Overview

Dataset statistics

Number of variables7
Number of observations87
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory61.5 B

Variable types

Categorical1
Text1
Numeric4
DateTime1

Dataset

Description경기도 포천시에서 제공하는 읍면동 법정리별 인구 및 세대현황(읍면동명, 법정동명, 세대수, 총인구수, 남자, 여자, 데이터기준일)데이터 입니다.
Author경기도 포천시
URLhttps://www.data.go.kr/data/15101890/fileData.do

Alerts

데이터기준일 has constant value ""Constant
세대수 is highly overall correlated with 총인구수 and 2 other fieldsHigh correlation
총인구수 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
남자 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
여자 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
법정동명 has unique valuesUnique
총인구수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:09:26.617248
Analysis finished2023-12-12 03:09:29.536352
Duration2.92 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동명
Categorical

Distinct14
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
신북면
12 
소흘읍
9 
군내면
8 
가산면
8 
영북면
7 
Other values (9)
43 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신북면 12
13.8%
소흘읍 9
10.3%
군내면 8
9.2%
가산면 8
9.2%
영북면 7
8.0%
내촌면 6
6.9%
창수면 6
6.9%
영중면 6
6.9%
일동면 6
6.9%
관인면 6
6.9%
Other values (4) 13
14.9%

Length

2023-12-12T12:09:29.642086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신북면 12
13.8%
소흘읍 9
10.3%
군내면 8
9.2%
가산면 8
9.2%
영북면 7
8.0%
내촌면 6
6.9%
창수면 6
6.9%
영중면 6
6.9%
일동면 6
6.9%
관인면 6
6.9%
Other values (4) 13
14.9%

법정동명
Text

UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size828.0 B
2023-12-12T12:09:30.031951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.091954
Min length2

Characters and Unicode

Total characters269
Distinct characters96
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

Unique87 ?
Unique (%)100.0%

Sample

1st row송우리
2nd row이동교리
3rd row무림리
4th row이곡리
5th row직동리
ValueCountFrequency (%)
송우리 1
 
1.1%
자일리 1
 
1.1%
연곡리 1
 
1.1%
도평리 1
 
1.1%
장암리 1
 
1.1%
수입리 1
 
1.1%
사직리 1
 
1.1%
화대리 1
 
1.1%
기산리 1
 
1.1%
유동리 1
 
1.1%
Other values (77) 77
88.5%
2023-12-12T12:09:30.666498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 81
30.1%
동 14
 
5.2%
산 8
 
3.0%
정 6
 
2.2%
가 6
 
2.2%
교 5
 
1.9%
성 5
 
1.9%
명 4
 
1.5%
곡 4
 
1.5%
현 4
 
1.5%
Other values (86) 132
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 269
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 81
30.1%
동 14
 
5.2%
산 8
 
3.0%
정 6
 
2.2%
가 6
 
2.2%
교 5
 
1.9%
성 5
 
1.9%
명 4
 
1.5%
곡 4
 
1.5%
현 4
 
1.5%
Other values (86) 132
49.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 269
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 81
30.1%
동 14
 
5.2%
산 8
 
3.0%
정 6
 
2.2%
가 6
 
2.2%
교 5
 
1.9%
성 5
 
1.9%
명 4
 
1.5%
곡 4
 
1.5%
현 4
 
1.5%
Other values (86) 132
49.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 269
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 81
30.1%
동 14
 
5.2%
산 8
 
3.0%
정 6
 
2.2%
가 6
 
2.2%
교 5
 
1.9%
성 5
 
1.9%
명 4
 
1.5%
곡 4
 
1.5%
현 4
 
1.5%
Other values (86) 132
49.1%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean848.1954
Minimum69
Maximum12208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-12T12:09:30.893344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum69
5-th percentile110
Q1261
median418
Q3650.5
95-th percentile2898.2
Maximum12208
Range12139
Interquartile range (IQR)389.5

Descriptive statistics

Standard deviation1667.7173
Coefficient of variation (CV)1.9661946
Kurtosis28.927307
Mean848.1954
Median Absolute Deviation (MAD)206
Skewness5.0391985
Sum73793
Variance2781280.9
MonotonicityNot monotonic
2023-12-12T12:09:31.133262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
289 3
 
3.4%
110 2
 
2.3%
183 2
 
2.3%
624 2
 
2.3%
1870 2
 
2.3%
400 1
 
1.1%
1065 1
 
1.1%
535 1
 
1.1%
558 1
 
1.1%
1216 1
 
1.1%
Other values (71) 71
81.6%
ValueCountFrequency (%)
69 1
1.1%
83 1
1.1%
96 1
1.1%
108 1
1.1%
110 2
2.3%
114 1
1.1%
124 1
1.1%
137 1
1.1%
139 1
1.1%
140 1
1.1%
ValueCountFrequency (%)
12208 1
1.1%
7966 1
1.1%
5779 1
1.1%
3012 1
1.1%
2984 1
1.1%
2698 1
1.1%
1870 2
2.3%
1657 1
1.1%
1365 1
1.1%
1216 1
1.1%

총인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct87
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1704.0805
Minimum116
Maximum29120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-12T12:09:31.350055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum116
5-th percentile194.9
Q1448.5
median726
Q31202
95-th percentile5868.7
Maximum29120
Range29004
Interquartile range (IQR)753.5

Descriptive statistics

Standard deviation3805.4603
Coefficient of variation (CV)2.2331459
Kurtosis34.407173
Mean1704.0805
Median Absolute Deviation (MAD)356
Skewness5.4742471
Sum148255
Variance14481528
MonotonicityNot monotonic
2023-12-12T12:09:31.571182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29120 1
 
1.1%
6068 1
 
1.1%
1918 1
 
1.1%
930 1
 
1.1%
894 1
 
1.1%
2454 1
 
1.1%
1064 1
 
1.1%
747 1
 
1.1%
726 1
 
1.1%
5896 1
 
1.1%
Other values (77) 77
88.5%
ValueCountFrequency (%)
116 1
1.1%
138 1
1.1%
145 1
1.1%
153 1
1.1%
194 1
1.1%
197 1
1.1%
199 1
1.1%
202 1
1.1%
233 1
1.1%
235 1
1.1%
ValueCountFrequency (%)
29120 1
1.1%
16993 1
1.1%
11761 1
1.1%
6068 1
1.1%
5896 1
1.1%
5805 1
1.1%
4270 1
1.1%
3702 1
1.1%
3489 1
1.1%
3265 1
1.1%

남자
Real number (ℝ)

HIGH CORRELATION 

Distinct86
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean903.27586
Minimum61
Maximum14567
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-12T12:09:31.805513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile108.6
Q1248.5
median396
Q3672.5
95-th percentile3104
Maximum14567
Range14506
Interquartile range (IQR)424

Descriptive statistics

Standard deviation1920.8526
Coefficient of variation (CV)2.1265404
Kurtosis32.803523
Mean903.27586
Median Absolute Deviation (MAD)202
Skewness5.3322583
Sum78585
Variance3689674.6
MonotonicityNot monotonic
2023-12-12T12:09:32.019354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127 2
 
2.3%
14567 1
 
1.1%
271 1
 
1.1%
1077 1
 
1.1%
501 1
 
1.1%
478 1
 
1.1%
1335 1
 
1.1%
532 1
 
1.1%
406 1
 
1.1%
387 1
 
1.1%
Other values (76) 76
87.4%
ValueCountFrequency (%)
61 1
1.1%
77 1
1.1%
83 1
1.1%
98 1
1.1%
108 1
1.1%
110 1
1.1%
111 1
1.1%
112 1
1.1%
126 1
1.1%
127 2
2.3%
ValueCountFrequency (%)
14567 1
1.1%
8573 1
1.1%
6230 1
1.1%
3154 1
1.1%
3113 1
1.1%
3083 1
1.1%
2279 1
1.1%
1948 1
1.1%
1867 1
1.1%
1674 1
1.1%

여자
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean836.50575
Minimum47
Maximum14553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size915.0 B
2023-12-12T12:09:32.245444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile83.2
Q1193.5
median303
Q3519.5
95-th percentile2912.4
Maximum14553
Range14506
Interquartile range (IQR)326

Descriptive statistics

Standard deviation1904.9746
Coefficient of variation (CV)2.2773
Kurtosis34.091092
Mean836.50575
Median Absolute Deviation (MAD)140
Skewness5.4203722
Sum72776
Variance3628928.1
MonotonicityNot monotonic
2023-12-12T12:09:32.848067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
428 2
 
2.3%
252 2
 
2.3%
283 2
 
2.3%
258 2
 
2.3%
240 2
 
2.3%
437 2
 
2.3%
477 2
 
2.3%
14553 1
 
1.1%
339 1
 
1.1%
416 1
 
1.1%
Other values (70) 70
80.5%
ValueCountFrequency (%)
47 1
1.1%
55 1
1.1%
61 1
1.1%
70 1
1.1%
82 1
1.1%
86 1
1.1%
91 1
1.1%
92 1
1.1%
107 1
1.1%
108 1
1.1%
ValueCountFrequency (%)
14553 1
1.1%
8420 1
1.1%
5531 1
1.1%
3359 1
1.1%
2955 1
1.1%
2813 1
1.1%
2651 1
1.1%
1991 1
1.1%
1754 1
1.1%
1622 1
1.1%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size828.0 B
Minimum2022-05-31 00:00:00
Maximum2022-05-31 00:00:00
2023-12-12T12:09:33.029898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:33.174865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T12:09:28.650740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:26.972380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.528987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.098989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.798831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.106899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.672191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.240476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.940596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.247759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.807506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.380058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:29.075225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.391952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:27.953940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:09:28.515706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:09:33.298911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동명법정동명세대수총인구수남자여자
읍면동명1.0001.0000.4890.4710.4890.467
법정동명1.0001.0001.0001.0001.0001.000
세대수0.4891.0001.0000.9971.0000.995
총인구수0.4711.0000.9971.0000.9970.999
남자0.4891.0001.0000.9971.0000.995
여자0.4671.0000.9950.9990.9951.000
2023-12-12T12:09:33.466559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수총인구수남자여자읍면동명
세대수1.0000.9950.9960.9620.248
총인구수0.9951.0000.9960.9750.238
남자0.9960.9961.0000.9660.248
여자0.9620.9750.9661.0000.235
읍면동명0.2480.2380.2480.2351.000

Missing values

2023-12-12T12:09:29.265685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:09:29.449010image/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

읍면동명법정동명세대수총인구수남자여자데이터기준일
0소흘읍송우리122082912014567145532022-05-31
1소흘읍이동교리26986068311329552022-05-31
2소흘읍무림리3928584304282022-05-31
3소흘읍이곡리4379184764422022-05-31
4소흘읍직동리65513326916412022-05-31
5소흘읍고모리59410625914712022-05-31
6소흘읍이가팔리82014608366242022-05-31
7소흘읍초가팔리16573702194817542022-05-31
8소흘읍무봉리4337374482892022-05-31
9군내면구읍리13653265167415912022-05-31
읍면동명법정동명세대수총인구수남자여자데이터기준일
77관인면사정리124199108912022-05-31
78화현면화현리85516918858062022-05-31
79화현면명덕리2654432352082022-05-31
80화현면지현리3285753172582022-05-31
81포천동신읍동796616993857384202022-05-31
82포천동어룡동921197110119602022-05-31
83선단동자작동4307333903432022-05-31
84선단동선단동577911761623055312022-05-31
85선단동설운동73212597525072022-05-31
86선단동동교동64611176404772022-05-31