Overview

Dataset statistics

Number of variables3
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)2.0%
Total size in memory2.7 KiB
Average record size in memory27.3 B

Variable types

Categorical1
Numeric2

Alerts

Dataset has 2 (2.0%) duplicate rowsDuplicates
공급량 is highly overall correlated with 시설명High correlation
시설명 is highly overall correlated with 공급량High correlation

Reproduction

Analysis started2023-12-10 13:09:53.760702
Analysis finished2023-12-10 13:09:54.751815
Duration0.99 seconds
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
31 
강진가압장
31 
계룡가압장
29 
광명가압장
9 

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 31
31.0%
강진가압장 31
31.0%
계룡가압장 29
29.0%
광명가압장 9
 
9.0%

Length

2023-12-10T22:09:55.215130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:09:55.369632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
갑천가압장 31
31.0%
강진가압장 31
31.0%
계룡가압장 29
29.0%
광명가압장 9
 
9.0%

공급날짜
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20210315
Minimum20210301
Maximum20210331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:55.593115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20210301
5-th percentile20210302
Q120210308
median20210314
Q320210323
95-th percentile20210330
Maximum20210331
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8721275
Coefficient of variation (CV)4.3899006 × 10-7
Kurtosis-1.1627818
Mean20210315
Median Absolute Deviation (MAD)7.5
Skewness0.12730589
Sum2.0210315 × 109
Variance78.714646
MonotonicityNot monotonic
2023-12-10T22:09:55.851584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20210309 5
 
5.0%
20210314 5
 
5.0%
20210320 4
 
4.0%
20210310 4
 
4.0%
20210307 4
 
4.0%
20210311 4
 
4.0%
20210303 4
 
4.0%
20210329 3
 
3.0%
20210321 3
 
3.0%
20210327 3
 
3.0%
Other values (21) 61
61.0%
ValueCountFrequency (%)
20210301 3
3.0%
20210302 3
3.0%
20210303 4
4.0%
20210304 3
3.0%
20210305 3
3.0%
20210306 3
3.0%
20210307 4
4.0%
20210308 3
3.0%
20210309 5
5.0%
20210310 4
4.0%
ValueCountFrequency (%)
20210331 3
3.0%
20210330 3
3.0%
20210329 3
3.0%
20210328 3
3.0%
20210327 3
3.0%
20210326 3
3.0%
20210325 3
3.0%
20210324 3
3.0%
20210323 2
2.0%
20210322 3
3.0%

공급량
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97169.18
Minimum1086
Maximum439762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:09:56.115936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1086
5-th percentile1148.85
Q11322
median64380
Q3134062
95-th percentile398236
Maximum439762
Range438676
Interquartile range (IQR)132740

Descriptive statistics

Standard deviation112115.65
Coefficient of variation (CV)1.153819
Kurtosis3.0558211
Mean97169.18
Median Absolute Deviation (MAD)63197.5
Skewness1.8377523
Sum9716918
Variance1.2569919 × 1010
MonotonicityNot monotonic
2023-12-10T22:09:56.350939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1149 2
 
2.0%
1249 2
 
2.0%
419871 2
 
2.0%
397743 2
 
2.0%
60100 1
 
1.0%
63880 1
 
1.0%
61830 1
 
1.0%
65580 1
 
1.0%
68060 1
 
1.0%
63230 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
1086 1
1.0%
1118 1
1.0%
1137 1
1.0%
1142 1
1.0%
1146 1
1.0%
1149 2
2.0%
1158 1
1.0%
1176 1
1.0%
1179 1
1.0%
1186 1
1.0%
ValueCountFrequency (%)
439762 1
1.0%
427151 1
1.0%
419871 2
2.0%
407603 1
1.0%
397743 2
2.0%
395453 1
1.0%
385936 1
1.0%
143912 1
1.0%
141336 1
1.0%
140352 1
1.0%

Interactions

2023-12-10T22:09:54.203555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:53.875108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:54.329093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:09:54.026998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:09:56.598687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명공급날짜공급량
시설명1.0000.0001.000
공급날짜0.0001.0000.000
공급량1.0000.0001.000
2023-12-10T22:09:56.744006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급날짜공급량시설명
공급날짜1.000-0.1940.000
공급량-0.1941.0000.990
시설명0.0000.9901.000

Missing values

2023-12-10T22:09:54.493726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:09:54.703641image/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
0갑천가압장202103291149
1갑천가압장202103181142
2갑천가압장202103061249
3갑천가압장202103231200
4갑천가압장202103131352
5갑천가압장202103041334
6갑천가압장202103301118
7갑천가압장202103121227
8갑천가압장202103241146
9갑천가압장202103011348
시설명공급날짜공급량
90계룡가압장2021032160910
91광명가압장20210310407603
92광명가압장20210311395453
93광명가압장20210309397743
94광명가압장20210309397743
95광명가압장20210303439762
96광명가압장20210307427151
97광명가압장20210314419871
98광명가압장20210314419871
99광명가압장20210320385936

Duplicate rows

Most frequently occurring

시설명공급날짜공급량# duplicates
0광명가압장202103093977432
1광명가압장202103144198712