Overview

Dataset statistics

Number of variables3
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory678.0 B
Average record size in memory32.3 B

Variable types

Categorical1
Numeric2

Dataset

Description대전광역시 2015년부터 2021년까지 도시재생지원센터 운영 및 지원현황입니다. 2022년 공공데이터 기업매칭지원사업으로 수행되었습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15111157/fileData.do

Alerts

년도 is highly overall correlated with 금액High correlation
금액 is highly overall correlated with 년도High correlation
금액 has unique valuesUnique
금액 has 1 (4.8%) zerosZeros

Reproduction

Analysis started2023-12-12 16:11:37.020884
Analysis finished2023-12-12 16:11:37.576142
Duration0.56 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
인건비
7 
운영비
7 
사업비
7 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인건비 7
33.3%
운영비 7
33.3%
사업비 7
33.3%

Length

2023-12-13T01:11:37.630209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:11:37.715384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인건비 7
33.3%
운영비 7
33.3%
사업비 7
33.3%

년도
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018
Minimum2015
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:11:37.799084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12016
median2018
Q32020
95-th percentile2021
Maximum2021
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0493902
Coefficient of variation (CV)0.0010155551
Kurtosis-1.2573099
Mean2018
Median Absolute Deviation (MAD)2
Skewness0
Sum42378
Variance4.2
MonotonicityNot monotonic
2023-12-13T01:11:37.908659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2015 3
14.3%
2016 3
14.3%
2017 3
14.3%
2018 3
14.3%
2019 3
14.3%
2020 3
14.3%
2021 3
14.3%
ValueCountFrequency (%)
2015 3
14.3%
2016 3
14.3%
2017 3
14.3%
2018 3
14.3%
2019 3
14.3%
2020 3
14.3%
2021 3
14.3%
ValueCountFrequency (%)
2021 3
14.3%
2020 3
14.3%
2019 3
14.3%
2018 3
14.3%
2017 3
14.3%
2016 3
14.3%
2015 3
14.3%

금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean273286.24
Minimum0
Maximum699070
Zeros1
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:11:38.024710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile69306
Q194462
median196659
Q3407750
95-th percentile570589
Maximum699070
Range699070
Interquartile range (IQR)313288

Descriptive statistics

Standard deviation198987.84
Coefficient of variation (CV)0.72812977
Kurtosis-0.80843091
Mean273286.24
Median Absolute Deviation (MAD)127353
Skewness0.54447843
Sum5739011
Variance3.9596162 × 1010
MonotonicityNot monotonic
2023-12-13T01:11:38.140512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
127351 1
 
4.8%
350460 1
 
4.8%
0 1
 
4.8%
395890 1
 
4.8%
418300 1
 
4.8%
407750 1
 
4.8%
699070 1
 
4.8%
169000 1
 
4.8%
129675 1
 
4.8%
196659 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
0 1
4.8%
69306 1
4.8%
86312 1
4.8%
86856 1
4.8%
93307 1
4.8%
94462 1
4.8%
125434 1
4.8%
127351 1
4.8%
129675 1
4.8%
169000 1
4.8%
ValueCountFrequency (%)
699070 1
4.8%
570589 1
4.8%
553253 1
4.8%
459510 1
4.8%
418300 1
4.8%
407750 1
4.8%
405332 1
4.8%
395890 1
4.8%
350460 1
4.8%
300495 1
4.8%

Interactions

2023-12-13T01:11:37.267888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:37.103406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:37.360652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:11:37.183434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:11:38.271018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분년도금액
구분1.0000.0000.644
년도0.0001.0000.000
금액0.6440.0001.000
2023-12-13T01:11:38.353689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도금액구분
년도1.0000.5740.000
금액0.5741.0000.261
구분0.0000.2611.000

Missing values

2023-12-13T01:11:37.472082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:11:37.548436image/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

구분년도금액
0인건비2015127351
1인건비2016350460
2인건비2017300495
3인건비2018459510
4인건비2019405332
5인건비2020570589
6인건비2021553253
7운영비201586312
8운영비201669306
9운영비201786856
구분년도금액
11운영비201993307
12운영비2020125434
13운영비2021196659
14사업비2016129675
15사업비2017169000
16사업비2018699070
17사업비2019407750
18사업비2020418300
19사업비2021395890
20사업비20150