Overview

Dataset statistics

Number of variables15
Number of observations9204
Missing cells45998
Missing cells (%)33.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory131.0 B

Variable types

Numeric5
Categorical3
Text3
Unsupported4

Dataset

Description2013, 2014년 1월 1일 기준 경상북도 덕곡면 개별공시지가
Author경상북도 고령군
URLhttps://www.data.go.kr/data/15051161/fileData.do

Alerts

법정동 has constant value ""Constant
No is highly overall correlated with 일련번호 and 1 other fieldsHigh correlation
일련번호 is highly overall correlated with No and 1 other fieldsHigh correlation
리 is highly overall correlated with No and 1 other fieldsHigh correlation
본번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 본번High correlation
행정동 is highly imbalanced (86.5%)Imbalance
Unnamed: 10 has 9204 (100.0%) missing valuesMissing
Unnamed: 11 has 9204 (100.0%) missing valuesMissing
Unnamed: 12 has 9204 (100.0%) missing valuesMissing
Unnamed: 13 has 9193 (99.9%) missing valuesMissing
Unnamed: 14 has 9193 (99.9%) missing valuesMissing
No has unique valuesUnique
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 12 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported
부번 has 6287 (68.3%) zerosZeros

Reproduction

Analysis started2023-12-12 21:48:09.014001
Analysis finished2023-12-12 21:48:13.606036
Duration4.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct9204
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4602.5
Minimum1
Maximum9204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.0 KiB
2023-12-13T06:48:13.677650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile461.15
Q12301.75
median4602.5
Q36903.25
95-th percentile8743.85
Maximum9204
Range9203
Interquartile range (IQR)4601.5

Descriptive statistics

Standard deviation2657.1103
Coefficient of variation (CV)0.57731891
Kurtosis-1.2
Mean4602.5
Median Absolute Deviation (MAD)2301
Skewness0
Sum42361410
Variance7060235
MonotonicityStrictly increasing
2023-12-13T06:48:13.816722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6141 1
 
< 0.1%
6135 1
 
< 0.1%
6136 1
 
< 0.1%
6137 1
 
< 0.1%
6138 1
 
< 0.1%
6139 1
 
< 0.1%
6140 1
 
< 0.1%
6142 1
 
< 0.1%
6133 1
 
< 0.1%
Other values (9194) 9194
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
9204 1
< 0.1%
9203 1
< 0.1%
9202 1
< 0.1%
9201 1
< 0.1%
9200 1
< 0.1%
9199 1
< 0.1%
9198 1
< 0.1%
9197 1
< 0.1%
9196 1
< 0.1%
9195 1
< 0.1%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9056
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34614.401
Minimum14202
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.0 KiB
2023-12-13T06:48:13.998597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14202
5-th percentile14662.15
Q116502.75
median18803.5
Q321104.25
95-th percentile22944.85
Maximum999999
Range985797
Interquartile range (IQR)4601.5

Descriptive statistics

Standard deviation123870.54
Coefficient of variation (CV)3.578584
Kurtosis56.767416
Mean34614.401
Median Absolute Deviation (MAD)2301
Skewness7.6633926
Sum3.1859095 × 108
Variance1.5343911 × 1010
MonotonicityNot monotonic
2023-12-13T06:48:14.147285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999999 149
 
1.6%
14202 1
 
< 0.1%
20241 1
 
< 0.1%
20235 1
 
< 0.1%
20236 1
 
< 0.1%
20237 1
 
< 0.1%
20238 1
 
< 0.1%
20239 1
 
< 0.1%
20240 1
 
< 0.1%
20242 1
 
< 0.1%
Other values (9046) 9046
98.3%
ValueCountFrequency (%)
14202 1
< 0.1%
14203 1
< 0.1%
14204 1
< 0.1%
14205 1
< 0.1%
14206 1
< 0.1%
14207 1
< 0.1%
14208 1
< 0.1%
14209 1
< 0.1%
14210 1
< 0.1%
14211 1
< 0.1%
ValueCountFrequency (%)
999999 149
1.6%
23256 1
 
< 0.1%
23255 1
 
< 0.1%
23254 1
 
< 0.1%
23253 1
 
< 0.1%
23252 1
 
< 0.1%
23251 1
 
< 0.1%
23250 1
 
< 0.1%
23249 1
 
< 0.1%
23248 1
 
< 0.1%

법정동
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.0 KiB
310
9204 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row310
2nd row310
3rd row310
4th row310
5th row310

Common Values

ValueCountFrequency (%)
310 9204
100.0%

Length

2023-12-13T06:48:14.274482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:48:14.354721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
310 9204
100.0%

행정동
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.0 KiB
0
9031 
310
 
173

Length

Max length3
Median length1
Mean length1.0375924
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9031
98.1%
310 173
 
1.9%

Length

2023-12-13T06:48:14.445270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:48:14.534562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9031
98.1%
310 173
 
1.9%

리
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.264885
Minimum31
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.0 KiB
2023-12-13T06:48:14.625384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile31
Q133
median35
Q337
95-th percentile40
Maximum40
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6633366
Coefficient of variation (CV)0.075523758
Kurtosis-1.1105448
Mean35.264885
Median Absolute Deviation (MAD)2
Skewness0.11176576
Sum324578
Variance7.093362
MonotonicityIncreasing
2023-12-13T06:48:14.737552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
33 1415
15.4%
34 1196
13.0%
37 1085
11.8%
39 957
10.4%
35 954
10.4%
36 820
8.9%
31 819
8.9%
38 810
8.8%
32 656
7.1%
40 492
 
5.3%
ValueCountFrequency (%)
31 819
8.9%
32 656
7.1%
33 1415
15.4%
34 1196
13.0%
35 954
10.4%
36 820
8.9%
37 1085
11.8%
38 810
8.8%
39 957
10.4%
40 492
 
5.3%
ValueCountFrequency (%)
40 492
 
5.3%
39 957
10.4%
38 810
8.8%
37 1085
11.8%
36 820
8.9%
35 954
10.4%
34 1196
13.0%
33 1415
15.4%
32 656
7.1%
31 819
8.9%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size72.0 KiB
1
8013 
2
1191 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8013
87.1%
2 1191
 
12.9%

Length

2023-12-13T06:48:14.855353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:48:14.956428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8013
87.1%
2 1191
 
12.9%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct1159
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.00206
Minimum1
Maximum1180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size81.0 KiB
2023-12-13T06:48:15.055140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q1111
median305
Q3527
95-th percentile818
Maximum1180
Range1179
Interquartile range (IQR)416

Descriptive statistics

Standard deviation270.18901
Coefficient of variation (CV)0.77417598
Kurtosis-0.19797299
Mean349.00206
Median Absolute Deviation (MAD)204
Skewness0.7137308
Sum3212215
Variance73002.104
MonotonicityNot monotonic
2023-12-13T06:48:15.200626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
818 149
 
1.6%
547 125
 
1.4%
21 47
 
0.5%
124 35
 
0.4%
32 35
 
0.4%
31 32
 
0.3%
27 31
 
0.3%
10 30
 
0.3%
43 28
 
0.3%
34 28
 
0.3%
Other values (1149) 8664
94.1%
ValueCountFrequency (%)
1 28
0.3%
2 21
0.2%
3 19
0.2%
4 23
0.2%
5 18
0.2%
6 21
0.2%
7 23
0.2%
8 22
0.2%
9 17
0.2%
10 30
0.3%
ValueCountFrequency (%)
1180 1
 
< 0.1%
1179 1
 
< 0.1%
1178 3
< 0.1%
1177 1
 
< 0.1%
1176 2
< 0.1%
1175 1
 
< 0.1%
1174 1
 
< 0.1%
1173 1
 
< 0.1%
1172 1
 
< 0.1%
1171 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct238
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2224033
Minimum0
Maximum345
Zeros6287
Zeros (%)68.3%
Negative0
Negative (%)0.0%
Memory size81.0 KiB
2023-12-13T06:48:15.348546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile7
Maximum345
Range345
Interquartile range (IQR)1

Descriptive statistics

Standard deviation28.531743
Coefficient of variation (CV)5.4633359
Kurtosis61.505654
Mean5.2224033
Median Absolute Deviation (MAD)0
Skewness7.471195
Sum48067
Variance814.06039
MonotonicityNot monotonic
2023-12-13T06:48:15.774180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6287
68.3%
1 1220
 
13.3%
2 633
 
6.9%
3 272
 
3.0%
4 154
 
1.7%
5 86
 
0.9%
6 63
 
0.7%
7 42
 
0.5%
8 27
 
0.3%
10 24
 
0.3%
Other values (228) 396
 
4.3%
ValueCountFrequency (%)
0 6287
68.3%
1 1220
 
13.3%
2 633
 
6.9%
3 272
 
3.0%
4 154
 
1.7%
5 86
 
0.9%
6 63
 
0.7%
7 42
 
0.5%
8 27
 
0.3%
9 22
 
0.2%
ValueCountFrequency (%)
345 1
< 0.1%
344 1
< 0.1%
343 1
< 0.1%
342 1
< 0.1%
341 1
< 0.1%
336 1
< 0.1%
335 1
< 0.1%
318 1
< 0.1%
317 1
< 0.1%
315 1
< 0.1%

2014
Text

Distinct1161
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size72.0 KiB
2023-12-13T06:48:16.156510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1488483
Min length2

Characters and Unicode

Total characters47390
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique344 ?
Unique (%)3.7%

Sample

1st row8,080
2nd row11,100
3rd row22,100
4th row9,100
5th row11,100
ValueCountFrequency (%)
11,600 206
 
2.2%
13,000 165
 
1.8%
12,600 115
 
1.2%
12,000 114
 
1.2%
19,000 108
 
1.2%
12,300 94
 
1.0%
14,000 92
 
1.0%
11,900 84
 
0.9%
5,900 78
 
0.8%
6,300 73
 
0.8%
Other values (1151) 8075
87.7%
2023-12-13T06:48:16.667973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14788
31.2%
, 7921
16.7%
1 5024
 
10.6%
2 3132
 
6.6%
6 2924
 
6.2%
4 2651
 
5.6%
3 2618
 
5.5%
7 2168
 
4.6%
5 2148
 
4.5%
8 2028
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39469
83.3%
Other Punctuation 7921
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14788
37.5%
1 5024
 
12.7%
2 3132
 
7.9%
6 2924
 
7.4%
4 2651
 
6.7%
3 2618
 
6.6%
7 2168
 
5.5%
5 2148
 
5.4%
8 2028
 
5.1%
9 1988
 
5.0%
Other Punctuation
ValueCountFrequency (%)
, 7921
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47390
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14788
31.2%
, 7921
16.7%
1 5024
 
10.6%
2 3132
 
6.6%
6 2924
 
6.2%
4 2651
 
5.6%
3 2618
 
5.5%
7 2168
 
4.6%
5 2148
 
4.5%
8 2028
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47390
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14788
31.2%
, 7921
16.7%
1 5024
 
10.6%
2 3132
 
6.6%
6 2924
 
6.2%
4 2651
 
5.6%
3 2618
 
5.5%
7 2168
 
4.6%
5 2148
 
4.5%
8 2028
 
4.3%

2013
Text

Distinct1193
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size72.0 KiB
2023-12-13T06:48:17.063802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1088657
Min length1

Characters and Unicode

Total characters47022
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique328 ?
Unique (%)3.6%

Sample

1st row7,230
2nd row10,200
3rd row20,100
4th row8,400
5th row10,200
ValueCountFrequency (%)
11,100 172
 
1.9%
11,500 131
 
1.4%
11,600 124
 
1.3%
11,000 104
 
1.1%
12,500 95
 
1.0%
12,000 93
 
1.0%
10,500 87
 
0.9%
10,700 86
 
0.9%
13,200 69
 
0.7%
6,140 66
 
0.7%
Other values (1183) 8177
88.8%
2023-12-13T06:48:17.579684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14465
30.8%
, 7873
16.7%
1 5470
 
11.6%
2 3040
 
6.5%
5 2827
 
6.0%
4 2543
 
5.4%
6 2495
 
5.3%
3 2372
 
5.0%
7 2205
 
4.7%
9 2027
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39149
83.3%
Other Punctuation 7873
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14465
36.9%
1 5470
 
14.0%
2 3040
 
7.8%
5 2827
 
7.2%
4 2543
 
6.5%
6 2495
 
6.4%
3 2372
 
6.1%
7 2205
 
5.6%
9 2027
 
5.2%
8 1705
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 7873
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 47022
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14465
30.8%
, 7873
16.7%
1 5470
 
11.6%
2 3040
 
6.5%
5 2827
 
6.0%
4 2543
 
5.4%
6 2495
 
5.3%
3 2372
 
5.0%
7 2205
 
4.7%
9 2027
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47022
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14465
30.8%
, 7873
16.7%
1 5470
 
11.6%
2 3040
 
6.5%
5 2827
 
6.0%
4 2543
 
5.4%
6 2495
 
5.3%
3 2372
 
5.0%
7 2205
 
4.7%
9 2027
 
4.3%

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9204
Missing (%)100.0%
Memory size81.0 KiB

Unnamed: 11
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9204
Missing (%)100.0%
Memory size81.0 KiB

Unnamed: 12
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9204
Missing (%)100.0%
Memory size81.0 KiB

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9193
Missing (%)99.9%
Memory size72.0 KiB

Unnamed: 14
Text

MISSING 

Distinct11
Distinct (%)100.0%
Missing9193
Missing (%)99.9%
Memory size72.0 KiB
2023-12-13T06:48:17.756791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8181818
Min length2

Characters and Unicode

Total characters31
Distinct characters19
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

Unique11 ?
Unique (%)100.0%

Sample

1st row리 명
2nd row가륜리
3rd row원송리
4th row노리
5th row백리
ValueCountFrequency (%)
리 1
8.3%
명 1
8.3%
가륜리 1
8.3%
원송리 1
8.3%
노리 1
8.3%
백리 1
8.3%
옥계리 1
8.3%
용흥리 1
8.3%
본리리 1
8.3%
예리 1
8.3%
Other values (2) 2
16.7%
2023-12-13T06:48:18.020576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 12
38.7%
2
 
6.5%
계 1
 
3.2%
후 1
 
3.2%
성 1
 
3.2%
반 1
 
3.2%
예 1
 
3.2%
본 1
 
3.2%
흥 1
 
3.2%
용 1
 
3.2%
Other values (9) 9
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 29
93.5%
Space Separator 2
 
6.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 12
41.4%
계 1
 
3.4%
후 1
 
3.4%
성 1
 
3.4%
반 1
 
3.4%
예 1
 
3.4%
본 1
 
3.4%
흥 1
 
3.4%
용 1
 
3.4%
옥 1
 
3.4%
Other values (8) 8
27.6%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 29
93.5%
Common 2
 
6.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 12
41.4%
계 1
 
3.4%
후 1
 
3.4%
성 1
 
3.4%
반 1
 
3.4%
예 1
 
3.4%
본 1
 
3.4%
흥 1
 
3.4%
용 1
 
3.4%
옥 1
 
3.4%
Other values (8) 8
27.6%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 29
93.5%
ASCII 2
 
6.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 12
41.4%
계 1
 
3.4%
후 1
 
3.4%
성 1
 
3.4%
반 1
 
3.4%
예 1
 
3.4%
본 1
 
3.4%
흥 1
 
3.4%
용 1
 
3.4%
옥 1
 
3.4%
Other values (8) 8
27.6%
ASCII
ValueCountFrequency (%)
2
100.0%

Interactions

2023-12-13T06:48:12.544628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:10.224195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:10.816556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:11.381584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:11.924459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:12.685741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:10.329909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:10.905326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:11.478214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:12.058815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:12.812091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:10.445736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:11.020605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:11.588880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:12.189166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:12.914460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:10.541349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:11.129857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:11.689967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:12.291709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:13.031224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:10.671803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:11.253498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:11.813075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:48:12.432478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:48:18.131907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호행정동리구분본번부번Unnamed: 14
No1.0000.0000.0350.9790.2370.6830.350NaN
일련번호0.0001.0000.6470.0000.0240.0000.000NaN
행정동0.0350.6471.0000.0700.0260.0270.0001.000
리0.9790.0000.0701.0000.2120.5730.342NaN
구분0.2370.0240.0260.2121.0000.6810.077NaN
본번0.6830.0000.0270.5730.6811.0000.393NaN
부번0.3500.0000.0000.3420.0770.3931.000NaN
Unnamed: 14NaNNaN1.000NaNNaNNaNNaN1.000
2023-12-13T06:48:18.262131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분행정동
구분1.0000.016
행정동0.0161.000
2023-12-13T06:48:18.346205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호리본번부번행정동구분
No1.0000.9720.9940.017-0.0280.0260.182
일련번호0.9721.0000.9660.015-0.0360.4480.015
리0.9940.9661.000-0.023-0.0320.0540.162
본번0.0170.015-0.0231.000-0.0170.0210.530
부번-0.028-0.036-0.032-0.0171.0000.0000.059
행정동0.0260.4480.0540.0210.0001.0000.016
구분0.1820.0150.1620.5300.0590.0161.000

Missing values

2023-12-13T06:48:13.187473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:48:13.410681image/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.
2023-12-13T06:48:13.549833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

No일련번호법정동행정동리구분본번부번20142013Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14
01142023100311108,0807,230<NA><NA><NA>NaN<NA>
121420331003112011,10010,200<NA><NA><NA>NaN<NA>
231420431003113222,10020,100<NA><NA><NA>NaN<NA>
34142053100311339,1008,400<NA><NA><NA>NaN<NA>
451420631003114011,10010,200<NA><NA><NA>NaN<NA>
561420731003114114,80013,800<NA><NA><NA>NaN<NA>
671420831003114414,80013,800<NA><NA><NA>리 NO리 명
78142093100311708,3407,460<NA><NA><NA>31가륜리
8914210310310311808,6007,700<NA><NA><NA>32원송리
9101421131003111008,6007,700<NA><NA><NA>33노리
No일련번호법정동행정동리구분본번부번20142013Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14
91949195232473100402480558521<NA><NA><NA>NaN<NA>
91959196232483100402490574536<NA><NA><NA>NaN<NA>
91969197232493100402500542506<NA><NA><NA>NaN<NA>
91979198232503100402510468439<NA><NA><NA>NaN<NA>
91989199232513100402511445409<NA><NA><NA>NaN<NA>
91999200232523100402520419385<NA><NA><NA>NaN<NA>
92009201232533100402530449415<NA><NA><NA>NaN<NA>
92019202232543100402540450420<NA><NA><NA>NaN<NA>
92029203232553100402550423389<NA><NA><NA>NaN<NA>
92039204232563100402560440405<NA><NA><NA>NaN<NA>