Overview

Dataset statistics

Number of variables19
Number of observations121
Missing cells1423
Missing cells (%)61.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.3 KiB
Average record size in memory172.1 B

Variable types

Numeric19

Dataset

DescriptionSRT 열차별 역별 하차인원 수에 따른 공공데이터로 SRT가 운행중인 경부선, 호남선 모두 포함되어 있습니다.(연간데이터)
URLhttps://www.data.go.kr/data/15081921/fileData.do

Alerts

열차번호 is highly overall correlated with 부산 and 1 other fieldsHigh correlation
수서 is highly overall correlated with 공주High correlation
동탄 is highly overall correlated with 김천구미 and 5 other fieldsHigh correlation
평택지제 is highly overall correlated with 부산 and 1 other fieldsHigh correlation
천안아산 is highly overall correlated with 서대구 and 3 other fieldsHigh correlation
오송 is highly overall correlated with 대전 and 9 other fieldsHigh correlation
대전 is highly overall correlated with 오송 and 5 other fieldsHigh correlation
김천구미 is highly overall correlated with 동탄 and 6 other fieldsHigh correlation
서대구 is highly overall correlated with 동탄 and 8 other fieldsHigh correlation
동대구 is highly overall correlated with 동탄 and 6 other fieldsHigh correlation
신경주 is highly overall correlated with 동탄 and 7 other fieldsHigh correlation
울산 is highly overall correlated with 동탄 and 6 other fieldsHigh correlation
부산 is highly overall correlated with 열차번호 and 3 other fieldsHigh correlation
공주 is highly overall correlated with 수서 and 5 other fieldsHigh correlation
익산 is highly overall correlated with 천안아산 and 5 other fieldsHigh correlation
정읍 is highly overall correlated with 천안아산 and 4 other fieldsHigh correlation
광주송정 is highly overall correlated with 열차번호 and 5 other fieldsHigh correlation
나주 is highly overall correlated with 동탄 and 4 other fieldsHigh correlation
목포 is highly overall correlated with 평택지제 and 2 other fieldsHigh correlation
수서 has 60 (49.6%) missing valuesMissing
동탄 has 49 (40.5%) missing valuesMissing
평택지제 has 75 (62.0%) missing valuesMissing
천안아산 has 57 (47.1%) missing valuesMissing
오송 has 60 (49.6%) missing valuesMissing
대전 has 41 (33.9%) missing valuesMissing
김천구미 has 100 (82.6%) missing valuesMissing
서대구 has 111 (91.7%) missing valuesMissing
동대구 has 41 (33.9%) missing valuesMissing
신경주 has 89 (73.6%) missing valuesMissing
울산 has 72 (59.5%) missing valuesMissing
부산 has 81 (66.9%) missing valuesMissing
공주 has 102 (84.3%) missing valuesMissing
익산 has 81 (66.9%) missing valuesMissing
정읍 has 97 (80.2%) missing valuesMissing
광주송정 has 92 (76.0%) missing valuesMissing
나주 has 103 (85.1%) missing valuesMissing
목포 has 112 (92.6%) missing valuesMissing
열차번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:27:31.332452
Analysis finished2023-12-12 06:28:11.410080
Duration40.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

열차번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.1157
Minimum301
Maximum690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:11.490852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile307
Q1331
median361
Q3611
95-th percentile663
Maximum690
Range389
Interquartile range (IQR)280

Descriptive statistics

Standard deviation141.79981
Coefficient of variation (CV)0.32218758
Kurtosis-1.4441791
Mean440.1157
Median Absolute Deviation (MAD)41
Skewness0.66972547
Sum53254
Variance20107.187
MonotonicityStrictly increasing
2023-12-12T15:28:11.640627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301 1
 
0.8%
612 1
 
0.8%
610 1
 
0.8%
609 1
 
0.8%
608 1
 
0.8%
607 1
 
0.8%
606 1
 
0.8%
605 1
 
0.8%
604 1
 
0.8%
603 1
 
0.8%
Other values (111) 111
91.7%
ValueCountFrequency (%)
301 1
0.8%
302 1
0.8%
303 1
0.8%
304 1
0.8%
305 1
0.8%
306 1
0.8%
307 1
0.8%
308 1
0.8%
309 1
0.8%
310 1
0.8%
ValueCountFrequency (%)
690 1
0.8%
668 1
0.8%
667 1
0.8%
666 1
0.8%
665 1
0.8%
664 1
0.8%
663 1
0.8%
662 1
0.8%
661 1
0.8%
660 1
0.8%

수서
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)100.0%
Missing60
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean123081.66
Minimum36217
Maximum206963
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:11.827706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36217
5-th percentile46280
Q1102385
median123418
Q3143487
95-th percentile193206
Maximum206963
Range170746
Interquartile range (IQR)41102

Descriptive statistics

Standard deviation38732.874
Coefficient of variation (CV)0.3146925
Kurtosis0.22967406
Mean123081.66
Median Absolute Deviation (MAD)20647
Skewness-0.13191814
Sum7507981
Variance1.5002356 × 109
MonotonicityNot monotonic
2023-12-12T15:28:12.013519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
142610 1
 
0.8%
101302 1
 
0.8%
61871 1
 
0.8%
65106 1
 
0.8%
92806 1
 
0.8%
56272 1
 
0.8%
36217 1
 
0.8%
42801 1
 
0.8%
103431 1
 
0.8%
135222 1
 
0.8%
Other values (51) 51
42.1%
(Missing) 60
49.6%
ValueCountFrequency (%)
36217 1
0.8%
42801 1
0.8%
43958 1
0.8%
46280 1
0.8%
56272 1
0.8%
61871 1
0.8%
65106 1
0.8%
76578 1
0.8%
85542 1
0.8%
92806 1
0.8%
ValueCountFrequency (%)
206963 1
0.8%
202565 1
0.8%
195010 1
0.8%
193206 1
0.8%
189109 1
0.8%
184242 1
0.8%
175881 1
0.8%
157252 1
0.8%
152226 1
0.8%
149842 1
0.8%

동탄
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct72
Distinct (%)100.0%
Missing49
Missing (%)40.5%
Infinite0
Infinite (%)0.0%
Mean23961.125
Minimum892
Maximum64196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:12.193287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum892
5-th percentile5502.7
Q111224.5
median17865
Q335410.75
95-th percentile55406.2
Maximum64196
Range63304
Interquartile range (IQR)24186.25

Descriptive statistics

Standard deviation16047.696
Coefficient of variation (CV)0.66973882
Kurtosis-0.43907228
Mean23961.125
Median Absolute Deviation (MAD)9958
Skewness0.70092204
Sum1725201
Variance2.5752854 × 108
MonotonicityNot monotonic
2023-12-12T15:28:12.332892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15767 1
 
0.8%
33105 1
 
0.8%
36271 1
 
0.8%
13279 1
 
0.8%
34787 1
 
0.8%
8975 1
 
0.8%
25007 1
 
0.8%
10236 1
 
0.8%
12388 1
 
0.8%
10477 1
 
0.8%
Other values (62) 62
51.2%
(Missing) 49
40.5%
ValueCountFrequency (%)
892 1
0.8%
1052 1
0.8%
1970 1
0.8%
4670 1
0.8%
6184 1
0.8%
6198 1
0.8%
6787 1
0.8%
7494 1
0.8%
8432 1
0.8%
8527 1
0.8%
ValueCountFrequency (%)
64196 1
0.8%
61678 1
0.8%
56217 1
0.8%
56196 1
0.8%
54760 1
0.8%
53118 1
0.8%
47603 1
0.8%
45063 1
0.8%
44891 1
0.8%
43628 1
0.8%

평택지제
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)100.0%
Missing75
Missing (%)62.0%
Infinite0
Infinite (%)0.0%
Mean23933.565
Minimum9284
Maximum50340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:12.476508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9284
5-th percentile11376.75
Q118318.75
median22519.5
Q329870.75
95-th percentile38593.25
Maximum50340
Range41056
Interquartile range (IQR)11552

Descriptive statistics

Standard deviation8897.2655
Coefficient of variation (CV)0.37174844
Kurtosis0.53915389
Mean23933.565
Median Absolute Deviation (MAD)5405
Skewness0.64003538
Sum1100944
Variance79161334
MonotonicityNot monotonic
2023-12-12T15:28:12.673117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
21351 1
 
0.8%
31332 1
 
0.8%
39247 1
 
0.8%
26716 1
 
0.8%
29067 1
 
0.8%
9284 1
 
0.8%
19927 1
 
0.8%
20139 1
 
0.8%
17727 1
 
0.8%
20481 1
 
0.8%
Other values (36) 36
29.8%
(Missing) 75
62.0%
ValueCountFrequency (%)
9284 1
0.8%
10898 1
0.8%
11286 1
0.8%
11649 1
0.8%
11748 1
0.8%
11759 1
0.8%
13352 1
0.8%
15708 1
0.8%
17130 1
0.8%
17727 1
0.8%
ValueCountFrequency (%)
50340 1
0.8%
42685 1
0.8%
39247 1
0.8%
36632 1
0.8%
33923 1
0.8%
33691 1
0.8%
32408 1
0.8%
32025 1
0.8%
31917 1
0.8%
31332 1
0.8%

천안아산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct64
Distinct (%)100.0%
Missing57
Missing (%)47.1%
Infinite0
Infinite (%)0.0%
Mean18428.5
Minimum3460
Maximum41400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:12.831619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3460
5-th percentile8179.15
Q112840.25
median18062.5
Q322236.5
95-th percentile32792.55
Maximum41400
Range37940
Interquartile range (IQR)9396.25

Descriptive statistics

Standard deviation7769.0912
Coefficient of variation (CV)0.42158023
Kurtosis0.48127557
Mean18428.5
Median Absolute Deviation (MAD)4991
Skewness0.71153642
Sum1179424
Variance60358778
MonotonicityNot monotonic
2023-12-12T15:28:12.973336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16643 1
 
0.8%
18800 1
 
0.8%
11346 1
 
0.8%
34382 1
 
0.8%
19792 1
 
0.8%
10368 1
 
0.8%
19158 1
 
0.8%
25833 1
 
0.8%
17708 1
 
0.8%
3460 1
 
0.8%
Other values (54) 54
44.6%
(Missing) 57
47.1%
ValueCountFrequency (%)
3460 1
0.8%
5800 1
0.8%
8132 1
0.8%
8176 1
0.8%
8197 1
0.8%
8900 1
0.8%
9734 1
0.8%
9767 1
0.8%
10368 1
0.8%
10665 1
0.8%
ValueCountFrequency (%)
41400 1
0.8%
37217 1
0.8%
34382 1
0.8%
32952 1
0.8%
31889 1
0.8%
30013 1
0.8%
29446 1
0.8%
28369 1
0.8%
27034 1
0.8%
25969 1
0.8%

오송
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)100.0%
Missing60
Missing (%)49.6%
Infinite0
Infinite (%)0.0%
Mean18282.443
Minimum3310
Maximum45361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:13.103511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3310
5-th percentile6491
Q111571
median17398
Q323563
95-th percentile32730
Maximum45361
Range42051
Interquartile range (IQR)11992

Descriptive statistics

Standard deviation8979.9996
Coefficient of variation (CV)0.49118161
Kurtosis0.14057761
Mean18282.443
Median Absolute Deviation (MAD)6091
Skewness0.62309297
Sum1115229
Variance80640393
MonotonicityNot monotonic
2023-12-12T15:28:13.225995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18523 1
 
0.8%
22180 1
 
0.8%
6379 1
 
0.8%
8412 1
 
0.8%
13754 1
 
0.8%
3310 1
 
0.8%
9991 1
 
0.8%
33561 1
 
0.8%
11351 1
 
0.8%
21134 1
 
0.8%
Other values (51) 51
42.1%
(Missing) 60
49.6%
ValueCountFrequency (%)
3310 1
0.8%
5498 1
0.8%
6379 1
0.8%
6491 1
0.8%
6607 1
0.8%
6750 1
0.8%
7162 1
0.8%
7419 1
0.8%
8389 1
0.8%
8412 1
0.8%
ValueCountFrequency (%)
45361 1
0.8%
38218 1
0.8%
33561 1
0.8%
32730 1
0.8%
31557 1
0.8%
30259 1
0.8%
30182 1
0.8%
30026 1
0.8%
29887 1
0.8%
27675 1
0.8%

대전
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)100.0%
Missing41
Missing (%)33.9%
Infinite0
Infinite (%)0.0%
Mean21377.925
Minimum4513
Maximum42044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:13.368112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4513
5-th percentile7431
Q114031.25
median20852
Q328385.5
95-th percentile35742.45
Maximum42044
Range37531
Interquartile range (IQR)14354.25

Descriptive statistics

Standard deviation8730.0832
Coefficient of variation (CV)0.40836906
Kurtosis-0.76541485
Mean21377.925
Median Absolute Deviation (MAD)6994.5
Skewness0.19442621
Sum1710234
Variance76214353
MonotonicityNot monotonic
2023-12-12T15:28:13.550740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19151 1
 
0.8%
16623 1
 
0.8%
27707 1
 
0.8%
14791 1
 
0.8%
36150 1
 
0.8%
22199 1
 
0.8%
35721 1
 
0.8%
26872 1
 
0.8%
27646 1
 
0.8%
20759 1
 
0.8%
Other values (70) 70
57.9%
(Missing) 41
33.9%
ValueCountFrequency (%)
4513 1
0.8%
6125 1
0.8%
6760 1
0.8%
7393 1
0.8%
7433 1
0.8%
8236 1
0.8%
10118 1
0.8%
10651 1
0.8%
11046 1
0.8%
11247 1
0.8%
ValueCountFrequency (%)
42044 1
0.8%
36648 1
0.8%
36152 1
0.8%
36150 1
0.8%
35721 1
0.8%
35488 1
0.8%
34615 1
0.8%
34174 1
0.8%
33697 1
0.8%
32741 1
0.8%

김천구미
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing100
Missing (%)82.6%
Infinite0
Infinite (%)0.0%
Mean12549.476
Minimum1293
Maximum28190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:13.700930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1293
5-th percentile3819
Q15819
median13259
Q317063
95-th percentile24217
Maximum28190
Range26897
Interquartile range (IQR)11244

Descriptive statistics

Standard deviation7478.7662
Coefficient of variation (CV)0.5959425
Kurtosis-0.76813913
Mean12549.476
Median Absolute Deviation (MAD)6453
Skewness0.35608326
Sum263539
Variance55931944
MonotonicityNot monotonic
2023-12-12T15:28:13.842724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
24217 1
 
0.8%
15594 1
 
0.8%
19272 1
 
0.8%
10498 1
 
0.8%
13259 1
 
0.8%
16815 1
 
0.8%
4510 1
 
0.8%
8805 1
 
0.8%
28190 1
 
0.8%
13627 1
 
0.8%
Other values (11) 11
 
9.1%
(Missing) 100
82.6%
ValueCountFrequency (%)
1293 1
0.8%
3819 1
0.8%
4204 1
0.8%
4510 1
0.8%
5450 1
0.8%
5819 1
0.8%
6199 1
0.8%
8380 1
0.8%
8805 1
0.8%
10498 1
0.8%
ValueCountFrequency (%)
28190 1
0.8%
24217 1
0.8%
21092 1
0.8%
19712 1
0.8%
19272 1
0.8%
17063 1
0.8%
16815 1
0.8%
15721 1
0.8%
15594 1
0.8%
13627 1
0.8%

서대구
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing111
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean9170.5
Minimum754
Maximum18139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:13.947863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum754
5-th percentile1229.65
Q14555
median7839
Q315579.75
95-th percentile17804.65
Maximum18139
Range17385
Interquartile range (IQR)11024.75

Descriptive statistics

Standard deviation6571.8659
Coefficient of variation (CV)0.71663114
Kurtosis-1.5350737
Mean9170.5
Median Absolute Deviation (MAD)4855.5
Skewness0.28901725
Sum91705
Variance43189421
MonotonicityNot monotonic
2023-12-12T15:28:14.117446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
754 1
 
0.8%
1811 1
 
0.8%
9922 1
 
0.8%
10869 1
 
0.8%
18139 1
 
0.8%
5752 1
 
0.8%
4156 1
 
0.8%
17150 1
 
0.8%
17396 1
 
0.8%
5756 1
 
0.8%
(Missing) 111
91.7%
ValueCountFrequency (%)
754 1
0.8%
1811 1
0.8%
4156 1
0.8%
5752 1
0.8%
5756 1
0.8%
9922 1
0.8%
10869 1
0.8%
17150 1
0.8%
17396 1
0.8%
18139 1
0.8%
ValueCountFrequency (%)
18139 1
0.8%
17396 1
0.8%
17150 1
0.8%
10869 1
0.8%
9922 1
0.8%
5756 1
0.8%
5752 1
0.8%
4156 1
0.8%
1811 1
0.8%
754 1
0.8%

동대구
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)100.0%
Missing41
Missing (%)33.9%
Infinite0
Infinite (%)0.0%
Mean30788.375
Minimum2201
Maximum74939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:14.289979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2201
5-th percentile9089.3
Q115789.5
median29081
Q345537.25
95-th percentile59511.05
Maximum74939
Range72738
Interquartile range (IQR)29747.75

Descriptive statistics

Standard deviation17855.93
Coefficient of variation (CV)0.57995688
Kurtosis-0.96719637
Mean30788.375
Median Absolute Deviation (MAD)14379
Skewness0.38690722
Sum2463070
Variance3.1883423 × 108
MonotonicityNot monotonic
2023-12-12T15:28:14.460541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18083 1
 
0.8%
19945 1
 
0.8%
54280 1
 
0.8%
17622 1
 
0.8%
63927 1
 
0.8%
22394 1
 
0.8%
52008 1
 
0.8%
29900 1
 
0.8%
48777 1
 
0.8%
26065 1
 
0.8%
Other values (70) 70
57.9%
(Missing) 41
33.9%
ValueCountFrequency (%)
2201 1
0.8%
3182 1
0.8%
5810 1
0.8%
9057 1
0.8%
9091 1
0.8%
9142 1
0.8%
9478 1
0.8%
9520 1
0.8%
9711 1
0.8%
10655 1
0.8%
ValueCountFrequency (%)
74939 1
0.8%
63927 1
0.8%
63322 1
0.8%
60291 1
0.8%
59470 1
0.8%
58972 1
0.8%
57346 1
0.8%
57190 1
0.8%
56135 1
0.8%
54280 1
0.8%

신경주
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)100.0%
Missing89
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean14723.531
Minimum346
Maximum40037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:14.618113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum346
5-th percentile2076.85
Q13592
median10513.5
Q322484.25
95-th percentile36231.5
Maximum40037
Range39691
Interquartile range (IQR)18892.25

Descriptive statistics

Standard deviation11820.834
Coefficient of variation (CV)0.8028532
Kurtosis-0.82252454
Mean14723.531
Median Absolute Deviation (MAD)8266
Skewness0.58997497
Sum471153
Variance1.3973212 × 108
MonotonicityNot monotonic
2023-12-12T15:28:14.754186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
31732 1
 
0.8%
2264 1
 
0.8%
18226 1
 
0.8%
2143 1
 
0.8%
19772 1
 
0.8%
2974 1
 
0.8%
17993 1
 
0.8%
3017 1
 
0.8%
21673 1
 
0.8%
2821 1
 
0.8%
Other values (22) 22
 
18.2%
(Missing) 89
73.6%
ValueCountFrequency (%)
346 1
0.8%
1996 1
0.8%
2143 1
0.8%
2264 1
0.8%
2821 1
0.8%
2974 1
0.8%
3017 1
0.8%
3346 1
0.8%
3674 1
0.8%
5101 1
0.8%
ValueCountFrequency (%)
40037 1
0.8%
37106 1
0.8%
35516 1
0.8%
31732 1
0.8%
27863 1
0.8%
25751 1
0.8%
25434 1
0.8%
24918 1
0.8%
21673 1
0.8%
21654 1
0.8%

울산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct49
Distinct (%)100.0%
Missing72
Missing (%)59.5%
Infinite0
Infinite (%)0.0%
Mean18956.449
Minimum1
Maximum53658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:14.925748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1261.6
Q13220
median9610
Q336275
95-th percentile42620.8
Maximum53658
Range53657
Interquartile range (IQR)33055

Descriptive statistics

Standard deviation17401.716
Coefficient of variation (CV)0.91798397
Kurtosis-1.4584506
Mean18956.449
Median Absolute Deviation (MAD)7835
Skewness0.44884033
Sum928866
Variance3.0281973 × 108
MonotonicityNot monotonic
2023-12-12T15:28:15.127588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
5700 1
 
0.8%
11362 1
 
0.8%
42808 1
 
0.8%
51637 1
 
0.8%
5973 1
 
0.8%
9610 1
 
0.8%
32328 1
 
0.8%
5398 1
 
0.8%
35269 1
 
0.8%
42069 1
 
0.8%
Other values (39) 39
32.2%
(Missing) 72
59.5%
ValueCountFrequency (%)
1 1
0.8%
778 1
0.8%
816 1
0.8%
1930 1
0.8%
2055 1
0.8%
2244 1
0.8%
2399 1
0.8%
2476 1
0.8%
2670 1
0.8%
2776 1
0.8%
ValueCountFrequency (%)
53658 1
0.8%
51637 1
0.8%
42808 1
0.8%
42340 1
0.8%
42069 1
0.8%
41711 1
0.8%
40420 1
0.8%
40246 1
0.8%
40060 1
0.8%
40016 1
0.8%

부산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)100.0%
Missing81
Missing (%)66.9%
Infinite0
Infinite (%)0.0%
Mean70608.1
Minimum32699
Maximum125768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:15.325818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32699
5-th percentile35524.5
Q157954.25
median71453.5
Q383724.75
95-th percentile101122.25
Maximum125768
Range93069
Interquartile range (IQR)25770.5

Descriptive statistics

Standard deviation21053.121
Coefficient of variation (CV)0.29816863
Kurtosis0.10861645
Mean70608.1
Median Absolute Deviation (MAD)12440
Skewness0.25479162
Sum2824324
Variance4.4323388 × 108
MonotonicityNot monotonic
2023-12-12T15:28:15.492575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
71719 1
 
0.8%
54520 1
 
0.8%
73909 1
 
0.8%
70501 1
 
0.8%
75445 1
 
0.8%
53447 1
 
0.8%
78588 1
 
0.8%
67887 1
 
0.8%
61350 1
 
0.8%
59099 1
 
0.8%
Other values (30) 30
 
24.8%
(Missing) 81
66.9%
ValueCountFrequency (%)
32699 1
0.8%
34717 1
0.8%
35567 1
0.8%
40308 1
0.8%
41624 1
0.8%
44200 1
0.8%
51007 1
0.8%
51153 1
0.8%
53447 1
0.8%
54520 1
0.8%
ValueCountFrequency (%)
125768 1
0.8%
110342 1
0.8%
100637 1
0.8%
99386 1
0.8%
96259 1
0.8%
94116 1
0.8%
88713 1
0.8%
85295 1
0.8%
84566 1
0.8%
83979 1
0.8%

공주
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing102
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean1924.4737
Minimum398
Maximum4817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:15.658271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum398
5-th percentile401.6
Q1732.5
median1458
Q33108.5
95-th percentile4106
Maximum4817
Range4419
Interquartile range (IQR)2376

Descriptive statistics

Standard deviation1383.7746
Coefficient of variation (CV)0.71904054
Kurtosis-0.74436642
Mean1924.4737
Median Absolute Deviation (MAD)949
Skewness0.66380176
Sum36565
Variance1914832.2
MonotonicityNot monotonic
2023-12-12T15:28:15.786374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
515 1
 
0.8%
2354 1
 
0.8%
402 1
 
0.8%
398 1
 
0.8%
1176 1
 
0.8%
2920 1
 
0.8%
1687 1
 
0.8%
473 1
 
0.8%
509 1
 
0.8%
950 1
 
0.8%
Other values (9) 9
 
7.4%
(Missing) 102
84.3%
ValueCountFrequency (%)
398 1
0.8%
402 1
0.8%
473 1
0.8%
509 1
0.8%
515 1
0.8%
950 1
0.8%
1176 1
0.8%
1234 1
0.8%
1432 1
0.8%
1458 1
0.8%
ValueCountFrequency (%)
4817 1
0.8%
4027 1
0.8%
3698 1
0.8%
3412 1
0.8%
3297 1
0.8%
2920 1
0.8%
2354 1
0.8%
1806 1
0.8%
1687 1
0.8%
1458 1
0.8%

익산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)100.0%
Missing81
Missing (%)66.9%
Infinite0
Infinite (%)0.0%
Mean19219.725
Minimum1323
Maximum48555
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:16.321684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1323
5-th percentile3571.9
Q16913
median12286
Q332371.5
95-th percentile42640.85
Maximum48555
Range47232
Interquartile range (IQR)25458.5

Descriptive statistics

Standard deviation14357.919
Coefficient of variation (CV)0.74704081
Kurtosis-1.3045582
Mean19219.725
Median Absolute Deviation (MAD)8290.5
Skewness0.47306697
Sum768789
Variance2.0614984 × 108
MonotonicityNot monotonic
2023-12-12T15:28:16.501317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
4487 1
 
0.8%
5841 1
 
0.8%
23186 1
 
0.8%
8785 1
 
0.8%
29790 1
 
0.8%
7977 1
 
0.8%
34893 1
 
0.8%
8039 1
 
0.8%
28779 1
 
0.8%
9429 1
 
0.8%
Other values (30) 30
 
24.8%
(Missing) 81
66.9%
ValueCountFrequency (%)
1323 1
0.8%
1898 1
0.8%
3660 1
0.8%
4331 1
0.8%
4487 1
0.8%
5664 1
0.8%
5823 1
0.8%
5841 1
0.8%
6158 1
0.8%
6691 1
0.8%
ValueCountFrequency (%)
48555 1
0.8%
43208 1
0.8%
42611 1
0.8%
40142 1
0.8%
39503 1
0.8%
36393 1
0.8%
35484 1
0.8%
35458 1
0.8%
34893 1
0.8%
32910 1
0.8%

정읍
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing97
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean5711.6667
Minimum287
Maximum17902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:16.690360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum287
5-th percentile635.5
Q11426
median2546
Q39845.75
95-th percentile16673
Maximum17902
Range17615
Interquartile range (IQR)8419.75

Descriptive statistics

Standard deviation6090.2306
Coefficient of variation (CV)1.0662791
Kurtosis-0.59837502
Mean5711.6667
Median Absolute Deviation (MAD)1355
Skewness1.0326917
Sum137080
Variance37090909
MonotonicityNot monotonic
2023-12-12T15:28:16.894930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1721 1
 
0.8%
4121 1
 
0.8%
1531 1
 
0.8%
2535 1
 
0.8%
1279 1
 
0.8%
15267 1
 
0.8%
1303 1
 
0.8%
1103 1
 
0.8%
1449 1
 
0.8%
16571 1
 
0.8%
Other values (14) 14
 
11.6%
(Missing) 97
80.2%
ValueCountFrequency (%)
287 1
0.8%
553 1
0.8%
1103 1
0.8%
1279 1
0.8%
1303 1
0.8%
1357 1
0.8%
1449 1
0.8%
1526 1
0.8%
1531 1
0.8%
1623 1
0.8%
ValueCountFrequency (%)
17902 1
0.8%
16691 1
0.8%
16571 1
0.8%
15267 1
0.8%
13526 1
0.8%
11819 1
0.8%
9188 1
0.8%
7582 1
0.8%
4121 1
0.8%
2926 1
0.8%

광주송정
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing92
Missing (%)76.0%
Infinite0
Infinite (%)0.0%
Mean46176.724
Minimum3566
Maximum100083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:17.046315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3566
5-th percentile4331
Q15421
median53219
Q371832
95-th percentile92815
Maximum100083
Range96517
Interquartile range (IQR)66411

Descriptive statistics

Standard deviation33909.301
Coefficient of variation (CV)0.73433751
Kurtosis-1.5255787
Mean46176.724
Median Absolute Deviation (MAD)30691
Skewness-0.082206892
Sum1339125
Variance1.1498407 × 109
MonotonicityNot monotonic
2023-12-12T15:28:17.222008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
70144 1
 
0.8%
4641 1
 
0.8%
56849 1
 
0.8%
4165 1
 
0.8%
64015 1
 
0.8%
4580 1
 
0.8%
72142 1
 
0.8%
5191 1
 
0.8%
71832 1
 
0.8%
4629 1
 
0.8%
Other values (19) 19
 
15.7%
(Missing) 92
76.0%
ValueCountFrequency (%)
3566 1
0.8%
4165 1
0.8%
4580 1
0.8%
4629 1
0.8%
4641 1
0.8%
4735 1
0.8%
5191 1
0.8%
5421 1
0.8%
6279 1
0.8%
9783 1
0.8%
ValueCountFrequency (%)
100083 1
0.8%
93395 1
0.8%
91945 1
0.8%
88025 1
0.8%
83910 1
0.8%
81199 1
0.8%
72142 1
0.8%
71832 1
0.8%
71815 1
0.8%
70144 1
0.8%

나주
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)94.4%
Missing103
Missing (%)85.1%
Infinite0
Infinite (%)0.0%
Mean8520.8333
Minimum242
Maximum21861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:17.375697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum242
5-th percentile510.6
Q1826.75
median2557
Q316260.25
95-th percentile20903.9
Maximum21861
Range21619
Interquartile range (IQR)15433.5

Descriptive statistics

Standard deviation8799.1005
Coefficient of variation (CV)1.0326573
Kurtosis-1.8434299
Mean8520.8333
Median Absolute Deviation (MAD)2157
Skewness0.37494283
Sum153375
Variance77424170
MonotonicityNot monotonic
2023-12-12T15:28:17.514706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
807 2
 
1.7%
18918 1
 
0.8%
886 1
 
0.8%
15319 1
 
0.8%
1004 1
 
0.8%
21861 1
 
0.8%
590 1
 
0.8%
14726 1
 
0.8%
4110 1
 
0.8%
242 1
 
0.8%
Other values (7) 7
 
5.8%
(Missing) 103
85.1%
ValueCountFrequency (%)
242 1
0.8%
558 1
0.8%
590 1
0.8%
807 2
1.7%
886 1
0.8%
912 1
0.8%
973 1
0.8%
1004 1
0.8%
4110 1
0.8%
14128 1
0.8%
ValueCountFrequency (%)
21861 1
0.8%
20735 1
0.8%
20225 1
0.8%
18918 1
0.8%
16574 1
0.8%
15319 1
0.8%
14726 1
0.8%
14128 1
0.8%
4110 1
0.8%
1004 1
0.8%

목포
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing112
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean31704.778
Minimum11688
Maximum58475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T15:28:17.652325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11688
5-th percentile15256
Q123780
median32895
Q335705
95-th percentile50508.2
Maximum58475
Range46787
Interquartile range (IQR)11925

Descriptive statistics

Standard deviation13176.748
Coefficient of variation (CV)0.41560766
Kurtosis1.6119839
Mean31704.778
Median Absolute Deviation (MAD)5663
Skewness0.6810629
Sum285343
Variance1.736267 × 108
MonotonicityNot monotonic
2023-12-12T15:28:17.797458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11688 1
 
0.8%
35084 1
 
0.8%
58475 1
 
0.8%
28550 1
 
0.8%
38558 1
 
0.8%
35705 1
 
0.8%
23780 1
 
0.8%
32895 1
 
0.8%
20608 1
 
0.8%
(Missing) 112
92.6%
ValueCountFrequency (%)
11688 1
0.8%
20608 1
0.8%
23780 1
0.8%
28550 1
0.8%
32895 1
0.8%
35084 1
0.8%
35705 1
0.8%
38558 1
0.8%
58475 1
0.8%
ValueCountFrequency (%)
58475 1
0.8%
38558 1
0.8%
35705 1
0.8%
35084 1
0.8%
32895 1
0.8%
28550 1
0.8%
23780 1
0.8%
20608 1
0.8%
11688 1
0.8%

Interactions

2023-12-12T15:28:08.153410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:31.856645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:33.774798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:36.198673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:38.293283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:40.227240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:42.071648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:44.111223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:46.117232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:48.007783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:50.007334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.906742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:54.018458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:56.105535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:58.388721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:00.081399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:02.018038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:04.266878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:06.197809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:08.250044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T15:27:47.331044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.076451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.125463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.251233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:55.324351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:57.576049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:59.369440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.228399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.101890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.401053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.277538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:09.457890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:32.959883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:35.088992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:37.614301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:39.574735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:41.419582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:43.526116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:45.377794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:47.416961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.153167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.217428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.372058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:55.442852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:57.679973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:59.465707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.334419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.197091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.473552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.380324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:09.565194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:33.049174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:35.182731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:37.708957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:39.695672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:41.502710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:43.617870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:45.481864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:47.507004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.248420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.307007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.479779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:55.532182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:57.772683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:59.544749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.433114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.291516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.566013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.480808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:09.700371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:33.170538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:35.315490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:37.790707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:39.785185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:41.581312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:43.698721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:45.613599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:47.587106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.618847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.463144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.580508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:55.625753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:57.877897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:59.619738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.525577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.400402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.648099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.574872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:09.825876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:33.281037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:35.399784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:37.887688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:39.872029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:41.655444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:43.775091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:45.713004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:47.668584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.696057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.561901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.670527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:55.721853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:57.995566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:59.696866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.622246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.494154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.744468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.680962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:09.994047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:33.405602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:35.520774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:37.984525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:39.968730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:41.755843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:43.862050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:45.820394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:47.755944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.773639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.635547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.746587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:55.822072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:58.107206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:59.788970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.725604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.608704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.839445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.802417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:10.110736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:33.537858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:35.956441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:38.070182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:40.057195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:41.861013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:43.938457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:45.916862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:47.853305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.848819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.710548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.825798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:55.923444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:58.192664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:59.864528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.828427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.704675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:05.927481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:07.931304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:10.246468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:33.656960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:36.080154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:38.159115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:40.132873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:41.961366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:44.024655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:46.014193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:47.930886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:49.922873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:51.798701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:53.907145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:56.025192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:58.295804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:27:59.956345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:01.918713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:03.812538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:06.062173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:28:08.065505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:28:17.922441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
열차번호수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
열차번호1.0000.5000.0000.0000.0000.0000.0000.0000.1920.0000.4110.0000.4970.4710.0000.3130.7701.000NaN
수서0.5001.0000.0000.0000.2790.0000.6610.0001.0000.2240.0000.000NaN0.7410.0000.000NaNNaNNaN
동탄0.0000.0001.0000.0000.0000.0000.4030.0001.0000.4770.0000.0000.7200.5850.5770.0000.3260.3060.647
평택지제0.0000.0000.0001.0000.4810.0000.5300.942NaN0.7750.8670.9030.2541.0000.0000.0000.0000.5320.827
천안아산0.0000.2790.0000.4811.0000.4810.8610.8731.0000.5780.8620.5860.0000.0000.3680.0000.0000.0000.913
오송0.0000.0000.0000.0000.4811.0000.5411.000NaN0.5850.7760.5190.0000.8560.4970.5050.4020.7810.913
대전0.0000.6610.4030.5300.8610.5411.0000.7161.0000.7590.6140.6190.000NaNNaNNaNNaNNaNNaN
김천구미0.0000.0000.0000.9420.8731.0000.7161.000NaN0.8130.8430.8010.000NaNNaNNaNNaNNaNNaN
서대구0.1921.0001.000NaN1.000NaN1.000NaN1.0000.6551.0000.0001.000NaNNaNNaNNaNNaNNaN
동대구0.0000.2240.4770.7750.5780.5850.7590.8130.6551.0000.8100.6670.000NaNNaNNaNNaNNaNNaN
신경주0.4110.0000.0000.8670.8620.7760.6140.8431.0000.8101.0000.8490.000NaNNaNNaNNaNNaNNaN
울산0.0000.0000.0000.9030.5860.5190.6190.8010.0000.6670.8491.0000.330NaNNaNNaNNaNNaNNaN
부산0.497NaN0.7200.2540.0000.0000.0000.0001.0000.0000.0000.3301.000NaNNaNNaNNaNNaNNaN
공주0.4710.7410.5851.0000.0000.856NaNNaNNaNNaNNaNNaNNaN1.0000.8830.8000.7161.0000.000
익산0.0000.0000.5770.0000.3680.497NaNNaNNaNNaNNaNNaNNaN0.8831.0000.7060.7740.7920.000
정읍0.3130.0000.0000.0000.0000.505NaNNaNNaNNaNNaNNaNNaN0.8000.7061.0000.8140.7721.000
광주송정0.770NaN0.3260.0000.0000.402NaNNaNNaNNaNNaNNaNNaN0.7160.7740.8141.0000.7630.486
나주1.000NaN0.3060.5320.0000.781NaNNaNNaNNaNNaNNaNNaN1.0000.7920.7720.7631.0000.000
목포NaNNaN0.6470.8270.9130.913NaNNaNNaNNaNNaNNaNNaN0.0000.0001.0000.4860.0001.000
2023-12-12T15:28:18.153032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
열차번호수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
열차번호1.000-0.471-0.0590.033-0.109-0.0080.0770.2120.4670.206-0.0860.046-0.550-0.328-0.050-0.052-0.5090.053-0.133
수서-0.4711.000-0.068-0.2610.3700.1120.447-0.264-0.300-0.0050.321-0.122NaN0.6000.332-0.0290.150-0.268NaN
동탄-0.059-0.0681.0000.3630.033-0.337-0.336-0.601-0.800-0.508-0.533-0.5380.347-0.095-0.430-0.434-0.374-0.6500.429
평택지제0.033-0.2610.3631.0000.2220.0020.2220.314NaN0.290-0.1030.277-0.654-0.1670.176-0.030-0.027-0.095-1.000
천안아산-0.1090.3700.0330.2221.0000.4680.4240.3761.0000.4130.2680.2840.1270.5550.6580.6360.2450.2910.100
오송-0.0080.112-0.3370.0020.4681.0000.7120.7500.5000.7640.9030.6960.3560.5380.7580.5910.5790.1520.029
대전0.0770.447-0.3360.2220.4240.7121.0000.6060.7090.8680.7090.6910.267NaNNaNNaNNaNNaNNaN
김천구미0.212-0.264-0.6010.3140.3760.7500.6061.0001.0000.7950.7860.6220.430NaNNaNNaNNaNNaNNaN
서대구0.467-0.300-0.800NaN1.0000.5000.7091.0001.0000.8910.5000.786-0.700NaNNaNNaNNaNNaNNaN
동대구0.206-0.005-0.5080.2900.4130.7640.8680.7950.8911.0000.8150.8490.270NaNNaNNaNNaNNaNNaN
신경주-0.0860.321-0.533-0.1030.2680.9030.7090.7860.5000.8151.0000.7680.753NaNNaNNaNNaNNaNNaN
울산0.046-0.122-0.5380.2770.2840.6960.6910.6220.7860.8490.7681.0000.245NaNNaNNaNNaNNaNNaN
부산-0.550NaN0.347-0.6540.1270.3560.2670.430-0.7000.2700.7530.2451.000NaNNaNNaNNaNNaNNaN
공주-0.3280.600-0.095-0.1670.5550.538NaNNaNNaNNaNNaNNaNNaN1.0000.6860.2550.783-0.0860.500
익산-0.0500.332-0.4300.1760.6580.758NaNNaNNaNNaNNaNNaNNaN0.6861.0000.7170.9250.6550.267
정읍-0.052-0.029-0.434-0.0300.6360.591NaNNaNNaNNaNNaNNaNNaN0.2550.7171.0000.7920.762-0.100
광주송정-0.5090.150-0.374-0.0270.2450.579NaNNaNNaNNaNNaNNaNNaN0.7830.9250.7921.0000.8570.317
나주0.053-0.268-0.650-0.0950.2910.152NaNNaNNaNNaNNaNNaNNaN-0.0860.6550.7620.8571.0000.767
목포-0.133NaN0.429-1.0000.1000.029NaNNaNNaNNaNNaNNaNNaN0.5000.267-0.1000.3170.7671.000

Missing values

2023-12-12T15:28:10.389970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:28:10.658795image/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.
2023-12-12T15:28:11.174786image/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

열차번호수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
0301<NA>1052<NA><NA>1710913601<NA><NA>18894106651744544200<NA><NA><NA><NA><NA><NA>
130213244314039117598197<NA>6125<NA><NA>5810<NA>816<NA><NA><NA><NA><NA><NA><NA>
2303<NA><NA>2008211470<NA>13671<NA><NA>28262<NA><NA>100637<NA><NA><NA><NA><NA><NA>
33041498421504815708<NA>66074513<NA><NA>2201346<NA><NA><NA><NA><NA><NA><NA><NA>
4305<NA>4670<NA><NA>2562721430<NA><NA>32461187962179551153<NA><NA><NA><NA><NA><NA>
530613789122001<NA>89005498743312937543182<NA>778<NA><NA><NA><NA><NA><NA><NA>
6307<NA><NA><NA>31889<NA>2138324217<NA>35633<NA><NA>83979<NA><NA><NA><NA><NA><NA>
7308189109<NA>20439<NA>6491112476199<NA>90913346<NA><NA><NA><NA><NA><NA><NA><NA>
8309<NA>618425822170302293919741<NA><NA>36588<NA>4234065135<NA><NA><NA><NA><NA><NA>
9310157252<NA><NA>13883907911258420418119142<NA>2055<NA><NA><NA><NA><NA><NA><NA>
열차번호수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
11166011964445063<NA>19217<NA><NA><NA><NA><NA><NA><NA><NA>1687942911034629558<NA>
112661<NA>9699<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>292036393<NA>718321891835705
11366211867636684<NA><NA>26221<NA><NA><NA><NA><NA><NA><NA><NA>1535313035191807<NA>
114663<NA>11238<NA><NA>30026<NA><NA><NA><NA><NA><NA><NA><NA>3545815267721421472623780
1156641015012900227940<NA>13301<NA><NA><NA><NA><NA><NA><NA>1176758812794580590<NA>
116665<NA>922132025<NA>22323<NA><NA><NA><NA><NA><NA><NA>398312522535640152186132895
11766685542<NA><NA><NA>9633<NA><NA><NA><NA><NA><NA><NA>4025664153141651004<NA>
118667<NA>6787<NA>17931<NA><NA><NA><NA><NA><NA><NA><NA>235428388<NA>568491531920608
1196684628027334<NA>8132<NA><NA><NA><NA><NA><NA><NA><NA><NA>582341214641886<NA>
120690105559<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>