Overview

Dataset statistics

Number of variables6
Number of observations689
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.1 KiB
Average record size in memory52.2 B

Variable types

Categorical2
Text1
Numeric3

Dataset

Description김해시에서 통계기반 도시현황 파악을 위해 개발한 통계지수 중 하나로서, 통계연도, 시도명, 시군구명, 귀농, 귀촌, 귀농 귀촌 가구수(가구)로 구성되어 있습니다. 김해시 중심의 통계지수로서, 데이터 수집, 가공 등의 어려움으로 김해시 외 지역의 정보는 누락될 수 있습니다.
Author경상남도 김해시
URLhttps://www.data.go.kr/data/15110102/fileData.do

Alerts

귀촌 is highly overall correlated with 귀농 귀촌 가구수(가구)High correlation
귀농 귀촌 가구수(가구) is highly overall correlated with 귀촌High correlation

Reproduction

Analysis started2023-12-12 13:30:56.626973
Analysis finished2023-12-12 13:30:58.244875
Duration1.62 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

통계연도
Categorical

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2017
138 
2018
138 
2019
138 
2021
138 
2020
137 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 138
20.0%
2018 138
20.0%
2019 138
20.0%
2021 138
20.0%
2020 137
19.9%

Length

2023-12-12T22:30:58.317717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:30:58.428254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 138
20.0%
2018 138
20.0%
2019 138
20.0%
2021 138
20.0%
2020 137
19.9%

시도명
Categorical

Distinct13
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
경상북도
114 
전라남도
105 
경상남도
90 
경기도
75 
강원도
75 
Other values (8)
230 

Length

Max length7
Median length4
Mean length3.862119
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시
2nd row대구광역시
3rd row인천광역시
4th row인천광역시
5th row울산광역시

Common Values

ValueCountFrequency (%)
경상북도 114
16.5%
전라남도 105
15.2%
경상남도 90
13.1%
경기도 75
10.9%
강원도 75
10.9%
충청남도 75
10.9%
전라북도 65
9.4%
충청북도 55
8.0%
인천광역시 10
 
1.5%
제주특별자치도 10
 
1.5%
Other values (3) 15
 
2.2%

Length

2023-12-12T22:30:58.589483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상북도 114
16.5%
전라남도 105
15.2%
경상남도 90
13.1%
경기도 75
10.9%
강원도 75
10.9%
충청남도 75
10.9%
전라북도 65
9.4%
충청북도 55
8.0%
인천광역시 10
 
1.5%
제주특별자치도 10
 
1.5%
Other values (3) 15
 
2.2%

시군구명
Text

Distinct137
Distinct (%)19.9%
Missing0
Missing (%)0.0%
Memory size5.5 KiB
2023-12-12T22:30:58.972407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0145138
Min length3

Characters and Unicode

Total characters2077
Distinct characters104
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

Unique0 ?
Unique (%)0.0%

Sample

1st row기장군
2nd row달성군
3rd row강화군
4th row옹진군
5th row울주군
ValueCountFrequency (%)
고성군 10
 
1.5%
상주시 5
 
0.7%
영천시 5
 
0.7%
영주시 5
 
0.7%
구미시 5
 
0.7%
안동시 5
 
0.7%
김천시 5
 
0.7%
경주시 5
 
0.7%
포항시 5
 
0.7%
신안군 5
 
0.7%
Other values (127) 634
92.0%
2023-12-12T22:30:59.558326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 419
20.2%
시 280
 
13.5%
주 95
 
4.6%
천 85
 
4.1%
양 70
 
3.4%
성 65
 
3.1%
산 55
 
2.6%
안 45
 
2.2%
영 45
 
2.2%
진 40
 
1.9%
Other values (94) 878
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2077
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 419
20.2%
시 280
 
13.5%
주 95
 
4.6%
천 85
 
4.1%
양 70
 
3.4%
성 65
 
3.1%
산 55
 
2.6%
안 45
 
2.2%
영 45
 
2.2%
진 40
 
1.9%
Other values (94) 878
42.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2077
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 419
20.2%
시 280
 
13.5%
주 95
 
4.6%
천 85
 
4.1%
양 70
 
3.4%
성 65
 
3.1%
산 55
 
2.6%
안 45
 
2.2%
영 45
 
2.2%
진 40
 
1.9%
Other values (94) 878
42.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2077
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 419
20.2%
시 280
 
13.5%
주 95
 
4.6%
천 85
 
4.1%
양 70
 
3.4%
성 65
 
3.1%
산 55
 
2.6%
안 45
 
2.2%
영 45
 
2.2%
진 40
 
1.9%
Other values (94) 878
42.3%

귀농
Real number (ℝ)

Distinct174
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.651669
Minimum4
Maximum227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-12T22:30:59.725718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile29.2
Q159
median90
Q3119
95-th percentile160.6
Maximum227
Range223
Interquartile range (IQR)60

Descriptive statistics

Standard deviation40.224342
Coefficient of variation (CV)0.44372423
Kurtosis-0.26480215
Mean90.651669
Median Absolute Deviation (MAD)30
Skewness0.2708436
Sum62459
Variance1617.9977
MonotonicityNot monotonic
2023-12-12T22:30:59.888648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 13
 
1.9%
104 13
 
1.9%
56 10
 
1.5%
95 10
 
1.5%
61 10
 
1.5%
72 10
 
1.5%
52 9
 
1.3%
130 9
 
1.3%
65 9
 
1.3%
90 9
 
1.3%
Other values (164) 587
85.2%
ValueCountFrequency (%)
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 2
0.3%
11 1
 
0.1%
12 2
0.3%
14 2
0.3%
15 3
0.4%
17 1
 
0.1%
ValueCountFrequency (%)
227 1
0.1%
223 1
0.1%
212 1
0.1%
211 1
0.1%
189 1
0.1%
188 1
0.1%
182 1
0.1%
181 1
0.1%
180 1
0.1%
178 1
0.1%

귀촌
Real number (ℝ)

HIGH CORRELATION 

Distinct614
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2419.1945
Minimum322
Maximum18706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-12T22:31:00.033800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile647.4
Q1969
median1359
Q32693
95-th percentile8085.8
Maximum18706
Range18384
Interquartile range (IQR)1724

Descriptive statistics

Standard deviation2588.0712
Coefficient of variation (CV)1.069807
Kurtosis7.9695693
Mean2419.1945
Median Absolute Deviation (MAD)566
Skewness2.6219476
Sum1666825
Variance6698112.5
MonotonicityNot monotonic
2023-12-12T22:31:00.212414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1163 4
 
0.6%
1184 3
 
0.4%
1316 3
 
0.4%
1101 3
 
0.4%
1315 3
 
0.4%
1187 3
 
0.4%
1150 2
 
0.3%
770 2
 
0.3%
1100 2
 
0.3%
648 2
 
0.3%
Other values (604) 662
96.1%
ValueCountFrequency (%)
322 1
0.1%
331 1
0.1%
340 1
0.1%
346 1
0.1%
361 1
0.1%
384 1
0.1%
447 1
0.1%
498 1
0.1%
501 1
0.1%
503 1
0.1%
ValueCountFrequency (%)
18706 1
0.1%
16233 1
0.1%
15082 1
0.1%
14033 1
0.1%
13750 1
0.1%
13675 1
0.1%
13136 1
0.1%
12892 1
0.1%
12676 1
0.1%
12248 1
0.1%

귀농 귀촌 가구수(가구)
Real number (ℝ)

HIGH CORRELATION 

Distinct608
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2509.8462
Minimum364
Maximum18863
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 KiB
2023-12-12T22:31:00.682020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum364
5-th percentile724.8
Q11060
median1477
Q32780
95-th percentile8176.2
Maximum18863
Range18499
Interquartile range (IQR)1720

Descriptive statistics

Standard deviation2585.6753
Coefficient of variation (CV)1.0302126
Kurtosis8.0349473
Mean2509.8462
Median Absolute Deviation (MAD)583
Skewness2.6255559
Sum1729284
Variance6685716.5
MonotonicityNot monotonic
2023-12-12T22:31:00.824887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1360 3
 
0.4%
1420 3
 
0.4%
795 3
 
0.4%
1524 3
 
0.4%
1281 3
 
0.4%
1138 3
 
0.4%
1275 3
 
0.4%
1040 3
 
0.4%
1308 3
 
0.4%
1289 2
 
0.3%
Other values (598) 660
95.8%
ValueCountFrequency (%)
364 1
0.1%
383 1
0.1%
389 1
0.1%
401 1
0.1%
404 1
0.1%
418 1
0.1%
451 1
0.1%
509 1
0.1%
548 1
0.1%
565 1
0.1%
ValueCountFrequency (%)
18863 1
0.1%
16389 1
0.1%
15210 1
0.1%
14122 1
0.1%
13866 1
0.1%
13739 1
0.1%
13227 1
0.1%
12960 1
0.1%
12780 1
0.1%
12307 1
0.1%

Interactions

2023-12-12T22:30:57.591913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:56.903918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.211397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.729564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.007623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.326053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.859314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.099158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:30:57.470707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:31:00.913127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명귀농귀촌귀농 귀촌 가구수(가구)
통계연도1.0000.0000.1880.0000.000
시도명0.0001.0000.4430.6050.609
귀농0.1880.4431.0000.2020.186
귀촌0.0000.6050.2021.0001.000
귀농 귀촌 가구수(가구)0.0000.6090.1861.0001.000
2023-12-12T22:31:01.003906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통계연도시도명
통계연도1.0000.000
시도명0.0001.000
2023-12-12T22:31:01.080623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
귀농귀촌귀농 귀촌 가구수(가구)통계연도시도명
귀농1.0000.0100.0550.0790.200
귀촌0.0101.0000.9980.0000.302
귀농 귀촌 가구수(가구)0.0550.9981.0000.0000.305
통계연도0.0790.0000.0001.0000.000
시도명0.2000.3020.3050.0001.000

Missing values

2023-12-12T22:30:58.030156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:30:58.190658image/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

통계연도시도명시군구명귀농귀촌귀농 귀촌 가구수(가구)
02017부산광역시기장군3363386371
12017대구광역시달성군891403314122
22017인천광역시강화군12321962319
32017인천광역시옹진군1514431458
42017울산광역시울주군9064846574
52017경기도평택시9669627058
62017경기도남양주시591224812307
72017경기도용인시6250815143
82017경기도파주시4861936241
92017경기도이천시7844134491
통계연도시도명시군구명귀농귀촌귀농 귀촌 가구수(가구)
6792021경상남도창녕군12615171643
6802021경상남도고성군10212241326
6812021경상남도남해군8110331114
6822021경상남도하동군1338931026
6832021경상남도산청군12010091129
6842021경상남도함양군1398641003
6852021경상남도거창군13712571394
6862021경상남도합천군1339491082
6872021제주특별자치도제주시11952175336
6882021제주특별자치도서귀포시12936893818