Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells49980
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

Numeric6
Categorical2
Text3
Unsupported4

Dataset

Description2013, 2014년 1월 1일 기준 경상북도 고령군 고령읍 개별공시지가
Author경상북도 고령군
URLhttps://www.data.go.kr/data/15051164/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
구분 is highly imbalanced (52.3%)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 9990 (99.9%) missing valuesMissing
Unnamed: 14 has 9990 (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
행정동 has 9505 (95.0%) zerosZeros
부번 has 4102 (41.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:45:40.993542
Analysis finished2023-12-12 06:45:46.632248
Duration5.64 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%
Mean7193.0613
Minimum2
Maximum14495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:46.717053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile724.95
Q13543.75
median7169.5
Q310827.25
95-th percentile13765.05
Maximum14495
Range14493
Interquartile range (IQR)7283.5

Descriptive statistics

Standard deviation4183.5301
Coefficient of variation (CV)0.58160634
Kurtosis-1.2006005
Mean7193.0613
Median Absolute Deviation (MAD)3642.5
Skewness0.017785636
Sum71930613
Variance17501924
MonotonicityNot monotonic
2023-12-12T15:45:46.865630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13047 1
 
< 0.1%
1509 1
 
< 0.1%
6290 1
 
< 0.1%
6575 1
 
< 0.1%
10616 1
 
< 0.1%
9007 1
 
< 0.1%
10475 1
 
< 0.1%
9519 1
 
< 0.1%
6041 1
 
< 0.1%
5429 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
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%
12 1
< 0.1%
ValueCountFrequency (%)
14495 1
< 0.1%
14494 1
< 0.1%
14493 1
< 0.1%
14492 1
< 0.1%
14491 1
< 0.1%
14489 1
< 0.1%
14488 1
< 0.1%
14487 1
< 0.1%
14486 1
< 0.1%
14483 1
< 0.1%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9804
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26598.785
Minimum2
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:47.026209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile730.9
Q13521.75
median7152.5
Q310791.25
95-th percentile13742.05
Maximum999999
Range999997
Interquartile range (IQR)7269.5

Descriptive statistics

Standard deviation138055.68
Coefficient of variation (CV)5.1903003
Kurtosis45.720898
Mean26598.785
Median Absolute Deviation (MAD)3633
Skewness6.9041384
Sum2.6598785 × 108
Variance1.9059372 × 1010
MonotonicityNot monotonic
2023-12-12T15:45:47.201377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999999 197
 
2.0%
1468 1
 
< 0.1%
6126 1
 
< 0.1%
6403 1
 
< 0.1%
10379 1
 
< 0.1%
8800 1
 
< 0.1%
10240 1
 
< 0.1%
9300 1
 
< 0.1%
5880 1
 
< 0.1%
12774 1
 
< 0.1%
Other values (9794) 9794
97.9%
ValueCountFrequency (%)
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%
12 1
< 0.1%
ValueCountFrequency (%)
999999 197
2.0%
14199 1
 
< 0.1%
14198 1
 
< 0.1%
14197 1
 
< 0.1%
14196 1
 
< 0.1%
14195 1
 
< 0.1%
14193 1
 
< 0.1%
14192 1
 
< 0.1%
14191 1
 
< 0.1%
14190 1
 
< 0.1%

법정동
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
250
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row250
2nd row250
3rd row250
4th row250
5th row250

Common Values

ValueCountFrequency (%)
250 10000
100.0%

Length

2023-12-12T15:45:47.384755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:45:47.501041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
250 10000
100.0%

행정동
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.471
Minimum0
Maximum250
Zeros9505
Zeros (%)95.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:47.589303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum250
Range250
Interquartile range (IQR)0

Descriptive statistics

Standard deviation33.688224
Coefficient of variation (CV)6.157599
Kurtosis47.56538
Mean5.471
Median Absolute Deviation (MAD)0
Skewness6.9697645
Sum54710
Variance1134.8964
MonotonicityNot monotonic
2023-12-12T15:45:47.706385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 9505
95.0%
250 182
 
1.8%
29 175
 
1.8%
31 98
 
1.0%
30 18
 
0.2%
26 14
 
0.1%
21 4
 
< 0.1%
33 3
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
0 9505
95.0%
10 1
 
< 0.1%
21 4
 
< 0.1%
26 14
 
0.1%
29 175
 
1.8%
30 18
 
0.2%
31 98
 
1.0%
33 3
 
< 0.1%
250 182
 
1.8%
ValueCountFrequency (%)
250 182
 
1.8%
33 3
 
< 0.1%
31 98
 
1.0%
30 18
 
0.2%
29 175
 
1.8%
26 14
 
0.1%
21 4
 
< 0.1%
10 1
 
< 0.1%
0 9505
95.0%

리
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.128
Minimum21
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:47.846045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median27
Q331
95-th percentile33
Maximum33
Range12
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.8160545
Coefficient of variation (CV)0.14066848
Kurtosis-1.1513667
Mean27.128
Median Absolute Deviation (MAD)3
Skewness-0.0080039683
Sum271280
Variance14.562272
MonotonicityNot monotonic
2023-12-12T15:45:47.963692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
26 1281
12.8%
27 1096
11.0%
32 985
9.8%
33 981
9.8%
21 897
9.0%
22 792
7.9%
29 772
7.7%
31 676
6.8%
25 604
6.0%
24 592
5.9%
Other values (3) 1324
13.2%
ValueCountFrequency (%)
21 897
9.0%
22 792
7.9%
23 442
 
4.4%
24 592
5.9%
25 604
6.0%
26 1281
12.8%
27 1096
11.0%
28 525
5.2%
29 772
7.7%
30 357
 
3.6%
ValueCountFrequency (%)
33 981
9.8%
32 985
9.8%
31 676
6.8%
30 357
 
3.6%
29 772
7.7%
28 525
5.2%
27 1096
11.0%
26 1281
12.8%
25 604
6.0%
24 592
5.9%

구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8974 
2
1026 

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 8974
89.7%
2 1026
 
10.3%

Length

2023-12-12T15:45:48.088156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:45:48.195288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8974
89.7%
2 1026
 
10.3%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct1074
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean364.7963
Minimum1
Maximum1198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:48.350066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q1130
median303
Q3548.25
95-th percentile905
Maximum1198
Range1197
Interquartile range (IQR)418.25

Descriptive statistics

Standard deviation281.9546
Coefficient of variation (CV)0.7729097
Kurtosis-0.39663309
Mean364.7963
Median Absolute Deviation (MAD)195
Skewness0.71523887
Sum3647963
Variance79498.396
MonotonicityNot monotonic
2023-12-12T15:45:48.538642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
925 69
 
0.7%
552 54
 
0.5%
232 49
 
0.5%
15 42
 
0.4%
4 41
 
0.4%
21 41
 
0.4%
457 41
 
0.4%
888 40
 
0.4%
183 38
 
0.4%
203 37
 
0.4%
Other values (1064) 9548
95.5%
ValueCountFrequency (%)
1 26
0.3%
2 24
0.2%
3 34
0.3%
4 41
0.4%
5 29
0.3%
6 22
0.2%
7 31
0.3%
8 29
0.3%
9 24
0.2%
10 31
0.3%
ValueCountFrequency (%)
1198 1
 
< 0.1%
1182 2
 
< 0.1%
1178 25
0.2%
1154 1
 
< 0.1%
1153 2
 
< 0.1%
1152 1
 
< 0.1%
1150 1
 
< 0.1%
1149 1
 
< 0.1%
1147 2
 
< 0.1%
1146 1
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct155
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2532
Minimum0
Maximum229
Zeros4102
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:45:48.737596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile21
Maximum229
Range229
Interquartile range (IQR)4

Descriptive statistics

Standard deviation16.02302
Coefficient of variation (CV)3.0501446
Kurtosis76.342084
Mean5.2532
Median Absolute Deviation (MAD)1
Skewness7.7421974
Sum52532
Variance256.73716
MonotonicityNot monotonic
2023-12-12T15:45:48.917476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4102
41.0%
1 1535
 
15.3%
2 988
 
9.9%
3 655
 
6.6%
4 430
 
4.3%
5 316
 
3.2%
6 256
 
2.6%
7 175
 
1.8%
8 170
 
1.7%
9 135
 
1.4%
Other values (145) 1238
 
12.4%
ValueCountFrequency (%)
0 4102
41.0%
1 1535
 
15.3%
2 988
 
9.9%
3 655
 
6.6%
4 430
 
4.3%
5 316
 
3.2%
6 256
 
2.6%
7 175
 
1.8%
8 170
 
1.7%
9 135
 
1.4%
ValueCountFrequency (%)
229 1
< 0.1%
228 1
< 0.1%
227 1
< 0.1%
226 1
< 0.1%
224 1
< 0.1%
223 1
< 0.1%
220 1
< 0.1%
214 1
< 0.1%
204 1
< 0.1%
202 1
< 0.1%

2014
Text

Distinct2246
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:45:49.355884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.8315
Min length3

Characters and Unicode

Total characters58315
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

Unique1024 ?
Unique (%)10.2%

Sample

1st row9,730
2nd row13,000
3rd row184,400
4th row321,700
5th row9,690
ValueCountFrequency (%)
13,000 140
 
1.4%
34,800 97
 
1.0%
14,000 86
 
0.9%
15,000 81
 
0.8%
21,700 75
 
0.8%
10,600 72
 
0.7%
11,500 61
 
0.6%
8,050 60
 
0.6%
17,800 58
 
0.6%
20,000 56
 
0.6%
Other values (2236) 9214
92.1%
2023-12-12T15:45:49.892198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 20157
34.6%
, 9316
16.0%
1 5542
 
9.5%
2 4139
 
7.1%
3 3286
 
5.6%
4 2968
 
5.1%
5 2920
 
5.0%
8 2834
 
4.9%
7 2488
 
4.3%
9 2343
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48999
84.0%
Other Punctuation 9316
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20157
41.1%
1 5542
 
11.3%
2 4139
 
8.4%
3 3286
 
6.7%
4 2968
 
6.1%
5 2920
 
6.0%
8 2834
 
5.8%
7 2488
 
5.1%
9 2343
 
4.8%
6 2322
 
4.7%
Other Punctuation
ValueCountFrequency (%)
, 9316
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58315
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20157
34.6%
, 9316
16.0%
1 5542
 
9.5%
2 4139
 
7.1%
3 3286
 
5.6%
4 2968
 
5.1%
5 2920
 
5.0%
8 2834
 
4.9%
7 2488
 
4.3%
9 2343
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20157
34.6%
, 9316
16.0%
1 5542
 
9.5%
2 4139
 
7.1%
3 3286
 
5.6%
4 2968
 
5.1%
5 2920
 
5.0%
8 2834
 
4.9%
7 2488
 
4.3%
9 2343
 
4.0%

2013
Text

Distinct1736
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T15:45:50.352788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.7699
Min length1

Characters and Unicode

Total characters57699
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

Unique599 ?
Unique (%)6.0%

Sample

1st row9,330
2nd row12,000
3rd row182,000
4th row317,000
5th row9,700
ValueCountFrequency (%)
13,000 107
 
1.1%
0 97
 
1.0%
20,700 89
 
0.9%
18,400 83
 
0.8%
33,900 82
 
0.8%
15,000 81
 
0.8%
12,000 80
 
0.8%
11,500 63
 
0.6%
19,600 60
 
0.6%
20,000 60
 
0.6%
Other values (1726) 9198
92.0%
2023-12-12T15:45:51.025161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21883
37.9%
, 9187
15.9%
1 5287
 
9.2%
2 4125
 
7.1%
3 3028
 
5.2%
4 2578
 
4.5%
5 2577
 
4.5%
8 2369
 
4.1%
7 2328
 
4.0%
6 2291
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48512
84.1%
Other Punctuation 9187
 
15.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21883
45.1%
1 5287
 
10.9%
2 4125
 
8.5%
3 3028
 
6.2%
4 2578
 
5.3%
5 2577
 
5.3%
8 2369
 
4.9%
7 2328
 
4.8%
6 2291
 
4.7%
9 2046
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 9187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57699
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21883
37.9%
, 9187
15.9%
1 5287
 
9.2%
2 4125
 
7.1%
3 3028
 
5.2%
4 2578
 
4.5%
5 2577
 
4.5%
8 2369
 
4.1%
7 2328
 
4.0%
6 2291
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57699
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21883
37.9%
, 9187
15.9%
1 5287
 
9.2%
2 4125
 
7.1%
3 3028
 
5.2%
4 2578
 
4.5%
5 2577
 
4.5%
8 2369
 
4.1%
7 2328
 
4.0%
6 2291
 
4.0%

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 

Missing9990
Missing (%)99.9%
Memory size156.2 KiB

Unnamed: 14
Text

MISSING 

Distinct10
Distinct (%)100.0%
Missing9990
Missing (%)99.9%
Memory size156.2 KiB
2023-12-12T15:45:51.228777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9
Min length2

Characters and Unicode

Total characters29
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

Unique10 ?
Unique (%)100.0%

Sample

1st row장기리
2nd row저전리
3rd row헌문리
4th row리 명
5th row연조리
ValueCountFrequency (%)
장기리 1
9.1%
저전리 1
9.1%
헌문리 1
9.1%
리 1
9.1%
명 1
9.1%
연조리 1
9.1%
쾌빈리 1
9.1%
신리 1
9.1%
내곡리 1
9.1%
내상리 1
9.1%
2023-12-12T15:45:51.561238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 10
34.5%
내 2
 
6.9%
장 1
 
3.4%
조 1
 
3.4%
고 1
 
3.4%
상 1
 
3.4%
곡 1
 
3.4%
신 1
 
3.4%
빈 1
 
3.4%
쾌 1
 
3.4%
Other values (9) 9
31.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28
96.6%
Space Separator 1
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 10
35.7%
내 2
 
7.1%
장 1
 
3.6%
조 1
 
3.6%
고 1
 
3.6%
상 1
 
3.6%
곡 1
 
3.6%
신 1
 
3.6%
빈 1
 
3.6%
쾌 1
 
3.6%
Other values (8) 8
28.6%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28
96.6%
Common 1
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 10
35.7%
내 2
 
7.1%
장 1
 
3.6%
조 1
 
3.6%
고 1
 
3.6%
상 1
 
3.6%
곡 1
 
3.6%
신 1
 
3.6%
빈 1
 
3.6%
쾌 1
 
3.6%
Other values (8) 8
28.6%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28
96.6%
ASCII 1
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 10
35.7%
내 2
 
7.1%
장 1
 
3.6%
조 1
 
3.6%
고 1
 
3.6%
상 1
 
3.6%
곡 1
 
3.6%
신 1
 
3.6%
빈 1
 
3.6%
쾌 1
 
3.6%
Other values (8) 8
28.6%
ASCII
ValueCountFrequency (%)
1
100.0%

Interactions

2023-12-12T15:45:45.379495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:42.469587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.145460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.740726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.280070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.833216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:45.466466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:42.581588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.255613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.849430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.386009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.923066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:45.564011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:42.697589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.352201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.940035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.490199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:45.009290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:45.646493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:42.842201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.457192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.036920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.573574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:45.092589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:45.729572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:42.949895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.555696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.119750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.662975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:45.190116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:45.837729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.041427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:43.661680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.204228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:44.748587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:45:45.300933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:45:51.678678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호행정동리구분본번부번Unnamed: 14
No1.0000.0240.3770.9810.3130.5740.258NaN
일련번호0.0241.0000.3160.0270.0030.0300.009NaN
행정동0.3770.3161.0000.4170.0350.0870.0201.000
리0.9810.0270.4171.0000.2350.5220.217NaN
구분0.3130.0030.0350.2351.0000.7060.085NaN
본번0.5740.0300.0870.5220.7061.0000.380NaN
부번0.2580.0090.0200.2170.0850.3801.000NaN
Unnamed: 14NaNNaN1.000NaNNaNNaNNaN1.000
2023-12-12T15:45:51.815986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호행정동리본번부번구분
No1.0000.9560.0560.9960.218-0.3330.240
일련번호0.9561.0000.1370.9520.202-0.3290.002
행정동0.0560.1371.0000.063-0.038-0.1100.058
리0.9960.9520.0631.0000.179-0.3410.189
본번0.2180.202-0.0380.1791.0000.0310.551
부번-0.333-0.329-0.110-0.3410.0311.0000.065
구분0.2400.0020.0580.1890.5510.0651.000

Missing values

2023-12-12T15:45:46.245547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:45:46.435277image/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.
2023-12-12T15:45:46.564783image/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
13046130471277425003311809,7309,330<NA><NA><NA>NaN<NA>
1324713248129722500331206013,00012,000<NA><NA><NA>NaN<NA>
603604594250021135026184,400182,000<NA><NA><NA>NaN<NA>
518751885049250026122516321,700317,000<NA><NA><NA>NaN<NA>
134111341213134250033134919,6909,700<NA><NA><NA>NaN<NA>
1143811439111892500312720332312<NA><NA><NA>NaN<NA>
1349413495132152500331430013,80013,300<NA><NA><NA>NaN<NA>
1318613187129122500331144019,50018,300<NA><NA><NA>NaN<NA>
1269212693124242500321855815,30015,300<NA><NA><NA>NaN<NA>
1063110632103952503131118014,00013,000<NA><NA><NA>NaN<NA>
No일련번호법정동행정동리구분본번부번20142013Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14
5809581056542500261640017,10015,500<NA><NA><NA>NaN<NA>
1058510586103492500302210769729<NA><NA><NA>NaN<NA>
5820582156642500261647017,10015,500<NA><NA><NA>NaN<NA>
668566866513250027161019,60019,100<NA><NA><NA>NaN<NA>
7492749373082500271581246,00043,900<NA><NA><NA>NaN<NA>
1402614027137412500331909012,20010,000<NA><NA><NA>NaN<NA>
2004200519502500221315065,60064,200<NA><NA><NA>NaN<NA>
8358835981632500281241132,40031,100<NA><NA><NA>NaN<NA>
39523953385125002511247498,000480,000<NA><NA><NA>NaN<NA>
4321432242072500251308011,50011,000<NA><NA><NA>NaN<NA>