Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells216
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory625.0 KiB
Average record size in memory64.0 B

Variable types

DateTime1
Categorical2
Text4

Dataset

Description한국전력공사와 "전기자동차 충전전력요금"으로 계약된 현황자료입니다. ○ 기간 : '19.1~'23.5(월별) ○ 항목 : 년월, 시도, 시군구, 충전요금제, 고객호수, 판매량, 판매수입
URLhttps://www.data.go.kr/data/15120784/fileData.do

Alerts

판매량 has 108 (1.1%) missing valuesMissing
판매수입 has 108 (1.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 04:57:54.630119
Analysis finished2023-12-12 04:57:55.982520
Duration1.35 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년월
Date

Distinct53
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2019-01-01 00:00:00
Maximum2023-05-01 00:00:00
2023-12-12T13:57:56.059695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:57:56.249516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

시도
Categorical

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도
1390 
서울특별시
1099 
경상북도
1021 
전라남도
962 
경상남도
799 
Other values (12)
4729 

Length

Max length7
Median length5
Mean length4.1223
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시
2nd row인천광역시
3rd row대구광역시
4th row부산광역시
5th row인천광역시

Common Values

ValueCountFrequency (%)
경기도 1390
13.9%
서울특별시 1099
11.0%
경상북도 1021
10.2%
전라남도 962
9.6%
경상남도 799
8.0%
강원도 779
7.8%
부산광역시 698
7.0%
충청남도 644
6.4%
전라북도 589
 
5.9%
충청북도 482
 
4.8%
Other values (7) 1537
15.4%

Length

2023-12-12T13:57:56.471659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 1390
13.9%
서울특별시 1099
11.0%
경상북도 1021
10.2%
전라남도 962
9.6%
경상남도 799
8.0%
강원도 779
7.8%
부산광역시 698
7.0%
충청남도 644
6.4%
전라북도 589
 
5.9%
충청북도 482
 
4.8%
Other values (7) 1537
15.4%

시군구
Text

Distinct207
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:57:56.913357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.9541
Min length2

Characters and Unicode

Total characters29541
Distinct characters137
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서구
2nd row남동구
3rd row서구
4th row동래구
5th row부평구
ValueCountFrequency (%)
동구 273
 
2.7%
중구 261
 
2.6%
서구 226
 
2.3%
북구 171
 
1.7%
남구 158
 
1.6%
고성군 101
 
1.0%
강서구 81
 
0.8%
청도군 59
 
0.6%
단양군 58
 
0.6%
가평군 58
 
0.6%
Other values (197) 8554
85.5%
2023-12-12T13:57:57.514743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 3753
 
12.7%
시 3443
 
11.7%
구 3211
 
10.9%
천 947
 
3.2%
주 883
 
3.0%
양 788
 
2.7%
성 784
 
2.7%
동 740
 
2.5%
산 673
 
2.3%
서 555
 
1.9%
Other values (127) 13764
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29541
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 3753
 
12.7%
시 3443
 
11.7%
구 3211
 
10.9%
천 947
 
3.2%
주 883
 
3.0%
양 788
 
2.7%
성 784
 
2.7%
동 740
 
2.5%
산 673
 
2.3%
서 555
 
1.9%
Other values (127) 13764
46.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29541
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 3753
 
12.7%
시 3443
 
11.7%
구 3211
 
10.9%
천 947
 
3.2%
주 883
 
3.0%
양 788
 
2.7%
성 784
 
2.7%
동 740
 
2.5%
산 673
 
2.3%
서 555
 
1.9%
Other values (127) 13764
46.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29541
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 3753
 
12.7%
시 3443
 
11.7%
구 3211
 
10.9%
천 947
 
3.2%
주 883
 
3.0%
양 788
 
2.7%
성 784
 
2.7%
동 740
 
2.5%
산 673
 
2.3%
서 555
 
1.9%
Other values (127) 13764
46.6%

충전요금
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
자가용
5018 
사업자용
4982 

Length

Max length4
Median length3
Mean length3.4982
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사업자용
2nd row자가용
3rd row자가용
4th row자가용
5th row자가용

Common Values

ValueCountFrequency (%)
자가용 5018
50.2%
사업자용 4982
49.8%

Length

2023-12-12T13:57:57.676510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:57:57.805366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자가용 5018
50.2%
사업자용 4982
49.8%

고객호수
Text

Distinct804
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:57:58.296576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.4141
Min length2

Characters and Unicode

Total characters34141
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique287 ?
Unique (%)2.9%

Sample

1st row203
2nd row266
3rd row72
4th row65
5th row108
ValueCountFrequency (%)
22 111
 
1.1%
20 111
 
1.1%
19 109
 
1.1%
5호미만제거 108
 
1.1%
27 106
 
1.1%
29 101
 
1.0%
28 97
 
1.0%
44 96
 
1.0%
16 93
 
0.9%
17 90
 
0.9%
Other values (794) 8978
89.8%
2023-12-12T13:57:58.954221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9892
29.0%
1 4539
13.3%
2 3313
 
9.7%
3 2629
 
7.7%
4 2338
 
6.8%
5 2234
 
6.5%
6 1952
 
5.7%
7 1823
 
5.3%
8 1686
 
4.9%
9 1608
 
4.7%
Other values (6) 2127
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23709
69.4%
Space Separator 9892
29.0%
Other Letter 540
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4539
19.1%
2 3313
14.0%
3 2629
11.1%
4 2338
9.9%
5 2234
9.4%
6 1952
8.2%
7 1823
7.7%
8 1686
 
7.1%
9 1608
 
6.8%
0 1587
 
6.7%
Other Letter
ValueCountFrequency (%)
호 108
20.0%
미 108
20.0%
만 108
20.0%
제 108
20.0%
거 108
20.0%
Space Separator
ValueCountFrequency (%)
9892
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33601
98.4%
Hangul 540
 
1.6%

Most frequent character per script

Common
ValueCountFrequency (%)
9892
29.4%
1 4539
13.5%
2 3313
 
9.9%
3 2629
 
7.8%
4 2338
 
7.0%
5 2234
 
6.6%
6 1952
 
5.8%
7 1823
 
5.4%
8 1686
 
5.0%
9 1608
 
4.8%
Hangul
ValueCountFrequency (%)
호 108
20.0%
미 108
20.0%
만 108
20.0%
제 108
20.0%
거 108
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33601
98.4%
Hangul 540
 
1.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9892
29.4%
1 4539
13.5%
2 3313
 
9.9%
3 2629
 
7.8%
4 2338
 
7.0%
5 2234
 
6.6%
6 1952
 
5.8%
7 1823
 
5.4%
8 1686
 
5.0%
9 1608
 
4.8%
Hangul
ValueCountFrequency (%)
호 108
20.0%
미 108
20.0%
만 108
20.0%
제 108
20.0%
거 108
20.0%

판매량
Text

MISSING 

Distinct9554
Distinct (%)96.6%
Missing108
Missing (%)1.1%
Memory size156.2 KiB
2023-12-12T13:57:59.463685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length6.2089567
Min length4

Characters and Unicode

Total characters61419
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9227 ?
Unique (%)93.3%

Sample

1st row133705
2nd row231714
3rd row22495
4th row49122
5th row66590
ValueCountFrequency (%)
24371 3
 
< 0.1%
21139 3
 
< 0.1%
19443 3
 
< 0.1%
4342 3
 
< 0.1%
75515 3
 
< 0.1%
24433 3
 
< 0.1%
6311 3
 
< 0.1%
16408 3
 
< 0.1%
3448 3
 
< 0.1%
15248 3
 
< 0.1%
Other values (9542) 9862
99.7%
2023-12-12T13:58:00.058691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9889
16.1%
1 7382
12.0%
2 6191
10.1%
3 5530
9.0%
4 5036
8.2%
5 4805
7.8%
6 4781
7.8%
8 4547
7.4%
7 4544
7.4%
9 4374
7.1%
Other values (3) 4340
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51524
83.9%
Space Separator 9889
 
16.1%
Open Punctuation 3
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7382
14.3%
2 6191
12.0%
3 5530
10.7%
4 5036
9.8%
5 4805
9.3%
6 4781
9.3%
8 4547
8.8%
7 4544
8.8%
9 4374
8.5%
0 4334
8.4%
Space Separator
ValueCountFrequency (%)
9889
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 61419
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9889
16.1%
1 7382
12.0%
2 6191
10.1%
3 5530
9.0%
4 5036
8.2%
5 4805
7.8%
6 4781
7.8%
8 4547
7.4%
7 4544
7.4%
9 4374
7.1%
Other values (3) 4340
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9889
16.1%
1 7382
12.0%
2 6191
10.1%
3 5530
9.0%
4 5036
8.2%
5 4805
7.8%
6 4781
7.8%
8 4547
7.4%
7 4544
7.4%
9 4374
7.1%
Other values (3) 4340
7.1%

판매수입
Text

MISSING 

Distinct9886
Distinct (%)99.9%
Missing108
Missing (%)1.1%
Memory size156.2 KiB
2023-12-12T13:58:00.549614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.1810554
Min length6

Characters and Unicode

Total characters80927
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9880 ?
Unique (%)99.9%

Sample

1st row5352994
2nd row44507048
3rd row1390721
4th row8545627
5th row7769508
ValueCountFrequency (%)
538272 2
 
< 0.1%
8251427 2
 
< 0.1%
316411 2
 
< 0.1%
468155 2
 
< 0.1%
3236125 2
 
< 0.1%
1127498 2
 
< 0.1%
185034 1
 
< 0.1%
1103764 1
 
< 0.1%
8373596 1
 
< 0.1%
303767 1
 
< 0.1%
Other values (9876) 9876
99.8%
2023-12-12T13:58:01.149339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9885
12.2%
1 9331
11.5%
2 7813
9.7%
3 7337
9.1%
4 7096
8.8%
5 6945
8.6%
6 6699
8.3%
8 6584
8.1%
7 6571
8.1%
0 6334
7.8%
Other values (3) 6332
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71028
87.8%
Space Separator 9885
 
12.2%
Open Punctuation 7
 
< 0.1%
Close Punctuation 7
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9331
13.1%
2 7813
11.0%
3 7337
10.3%
4 7096
10.0%
5 6945
9.8%
6 6699
9.4%
8 6584
9.3%
7 6571
9.3%
0 6334
8.9%
9 6318
8.9%
Space Separator
ValueCountFrequency (%)
9885
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80927
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9885
12.2%
1 9331
11.5%
2 7813
9.7%
3 7337
9.1%
4 7096
8.8%
5 6945
8.6%
6 6699
8.3%
8 6584
8.1%
7 6571
8.1%
0 6334
7.8%
Other values (3) 6332
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9885
12.2%
1 9331
11.5%
2 7813
9.7%
3 7337
9.1%
4 7096
8.8%
5 6945
8.6%
6 6699
8.3%
8 6584
8.1%
7 6571
8.1%
0 6334
7.8%
Other values (3) 6332
7.8%

Correlations

2023-12-12T13:58:01.276247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년월시도충전요금
년월1.0000.0000.000
시도0.0001.0000.000
충전요금0.0000.0001.000
2023-12-12T13:58:01.380364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도충전요금
시도1.0000.000
충전요금0.0001.000
2023-12-12T13:58:01.469899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도충전요금
시도1.0000.000
충전요금0.0001.000

Missing values

2023-12-12T13:57:55.594544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:57:55.778374image/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.
2023-12-12T13:57:55.923215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

년월시도시군구충전요금고객호수판매량판매수입
110612020-03대전광역시서구사업자용2031337055352994
174092023-02인천광역시남동구자가용26623171444507048
100802019-01대구광역시서구자가용72224951390721
128342022-08부산광역시동래구자가용65491228545627
172152022-04인천광역시부평구자가용108665907769508
114902019-02부산광역시동래구자가용286312349467
56142019-12경상남도창원시사업자용45471984032432521
217062019-07충청남도서산시사업자용47273881331247
132102019-02서울특별시노원구자가용3314184741627
101202019-04대구광역시달서구자가용210931652881198
년월시도시군구충전요금고객호수판매량판매수입
238342021-10충청북도영동군사업자용35287958086355
88282022-02경상북도영양군자가용2094461491926
592019-02강원도철원군자가용8136985713
83412021-03경상북도포항시사업자용54459750453924323
166242019-11인천광역시강화군사업자용188649324100
228422022-09충청남도보령시사업자용11915326038708588
8712021-01강원도삼척시자가용117343314171768
49502023-02경기도과천시사업자용7715349330587482
164212023-05울산광역시동구사업자용13911189518957481
242562023-05충청북도음성군자가용18910316115168333