Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells49976
Missing cells (%)33.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory139.0 B

Variable types

Numeric5
Categorical3
Text3
Unsupported4

Dataset

Description2013, 2014년 1월 1일 기준 경상북도 고령군 쌍림면 개별공시지가
Author경상북도 고령군
URLhttps://www.data.go.kr/data/15051165/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 2 other fieldsHigh correlation
리 is highly overall correlated with No and 1 other fieldsHigh correlation
행정동 is highly overall correlated with 일련번호High correlation
행정동 is highly imbalanced (87.5%)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 9988 (99.9%) missing valuesMissing
Unnamed: 14 has 9988 (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 4964 (49.6%) zerosZeros

Reproduction

Analysis started2023-12-12 13:09:25.742252
Analysis finished2023-12-12 13:09:29.954547
Duration4.21 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%
Mean8274.058
Minimum3
Maximum16568
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:09:30.026429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile833.9
Q14156.75
median8265.5
Q312394.25
95-th percentile15705.15
Maximum16568
Range16565
Interquartile range (IQR)8237.5

Descriptive statistics

Standard deviation4774.5537
Coefficient of variation (CV)0.57705103
Kurtosis-1.1963906
Mean8274.058
Median Absolute Deviation (MAD)4119.5
Skewness-0.00045446663
Sum82740580
Variance22796363
MonotonicityNot monotonic
2023-12-12T22:09:30.159841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7764 1
 
< 0.1%
8415 1
 
< 0.1%
14737 1
 
< 0.1%
14139 1
 
< 0.1%
5562 1
 
< 0.1%
13315 1
 
< 0.1%
12051 1
 
< 0.1%
6804 1
 
< 0.1%
9533 1
 
< 0.1%
2488 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
10 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
ValueCountFrequency (%)
16568 1
< 0.1%
16567 1
< 0.1%
16566 1
< 0.1%
16565 1
< 0.1%
16563 1
< 0.1%
16561 1
< 0.1%
16560 1
< 0.1%
16557 1
< 0.1%
16556 1
< 0.1%
16555 1
< 0.1%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9831
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106940.13
Minimum83367
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:09:30.324329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum83367
5-th percentile84196.9
Q187520.25
median91628
Q395750.25
95-th percentile99079.05
Maximum999999
Range916632
Interquartile range (IQR)8230

Descriptive statistics

Standard deviation117541.29
Coefficient of variation (CV)1.0991317
Kurtosis53.690224
Mean106940.13
Median Absolute Deviation (MAD)4115.5
Skewness7.4555935
Sum1.0694013 × 109
Variance1.3815954 × 1010
MonotonicityNot monotonic
2023-12-12T22:09:30.504031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999999 170
 
1.7%
91010 1
 
< 0.1%
93697 1
 
< 0.1%
90071 1
 
< 0.1%
92747 1
 
< 0.1%
91649 1
 
< 0.1%
85810 1
 
< 0.1%
87632 1
 
< 0.1%
84706 1
 
< 0.1%
90134 1
 
< 0.1%
Other values (9821) 9821
98.2%
ValueCountFrequency (%)
83367 1
< 0.1%
83368 1
< 0.1%
83370 1
< 0.1%
83371 1
< 0.1%
83373 1
< 0.1%
83374 1
< 0.1%
83375 1
< 0.1%
83377 1
< 0.1%
83378 1
< 0.1%
83379 1
< 0.1%
ValueCountFrequency (%)
999999 170
1.7%
99655 1
 
< 0.1%
99654 1
 
< 0.1%
99653 1
 
< 0.1%
99652 1
 
< 0.1%
99650 1
 
< 0.1%
99648 1
 
< 0.1%
99647 1
 
< 0.1%
99644 1
 
< 0.1%
99643 1
 
< 0.1%

법정동
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
370
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row370
2nd row370
3rd row370
4th row370
5th row370

Common Values

ValueCountFrequency (%)
370 10000
100.0%

Length

2023-12-12T22:09:30.636703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:09:30.736345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
370 10000
100.0%

행정동
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9828 
370
 
172

Length

Max length3
Median length1
Mean length1.0344
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9828
98.3%
370 172
 
1.7%

Length

2023-12-12T22:09:30.869007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:09:30.996603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9828
98.3%
370 172
 
1.7%

리
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.2688
Minimum37
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:09:31.121161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile38
Q142
median46
Q349
95-th percentile52
Maximum52
Range15
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.4146229
Coefficient of variation (CV)0.097520211
Kurtosis-0.99316374
Mean45.2688
Median Absolute Deviation (MAD)4
Skewness-0.22866903
Sum452688
Variance19.488895
MonotonicityNot monotonic
2023-12-12T22:09:31.250846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
46 1051
10.5%
45 900
9.0%
49 867
8.7%
47 841
 
8.4%
52 804
 
8.0%
38 758
 
7.6%
50 751
 
7.5%
42 661
 
6.6%
43 627
 
6.3%
51 581
 
5.8%
Other values (6) 2159
21.6%
ValueCountFrequency (%)
37 354
 
3.5%
38 758
7.6%
39 360
 
3.6%
40 247
 
2.5%
41 498
5.0%
42 661
6.6%
43 627
6.3%
44 430
4.3%
45 900
9.0%
46 1051
10.5%
ValueCountFrequency (%)
52 804
8.0%
51 581
5.8%
50 751
7.5%
49 867
8.7%
48 270
 
2.7%
47 841
8.4%
46 1051
10.5%
45 900
9.0%
44 430
4.3%
43 627
6.3%

구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8714 
2
1286 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8714
87.1%
2 1286
 
12.9%

Length

2023-12-12T22:09:31.409419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:09:31.516984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8714
87.1%
2 1286
 
12.9%

본번
Real number (ℝ)

Distinct1034
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean338.3992
Minimum1
Maximum1050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:09:31.637407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q1101
median273
Q3532
95-th percentile870.05
Maximum1050
Range1049
Interquartile range (IQR)431

Descriptive statistics

Standard deviation269.99078
Coefficient of variation (CV)0.79784698
Kurtosis-0.54158173
Mean338.3992
Median Absolute Deviation (MAD)197
Skewness0.69431965
Sum3383992
Variance72895.021
MonotonicityNot monotonic
2023-12-12T22:09:31.812502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
773 74
 
0.7%
38 72
 
0.7%
43 72
 
0.7%
665 63
 
0.6%
348 62
 
0.6%
40 61
 
0.6%
42 60
 
0.6%
55 55
 
0.5%
479 48
 
0.5%
86 45
 
0.4%
Other values (1024) 9388
93.9%
ValueCountFrequency (%)
1 22
0.2%
2 29
0.3%
3 29
0.3%
4 15
0.1%
5 23
0.2%
6 23
0.2%
7 26
0.3%
8 36
0.4%
9 31
0.3%
10 15
0.1%
ValueCountFrequency (%)
1050 7
0.1%
1047 1
 
< 0.1%
1046 1
 
< 0.1%
1045 1
 
< 0.1%
1041 1
 
< 0.1%
1040 1
 
< 0.1%
1038 2
 
< 0.1%
1037 2
 
< 0.1%
1036 1
 
< 0.1%
1035 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct180
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4642
Minimum0
Maximum222
Zeros4964
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T22:09:32.020413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile24
Maximum222
Range222
Interquartile range (IQR)2

Descriptive statistics

Standard deviation19.918298
Coefficient of variation (CV)3.6452359
Kurtosis43.997357
Mean5.4642
Median Absolute Deviation (MAD)1
Skewness6.2109489
Sum54642
Variance396.73859
MonotonicityNot monotonic
2023-12-12T22:09:32.185928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4964
49.6%
1 1674
 
16.7%
2 946
 
9.5%
3 553
 
5.5%
4 340
 
3.4%
5 220
 
2.2%
6 169
 
1.7%
7 130
 
1.3%
8 76
 
0.8%
9 64
 
0.6%
Other values (170) 864
 
8.6%
ValueCountFrequency (%)
0 4964
49.6%
1 1674
 
16.7%
2 946
 
9.5%
3 553
 
5.5%
4 340
 
3.4%
5 220
 
2.2%
6 169
 
1.7%
7 130
 
1.3%
8 76
 
0.8%
9 64
 
0.6%
ValueCountFrequency (%)
222 1
 
< 0.1%
221 1
 
< 0.1%
220 1
 
< 0.1%
210 1
 
< 0.1%
208 1
 
< 0.1%
207 2
< 0.1%
206 1
 
< 0.1%
204 3
< 0.1%
203 1
 
< 0.1%
202 1
 
< 0.1%

2014
Text

Distinct1282
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:09:32.605075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.314
Min length3

Characters and Unicode

Total characters53140
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

Unique385 ?
Unique (%)3.9%

Sample

1st row515
2nd row2,120
3rd row3,780
4th row7,500
5th row5,130
ValueCountFrequency (%)
13,500 131
 
1.3%
12,500 125
 
1.2%
11,800 123
 
1.2%
15,000 121
 
1.2%
18,000 113
 
1.1%
14,000 109
 
1.1%
12,800 107
 
1.1%
12,000 90
 
0.9%
15,500 90
 
0.9%
9,400 84
 
0.8%
Other values (1272) 8907
89.1%
2023-12-12T22:09:33.188984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17559
33.0%
, 8692
16.4%
1 6097
 
11.5%
2 3517
 
6.6%
5 3101
 
5.8%
3 2816
 
5.3%
4 2473
 
4.7%
8 2365
 
4.5%
6 2314
 
4.4%
7 2199
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44448
83.6%
Other Punctuation 8692
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17559
39.5%
1 6097
 
13.7%
2 3517
 
7.9%
5 3101
 
7.0%
3 2816
 
6.3%
4 2473
 
5.6%
8 2365
 
5.3%
6 2314
 
5.2%
7 2199
 
4.9%
9 2007
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 8692
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53140
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17559
33.0%
, 8692
16.4%
1 6097
 
11.5%
2 3517
 
6.6%
5 3101
 
5.8%
3 2816
 
5.3%
4 2473
 
4.7%
8 2365
 
4.5%
6 2314
 
4.4%
7 2199
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17559
33.0%
, 8692
16.4%
1 6097
 
11.5%
2 3517
 
6.6%
5 3101
 
5.8%
3 2816
 
5.3%
4 2473
 
4.7%
8 2365
 
4.5%
6 2314
 
4.4%
7 2199
 
4.1%

2013
Text

Distinct1287
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T22:09:33.633289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.2765
Min length1

Characters and Unicode

Total characters52765
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

Unique363 ?
Unique (%)3.6%

Sample

1st row498
2nd row2,120
3rd row3,490
4th row7,300
5th row5,030
ValueCountFrequency (%)
12,300 178
 
1.8%
14,000 141
 
1.4%
13,000 116
 
1.2%
14,500 114
 
1.1%
13,500 113
 
1.1%
14,700 109
 
1.1%
5,100 102
 
1.0%
16,600 96
 
1.0%
9,400 87
 
0.9%
11,500 86
 
0.9%
Other values (1277) 8858
88.6%
2023-12-12T22:09:34.107178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17107
32.4%
, 8637
16.4%
1 6292
 
11.9%
2 3192
 
6.0%
3 3151
 
6.0%
5 2722
 
5.2%
4 2680
 
5.1%
7 2405
 
4.6%
6 2241
 
4.2%
8 2198
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44128
83.6%
Other Punctuation 8637
 
16.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17107
38.8%
1 6292
 
14.3%
2 3192
 
7.2%
3 3151
 
7.1%
5 2722
 
6.2%
4 2680
 
6.1%
7 2405
 
5.5%
6 2241
 
5.1%
8 2198
 
5.0%
9 2140
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 8637
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 52765
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17107
32.4%
, 8637
16.4%
1 6292
 
11.9%
2 3192
 
6.0%
3 3151
 
6.0%
5 2722
 
5.2%
4 2680
 
5.1%
7 2405
 
4.6%
6 2241
 
4.2%
8 2198
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52765
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17107
32.4%
, 8637
16.4%
1 6292
 
11.9%
2 3192
 
6.0%
3 3151
 
6.0%
5 2722
 
5.2%
4 2680
 
5.1%
7 2405
 
4.6%
6 2241
 
4.2%
8 2198
 
4.2%

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 

Missing9988
Missing (%)99.9%
Memory size156.2 KiB

Unnamed: 14
Text

MISSING 

Distinct12
Distinct (%)100.0%
Missing9988
Missing (%)99.9%
Memory size156.2 KiB
2023-12-12T22:09:34.287178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9166667
Min length2

Characters and Unicode

Total characters35
Distinct characters20
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

Unique12 ?
Unique (%)100.0%

Sample

1st row용리
2nd row안림리
3rd row산당리
4th row월막리
5th row신촌리
ValueCountFrequency (%)
용리 1
 
7.7%
안림리 1
 
7.7%
산당리 1
 
7.7%
월막리 1
 
7.7%
신촌리 1
 
7.7%
매촌리 1
 
7.7%
송림리 1
 
7.7%
리 1
 
7.7%
명 1
 
7.7%
백산리 1
 
7.7%
Other values (3) 3
23.1%
2023-12-12T22:09:34.566553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 12
34.3%
산 3
 
8.6%
림 2
 
5.7%
촌 2
 
5.7%
용 1
 
2.9%
1
 
2.9%
곡 1
 
2.9%
고 1
 
2.9%
지 1
 
2.9%
평 1
 
2.9%
Other values (10) 10
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34
97.1%
Space Separator 1
 
2.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 12
35.3%
산 3
 
8.8%
림 2
 
5.9%
촌 2
 
5.9%
용 1
 
2.9%
곡 1
 
2.9%
고 1
 
2.9%
지 1
 
2.9%
평 1
 
2.9%
백 1
 
2.9%
Other values (9) 9
26.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34
97.1%
Common 1
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 12
35.3%
산 3
 
8.8%
림 2
 
5.9%
촌 2
 
5.9%
용 1
 
2.9%
곡 1
 
2.9%
고 1
 
2.9%
지 1
 
2.9%
평 1
 
2.9%
백 1
 
2.9%
Other values (9) 9
26.5%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34
97.1%
ASCII 1
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 12
35.3%
산 3
 
8.8%
림 2
 
5.9%
촌 2
 
5.9%
용 1
 
2.9%
곡 1
 
2.9%
고 1
 
2.9%
지 1
 
2.9%
평 1
 
2.9%
백 1
 
2.9%
Other values (9) 9
26.5%
ASCII
ValueCountFrequency (%)
1
100.0%

Interactions

2023-12-12T22:09:28.865413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:26.899202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:27.430204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:27.951869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.473542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.943264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:26.988353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:27.555627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.054392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.557478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:29.016564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:27.092770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:27.652312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.170542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.633176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:29.105592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:27.227826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:27.761864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.297200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.717634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:29.184486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:27.338938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:27.860013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.386867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T22:09:28.795617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:09:34.653866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호행정동리구분본번부번Unnamed: 14
No1.0000.0000.0000.9880.2280.4760.341NaN
일련번호0.0001.0000.7910.0050.0270.0000.000NaN
행정동0.0000.7911.0000.0000.0220.0080.036NaN
리0.9880.0050.0001.0000.1630.4350.325NaN
구분0.2280.0270.0220.1631.0000.6380.088NaN
본번0.4760.0000.0080.4350.6381.0000.268NaN
부번0.3410.0000.0360.3250.0880.2681.000NaN
Unnamed: 14NaNNaNNaNNaNNaNNaNNaN1.000
2023-12-12T22:09:34.769304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동구분
행정동1.0000.014
구분0.0141.000
2023-12-12T22:09:34.857845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호리본번부번행정동구분
No1.0000.9700.9970.217-0.1460.0000.174
일련번호0.9701.0000.9670.207-0.1470.5810.017
리0.9970.9671.0000.193-0.1420.0000.121
본번0.2170.2070.1931.000-0.0930.0060.495
부번-0.146-0.147-0.142-0.0931.0000.0270.068
행정동0.0000.5810.0000.0060.0271.0000.014
구분0.1740.0170.1210.4950.0680.0141.000

Missing values

2023-12-12T22:09:29.292372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:09:29.463861image/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.
2023-12-12T22:09:29.894435image/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
7763776491010370045240515498<NA><NA><NA>NaN<NA>
162171621899312370052188902,1202,120<NA><NA><NA>NaN<NA>
192719288525837003919103,7803,490<NA><NA><NA>NaN<NA>
160191602099117370052172317,5007,300<NA><NA><NA>NaN<NA>
5113511488403370043125005,1305,030<NA><NA><NA>NaN<NA>
104991050093699370047169588,2407,520<NA><NA><NA>NaN<NA>
1457014571976973700511241014,80013,700<NA><NA><NA>NaN<NA>
1174411745949203700491162215,70014,700<NA><NA><NA>NaN<NA>
78297830910753700452405377447<NA><NA><NA>NaN<NA>
5500550188784370043158305,3005,100<NA><NA><NA>NaN<NA>
No일련번호법정동행정동리구분본번부번20142013Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14
1149311494946743700482190385366<NA><NA><NA>NaN<NA>
18121813851453700382483357336<NA><NA><NA>NaN<NA>
5885588689163370044131233,10032,900<NA><NA><NA>NaN<NA>
85868587918193700461371015,20016,400<NA><NA><NA>NaN<NA>
1257312574957403700491632217,60015,700<NA><NA><NA>NaN<NA>
6902690390167370045130509,6508,750<NA><NA><NA>NaN<NA>
26912692860113700401162156,60056,600<NA><NA><NA>NaN<NA>
17431744850783700382268342322<NA><NA><NA>NaN<NA>
1401314014971503700501948133,80033,800<NA><NA><NA>NaN<NA>
5196519788484370043131917,2007,300<NA><NA><NA>NaN<NA>