Overview

Dataset statistics

Number of variables4
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory37.4 B

Variable types

DateTime1
Numeric2
Text1

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/0ed09969-08a8-4bd2-be9b-8f0426f42a1a

Alerts

기준년월 has constant value ""Constant
외국인 인구 has 1 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:15:49.725897
Analysis finished2023-12-10 14:15:53.432047
Duration3.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2019-01-01 00:00:00
Maximum2019-01-01 00:00:00
2023-12-10T23:15:53.529805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:53.747910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

블록 ID
Real number (ℝ)

Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1050512 × 1019
Minimum3.105051 × 1019
Maximum3.105052 × 1019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:15:53.962913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.105051 × 1019
5-th percentile3.105051 × 1019
Q13.105051 × 1019
median3.105051 × 1019
Q33.105051 × 1019
95-th percentile3.105052 × 1019
Maximum3.105052 × 1019
Range1.000002 × 1013
Interquartile range (IQR)1.00105 × 1011

Descriptive statistics

Standard deviation3.7694146 × 1012
Coefficient of variation (CV)1.2139621 × 10-7
Kurtosis1.6558494
Mean3.1050512 × 1019
Median Absolute Deviation (MAD)9.9979997 × 1010
Skewness1.8837736
Sum9.3151535 × 1020
Variance1.4208486 × 1025
MonotonicityNot monotonic
2023-12-10T23:15:54.216303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
3.10505101001e+19 7
23.3%
3.105051020019e+19 4
13.3%
3.105051020009e+19 2
 
6.7%
3.105052010003e+19 2
 
6.7%
3.105051010008e+19 2
 
6.7%
3.105051020004e+19 2
 
6.7%
3.105051020005e+19 1
 
3.3%
3.105051020007e+19 1
 
3.3%
3.105051020012e+19 1
 
3.3%
3.105051010003e+19 1
 
3.3%
Other values (7) 7
23.3%
ValueCountFrequency (%)
3.105051010003e+19 1
 
3.3%
3.105051010008e+19 2
 
6.7%
3.105051010009e+19 1
 
3.3%
3.10505101001e+19 7
23.3%
3.105051020004e+19 2
 
6.7%
3.105051020005e+19 1
 
3.3%
3.105051020007e+19 1
 
3.3%
3.105051020009e+19 2
 
6.7%
3.105051020012e+19 1
 
3.3%
3.105051020019e+19 4
13.3%
ValueCountFrequency (%)
3.105052010005e+19 1
 
3.3%
3.105052010004e+19 1
 
3.3%
3.105052010003e+19 2
6.7%
3.105052010002e+19 1
 
3.3%
3.105051020701e+19 1
 
3.3%
3.105051020022e+19 1
 
3.3%
3.105051020021e+19 1
 
3.3%
3.105051020019e+19 4
13.3%
3.105051020012e+19 1
 
3.3%
3.105051020009e+19 2
6.7%

국가 코드
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:15:54.478550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st rowGBR
2nd rowSGP
3rd rowSGP
4th rowAUT
5th rowAUT
ValueCountFrequency (%)
sgp 4
13.3%
jpn 3
 
10.0%
nzl 3
 
10.0%
nld 2
 
6.7%
aut 2
 
6.7%
aus 2
 
6.7%
chn 2
 
6.7%
gbr 1
 
3.3%
bel 1
 
3.3%
lka 1
 
3.3%
Other values (9) 9
30.0%
2023-12-10T23:15:54.894363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 13
14.4%
S 9
10.0%
L 8
 
8.9%
U 8
 
8.9%
P 7
 
7.8%
A 6
 
6.7%
G 5
 
5.6%
D 5
 
5.6%
T 4
 
4.4%
J 3
 
3.3%
Other values (10) 22
24.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 90
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 13
14.4%
S 9
10.0%
L 8
 
8.9%
U 8
 
8.9%
P 7
 
7.8%
A 6
 
6.7%
G 5
 
5.6%
D 5
 
5.6%
T 4
 
4.4%
J 3
 
3.3%
Other values (10) 22
24.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 90
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 13
14.4%
S 9
10.0%
L 8
 
8.9%
U 8
 
8.9%
P 7
 
7.8%
A 6
 
6.7%
G 5
 
5.6%
D 5
 
5.6%
T 4
 
4.4%
J 3
 
3.3%
Other values (10) 22
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 13
14.4%
S 9
10.0%
L 8
 
8.9%
U 8
 
8.9%
P 7
 
7.8%
A 6
 
6.7%
G 5
 
5.6%
D 5
 
5.6%
T 4
 
4.4%
J 3
 
3.3%
Other values (10) 22
24.4%

외국인 인구
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48333333
Minimum0
Maximum4.61
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:15:55.040068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0145
Q10.04
median0.125
Q30.3125
95-th percentile1.868
Maximum4.61
Range4.61
Interquartile range (IQR)0.2725

Descriptive statistics

Standard deviation0.93766156
Coefficient of variation (CV)1.9399894
Kurtosis13.112008
Mean0.48333333
Median Absolute Deviation (MAD)0.095
Skewness3.3743117
Sum14.5
Variance0.8792092
MonotonicityNot monotonic
2023-12-10T23:15:55.183977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.03 3
 
10.0%
0.04 3
 
10.0%
0.05 2
 
6.7%
0.08 2
 
6.7%
1.56 1
 
3.3%
0.21 1
 
3.3%
1.31 1
 
3.3%
0.24 1
 
3.3%
2.12 1
 
3.3%
0.15 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
0.0 1
 
3.3%
0.01 1
 
3.3%
0.02 1
 
3.3%
0.03 3
10.0%
0.04 3
10.0%
0.05 2
6.7%
0.08 2
6.7%
0.09 1
 
3.3%
0.1 1
 
3.3%
0.15 1
 
3.3%
ValueCountFrequency (%)
4.61 1
3.3%
2.12 1
3.3%
1.56 1
3.3%
1.31 1
3.3%
1.11 1
3.3%
0.72 1
3.3%
0.64 1
3.3%
0.32 1
3.3%
0.29 1
3.3%
0.24 1
3.3%

Interactions

2023-12-10T23:15:52.683119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:52.027905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:53.005849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:15:52.401727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:15:55.282004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
블록 ID국가 코드외국인 인구
블록 ID1.0000.0000.000
국가 코드0.0001.0000.000
외국인 인구0.0000.0001.000
2023-12-10T23:15:55.373018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
블록 ID외국인 인구
블록 ID1.000-0.004
외국인 인구-0.0041.000

Missing values

2023-12-10T23:15:53.178683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:15:53.348793image/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

기준년월블록 ID국가 코드외국인 인구
02019-0131050510100080000001GBR0.05
12019-0131050510100030000001SGP0.17
22019-0131050510100080000001SGP0.16
32019-0131050510100100000001AUT0.01
42019-0131050510100090000001AUT0.0
52019-0131050510100100000001VNM0.29
62019-0131050510100100000003AUS0.32
72019-0131050510100100000004JPN0.64
82019-0131050510100100000004SGP0.03
92019-0131050510100100000006IND0.72
기준년월블록 ID국가 코드외국인 인구
202019-0131050510200190000004MLT0.02
212019-0131050510200190000004NZL0.15
222019-0131050510200210000001JPN2.12
232019-0131050510200220000001RUS0.24
242019-0131050510207010000001CHN1.31
252019-0131050520100030000001LKA0.08
262019-0131050520100020000001SGP0.03
272019-0131050520100030000001NLD0.08
282019-0131050520100040000005IDN0.21
292019-0131050520100050000001NZL0.04