Overview

Dataset statistics

Number of variables3
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory332.0 KiB
Average record size in memory34.0 B

Variable types

Text1
Numeric2

Dataset

Description국토지리정보원에서 생성하는 국토통계지도(인구, 건물, 토지, 주택, 국토지표) 정보를 격자(250M)로 제공합니다.
Author국토교통부 국토지리정보원
URLhttps://www.data.go.kr/data/15122659/fileData.do

Alerts

x좌표 is highly overall correlated with y좌표High correlation
y좌표 is highly overall correlated with x좌표High correlation
격자ID has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:36:44.934360
Analysis finished2023-12-13 00:36:45.614483
Duration0.68 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

격자ID
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T09:36:45.776094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters100000
Distinct characters19
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row가마72aa30aa
2nd row가마94aa97aa
3rd row가마43aa99aa
4th row가나19aa87aa
5th row가마80aa02aa
ValueCountFrequency (%)
가마72aa30aa 1
 
< 0.1%
사바91aa02aa 1
 
< 0.1%
가가98aa93aa 1
 
< 0.1%
가바62aa93aa 1
 
< 0.1%
사사36aa05aa 1
 
< 0.1%
가가00aa10aa 1
 
< 0.1%
가나93aa72aa 1
 
< 0.1%
사바60aa22aa 1
 
< 0.1%
가라86aa70aa 1
 
< 0.1%
가나37aa40aa 1
 
< 0.1%
Other values (9990) 9990
99.9%
2023-12-13T09:36:46.079048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 40000
40.0%
가 7840
 
7.8%
사 4548
 
4.5%
2 4108
 
4.1%
5 4084
 
4.1%
9 4053
 
4.1%
8 4034
 
4.0%
1 4016
 
4.0%
0 3995
 
4.0%
3 3965
 
4.0%
Other values (9) 19357
19.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40000
40.0%
Decimal Number 40000
40.0%
Other Letter 20000
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4108
10.3%
5 4084
10.2%
9 4053
10.1%
8 4034
10.1%
1 4016
10.0%
0 3995
10.0%
3 3965
9.9%
7 3961
9.9%
4 3899
9.7%
6 3885
9.7%
Other Letter
ValueCountFrequency (%)
가 7840
39.2%
사 4548
22.7%
바 2303
 
11.5%
아 1111
 
5.6%
라 1086
 
5.4%
다 1052
 
5.3%
나 1033
 
5.2%
마 1027
 
5.1%
Lowercase Letter
ValueCountFrequency (%)
a 40000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40000
40.0%
Common 40000
40.0%
Hangul 20000
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4108
10.3%
5 4084
10.2%
9 4053
10.1%
8 4034
10.1%
1 4016
10.0%
0 3995
10.0%
3 3965
9.9%
7 3961
9.9%
4 3899
9.7%
6 3885
9.7%
Hangul
ValueCountFrequency (%)
가 7840
39.2%
사 4548
22.7%
바 2303
 
11.5%
아 1111
 
5.6%
라 1086
 
5.4%
다 1052
 
5.3%
나 1033
 
5.2%
마 1027
 
5.1%
Latin
ValueCountFrequency (%)
a 40000
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
80.0%
Hangul 20000
 
20.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 40000
50.0%
2 4108
 
5.1%
5 4084
 
5.1%
9 4053
 
5.1%
8 4034
 
5.0%
1 4016
 
5.0%
0 3995
 
5.0%
3 3965
 
5.0%
7 3961
 
5.0%
4 3899
 
4.9%
Hangul
ValueCountFrequency (%)
가 7840
39.2%
사 4548
22.7%
바 2303
 
11.5%
아 1111
 
5.6%
라 1086
 
5.4%
다 1052
 
5.3%
나 1033
 
5.2%
마 1027
 
5.1%

x좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct210
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean938986.1
Minimum700000
Maximum1399000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T09:36:46.190398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700000
5-th percentile706000
Q1734000
median771000
Q31319000
95-th percentile1384000
Maximum1399000
Range699000
Interquartile range (IQR)585000

Descriptive statistics

Standard deviation281172.97
Coefficient of variation (CV)0.29944317
Kurtosis-1.361956
Mean938986.1
Median Absolute Deviation (MAD)47000
Skewness0.7676882
Sum9.389861 × 109
Variance7.905824 × 1010
MonotonicityNot monotonic
2023-12-13T09:36:46.296680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
726000 93
 
0.9%
739000 90
 
0.9%
734000 87
 
0.9%
752000 85
 
0.9%
724000 83
 
0.8%
721000 83
 
0.8%
707000 82
 
0.8%
725000 81
 
0.8%
742000 81
 
0.8%
703000 81
 
0.8%
Other values (200) 9154
91.5%
ValueCountFrequency (%)
700000 75
0.8%
701000 66
0.7%
702000 65
0.7%
703000 81
0.8%
704000 71
0.7%
705000 78
0.8%
706000 68
0.7%
707000 82
0.8%
708000 76
0.8%
709000 59
0.6%
ValueCountFrequency (%)
1399000 31
0.3%
1398000 25
0.2%
1397000 44
0.4%
1396000 38
0.4%
1395000 39
0.4%
1394000 29
0.3%
1393000 30
0.3%
1392000 31
0.3%
1391000 29
0.3%
1390000 26
0.3%

y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct800
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1730572.2
Minimum1300000
Maximum2099000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T09:36:46.408021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300000
5-th percentile1349000
Q11540000
median1779000
Q31905000
95-th percentile2056000
Maximum2099000
Range799000
Interquartile range (IQR)365000

Descriptive statistics

Standard deviation222930.67
Coefficient of variation (CV)0.12881905
Kurtosis-1.0905924
Mean1730572.2
Median Absolute Deviation (MAD)173000
Skewness-0.27787172
Sum1.7305722 × 1010
Variance4.9698086 × 1010
MonotonicityNot monotonic
2023-12-13T09:36:46.518674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1824000 32
 
0.3%
1872000 32
 
0.3%
1838000 30
 
0.3%
1871000 29
 
0.3%
1862000 29
 
0.3%
1859000 29
 
0.3%
1802000 29
 
0.3%
1811000 28
 
0.3%
1882000 27
 
0.3%
1895000 27
 
0.3%
Other values (790) 9708
97.1%
ValueCountFrequency (%)
1300000 12
0.1%
1301000 13
0.1%
1302000 13
0.1%
1303000 13
0.1%
1304000 8
0.1%
1305000 6
0.1%
1306000 8
0.1%
1307000 7
0.1%
1308000 7
0.1%
1309000 11
0.1%
ValueCountFrequency (%)
2099000 10
0.1%
2098000 11
0.1%
2097000 14
0.1%
2096000 14
0.1%
2095000 12
0.1%
2094000 7
 
0.1%
2093000 14
0.1%
2092000 18
0.2%
2091000 10
0.1%
2090000 12
0.1%

Interactions

2023-12-13T09:36:45.326114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:36:45.156301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:36:45.406815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:36:45.235021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:36:46.592276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
x좌표y좌표
x좌표1.0000.636
y좌표0.6361.000
2023-12-13T09:36:46.650464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
x좌표y좌표
x좌표1.0000.510
y좌표0.5101.000

Missing values

2023-12-13T09:36:45.513329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:36:45.584793image/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

격자IDx좌표y좌표
78202가마72aa30aa7720001730000
80469가마94aa97aa7940001797000
75371가마43aa99aa7430001799000
42959가나19aa87aa7190001487000
78974가마80aa02aa7800001702000
6806사바58aa34aa13580001834000
9834사바88aa62aa13880001862000
47055가나60aa83aa7600001483000
59148가다81aa76aa7810001576000
12421사사14aa49aa13140001949000
격자IDx좌표y좌표
944바아99aa72aa12990002072000
70132가라91aa60aa7910001660000
50163가나91aa91aa7910001491000
28868사아78aa96aa13780002096000
1344사바03aa72aa13030001872000
85875가바49aa03aa7490001803000
67889가라69aa17aa7690001617000
80272가마93aa00aa7930001700000
92322가사13aa50aa7130001950000
47500가나65aa28aa7650001428000