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 03:14:54.837240
Analysis finished2024-05-18 03:14:57.759060
Duration2.92 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

2024-05-18T12:14:57.877990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:14:58.049477image/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

2024-05-18T12:14:58.357758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:14:58.647215image/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 row111467200001
3rd row111467200000
4th row111467200002
5th row111467200002

Common Values

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

Length

2024-05-18T12:14:58.962192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:14:59.261081image/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%
Mean2.0240118 × 109
Minimum2.0240115 × 109
Maximum2.0240121 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-18T12:14:59.576636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0240115 × 109
5-th percentile2.0240115 × 109
Q12.0240116 × 109
median2.0240118 × 109
Q32.0240119 × 109
95-th percentile2.0240121 × 109
Maximum2.0240121 × 109
Range613
Interquartile range (IQR)356.5

Descriptive statistics

Standard deviation212.11768
Coefficient of variation (CV)1.0480062 × 10-7
Kurtosis-1.3399114
Mean2.0240118 × 109
Median Absolute Deviation (MAD)200
Skewness0.18211902
Sum6.8006796 × 1011
Variance44993.91
MonotonicityIncreasing
2024-05-18T12:14:59.889182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2024011509 6
 
1.8%
2024011517 6
 
1.8%
2024011510 6
 
1.8%
2024011522 6
 
1.8%
2024011521 6
 
1.8%
2024011519 6
 
1.8%
2024011518 6
 
1.8%
2024011520 6
 
1.8%
2024011516 6
 
1.8%
2024011515 6
 
1.8%
Other values (88) 276
82.1%
ValueCountFrequency (%)
2024011509 6
1.8%
2024011510 6
1.8%
2024011511 6
1.8%
2024011512 6
1.8%
2024011513 6
1.8%
2024011514 6
1.8%
2024011515 6
1.8%
2024011516 6
1.8%
2024011517 6
1.8%
2024011518 6
1.8%
ValueCountFrequency (%)
2024012122 3
0.9%
2024012121 3
0.9%
2024012120 3
0.9%
2024012119 3
0.9%
2024012118 3
0.9%
2024012117 3
0.9%
2024012116 3
0.9%
2024012115 3
0.9%
2024012114 3
0.9%
2024012113 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 row102
3rd row38
4th row52
5th row52

Common Values

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

Length

2024-05-18T12:15:00.177443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:15:00.434806image/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 

Distinct66
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.166667
Minimum7
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-18T12:15:00.646031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile14
Q114
median24
Q340
95-th percentile69.5
Maximum95
Range88
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.431369
Coefficient of variation (CV)0.64413379
Kurtosis1.6510867
Mean30.166667
Median Absolute Deviation (MAD)10
Skewness1.369507
Sum10136
Variance377.57811
MonotonicityNot monotonic
2024-05-18T12:15:00.885935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 116
34.5%
30 10
 
3.0%
36 9
 
2.7%
32 7
 
2.1%
45 7
 
2.1%
18 7
 
2.1%
29 7
 
2.1%
24 7
 
2.1%
37 7
 
2.1%
40 7
 
2.1%
Other values (56) 152
45.2%
ValueCountFrequency (%)
7 2
 
0.6%
9 2
 
0.6%
10 3
 
0.9%
12 1
 
0.3%
13 2
 
0.6%
14 116
34.5%
15 3
 
0.9%
16 3
 
0.9%
17 5
 
1.5%
18 7
 
2.1%
ValueCountFrequency (%)
95 1
 
0.3%
94 2
0.6%
92 2
0.6%
91 3
0.9%
90 1
 
0.3%
89 1
 
0.3%
86 1
 
0.3%
84 1
 
0.3%
82 1
 
0.3%
78 1
 
0.3%

빈주차면수
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.833333
Minimum2
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 KiB
2024-05-18T12:15:01.129270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q124
median24
Q343.25
95-th percentile78
Maximum87
Range85
Interquartile range (IQR)19.25

Descriptive statistics

Standard deviation21.23252
Coefficient of variation (CV)0.62756216
Kurtosis-0.10323507
Mean33.833333
Median Absolute Deviation (MAD)9
Skewness0.9837244
Sum11368
Variance450.8199
MonotonicityNot monotonic
2024-05-18T12:15:01.502603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 114
33.9%
38 8
 
2.4%
8 7
 
2.1%
7 7
 
2.1%
11 7
 
2.1%
16 7
 
2.1%
72 6
 
1.8%
42 6
 
1.8%
23 5
 
1.5%
20 5
 
1.5%
Other values (66) 164
48.8%
ValueCountFrequency (%)
2 1
 
0.3%
3 2
 
0.6%
5 3
0.9%
6 1
 
0.3%
7 7
2.1%
8 7
2.1%
9 4
1.2%
10 3
0.9%
11 7
2.1%
12 4
1.2%
ValueCountFrequency (%)
87 1
 
0.3%
86 2
 
0.6%
84 5
1.5%
83 3
0.9%
81 1
 
0.3%
80 3
0.9%
78 3
0.9%
77 1
 
0.3%
76 1
 
0.3%
75 2
 
0.6%

등록일자
Date

Distinct127
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2024-01-15 08:59:21
Maximum2024-01-21 21:58:59
2024-05-18T12:15:01.879633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:15:02.286060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-18T12:14:56.698258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:55.201385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:56.135135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:56.860763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:55.667327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:56.424682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:57.071893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:55.937465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:14:56.564705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T12:15:02.530320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼데이터요청일시총주차면수주차면수빈주차면수
시리얼1.0000.0001.0000.8130.862
데이터요청일시0.0001.0000.0000.3110.251
총주차면수1.0000.0001.0000.8130.862
주차면수0.8130.3110.8131.0000.954
빈주차면수0.8620.2510.8620.9541.000
2024-05-18T12:15:02.734519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼총주차면수
시리얼1.0001.000
총주차면수1.0001.000
2024-05-18T12:15:02.977769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터요청일시주차면수빈주차면수시리얼총주차면수
데이터요청일시1.0000.085-0.1580.0000.000
주차면수0.0851.000-0.0760.6970.697
빈주차면수-0.158-0.0761.0000.7860.786
시리얼0.0000.6970.7861.0001.000
총주차면수0.0000.6970.7861.0001.000

Missing values

2024-05-18T12:14:57.398057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T12:14:57.671906image/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-SENSOR111467200001202401150910230722024-01-15 08:59:21
1마포구PARKING-SENSOR111467200001202401150910230722024-01-15 08:59:21
2마포구PARKING-SENSOR11146720000020240115093814242024-01-15 08:59:21
3마포구PARKING-SENSOR11146720000220240115095217352024-01-15 08:59:21
4마포구PARKING-SENSOR11146720000220240115095217352024-01-15 08:59:21
5마포구PARKING-SENSOR11146720000020240115093814242024-01-15 08:59:21
6마포구PARKING-SENSOR11146720000020240115103814242024-01-15 09:59:14
7마포구PARKING-SENSOR11146720000220240115105223292024-01-15 09:59:14
8마포구PARKING-SENSOR11146720000020240115103814242024-01-15 09:59:14
9마포구PARKING-SENSOR111467200001202401151010230722024-01-15 09:59:14
기관 명모델명시리얼데이터요청일시총주차면수주차면수빈주차면수등록일자
326마포구PARKING-SENSOR11146720000020240121193814242024-01-21 18:58:59
327마포구PARKING-SENSOR111467200001202401212010231712024-01-21 19:59:05
328마포구PARKING-SENSOR11146720000020240121203814242024-01-21 19:59:06
329마포구PARKING-SENSOR11146720000220240121205221312024-01-21 19:59:06
330마포구PARKING-SENSOR11146720000020240121213814242024-01-21 20:58:59
331마포구PARKING-SENSOR11146720000220240121215214382024-01-21 20:58:59
332마포구PARKING-SENSOR111467200001202401212110219832024-01-21 20:58:59
333마포구PARKING-SENSOR111467200001202401212210215872024-01-21 21:58:59
334마포구PARKING-SENSOR11146720000020240121223814242024-01-21 21:58:59
335마포구PARKING-SENSOR1114672000022024012122529432024-01-21 21:58:59

Duplicate rows

Most frequently occurring

기관 명모델명시리얼데이터요청일시총주차면수주차면수빈주차면수등록일자# duplicates
0마포구PARKING-SENSOR11146720000020240115093814242024-01-15 08:59:212
1마포구PARKING-SENSOR11146720000020240115103814242024-01-15 09:59:142
2마포구PARKING-SENSOR11146720000020240115113814242024-01-15 10:59:202
3마포구PARKING-SENSOR11146720000020240115123814242024-01-15 11:59:202
4마포구PARKING-SENSOR11146720000020240115133814242024-01-15 12:59:202
5마포구PARKING-SENSOR11146720000020240115143814242024-01-15 13:59:202
6마포구PARKING-SENSOR11146720000020240115153814242024-01-15 14:59:202
7마포구PARKING-SENSOR11146720000020240115163814242024-01-15 15:59:132
8마포구PARKING-SENSOR11146720000020240115173814242024-01-15 16:59:202
9마포구PARKING-SENSOR11146720000020240115183814242024-01-15 17:59:202