Overview

Dataset statistics

Number of variables3
Number of observations54
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory28.4 B

Variable types

DateTime1
Numeric2

Dataset

Description대구광역시 상수도사업본부에서 활용 중인 PDA의 월별 검침데이터 작업 건수와 동기화 시 발생한 오류의 2019년 1월부터 2023년 6월까지의 건수입니다.
URLhttps://www.data.go.kr/data/15116792/fileData.do

Alerts

납기년월 has unique valuesUnique
PDA작업건수 has unique valuesUnique
동기화오류건수 has unique valuesUnique

Reproduction

Analysis started2023-12-11 23:54:33.857633
Analysis finished2023-12-11 23:54:34.526078
Duration0.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

납기년월
Date

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size564.0 B
Minimum2019-01-01 00:00:00
Maximum2023-06-01 00:00:00
2023-12-12T08:54:34.901426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:35.061229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PDA작업건수
Real number (ℝ)

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157099.69
Minimum97883
Maximum216182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T08:54:35.244413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum97883
5-th percentile131959
Q1142297
median155958.5
Q3168566.25
95-th percentile192504.35
Maximum216182
Range118299
Interquartile range (IQR)26269.25

Descriptive statistics

Standard deviation20349.217
Coefficient of variation (CV)0.1295306
Kurtosis1.2918952
Mean157099.69
Median Absolute Deviation (MAD)13509.5
Skewness0.25002457
Sum8483383
Variance4.1409062 × 108
MonotonicityNot monotonic
2023-12-12T08:54:35.418123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189641 1
 
1.9%
139301 1
 
1.9%
164371 1
 
1.9%
154181 1
 
1.9%
152026 1
 
1.9%
152739 1
 
1.9%
156283 1
 
1.9%
174931 1
 
1.9%
136405 1
 
1.9%
144260 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
97883 1
1.9%
126309 1
1.9%
126707 1
1.9%
134787 1
1.9%
136088 1
1.9%
136405 1
1.9%
136724 1
1.9%
137507 1
1.9%
138506 1
1.9%
139301 1
1.9%
ValueCountFrequency (%)
216182 1
1.9%
198513 1
1.9%
197822 1
1.9%
189641 1
1.9%
185412 1
1.9%
178286 1
1.9%
177444 1
1.9%
176882 1
1.9%
176595 1
1.9%
174931 1
1.9%

동기화오류건수
Real number (ℝ)

UNIQUE 

Distinct54
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4331.1667
Minimum2657
Maximum15563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size618.0 B
2023-12-12T08:54:35.576158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2657
5-th percentile2802.25
Q13312
median3613.5
Q34298.5
95-th percentile9022.5
Maximum15563
Range12906
Interquartile range (IQR)986.5

Descriptive statistics

Standard deviation2337.1711
Coefficient of variation (CV)0.53961699
Kurtosis12.015229
Mean4331.1667
Median Absolute Deviation (MAD)386
Skewness3.3019007
Sum233883
Variance5462368.9
MonotonicityNot monotonic
2023-12-12T08:54:35.728606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2962 1
 
1.9%
3877 1
 
1.9%
4385 1
 
1.9%
4995 1
 
1.9%
3831 1
 
1.9%
3554 1
 
1.9%
3300 1
 
1.9%
3392 1
 
1.9%
3502 1
 
1.9%
3897 1
 
1.9%
Other values (44) 44
81.5%
ValueCountFrequency (%)
2657 1
1.9%
2770 1
1.9%
2773 1
1.9%
2818 1
1.9%
2902 1
1.9%
2904 1
1.9%
2962 1
1.9%
3009 1
1.9%
3032 1
1.9%
3079 1
1.9%
ValueCountFrequency (%)
15563 1
1.9%
12418 1
1.9%
9666 1
1.9%
8676 1
1.9%
6535 1
1.9%
6002 1
1.9%
5471 1
1.9%
5456 1
1.9%
4995 1
1.9%
4816 1
1.9%

Interactions

2023-12-12T08:54:34.154331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:33.952475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:34.260036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T08:54:34.062187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T08:54:35.839409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납기년월PDA작업건수동기화오류건수
납기년월1.0001.0001.000
PDA작업건수1.0001.0000.284
동기화오류건수1.0000.2841.000
2023-12-12T08:54:35.943727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PDA작업건수동기화오류건수
PDA작업건수1.000-0.263
동기화오류건수-0.2631.000

Missing values

2023-12-12T08:54:34.401558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T08:54:34.490269image/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

납기년월PDA작업건수동기화오류건수
02019-011896412962
12019-021421452773
22019-031701842770
32019-041673463032
42019-051854123514
52019-061263093822
62019-071985133360
72019-081548663603
82019-091605112904
92019-101739863141
납기년월PDA작업건수동기화오류건수
442022-091395324548
452022-101465836002
462022-111427535456
472022-121399854728
482023-011385063800
492023-021375073140
502023-031609634816
512023-041267079666
522023-051680788676
532023-0614637615563