Mean779.06829
Median Absolute Deviation (MAD)264
Skewness1.3697467
Sum159709
Variance729278.59
MonotonicityNot monotonic
2023-12-12T23:34:27.146907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
282 12
 
5.9%
60 10
 
4.9%
173 9
 
4.4%
152 8
 
3.9%
120 7
 
3.4%
157 6
 
2.9%
53 5
 
2.4%
232 4
 
2.0%
13 4
 
2.0%
196 3
 
1.5%
Other values (124) 137
66.8%
ValueCountFrequency (%)
0 3
 
1.5%
1 2
 
1.0%
2 1
 
0.5%
4 1
 
0.5%
13 4
 
2.0%
40 1
 
0.5%
44 1
 
0.5%
47 2
 
1.0%
53 5
2.4%
60 10
4.9%
ValueCountFrequency (%)
4170 1
0.5%
3359 1
0.5%
3277 1
0.5%
3132 1
0.5%
3112 1
0.5%
2944 1
0.5%
2761 1
0.5%
2647 1
0.5%
2645 1
0.5%
2579 1
0.5%

남은시간
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean955.57561
Minimum0
Maximum4813
Zeros27
Zeros (%)13.2%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T23:34:27.279825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1144
median370
Q31494
95-th percentile3206.8
Maximum4813
Range4813
Interquartile range (IQR)1350

Descriptive statistics

Standard deviation1084.172
Coefficient of variation (CV)1.1345747
Kurtosis0.72222051
Mean955.57561
Median Absolute Deviation (MAD)370
Skewness1.2405899
Sum195893
Variance1175428.8
MonotonicityNot monotonic
2023-12-12T23:34:27.428351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27
 
13.2%
271 12
 
5.9%
162 11
 
5.4%
144 8
 
3.9%
94 7
 
3.4%
54 5
 
2.4%
211 3
 
1.5%
235 3
 
1.5%
51 2
 
1.0%
670 2
 
1.0%
Other values (118) 125
61.0%
ValueCountFrequency (%)
0 27
13.2%
43 2
 
1.0%
47 1
 
0.5%
50 1
 
0.5%
51 2
 
1.0%
54 5
 
2.4%
94 7
 
3.4%
125 1
 
0.5%
141 1
 
0.5%
142 1
 
0.5%
ValueCountFrequency (%)
4813 1
0.5%
4239 1
0.5%
4003 1
0.5%
3724 1
0.5%
3559 1
0.5%
3528 1
0.5%
3496 1
0.5%
3432 1
0.5%
3421 1
0.5%
3287 1
0.5%

Interactions

2023-12-12T23:34:21.435221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:08.813724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.145722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.376269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.608230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.707573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.617869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.709704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.875621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.870245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.907490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:20.080120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:21.546711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:08.933775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.249128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.487995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.718435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.798257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.708585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.813905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.965775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.950319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.979281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:20.202251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:21.625815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:09.042417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.338031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.592129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.807647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.874470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.772243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.908880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.038665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.065504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.070591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:20.301423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:21.707062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:09.139921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.430933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.693420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.901020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.949084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.839531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.996282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.116261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.132654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.157505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:20.383009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:21.792763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:09.231980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.537698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.795609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.991626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.033799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.904492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.128286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.194937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.239913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.262762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:20.466738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:21.877143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:09.319984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.649460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.892137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.076535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.111300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.966676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.214358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.275217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.332621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.354669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:20.546876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:21.972202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:09.450795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.738890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.984097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.165878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.192291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.252987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.297837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.354112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.421701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.446988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:20.632965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:22.064786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:09.590785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.847561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.112931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.274396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.271373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.327554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.399518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.455029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.528507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.547731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:20.732127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:22.163693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:09.714353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.947264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.212914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.368481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.345789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.397103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.523977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.533324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.600061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.640446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:20.814774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:22.245553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:09.805469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.064493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.323118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.459506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.411517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.478570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.614081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.614375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.672156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.794314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:21.186481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:22.336440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:09.923138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.174583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.419777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.535597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.488012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.559100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.697585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.698579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.747622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.918995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:21.263359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:22.426897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:10.034639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:11.277933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:12.512976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:13.612612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:14.552859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:15.630841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:16.785062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:17.780795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:18.817833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:19.998204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:34:21.339882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:34:27.522180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
버스단말기번호앞차이격거리앞차이격시간앞차통과정류소(ID)앞차차량번호뒤차이격거리뒤차이격시간뒤차통과정류소식별자뒤차차량번호소통정보잔여거리남은시간
버스단말기번호1.0000.0000.0000.0000.4770.0000.0000.0000.4900.6750.2300.297
앞차이격거리0.0001.0000.8720.7720.9510.0000.0000.0000.0000.0000.6110.709
앞차이격시간0.0000.8721.0000.7590.4200.0000.0000.0000.0000.0000.5640.682
앞차통과정류소(ID)0.0000.7720.7591.0000.7470.0000.0000.0000.0000.0000.5040.519
앞차차량번호0.4770.9510.4200.7471.0000.0000.0000.0000.1990.0660.6250.615
뒤차이격거리0.0000.0000.0000.0000.0001.0000.8480.8130.9260.0000.0000.000
뒤차이격시간0.0000.0000.0000.0000.0000.8481.0000.5780.8810.0000.0000.000
뒤차통과정류소식별자0.0000.0000.0000.0000.0000.8130.5781.0000.5480.0000.0000.000
뒤차차량번호0.4900.0000.0000.0000.1990.9260.8810.5481.0000.0000.2030.266
소통정보0.6750.0000.0000.0000.0660.0000.0000.0000.0001.0000.4730.443
잔여거리0.2300.6110.5640.5040.6250.0000.0000.0000.2030.4731.0000.984
남은시간0.2970.7090.6820.5190.6150.0000.0000.0000.2660.4430.9841.000
2023-12-12T23:34:27.660909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
앞차통과시각뒤차통과시각
앞차통과시각1.0001.000
뒤차통과시각1.0001.000
2023-12-12T23:34:27.750857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
버스단말기번호앞차이격거리앞차이격시간앞차통과정류소(ID)앞차차량번호뒤차이격거리뒤차이격시간뒤차통과정류소식별자뒤차차량번호소통정보잔여거리남은시간앞차통과시각뒤차통과시각
버스단말기번호1.0000.0620.0560.0820.1180.0870.1050.1150.1780.1190.0110.0241.0001.000
앞차이격거리0.0621.0000.9670.9860.982-0.120-0.122-0.079-0.0650.3300.5120.5151.0001.000
앞차이격시간0.0560.9671.0000.9630.958-0.103-0.106-0.061-0.0470.3400.5030.5071.0001.000
앞차통과정류소(ID)0.0820.9860.9631.0000.986-0.088-0.092-0.043-0.0260.3300.5020.5051.0001.000
앞차차량번호0.1180.9820.9580.9861.000-0.096-0.100-0.046-0.0220.3260.4970.5001.0001.000
뒤차이격거리0.087-0.120-0.103-0.088-0.0961.0000.9740.9610.9470.004-0.175-0.1811.0001.000
뒤차이격시간0.105-0.122-0.106-0.092-0.1000.9741.0000.9580.935-0.013-0.187-0.1891.0001.000
뒤차통과정류소식별자0.115-0.079-0.061-0.043-0.0460.9610.9581.0000.948-0.003-0.181-0.1821.0001.000
뒤차차량번호0.178-0.065-0.047-0.026-0.0220.9470.9350.9481.0000.016-0.178-0.1831.0001.000
소통정보0.1190.3300.3400.3300.3260.004-0.013-0.0030.0161.0000.7820.8121.0001.000
잔여거리0.0110.5120.5030.5020.497-0.175-0.187-0.181-0.1780.7821.0000.9851.0001.000
남은시간0.0240.5150.5070.5050.500-0.181-0.189-0.182-0.1830.8120.9851.0001.0001.000
앞차통과시각1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
뒤차통과시각1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T23:34:22.583093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:34:22.844859image/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.
2023-12-12T23:34:23.013005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

버스단말기번호앞차이격거리앞차이격시간앞차통과정류소(ID)앞차통과시각앞차차량번호뒤차이격거리뒤차이격시간뒤차통과정류소식별자뒤차통과시각뒤차차량번호소통정보잔여거리남은시간
01111000<NA><NA>000<NA><NA><NA>00
133330000000000133126453724
25001000001853781050602222442501
3500200000132346050672220173162
4500300000000002000282271
5500400000000002000282271
6500500000000000130
750060000000000222215391625
850070000000000222210721326
9500826365590506000000222220562666
버스단말기번호앞차이격거리앞차이격시간앞차통과정류소(ID)앞차통과시각앞차차량번호뒤차이격거리뒤차이격시간뒤차통과정류소식별자뒤차통과시각뒤차차량번호소통정보잔여거리남은시간
19555938108570560000000222226473559
1965597000002637861055052222392596
19756007124960556391013405593222223383099
1985602000000000001570
1995604000001950399055022222279338
200560500000000002000282271
2015606000001523366055082222232235
2025607000002227652055502200227245
2037777000<NA><NA>000<NA><NA><NA>00
2049135000<NA><NA>000<NA><NA><NA>00