Overview

Dataset statistics

Number of variables9
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory84.3 B

Variable types

Text2
Numeric6
DateTime1

Dataset

Description경기도 평택시 내에 있는 공동묘지 현황(묘지명, 소재지, 면적(제곱미터), 매장가능 면적, 맨장가능 기수, 기매장 기수, 위도, 경도 등)데이터를 제공합니다.※ 문의 : 평택시청 노인장애인과(031-8024-3124)
Author경기도 평택시
URLhttps://www.data.go.kr/data/3064145/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
면적(제곱미터) is highly overall correlated with 매장가능면적High correlation
매장가능면적 is highly overall correlated with 면적(제곱미터)High correlation
매장가능기수 is highly overall correlated with 기매장기수High correlation
기매장기수 is highly overall correlated with 매장가능기수High correlation
묘지명 has unique valuesUnique
소재지 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique
매장가능기수 has unique valuesUnique
기매장기수 has unique valuesUnique

Reproduction

Analysis started2024-03-16 04:25:27.984348
Analysis finished2024-03-16 04:25:31.722909
Duration3.74 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

묘지명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-16T13:25:31.821469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.8095238
Min length3

Characters and Unicode

Total characters101
Distinct characters27
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

Unique21 ?
Unique (%)100.0%

Sample

1st row팽성읍제1
2nd row팽성읍제3
3rd row팽성읍제5
4th row팽성읍제6
5th row칠원동
ValueCountFrequency (%)
팽성읍제1 1
 
4.8%
진위면제5 1
 
4.8%
포승읍제2 1
 
4.8%
포승읍제1 1
 
4.8%
고덕면제3 1
 
4.8%
고덕면제1 1
 
4.8%
서탄면제4 1
 
4.8%
서탄면제3 1
 
4.8%
서탄면제2 1
 
4.8%
서탄면제1 1
 
4.8%
Other values (11) 11
52.4%
2024-03-16T13:25:32.084460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 19
18.8%
면 11
 
10.9%
읍 6
 
5.9%
1 6
 
5.9%
3 5
 
5.0%
팽 4
 
4.0%
성 4
 
4.0%
탄 4
 
4.0%
서 4
 
4.0%
동 4
 
4.0%
Other values (17) 34
33.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 82
81.2%
Decimal Number 19
 
18.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 19
23.2%
면 11
13.4%
읍 6
 
7.3%
팽 4
 
4.9%
성 4
 
4.9%
탄 4
 
4.9%
서 4
 
4.9%
동 4
 
4.9%
위 4
 
4.9%
진 4
 
4.9%
Other values (11) 18
22.0%
Decimal Number
ValueCountFrequency (%)
1 6
31.6%
3 5
26.3%
2 3
15.8%
4 2
 
10.5%
5 2
 
10.5%
6 1
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 82
81.2%
Common 19
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 19
23.2%
면 11
13.4%
읍 6
 
7.3%
팽 4
 
4.9%
성 4
 
4.9%
탄 4
 
4.9%
서 4
 
4.9%
동 4
 
4.9%
위 4
 
4.9%
진 4
 
4.9%
Other values (11) 18
22.0%
Common
ValueCountFrequency (%)
1 6
31.6%
3 5
26.3%
2 3
15.8%
4 2
 
10.5%
5 2
 
10.5%
6 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 82
81.2%
ASCII 19
 
18.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 19
23.2%
면 11
13.4%
읍 6
 
7.3%
팽 4
 
4.9%
성 4
 
4.9%
탄 4
 
4.9%
서 4
 
4.9%
동 4
 
4.9%
위 4
 
4.9%
진 4
 
4.9%
Other values (11) 18
22.0%
ASCII
ValueCountFrequency (%)
1 6
31.6%
3 5
26.3%
2 3
15.8%
4 2
 
10.5%
5 2
 
10.5%
6 1
 
5.3%

소재지
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2024-03-16T13:25:32.255953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length20
Min length14

Characters and Unicode

Total characters420
Distinct characters62
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

Unique21 ?
Unique (%)100.0%

Sample

1st row경기도 평택시 팽성읍 남산리 산29-1
2nd row경기도 평택시 팽성읍 본정리 산9-2
3rd row경기도 평택시 팽성읍 신대리 산23-5
4th row경기도 평택시 팽성읍 신대리 산28외 2필지
5th row경기도 평택시 칠원동 산43
ValueCountFrequency (%)
경기도 21
19.8%
평택시 21
19.8%
팽성읍 4
 
3.8%
진위면 4
 
3.8%
서탄면 4
 
3.8%
포승읍 2
 
1.9%
고덕면 2
 
1.9%
수월암리 2
 
1.9%
홍원리 2
 
1.9%
외 2
 
1.9%
Other values (40) 42
39.6%
2024-03-16T13:25:32.557052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
20.2%
도 23
 
5.5%
경 21
 
5.0%
평 21
 
5.0%
택 21
 
5.0%
시 21
 
5.0%
기 21
 
5.0%
산 20
 
4.8%
리 17
 
4.0%
1 14
 
3.3%
Other values (52) 156
37.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 270
64.3%
Space Separator 85
 
20.2%
Decimal Number 58
 
13.8%
Dash Punctuation 7
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 23
 
8.5%
경 21
 
7.8%
평 21
 
7.8%
택 21
 
7.8%
시 21
 
7.8%
기 21
 
7.8%
산 20
 
7.4%
리 17
 
6.3%
면 11
 
4.1%
읍 6
 
2.2%
Other values (40) 88
32.6%
Decimal Number
ValueCountFrequency (%)
1 14
24.1%
9 8
13.8%
2 7
12.1%
7 6
10.3%
0 5
 
8.6%
3 5
 
8.6%
8 5
 
8.6%
4 4
 
6.9%
6 3
 
5.2%
5 1
 
1.7%
Space Separator
ValueCountFrequency (%)
85
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 270
64.3%
Common 150
35.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 23
 
8.5%
경 21
 
7.8%
평 21
 
7.8%
택 21
 
7.8%
시 21
 
7.8%
기 21
 
7.8%
산 20
 
7.4%
리 17
 
6.3%
면 11
 
4.1%
읍 6
 
2.2%
Other values (40) 88
32.6%
Common
ValueCountFrequency (%)
85
56.7%
1 14
 
9.3%
9 8
 
5.3%
2 7
 
4.7%
- 7
 
4.7%
7 6
 
4.0%
0 5
 
3.3%
3 5
 
3.3%
8 5
 
3.3%
4 4
 
2.7%
Other values (2) 4
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 270
64.3%
ASCII 150
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
56.7%
1 14
 
9.3%
9 8
 
5.3%
2 7
 
4.7%
- 7
 
4.7%
7 6
 
4.0%
0 5
 
3.3%
3 5
 
3.3%
8 5
 
3.3%
4 4
 
2.7%
Other values (2) 4
 
2.7%
Hangul
ValueCountFrequency (%)
도 23
 
8.5%
경 21
 
7.8%
평 21
 
7.8%
택 21
 
7.8%
시 21
 
7.8%
기 21
 
7.8%
산 20
 
7.4%
리 17
 
6.3%
면 11
 
4.1%
읍 6
 
2.2%
Other values (40) 88
32.6%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.042892
Minimum36.942325
Maximum37.123859
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-16T13:25:32.686870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.942325
5-th percentile36.945566
Q137.010516
median37.034373
Q337.108531
95-th percentile37.120419
Maximum37.123859
Range0.181534
Interquartile range (IQR)0.098015

Descriptive statistics

Standard deviation0.066063119
Coefficient of variation (CV)0.0017834223
Kurtosis-1.420223
Mean37.042892
Median Absolute Deviation (MAD)0.074158
Skewness-0.25683917
Sum777.90074
Variance0.0043643357
MonotonicityNot monotonic
2024-03-16T13:25:32.817589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
36.95261 1
 
4.8%
36.948816 1
 
4.8%
36.96015 1
 
4.8%
37.010516 1
 
4.8%
37.010526 1
 
4.8%
37.020904 1
 
4.8%
37.022809 1
 
4.8%
37.118452 1
 
4.8%
37.120419 1
 
4.8%
37.108747 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
36.942325 1
4.8%
36.945566 1
4.8%
36.948816 1
4.8%
36.95261 1
4.8%
36.96015 1
4.8%
37.010516 1
4.8%
37.010526 1
4.8%
37.020904 1
4.8%
37.021915 1
4.8%
37.022809 1
4.8%
ValueCountFrequency (%)
37.123859 1
4.8%
37.120419 1
4.8%
37.118452 1
4.8%
37.111736 1
4.8%
37.108747 1
4.8%
37.108531 1
4.8%
37.106943 1
4.8%
37.103808 1
4.8%
37.065417 1
4.8%
37.062313 1
4.8%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.03622
Minimum126.88837
Maximum127.10616
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-16T13:25:32.939430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.88837
5-th percentile126.8892
Q1127.01061
median127.04396
Q3127.08692
95-th percentile127.10255
Maximum127.10616
Range0.217789
Interquartile range (IQR)0.076316

Descriptive statistics

Standard deviation0.064867333
Coefficient of variation (CV)0.00051062077
Kurtosis0.80379667
Mean127.03622
Median Absolute Deviation (MAD)0.04296
Skewness-1.1905035
Sum2667.7606
Variance0.0042077709
MonotonicityNot monotonic
2024-03-16T13:25:33.052232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
127.068753 1
 
4.8%
127.010606 1
 
4.8%
126.940311 1
 
4.8%
126.889195 1
 
4.8%
126.888375 1
 
4.8%
127.04223 1
 
4.8%
127.037104 1
 
4.8%
127.040183 1
 
4.8%
127.043962 1
 
4.8%
127.045796 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
126.888375 1
4.8%
126.889195 1
4.8%
126.940311 1
4.8%
126.994243 1
4.8%
126.994897 1
4.8%
127.010606 1
4.8%
127.025551 1
4.8%
127.037104 1
4.8%
127.040183 1
4.8%
127.04223 1
4.8%
ValueCountFrequency (%)
127.106164 1
4.8%
127.102549 1
4.8%
127.099476 1
4.8%
127.094792 1
4.8%
127.088307 1
4.8%
127.086922 1
4.8%
127.084556 1
4.8%
127.076672 1
4.8%
127.068753 1
4.8%
127.045796 1
4.8%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13454.524
Minimum709
Maximum95156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-16T13:25:33.165904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum709
5-th percentile1488
Q13703
median5950
Q310116
95-th percentile31935
Maximum95156
Range94447
Interquartile range (IQR)6413

Descriptive statistics

Standard deviation20534.097
Coefficient of variation (CV)1.5261853
Kurtosis13.516699
Mean13454.524
Median Absolute Deviation (MAD)2677
Skewness3.4681917
Sum282545
Variance4.2164914 × 108
MonotonicityNot monotonic
2024-03-16T13:25:33.283774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5950 2
 
9.5%
10116 1
 
4.8%
3703 1
 
4.8%
5256 1
 
4.8%
31935 1
 
4.8%
709 1
 
4.8%
6235 1
 
4.8%
9917 1
 
4.8%
20727 1
 
4.8%
7835 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
709 1
4.8%
1488 1
4.8%
3273 1
4.8%
3449 1
4.8%
3471 1
4.8%
3703 1
4.8%
4491 1
4.8%
5256 1
4.8%
5714 1
4.8%
5950 2
9.5%
ValueCountFrequency (%)
95156 1
4.8%
31935 1
4.8%
25309 1
4.8%
22215 1
4.8%
20727 1
4.8%
10116 1
4.8%
9917 1
4.8%
9646 1
4.8%
7835 1
4.8%
6235 1
4.8%

매장가능면적
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13454.524
Minimum709
Maximum95156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-16T13:25:33.403371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum709
5-th percentile1488
Q13703
median5950
Q310116
95-th percentile31935
Maximum95156
Range94447
Interquartile range (IQR)6413

Descriptive statistics

Standard deviation20534.097
Coefficient of variation (CV)1.5261853
Kurtosis13.516699
Mean13454.524
Median Absolute Deviation (MAD)2677
Skewness3.4681917
Sum282545
Variance4.2164914 × 108
MonotonicityNot monotonic
2024-03-16T13:25:33.522028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
5950 2
 
9.5%
10116 1
 
4.8%
3703 1
 
4.8%
5256 1
 
4.8%
31935 1
 
4.8%
709 1
 
4.8%
6235 1
 
4.8%
9917 1
 
4.8%
20727 1
 
4.8%
7835 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
709 1
4.8%
1488 1
4.8%
3273 1
4.8%
3449 1
4.8%
3471 1
4.8%
3703 1
4.8%
4491 1
4.8%
5256 1
4.8%
5714 1
4.8%
5950 2
9.5%
ValueCountFrequency (%)
95156 1
4.8%
31935 1
4.8%
25309 1
4.8%
22215 1
4.8%
20727 1
4.8%
10116 1
4.8%
9917 1
4.8%
9646 1
4.8%
7835 1
4.8%
6235 1
4.8%

매장가능기수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.71429
Minimum31
Maximum1917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-16T13:25:33.614195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile58
Q1106
median182
Q3300
95-th percentile406
Maximum1917
Range1886
Interquartile range (IQR)194

Descriptive statistics

Standard deviation391.08633
Coefficient of variation (CV)1.4340515
Kurtosis17.631262
Mean272.71429
Median Absolute Deviation (MAD)85
Skewness4.0555162
Sum5727
Variance152948.51
MonotonicityNot monotonic
2024-03-16T13:25:33.706729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
359 1
 
4.8%
186 1
 
4.8%
165 1
 
4.8%
190 1
 
4.8%
1917 1
 
4.8%
31 1
 
4.8%
97 1
 
4.8%
102 1
 
4.8%
106 1
 
4.8%
305 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
31 1
4.8%
58 1
4.8%
60 1
4.8%
97 1
4.8%
102 1
4.8%
106 1
4.8%
131 1
4.8%
135 1
4.8%
153 1
4.8%
165 1
4.8%
ValueCountFrequency (%)
1917 1
4.8%
406 1
4.8%
359 1
4.8%
310 1
4.8%
305 1
4.8%
300 1
4.8%
296 1
4.8%
238 1
4.8%
190 1
4.8%
186 1
4.8%

기매장기수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean272.71429
Minimum31
Maximum1917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2024-03-16T13:25:33.801043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile58
Q1106
median182
Q3300
95-th percentile406
Maximum1917
Range1886
Interquartile range (IQR)194

Descriptive statistics

Standard deviation391.08633
Coefficient of variation (CV)1.4340515
Kurtosis17.631262
Mean272.71429
Median Absolute Deviation (MAD)85
Skewness4.0555162
Sum5727
Variance152948.51
MonotonicityNot monotonic
2024-03-16T13:25:33.892994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
359 1
 
4.8%
186 1
 
4.8%
165 1
 
4.8%
190 1
 
4.8%
1917 1
 
4.8%
31 1
 
4.8%
97 1
 
4.8%
102 1
 
4.8%
106 1
 
4.8%
305 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
31 1
4.8%
58 1
4.8%
60 1
4.8%
97 1
4.8%
102 1
4.8%
106 1
4.8%
131 1
4.8%
135 1
4.8%
153 1
4.8%
165 1
4.8%
ValueCountFrequency (%)
1917 1
4.8%
406 1
4.8%
359 1
4.8%
310 1
4.8%
305 1
4.8%
300 1
4.8%
296 1
4.8%
238 1
4.8%
190 1
4.8%
186 1
4.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum2024-03-11 00:00:00
Maximum2024-03-11 00:00:00
2024-03-16T13:25:33.976363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:34.042437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-16T13:25:31.111638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:28.790999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.279132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.653765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.022616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.464922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:31.197632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:28.865372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.343928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.718740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.088057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.545706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:31.278228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:28.933875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.405189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.775638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.150768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.621429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:31.351146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.012090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.465537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.833912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.241529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.901160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:31.411670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.116034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.522382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.889138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.317363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.966553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:31.488983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.206734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.587506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:29.957119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:30.385352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:25:31.040926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:25:34.098768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
묘지명소재지위도경도면적(제곱미터)매장가능면적매장가능기수기매장기수
묘지명1.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0000.4520.3670.3670.5300.530
경도1.0001.0000.4521.0000.4960.4960.0000.000
면적(제곱미터)1.0001.0000.3670.4961.0001.0000.6580.658
매장가능면적1.0001.0000.3670.4961.0001.0000.6580.658
매장가능기수1.0001.0000.5300.0000.6580.6581.0001.000
기매장기수1.0001.0000.5300.0000.6580.6581.0001.000
2024-03-16T13:25:34.186332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도면적(제곱미터)매장가능면적매장가능기수기매장기수
위도1.0000.5030.2940.294-0.268-0.268
경도0.5031.000-0.212-0.212-0.351-0.351
면적(제곱미터)0.294-0.2121.0001.0000.4170.417
매장가능면적0.294-0.2121.0001.0000.4170.417
매장가능기수-0.268-0.3510.4170.4171.0001.000
기매장기수-0.268-0.3510.4170.4171.0001.000

Missing values

2024-03-16T13:25:31.586325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:25:31.684307image/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

묘지명소재지위도경도면적(제곱미터)매장가능면적매장가능기수기매장기수데이터기준일자
0팽성읍제1경기도 평택시 팽성읍 남산리 산29-136.95261127.068753595059503593592024-03-11
1팽성읍제3경기도 평택시 팽성읍 본정리 산9-236.948816127.010606344934491861862024-03-11
2팽성읍제5경기도 평택시 팽성읍 신대리 산23-536.945566126.99489795156951561821822024-03-11
3팽성읍제6경기도 평택시 팽성읍 신대리 산28외 2필지36.942325126.994243571457143103102024-03-11
4칠원동경기도 평택시 칠원동 산4337.021915127.106164347134711311312024-03-11
5도일동제1경기도 평택시 도일동 산14837.065417127.0947921488148858582024-03-11
6도일동제2경기도 평택시 도일동1009-737.034373127.099476449144912382382024-03-11
7장안동경기도 평택시 장안동 산837.062313127.088307327332731351352024-03-11
8진위면제1경기도 평택시 진위면 봉남리 산2637.103808127.08692225309253092962962024-03-11
9진위면제3경기도 평택시 진위면 동천리 산117-137.123859127.1025499646964660602024-03-11
묘지명소재지위도경도면적(제곱미터)매장가능면적매장가능기수기매장기수데이터기준일자
11진위면제5경기도 평택시 진위면 동청리 산18037.106943127.08455622215222153003002024-03-11
12서탄면제1경기도 평택시 서탄면 마두리 산1737.111736127.02555110116101164064062024-03-11
13서탄면제2경기도 평택시 서탄면 사리 산3737.108747127.045796783578353053052024-03-11
14서탄면제3경기도 평택시 서탄면 수월암리 산9437.120419127.04396220727207271061062024-03-11
15서탄면제4경기도 평택시 서탄면 수월암리 산97 외 1필지37.118452127.040183991799171021022024-03-11
16고덕면제1경기도 평택시 고덕면 궁리 39-237.022809127.0371046235623597972024-03-11
17고덕면제3경기도 평택시 고덕면 방축리 470-10외 6필지37.020904127.0422370970931312024-03-11
18포승읍제1경기도 평택시 포승읍 홍원리 산1837.010526126.8883753193531935191719172024-03-11
19포승읍제2경기도 평택시 포승읍 홍원리 산1937.010516126.889195525652561901902024-03-11
20현덕면제3경기도 평택시 현덕면 덕목리 산69 외 1필지36.96015126.940311370337031651652024-03-11