Overview

Dataset statistics

Number of variables9
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory830.1 KiB
Average record size in memory85.0 B

Variable types

DateTime1
Text2
Numeric4
Categorical2

Dataset

Description대구광역시에서 관리·운영하는 전기차 충전소의 위치(위도, 경도), 충전기 타입(급속, 완속) 현황과 일별, 충전소별 사용횟수 및 전력사용량 현황 입니다.
Author대구광역시
URLhttps://www.data.go.kr/data/15067156/fileData.do

Alerts

사용횟수 is highly overall correlated with 충전량 and 1 other fieldsHigh correlation
충전량 is highly overall correlated with 사용횟수 and 1 other fieldsHigh correlation
충전기ID is highly overall correlated with 충전기타입High correlation
충전기타입 is highly overall correlated with 사용횟수 and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 02:04:38.244875
Analysis finished2023-12-12 02:04:41.866019
Duration3.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

Distinct433
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2022-06-25 00:00:00
Maximum2023-08-31 00:00:00
2023-12-12T11:04:41.961873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:42.137129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

충전소ID
Text

Distinct204
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T11:04:42.506458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters34
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

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowDGYT0001
2nd row27230023
3rd rowSDJC0001
4th row27290044
5th rowDGNG0001
ValueCountFrequency (%)
27230001 190
 
1.9%
dgug0001 187
 
1.9%
27290004 176
 
1.8%
27230004 169
 
1.7%
27110003 166
 
1.7%
27290005 154
 
1.5%
27140004 145
 
1.5%
dgyt0001 140
 
1.4%
dgss0002 138
 
1.4%
27140007 126
 
1.3%
Other values (194) 8409
84.1%
2023-12-12T11:04:43.044548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 27881
34.9%
2 12070
15.1%
1 7717
 
9.6%
7 7538
 
9.4%
D 3992
 
5.0%
G 3890
 
4.9%
3 3033
 
3.8%
4 2386
 
3.0%
S 1907
 
2.4%
9 1845
 
2.3%
Other values (24) 7741
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65276
81.6%
Uppercase Letter 14724
 
18.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 3992
27.1%
G 3890
26.4%
S 1907
13.0%
C 600
 
4.1%
J 552
 
3.7%
H 487
 
3.3%
B 416
 
2.8%
M 414
 
2.8%
T 395
 
2.7%
W 370
 
2.5%
Other values (14) 1701
11.6%
Decimal Number
ValueCountFrequency (%)
0 27881
42.7%
2 12070
18.5%
1 7717
 
11.8%
7 7538
 
11.5%
3 3033
 
4.6%
4 2386
 
3.7%
9 1845
 
2.8%
6 1467
 
2.2%
5 829
 
1.3%
8 510
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 65276
81.6%
Latin 14724
 
18.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 3992
27.1%
G 3890
26.4%
S 1907
13.0%
C 600
 
4.1%
J 552
 
3.7%
H 487
 
3.3%
B 416
 
2.8%
M 414
 
2.8%
T 395
 
2.7%
W 370
 
2.5%
Other values (14) 1701
11.6%
Common
ValueCountFrequency (%)
0 27881
42.7%
2 12070
18.5%
1 7717
 
11.8%
7 7538
 
11.5%
3 3033
 
4.6%
4 2386
 
3.7%
9 1845
 
2.8%
6 1467
 
2.2%
5 829
 
1.3%
8 510
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 27881
34.9%
2 12070
15.1%
1 7717
 
9.6%
7 7538
 
9.4%
D 3992
 
5.0%
G 3890
 
4.9%
3 3033
 
3.8%
4 2386
 
3.0%
S 1907
 
2.4%
9 1845
 
2.3%
Other values (24) 7741
 
9.7%

충전소명칭
Text

Distinct206
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T11:04:43.360321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length10.1325
Min length3

Characters and Unicode

Total characters101325
Distinct characters243
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row대구시_대구유통단지 전자관
2nd row대구체육관 주차장
3rd row대구시_서대구역(남측) 주차장
4th row본동행정복지센터
5th row대구시_남구청
ValueCountFrequency (%)
행정복지센터 1445
 
10.4%
주차장 472
 
3.4%
농수산물도매시장 190
 
1.4%
대구시_유가읍 187
 
1.3%
대구은행 178
 
1.3%
공영주차장 176
 
1.3%
두류공원(성당휴게소 176
 
1.3%
시청별관 169
 
1.2%
서문주차빌딩 166
 
1.2%
앞(롯데캐슬입구 154
 
1.1%
Other values (223) 10594
76.2%
2023-12-12T11:04:43.949401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 6363
 
6.3%
대 5712
 
5.6%
시 4591
 
4.5%
지 4264
 
4.2%
3907
 
3.9%
동 3711
 
3.7%
_ 3681
 
3.6%
터 3446
 
3.4%
센 3439
 
3.4%
행 3001
 
3.0%
Other values (233) 59210
58.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89334
88.2%
Space Separator 3907
 
3.9%
Connector Punctuation 3681
 
3.6%
Decimal Number 2130
 
2.1%
Close Punctuation 912
 
0.9%
Open Punctuation 912
 
0.9%
Uppercase Letter 349
 
0.3%
Math Symbol 100
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 6363
 
7.1%
대 5712
 
6.4%
시 4591
 
5.1%
지 4264
 
4.8%
동 3711
 
4.2%
터 3446
 
3.9%
센 3439
 
3.8%
행 3001
 
3.4%
복 2978
 
3.3%
정 2683
 
3.0%
Other values (216) 49146
55.0%
Decimal Number
ValueCountFrequency (%)
2 714
33.5%
1 711
33.4%
3 450
21.1%
4 148
 
6.9%
5 79
 
3.7%
9 28
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
C 114
32.7%
I 69
19.8%
D 45
 
12.9%
T 45
 
12.9%
K 38
 
10.9%
S 38
 
10.9%
Space Separator
ValueCountFrequency (%)
3907
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3681
100.0%
Close Punctuation
ValueCountFrequency (%)
) 912
100.0%
Open Punctuation
ValueCountFrequency (%)
( 912
100.0%
Math Symbol
ValueCountFrequency (%)
+ 100
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89334
88.2%
Common 11642
 
11.5%
Latin 349
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 6363
 
7.1%
대 5712
 
6.4%
시 4591
 
5.1%
지 4264
 
4.8%
동 3711
 
4.2%
터 3446
 
3.9%
센 3439
 
3.8%
행 3001
 
3.4%
복 2978
 
3.3%
정 2683
 
3.0%
Other values (216) 49146
55.0%
Common
ValueCountFrequency (%)
3907
33.6%
_ 3681
31.6%
) 912
 
7.8%
( 912
 
7.8%
2 714
 
6.1%
1 711
 
6.1%
3 450
 
3.9%
4 148
 
1.3%
+ 100
 
0.9%
5 79
 
0.7%
Latin
ValueCountFrequency (%)
C 114
32.7%
I 69
19.8%
D 45
 
12.9%
T 45
 
12.9%
K 38
 
10.9%
S 38
 
10.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89334
88.2%
ASCII 11991
 
11.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 6363
 
7.1%
대 5712
 
6.4%
시 4591
 
5.1%
지 4264
 
4.8%
동 3711
 
4.2%
터 3446
 
3.9%
센 3439
 
3.8%
행 3001
 
3.4%
복 2978
 
3.3%
정 2683
 
3.0%
Other values (216) 49146
55.0%
ASCII
ValueCountFrequency (%)
3907
32.6%
_ 3681
30.7%
) 912
 
7.6%
( 912
 
7.6%
2 714
 
6.0%
1 711
 
5.9%
3 450
 
3.8%
4 148
 
1.2%
C 114
 
1.0%
+ 100
 
0.8%
Other values (7) 342
 
2.9%

위도
Real number (ℝ)

Distinct203
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.857259
Minimum35.64072
Maximum35.9908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:04:44.515324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.64072
5-th percentile35.78256
Q135.83456
median35.85958
Q335.88511
95-th percentile35.92801
Maximum35.9908
Range0.35008
Interquartile range (IQR)0.05055

Descriptive statistics

Standard deviation0.050218061
Coefficient of variation (CV)0.0014004992
Kurtosis4.2304907
Mean35.857259
Median Absolute Deviation (MAD)0.0253
Skewness-1.3328516
Sum358572.59
Variance0.0025218537
MonotonicityNot monotonic
2023-12-12T11:04:44.723096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.90165 190
 
1.9%
35.69385 187
 
1.9%
35.84471 176
 
1.8%
35.89063 169
 
1.7%
35.86899 166
 
1.7%
35.85193 154
 
1.5%
35.87973 145
 
1.5%
35.90816 140
 
1.4%
35.83428 138
 
1.4%
35.86861 126
 
1.3%
Other values (193) 8409
84.1%
ValueCountFrequency (%)
35.64072 31
 
0.3%
35.64603 46
 
0.5%
35.6852 61
 
0.6%
35.69385 187
1.9%
35.69407 12
 
0.1%
35.69692 38
 
0.4%
35.72968 42
 
0.4%
35.77387 50
 
0.5%
35.77409 16
 
0.2%
35.78256 43
 
0.4%
ValueCountFrequency (%)
35.9908 65
0.7%
35.97027 38
0.4%
35.96938 41
0.4%
35.94423 40
0.4%
35.9411 37
0.4%
35.94005 28
 
0.3%
35.93891 45
0.4%
35.93728 39
0.4%
35.93436 35
0.4%
35.93149 78
0.8%

경도
Real number (ℝ)

Distinct204
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.58133
Minimum128.40098
Maximum128.73659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:04:44.897981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.40098
5-th percentile128.45989
Q1128.54101
median128.58538
Q3128.62578
95-th percentile128.69518
Maximum128.73659
Range0.335616
Interquartile range (IQR)0.084775

Descriptive statistics

Standard deviation0.065961049
Coefficient of variation (CV)0.00051299088
Kurtosis-0.22515591
Mean128.58133
Median Absolute Deviation (MAD)0.043552
Skewness-0.13992661
Sum1285813.3
Variance0.00435086
MonotonicityNot monotonic
2023-12-12T11:04:45.063975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.541827 190
 
1.9%
128.459888 187
 
1.9%
128.561737 176
 
1.8%
128.60057 169
 
1.7%
128.581015 166
 
1.7%
128.525938 154
 
1.5%
128.632998 145
 
1.5%
128.606681 140
 
1.4%
128.693463 138
 
1.4%
128.680791 126
 
1.3%
Other values (194) 8409
84.1%
ValueCountFrequency (%)
128.400977 46
0.5%
128.419638 31
0.3%
128.420312 16
 
0.2%
128.430902 50
0.5%
128.437936 39
0.4%
128.445517 38
0.4%
128.446878 43
0.4%
128.447912 38
0.4%
128.453025 42
0.4%
128.454281 42
0.4%
ValueCountFrequency (%)
128.736593 30
0.3%
128.726065 41
0.4%
128.725839 38
0.4%
128.712585 1
 
< 0.1%
128.71083 42
0.4%
128.709609 68
0.7%
128.706596 36
0.4%
128.706352 34
0.3%
128.70578 58
0.6%
128.70203 56
0.6%

충전기ID
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
7160 
2
1571 
3
 
691
4
 
466
5
 
112

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 7160
71.6%
2 1571
 
15.7%
3 691
 
6.9%
4 466
 
4.7%
5 112
 
1.1%

Length

2023-12-12T11:04:45.207785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:04:45.321995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 7160
71.6%
2 1571
 
15.7%
3 691
 
6.9%
4 466
 
4.7%
5 112
 
1.1%

충전기타입
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
급속
5925 
완속
4075 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row급속
2nd row급속
3rd row급속
4th row완속
5th row완속

Common Values

ValueCountFrequency (%)
급속 5925
59.2%
완속 4075
40.8%

Length

2023-12-12T11:04:45.445464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:04:45.545084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
급속 5925
59.2%
완속 4075
40.8%

사용횟수
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6742
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:04:45.666803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile9
Maximum23
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.8384256
Coefficient of variation (CV)0.77252888
Kurtosis1.5626169
Mean3.6742
Median Absolute Deviation (MAD)2
Skewness1.3010932
Sum36742
Variance8.05666
MonotonicityNot monotonic
2023-12-12T11:04:45.794075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 2654
26.5%
2 1975
19.8%
3 1381
13.8%
4 926
 
9.3%
5 764
 
7.6%
6 656
 
6.6%
7 504
 
5.0%
8 394
 
3.9%
9 285
 
2.9%
10 163
 
1.6%
Other values (10) 298
 
3.0%
ValueCountFrequency (%)
1 2654
26.5%
2 1975
19.8%
3 1381
13.8%
4 926
 
9.3%
5 764
 
7.6%
6 656
 
6.6%
7 504
 
5.0%
8 394
 
3.9%
9 285
 
2.9%
10 163
 
1.6%
ValueCountFrequency (%)
23 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 3
 
< 0.1%
16 6
 
0.1%
15 17
 
0.2%
14 28
 
0.3%
13 44
 
0.4%
12 67
0.7%
11 130
1.3%

충전량
Real number (ℝ)

HIGH CORRELATION 

Distinct7860
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.22395
Minimum0
Maximum565.89
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T11:04:45.942791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.4495
Q131.4275
median60.52
Q3113.655
95-th percentile216.6205
Maximum565.89
Range565.89
Interquartile range (IQR)82.2275

Descriptive statistics

Standard deviation72.73171
Coefficient of variation (CV)0.88455626
Kurtosis6.4511948
Mean82.22395
Median Absolute Deviation (MAD)36.46
Skewness1.9764683
Sum822239.5
Variance5289.9017
MonotonicityNot monotonic
2023-12-12T11:04:46.105740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.25 16
 
0.2%
30.83 14
 
0.1%
0.0 13
 
0.1%
6.17 13
 
0.1%
500.0 13
 
0.1%
15.41 10
 
0.1%
21.58 8
 
0.1%
36.99 7
 
0.1%
61.65 7
 
0.1%
3.08 6
 
0.1%
Other values (7850) 9893
98.9%
ValueCountFrequency (%)
0.0 13
0.1%
0.01 1
 
< 0.1%
0.05 1
 
< 0.1%
0.06 2
 
< 0.1%
0.09 1
 
< 0.1%
0.1 2
 
< 0.1%
0.11 2
 
< 0.1%
0.12 1
 
< 0.1%
0.13 1
 
< 0.1%
0.16 1
 
< 0.1%
ValueCountFrequency (%)
565.89 1
< 0.1%
561.08 1
< 0.1%
553.1 1
< 0.1%
550.86 1
< 0.1%
549.52 1
< 0.1%
547.96 1
< 0.1%
545.97 1
< 0.1%
538.53 1
< 0.1%
537.47 1
< 0.1%
533.25 1
< 0.1%

Interactions

2023-12-12T11:04:41.000025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:39.431411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:39.938277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:40.493649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:41.117778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:39.546710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:40.074213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:40.627705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:41.258276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:39.691049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:40.219573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:40.754133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:41.394819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:39.820511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:40.371345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:04:40.859463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:04:46.205662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도충전기ID충전기타입사용횟수충전량
위도1.0000.8850.3400.2650.2420.255
경도0.8851.0000.2960.2200.1340.178
충전기ID0.3400.2961.0000.4680.3800.402
충전기타입0.2650.2200.4681.0000.7240.702
사용횟수0.2420.1340.3800.7241.0000.838
충전량0.2550.1780.4020.7020.8381.000
2023-12-12T11:04:46.321429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전기ID충전기타입
충전기ID1.0000.569
충전기타입0.5691.000
2023-12-12T11:04:46.427386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도사용횟수충전량충전기ID충전기타입
위도1.0000.2620.0300.0310.1480.203
경도0.2621.0000.0320.0400.1300.171
사용횟수0.0300.0321.0000.8440.1670.566
충전량0.0310.0400.8441.0000.1780.548
충전기ID0.1480.1300.1670.1781.0000.569
충전기타입0.2030.1710.5660.5480.5691.000

Missing values

2023-12-12T11:04:41.578895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:04:41.784874image/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

일자충전소ID충전소명칭위도경도충전기ID충전기타입사용횟수충전량
943212022-06-29DGYT0001대구시_대구유통단지 전자관35.90816128.6066811급속13230.69
34762023-08-0627230023대구체육관 주차장35.89429128.6029681급속6144.38
286992023-04-05SDJC0001대구시_서대구역(남측) 주차장35.88055128.5390391급속239.43
221342023-05-0327290044본동행정복지센터35.83489128.5406911완속241.38
487342023-01-10DGNG0001대구시_남구청35.84602128.5976952완속380.81
249432023-04-2127230022아이빌35.90177128.5768581급속256.44
478972023-01-1327260030만촌3동행정복지센터35.85551128.6506461완속18.39
851662022-08-0727170003서구청35.87197128.5587561급속562.09
706872022-10-0927170004충전인프라 관제센터35.88511128.5312754완속162.19
664462022-10-28DGAS0002대구시_안심3+4동 행정복지센터35.87053128.710831급속9150.97
일자충전소ID충전소명칭위도경도충전기ID충전기타입사용횟수충전량
387382023-02-21DGSS0002대구시_수성의료지구35.83428128.6934631급속379.19
160052023-05-3027290006성서체육공원주차장35.83233128.4946883완속140.41
392422023-02-19DGSR0001대구시_진천삼성래미안35.80702128.5174711급속222.19
593222022-11-2727260015대구환경공단(지산)35.82968128.6214251급속8173.85
637752022-11-0827230004시청별관35.89063128.600574완속349.0
257732023-04-18DGHM0001대구시_효목1동 행정복지센터35.88186128.644951급속259.8
866652022-08-01DHDH0001대구시_대현동 행정복지센터35.88233128.6059841급속327.8
38192023-08-0427230004시청별관35.89063128.600572완속150.41
480162023-01-13DGRS0001대구시_두류3동35.85367128.5554711급속6147.229998
572952022-12-06HGDR0001대구시_한국도로공사(유천IC)35.81086128.5015561급속11237.709997