Overview

Dataset statistics

Number of variables17
Number of observations10000
Missing cells69984
Missing cells (%)41.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory157.0 B

Variable types

Numeric5
Categorical3
Text3
Unsupported6

Dataset

Description2013, 2014년 1월 1일 기준 경상북도 고령군 다산면 개별공시지가
Author경상북도 고령군
URLhttps://www.data.go.kr/data/15051166/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
행정동 is highly imbalanced (86.4%)Imbalance
구분 is highly imbalanced (60.2%)Imbalance
Unnamed: 10 has 10000 (100.0%) missing valuesMissing
Unnamed: 11 has 10000 (100.0%) missing valuesMissing
Unnamed: 12 has 10000 (100.0%) missing valuesMissing
Unnamed: 13 has 10000 (100.0%) missing valuesMissing
Unnamed: 14 has 10000 (100.0%) missing valuesMissing
Unnamed: 15 has 9992 (99.9%) missing valuesMissing
Unnamed: 16 has 9992 (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
Unnamed: 14 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 15 is an unsupported type, check if it needs cleaning or further analysisUnsupported
부번 has 4598 (46.0%) zerosZeros

Reproduction

Analysis started2023-12-12 09:11:33.334185
Analysis finished2023-12-12 09:11:38.157653
Duration4.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6950.9566
Minimum1
Maximum13864
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:11:38.227431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile715.95
Q13463.75
median6956.5
Q310453.25
95-th percentile13173.05
Maximum13864
Range13863
Interquartile range (IQR)6989.5

Descriptive statistics

Standard deviation4005.518
Coefficient of variation (CV)0.57625421
Kurtosis-1.2091638
Mean6950.9566
Median Absolute Deviation (MAD)3494.5
Skewness-0.0026055332
Sum69509566
Variance16044174
MonotonicityNot monotonic
2023-12-12T18:11:38.385091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11613 1
 
< 0.1%
2399 1
 
< 0.1%
11982 1
 
< 0.1%
3158 1
 
< 0.1%
10547 1
 
< 0.1%
11594 1
 
< 0.1%
13263 1
 
< 0.1%
552 1
 
< 0.1%
4009 1
 
< 0.1%
11494 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 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%
11 1
< 0.1%
ValueCountFrequency (%)
13864 1
< 0.1%
13862 1
< 0.1%
13861 1
< 0.1%
13860 1
< 0.1%
13859 1
< 0.1%
13858 1
< 0.1%
13855 1
< 0.1%
13854 1
< 0.1%
13853 1
< 0.1%
13851 1
< 0.1%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9818
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71437.802
Minimum47305
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:11:38.556183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47305
5-th percentile48021.95
Q150767.75
median54257.5
Q357754.25
95-th percentile60474.05
Maximum999999
Range952694
Interquartile range (IQR)6986.5

Descriptive statistics

Standard deviation126844.98
Coefficient of variation (CV)1.7756002
Kurtosis49.589666
Mean71437.802
Median Absolute Deviation (MAD)3493.5
Skewness7.1782546
Sum7.1437802 × 108
Variance1.6089648 × 1010
MonotonicityNot monotonic
2023-12-12T18:11:38.709456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999999 183
 
1.8%
58705 1
 
< 0.1%
56366 1
 
< 0.1%
53814 1
 
< 0.1%
50232 1
 
< 0.1%
48841 1
 
< 0.1%
52233 1
 
< 0.1%
47555 1
 
< 0.1%
57577 1
 
< 0.1%
54461 1
 
< 0.1%
Other values (9808) 9808
98.1%
ValueCountFrequency (%)
47305 1
< 0.1%
47306 1
< 0.1%
47307 1
< 0.1%
47309 1
< 0.1%
47310 1
< 0.1%
47311 1
< 0.1%
47312 1
< 0.1%
47313 1
< 0.1%
47314 1
< 0.1%
47315 1
< 0.1%
ValueCountFrequency (%)
999999 183
1.8%
60912 1
 
< 0.1%
60910 1
 
< 0.1%
60909 1
 
< 0.1%
60908 1
 
< 0.1%
60907 1
 
< 0.1%
60906 1
 
< 0.1%
60903 1
 
< 0.1%
60902 1
 
< 0.1%
60901 1
 
< 0.1%

법정동
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
340
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row340
2nd row340
3rd row340
4th row340
5th row340

Common Values

ValueCountFrequency (%)
340 10000
100.0%

Length

2023-12-12T18:11:38.834669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:38.929775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
340 10000
100.0%

행정동
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9809 
340
 
191

Length

Max length3
Median length1
Mean length1.0382
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9809
98.1%
340 191
 
1.9%

Length

2023-12-12T18:11:39.054571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:39.218053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9809
98.1%
340 191
 
1.9%

리
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.8054
Minimum31
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:11:39.323019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile31
Q133
median36
Q338
95-th percentile40
Maximum40
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.9041994
Coefficient of variation (CV)0.081110654
Kurtosis-1.2955346
Mean35.8054
Median Absolute Deviation (MAD)3
Skewness-0.13694743
Sum358054
Variance8.4343743
MonotonicityNot monotonic
2023-12-12T18:11:39.438861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
36 1237
12.4%
33 1225
12.2%
37 1224
12.2%
40 1186
11.9%
39 1163
11.6%
32 1146
11.5%
38 1039
10.4%
31 702
7.0%
34 634
6.3%
35 444
 
4.4%
ValueCountFrequency (%)
31 702
7.0%
32 1146
11.5%
33 1225
12.2%
34 634
6.3%
35 444
 
4.4%
36 1237
12.4%
37 1224
12.2%
38 1039
10.4%
39 1163
11.6%
40 1186
11.9%
ValueCountFrequency (%)
40 1186
11.9%
39 1163
11.6%
38 1039
10.4%
37 1224
12.2%
36 1237
12.4%
35 444
 
4.4%
34 634
6.3%
33 1225
12.2%
32 1146
11.5%
31 702
7.0%

구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9213 
2
 
787

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 9213
92.1%
2 787
 
7.9%

Length

2023-12-12T18:11:39.561243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T18:11:39.658913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9213
92.1%
2 787
 
7.9%

본번
Real number (ℝ)

Distinct1790
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean513.6983
Minimum1
Maximum2166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:11:39.770792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q1126
median339
Q3803
95-th percentile1537
Maximum2166
Range2165
Interquartile range (IQR)677

Descriptive statistics

Standard deviation487.65484
Coefficient of variation (CV)0.94930202
Kurtosis1.0101611
Mean513.6983
Median Absolute Deviation (MAD)260
Skewness1.2340459
Sum5136983
Variance237807.24
MonotonicityNot monotonic
2023-12-12T18:11:39.906609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 130
 
1.3%
42 112
 
1.1%
151 53
 
0.5%
148 52
 
0.5%
230 45
 
0.4%
136 44
 
0.4%
161 43
 
0.4%
175 42
 
0.4%
642 41
 
0.4%
81 41
 
0.4%
Other values (1780) 9397
94.0%
ValueCountFrequency (%)
1 18
0.2%
2 16
0.2%
3 12
0.1%
4 13
0.1%
5 14
0.1%
6 16
0.2%
7 11
0.1%
8 20
0.2%
9 13
0.1%
10 14
0.1%
ValueCountFrequency (%)
2166 1
< 0.1%
2165 1
< 0.1%
2164 1
< 0.1%
2163 1
< 0.1%
2161 1
< 0.1%
2160 1
< 0.1%
2158 1
< 0.1%
2157 1
< 0.1%
2156 1
< 0.1%
2152 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct200
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7204
Minimum0
Maximum332
Zeros4598
Zeros (%)46.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T18:11:40.047148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile23
Maximum332
Range332
Interquartile range (IQR)4

Descriptive statistics

Standard deviation24.621487
Coefficient of variation (CV)3.6636937
Kurtosis79.382335
Mean6.7204
Median Absolute Deviation (MAD)1
Skewness8.1573127
Sum67204
Variance606.21765
MonotonicityNot monotonic
2023-12-12T18:11:40.482069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4598
46.0%
1 1230
 
12.3%
2 824
 
8.2%
3 526
 
5.3%
4 358
 
3.6%
5 309
 
3.1%
6 249
 
2.5%
7 203
 
2.0%
8 180
 
1.8%
9 145
 
1.5%
Other values (190) 1378
 
13.8%
ValueCountFrequency (%)
0 4598
46.0%
1 1230
 
12.3%
2 824
 
8.2%
3 526
 
5.3%
4 358
 
3.6%
5 309
 
3.1%
6 249
 
2.5%
7 203
 
2.0%
8 180
 
1.8%
9 145
 
1.5%
ValueCountFrequency (%)
332 1
< 0.1%
330 1
< 0.1%
329 1
< 0.1%
328 1
< 0.1%
326 1
< 0.1%
325 1
< 0.1%
324 1
< 0.1%
323 1
< 0.1%
322 1
< 0.1%
321 1
< 0.1%

2014
Text

Distinct1397
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T18:11:40.825150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6.0005
Min length3

Characters and Unicode

Total characters60005
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

Unique506 ?
Unique (%)5.1%

Sample

1st row23,600
2nd row218,000
3rd row30,700
4th row14,100
5th row34,000
ValueCountFrequency (%)
34,000 232
 
2.3%
57,700 148
 
1.5%
55,000 139
 
1.4%
37,500 114
 
1.1%
48,000 113
 
1.1%
58,000 112
 
1.1%
56,500 96
 
1.0%
163,200 90
 
0.9%
37,000 80
 
0.8%
39,000 74
 
0.7%
Other values (1387) 8802
88.0%
2023-12-12T18:11:41.335458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22022
36.7%
, 9836
16.4%
5 4569
 
7.6%
2 3845
 
6.4%
1 3716
 
6.2%
3 3509
 
5.8%
4 3451
 
5.8%
6 2706
 
4.5%
7 2446
 
4.1%
8 2093
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50169
83.6%
Other Punctuation 9836
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22022
43.9%
5 4569
 
9.1%
2 3845
 
7.7%
1 3716
 
7.4%
3 3509
 
7.0%
4 3451
 
6.9%
6 2706
 
5.4%
7 2446
 
4.9%
8 2093
 
4.2%
9 1812
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 9836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60005
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22022
36.7%
, 9836
16.4%
5 4569
 
7.6%
2 3845
 
6.4%
1 3716
 
6.2%
3 3509
 
5.8%
4 3451
 
5.8%
6 2706
 
4.5%
7 2446
 
4.1%
8 2093
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22022
36.7%
, 9836
16.4%
5 4569
 
7.6%
2 3845
 
6.4%
1 3716
 
6.2%
3 3509
 
5.8%
4 3451
 
5.8%
6 2706
 
4.5%
7 2446
 
4.1%
8 2093
 
3.5%

2013
Text

Distinct1197
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T18:11:41.688269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.8556
Min length1

Characters and Unicode

Total characters58556
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

Unique370 ?
Unique (%)3.7%

Sample

1st row22,500
2nd row208,000
3rd row28,000
4th row12,700
5th row33,500
ValueCountFrequency (%)
0 260
 
2.6%
53,500 207
 
2.1%
33,500 137
 
1.4%
33,000 114
 
1.1%
36,500 105
 
1.1%
152,000 94
 
0.9%
47,000 92
 
0.9%
52,000 88
 
0.9%
57,000 87
 
0.9%
55,500 86
 
0.9%
Other values (1187) 8730
87.3%
2023-12-12T18:11:42.145674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22948
39.2%
, 9537
16.3%
5 4486
 
7.7%
3 3699
 
6.3%
2 3575
 
6.1%
1 3398
 
5.8%
4 2924
 
5.0%
6 2189
 
3.7%
7 2142
 
3.7%
8 1848
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49019
83.7%
Other Punctuation 9537
 
16.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22948
46.8%
5 4486
 
9.2%
3 3699
 
7.5%
2 3575
 
7.3%
1 3398
 
6.9%
4 2924
 
6.0%
6 2189
 
4.5%
7 2142
 
4.4%
8 1848
 
3.8%
9 1810
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 9537
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58556
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22948
39.2%
, 9537
16.3%
5 4486
 
7.7%
3 3699
 
6.3%
2 3575
 
6.1%
1 3398
 
5.8%
4 2924
 
5.0%
6 2189
 
3.7%
7 2142
 
3.7%
8 1848
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58556
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22948
39.2%
, 9537
16.3%
5 4486
 
7.7%
3 3699
 
6.3%
2 3575
 
6.1%
1 3398
 
5.8%
4 2924
 
5.0%
6 2189
 
3.7%
7 2142
 
3.7%
8 1848
 
3.2%

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Unnamed: 11
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Unnamed: 12
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Unnamed: 14
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

Unnamed: 15
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9992
Missing (%)99.9%
Memory size156.2 KiB

Unnamed: 16
Text

MISSING 

Distinct8
Distinct (%)100.0%
Missing9992
Missing (%)99.9%
Memory size156.2 KiB
2023-12-12T18:11:42.367792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24
Distinct characters14
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

Unique8 ?
Unique (%)100.0%

Sample

1st row송곡리
2nd row호촌리
3rd row평리리
4th row리 명
5th row나정리
ValueCountFrequency (%)
송곡리 1
11.1%
호촌리 1
11.1%
평리리 1
11.1%
리 1
11.1%
명 1
11.1%
나정리 1
11.1%
곽촌리 1
11.1%
좌학리 1
11.1%
상곡리 1
11.1%
2023-12-12T18:11:42.680325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 9
37.5%
곡 2
 
8.3%
촌 2
 
8.3%
송 1
 
4.2%
호 1
 
4.2%
평 1
 
4.2%
1
 
4.2%
명 1
 
4.2%
나 1
 
4.2%
정 1
 
4.2%
Other values (4) 4
16.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 23
95.8%
Space Separator 1
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 9
39.1%
곡 2
 
8.7%
촌 2
 
8.7%
송 1
 
4.3%
호 1
 
4.3%
평 1
 
4.3%
명 1
 
4.3%
나 1
 
4.3%
정 1
 
4.3%
곽 1
 
4.3%
Other values (3) 3
 
13.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 23
95.8%
Common 1
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 9
39.1%
곡 2
 
8.7%
촌 2
 
8.7%
송 1
 
4.3%
호 1
 
4.3%
평 1
 
4.3%
명 1
 
4.3%
나 1
 
4.3%
정 1
 
4.3%
곽 1
 
4.3%
Other values (3) 3
 
13.0%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 23
95.8%
ASCII 1
 
4.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 9
39.1%
곡 2
 
8.7%
촌 2
 
8.7%
송 1
 
4.3%
호 1
 
4.3%
평 1
 
4.3%
명 1
 
4.3%
나 1
 
4.3%
정 1
 
4.3%
곽 1
 
4.3%
Other values (3) 3
 
13.0%
ASCII
ValueCountFrequency (%)
1
100.0%

Interactions

2023-12-12T18:11:36.849434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:34.282970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:34.943993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:35.480184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.069206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.005676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:34.437236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:35.052597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:35.599545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.259555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.149998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:34.552532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:35.151296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:35.708287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.400560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.293740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:34.670414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:35.252287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:35.811507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.542015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:37.448208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:34.818202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:35.353246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:35.920509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T18:11:36.689312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:11:42.781592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호행정동리구분본번부번Unnamed: 16
No1.0000.0220.0540.9860.3390.7090.332NaN
일련번호0.0221.0000.6780.0350.0460.0070.000NaN
행정동0.0540.6781.0000.0600.0370.0400.000NaN
리0.9860.0350.0601.0000.2780.6780.372NaN
구분0.3390.0460.0370.2781.0000.4770.054NaN
본번0.7090.0070.0400.6780.4771.0000.190NaN
부번0.3320.0000.0000.3720.0540.1901.000NaN
Unnamed: 16NaNNaNNaNNaNNaNNaNNaN1.000
2023-12-12T18:11:42.919244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동구분
행정동1.0000.024
구분0.0241.000
2023-12-12T18:11:43.016896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호리본번부번행정동구분
No1.0000.9670.9940.288-0.4820.0410.260
일련번호0.9671.0000.9600.278-0.4760.4740.029
리0.9940.9601.0000.237-0.4640.0460.213
본번0.2880.2780.2371.000-0.3430.0310.367
부번-0.482-0.476-0.464-0.3431.0000.0000.042
행정동0.0410.4740.0460.0310.0001.0000.024
구분0.2600.0290.2130.3670.0420.0241.000

Missing values

2023-12-12T18:11:37.667634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:11:37.940136image/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.
2023-12-12T18:11:38.101762image/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: 14Unnamed: 15Unnamed: 16
1161211613587053400391857023,60022,500<NA><NA><NA><NA><NA>NaN<NA>
230823094958334003216401218,000208,000<NA><NA><NA><NA><NA>NaN<NA>
848884895564634003711000030,70028,000<NA><NA><NA><NA><NA>NaN<NA>
83748375555333400371885014,10012,700<NA><NA><NA><NA><NA>NaN<NA>
908590865623534003711911034,00033,500<NA><NA><NA><NA><NA>NaN<NA>
476047615199234003412387183,300176,000<NA><NA><NA><NA><NA>NaN<NA>
7475747654644340036212951,5101,390<NA><NA><NA><NA><NA>NaN<NA>
65186519537093400361620020,50019,500<NA><NA><NA><NA><NA>NaN<NA>
191619174919534003213761754,20053,200<NA><NA><NA><NA><NA>NaN<NA>
599359945319634003612006149,100145,000<NA><NA><NA><NA><NA>NaN<NA>
No일련번호법정동행정동리구분본번부번20142013Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16
78747875550403400371912734,00033,000<NA><NA><NA><NA><NA>NaN<NA>
11818118195890934003911088081,00074,000<NA><NA><NA><NA><NA>NaN<NA>
11838118395892934003911109134,70033,700<NA><NA><NA><NA><NA>NaN<NA>
94609461566003400381218041,50040,000<NA><NA><NA><NA><NA>NaN<NA>
215621574943334003215835182,300175,000<NA><NA><NA><NA><NA>NaN<NA>
450945105174634003411509455,900426,000<NA><NA><NA><NA><NA>NaN<NA>
510651075232834003415221229,200213,000<NA><NA><NA><NA><NA>NaN<NA>
645646479383400311293455,00053,500<NA><NA><NA><NA><NA>NaN<NA>
60906091532903400361264042,00041,000<NA><NA><NA><NA><NA>NaN<NA>
7737744806534003113101753,00052,000<NA><NA><NA><NA><NA>NaN<NA>