Overview

Dataset statistics

Number of variables12
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory104.6 B

Variable types

DateTime2
Categorical8
Text1
Numeric1

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://www.bigdata-region.kr/#/dataset/d94b586e-6c33-46ee-b064-e4b05a760e25

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
시군구명 has constant value ""Constant
행정동명 has constant value ""Constant
등록일자 has constant value ""Constant
작업자명 has constant value ""Constant
업종대분류코드 is highly imbalanced (56.4%)Imbalance

Reproduction

Analysis started2023-12-10 14:18:32.139626
Analysis finished2023-12-10 14:18:32.806916
Duration0.67 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum2019-01-01 00:00:00
Maximum2019-01-01 00:00:00
2023-12-10T23:18:32.846102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:32.928503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
[미분류]
29 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[미분류]
2nd row[미분류]
3rd row[미분류]
4th row[미분류]
5th row[미분류]

Common Values

ValueCountFrequency (%)
[미분류] 29
100.0%

Length

2023-12-10T23:18:33.036065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:33.139745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 29
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
[미분류]
29 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[미분류]
2nd row[미분류]
3rd row[미분류]
4th row[미분류]
5th row[미분류]

Common Values

ValueCountFrequency (%)
[미분류] 29
100.0%

Length

2023-12-10T23:18:33.254215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:33.352305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 29
100.0%

행정동명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
[미분류]
29 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row[미분류]
2nd row[미분류]
3rd row[미분류]
4th row[미분류]
5th row[미분류]

Common Values

ValueCountFrequency (%)
[미분류] 29
100.0%

Length

2023-12-10T23:18:33.456314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:33.563213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 29
100.0%

업종대분류코드
Categorical

IMBALANCE 

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
C
25 
B
3 
A
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.4%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowB
5th rowC

Common Values

ValueCountFrequency (%)
C 25
86.2%
B 3
 
10.3%
A 1
 
3.4%

Length

2023-12-10T23:18:33.656985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:33.751860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 25
86.2%
b 3
 
10.3%
a 1
 
3.4%

업종중분류코드
Text

Distinct17
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-10T23:18:33.912139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters87
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)34.5%

Sample

1st rowA03
2nd rowB07
3rd rowB07
4th rowB07
5th rowC10
ValueCountFrequency (%)
c10 5
17.2%
c28 3
10.3%
b07 3
10.3%
c24 2
 
6.9%
c31 2
 
6.9%
c20 2
 
6.9%
c26 2
 
6.9%
c23 1
 
3.4%
a03 1
 
3.4%
c29 1
 
3.4%
Other values (7) 7
24.1%
2023-12-10T23:18:34.240348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 25
28.7%
2 15
17.2%
1 11
12.6%
0 11
12.6%
3 7
 
8.0%
7 4
 
4.6%
B 3
 
3.4%
8 3
 
3.4%
6 3
 
3.4%
4 2
 
2.3%
Other values (3) 3
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58
66.7%
Uppercase Letter 29
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15
25.9%
1 11
19.0%
0 11
19.0%
3 7
12.1%
7 4
 
6.9%
8 3
 
5.2%
6 3
 
5.2%
4 2
 
3.4%
5 1
 
1.7%
9 1
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
C 25
86.2%
B 3
 
10.3%
A 1
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 58
66.7%
Latin 29
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15
25.9%
1 11
19.0%
0 11
19.0%
3 7
12.1%
7 4
 
6.9%
8 3
 
5.2%
6 3
 
5.2%
4 2
 
3.4%
5 1
 
1.7%
9 1
 
1.7%
Latin
ValueCountFrequency (%)
C 25
86.2%
B 3
 
10.3%
A 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 87
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 25
28.7%
2 15
17.2%
1 11
12.6%
0 11
12.6%
3 7
 
8.0%
7 4
 
4.6%
B 3
 
3.4%
8 3
 
3.4%
6 3
 
3.4%
4 2
 
2.3%
Other values (3) 3
 
3.4%

가공기업구분코드
Real number (ℝ)

Distinct6
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1724138
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-10T23:18:34.421466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q310
95-th percentile20
Maximum30
Range29
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.1717151
Coefficient of variation (CV)0.99990259
Kurtosis2.742293
Mean7.1724138
Median Absolute Deviation (MAD)3
Skewness1.7025802
Sum208
Variance51.433498
MonotonicityNot monotonic
2023-12-10T23:18:34.548395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 8
27.6%
2 7
24.1%
10 6
20.7%
1 4
13.8%
20 3
 
10.3%
30 1
 
3.4%
ValueCountFrequency (%)
1 4
13.8%
2 7
24.1%
5 8
27.6%
10 6
20.7%
20 3
 
10.3%
30 1
 
3.4%
ValueCountFrequency (%)
30 1
 
3.4%
20 3
 
10.3%
10 6
20.7%
5 8
27.6%
2 7
24.1%
1 4
13.8%

업력구간코드
Categorical

Distinct4
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
4
17 
99
8 
3
3 
1
 
1

Length

Max length2
Median length1
Mean length1.2758621
Min length1

Unique

Unique1 ?
Unique (%)3.4%

Sample

1st row4
2nd row99
3rd row99
4th row99
5th row4

Common Values

ValueCountFrequency (%)
4 17
58.6%
99 8
27.6%
3 3
 
10.3%
1 1
 
3.4%

Length

2023-12-10T23:18:34.729136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:34.886750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 17
58.6%
99 8
27.6%
3 3
 
10.3%
1 1
 
3.4%

성별코드
Categorical

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
M
17 
N
12 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowM
4th rowN
5th rowM

Common Values

ValueCountFrequency (%)
M 17
58.6%
N 12
41.4%

Length

2023-12-10T23:18:35.063626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:35.197262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
m 17
58.6%
n 12
41.4%

총기업수
Categorical

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
1
22 
3
4 
2
3 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 22
75.9%
3 4
 
13.8%
2 3
 
10.3%

Length

2023-12-10T23:18:35.308288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:35.444601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 22
75.9%
3 4
 
13.8%
2 3
 
10.3%

등록일자
Date

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
Minimum2019-12-11 00:00:00
Maximum2019-12-11 00:00:00
2023-12-10T23:18:35.563584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:35.699471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

작업자명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
20164
29 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20164
2nd row20164
3rd row20164
4th row20164
5th row20164

Common Values

ValueCountFrequency (%)
20164 29
100.0%

Length

2023-12-10T23:18:35.862433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:35.953913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20164 29
100.0%

Interactions

2023-12-10T23:18:32.465060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:18:36.022663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대분류코드업종중분류코드가공기업구분코드업력구간코드성별코드총기업수
업종대분류코드1.0001.0000.0000.2640.0830.670
업종중분류코드1.0001.0000.0000.0000.5640.863
가공기업구분코드0.0000.0001.0000.0000.3410.000
업력구간코드0.2640.0000.0001.0000.0000.105
성별코드0.0830.5640.3410.0001.0000.000
총기업수0.6700.8630.0000.1050.0001.000
2023-12-10T23:18:36.153106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
성별코드총기업수업력구간코드업종대분류코드
성별코드1.0000.0000.0000.125
총기업수0.0001.0000.0750.325
업력구간코드0.0000.0751.0000.240
업종대분류코드0.1250.3250.2401.000
2023-12-10T23:18:36.268115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가공기업구분코드업종대분류코드업력구간코드성별코드총기업수
가공기업구분코드1.0000.0000.0000.3870.000
업종대분류코드0.0001.0000.2400.1250.325
업력구간코드0.0000.2401.0000.0000.075
성별코드0.3870.1250.0001.0000.000
총기업수0.0000.3250.0750.0001.000

Missing values

2023-12-10T23:18:32.580027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:18:32.740219image/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

기준년월시도명시군구명행정동명업종대분류코드업종중분류코드가공기업구분코드업력구간코드성별코드총기업수등록일자작업자명
02019-01[미분류][미분류][미분류]AA0324N22019-12-1120164
12019-01[미분류][미분류][미분류]BB071099N12019-12-1120164
22019-01[미분류][미분류][미분류]BB07299M12019-12-1120164
32019-01[미분류][미분류][미분류]BB07299N12019-12-1120164
42019-01[미분류][미분류][미분류]CC1014M32019-12-1120164
52019-01[미분류][미분류][미분류]CC10199M12019-12-1120164
62019-01[미분류][미분류][미분류]CC10103M12019-12-1120164
72019-01[미분류][미분류][미분류]CC10204M12019-12-1120164
82019-01[미분류][미분류][미분류]CC102099M12019-12-1120164
92019-01[미분류][미분류][미분류]CC15104N12019-12-1120164
기준년월시도명시군구명행정동명업종대분류코드업종중분류코드가공기업구분코드업력구간코드성별코드총기업수등록일자작업자명
192019-01[미분류][미분류][미분류]CC26204M12019-12-1120164
202019-01[미분류][미분류][미분류]CC2653M12019-12-1120164
212019-01[미분류][미분류][미분류]CC28304M12019-12-1120164
222019-01[미분류][미분류][미분류]CC2854N12019-12-1120164
232019-01[미분류][미분류][미분류]CC28599N12019-12-1120164
242019-01[미분류][미분류][미분류]CC2914M22019-12-1120164
252019-01[미분류][미분류][미분류]CC31299M12019-12-1120164
262019-01[미분류][미분류][미분류]CC3154M22019-12-1120164
272019-01[미분류][미분류][미분류]CC3224M32019-12-1120164
282019-01[미분류][미분류][미분류]CC33104N32019-12-1120164