Overview

Dataset statistics

Number of variables3
Number of observations1715
Missing cells0
Missing cells (%)0.0%
Duplicate rows189
Duplicate rows (%)11.0%
Total size in memory43.7 KiB
Average record size in memory26.1 B

Variable types

DateTime1
Numeric2

Dataset

Description경상북도 구미시 유수율제고블럭 시스템의 수요예측블록시간대별유량 테이블 정보로서 시간대별 유량값정보를 제공합니다.
Author경상북도 구미시
URLhttps://www.data.go.kr/data/15049703/fileData.do

Alerts

수요예측일자 has constant value ""Constant
Dataset has 189 (11.0%) duplicate rowsDuplicates
시간HH24 has 72 (4.2%) zerosZeros

Reproduction

Analysis started2023-12-12 15:16:47.515454
Analysis finished2023-12-12 15:16:48.182329
Duration0.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

수요예측일자
Date

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size13.5 KiB
Minimum2020-08-31 00:00:00
Maximum2020-08-31 00:00:00
2023-12-13T00:16:48.229605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:48.321515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시간HH24
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.447813
Minimum0
Maximum23
Zeros72
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-13T00:16:48.465530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median11
Q317
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9343512
Coefficient of variation (CV)0.60573587
Kurtosis-1.2095514
Mean11.447813
Median Absolute Deviation (MAD)6
Skewness0.010346378
Sum19633
Variance48.085227
MonotonicityNot monotonic
2023-12-13T00:16:48.602658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3 73
 
4.3%
4 73
 
4.3%
2 73
 
4.3%
1 72
 
4.2%
5 72
 
4.2%
6 72
 
4.2%
7 72
 
4.2%
0 72
 
4.2%
17 71
 
4.1%
23 71
 
4.1%
Other values (14) 994
58.0%
ValueCountFrequency (%)
0 72
4.2%
1 72
4.2%
2 73
4.3%
3 73
4.3%
4 73
4.3%
5 72
4.2%
6 72
4.2%
7 72
4.2%
8 71
4.1%
9 71
4.1%
ValueCountFrequency (%)
23 71
4.1%
22 71
4.1%
21 71
4.1%
20 71
4.1%
19 71
4.1%
18 71
4.1%
17 71
4.1%
16 71
4.1%
15 71
4.1%
14 71
4.1%

실제유량값
Real number (ℝ)

Distinct1523
Distinct (%)88.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.87151
Minimum1.0667
Maximum6485.0087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size15.2 KiB
2023-12-13T00:16:48.737331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0667
5-th percentile10.92765
Q138.15425
median87.1769
Q3223.86125
95-th percentile867.74349
Maximum6485.0087
Range6483.942
Interquartile range (IQR)185.707

Descriptive statistics

Standard deviation610.92707
Coefficient of variation (CV)2.435219
Kurtosis51.067787
Mean250.87151
Median Absolute Deviation (MAD)63.1314
Skewness6.5657368
Sum430244.63
Variance373231.88
MonotonicityNot monotonic
2023-12-13T00:16:48.899468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.25 3
 
0.2%
194.6032 2
 
0.1%
55.56 2
 
0.1%
46.7624 2
 
0.1%
72.3304 2
 
0.1%
69.3871 2
 
0.1%
90.7259 2
 
0.1%
46.449 2
 
0.1%
27.6892 2
 
0.1%
27.2081 2
 
0.1%
Other values (1513) 1694
98.8%
ValueCountFrequency (%)
1.0667 1
0.1%
1.0682 1
0.1%
1.0714 1
0.1%
1.075 1
0.1%
1.0833 1
0.1%
1.087 1
0.1%
1.1071 1
0.1%
1.1333 1
0.1%
1.1489 1
0.1%
1.15 1
0.1%
ValueCountFrequency (%)
6485.0087 1
0.1%
6315.3926 1
0.1%
6138.7047 1
0.1%
6079.5558 1
0.1%
6045.5153 1
0.1%
6026.7129 1
0.1%
5549.4207 1
0.1%
5294.8657 1
0.1%
5251.6934 1
0.1%
4980.7192 1
0.1%

Interactions

2023-12-13T00:16:47.804174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:47.605110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:47.902976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:16:47.695677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:16:49.002427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간HH24실제유량값
시간HH241.0000.133
실제유량값0.1331.000
2023-12-13T00:16:49.094541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간HH24실제유량값
시간HH241.0000.186
실제유량값0.1861.000

Missing values

2023-12-13T00:16:48.057833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:16:48.134066image/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

수요예측일자시간HH24실제유량값
02020-08-311264.036
12020-08-312204.9777
22020-08-313198.0914
32020-08-314173.2973
42020-08-315208.28
52020-08-316370.2442
62020-08-317510.5816
72020-08-318697.9512
82020-08-319669.8957
92020-08-3110492.5153
수요예측일자시간HH24실제유량값
17052020-08-31234292.1266
17062020-08-3103421.4469
17072020-08-3186485.0087
17082020-08-3196026.7129
17092020-08-31196138.7047
17102020-08-3141861.5465
17112020-08-3163826.7953
17122020-08-3176045.5153
17132020-08-31144313.4741
17142020-08-3131967.0284

Duplicate rows

Most frequently occurring

수요예측일자시간HH24실제유량값# duplicates
02020-08-31032.67632
12020-08-31034.22772
22020-08-31037.33332
32020-08-31094.62542
42020-08-310125.51612
52020-08-310162.26792
62020-08-310168.10712
72020-08-310423.26442
82020-08-31121.72
92020-08-31126.29382