Overview

Dataset statistics

Number of variables8
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory73.3 B

Variable types

Categorical2
Numeric6

Dataset

Description(주)한국가스기술공사의 수소충전소별 경향성 분석자료 월별충전댓수, 월간충전량, 일간최대충전대수, 일간 최대충전량 분석자료를 제공합니다.
URLhttps://www.data.go.kr/data/15103340/fileData.do

Alerts

1월 is highly overall correlated with 2월 and 4 other fieldsHigh correlation
2월 is highly overall correlated with 1월 and 4 other fieldsHigh correlation
3월 is highly overall correlated with 1월 and 5 other fieldsHigh correlation
4월 is highly overall correlated with 1월 and 5 other fieldsHigh correlation
5월 is highly overall correlated with 1월 and 5 other fieldsHigh correlation
6월 is highly overall correlated with 1월 and 5 other fieldsHigh correlation
구분 is highly overall correlated with 3월 and 3 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 16:53:59.931801
Analysis finished2023-12-12 16:54:04.658064
Duration4.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

충전소명
Categorical

Distinct10
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
서산
4 
안성
4 
화성
4 
춘천
4 
평창
4 
Other values (5)
20 

Length

Max length6
Median length2
Mean length2.4
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서산
2nd row서산
3rd row서산
4th row서산
5th row안성

Common Values

ValueCountFrequency (%)
서산 4
10.0%
안성 4
10.0%
화성 4
10.0%
춘천 4
10.0%
평창 4
10.0%
천안 4
10.0%
부안 4
10.0%
전주 4
10.0%
서울(강서) 4
10.0%
충주 4
10.0%

Length

2023-12-13T01:54:04.765111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:54:04.908127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서산 4
10.0%
안성 4
10.0%
화성 4
10.0%
춘천 4
10.0%
평창 4
10.0%
천안 4
10.0%
부안 4
10.0%
전주 4
10.0%
서울(강서 4
10.0%
충주 4
10.0%

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
월별충전(댓수)
10 
일간최대충전(댓수)
10 
월간 충전량(kg)
10 
일간 최대충전량(kg)
10 

Length

Max length12
Median length11
Mean length10
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row월별충전(댓수)
2nd row일간최대충전(댓수)
3rd row월간 충전량(kg)
4th row일간 최대충전량(kg)
5th row월별충전(댓수)

Common Values

ValueCountFrequency (%)
월별충전(댓수) 10
25.0%
일간최대충전(댓수) 10
25.0%
월간 충전량(kg) 10
25.0%
일간 최대충전량(kg) 10
25.0%

Length

2023-12-13T01:54:05.095688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:54:05.235609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
월별충전(댓수 10
16.7%
일간최대충전(댓수 10
16.7%
월간 10
16.7%
충전량(kg 10
16.7%
일간 10
16.7%
최대충전량(kg 10
16.7%

1월
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2001.85
Minimum22
Maximum11148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:54:05.370180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile42.95
Q1138.25
median545
Q32758.75
95-th percentile8134.25
Maximum11148
Range11126
Interquartile range (IQR)2620.5

Descriptive statistics

Standard deviation2928.1938
Coefficient of variation (CV)1.4627438
Kurtosis2.6446986
Mean2001.85
Median Absolute Deviation (MAD)487.5
Skewness1.8293844
Sum80074
Variance8574318.7
MonotonicityNot monotonic
2023-12-13T01:54:05.528293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
61 2
 
5.0%
1307 1
 
2.5%
82 1
 
2.5%
6900 1
 
2.5%
286 1
 
2.5%
1270 1
 
2.5%
4875 1
 
2.5%
205 1
 
2.5%
2556 1
 
2.5%
112 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
22 1
2.5%
42 1
2.5%
43 1
2.5%
54 1
2.5%
61 2
5.0%
79 1
2.5%
82 1
2.5%
111 1
2.5%
112 1
2.5%
147 1
2.5%
ValueCountFrequency (%)
11148 1
2.5%
10305 1
2.5%
8020 1
2.5%
6900 1
2.5%
6111 1
2.5%
5907 1
2.5%
4875 1
2.5%
3909 1
2.5%
2965 1
2.5%
2776 1
2.5%

2월
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1726.75
Minimum22
Maximum9526
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:54:05.704978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile37.85
Q1133
median476
Q32214.5
95-th percentile7348.35
Maximum9526
Range9504
Interquartile range (IQR)2081.5

Descriptive statistics

Standard deviation2515.4315
Coefficient of variation (CV)1.4567433
Kurtosis2.3937104
Mean1726.75
Median Absolute Deviation (MAD)428.5
Skewness1.7992648
Sum69070
Variance6327395.6
MonotonicityNot monotonic
2023-12-13T01:54:05.864549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
53 2
 
5.0%
1147 1
 
2.5%
320 1
 
2.5%
947 1
 
2.5%
47 1
 
2.5%
3679 1
 
2.5%
187 1
 
2.5%
2030 1
 
2.5%
88 1
 
2.5%
9526 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
22 1
2.5%
35 1
2.5%
38 1
2.5%
47 1
2.5%
52 1
2.5%
53 2
5.0%
83 1
2.5%
88 1
2.5%
97 1
2.5%
145 1
2.5%
ValueCountFrequency (%)
9526 1
2.5%
8248 1
2.5%
7301 1
2.5%
6793 1
2.5%
5306 1
2.5%
4861 1
2.5%
3679 1
2.5%
3364 1
2.5%
2895 1
2.5%
2381 1
2.5%

3월
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1765.7
Minimum25
Maximum8681
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:54:06.036461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile41.75
Q1136
median498.5
Q32266.5
95-th percentile7522.3
Maximum8681
Range8656
Interquartile range (IQR)2130.5

Descriptive statistics

Standard deviation2493.575
Coefficient of variation (CV)1.4122303
Kurtosis1.5123123
Mean1765.7
Median Absolute Deviation (MAD)448
Skewness1.6183106
Sum70628
Variance6217916.5
MonotonicityNot monotonic
2023-12-13T01:54:06.195046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
59 2
 
5.0%
1342 1
 
2.5%
5983 1
 
2.5%
274 1
 
2.5%
942 1
 
2.5%
42 1
 
2.5%
3711 1
 
2.5%
170 1
 
2.5%
1752 1
 
2.5%
88 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
25 1
2.5%
37 1
2.5%
42 1
2.5%
49 1
2.5%
59 2
5.0%
62 1
2.5%
63 1
2.5%
88 1
2.5%
91 1
2.5%
151 1
2.5%
ValueCountFrequency (%)
8681 1
2.5%
8231 1
2.5%
7485 1
2.5%
6018 1
2.5%
5983 1
2.5%
5595 1
2.5%
3885 1
2.5%
3711 1
2.5%
3549 1
2.5%
2544 1
2.5%

4월
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1640.575
Minimum25
Maximum7867
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:54:06.331076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile37.35
Q199
median351.5
Q31657.75
95-th percentile6770.2
Maximum7867
Range7842
Interquartile range (IQR)1558.75

Descriptive statistics

Standard deviation2358.0075
Coefficient of variation (CV)1.4373055
Kurtosis1.2755388
Mean1640.575
Median Absolute Deviation (MAD)320
Skewness1.6066103
Sum65623
Variance5560199.5
MonotonicityNot monotonic
2023-12-13T01:54:06.482149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
68 2
 
5.0%
25 2
 
5.0%
60 1
 
2.5%
5786 1
 
2.5%
252 1
 
2.5%
978 1
 
2.5%
47 1
 
2.5%
3877 1
 
2.5%
191 1
 
2.5%
1479 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
25 2
5.0%
38 1
2.5%
47 1
2.5%
48 1
2.5%
60 1
2.5%
66 1
2.5%
68 2
5.0%
90 1
2.5%
102 1
2.5%
138 1
2.5%
ValueCountFrequency (%)
7867 1
2.5%
7591 1
2.5%
6727 1
2.5%
6154 1
2.5%
6094 1
2.5%
5786 1
2.5%
3877 1
2.5%
3370 1
2.5%
2770 1
2.5%
2104 1
2.5%

5월
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1711.125
Minimum19
Maximum8637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:54:06.646104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile35.35
Q194.75
median353
Q31668.25
95-th percentile7010.6
Maximum8637
Range8618
Interquartile range (IQR)1573.5

Descriptive statistics

Standard deviation2507.1268
Coefficient of variation (CV)1.4651921
Kurtosis1.3733537
Mean1711.125
Median Absolute Deviation (MAD)323.5
Skewness1.6403021
Sum68445
Variance6285684.8
MonotonicityNot monotonic
2023-12-13T01:54:06.800278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
74 2
 
5.0%
68 1
 
2.5%
6139 1
 
2.5%
285 1
 
2.5%
1024 1
 
2.5%
61 1
 
2.5%
3855 1
 
2.5%
205 1
 
2.5%
1456 1
 
2.5%
55 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
19 1
2.5%
23 1
2.5%
36 1
2.5%
49 1
2.5%
55 1
2.5%
61 1
2.5%
68 1
2.5%
74 2
5.0%
76 1
2.5%
101 1
2.5%
ValueCountFrequency (%)
8637 1
2.5%
7478 1
2.5%
6986 1
2.5%
6927 1
2.5%
6734 1
2.5%
6139 1
2.5%
3855 1
2.5%
3210 1
2.5%
3005 1
2.5%
2209 1
2.5%

6월
Real number (ℝ)

HIGH CORRELATION 

Distinct39
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1658.725
Minimum18
Maximum8231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:54:06.941445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile43.9
Q195.5
median350.5
Q31594.5
95-th percentile7257.35
Maximum8231
Range8213
Interquartile range (IQR)1499

Descriptive statistics

Standard deviation2428.5198
Coefficient of variation (CV)1.4640883
Kurtosis1.4746089
Mean1658.725
Median Absolute Deviation (MAD)316.5
Skewness1.6576014
Sum66349
Variance5897708.5
MonotonicityNot monotonic
2023-12-13T01:54:07.069494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
298 2
 
5.0%
1387 1
 
2.5%
58 1
 
2.5%
4789 1
 
2.5%
261 1
 
2.5%
1033 1
 
2.5%
45 1
 
2.5%
3865 1
 
2.5%
173 1
 
2.5%
1415 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
18 1
2.5%
23 1
2.5%
45 1
2.5%
46 1
2.5%
58 1
2.5%
60 1
2.5%
61 1
2.5%
64 1
2.5%
69 1
2.5%
70 1
2.5%
ValueCountFrequency (%)
8231 1
2.5%
7549 1
2.5%
7242 1
2.5%
6746 1
2.5%
6455 1
2.5%
4789 1
2.5%
3865 1
2.5%
3487 1
2.5%
3140 1
2.5%
2133 1
2.5%

Interactions

2023-12-13T01:54:03.909749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:00.250060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:00.811887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:01.887397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:02.624572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.369624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.984647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:00.329980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:00.922187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:01.992879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:02.770444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.459568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:04.063053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:00.415859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:01.393860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:02.116673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:02.895034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.548054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:04.141519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:00.511970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:01.498246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:02.235347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.039077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.631740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:04.226213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:00.630720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:01.647739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:02.374953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.167593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.734635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:04.299844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:00.733462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:01.765368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:02.504932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.266410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:54:03.824477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:54:07.173058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소명구분1월2월3월4월5월6월
충전소명1.0000.0000.0000.0000.0000.0000.0000.000
구분0.0001.0000.6690.8400.7870.9040.8830.747
1월0.0000.6691.0000.9390.9340.9330.9030.979
2월0.0000.8400.9391.0000.9220.9660.9630.893
3월0.0000.7870.9340.9221.0000.9680.9000.932
4월0.0000.9040.9330.9660.9681.0000.9840.979
5월0.0000.8830.9030.9630.9000.9841.0000.949
6월0.0000.7470.9790.8930.9320.9790.9491.000
2023-12-13T01:54:07.569672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
충전소명구분
충전소명1.0000.000
구분0.0001.000
2023-12-13T01:54:07.675768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월2월3월4월5월6월충전소명구분
1월1.0000.9970.9910.9950.9820.9810.0000.459
2월0.9971.0000.9940.9950.9830.9820.0000.479
3월0.9910.9941.0000.9920.9790.9790.0000.649
4월0.9950.9950.9921.0000.9880.9870.0000.570
5월0.9820.9830.9790.9881.0000.9930.0000.536
6월0.9810.9820.9790.9870.9931.0000.0000.545
충전소명0.0000.0000.0000.0000.0000.0001.0000.000
구분0.4590.4790.6490.5700.5360.5450.0001.000

Missing values

2023-12-13T01:54:04.411482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:54:04.585926image/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

충전소명구분1월2월3월4월5월6월
0서산월별충전(댓수)130711471342137714881387
1서산일간최대충전(댓수)825359686869
2서산월간 충전량(kg)611153066018615467346455
3서산일간 최대충전량(kg)336247268284293306
4안성월별충전(댓수)788747945726759813
5안성일간최대충전(댓수)423863383646
6안성월간 충전량(kg)296528953549277030053140
7안성일간 최대충전량(kg)147157224138151172
8화성월별충전(댓수)9619041002824792859
9화성일간최대충전(댓수)545349484961
충전소명구분1월2월3월4월5월6월
30전주월간 충전량(kg)1114895268681759174787549
31전주일간 최대충전량(kg)493397394334297298
32서울(강서)월별충전(댓수)597578648631632627
33서울(강서)일간최대충전(댓수)222225252323
34서울(강서)월간 충전량(kg)802073017485672769277242
35서울(강서)일간 최대충전량(kg)341286278249255300
36충주월별충전(댓수)11169431103124813981314
37충주일간최대충전(댓수)615259667464
38충주월간 충전량(kg)590748615595609469866746
39충주일간 최대충전량(kg)289238263313309298