Overview

Dataset statistics

Number of variables4
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory880.0 B
Average record size in memory40.0 B

Variable types

Numeric2
DateTime1
Categorical1

Dataset

Description인천광역시 부평구 매연단속 현황 데이터는 부평구 내의 단속 장소 및 단속 일시, 단속 대 수 데이터를 제공합니다.(예. 1, 2023-01-06, 부평구청 사거리 인근 도로, 273)
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15118785&srcSe=7661IVAWM27C61E190

Alerts

연번 has unique valuesUnique
단속일시 has unique valuesUnique

Reproduction

Analysis started2024-03-18 03:40:10.492737
Analysis finished2024-03-18 03:40:12.146096
Duration1.65 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:40:12.197900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q16.25
median11.5
Q316.75
95-th percentile20.95
Maximum22
Range21
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation6.4935866
Coefficient of variation (CV)0.5646597
Kurtosis-1.2
Mean11.5
Median Absolute Deviation (MAD)5.5
Skewness0
Sum253
Variance42.166667
MonotonicityStrictly increasing
2024-03-18T12:40:12.291979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1 1
 
4.5%
13 1
 
4.5%
22 1
 
4.5%
21 1
 
4.5%
20 1
 
4.5%
19 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
16 1
 
4.5%
15 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
1 1
4.5%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
6 1
4.5%
7 1
4.5%
8 1
4.5%
9 1
4.5%
10 1
4.5%
ValueCountFrequency (%)
22 1
4.5%
21 1
4.5%
20 1
4.5%
19 1
4.5%
18 1
4.5%
17 1
4.5%
16 1
4.5%
15 1
4.5%
14 1
4.5%
13 1
4.5%

단속일시
Date

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
Minimum2023-01-06 00:00:00
Maximum2023-07-21 00:00:00
2024-03-18T12:40:12.381322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:40:12.468550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)

단속장소
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
부평구청 사거리 인근 도로
12 
명신여고
10 

Length

Max length14
Median length14
Mean length9.4545455
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부평구청 사거리 인근 도로
2nd row명신여고
3rd row명신여고
4th row명신여고
5th row부평구청 사거리 인근 도로

Common Values

ValueCountFrequency (%)
부평구청 사거리 인근 도로 12
54.5%
명신여고 10
45.5%

Length

2024-03-18T12:40:12.613149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:40:12.713923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부평구청 12
20.7%
사거리 12
20.7%
인근 12
20.7%
도로 12
20.7%
명신여고 10
17.2%

단속대수
Real number (ℝ)

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean365.04545
Minimum248
Maximum746
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2024-03-18T12:40:12.791568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum248
5-th percentile266.15
Q1277.5
median309.5
Q3432
95-th percentile517.5
Maximum746
Range498
Interquartile range (IQR)154.5

Descriptive statistics

Standard deviation121.38074
Coefficient of variation (CV)0.33250856
Kurtosis3.2826618
Mean365.04545
Median Absolute Deviation (MAD)42
Skewness1.6908807
Sum8031
Variance14733.284
MonotonicityNot monotonic
2024-03-18T12:40:12.877530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
273 2
 
9.1%
298 2
 
9.1%
498 1
 
4.5%
293 1
 
4.5%
354 1
 
4.5%
266 1
 
4.5%
508 1
 
4.5%
248 1
 
4.5%
291 1
 
4.5%
269 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
248 1
4.5%
266 1
4.5%
269 1
4.5%
272 1
4.5%
273 2
9.1%
291 1
4.5%
293 1
4.5%
294 1
4.5%
298 2
9.1%
321 1
4.5%
ValueCountFrequency (%)
746 1
4.5%
518 1
4.5%
508 1
4.5%
498 1
4.5%
455 1
4.5%
434 1
4.5%
426 1
4.5%
364 1
4.5%
354 1
4.5%
332 1
4.5%

Interactions

2024-03-18T12:40:11.786908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:40:11.618791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:40:11.962086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T12:40:11.725228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T12:40:12.950502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번단속일시단속장소단속대수
연번1.0001.0000.0000.000
단속일시1.0001.0001.0001.000
단속장소0.0001.0001.0000.165
단속대수0.0001.0000.1651.000
2024-03-18T12:40:13.030010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번단속대수단속장소
연번1.000-0.3840.000
단속대수-0.3841.0000.168
단속장소0.0000.1681.000

Missing values

2024-03-18T12:40:12.038218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T12:40:12.111948image/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

연번단속일시단속장소단속대수
012023-01-06부평구청 사거리 인근 도로273
122023-01-09명신여고426
232023-01-17명신여고498
342023-01-31명신여고518
452023-02-07부평구청 사거리 인근 도로321
562023-02-21부평구청 사거리 인근 도로455
672023-03-03부평구청 사거리 인근 도로434
782023-03-20부평구청 사거리 인근 도로332
892023-03-27부평구청 사거리 인근 도로272
9102023-04-13명신여고746
연번단속일시단속장소단속대수
12132023-05-17부평구청 사거리 인근 도로293
13142023-06-02명신여고364
14152023-06-07부평구청 사거리 인근 도로269
15162023-06-08명신여고273
16172023-06-16명신여고298
17182023-06-23명신여고291
18192023-06-26부평구청 사거리 인근 도로248
19202023-07-06명신여고508
20212023-07-17명신여고266
21222023-07-21부평구청 사거리 인근 도로354