Overview

Dataset statistics

Number of variables5
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory48.7 B

Variable types

Text1
Numeric2
Categorical2

Dataset

Description경상북도 시군단위지역내 총샌상 1인당 생산액에 대한 데이터로 경상북도_시군단위지역내총생산데이터 중 1인당생산액에 대한 정보를 제공합니다.
Author경상북도
URLhttps://www.data.go.kr/data/15052024/fileData.do

Alerts

조사연도 has constant value ""Constant
단위 has constant value ""Constant
시군 has unique valuesUnique
시군코드 has unique valuesUnique
1인당생산액 has unique valuesUnique

Reproduction

Analysis started2023-12-16 15:38:19.431285
Analysis finished2023-12-16 15:38:22.757628
Duration3.33 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-16T15:38:23.498296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters46
Distinct characters33
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

Unique23 ?
Unique (%)100.0%

Sample

1st row포항
2nd row경주
3rd row김천
4th row안동
5th row구미
ValueCountFrequency (%)
포항 1
 
4.3%
청송 1
 
4.3%
울진 1
 
4.3%
봉화 1
 
4.3%
예천 1
 
4.3%
칠곡 1
 
4.3%
성주 1
 
4.3%
고령 1
 
4.3%
청도 1
 
4.3%
영덕 1
 
4.3%
Other values (13) 13
56.5%
2023-12-16T15:38:24.623301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 4
 
8.7%
영 4
 
8.7%
경 3
 
6.5%
천 3
 
6.5%
청 2
 
4.3%
성 2
 
4.3%
울 2
 
4.3%
칠 1
 
2.2%
덕 1
 
2.2%
도 1
 
2.2%
Other values (23) 23
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 4
 
8.7%
영 4
 
8.7%
경 3
 
6.5%
천 3
 
6.5%
청 2
 
4.3%
성 2
 
4.3%
울 2
 
4.3%
칠 1
 
2.2%
덕 1
 
2.2%
도 1
 
2.2%
Other values (23) 23
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 4
 
8.7%
영 4
 
8.7%
경 3
 
6.5%
천 3
 
6.5%
청 2
 
4.3%
성 2
 
4.3%
울 2
 
4.3%
칠 1
 
2.2%
덕 1
 
2.2%
도 1
 
2.2%
Other values (23) 23
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 4
 
8.7%
영 4
 
8.7%
경 3
 
6.5%
천 3
 
6.5%
청 2
 
4.3%
성 2
 
4.3%
울 2
 
4.3%
칠 1
 
2.2%
덕 1
 
2.2%
도 1
 
2.2%
Other values (23) 23
50.0%

시군코드
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3723.3043
Minimum3701
Maximum3743
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-16T15:38:25.664447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3701
5-th percentile3702.1
Q13706.5
median3732
Q33737.5
95-th percentile3741.9
Maximum3743
Range42
Interquartile range (IQR)31

Descriptive statistics

Standard deviation16.338562
Coefficient of variation (CV)0.0043881888
Kurtosis-1.9232348
Mean3723.3043
Median Absolute Deviation (MAD)10
Skewness-0.22796191
Sum85636
Variance266.94862
MonotonicityStrictly increasing
2023-12-16T15:38:26.439186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3701 1
 
4.3%
3702 1
 
4.3%
3743 1
 
4.3%
3742 1
 
4.3%
3741 1
 
4.3%
3740 1
 
4.3%
3739 1
 
4.3%
3738 1
 
4.3%
3737 1
 
4.3%
3736 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
3701 1
4.3%
3702 1
4.3%
3703 1
4.3%
3704 1
4.3%
3705 1
4.3%
3706 1
4.3%
3707 1
4.3%
3708 1
4.3%
3709 1
4.3%
3710 1
4.3%
ValueCountFrequency (%)
3743 1
4.3%
3742 1
4.3%
3741 1
4.3%
3740 1
4.3%
3739 1
4.3%
3738 1
4.3%
3737 1
4.3%
3736 1
4.3%
3735 1
4.3%
3734 1
4.3%

조사연도
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
2020
23 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 23
100.0%

Length

2023-12-16T15:38:27.368310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:38:27.877780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 23
100.0%

단위
Categorical

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
만원
23 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
만원 23
100.0%

Length

2023-12-16T15:38:28.520447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:38:29.147079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
만원 23
100.0%

1인당생산액
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3581.2609
Minimum2142
Maximum6717
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-16T15:38:29.782343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2142
5-th percentile2470.6
Q12804
median3716
Q33923.5
95-th percentile5210.6
Maximum6717
Range4575
Interquartile range (IQR)1119.5

Descriptive statistics

Standard deviation1038.5369
Coefficient of variation (CV)0.28999198
Kurtosis2.5861236
Mean3581.2609
Median Absolute Deviation (MAD)789
Skewness1.3269631
Sum82369
Variance1078558.9
MonotonicityNot monotonic
2023-12-16T15:38:30.549123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3716 1
 
4.3%
3790 1
 
4.3%
3293 1
 
4.3%
5236 1
 
4.3%
3886 1
 
4.3%
2142 1
 
4.3%
3961 1
 
4.3%
4982 1
 
4.3%
4027 1
 
4.3%
2455 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2142 1
4.3%
2455 1
4.3%
2611 1
4.3%
2631 1
4.3%
2662 1
4.3%
2789 1
4.3%
2819 1
4.3%
2859 1
4.3%
2927 1
4.3%
3293 1
4.3%
ValueCountFrequency (%)
6717 1
4.3%
5236 1
4.3%
4982 1
4.3%
4064 1
4.3%
4027 1
4.3%
3961 1
4.3%
3886 1
4.3%
3833 1
4.3%
3811 1
4.3%
3790 1
4.3%

Interactions

2023-12-16T15:38:20.546333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:38:19.829530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:38:21.085621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:38:20.178532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:38:30.905040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군시군코드1인당생산액
시군1.0001.0001.000
시군코드1.0001.0000.000
1인당생산액1.0000.0001.000
2023-12-16T15:38:31.441895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군코드1인당생산액
시군코드1.000-0.016
1인당생산액-0.0161.000

Missing values

2023-12-16T15:38:21.630420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:38:22.395114image/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

시군시군코드조사연도단위1인당생산액
0포항37012020만원3716
1경주37022020만원3790
2김천37032020만원4064
3안동37042020만원3776
4구미37052020만원6717
5영주37062020만원2927
6영천37072020만원3811
7상주37082020만원2611
8문경37092020만원2631
9경산37102020만원2819
시군시군코드조사연도단위1인당생산액
13영양37342020만원2789
14영덕37352020만원2662
15청도37362020만원2455
16고령37372020만원4027
17성주37382020만원4982
18칠곡37392020만원3961
19예천37402020만원2142
20봉화37412020만원3886
21울진37422020만원5236
22울릉37432020만원3293