Overview

Dataset statistics

Number of variables9
Number of observations2928
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory226.0 KiB
Average record size in memory79.0 B

Variable types

Numeric7
Categorical1
DateTime1

Dataset

Description서울교통공사에서 운영중인 1~8호선 2020년 열차 전력사용량정보 입니다. (공공데이터 제공 요청으로 지하철 시간별 운행 전력 사용량데이터의 요청이 있었으나,)열차 전력사용량을 시간대별로 측정하는 별도의 계측기가 설치되어 있지 않아 전체 열차 전력사용량과 열차운행 거리를 바탕으로 일별 전력사용량을 계산한 정보입니다.(공공데이터 신청에 의한 일시성 데이터로 업데이트 합니다.) 열차운행횟수 = 본선운행열차, 회송열차, 임시열차의 총합 일자별 전력사용량 = 월별 전력사용량 / 월별 열차실적 * 일자별열차실적
Author서울교통공사
URLhttps://www.data.go.kr/data/15087546/fileData.do

Alerts

연번 is highly overall correlated with 호선 and 1 other fieldsHigh correlation
호선 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
열차운행횟수 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
열차운행거리(km) is highly overall correlated with 열차운행횟수 and 2 other fieldsHigh correlation
차량운행거리(km) is highly overall correlated with 열차운행횟수 and 2 other fieldsHigh correlation
일별전력사용량(kWh) is highly overall correlated with 열차운행횟수 and 2 other fieldsHigh correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:55:56.424885
Analysis finished2023-12-11 23:56:02.970943
Duration6.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2928
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1464.5
Minimum1
Maximum2928
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-12-12T08:56:03.054587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile147.35
Q1732.75
median1464.5
Q32196.25
95-th percentile2781.65
Maximum2928
Range2927
Interquartile range (IQR)1463.5

Descriptive statistics

Standard deviation845.38512
Coefficient of variation (CV)0.5772517
Kurtosis-1.2
Mean1464.5
Median Absolute Deviation (MAD)732
Skewness0
Sum4288056
Variance714676
MonotonicityStrictly increasing
2023-12-12T08:56:03.213544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1947 1
 
< 0.1%
1949 1
 
< 0.1%
1950 1
 
< 0.1%
1951 1
 
< 0.1%
1952 1
 
< 0.1%
1953 1
 
< 0.1%
1954 1
 
< 0.1%
1955 1
 
< 0.1%
1956 1
 
< 0.1%
Other values (2918) 2918
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2928 1
< 0.1%
2927 1
< 0.1%
2926 1
< 0.1%
2925 1
< 0.1%
2924 1
< 0.1%
2923 1
< 0.1%
2922 1
< 0.1%
2921 1
< 0.1%
2920 1
< 0.1%
2919 1
< 0.1%

호선
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-12-12T08:56:03.369916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.75
median4.5
Q36.25
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.2916792
Coefficient of variation (CV)0.50926205
Kurtosis-1.2381601
Mean4.5
Median Absolute Deviation (MAD)2
Skewness0
Sum13176
Variance5.2517936
MonotonicityIncreasing
2023-12-12T08:56:03.511967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 366
12.5%
2 366
12.5%
3 366
12.5%
4 366
12.5%
5 366
12.5%
6 366
12.5%
7 366
12.5%
8 366
12.5%
ValueCountFrequency (%)
1 366
12.5%
2 366
12.5%
3 366
12.5%
4 366
12.5%
5 366
12.5%
6 366
12.5%
7 366
12.5%
8 366
12.5%
ValueCountFrequency (%)
8 366
12.5%
7 366
12.5%
6 366
12.5%
5 366
12.5%
4 366
12.5%
3 366
12.5%
2 366
12.5%
1 366
12.5%

전력산정월
Real number (ℝ)

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4972678
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-12-12T08:56:03.654617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median6.5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4534326
Coefficient of variation (CV)0.53152075
Kurtosis-1.215599
Mean6.4972678
Median Absolute Deviation (MAD)2.5
Skewness-0.0056102676
Sum19024
Variance11.926197
MonotonicityNot monotonic
2023-12-12T08:56:03.770193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 248
8.5%
2 248
8.5%
4 248
8.5%
6 248
8.5%
8 248
8.5%
9 248
8.5%
11 248
8.5%
5 240
8.2%
7 240
8.2%
10 240
8.2%
Other values (2) 472
16.1%
ValueCountFrequency (%)
1 248
8.5%
2 248
8.5%
3 232
7.9%
4 248
8.5%
5 240
8.2%
6 248
8.5%
7 240
8.2%
8 248
8.5%
9 248
8.5%
10 240
8.2%
ValueCountFrequency (%)
12 240
8.2%
11 248
8.5%
10 240
8.2%
9 248
8.5%
8 248
8.5%
7 240
8.2%
6 248
8.5%
5 240
8.2%
4 248
8.5%
3 232
7.9%

휴일구분
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
평
2008 
일
400 
토
392 
휴
 
128

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row평
2nd row평
3rd row토
4th row일
5th row평

Common Values

ValueCountFrequency (%)
평 2008
68.6%
일 400
 
13.7%
토 392
 
13.4%
휴 128
 
4.4%

Length

2023-12-12T08:56:03.901056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T08:56:03.998458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평 2008
68.6%
일 400
 
13.7%
토 392
 
13.4%
휴 128
 
4.4%

일자
Date

Distinct366
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size23.0 KiB
Minimum2019-12-19 00:00:00
Maximum2020-12-18 00:00:00
2023-12-12T08:56:04.137826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:04.267068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

열차운행횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct328
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean553.2097
Minimum237
Maximum1164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-12-12T08:56:04.395908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum237
5-th percentile306
Q1410
median513
Q3587
95-th percentile1124
Maximum1164
Range927
Interquartile range (IQR)177

Descriptive statistics

Standard deviation223.96823
Coefficient of variation (CV)0.40485233
Kurtosis1.4396723
Mean553.2097
Median Absolute Deviation (MAD)92
Skewness1.4414848
Sum1619798
Variance50161.77
MonotonicityNot monotonic
2023-12-12T08:56:04.541683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 90
 
3.1%
548 83
 
2.8%
509 75
 
2.6%
293 75
 
2.6%
401 73
 
2.5%
421 70
 
2.4%
961 70
 
2.4%
423 67
 
2.3%
410 57
 
1.9%
546 48
 
1.6%
Other values (318) 2220
75.8%
ValueCountFrequency (%)
237 4
 
0.1%
239 1
 
< 0.1%
245 3
 
0.1%
246 1
 
< 0.1%
266 4
 
0.1%
268 1
 
< 0.1%
274 3
 
0.1%
276 1
 
< 0.1%
293 75
2.6%
295 22
 
0.8%
ValueCountFrequency (%)
1164 1
 
< 0.1%
1163 1
 
< 0.1%
1162 4
 
0.1%
1161 9
0.3%
1160 12
0.4%
1159 3
 
0.1%
1158 2
 
0.1%
1157 1
 
< 0.1%
1156 9
0.3%
1154 1
 
< 0.1%

열차운행거리(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct889
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13858.536
Minimum3727.6
Maximum27112.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-12-12T08:56:04.695563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3727.6
5-th percentile4785.4
Q16885.05
median13309.8
Q320243.6
95-th percentile25946.35
Maximum27112.2
Range23384.6
Interquartile range (IQR)13358.55

Descriptive statistics

Standard deviation6845.3853
Coefficient of variation (CV)0.49394721
Kurtosis-1.11663
Mean13858.536
Median Absolute Deviation (MAD)6933.8
Skewness0.19351999
Sum40577794
Variance46859300
MonotonicityNot monotonic
2023-12-12T08:56:04.842678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5083.4 105
 
3.6%
8685.6 90
 
3.1%
4785.4 75
 
2.6%
14041.0 73
 
2.5%
10849.2 73
 
2.5%
22303.8 70
 
2.4%
11172.0 56
 
1.9%
13259.5 52
 
1.8%
4678.8 50
 
1.7%
10396.8 47
 
1.6%
Other values (879) 2237
76.4%
ValueCountFrequency (%)
3727.6 1
 
< 0.1%
3920.6 4
 
0.1%
3924.4 1
 
< 0.1%
4062.2 4
 
0.1%
4618.9 1
 
< 0.1%
4652.4 1
 
< 0.1%
4656.2 39
1.3%
4660.7 1
 
< 0.1%
4663.2 1
 
< 0.1%
4670.2 1
 
< 0.1%
ValueCountFrequency (%)
27112.2 1
 
< 0.1%
27068.8 1
 
< 0.1%
27030.9 1
 
< 0.1%
27015.4 3
0.1%
27014.6 3
0.1%
27001.2 1
 
< 0.1%
26986.0 1
 
< 0.1%
26970.4 1
 
< 0.1%
26966.6 6
0.2%
26965.8 5
0.2%

차량운행거리(km)
Real number (ℝ)

HIGH CORRELATION 

Distinct894
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122095.28
Minimum23523.6
Maximum258246.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-12-12T08:56:04.956943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23523.6
5-th percentile30100.8
Q157189.8
median132753.5
Q3161948.8
95-th percentile246822.4
Maximum258246.4
Range234722.8
Interquartile range (IQR)104759

Descriptive statistics

Standard deviation63406.037
Coefficient of variation (CV)0.51931603
Kurtosis-0.68375228
Mean122095.28
Median Absolute Deviation (MAD)44104.9
Skewness0.24159938
Sum3.5749498 × 108
Variance4.0203256 × 109
MonotonicityNot monotonic
2023-12-12T08:56:05.345834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50834.0 105
 
3.6%
69484.8 90
 
3.1%
28712.4 75
 
2.6%
86793.6 73
 
2.5%
140410.0 73
 
2.5%
211060.0 70
 
2.4%
89376.0 56
 
1.9%
132595.0 52
 
1.8%
46788.0 50
 
1.7%
83174.4 47
 
1.6%
Other values (884) 2237
76.4%
ValueCountFrequency (%)
23523.6 4
 
0.1%
23546.4 1
 
< 0.1%
24373.2 4
 
0.1%
28712.4 75
2.6%
28735.2 22
 
0.8%
28758.0 4
 
0.1%
28801.2 1
 
< 0.1%
28947.6 2
 
0.1%
29049.6 1
 
< 0.1%
29072.4 1
 
< 0.1%
ValueCountFrequency (%)
258246.4 1
 
< 0.1%
257812.4 1
 
< 0.1%
257433.4 1
 
< 0.1%
257278.4 3
0.1%
257270.4 3
0.1%
257136.4 1
 
< 0.1%
256984.4 1
 
< 0.1%
256828.4 1
 
< 0.1%
256790.4 6
0.2%
256782.4 5
0.2%

일별전력사용량(kWh)
Real number (ℝ)

HIGH CORRELATION 

Distinct1356
Distinct (%)46.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269628.71
Minimum61585.825
Maximum601708.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.9 KiB
2023-12-12T08:56:05.458051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61585.825
5-th percentile80105.702
Q1128370.29
median291171.68
Q3355427.46
95-th percentile501566.07
Maximum601708.31
Range540122.49
Interquartile range (IQR)227057.17

Descriptive statistics

Standard deviation132610.72
Coefficient of variation (CV)0.49182716
Kurtosis-0.81254703
Mean269628.71
Median Absolute Deviation (MAD)96555.029
Skewness0.13144122
Sum7.8947287 × 108
Variance1.7585604 × 1010
MonotonicityNot monotonic
2023-12-12T08:56:05.576379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360540.8658 19
 
0.6%
199116.4576 18
 
0.6%
181302.6213 18
 
0.6%
97607.42078 18
 
0.6%
193671.7096 17
 
0.6%
110084.0806 17
 
0.6%
214238.0103 16
 
0.5%
101664.8699 15
 
0.5%
113637.0596 15
 
0.5%
208903.3503 15
 
0.5%
Other values (1346) 2760
94.3%
ValueCountFrequency (%)
61585.82463 2
 
0.1%
61645.51604 1
 
< 0.1%
63810.11499 2
 
0.1%
67599.84515 2
 
0.1%
70041.34341 2
 
0.1%
70674.32566 1
 
< 0.1%
73815.50585 9
0.3%
73874.12141 1
 
< 0.1%
74420.17167 1
 
< 0.1%
74521.54913 8
0.3%
ValueCountFrequency (%)
601708.3143 1
 
< 0.1%
600745.1265 1
 
< 0.1%
599560.006 1
 
< 0.1%
598476.9745 1
 
< 0.1%
598459.2199 1
 
< 0.1%
598272.7965 1
 
< 0.1%
597496.0321 1
 
< 0.1%
596191.0679 5
0.2%
587320.1948 1
 
< 0.1%
585043.4858 1
 
< 0.1%

Interactions

2023-12-12T08:56:01.946363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.064376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.784492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:58.579508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:59.612776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:00.364756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:01.141780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:02.063467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.170814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.891731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:58.932278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:59.728785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:00.471388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:01.260122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:02.185731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.269731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.993069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:59.051185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:59.834260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:00.579954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:01.383155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:02.297602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.388674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:58.114862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:59.151591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:59.927202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:00.686148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:01.496216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:02.441209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.510643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:58.260472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:59.267767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:00.012895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:00.809314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:01.622569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:02.547127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.596453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:58.374371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:59.374467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:00.144989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:00.918278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:01.734655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:02.641022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:57.681661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:58.466473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:55:59.483572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:00.239473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:01.023587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:56:01.830478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:56:05.654741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선전력산정월휴일구분열차운행횟수열차운행거리(km)차량운행거리(km)일별전력사용량(kWh)
연번1.0000.9490.4250.0000.7840.9110.9360.898
호선0.9491.0000.0000.0000.9410.8850.9240.875
전력산정월0.4250.0001.0000.2130.1480.3140.0000.447
휴일구분0.0000.0000.2131.0000.7510.4710.5010.427
열차운행횟수0.7840.9410.1480.7511.0000.8120.8990.837
열차운행거리(km)0.9110.8850.3140.4710.8121.0000.9590.942
차량운행거리(km)0.9360.9240.0000.5010.8990.9591.0000.963
일별전력사용량(kWh)0.8980.8750.4470.4270.8370.9420.9631.000
2023-12-12T08:56:05.758553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번호선전력산정월열차운행횟수열차운행거리(km)차량운행거리(km)일별전력사용량(kWh)휴일구분
연번1.0000.9920.125-0.526-0.089-0.219-0.2540.000
호선0.9921.0000.000-0.526-0.083-0.216-0.2510.000
전력산정월0.1250.0001.000-0.035-0.054-0.041-0.0470.128
열차운행횟수-0.526-0.526-0.0351.0000.7420.8080.8250.419
열차운행거리(km)-0.089-0.083-0.0540.7421.0000.9810.9420.300
차량운행거리(km)-0.219-0.216-0.0410.8080.9811.0000.9570.322
일별전력사용량(kWh)-0.254-0.251-0.0470.8250.9420.9571.0000.268
휴일구분0.0000.0000.1280.4190.3000.3220.2681.000

Missing values

2023-12-12T08:56:02.757373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:56:02.902196image/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

연번호선전력산정월휴일구분일자열차운행횟수열차운행거리(km)차량운행거리(km)일별전력사용량(kWh)
0111평2019-12-195495108.051080.0120599.8482
1211평2019-12-205475150.851508.0121610.3559
2311토2019-12-215004702.647026.0111028.3567
3411일2019-12-225004702.647026.0111028.3567
4511평2019-12-235475150.851508.0121610.3559
5611평2019-12-245475150.851508.0121610.3559
6711휴2019-12-255034725.847258.0111576.1086
7811평2019-12-265455150.851508.0121610.3559
8911평2019-12-275485159.851598.0121822.8459
91011토2019-12-285004702.647026.0111028.3567
연번호선전력산정월휴일구분일자열차운행횟수열차운행거리(km)차량운행거리(km)일별전력사용량(kWh)
29182919812평2020-12-093134949.829698.880167.2475
29192920812평2020-12-103114914.429486.479593.90705
29202921812평2020-12-113134949.829698.880167.2475
29212922812토2020-12-122934785.428712.477504.61558
29222923812일2020-12-132934785.428712.477504.61558
29232924812평2020-12-143094910.629463.679532.36203
29242925812평2020-12-153094910.629463.679532.36203
29252926812평2020-12-163114914.429486.479593.90705
29262927812평2020-12-173144933.429600.479901.63215
29272928812평2020-12-183114914.429486.479593.90705