Overview

Dataset statistics

Number of variables4
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory36.8 B

Variable types

DateTime1
Numeric3

Dataset

Description광주교통공사 지하수유출현황에 대한 데이터로 월별, 역별(문화전당역, 양동시장역, 공항역)의 지하수 유출정보를 제공합니다.
Author광주교통공사
URLhttps://www.data.go.kr/data/15045354/fileData.do

Alerts

구분 has unique valuesUnique
양동시장역 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:10:09.450498
Analysis finished2024-04-06 08:10:12.179116
Duration2.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Date

UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size708.0 B
Minimum2018-01-01 00:00:00
Maximum2023-12-01 00:00:00
2024-04-06T17:10:12.316445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:12.576170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

문화전당역
Real number (ℝ)

Distinct57
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20828.637
Minimum13326
Maximum24988.171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2024-04-06T17:10:12.852407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13326
5-th percentile18001.794
Q119514.382
median20982.184
Q322017.573
95-th percentile23463.259
Maximum24988.171
Range11662.171
Interquartile range (IQR)2503.1901

Descriptive statistics

Standard deviation1908.8464
Coefficient of variation (CV)0.091645282
Kurtosis2.4218421
Mean20828.637
Median Absolute Deviation (MAD)1191.1994
Skewness-0.81096456
Sum1499661.9
Variance3643694.4
MonotonicityNot monotonic
2024-04-06T17:10:13.194432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21999.62972 2
 
2.8%
22492.22652 2
 
2.8%
16951.87663 2
 
2.8%
18554.79913 2
 
2.8%
18001.79398 2
 
2.8%
19289.4638 2
 
2.8%
19197.14461 2
 
2.8%
21496.5521 2
 
2.8%
22006.76341 2
 
2.8%
20879.91987 2
 
2.8%
Other values (47) 52
72.2%
ValueCountFrequency (%)
13326.0 1
1.4%
16951.87663 2
2.8%
18001.79398 2
2.8%
18330.0 1
1.4%
18554.79913 2
2.8%
18809.0 1
1.4%
19026.0 1
1.4%
19170.0 1
1.4%
19197.14461 2
2.8%
19289.4638 2
2.8%
ValueCountFrequency (%)
24988.1709 1
1.4%
24569.69 1
1.4%
24374.45784 1
1.4%
23675.65409 1
1.4%
23289.48052 1
1.4%
23079.72874 1
1.4%
22953.67575 1
1.4%
22788.39135 1
1.4%
22633.20567 1
1.4%
22609.03436 1
1.4%

양동시장역
Real number (ℝ)

UNIQUE 

Distinct72
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9980.2812
Minimum5914.7914
Maximum15510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2024-04-06T17:10:13.490927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5914.7914
5-th percentile6558.5618
Q18327.0632
median9822.415
Q311291.5
95-th percentile14445.955
Maximum15510
Range9595.2086
Interquartile range (IQR)2964.4368

Descriptive statistics

Standard deviation2266.6934
Coefficient of variation (CV)0.22711718
Kurtosis-0.14142842
Mean9980.2812
Median Absolute Deviation (MAD)1475.585
Skewness0.43475219
Sum718580.25
Variance5137898.9
MonotonicityNot monotonic
2024-04-06T17:10:13.780179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12150.0 1
 
1.4%
7637.743339 1
 
1.4%
6327.269028 1
 
1.4%
6645.430442 1
 
1.4%
6452.389028 1
 
1.4%
6659.731658 1
 
1.4%
6183.380983 1
 
1.4%
6968.959482 1
 
1.4%
8859.454116 1
 
1.4%
8696.933547 1
 
1.4%
Other values (62) 62
86.1%
ValueCountFrequency (%)
5914.791368 1
1.4%
6183.380983 1
1.4%
6327.269028 1
1.4%
6452.389028 1
1.4%
6645.430442 1
1.4%
6659.731658 1
1.4%
6968.959482 1
1.4%
7287.082671 1
1.4%
7347.937071 1
1.4%
7462.109837 1
1.4%
ValueCountFrequency (%)
15510.0 1
1.4%
15150.0 1
1.4%
14909.12747 1
1.4%
14640.0 1
1.4%
14287.19144 1
1.4%
13920.0 1
1.4%
12908.21867 1
1.4%
12690.0 1
1.4%
12645.0 1
1.4%
12630.0 1
1.4%

공항역
Real number (ℝ)

Distinct60
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50470.087
Minimum37376.393
Maximum65370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2024-04-06T17:10:14.054909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37376.393
5-th percentile39164.182
Q146424.829
median50569.351
Q353931.442
95-th percentile60837.568
Maximum65370
Range27993.607
Interquartile range (IQR)7506.6137

Descriptive statistics

Standard deviation5865.0414
Coefficient of variation (CV)0.11620827
Kurtosis0.29166438
Mean50470.087
Median Absolute Deviation (MAD)3667.0642
Skewness0.10606082
Sum3633846.3
Variance34398711
MonotonicityNot monotonic
2024-04-06T17:10:14.321943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48589.71405 2
 
2.8%
53175.40221 2
 
2.8%
53494.64878 2
 
2.8%
55639.93894 2
 
2.8%
57121.53176 2
 
2.8%
61660.54008 2
 
2.8%
44169.69118 2
 
2.8%
45849.00199 2
 
2.8%
48046.28047 2
 
2.8%
50444.70237 2
 
2.8%
Other values (50) 52
72.2%
ValueCountFrequency (%)
37376.39304 1
1.4%
38344.84242 1
1.4%
38688.50511 1
1.4%
38977.27727 1
1.4%
39317.10373 1
1.4%
43436.0 1
1.4%
44169.69118 2
2.8%
44250.0 1
1.4%
44543.83702 1
1.4%
44938.17895 1
1.4%
ValueCountFrequency (%)
65370.0 1
1.4%
64792.0 1
1.4%
61660.54008 2
2.8%
60164.22795 1
1.4%
58456.0 1
1.4%
57395.56035 1
1.4%
57121.53176 2
2.8%
56100.0 1
1.4%
55674.0 1
1.4%
55639.93894 2
2.8%

Interactions

2024-04-06T17:10:11.287739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:09.724605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:10.750118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:11.453546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:10.418258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:10.920803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:11.606022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:10.582460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:10:11.095782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:10:14.506209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분문화전당역양동시장역공항역
구분1.0001.0001.0001.000
문화전당역1.0001.0000.5120.505
양동시장역1.0000.5121.0000.579
공항역1.0000.5050.5791.000
2024-04-06T17:10:14.673742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
문화전당역양동시장역공항역
문화전당역1.000-0.0220.166
양동시장역-0.0221.0000.437
공항역0.1660.4371.000

Missing values

2024-04-06T17:10:11.908844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:10:12.085908image/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

구분문화전당역양동시장역공항역
02018-01-0113326.012150.050730.0
12018-02-0118330.010650.049440.0
22018-03-0119920.010860.050850.0
32018-04-0119920.09990.044250.0
42018-05-0120700.010560.046080.0
52018-06-0120160.010080.046920.0
62018-07-0120370.012690.049950.0
72018-08-0120700.013920.053910.0
82018-09-0121960.015150.054840.0
92018-10-0122050.015510.056100.0
구분문화전당역양동시장역공항역
622023-03-0123079.728747287.08267139317.10373
632023-04-0122247.619835914.79136837376.39304
642023-05-0122788.391357347.93707138344.84242
652023-06-0122337.183927462.10983738977.27727
662023-07-0124988.170910336.1098748435.39573
672023-08-0124374.4578410420.1098451229.03175
682023-09-0122121.236269526.10983747216.43439
692023-10-0123675.654099934.28267146791.41854
702023-11-0122609.034369490.10983744938.17895
712023-12-0123289.480529392.39147146520.17827