Overview

Dataset statistics

Number of variables8
Number of observations27
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory74.9 B

Variable types

Text1
Numeric6
Categorical1

Dataset

Description전라북도 군산시 읍면동별 기초생활수급자 독거노인 전출입자 현황(읍면동,기초생활수급자수, 독거노인수, 전입자수, 전출자수 등)
Author전라북도 군산시
URLhttps://www.data.go.kr/data/15037820/fileData.do

Alerts

전출입기준일 has constant value ""Constant
기초생활수급자수 is highly overall correlated with 독거노인수 and 4 other fieldsHigh correlation
독거노인수 is highly overall correlated with 기초생활수급자수 and 4 other fieldsHigh correlation
관내전입자수 is highly overall correlated with 기초생활수급자수 and 4 other fieldsHigh correlation
관외전입자수 is highly overall correlated with 기초생활수급자수 and 4 other fieldsHigh correlation
관내전출자수 is highly overall correlated with 기초생활수급자수 and 4 other fieldsHigh correlation
관외전출자수 is highly overall correlated with 기초생활수급자수 and 4 other fieldsHigh correlation
읍면동 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:57:04.512480
Analysis finished2023-12-12 09:57:08.745678
Duration4.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-12T18:57:08.901721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1111111
Min length3

Characters and Unicode

Total characters84
Distinct characters43
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)100.0%

Sample

1st row옥구읍
2nd row옥산면
3rd row회현면
4th row임피면
5th row서수면
ValueCountFrequency (%)
옥구읍 1
 
3.7%
신풍동 1
 
3.7%
소룡동 1
 
3.7%
나운3동 1
 
3.7%
나운2동 1
 
3.7%
나운1동 1
 
3.7%
수송동 1
 
3.7%
개정동 1
 
3.7%
구암동 1
 
3.7%
경암동 1
 
3.7%
Other values (17) 17
63.0%
2023-12-12T18:57:09.293787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 16
19.0%
면 10
 
11.9%
나 4
 
4.8%
옥 4
 
4.8%
운 3
 
3.6%
산 2
 
2.4%
개 2
 
2.4%
구 2
 
2.4%
신 2
 
2.4%
암 2
 
2.4%
Other values (33) 37
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 81
96.4%
Decimal Number 3
 
3.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 16
19.8%
면 10
 
12.3%
나 4
 
4.9%
옥 4
 
4.9%
운 3
 
3.7%
산 2
 
2.5%
개 2
 
2.5%
구 2
 
2.5%
신 2
 
2.5%
암 2
 
2.5%
Other values (30) 34
42.0%
Decimal Number
ValueCountFrequency (%)
1 1
33.3%
2 1
33.3%
3 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 81
96.4%
Common 3
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 16
19.8%
면 10
 
12.3%
나 4
 
4.9%
옥 4
 
4.9%
운 3
 
3.7%
산 2
 
2.5%
개 2
 
2.5%
구 2
 
2.5%
신 2
 
2.5%
암 2
 
2.5%
Other values (30) 34
42.0%
Common
ValueCountFrequency (%)
1 1
33.3%
2 1
33.3%
3 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 81
96.4%
ASCII 3
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 16
19.8%
면 10
 
12.3%
나 4
 
4.9%
옥 4
 
4.9%
운 3
 
3.7%
산 2
 
2.5%
개 2
 
2.5%
구 2
 
2.5%
신 2
 
2.5%
암 2
 
2.5%
Other values (30) 34
42.0%
ASCII
ValueCountFrequency (%)
1 1
33.3%
2 1
33.3%
3 1
33.3%

기초생활수급자수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean425.85185
Minimum79
Maximum1619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T18:57:09.431257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79
5-th percentile95.7
Q1155
median326
Q3558.5
95-th percentile1071
Maximum1619
Range1540
Interquartile range (IQR)403.5

Descriptive statistics

Standard deviation372.91169
Coefficient of variation (CV)0.87568409
Kurtosis2.8136939
Mean425.85185
Median Absolute Deviation (MAD)191
Skewness1.6109132
Sum11498
Variance139063.13
MonotonicityNot monotonic
2023-12-12T18:57:09.646023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
155 2
 
7.4%
233 1
 
3.7%
765 1
 
3.7%
753 1
 
3.7%
1022 1
 
3.7%
1619 1
 
3.7%
719 1
 
3.7%
1092 1
 
3.7%
79 1
 
3.7%
361 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
79 1
3.7%
93 1
3.7%
102 1
3.7%
119 1
3.7%
120 1
3.7%
150 1
3.7%
155 2
7.4%
158 1
3.7%
164 1
3.7%
171 1
3.7%
ValueCountFrequency (%)
1619 1
3.7%
1092 1
3.7%
1022 1
3.7%
765 1
3.7%
753 1
3.7%
719 1
3.7%
583 1
3.7%
534 1
3.7%
517 1
3.7%
438 1
3.7%

독거노인수
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean361.11111
Minimum103
Maximum1229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T18:57:09.818460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile113.7
Q1174
median258
Q3459
95-th percentile744.8
Maximum1229
Range1126
Interquartile range (IQR)285

Descriptive statistics

Standard deviation253.68732
Coefficient of variation (CV)0.70251873
Kurtosis4.0299596
Mean361.11111
Median Absolute Deviation (MAD)139
Skewness1.7279026
Sum9750
Variance64357.256
MonotonicityNot monotonic
2023-12-12T18:57:09.965119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
166 2
 
7.4%
180 1
 
3.7%
258 1
 
3.7%
609 1
 
3.7%
397 1
 
3.7%
776 1
 
3.7%
1229 1
 
3.7%
452 1
 
3.7%
672 1
 
3.7%
105 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
103 1
3.7%
105 1
3.7%
134 1
3.7%
154 1
3.7%
166 2
7.4%
168 1
3.7%
180 1
3.7%
187 1
3.7%
203 1
3.7%
205 1
3.7%
ValueCountFrequency (%)
1229 1
3.7%
776 1
3.7%
672 1
3.7%
609 1
3.7%
527 1
3.7%
509 1
3.7%
466 1
3.7%
452 1
3.7%
449 1
3.7%
442 1
3.7%

관내전입자수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean313.51852
Minimum10
Maximum1645
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T18:57:10.101816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile21
Q164
median111
Q3435.5
95-th percentile1384
Maximum1645
Range1635
Interquartile range (IQR)371.5

Descriptive statistics

Standard deviation436.25922
Coefficient of variation (CV)1.3914943
Kurtosis4.9352832
Mean313.51852
Median Absolute Deviation (MAD)75
Skewness2.2686878
Sum8465
Variance190322.11
MonotonicityNot monotonic
2023-12-12T18:57:10.268898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
79 3
 
11.1%
64 2
 
7.4%
133 2
 
7.4%
388 1
 
3.7%
504 1
 
3.7%
644 1
 
3.7%
831 1
 
3.7%
450 1
 
3.7%
453 1
 
3.7%
1645 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
10 1
 
3.7%
18 1
 
3.7%
28 1
 
3.7%
36 1
 
3.7%
46 1
 
3.7%
50 1
 
3.7%
64 2
7.4%
79 3
11.1%
97 1
 
3.7%
100 1
 
3.7%
ValueCountFrequency (%)
1645 1
3.7%
1621 1
3.7%
831 1
3.7%
644 1
3.7%
504 1
3.7%
453 1
3.7%
450 1
3.7%
421 1
3.7%
388 1
3.7%
198 1
3.7%

관외전입자수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.88889
Minimum14
Maximum734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T18:57:10.465133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15.2
Q131
median54
Q3159.5
95-th percentile684.1
Maximum734
Range720
Interquartile range (IQR)128.5

Descriptive statistics

Standard deviation238.31029
Coefficient of variation (CV)1.3704745
Kurtosis0.75966051
Mean173.88889
Median Absolute Deviation (MAD)33
Skewness1.5280882
Sum4695
Variance56791.795
MonotonicityNot monotonic
2023-12-12T18:57:10.636655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
32 2
 
7.4%
61 2
 
7.4%
14 2
 
7.4%
43 2
 
7.4%
30 1
 
3.7%
93 1
 
3.7%
712 1
 
3.7%
367 1
 
3.7%
536 1
 
3.7%
136 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
14 2
7.4%
18 1
3.7%
21 1
3.7%
25 1
3.7%
28 1
3.7%
30 1
3.7%
32 2
7.4%
39 1
3.7%
43 2
7.4%
46 1
3.7%
ValueCountFrequency (%)
734 1
3.7%
712 1
3.7%
619 1
3.7%
586 1
3.7%
536 1
3.7%
367 1
3.7%
183 1
3.7%
136 1
3.7%
104 1
3.7%
93 1
3.7%

관내전출자수
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)81.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean345.85185
Minimum32
Maximum1285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T18:57:10.835089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile47.2
Q181
median158
Q3435.5
95-th percentile1209
Maximum1285
Range1253
Interquartile range (IQR)354.5

Descriptive statistics

Standard deviation394.87902
Coefficient of variation (CV)1.1417577
Kurtosis0.95889022
Mean345.85185
Median Absolute Deviation (MAD)97
Skewness1.4867321
Sum9338
Variance155929.44
MonotonicityNot monotonic
2023-12-12T18:57:10.990217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
126 2
 
7.4%
61 2
 
7.4%
1083 2
 
7.4%
90 2
 
7.4%
226 2
 
7.4%
32 1
 
3.7%
50 1
 
3.7%
500 1
 
3.7%
64 1
 
3.7%
753 1
 
3.7%
Other values (12) 12
44.4%
ValueCountFrequency (%)
32 1
3.7%
46 1
3.7%
50 1
3.7%
61 2
7.4%
64 1
3.7%
72 1
3.7%
90 2
7.4%
97 1
3.7%
126 2
7.4%
129 1
3.7%
ValueCountFrequency (%)
1285 1
3.7%
1263 1
3.7%
1083 2
7.4%
753 1
3.7%
500 1
3.7%
439 1
3.7%
432 1
3.7%
418 1
3.7%
266 1
3.7%
226 2
7.4%

관외전출자수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)85.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.66667
Minimum14
Maximum918
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-12T18:57:11.141124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile15.2
Q129
median51
Q3157
95-th percentile738.7
Maximum918
Range904
Interquartile range (IQR)128

Descriptive statistics

Standard deviation239.33578
Coefficient of variation (CV)1.5574997
Kurtosis6.4479555
Mean153.66667
Median Absolute Deviation (MAD)33
Skewness2.5977466
Sum4149
Variance57281.615
MonotonicityNot monotonic
2023-12-12T18:57:11.280795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
21 3
 
11.1%
14 2
 
7.4%
36 2
 
7.4%
904 1
 
3.7%
162 1
 
3.7%
353 1
 
3.7%
352 1
 
3.7%
288 1
 
3.7%
152 1
 
3.7%
918 1
 
3.7%
Other values (13) 13
48.1%
ValueCountFrequency (%)
14 2
7.4%
18 1
 
3.7%
21 3
11.1%
25 1
 
3.7%
33 1
 
3.7%
36 2
7.4%
39 1
 
3.7%
40 1
 
3.7%
43 1
 
3.7%
51 1
 
3.7%
ValueCountFrequency (%)
918 1
3.7%
904 1
3.7%
353 1
3.7%
352 1
3.7%
288 1
3.7%
180 1
3.7%
162 1
3.7%
152 1
3.7%
115 1
3.7%
105 1
3.7%

전출입기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-08-31
27 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-08-31
2nd row2023-08-31
3rd row2023-08-31
4th row2023-08-31
5th row2023-08-31

Common Values

ValueCountFrequency (%)
2023-08-31 27
100.0%

Length

2023-12-12T18:57:11.437188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:57:11.547621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-08-31 27
100.0%

Interactions

2023-12-12T18:57:07.569511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:04.754236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.307933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.856648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.411690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.994619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:07.667974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:04.851839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.428851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.947169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.518671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:07.099114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:07.750783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:04.945204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.498159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.032634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.605191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:07.177516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:07.852679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.040993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.593724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.135439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.713518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:07.270443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:07.972656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.144189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.686679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.225297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.817461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:07.374917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:08.442897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.228340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:05.766216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.312297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:06.912383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:57:07.466181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:57:11.634721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동기초생활수급자수독거노인수관내전입자수관외전입자수관내전출자수관외전출자수
읍면동1.0001.0001.0001.0001.0001.0001.000
기초생활수급자수1.0001.0000.9500.7460.9270.8190.787
독거노인수1.0000.9501.0000.6430.8060.7430.655
관내전입자수1.0000.7460.6431.0000.7990.9630.907
관외전입자수1.0000.9270.8060.7991.0000.7610.843
관내전출자수1.0000.8190.7430.9630.7611.0000.996
관외전출자수1.0000.7870.6550.9070.8430.9961.000
2023-12-12T18:57:11.750290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기초생활수급자수독거노인수관내전입자수관외전입자수관내전출자수관외전출자수
기초생활수급자수1.0000.9230.7920.7600.7930.810
독거노인수0.9231.0000.6790.7320.6870.742
관내전입자수0.7920.6791.0000.7560.9520.786
관외전입자수0.7600.7320.7561.0000.7130.795
관내전출자수0.7930.6870.9520.7131.0000.882
관외전출자수0.8100.7420.7860.7950.8821.000

Missing values

2023-12-12T18:57:08.556743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:57:08.688322image/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

읍면동기초생활수급자수독거노인수관내전입자수관외전입자수관내전출자수관외전출자수전출입기준일
0옥구읍1551807930126212023-08-31
1옥산면15810319839266542023-08-31
2회현면150205363264252023-08-31
3임피면171187182532182023-08-31
4서수면164203104661572023-08-31
5대야면234449796190392023-08-31
6개정면120168796161142023-08-31
7성산면102166502850212023-08-31
8나포면119154286446362023-08-31
9옥도면93134971497142023-08-31
읍면동기초생활수급자수독거노인수관내전입자수관외전입자수관내전출자수관외전출자수전출입기준일
17조촌동583509162161912639042023-08-31
18경암동534527100932261152023-08-31
19구암동3612074215864391052023-08-31
20개정동791056418129432023-08-31
21수송동1092672164573412859182023-08-31
22나운1동7194524531834321522023-08-31
23나운2동161912294501367532882023-08-31
24나운3동102277683153610833522023-08-31
25소룡동75339764436710833532023-08-31
26미성동7656095047125001622023-08-31