Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory498.0 KiB
Average record size in memory51.0 B

Variable types

Numeric3
Categorical1
Text1

Dataset

Description송파구 관내 조명시설 데이터로 순번, 구분, 일련번호, 위도, 경도로 구성되어 있습니다. 순번은 조명시설의 순번, 구분은 가로등/보안등 구분, 일련번호는 기 부여된 조명시설의 일련번호, 위도와 경도 입니다.
Author서울특별시 송파구
URLhttps://www.data.go.kr/data/15097746/fileData.do

Alerts

순번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 순번High correlation
순번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:31:09.362903
Analysis finished2023-12-12 11:31:12.985687
Duration3.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5639.1343
Minimum1
Maximum11268
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:31:13.114255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile560.95
Q12831.75
median5646.5
Q38462.5
95-th percentile10715.05
Maximum11268
Range11267
Interquartile range (IQR)5630.75

Descriptive statistics

Standard deviation3257.9427
Coefficient of variation (CV)0.57773809
Kurtosis-1.1994317
Mean5639.1343
Median Absolute Deviation (MAD)2815.5
Skewness-0.00094389543
Sum56391343
Variance10614191
MonotonicityNot monotonic
2023-12-12T20:31:13.333608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1991 1
 
< 0.1%
7296 1
 
< 0.1%
2811 1
 
< 0.1%
9110 1
 
< 0.1%
2721 1
 
< 0.1%
5680 1
 
< 0.1%
1924 1
 
< 0.1%
5538 1
 
< 0.1%
8563 1
 
< 0.1%
6313 1
 
< 0.1%
Other values (9990) 9990
99.9%
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 (%)
11268 1
< 0.1%
11267 1
< 0.1%
11266 1
< 0.1%
11263 1
< 0.1%
11262 1
< 0.1%
11261 1
< 0.1%
11260 1
< 0.1%
11259 1
< 0.1%
11258 1
< 0.1%
11257 1
< 0.1%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
보안등
7339 
가로등
2661 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가로등
2nd row보안등
3rd row보안등
4th row보안등
5th row보안등

Common Values

ValueCountFrequency (%)
보안등 7339
73.4%
가로등 2661
 
26.6%

Length

2023-12-12T20:31:13.526474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:31:13.680341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보안등 7339
73.4%
가로등 2661
 
26.6%

일련번호
Text

Distinct9954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T20:31:14.125766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length8
Mean length7.6619
Min length5

Characters and Unicode

Total characters76619
Distinct characters65
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9911 ?
Unique (%)99.1%

Sample

1st row오금로-157
2nd row마천2동-279
3rd row거여1동-250
4th row마천1동-281
5th row가락본동-278
ValueCountFrequency (%)
방이2동-000 3
 
< 0.1%
오금동-546 3
 
< 0.1%
장지동-000 3
 
< 0.1%
오금동-558 2
 
< 0.1%
오금동-538 2
 
< 0.1%
오금동-539 2
 
< 0.1%
거여1동-263 2
 
< 0.1%
오금동-542 2
 
< 0.1%
송파2동-043 2
 
< 0.1%
송파2동-051 2
 
< 0.1%
Other values (9944) 9977
99.8%
2023-12-12T20:31:15.075388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 10084
 
13.2%
동 7406
 
9.7%
1 6587
 
8.6%
2 6247
 
8.2%
0 4708
 
6.1%
3 3572
 
4.7%
4 2918
 
3.8%
로 2661
 
3.5%
5 2374
 
3.1%
6 2026
 
2.6%
Other values (55) 28036
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34101
44.5%
Other Letter 32434
42.3%
Dash Punctuation 10084
 
13.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 7406
22.8%
로 2661
 
8.2%
천 1394
 
4.3%
대 1168
 
3.6%
송 1045
 
3.2%
파 1045
 
3.2%
마 877
 
2.7%
오 861
 
2.7%
금 855
 
2.6%
풍 804
 
2.5%
Other values (44) 14318
44.1%
Decimal Number
ValueCountFrequency (%)
1 6587
19.3%
2 6247
18.3%
0 4708
13.8%
3 3572
10.5%
4 2918
8.6%
5 2374
 
7.0%
6 2026
 
5.9%
7 1916
 
5.6%
8 1897
 
5.6%
9 1856
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 10084
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44185
57.7%
Hangul 32434
42.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 7406
22.8%
로 2661
 
8.2%
천 1394
 
4.3%
대 1168
 
3.6%
송 1045
 
3.2%
파 1045
 
3.2%
마 877
 
2.7%
오 861
 
2.7%
금 855
 
2.6%
풍 804
 
2.5%
Other values (44) 14318
44.1%
Common
ValueCountFrequency (%)
- 10084
22.8%
1 6587
14.9%
2 6247
14.1%
0 4708
10.7%
3 3572
 
8.1%
4 2918
 
6.6%
5 2374
 
5.4%
6 2026
 
4.6%
7 1916
 
4.3%
8 1897
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44185
57.7%
Hangul 32434
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 10084
22.8%
1 6587
14.9%
2 6247
14.1%
0 4708
10.7%
3 3572
 
8.1%
4 2918
 
6.6%
5 2374
 
5.4%
6 2026
 
4.6%
7 1916
 
4.3%
8 1897
 
4.3%
Hangul
ValueCountFrequency (%)
동 7406
22.8%
로 2661
 
8.2%
천 1394
 
4.3%
대 1168
 
3.6%
송 1045
 
3.2%
파 1045
 
3.2%
마 877
 
2.7%
오 861
 
2.7%
금 855
 
2.6%
풍 804
 
2.5%
Other values (44) 14318
44.1%

위도
Real number (ℝ)

Distinct8671
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.505643
Minimum37.466917
Maximum37.540725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:31:15.370680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.466917
5-th percentile37.486696
Q137.497622
median37.50402
Q337.51155
95-th percentile37.534139
Maximum37.540725
Range0.07380765
Interquartile range (IQR)0.01392763

Descriptive statistics

Standard deviation0.012946604
Coefficient of variation (CV)0.00034519084
Kurtosis0.43249948
Mean37.505643
Median Absolute Deviation (MAD)0.00698504
Skewness0.62869045
Sum375056.43
Variance0.00016761456
MonotonicityNot monotonic
2023-12-12T20:31:15.651925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.51542696 19
 
0.2%
37.50337502 16
 
0.2%
37.50839878 13
 
0.1%
37.50110605 12
 
0.1%
37.50677128 12
 
0.1%
37.49938766 12
 
0.1%
37.48402568 11
 
0.1%
37.53247458 11
 
0.1%
37.53662835 11
 
0.1%
37.48997934 11
 
0.1%
Other values (8661) 9872
98.7%
ValueCountFrequency (%)
37.46691735 1
< 0.1%
37.46974957 1
< 0.1%
37.47105736 1
< 0.1%
37.47155095 1
< 0.1%
37.47171868 1
< 0.1%
37.471943 1
< 0.1%
37.47219394 1
< 0.1%
37.47240983 1
< 0.1%
37.47269715 1
< 0.1%
37.47303486 1
< 0.1%
ValueCountFrequency (%)
37.540725 1
< 0.1%
37.540702 1
< 0.1%
37.540664 1
< 0.1%
37.54061846 1
< 0.1%
37.540592 1
< 0.1%
37.54055893 1
< 0.1%
37.540519 1
< 0.1%
37.54050842 1
< 0.1%
37.540451 1
< 0.1%
37.540435 1
< 0.1%

경도
Real number (ℝ)

Distinct8622
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.11938
Minimum127.07084
Maximum127.15856
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T20:31:15.960242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.07084
5-th percentile127.08419
Q1127.10823
median127.11972
Q3127.13247
95-th percentile127.15171
Maximum127.15856
Range0.0877231
Interquartile range (IQR)0.024247

Descriptive statistics

Standard deviation0.019184793
Coefficient of variation (CV)0.0001509195
Kurtosis-0.46785352
Mean127.11938
Median Absolute Deviation (MAD)0.0120722
Skewness-0.16994616
Sum1271193.8
Variance0.00036805629
MonotonicityNot monotonic
2023-12-12T20:31:16.192447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1372089 19
 
0.2%
127.1314386 16
 
0.2%
127.1204692 13
 
0.1%
127.1507878 12
 
0.1%
127.1154666 12
 
0.1%
127.0841878 12
 
0.1%
127.1311715 11
 
0.1%
127.1201682 11
 
0.1%
127.1362521 11
 
0.1%
127.1150591 11
 
0.1%
Other values (8612) 9872
98.7%
ValueCountFrequency (%)
127.070839 1
< 0.1%
127.0710577 1
< 0.1%
127.0712413 1
< 0.1%
127.0714874 1
< 0.1%
127.0717628 1
< 0.1%
127.0719298 1
< 0.1%
127.072062 1
< 0.1%
127.0721832 1
< 0.1%
127.072297 1
< 0.1%
127.072472 1
< 0.1%
ValueCountFrequency (%)
127.1585621 1
< 0.1%
127.1584149 1
< 0.1%
127.1583926 1
< 0.1%
127.1583188 1
< 0.1%
127.1582856 1
< 0.1%
127.1581743 1
< 0.1%
127.1581383 1
< 0.1%
127.1581365 1
< 0.1%
127.158099 1
< 0.1%
127.1580903 1
< 0.1%

Interactions

2023-12-12T20:31:12.155893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:10.420817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:11.006656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:12.340203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:10.599077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:11.219680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:12.549084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:10.814695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:31:11.436152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:31:16.380791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번구분위도경도
순번1.0000.9960.8410.860
구분0.9961.0000.3920.302
위도0.8410.3921.0000.729
경도0.8600.3020.7291.000
2023-12-12T20:31:16.553604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번위도경도구분
순번1.0000.189-0.1420.941
위도0.1891.000-0.3950.301
경도-0.142-0.3951.0000.232
구분0.9410.3010.2321.000

Missing values

2023-12-12T20:31:12.734151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:31:12.917305image/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

순번구분일련번호위도경도
19901991가로등오금로-15737.515201127.107014
48984899보안등마천2동-27937.49802127.150952
39663967보안등거여1동-25037.492881127.143857
44514452보안등마천1동-28137.496561127.154925
32553256보안등가락본동-27837.493006127.123593
99529953보안등장지동-03937.489405127.134
56185619보안등방이1동-14437.512628127.12052
23432344가로등올림픽로-14137.517709127.113053
84348435보안등송파1동-49537.503211127.110633
48474848보안등마천2동-22937.497928127.14813
순번구분일련번호위도경도
73687369보안등석촌동-53737.503693127.10591
78가로등강동대로-00837.527874127.11132
39523953보안등거여1동-23637.493667127.142367
67026703보안등삼전동-42937.500208127.092687
52725273보안등문정1동-10937.487208127.125182
16931694가로등양재대로-16937.496181127.109329
88488849보안등오금동-12637.505625127.130715
3031가로등강동대로-03137.526051127.117012
30713072보안등가락본동-06937.500732127.126557
1034210343보안등장지천-5137.476112127.128257