Overview

Dataset statistics

Number of variables5
Number of observations3377
Missing cells5
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory138.6 KiB
Average record size in memory42.0 B

Variable types

Categorical1
Text2
Numeric2

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. 10. 1. 기준)
Author강서구시설관리공단
URLhttps://www.data.go.kr/data/15124411/fileData.do

Alerts

위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
행정동 is highly overall correlated with 위도 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 12:35:32.367273
Analysis finished2023-12-12 12:35:33.360384
Duration0.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
공항동
334 
화곡6동
284 
화곡1동
251 
방화2동
245 
우장산동
241 
Other values (15)
2022 

Length

Max length4
Median length4
Mean length3.8777021
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공항동 334
 
9.9%
화곡6동 284
 
8.4%
화곡1동 251
 
7.4%
방화2동 245
 
7.3%
우장산동 241
 
7.1%
화곡4동 238
 
7.0%
등촌1동 203
 
6.0%
방화1동 193
 
5.7%
화곡3동 183
 
5.4%
발산1동 180
 
5.3%
Other values (10) 1025
30.4%

Length

2023-12-12T21:35:33.451696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공항동 334
 
9.9%
화곡6동 284
 
8.4%
화곡1동 251
 
7.4%
방화2동 245
 
7.3%
우장산동 241
 
7.1%
화곡4동 238
 
7.0%
등촌1동 203
 
6.0%
방화1동 193
 
5.7%
화곡3동 183
 
5.4%
발산1동 180
 
5.3%
Other values (10) 1025
30.4%

구간명
Text

Distinct581
Distinct (%)17.2%
Missing5
Missing (%)0.1%
Memory size26.5 KiB
2023-12-12T21:35:33.926727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length4.7502966
Min length3

Characters and Unicode

Total characters16018
Distinct characters22
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

Unique78 ?
Unique (%)2.3%

Sample

1st row1_13
2nd row1_13
3rd row1_13
4th row1_13
5th row1_13
ValueCountFrequency (%)
홈플러스(전일 110
 
3.3%
15_13 32
 
0.9%
15_14 29
 
0.9%
6_63 29
 
0.9%
18_48 27
 
0.8%
14_12 26
 
0.8%
11_79 25
 
0.7%
15_11 25
 
0.7%
17_51 25
 
0.7%
22_25 23
 
0.7%
Other values (571) 3021
89.6%
2023-12-12T21:35:34.616308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 3250
20.3%
1 3049
19.0%
2 1853
11.6%
3 1048
 
6.5%
0 1015
 
6.3%
9 922
 
5.8%
4 886
 
5.5%
5 872
 
5.4%
8 772
 
4.8%
7 770
 
4.8%
Other values (12) 1581
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11849
74.0%
Connector Punctuation 3250
 
20.3%
Other Letter 699
 
4.4%
Close Punctuation 110
 
0.7%
Open Punctuation 110
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3049
25.7%
2 1853
15.6%
3 1048
 
8.8%
0 1015
 
8.6%
9 922
 
7.8%
4 886
 
7.5%
5 872
 
7.4%
8 772
 
6.5%
7 770
 
6.5%
6 662
 
5.6%
Other Letter
ValueCountFrequency (%)
홈 110
15.7%
플 110
15.7%
일 110
15.7%
전 110
15.7%
스 110
15.7%
러 110
15.7%
한 13
 
1.9%
광 13
 
1.9%
고 13
 
1.9%
Connector Punctuation
ValueCountFrequency (%)
_ 3250
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15319
95.6%
Hangul 699
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 3250
21.2%
1 3049
19.9%
2 1853
12.1%
3 1048
 
6.8%
0 1015
 
6.6%
9 922
 
6.0%
4 886
 
5.8%
5 872
 
5.7%
8 772
 
5.0%
7 770
 
5.0%
Other values (3) 882
 
5.8%
Hangul
ValueCountFrequency (%)
홈 110
15.7%
플 110
15.7%
일 110
15.7%
전 110
15.7%
스 110
15.7%
러 110
15.7%
한 13
 
1.9%
광 13
 
1.9%
고 13
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15319
95.6%
Hangul 699
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 3250
21.2%
1 3049
19.9%
2 1853
12.1%
3 1048
 
6.8%
0 1015
 
6.6%
9 922
 
6.0%
4 886
 
5.8%
5 872
 
5.7%
8 772
 
5.0%
7 770
 
5.0%
Other values (3) 882
 
5.8%
Hangul
ValueCountFrequency (%)
홈 110
15.7%
플 110
15.7%
일 110
15.7%
전 110
15.7%
스 110
15.7%
러 110
15.7%
한 13
 
1.9%
광 13
 
1.9%
고 13
 
1.9%

구획번호
Text

Distinct3373
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size26.5 KiB
2023-12-12T21:35:35.044136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length7.9523245
Min length5

Characters and Unicode

Total characters26855
Distinct characters22
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

Unique3369 ?
Unique (%)99.8%

Sample

1st row1_13_05
2nd row1_13_01
3rd row1_13_02
4th row1_13_03
5th row1_13_04
ValueCountFrequency (%)
8_45_106 2
 
0.1%
21_92_103 2
 
0.1%
6_10_118 2
 
0.1%
8_31_102 2
 
0.1%
19_06_103 1
 
< 0.1%
19_04_103 1
 
< 0.1%
19_03_102 1
 
< 0.1%
19_06_06 1
 
< 0.1%
19_09_06 1
 
< 0.1%
1_13_05 1
 
< 0.1%
Other values (3363) 3363
99.6%
2023-12-12T21:35:35.633523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 6631
24.7%
1 5087
18.9%
0 3444
12.8%
2 2549
 
9.5%
3 1527
 
5.7%
4 1286
 
4.8%
5 1215
 
4.5%
9 1134
 
4.2%
7 1051
 
3.9%
8 1026
 
3.8%
Other values (12) 1905
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19305
71.9%
Connector Punctuation 6631
 
24.7%
Other Letter 699
 
2.6%
Open Punctuation 110
 
0.4%
Close Punctuation 110
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5087
26.4%
0 3444
17.8%
2 2549
13.2%
3 1527
 
7.9%
4 1286
 
6.7%
5 1215
 
6.3%
9 1134
 
5.9%
7 1051
 
5.4%
8 1026
 
5.3%
6 986
 
5.1%
Other Letter
ValueCountFrequency (%)
일 110
15.7%
전 110
15.7%
홈 110
15.7%
스 110
15.7%
러 110
15.7%
플 110
15.7%
한 13
 
1.9%
광 13
 
1.9%
고 13
 
1.9%
Connector Punctuation
ValueCountFrequency (%)
_ 6631
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26156
97.4%
Hangul 699
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 6631
25.4%
1 5087
19.4%
0 3444
13.2%
2 2549
 
9.7%
3 1527
 
5.8%
4 1286
 
4.9%
5 1215
 
4.6%
9 1134
 
4.3%
7 1051
 
4.0%
8 1026
 
3.9%
Other values (3) 1206
 
4.6%
Hangul
ValueCountFrequency (%)
일 110
15.7%
전 110
15.7%
홈 110
15.7%
스 110
15.7%
러 110
15.7%
플 110
15.7%
한 13
 
1.9%
광 13
 
1.9%
고 13
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26156
97.4%
Hangul 699
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 6631
25.4%
1 5087
19.4%
0 3444
13.2%
2 2549
 
9.7%
3 1527
 
5.8%
4 1286
 
4.9%
5 1215
 
4.6%
9 1134
 
4.3%
7 1051
 
4.0%
8 1026
 
3.9%
Other values (3) 1206
 
4.6%
Hangul
ValueCountFrequency (%)
일 110
15.7%
전 110
15.7%
홈 110
15.7%
스 110
15.7%
러 110
15.7%
플 110
15.7%
한 13
 
1.9%
광 13
 
1.9%
고 13
 
1.9%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct3254
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.552358
Minimum37.527011
Maximum37.589388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-12-12T21:35:35.801578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.527011
5-th percentile37.530496
Q137.541772
median37.552432
Q337.562791
95-th percentile37.575348
Maximum37.589388
Range0.06237696
Interquartile range (IQR)0.02101872

Descriptive statistics

Standard deviation0.013650407
Coefficient of variation (CV)0.00036350332
Kurtosis-0.88065007
Mean37.552358
Median Absolute Deviation (MAD)0.01046755
Skewness0.064226719
Sum126814.31
Variance0.00018633361
MonotonicityNot monotonic
2023-12-12T21:35:35.986971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5584621 110
 
3.3%
37.5306077 3
 
0.1%
37.56743733 3
 
0.1%
37.5564419 2
 
0.1%
37.53692884 2
 
0.1%
37.53204586 2
 
0.1%
37.54178068 2
 
0.1%
37.55071728 2
 
0.1%
37.543432 2
 
0.1%
37.57840834 2
 
0.1%
Other values (3244) 3247
96.2%
ValueCountFrequency (%)
37.52701072 1
< 0.1%
37.52728998 1
< 0.1%
37.52742757 1
< 0.1%
37.52743891 1
< 0.1%
37.52746158 1
< 0.1%
37.52747067 1
< 0.1%
37.52748654 1
< 0.1%
37.52749789 1
< 0.1%
37.52750473 1
< 0.1%
37.52751833 1
< 0.1%
ValueCountFrequency (%)
37.58938768 1
< 0.1%
37.58003004 1
< 0.1%
37.58002882 1
< 0.1%
37.58002735 1
< 0.1%
37.58002726 1
< 0.1%
37.58002717 1
< 0.1%
37.58002674 1
< 0.1%
37.58002663 1
< 0.1%
37.58002654 1
< 0.1%
37.58001093 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct3239
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.83817
Minimum126.80148
Maximum126.87729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.8 KiB
2023-12-12T21:35:36.137050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80148
5-th percentile126.80869
Q1126.81606
median126.84215
Q3126.85365
95-th percentile126.8627
Maximum126.87729
Range0.0758037
Interquartile range (IQR)0.0375843

Descriptive statistics

Standard deviation0.018373627
Coefficient of variation (CV)0.00014485882
Kurtosis-1.1599833
Mean126.83817
Median Absolute Deviation (MAD)0.0127782
Skewness-0.31762901
Sum428332.48
Variance0.00033759018
MonotonicityNot monotonic
2023-12-12T21:35:36.571765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8549234 111
 
3.3%
126.8549423 3
 
0.1%
126.8061483 3
 
0.1%
126.876454 2
 
0.1%
126.8151631 2
 
0.1%
126.8226123 2
 
0.1%
126.8377659 2
 
0.1%
126.8072604 2
 
0.1%
126.83206 2
 
0.1%
126.8568814 2
 
0.1%
Other values (3229) 3246
96.1%
ValueCountFrequency (%)
126.8014848 1
< 0.1%
126.8015543 1
< 0.1%
126.8016551 1
< 0.1%
126.8016822 1
< 0.1%
126.8020092 1
< 0.1%
126.802021 1
< 0.1%
126.8020376 1
< 0.1%
126.8021172 1
< 0.1%
126.8021371 1
< 0.1%
126.8052895 1
< 0.1%
ValueCountFrequency (%)
126.8772885 1
< 0.1%
126.8764668 1
< 0.1%
126.8764633 1
< 0.1%
126.8764587 1
< 0.1%
126.8764553 1
< 0.1%
126.876454 2
0.1%
126.8764458 1
< 0.1%
126.8762511 1
< 0.1%
126.8761945 1
< 0.1%
126.8761379 1
< 0.1%

Interactions

2023-12-12T21:35:32.872380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:32.631094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:33.049317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:35:32.739479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:35:36.697798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동위도경도
행정동1.0000.9570.957
위도0.9571.0000.732
경도0.9570.7321.000
2023-12-12T21:35:36.778293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동
위도1.000-0.5310.670
경도-0.5311.0000.671
행정동0.6700.6711.000

Missing values

2023-12-12T21:35:33.206997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:35:33.314311image/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

행정동구간명구획번호위도경도
0염창동1_131_13_0537.552936126.874375
1염창동1_131_13_0137.553118126.874342
2염창동1_131_13_0237.552984126.87437
3염창동1_131_13_0337.553033126.874365
4염창동1_131_13_0437.553083126.874361
5염창동1_151_15_0237.550514126.876251
6염창동1_151_15_0337.550528126.876194
7염창동1_151_15_0437.550543126.876138
8염창동1_151_15_1137.550577126.876005
9염창동1_201_20_10437.547999126.877289
행정동구간명구획번호위도경도
3367우장산동18_5018_50_0337.554419126.843277
3368우장산동18_5018_50_0437.554442126.843319
3369우장산동18_5018_50_0537.554453126.843367
3370우장산동18_5018_50_0637.554433126.843495
3371우장산동18_5018_50_0737.554403126.843467
3372우장산동18_5018_50_0837.55437126.843438
3373우장산동18_5018_50_0937.554336126.843407
3374우장산동18_5018_50_1037.554307126.843371
3375우장산동18_5018_50_1137.554315126.843249
3376우장산동18_5018_50_1237.554324126.843187