Overview

Dataset statistics

Number of variables5
Number of observations3363
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory138.1 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. (2023.7.1.기준)
URLhttps://www.data.go.kr/data/15117019/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
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 10:19:33.432236
Analysis finished2023-12-12 10:19:34.840556
Duration1.41 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
공항동
330 
화곡6동
284 
화곡1동
250 
방화2동
243 
우장산동
241 
Other values (15)
2015 

Length

Max length4
Median length4
Mean length3.8783824
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공항동 330
 
9.8%
화곡6동 284
 
8.4%
화곡1동 250
 
7.4%
방화2동 243
 
7.2%
우장산동 241
 
7.2%
화곡4동 238
 
7.1%
등촌1동 203
 
6.0%
방화1동 192
 
5.7%
화곡3동 183
 
5.4%
발산1동 180
 
5.4%
Other values (10) 1019
30.3%

Length

2023-12-12T19:19:34.943629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공항동 330
 
9.8%
화곡6동 284
 
8.4%
화곡1동 250
 
7.4%
방화2동 243
 
7.2%
우장산동 241
 
7.2%
화곡4동 238
 
7.1%
등촌1동 203
 
6.0%
방화1동 192
 
5.7%
화곡3동 183
 
5.4%
발산1동 180
 
5.4%
Other values (10) 1019
30.3%

구간명
Text

Distinct580
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
2023-12-12T19:19:35.437662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length4.7481415
Min length3

Characters and Unicode

Total characters15968
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

Unique80 ?
Unique (%)2.4%

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
 
1.0%
6_63 29
 
0.9%
15_14 29
 
0.9%
18_48 27
 
0.8%
14_12 26
 
0.8%
11_79 25
 
0.7%
17_51 25
 
0.7%
15_11 25
 
0.7%
22_25 23
 
0.7%
Other values (570) 3012
89.6%
2023-12-12T19:19:36.081579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 3239
20.3%
1 3041
19.0%
2 1851
11.6%
3 1044
 
6.5%
0 1007
 
6.3%
9 916
 
5.7%
4 886
 
5.5%
5 865
 
5.4%
8 770
 
4.8%
7 767
 
4.8%
Other values (12) 1582
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11807
73.9%
Connector Punctuation 3239
 
20.3%
Other Letter 702
 
4.4%
Close Punctuation 110
 
0.7%
Open Punctuation 110
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3041
25.8%
2 1851
15.7%
3 1044
 
8.8%
0 1007
 
8.5%
9 916
 
7.8%
4 886
 
7.5%
5 865
 
7.3%
8 770
 
6.5%
7 767
 
6.5%
6 660
 
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 14
 
2.0%
광 14
 
2.0%
고 14
 
2.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3239
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15266
95.6%
Hangul 702
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 3239
21.2%
1 3041
19.9%
2 1851
12.1%
3 1044
 
6.8%
0 1007
 
6.6%
9 916
 
6.0%
4 886
 
5.8%
5 865
 
5.7%
8 770
 
5.0%
7 767
 
5.0%
Other values (3) 880
 
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 14
 
2.0%
광 14
 
2.0%
고 14
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15266
95.6%
Hangul 702
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 3239
21.2%
1 3041
19.9%
2 1851
12.1%
3 1044
 
6.8%
0 1007
 
6.6%
9 916
 
6.0%
4 886
 
5.8%
5 865
 
5.7%
8 770
 
5.0%
7 767
 
5.0%
Other values (3) 880
 
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 14
 
2.0%
광 14
 
2.0%
고 14
 
2.0%

구획번호
Text

Distinct3358
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
2023-12-12T19:19:36.483909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length7.9515314
Min length5

Characters and Unicode

Total characters26741
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

Unique3353 ?
Unique (%)99.7%

Sample

1st row1_13_05
2nd row1_13_01
3rd row1_13_02
4th row1_13_03
5th row1_13_04
ValueCountFrequency (%)
6_10_118 2
 
0.1%
8_31_102 2
 
0.1%
21_92_103 2
 
0.1%
8_45_106 2
 
0.1%
17_06_102 2
 
0.1%
19_06_101 1
 
< 0.1%
19_09_05 1
 
< 0.1%
19_09_04 1
 
< 0.1%
19_06_103 1
 
< 0.1%
19_06_102 1
 
< 0.1%
Other values (3348) 3348
99.6%
2023-12-12T19:19:36.965472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 6602
24.7%
1 5066
18.9%
0 3426
12.8%
2 2543
 
9.5%
3 1520
 
5.7%
4 1285
 
4.8%
5 1205
 
4.5%
9 1126
 
4.2%
7 1046
 
3.9%
8 1022
 
3.8%
Other values (12) 1900
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19217
71.9%
Connector Punctuation 6602
 
24.7%
Other Letter 702
 
2.6%
Open Punctuation 110
 
0.4%
Close Punctuation 110
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5066
26.4%
0 3426
17.8%
2 2543
13.2%
3 1520
 
7.9%
4 1285
 
6.7%
5 1205
 
6.3%
9 1126
 
5.9%
7 1046
 
5.4%
8 1022
 
5.3%
6 978
 
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 14
 
2.0%
광 14
 
2.0%
고 14
 
2.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6602
100.0%
Open Punctuation
ValueCountFrequency (%)
( 110
100.0%
Close Punctuation
ValueCountFrequency (%)
) 110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26039
97.4%
Hangul 702
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
_ 6602
25.4%
1 5066
19.5%
0 3426
13.2%
2 2543
 
9.8%
3 1520
 
5.8%
4 1285
 
4.9%
5 1205
 
4.6%
9 1126
 
4.3%
7 1046
 
4.0%
8 1022
 
3.9%
Other values (3) 1198
 
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 14
 
2.0%
광 14
 
2.0%
고 14
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26039
97.4%
Hangul 702
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 6602
25.4%
1 5066
19.5%
0 3426
13.2%
2 2543
 
9.8%
3 1520
 
5.8%
4 1285
 
4.9%
5 1205
 
4.6%
9 1126
 
4.3%
7 1046
 
4.0%
8 1022
 
3.9%
Other values (3) 1198
 
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 14
 
2.0%
광 14
 
2.0%
고 14
 
2.0%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct3243
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.552346
Minimum37.527011
Maximum37.589388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2023-12-12T19:19:37.120359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.527011
5-th percentile37.530492
Q137.541724
median37.552413
Q337.562791
95-th percentile37.575359
Maximum37.589388
Range0.062376959
Interquartile range (IQR)0.021067612

Descriptive statistics

Standard deviation0.013658884
Coefficient of variation (CV)0.00036372917
Kurtosis-0.88099565
Mean37.552346
Median Absolute Deviation (MAD)0.010487909
Skewness0.068143146
Sum126288.54
Variance0.0001865651
MonotonicityNot monotonic
2023-12-12T19:19:37.282631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5584621 110
 
3.3%
37.5306076975 3
 
0.1%
37.543432 2
 
0.1%
37.5369288396 2
 
0.1%
37.532045855224 2
 
0.1%
37.550717279053 2
 
0.1%
37.5417806767 2
 
0.1%
37.5541896041038 2
 
0.1%
37.5538877665607 2
 
0.1%
37.5564418952762 2
 
0.1%
Other values (3233) 3234
96.2%
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 (%)
37.5893876771916 1
< 0.1%
37.5800300356645 1
< 0.1%
37.5800288202083 1
< 0.1%
37.5800273481 1
< 0.1%
37.5800272576 1
< 0.1%
37.5800271671 1
< 0.1%
37.5800267372299 1
< 0.1%
37.580026628 1
< 0.1%
37.5800265375 1
< 0.1%
37.5800109271 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct3243
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.83818
Minimum126.80148
Maximum126.87729
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.7 KiB
2023-12-12T19:19:37.438090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.80148
5-th percentile126.80872
Q1126.81614
median126.84215
Q3126.85364
95-th percentile126.8627
Maximum126.87729
Range0.075803691
Interquartile range (IQR)0.037496179

Descriptive statistics

Standard deviation0.018353175
Coefficient of variation (CV)0.00014469755
Kurtosis-1.1554005
Mean126.83818
Median Absolute Deviation (MAD)0.012778188
Skewness-0.31932946
Sum426556.81
Variance0.00033683901
MonotonicityNot monotonic
2023-12-12T19:19:37.616492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8549234 110
 
3.3%
126.8549422569 3
 
0.1%
126.835838 2
 
0.1%
126.8568814487 2
 
0.1%
126.842257410005 2
 
0.1%
126.844311641143 2
 
0.1%
126.8373064384 2
 
0.1%
126.843540511292 2
 
0.1%
126.832059956267 2
 
0.1%
126.819706861684 2
 
0.1%
Other values (3233) 3234
96.2%
ValueCountFrequency (%)
126.801484784142 1
< 0.1%
126.801554339628 1
< 0.1%
126.801655051164 1
< 0.1%
126.80168224203 1
< 0.1%
126.802009238679 1
< 0.1%
126.802021000273 1
< 0.1%
126.802037626772 1
< 0.1%
126.802117187216 1
< 0.1%
126.802137096658 1
< 0.1%
126.8052895 1
< 0.1%
ValueCountFrequency (%)
126.8772884749 1
< 0.1%
126.876466818 1
< 0.1%
126.8764633421 1
< 0.1%
126.8764587313 1
< 0.1%
126.8764552524 1
< 0.1%
126.8764540369 1
< 0.1%
126.8764539578 1
< 0.1%
126.8764458391 1
< 0.1%
126.8762510976 1
< 0.1%
126.8761944836 1
< 0.1%

Interactions

2023-12-12T19:19:34.334240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:34.046665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:34.489649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:19:34.175631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:19:37.755713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동위도경도
행정동1.0000.9560.956
위도0.9561.0000.732
경도0.9560.7321.000
2023-12-12T19:19:37.888955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도행정동
위도1.000-0.5320.670
경도-0.5321.0000.670
행정동0.6700.6701.000

Missing values

2023-12-12T19:19:34.669538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:19:34.787692image/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_10437.547999126.877288
행정동구간명구획번호위도경도
3353우장산동18_5018_50_0337.554419126.843277
3354우장산동18_5018_50_0437.554442126.843319
3355우장산동18_5018_50_0537.554453126.843367
3356우장산동18_5018_50_0637.554433126.843495
3357우장산동18_5018_50_0737.554403126.843467
3358우장산동18_5018_50_0837.55437126.843438
3359우장산동18_5018_50_0937.554336126.843407
3360우장산동18_5018_50_1037.554307126.843371
3361우장산동18_5018_50_1137.554315126.843249
3362우장산동18_5018_50_1237.554324126.843187

Duplicate rows

Most frequently occurring

행정동구간명구획번호위도경도# duplicates
0화곡1동6_106_10_11837.532046126.8422572