Overview

Dataset statistics

Number of variables3
Number of observations366
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.4 KiB
Average record size in memory26.4 B

Variable types

DateTime1
Numeric2

Dataset

Description광주교통공사 2016년 일별 승하차 인원에 대한 데이터로 일자 별 승차 인원수, 하차 인원수 현황 정보를 제공합니다.
Author광주교통공사
URLhttps://www.data.go.kr/data/15069325/fileData.do

Alerts

승차 is highly overall correlated with 하차High correlation
하차 is highly overall correlated with 승차High correlation
일자 has unique valuesUnique

Reproduction

Analysis started2023-12-12 05:18:09.698318
Analysis finished2023-12-12 05:18:10.738644
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자
Date

UNIQUE 

Distinct366
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum2016-01-01 00:00:00
Maximum2016-12-31 00:00:00
2023-12-12T14:18:10.829529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:10.980154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

승차
Real number (ℝ)

HIGH CORRELATION 

Distinct361
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50958.653
Minimum16616
Maximum67242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T14:18:11.173499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16616
5-th percentile30585.5
Q148263.5
median54436
Q357356.25
95-th percentile60942.75
Maximum67242
Range50626
Interquartile range (IQR)9092.75

Descriptive statistics

Standard deviation9920.6231
Coefficient of variation (CV)0.19467985
Kurtosis0.5917304
Mean50958.653
Median Absolute Deviation (MAD)3471.5
Skewness-1.2242321
Sum18650867
Variance98418763
MonotonicityNot monotonic
2023-12-12T14:18:11.394603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
57098 2
 
0.5%
34631 2
 
0.5%
54902 2
 
0.5%
57193 2
 
0.5%
54477 2
 
0.5%
54378 1
 
0.3%
56817 1
 
0.3%
56778 1
 
0.3%
32447 1
 
0.3%
50152 1
 
0.3%
Other values (351) 351
95.9%
ValueCountFrequency (%)
16616 1
0.3%
18150 1
0.3%
22748 1
0.3%
23734 1
0.3%
25149 1
0.3%
26263 1
0.3%
26462 1
0.3%
27321 1
0.3%
27468 1
0.3%
27502 1
0.3%
ValueCountFrequency (%)
67242 1
0.3%
66545 1
0.3%
66499 1
0.3%
65248 1
0.3%
64871 1
0.3%
64727 1
0.3%
64268 1
0.3%
63807 1
0.3%
63547 1
0.3%
63063 1
0.3%

하차
Real number (ℝ)

HIGH CORRELATION 

Distinct361
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50224.664
Minimum16557
Maximum79816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 KiB
2023-12-12T14:18:11.581062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16557
5-th percentile30354
Q147293.25
median53531
Q356328.75
95-th percentile59873.5
Maximum79816
Range63259
Interquartile range (IQR)9035.5

Descriptive statistics

Standard deviation9773.3009
Coefficient of variation (CV)0.19459166
Kurtosis0.74084277
Mean50224.664
Median Absolute Deviation (MAD)3378
Skewness-1.1367222
Sum18382227
Variance95517411
MonotonicityNot monotonic
2023-12-12T14:18:11.780373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56350 2
 
0.5%
49764 2
 
0.5%
52844 2
 
0.5%
52969 2
 
0.5%
56835 2
 
0.5%
35538 1
 
0.3%
58278 1
 
0.3%
57036 1
 
0.3%
55918 1
 
0.3%
55590 1
 
0.3%
Other values (351) 351
95.9%
ValueCountFrequency (%)
16557 1
0.3%
18001 1
0.3%
22562 1
0.3%
23554 1
0.3%
24880 1
0.3%
25864 1
0.3%
26066 1
0.3%
26786 1
0.3%
27169 1
0.3%
27286 1
0.3%
ValueCountFrequency (%)
79816 1
0.3%
66738 1
0.3%
65206 1
0.3%
65205 1
0.3%
64793 1
0.3%
64004 1
0.3%
63530 1
0.3%
62966 1
0.3%
62460 1
0.3%
62408 1
0.3%

Interactions

2023-12-12T14:18:10.007598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:09.784911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:10.125215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:18:09.897680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:18:11.892548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승차하차
승차1.0000.927
하차0.9271.000
2023-12-12T14:18:11.973524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승차하차
승차1.0000.983
하차0.9831.000

Missing values

2023-12-12T14:18:10.281935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:18:10.690795image/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

일자승차하차
02016-01-012768127489
12016-01-023787537533
22016-01-033025130164
32016-01-045399053144
42016-01-055263451960
52016-01-065251752261
62016-01-075214451779
72016-01-085355953193
82016-01-094374343070
92016-01-103098230662
일자승차하차
3562016-12-225409953142
3572016-12-235836257423
3582016-12-245719356304
3592016-12-253988039129
3602016-12-265038749576
3612016-12-275371552677
3622016-12-285415953132
3632016-12-295240051427
3642016-12-305635755338
3652016-12-314847847649