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 10:20:34.856497
Analysis finished2023-12-10 10:20:35.961141
Duration1.1 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
광동취수장
31 
다압취수장
31 
대청취수장
31 
덕소취수장
7 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광동취수장 31
31.0%
다압취수장 31
31.0%
대청취수장 31
31.0%
덕소취수장 7
 
7.0%

Length

2023-12-10T19:20:36.058681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:20:36.228735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광동취수장 31
31.0%
다압취수장 31
31.0%
대청취수장 31
31.0%
덕소취수장 7
 
7.0%

공급날짜
Real number (ℝ)

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20201215
Minimum20201201
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:20:36.426978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20201201
5-th percentile20201202
Q120201208
median20201216
Q320201223
95-th percentile20201230
Maximum20201231
Range30
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation9.0725193
Coefficient of variation (CV)4.4910759 × 10-7
Kurtosis-1.1843523
Mean20201215
Median Absolute Deviation (MAD)7.5
Skewness0.044927891
Sum2.0201215 × 109
Variance82.310606
MonotonicityNot monotonic
2023-12-10T19:20:36.674105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20201201 5
 
5.0%
20201216 5
 
5.0%
20201204 5
 
5.0%
20201215 4
 
4.0%
20201223 3
 
3.0%
20201221 3
 
3.0%
20201212 3
 
3.0%
20201228 3
 
3.0%
20201209 3
 
3.0%
20201214 3
 
3.0%
Other values (21) 63
63.0%
ValueCountFrequency (%)
20201201 5
5.0%
20201202 3
3.0%
20201203 3
3.0%
20201204 5
5.0%
20201205 3
3.0%
20201206 3
3.0%
20201207 3
3.0%
20201208 3
3.0%
20201209 3
3.0%
20201210 3
3.0%
ValueCountFrequency (%)
20201231 3
3.0%
20201230 3
3.0%
20201229 3
3.0%
20201228 3
3.0%
20201227 3
3.0%
20201226 3
3.0%
20201225 3
3.0%
20201224 3
3.0%
20201223 3
3.0%
20201222 3
3.0%

공급량
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232188.91
Minimum26116
Maximum397400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:20:36.923450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26116
5-th percentile26877.4
Q131489
median309902
Q3331392
95-th percentile362115
Maximum397400
Range371284
Interquartile range (IQR)299903

Descriptive statistics

Standard deviation138061.13
Coefficient of variation (CV)0.59460691
Kurtosis-1.3296365
Mean232188.91
Median Absolute Deviation (MAD)24530
Skewness-0.7674643
Sum23218891
Variance1.9060876 × 1010
MonotonicityNot monotonic
2023-12-10T19:20:37.224493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29568 2
 
2.0%
331392 2
 
2.0%
361500 2
 
2.0%
377200 2
 
2.0%
397400 2
 
2.0%
334464 2
 
2.0%
334400 2
 
2.0%
28774 1
 
1.0%
311032 1
 
1.0%
313436 1
 
1.0%
Other values (83) 83
83.0%
ValueCountFrequency (%)
26116 1
1.0%
26432 1
1.0%
26468 1
1.0%
26490 1
1.0%
26524 1
1.0%
26896 1
1.0%
27168 1
1.0%
27640 1
1.0%
27850 1
1.0%
27870 1
1.0%
ValueCountFrequency (%)
397400 2
2.0%
377200 2
2.0%
373800 1
1.0%
361500 2
2.0%
335296 1
1.0%
335232 1
1.0%
334976 1
1.0%
334912 1
1.0%
334848 1
1.0%
334720 1
1.0%

Interactions

2023-12-10T19:20:35.318898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:35.001721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:35.498932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:20:35.164301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:20:37.406002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명공급날짜공급량
시설명1.0000.0000.921
공급날짜0.0001.0000.000
공급량0.9210.0001.000
2023-12-10T19:20:37.561794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공급날짜공급량시설명
공급날짜1.000-0.0510.000
공급량-0.0511.0000.929
시설명0.0000.9291.000

Missing values

2023-12-10T19:20:35.769674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:20:35.903987image/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
0광동취수장2020121428774
1광동취수장2020121931090
2광동취수장2020121629568
3광동취수장2020121328794
4광동취수장2020122431896
5광동취수장2020122729582
6광동취수장2020123029288
7광동취수장2020122631226
8광동취수장2020122231858
9광동취수장2020120126468
시설명공급날짜공급량
90대청취수장20201219310552
91대청취수장20201215311188
92대청취수장20201228308856
93덕소취수장20201216397400
94덕소취수장20201204377200
95덕소취수장20201204377200
96덕소취수장20201201361500
97덕소취수장20201215373800
98덕소취수장20201201361500
99덕소취수장20201216397400

Duplicate rows

Most frequently occurring

시설명공급날짜공급량# duplicates
0덕소취수장202012013615002
1덕소취수장202012043772002
2덕소취수장202012163974002