Overview

Dataset statistics

Number of variables3
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)3.0%
Total size in memory2.7 KiB
Average record size in memory27.3 B

Variable types

Categorical1
Numeric2

Alerts

Dataset has 3 (3.0%) duplicate rowsDuplicates
공급량 is highly overall correlated with 시설명High correlation
시설명 is highly overall correlated with 공급량High correlation

Reproduction

Analysis started2023-12-10 13:10:08.653055
Analysis finished2023-12-10 13:10:09.659687
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
갑천가압장
30 
강진가압장
30 
계룡가압장
30 
광명가압장
10 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
갑천가압장 30
30.0%
강진가압장 30
30.0%
계룡가압장 30
30.0%
광명가압장 10
 
10.0%

Length

2023-12-10T22:10:09.784474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:10:10.061089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
갑천가압장 30
30.0%
강진가압장 30
30.0%
계룡가압장 30
30.0%
광명가압장 10
 
10.0%

공급날짜
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20201115
Minimum20201101
Maximum20201130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:10.450413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20201101
5-th percentile20201102
Q120201108
median20201115
Q320201122
95-th percentile20201129
Maximum20201130
Range29
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.4622215
Coefficient of variation (CV)4.1889873 × 10-7
Kurtosis-1.1399752
Mean20201115
Median Absolute Deviation (MAD)7
Skewness0.089331804
Sum2.0201115 × 109
Variance71.609192
MonotonicityNot monotonic
2023-12-10T22:10:10.692873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20201109 5
 
5.0%
20201116 5
 
5.0%
20201108 5
 
5.0%
20201105 4
 
4.0%
20201110 4
 
4.0%
20201120 4
 
4.0%
20201117 4
 
4.0%
20201126 3
 
3.0%
20201103 3
 
3.0%
20201123 3
 
3.0%
Other values (20) 60
60.0%
ValueCountFrequency (%)
20201101 3
3.0%
20201102 3
3.0%
20201103 3
3.0%
20201104 3
3.0%
20201105 4
4.0%
20201106 3
3.0%
20201107 3
3.0%
20201108 5
5.0%
20201109 5
5.0%
20201110 4
4.0%
ValueCountFrequency (%)
20201130 3
3.0%
20201129 3
3.0%
20201128 3
3.0%
20201127 3
3.0%
20201126 3
3.0%
20201125 3
3.0%
20201124 3
3.0%
20201123 3
3.0%
20201122 3
3.0%
20201121 3
3.0%

공급량
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96049.51
Minimum972
Maximum385103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:10:10.950103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum972
5-th percentile1090.9
Q11297.25
median64860
Q3131805
95-th percentile365022.25
Maximum385103
Range384131
Interquartile range (IQR)130507.75

Descriptive statistics

Standard deviation104903.9
Coefficient of variation (CV)1.0921857
Kurtosis2.1536454
Mean96049.51
Median Absolute Deviation (MAD)63701
Skewness1.615181
Sum9604951
Variance1.1004829 × 1010
MonotonicityNot monotonic
2023-12-10T22:10:11.265561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1159 2
 
2.0%
380502 2
 
2.0%
364068 2
 
2.0%
354831 2
 
2.0%
132408 2
 
2.0%
1179 1
 
1.0%
62910 1
 
1.0%
64500 1
 
1.0%
62060 1
 
1.0%
65440 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
972 1
1.0%
984 1
1.0%
1082 1
1.0%
1086 1
1.0%
1089 1
1.0%
1091 1
1.0%
1097 1
1.0%
1098 1
1.0%
1104 1
1.0%
1113 1
1.0%
ValueCountFrequency (%)
385103 1
1.0%
383771 1
1.0%
380502 2
2.0%
365122 1
1.0%
365017 1
1.0%
364068 2
2.0%
354831 2
2.0%
137032 1
1.0%
136872 1
1.0%
136216 1
1.0%

Interactions

2023-12-10T22:10:09.071434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:08.791037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:09.233770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:10:08.920492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:10:11.435616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명공급날짜공급량
시설명1.0000.0001.000
공급날짜0.0001.0000.000
공급량1.0000.0001.000
2023-12-10T22:10:11.605794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급날짜공급량시설명
공급날짜1.000-0.0930.000
공급량-0.0931.0001.000
시설명0.0001.0001.000

Missing values

2023-12-10T22:10:09.505608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:10:09.618120image/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

시설명공급날짜공급량
0갑천가압장202011221179
1갑천가압장202011011160
2갑천가압장202011051159
3갑천가압장202011081280
4갑천가압장202011151363
5갑천가압장202011101113
6갑천가압장202011131303
7갑천가압장202011211246
8갑천가압장202011071342
9갑천가압장20201124972
시설명공급날짜공급량
90광명가압장20201109354831
91광명가압장20201120365017
92광명가압장20201108364068
93광명가압장20201109354831
94광명가압장20201110365122
95광명가압장20201108364068
96광명가압장20201116380502
97광명가압장20201105383771
98광명가압장20201117385103
99광명가압장20201116380502

Duplicate rows

Most frequently occurring

시설명공급날짜공급량# duplicates
0광명가압장202011083640682
1광명가압장202011093548312
2광명가압장202011163805022