Overview

Dataset statistics

Number of variables9
Number of observations84
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory81.6 B

Variable types

DateTime1
Numeric8

Dataset

Description광주교통공사 수송전력분석에 대한 데이터로 월별 ,영업 운행거리(km),운행횟수,전체 전력 사용량(kwh),전동차 전력사용량(kwh),전기요금 등 정보를 제공합니다.
Author광주교통공사
URLhttps://www.data.go.kr/data/15048342/fileData.do

Alerts

영업 운행거리(km) is highly overall correlated with 운행횟수 and 1 other fieldsHigh correlation
운행횟수 is highly overall correlated with 영업 운행거리(km) and 1 other fieldsHigh correlation
전체 전력 사용량(kwh) is highly overall correlated with 영업 운행거리(km) and 5 other fieldsHigh correlation
전동차 전력사용량(kwh) is highly overall correlated with 전체 전력 사용량(kwh)High correlation
전기요금 is highly overall correlated with 전체 전력 사용량(kwh) and 1 other fieldsHigh correlation
총 승객수 is highly overall correlated with 승객 1인당 전기사용요금High correlation
승객 1인당 전기사용량 is highly overall correlated with 전체 전력 사용량(kwh)High correlation
승객 1인당 전기사용요금 is highly overall correlated with 전체 전력 사용량(kwh) and 2 other fieldsHigh correlation
구분 has unique valuesUnique
전체 전력 사용량(kwh) has unique valuesUnique
전동차 전력사용량(kwh) has unique valuesUnique
전기요금 has unique valuesUnique
총 승객수 has unique valuesUnique
승객 1인당 전기사용요금 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:10:41.150597
Analysis finished2024-04-06 08:10:55.692072
Duration14.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size804.0 B
Minimum2017-01-01 00:00:00
Maximum2023-12-01 00:00:00
2024-04-06T17:10:55.821367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:56.533585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업 운행거리(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134042.69
Minimum120065
Maximum138492.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-04-06T17:10:56.880352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120065
5-th percentile124559.6
Q1133080.45
median134641.45
Q3137047.15
95-th percentile137949.55
Maximum138492.8
Range18427.8
Interquartile range (IQR)3966.7

Descriptive statistics

Standard deviation4244.3423
Coefficient of variation (CV)0.031664108
Kurtosis2.9030096
Mean134042.69
Median Absolute Deviation (MAD)2364.9
Skewness-1.6955717
Sum11259586
Variance18014442
MonotonicityNot monotonic
2024-04-06T17:10:57.163435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133776.2 2
 
2.4%
137096.0 2
 
2.4%
133936.0 1
 
1.2%
137014.6 1
 
1.2%
121539.2 1
 
1.2%
135424.8 1
 
1.2%
138407.0 1
 
1.2%
137033.2 1
 
1.2%
129241.2 1
 
1.2%
137012.6 1
 
1.2%
Other values (72) 72
85.7%
ValueCountFrequency (%)
120065.0 1
1.2%
120672.0 1
1.2%
120789.4 1
1.2%
121539.2 1
1.2%
124547.0 1
1.2%
124631.0 1
1.2%
127947.0 1
1.2%
128675.4 1
1.2%
129241.2 1
1.2%
129332.0 1
1.2%
ValueCountFrequency (%)
138492.8 1
1.2%
138474.0 1
1.2%
138407.0 1
1.2%
137990.0 1
1.2%
137967.3 1
1.2%
137849.0 1
1.2%
137815.6 1
1.2%
137802.2 1
1.2%
137797.0 1
1.2%
137751.8 1
1.2%

운행횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8451.5119
Minimum7577
Maximum8717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-04-06T17:10:57.436129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7577
5-th percentile7841.5
Q18381.5
median8542
Q38637.5
95-th percentile8690.55
Maximum8717
Range1140
Interquartile range (IQR)256

Descriptive statistics

Standard deviation264.51235
Coefficient of variation (CV)0.031297637
Kurtosis2.9805178
Mean8451.5119
Median Absolute Deviation (MAD)123.5
Skewness-1.742012
Sum709927
Variance69966.783
MonotonicityNot monotonic
2024-04-06T17:10:57.702656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8667 4
 
4.8%
8585 3
 
3.6%
8635 3
 
3.6%
8428 2
 
2.4%
8589 2
 
2.4%
8334 2
 
2.4%
8645 2
 
2.4%
8386 2
 
2.4%
8424 2
 
2.4%
8663 2
 
2.4%
Other values (59) 60
71.4%
ValueCountFrequency (%)
7577 1
1.2%
7619 1
1.2%
7630 1
1.2%
7666 1
1.2%
7840 1
1.2%
7850 1
1.2%
8079 1
1.2%
8101 1
1.2%
8154 1
1.2%
8159 1
1.2%
ValueCountFrequency (%)
8717 1
1.2%
8707 2
2.4%
8703 1
1.2%
8691 1
1.2%
8688 1
1.2%
8685 1
1.2%
8681 1
1.2%
8677 1
1.2%
8675 1
1.2%
8671 1
1.2%

전체 전력 사용량(kwh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3143569.7
Minimum2787894
Maximum4235231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-04-06T17:10:57.965790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2787894
5-th percentile2860379.8
Q12973404.2
median3055356
Q33320680.5
95-th percentile3504999.6
Maximum4235231
Range1447337
Interquartile range (IQR)347276.25

Descriptive statistics

Standard deviation249534.26
Coefficient of variation (CV)0.079379267
Kurtosis3.0902037
Mean3143569.7
Median Absolute Deviation (MAD)145755
Skewness1.342584
Sum2.6405986 × 108
Variance6.2267348 × 1010
MonotonicityNot monotonic
2024-04-06T17:10:58.267663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3255727 1
 
1.2%
3521944 1
 
1.2%
3198695 1
 
1.2%
3135970 1
 
1.2%
3454142 1
 
1.2%
3425516 1
 
1.2%
3065000 1
 
1.2%
2974752 1
 
1.2%
2974693 1
 
1.2%
3442078 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
2787894 1
1.2%
2820676 1
1.2%
2829577 1
1.2%
2844885 1
1.2%
2857883 1
1.2%
2874528 1
1.2%
2877409 1
1.2%
2880747 1
1.2%
2880815 1
1.2%
2881216 1
1.2%
ValueCountFrequency (%)
4235231 1
1.2%
3753923 1
1.2%
3573974 1
1.2%
3521944 1
1.2%
3507476 1
1.2%
3490967 1
1.2%
3484389 1
1.2%
3482456 1
1.2%
3478728 1
1.2%
3456885 1
1.2%

전동차 전력사용량(kwh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1557557.3
Minimum1344402
Maximum1831941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-04-06T17:10:58.535017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1344402
5-th percentile1418467.2
Q11471401.5
median1514154
Q31642360.8
95-th percentile1769056.6
Maximum1831941
Range487539
Interquartile range (IQR)170959.25

Descriptive statistics

Standard deviation117314.07
Coefficient of variation (CV)0.07531926
Kurtosis-0.55977888
Mean1557557.3
Median Absolute Deviation (MAD)65489.5
Skewness0.64078788
Sum1.3083482 × 108
Variance1.376259 × 1010
MonotonicityNot monotonic
2024-04-06T17:10:58.833721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1756046.0 1
 
1.2%
1691952.0 1
 
1.2%
1564018.0 1
 
1.2%
1617493.0 1
 
1.2%
1755125.0 1
 
1.2%
1756264.0 1
 
1.2%
1512991.0 1
 
1.2%
1481891.0 1
 
1.2%
1500688.0 1
 
1.2%
1649131.0 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
1344402.0 1
1.2%
1378709.0 1
1.2%
1388544.5 1
1.2%
1403574.0 1
1.2%
1418204.0 1
1.2%
1419959.0 1
1.2%
1428083.0 1
1.2%
1428338.0 1
1.2%
1434410.0 1
1.2%
1437482.0 1
1.2%
ValueCountFrequency (%)
1831941.0 1
1.2%
1829934.0 1
1.2%
1780181.0 1
1.2%
1771412.0 1
1.2%
1769236.0 1
1.2%
1768040.0 1
1.2%
1756264.0 1
1.2%
1756046.0 1
1.2%
1755125.0 1
1.2%
1748180.0 1
1.2%

전기요금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5264522 × 108
Minimum3.1543822 × 108
Maximum7.1414844 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-04-06T17:10:59.139393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1543822 × 108
5-th percentile3.3311455 × 108
Q13.7568622 × 108
median4.5175074 × 108
Q35.0605162 × 108
95-th percentile6.4240644 × 108
Maximum7.1414844 × 108
Range3.9871022 × 108
Interquartile range (IQR)1.303654 × 108

Descriptive statistics

Standard deviation94602491
Coefficient of variation (CV)0.20899921
Kurtosis0.0056206645
Mean4.5264522 × 108
Median Absolute Deviation (MAD)65165245
Skewness0.66000517
Sum3.8022198 × 1010
Variance8.9496314 × 1015
MonotonicityNot monotonic
2024-04-06T17:10:59.420336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
430169270 1
 
1.2%
524878670 1
 
1.2%
377602390 1
 
1.2%
459202440 1
 
1.2%
492098290 1
 
1.2%
500841030 1
 
1.2%
465063970 1
 
1.2%
355349500 1
 
1.2%
348915210 1
 
1.2%
507740910 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
315438220 1
1.2%
315788000 1
1.2%
320440060 1
1.2%
332535620 1
1.2%
333096950 1
1.2%
333214310 1
1.2%
336406160 1
1.2%
337209920 1
1.2%
339947830 1
1.2%
340802860 1
1.2%
ValueCountFrequency (%)
714148440 1
1.2%
675603400 1
1.2%
666453670 1
1.2%
649937160 1
1.2%
644591180 1
1.2%
630026270 1
1.2%
620499510 1
1.2%
608474740 1
1.2%
595400410 1
1.2%
591349580 1
1.2%

총 승객수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1403033
Minimum961771
Maximum1767026
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-04-06T17:10:59.712072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum961771
5-th percentile1003303.3
Q11249380.8
median1440719
Q31567934.2
95-th percentile1692765.7
Maximum1767026
Range805255
Interquartile range (IQR)318553.5

Descriptive statistics

Standard deviation213074.34
Coefficient of variation (CV)0.15186695
Kurtosis-0.75598087
Mean1403033
Median Absolute Deviation (MAD)151024
Skewness-0.39737331
Sum1.1785477 × 108
Variance4.5400676 × 1010
MonotonicityNot monotonic
2024-04-06T17:11:00.005296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1461310 1
 
1.2%
1175570 1
 
1.2%
1181231 1
 
1.2%
1001971 1
 
1.2%
1195341 1
 
1.2%
1320834 1
 
1.2%
1364707 1
 
1.2%
1290456 1
 
1.2%
1168367 1
 
1.2%
1088830 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
961771 1
1.2%
975399 1
1.2%
981349 1
1.2%
982962 1
1.2%
1001971 1
1.2%
1010853 1
1.2%
1044674 1
1.2%
1054621 1
1.2%
1061253 1
1.2%
1088830 1
1.2%
ValueCountFrequency (%)
1767026 1
1.2%
1749598 1
1.2%
1733309 1
1.2%
1709250 1
1.2%
1696248 1
1.2%
1673033 1
1.2%
1669180 1
1.2%
1668749 1
1.2%
1668687 1
1.2%
1657000 1
1.2%

승객 1인당 전기사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1071429
Minimum1.14
Maximum3.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-04-06T17:11:00.457945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.14
5-th percentile1.2725
Q11.8
median2.095
Q32.3
95-th percentile3.017
Maximum3.28
Range2.14
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.47285488
Coefficient of variation (CV)0.22440571
Kurtosis0.093478886
Mean2.1071429
Median Absolute Deviation (MAD)0.295
Skewness0.3343826
Sum177
Variance0.22359174
MonotonicityNot monotonic
2024-04-06T17:11:00.807193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.8 6
 
7.1%
2.3 5
 
6.0%
2.2 3
 
3.6%
1.9 3
 
3.6%
1.7 3
 
3.6%
2.1 3
 
3.6%
2.25 3
 
3.6%
1.71 2
 
2.4%
1.25 2
 
2.4%
2.01 2
 
2.4%
Other values (45) 52
61.9%
ValueCountFrequency (%)
1.14 1
1.2%
1.17 1
1.2%
1.2 1
1.2%
1.25 2
2.4%
1.4 1
1.2%
1.42 1
1.2%
1.52 1
1.2%
1.55 1
1.2%
1.62 1
1.2%
1.67 1
1.2%
ValueCountFrequency (%)
3.28 1
1.2%
3.16 1
1.2%
3.13 1
1.2%
3.03 1
1.2%
3.02 1
1.2%
3.0 2
2.4%
2.89 1
1.2%
2.72 1
1.2%
2.71 1
1.2%
2.66 1
1.2%

승객 1인당 전기사용요금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean330.87405
Minimum192
Maximum543.32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-04-06T17:11:01.098640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum192
5-th percentile204.925
Q1273.9575
median319.085
Q3377.8475
95-th percentile486.062
Maximum543.32
Range351.32
Interquartile range (IQR)103.89

Descriptive statistics

Standard deviation86.656351
Coefficient of variation (CV)0.26190132
Kurtosis-0.53439807
Mean330.87405
Median Absolute Deviation (MAD)53.28
Skewness0.4467745
Sum27793.42
Variance7509.3231
MonotonicityNot monotonic
2024-04-06T17:11:01.472891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
294.4 1
 
1.2%
446.49 1
 
1.2%
319.67 1
 
1.2%
458.3 1
 
1.2%
411.68 1
 
1.2%
379.19 1
 
1.2%
340.78 1
 
1.2%
275.37 1
 
1.2%
298.63 1
 
1.2%
466.32 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
192.0 1
1.2%
193.3 1
1.2%
200.42 1
1.2%
203.7 1
1.2%
204.4 1
1.2%
207.9 1
1.2%
212.8 1
1.2%
213.21 1
1.2%
215.3 1
1.2%
218.6 1
1.2%
ValueCountFrequency (%)
543.32 1
1.2%
514.25 1
1.2%
506.95 1
1.2%
489.87 1
1.2%
489.02 1
1.2%
469.3 1
1.2%
466.32 1
1.2%
465.15 1
1.2%
458.3 1
1.2%
453.9 1
1.2%

Interactions

2024-04-06T17:10:53.631065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:41.668777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:43.695261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:45.774310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:47.238019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:48.824631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:50.469009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:52.168380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:53.844407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:41.849058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:43.913799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:45.948582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:47.434458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:49.004943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:50.701073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:52.338449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:53.999778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:42.054729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:44.090571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:46.121376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:47.608099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:49.179711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:50.881042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:52.495390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:54.221349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:42.425771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:44.356071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:46.307667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:47.782242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:49.386732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:51.073786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:52.675871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:54.444632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:42.626142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:44.603126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:46.472722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:47.967492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:49.585821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:51.325372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:52.927578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:54.654797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:42.908030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:44.856355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:46.677573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:48.216373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:49.822228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:51.552330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:53.098338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:54.856493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:43.196933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:45.061751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:46.877115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:48.404641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:50.046226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:51.810551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:53.289266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:55.080540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:43.371215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:45.212428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:47.033975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:48.619556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:50.231719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:51.990422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:53.436133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:11:01.693779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분영업 운행거리(km)운행횟수전체 전력 사용량(kwh)전동차 전력사용량(kwh)전기요금총 승객수승객 1인당 전기사용량승객 1인당 전기사용요금
구분1.0001.0001.0001.0001.0001.0001.0001.0001.000
영업 운행거리(km)1.0001.0000.9970.3230.3400.0000.3140.2760.106
운행횟수1.0000.9971.0000.3420.3360.0930.2020.2520.230
전체 전력 사용량(kwh)1.0000.3230.3421.0000.5460.6420.3200.5060.547
전동차 전력사용량(kwh)1.0000.3400.3360.5461.0000.1020.4130.3170.427
전기요금1.0000.0000.0930.6420.1021.0000.0000.4400.744
총 승객수1.0000.3140.2020.3200.4130.0001.0000.8370.660
승객 1인당 전기사용량1.0000.2760.2520.5060.3170.4400.8371.0000.745
승객 1인당 전기사용요금1.0000.1060.2300.5470.4270.7440.6600.7451.000
2024-04-06T17:11:02.034517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업 운행거리(km)운행횟수전체 전력 사용량(kwh)전동차 전력사용량(kwh)전기요금총 승객수승객 1인당 전기사용량승객 1인당 전기사용요금
영업 운행거리(km)1.0000.9790.5850.4830.3580.0820.1430.188
운행횟수0.9791.0000.5870.4830.3350.0800.1380.176
전체 전력 사용량(kwh)0.5850.5871.0000.7830.694-0.1300.5250.586
전동차 전력사용량(kwh)0.4830.4830.7831.0000.400-0.2040.3630.450
전기요금0.3580.3350.6940.4001.000-0.0920.4630.785
총 승객수0.0820.080-0.130-0.204-0.0921.000-0.322-0.648
승객 1인당 전기사용량0.1430.1380.5250.3630.463-0.3221.0000.470
승객 1인당 전기사용요금0.1880.1760.5860.4500.785-0.6480.4701.000

Missing values

2024-04-06T17:10:55.326705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:10:55.582594image/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

구분영업 운행거리(km)운행횟수전체 전력 사용량(kwh)전동차 전력사용량(kwh)전기요금총 승객수승객 1인당 전기사용량승객 1인당 전기사용요금
02017-01133936.0845032557271756046.043016927014613102.2294.4
12017-02124547.0784029487781557270.041268039014461112.0285.4
22017-03133847.0862831215851650392.033994783016687491.9203.7
32017-04132348.0833428206761456348.031543822016318451.7193.3
42017-05136192.0858528808151500333.032044006016686871.7192.0
52017-06133311.0839028295771403574.041171896015690721.8262.4
62017-07137096.0864533254911680053.047861983015027162.2318.5
72017-08137670.0866734144751597557.049998757014759142.3338.8
82017-09133143.0838629752361481376.034574255016057571.9215.3
92017-10131058.0829227878941378709.031578800015191461.8207.9
구분영업 운행거리(km)운행횟수전체 전력 사용량(kwh)전동차 전력사용량(kwh)전기요금총 승객수승객 1인당 전기사용량승객 1인당 전기사용요금
742023-03137751.8867531949731465059.051055258014986492.13340.68
752023-04132357.2833829860131450051.048306729014295262.09337.92
762023-05137031.7854131161991440232.051172676015135412.06338.1
772023-06133109.6838630260681434410.062049951014236092.13435.86
782023-07137018.4863733680081578884.067560340013326902.53506.95
792023-08137744.2867135739741653554.071414844013144162.72543.32
802023-09129375.8816930435071470479.051243680013578242.24377.4
812023-10135448.6854329695381418204.050147242015501341.92323.5
822023-11133780.0842630550201469852.063002627015027472.03419.25
832023-12136429.2858932975501640104.066645367015308642.15435.34