Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory80.4 B

Variable types

Categorical4
Text2
Numeric3

Dataset

Description샘플 데이터
Author펌프킨
URLhttps://bigdata-region.kr/#/dataset/3508c740-6739-45ac-a082-255f1751b0f9

Alerts

운행일자 has constant value ""Constant
비고 has constant value ""Constant
생산_일시 has constant value ""Constant
SOC_충전_량 is highly overall correlated with 충전_전력량 and 2 other fieldsHigh correlation
충전_전력량 is highly overall correlated with SOC_충전_량 and 2 other fieldsHigh correlation
총_충전_소요시간 is highly overall correlated with SOC_충전_량 and 1 other fieldsHigh correlation
충전_수 is highly overall correlated with SOC_충전_량 and 1 other fieldsHigh correlation
충전_수 is highly imbalanced (52.4%)Imbalance
노선ID has unique valuesUnique

Reproduction

Analysis started2024-03-13 11:51:38.437536
Analysis finished2024-03-13 11:51:40.132299
Duration1.69 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

운행일자
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-10-01
30 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-10-01
2nd row2023-10-01
3rd row2023-10-01
4th row2023-10-01
5th row2023-10-01

Common Values

ValueCountFrequency (%)
2023-10-01 30
100.0%

Length

2024-03-13T20:51:40.239215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:51:40.389099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-10-01 30
100.0%

노선ID
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:51:40.632964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters360
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowGGB207000019
2nd rowGGB219000001
3rd rowGGB222000023
4th rowGGB222000029
5th rowGGB224000003
ValueCountFrequency (%)
ggb207000019 1
 
3.3%
ggb219000001 1
 
3.3%
ggb204000032 1
 
3.3%
ggb200000107 1
 
3.3%
ggb200000085 1
 
3.3%
ggb200000060 1
 
3.3%
cwb379000320 1
 
3.3%
ggb238000024 1
 
3.3%
ggb236000057 1
 
3.3%
ggb235000113 1
 
3.3%
Other values (20) 20
66.7%
2024-03-13T20:51:41.120598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 127
35.3%
G 58
16.1%
2 51
14.2%
B 30
 
8.3%
3 29
 
8.1%
1 13
 
3.6%
4 11
 
3.1%
8 10
 
2.8%
9 9
 
2.5%
7 7
 
1.9%
Other values (4) 15
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
75.0%
Uppercase Letter 90
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 127
47.0%
2 51
18.9%
3 29
 
10.7%
1 13
 
4.8%
4 11
 
4.1%
8 10
 
3.7%
9 9
 
3.3%
7 7
 
2.6%
6 7
 
2.6%
5 6
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
G 58
64.4%
B 30
33.3%
C 1
 
1.1%
W 1
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 270
75.0%
Latin 90
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 127
47.0%
2 51
18.9%
3 29
 
10.7%
1 13
 
4.8%
4 11
 
4.1%
8 10
 
3.7%
9 9
 
3.3%
7 7
 
2.6%
6 7
 
2.6%
5 6
 
2.2%
Latin
ValueCountFrequency (%)
G 58
64.4%
B 30
33.3%
C 1
 
1.1%
W 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 127
35.3%
G 58
16.1%
2 51
14.2%
B 30
 
8.3%
3 29
 
8.1%
1 13
 
3.6%
4 11
 
3.1%
8 10
 
2.8%
9 9
 
2.5%
7 7
 
1.9%
Other values (4) 15
 
4.2%

노선_번호
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-03-13T20:51:41.311167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.0666667
Min length1

Characters and Unicode

Total characters62
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)53.3%

Sample

1st row3
2nd row11
3rd row55
4th row6
5th row23
ValueCountFrequency (%)
10 4
 
13.3%
88 2
 
6.7%
20 2
 
6.7%
11 2
 
6.7%
70 2
 
6.7%
30 2
 
6.7%
3 1
 
3.3%
27 1
 
3.3%
98 1
 
3.3%
32 1
 
3.3%
Other values (12) 12
40.0%
2024-03-13T20:51:41.651128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14
22.6%
1 10
16.1%
2 8
12.9%
3 7
11.3%
8 7
11.3%
7 4
 
6.5%
9 4
 
6.5%
H 2
 
3.2%
4 2
 
3.2%
5 2
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
96.8%
Uppercase Letter 2
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14
23.3%
1 10
16.7%
2 8
13.3%
3 7
11.7%
8 7
11.7%
7 4
 
6.7%
9 4
 
6.7%
4 2
 
3.3%
5 2
 
3.3%
6 2
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
H 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60
96.8%
Latin 2
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14
23.3%
1 10
16.7%
2 8
13.3%
3 7
11.7%
8 7
11.7%
7 4
 
6.7%
9 4
 
6.7%
4 2
 
3.3%
5 2
 
3.3%
6 2
 
3.3%
Latin
ValueCountFrequency (%)
H 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14
22.6%
1 10
16.1%
2 8
12.9%
3 7
11.3%
8 7
11.3%
7 4
 
6.5%
9 4
 
6.5%
H 2
 
3.2%
4 2
 
3.2%
5 2
 
3.2%

충전_수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
0
24 
3
 
2
4
 
2
5
 
1
7
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row0
2nd row0
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 24
80.0%
3 2
 
6.7%
4 2
 
6.7%
5 1
 
3.3%
7 1
 
3.3%

Length

2024-03-13T20:51:41.811619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:51:41.934538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 24
80.0%
3 2
 
6.7%
4 2
 
6.7%
5 1
 
3.3%
7 1
 
3.3%

SOC_충전_량
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.233333
Minimum7
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:51:42.053627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12.45
Q118.25
median25
Q331.75
95-th percentile35.2
Maximum43
Range36
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.3363902
Coefficient of variation (CV)0.3440051
Kurtosis-0.35672874
Mean24.233333
Median Absolute Deviation (MAD)7
Skewness0.011470202
Sum727
Variance69.495402
MonotonicityNot monotonic
2024-03-13T20:51:42.177037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
25 7
23.3%
32 5
16.7%
16 2
 
6.7%
21 2
 
6.7%
31 2
 
6.7%
14 2
 
6.7%
43 1
 
3.3%
12 1
 
3.3%
26 1
 
3.3%
13 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
7 1
 
3.3%
12 1
 
3.3%
13 1
 
3.3%
14 2
 
6.7%
16 2
 
6.7%
18 1
 
3.3%
19 1
 
3.3%
20 1
 
3.3%
21 2
 
6.7%
25 7
23.3%
ValueCountFrequency (%)
43 1
 
3.3%
37 1
 
3.3%
33 1
 
3.3%
32 5
16.7%
31 2
 
6.7%
26 1
 
3.3%
25 7
23.3%
21 2
 
6.7%
20 1
 
3.3%
19 1
 
3.3%

충전_전력량
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.566667
Minimum19
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:51:42.308541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile23.6
Q137
median47
Q355.25
95-th percentile85.2
Maximum96
Range77
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation17.837742
Coefficient of variation (CV)0.36728365
Kurtosis1.1356004
Mean48.566667
Median Absolute Deviation (MAD)9.5
Skewness0.86954148
Sum1457
Variance318.18506
MonotonicityNot monotonic
2024-03-13T20:51:42.444546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
53 5
16.7%
45 3
 
10.0%
37 2
 
6.7%
61 2
 
6.7%
56 2
 
6.7%
31 2
 
6.7%
48 2
 
6.7%
20 1
 
3.3%
35 1
 
3.3%
83 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
19 1
 
3.3%
20 1
 
3.3%
28 1
 
3.3%
31 2
6.7%
34 1
 
3.3%
35 1
 
3.3%
37 2
6.7%
39 1
 
3.3%
41 1
 
3.3%
45 3
10.0%
ValueCountFrequency (%)
96 1
 
3.3%
87 1
 
3.3%
83 1
 
3.3%
63 1
 
3.3%
61 2
 
6.7%
56 2
 
6.7%
53 5
16.7%
48 2
 
6.7%
46 1
 
3.3%
45 3
10.0%

총_충전_소요시간
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.166667
Minimum13
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-03-13T20:51:42.575936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile19.45
Q125.25
median29.5
Q334.75
95-th percentile55.25
Maximum66
Range53
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation11.98874
Coefficient of variation (CV)0.37270694
Kurtosis1.7844904
Mean32.166667
Median Absolute Deviation (MAD)5
Skewness1.2983537
Sum965
Variance143.72989
MonotonicityNot monotonic
2024-03-13T20:51:42.723042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
31 6
20.0%
28 5
16.7%
46 3
10.0%
22 3
10.0%
25 1
 
3.3%
62 1
 
3.3%
19 1
 
3.3%
38 1
 
3.3%
47 1
 
3.3%
34 1
 
3.3%
Other values (7) 7
23.3%
ValueCountFrequency (%)
13 1
 
3.3%
19 1
 
3.3%
20 1
 
3.3%
22 3
10.0%
23 1
 
3.3%
25 1
 
3.3%
26 1
 
3.3%
27 1
 
3.3%
28 5
16.7%
31 6
20.0%
ValueCountFrequency (%)
66 1
 
3.3%
62 1
 
3.3%
47 1
 
3.3%
46 3
10.0%
38 1
 
3.3%
35 1
 
3.3%
34 1
 
3.3%
31 6
20.0%
28 5
16.7%
27 1
 
3.3%

비고
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-01-10 전기버스 노선별 충전이력
30 

Length

Max length24
Median length24
Mean length24
Min length24

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-10 전기버스 노선별 충전이력
2nd row2024-01-10 전기버스 노선별 충전이력
3rd row2024-01-10 전기버스 노선별 충전이력
4th row2024-01-10 전기버스 노선별 충전이력
5th row2024-01-10 전기버스 노선별 충전이력

Common Values

ValueCountFrequency (%)
2024-01-10 전기버스 노선별 충전이력 30
100.0%

Length

2024-03-13T20:51:42.862907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:51:42.980057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-10 30
25.0%
전기버스 30
25.0%
노선별 30
25.0%
충전이력 30
25.0%

생산_일시
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-01-10 14:26:05
30 

Length

Max length19
Median length19
Mean length19
Min length19

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-10 14:26:05
2nd row2024-01-10 14:26:05
3rd row2024-01-10 14:26:05
4th row2024-01-10 14:26:05
5th row2024-01-10 14:26:05

Common Values

ValueCountFrequency (%)
2024-01-10 14:26:05 30
100.0%

Length

2024-03-13T20:51:43.094159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:51:43.237662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-10 30
50.0%
14:26:05 30
50.0%

Interactions

2024-03-13T20:51:39.437681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:38.750205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:39.103387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:39.546764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:38.863199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:39.223567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:39.713168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:39.008102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:51:39.341063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:51:43.331759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선ID노선_번호충전_수SOC_충전_량충전_전력량총_충전_소요시간
노선ID1.0001.0001.0001.0001.0001.000
노선_번호1.0001.0001.0001.0001.0001.000
충전_수1.0001.0001.0000.8370.7320.579
SOC_충전_량1.0001.0000.8371.0000.8430.849
충전_전력량1.0001.0000.7320.8431.0000.857
총_충전_소요시간1.0001.0000.5790.8490.8571.000
2024-03-13T20:51:43.441428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
SOC_충전_량충전_전력량총_충전_소요시간충전_수
SOC_충전_량1.0000.8120.8900.587
충전_전력량0.8121.0000.9530.525
총_충전_소요시간0.8900.9531.0000.395
충전_수0.5870.5250.3951.000

Missing values

2024-03-13T20:51:39.898592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:51:40.072445image/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

운행일자노선ID노선_번호충전_수SOC_충전_량충전_전력량총_충전_소요시간비고생산_일시
02023-10-01GGB207000019301941252024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
12023-10-01GGB2190000011101431222024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
22023-10-01GGB2220000235531646272024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
32023-10-01GGB22200002960719132024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
42023-10-01GGB2240000032301639262024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
52023-10-01GGB2270000033003245312024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
62023-10-01GGB2280000198802137282024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
72023-10-01GGB2280002382003261462024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
82023-10-01GGB2280002879902028202024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
92023-10-01GGB2290000103003245312024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
운행일자노선ID노선_번호충전_수SOC_충전_량충전_전력량총_충전_소요시간비고생산_일시
202023-10-01GGB2340003166074387622024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
212023-10-01GGB2350001138701334232024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
222023-10-01GGB236000057202653342024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
232023-10-01GGB2380000247002548282024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
242023-10-01CWB3790003203243183472024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
252023-10-01GGB2000000601101431222024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
262023-10-01GGB2000000859803156382024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
272023-10-01GGB2000001078802137282024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
282023-10-01GGB20400003224001235192024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05
292023-10-01GGB2080000251002553312024-01-10 전기버스 노선별 충전이력2024-01-10 14:26:05