Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory478.5 KiB
Average record size in memory49.0 B

Variable types

Numeric1
Text3
Categorical1

Dataset

Description순번,사업예정지 일련번호,입력년도,X좌표,Y좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-11686/S/1/datasetView.do

Alerts

순번 has unique valuesUnique

Reproduction

Analysis started2024-04-21 12:42:55.587945
Analysis finished2024-04-21 12:42:56.958070
Duration1.37 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28412.256
Minimum1
Maximum56800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-04-21T21:42:57.145893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2880.85
Q114099.75
median28367
Q342887.25
95-th percentile53818.05
Maximum56800
Range56799
Interquartile range (IQR)28787.5

Descriptive statistics

Standard deviation16431.557
Coefficient of variation (CV)0.57832639
Kurtosis-1.2149296
Mean28412.256
Median Absolute Deviation (MAD)14391.5
Skewness-0.00089030067
Sum2.8412256 × 108
Variance2.6999608 × 108
MonotonicityNot monotonic
2024-04-21T21:42:57.549248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8957 1
 
< 0.1%
15127 1
 
< 0.1%
43537 1
 
< 0.1%
53356 1
 
< 0.1%
6564 1
 
< 0.1%
10178 1
 
< 0.1%
42224 1
 
< 0.1%
13403 1
 
< 0.1%
11347 1
 
< 0.1%
41791 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
8 1
< 0.1%
13 1
< 0.1%
30 1
< 0.1%
31 1
< 0.1%
36 1
< 0.1%
47 1
< 0.1%
48 1
< 0.1%
52 1
< 0.1%
ValueCountFrequency (%)
56800 1
< 0.1%
56799 1
< 0.1%
56798 1
< 0.1%
56793 1
< 0.1%
56787 1
< 0.1%
56782 1
< 0.1%
56777 1
< 0.1%
56776 1
< 0.1%
56752 1
< 0.1%
56750 1
< 0.1%

사업예정지 일련번호
Text

Distinct9983
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T21:42:58.343181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.3403
Min length1

Characters and Unicode

Total characters173403
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9972 ?
Unique (%)99.7%

Sample

1st rowSVR001200509200002
2nd rowSVR001200707110082
3rd rowSVR001201302010050
4th rowSVR001200508010449
5th rowSVR001200701170136
ValueCountFrequency (%)
1 8
 
0.1%
4716 2
 
< 0.1%
4760 2
 
< 0.1%
4809 2
 
< 0.1%
svr001201307100105 2
 
< 0.1%
svr001201307090068 2
 
< 0.1%
svr001201307080134 2
 
< 0.1%
svr001201307090122 2
 
< 0.1%
svr001201307090052 2
 
< 0.1%
svr001201307090134 2
 
< 0.1%
Other values (9972) 9973
99.7%
2024-04-21T21:42:59.549501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 66084
38.1%
1 29066
16.8%
2 17092
 
9.9%
S 9530
 
5.5%
V 9530
 
5.5%
R 9530
 
5.5%
3 6325
 
3.6%
7 5597
 
3.2%
5 4799
 
2.8%
6 4044
 
2.3%
Other values (5) 11806
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 144804
83.5%
Uppercase Letter 28590
 
16.5%
Dash Punctuation 8
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66084
45.6%
1 29066
20.1%
2 17092
 
11.8%
3 6325
 
4.4%
7 5597
 
3.9%
5 4799
 
3.3%
6 4044
 
2.8%
8 4021
 
2.8%
9 3903
 
2.7%
4 3873
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
S 9530
33.3%
V 9530
33.3%
R 9530
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 144813
83.5%
Latin 28590
 
16.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66084
45.6%
1 29066
20.1%
2 17092
 
11.8%
3 6325
 
4.4%
7 5597
 
3.9%
5 4799
 
3.3%
6 4044
 
2.8%
8 4021
 
2.8%
9 3903
 
2.7%
4 3873
 
2.7%
Other values (2) 9
 
< 0.1%
Latin
ValueCountFrequency (%)
S 9530
33.3%
V 9530
33.3%
R 9530
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66084
38.1%
1 29066
16.8%
2 17092
 
9.9%
S 9530
 
5.5%
V 9530
 
5.5%
R 9530
 
5.5%
3 6325
 
3.6%
7 5597
 
3.2%
5 4799
 
2.8%
6 4044
 
2.3%
Other values (5) 11806
 
6.8%

입력년도
Categorical

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2005
2168 
2013
1442 
2007
1301 
2008
1087 
2009
1042 
Other values (5)
2960 

Length

Max length4
Median length4
Mean length3.8572
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2005
2nd row2007
3rd row2013
4th row2005
5th row2007

Common Values

ValueCountFrequency (%)
2005 2168
21.7%
2013 1442
14.4%
2007 1301
13.0%
2008 1087
10.9%
2009 1042
10.4%
2012 893
8.9%
2011 890
8.9%
2010 691
 
6.9%
476
 
4.8%
2006 10
 
0.1%

Length

2024-04-21T21:42:59.972409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T21:43:00.201041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2005 2168
22.8%
2013 1442
15.1%
2007 1301
13.7%
2008 1087
11.4%
2009 1042
10.9%
2012 893
9.4%
2011 890
9.3%
2010 691
 
7.3%
2006 10
 
0.1%

X좌표
Text

Distinct9637
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T21:43:01.336360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.3945
Min length1

Characters and Unicode

Total characters83945
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9374 ?
Unique (%)93.7%

Sample

1st row193411.9
2nd row201054.5
3rd row188796.4
4th row196690.05
5th row198244.6
ValueCountFrequency (%)
201077.2 7
 
0.1%
200886.75 6
 
0.1%
200624.85 5
 
0.1%
196745.7 5
 
0.1%
197642.65 5
 
0.1%
196056.25 5
 
0.1%
197814.3 5
 
0.1%
196478.6 5
 
0.1%
197971.95 5
 
0.1%
198101.45 4
 
< 0.1%
Other values (9626) 9946
99.5%
2024-04-21T21:43:03.102570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10412
12.4%
1 10224
12.2%
5 9970
11.9%
. 9500
11.3%
0 9059
10.8%
9 8240
9.8%
8 6201
7.4%
3 5327
6.3%
7 5252
6.3%
4 4880
5.8%
Other values (2) 4880
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74443
88.7%
Other Punctuation 9500
 
11.3%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10412
14.0%
1 10224
13.7%
5 9970
13.4%
0 9059
12.2%
9 8240
11.1%
8 6201
8.3%
3 5327
7.2%
7 5252
7.1%
4 4880
6.6%
6 4878
6.6%
Other Punctuation
ValueCountFrequency (%)
. 9500
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 83945
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10412
12.4%
1 10224
12.2%
5 9970
11.9%
. 9500
11.3%
0 9059
10.8%
9 8240
9.8%
8 6201
7.4%
3 5327
6.3%
7 5252
6.3%
4 4880
5.8%
Other values (2) 4880
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83945
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10412
12.4%
1 10224
12.2%
5 9970
11.9%
. 9500
11.3%
0 9059
10.8%
9 8240
9.8%
8 6201
7.4%
3 5327
6.3%
7 5252
6.3%
4 4880
5.8%
Other values (2) 4880
5.8%

Y좌표
Text

Distinct9630
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-21T21:43:04.334623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.3853
Min length1

Characters and Unicode

Total characters83853
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9358 ?
Unique (%)93.6%

Sample

1st row444495.5
2nd row460111.1
3rd row449752.1
4th row442045.35
5th row441878.6
ValueCountFrequency (%)
452162.85 7
 
0.1%
452198.65 6
 
0.1%
442254.5 5
 
0.1%
450363.55 5
 
0.1%
450133.45 5
 
0.1%
447750.7 5
 
0.1%
441451.65 5
 
0.1%
442039.5 4
 
< 0.1%
441913.9 4
 
< 0.1%
441555.85 4
 
< 0.1%
Other values (9619) 9948
99.5%
2024-04-21T21:43:06.012566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 19988
23.8%
5 14217
17.0%
. 9500
11.3%
2 5412
 
6.5%
1 5292
 
6.3%
6 5239
 
6.2%
3 5170
 
6.2%
9 4976
 
5.9%
8 4828
 
5.8%
7 4799
 
5.7%
Other values (2) 4432
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74351
88.7%
Other Punctuation 9500
 
11.3%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 19988
26.9%
5 14217
19.1%
2 5412
 
7.3%
1 5292
 
7.1%
6 5239
 
7.0%
3 5170
 
7.0%
9 4976
 
6.7%
8 4828
 
6.5%
7 4799
 
6.5%
0 4430
 
6.0%
Other Punctuation
ValueCountFrequency (%)
. 9500
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 83853
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 19988
23.8%
5 14217
17.0%
. 9500
11.3%
2 5412
 
6.5%
1 5292
 
6.3%
6 5239
 
6.2%
3 5170
 
6.2%
9 4976
 
5.9%
8 4828
 
5.8%
7 4799
 
5.7%
Other values (2) 4432
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83853
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 19988
23.8%
5 14217
17.0%
. 9500
11.3%
2 5412
 
6.5%
1 5292
 
6.3%
6 5239
 
6.2%
3 5170
 
6.2%
9 4976
 
5.9%
8 4828
 
5.8%
7 4799
 
5.7%
Other values (2) 4432
 
5.3%

Interactions

2024-04-21T21:42:56.157225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T21:43:06.273139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번입력년도
순번1.0000.874
입력년도0.8741.000
2024-04-21T21:43:06.490540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번입력년도
순번1.0000.455
입력년도0.4551.000

Missing values

2024-04-21T21:42:56.487014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T21:42:56.811738image/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

순번사업예정지 일련번호입력년도X좌표Y좌표
478458957SVR0012005092000022005193411.9444495.5
3237824424SVR0012007071100822007201054.5460111.1
1336143441SVR0012013020100502013188796.4449752.1
484568346SVR0012005080104492005196690.05442045.35
3775919043SVR0012007011701362007198244.6441878.6
3707519727SVR0012008011401542008200191.95452893.3
4483911963SVR0012007032400132007185984.75450470.15
4262814174SVR0012007070400432007197560.7450940.65
50372643047382005201228.25452174.7
2224134561SVR0012012011500132012196943450419.95
순번사업예정지 일련번호입력년도X좌표Y좌표
2184534957SVR0012012011201462012201781.15449178.5
4228114521SVR0012007012000082007210590.45443614
1543441368SVR0012013012202452013197296.45454216.8
549721830SVR0012005040102612005195105.45448701.95
2459932203SVR0012009091700132009202059.35446442.1
494697333SVR0012005050200852005193128.95445987.1
4274214060SVR0012008012401852008202798.45447610.55
480951993SVR0012009010200112009207511.45455049
537251430SVR0012007012800162007208852.6446232.35
4619910603SVR0012005080118742005197371.3453848.3