Overview

Dataset statistics

Number of variables9
Number of observations3000
Missing cells6000
Missing cells (%)22.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory228.6 KiB
Average record size in memory78.0 B

Variable types

DateTime2
Numeric2
Categorical3
Unsupported2

Dataset

Description보행자Care검지 내역 조회
Author차세대융합기술연구원
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=AM7QR8JEOI1J8KFWD55W31986856&infSeq=1

Alerts

상태값 has constant value ""Constant
운영코드 has constant value ""Constant
보행자적외선검지유형 is highly overall correlated with 카메라명High correlation
카메라명 is highly overall correlated with 보행자적외선검지유형High correlation
함체ID has 3000 (100.0%) missing valuesMissing
볼라드ID has 3000 (100.0%) missing valuesMissing
이벤트발생년월일시 is highly skewed (γ1 = -38.70036717)Skewed
발생일시 has unique valuesUnique
함체ID is an unsupported type, check if it needs cleaning or further analysisUnsupported
볼라드ID is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-23 01:31:14.198564
Analysis finished2024-03-23 01:31:17.063437
Duration2.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

발생일시
Date

UNIQUE 

Distinct3000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Minimum2019-07-31 23:59:31.885000
Maximum2019-08-01 07:43:49.293000
2024-03-23T01:31:17.424068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:17.853900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

보행자적외선검지유형
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2803333
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-03-23T01:31:18.249555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median3
Q33
95-th percentile9
Maximum12
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.0532458
Coefficient of variation (CV)0.62592598
Kurtosis5.7500189
Mean3.2803333
Median Absolute Deviation (MAD)0
Skewness2.6245843
Sum9841
Variance4.2158185
MonotonicityNot monotonic
2024-03-23T01:31:18.606093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 1738
57.9%
2 976
32.5%
9 221
 
7.4%
11 54
 
1.8%
12 6
 
0.2%
4 5
 
0.2%
ValueCountFrequency (%)
2 976
32.5%
3 1738
57.9%
4 5
 
0.2%
9 221
 
7.4%
11 54
 
1.8%
12 6
 
0.2%
ValueCountFrequency (%)
12 6
 
0.2%
11 54
 
1.8%
9 221
 
7.4%
4 5
 
0.2%
3 1738
57.9%
2 976
32.5%

일시
Date

Distinct2781
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
Minimum2019-07-31 23:59:41
Maximum2019-08-01 07:43:48
2024-03-23T01:31:18.988786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:19.431912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

상태값
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
2
3000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 3000
100.0%

Length

2024-03-23T01:31:19.825820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:31:20.185910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 3000
100.0%

카메라명
Categorical

HIGH CORRELATION 

Distinct43
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
CIT-SDB-3104
1353 
CIT-SDB-3074
521 
CIT-SDB-3045
199 
CIT-SDB-1011
174 
CIT-SDB-1012
152 
Other values (38)
601 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowCIT-SDB-3074
2nd rowCIT-SDB-3104
3rd rowCIT-SDB-3101
4th rowCIT-SDB-3045
5th rowCIT-SDB-3045

Common Values

ValueCountFrequency (%)
CIT-SDB-3104 1353
45.1%
CIT-SDB-3074 521
 
17.4%
CIT-SDB-3045 199
 
6.6%
CIT-SDB-1011 174
 
5.8%
CIT-SDB-1012 152
 
5.1%
CIT-SDB-1032 69
 
2.3%
CIT-SDB-3102 49
 
1.6%
CIT-SDB-3042 37
 
1.2%
CIT-SDB-1031 34
 
1.1%
CIT-SDB-3075 32
 
1.1%
Other values (33) 380
 
12.7%

Length

2024-03-23T01:31:20.564888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cit-sdb-3104 1353
45.1%
cit-sdb-3074 521
 
17.4%
cit-sdb-3045 199
 
6.6%
cit-sdb-1011 174
 
5.8%
cit-sdb-1012 152
 
5.1%
cit-sdb-1032 69
 
2.3%
cit-sdb-3102 49
 
1.6%
cit-sdb-3042 37
 
1.2%
cit-sdb-1031 34
 
1.1%
cit-sdb-3075 32
 
1.1%
Other values (33) 380
 
12.7%

함체ID
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3000
Missing (%)100.0%
Memory size26.5 KiB

볼라드ID
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3000
Missing (%)100.0%
Memory size26.5 KiB

운영코드
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.6 KiB
1
3000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3000
100.0%

Length

2024-03-23T01:31:20.933494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T01:31:21.355452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 3000
100.0%

이벤트발생년월일시
Real number (ℝ)

SKEWED 

Distinct2781
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0190801 × 1013
Minimum2.0190731 × 1013
Maximum2.0190801 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.5 KiB
2024-03-23T01:31:21.750513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0190731 × 1013
5-th percentile2.0190801 × 1013
Q12.0190801 × 1013
median2.0190801 × 1013
Q32.0190801 × 1013
95-th percentile2.0190801 × 1013
Maximum2.0190801 × 1013
Range69838407
Interquartile range (IQR)42390

Descriptive statistics

Standard deviation1802086.3
Coefficient of variation (CV)8.9252841 × 10-8
Kurtosis1496.9769
Mean2.0190801 × 1013
Median Absolute Deviation (MAD)21098.5
Skewness-38.700367
Sum6.0572403 × 1016
Variance3.2475152 × 1012
MonotonicityNot monotonic
2024-03-23T01:31:22.347456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20190801010226 8
 
0.3%
20190801031051 4
 
0.1%
20190801000632 4
 
0.1%
20190801072259 3
 
0.1%
20190801011833 3
 
0.1%
20190801055005 3
 
0.1%
20190801040818 3
 
0.1%
20190801035740 3
 
0.1%
20190801072346 3
 
0.1%
20190801003952 3
 
0.1%
Other values (2771) 2963
98.8%
ValueCountFrequency (%)
20190731235941 1
< 0.1%
20190731235954 1
< 0.1%
20190801000021 1
< 0.1%
20190801000043 1
< 0.1%
20190801000044 1
< 0.1%
20190801000047 1
< 0.1%
20190801000050 1
< 0.1%
20190801000052 1
< 0.1%
20190801000053 1
< 0.1%
20190801000055 1
< 0.1%
ValueCountFrequency (%)
20190801074348 1
< 0.1%
20190801074345 1
< 0.1%
20190801074342 1
< 0.1%
20190801074340 1
< 0.1%
20190801074337 2
0.1%
20190801074334 1
< 0.1%
20190801074329 1
< 0.1%
20190801074319 1
< 0.1%
20190801074315 1
< 0.1%
20190801074313 1
< 0.1%

Interactions

2024-03-23T01:31:15.438640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:14.712162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:15.790341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T01:31:15.033632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T01:31:22.636141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보행자적외선검지유형카메라명이벤트발생년월일시
보행자적외선검지유형1.0000.9120.000
카메라명0.9121.0000.000
이벤트발생년월일시0.0000.0001.000
2024-03-23T01:31:22.923406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
보행자적외선검지유형이벤트발생년월일시카메라명
보행자적외선검지유형1.000-0.3060.628
이벤트발생년월일시-0.3061.0000.000
카메라명0.6280.0001.000

Missing values

2024-03-23T01:31:16.270083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T01:31:16.880157image/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

발생일시보행자적외선검지유형일시상태값카메라명함체ID볼라드ID운영코드이벤트발생년월일시
02019-08-01 00:19:47.63432019-08-01 00:19:572CIT-SDB-3074<NA><NA>120190801001957
12019-08-01 00:19:51.95422019-08-01 00:20:012CIT-SDB-3104<NA><NA>120190801002001
22019-08-01 00:19:56.54622019-08-01 00:20:062CIT-SDB-3101<NA><NA>120190801002006
32019-08-01 00:20:01.46992019-08-01 00:20:112CIT-SDB-3045<NA><NA>120190801002011
42019-08-01 00:20:16.78292019-08-01 00:20:262CIT-SDB-3045<NA><NA>120190801002026
52019-08-01 00:20:19.07922019-08-01 00:20:282CIT-SDB-3104<NA><NA>120190801002028
62019-08-01 00:20:37.34522019-08-01 00:20:472CIT-SDB-3071<NA><NA>120190801002047
72019-08-01 00:21:54.45422019-08-01 00:22:042CIT-SDB-3094<NA><NA>120190801002204
82019-08-01 00:22:07.79832019-08-01 00:22:172CIT-SDB-3074<NA><NA>120190801002217
92019-08-01 00:22:10.53492019-08-01 00:22:202CIT-SDB-3045<NA><NA>120190801002220
발생일시보행자적외선검지유형일시상태값카메라명함체ID볼라드ID운영코드이벤트발생년월일시
29902019-08-01 07:07:19.94492019-08-01 07:07:192CIT-SDB-3045<NA><NA>120190801070719
29912019-08-01 07:07:20.54692019-08-01 07:07:192CIT-SDB-3045<NA><NA>120190801070719
29922019-08-01 07:07:21.58492019-08-01 07:07:212CIT-SDB-3045<NA><NA>120190801070721
29932019-08-01 07:07:22.18792019-08-01 07:07:212CIT-SDB-3045<NA><NA>120190801070721
29942019-08-01 07:07:23.22592019-08-01 07:07:222CIT-SDB-3045<NA><NA>120190801070722
29952019-08-01 07:07:23.82892019-08-01 07:07:222CIT-SDB-3045<NA><NA>120190801070722
29962019-08-01 07:07:24.4322019-08-01 07:07:222CIT-SDB-1012<NA><NA>120190801070722
29972019-08-01 07:07:25.03322019-08-01 07:07:232CIT-SDB-1013<NA><NA>120190801070723
29982019-08-01 07:08:36.50822019-08-01 07:08:362CIT-SDB-1033<NA><NA>120190801070836
29992019-08-01 07:09:02.86722019-08-01 07:09:022CIT-SDB-1032<NA><NA>120190801070902