Overview

Dataset statistics

Number of variables6
Number of observations6105
Missing cells6353
Missing cells (%)17.3%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory304.2 KiB
Average record size in memory51.0 B

Variable types

Categorical1
Text2
Numeric2
Unsupported1

Dataset

Description서대문구 관내 거주자우선 주차장 GIS 좌표 현황
Author서울특별시서대문구도시관리공단
URLhttps://www.data.go.kr/data/15001517/fileData.do

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
경도 is highly overall correlated with 동명High correlation
위도 is highly overall correlated with 동명High correlation
동명 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
구간명 has 62 (1.0%) missing valuesMissing
구획명 has 62 (1.0%) missing valuesMissing
경도 has 62 (1.0%) missing valuesMissing
위도 has 62 (1.0%) missing valuesMissing
Unnamed: 5 has 6105 (100.0%) missing valuesMissing
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 16:58:32.110004
Analysis finished2023-12-12 16:58:33.040451
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

동명
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size47.8 KiB
연희동(1지구)
878 
북가좌2동
536 
홍은1동(2지구)
500 
홍은2동
456 
연희동(2지구)
455 
Other values (17)
3280 

Length

Max length9
Median length8
Mean length6.9038493
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남가좌2동
2nd row남가좌2동
3rd row남가좌2동
4th row남가좌2동
5th row남가좌2동

Common Values

ValueCountFrequency (%)
연희동(1지구) 878
14.4%
북가좌2동 536
 
8.8%
홍은1동(2지구) 500
 
8.2%
홍은2동 456
 
7.5%
연희동(2지구) 455
 
7.5%
남가좌2동 452
 
7.4%
홍제1동(1지구) 440
 
7.2%
연희동(3지구) 310
 
5.1%
홍제3동 273
 
4.5%
충현동(1지구) 264
 
4.3%
Other values (12) 1541
25.2%

Length

2023-12-13T01:58:33.098266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연희동(1지구 878
14.4%
북가좌2동 536
 
8.8%
홍은1동(2지구 500
 
8.2%
홍은2동 456
 
7.5%
연희동(2지구 455
 
7.5%
남가좌2동 452
 
7.4%
홍제1동(1지구 440
 
7.2%
연희동(3지구 310
 
5.1%
홍제3동 273
 
4.5%
충현동(1지구 264
 
4.3%
Other values (12) 1541
25.2%

구간명
Text

MISSING 

Distinct196
Distinct (%)3.2%
Missing62
Missing (%)1.0%
Memory size47.8 KiB
2023-12-13T01:58:33.694726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length3.2139666
Min length3

Characters and Unicode

Total characters19422
Distinct characters88
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.2%

Sample

1st row021
2nd row022
3rd row022
4th row022
5th row022
ValueCountFrequency (%)
공영1 431
 
7.1%
공영2 340
 
5.6%
005 148
 
2.4%
009 140
 
2.3%
001 131
 
2.2%
공영4 121
 
2.0%
북성초 105
 
1.7%
003 105
 
1.7%
013 104
 
1.7%
007 95
 
1.6%
Other values (186) 4323
71.5%
2023-12-13T01:58:34.271484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5460
28.1%
1 2388
12.3%
2 1392
 
7.2%
3 1164
 
6.0%
공 1130
 
5.8%
영 1130
 
5.8%
4 943
 
4.9%
5 904
 
4.7%
7 564
 
2.9%
6 485
 
2.5%
Other values (78) 3862
19.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14202
73.1%
Other Letter 4738
 
24.4%
Connector Punctuation 277
 
1.4%
Uppercase Letter 135
 
0.7%
Other Punctuation 40
 
0.2%
Close Punctuation 15
 
0.1%
Open Punctuation 15
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 1130
23.8%
영 1130
23.8%
교 269
 
5.7%
초 194
 
4.1%
연 151
 
3.2%
홍 140
 
3.0%
회 128
 
2.7%
북 105
 
2.2%
성 105
 
2.2%
대 70
 
1.5%
Other values (59) 1316
27.8%
Decimal Number
ValueCountFrequency (%)
0 5460
38.4%
1 2388
16.8%
2 1392
 
9.8%
3 1164
 
8.2%
4 943
 
6.6%
5 904
 
6.4%
7 564
 
4.0%
6 485
 
3.4%
9 479
 
3.4%
8 423
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
K 45
33.3%
T 40
29.6%
G 40
29.6%
R 5
 
3.7%
A 5
 
3.7%
Connector Punctuation
ValueCountFrequency (%)
_ 277
100.0%
Other Punctuation
ValueCountFrequency (%)
& 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14549
74.9%
Hangul 4738
 
24.4%
Latin 135
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 1130
23.8%
영 1130
23.8%
교 269
 
5.7%
초 194
 
4.1%
연 151
 
3.2%
홍 140
 
3.0%
회 128
 
2.7%
북 105
 
2.2%
성 105
 
2.2%
대 70
 
1.5%
Other values (59) 1316
27.8%
Common
ValueCountFrequency (%)
0 5460
37.5%
1 2388
16.4%
2 1392
 
9.6%
3 1164
 
8.0%
4 943
 
6.5%
5 904
 
6.2%
7 564
 
3.9%
6 485
 
3.3%
9 479
 
3.3%
8 423
 
2.9%
Other values (4) 347
 
2.4%
Latin
ValueCountFrequency (%)
K 45
33.3%
T 40
29.6%
G 40
29.6%
R 5
 
3.7%
A 5
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14684
75.6%
Hangul 4738
 
24.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5460
37.2%
1 2388
16.3%
2 1392
 
9.5%
3 1164
 
7.9%
4 943
 
6.4%
5 904
 
6.2%
7 564
 
3.8%
6 485
 
3.3%
9 479
 
3.3%
8 423
 
2.9%
Other values (9) 482
 
3.3%
Hangul
ValueCountFrequency (%)
공 1130
23.8%
영 1130
23.8%
교 269
 
5.7%
초 194
 
4.1%
연 151
 
3.2%
홍 140
 
3.0%
회 128
 
2.7%
북 105
 
2.2%
성 105
 
2.2%
대 70
 
1.5%
Other values (59) 1316
27.8%

구획명
Text

MISSING 

Distinct3204
Distinct (%)53.0%
Missing62
Missing (%)1.0%
Memory size47.8 KiB
2023-12-13T01:58:34.642758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length6.0916763
Min length2

Characters and Unicode

Total characters36812
Distinct characters79
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2187 ?
Unique (%)36.2%

Sample

1st row021-02
2nd row022-01
3rd row022-10
4th row022-11
5th row022-12
ValueCountFrequency (%)
005-04 13
 
0.2%
009-04 12
 
0.2%
009-01 12
 
0.2%
009-05 12
 
0.2%
002-02 11
 
0.2%
011-03 11
 
0.2%
009-06 11
 
0.2%
009-03 11
 
0.2%
009-02 11
 
0.2%
001-06 11
 
0.2%
Other values (3193) 5928
98.1%
2023-12-13T01:58:35.166164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9420
25.6%
- 5996
16.3%
1 4281
11.6%
2 2726
 
7.4%
3 2193
 
6.0%
4 1819
 
4.9%
5 1706
 
4.6%
6 1181
 
3.2%
7 1173
 
3.2%
공 1047
 
2.8%
Other values (69) 5270
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26452
71.9%
Dash Punctuation 5996
 
16.3%
Other Letter 4051
 
11.0%
Uppercase Letter 135
 
0.4%
Close Punctuation 68
 
0.2%
Open Punctuation 68
 
0.2%
Other Punctuation 40
 
0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 1047
25.8%
영 1047
25.8%
초 194
 
4.8%
교 124
 
3.1%
성 105
 
2.6%
북 105
 
2.6%
홍 102
 
2.5%
연 94
 
2.3%
회 79
 
2.0%
가 75
 
1.9%
Other values (49) 1079
26.6%
Decimal Number
ValueCountFrequency (%)
0 9420
35.6%
1 4281
16.2%
2 2726
 
10.3%
3 2193
 
8.3%
4 1819
 
6.9%
5 1706
 
6.4%
6 1181
 
4.5%
7 1173
 
4.4%
8 985
 
3.7%
9 968
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
K 45
33.3%
G 40
29.6%
T 40
29.6%
R 5
 
3.7%
A 5
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 5996
100.0%
Close Punctuation
ValueCountFrequency (%)
) 68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 68
100.0%
Other Punctuation
ValueCountFrequency (%)
& 40
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32626
88.6%
Hangul 4051
 
11.0%
Latin 135
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 1047
25.8%
영 1047
25.8%
초 194
 
4.8%
교 124
 
3.1%
성 105
 
2.6%
북 105
 
2.6%
홍 102
 
2.5%
연 94
 
2.3%
회 79
 
2.0%
가 75
 
1.9%
Other values (49) 1079
26.6%
Common
ValueCountFrequency (%)
0 9420
28.9%
- 5996
18.4%
1 4281
13.1%
2 2726
 
8.4%
3 2193
 
6.7%
4 1819
 
5.6%
5 1706
 
5.2%
6 1181
 
3.6%
7 1173
 
3.6%
8 985
 
3.0%
Other values (5) 1146
 
3.5%
Latin
ValueCountFrequency (%)
K 45
33.3%
G 40
29.6%
T 40
29.6%
R 5
 
3.7%
A 5
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32761
89.0%
Hangul 4051
 
11.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9420
28.8%
- 5996
18.3%
1 4281
13.1%
2 2726
 
8.3%
3 2193
 
6.7%
4 1819
 
5.6%
5 1706
 
5.2%
6 1181
 
3.6%
7 1173
 
3.6%
8 985
 
3.0%
Other values (10) 1281
 
3.9%
Hangul
ValueCountFrequency (%)
공 1047
25.8%
영 1047
25.8%
초 194
 
4.8%
교 124
 
3.1%
성 105
 
2.6%
북 105
 
2.6%
홍 102
 
2.5%
연 94
 
2.3%
회 79
 
2.0%
가 75
 
1.9%
Other values (49) 1079
26.6%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4795
Distinct (%)79.3%
Missing62
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean126.93502
Minimum126.9036
Maximum126.96859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.8 KiB
2023-12-13T01:58:35.358935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.9036
5-th percentile126.90927
Q1126.92341
median126.93422
Q3126.94756
95-th percentile126.96035
Maximum126.96859
Range0.064992192
Interquartile range (IQR)0.024154386

Descriptive statistics

Standard deviation0.015373285
Coefficient of variation (CV)0.00012111146
Kurtosis-0.72316713
Mean126.93502
Median Absolute Deviation (MAD)0.011492402
Skewness0.046058137
Sum767068.33
Variance0.0002363379
MonotonicityNot monotonic
2023-12-13T01:58:35.518064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.949120003075 107
 
1.8%
126.923189167414 105
 
1.7%
126.952529745631 100
 
1.6%
126.931130073017 89
 
1.5%
126.921783152928 81
 
1.3%
126.949546497904 80
 
1.3%
126.964538173228 70
 
1.1%
126.922884480053 61
 
1.0%
126.940841704727 55
 
0.9%
126.958434545787 54
 
0.9%
Other values (4785) 5241
85.8%
(Missing) 62
 
1.0%
ValueCountFrequency (%)
126.903596632364 1
< 0.1%
126.903605064727 1
< 0.1%
126.903624852532 1
< 0.1%
126.903633328461 1
< 0.1%
126.903650289034 1
< 0.1%
126.903664413661 1
< 0.1%
126.90368703745 1
< 0.1%
126.903695507594 1
< 0.1%
126.903709646745 1
< 0.1%
126.903726601551 1
< 0.1%
ValueCountFrequency (%)
126.968588824041 1
 
< 0.1%
126.96850392051 1
 
< 0.1%
126.968427508494 1
 
< 0.1%
126.968390682043 1
 
< 0.1%
126.968339395844 1
 
< 0.1%
126.968319966218 1
 
< 0.1%
126.968302906642 29
0.5%
126.968299807911 1
 
< 0.1%
126.968291627414 1
 
< 0.1%
126.968237900615 1
 
< 0.1%

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct4798
Distinct (%)79.4%
Missing62
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean37.577486
Minimum37.55597
Maximum37.6057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size53.8 KiB
2023-12-13T01:58:35.698772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.55597
5-th percentile37.55961
Q137.568735
median37.577018
Q337.584702
95-th percentile37.598609
Maximum37.6057
Range0.049729598
Interquartile range (IQR)0.015966664

Descriptive statistics

Standard deviation0.011387467
Coefficient of variation (CV)0.00030303961
Kurtosis-0.56567511
Mean37.577486
Median Absolute Deviation (MAD)0.0079959289
Skewness0.32364526
Sum227080.75
Variance0.00012967439
MonotonicityNot monotonic
2023-12-13T01:58:35.852899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.6013254599891 107
 
1.8%
37.5830821156547 105
 
1.7%
37.559419408817 100
 
1.6%
37.5812444842571 89
 
1.5%
37.5770175050235 81
 
1.3%
37.5986091559918 80
 
1.3%
37.5651832466826 70
 
1.1%
37.5711414924328 61
 
1.0%
37.5909128988969 55
 
0.9%
37.5600185456999 54
 
0.9%
Other values (4788) 5241
85.8%
(Missing) 62
 
1.0%
ValueCountFrequency (%)
37.5559704649146 1
< 0.1%
37.5559881280707 1
< 0.1%
37.5560699394958 1
< 0.1%
37.5560925030671 1
< 0.1%
37.5561489931523 1
< 0.1%
37.5563361723405 1
< 0.1%
37.5563609805527 1
< 0.1%
37.5563653827716 1
< 0.1%
37.5563790297933 1
< 0.1%
37.556402275226 1
< 0.1%
ValueCountFrequency (%)
37.6057000631458 1
< 0.1%
37.6056978203018 1
< 0.1%
37.6056933261824 1
< 0.1%
37.6056933153538 1
< 0.1%
37.6056911085632 1
< 0.1%
37.6056888464798 1
< 0.1%
37.6056888404717 1
< 0.1%
37.6056640331914 1
< 0.1%
37.6054680619286 1
< 0.1%
37.6054456260839 1
< 0.1%

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing6105
Missing (%)100.0%
Memory size53.8 KiB

Interactions

2023-12-13T01:58:32.583787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:32.409778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:32.682827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:58:32.501491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:58:35.959378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명경도위도
동명1.0000.9500.925
경도0.9501.0000.829
위도0.9250.8291.000
2023-12-13T01:58:36.065806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경도위도동명
경도1.000-0.0700.757
위도-0.0701.0000.680
동명0.7570.6801.000

Missing values

2023-12-13T01:58:32.803763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:58:32.900921image/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.
2023-12-13T01:58:32.985784image/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

동명구간명구획명경도위도Unnamed: 5
0남가좌2동021021-02126.92446137.579065<NA>
1남가좌2동022022-01126.92242137.578365<NA>
2남가좌2동022022-10126.92371237.578494<NA>
3남가좌2동022022-11126.92374337.578521<NA>
4남가좌2동022022-12126.92376837.578548<NA>
5남가좌2동022022-18126.92379437.578618<NA>
6남가좌2동022022-19126.92384737.57865<NA>
7남가좌2동022022-20126.92387937.578681<NA>
8남가좌2동023023-01126.92433737.578616<NA>
9남가좌2동023023-02126.92437737.578573<NA>
동명구간명구획명경도위도Unnamed: 5
6095<NA><NA><NA><NA><NA><NA>
6096<NA><NA><NA><NA><NA><NA>
6097<NA><NA><NA><NA><NA><NA>
6098<NA><NA><NA><NA><NA><NA>
6099<NA><NA><NA><NA><NA><NA>
6100<NA><NA><NA><NA><NA><NA>
6101<NA><NA><NA><NA><NA><NA>
6102<NA><NA><NA><NA><NA><NA>
6103<NA><NA><NA><NA><NA><NA>
6104<NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

동명구간명구획명경도위도# duplicates
0<NA><NA><NA><NA><NA>62