Overview

Dataset statistics

Number of variables19
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory171.4 B

Variable types

Text1
Categorical1
Numeric17

Dataset

Description(주)에스알에서 제공하는 SRT 역별 주별 승하차인원 데이터입니다. 2020년 1~6월 기간을 주별로 나누어 각 역의 승하차인원에 대한 정보를 조회할 수 있습니다.
Author(주)에스알
URLhttps://www.data.go.kr/data/15061851/fileData.do

Alerts

수서 is highly overall correlated with 동탄 and 15 other fieldsHigh correlation
동탄 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
지제 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
천안아산 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
오송 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
대전 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
김천구미 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
동대구 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
신경주 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
울산 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
부산 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
공주 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
익산 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
정읍 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
광주송정 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
나주 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
목포 is highly overall correlated with 수서 and 15 other fieldsHigh correlation
동탄 has unique valuesUnique
동대구 has unique valuesUnique
부산 has unique valuesUnique
광주송정 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:30:35.450897
Analysis finished2023-12-12 13:31:07.432960
Duration31.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct27
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size564.0 B
2023-12-12T22:31:07.569176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.6666667
Min length8

Characters and Unicode

Total characters468
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020년 1주
2nd row2020년 1주
3rd row2020년 2주
4th row2020년 2주
5th row2020년 3주
ValueCountFrequency (%)
2020년 54
50.0%
1주 2
 
1.9%
26주 2
 
1.9%
25주 2
 
1.9%
24주 2
 
1.9%
23주 2
 
1.9%
22주 2
 
1.9%
21주 2
 
1.9%
20주 2
 
1.9%
19주 2
 
1.9%
Other values (18) 36
33.3%
2023-12-12T22:31:07.960121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 130
27.8%
0 112
23.9%
54
11.5%
54
11.5%
54
11.5%
1 26
 
5.6%
3 6
 
1.3%
4 6
 
1.3%
5 6
 
1.3%
6 6
 
1.3%
Other values (3) 14
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 306
65.4%
Other Letter 108
 
23.1%
Space Separator 54
 
11.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 130
42.5%
0 112
36.6%
1 26
 
8.5%
3 6
 
2.0%
4 6
 
2.0%
5 6
 
2.0%
6 6
 
2.0%
7 6
 
2.0%
8 4
 
1.3%
9 4
 
1.3%
Other Letter
ValueCountFrequency (%)
54
50.0%
54
50.0%
Space Separator
ValueCountFrequency (%)
54
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 360
76.9%
Hangul 108
 
23.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 130
36.1%
0 112
31.1%
54
15.0%
1 26
 
7.2%
3 6
 
1.7%
4 6
 
1.7%
5 6
 
1.7%
6 6
 
1.7%
7 6
 
1.7%
8 4
 
1.1%
Hangul
ValueCountFrequency (%)
54
50.0%
54
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
76.9%
Hangul 108
 
23.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 130
36.1%
0 112
31.1%
54
15.0%
1 26
 
7.2%
3 6
 
1.7%
4 6
 
1.7%
5 6
 
1.7%
6 6
 
1.7%
7 6
 
1.7%
8 4
 
1.1%
Hangul
ValueCountFrequency (%)
54
50.0%
54
50.0%

승하차
Categorical

Distinct2
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size564.0 B
승차
27 
하차
27 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row승차
2nd row하차
3rd row승차
4th row하차
5th row승차

Common Values

ValueCountFrequency (%)
승차 27
50.0%
하차 27
50.0%

Length

2023-12-12T22:31:08.123786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:31:08.249545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
승차 27
50.0%
하차 27
50.0%

수서
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14805.852
Minimum6714
Maximum22140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:08.403156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6714
5-th percentile7194.95
Q110677
median15916
Q317247.25
95-th percentile21174.25
Maximum22140
Range15426
Interquartile range (IQR)6570.25

Descriptive statistics

Standard deviation4410.7131
Coefficient of variation (CV)0.29790337
Kurtosis-0.88826309
Mean14805.852
Median Absolute Deviation (MAD)2608.5
Skewness-0.35393734
Sum799516
Variance19454390
MonotonicityNot monotonic
2023-12-12T22:31:08.571035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16041 2
 
3.7%
20114 1
 
1.9%
15830 1
 
1.9%
10528 1
 
1.9%
11124 1
 
1.9%
11329 1
 
1.9%
12823 1
 
1.9%
13045 1
 
1.9%
14878 1
 
1.9%
15066 1
 
1.9%
Other values (43) 43
79.6%
ValueCountFrequency (%)
6714 1
1.9%
6724 1
1.9%
7193 1
1.9%
7196 1
1.9%
7926 1
1.9%
7980 1
1.9%
8786 1
1.9%
8788 1
1.9%
9224 1
1.9%
9276 1
1.9%
ValueCountFrequency (%)
22140 1
1.9%
21350 1
1.9%
21223 1
1.9%
21148 1
1.9%
21044 1
1.9%
21008 1
1.9%
20511 1
1.9%
20121 1
1.9%
20114 1
1.9%
18262 1
1.9%

동탄
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2938.2778
Minimum1260
Maximum4978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:08.732471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1260
5-th percentile1340.45
Q12056.5
median3087
Q33398.75
95-th percentile4555.55
Maximum4978
Range3718
Interquartile range (IQR)1342.25

Descriptive statistics

Standard deviation970.19252
Coefficient of variation (CV)0.33019088
Kurtosis-0.6656046
Mean2938.2778
Median Absolute Deviation (MAD)555
Skewness-0.0057266752
Sum158667
Variance941273.53
MonotonicityNot monotonic
2023-12-12T22:31:08.935291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4590 1
 
1.9%
3123 1
 
1.9%
2181 1
 
1.9%
2229 1
 
1.9%
2439 1
 
1.9%
2464 1
 
1.9%
3344 1
 
1.9%
3089 1
 
1.9%
2994 1
 
1.9%
3224 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
1260 1
1.9%
1293 1
1.9%
1306 1
1.9%
1359 1
1.9%
1536 1
1.9%
1537 1
1.9%
1685 1
1.9%
1743 1
1.9%
1771 1
1.9%
1803 1
1.9%
ValueCountFrequency (%)
4978 1
1.9%
4644 1
1.9%
4590 1
1.9%
4537 1
1.9%
4350 1
1.9%
4323 1
1.9%
4243 1
1.9%
4202 1
1.9%
4152 1
1.9%
3574 1
1.9%

지제
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1529.0926
Minimum818
Maximum2256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:09.121791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum818
5-th percentile835.9
Q11160
median1646.5
Q31796
95-th percentile2044.9
Maximum2256
Range1438
Interquartile range (IQR)636

Descriptive statistics

Standard deviation389.07565
Coefficient of variation (CV)0.25444872
Kurtosis-0.88805972
Mean1529.0926
Median Absolute Deviation (MAD)238.5
Skewness-0.40471422
Sum82571
Variance151379.86
MonotonicityNot monotonic
2023-12-12T22:31:09.292736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1737 2
 
3.7%
1885 2
 
3.7%
1690 2
 
3.7%
2147 1
 
1.9%
1616 1
 
1.9%
1128 1
 
1.9%
1260 1
 
1.9%
1256 1
 
1.9%
1318 1
 
1.9%
1319 1
 
1.9%
Other values (41) 41
75.9%
ValueCountFrequency (%)
818 1
1.9%
822 1
1.9%
832 1
1.9%
838 1
1.9%
907 1
1.9%
940 1
1.9%
1002 1
1.9%
1017 1
1.9%
1032 1
1.9%
1037 1
1.9%
ValueCountFrequency (%)
2256 1
1.9%
2147 1
1.9%
2106 1
1.9%
2012 1
1.9%
2000 1
1.9%
1961 1
1.9%
1947 1
1.9%
1940 1
1.9%
1885 2
3.7%
1844 1
1.9%

천안아산
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.1852
Minimum900
Maximum2843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:09.492979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum900
5-th percentile1002.65
Q11576.5
median2184
Q32369.25
95-th percentile2743.85
Maximum2843
Range1943
Interquartile range (IQR)792.75

Descriptive statistics

Standard deviation544.33151
Coefficient of variation (CV)0.2705176
Kurtosis-0.70143906
Mean2012.1852
Median Absolute Deviation (MAD)277.5
Skewness-0.59991907
Sum108658
Variance296296.8
MonotonicityNot monotonic
2023-12-12T22:31:09.686079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2184 2
 
3.7%
2193 2
 
3.7%
2740 2
 
3.7%
1990 1
 
1.9%
2435 1
 
1.9%
1647 1
 
1.9%
1720 1
 
1.9%
1789 1
 
1.9%
1862 1
 
1.9%
2025 1
 
1.9%
Other values (41) 41
75.9%
ValueCountFrequency (%)
900 1
1.9%
941 1
1.9%
963 1
1.9%
1024 1
1.9%
1081 1
1.9%
1143 1
1.9%
1229 1
1.9%
1263 1
1.9%
1318 1
1.9%
1355 1
1.9%
ValueCountFrequency (%)
2843 1
1.9%
2815 1
1.9%
2751 1
1.9%
2740 2
3.7%
2710 1
1.9%
2593 1
1.9%
2465 1
1.9%
2462 1
1.9%
2461 1
1.9%
2435 1
1.9%

오송
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1920.537
Minimum941
Maximum2692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:09.857606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum941
5-th percentile1013.4
Q11479.25
median2134
Q32265
95-th percentile2623.45
Maximum2692
Range1751
Interquartile range (IQR)785.75

Descriptive statistics

Standard deviation511.68419
Coefficient of variation (CV)0.26642766
Kurtosis-0.91064744
Mean1920.537
Median Absolute Deviation (MAD)197
Skewness-0.61819582
Sum103709
Variance261820.71
MonotonicityNot monotonic
2023-12-12T22:31:10.045959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2134 2
 
3.7%
2320 2
 
3.7%
2442 1
 
1.9%
1410 1
 
1.9%
1544 1
 
1.9%
1531 1
 
1.9%
1740 1
 
1.9%
1713 1
 
1.9%
1942 1
 
1.9%
1831 1
 
1.9%
Other values (42) 42
77.8%
ValueCountFrequency (%)
941 1
1.9%
953 1
1.9%
1003 1
1.9%
1019 1
1.9%
1083 1
1.9%
1111 1
1.9%
1149 1
1.9%
1207 1
1.9%
1211 1
1.9%
1228 1
1.9%
ValueCountFrequency (%)
2692 1
1.9%
2688 1
1.9%
2628 1
1.9%
2621 1
1.9%
2442 1
1.9%
2415 1
1.9%
2383 1
1.9%
2336 1
1.9%
2320 2
3.7%
2306 1
1.9%

대전
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3075.1111
Minimum1265
Maximum4452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:10.195079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1265
5-th percentile1313.4
Q12318.75
median3395.5
Q33610.25
95-th percentile4349.1
Maximum4452
Range3187
Interquartile range (IQR)1291.5

Descriptive statistics

Standard deviation917.87373
Coefficient of variation (CV)0.29848474
Kurtosis-0.73874115
Mean3075.1111
Median Absolute Deviation (MAD)550
Skewness-0.54073752
Sum166056
Variance842492.18
MonotonicityNot monotonic
2023-12-12T22:31:10.333066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3330 2
 
3.7%
4193 1
 
1.9%
2511 1
 
1.9%
2465 1
 
1.9%
2792 1
 
1.9%
2759 1
 
1.9%
3119 1
 
1.9%
3064 1
 
1.9%
3377 1
 
1.9%
3483 1
 
1.9%
Other values (43) 43
79.6%
ValueCountFrequency (%)
1265 1
1.9%
1274 1
1.9%
1277 1
1.9%
1333 1
1.9%
1607 1
1.9%
1636 1
1.9%
1837 1
1.9%
1863 1
1.9%
1908 1
1.9%
1955 1
1.9%
ValueCountFrequency (%)
4452 1
1.9%
4412 1
1.9%
4379 1
1.9%
4333 1
1.9%
4281 1
1.9%
4247 1
1.9%
4193 1
1.9%
3960 1
1.9%
3931 1
1.9%
3921 1
1.9%

김천구미
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean547.16667
Minimum184
Maximum847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:10.502542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184
5-th percentile192.6
Q1400
median604.5
Q3661.25
95-th percentile777.25
Maximum847
Range663
Interquartile range (IQR)261.25

Descriptive statistics

Standard deviation184.20372
Coefficient of variation (CV)0.33665011
Kurtosis-0.61517773
Mean547.16667
Median Absolute Deviation (MAD)95.5
Skewness-0.66460646
Sum29547
Variance33931.009
MonotonicityNot monotonic
2023-12-12T22:31:10.986264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
673 2
 
3.7%
184 2
 
3.7%
596 1
 
1.9%
445 1
 
1.9%
506 1
 
1.9%
482 1
 
1.9%
580 1
 
1.9%
610 1
 
1.9%
621 1
 
1.9%
597 1
 
1.9%
Other values (42) 42
77.8%
ValueCountFrequency (%)
184 2
3.7%
190 1
1.9%
194 1
1.9%
217 1
1.9%
222 1
1.9%
277 1
1.9%
282 1
1.9%
309 1
1.9%
331 1
1.9%
351 1
1.9%
ValueCountFrequency (%)
847 1
1.9%
817 1
1.9%
813 1
1.9%
758 1
1.9%
752 1
1.9%
746 1
1.9%
744 1
1.9%
729 1
1.9%
717 1
1.9%
697 1
1.9%

동대구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4391
Minimum855
Maximum7931
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:11.172136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum855
5-th percentile875.9
Q12625.5
median4839
Q35681
95-th percentile7356.75
Maximum7931
Range7076
Interquartile range (IQR)3055.5

Descriptive statistics

Standard deviation2041.4803
Coefficient of variation (CV)0.46492377
Kurtosis-0.92648487
Mean4391
Median Absolute Deviation (MAD)1283
Skewness-0.26726827
Sum237114
Variance4167641.8
MonotonicityNot monotonic
2023-12-12T22:31:11.357094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7423 1
 
1.9%
4841 1
 
1.9%
3026 1
 
1.9%
3005 1
 
1.9%
3618 1
 
1.9%
3563 1
 
1.9%
4373 1
 
1.9%
4651 1
 
1.9%
4930 1
 
1.9%
4626 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
855 1
1.9%
871 1
1.9%
872 1
1.9%
878 1
1.9%
1161 1
1.9%
1166 1
1.9%
1512 1
1.9%
1547 1
1.9%
1798 1
1.9%
1841 1
1.9%
ValueCountFrequency (%)
7931 1
1.9%
7423 1
1.9%
7373 1
1.9%
7348 1
1.9%
7239 1
1.9%
7111 1
1.9%
7087 1
1.9%
7026 1
1.9%
7003 1
1.9%
6185 1
1.9%

신경주
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean613.07407
Minimum156
Maximum1031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:11.528738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum156
5-th percentile172
Q1392.5
median686.5
Q3770.5
95-th percentile969.4
Maximum1031
Range875
Interquartile range (IQR)378

Descriptive statistics

Standard deviation242.63248
Coefficient of variation (CV)0.39576373
Kurtosis-0.83111546
Mean613.07407
Median Absolute Deviation (MAD)150.5
Skewness-0.38578914
Sum33106
Variance58870.523
MonotonicityNot monotonic
2023-12-12T22:31:11.702940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
972 2
 
3.7%
687 2
 
3.7%
172 2
 
3.7%
1031 1
 
1.9%
692 1
 
1.9%
428 1
 
1.9%
498 1
 
1.9%
502 1
 
1.9%
712 1
 
1.9%
810 1
 
1.9%
Other values (41) 41
75.9%
ValueCountFrequency (%)
156 1
1.9%
166 1
1.9%
172 2
3.7%
230 1
1.9%
234 1
1.9%
275 1
1.9%
285 1
1.9%
308 1
1.9%
324 1
1.9%
352 1
1.9%
ValueCountFrequency (%)
1031 1
1.9%
972 2
3.7%
968 1
1.9%
954 1
1.9%
925 1
1.9%
897 1
1.9%
890 1
1.9%
864 1
1.9%
810 1
1.9%
808 1
1.9%

울산
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1948.4074
Minimum707
Maximum3206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:11.862806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum707
5-th percentile736.55
Q11430.5
median2113.5
Q32283
95-th percentile2827.5
Maximum3206
Range2499
Interquartile range (IQR)852.5

Descriptive statistics

Standard deviation643.58526
Coefficient of variation (CV)0.33031349
Kurtosis-0.65218609
Mean1948.4074
Median Absolute Deviation (MAD)411.5
Skewness-0.37881379
Sum105214
Variance414201.98
MonotonicityNot monotonic
2023-12-12T22:31:12.000634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2786 2
 
3.7%
2181 2
 
3.7%
2190 2
 
3.7%
2899 1
 
1.9%
2074 1
 
1.9%
1492 1
 
1.9%
1506 1
 
1.9%
1680 1
 
1.9%
1716 1
 
1.9%
2040 1
 
1.9%
Other values (41) 41
75.9%
ValueCountFrequency (%)
707 1
1.9%
709 1
1.9%
732 1
1.9%
739 1
1.9%
950 1
1.9%
972 1
1.9%
1085 1
1.9%
1102 1
1.9%
1166 1
1.9%
1204 1
1.9%
ValueCountFrequency (%)
3206 1
1.9%
2908 1
1.9%
2899 1
1.9%
2789 1
1.9%
2786 2
3.7%
2785 1
1.9%
2696 1
1.9%
2607 1
1.9%
2539 1
1.9%
2498 1
1.9%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5333.3889
Minimum1928
Maximum9345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:12.129085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1928
5-th percentile2025.25
Q13604.75
median5593
Q36265
95-th percentile8446.1
Maximum9345
Range7417
Interquartile range (IQR)2660.25

Descriptive statistics

Standard deviation1985.2462
Coefficient of variation (CV)0.37222978
Kurtosis-0.68746462
Mean5333.3889
Median Absolute Deviation (MAD)1297
Skewness0.050307349
Sum288003
Variance3941202.5
MonotonicityNot monotonic
2023-12-12T22:31:12.265315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9027 1
 
1.9%
5585 1
 
1.9%
3864 1
 
1.9%
3808 1
 
1.9%
4331 1
 
1.9%
4261 1
 
1.9%
5610 1
 
1.9%
6319 1
 
1.9%
5700 1
 
1.9%
5154 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
1928 1
1.9%
1955 1
1.9%
1957 1
1.9%
2062 1
1.9%
2478 1
1.9%
2532 1
1.9%
2849 1
1.9%
2887 1
1.9%
3092 1
1.9%
3124 1
1.9%
ValueCountFrequency (%)
9345 1
1.9%
9027 1
1.9%
8541 1
1.9%
8395 1
1.9%
8374 1
1.9%
8217 1
1.9%
8192 1
1.9%
8100 1
1.9%
8087 1
1.9%
7046 1
1.9%

공주
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)68.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.722222
Minimum27
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:12.414134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile30.3
Q143
median64
Q369
95-th percentile82.7
Maximum115
Range88
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.935259
Coefficient of variation (CV)0.3170555
Kurtosis0.36902064
Mean59.722222
Median Absolute Deviation (MAD)11
Skewness0.23473769
Sum3225
Variance358.54403
MonotonicityNot monotonic
2023-12-12T22:31:12.574114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
67 4
 
7.4%
64 4
 
7.4%
32 3
 
5.6%
69 3
 
5.6%
60 2
 
3.7%
82 2
 
3.7%
70 2
 
3.7%
65 2
 
3.7%
41 2
 
3.7%
75 2
 
3.7%
Other values (27) 28
51.9%
ValueCountFrequency (%)
27 1
 
1.9%
28 1
 
1.9%
29 1
 
1.9%
31 1
 
1.9%
32 3
5.6%
35 1
 
1.9%
37 1
 
1.9%
39 1
 
1.9%
40 1
 
1.9%
41 2
3.7%
ValueCountFrequency (%)
115 1
1.9%
104 1
1.9%
84 1
1.9%
82 2
3.7%
81 1
1.9%
80 1
1.9%
77 1
1.9%
75 2
3.7%
73 1
1.9%
70 2
3.7%

익산
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1389.9074
Minimum763
Maximum2065
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:12.700055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum763
5-th percentile810.95
Q11085.75
median1478.5
Q31583
95-th percentile1905.05
Maximum2065
Range1302
Interquartile range (IQR)497.25

Descriptive statistics

Standard deviation342.87565
Coefficient of variation (CV)0.24668956
Kurtosis-0.81932764
Mean1389.9074
Median Absolute Deviation (MAD)219.5
Skewness-0.18986336
Sum75055
Variance117563.71
MonotonicityNot monotonic
2023-12-12T22:31:12.862315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1396 2
 
3.7%
1946 1
 
1.9%
1475 1
 
1.9%
1148 1
 
1.9%
1097 1
 
1.9%
1228 1
 
1.9%
1194 1
 
1.9%
1498 1
 
1.9%
1602 1
 
1.9%
1667 1
 
1.9%
Other values (43) 43
79.6%
ValueCountFrequency (%)
763 1
1.9%
775 1
1.9%
809 1
1.9%
812 1
1.9%
885 1
1.9%
916 1
1.9%
921 1
1.9%
955 1
1.9%
960 1
1.9%
988 1
1.9%
ValueCountFrequency (%)
2065 1
1.9%
1955 1
1.9%
1946 1
1.9%
1883 1
1.9%
1880 1
1.9%
1841 1
1.9%
1837 1
1.9%
1819 1
1.9%
1733 1
1.9%
1667 1
1.9%

정읍
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)87.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.7963
Minimum161
Maximum497
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:13.006820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum161
5-th percentile166.6
Q1221.5
median289.5
Q3320.5
95-th percentile384.15
Maximum497
Range336
Interquartile range (IQR)99

Descriptive statistics

Standard deviation72.393156
Coefficient of variation (CV)0.25781379
Kurtosis0.46061846
Mean280.7963
Median Absolute Deviation (MAD)48.5
Skewness0.43259077
Sum15163
Variance5240.769
MonotonicityNot monotonic
2023-12-12T22:31:13.137588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
289 2
 
3.7%
164 2
 
3.7%
381 2
 
3.7%
234 2
 
3.7%
343 2
 
3.7%
321 2
 
3.7%
292 2
 
3.7%
229 1
 
1.9%
260 1
 
1.9%
244 1
 
1.9%
Other values (37) 37
68.5%
ValueCountFrequency (%)
161 1
1.9%
164 2
3.7%
168 1
1.9%
179 1
1.9%
183 1
1.9%
191 1
1.9%
196 1
1.9%
200 1
1.9%
204 1
1.9%
209 1
1.9%
ValueCountFrequency (%)
497 1
1.9%
449 1
1.9%
390 1
1.9%
381 2
3.7%
368 1
1.9%
363 1
1.9%
343 2
3.7%
341 1
1.9%
331 1
1.9%
324 1
1.9%

광주송정
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2840.9444
Minimum1533
Maximum4460
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:13.286651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1533
5-th percentile1632.65
Q12164.5
median2982.5
Q33170.5
95-th percentile4035.8
Maximum4460
Range2927
Interquartile range (IQR)1006

Descriptive statistics

Standard deviation737.81926
Coefficient of variation (CV)0.25970915
Kurtosis-0.60855797
Mean2840.9444
Median Absolute Deviation (MAD)378.5
Skewness0.0040164745
Sum153411
Variance544377.26
MonotonicityNot monotonic
2023-12-12T22:31:13.449485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4096 1
 
1.9%
2976 1
 
1.9%
2317 1
 
1.9%
2208 1
 
1.9%
2533 1
 
1.9%
2449 1
 
1.9%
3009 1
 
1.9%
3177 1
 
1.9%
3290 1
 
1.9%
2975 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
1533 1
1.9%
1548 1
1.9%
1632 1
1.9%
1633 1
1.9%
1805 1
1.9%
1815 1
1.9%
1888 1
1.9%
1930 1
1.9%
1948 1
1.9%
1974 1
1.9%
ValueCountFrequency (%)
4460 1
1.9%
4096 1
1.9%
4067 1
1.9%
4019 1
1.9%
3992 1
1.9%
3909 1
1.9%
3862 1
1.9%
3819 1
1.9%
3711 1
1.9%
3290 1
1.9%

나주
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean326.38889
Minimum203
Maximum452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:13.591793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum203
5-th percentile224.95
Q1258.75
median348
Q3360.75
95-th percentile421.8
Maximum452
Range249
Interquartile range (IQR)102

Descriptive statistics

Standard deviation67.093969
Coefficient of variation (CV)0.2055645
Kurtosis-0.93266266
Mean326.38889
Median Absolute Deviation (MAD)43
Skewness-0.25514501
Sum17625
Variance4501.6006
MonotonicityNot monotonic
2023-12-12T22:31:13.737357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
355 2
 
3.7%
391 2
 
3.7%
226 2
 
3.7%
358 2
 
3.7%
339 2
 
3.7%
353 2
 
3.7%
348 2
 
3.7%
352 2
 
3.7%
350 1
 
1.9%
253 1
 
1.9%
Other values (36) 36
66.7%
ValueCountFrequency (%)
203 1
1.9%
204 1
1.9%
223 1
1.9%
226 2
3.7%
229 1
1.9%
231 1
1.9%
233 1
1.9%
238 1
1.9%
241 1
1.9%
249 1
1.9%
ValueCountFrequency (%)
452 1
1.9%
446 1
1.9%
427 1
1.9%
419 1
1.9%
416 1
1.9%
409 1
1.9%
403 1
1.9%
394 1
1.9%
391 2
3.7%
387 1
1.9%

목포
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean596.85185
Minimum321
Maximum1043
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T22:31:13.863737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum321
5-th percentile348.3
Q1435.5
median621.5
Q3691.25
95-th percentile874.45
Maximum1043
Range722
Interquartile range (IQR)255.75

Descriptive statistics

Standard deviation169.87114
Coefficient of variation (CV)0.2846119
Kurtosis-0.39179093
Mean596.85185
Median Absolute Deviation (MAD)118.5
Skewness0.15554247
Sum32230
Variance28856.204
MonotonicityNot monotonic
2023-12-12T22:31:14.049685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
645 3
 
5.6%
785 2
 
3.7%
366 2
 
3.7%
606 2
 
3.7%
909 1
 
1.9%
650 1
 
1.9%
434 1
 
1.9%
505 1
 
1.9%
501 1
 
1.9%
734 1
 
1.9%
Other values (39) 39
72.2%
ValueCountFrequency (%)
321 1
1.9%
324 1
1.9%
347 1
1.9%
349 1
1.9%
352 1
1.9%
366 2
3.7%
385 1
1.9%
387 1
1.9%
390 1
1.9%
396 1
1.9%
ValueCountFrequency (%)
1043 1
1.9%
909 1
1.9%
892 1
1.9%
865 1
1.9%
794 1
1.9%
785 2
3.7%
779 1
1.9%
769 1
1.9%
767 1
1.9%
734 1
1.9%

Interactions

2023-12-12T22:31:04.972553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:36.197586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:38.079599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:40.136549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:41.961374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:43.687416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:45.428878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:47.470861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:49.286998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:50.588024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:52.642530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:54.408835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:56.081185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.623793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:59.770551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:01.351971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:03.080189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:05.089611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:36.396262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:38.212247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:40.252244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:42.070512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:43.793153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:45.526217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:47.577186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:49.372543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:50.683803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:52.756619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:54.565265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:56.166158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.760416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:59.901595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:01.445426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:03.189342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:05.479395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:36.508340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:38.321843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:40.362065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T22:31:04.530592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:06.648336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:37.768424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:39.777435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:41.704513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:43.364118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:45.098041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:47.170114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:48.943209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:50.372981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:51.994140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:54.127546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:55.828569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.286898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:59.495181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:01.107146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:02.631788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:04.638633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:06.746503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:37.868427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:39.914715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:41.791604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:43.471054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:45.204759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:47.260343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:49.082621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:50.441233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:52.418422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:54.211926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:55.910626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.416064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:59.589476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:01.197001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:02.867759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:04.743550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:06.850869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:37.965368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:40.026356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:41.873961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:43.577365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:45.308254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:47.368418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:49.204766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:50.507099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:52.527716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:54.309807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:55.997872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.516858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:59.669578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:01.272754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:02.963463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:31:04.871937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:31:14.166009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분승하차수서동탄지제천안아산오송대전김천구미동대구신경주울산부산공주익산정읍광주송정나주목포
구분1.0000.0000.9500.9670.9730.9640.9850.9850.9480.9780.9680.9470.9860.9790.9600.9480.9350.9280.929
승하차0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3420.0000.000
수서0.9500.0001.0000.9890.9530.9580.9660.9670.9490.9570.9460.9410.9550.8590.9670.7670.9750.9280.944
동탄0.9670.0000.9891.0000.9430.9490.9610.9490.9540.9580.9600.9270.9210.8600.9480.8210.9590.8820.931
지제0.9730.0000.9530.9431.0000.9520.9760.9420.9440.9500.9510.9350.8720.8480.9280.8210.9440.9150.903
천안아산0.9640.0000.9580.9490.9521.0000.9590.9820.9390.9640.9410.9430.8890.8770.9530.8050.9480.9060.924
오송0.9850.0000.9660.9610.9760.9591.0000.9550.9520.9500.9440.9160.8980.8440.9540.8040.9630.8830.932
대전0.9850.0000.9670.9490.9420.9820.9551.0000.9330.9610.9290.9450.9150.8440.9410.7930.9500.9180.915
김천구미0.9480.0000.9490.9540.9440.9390.9520.9331.0000.9680.9650.9390.8900.8690.9500.8290.9570.8970.906
동대구0.9780.0000.9570.9580.9500.9640.9500.9610.9681.0000.9630.9430.9200.8640.9190.7900.9330.8550.869
신경주0.9680.0000.9460.9600.9510.9410.9440.9290.9650.9631.0000.9660.9030.8680.9280.8070.9420.8310.904
울산0.9470.0000.9410.9270.9350.9430.9160.9450.9390.9430.9661.0000.9040.8960.9490.8670.9500.9100.918
부산0.9860.0000.9550.9210.8720.8890.8980.9150.8900.9200.9030.9041.0000.9370.9130.9210.9320.8090.880
공주0.9790.0000.8590.8600.8480.8770.8440.8440.8690.8640.8680.8960.9371.0000.8970.9830.9080.8310.915
익산0.9600.0000.9670.9480.9280.9530.9540.9410.9500.9190.9280.9490.9130.8971.0000.8360.9750.9190.975
정읍0.9480.0000.7670.8210.8210.8050.8040.7930.8290.7900.8070.8670.9210.9830.8361.0000.8560.7760.852
광주송정0.9350.3420.9750.9590.9440.9480.9630.9500.9570.9330.9420.9500.9320.9080.9750.8561.0000.9200.975
나주0.9280.0000.9280.8820.9150.9060.8830.9180.8970.8550.8310.9100.8090.8310.9190.7760.9201.0000.933
목포0.9290.0000.9440.9310.9030.9240.9320.9150.9060.8690.9040.9180.8800.9150.9750.8520.9750.9331.000
2023-12-12T22:31:14.707229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수서동탄지제천안아산오송대전김천구미동대구신경주울산부산공주익산정읍광주송정나주목포승하차
수서1.0000.9810.9600.9730.9050.9390.9340.9420.9300.9420.9520.8780.9030.8200.8870.9010.8710.000
동탄0.9811.0000.9680.9550.8840.9230.9430.9450.9580.9460.9610.8900.9220.8620.8980.9110.9060.000
지제0.9600.9681.0000.9670.9390.9300.9500.9460.9390.9320.9210.9000.9110.8640.8950.9150.8920.000
천안아산0.9730.9550.9671.0000.9310.9450.9260.9370.9240.9430.9400.8770.8990.8080.8870.9140.8670.000
오송0.9050.8840.9390.9311.0000.9010.9210.9120.8660.8610.8630.8240.8500.7710.8560.8480.8230.000
대전0.9390.9230.9300.9450.9011.0000.9310.9410.9140.9360.9230.8800.8610.7980.8660.8680.8410.000
김천구미0.9340.9430.9500.9260.9210.9311.0000.9870.9500.9420.9420.8690.9180.8660.9090.8960.8910.000
동대구0.9420.9450.9460.9370.9120.9410.9871.0000.9540.9580.9490.8570.8980.8390.8910.8890.8700.000
신경주0.9300.9580.9390.9240.8660.9140.9500.9541.0000.9740.9700.9050.9170.8930.9030.9050.9200.000
울산0.9420.9460.9320.9430.8610.9360.9420.9580.9741.0000.9690.9030.9070.8730.9020.9220.8880.000
부산0.9520.9610.9210.9400.8630.9230.9420.9490.9700.9691.0000.9030.9390.8690.9340.9330.9320.000
공주0.8780.8900.9000.8770.8240.8800.8690.8570.9050.9030.9031.0000.9250.9220.9400.9170.9340.000
익산0.9030.9220.9110.8990.8500.8610.9180.8980.9170.9070.9390.9251.0000.9460.9850.9460.9700.000
정읍0.8200.8620.8640.8080.7710.7980.8660.8390.8930.8730.8690.9220.9461.0000.9350.8930.9510.000
광주송정0.8870.8980.8950.8870.8560.8660.9090.8910.9030.9020.9340.9400.9850.9351.0000.9540.9710.225
나주0.9010.9110.9150.9140.8480.8680.8960.8890.9050.9220.9330.9170.9460.8930.9541.0000.9470.000
목포0.8710.9060.8920.8670.8230.8410.8910.8700.9200.8880.9320.9340.9700.9510.9710.9471.0000.000
승하차0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.2250.0000.0001.000

Missing values

2023-12-12T22:31:07.003583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:31:07.310414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

구분승하차수서동탄지제천안아산오송대전김천구미동대구신경주울산부산공주익산정읍광주송정나주목포
02020년 1주승차201144590214727102442419375274231031289990277319463904096409909
12020년 1주하차21044464422562740238342818137348968290885418218373813711419794
22020년 2주승차21008432320122740268843337587239954269680878218833414019391767
32020년 2주하차21148435020002815262143797447111972278681928118193193819394769
42020년 3주승차21350420219612751269244127467087897260782178018803433992385785
52020년 3주하차21223415219472843262844527297026925278983748418413313862403779
62020년 4주승차221404978210625932300424781773738642785810011519554493909446865
72020년 4주하차2012145371940246520993960847793197232069345104206549744604521043
82020년 5주승차17643354317372323223937217177003890278683957717333814067416892
92020년 5주하차20511424318852461228239076556025808249870467515432913180427726
구분승하차수서동탄지제천안아산오송대전김천구미동대구신경주울산부산공주익산정읍광주송정나주목포
442020년 23주승차15739308116542152220034816384912689207955326314963062983327645
452020년 23주하차15849305416722225213434886054863704212555626514623012918331619
462020년 24주승차16288317217272249233636386625189686209556926415252893074355643
472020년 24주하차16358320917002343225936226135133687218157556714822923001353628
482020년 25주승차16989333118342333232035106735370708218158826915773213151352689
492020년 25주하차16872328518002399225234896415435727225060986815473103083361672
502020년 26주승차17294341718852378241532536935693754219060996616313213216391692
512020년 26주하차17333343818442435232032656575614776232062116616013243117387681
522020년 27주승차15398291114381990212227906334906639191452725813662512844298584
532020년 27주하차15680291416042142220230325874786623189647405613022342726330559