Overview

Dataset statistics

Number of variables6
Number of observations1347
Missing cells5
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory67.2 KiB
Average record size in memory51.1 B

Variable types

Numeric3
Categorical1
Text2

Dataset

Description부산광역시 금정구 관내의 지적 측량에 관한 자료 입니다. 지적측량 기준점 데이터는 측량 기준점의 좌표, 주소 등의 정보가 있습니다.
Author부산광역시 금정구
URLhttps://www.data.go.kr/data/3070501/fileData.do

Alerts

순번 is highly overall correlated with 종류High correlation
종류 is highly overall correlated with 순번High correlation
종류 is highly imbalanced (77.0%)Imbalance
순번 has unique valuesUnique
기준점명 has unique valuesUnique

Reproduction

Analysis started2024-03-14 19:32:07.062592
Analysis finished2024-03-14 19:32:09.816493
Duration2.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1347
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean674
Minimum1
Maximum1347
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-03-15T04:32:10.280361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile68.3
Q1337.5
median674
Q31010.5
95-th percentile1279.7
Maximum1347
Range1346
Interquartile range (IQR)673

Descriptive statistics

Standard deviation388.98972
Coefficient of variation (CV)0.57713608
Kurtosis-1.2
Mean674
Median Absolute Deviation (MAD)337
Skewness0
Sum907878
Variance151313
MonotonicityStrictly increasing
2024-03-15T04:32:10.625325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.1%
897 1
 
0.1%
905 1
 
0.1%
904 1
 
0.1%
903 1
 
0.1%
902 1
 
0.1%
901 1
 
0.1%
900 1
 
0.1%
899 1
 
0.1%
898 1
 
0.1%
Other values (1337) 1337
99.3%
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 (%)
1347 1
0.1%
1346 1
0.1%
1345 1
0.1%
1344 1
0.1%
1343 1
0.1%
1342 1
0.1%
1341 1
0.1%
1340 1
0.1%
1339 1
0.1%
1338 1
0.1%

종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
도근점
1264 
삼각보조점
 
74
삼각점
 
9

Length

Max length5
Median length3
Mean length3.1098738
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
도근점 1264
93.8%
삼각보조점 74
 
5.5%
삼각점 9
 
0.7%

Length

2024-03-15T04:32:10.886342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T04:32:11.241448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도근점 1264
93.8%
삼각보조점 74
 
5.5%
삼각점 9
 
0.7%

기준점명
Text

UNIQUE 

Distinct1347
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size10.6 KiB
2024-03-15T04:32:12.737743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.7520416
Min length2

Characters and Unicode

Total characters6401
Distinct characters15
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1347 ?
Unique (%)100.0%

Sample

1st row부산116
2nd row부산121
3rd roww부산111
4th roww부산112
5th roww부산114
ValueCountFrequency (%)
w5998 2
 
0.1%
부산116 1
 
0.1%
w5626 1
 
0.1%
w5493 1
 
0.1%
w5492 1
 
0.1%
w5491 1
 
0.1%
w5490 1
 
0.1%
w5489 1
 
0.1%
w5488 1
 
0.1%
w5487 1
 
0.1%
Other values (1336) 1336
99.2%
2024-03-15T04:32:14.389979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 1316
20.6%
5 1151
18.0%
2 558
8.7%
3 462
 
7.2%
1 440
 
6.9%
7 425
 
6.6%
8 400
 
6.2%
9 397
 
6.2%
6 389
 
6.1%
0 386
 
6.0%
Other values (5) 477
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4992
78.0%
Lowercase Letter 1316
 
20.6%
Other Letter 92
 
1.4%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 1151
23.1%
2 558
11.2%
3 462
9.3%
1 440
 
8.8%
7 425
 
8.5%
8 400
 
8.0%
9 397
 
8.0%
6 389
 
7.8%
0 386
 
7.7%
4 384
 
7.7%
Other Letter
ValueCountFrequency (%)
보 74
80.4%
부 9
 
9.8%
산 9
 
9.8%
Lowercase Letter
ValueCountFrequency (%)
w 1316
100.0%
Uppercase Letter
ValueCountFrequency (%)
W 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4992
78.0%
Latin 1317
 
20.6%
Hangul 92
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
5 1151
23.1%
2 558
11.2%
3 462
9.3%
1 440
 
8.8%
7 425
 
8.5%
8 400
 
8.0%
9 397
 
8.0%
6 389
 
7.8%
0 386
 
7.7%
4 384
 
7.7%
Hangul
ValueCountFrequency (%)
보 74
80.4%
부 9
 
9.8%
산 9
 
9.8%
Latin
ValueCountFrequency (%)
w 1316
99.9%
W 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6309
98.6%
Hangul 92
 
1.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 1316
20.9%
5 1151
18.2%
2 558
8.8%
3 462
 
7.3%
1 440
 
7.0%
7 425
 
6.7%
8 400
 
6.3%
9 397
 
6.3%
6 389
 
6.2%
0 386
 
6.1%
Other values (2) 385
 
6.1%
Hangul
ValueCountFrequency (%)
보 74
80.4%
부 9
 
9.8%
산 9
 
9.8%

X좌표
Real number (ℝ)

Distinct1344
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean294958.02
Minimum191581.35
Maximum300991.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-03-15T04:32:14.824948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum191581.35
5-th percentile291273.01
Q1292490.49
median294844.52
Q3297585.67
95-th percentile299710.22
Maximum300991.7
Range109410.35
Interquartile range (IQR)5095.18

Descriptive statistics

Standard deviation4827.1738
Coefficient of variation (CV)0.01636563
Kurtosis297.2509
Mean294958.02
Median Absolute Deviation (MAD)2437.12
Skewness-14.051284
Sum3.9730846 × 108
Variance23301607
MonotonicityNot monotonic
2024-03-15T04:32:15.189008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
290916.95 2
 
0.1%
293330.16 2
 
0.1%
292348.5 2
 
0.1%
193948.43 1
 
0.1%
291051.48 1
 
0.1%
291353.91 1
 
0.1%
291299.1 1
 
0.1%
291266.95 1
 
0.1%
291233.17 1
 
0.1%
291201.34 1
 
0.1%
Other values (1334) 1334
99.0%
ValueCountFrequency (%)
191581.35 1
0.1%
193948.43 1
0.1%
290601.56 1
0.1%
290628.39 1
0.1%
290631.17 1
0.1%
290644.54 1
0.1%
290664.68 1
0.1%
290665.8 1
0.1%
290679.5 1
0.1%
290680.48 1
0.1%
ValueCountFrequency (%)
300991.7 1
0.1%
300853.59 1
0.1%
300808.26 1
0.1%
300781.55 1
0.1%
300767.97 1
0.1%
300764.84 1
0.1%
300749.32 1
0.1%
300724.12 1
0.1%
300702.15 1
0.1%
300701.04 1
0.1%

Y좌표
Real number (ℝ)

Distinct1341
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208710.32
Minimum204232.4
Maximum212584.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.0 KiB
2024-03-15T04:32:15.624942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum204232.4
5-th percentile206695.89
Q1207808.46
median208560.94
Q3209633.95
95-th percentile210829.08
Maximum212584.38
Range8351.98
Interquartile range (IQR)1825.485

Descriptive statistics

Standard deviation1388.1683
Coefficient of variation (CV)0.0066511722
Kurtosis0.72482791
Mean208710.32
Median Absolute Deviation (MAD)838.69
Skewness-0.11379891
Sum2.811328 × 108
Variance1927011.2
MonotonicityNot monotonic
2024-03-15T04:32:16.022495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207801.88 2
 
0.1%
209319.15 2
 
0.1%
207701.54 2
 
0.1%
208094.54 2
 
0.1%
210703.66 2
 
0.1%
207550.46 2
 
0.1%
208989.51 1
 
0.1%
208602.34 1
 
0.1%
208608.54 1
 
0.1%
208474.56 1
 
0.1%
Other values (1331) 1331
98.8%
ValueCountFrequency (%)
204232.4 1
0.1%
204369.33 1
0.1%
204613.55 1
0.1%
204622.64 1
0.1%
204641.01 1
0.1%
204652.66 1
0.1%
204652.8 1
0.1%
204665.77 1
0.1%
204679.73 1
0.1%
204699.29 1
0.1%
ValueCountFrequency (%)
212584.38 1
0.1%
212571.3 1
0.1%
212569.72 1
0.1%
212563.49 1
0.1%
212535.2 1
0.1%
212515.13 1
0.1%
212474.97 1
0.1%
212469.46 1
0.1%
212468.55 1
0.1%
212433.6 1
0.1%

상세주소
Text

Distinct787
Distinct (%)58.6%
Missing5
Missing (%)0.4%
Memory size10.6 KiB
2024-03-15T04:32:17.112299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length22
Mean length8.9187779
Min length3

Characters and Unicode

Total characters11969
Distinct characters44
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique604 ?
Unique (%)45.0%

Sample

1st row장전동 산2-1
2nd row서동 산94
3rd row금성동 산69-4
4th row금성동 산1-1
5th row청룡동 산2-1
ValueCountFrequency (%)
장전동 200
 
7.1%
부곡동 159
 
5.6%
선동 156
 
5.5%
구서동 151
 
5.3%
노포동 113
 
4.0%
금정구 111
 
3.9%
두구동 107
 
3.8%
남산동 102
 
3.6%
회동동 68
 
2.4%
서동 66
 
2.3%
Other values (767) 1590
56.3%
2024-03-15T04:32:18.780655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1495
 
12.5%
동 1402
 
11.7%
1 931
 
7.8%
- 795
 
6.6%
2 596
 
5.0%
3 529
 
4.4%
4 507
 
4.2%
5 491
 
4.1%
6 483
 
4.0%
7 402
 
3.4%
Other values (34) 4338
36.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5014
41.9%
Other Letter 4665
39.0%
Space Separator 1495
 
12.5%
Dash Punctuation 795
 
6.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1402
30.1%
구 380
 
8.1%
산 270
 
5.8%
부 225
 
4.8%
서 224
 
4.8%
금 212
 
4.5%
장 206
 
4.4%
전 204
 
4.4%
곡 162
 
3.5%
선 158
 
3.4%
Other values (22) 1222
26.2%
Decimal Number
ValueCountFrequency (%)
1 931
18.6%
2 596
11.9%
3 529
10.6%
4 507
10.1%
5 491
9.8%
6 483
9.6%
7 402
8.0%
0 386
7.7%
9 368
 
7.3%
8 321
 
6.4%
Space Separator
ValueCountFrequency (%)
1495
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 795
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7304
61.0%
Hangul 4665
39.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1402
30.1%
구 380
 
8.1%
산 270
 
5.8%
부 225
 
4.8%
서 224
 
4.8%
금 212
 
4.5%
장 206
 
4.4%
전 204
 
4.4%
곡 162
 
3.5%
선 158
 
3.4%
Other values (22) 1222
26.2%
Common
ValueCountFrequency (%)
1495
20.5%
1 931
12.7%
- 795
10.9%
2 596
 
8.2%
3 529
 
7.2%
4 507
 
6.9%
5 491
 
6.7%
6 483
 
6.6%
7 402
 
5.5%
0 386
 
5.3%
Other values (2) 689
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7304
61.0%
Hangul 4665
39.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1495
20.5%
1 931
12.7%
- 795
10.9%
2 596
 
8.2%
3 529
 
7.2%
4 507
 
6.9%
5 491
 
6.7%
6 483
 
6.6%
7 402
 
5.5%
0 386
 
5.3%
Other values (2) 689
9.4%
Hangul
ValueCountFrequency (%)
동 1402
30.1%
구 380
 
8.1%
산 270
 
5.8%
부 225
 
4.8%
서 224
 
4.8%
금 212
 
4.5%
장 206
 
4.4%
전 204
 
4.4%
곡 162
 
3.5%
선 158
 
3.4%
Other values (22) 1222
26.2%

Interactions

2024-03-15T04:32:08.523234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:32:07.366348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:32:08.009872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:32:08.784133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:32:07.650009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:32:08.184378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:32:09.045747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:32:07.849152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T04:32:08.360277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T04:32:18.954826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번종류X좌표Y좌표
순번1.0000.6850.0080.586
종류0.6851.0000.2140.225
X좌표0.0080.2141.0000.016
Y좌표0.5860.2250.0161.000
2024-03-15T04:32:19.173064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번X좌표Y좌표종류
순번1.0000.0300.0060.537
X좌표0.0301.0000.1270.469
Y좌표0.0060.1271.0000.135
종류0.5370.4690.1351.000

Missing values

2024-03-15T04:32:09.391411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T04:32:09.706566image/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

순번종류기준점명X좌표Y좌표상세주소
01삼각점부산116193948.43207235.28장전동 산2-1
12삼각점부산121191581.35209210.31서동 산94
23삼각점w부산111293422.27204369.33금성동 산69-4
34삼각점w부산112298180.34204613.55금성동 산1-1
45삼각점w부산114298933.12207043.31청룡동 산2-1
56삼각점w부산115294279.74208780.24구서동 산60-1
67삼각점w부산117292777.28208384.67부곡동 398
78삼각점w부산120294736.77206120.85장전동 산2-3
89삼각점w부산122292157.65205645.39장전동 산46-2
910삼각보조점w보1299765.27209444.56두구동 1344-1
순번종류기준점명X좌표Y좌표상세주소
13371338도근점w5992294890.67205108.98금성동 554-2
13381339도근점w5993294958.57205105.49금성동 1038-3
13391340도근점w5994294963.49204938.11금성동 1074-11
13401341도근점w5995295001.08204841.82금성동 247-2
13411342도근점w5996293391.12207378.07장전동 26-8번지 인접
13421343도근점w5997293386.7207558.68장전동 56-8번지 인접
13431344도근점W5998293330.16207550.46장전동 263-146번지 인접
13441345도근점w5999293234.36207549.97장전동 269-18번지 인접
13451346도근점w6000293208.71207626.07장전동 269-12번지 인접
13461347도근점w5998293330.16207550.46장전동 263-146번지 인점