Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory566.4 KiB
Average record size in memory58.0 B

Variable types

Categorical5
Text1

Alerts

1차순번 has constant value ""Constant
1차본부 has constant value ""Constant
2차사업소 is highly overall correlated with 2차순번High correlation
2차순번 is highly overall correlated with 2차사업소High correlation
전산화번호 has unique valuesUnique

Reproduction

Analysis started2024-04-21 21:33:01.465826
Analysis finished2024-04-21 21:33:02.480821
Duration1.01 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

1차순번
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
3
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 10000
100.0%

Length

2024-04-22T06:33:02.588542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T06:33:02.743376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 10000
100.0%

1차본부
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
인천본부
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천본부 10000
100.0%

Length

2024-04-22T06:33:02.907825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T06:33:03.067281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천본부 10000
100.0%

2차순번
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4
3482 
1
2747 
2
1928 
3
1843 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row1

Common Values

ValueCountFrequency (%)
4 3482
34.8%
1 2747
27.5%
2 1928
19.3%
3 1843
18.4%

Length

2024-04-22T06:33:03.229966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T06:33:03.402319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 3482
34.8%
1 2747
27.5%
2 1928
19.3%
3 1843
18.4%

2차사업소
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
김포지사
3482 
인천본부직할
2747 
남인천지사
1928 
부천지사
1843 

Length

Max length6
Median length4
Mean length4.7422
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부천지사
2nd row남인천지사
3rd row남인천지사
4th row남인천지사
5th row인천본부직할

Common Values

ValueCountFrequency (%)
김포지사 3482
34.8%
인천본부직할 2747
27.5%
남인천지사 1928
19.3%
부천지사 1843
18.4%

Length

2024-04-22T06:33:03.616075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T06:33:03.811290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
김포지사 3482
34.8%
인천본부직할 2747
27.5%
남인천지사 1928
19.3%
부천지사 1843
18.4%

전산화번호
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-04-22T06:33:04.907879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters80000
Distinct characters29
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 row9022Y371
2nd row8717G062
3rd row8620E571
4th row8718R931
5th row8826P411
ValueCountFrequency (%)
9022y371 1
 
< 0.1%
8925b201 1
 
< 0.1%
8721s783 1
 
< 0.1%
8336q261 1
 
< 0.1%
8716h792 1
 
< 0.1%
9224s092 1
 
< 0.1%
8720e071 1
 
< 0.1%
8723a962 1
 
< 0.1%
8332p742 1
 
< 0.1%
8334p831 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-04-22T06:33:06.243347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 12507
15.6%
8 12042
15.1%
1 10724
13.4%
3 8059
10.1%
9 5967
7.5%
0 4774
 
6.0%
7 4690
 
5.9%
4 4630
 
5.8%
6 3546
 
4.4%
5 3242
 
4.1%
Other values (19) 9819
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70181
87.7%
Uppercase Letter 9816
 
12.3%
Lowercase Letter 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 687
 
7.0%
F 666
 
6.8%
S 663
 
6.8%
E 663
 
6.8%
A 647
 
6.6%
G 647
 
6.6%
H 645
 
6.6%
R 623
 
6.3%
C 623
 
6.3%
B 615
 
6.3%
Other values (6) 3337
34.0%
Decimal Number
ValueCountFrequency (%)
2 12507
17.8%
8 12042
17.2%
1 10724
15.3%
3 8059
11.5%
9 5967
8.5%
0 4774
 
6.8%
7 4690
 
6.7%
4 4630
 
6.6%
6 3546
 
5.1%
5 3242
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
h 1
33.3%
q 1
33.3%
f 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 70181
87.7%
Latin 9819
 
12.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 687
 
7.0%
F 666
 
6.8%
S 663
 
6.8%
E 663
 
6.8%
A 647
 
6.6%
G 647
 
6.6%
H 645
 
6.6%
R 623
 
6.3%
C 623
 
6.3%
B 615
 
6.3%
Other values (9) 3340
34.0%
Common
ValueCountFrequency (%)
2 12507
17.8%
8 12042
17.2%
1 10724
15.3%
3 8059
11.5%
9 5967
8.5%
0 4774
 
6.8%
7 4690
 
6.7%
4 4630
 
6.6%
6 3546
 
5.1%
5 3242
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 12507
15.6%
8 12042
15.1%
1 10724
13.4%
3 8059
10.1%
9 5967
7.5%
0 4774
 
6.0%
7 4690
 
5.9%
4 4630
 
5.8%
6 3546
 
4.4%
5 3242
 
4.1%
Other values (19) 9819
12.3%

지역구분
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
4090 
주택가
3896 
농어촌
1542 
번화가
472 

Length

Max length4
Median length3
Mean length3.409
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row번화가
4th row<NA>
5th row주택가

Common Values

ValueCountFrequency (%)
<NA> 4090
40.9%
주택가 3896
39.0%
농어촌 1542
 
15.4%
번화가 472
 
4.7%

Length

2024-04-22T06:33:06.482302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-22T06:33:06.663418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4090
40.9%
주택가 3896
39.0%
농어촌 1542
 
15.4%
번화가 472
 
4.7%

Correlations

2024-04-22T06:33:07.053609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2차순번2차사업소지역구분
2차순번1.0001.0000.282
2차사업소1.0001.0000.282
지역구분0.2820.2821.000
2024-04-22T06:33:07.200612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2차사업소2차순번지역구분
2차사업소1.0001.0000.270
2차순번1.0001.0000.270
지역구분0.2700.2701.000
2024-04-22T06:33:07.349952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2차순번2차사업소지역구분
2차순번1.0001.0000.270
2차사업소1.0001.0000.270
지역구분0.2700.2701.000

Missing values

2024-04-22T06:33:02.395179image/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

1차순번1차본부2차순번2차사업소전산화번호지역구분
524023인천본부3부천지사9022Y371<NA>
300053인천본부2남인천지사8717G062<NA>
434933인천본부2남인천지사8620E571번화가
370443인천본부2남인천지사8718R931<NA>
224793인천본부1인천본부직할8826P411주택가
115063인천본부1인천본부직할8825R102<NA>
725033인천본부4김포지사8729Q051주택가
233453인천본부1인천본부직할3132H551주택가
745863인천본부4김포지사8131H331농어촌
289283인천본부2남인천지사8716A943<NA>
1차순번1차본부2차순번2차사업소전산화번호지역구분
322473인천본부2남인천지사8720G772번화가
875313인천본부4김포지사8130E761주택가
72873인천본부1인천본부직할8721R881주택가
841573인천본부4김포지사8331X334주택가
170933인천본부1인천본부직할8721S333주택가
991103인천본부4김포지사8328H352<NA>
547843인천본부3부천지사9122B231<NA>
722523인천본부4김포지사8828W401주택가
563973인천본부3부천지사9122S161주택가
645163인천본부3부천지사9024G281<NA>