Overview

Dataset statistics

Number of variables5
Number of observations84
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory44.6 B

Variable types

Categorical2
Numeric3

Dataset

Description인천광역시 관내 석유류에 대한 인천광역시 관내 석유류(휘발유, 등유, 경유 등)의 월별 소비량을 제공하는 데이터입니다.(단위 : ㎘)
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15055170&srcSe=7661IVAWM27C61E190

Alerts

2017년 is highly overall correlated with 2018년 and 2 other fieldsHigh correlation
2018년 is highly overall correlated with 2017년 and 2 other fieldsHigh correlation
2019년 is highly overall correlated with 2017년 and 2 other fieldsHigh correlation
석유류별 is highly overall correlated with 2017년 and 2 other fieldsHigh correlation
2018년 has unique valuesUnique

Reproduction

Analysis started2024-01-28 17:36:28.003216
Analysis finished2024-01-28 17:36:29.071676
Duration1.07 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

석유류별
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size804.0 B
휘발유
12 
등유
12 
경유
12 
중유
12 
벙커C유
12 
Other values (2)
24 

Length

Max length4
Median length2
Mean length2.5714286
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
휘발유 12
14.3%
등유 12
14.3%
경유 12
14.3%
중유 12
14.3%
벙커C유 12
14.3%
LPG 12
14.3%
기타 12
14.3%

Length

2024-01-29T02:36:29.164248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T02:36:29.296127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
휘발유 12
14.3%
등유 12
14.3%
경유 12
14.3%
중유 12
14.3%
벙커c유 12
14.3%
lpg 12
14.3%
기타 12
14.3%

월별
Categorical

Distinct12
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size804.0 B
01월
7 
02월
7 
03월
7 
04월
7 
05월
7 
Other values (7)
49 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01월
2nd row02월
3rd row03월
4th row04월
5th row05월

Common Values

ValueCountFrequency (%)
01월 7
8.3%
02월 7
8.3%
03월 7
8.3%
04월 7
8.3%
05월 7
8.3%
06월 7
8.3%
07월 7
8.3%
08월 7
8.3%
09월 7
8.3%
10월 7
8.3%
Other values (2) 14
16.7%

Length

2024-01-29T02:36:29.411150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01월 7
8.3%
02월 7
8.3%
03월 7
8.3%
04월 7
8.3%
05월 7
8.3%
06월 7
8.3%
07월 7
8.3%
08월 7
8.3%
09월 7
8.3%
10월 7
8.3%
Other values (2) 14
16.7%

2017년
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134295.12
Minimum1344
Maximum783323
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-01-29T02:36:29.528071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1344
5-th percentile1896.05
Q110522
median41711
Q3102093.25
95-th percentile701532.05
Maximum783323
Range781979
Interquartile range (IQR)91571.25

Descriptive statistics

Standard deviation234208.69
Coefficient of variation (CV)1.7439851
Kurtosis2.2784998
Mean134295.12
Median Absolute Deviation (MAD)35771
Skewness2.0132044
Sum11280790
Variance5.485371 × 1010
MonotonicityNot monotonic
2024-01-29T02:36:29.677400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45763 2
 
2.4%
35753 2
 
2.4%
32575 1
 
1.2%
41155 1
 
1.2%
46240 1
 
1.2%
36547 1
 
1.2%
42267 1
 
1.2%
43062 1
 
1.2%
28284 1
 
1.2%
31462 1
 
1.2%
Other values (72) 72
85.7%
ValueCountFrequency (%)
1344 1
1.2%
1552 1
1.2%
1595 1
1.2%
1606 1
1.2%
1856 1
1.2%
2123 1
1.2%
2155 1
1.2%
2172 1
1.2%
2182 1
1.2%
2185 1
1.2%
ValueCountFrequency (%)
783323 1
1.2%
732457 1
1.2%
730783 1
1.2%
705303 1
1.2%
703115 1
1.2%
692562 1
1.2%
688304 1
1.2%
685584 1
1.2%
672479 1
1.2%
664455 1
1.2%

2018년
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct84
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138231.62
Minimum1377
Maximum821243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-01-29T02:36:29.806967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1377
5-th percentile1697.35
Q110870.25
median40030.5
Q3104215.5
95-th percentile747817.45
Maximum821243
Range819866
Interquartile range (IQR)93345.25

Descriptive statistics

Standard deviation247085.99
Coefficient of variation (CV)1.7874781
Kurtosis2.3084427
Mean138231.62
Median Absolute Deviation (MAD)33716
Skewness2.0203708
Sum11611456
Variance6.1051488 × 1010
MonotonicityNot monotonic
2024-01-29T02:36:29.933092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52240 1
 
1.2%
20549 1
 
1.2%
45591 1
 
1.2%
40100 1
 
1.2%
43297 1
 
1.2%
28920 1
 
1.2%
22276 1
 
1.2%
24575 1
 
1.2%
20196 1
 
1.2%
26896 1
 
1.2%
Other values (74) 74
88.1%
ValueCountFrequency (%)
1377 1
1.2%
1630 1
1.2%
1652 1
1.2%
1682 1
1.2%
1684 1
1.2%
1773 1
1.2%
1825 1
1.2%
2174 1
1.2%
2192 1
1.2%
2317 1
1.2%
ValueCountFrequency (%)
821243 1
1.2%
776215 1
1.2%
764442 1
1.2%
755127 1
1.2%
749605 1
1.2%
737688 1
1.2%
736369 1
1.2%
704499 1
1.2%
703746 1
1.2%
699870 1
1.2%

2019년
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136671.33
Minimum99
Maximum853336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2024-01-29T02:36:30.061873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum99
5-th percentile220.5
Q15855.5
median35772
Q3104454.75
95-th percentile793089.8
Maximum853336
Range853237
Interquartile range (IQR)98599.25

Descriptive statistics

Standard deviation250312.04
Coefficient of variation (CV)1.831489
Kurtosis2.7833647
Mean136671.33
Median Absolute Deviation (MAD)30567
Skewness2.107095
Sum11480392
Variance6.2656117 × 1010
MonotonicityNot monotonic
2024-01-29T02:36:30.181983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35772 2
 
2.4%
56281 2
 
2.4%
123215 2
 
2.4%
22576 2
 
2.4%
5724 1
 
1.2%
35931 1
 
1.2%
34341 1
 
1.2%
41178 1
 
1.2%
5883 1
 
1.2%
6995 1
 
1.2%
Other values (70) 70
83.3%
ValueCountFrequency (%)
99 1
1.2%
100 1
1.2%
172 1
1.2%
215 1
1.2%
219 1
1.2%
229 1
1.2%
278 1
1.2%
304 1
1.2%
440 1
1.2%
447 1
1.2%
ValueCountFrequency (%)
853336 1
1.2%
809058 1
1.2%
804061 1
1.2%
799007 1
1.2%
797228 1
1.2%
769640 1
1.2%
762176 1
1.2%
739056 1
1.2%
717325 1
1.2%
684870 1
1.2%

Interactions

2024-01-29T02:36:28.659602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:36:28.182442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:36:28.429765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:36:28.750668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:36:28.268232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:36:28.524856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:36:28.818693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:36:28.350743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:36:28.592506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T02:36:30.259959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
석유류별월별2017년2018년2019년
석유류별1.0000.0000.8660.8660.770
월별0.0001.0000.0000.0000.000
2017년0.8660.0001.0000.9810.994
2018년0.8660.0000.9811.0000.946
2019년0.7700.0000.9940.9461.000
2024-01-29T02:36:30.344235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월별석유류별
월별1.0000.000
석유류별0.0001.000
2024-01-29T02:36:30.418400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2017년2018년2019년석유류별월별
2017년1.0000.9540.9440.7850.000
2018년0.9541.0000.9740.7850.000
2019년0.9440.9741.0000.5910.000
석유류별0.7850.7850.5911.0000.000
월별0.0000.0000.0000.0001.000

Missing values

2024-01-29T02:36:28.907459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T02:36:29.000134image/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

석유류별월별2017년2018년2019년
0휘발유01월463995224062323
1휘발유02월457634692651671
2휘발유03월510075119055327
3휘발유04월481474961656281
4휘발유05월522785022646106
5휘발유06월514845260958189
6휘발유07월562515498754851
7휘발유08월543445773368841
8휘발유09월533905372445947
9휘발유10월497364457256758
석유류별월별2017년2018년2019년
74기타03월692562704499769640
75기타04월664455703746799007
76기타05월662824680717853336
77기타06월657589755127804061
78기타07월730783764442762176
79기타08월783323821243684870
80기타09월685584776215659699
81기타10월705303737688325101
82기타11월688304699870717325
83기타12월672479749605797228