Overview

Dataset statistics

Number of variables8
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory75.3 B

Variable types

DateTime1
Numeric7

Dataset

DescriptionHEPI, EIA 등의 자료를 가공하여 주요국(미국, 영국, 독일, 프랑스 등)의 소매 전기요금 가격지수를 생성한 것으로, 각국의 소매 전기요금의 등락 정도를 교차 비교할 수 있다. 2020년 6월 가격을 100으로 설정하여 비교한 것이다.
URLhttps://www.data.go.kr/data/15117850/fileData.do

Alerts

미국 is highly overall correlated with 유럽연합27개국 및 영국 and 4 other fieldsHigh correlation
유럽연합27개국 및 영국 is highly overall correlated with 미국 and 5 other fieldsHigh correlation
유럽연합27개국 is highly overall correlated with 미국 and 5 other fieldsHigh correlation
독일 is highly overall correlated with 유럽연합27개국 및 영국 and 4 other fieldsHigh correlation
스페인 is highly overall correlated with 미국 and 5 other fieldsHigh correlation
프랑스 is highly overall correlated with 미국 and 5 other fieldsHigh correlation
영국 is highly overall correlated with 미국 and 5 other fieldsHigh correlation
연월 has unique valuesUnique
미국 has 2 (6.5%) zerosZeros

Reproduction

Analysis started2023-12-12 17:37:49.816053
Analysis finished2023-12-12 17:37:56.398595
Duration6.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연월
Date

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
Minimum2020-06-01 00:00:00
Maximum2022-12-01 00:00:00
2023-12-13T02:37:56.482886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:56.670443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

미국
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.451613
Minimum0
Maximum123
Zeros2
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T02:37:56.857589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.5
Q1100
median105
Q3108
95-th percentile121.5
Maximum123
Range123
Interquartile range (IQR)8

Descriptive statistics

Standard deviation27.491621
Coefficient of variation (CV)0.27643213
Kurtosis10.694469
Mean99.451613
Median Absolute Deviation (MAD)5
Skewness-3.2869684
Sum3083
Variance755.78925
MonotonicityNot monotonic
2023-12-13T02:37:57.042154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
105 5
16.1%
100 4
12.9%
104 3
 
9.7%
0 2
 
6.5%
116 2
 
6.5%
106 2
 
6.5%
98 1
 
3.2%
95 1
 
3.2%
97 1
 
3.2%
107 1
 
3.2%
Other values (9) 9
29.0%
ValueCountFrequency (%)
0 2
 
6.5%
95 1
 
3.2%
97 1
 
3.2%
98 1
 
3.2%
100 4
12.9%
101 1
 
3.2%
102 1
 
3.2%
103 1
 
3.2%
104 3
9.7%
105 5
16.1%
ValueCountFrequency (%)
123 1
 
3.2%
122 1
 
3.2%
121 1
 
3.2%
116 2
 
6.5%
113 1
 
3.2%
111 1
 
3.2%
109 1
 
3.2%
107 1
 
3.2%
106 2
 
6.5%
105 5
16.1%

유럽연합27개국 및 영국
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.96774
Minimum99
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T02:37:57.203771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile100
Q1104
median112
Q3149
95-th percentile180.5
Maximum188
Range89
Interquartile range (IQR)45

Descriptive statistics

Standard deviation28.914222
Coefficient of variation (CV)0.22772889
Kurtosis-0.70222097
Mean126.96774
Median Absolute Deviation (MAD)12
Skewness0.81640359
Sum3936
Variance836.03226
MonotonicityNot monotonic
2023-12-13T02:37:57.386014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
104 4
 
12.9%
100 3
 
9.7%
101 3
 
9.7%
149 2
 
6.5%
140 2
 
6.5%
132 1
 
3.2%
171 1
 
3.2%
177 1
 
3.2%
188 1
 
3.2%
184 1
 
3.2%
Other values (12) 12
38.7%
ValueCountFrequency (%)
99 1
 
3.2%
100 3
9.7%
101 3
9.7%
104 4
12.9%
105 1
 
3.2%
106 1
 
3.2%
108 1
 
3.2%
110 1
 
3.2%
112 1
 
3.2%
118 1
 
3.2%
ValueCountFrequency (%)
188 1
3.2%
184 1
3.2%
177 1
3.2%
171 1
3.2%
170 1
3.2%
158 1
3.2%
150 1
3.2%
149 2
6.5%
140 2
6.5%
132 1
3.2%

유럽연합27개국
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.41935
Minimum99
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T02:37:57.577067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile99.5
Q1104
median113
Q3151
95-th percentile188
Maximum197
Range98
Interquartile range (IQR)47

Descriptive statistics

Standard deviation31.011583
Coefficient of variation (CV)0.24148683
Kurtosis-0.42233959
Mean128.41935
Median Absolute Deviation (MAD)13
Skewness0.91260303
Sum3981
Variance961.71828
MonotonicityNot monotonic
2023-12-13T02:37:57.744878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
101 3
 
9.7%
105 3
 
9.7%
151 3
 
9.7%
100 2
 
6.5%
104 2
 
6.5%
99 2
 
6.5%
140 1
 
3.2%
177 1
 
3.2%
183 1
 
3.2%
197 1
 
3.2%
Other values (12) 12
38.7%
ValueCountFrequency (%)
99 2
6.5%
100 2
6.5%
101 3
9.7%
104 2
6.5%
105 3
9.7%
106 1
 
3.2%
109 1
 
3.2%
111 1
 
3.2%
113 1
 
3.2%
119 1
 
3.2%
ValueCountFrequency (%)
197 1
 
3.2%
193 1
 
3.2%
183 1
 
3.2%
177 1
 
3.2%
172 1
 
3.2%
159 1
 
3.2%
151 3
9.7%
141 1
 
3.2%
140 1
 
3.2%
132 1
 
3.2%

독일
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.83871
Minimum99
Maximum198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T02:37:57.894783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile99
Q1102
median103
Q3129
95-th percentile181.5
Maximum198
Range99
Interquartile range (IQR)27

Descriptive statistics

Standard deviation27.905909
Coefficient of variation (CV)0.23286223
Kurtosis2.0050033
Mean119.83871
Median Absolute Deviation (MAD)4
Skewness1.6391249
Sum3715
Variance778.73978
MonotonicityNot monotonic
2023-12-13T02:37:58.049276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
102 5
16.1%
103 5
16.1%
99 4
12.9%
100 3
9.7%
123 2
 
6.5%
122 1
 
3.2%
159 1
 
3.2%
198 1
 
3.2%
194 1
 
3.2%
169 1
 
3.2%
Other values (7) 7
22.6%
ValueCountFrequency (%)
99 4
12.9%
100 3
9.7%
102 5
16.1%
103 5
16.1%
112 1
 
3.2%
114 1
 
3.2%
122 1
 
3.2%
123 2
 
6.5%
128 1
 
3.2%
130 1
 
3.2%
ValueCountFrequency (%)
198 1
3.2%
194 1
3.2%
169 1
3.2%
159 1
3.2%
154 1
3.2%
137 1
3.2%
131 1
3.2%
130 1
3.2%
128 1
3.2%
123 2
6.5%

스페인
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.3871
Minimum100
Maximum197
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T02:37:58.231365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile102.5
Q1109
median125
Q3158
95-th percentile173
Maximum197
Range97
Interquartile range (IQR)49

Descriptive statistics

Standard deviation27.813279
Coefficient of variation (CV)0.20696391
Kurtosis-1.0321981
Mean134.3871
Median Absolute Deviation (MAD)22
Skewness0.41326208
Sum4166
Variance773.57849
MonotonicityNot monotonic
2023-12-13T02:37:58.361715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
103 3
 
9.7%
104 2
 
6.5%
115 2
 
6.5%
167 2
 
6.5%
100 1
 
3.2%
164 1
 
3.2%
149 1
 
3.2%
125 1
 
3.2%
152 1
 
3.2%
168 1
 
3.2%
Other values (16) 16
51.6%
ValueCountFrequency (%)
100 1
 
3.2%
102 1
 
3.2%
103 3
9.7%
104 2
6.5%
106 1
 
3.2%
112 1
 
3.2%
115 2
6.5%
116 1
 
3.2%
118 1
 
3.2%
121 1
 
3.2%
ValueCountFrequency (%)
197 1
3.2%
176 1
3.2%
170 1
3.2%
168 1
3.2%
167 2
6.5%
164 1
3.2%
160 1
3.2%
156 1
3.2%
152 1
3.2%
150 1
3.2%

프랑스
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.67742
Minimum100
Maximum146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T02:37:58.500779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100.5
Q1102.5
median104
Q3117.5
95-th percentile132.5
Maximum146
Range46
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.931435
Coefficient of variation (CV)0.1078037
Kurtosis1.2530116
Mean110.67742
Median Absolute Deviation (MAD)4
Skewness1.3503559
Sum3431
Variance142.35914
MonotonicityNot monotonic
2023-12-13T02:37:58.637425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
103 6
19.4%
101 5
16.1%
108 3
 
9.7%
100 2
 
6.5%
104 2
 
6.5%
122 1
 
3.2%
129 1
 
3.2%
146 1
 
3.2%
131 1
 
3.2%
134 1
 
3.2%
Other values (8) 8
25.8%
ValueCountFrequency (%)
100 2
 
6.5%
101 5
16.1%
102 1
 
3.2%
103 6
19.4%
104 2
 
6.5%
105 1
 
3.2%
108 3
9.7%
115 1
 
3.2%
116 1
 
3.2%
117 1
 
3.2%
ValueCountFrequency (%)
146 1
3.2%
134 1
3.2%
131 1
3.2%
129 1
3.2%
122 1
3.2%
121 1
3.2%
120 1
3.2%
118 1
3.2%
117 1
3.2%
116 1
3.2%

영국
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.25806
Minimum99
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2023-12-13T02:37:58.770322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile99.5
Q1106
median125
Q3211.5
95-th percentile263
Maximum289
Range190
Interquartile range (IQR)105.5

Descriptive statistics

Standard deviation59.660689
Coefficient of variation (CV)0.37461644
Kurtosis-0.81815099
Mean159.25806
Median Absolute Deviation (MAD)26
Skewness0.6374887
Sum4937
Variance3559.3978
MonotonicityNot monotonic
2023-12-13T02:37:58.928995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
100 4
 
12.9%
178 2
 
6.5%
99 2
 
6.5%
216 1
 
3.2%
211 1
 
3.2%
191 1
 
3.2%
289 1
 
3.2%
284 1
 
3.2%
228 1
 
3.2%
224 1
 
3.2%
Other values (16) 16
51.6%
ValueCountFrequency (%)
99 2
6.5%
100 4
12.9%
101 1
 
3.2%
104 1
 
3.2%
108 1
 
3.2%
110 1
 
3.2%
112 1
 
3.2%
117 1
 
3.2%
118 1
 
3.2%
120 1
 
3.2%
ValueCountFrequency (%)
289 1
3.2%
284 1
3.2%
242 1
3.2%
228 1
3.2%
224 1
3.2%
219 1
3.2%
216 1
3.2%
212 1
3.2%
211 1
3.2%
199 1
3.2%

Interactions

2023-12-13T02:37:55.023100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:50.075391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:50.848998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.481264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:52.279668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:53.101763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:54.082113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:55.132322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:50.181882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:50.958558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.568639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:52.418542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:53.300278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:54.262622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:55.241642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:50.290045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.048949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.671485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:52.545648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:53.422503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:54.403632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:55.344589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:50.410242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.143783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.810404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:52.654988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:53.533159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:54.519822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:55.456632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:50.527383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.228281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.947970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:52.762244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:53.677920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:54.638362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:55.549127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:50.621273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.313399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:52.056796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:52.861626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:53.830886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:54.746752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:55.665289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:50.712491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:51.391844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:52.173833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:52.959224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:53.952874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:37:54.884600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:37:59.045257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연월미국유럽연합27개국 및 영국유럽연합27개국독일스페인프랑스영국
연월1.0001.0001.0001.0001.0001.0001.0001.000
미국1.0001.0000.8890.8230.9040.4610.7510.752
유럽연합27개국 및 영국1.0000.8891.0000.9960.8340.7970.8550.911
유럽연합27개국1.0000.8230.9961.0000.8680.7690.9040.911
독일1.0000.9040.8340.8681.0000.5640.8970.849
스페인1.0000.4610.7970.7690.5641.0000.7190.829
프랑스1.0000.7510.8550.9040.8970.7191.0000.766
영국1.0000.7520.9110.9110.8490.8290.7661.000
2023-12-13T02:37:59.173194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미국유럽연합27개국 및 영국유럽연합27개국독일스페인프랑스영국
미국1.0000.5880.5880.4610.7130.5570.617
유럽연합27개국 및 영국0.5881.0000.9970.9430.8790.9860.911
유럽연합27개국0.5880.9971.0000.9450.8770.9840.911
독일0.4610.9430.9451.0000.8140.9430.861
스페인0.7130.8790.8770.8141.0000.8640.921
프랑스0.5570.9860.9840.9430.8641.0000.903
영국0.6170.9110.9110.8610.9210.9031.000

Missing values

2023-12-13T02:37:55.837934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:37:56.345885image/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

연월미국유럽연합27개국 및 영국유럽연합27개국독일스페인프랑스영국
02020-06-01100100100100100100100
12020-07-01100999999102100100
22020-08-011001009910010310199
32020-09-0110210010010010410199
42020-10-0110310110199103101100
52020-11-0110110110199104101100
62020-12-019710110199106101101
72021-01-0195104104102118102104
82021-02-0198104104103103103108
92021-03-01100104105103112103110
연월미국유럽연합27개국 및 영국유럽연합27개국독일스페인프랑스영국
212022-03-01109140140128197117242
222022-04-01111149151130176118219
232022-05-01113149151131167115224
242022-06-01116150151122170120228
252022-07-01116158159123156122284
262022-08-01121170172137167121289
272022-09-01123184193169168134178
282022-10-01122188197194152131191
292022-11-010177183198125146211
302022-12-010171177159149129216