Overview

Dataset statistics

Number of variables3
Number of observations1915
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.8 KiB
Average record size in memory26.1 B

Variable types

DateTime1
Numeric2

Dataset

Description부산광역시 해운대구의 재정정보시스템에 대한 지출내역 데이터를 제공합니다.(2023년 12월, 기획조정실 예산팀)
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/15050172/fileData.do

Alerts

지급일자(pay_cmd_ymd) has unique valuesUnique
지출액(expd_resol_amt) has unique valuesUnique

Reproduction

Analysis started2023-12-23 07:00:33.744518
Analysis finished2023-12-23 07:00:36.732036
Duration2.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지급일자(pay_cmd_ymd)
Date

UNIQUE 

Distinct1915
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
Minimum2016-01-05 00:00:00
Maximum2023-12-11 00:00:00
2023-12-23T07:00:37.063405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:37.834424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

세입회계연도(tax_accounting_year)
Real number (ℝ)

Distinct8
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5055
Minimum2016
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2023-12-23T07:00:38.810474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12018
median2020
Q32021.5
95-th percentile2023
Maximum2023
Range7
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.2874302
Coefficient of variation (CV)0.0011326685
Kurtosis-1.2337244
Mean2019.5055
Median Absolute Deviation (MAD)2
Skewness-0.015211855
Sum3867353
Variance5.2323367
MonotonicityNot monotonic
2023-12-23T07:00:39.735321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2022 246
12.8%
2020 243
12.7%
2016 242
12.6%
2021 242
12.6%
2019 241
12.6%
2017 235
12.3%
2018 233
12.2%
2023 233
12.2%
ValueCountFrequency (%)
2016 242
12.6%
2017 235
12.3%
2018 233
12.2%
2019 241
12.6%
2020 243
12.7%
2021 242
12.6%
2022 246
12.8%
2023 233
12.2%
ValueCountFrequency (%)
2023 233
12.2%
2022 246
12.8%
2021 242
12.6%
2020 243
12.7%
2019 241
12.6%
2018 233
12.2%
2017 235
12.3%
2016 242
12.6%

지출액(expd_resol_amt)
Real number (ℝ)

UNIQUE 

Distinct1915
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7480885 × 109
Minimum-6.1547296 × 109
Maximum7.1536365 × 1010
Zeros0
Zeros (%)0.0%
Negative19
Negative (%)1.0%
Memory size17.0 KiB
2023-12-23T07:00:41.132577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.1547296 × 109
5-th percentile1.8259614 × 108
Q15.110815 × 108
median9.9617992 × 108
Q32.2781658 × 109
95-th percentile1.3364051 × 1010
Maximum7.1536365 × 1010
Range7.7691095 × 1010
Interquartile range (IQR)1.7670843 × 109

Descriptive statistics

Standard deviation4.8026256 × 109
Coefficient of variation (CV)1.7476241
Kurtosis32.73606
Mean2.7480885 × 109
Median Absolute Deviation (MAD)6.2795435 × 108
Skewness4.2529745
Sum5.2625894 × 1012
Variance2.3065212 × 1019
MonotonicityNot monotonic
2023-12-23T07:00:43.115602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4400755140 1
 
0.1%
292539850 1
 
0.1%
1331313750 1
 
0.1%
973376870 1
 
0.1%
1064136880 1
 
0.1%
356834370 1
 
0.1%
636548360 1
 
0.1%
229014150 1
 
0.1%
495169420 1
 
0.1%
183729630 1
 
0.1%
Other values (1905) 1905
99.5%
ValueCountFrequency (%)
-6154729580 1
0.1%
-1844585530 1
0.1%
-928419520 1
0.1%
-238625910 1
0.1%
-132237150 1
0.1%
-28400000 1
0.1%
-25000000 1
0.1%
-19981370 1
0.1%
-4505880 1
0.1%
-2172460 1
0.1%
ValueCountFrequency (%)
71536365230 1
0.1%
48863781720 1
0.1%
38171586910 1
0.1%
33921587210 1
0.1%
32175771280 1
0.1%
30812346490 1
0.1%
25570675960 1
0.1%
23059274460 1
0.1%
22996188720 1
0.1%
22926238590 1
0.1%

Interactions

2023-12-23T07:00:34.957307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:33.961562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:35.426206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:00:34.492764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:00:44.120644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세입회계연도(tax_accounting_year)지출액(expd_resol_amt)
세입회계연도(tax_accounting_year)1.0000.129
지출액(expd_resol_amt)0.1291.000
2023-12-23T07:00:45.089392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세입회계연도(tax_accounting_year)지출액(expd_resol_amt)
세입회계연도(tax_accounting_year)1.0000.149
지출액(expd_resol_amt)0.1491.000

Missing values

2023-12-23T07:00:35.991978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:00:36.568367image/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

지급일자(pay_cmd_ymd)세입회계연도(tax_accounting_year)지출액(expd_resol_amt)
02016-12-3020164400755140
12016-12-2920162953440080
22016-12-2820162214580480
32016-12-2720161456042690
42016-12-262016768832460
52016-12-23201610976840810
62016-12-2220162928182460
72016-12-2120161270193590
82016-12-2020168994288450
92016-12-192016399323060
지급일자(pay_cmd_ymd)세입회계연도(tax_accounting_year)지출액(expd_resol_amt)
19052023-01-172023859837720
19062023-01-162023669308430
19072023-01-13202341498150
19082023-01-122023237754550
19092023-01-1120233821780
19102023-01-1020231448110
19112023-01-0920232578700
19122023-01-06202321357340
19132023-01-0420235020000
19142023-01-03202313223820