Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory732.4 KiB
Average record size in memory75.0 B

Variable types

Categorical5
Numeric3

Dataset

Description경기도 포천시에서 제공하는 개별공시지기(시군구, 읍면동, 리, 구분, 본번, 부번, 지가, 데이터기준일 등)자료 입니다.
URLhttps://www.data.go.kr/data/15113631/fileData.do

Alerts

시군구 has constant value ""Constant
데이터기준일 has constant value ""Constant
리 is highly overall correlated with 읍면동High correlation
읍면동 is highly overall correlated with 리High correlation
구분 is highly imbalanced (67.8%)Imbalance
부번 has 1465 (14.6%) zerosZeros

Reproduction

Analysis started2023-12-11 22:52:07.407551
Analysis finished2023-12-11 22:52:09.220404
Duration1.81 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
포천시
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
포천시 10000
100.0%

Length

2023-12-12T07:52:09.288889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:52:09.375607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포천시 10000
100.0%

읍면동
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
소흘읍
3273 
군내면
2006 
내촌면
1865 
가산면
550 
신읍동
550 
Other values (6)
1756 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row군내면
2nd row군내면
3rd row내촌면
4th row동교동
5th row내촌면

Common Values

ValueCountFrequency (%)
소흘읍 3273
32.7%
군내면 2006
20.1%
내촌면 1865
18.6%
가산면 550
 
5.5%
신읍동 550
 
5.5%
선단동 479
 
4.8%
동교동 365
 
3.6%
어룡동 337
 
3.4%
자작동 291
 
2.9%
설운동 275
 
2.8%

Length

2023-12-12T07:52:09.464218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
소흘읍 3273
32.7%
군내면 2006
20.1%
내촌면 1865
18.6%
가산면 550
 
5.5%
신읍동 550
 
5.5%
선단동 479
 
4.8%
동교동 365
 
3.6%
어룡동 337
 
3.4%
자작동 291
 
2.9%
설운동 275
 
2.8%

리
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
2297 
이동교리
732 
무봉리
 
457
마산리
 
449
송우리
 
419
Other values (22)
5646 

Length

Max length4
Median length3
Mean length3.3597
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구읍리
2nd row명산리
3rd row신팔리
4th row<NA>
5th row소학리

Common Values

ValueCountFrequency (%)
<NA> 2297
23.0%
이동교리 732
 
7.3%
무봉리 457
 
4.6%
마산리 449
 
4.5%
송우리 419
 
4.2%
음현리 391
 
3.9%
고모리 390
 
3.9%
유교리 379
 
3.8%
진목리 357
 
3.6%
내리 356
 
3.6%
Other values (17) 3773
37.7%

Length

2023-12-12T07:52:09.568920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 2297
23.0%
이동교리 732
 
7.3%
무봉리 457
 
4.6%
마산리 449
 
4.5%
송우리 419
 
4.2%
음현리 391
 
3.9%
고모리 390
 
3.9%
유교리 379
 
3.8%
진목리 357
 
3.6%
내리 356
 
3.6%
Other values (17) 3773
37.7%

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반
9414 
산
 
586

Length

Max length2
Median length2
Mean length1.9414
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
일반 9414
94.1%
산 586
 
5.9%

Length

2023-12-12T07:52:09.680475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:52:09.753065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반 9414
94.1%
산 586
 
5.9%

본번
Real number (ℝ)

Distinct936
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342.695
Minimum1
Maximum1285
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:52:09.841331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q1138
median304
Q3523
95-th percentile763
Maximum1285
Range1284
Interquartile range (IQR)385

Descriptive statistics

Standard deviation239.89393
Coefficient of variation (CV)0.70002168
Kurtosis-0.59457276
Mean342.695
Median Absolute Deviation (MAD)187
Skewness0.50556655
Sum3426950
Variance57549.098
MonotonicityNot monotonic
2023-12-12T07:52:09.958433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 37
 
0.4%
1 33
 
0.3%
23 31
 
0.3%
2 30
 
0.3%
99 29
 
0.3%
144 28
 
0.3%
473 28
 
0.3%
27 27
 
0.3%
88 27
 
0.3%
696 26
 
0.3%
Other values (926) 9704
97.0%
ValueCountFrequency (%)
1 33
0.3%
2 30
0.3%
3 13
 
0.1%
4 13
 
0.1%
5 12
 
0.1%
6 21
0.2%
7 19
0.2%
8 25
0.2%
9 26
0.3%
10 23
0.2%
ValueCountFrequency (%)
1285 1
< 0.1%
1279 1
< 0.1%
1278 1
< 0.1%
1276 1
< 0.1%
1269 1
< 0.1%
1246 1
< 0.1%
1237 1
< 0.1%
1231 1
< 0.1%
1228 1
< 0.1%
1049 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct127
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2779
Minimum0
Maximum176
Zeros1465
Zeros (%)14.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:52:10.074836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q39
95-th percentile31
Maximum176
Range176
Interquartile range (IQR)8

Descriptive statistics

Standard deviation14.266136
Coefficient of variation (CV)1.7234003
Kurtosis28.680805
Mean8.2779
Median Absolute Deviation (MAD)3
Skewness4.5227155
Sum82779
Variance203.52262
MonotonicityNot monotonic
2023-12-12T07:52:10.199451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1465
14.6%
1 1389
13.9%
2 1148
11.5%
3 907
 
9.1%
4 679
 
6.8%
5 557
 
5.6%
6 444
 
4.4%
7 387
 
3.9%
8 301
 
3.0%
9 268
 
2.7%
Other values (117) 2455
24.6%
ValueCountFrequency (%)
0 1465
14.6%
1 1389
13.9%
2 1148
11.5%
3 907
9.1%
4 679
6.8%
5 557
 
5.6%
6 444
 
4.4%
7 387
 
3.9%
8 301
 
3.0%
9 268
 
2.7%
ValueCountFrequency (%)
176 1
< 0.1%
166 1
< 0.1%
165 1
< 0.1%
163 1
< 0.1%
162 1
< 0.1%
148 1
< 0.1%
147 1
< 0.1%
146 1
< 0.1%
145 1
< 0.1%
144 1
< 0.1%

지가
Real number (ℝ)

Distinct3411
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192310.25
Minimum1240
Maximum5292000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T07:52:10.348129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1240
5-th percentile15490
Q156500
median106700
Q3231250
95-th percentile606605
Maximum5292000
Range5290760
Interquartile range (IQR)174750

Descriptive statistics

Standard deviation296022.54
Coefficient of variation (CV)1.5392968
Kurtosis64.336323
Mean192310.25
Median Absolute Deviation (MAD)72600
Skewness6.3698548
Sum1.9231025 × 109
Variance8.7629347 × 1010
MonotonicityNot monotonic
2023-12-12T07:52:10.460891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60500 37
 
0.4%
26000 34
 
0.3%
63000 29
 
0.3%
11300 28
 
0.3%
23800 27
 
0.3%
29700 26
 
0.3%
32700 26
 
0.3%
37300 25
 
0.2%
73500 24
 
0.2%
49700 24
 
0.2%
Other values (3401) 9720
97.2%
ValueCountFrequency (%)
1240 1
 
< 0.1%
2040 1
 
< 0.1%
2110 3
< 0.1%
2140 1
 
< 0.1%
2430 1
 
< 0.1%
2760 1
 
< 0.1%
2790 1
 
< 0.1%
2840 1
 
< 0.1%
2850 1
 
< 0.1%
2870 1
 
< 0.1%
ValueCountFrequency (%)
5292000 1
< 0.1%
4992000 1
< 0.1%
4872000 2
< 0.1%
4170000 1
< 0.1%
4043000 1
< 0.1%
3826000 1
< 0.1%
3819000 1
< 0.1%
3814000 2
< 0.1%
3630000 1
< 0.1%
3571000 1
< 0.1%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-01-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-01-01
2nd row2023-01-01
3rd row2023-01-01
4th row2023-01-01
5th row2023-01-01

Common Values

ValueCountFrequency (%)
2023-01-01 10000
100.0%

Length

2023-12-12T07:52:10.564155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:52:10.637098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-01-01 10000
100.0%

Interactions

2023-12-12T07:52:08.741867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:07.942993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:08.439211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:08.826253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:08.268252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:08.524069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:08.930459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:08.357142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:52:08.633368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:52:10.682941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동리구분본번부번지가
읍면동1.0001.0000.1230.6540.1500.289
리1.0001.0000.1790.7520.2110.422
구분0.1230.1791.0000.4800.0100.056
본번0.6540.7520.4801.0000.2220.088
부번0.1500.2110.0100.2221.0000.148
지가0.2890.4220.0560.0880.1481.000
2023-12-12T07:52:10.760739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
리읍면동구분
리1.0000.9990.142
읍면동0.9991.0000.118
구분0.1420.1181.000
2023-12-12T07:52:10.830411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
본번부번지가읍면동리구분
본번1.000-0.0200.0680.3480.3880.369
부번-0.0201.0000.1100.0640.0780.008
지가0.0680.1101.0000.1340.1720.056
읍면동0.3480.0640.1341.0000.9990.118
리0.3880.0780.1720.9991.0000.142
구분0.3690.0080.0560.1180.1421.000

Missing values

2023-12-12T07:52:09.045994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:52:09.172602image/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

시군구읍면동리구분본번부번지가데이터기준일
54085포천시군내면구읍리일반4100896002023-01-01
63306포천시군내면명산리일반1115260002023-01-01
88950포천시내촌면신팔리일반1420656002023-01-01
21319포천시동교동<NA>일반57415427002023-01-01
87001포천시내촌면소학리일반2044935002023-01-01
18807포천시동교동<NA>일반374351002023-01-01
38676포천시소흘읍직동리일반2074343002023-01-01
6501포천시어룡동<NA>일반30616860002023-01-01
76616포천시내촌면마명리일반1323320002023-01-01
16470포천시설운동<NA>일반1261876002023-01-01
시군구읍면동리구분본번부번지가데이터기준일
55473포천시군내면구읍리일반73829417002023-01-01
49182포천시소흘읍무봉리일반19081580002023-01-01
60449포천시군내면유교리일반7951649002023-01-01
3500포천시신읍동<NA>일반30832722002023-01-01
91130포천시가산면마산리일반14345381002023-01-01
28880포천시소흘읍이동교리일반38049579002023-01-01
28112포천시소흘읍이동교리일반294201468002023-01-01
91138포천시가산면마산리일반14491094002023-01-01
31024포천시소흘읍이동교리일반69303367002023-01-01
27621포천시소흘읍이동교리일반21243004002023-01-01