Overview

Dataset statistics

Number of variables9
Number of observations21
Missing cells27
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory86.3 B

Variable types

Numeric5
Text1
Categorical3

Dataset

Description서울특별시 중랑구 공영주차장의 주차구획 정보를 제공합니다. 주차장명, 여성,장애인,저공해,충전소,비상벨 현황을 제공합니다. 참고해주시기 바랍니다.
URLhttps://www.data.go.kr/data/15101098/fileData.do

Alerts

저공해 is highly overall correlated with 연번 and 4 other fieldsHigh correlation
비상벨 is highly overall correlated with 여성 and 2 other fieldsHigh correlation
연번 is highly overall correlated with 총구획수 and 2 other fieldsHigh correlation
총구획수 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
여성 is highly overall correlated with 총구획수 and 5 other fieldsHigh correlation
장애인 is highly overall correlated with 총구획수 and 2 other fieldsHigh correlation
경차 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
충전소 is highly overall correlated with 여성 and 1 other fieldsHigh correlation
저공해 is highly imbalanced (65.4%)Imbalance
여성 has 9 (42.9%) missing valuesMissing
장애인 has 3 (14.3%) missing valuesMissing
경차 has 15 (71.4%) missing valuesMissing
연번 has unique valuesUnique
주차장명 has unique valuesUnique
총구획수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:24:58.820313
Analysis finished2023-12-12 15:25:02.400035
Duration3.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T00:25:02.478251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.56407607
Kurtosis-1.2
Mean11
Median Absolute Deviation (MAD)5
Skewness0
Sum231
Variance38.5
MonotonicityStrictly increasing
2023-12-13T00:25:02.622361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
21 1
 
4.8%
20 1
 
4.8%
19 1
 
4.8%
18 1
 
4.8%
17 1
 
4.8%
16 1
 
4.8%
15 1
 
4.8%
14 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
3 1
4.8%
4 1
4.8%
5 1
4.8%
6 1
4.8%
7 1
4.8%
8 1
4.8%
9 1
4.8%
10 1
4.8%
ValueCountFrequency (%)
21 1
4.8%
20 1
4.8%
19 1
4.8%
18 1
4.8%
17 1
4.8%
16 1
4.8%
15 1
4.8%
14 1
4.8%
13 1
4.8%
12 1
4.8%

주차장명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T00:25:02.820657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.5238095
Min length2

Characters and Unicode

Total characters95
Distinct characters49
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row우림시장
2nd row동원시장
3rd row면목시장
4th row망우본동
5th row은행나무
ValueCountFrequency (%)
우림시장 1
 
4.3%
중랑초교 1
 
4.3%
봉화산역 1
 
4.3%
제1~2 1
 
4.3%
중랑역 1
 
4.3%
면목유수지 1
 
4.3%
제1~4 1
 
4.3%
중화 1
 
4.3%
망우3동 1
 
4.3%
면목5동 1
 
4.3%
Other values (13) 13
56.5%
2023-12-13T00:25:03.269681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 8
 
8.4%
면 6
 
6.3%
목 5
 
5.3%
중 4
 
4.2%
시 4
 
4.2%
장 4
 
4.2%
2 4
 
4.2%
우 3
 
3.2%
초 3
 
3.2%
교 3
 
3.2%
Other values (39) 51
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80
84.2%
Decimal Number 9
 
9.5%
Space Separator 2
 
2.1%
Math Symbol 2
 
2.1%
Open Punctuation 1
 
1.1%
Close Punctuation 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8
 
10.0%
면 6
 
7.5%
목 5
 
6.2%
중 4
 
5.0%
시 4
 
5.0%
장 4
 
5.0%
우 3
 
3.8%
초 3
 
3.8%
교 3
 
3.8%
봉 3
 
3.8%
Other values (30) 37
46.2%
Decimal Number
ValueCountFrequency (%)
2 4
44.4%
1 2
22.2%
3 1
 
11.1%
5 1
 
11.1%
4 1
 
11.1%
Space Separator
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 80
84.2%
Common 15
 
15.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8
 
10.0%
면 6
 
7.5%
목 5
 
6.2%
중 4
 
5.0%
시 4
 
5.0%
장 4
 
5.0%
우 3
 
3.8%
초 3
 
3.8%
교 3
 
3.8%
봉 3
 
3.8%
Other values (30) 37
46.2%
Common
ValueCountFrequency (%)
2 4
26.7%
2
13.3%
1 2
13.3%
~ 2
13.3%
( 1
 
6.7%
3 1
 
6.7%
5 1
 
6.7%
4 1
 
6.7%
) 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 80
84.2%
ASCII 15
 
15.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 8
 
10.0%
면 6
 
7.5%
목 5
 
6.2%
중 4
 
5.0%
시 4
 
5.0%
장 4
 
5.0%
우 3
 
3.8%
초 3
 
3.8%
교 3
 
3.8%
봉 3
 
3.8%
Other values (30) 37
46.2%
ASCII
ValueCountFrequency (%)
2 4
26.7%
2
13.3%
1 2
13.3%
~ 2
13.3%
( 1
 
6.7%
3 1
 
6.7%
5 1
 
6.7%
4 1
 
6.7%
) 1
 
6.7%

총구획수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128
Minimum25
Maximum567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T00:25:03.463541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile26
Q167
median90
Q3129
95-th percentile372
Maximum567
Range542
Interquartile range (IQR)62

Descriptive statistics

Standard deviation127.70278
Coefficient of variation (CV)0.99767797
Kurtosis6.9564087
Mean128
Median Absolute Deviation (MAD)35
Skewness2.5281509
Sum2688
Variance16308
MonotonicityNot monotonic
2023-12-13T00:25:03.686377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
44 1
 
4.8%
26 1
 
4.8%
129 1
 
4.8%
100 1
 
4.8%
217 1
 
4.8%
567 1
 
4.8%
372 1
 
4.8%
55 1
 
4.8%
67 1
 
4.8%
90 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
25 1
4.8%
26 1
4.8%
31 1
4.8%
44 1
4.8%
55 1
4.8%
67 1
4.8%
72 1
4.8%
77 1
4.8%
80 1
4.8%
82 1
4.8%
ValueCountFrequency (%)
567 1
4.8%
372 1
4.8%
217 1
4.8%
199 1
4.8%
140 1
4.8%
129 1
4.8%
119 1
4.8%
100 1
4.8%
99 1
4.8%
97 1
4.8%

여성
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing9
Missing (%)42.9%
Infinite0
Infinite (%)0.0%
Mean17.333333
Minimum7
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T00:25:03.828979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.55
Q19.75
median16.5
Q326.25
95-th percentile29.35
Maximum31
Range24
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation8.6794777
Coefficient of variation (CV)0.5007391
Kurtosis-1.5199019
Mean17.333333
Median Absolute Deviation (MAD)8
Skewness0.36286481
Sum208
Variance75.333333
MonotonicityNot monotonic
2023-12-13T00:25:03.970545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 1
 
4.8%
11 1
 
4.8%
31 1
 
4.8%
8 1
 
4.8%
27 1
 
4.8%
26 1
 
4.8%
18 1
 
4.8%
16 1
 
4.8%
7 1
 
4.8%
28 1
 
4.8%
Other values (2) 2
 
9.5%
(Missing) 9
42.9%
ValueCountFrequency (%)
7 1
4.8%
8 1
4.8%
9 1
4.8%
10 1
4.8%
11 1
4.8%
16 1
4.8%
17 1
4.8%
18 1
4.8%
26 1
4.8%
27 1
4.8%
ValueCountFrequency (%)
31 1
4.8%
28 1
4.8%
27 1
4.8%
26 1
4.8%
18 1
4.8%
17 1
4.8%
16 1
4.8%
11 1
4.8%
10 1
4.8%
9 1
4.8%

장애인
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)44.4%
Missing3
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean5.3888889
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T00:25:04.113767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.85
Q12.25
median4
Q34.75
95-th percentile18.05
Maximum24
Range23
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation5.8424299
Coefficient of variation (CV)1.0841622
Kurtosis6.4028989
Mean5.3888889
Median Absolute Deviation (MAD)1
Skewness2.5667326
Sum97
Variance34.133987
MonotonicityNot monotonic
2023-12-13T00:25:04.239666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 5
23.8%
2 4
19.0%
3 3
14.3%
5 2
 
9.5%
1 1
 
4.8%
17 1
 
4.8%
24 1
 
4.8%
8 1
 
4.8%
(Missing) 3
14.3%
ValueCountFrequency (%)
1 1
 
4.8%
2 4
19.0%
3 3
14.3%
4 5
23.8%
5 2
 
9.5%
8 1
 
4.8%
17 1
 
4.8%
24 1
 
4.8%
ValueCountFrequency (%)
24 1
 
4.8%
17 1
 
4.8%
8 1
 
4.8%
5 2
 
9.5%
4 5
23.8%
3 3
14.3%
2 4
19.0%
1 1
 
4.8%

경차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)100.0%
Missing15
Missing (%)71.4%
Infinite0
Infinite (%)0.0%
Mean11.833333
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T00:25:04.376858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16.25
median13
Q317.5
95-th percentile20.25
Maximum21
Range20
Interquartile range (IQR)11.25

Descriptive statistics

Standard deviation7.8336879
Coefficient of variation (CV)0.6620018
Kurtosis-1.6451011
Mean11.833333
Median Absolute Deviation (MAD)6.5
Skewness-0.32423093
Sum71
Variance61.366667
MonotonicityNot monotonic
2023-12-13T00:25:04.550844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
18 1
 
4.8%
10 1
 
4.8%
21 1
 
4.8%
5 1
 
4.8%
16 1
 
4.8%
1 1
 
4.8%
(Missing) 15
71.4%
ValueCountFrequency (%)
1 1
4.8%
5 1
4.8%
10 1
4.8%
16 1
4.8%
18 1
4.8%
21 1
4.8%
ValueCountFrequency (%)
21 1
4.8%
18 1
4.8%
16 1
4.8%
10 1
4.8%
5 1
4.8%
1 1
4.8%

저공해
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
19 
1
 
1
10
 
1

Length

Max length4
Median length4
Mean length3.7619048
Min length1

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 19
90.5%
1 1
 
4.8%
10 1
 
4.8%

Length

2023-12-13T00:25:04.717850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:04.846845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
90.5%
1 1
 
4.8%
10 1
 
4.8%

충전소
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
13 
4
4 
1
2 
20
 
1
3
 
1

Length

Max length4
Median length4
Mean length2.9047619
Min length1

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row4
2nd row<NA>
3rd row<NA>
4th row1
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 13
61.9%
4 4
 
19.0%
1 2
 
9.5%
20 1
 
4.8%
3 1
 
4.8%

Length

2023-12-13T00:25:04.969413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:05.074381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 13
61.9%
4 4
 
19.0%
1 2
 
9.5%
20 1
 
4.8%
3 1
 
4.8%

비상벨
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size300.0 B
<NA>
14 
1
6 
8
 
1

Length

Max length4
Median length4
Mean length3
Min length1

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 14
66.7%
1 6
28.6%
8 1
 
4.8%

Length

2023-12-13T00:25:05.197017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:25:05.310774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 14
66.7%
1 6
28.6%
8 1
 
4.8%

Interactions

2023-12-13T00:25:01.422903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:59.180964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:59.980848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.523342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.963714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:01.530723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:59.286358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.091115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.616232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:01.053763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:01.619428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:59.383422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.250925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.710495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:01.132469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:01.726540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:59.470438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.336066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.800541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:01.228635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:01.830469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:24:59.564126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.419175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:00.888948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:25:01.321231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:25:05.397621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주차장명총구획수여성장애인경차저공해충전소비상벨
연번1.0001.0000.2800.0000.0001.0000.0000.0000.000
주차장명1.0001.0001.0001.0001.0001.0000.0001.0001.000
총구획수0.2801.0001.0000.5390.8341.0000.0000.0000.000
여성0.0001.0000.5391.0000.4421.0000.0001.000NaN
장애인0.0001.0000.8340.4421.0001.0000.0000.7490.000
경차1.0001.0001.0001.0001.0001.000NaN1.0000.000
저공해0.0000.0000.0000.0000.000NaN1.000NaNNaN
충전소0.0001.0000.0001.0000.7491.000NaN1.0000.000
비상벨0.0001.0000.000NaN0.0000.000NaN0.0001.000
2023-12-13T00:25:05.518878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
저공해충전소비상벨
저공해1.000NaN1.000
충전소NaN1.0000.000
비상벨1.0000.0001.000
2023-12-13T00:25:05.635077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번총구획수여성장애인경차저공해충전소비상벨
연번1.0000.5780.0140.455-0.6001.0000.0000.000
총구획수0.5781.0000.6780.919-0.1431.0000.0000.000
여성0.0140.6781.0000.6141.0001.0000.5771.000
장애인0.4550.9190.6141.0000.0911.0000.1670.000
경차-0.600-0.1431.0000.0911.0000.0001.0001.000
저공해1.0001.0001.0001.0000.0001.000NaN1.000
충전소0.0000.0000.5770.1671.000NaN1.0000.000
비상벨0.0000.0001.0000.0001.0001.0000.0001.000

Missing values

2023-12-13T00:25:01.998757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:25:02.162185image/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.
2023-12-13T00:25:02.301725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번주차장명총구획수여성장애인경차저공해충전소비상벨
01우림시장449<NA><NA><NA>4<NA>
12동원시장26<NA>1<NA><NA><NA><NA>
23면목시장25<NA><NA><NA><NA><NA><NA>
34망우본동99<NA>418<NA>1<NA>
45은행나무97<NA>2<NA><NA><NA><NA>
56늘봄72<NA>2<NA><NA><NA><NA>
67봉수대31<NA><NA><NA><NA><NA><NA>
78용마폭포119114<NA><NA><NA><NA>
89면목2동199315<NA>141
910상봉2동8283<NA><NA><NA><NA>
연번주차장명총구획수여성장애인경차저공해충전소비상벨
1112면일초교80263<NA>10<NA>1
1213중랑초교77<NA>4<NA><NA><NA>1
1314까치공원90183<NA><NA><NA>1
1415면목5동67162<NA><NA><NA><NA>
1516망우3동557210<NA><NA><NA>
1617중화 제1~4372281721<NA>4<NA>
1718면목유수지567<NA>245<NA>20<NA>
1819중랑역 제1~2217178<NA><NA>1<NA>
1920봉화산역10010416<NA><NA>1
2021면목2동(동부시장)129<NA>41<NA>38