Overview

Dataset statistics

Number of variables7
Number of observations3495
Missing cells121
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory201.5 KiB
Average record size in memory59.0 B

Variable types

Numeric3
Text2
Categorical2

Dataset

Description서울특별시 강서구 내 강서구시설관리공단이 관리 및 운영하고 있는 거주자우선주차 구획선 정보입니다. 구획명, 동명, 구간명 등 거주자우선주차장 구획선에 대한 경도 및 위도에 대한 정보가 있습니다.(2023.4.1.기준)
URLhttps://www.data.go.kr/data/15086251/fileData.do

Alerts

연번 is highly overall correlated with 동명 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
동명 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 연번High correlation
경도 has 76 (2.2%) missing valuesMissing
위도 has 45 (1.3%) missing valuesMissing
연번 has unique valuesUnique
구획명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:54:47.201050
Analysis finished2023-12-12 13:54:49.753364
Duration2.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3495
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1748
Minimum1
Maximum3495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T22:54:49.842717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile175.7
Q1874.5
median1748
Q32621.5
95-th percentile3320.3
Maximum3495
Range3494
Interquartile range (IQR)1747

Descriptive statistics

Standard deviation1009.0639
Coefficient of variation (CV)0.57726769
Kurtosis-1.2
Mean1748
Median Absolute Deviation (MAD)874
Skewness0
Sum6109260
Variance1018210
MonotonicityStrictly increasing
2023-12-12T22:54:50.054605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2335 1
 
< 0.1%
2324 1
 
< 0.1%
2325 1
 
< 0.1%
2326 1
 
< 0.1%
2327 1
 
< 0.1%
2328 1
 
< 0.1%
2329 1
 
< 0.1%
2330 1
 
< 0.1%
2331 1
 
< 0.1%
Other values (3485) 3485
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3495 1
< 0.1%
3494 1
< 0.1%
3493 1
< 0.1%
3492 1
< 0.1%
3491 1
< 0.1%
3490 1
< 0.1%
3489 1
< 0.1%
3488 1
< 0.1%
3487 1
< 0.1%
3486 1
< 0.1%

구획명
Text

UNIQUE 

Distinct3495
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
2023-12-12T22:54:50.431528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length7.9473534
Min length7

Characters and Unicode

Total characters27776
Distinct characters29
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3495 ?
Unique (%)100.0%

Sample

1st row1_13_01
2nd row1_13_02
3rd row1_13_03
4th row1_13_04
5th row1_13_05
ValueCountFrequency (%)
1_13_01 1
 
< 0.1%
21_16_13 1
 
< 0.1%
21_14_09 1
 
< 0.1%
21_21_02 1
 
< 0.1%
21_14_10 1
 
< 0.1%
21_14_11 1
 
< 0.1%
21_14_12 1
 
< 0.1%
21_14_13 1
 
< 0.1%
21_14_14 1
 
< 0.1%
21_14_15 1
 
< 0.1%
Other values (3485) 3485
99.7%
2023-12-12T22:54:50.988601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 6810
24.5%
1 5138
18.5%
0 3522
12.7%
2 2642
 
9.5%
3 1579
 
5.7%
4 1331
 
4.8%
5 1268
 
4.6%
9 1131
 
4.1%
7 1072
 
3.9%
8 1059
 
3.8%
Other values (19) 2224
 
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19732
71.0%
Connector Punctuation 6810
 
24.5%
Other Letter 1014
 
3.7%
Close Punctuation 110
 
0.4%
Open Punctuation 110
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 155
15.3%
홈 110
10.8%
일 110
10.8%
전 110
10.8%
러 110
10.8%
플 110
10.8%
퀘 45
 
4.4%
어 45
 
4.4%
터 45
 
4.4%
센 45
 
4.4%
Other values (6) 129
12.7%
Decimal Number
ValueCountFrequency (%)
1 5138
26.0%
0 3522
17.8%
2 2642
13.4%
3 1579
 
8.0%
4 1331
 
6.7%
5 1268
 
6.4%
9 1131
 
5.7%
7 1072
 
5.4%
8 1059
 
5.4%
6 990
 
5.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6810
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26762
96.3%
Hangul 1014
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 155
15.3%
홈 110
10.8%
일 110
10.8%
전 110
10.8%
러 110
10.8%
플 110
10.8%
퀘 45
 
4.4%
어 45
 
4.4%
터 45
 
4.4%
센 45
 
4.4%
Other values (6) 129
12.7%
Common
ValueCountFrequency (%)
_ 6810
25.4%
1 5138
19.2%
0 3522
13.2%
2 2642
 
9.9%
3 1579
 
5.9%
4 1331
 
5.0%
5 1268
 
4.7%
9 1131
 
4.2%
7 1072
 
4.0%
8 1059
 
4.0%
Other values (3) 1210
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26762
96.3%
Hangul 1014
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 6810
25.4%
1 5138
19.2%
0 3522
13.2%
2 2642
 
9.9%
3 1579
 
5.9%
4 1331
 
5.0%
5 1268
 
4.7%
9 1131
 
4.2%
7 1072
 
4.0%
8 1059
 
4.0%
Other values (3) 1210
 
4.5%
Hangul
ValueCountFrequency (%)
스 155
15.3%
홈 110
10.8%
일 110
10.8%
전 110
10.8%
러 110
10.8%
플 110
10.8%
퀘 45
 
4.4%
어 45
 
4.4%
터 45
 
4.4%
센 45
 
4.4%
Other values (6) 129
12.7%

동명
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
공항동
322 
화곡6동
281 
우장산동
256 
방화2동
252 
화곡1동
246 
Other values (15)
2138 

Length

Max length4
Median length4
Mean length3.8712446
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공항동 322
 
9.2%
화곡6동 281
 
8.0%
우장산동 256
 
7.3%
방화2동 252
 
7.2%
화곡1동 246
 
7.0%
화곡4동 230
 
6.6%
화곡3동 205
 
5.9%
등촌1동 203
 
5.8%
방화1동 201
 
5.8%
발산1동 184
 
5.3%
Other values (10) 1115
31.9%

Length

2023-12-12T22:54:51.185448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공항동 322
 
9.2%
화곡6동 281
 
8.0%
우장산동 256
 
7.3%
방화2동 252
 
7.2%
화곡1동 246
 
7.0%
화곡4동 230
 
6.6%
화곡3동 205
 
5.9%
등촌1동 203
 
5.8%
방화1동 201
 
5.8%
발산1동 184
 
5.3%
Other values (10) 1115
31.9%

구간명
Text

Distinct596
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
2023-12-12T22:54:51.714849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.7525036
Min length4

Characters and Unicode

Total characters16610
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)2.4%

Sample

1st row1_13
2nd row1_13
3rd row1_13
4th row1_13
5th row1_13
ValueCountFrequency (%)
홈플러스(전일 110
 
3.1%
센터스퀘어 45
 
1.3%
15_13 32
 
0.9%
15_14 30
 
0.9%
6_63 29
 
0.8%
18_48 27
 
0.8%
14_12 26
 
0.7%
헤링턴타워 25
 
0.7%
17_51 25
 
0.7%
11_79 25
 
0.7%
Other values (586) 3121
89.3%
2023-12-12T22:54:52.442404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 3315
20.0%
1 3064
18.4%
2 1912
11.5%
3 1079
 
6.5%
0 1032
 
6.2%
5 918
 
5.5%
4 915
 
5.5%
9 914
 
5.5%
8 796
 
4.8%
7 778
 
4.7%
Other values (18) 1887
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12065
72.6%
Connector Punctuation 3315
 
20.0%
Other Letter 1010
 
6.1%
Open Punctuation 110
 
0.7%
Close Punctuation 110
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 155
15.3%
러 110
10.9%
일 110
10.9%
전 110
10.9%
플 110
10.9%
홈 110
10.9%
어 45
 
4.5%
퀘 45
 
4.5%
터 45
 
4.5%
센 45
 
4.5%
Other values (5) 125
12.4%
Decimal Number
ValueCountFrequency (%)
1 3064
25.4%
2 1912
15.8%
3 1079
 
8.9%
0 1032
 
8.6%
5 918
 
7.6%
4 915
 
7.6%
9 914
 
7.6%
8 796
 
6.6%
7 778
 
6.4%
6 657
 
5.4%
Connector Punctuation
ValueCountFrequency (%)
_ 3315
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15600
93.9%
Hangul 1010
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 155
15.3%
러 110
10.9%
일 110
10.9%
전 110
10.9%
플 110
10.9%
홈 110
10.9%
어 45
 
4.5%
퀘 45
 
4.5%
터 45
 
4.5%
센 45
 
4.5%
Other values (5) 125
12.4%
Common
ValueCountFrequency (%)
_ 3315
21.2%
1 3064
19.6%
2 1912
12.3%
3 1079
 
6.9%
0 1032
 
6.6%
5 918
 
5.9%
4 915
 
5.9%
9 914
 
5.9%
8 796
 
5.1%
7 778
 
5.0%
Other values (3) 877
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15600
93.9%
Hangul 1010
 
6.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 3315
21.2%
1 3064
19.6%
2 1912
12.3%
3 1079
 
6.9%
0 1032
 
6.6%
5 918
 
5.9%
4 915
 
5.9%
9 914
 
5.9%
8 796
 
5.1%
7 778
 
5.0%
Other values (3) 877
 
5.6%
Hangul
ValueCountFrequency (%)
스 155
15.3%
러 110
10.9%
일 110
10.9%
전 110
10.9%
플 110
10.9%
홈 110
10.9%
어 45
 
4.5%
퀘 45
 
4.5%
터 45
 
4.5%
센 45
 
4.5%
Other values (5) 125
12.4%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size27.4 KiB
일반
2799 
지정
696 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 2799
80.1%
지정 696
 
19.9%

Length

2023-12-12T22:54:52.616673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:54:52.732879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 2799
80.1%
지정 696
 
19.9%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct3307
Distinct (%)96.7%
Missing76
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean37.552372
Minimum37.527011
Maximum37.580027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T22:54:52.896822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.527011
5-th percentile37.53051
Q137.542382
median37.552295
Q337.562807
95-th percentile37.574238
Maximum37.580027
Range0.05301663
Interquartile range (IQR)0.020424989

Descriptive statistics

Standard deviation0.013501756
Coefficient of variation (CV)0.00035954468
Kurtosis-0.86656659
Mean37.552372
Median Absolute Deviation (MAD)0.010258085
Skewness0.05584918
Sum128391.56
Variance0.00018229741
MonotonicityNot monotonic
2023-12-12T22:54:53.087333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5584621 110
 
3.1%
37.532045855224 2
 
0.1%
37.550717279053 2
 
0.1%
37.5541896041038 2
 
0.1%
37.5643745074544 1
 
< 0.1%
37.5646233334 1
 
< 0.1%
37.565150001262 1
 
< 0.1%
37.5651440195959 1
 
< 0.1%
37.5651463316627 1
 
< 0.1%
37.5651489502262 1
 
< 0.1%
Other values (3297) 3297
94.3%
(Missing) 76
 
2.2%
ValueCountFrequency (%)
37.5270107179736 1
< 0.1%
37.5272899782171 1
< 0.1%
37.5274275700365 1
< 0.1%
37.527438912078 1
< 0.1%
37.527461584716 1
< 0.1%
37.527470674176 1
< 0.1%
37.5274865400059 1
< 0.1%
37.527497885676 1
< 0.1%
37.5275047338789 1
< 0.1%
37.5275183281993 1
< 0.1%
ValueCountFrequency (%)
37.5800273481 1
< 0.1%
37.5800272576 1
< 0.1%
37.5800271671 1
< 0.1%
37.580026628 1
< 0.1%
37.5800265375 1
< 0.1%
37.5800109271 1
< 0.1%
37.580010882 1
< 0.1%
37.5800101163 1
< 0.1%
37.5800100712 1
< 0.1%
37.5800094859 1
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct597
Distinct (%)17.3%
Missing45
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean126.83836
Minimum126.80155
Maximum126.87647
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.8 KiB
2023-12-12T22:54:53.256305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80155
5-th percentile126.80873
Q1126.81701
median126.84198
Q3126.85413
95-th percentile126.8628
Maximum126.87647
Range0.074912478
Interquartile range (IQR)0.03711757

Descriptive statistics

Standard deviation0.018372857
Coefficient of variation (CV)0.00014485253
Kurtosis-1.1564048
Mean126.83836
Median Absolute Deviation (MAD)0.0129431
Skewness-0.31798429
Sum437592.33
Variance0.00033756186
MonotonicityNot monotonic
2023-12-12T22:54:53.445621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8549234 110
 
3.1%
126.854131511 32
 
0.9%
126.8516652535 30
 
0.9%
126.837130060556 29
 
0.8%
126.848385722 29
 
0.8%
126.844311641143 27
 
0.8%
126.8421250512 26
 
0.7%
126.83607486323 25
 
0.7%
126.856726768 25
 
0.7%
126.822793245568 25
 
0.7%
Other values (587) 3092
88.5%
(Missing) 45
 
1.3%
ValueCountFrequency (%)
126.801554339628 2
 
0.1%
126.801655298132 1
 
< 0.1%
126.8056710066 7
0.2%
126.806148338568 10
0.3%
126.806557095467 1
 
< 0.1%
126.806725646168 4
 
0.1%
126.8067262509 7
0.2%
126.8069914281 2
 
0.1%
126.8072658279 13
0.4%
126.807275599537 13
0.4%
ValueCountFrequency (%)
126.876466818 7
0.2%
126.8762510976 5
0.1%
126.8743746638 5
0.1%
126.873978363663 4
 
0.1%
126.870705356482 8
0.2%
126.870556924747 12
0.3%
126.8698639411 7
0.2%
126.8696426693 7
0.2%
126.8695068654 7
0.2%
126.8681971 5
0.1%

Interactions

2023-12-12T22:54:48.612616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:47.749784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.218484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.770104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:47.907324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.363329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.911942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.050716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:54:48.467501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:54:53.553372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동명구분경도위도
연번1.0000.9560.9340.8600.817
동명0.9561.0000.3550.9620.962
구분0.9340.3551.0000.3120.115
경도0.8600.9620.3121.0000.759
위도0.8170.9620.1150.7591.000
2023-12-12T22:54:53.653797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분동명
구분1.0000.280
동명0.2801.000
2023-12-12T22:54:53.742676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번경도위도동명구분
연번1.0000.356-0.4060.6670.783
경도0.3561.000-0.5240.6880.239
위도-0.406-0.5241.0000.6870.088
동명0.6670.6880.6871.0000.280
구분0.7830.2390.0880.2801.000

Missing values

2023-12-12T22:54:49.093711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:54:49.579498image/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.
2023-12-12T22:54:49.691507image/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

연번구획명동명구간명구분경도위도
011_13_01염창동1_13일반37.553118126.874375
121_13_02염창동1_13일반37.552984126.874375
231_13_03염창동1_13일반37.553033126.874375
341_13_04염창동1_13일반37.553083126.874375
451_13_05염창동1_13일반37.552936126.874375
561_15_02염창동1_15일반37.550514126.876251
671_15_03염창동1_15일반37.550528126.876251
781_15_04염창동1_15일반37.550543126.876251
891_15_11염창동1_15일반37.550577126.876251
9101_22_01염창동1_22일반37.548141126.876467
연번구획명동명구간명구분경도위도
34853486홈플러스(전일)_101등촌1동홈플러스(전일)일반37.558462126.854923
34863487홈플러스(전일)_102등촌1동홈플러스(전일)일반37.558462126.854923
34873488홈플러스(전일)_103등촌1동홈플러스(전일)일반37.558462126.854923
34883489홈플러스(전일)_104등촌1동홈플러스(전일)일반37.558462126.854923
34893490홈플러스(전일)_105등촌1동홈플러스(전일)일반37.558462126.854923
34903491홈플러스(전일)_106등촌1동홈플러스(전일)일반37.558462126.854923
34913492홈플러스(전일)_107등촌1동홈플러스(전일)일반37.558462126.854923
34923493홈플러스(전일)_108등촌1동홈플러스(전일)일반37.558462126.854923
34933494홈플러스(전일)_109등촌1동홈플러스(전일)일반37.558462126.854923
34943495홈플러스(전일)_110등촌1동홈플러스(전일)일반37.558462126.854923