Overview

Dataset statistics

Number of variables10
Number of observations161
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory88.8 B

Variable types

Categorical1
Text1
Numeric8

Dataset

Description한국환경공단_연도별 전기차 충전소 전력 충전량 자료입니다.시도, 시군구별 자료입니다.2016년~2023년까지의 자료입니다.
Author한국환경공단
URLhttps://www.data.go.kr/data/15127192/fileData.do

Alerts

2016년 충전량(kWh) is highly overall correlated with 2017년 충전량(kWh) and 7 other fieldsHigh correlation
2017년 충전량(kWh) is highly overall correlated with 2016년 충전량(kWh) and 7 other fieldsHigh correlation
2018년 충전량(kWh) is highly overall correlated with 2016년 충전량(kWh) and 7 other fieldsHigh correlation
2019년 충전량(kWh) is highly overall correlated with 2016년 충전량(kWh) and 7 other fieldsHigh correlation
2020년 충전량(kWh) is highly overall correlated with 2016년 충전량(kWh) and 7 other fieldsHigh correlation
2021년 충전량(kWh) is highly overall correlated with 2016년 충전량(kWh) and 7 other fieldsHigh correlation
2022년 충전량(kWh) is highly overall correlated with 2016년 충전량(kWh) and 7 other fieldsHigh correlation
2023년 충전량(kWh) is highly overall correlated with 2016년 충전량(kWh) and 7 other fieldsHigh correlation
지역 is highly overall correlated with 2016년 충전량(kWh) and 7 other fieldsHigh correlation
2019년 충전량(kWh) has unique valuesUnique
2020년 충전량(kWh) has unique valuesUnique
2021년 충전량(kWh) has unique valuesUnique
2022년 충전량(kWh) has unique valuesUnique
2023년 충전량(kWh) has unique valuesUnique
2016년 충전량(kWh) has 24 (14.9%) zerosZeros
2017년 충전량(kWh) has 4 (2.5%) zerosZeros
2018년 충전량(kWh) has 2 (1.2%) zerosZeros

Reproduction

Analysis started2024-03-30 07:44:19.750303
Analysis finished2024-03-30 07:44:41.898541
Duration22.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
경기도
31 
경상북도
23 
전라남도
22 
강원도
18 
경상남도
18 
Other values (12)
49 

Length

Max length7
Median length4
Mean length3.7763975
Min length3

Unique

Unique9 ?
Unique (%)5.6%

Sample

1st row강원도
2nd row강원도
3rd row강원도
4th row강원도
5th row강원도

Common Values

ValueCountFrequency (%)
경기도 31
19.3%
경상북도 23
14.3%
전라남도 22
13.7%
강원도 18
11.2%
경상남도 18
11.2%
충청남도 15
9.3%
전라북도 14
8.7%
충청북도 11
 
6.8%
대전광역시 1
 
0.6%
광주광역시 1
 
0.6%
Other values (7) 7
 
4.3%

Length

2024-03-30T07:44:42.132184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 31
19.3%
경상북도 23
14.3%
전라남도 22
13.7%
강원도 18
11.2%
경상남도 18
11.2%
충청남도 15
9.3%
전라북도 14
8.7%
충청북도 11
 
6.8%
부산광역시 1
 
0.6%
세종특별자치시 1
 
0.6%
Other values (7) 7
 
4.3%

시군구
Text

Distinct160
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-03-30T07:44:43.223993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.1552795
Min length3

Characters and Unicode

Total characters508
Distinct characters119
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

Unique159 ?
Unique (%)98.8%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
고성군 2
 
1.2%
강릉시 1
 
0.6%
여수시 1
 
0.6%
나주시 1
 
0.6%
담양군 1
 
0.6%
목포시 1
 
0.6%
무안군 1
 
0.6%
보성군 1
 
0.6%
순천시 1
 
0.6%
신안군 1
 
0.6%
Other values (150) 150
93.2%
2024-03-30T07:44:45.254459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 84
 
16.5%
군 80
 
15.7%
천 21
 
4.1%
주 20
 
3.9%
양 16
 
3.1%
산 15
 
3.0%
성 13
 
2.6%
광 11
 
2.2%
안 11
 
2.2%
영 9
 
1.8%
Other values (109) 228
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 508
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 84
 
16.5%
군 80
 
15.7%
천 21
 
4.1%
주 20
 
3.9%
양 16
 
3.1%
산 15
 
3.0%
성 13
 
2.6%
광 11
 
2.2%
안 11
 
2.2%
영 9
 
1.8%
Other values (109) 228
44.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 508
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 84
 
16.5%
군 80
 
15.7%
천 21
 
4.1%
주 20
 
3.9%
양 16
 
3.1%
산 15
 
3.0%
성 13
 
2.6%
광 11
 
2.2%
안 11
 
2.2%
영 9
 
1.8%
Other values (109) 228
44.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 508
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 84
 
16.5%
군 80
 
15.7%
천 21
 
4.1%
주 20
 
3.9%
양 16
 
3.1%
산 15
 
3.0%
성 13
 
2.6%
광 11
 
2.2%
안 11
 
2.2%
영 9
 
1.8%
Other values (109) 228
44.9%

2016년 충전량(kWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct134
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11669.807
Minimum0
Maximum909837
Zeros24
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-30T07:44:45.854088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1220
median1085
Q33994
95-th percentile25719
Maximum909837
Range909837
Interquartile range (IQR)3774

Descriptive statistics

Standard deviation75701.897
Coefficient of variation (CV)6.4869877
Kurtosis127.09877
Mean11669.807
Median Absolute Deviation (MAD)1085
Skewness10.960107
Sum1878839
Variance5.7307772 × 109
MonotonicityNot monotonic
2024-03-30T07:44:46.350577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24
 
14.9%
226 2
 
1.2%
2438 2
 
1.2%
996 2
 
1.2%
1010 2
 
1.2%
4728 1
 
0.6%
6216 1
 
0.6%
602 1
 
0.6%
4662 1
 
0.6%
2839 1
 
0.6%
Other values (124) 124
77.0%
ValueCountFrequency (%)
0 24
14.9%
40 1
 
0.6%
50 1
 
0.6%
53 1
 
0.6%
57 1
 
0.6%
66 1
 
0.6%
71 1
 
0.6%
72 1
 
0.6%
73 1
 
0.6%
83 1
 
0.6%
ValueCountFrequency (%)
909837 1
0.6%
310473 1
0.6%
60893 1
0.6%
51698 1
0.6%
45567 1
0.6%
32021 1
0.6%
29536 1
0.6%
27774 1
0.6%
25719 1
0.6%
15889 1
0.6%

2017년 충전량(kWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct158
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33535.913
Minimum0
Maximum2191204
Zeros4
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-30T07:44:46.961330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1011
Q13785
median9070
Q323470
95-th percentile62762
Maximum2191204
Range2191204
Interquartile range (IQR)19685

Descriptive statistics

Standard deviation176743.08
Coefficient of variation (CV)5.2702629
Kurtosis141.38548
Mean33535.913
Median Absolute Deviation (MAD)6118
Skewness11.631995
Sum5399282
Variance3.1238115 × 1010
MonotonicityNot monotonic
2024-03-30T07:44:47.685624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
2.5%
2565 1
 
0.6%
10928 1
 
0.6%
3785 1
 
0.6%
12492 1
 
0.6%
6593 1
 
0.6%
8313 1
 
0.6%
52387 1
 
0.6%
1035 1
 
0.6%
15035 1
 
0.6%
Other values (148) 148
91.9%
ValueCountFrequency (%)
0 4
2.5%
544 1
 
0.6%
741 1
 
0.6%
784 1
 
0.6%
966 1
 
0.6%
1011 1
 
0.6%
1035 1
 
0.6%
1102 1
 
0.6%
1497 1
 
0.6%
1703 1
 
0.6%
ValueCountFrequency (%)
2191204 1
0.6%
500464 1
0.6%
156495 1
0.6%
114734 1
0.6%
111957 1
0.6%
104250 1
0.6%
65413 1
0.6%
64201 1
0.6%
62762 1
0.6%
55682 1
0.6%

2018년 충전량(kWh)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct160
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102359.12
Minimum0
Maximum4147601
Zeros2
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-30T07:44:48.347302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6800
Q123491
median51019
Q384286
95-th percentile245266
Maximum4147601
Range4147601
Interquartile range (IQR)60795

Descriptive statistics

Standard deviation340047.01
Coefficient of variation (CV)3.3220977
Kurtosis127.492
Mean102359.12
Median Absolute Deviation (MAD)31636
Skewness10.847907
Sum16479819
Variance1.1563197 × 1011
MonotonicityNot monotonic
2024-03-30T07:44:49.397719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
1.2%
138373 1
 
0.6%
19383 1
 
0.6%
9975 1
 
0.6%
40174 1
 
0.6%
33786 1
 
0.6%
42032 1
 
0.6%
228969 1
 
0.6%
2447 1
 
0.6%
61464 1
 
0.6%
Other values (150) 150
93.2%
ValueCountFrequency (%)
0 2
1.2%
254 1
0.6%
2447 1
0.6%
3294 1
0.6%
6270 1
0.6%
6364 1
0.6%
6709 1
0.6%
6800 1
0.6%
7227 1
0.6%
7416 1
0.6%
ValueCountFrequency (%)
4147601 1
0.6%
1147611 1
0.6%
461277 1
0.6%
368120 1
0.6%
348898 1
0.6%
289296 1
0.6%
275057 1
0.6%
245954 1
0.6%
245266 1
0.6%
233777 1
0.6%

2019년 충전량(kWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean217395.34
Minimum53
Maximum5758754
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-30T07:44:50.117368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile20400
Q156423
median122483
Q3218703
95-th percentile544766
Maximum5758754
Range5758701
Interquartile range (IQR)162280

Descriptive statistics

Standard deviation496319.08
Coefficient of variation (CV)2.2830254
Kurtosis99.115375
Mean217395.34
Median Absolute Deviation (MAD)76553
Skewness9.1984371
Sum35000650
Variance2.4633263 × 1011
MonotonicityNot monotonic
2024-03-30T07:44:51.013352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220209 1
 
0.6%
191363 1
 
0.6%
75553 1
 
0.6%
20400 1
 
0.6%
32992 1
 
0.6%
116714 1
 
0.6%
94329 1
 
0.6%
477007 1
 
0.6%
7177 1
 
0.6%
180589 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
53 1
0.6%
7177 1
0.6%
7293 1
0.6%
9541 1
0.6%
10296 1
0.6%
12418 1
0.6%
18135 1
0.6%
19975 1
0.6%
20400 1
0.6%
20881 1
0.6%
ValueCountFrequency (%)
5758754 1
0.6%
2051618 1
0.6%
1124492 1
0.6%
827153 1
0.6%
788269 1
0.6%
759649 1
0.6%
755992 1
0.6%
623056 1
0.6%
544766 1
0.6%
523356 1
0.6%

2020년 충전량(kWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean297198.81
Minimum24
Maximum4816732
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-30T07:44:51.595353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile28949
Q186341
median156192
Q3302560
95-th percentile808804
Maximum4816732
Range4816708
Interquartile range (IQR)216219

Descriptive statistics

Standard deviation517893.18
Coefficient of variation (CV)1.7425816
Kurtosis43.905087
Mean297198.81
Median Absolute Deviation (MAD)98349
Skewness5.9609488
Sum47849008
Variance2.6821334 × 1011
MonotonicityNot monotonic
2024-03-30T07:44:52.189416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298971 1
 
0.6%
242801 1
 
0.6%
156192 1
 
0.6%
40925 1
 
0.6%
71808 1
 
0.6%
158884 1
 
0.6%
125316 1
 
0.6%
543855 1
 
0.6%
21019 1
 
0.6%
319086 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
24 1
0.6%
9771 1
0.6%
10944 1
0.6%
13044 1
0.6%
21019 1
0.6%
21841 1
0.6%
24649 1
0.6%
25484 1
0.6%
28949 1
0.6%
32457 1
0.6%
ValueCountFrequency (%)
4816732 1
0.6%
3354833 1
0.6%
2146127 1
0.6%
1445628 1
0.6%
1344742 1
0.6%
1110776 1
0.6%
1073232 1
0.6%
878671 1
0.6%
808804 1
0.6%
803011 1
0.6%

2021년 충전량(kWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean510456.1
Minimum0
Maximum5880806
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-30T07:44:53.212606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile59823
Q1155482
median287320
Q3558710
95-th percentile1426026
Maximum5880806
Range5880806
Interquartile range (IQR)403228

Descriptive statistics

Standard deviation718979.8
Coefficient of variation (CV)1.4085047
Kurtosis26.694153
Mean510456.1
Median Absolute Deviation (MAD)175258
Skewness4.5718829
Sum82183432
Variance5.1693196 × 1011
MonotonicityNot monotonic
2024-03-30T07:44:54.390219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700741 1
 
0.6%
501685 1
 
0.6%
231282 1
 
0.6%
155881 1
 
0.6%
117114 1
 
0.6%
276112 1
 
0.6%
227770 1
 
0.6%
645878 1
 
0.6%
55968 1
 
0.6%
562172 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
0 1
0.6%
26143 1
0.6%
34313 1
0.6%
37878 1
0.6%
41573 1
0.6%
42547 1
0.6%
53065 1
0.6%
55968 1
0.6%
59823 1
0.6%
66667 1
0.6%
ValueCountFrequency (%)
5880806 1
0.6%
4651415 1
0.6%
3141773 1
0.6%
2783648 1
0.6%
2352008 1
0.6%
2029734 1
0.6%
1835621 1
0.6%
1455031 1
0.6%
1426026 1
0.6%
1331305 1
0.6%

2022년 충전량(kWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1008451.2
Minimum0
Maximum11155807
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-30T07:44:55.052152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile116093
Q1344770
median609409
Q31153787
95-th percentile2881709
Maximum11155807
Range11155807
Interquartile range (IQR)809017

Descriptive statistics

Standard deviation1319662.5
Coefficient of variation (CV)1.3086032
Kurtosis25.185311
Mean1008451.2
Median Absolute Deviation (MAD)313903
Skewness4.2733133
Sum1.6236065 × 108
Variance1.7415091 × 1012
MonotonicityNot monotonic
2024-03-30T07:44:55.645382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1317851 1
 
0.6%
1271286 1
 
0.6%
396684 1
 
0.6%
281498 1
 
0.6%
317089 1
 
0.6%
515785 1
 
0.6%
343696 1
 
0.6%
1259755 1
 
0.6%
149275 1
 
0.6%
1153787 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
0 1
0.6%
48780 1
0.6%
75911 1
0.6%
89553 1
0.6%
97938 1
0.6%
103530 1
0.6%
113674 1
0.6%
115911 1
0.6%
116093 1
0.6%
149275 1
0.6%
ValueCountFrequency (%)
11155807 1
0.6%
7397799 1
0.6%
5279472 1
0.6%
4791800 1
0.6%
4261040 1
0.6%
4031018 1
0.6%
3846553 1
0.6%
3785988 1
0.6%
2881709 1
0.6%
2823972 1
0.6%

2023년 충전량(kWh)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1086719.7
Minimum0
Maximum9859964
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.5 KiB
2024-03-30T07:44:56.238375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile171879
Q1381864
median655978
Q31263028
95-th percentile3307927
Maximum9859964
Range9859964
Interquartile range (IQR)881164

Descriptive statistics

Standard deviation1317088.4
Coefficient of variation (CV)1.2119854
Kurtosis17.279835
Mean1086719.7
Median Absolute Deviation (MAD)331984
Skewness3.6380797
Sum1.7496187 × 108
Variance1.7347218 × 1012
MonotonicityNot monotonic
2024-03-30T07:44:56.932507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1174703 1
 
0.6%
1487080 1
 
0.6%
478993 1
 
0.6%
384883 1
 
0.6%
412261 1
 
0.6%
639874 1
 
0.6%
346839 1
 
0.6%
1269913 1
 
0.6%
137816 1
 
0.6%
1435517 1
 
0.6%
Other values (151) 151
93.8%
ValueCountFrequency (%)
0 1
0.6%
111138 1
0.6%
114448 1
0.6%
115274 1
0.6%
116962 1
0.6%
137816 1
0.6%
159517 1
0.6%
160486 1
0.6%
171879 1
0.6%
176810 1
0.6%
ValueCountFrequency (%)
9859964 1
0.6%
7374939 1
0.6%
7182011 1
0.6%
4609533 1
0.6%
4369861 1
0.6%
4118343 1
0.6%
3698907 1
0.6%
3677699 1
0.6%
3307927 1
0.6%
3023309 1
0.6%

Interactions

2024-03-30T07:44:38.335527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:20.793014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:23.111884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:25.392361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:27.963894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:30.560035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:33.253793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:36.418749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:38.618952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:21.083158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:23.367645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:25.627105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:28.211120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:30.960398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:33.540495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:36.678553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:38.873945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:21.331783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:23.642528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:25.966099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:28.457888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:31.399455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:33.863693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:36.918450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:39.125283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:21.599161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:23.963852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:26.295094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:28.711359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:31.617462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:34.190599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:37.159860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:39.385644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:21.858565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:24.234274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:26.669909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:29.065741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:31.876237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:34.587651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:37.466282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:39.767645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:22.233851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:24.479236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:26.954004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:29.375219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:32.125874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:34.936969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:37.682800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:40.172332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:22.556166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:24.730933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:27.270135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:29.724018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:32.536435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:35.635682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:37.896657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:40.599777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:22.839854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:25.089907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:27.711930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:30.098157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:32.920786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:36.025491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T07:44:38.094327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T07:44:57.213683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역2016년 충전량(kWh)2017년 충전량(kWh)2018년 충전량(kWh)2019년 충전량(kWh)2020년 충전량(kWh)2021년 충전량(kWh)2022년 충전량(kWh)2023년 충전량(kWh)
지역1.0001.0001.0001.0000.9770.9690.9650.9410.917
2016년 충전량(kWh)1.0001.0001.0001.0001.0001.0001.0000.8720.872
2017년 충전량(kWh)1.0001.0001.0001.0001.0001.0001.0000.8720.872
2018년 충전량(kWh)1.0001.0001.0001.0000.9871.0001.0000.8710.848
2019년 충전량(kWh)0.9771.0001.0000.9871.0000.9651.0000.8890.901
2020년 충전량(kWh)0.9691.0001.0001.0000.9651.0000.9820.9890.971
2021년 충전량(kWh)0.9651.0001.0001.0001.0000.9821.0000.9500.906
2022년 충전량(kWh)0.9410.8720.8720.8710.8890.9890.9501.0000.975
2023년 충전량(kWh)0.9170.8720.8720.8480.9010.9710.9060.9751.000
2024-03-30T07:44:57.661016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2016년 충전량(kWh)2017년 충전량(kWh)2018년 충전량(kWh)2019년 충전량(kWh)2020년 충전량(kWh)2021년 충전량(kWh)2022년 충전량(kWh)2023년 충전량(kWh)지역
2016년 충전량(kWh)1.0000.8320.7030.6650.6820.6730.6480.6350.955
2017년 충전량(kWh)0.8321.0000.9020.8660.8630.8330.8080.7980.955
2018년 충전량(kWh)0.7030.9021.0000.9560.9270.8970.8710.8570.958
2019년 충전량(kWh)0.6650.8660.9561.0000.9760.9450.9180.8930.899
2020년 충전량(kWh)0.6820.8630.9270.9761.0000.9750.9430.9180.857
2021년 충전량(kWh)0.6730.8330.8970.9450.9751.0000.9780.9440.823
2022년 충전량(kWh)0.6480.8080.8710.9180.9430.9781.0000.9760.773
2023년 충전량(kWh)0.6350.7980.8570.8930.9180.9440.9761.0000.712
지역0.9550.9550.9580.8990.8570.8230.7730.7121.000

Missing values

2024-03-30T07:44:41.080142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T07:44:41.720613image/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

지역시군구2016년 충전량(kWh)2017년 충전량(kWh)2018년 충전량(kWh)2019년 충전량(kWh)2020년 충전량(kWh)2021년 충전량(kWh)2022년 충전량(kWh)2023년 충전량(kWh)
0강원도강릉시119843162413837322020929897170074113178511174703
1강원도고성군096672273330147163129778295506285498
2강원도동해시6636732410759060111468250876576712547944
3강원도삼척시4021096757784116026130089280046490207546132
4강원도속초시01314257362111946142169287320652896583645
5강원도양구군02790178713933452068104085203568307397
6강원도양양군22651641515662412108057197516510575584028
7강원도영월군6484483148183272637051132888308022347150
8강원도원주시35632988515953543993658142199093222047492277148
9강원도인제군437906463143210240261208353004639627585512
지역시군구2016년 충전량(kWh)2017년 충전량(kWh)2018년 충전량(kWh)2019년 충전량(kWh)2020년 충전량(kWh)2021년 충전량(kWh)2022년 충전량(kWh)2023년 충전량(kWh)
151충청북도단양군4681076666076123075141801268519615404905479
152충청북도보은군305918457141109364119188191751581557941435
153충청북도영동군938627136052114584150009286849650020732924
154충청북도옥천군24382429078041181853274328530579885023881227
155충청북도음성군1468119568993520205327109355168812136661670136
156충청북도제천시99650592992676926138638448584691862627625
157충청북도증평군1703533627021137254846666797938114448
158충청북도진천군83838522481561859102806216687457459452941
159충청북도청주시7745414282892967882691073232183562137859883698907
160충청북도충주시50624701614651238246447405786395821551083023309