Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory644.5 KiB
Average record size in memory66.0 B

Variable types

Numeric2
Text3
Categorical2

Dataset

Description한국전력공사에서 운영중인 전기차 충전기에 대한 코드 정보 입니다. 한국전력공사에서 관리하는 코드 값 및 한국환경공단에서 관리하고 있는 전기차 충전기과 연계된 코드값 데이터 입니다.
URLhttps://www.data.go.kr/data/15114166/fileData.do

Alerts

충전소ID is highly overall correlated with 충전기ID and 1 other fieldsHigh correlation
충전기ID is highly overall correlated with 충전소ID and 1 other fieldsHigh correlation
충전소용도 is highly overall correlated with 충전소ID and 1 other fieldsHigh correlation
충전기ID has unique valuesUnique
한국전력공사 관리코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:59:59.710131
Analysis finished2023-12-12 18:00:01.143068
Duration1.43 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

충전소ID
Real number (ℝ)

HIGH CORRELATION 

Distinct4558
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2402.5147
Minimum2
Maximum5693
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:00:01.205877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile102
Q11029
median2239
Q33669
95-th percentile5239.05
Maximum5693
Range5691
Interquartile range (IQR)2640

Descriptive statistics

Standard deviation1609.1322
Coefficient of variation (CV)0.66976999
Kurtosis-1.0494833
Mean2402.5147
Median Absolute Deviation (MAD)1302.5
Skewness0.30927723
Sum24025147
Variance2589306.6
MonotonicityNot monotonic
2023-12-13T03:00:01.323564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137 18
 
0.2%
74 15
 
0.1%
4127 15
 
0.1%
295 15
 
0.1%
47 14
 
0.1%
46 14
 
0.1%
62 14
 
0.1%
102 14
 
0.1%
2229 13
 
0.1%
22 13
 
0.1%
Other values (4548) 9855
98.6%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
10 8
0.1%
11 2
 
< 0.1%
12 7
0.1%
13 7
0.1%
14 8
0.1%
ValueCountFrequency (%)
5693 7
0.1%
5691 1
 
< 0.1%
5678 1
 
< 0.1%
5677 1
 
< 0.1%
5671 1
 
< 0.1%
5670 1
 
< 0.1%
5669 1
 
< 0.1%
5668 1
 
< 0.1%
5648 10
0.1%
5647 1
 
< 0.1%

충전소명
Text

Distinct4525
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:00:01.522135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length16
Mean length8.9855
Min length3

Characters and Unicode

Total characters89855
Distinct characters645
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1885 ?
Unique (%)18.9%

Sample

1st row인제지사(공용)
2nd row산수화아파트
3rd row대림운동장 공영주차장
4th row유현마을현대프라임빌
5th row양산시청
ValueCountFrequency (%)
아파트 1741
 
12.3%
공영주차장 201
 
1.4%
주차장 140
 
1.0%
충전소 70
 
0.5%
행정복지센터 66
 
0.5%
홈플러스 47
 
0.3%
하나로마트 31
 
0.2%
이마트 30
 
0.2%
한전본사 29
 
0.2%
3단지 26
 
0.2%
Other values (4957) 11770
83.2%
2023-12-13T03:00:01.915935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4152
 
4.6%
아 3883
 
4.3%
트 3836
 
4.3%
파 3759
 
4.2%
지 3273
 
3.6%
차 1615
 
1.8%
단 1602
 
1.8%
주 1558
 
1.7%
사 1378
 
1.5%
동 1224
 
1.4%
Other values (635) 63575
70.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80158
89.2%
Space Separator 4152
 
4.6%
Decimal Number 3162
 
3.5%
Uppercase Letter 1051
 
1.2%
Open Punctuation 447
 
0.5%
Close Punctuation 445
 
0.5%
Lowercase Letter 276
 
0.3%
Dash Punctuation 104
 
0.1%
Other Punctuation 51
 
0.1%
Connector Punctuation 5
 
< 0.1%
Other values (2) 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 3883
 
4.8%
트 3836
 
4.8%
파 3759
 
4.7%
지 3273
 
4.1%
차 1615
 
2.0%
단 1602
 
2.0%
주 1558
 
1.9%
사 1378
 
1.7%
동 1224
 
1.5%
스 1175
 
1.5%
Other values (575) 56855
70.9%
Uppercase Letter
ValueCountFrequency (%)
L 217
20.6%
H 192
18.3%
S 147
14.0%
K 92
8.8%
C 66
 
6.3%
G 49
 
4.7%
B 39
 
3.7%
A 36
 
3.4%
E 30
 
2.9%
I 29
 
2.8%
Other values (12) 154
14.7%
Lowercase Letter
ValueCountFrequency (%)
e 165
59.8%
k 22
 
8.0%
w 14
 
5.1%
s 13
 
4.7%
r 12
 
4.3%
l 9
 
3.3%
c 8
 
2.9%
h 7
 
2.5%
a 6
 
2.2%
o 6
 
2.2%
Other values (7) 14
 
5.1%
Decimal Number
ValueCountFrequency (%)
1 946
29.9%
2 898
28.4%
3 436
13.8%
5 195
 
6.2%
4 178
 
5.6%
6 146
 
4.6%
7 107
 
3.4%
0 107
 
3.4%
8 75
 
2.4%
9 74
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 31
60.8%
, 13
25.5%
· 4
 
7.8%
& 3
 
5.9%
Space Separator
ValueCountFrequency (%)
4152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 447
100.0%
Close Punctuation
ValueCountFrequency (%)
) 445
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 104
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%
Letter Number
ValueCountFrequency (%)
Ⅳ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80160
89.2%
Common 8366
 
9.3%
Latin 1329
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 3883
 
4.8%
트 3836
 
4.8%
파 3759
 
4.7%
지 3273
 
4.1%
차 1615
 
2.0%
단 1602
 
2.0%
주 1558
 
1.9%
사 1378
 
1.7%
동 1224
 
1.5%
스 1175
 
1.5%
Other values (576) 56857
70.9%
Latin
ValueCountFrequency (%)
L 217
16.3%
H 192
14.4%
e 165
12.4%
S 147
11.1%
K 92
 
6.9%
C 66
 
5.0%
G 49
 
3.7%
B 39
 
2.9%
A 36
 
2.7%
E 30
 
2.3%
Other values (30) 296
22.3%
Common
ValueCountFrequency (%)
4152
49.6%
1 946
 
11.3%
2 898
 
10.7%
( 447
 
5.3%
) 445
 
5.3%
3 436
 
5.2%
5 195
 
2.3%
4 178
 
2.1%
6 146
 
1.7%
7 107
 
1.3%
Other values (9) 416
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80158
89.2%
ASCII 9689
 
10.8%
None 6
 
< 0.1%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4152
42.9%
1 946
 
9.8%
2 898
 
9.3%
( 447
 
4.6%
) 445
 
4.6%
3 436
 
4.5%
L 217
 
2.2%
5 195
 
2.0%
H 192
 
2.0%
4 178
 
1.8%
Other values (47) 1583
 
16.3%
Hangul
ValueCountFrequency (%)
아 3883
 
4.8%
트 3836
 
4.8%
파 3759
 
4.7%
지 3273
 
4.1%
차 1615
 
2.0%
단 1602
 
2.0%
주 1558
 
1.9%
사 1378
 
1.7%
동 1224
 
1.5%
스 1175
 
1.5%
Other values (575) 56855
70.9%
None
ValueCountFrequency (%)
· 4
66.7%
㈜ 2
33.3%
Number Forms
ValueCountFrequency (%)
Ⅳ 2
100.0%

충전기ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6199.9625
Minimum5
Maximum12556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T03:00:02.083818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile617.95
Q13096.75
median6122.5
Q39358.25
95-th percentile11845.1
Maximum12556
Range12551
Interquartile range (IQR)6261.5

Descriptive statistics

Standard deviation3606.8874
Coefficient of variation (CV)0.58175955
Kurtosis-1.2070976
Mean6199.9625
Median Absolute Deviation (MAD)3112
Skewness0.037528397
Sum61999625
Variance13009637
MonotonicityNot monotonic
2023-12-13T03:00:02.238451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1308 1
 
< 0.1%
8018 1
 
< 0.1%
469 1
 
< 0.1%
4101 1
 
< 0.1%
5901 1
 
< 0.1%
8741 1
 
< 0.1%
8001 1
 
< 0.1%
12074 1
 
< 0.1%
7779 1
 
< 0.1%
10850 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
5 1
< 0.1%
7 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
ValueCountFrequency (%)
12556 1
< 0.1%
12555 1
< 0.1%
12554 1
< 0.1%
12553 1
< 0.1%
12543 1
< 0.1%
12537 1
< 0.1%
12522 1
< 0.1%
12521 1
< 0.1%
12520 1
< 0.1%
12519 1
< 0.1%

충전기명
Categorical

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
급속01
3464 
완속01
2135 
완속02
1654 
완속03
826 
급속02
801 
Other values (26)
1120 

Length

Max length10
Median length4
Mean length3.9998
Min length3

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row완속03
2nd row완속01
3rd row급속01
4th row완속03
5th row급속02

Common Values

ValueCountFrequency (%)
급속01 3464
34.6%
완속01 2135
21.3%
완속02 1654
16.5%
완속03 826
 
8.3%
급속02 801
 
8.0%
완속04 312
 
3.1%
완속05 161
 
1.6%
급속03 141
 
1.4%
완속06 100
 
1.0%
급속04 81
 
0.8%
Other values (21) 325
 
3.2%

Length

2023-12-13T03:00:02.419283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
급속01 3464
34.6%
완속01 2135
21.3%
완속02 1654
16.5%
완속03 826
 
8.3%
급속02 801
 
8.0%
완속04 312
 
3.1%
완속05 161
 
1.6%
급속03 141
 
1.4%
완속06 100
 
1.0%
급속04 81
 
0.8%
Other values (21) 325
 
3.2%

충전소용도
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
아파트용
6071 
공용
3117 
업무용
731 
전기버스
 
81

Length

Max length4
Median length4
Mean length3.3035
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공용
2nd row아파트용
3rd row공용
4th row아파트용
5th row공용

Common Values

ValueCountFrequency (%)
아파트용 6071
60.7%
공용 3117
31.2%
업무용 731
 
7.3%
전기버스 81
 
0.8%

Length

2023-12-13T03:00:02.569883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:00:02.694624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아파트용 6071
60.7%
공용 3117
31.2%
업무용 731
 
7.3%
전기버스 81
 
0.8%

한국환경공단 연계 코드
Text

Distinct4557
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:00:02.902257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
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

Unique1915 ?
Unique (%)19.1%

Sample

1st rowKEPS000189
2nd rowKEPS000762
3rd rowKEPS003266
4th rowKEPS001406
5th rowKEPS002203
ValueCountFrequency (%)
keps000137 18
 
0.2%
keps004127 15
 
0.1%
keps000295 15
 
0.1%
keps000074 15
 
0.1%
keps000062 14
 
0.1%
keps000047 14
 
0.1%
keps000046 14
 
0.1%
keps000102 14
 
0.1%
keps000068 13
 
0.1%
keps000298 13
 
0.1%
Other values (4547) 9855
98.6%
2023-12-13T03:00:03.308998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25789
25.8%
K 10000
 
10.0%
E 10000
 
10.0%
P 10000
 
10.0%
S 10000
 
10.0%
1 5247
 
5.2%
2 4816
 
4.8%
3 4407
 
4.4%
4 4212
 
4.2%
5 3775
 
3.8%
Other values (4) 11754
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
60.0%
Uppercase Letter 40000
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25789
43.0%
1 5247
 
8.7%
2 4816
 
8.0%
3 4407
 
7.3%
4 4212
 
7.0%
5 3775
 
6.3%
6 3143
 
5.2%
8 2886
 
4.8%
7 2879
 
4.8%
9 2846
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
K 10000
25.0%
E 10000
25.0%
P 10000
25.0%
S 10000
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
60.0%
Latin 40000
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25789
43.0%
1 5247
 
8.7%
2 4816
 
8.0%
3 4407
 
7.3%
4 4212
 
7.0%
5 3775
 
6.3%
6 3143
 
5.2%
8 2886
 
4.8%
7 2879
 
4.8%
9 2846
 
4.7%
Latin
ValueCountFrequency (%)
K 10000
25.0%
E 10000
25.0%
P 10000
25.0%
S 10000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25789
25.8%
K 10000
 
10.0%
E 10000
 
10.0%
P 10000
 
10.0%
S 10000
 
10.0%
1 5247
 
5.2%
2 4816
 
4.8%
3 4407
 
4.4%
4 4212
 
4.2%
5 3775
 
3.8%
Other values (4) 11754
11.8%

한국전력공사 관리코드
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T03:00:03.617021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters13
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

Unique10000 ?
Unique (%)100.0%

Sample

1st rowKEPE001308
2nd rowKEPE002402
3rd rowKEPE008449
4th rowKEPE004208
5th rowKEPE006089
ValueCountFrequency (%)
kepe001308 1
 
< 0.1%
kepe012074 1
 
< 0.1%
kepe003376 1
 
< 0.1%
kepe011864 1
 
< 0.1%
kepe000469 1
 
< 0.1%
kepe004101 1
 
< 0.1%
kepe005901 1
 
< 0.1%
kepe008741 1
 
< 0.1%
kepe008001 1
 
< 0.1%
kepe008018 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-13T03:00:04.015467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22588
22.6%
E 20000
20.0%
K 10000
10.0%
P 10000
10.0%
1 6867
 
6.9%
2 4187
 
4.2%
3 3937
 
3.9%
4 3842
 
3.8%
5 3787
 
3.8%
8 3782
 
3.8%
Other values (3) 11010
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60000
60.0%
Uppercase Letter 40000
40.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22588
37.6%
1 6867
 
11.4%
2 4187
 
7.0%
3 3937
 
6.6%
4 3842
 
6.4%
5 3787
 
6.3%
8 3782
 
6.3%
6 3730
 
6.2%
9 3642
 
6.1%
7 3638
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
E 20000
50.0%
K 10000
25.0%
P 10000
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60000
60.0%
Latin 40000
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22588
37.6%
1 6867
 
11.4%
2 4187
 
7.0%
3 3937
 
6.6%
4 3842
 
6.4%
5 3787
 
6.3%
8 3782
 
6.3%
6 3730
 
6.2%
9 3642
 
6.1%
7 3638
 
6.1%
Latin
ValueCountFrequency (%)
E 20000
50.0%
K 10000
25.0%
P 10000
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22588
22.6%
E 20000
20.0%
K 10000
10.0%
P 10000
10.0%
1 6867
 
6.9%
2 4187
 
4.2%
3 3937
 
3.9%
4 3842
 
3.8%
5 3787
 
3.8%
8 3782
 
3.8%
Other values (3) 11010
11.0%

Interactions

2023-12-13T03:00:00.788741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:00:00.629962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:00:00.875075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:00:00.702932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:00:04.114919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소ID충전기ID충전기명충전소용도
충전소ID1.0000.9890.4870.698
충전기ID0.9891.0000.4760.739
충전기명0.4870.4761.0000.741
충전소용도0.6980.7390.7411.000
2023-12-13T03:00:04.211600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소용도충전기명
충전소용도1.0000.485
충전기명0.4851.000
2023-12-13T03:00:04.289485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소ID충전기ID충전기명충전소용도
충전소ID1.0000.9130.1930.500
충전기ID0.9131.0000.1880.546
충전기명0.1930.1881.0000.485
충전소용도0.5000.5460.4851.000

Missing values

2023-12-13T03:00:00.982592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:00:01.087339image/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

충전소ID충전소명충전기ID충전기명충전소용도한국환경공단 연계 코드한국전력공사 관리코드
793189인제지사(공용)1308완속03공용KEPS000189KEPE001308
2134762산수화아파트2402완속01아파트용KEPS000762KEPE002402
77103266대림운동장 공영주차장8449급속01공용KEPS003266KEPE008449
38521406유현마을현대프라임빌4208완속03아파트용KEPS001406KEPE004208
55632203양산시청6089급속02공용KEPS002203KEPE006089
1395495첫마을7단지1591완속03아파트용KEPS000495KEPE001591
1292459삼계현대아파트1695완속03아파트용KEPS000459KEPE001695
88523937거제2차아이파크10304완속03아파트용KEPS003937KEPE010304
49441838동광모닝스카이아파트5383급속01아파트용KEPS001838KEPE005383
55752212남창원농협 농수산물유통센터6100급속01공용KEPS002212KEPE006100
충전소ID충전소명충전기ID충전기명충전소용도한국환경공단 연계 코드한국전력공사 관리코드
107775302해남군청 전기차충전소11901급속01공용KEPS005302KEPE011901
74703148세곡리엔파크1단지8208완속02아파트용KEPS003148KEPE008208
41571520대덕마을5단지4533완속01아파트용KEPS001520KEPE004533
111785618(전주거치형)시민공원옆주거지12405완속01공용KEPS005618KEPE012405
94224283농어촌공사 강원본부10857급속01공용KEPS004283KEPE010857
1612569수원센트럴타운2단지1879완속01아파트용KEPS000569KEPE001879
35464김해지사306완속01업무용KEPS000064KEPE000306
2054732대림동현대3차 아파트2322완속04아파트용KEPS000732KEPE002322
99544715강천면 주민자치센터11015급속01공용KEPS004715KEPE011015
95684434포항지방해양수산청10606급속01공용KEPS004434KEPE010606