Overview

Dataset statistics

Number of variables8
Number of observations170
Missing cells340
Missing cells (%)25.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.6 KiB
Average record size in memory69.8 B

Variable types

Categorical2
Text1
Numeric3
Unsupported2

Dataset

Description부산광역시_기장군_읍면별인구현황_20171031
Author부산광역시 기장군
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3072537

Alerts

행정구역(군) has constant value ""Constant
세대수 is highly overall correlated with 인구수(남) and 1 other fieldsHigh correlation
인구수(남) is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
인구수(여) is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
Unnamed: 6 has 170 (100.0%) missing valuesMissing
has 170 (100.0%) missing valuesMissing
행정구역(리(통)) has unique valuesUnique
Unnamed: 6 is an unsupported type, check if it needs cleaning or further analysisUnsupported
is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 17:32:01.674138
Analysis finished2023-12-10 17:32:05.034700
Duration3.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정구역(군)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
부산광역시 기장군
170 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산광역시 기장군
2nd row부산광역시 기장군
3rd row부산광역시 기장군
4th row부산광역시 기장군
5th row부산광역시 기장군

Common Values

ValueCountFrequency (%)
부산광역시 기장군 170
100.0%

Length

2023-12-11T02:32:05.287161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:32:05.508211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 170
50.0%
기장군 170
50.0%

행정구역(읍.면)
Categorical

Distinct5
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
정관읍
53 
기장읍
44 
일광면
26 
장안읍
25 
철마면
22 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정관읍 53
31.2%
기장읍 44
25.9%
일광면 26
15.3%
장안읍 25
14.7%
철마면 22
12.9%

Length

2023-12-11T02:32:05.744842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:32:06.024876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정관읍 53
31.2%
기장읍 44
25.9%
일광면 26
15.3%
장안읍 25
14.7%
철마면 22
12.9%

행정구역(리(통))
Text

UNIQUE 

Distinct170
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-11T02:32:06.607901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.2
Min length6

Characters and Unicode

Total characters1224
Distinct characters112
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)100.0%

Sample

1st row동부1(1통)
2nd row동부2(2통)
3rd row서부(3통)
4th row동서(4통)
5th row두화(5통)
ValueCountFrequency (%)
동부1(1통 1
 
0.6%
방곡3(46통 1
 
0.6%
달산5(39통 1
 
0.6%
후동(4통 1
 
0.6%
용수7(40통 1
 
0.6%
매학(41통 1
 
0.6%
모전8(42통 1
 
0.6%
용수8(43통 1
 
0.6%
달산6(44통 1
 
0.6%
대전(45통 1
 
0.6%
Other values (160) 160
94.1%
2023-12-11T02:32:07.680118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 170
13.9%
통 170
13.9%
) 170
13.9%
1 84
 
6.9%
2 73
 
6.0%
3 51
 
4.2%
4 41
 
3.3%
5 26
 
2.1%
6 21
 
1.7%
0 17
 
1.4%
Other values (102) 401
32.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 521
42.6%
Decimal Number 363
29.7%
Open Punctuation 170
 
13.9%
Close Punctuation 170
 
13.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
통 170
32.6%
곡 17
 
3.3%
동 16
 
3.1%
전 16
 
3.1%
수 13
 
2.5%
산 13
 
2.5%
리 13
 
2.5%
용 11
 
2.1%
모 10
 
1.9%
덕 9
 
1.7%
Other values (90) 233
44.7%
Decimal Number
ValueCountFrequency (%)
1 84
23.1%
2 73
20.1%
3 51
14.0%
4 41
11.3%
5 26
 
7.2%
6 21
 
5.8%
0 17
 
4.7%
9 17
 
4.7%
7 17
 
4.7%
8 16
 
4.4%
Open Punctuation
ValueCountFrequency (%)
( 170
100.0%
Close Punctuation
ValueCountFrequency (%)
) 170
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 703
57.4%
Hangul 521
42.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
통 170
32.6%
곡 17
 
3.3%
동 16
 
3.1%
전 16
 
3.1%
수 13
 
2.5%
산 13
 
2.5%
리 13
 
2.5%
용 11
 
2.1%
모 10
 
1.9%
덕 9
 
1.7%
Other values (90) 233
44.7%
Common
ValueCountFrequency (%)
( 170
24.2%
) 170
24.2%
1 84
11.9%
2 73
10.4%
3 51
 
7.3%
4 41
 
5.8%
5 26
 
3.7%
6 21
 
3.0%
0 17
 
2.4%
9 17
 
2.4%
Other values (2) 33
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 703
57.4%
Hangul 521
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 170
24.2%
) 170
24.2%
1 84
11.9%
2 73
10.4%
3 51
 
7.3%
4 41
 
5.8%
5 26
 
3.7%
6 21
 
3.0%
0 17
 
2.4%
9 17
 
2.4%
Other values (2) 33
 
4.7%
Hangul
ValueCountFrequency (%)
통 170
32.6%
곡 17
 
3.3%
동 16
 
3.1%
전 16
 
3.1%
수 13
 
2.5%
산 13
 
2.5%
리 13
 
2.5%
용 11
 
2.1%
모 10
 
1.9%
덕 9
 
1.7%
Other values (90) 233
44.7%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct137
Distinct (%)80.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean383.23529
Minimum3
Maximum1940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T02:32:08.027266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile41
Q167
median168.5
Q3644.5
95-th percentile1124.05
Maximum1940
Range1937
Interquartile range (IQR)577.5

Descriptive statistics

Standard deviation419.62027
Coefficient of variation (CV)1.0949416
Kurtosis1.5844544
Mean383.23529
Median Absolute Deviation (MAD)121.5
Skewness1.4101411
Sum65150
Variance176081.18
MonotonicityNot monotonic
2023-12-11T02:32:08.376751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 5
 
2.9%
41 4
 
2.4%
67 4
 
2.4%
124 4
 
2.4%
46 3
 
1.8%
51 3
 
1.8%
1071 2
 
1.2%
56 2
 
1.2%
55 2
 
1.2%
65 2
 
1.2%
Other values (127) 139
81.8%
ValueCountFrequency (%)
3 1
 
0.6%
13 1
 
0.6%
21 1
 
0.6%
29 1
 
0.6%
31 1
 
0.6%
33 2
1.2%
39 1
 
0.6%
41 4
2.4%
43 2
1.2%
44 1
 
0.6%
ValueCountFrequency (%)
1940 1
0.6%
1871 1
0.6%
1695 1
0.6%
1578 1
0.6%
1513 1
0.6%
1452 1
0.6%
1191 1
0.6%
1138 1
0.6%
1129 1
0.6%
1118 1
0.6%

인구수(남)
Real number (ℝ)

HIGH CORRELATION 

Distinct152
Distinct (%)89.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean468.54706
Minimum4
Maximum2667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T02:32:08.760655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile40.9
Q168.75
median171
Q3749.25
95-th percentile1520.65
Maximum2667
Range2663
Interquartile range (IQR)680.5

Descriptive statistics

Standard deviation561.2886
Coefficient of variation (CV)1.1979343
Kurtosis2.2952789
Mean468.54706
Median Absolute Deviation (MAD)123
Skewness1.6132854
Sum79653
Variance315044.89
MonotonicityNot monotonic
2023-12-11T02:32:09.111720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 4
 
2.4%
48 4
 
2.4%
55 3
 
1.8%
49 3
 
1.8%
166 2
 
1.2%
59 2
 
1.2%
66 2
 
1.2%
43 2
 
1.2%
50 2
 
1.2%
75 2
 
1.2%
Other values (142) 144
84.7%
ValueCountFrequency (%)
4 1
0.6%
13 1
0.6%
20 1
0.6%
21 1
0.6%
28 1
0.6%
37 1
0.6%
38 1
0.6%
39 1
0.6%
40 1
0.6%
42 1
0.6%
ValueCountFrequency (%)
2667 1
0.6%
2442 1
0.6%
2281 1
0.6%
2231 1
0.6%
2029 1
0.6%
1876 1
0.6%
1811 1
0.6%
1567 1
0.6%
1540 1
0.6%
1497 1
0.6%

인구수(여)
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean477.4
Minimum4
Maximum2646
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-11T02:32:09.407412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile33.25
Q159.25
median170.5
Q3733
95-th percentile1617.85
Maximum2646
Range2642
Interquartile range (IQR)673.75

Descriptive statistics

Standard deviation584.8096
Coefficient of variation (CV)1.2249887
Kurtosis2.0231211
Mean477.4
Median Absolute Deviation (MAD)128.5
Skewness1.5817597
Sum81158
Variance342002.27
MonotonicityNot monotonic
2023-12-11T02:32:09.759679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 4
 
2.4%
48 4
 
2.4%
733 3
 
1.8%
52 3
 
1.8%
51 3
 
1.8%
36 3
 
1.8%
46 2
 
1.2%
74 2
 
1.2%
59 2
 
1.2%
27 2
 
1.2%
Other values (134) 142
83.5%
ValueCountFrequency (%)
4 1
 
0.6%
6 1
 
0.6%
17 2
1.2%
18 1
 
0.6%
27 2
1.2%
30 1
 
0.6%
31 1
 
0.6%
36 3
1.8%
37 1
 
0.6%
39 1
 
0.6%
ValueCountFrequency (%)
2646 1
0.6%
2524 1
0.6%
2307 1
0.6%
2260 1
0.6%
2240 1
0.6%
1903 1
0.6%
1812 1
0.6%
1686 1
0.6%
1666 1
0.6%
1559 1
0.6%

Unnamed: 6
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing170
Missing (%)100.0%
Memory size1.6 KiB


Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing170
Missing (%)100.0%
Memory size1.6 KiB

Interactions

2023-12-11T02:32:04.066210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:02.150267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:03.455368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:04.256886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:02.484590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:03.722065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:04.419531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:02.686454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:32:03.903250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:32:10.008155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역(읍.면)세대수인구수(남)인구수(여)
행정구역(읍.면)1.0000.4230.3240.393
세대수0.4231.0000.9740.972
인구수(남)0.3240.9741.0000.989
인구수(여)0.3930.9720.9891.000
2023-12-11T02:32:10.214704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구수(남)인구수(여)행정구역(읍.면)
세대수1.0000.9820.9830.185
인구수(남)0.9821.0000.9880.137
인구수(여)0.9830.9881.0000.170
행정구역(읍.면)0.1850.1370.1701.000

Missing values

2023-12-11T02:32:04.652657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:32:04.929506image/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

행정구역(군)행정구역(읍.면)행정구역(리(통))세대수인구수(남)인구수(여)Unnamed: 6
0부산광역시 기장군기장읍동부1(1통)90211161013<NA><NA>
1부산광역시 기장군기장읍동부2(2통)8411018979<NA><NA>
2부산광역시 기장군기장읍서부(3통)175192200<NA><NA>
3부산광역시 기장군기장읍동서(4통)11511288<NA><NA>
4부산광역시 기장군기장읍두화(5통)13136<NA><NA>
5부산광역시 기장군기장읍교리1(6통)92012461261<NA><NA>
6부산광역시 기장군기장읍소정1(7통)366471458<NA><NA>
7부산광역시 기장군기장읍소정2(8통)434339<NA><NA>
8부산광역시 기장군기장읍오신(9통)514851<NA><NA>
9부산광역시 기장군기장읍내동(10통)535336<NA><NA>
행정구역(군)행정구역(읍.면)행정구역(리(통))세대수인구수(남)인구수(여)Unnamed: 6
160부산광역시 기장군철마면대곡(13통)525852<NA><NA>
161부산광역시 기장군철마면고촌(14통)124133138<NA><NA>
162부산광역시 기장군철마면사등(15통)464327<NA><NA>
163부산광역시 기장군철마면안평(16통)182176158<NA><NA>
164부산광역시 기장군철마면송정1(17통)209221165<NA><NA>
165부산광역시 기장군철마면입석(19통)137152143<NA><NA>
166부산광역시 기장군철마면임기(20통)272289256<NA><NA>
167부산광역시 기장군철마면고촌2(21통)107110611162<NA><NA>
168부산광역시 기장군철마면고촌3(22통)457669689<NA><NA>
169부산광역시 기장군철마면고촌4(23통)454662655<NA><NA>