Overview

Dataset statistics

Number of variables13
Number of observations42
Missing cells41
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 KiB
Average record size in memory117.1 B

Variable types

Categorical3
Numeric8
Text1
DateTime1

Dataset

Description본 공공데이터는 완주군에 위치한 수소충전소의 운영 통계를 제공하며 일일, 월간 이용 대수와 판매가격 판매량 등의 데이터를 제공합니다
Author전북특별자치도 완주군
URLhttps://www.data.go.kr/data/15113287/fileData.do

Alerts

충전소명 has constant value ""Constant
비고 has constant value ""Constant
데이터기준일 has constant value ""Constant
판매연 is highly overall correlated with 상용차 일일 이용 and 4 other fieldsHigh correlation
판매단가(원) is highly overall correlated with 판매연High correlation
상용차 일일 이용 is highly overall correlated with 일평균판매(kg) and 3 other fieldsHigh correlation
승용차 일일 이용 is highly overall correlated with 승용차 월간 이용 and 1 other fieldsHigh correlation
일평균판매(kg) is highly overall correlated with 상용차 일일 이용 and 4 other fieldsHigh correlation
상용차 월간 이용 is highly overall correlated with 상용차 일일 이용 and 3 other fieldsHigh correlation
승용차 월간 이용 is highly overall correlated with 승용차 일일 이용 and 1 other fieldsHigh correlation
합계 is highly overall correlated with 승용차 일일 이용 and 3 other fieldsHigh correlation
월간판매량(kg) is highly overall correlated with 상용차 일일 이용 and 4 other fieldsHigh correlation
비고 has 41 (97.6%) missing valuesMissing
월간판매량(kg) has unique valuesUnique
상용차 일일 이용 has 1 (2.4%) zerosZeros
승용차 일일 이용 has 1 (2.4%) zerosZeros
일평균판매(kg) has 1 (2.4%) zerosZeros
상용차 월간 이용 has 1 (2.4%) zerosZeros
승용차 월간 이용 has 1 (2.4%) zerosZeros
합계 has 1 (2.4%) zerosZeros
월간판매량(kg) has 1 (2.4%) zerosZeros

Reproduction

Analysis started2024-04-21 02:18:17.469488
Analysis finished2024-04-21 02:18:25.570217
Duration8.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

충전소명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
H완주
42 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
H완주 42
100.0%

Length

2024-04-21T11:18:25.647508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:18:25.744622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
h완주 42
100.0%

판매연
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size468.0 B
2021
12 
2022
12 
2023
11 
2020
7 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2021 12
28.6%
2022 12
28.6%
2023 11
26.2%
2020 7
16.7%

Length

2024-04-21T11:18:25.837591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:18:25.959503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 12
28.6%
2022 12
28.6%
2023 11
26.2%
2020 7
16.7%

판매월
Real number (ℝ)

Distinct12
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7857143
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T11:18:26.094036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.05
Q14
median7
Q39.75
95-th percentile11.95
Maximum12
Range11
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.3752016
Coefficient of variation (CV)0.49739814
Kurtosis-1.1058077
Mean6.7857143
Median Absolute Deviation (MAD)3
Skewness-0.16539767
Sum285
Variance11.391986
MonotonicityNot monotonic
2024-04-21T11:18:26.220701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 4
9.5%
7 4
9.5%
8 4
9.5%
9 4
9.5%
10 4
9.5%
11 4
9.5%
12 3
7.1%
1 3
7.1%
2 3
7.1%
3 3
7.1%
Other values (2) 6
14.3%
ValueCountFrequency (%)
1 3
7.1%
2 3
7.1%
3 3
7.1%
4 3
7.1%
5 3
7.1%
6 4
9.5%
7 4
9.5%
8 4
9.5%
9 4
9.5%
10 4
9.5%
ValueCountFrequency (%)
12 3
7.1%
11 4
9.5%
10 4
9.5%
9 4
9.5%
8 4
9.5%
7 4
9.5%
6 4
9.5%
5 3
7.1%
4 3
7.1%
3 3
7.1%

판매단가(원)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
8800
32 
9900
10 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row8800
2nd row8800
3rd row8800
4th row8800
5th row8800

Common Values

ValueCountFrequency (%)
8800 32
76.2%
9900 10
 
23.8%

Length

2024-04-21T11:18:26.351192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T11:18:26.459124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
8800 32
76.2%
9900 10
 
23.8%

상용차 일일 이용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5
Minimum0
Maximum27
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T11:18:26.560850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q17
median9
Q321.5
95-th percentile24
Maximum27
Range27
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation7.5876986
Coefficient of variation (CV)0.60701589
Kurtosis-1.1923339
Mean12.5
Median Absolute Deviation (MAD)3
Skewness0.5984921
Sum525
Variance57.573171
MonotonicityNot monotonic
2024-04-21T11:18:26.683265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
9 8
19.0%
6 5
11.9%
7 5
11.9%
23 5
11.9%
24 4
9.5%
8 3
 
7.1%
5 2
 
4.8%
0 1
 
2.4%
4 1
 
2.4%
10 1
 
2.4%
Other values (7) 7
16.7%
ValueCountFrequency (%)
0 1
 
2.4%
4 1
 
2.4%
5 2
 
4.8%
6 5
11.9%
7 5
11.9%
8 3
 
7.1%
9 8
19.0%
10 1
 
2.4%
11 1
 
2.4%
14 1
 
2.4%
ValueCountFrequency (%)
27 1
 
2.4%
24 4
9.5%
23 5
11.9%
22 1
 
2.4%
20 1
 
2.4%
18 1
 
2.4%
17 1
 
2.4%
14 1
 
2.4%
11 1
 
2.4%
10 1
 
2.4%

승용차 일일 이용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.642857
Minimum0
Maximum72
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T11:18:26.794173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q129.5
median44
Q351
95-th percentile60
Maximum72
Range72
Interquartile range (IQR)21.5

Descriptive statistics

Standard deviation14.666739
Coefficient of variation (CV)0.3608688
Kurtosis0.14645387
Mean40.642857
Median Absolute Deviation (MAD)9
Skewness-0.41320843
Sum1707
Variance215.11324
MonotonicityNot monotonic
2024-04-21T11:18:26.910674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
51 3
 
7.1%
60 3
 
7.1%
45 3
 
7.1%
48 2
 
4.8%
36 2
 
4.8%
23 2
 
4.8%
21 2
 
4.8%
29 2
 
4.8%
24 2
 
4.8%
47 2
 
4.8%
Other values (18) 19
45.2%
ValueCountFrequency (%)
0 1
2.4%
16 1
2.4%
21 2
4.8%
23 2
4.8%
24 2
4.8%
26 1
2.4%
29 2
4.8%
31 1
2.4%
32 1
2.4%
35 1
2.4%
ValueCountFrequency (%)
72 1
 
2.4%
60 3
7.1%
58 1
 
2.4%
57 1
 
2.4%
56 1
 
2.4%
53 1
 
2.4%
52 1
 
2.4%
51 3
7.1%
49 1
 
2.4%
48 2
4.8%

일평균판매(kg)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean331.04762
Minimum0
Maximum563
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T11:18:27.028031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile153.05
Q1274
median334.5
Q3382
95-th percentile490.7
Maximum563
Range563
Interquartile range (IQR)108

Descriptive statistics

Standard deviation111.19022
Coefficient of variation (CV)0.33587381
Kurtosis0.89000091
Mean331.04762
Median Absolute Deviation (MAD)59
Skewness-0.47595239
Sum13904
Variance12363.266
MonotonicityNot monotonic
2024-04-21T11:18:27.189807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
307 3
 
7.1%
340 2
 
4.8%
379 2
 
4.8%
319 2
 
4.8%
313 1
 
2.4%
363 1
 
2.4%
329 1
 
2.4%
375 1
 
2.4%
221 1
 
2.4%
358 1
 
2.4%
Other values (27) 27
64.3%
ValueCountFrequency (%)
0 1
2.4%
137 1
2.4%
152 1
2.4%
173 1
2.4%
213 1
2.4%
215 1
2.4%
220 1
2.4%
221 1
2.4%
224 1
2.4%
251 1
2.4%
ValueCountFrequency (%)
563 1
2.4%
521 1
2.4%
491 1
2.4%
485 1
2.4%
467 1
2.4%
447 1
2.4%
446 1
2.4%
429 1
2.4%
427 1
2.4%
417 1
2.4%

상용차 월간 이용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean379.92857
Minimum0
Maximum837
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T11:18:27.317777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile148.85
Q1209.25
median272
Q3641.5
95-th percentile746.25
Maximum837
Range837
Interquartile range (IQR)432.25

Descriptive statistics

Standard deviation232.20685
Coefficient of variation (CV)0.61118553
Kurtosis-1.1419899
Mean379.92857
Median Absolute Deviation (MAD)94
Skewness0.61836631
Sum15957
Variance53920.019
MonotonicityNot monotonic
2024-04-21T11:18:27.462451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
216 3
 
7.1%
702 2
 
4.8%
256 2
 
4.8%
276 1
 
2.4%
148 1
 
2.4%
207 1
 
2.4%
165 1
 
2.4%
200 1
 
2.4%
125 1
 
2.4%
0 1
 
2.4%
Other values (28) 28
66.7%
ValueCountFrequency (%)
0 1
2.4%
125 1
2.4%
148 1
2.4%
165 1
2.4%
173 1
2.4%
177 1
2.4%
179 1
2.4%
180 1
2.4%
182 1
2.4%
200 1
2.4%
ValueCountFrequency (%)
837 1
2.4%
753 1
2.4%
747 1
2.4%
732 1
2.4%
715 1
2.4%
714 1
2.4%
702 2
4.8%
691 1
2.4%
682 1
2.4%
645 1
2.4%

승용차 월간 이용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1233.881
Minimum0
Maximum2148
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T11:18:27.627460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile645.05
Q1920.5
median1263
Q31571.75
95-th percentile1809.9
Maximum2148
Range2148
Interquartile range (IQR)651.25

Descriptive statistics

Standard deviation447.4117
Coefficient of variation (CV)0.36260524
Kurtosis0.054984292
Mean1233.881
Median Absolute Deviation (MAD)322
Skewness-0.43142901
Sum51823
Variance200177.23
MonotonicityNot monotonic
2024-04-21T11:18:27.760207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1103 2
 
4.8%
468 1
 
2.4%
1342 1
 
2.4%
1811 1
 
2.4%
1190 1
 
2.4%
1789 1
 
2.4%
1861 1
 
2.4%
2148 1
 
2.4%
1571 1
 
2.4%
0 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
0 1
2.4%
468 1
2.4%
645 1
2.4%
646 1
2.4%
664 1
2.4%
691 1
2.4%
702 1
2.4%
737 1
2.4%
787 1
2.4%
899 1
2.4%
ValueCountFrequency (%)
2148 1
2.4%
1861 1
2.4%
1811 1
2.4%
1789 1
2.4%
1787 1
2.4%
1722 1
2.4%
1667 1
2.4%
1648 1
2.4%
1606 1
2.4%
1596 1
2.4%

합계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct41
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1613.8095
Minimum0
Maximum2348
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T11:18:27.888325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile875.9
Q11353
median1684.5
Q31938.75
95-th percentile2157.05
Maximum2348
Range2348
Interquartile range (IQR)585.75

Descriptive statistics

Standard deviation454.96091
Coefficient of variation (CV)0.28191735
Kurtosis2.7415494
Mean1613.8095
Median Absolute Deviation (MAD)282.5
Skewness-1.3070976
Sum67780
Variance206989.43
MonotonicityNot monotonic
2024-04-21T11:18:28.015959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1319 2
 
4.8%
645 1
 
2.4%
1618 1
 
2.4%
2101 1
 
2.4%
1338 1
 
2.4%
1996 1
 
2.4%
2026 1
 
2.4%
2348 1
 
2.4%
1696 1
 
2.4%
0 1
 
2.4%
Other values (31) 31
73.8%
ValueCountFrequency (%)
0 1
2.4%
645 1
2.4%
870 1
2.4%
988 1
2.4%
1078 1
2.4%
1145 1
2.4%
1245 1
2.4%
1277 1
2.4%
1319 2
4.8%
1338 1
2.4%
ValueCountFrequency (%)
2348 1
2.4%
2225 1
2.4%
2160 1
2.4%
2101 1
2.4%
2048 1
2.4%
2026 1
2.4%
2001 1
2.4%
1996 1
2.4%
1978 1
2.4%
1963 1
2.4%

월간판매량(kg)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct42
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10068.452
Minimum0
Maximum16881
Zeros1
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2024-04-21T11:18:28.160929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4589.15
Q18309
median9990
Q311600.25
95-th percentile15018.1
Maximum16881
Range16881
Interquartile range (IQR)3291.25

Descriptive statistics

Standard deviation3401.0774
Coefficient of variation (CV)0.33779545
Kurtosis0.84288903
Mean10068.452
Median Absolute Deviation (MAD)1711
Skewness-0.45090004
Sum422875
Variance11567328
MonotonicityNot monotonic
2024-04-21T11:18:28.287279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3970 1
 
2.4%
8600 1
 
2.4%
9876 1
 
2.4%
11634 1
 
2.4%
6865 1
 
2.4%
10021 1
 
2.4%
9717 1
 
2.4%
11499 1
 
2.4%
7790 1
 
2.4%
0 1
 
2.4%
Other values (32) 32
76.2%
ValueCountFrequency (%)
0 1
2.4%
3970 1
2.4%
4549 1
2.4%
5352 1
2.4%
6603 1
2.4%
6674 1
2.4%
6734 1
2.4%
6818 1
2.4%
6865 1
2.4%
7790 1
2.4%
ValueCountFrequency (%)
16881 1
2.4%
16153 1
2.4%
15033 1
2.4%
14735 1
2.4%
14477 1
2.4%
13834 1
2.4%
13424 1
2.4%
13285 1
2.4%
12935 1
2.4%
12812 1
2.4%

비고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing41
Missing (%)97.6%
Memory size468.0 B
2024-04-21T11:18:28.401165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3
Distinct characters3
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

Unique1 ?
Unique (%)100.0%

Sample

1st row미운영
ValueCountFrequency (%)
미운영 1
100.0%
2024-04-21T11:18:28.627859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
미 1
33.3%
운 1
33.3%
영 1
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 1
33.3%
운 1
33.3%
영 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
미 1
33.3%
운 1
33.3%
영 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
미 1
33.3%
운 1
33.3%
영 1
33.3%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size468.0 B
Minimum2023-12-28 00:00:00
Maximum2023-12-28 00:00:00
2024-04-21T11:18:28.750645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:28.862309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-21T11:18:24.528258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:19.552090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.268440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.939532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.657593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.332102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.970932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.652905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:24.614916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:19.696623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.346956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.017107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.751942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.408457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.066534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.728558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:24.699092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:19.798417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.421001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.098119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.860149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.482867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.157082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.807993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:24.784495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:19.882608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.508959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.182442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.947307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.571559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.237219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.922031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:24.865435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:19.955874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.609826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.280670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.025172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.651492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.318949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:24.217775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:24.941579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.028960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.689156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.376626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.099865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.721000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.407888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:24.298730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:25.030007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.104332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.762577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.459921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.176207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.793543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.484541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:24.371937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:25.110220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.179589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:20.849270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:21.552357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.248867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:22.861956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:23.570045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T11:18:24.440401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T11:18:29.188731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
판매연판매월판매단가(원)상용차 일일 이용승용차 일일 이용일평균판매(kg)상용차 월간 이용승용차 월간 이용합계월간판매량(kg)
판매연1.0000.0000.9950.7750.6740.7720.7520.6890.5590.761
판매월0.0001.0000.0000.0000.0000.0000.1360.0000.0000.000
판매단가(원)0.9950.0001.0000.4410.5260.4450.4410.4330.1980.410
상용차 일일 이용0.7750.0000.4411.0000.7170.8040.9970.6830.5470.807
승용차 일일 이용0.6740.0000.5260.7171.0000.8770.6330.9890.8850.863
일평균판매(kg)0.7720.0000.4450.8040.8771.0000.7770.9300.9360.997
상용차 월간 이용0.7520.1360.4410.9970.6330.7771.0000.6870.6220.782
승용차 월간 이용0.6890.0000.4330.6830.9890.9300.6871.0000.9450.909
합계0.5590.0000.1980.5470.8850.9360.6220.9451.0000.924
월간판매량(kg)0.7610.0000.4100.8070.8630.9970.7820.9090.9241.000
2024-04-21T11:18:29.297791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
판매연판매단가(원)
판매연1.0000.910
판매단가(원)0.9101.000
2024-04-21T11:18:29.387915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
판매월상용차 일일 이용승용차 일일 이용일평균판매(kg)상용차 월간 이용승용차 월간 이용합계월간판매량(kg)판매연판매단가(원)
판매월1.000-0.1400.232-0.007-0.1460.2350.239-0.0010.0000.000
상용차 일일 이용-0.1401.000-0.2110.7590.993-0.2150.2640.7540.5380.295
승용차 일일 이용0.232-0.2111.0000.329-0.2330.9970.8360.3360.4670.476
일평균판매(kg)-0.0070.7590.3291.0000.7390.3220.7190.9910.5780.400
상용차 월간 이용-0.1460.993-0.2330.7391.000-0.2330.2400.7400.5110.295
승용차 월간 이용0.235-0.2150.9970.322-0.2331.0000.8340.3370.4820.389
합계0.2390.2640.8360.7190.2400.8341.0000.7320.3610.166
월간판매량(kg)-0.0010.7540.3360.9910.7400.3370.7321.0000.5640.368
판매연0.0000.5380.4670.5780.5110.4820.3610.5641.0000.910
판매단가(원)0.0000.2950.4760.4000.2950.3890.1660.3680.9101.000

Missing values

2024-04-21T11:18:25.244261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T11:18:25.488183image/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

충전소명판매연판매월판매단가(원)상용차 일일 이용승용차 일일 이용일평균판매(kg)상용차 월간 이용승용차 월간 이용합계월간판매량(kg)비고데이터기준일
0H완주2020688006161371774686453970<NA>2023-12-28
1H완주2020788008241732517379885352<NA>2023-12-28
2H완주202088800736215216110313196674<NA>2023-12-28
3H완주202098800739224216118213986734<NA>2023-12-28
4H완주2020108800736213216110313196603<NA>2023-12-28
5H완주20201188006231521796918704549<NA>2023-12-28
6H완주2020128800142122043364510786818<NA>2023-12-28
7H완주202118800202126563164612778212<NA>2023-12-28
8H완주202128800172431948166411458945<NA>2023-12-28
9H완주202138800182331554370212459772<NA>2023-12-28
충전소명판매연판매월판매단가(원)상용차 일일 이용승용차 일일 이용일평균판매(kg)상용차 월간 이용승용차 월간 이용합계월간판매량(kg)비고데이터기준일
32H완주202329900645307180126114418600<NA>2023-12-28
33H완주2023399009433702761342161811489<NA>2023-12-28
34H완주2023499009493712561479173511148<NA>2023-12-28
35H완주202359900652307173160617799531<NA>2023-12-28
36H완주202369900646307182137915619221<NA>2023-12-28
37H완주202379900947319283146017439911<NA>2023-12-28
38H완주202389900851301242157218149346<NA>2023-12-28
39H완주2023999009563402681667193510211<NA>2023-12-28
40H완주2023109900853321237164818859959<NA>2023-12-28
41H완주20231199009573792561722197811392<NA>2023-12-28