Overview

Dataset statistics

Number of variables19
Number of observations121
Missing cells1419
Missing cells (%)61.7%
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 승차인원 수에 따른 공공데이터로 SRT가 운행중인 경부선, 호남선 모두 포함되어 있습니다.(연간데이터)
URLhttps://www.data.go.kr/data/15081920/fileData.do

Alerts

열차번호 is highly overall correlated with 광주송정High correlation
수서 is highly overall correlated with 평택지제 and 3 other fieldsHigh correlation
동탄 is highly overall correlated with 김천구미 and 7 other fieldsHigh correlation
평택지제 is highly overall correlated with 수서 and 1 other fieldsHigh correlation
천안아산 is highly overall correlated with 대전 and 6 other fieldsHigh correlation
오송 is highly overall correlated with 수서 and 7 other fieldsHigh correlation
대전 is highly overall correlated with 수서 and 8 other fieldsHigh correlation
김천구미 is highly overall correlated with 동탄 and 6 other fieldsHigh correlation
서대구 is highly overall correlated with 동탄 and 7 other fieldsHigh correlation
동대구 is highly overall correlated with 동탄 and 8 other fieldsHigh correlation
신경주 is highly overall correlated with 동탄 and 5 other fieldsHigh correlation
울산 is highly overall correlated with 동탄 and 7 other fieldsHigh correlation
부산 is highly overall correlated with 대전 and 3 other fieldsHigh correlation
공주 is highly overall correlated with 천안아산 and 6 other fieldsHigh correlation
익산 is highly overall correlated with 수서 and 8 other fieldsHigh correlation
정읍 is highly overall correlated with 동탄 and 6 other fieldsHigh correlation
광주송정 is highly overall correlated with 열차번호 and 4 other fieldsHigh correlation
나주 is highly overall correlated with 동탄 and 6 other fieldsHigh correlation
목포 is highly overall correlated with 오송 and 3 other fieldsHigh correlation
수서 has 61 (50.4%) missing valuesMissing
동탄 has 46 (38.0%) missing valuesMissing
평택지제 has 75 (62.0%) missing valuesMissing
천안아산 has 56 (46.3%) missing valuesMissing
오송 has 59 (48.8%) 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 73 (60.3%) missing valuesMissing
부산 has 80 (66.1%) 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 13:37:59.213297
Analysis finished2023-12-12 13:38:36.044298
Duration36.83 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
2023-12-12T22:38:36.133870image/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
2023-12-12T22:38:36.385755image/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 

Distinct60
Distinct (%)100.0%
Missing61
Missing (%)50.4%
Infinite0
Infinite (%)0.0%
Mean126022.47
Minimum30108
Maximum206766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:36.551802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30108
5-th percentile64032.15
Q1104955
median123463.5
Q3146705.25
95-th percentile187444.85
Maximum206766
Range176658
Interquartile range (IQR)41750.25

Descriptive statistics

Standard deviation35683.993
Coefficient of variation (CV)0.28315581
Kurtosis1.0856345
Mean126022.47
Median Absolute Deviation (MAD)19699.5
Skewness-0.17562203
Sum7561348
Variance1.2733474 × 109
MonotonicityNot monotonic
2023-12-12T22:38:36.700499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127314 1
 
0.8%
99563 1
 
0.8%
186801 1
 
0.8%
150375 1
 
0.8%
117427 1
 
0.8%
122337 1
 
0.8%
118134 1
 
0.8%
109431 1
 
0.8%
31531 1
 
0.8%
123461 1
 
0.8%
Other values (50) 50
41.3%
(Missing) 61
50.4%
ValueCountFrequency (%)
30108 1
0.8%
31531 1
0.8%
53167 1
0.8%
64604 1
0.8%
83927 1
0.8%
84726 1
0.8%
90617 1
0.8%
99563 1
0.8%
99690 1
0.8%
100729 1
0.8%
ValueCountFrequency (%)
206766 1
0.8%
201830 1
0.8%
199678 1
0.8%
186801 1
0.8%
186452 1
0.8%
180196 1
0.8%
174470 1
0.8%
153206 1
0.8%
151836 1
0.8%
150375 1
0.8%

동탄
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct75
Distinct (%)100.0%
Missing46
Missing (%)38.0%
Infinite0
Infinite (%)0.0%
Mean25247.133
Minimum1
Maximum105559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:36.860094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1742.5
Q18435
median16405
Q341460.5
95-th percentile66319.7
Maximum105559
Range105558
Interquartile range (IQR)33025.5

Descriptive statistics

Standard deviation23107.036
Coefficient of variation (CV)0.91523403
Kurtosis1.4329925
Mean25247.133
Median Absolute Deviation (MAD)10484
Skewness1.3012971
Sum1893535
Variance5.339351 × 108
MonotonicityNot monotonic
2023-12-12T22:38:37.018770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9697 1
 
0.8%
12465 1
 
0.8%
15058 1
 
0.8%
47187 1
 
0.8%
11508 1
 
0.8%
52030 1
 
0.8%
23646 1
 
0.8%
43881 1
 
0.8%
11054 1
 
0.8%
11519 1
 
0.8%
Other values (65) 65
53.7%
(Missing) 46
38.0%
ValueCountFrequency (%)
1 1
0.8%
5 1
0.8%
757 1
0.8%
1277 1
0.8%
1942 1
0.8%
2011 1
0.8%
2517 1
0.8%
3032 1
0.8%
4283 1
0.8%
4376 1
0.8%
ValueCountFrequency (%)
105559 1
0.8%
87228 1
0.8%
86910 1
0.8%
66900 1
0.8%
66071 1
0.8%
63079 1
0.8%
55753 1
0.8%
53676 1
0.8%
52556 1
0.8%
52030 1
0.8%

평택지제
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct46
Distinct (%)100.0%
Missing75
Missing (%)62.0%
Infinite0
Infinite (%)0.0%
Mean23877.478
Minimum4026
Maximum52598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:37.180474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4026
5-th percentile8612
Q118273
median23377
Q328730
95-th percentile35408.75
Maximum52598
Range48572
Interquartile range (IQR)10457

Descriptive statistics

Standard deviation9291.54
Coefficient of variation (CV)0.38913406
Kurtosis1.586557
Mean23877.478
Median Absolute Deviation (MAD)5561
Skewness0.50854591
Sum1098364
Variance86332716
MonotonicityNot monotonic
2023-12-12T22:38:37.332264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
31878 1
 
0.8%
8590 1
 
0.8%
35060 1
 
0.8%
21277 1
 
0.8%
13636 1
 
0.8%
28472 1
 
0.8%
16862 1
 
0.8%
17508 1
 
0.8%
22128 1
 
0.8%
25832 1
 
0.8%
Other values (36) 36
29.8%
(Missing) 75
62.0%
ValueCountFrequency (%)
4026 1
0.8%
6802 1
0.8%
8590 1
0.8%
8678 1
0.8%
13636 1
0.8%
13764 1
0.8%
14050 1
0.8%
15577 1
0.8%
16862 1
0.8%
17508 1
0.8%
ValueCountFrequency (%)
52598 1
0.8%
46706 1
0.8%
35525 1
0.8%
35060 1
0.8%
33463 1
0.8%
33246 1
0.8%
32397 1
0.8%
31878 1
0.8%
29845 1
0.8%
29508 1
0.8%

천안아산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct65
Distinct (%)100.0%
Missing56
Missing (%)46.3%
Infinite0
Infinite (%)0.0%
Mean17603.292
Minimum1
Maximum33731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:37.474148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4688.4
Q111712
median16563
Q323907
95-th percentile32154.4
Maximum33731
Range33730
Interquartile range (IQR)12195

Descriptive statistics

Standard deviation8247.741
Coefficient of variation (CV)0.468534
Kurtosis-0.63127284
Mean17603.292
Median Absolute Deviation (MAD)6139
Skewness0.2024742
Sum1144214
Variance68025231
MonotonicityNot monotonic
2023-12-12T22:38:37.654253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16251 1
 
0.8%
12551 1
 
0.8%
21117 1
 
0.8%
9568 1
 
0.8%
19682 1
 
0.8%
12768 1
 
0.8%
14156 1
 
0.8%
7559 1
 
0.8%
4497 1
 
0.8%
4349 1
 
0.8%
Other values (55) 55
45.5%
(Missing) 56
46.3%
ValueCountFrequency (%)
1 1
0.8%
3184 1
0.8%
4349 1
0.8%
4497 1
0.8%
5454 1
0.8%
7189 1
0.8%
7559 1
0.8%
9082 1
0.8%
9127 1
0.8%
9291 1
0.8%
ValueCountFrequency (%)
33731 1
0.8%
33213 1
0.8%
33162 1
0.8%
32354 1
0.8%
31356 1
0.8%
31190 1
0.8%
30565 1
0.8%
29401 1
0.8%
28229 1
0.8%
26557 1
0.8%

오송
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)98.4%
Missing59
Missing (%)48.8%
Infinite0
Infinite (%)0.0%
Mean17565.081
Minimum1
Maximum36514
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:37.811379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5799.95
Q111655.5
median16901
Q322768.75
95-th percentile30526.2
Maximum36514
Range36513
Interquartile range (IQR)11113.25

Descriptive statistics

Standard deviation8108.6345
Coefficient of variation (CV)0.46163378
Kurtosis-0.45137891
Mean17565.081
Median Absolute Deviation (MAD)5610
Skewness0.18261806
Sum1089035
Variance65749953
MonotonicityNot monotonic
2023-12-12T22:38:37.954013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9775 2
 
1.7%
11008 1
 
0.8%
16417 1
 
0.8%
5797 1
 
0.8%
5856 1
 
0.8%
4955 1
 
0.8%
1 1
 
0.8%
21444 1
 
0.8%
25823 1
 
0.8%
32349 1
 
0.8%
Other values (51) 51
42.1%
(Missing) 59
48.8%
ValueCountFrequency (%)
1 1
0.8%
1824 1
0.8%
4955 1
0.8%
5797 1
0.8%
5856 1
0.8%
7667 1
0.8%
7701 1
0.8%
8323 1
0.8%
9618 1
0.8%
9775 2
1.7%
ValueCountFrequency (%)
36514 1
0.8%
33661 1
0.8%
32349 1
0.8%
30597 1
0.8%
29181 1
0.8%
28406 1
0.8%
28293 1
0.8%
28182 1
0.8%
27864 1
0.8%
27134 1
0.8%

대전
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)100.0%
Missing41
Missing (%)33.9%
Infinite0
Infinite (%)0.0%
Mean19639.25
Minimum2893
Maximum50777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:38.092332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2893
5-th percentile5744.1
Q113498
median18583
Q324592.75
95-th percentile35152.25
Maximum50777
Range47884
Interquartile range (IQR)11094.75

Descriptive statistics

Standard deviation9040.5569
Coefficient of variation (CV)0.46033107
Kurtosis0.93929794
Mean19639.25
Median Absolute Deviation (MAD)5436
Skewness0.74513168
Sum1571140
Variance81731669
MonotonicityNot monotonic
2023-12-12T22:38:38.249687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21820 1
 
0.8%
21728 1
 
0.8%
17457 1
 
0.8%
18593 1
 
0.8%
22949 1
 
0.8%
39816 1
 
0.8%
17781 1
 
0.8%
35366 1
 
0.8%
16122 1
 
0.8%
18983 1
 
0.8%
Other values (70) 70
57.9%
(Missing) 41
33.9%
ValueCountFrequency (%)
2893 1
0.8%
3785 1
0.8%
4980 1
0.8%
5062 1
0.8%
5780 1
0.8%
8023 1
0.8%
9791 1
0.8%
10067 1
0.8%
10371 1
0.8%
10447 1
0.8%
ValueCountFrequency (%)
50777 1
0.8%
40755 1
0.8%
39816 1
0.8%
35366 1
0.8%
35141 1
0.8%
34739 1
0.8%
32861 1
0.8%
32029 1
0.8%
31458 1
0.8%
30629 1
0.8%

김천구미
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing100
Missing (%)82.6%
Infinite0
Infinite (%)0.0%
Mean12376.19
Minimum1611
Maximum26748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:38.740343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1611
5-th percentile2298
Q16392
median13084
Q315378
95-th percentile26027
Maximum26748
Range25137
Interquartile range (IQR)8986

Descriptive statistics

Standard deviation6960.3169
Coefficient of variation (CV)0.56239575
Kurtosis-0.13449982
Mean12376.19
Median Absolute Deviation (MAD)4882
Skewness0.39188124
Sum259900
Variance48446011
MonotonicityNot monotonic
2023-12-12T22:38:38.861500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6143 1
 
0.8%
1611 1
 
0.8%
4182 1
 
0.8%
15378 1
 
0.8%
2298 1
 
0.8%
4469 1
 
0.8%
12755 1
 
0.8%
19630 1
 
0.8%
10481 1
 
0.8%
26748 1
 
0.8%
Other values (11) 11
 
9.1%
(Missing) 100
82.6%
ValueCountFrequency (%)
1611 1
0.8%
2298 1
0.8%
4182 1
0.8%
4469 1
0.8%
6143 1
0.8%
6392 1
0.8%
8202 1
0.8%
10481 1
0.8%
10758 1
0.8%
12755 1
0.8%
ValueCountFrequency (%)
26748 1
0.8%
26027 1
0.8%
19630 1
0.8%
18476 1
0.8%
16479 1
0.8%
15378 1
0.8%
14854 1
0.8%
14502 1
0.8%
13860 1
0.8%
13571 1
0.8%

서대구
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing111
Missing (%)91.7%
Infinite0
Infinite (%)0.0%
Mean9295.6
Minimum5799
Maximum16559
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:38.992303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5799
5-th percentile5802.6
Q16195.25
median7868.5
Q311308.5
95-th percentile16052.3
Maximum16559
Range10760
Interquartile range (IQR)5113.25

Descriptive statistics

Standard deviation4040.2518
Coefficient of variation (CV)0.43464131
Kurtosis-0.43864382
Mean9295.6
Median Absolute Deviation (MAD)1904.5
Skewness1.0131684
Sum92956
Variance16323634
MonotonicityNot monotonic
2023-12-12T22:38:39.111556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6908 1
 
0.8%
12076 1
 
0.8%
8829 1
 
0.8%
9006 1
 
0.8%
5807 1
 
0.8%
15433 1
 
0.8%
16559 1
 
0.8%
6418 1
 
0.8%
6121 1
 
0.8%
5799 1
 
0.8%
(Missing) 111
91.7%
ValueCountFrequency (%)
5799 1
0.8%
5807 1
0.8%
6121 1
0.8%
6418 1
0.8%
6908 1
0.8%
8829 1
0.8%
9006 1
0.8%
12076 1
0.8%
15433 1
0.8%
16559 1
0.8%
ValueCountFrequency (%)
16559 1
0.8%
15433 1
0.8%
12076 1
0.8%
9006 1
0.8%
8829 1
0.8%
6908 1
0.8%
6418 1
0.8%
6121 1
0.8%
5807 1
0.8%
5799 1
0.8%

동대구
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)100.0%
Missing41
Missing (%)33.9%
Infinite0
Infinite (%)0.0%
Mean30479.812
Minimum1342
Maximum74657
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:39.264533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1342
5-th percentile7965.35
Q113311.25
median24635.5
Q347422.5
95-th percentile62024.3
Maximum74657
Range73315
Interquartile range (IQR)34111.25

Descriptive statistics

Standard deviation19235.959
Coefficient of variation (CV)0.6311049
Kurtosis-1.1646579
Mean30479.812
Median Absolute Deviation (MAD)13513
Skewness0.43801403
Sum2438385
Variance3.7002212 × 108
MonotonicityNot monotonic
2023-12-12T22:38:39.437517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40125 1
 
0.8%
43070 1
 
0.8%
13776 1
 
0.8%
43194 1
 
0.8%
20311 1
 
0.8%
65322 1
 
0.8%
14707 1
 
0.8%
57905 1
 
0.8%
15250 1
 
0.8%
36377 1
 
0.8%
Other values (70) 70
57.9%
(Missing) 41
33.9%
ValueCountFrequency (%)
1342 1
0.8%
3129 1
0.8%
6830 1
0.8%
7345 1
0.8%
7998 1
0.8%
8718 1
0.8%
9721 1
0.8%
10257 1
0.8%
10612 1
0.8%
11039 1
0.8%
ValueCountFrequency (%)
74657 1
0.8%
65425 1
0.8%
65322 1
0.8%
62429 1
0.8%
62003 1
0.8%
61487 1
0.8%
58938 1
0.8%
57972 1
0.8%
57905 1
0.8%
57210 1
0.8%

신경주
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)100.0%
Missing89
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean14520.469
Minimum1342
Maximum42638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:39.591183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1342
5-th percentile2164.6
Q13848
median7579.5
Q326372.25
95-th percentile34668
Maximum42638
Range41296
Interquartile range (IQR)22524.25

Descriptive statistics

Standard deviation12674.038
Coefficient of variation (CV)0.87283948
Kurtosis-0.97178265
Mean14520.469
Median Absolute Deviation (MAD)5470.5
Skewness0.70871155
Sum464655
Variance1.6063125 × 108
MonotonicityNot monotonic
2023-12-12T22:38:39.755276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
6322 1
 
0.8%
8146 1
 
0.8%
1553 1
 
0.8%
24471 1
 
0.8%
4518 1
 
0.8%
30759 1
 
0.8%
3320 1
 
0.8%
26195 1
 
0.8%
3905 1
 
0.8%
33309 1
 
0.8%
Other values (22) 22
 
18.2%
(Missing) 89
73.6%
ValueCountFrequency (%)
1342 1
0.8%
1553 1
0.8%
2665 1
0.8%
3203 1
0.8%
3307 1
0.8%
3320 1
0.8%
3351 1
0.8%
3677 1
0.8%
3905 1
0.8%
4518 1
0.8%
ValueCountFrequency (%)
42638 1
0.8%
36329 1
0.8%
33309 1
0.8%
33285 1
0.8%
30765 1
0.8%
30759 1
0.8%
27405 1
0.8%
26904 1
0.8%
26195 1
0.8%
24471 1
0.8%

울산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)100.0%
Missing73
Missing (%)60.3%
Infinite0
Infinite (%)0.0%
Mean19242.792
Minimum732
Maximum55057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:39.928792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum732
5-th percentile1669
Q14006.25
median9350
Q334491.25
95-th percentile52874.6
Maximum55057
Range54325
Interquartile range (IQR)30485

Descriptive statistics

Standard deviation17743.725
Coefficient of variation (CV)0.92209722
Kurtosis-1.0734598
Mean19242.792
Median Absolute Deviation (MAD)7645
Skewness0.61483663
Sum923654
Variance3.1483976 × 108
MonotonicityNot monotonic
2023-12-12T22:38:40.095619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
34921 1
 
0.8%
52495 1
 
0.8%
4698 1
 
0.8%
2831 1
 
0.8%
37260 1
 
0.8%
53079 1
 
0.8%
3277 1
 
0.8%
37502 1
 
0.8%
2657 1
 
0.8%
1825 1
 
0.8%
Other values (38) 38
31.4%
(Missing) 73
60.3%
ValueCountFrequency (%)
732 1
0.8%
1554 1
0.8%
1585 1
0.8%
1825 1
0.8%
2302 1
0.8%
2498 1
0.8%
2657 1
0.8%
2788 1
0.8%
2831 1
0.8%
3011 1
0.8%
ValueCountFrequency (%)
55057 1
0.8%
54612 1
0.8%
53079 1
0.8%
52495 1
0.8%
46433 1
0.8%
41237 1
0.8%
38485 1
0.8%
37502 1
0.8%
37497 1
0.8%
37260 1
0.8%

부산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)100.0%
Missing80
Missing (%)66.1%
Infinite0
Infinite (%)0.0%
Mean68761.829
Minimum1
Maximum123327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:40.244871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile37441
Q155407
median70451
Q380468
95-th percentile104678
Maximum123327
Range123326
Interquartile range (IQR)25061

Descriptive statistics

Standard deviation22471.102
Coefficient of variation (CV)0.32679617
Kurtosis1.6848642
Mean68761.829
Median Absolute Deviation (MAD)10517
Skewness-0.25158311
Sum2819235
Variance5.0495044 × 108
MonotonicityNot monotonic
2023-12-12T22:38:40.385037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
100516 1
 
0.8%
75650 1
 
0.8%
75362 1
 
0.8%
83987 1
 
0.8%
73800 1
 
0.8%
92878 1
 
0.8%
91370 1
 
0.8%
80468 1
 
0.8%
72988 1
 
0.8%
70908 1
 
0.8%
Other values (31) 31
 
25.6%
(Missing) 80
66.1%
ValueCountFrequency (%)
1 1
0.8%
33169 1
0.8%
37441 1
0.8%
39729 1
0.8%
42523 1
0.8%
47373 1
0.8%
48056 1
0.8%
49558 1
0.8%
51846 1
0.8%
53840 1
0.8%
ValueCountFrequency (%)
123327 1
0.8%
113652 1
0.8%
104678 1
0.8%
100516 1
0.8%
92878 1
0.8%
91370 1
0.8%
85429 1
0.8%
83987 1
0.8%
81624 1
0.8%
80968 1
0.8%

공주
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing102
Missing (%)84.3%
Infinite0
Infinite (%)0.0%
Mean1920.7895
Minimum332
Maximum4467
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:40.537659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum332
5-th percentile512
Q1925
median1330
Q33087
95-th percentile4057.5
Maximum4467
Range4135
Interquartile range (IQR)2162

Descriptive statistics

Standard deviation1339.108
Coefficient of variation (CV)0.69716541
Kurtosis-0.93372695
Mean1920.7895
Median Absolute Deviation (MAD)700
Skewness0.70369624
Sum36495
Variance1793210.2
MonotonicityNot monotonic
2023-12-12T22:38:40.687232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3182 1
 
0.8%
566 1
 
0.8%
630 1
 
0.8%
332 1
 
0.8%
1330 1
 
0.8%
1321 1
 
0.8%
4012 1
 
0.8%
532 1
 
0.8%
1128 1
 
0.8%
722 1
 
0.8%
Other values (9) 9
 
7.4%
(Missing) 102
84.3%
ValueCountFrequency (%)
332 1
0.8%
532 1
0.8%
566 1
0.8%
630 1
0.8%
722 1
0.8%
1128 1
0.8%
1238 1
0.8%
1315 1
0.8%
1321 1
0.8%
1330 1
0.8%
ValueCountFrequency (%)
4467 1
0.8%
4012 1
0.8%
3996 1
0.8%
3514 1
0.8%
3182 1
0.8%
2992 1
0.8%
1930 1
0.8%
1892 1
0.8%
1396 1
0.8%
1330 1
0.8%

익산
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)100.0%
Missing81
Missing (%)66.9%
Infinite0
Infinite (%)0.0%
Mean17623.175
Minimum525
Maximum53765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:40.826228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum525
5-th percentile2066.8
Q15884.5
median10941
Q328685.75
95-th percentile46105.55
Maximum53765
Range53240
Interquartile range (IQR)22801.25

Descriptive statistics

Standard deviation14538.814
Coefficient of variation (CV)0.82498267
Kurtosis-0.35691776
Mean17623.175
Median Absolute Deviation (MAD)7916.5
Skewness0.80337337
Sum704927
Variance2.1137711 × 108
MonotonicityNot monotonic
2023-12-12T22:38:41.008030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
12121 1
 
0.8%
27968 1
 
0.8%
7542 1
 
0.8%
32188 1
 
0.8%
8667 1
 
0.8%
24729 1
 
0.8%
6231 1
 
0.8%
27687 1
 
0.8%
7309 1
 
0.8%
28385 1
 
0.8%
Other values (30) 30
 
24.8%
(Missing) 81
66.9%
ValueCountFrequency (%)
525 1
0.8%
2025 1
0.8%
2069 1
0.8%
2772 1
0.8%
3277 1
0.8%
3674 1
0.8%
4688 1
0.8%
4739 1
0.8%
5140 1
0.8%
5322 1
0.8%
ValueCountFrequency (%)
53765 1
0.8%
49023 1
0.8%
45952 1
0.8%
36214 1
0.8%
35818 1
0.8%
33710 1
0.8%
32188 1
0.8%
30698 1
0.8%
30489 1
0.8%
29588 1
0.8%

정읍
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)100.0%
Missing97
Missing (%)80.2%
Infinite0
Infinite (%)0.0%
Mean6053.5833
Minimum263
Maximum17729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:41.148462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum263
5-th percentile541.5
Q11620.25
median4141.5
Q310521
95-th percentile15062.75
Maximum17729
Range17466
Interquartile range (IQR)8900.75

Descriptive statistics

Standard deviation5274.7275
Coefficient of variation (CV)0.8713397
Kurtosis-0.67703073
Mean6053.5833
Median Absolute Deviation (MAD)3237.5
Skewness0.70522098
Sum145286
Variance27822750
MonotonicityNot monotonic
2023-12-12T22:38:41.296559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10297 1
 
0.8%
3711 1
 
0.8%
6165 1
 
0.8%
263 1
 
0.8%
7320 1
 
0.8%
1989 1
 
0.8%
8869 1
 
0.8%
11876 1
 
0.8%
13265 1
 
0.8%
1654 1
 
0.8%
Other values (14) 14
 
11.6%
(Missing) 97
80.2%
ValueCountFrequency (%)
263 1
0.8%
516 1
0.8%
686 1
0.8%
845 1
0.8%
1173 1
0.8%
1519 1
0.8%
1654 1
0.8%
1726 1
0.8%
1989 1
0.8%
2284 1
0.8%
ValueCountFrequency (%)
17729 1
0.8%
15380 1
0.8%
13265 1
0.8%
11876 1
0.8%
11425 1
0.8%
11193 1
0.8%
10297 1
0.8%
8869 1
0.8%
7320 1
0.8%
7157 1
0.8%

광주송정
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing92
Missing (%)76.0%
Infinite0
Infinite (%)0.0%
Mean48560.586
Minimum2245
Maximum115622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:41.456232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2245
5-th percentile2423.6
Q14875
median49193
Q370097
95-th percentile101337
Maximum115622
Range113377
Interquartile range (IQR)65222

Descriptive statistics

Standard deviation35864.438
Coefficient of variation (CV)0.73855035
Kurtosis-1.1462974
Mean48560.586
Median Absolute Deviation (MAD)36152
Skewness0.066614572
Sum1408257
Variance1.2862579 × 109
MonotonicityNot monotonic
2023-12-12T22:38:41.612743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
44140 1
 
0.8%
44316 1
 
0.8%
2284 1
 
0.8%
39294 1
 
0.8%
4875 1
 
0.8%
57961 1
 
0.8%
3515 1
 
0.8%
55266 1
 
0.8%
4492 1
 
0.8%
62986 1
 
0.8%
Other values (19) 19
 
15.7%
(Missing) 92
76.0%
ValueCountFrequency (%)
2245 1
0.8%
2284 1
0.8%
2633 1
0.8%
3116 1
0.8%
3235 1
0.8%
3515 1
0.8%
4492 1
0.8%
4875 1
0.8%
11344 1
0.8%
36792 1
0.8%
ValueCountFrequency (%)
115622 1
0.8%
101779 1
0.8%
100674 1
0.8%
97570 1
0.8%
86324 1
0.8%
85345 1
0.8%
79781 1
0.8%
70097 1
0.8%
68430 1
0.8%
66648 1
0.8%

나주
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)100.0%
Missing103
Missing (%)85.1%
Infinite0
Infinite (%)0.0%
Mean8496.6667
Minimum268
Maximum23856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:41.763047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum268
5-th percentile505.15
Q1597.75
median3799
Q316059.75
95-th percentile23695.35
Maximum23856
Range23588
Interquartile range (IQR)15462

Descriptive statistics

Standard deviation8946.3909
Coefficient of variation (CV)1.0529295
Kurtosis-1.4101767
Mean8496.6667
Median Absolute Deviation (MAD)3391.5
Skewness0.53142123
Sum152940
Variance80037911
MonotonicityNot monotonic
2023-12-12T22:38:41.928078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
582 1
 
0.8%
5717 1
 
0.8%
268 1
 
0.8%
10360 1
 
0.8%
631 1
 
0.8%
19213 1
 
0.8%
547 1
 
0.8%
23856 1
 
0.8%
795 1
 
0.8%
14638 1
 
0.8%
Other values (8) 8
 
6.6%
(Missing) 103
85.1%
ValueCountFrequency (%)
268 1
0.8%
547 1
0.8%
570 1
0.8%
582 1
0.8%
592 1
0.8%
615 1
0.8%
631 1
0.8%
795 1
0.8%
1881 1
0.8%
5717 1
0.8%
ValueCountFrequency (%)
23856 1
0.8%
23667 1
0.8%
19213 1
0.8%
16933 1
0.8%
16082 1
0.8%
15993 1
0.8%
14638 1
0.8%
10360 1
0.8%
5717 1
0.8%
1881 1
0.8%

목포
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing112
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean33069
Minimum21264
Maximum42450
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-12T22:38:42.056455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21264
5-th percentile23056
Q127402
median34448
Q338562
95-th percentile42280.8
Maximum42450
Range21186
Interquartile range (IQR)11160

Descriptive statistics

Standard deviation7465.1382
Coefficient of variation (CV)0.2257443
Kurtosis-1.2387887
Mean33069
Median Absolute Deviation (MAD)7046
Skewness-0.20088302
Sum297621
Variance55728288
MonotonicityNot monotonic
2023-12-12T22:38:42.179349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
27402 1
 
0.8%
29677 1
 
0.8%
42450 1
 
0.8%
34448 1
 
0.8%
36047 1
 
0.8%
42027 1
 
0.8%
38562 1
 
0.8%
25744 1
 
0.8%
21264 1
 
0.8%
(Missing) 112
92.6%
ValueCountFrequency (%)
21264 1
0.8%
25744 1
0.8%
27402 1
0.8%
29677 1
0.8%
34448 1
0.8%
36047 1
0.8%
38562 1
0.8%
42027 1
0.8%
42450 1
0.8%
ValueCountFrequency (%)
42450 1
0.8%
42027 1
0.8%
38562 1
0.8%
36047 1
0.8%
34448 1
0.8%
29677 1
0.8%
27402 1
0.8%
25744 1
0.8%
21264 1
0.8%

Interactions

2023-12-12T22:38:33.190878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:37:59.725665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:01.795444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:03.720350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:05.479750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:07.642721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:09.354889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:11.218474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:12.860556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:14.987396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:16.653318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:18.331643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:20.074907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:22.234111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:24.103539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:25.945971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:27.924985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:29.531605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T22:38:17.616202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:19.433997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:21.459903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:23.354921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:25.166669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:26.924862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:28.811281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:30.521364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:32.382881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:34.610562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:00.862220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:03.053765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:04.852956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:06.575102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:08.784252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:10.463023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:12.260005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:14.380601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:16.057544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:17.693815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:19.514935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:21.541944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:23.447077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:25.260353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:26.992354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:28.881496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:30.601937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:32.473848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:34.715175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:00.945000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:03.152514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:04.928381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:06.664629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:08.861324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:10.562297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:12.337997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:14.483914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:16.148990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:17.816309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:19.631541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:21.640889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:23.558331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:25.356884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:27.064663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:28.954803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:30.683685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:32.570406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:34.830328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:01.027164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:03.247818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:05.021838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:06.767222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:08.944278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:10.653917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:12.429299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:14.565596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:16.247173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:17.912688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:19.710098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:21.730881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:23.652074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:25.444983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:27.134325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:29.064025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:30.762017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:32.690004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:34.920093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:01.119543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:03.327566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:05.107939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:06.848954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:09.040953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:10.745926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:12.522650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:14.662521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:16.332309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:18.023533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:19.780347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:21.809737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:23.752472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:25.548909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:27.207717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:29.146265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:30.845384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:32.790335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:35.007499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:01.501838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:03.434606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:05.201821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:06.973512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:09.119567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:10.854880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:12.619804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:14.749778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:16.407170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:18.103014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:19.852691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:21.939517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:23.836556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:25.662240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:27.303365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:29.230876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:30.967445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:32.897569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:35.081214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:01.594010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:03.526493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:05.298893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:07.078542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:09.199624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:10.938192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:12.697759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:14.835450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:16.486996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:18.174964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:19.932982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:22.043879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:23.924628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:25.748688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:27.389459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:29.327737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:31.084514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:32.986642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:35.164293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:01.705864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:03.628619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:05.390419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:07.185660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:09.271708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:11.067816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:12.772394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:14.914499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:16.568961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:18.251204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:20.000876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:22.148399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:24.012022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:25.842287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:27.818834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:29.416317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:31.204785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:38:33.089360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:38:42.322171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
열차번호수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
열차번호1.0000.2300.2350.3480.5480.3350.6560.0000.6810.0000.0000.0000.0000.0000.5640.0000.7420.000NaN
수서0.2301.0000.5370.4290.6690.1910.7500.3280.1650.0000.0000.000NaN0.7790.0741.000NaNNaNNaN
동탄0.2350.5371.0000.9340.0000.2730.6110.0001.0000.5600.0000.0000.0000.3130.5690.7000.2410.0001.000
평택지제0.3480.4290.9341.0000.1380.0000.0000.573NaN0.0000.0000.8310.7380.5680.5141.0000.2750.0001.000
천안아산0.5480.6690.0000.1381.0000.1200.6330.8201.0000.5480.0000.5930.5220.6340.6930.0000.7390.1800.416
오송0.3350.1910.2730.0000.1201.0000.5740.5440.0000.6600.6370.7430.2520.8540.6230.0000.7870.5421.000
대전0.6560.7500.6110.0000.6330.5741.0000.7890.7490.6770.3860.5100.722NaNNaNNaNNaNNaNNaN
김천구미0.0000.3280.0000.5730.8200.5440.7891.0000.0000.9250.5440.7870.674NaNNaNNaNNaNNaNNaN
서대구0.6810.1651.000NaN1.0000.0000.7490.0001.0000.9240.0001.0001.000NaNNaNNaNNaNNaNNaN
동대구0.0000.0000.5600.0000.5480.6600.6770.9250.9241.0000.7760.7050.735NaNNaNNaNNaNNaNNaN
신경주0.0000.0000.0000.0000.0000.6370.3860.5440.0000.7761.0000.8620.289NaNNaNNaNNaNNaNNaN
울산0.0000.0000.0000.8310.5930.7430.5100.7871.0000.7050.8621.0000.640NaNNaNNaNNaNNaNNaN
부산0.000NaN0.0000.7380.5220.2520.7220.6741.0000.7350.2890.6401.000NaNNaNNaNNaNNaNNaN
공주0.0000.7790.3130.5680.6340.854NaNNaNNaNNaNNaNNaNNaN1.0000.6710.4050.6931.0001.000
익산0.5640.0740.5690.5140.6930.623NaNNaNNaNNaNNaNNaNNaN0.6711.0000.8230.8600.8070.284
정읍0.0001.0000.7001.0000.0000.000NaNNaNNaNNaNNaNNaNNaN0.4050.8231.0000.7960.8510.719
광주송정0.742NaN0.2410.2750.7390.787NaNNaNNaNNaNNaNNaNNaN0.6930.8600.7961.0001.0000.647
나주0.000NaN0.0000.0000.1800.542NaNNaNNaNNaNNaNNaNNaN1.0000.8070.8511.0001.0000.905
목포NaNNaN1.0001.0000.4161.000NaNNaNNaNNaNNaNNaNNaN1.0000.2840.7190.6470.9051.000
2023-12-12T22:38:42.555542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
열차번호수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
열차번호1.000-0.124-0.090-0.364-0.208-0.171-0.304-0.240-0.394-0.1980.142-0.1510.276-0.419-0.0060.085-0.527-0.119-0.250
수서-0.1241.000-0.1520.5600.3620.5310.5630.309-0.1000.3380.182-0.206NaN0.0830.511-0.0910.200-0.283NaN
동탄-0.090-0.1521.0000.278-0.377-0.059-0.434-0.6260.600-0.603-0.689-0.523-0.178-0.187-0.546-0.561-0.448-0.6920.086
평택지제-0.3640.5600.2781.000-0.1940.477-0.226-0.486NaN-0.377-0.362-0.483-0.0250.4290.5710.3820.2940.000-0.400
천안아산-0.2080.362-0.377-0.1941.0000.3190.6800.6880.4000.6390.2210.6670.2050.5640.7270.2940.4710.7730.486
오송-0.1710.531-0.0590.4770.3191.0000.7040.3210.5000.5960.3790.2850.2510.6150.7060.5310.4700.1880.800
대전-0.3040.563-0.434-0.2260.6800.7041.0000.7250.7820.8570.6360.5710.513NaNNaNNaNNaNNaNNaN
김천구미-0.2400.309-0.626-0.4860.6880.3210.7251.0001.0000.8480.7500.8450.345NaNNaNNaNNaNNaNNaN
서대구-0.394-0.1000.600NaN0.4000.5000.7821.0001.0000.8670.5000.6430.700NaNNaNNaNNaNNaNNaN
동대구-0.1980.338-0.603-0.3770.6390.5960.8570.8480.8671.0000.7930.8950.649NaNNaNNaNNaNNaNNaN
신경주0.1420.182-0.689-0.3620.2210.3790.6360.7500.5000.7931.0000.8570.462NaNNaNNaNNaNNaNNaN
울산-0.151-0.206-0.523-0.4830.6670.2850.5710.8450.6430.8950.8571.0000.536NaNNaNNaNNaNNaNNaN
부산0.276NaN-0.178-0.0250.2050.2510.5130.3450.7000.6490.4620.5361.000NaNNaNNaNNaNNaNNaN
공주-0.4190.083-0.1870.4290.5640.615NaNNaNNaNNaNNaNNaNNaN1.0000.5770.8730.7310.6570.500
익산-0.0060.511-0.5460.5710.7270.706NaNNaNNaNNaNNaNNaNNaN0.5771.0000.8910.9670.8290.367
정읍0.085-0.091-0.5610.3820.2940.531NaNNaNNaNNaNNaNNaNNaN0.8730.8911.0000.8600.7910.619
광주송정-0.5270.200-0.4480.2940.4710.470NaNNaNNaNNaNNaNNaNNaN0.7310.9670.8601.0000.8080.067
나주-0.119-0.283-0.6920.0000.7730.188NaNNaNNaNNaNNaNNaNNaN0.6570.8290.7910.8081.0000.967
목포-0.250NaN0.086-0.4000.4860.800NaNNaNNaNNaNNaNNaNNaN0.5000.3670.6190.0670.9671.000

Missing values

2023-12-12T22:38:35.282445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:38:35.535540image/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.
2023-12-12T22:38:35.808707image/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
03015316738919<NA><NA>83234980<NA><NA>1257313423662<NA><NA><NA><NA><NA><NA><NA>
1302<NA>115192077722702<NA>25543<NA><NA>35151<NA>2376839729<NA><NA><NA><NA><NA><NA>
2303121126<NA>246317189<NA>10564<NA><NA>10612<NA><NA><NA><NA><NA><NA><NA><NA><NA>
3304<NA>988020307<NA>2333632029<NA><NA>4977710880<NA>48056<NA><NA><NA><NA><NA><NA>
43058472649218<NA><NA>1100811297<NA><NA>1287726654141<NA><NA><NA><NA><NA><NA><NA>
5306<NA>11779<NA>20308164172287410758690837859<NA>2765833169<NA><NA><NA><NA><NA><NA>
6307149271<NA><NA>15186<NA>154626143<NA>11039<NA><NA><NA><NA><NA><NA><NA><NA><NA>
7308<NA><NA>28233<NA>281823514113860<NA>5797215750<NA>66784<NA><NA><NA><NA><NA><NA>
830990617630793239716055961810067<NA><NA>8718<NA>5228<NA><NA><NA><NA><NA><NA><NA>
9310<NA><NA><NA>331621312317526164791207640392<NA>3848537441<NA><NA><NA><NA><NA><NA>
열차번호수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
111660<NA>14958<NA>26133<NA><NA><NA><NA><NA><NA><NA><NA>40122838511876629861693336047
11266112293637169<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>13218967<NA>4492582<NA>
113662<NA>10067<NA><NA>33661<NA><NA><NA><NA><NA><NA><NA><NA>304898869552662385642027
11466313400527434<NA><NA>27134<NA><NA><NA><NA><NA><NA><NA><NA>801319893515547<NA>
115664<NA>437617694<NA>16117<NA><NA><NA><NA><NA><NA><NA>1330243847320579611921338562
1166651337241855628816<NA>21544<NA><NA><NA><NA><NA><NA><NA>33277842634875631<NA>
117666<NA><NA><NA><NA>10126<NA><NA><NA><NA><NA><NA><NA>630156226165392941036025744
11866710863823108<NA>10095<NA><NA><NA><NA><NA><NA><NA><NA>5663277<NA>2284268<NA>
119668<NA>757<NA>9291<NA><NA><NA><NA><NA><NA><NA><NA><NA>12161371144316571721264
120690<NA>105559<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>