Overview

Dataset statistics

Number of variables8
Number of observations336
Missing cells0
Missing cells (%)0.0%
Duplicate rows42
Duplicate rows (%)12.5%
Total size in memory22.8 KiB
Average record size in memory69.4 B

Variable types

Categorical4
Numeric3
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15980/S/1/datasetView.do

Alerts

기관 명 has constant value ""Constant
모델명 has constant value ""Constant
Dataset has 42 (12.5%) duplicate rowsDuplicates
시리얼 is highly overall correlated with 주차면수 and 2 other fieldsHigh correlation
총주차면수 is highly overall correlated with 주차면수 and 2 other fieldsHigh correlation
주차면수 is highly overall correlated with 시리얼 and 1 other fieldsHigh correlation
빈주차면수 is highly overall correlated with 시리얼 and 1 other fieldsHigh correlation

Reproduction

Analysis started2024-05-18 03:14:44.345814
Analysis finished2024-05-18 03:14:47.082494
Duration2.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
마포구
336 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마포구 336
100.0%

Length

2024-05-18T12:14:47.211989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:14:47.532635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 336
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
PARKING-SENSOR
336 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPARKING-SENSOR
2nd rowPARKING-SENSOR
3rd rowPARKING-SENSOR
4th rowPARKING-SENSOR
5th rowPARKING-SENSOR

Common Values

ValueCountFrequency (%)
PARKING-SENSOR 336
100.0%

Length

2024-05-18T12:14:47.885266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:14:48.198618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
parking-sensor 336
100.0%

시리얼
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
111467200001
112 
111467200000
112 
111467200002
112 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row111467200001
2nd row111467200000
3rd row111467200002
4th row111467200001
5th row111467200002

Common Values

ValueCountFrequency (%)
111467200001 112
33.3%
111467200000 112
33.3%
111467200002 112
33.3%

Length

2024-05-18T12:14:48.504325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:14:48.813493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
111467200001 112
33.3%
111467200000 112
33.3%
111467200002 112
33.3%

데이터요청일시
Real number (ℝ)

Distinct98
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0240111 × 109
Minimum2.0240108 × 109
Maximum2.0240114 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-18T12:14:49.178968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0240108 × 109
5-th percentile2.0240108 × 109
Q12.024011 × 109
median2.0240112 × 109
Q32.0240113 × 109
95-th percentile2.0240114 × 109
Maximum2.0240114 × 109
Range613
Interquartile range (IQR)328

Descriptive statistics

Standard deviation198.7683
Coefficient of variation (CV)9.820514 × 10-8
Kurtosis-1.2590749
Mean2.0240111 × 109
Median Absolute Deviation (MAD)152.5
Skewness-0.25295106
Sum6.8006774 × 1011
Variance39508.836
MonotonicityIncreasing
2024-05-18T12:14:49.647418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024011314 6
 
1.8%
2024011322 6
 
1.8%
2024011309 6
 
1.8%
2024011310 6
 
1.8%
2024011311 6
 
1.8%
2024011312 6
 
1.8%
2024011313 6
 
1.8%
2024011315 6
 
1.8%
2024011316 6
 
1.8%
2024011317 6
 
1.8%
Other values (88) 276
82.1%
ValueCountFrequency (%)
2024010809 3
0.9%
2024010810 3
0.9%
2024010811 3
0.9%
2024010812 3
0.9%
2024010813 3
0.9%
2024010814 3
0.9%
2024010815 3
0.9%
2024010816 3
0.9%
2024010817 3
0.9%
2024010818 3
0.9%
ValueCountFrequency (%)
2024011422 3
0.9%
2024011421 3
0.9%
2024011420 3
0.9%
2024011419 3
0.9%
2024011418 3
0.9%
2024011417 3
0.9%
2024011416 3
0.9%
2024011415 3
0.9%
2024011414 3
0.9%
2024011413 3
0.9%

총주차면수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
102
112 
38
112 
52
112 

Length

Max length3
Median length2
Mean length2.3333333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row102
2nd row38
3rd row52
4th row102
5th row52

Common Values

ValueCountFrequency (%)
102 112
33.3%
38 112
33.3%
52 112
33.3%

Length

2024-05-18T12:14:50.076853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:14:50.389142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
102 112
33.3%
38 112
33.3%
52 112
33.3%

주차면수
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)20.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.297619
Minimum8
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-18T12:14:50.782950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q114
median23
Q341
95-th percentile76
Maximum93
Range85
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.507231
Coefficient of variation (CV)0.64385359
Kurtosis0.6211321
Mean30.297619
Median Absolute Deviation (MAD)9
Skewness1.1308872
Sum10180
Variance380.53205
MonotonicityNot monotonic
2024-05-18T12:14:51.198123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 114
33.9%
41 14
 
4.2%
30 8
 
2.4%
38 8
 
2.4%
39 8
 
2.4%
36 7
 
2.1%
31 7
 
2.1%
43 7
 
2.1%
33 6
 
1.8%
28 6
 
1.8%
Other values (58) 151
44.9%
ValueCountFrequency (%)
8 1
 
0.3%
9 6
 
1.8%
10 5
 
1.5%
12 2
 
0.6%
13 2
 
0.6%
14 114
33.9%
15 5
 
1.5%
16 4
 
1.2%
17 6
 
1.8%
18 3
 
0.9%
ValueCountFrequency (%)
93 1
 
0.3%
88 1
 
0.3%
87 1
 
0.3%
86 3
0.9%
82 2
0.6%
81 1
 
0.3%
80 2
0.6%
78 2
0.6%
77 1
 
0.3%
76 4
1.2%

빈주차면수
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.702381
Minimum5
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-18T12:14:51.599694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile9
Q124
median24
Q343
95-th percentile80.25
Maximum86
Range81
Interquartile range (IQR)19

Descriptive statistics

Standard deviation20.665801
Coefficient of variation (CV)0.6131852
Kurtosis0.14071447
Mean33.702381
Median Absolute Deviation (MAD)9
Skewness1.0629127
Sum11324
Variance427.07534
MonotonicityNot monotonic
2024-05-18T12:14:52.047719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 120
35.7%
11 10
 
3.0%
43 9
 
2.7%
9 7
 
2.1%
14 7
 
2.1%
22 7
 
2.1%
42 7
 
2.1%
16 6
 
1.8%
6 5
 
1.5%
13 5
 
1.5%
Other values (60) 153
45.5%
ValueCountFrequency (%)
5 1
 
0.3%
6 5
1.5%
7 4
 
1.2%
8 3
 
0.9%
9 7
2.1%
10 4
 
1.2%
11 10
3.0%
13 5
1.5%
14 7
2.1%
15 4
 
1.2%
ValueCountFrequency (%)
86 1
 
0.3%
85 4
1.2%
84 2
0.6%
83 4
1.2%
82 4
1.2%
81 2
0.6%
80 2
0.6%
79 2
0.6%
76 1
 
0.3%
75 2
0.6%

등록일자
Date

Distinct119
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2024-01-08 08:59:30
Maximum2024-01-14 21:59:15
2024-05-18T12:14:52.375661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:52.659390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-18T12:14:45.717136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:44.663644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:45.271485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:45.892652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:44.839727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:45.426676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:46.084004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:45.002620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:45.556691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T12:14:52.823013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼데이터요청일시총주차면수주차면수빈주차면수
시리얼1.0000.0001.0000.8040.865
데이터요청일시0.0001.0000.0000.2670.067
총주차면수1.0000.0001.0000.8040.865
주차면수0.8040.2670.8041.0000.933
빈주차면수0.8650.0670.8650.9331.000
2024-05-18T12:14:52.992752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼총주차면수
시리얼1.0001.000
총주차면수1.0001.000
2024-05-18T12:14:53.143163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터요청일시주차면수빈주차면수시리얼총주차면수
데이터요청일시1.0000.003-0.0780.0000.000
주차면수0.0031.000-0.0660.6860.686
빈주차면수-0.078-0.0661.0000.7840.784
시리얼0.0000.6860.7841.0001.000
총주차면수0.0000.6860.7841.0001.000

Missing values

2024-05-18T12:14:46.517754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T12:14:46.925147image/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

기관 명모델명시리얼데이터요청일시총주차면수주차면수빈주차면수등록일자
0마포구PARKING-SENSOR111467200001202401080910230722024-01-08 08:59:30
1마포구PARKING-SENSOR11146720000020240108093814242024-01-08 08:59:30
2마포구PARKING-SENSOR11146720000220240108095218342024-01-08 08:59:30
3마포구PARKING-SENSOR111467200001202401081010232702024-01-08 09:59:36
4마포구PARKING-SENSOR11146720000220240108105222302024-01-08 09:59:37
5마포구PARKING-SENSOR11146720000020240108103814242024-01-08 09:59:37
6마포구PARKING-SENSOR11146720000020240108113814242024-01-08 10:59:36
7마포구PARKING-SENSOR111467200001202401081110234682024-01-08 10:59:36
8마포구PARKING-SENSOR11146720000220240108115230222024-01-08 10:59:36
9마포구PARKING-SENSOR11146720000020240108123814242024-01-08 11:59:36
기관 명모델명시리얼데이터요청일시총주차면수주차면수빈주차면수등록일자
326마포구PARKING-SENSOR111467200001202401141910236662024-01-14 18:59:22
327마포구PARKING-SENSOR111467200001202401142010227752024-01-14 19:59:15
328마포구PARKING-SENSOR11146720000220240114205213392024-01-14 19:59:15
329마포구PARKING-SENSOR11146720000020240114203814242024-01-14 19:59:15
330마포구PARKING-SENSOR1114672000022024011421529432024-01-14 20:59:15
331마포구PARKING-SENSOR11146720000020240114213814242024-01-14 20:59:15
332마포구PARKING-SENSOR111467200001202401142110219832024-01-14 20:59:15
333마포구PARKING-SENSOR11146720000020240114223814242024-01-14 21:59:15
334마포구PARKING-SENSOR111467200001202401142210217852024-01-14 21:59:15
335마포구PARKING-SENSOR1114672000022024011422529432024-01-14 21:59:15

Duplicate rows

Most frequently occurring

기관 명모델명시리얼데이터요청일시총주차면수주차면수빈주차면수등록일자# duplicates
0마포구PARKING-SENSOR11146720000020240113093814242024-01-13 08:59:192
1마포구PARKING-SENSOR11146720000020240113103814242024-01-13 09:59:252
2마포구PARKING-SENSOR11146720000020240113113814242024-01-13 10:59:182
3마포구PARKING-SENSOR11146720000020240113123814242024-01-13 11:59:182
4마포구PARKING-SENSOR11146720000020240113133814242024-01-13 12:59:182
5마포구PARKING-SENSOR11146720000020240113143814242024-01-13 13:59:252
6마포구PARKING-SENSOR11146720000020240113153814242024-01-13 14:59:182
7마포구PARKING-SENSOR11146720000020240113163814242024-01-13 15:59:252
8마포구PARKING-SENSOR11146720000020240113173814242024-01-13 16:59:242
9마포구PARKING-SENSOR11146720000020240113183814242024-01-13 17:59:182