Overview

Dataset statistics

Number of variables19
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory175.1 B

Variable types

Text1
Numeric18

Dataset

Description이 데이터는 고속열차 SRT에 대한 자료로, 2022년 주별 평균 하차인원을 나타낸 자료입니다. 해당 자료는 일반권을 기준으로 작성되었습니다.(정기/회수권 불포함)
URLhttps://www.data.go.kr/data/15071944/fileData.do

Alerts

수서 is highly overall correlated with 동탄 and 9 other fieldsHigh correlation
동탄 is highly overall correlated with 수서 and 4 other fieldsHigh correlation
평택지제 is highly overall correlated with 수서 and 9 other fieldsHigh correlation
천안아산 is highly overall correlated with 수서 and 7 other fieldsHigh correlation
오송 is highly overall correlated with 수서 and 7 other fieldsHigh correlation
대전 is highly overall correlated with 수서 and 7 other fieldsHigh correlation
동대구 is highly overall correlated with 수서 and 9 other fieldsHigh correlation
신경주 is highly overall correlated with 울산 and 3 other fieldsHigh correlation
울산 is highly overall correlated with 수서 and 11 other fieldsHigh correlation
부산 is highly overall correlated with 신경주High correlation
공주 is highly overall correlated with 수서 and 6 other fieldsHigh correlation
익산 is highly overall correlated with 수서 and 8 other fieldsHigh correlation
정읍 is highly overall correlated with 신경주 and 1 other fieldsHigh correlation
광주송정 is highly overall correlated with 수서 and 8 other fieldsHigh correlation
목포 is highly overall correlated with 신경주 and 2 other fieldsHigh correlation
구분 has unique valuesUnique
수서 has unique valuesUnique
동탄 has unique valuesUnique
천안아산 has unique valuesUnique
대전 has unique valuesUnique
신경주 has unique valuesUnique
부산 has unique valuesUnique
익산 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:15:35.173398
Analysis finished2023-12-12 15:16:09.020108
Duration33.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-13T00:16:09.148442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters234
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

Unique26 ?
Unique (%)100.0%

Sample

1st row2022년 28주
2nd row2022년 29주
3rd row2022년 30주
4th row2022년 31주
5th row2022년 32주
ValueCountFrequency (%)
2022년 26
50.0%
41주 1
 
1.9%
52주 1
 
1.9%
51주 1
 
1.9%
50주 1
 
1.9%
49주 1
 
1.9%
48주 1
 
1.9%
47주 1
 
1.9%
46주 1
 
1.9%
45주 1
 
1.9%
Other values (17) 17
32.7%
2023-12-13T00:16:09.494082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 83
35.5%
0 29
 
12.4%
26
 
11.1%
26
 
11.1%
26
 
11.1%
3 13
 
5.6%
4 12
 
5.1%
5 6
 
2.6%
8 3
 
1.3%
9 3
 
1.3%
Other values (3) 7
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 156
66.7%
Other Letter 52
 
22.2%
Space Separator 26
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 83
53.2%
0 29
 
18.6%
3 13
 
8.3%
4 12
 
7.7%
5 6
 
3.8%
8 3
 
1.9%
9 3
 
1.9%
1 3
 
1.9%
6 2
 
1.3%
7 2
 
1.3%
Other Letter
ValueCountFrequency (%)
26
50.0%
26
50.0%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 182
77.8%
Hangul 52
 
22.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 83
45.6%
0 29
 
15.9%
26
 
14.3%
3 13
 
7.1%
4 12
 
6.6%
5 6
 
3.3%
8 3
 
1.6%
9 3
 
1.6%
1 3
 
1.6%
6 2
 
1.1%
Hangul
ValueCountFrequency (%)
26
50.0%
26
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 182
77.8%
Hangul 52
 
22.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 83
45.6%
0 29
 
15.9%
26
 
14.3%
3 13
 
7.1%
4 12
 
6.6%
5 6
 
3.3%
8 3
 
1.6%
9 3
 
1.6%
1 3
 
1.6%
6 2
 
1.1%
Hangul
ValueCountFrequency (%)
26
50.0%
26
50.0%

수서
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21414.577
Minimum17953
Maximum22536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:09.630406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17953
5-th percentile19982.75
Q121113.75
median21589.5
Q322042.5
95-th percentile22222
Maximum22536
Range4583
Interquartile range (IQR)928.75

Descriptive statistics

Standard deviation925.0396
Coefficient of variation (CV)0.043196725
Kurtosis7.3649775
Mean21414.577
Median Absolute Deviation (MAD)468
Skewness-2.3262005
Sum556779
Variance855698.25
MonotonicityNot monotonic
2023-12-13T00:16:09.746195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
21097 1
 
3.8%
22163 1
 
3.8%
19772 1
 
3.8%
21252 1
 
3.8%
22011 1
 
3.8%
21697 1
 
3.8%
21529 1
 
3.8%
22102 1
 
3.8%
22227 1
 
3.8%
21964 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
17953 1
3.8%
19772 1
3.8%
20615 1
3.8%
20929 1
3.8%
21042 1
3.8%
21076 1
3.8%
21097 1
3.8%
21164 1
3.8%
21252 1
3.8%
21361 1
3.8%
ValueCountFrequency (%)
22536 1
3.8%
22227 1
3.8%
22207 1
3.8%
22163 1
3.8%
22102 1
3.8%
22062 1
3.8%
22053 1
3.8%
22011 1
3.8%
21964 1
3.8%
21734 1
3.8%

동탄
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4821.6538
Minimum4442
Maximum5121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:09.869758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4442
5-th percentile4476.5
Q14684
median4829.5
Q34973.25
95-th percentile5090.75
Maximum5121
Range679
Interquartile range (IQR)289.25

Descriptive statistics

Standard deviation189.50102
Coefficient of variation (CV)0.039302078
Kurtosis-0.54147103
Mean4821.6538
Median Absolute Deviation (MAD)156.5
Skewness-0.33448764
Sum125363
Variance35910.635
MonotonicityNot monotonic
2023-12-13T00:16:10.007023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4662 1
 
3.8%
5091 1
 
3.8%
4458 1
 
3.8%
4798 1
 
3.8%
5090 1
 
3.8%
4915 1
 
3.8%
4711 1
 
3.8%
4929 1
 
3.8%
4988 1
 
3.8%
4853 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
4442 1
3.8%
4458 1
3.8%
4532 1
3.8%
4638 1
3.8%
4651 1
3.8%
4662 1
3.8%
4675 1
3.8%
4711 1
3.8%
4744 1
3.8%
4770 1
3.8%
ValueCountFrequency (%)
5121 1
3.8%
5091 1
3.8%
5090 1
3.8%
5027 1
3.8%
5025 1
3.8%
4989 1
3.8%
4988 1
3.8%
4929 1
3.8%
4915 1
3.8%
4900 1
3.8%

평택지제
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3087.9231
Minimum2742
Maximum3321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:10.136076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2742
5-th percentile2817
Q12974.5
median3111.5
Q33200.75
95-th percentile3270
Maximum3321
Range579
Interquartile range (IQR)226.25

Descriptive statistics

Standard deviation157.79073
Coefficient of variation (CV)0.051099307
Kurtosis-0.55723449
Mean3087.9231
Median Absolute Deviation (MAD)128
Skewness-0.62710148
Sum80286
Variance24897.914
MonotonicityNot monotonic
2023-12-13T00:16:10.289577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
3249 2
 
7.7%
2973 1
 
3.8%
2979 1
 
3.8%
2803 1
 
3.8%
3277 1
 
3.8%
3112 1
 
3.8%
3182 1
 
3.8%
3201 1
 
3.8%
3200 1
 
3.8%
3242 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
2742 1
3.8%
2803 1
3.8%
2859 1
3.8%
2869 1
3.8%
2926 1
3.8%
2943 1
3.8%
2973 1
3.8%
2979 1
3.8%
3045 1
3.8%
3050 1
3.8%
ValueCountFrequency (%)
3321 1
3.8%
3277 1
3.8%
3249 2
7.7%
3242 1
3.8%
3237 1
3.8%
3201 1
3.8%
3200 1
3.8%
3182 1
3.8%
3180 1
3.8%
3172 1
3.8%

천안아산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3189.9231
Minimum2536
Maximum3398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:10.436261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2536
5-th percentile2944.5
Q13089
median3285.5
Q33323
95-th percentile3391.5
Maximum3398
Range862
Interquartile range (IQR)234

Descriptive statistics

Standard deviation199.09353
Coefficient of variation (CV)0.06241327
Kurtosis3.1841898
Mean3189.9231
Median Absolute Deviation (MAD)105.5
Skewness-1.5298329
Sum82938
Variance39638.234
MonotonicityNot monotonic
2023-12-13T00:16:10.568672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3095 1
 
3.8%
3283 1
 
3.8%
2942 1
 
3.8%
3315 1
 
3.8%
3392 1
 
3.8%
3324 1
 
3.8%
3300 1
 
3.8%
3398 1
 
3.8%
3310 1
 
3.8%
3357 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
2536 1
3.8%
2942 1
3.8%
2952 1
3.8%
2957 1
3.8%
3014 1
3.8%
3083 1
3.8%
3088 1
3.8%
3092 1
3.8%
3095 1
3.8%
3132 1
3.8%
ValueCountFrequency (%)
3398 1
3.8%
3392 1
3.8%
3390 1
3.8%
3379 1
3.8%
3357 1
3.8%
3328 1
3.8%
3324 1
3.8%
3320 1
3.8%
3315 1
3.8%
3310 1
3.8%

오송
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2882.3846
Minimum2236
Maximum3133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:10.690940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2236
5-th percentile2692.75
Q12805.5
median2894
Q33014
95-th percentile3096.75
Maximum3133
Range897
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation182.42962
Coefficient of variation (CV)0.063291213
Kurtosis5.2873882
Mean2882.3846
Median Absolute Deviation (MAD)89.5
Skewness-1.6761759
Sum74942
Variance33280.566
MonotonicityNot monotonic
2023-12-13T00:16:10.802528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2901 2
 
7.7%
2741 2
 
7.7%
3081 2
 
7.7%
2855 1
 
3.8%
2686 1
 
3.8%
3102 1
 
3.8%
3133 1
 
3.8%
3027 1
 
3.8%
3041 1
 
3.8%
3044 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
2236 1
3.8%
2686 1
3.8%
2713 1
3.8%
2741 2
7.7%
2804 1
3.8%
2805 1
3.8%
2807 1
3.8%
2819 1
3.8%
2855 1
3.8%
2858 1
3.8%
ValueCountFrequency (%)
3133 1
3.8%
3102 1
3.8%
3081 2
7.7%
3044 1
3.8%
3041 1
3.8%
3027 1
3.8%
2975 1
3.8%
2966 1
3.8%
2951 1
3.8%
2908 1
3.8%

대전
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4398.5
Minimum3681
Maximum4721
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:10.909037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3681
5-th percentile3928.75
Q14298.5
median4465.5
Q34587.5
95-th percentile4676.25
Maximum4721
Range1040
Interquartile range (IQR)289

Descriptive statistics

Standard deviation256.51546
Coefficient of variation (CV)0.058318849
Kurtosis1.2700387
Mean4398.5
Median Absolute Deviation (MAD)146
Skewness-1.2042696
Sum114361
Variance65800.18
MonotonicityNot monotonic
2023-12-13T00:16:11.019737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4293 1
 
3.8%
4499 1
 
3.8%
3886 1
 
3.8%
4607 1
 
3.8%
4655 1
 
3.8%
4600 1
 
3.8%
4550 1
 
3.8%
4680 1
 
3.8%
4502 1
 
3.8%
4721 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
3681 1
3.8%
3886 1
3.8%
4057 1
3.8%
4089 1
3.8%
4121 1
3.8%
4256 1
3.8%
4293 1
3.8%
4315 1
3.8%
4370 1
3.8%
4385 1
3.8%
ValueCountFrequency (%)
4721 1
3.8%
4680 1
3.8%
4665 1
3.8%
4655 1
3.8%
4626 1
3.8%
4607 1
3.8%
4600 1
3.8%
4550 1
3.8%
4523 1
3.8%
4515 1
3.8%

김천구미
Real number (ℝ)

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.30769
Minimum649
Maximum768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:11.141074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum649
5-th percentile658.75
Q1687
median699.5
Q3717.75
95-th percentile752
Maximum768
Range119
Interquartile range (IQR)30.75

Descriptive statistics

Standard deviation28.576591
Coefficient of variation (CV)0.040747579
Kurtosis0.23178453
Mean701.30769
Median Absolute Deviation (MAD)16.5
Skewness0.41242738
Sum18234
Variance816.62154
MonotonicityNot monotonic
2023-12-13T00:16:11.267724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
700 2
 
7.7%
687 2
 
7.7%
711 1
 
3.8%
670 1
 
3.8%
720 1
 
3.8%
710 1
 
3.8%
696 1
 
3.8%
704 1
 
3.8%
731 1
 
3.8%
756 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
649 1
3.8%
656 1
3.8%
667 1
3.8%
670 1
3.8%
673 1
3.8%
677 1
3.8%
687 2
7.7%
688 1
3.8%
693 1
3.8%
696 1
3.8%
ValueCountFrequency (%)
768 1
3.8%
756 1
3.8%
740 1
3.8%
731 1
3.8%
726 1
3.8%
721 1
3.8%
720 1
3.8%
711 1
3.8%
710 1
3.8%
707 1
3.8%

서대구
Real number (ℝ)

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336
Minimum293
Maximum383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:11.400690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum293
5-th percentile308.75
Q1320.75
median337
Q3343
95-th percentile378.75
Maximum383
Range90
Interquartile range (IQR)22.25

Descriptive statistics

Standard deviation21.989088
Coefficient of variation (CV)0.065443715
Kurtosis0.19274215
Mean336
Median Absolute Deviation (MAD)13.5
Skewness0.49761648
Sum8736
Variance483.52
MonotonicityNot monotonic
2023-12-13T00:16:11.525116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
337 3
 
11.5%
328 2
 
7.7%
338 2
 
7.7%
343 2
 
7.7%
318 1
 
3.8%
372 1
 
3.8%
354 1
 
3.8%
320 1
 
3.8%
313 1
 
3.8%
308 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
293 1
3.8%
308 1
3.8%
311 1
3.8%
313 1
3.8%
316 1
3.8%
318 1
3.8%
320 1
3.8%
323 1
3.8%
328 2
7.7%
329 1
3.8%
ValueCountFrequency (%)
383 1
 
3.8%
381 1
 
3.8%
372 1
 
3.8%
364 1
 
3.8%
354 1
 
3.8%
350 1
 
3.8%
343 2
7.7%
340 1
 
3.8%
338 2
7.7%
337 3
11.5%

동대구
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6850.5769
Minimum6244
Maximum7349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:11.650083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6244
5-th percentile6530
Q16695.25
median6899
Q36993
95-th percentile7235.75
Maximum7349
Range1105
Interquartile range (IQR)297.75

Descriptive statistics

Standard deviation243.8531
Coefficient of variation (CV)0.035595995
Kurtosis0.4943252
Mean6850.5769
Median Absolute Deviation (MAD)146
Skewness-0.2132263
Sum178115
Variance59464.334
MonotonicityNot monotonic
2023-12-13T00:16:11.817088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
6630 2
 
7.7%
6744 1
 
3.8%
6244 1
 
3.8%
6902 1
 
3.8%
6948 1
 
3.8%
6916 1
 
3.8%
6808 1
 
3.8%
7260 1
 
3.8%
6999 1
 
3.8%
7349 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
6244 1
3.8%
6526 1
3.8%
6542 1
3.8%
6622 1
3.8%
6630 2
7.7%
6683 1
3.8%
6732 1
3.8%
6744 1
3.8%
6772 1
3.8%
6789 1
3.8%
ValueCountFrequency (%)
7349 1
3.8%
7260 1
3.8%
7163 1
3.8%
7106 1
3.8%
7036 1
3.8%
7009 1
3.8%
6999 1
3.8%
6975 1
3.8%
6948 1
3.8%
6923 1
3.8%

신경주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1398.5385
Minimum1156
Maximum1622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:11.986121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1156
5-th percentile1198.75
Q11280.75
median1406.5
Q31497.5
95-th percentile1615.25
Maximum1622
Range466
Interquartile range (IQR)216.75

Descriptive statistics

Standard deviation143.17604
Coefficient of variation (CV)0.10237548
Kurtosis-1.1134369
Mean1398.5385
Median Absolute Deviation (MAD)120
Skewness0.087742902
Sum36362
Variance20499.378
MonotonicityNot monotonic
2023-12-13T00:16:12.105948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1298 1
 
3.8%
1506 1
 
3.8%
1255 1
 
3.8%
1320 1
 
3.8%
1204 1
 
3.8%
1244 1
 
3.8%
1237 1
 
3.8%
1401 1
 
3.8%
1470 1
 
3.8%
1553 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1156 1
3.8%
1197 1
3.8%
1204 1
3.8%
1237 1
3.8%
1244 1
3.8%
1255 1
3.8%
1275 1
3.8%
1298 1
3.8%
1313 1
3.8%
1320 1
3.8%
ValueCountFrequency (%)
1622 1
3.8%
1617 1
3.8%
1610 1
3.8%
1605 1
3.8%
1570 1
3.8%
1553 1
3.8%
1506 1
3.8%
1472 1
3.8%
1470 1
3.8%
1444 1
3.8%

울산
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2759.6538
Minimum2475
Maximum3018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:12.213475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2475
5-th percentile2516.75
Q12661.25
median2775
Q32824.5
95-th percentile2972.5
Maximum3018
Range543
Interquartile range (IQR)163.25

Descriptive statistics

Standard deviation137.00276
Coefficient of variation (CV)0.049644906
Kurtosis-0.14595874
Mean2759.6538
Median Absolute Deviation (MAD)95.5
Skewness-0.25718372
Sum71751
Variance18769.755
MonotonicityNot monotonic
2023-12-13T00:16:12.335256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2884 2
 
7.7%
2582 1
 
3.8%
2606 1
 
3.8%
2475 1
 
3.8%
2811 1
 
3.8%
2780 1
 
3.8%
2756 1
 
3.8%
2817 1
 
3.8%
2826 1
 
3.8%
2980 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
2475 1
3.8%
2495 1
3.8%
2582 1
3.8%
2606 1
3.8%
2625 1
3.8%
2646 1
3.8%
2654 1
3.8%
2683 1
3.8%
2722 1
3.8%
2741 1
3.8%
ValueCountFrequency (%)
3018 1
3.8%
2980 1
3.8%
2950 1
3.8%
2884 2
7.7%
2874 1
3.8%
2826 1
3.8%
2820 1
3.8%
2817 1
3.8%
2812 1
3.8%
2811 1
3.8%

부산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8529.7692
Minimum7397
Maximum9527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:12.460828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7397
5-th percentile7587
Q18295.25
median8511
Q38812
95-th percentile9338.5
Maximum9527
Range2130
Interquartile range (IQR)516.75

Descriptive statistics

Standard deviation502.73375
Coefficient of variation (CV)0.058938728
Kurtosis0.53326515
Mean8529.7692
Median Absolute Deviation (MAD)231
Skewness-0.27132886
Sum221774
Variance252741.22
MonotonicityNot monotonic
2023-12-13T00:16:12.588037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
8282 1
 
3.8%
8606 1
 
3.8%
8413 1
 
3.8%
8660 1
 
3.8%
8463 1
 
3.8%
8192 1
 
3.8%
8365 1
 
3.8%
8512 1
 
3.8%
9383 1
 
3.8%
8841 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
7397 1
3.8%
7502 1
3.8%
7842 1
3.8%
8070 1
3.8%
8192 1
3.8%
8278 1
3.8%
8282 1
3.8%
8335 1
3.8%
8365 1
3.8%
8413 1
3.8%
ValueCountFrequency (%)
9527 1
3.8%
9383 1
3.8%
9205 1
3.8%
9099 1
3.8%
8932 1
3.8%
8854 1
3.8%
8841 1
3.8%
8725 1
3.8%
8690 1
3.8%
8660 1
3.8%

공주
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.69231
Minimum85
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:12.741428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum85
5-th percentile87.25
Q197
median105.5
Q3111.5
95-th percentile119.75
Maximum129
Range44
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation10.646198
Coefficient of variation (CV)0.10169036
Kurtosis-0.17603471
Mean104.69231
Median Absolute Deviation (MAD)7
Skewness0.094532034
Sum2722
Variance113.34154
MonotonicityNot monotonic
2023-12-13T00:16:12.878372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
110 4
15.4%
95 2
 
7.7%
116 2
 
7.7%
97 2
 
7.7%
112 2
 
7.7%
101 2
 
7.7%
109 1
 
3.8%
91 1
 
3.8%
106 1
 
3.8%
121 1
 
3.8%
Other values (8) 8
30.8%
ValueCountFrequency (%)
85 1
3.8%
86 1
3.8%
91 1
3.8%
94 1
3.8%
95 2
7.7%
97 2
7.7%
99 1
3.8%
101 2
7.7%
102 1
3.8%
105 1
3.8%
ValueCountFrequency (%)
129 1
 
3.8%
121 1
 
3.8%
116 2
7.7%
113 1
 
3.8%
112 2
7.7%
110 4
15.4%
109 1
 
3.8%
106 1
 
3.8%
105 1
 
3.8%
102 1
 
3.8%

익산
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1997.5769
Minimum1868
Maximum2140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:13.026534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1868
5-th percentile1875.25
Q11911.75
median2002.5
Q32068.75
95-th percentile2122.25
Maximum2140
Range272
Interquartile range (IQR)157

Descriptive statistics

Standard deviation85.935871
Coefficient of variation (CV)0.043020056
Kurtosis-1.2613781
Mean1997.5769
Median Absolute Deviation (MAD)72.5
Skewness-0.11769441
Sum51937
Variance7384.9738
MonotonicityNot monotonic
2023-12-13T00:16:13.234292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1897 1
 
3.8%
2070 1
 
3.8%
2008 1
 
3.8%
2051 1
 
3.8%
2081 1
 
3.8%
1999 1
 
3.8%
2003 1
 
3.8%
2134 1
 
3.8%
2086 1
 
3.8%
2140 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1868 1
3.8%
1874 1
3.8%
1879 1
3.8%
1882 1
3.8%
1887 1
3.8%
1897 1
3.8%
1905 1
3.8%
1932 1
3.8%
1957 1
3.8%
1960 1
3.8%
ValueCountFrequency (%)
2140 1
3.8%
2134 1
3.8%
2087 1
3.8%
2086 1
3.8%
2081 1
3.8%
2077 1
3.8%
2070 1
3.8%
2065 1
3.8%
2064 1
3.8%
2051 1
3.8%

정읍
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean389.03846
Minimum348
Maximum515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:13.394892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum348
5-th percentile354.5
Q1361
median372
Q3401.75
95-th percentile485.5
Maximum515
Range167
Interquartile range (IQR)40.75

Descriptive statistics

Standard deviation43.638039
Coefficient of variation (CV)0.11216896
Kurtosis2.4624861
Mean389.03846
Median Absolute Deviation (MAD)15
Skewness1.7227939
Sum10115
Variance1904.2785
MonotonicityNot monotonic
2023-12-13T00:16:13.537914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
357 3
 
11.5%
361 3
 
11.5%
409 2
 
7.7%
386 2
 
7.7%
428 1
 
3.8%
380 1
 
3.8%
354 1
 
3.8%
348 1
 
3.8%
370 1
 
3.8%
398 1
 
3.8%
Other values (10) 10
38.5%
ValueCountFrequency (%)
348 1
 
3.8%
354 1
 
3.8%
356 1
 
3.8%
357 3
11.5%
361 3
11.5%
362 1
 
3.8%
364 1
 
3.8%
365 1
 
3.8%
370 1
 
3.8%
374 1
 
3.8%
ValueCountFrequency (%)
515 1
3.8%
490 1
3.8%
472 1
3.8%
428 1
3.8%
409 2
7.7%
403 1
3.8%
398 1
3.8%
392 1
3.8%
386 2
7.7%
380 1
3.8%

광주송정
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3968.8846
Minimum3706
Maximum4182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:13.693522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3706
5-th percentile3737
Q13827.75
median3994
Q34123.5
95-th percentile4175.75
Maximum4182
Range476
Interquartile range (IQR)295.75

Descriptive statistics

Standard deviation162.02452
Coefficient of variation (CV)0.040823692
Kurtosis-1.5175684
Mean3968.8846
Median Absolute Deviation (MAD)147.5
Skewness-0.13081828
Sum103191
Variance26251.946
MonotonicityNot monotonic
2023-12-13T00:16:13.838312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4172 2
 
7.7%
3770 2
 
7.7%
4026 1
 
3.8%
3888 1
 
3.8%
4135 1
 
3.8%
4177 1
 
3.8%
4098 1
 
3.8%
4086 1
 
3.8%
4148 1
 
3.8%
4182 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
3706 1
3.8%
3729 1
3.8%
3761 1
3.8%
3770 2
7.7%
3802 1
3.8%
3827 1
3.8%
3830 1
3.8%
3869 1
3.8%
3877 1
3.8%
3888 1
3.8%
ValueCountFrequency (%)
4182 1
3.8%
4177 1
3.8%
4172 2
7.7%
4148 1
3.8%
4135 1
3.8%
4132 1
3.8%
4098 1
3.8%
4097 1
3.8%
4086 1
3.8%
4028 1
3.8%

나주
Real number (ℝ)

Distinct22
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean447.80769
Minimum385
Maximum490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:13.984890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum385
5-th percentile395.5
Q1436.5
median451.5
Q3461
95-th percentile477.75
Maximum490
Range105
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation24.408227
Coefficient of variation (CV)0.054506046
Kurtosis1.6770081
Mean447.80769
Median Absolute Deviation (MAD)11.5
Skewness-1.0421233
Sum11643
Variance595.76154
MonotonicityNot monotonic
2023-12-13T00:16:14.140095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
443 2
 
7.7%
461 2
 
7.7%
435 2
 
7.7%
450 2
 
7.7%
455 1
 
3.8%
387 1
 
3.8%
434 1
 
3.8%
478 1
 
3.8%
469 1
 
3.8%
445 1
 
3.8%
Other values (12) 12
46.2%
ValueCountFrequency (%)
385 1
3.8%
387 1
3.8%
421 1
3.8%
426 1
3.8%
434 1
3.8%
435 2
7.7%
441 1
3.8%
443 2
7.7%
445 1
3.8%
450 2
7.7%
ValueCountFrequency (%)
490 1
3.8%
478 1
3.8%
477 1
3.8%
470 1
3.8%
469 1
3.8%
464 1
3.8%
461 2
7.7%
460 1
3.8%
456 1
3.8%
455 1
3.8%

목포
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean854.19231
Minimum769
Maximum964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-13T00:16:14.614686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum769
5-th percentile773
Q1817.25
median845.5
Q3891.25
95-th percentile944.25
Maximum964
Range195
Interquartile range (IQR)74

Descriptive statistics

Standard deviation54.851085
Coefficient of variation (CV)0.064213977
Kurtosis-0.72152136
Mean854.19231
Median Absolute Deviation (MAD)36
Skewness0.34131337
Sum22209
Variance3008.6415
MonotonicityNot monotonic
2023-12-13T00:16:14.762420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
769 2
 
7.7%
880 2
 
7.7%
895 1
 
3.8%
842 1
 
3.8%
820 1
 
3.8%
824 1
 
3.8%
785 1
 
3.8%
818 1
 
3.8%
862 1
 
3.8%
868 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
769 2
7.7%
785 1
3.8%
790 1
3.8%
807 1
3.8%
808 1
3.8%
817 1
3.8%
818 1
3.8%
820 1
3.8%
824 1
3.8%
840 1
3.8%
ValueCountFrequency (%)
964 1
3.8%
946 1
3.8%
939 1
3.8%
922 1
3.8%
916 1
3.8%
907 1
3.8%
895 1
3.8%
880 2
7.7%
868 1
3.8%
862 1
3.8%

Interactions

2023-12-13T00:16:06.063055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:35.808606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:37.598332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:39.266131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:41.261807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:42.916640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:44.709713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:46.442055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:48.572437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:50.571142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:52.342433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:54.001680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:55.513208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:57.171435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:58.811341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:00.992886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.911901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.340174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:06.172004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:35.899157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:37.687848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:39.345053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:41.341043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:43.024731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:44.819484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:46.541561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T00:15:50.657425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-13T00:15:51.719579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:53.278972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:54.962365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:56.608192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:58.235141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:00.090614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.283566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.876359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.424896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:07.499845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:37.092178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:38.845064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:40.757981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:42.404279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:44.224853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:45.988205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:47.950989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:50.096124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:51.814187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:53.357654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:55.034275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:56.712100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:58.327503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:00.192614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.392050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:03.947933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.541085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:07.624906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:37.195404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:38.934321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:40.840040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:42.497851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:44.306043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:46.082682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:48.062737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:50.195324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:51.916822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:53.434714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:55.130759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:56.795516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:58.415227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:00.277154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.523508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.024481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.641912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:08.094276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:37.291605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:39.026227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:40.944410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:42.587610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:44.392046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:46.184580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:48.194300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:50.283771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:52.012579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:53.509379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:55.228136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:56.891016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:58.507225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:00.377418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.623295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.101121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.742347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:08.224043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:37.380519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:39.109565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:41.078007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:42.673898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:44.481053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:46.266372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:48.315073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:50.381247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:52.112119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:53.582086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:55.320179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:56.978740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:58.583942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:00.463474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.716372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.175881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.838215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:08.333608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:37.472728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:39.178490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:41.165568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:42.781827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:44.581347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:46.355322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:48.441374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:50.476927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:52.228337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:53.655502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:55.414820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:57.072340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:15:58.680290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:00.550731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:02.814648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:04.253085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:05.944551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:16:14.906726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
수서1.0001.0000.6810.7920.7450.2740.7880.7090.7170.5590.2560.5070.6480.7660.7080.0000.3530.7000.000
동탄1.0000.6811.0000.8720.0000.3710.5460.6820.7260.7000.6270.5020.0000.6980.8210.6390.5410.5540.000
평택지제1.0000.7920.8721.0000.6210.4590.7020.8600.6780.1330.5880.7340.5360.7090.6510.0000.6920.4530.000
천안아산1.0000.7450.0000.6211.0000.6700.8420.0000.3240.6690.1330.5930.5060.6620.3720.0000.8890.2690.000
오송1.0000.2740.3710.4590.6701.0000.7430.4270.6060.5130.0000.0000.0000.4630.7630.4630.9840.4020.425
대전1.0000.7880.5460.7020.8420.7431.0000.6310.8220.4840.0000.3390.4710.6450.7750.0000.7320.8510.576
김천구미1.0000.7090.6820.8600.0000.4270.6311.0000.5810.0000.0000.4060.7060.6380.7550.7650.5790.7280.740
서대구1.0000.7170.7260.6780.3240.6060.8220.5811.0000.0000.1570.1490.0000.5860.6060.5740.5890.7810.000
동대구1.0000.5590.7000.1330.6690.5130.4840.0000.0001.0000.6250.6270.0740.6270.5950.4870.3020.4970.664
신경주1.0000.2560.6270.5880.1330.0000.0000.0000.1570.6251.0000.8330.5400.6850.4820.0000.0000.3310.622
울산1.0000.5070.5020.7340.5930.0000.3390.4060.1490.6270.8331.0000.6610.4980.6640.0000.2120.0000.511
부산1.0000.6480.0000.5360.5060.0000.4710.7060.0000.0740.5400.6611.0000.4330.0000.0000.0000.3440.739
공주1.0000.7660.6980.7090.6620.4630.6450.6380.5860.6270.6850.4980.4331.0000.6620.4940.4670.4820.000
익산1.0000.7080.8210.6510.3720.7630.7750.7550.6060.5950.4820.6640.0000.6621.0000.2140.7640.8320.596
정읍1.0000.0000.6390.0000.0000.4630.0000.7650.5740.4870.0000.0000.0000.4940.2141.0000.2740.0000.430
광주송정1.0000.3530.5410.6920.8890.9840.7320.5790.5890.3020.0000.2120.0000.4670.7640.2741.0000.6920.760
나주1.0000.7000.5540.4530.2690.4020.8510.7280.7810.4970.3310.0000.3440.4820.8320.0000.6921.0000.597
목포1.0000.0000.0000.0000.0000.4250.5760.7400.0000.6640.6220.5110.7390.0000.5960.4300.7600.5971.000
2023-12-13T00:16:15.154361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
수서1.0000.7110.7850.7710.6470.7680.207-0.3150.8360.4430.7300.0320.6840.7320.0200.5560.4560.073
동탄0.7111.0000.7550.3940.3650.4020.4290.1880.6060.4090.6280.4010.5940.3980.0270.4480.4370.304
평택지제0.7850.7551.0000.6900.7660.7810.4090.0210.7340.2930.7280.1580.5540.645-0.1230.5950.3240.034
천안아산0.7710.3940.6901.0000.8830.9400.192-0.3870.7970.1790.593-0.2250.4330.730-0.0950.6530.326-0.163
오송0.6470.3650.7660.8831.0000.9090.268-0.2330.6870.1150.581-0.0610.3870.698-0.1520.6720.287-0.187
대전0.7680.4020.7810.9400.9091.0000.242-0.2320.7600.1420.570-0.1690.4330.708-0.1430.5730.339-0.255
김천구미0.2070.4290.4090.1920.2680.2421.0000.3780.3100.0930.4670.1490.4960.3250.3590.4000.2820.269
서대구-0.3150.1880.021-0.387-0.233-0.2320.3781.000-0.235-0.271-0.1810.302-0.077-0.2110.066-0.075-0.1810.120
동대구0.8360.6060.7340.7970.6870.7600.310-0.2351.0000.4590.8350.0940.7270.8040.1690.7310.3370.232
신경주0.4430.4090.2930.1790.1150.1420.093-0.2710.4591.0000.6050.5120.4520.3550.5270.2520.3030.525
울산0.7300.6280.7280.5930.5810.5700.467-0.1810.8350.6051.0000.2690.7800.7830.3990.7730.4040.510
부산0.0320.4010.158-0.225-0.061-0.1690.1490.3020.0940.5120.2691.0000.0750.0050.3330.1590.0720.483
공주0.6840.5940.5540.4330.3870.4330.496-0.0770.7270.4520.7800.0751.0000.6550.4490.5930.3800.396
익산0.7320.3980.6450.7300.6980.7080.325-0.2110.8040.3550.7830.0050.6551.0000.2900.8410.1730.252
정읍0.0200.027-0.123-0.095-0.152-0.1430.3590.0660.1690.5270.3990.3330.4490.2901.0000.2790.0330.689
광주송정0.5560.4480.5950.6530.6720.5730.400-0.0750.7310.2520.7730.1590.5930.8410.2791.0000.1540.273
나주0.4560.4370.3240.3260.2870.3390.282-0.1810.3370.3030.4040.0720.3800.1730.0330.1541.000-0.021
목포0.0730.3040.034-0.163-0.187-0.2550.2690.1200.2320.5250.5100.4830.3960.2520.6890.273-0.0211.000

Missing values

2023-12-13T00:16:08.546216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:16:08.923945image/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

구분수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
02022년 28주210974662297330952855429370032867441298258282829518973573830443769
12022년 29주2104247702979313228794315693316663012752606844910118793573770460817
22022년 30주209294638292630882858425668732965421372265486429418823643761461844
32022년 31주2149850273045308327134121740383677214122772952710219574093921490939
42022년 32주2107649893170295727414089726381663014722760893210518684033869435907
52022년 33주206154675285929522741405765634067321419264692058518873863802421946
62022년 34주214054900309530142805437070735066221444268388549718743653729461808
72022년 35주213614651294330922807442866733267891427262587259919323613770426790
82022년 36주211644442286931552804440664929365261197249575028619053623827441769
92022년 37주1795345322742253622363681768364668311562741739711319604723877385922
구분수서동탄평택지제천안아산오송대전김천구미서대구동대구신경주울산부산공주익산정읍광주송정나주목포
162022년 44주2220748923180339029664626721313703616222874851011620874284132470880
172022년 45주2168048033099332829754523698320697516172812827811020655154097456847
182022년 46주2196448533242335730814721756338734915532980884111621404904172464850
192022년 47주2222749883200331030444502700328699914702826938312120864094182445868
202022년 48주2210249293201339830814680731337726014012884851211221343984148450862
212022년 49주2152947113182330030414550704337680812372817836510620033704086469818
222022년 50주2169749153112332430274600696343691612442756819211019993484098478785
232022년 51주2201150903249339231334655710338694812042780846310120813544177435824
242022년 52주212524798327733153102460772035469021320281186609520513574135434820
252022년 53주197724458280329422686388667037262441255247584139120083803888387842