Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells59978
Missing cells (%)37.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory149.0 B

Variable types

Numeric6
Categorical3
Text3
Unsupported4

Dataset

Description2013, 2014년 1월 1일 기준 경상북도 고령군 성산면 개별공시지가
Author경상북도 고령군
URLhttps://www.data.go.kr/data/15051162/fileData.do

Alerts

법정동 has constant value ""Constant
No is highly overall correlated with 일련번호 and 2 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
본번 is highly overall correlated with Unnamed: 14 and 1 other fieldsHigh correlation
Unnamed: 14 is highly overall correlated with No and 3 other fieldsHigh correlation
구분 is highly overall correlated with 본번 and 1 other fieldsHigh correlation
행정동 is highly imbalanced (87.1%)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 9989 (99.9%) missing valuesMissing
Unnamed: 15 has 9989 (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 5535 (55.4%) zerosZeros

Reproduction

Analysis started2023-12-12 08:58:09.297269
Analysis finished2023-12-12 08:58:18.615278
Duration9.32 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%
Mean6622.666
Minimum1
Maximum13233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:58:18.768054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile650.9
Q13314.75
median6611
Q39960.25
95-th percentile12571.1
Maximum13233
Range13232
Interquartile range (IQR)6645.5

Descriptive statistics

Standard deviation3834.7292
Coefficient of variation (CV)0.57903104
Kurtosis-1.2050124
Mean6622.666
Median Absolute Deviation (MAD)3323.5
Skewness-0.006090885
Sum66226660
Variance14705148
MonotonicityNot monotonic
2023-12-12T17:58:19.018634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11332 1
 
< 0.1%
12289 1
 
< 0.1%
11736 1
 
< 0.1%
5409 1
 
< 0.1%
4537 1
 
< 0.1%
130 1
 
< 0.1%
7224 1
 
< 0.1%
1558 1
 
< 0.1%
2608 1
 
< 0.1%
10556 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
ValueCountFrequency (%)
13233 1
< 0.1%
13232 1
< 0.1%
13230 1
< 0.1%
13229 1
< 0.1%
13226 1
< 0.1%
13224 1
< 0.1%
13223 1
< 0.1%
13222 1
< 0.1%
13221 1
< 0.1%
13220 1
< 0.1%

일련번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9832
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57014.428
Minimum34300
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:58:19.256108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34300
5-th percentile34946.9
Q137608.75
median40918.5
Q344246.25
95-th percentile46876.05
Maximum999999
Range965699
Interquartile range (IQR)6637.5

Descriptive statistics

Standard deviation123699.83
Coefficient of variation (CV)2.1696233
Kurtosis54.112183
Mean57014.428
Median Absolute Deviation (MAD)3318.5
Skewness7.4864214
Sum5.7014428 × 108
Variance1.5301648 × 1010
MonotonicityNot monotonic
2023-12-12T17:58:19.480744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
999999 169
 
1.7%
45433 1
 
< 0.1%
44671 1
 
< 0.1%
39616 1
 
< 0.1%
38756 1
 
< 0.1%
34426 1
 
< 0.1%
41393 1
 
< 0.1%
35826 1
 
< 0.1%
36855 1
 
< 0.1%
46375 1
 
< 0.1%
Other values (9822) 9822
98.2%
ValueCountFrequency (%)
34300 1
< 0.1%
34302 1
< 0.1%
34303 1
< 0.1%
34304 1
< 0.1%
34306 1
< 0.1%
34307 1
< 0.1%
34308 1
< 0.1%
34310 1
< 0.1%
34311 1
< 0.1%
34312 1
< 0.1%
ValueCountFrequency (%)
999999 169
1.7%
47304 1
 
< 0.1%
47303 1
 
< 0.1%
47301 1
 
< 0.1%
47300 1
 
< 0.1%
47297 1
 
< 0.1%
47295 1
 
< 0.1%
47294 1
 
< 0.1%
47293 1
 
< 0.1%
47292 1
 
< 0.1%

법정동
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
330
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row330
2nd row330
3rd row330
4th row330
5th row330

Common Values

ValueCountFrequency (%)
330 10000
100.0%

Length

2023-12-12T17:58:19.712187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:58:19.933808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
330 10000
100.0%

행정동
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9821 
330
 
179

Length

Max length3
Median length1
Mean length1.0358
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 9821
98.2%
330 179
 
1.8%

Length

2023-12-12T17:58:20.115988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:58:20.283493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9821
98.2%
330 179
 
1.8%

리
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.106
Minimum35
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:58:20.404064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile35
Q138
median43
Q346
95-th percentile48
Maximum48
Range13
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.1369645
Coefficient of variation (CV)0.098251188
Kurtosis-1.1979033
Mean42.106
Median Absolute Deviation (MAD)3
Skewness-0.2166783
Sum421060
Variance17.114475
MonotonicityNot monotonic
2023-12-12T17:58:20.599688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
47 983
9.8%
48 952
9.5%
40 895
8.9%
46 849
8.5%
43 844
8.4%
44 801
8.0%
35 795
8.0%
37 780
7.8%
41 694
6.9%
45 680
 
6.8%
Other values (4) 1727
17.3%
ValueCountFrequency (%)
35 795
8.0%
36 388
3.9%
37 780
7.8%
38 607
6.1%
39 248
 
2.5%
40 895
8.9%
41 694
6.9%
42 484
4.8%
43 844
8.4%
44 801
8.0%
ValueCountFrequency (%)
48 952
9.5%
47 983
9.8%
46 849
8.5%
45 680
6.8%
44 801
8.0%
43 844
8.4%
42 484
4.8%
41 694
6.9%
40 895
8.9%
39 248
 
2.5%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8497 
2
1503 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 8497
85.0%
2 1503
 
15.0%

Length

2023-12-12T17:58:20.796768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:58:20.961752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8497
85.0%
2 1503
 
15.0%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct974
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean320.0815
Minimum1
Maximum990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:58:21.129150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q1115
median271
Q3496
95-th percentile774
Maximum990
Range989
Interquartile range (IQR)381

Descriptive statistics

Standard deviation241.26652
Coefficient of variation (CV)0.75376589
Kurtosis-0.56237924
Mean320.0815
Median Absolute Deviation (MAD)181
Skewness0.63120257
Sum3200815
Variance58209.532
MonotonicityNot monotonic
2023-12-12T17:58:21.330630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 57
 
0.6%
35 51
 
0.5%
835 43
 
0.4%
154 39
 
0.4%
69 38
 
0.4%
315 38
 
0.4%
185 38
 
0.4%
146 37
 
0.4%
56 35
 
0.4%
37 34
 
0.3%
Other values (964) 9590
95.9%
ValueCountFrequency (%)
1 34
0.3%
2 30
0.3%
3 29
0.3%
4 23
0.2%
5 28
0.3%
6 23
0.2%
7 18
0.2%
8 33
0.3%
9 23
0.2%
10 27
0.3%
ValueCountFrequency (%)
990 1
< 0.1%
988 1
< 0.1%
987 1
< 0.1%
986 1
< 0.1%
985 1
< 0.1%
984 1
< 0.1%
983 1
< 0.1%
981 1
< 0.1%
979 1
< 0.1%
978 1
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct80
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.062
Minimum0
Maximum139
Zeros5535
Zeros (%)55.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:58:21.524204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile8
Maximum139
Range139
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.4486366
Coefficient of variation (CV)3.1273699
Kurtosis114.70145
Mean2.062
Median Absolute Deviation (MAD)0
Skewness8.916733
Sum20620
Variance41.584914
MonotonicityNot monotonic
2023-12-12T17:58:21.705168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5535
55.4%
1 1596
 
16.0%
2 1031
 
10.3%
3 512
 
5.1%
4 312
 
3.1%
5 200
 
2.0%
6 156
 
1.6%
7 111
 
1.1%
8 69
 
0.7%
9 56
 
0.6%
Other values (70) 422
 
4.2%
ValueCountFrequency (%)
0 5535
55.4%
1 1596
 
16.0%
2 1031
 
10.3%
3 512
 
5.1%
4 312
 
3.1%
5 200
 
2.0%
6 156
 
1.6%
7 111
 
1.1%
8 69
 
0.7%
9 56
 
0.6%
ValueCountFrequency (%)
139 1
< 0.1%
130 1
< 0.1%
127 1
< 0.1%
113 1
< 0.1%
105 1
< 0.1%
97 1
< 0.1%
93 1
< 0.1%
90 1
< 0.1%
89 1
< 0.1%
82 1
< 0.1%

2014
Text

Distinct1726
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:58:22.154750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.5499
Min length3

Characters and Unicode

Total characters55499
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

Unique558 ?
Unique (%)5.6%

Sample

1st row21,900
2nd row9,500
3rd row829
4th row13,300
5th row529
ValueCountFrequency (%)
48,000 133
 
1.3%
6,200 103
 
1.0%
11,000 97
 
1.0%
16,000 84
 
0.8%
18,000 72
 
0.7%
10,400 64
 
0.6%
33,900 56
 
0.6%
34,700 56
 
0.6%
38,500 55
 
0.5%
17,000 53
 
0.5%
Other values (1716) 9227
92.3%
2023-12-12T17:58:22.648736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 19476
35.1%
, 8979
16.2%
1 4763
 
8.6%
2 3587
 
6.5%
3 3476
 
6.3%
4 3009
 
5.4%
5 2820
 
5.1%
6 2513
 
4.5%
8 2392
 
4.3%
7 2351
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46520
83.8%
Other Punctuation 8979
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19476
41.9%
1 4763
 
10.2%
2 3587
 
7.7%
3 3476
 
7.5%
4 3009
 
6.5%
5 2820
 
6.1%
6 2513
 
5.4%
8 2392
 
5.1%
7 2351
 
5.1%
9 2133
 
4.6%
Other Punctuation
ValueCountFrequency (%)
, 8979
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55499
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19476
35.1%
, 8979
16.2%
1 4763
 
8.6%
2 3587
 
6.5%
3 3476
 
6.3%
4 3009
 
5.4%
5 2820
 
5.1%
6 2513
 
4.5%
8 2392
 
4.3%
7 2351
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55499
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19476
35.1%
, 8979
16.2%
1 4763
 
8.6%
2 3587
 
6.5%
3 3476
 
6.3%
4 3009
 
5.4%
5 2820
 
5.1%
6 2513
 
4.5%
8 2392
 
4.3%
7 2351
 
4.2%

2013
Text

Distinct1564
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T17:58:22.965562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.4501
Min length1

Characters and Unicode

Total characters54501
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

Unique432 ?
Unique (%)4.3%

Sample

1st row18,200
2nd row8,930
3rd row792
4th row12,900
5th row521
ValueCountFrequency (%)
5,200 113
 
1.1%
45,500 101
 
1.0%
14,500 94
 
0.9%
0 88
 
0.9%
33,800 72
 
0.7%
39,600 59
 
0.6%
12,500 53
 
0.5%
37,200 52
 
0.5%
31,800 50
 
0.5%
5,850 48
 
0.5%
Other values (1554) 9270
92.7%
2023-12-12T17:58:23.433780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18433
33.8%
, 8855
16.2%
1 4579
 
8.4%
2 3636
 
6.7%
3 3433
 
6.3%
5 3370
 
6.2%
4 3223
 
5.9%
8 2370
 
4.3%
6 2368
 
4.3%
9 2241
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45646
83.8%
Other Punctuation 8855
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18433
40.4%
1 4579
 
10.0%
2 3636
 
8.0%
3 3433
 
7.5%
5 3370
 
7.4%
4 3223
 
7.1%
8 2370
 
5.2%
6 2368
 
5.2%
9 2241
 
4.9%
7 1993
 
4.4%
Other Punctuation
ValueCountFrequency (%)
, 8855
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 54501
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18433
33.8%
, 8855
16.2%
1 4579
 
8.4%
2 3636
 
6.7%
3 3433
 
6.3%
5 3370
 
6.2%
4 3223
 
5.9%
8 2370
 
4.3%
6 2368
 
4.3%
9 2241
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54501
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18433
33.8%
, 8855
16.2%
1 4579
 
8.4%
2 3636
 
6.7%
3 3433
 
6.3%
5 3370
 
6.2%
4 3223
 
5.9%
8 2370
 
4.3%
6 2368
 
4.3%
9 2241
 
4.1%

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
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)100.0%
Missing9989
Missing (%)99.9%
Infinite0
Infinite (%)0.0%
Mean41.909091
Minimum35
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T17:58:23.542779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile35.5
Q137.5
median43
Q345.5
95-th percentile47.5
Maximum48
Range13
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.65735
Coefficient of variation (CV)0.11112983
Kurtosis-1.5356343
Mean41.909091
Median Absolute Deviation (MAD)4
Skewness-0.29984422
Sum461
Variance21.690909
MonotonicityNot monotonic
2023-12-12T17:58:23.631769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
35 1
 
< 0.1%
45 1
 
< 0.1%
37 1
 
< 0.1%
47 1
 
< 0.1%
43 1
 
< 0.1%
42 1
 
< 0.1%
44 1
 
< 0.1%
48 1
 
< 0.1%
46 1
 
< 0.1%
38 1
 
< 0.1%
(Missing) 9989
99.9%
ValueCountFrequency (%)
35 1
< 0.1%
36 1
< 0.1%
37 1
< 0.1%
38 1
< 0.1%
42 1
< 0.1%
43 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
ValueCountFrequency (%)
48 1
< 0.1%
47 1
< 0.1%
46 1
< 0.1%
45 1
< 0.1%
44 1
< 0.1%
43 1
< 0.1%
42 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%
36 1
< 0.1%

Unnamed: 15
Text

MISSING 

Distinct11
Distinct (%)100.0%
Missing9989
Missing (%)99.9%
Memory size156.2 KiB
2023-12-12T17:58:23.763682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters33
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
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 row어곡리
2nd row고탄리
3rd row삼대리
4th row기산리
5th row용소리
ValueCountFrequency (%)
어곡리 1
9.1%
고탄리 1
9.1%
삼대리 1
9.1%
기산리 1
9.1%
용소리 1
9.1%
대흥리 1
9.1%
상용리 1
9.1%
사부리 1
9.1%
기족리 1
9.1%
오곡리 1
9.1%
2023-12-12T17:58:24.017391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 11
33.3%
용 2
 
6.1%
대 2
 
6.1%
기 2
 
6.1%
곡 2
 
6.1%
상 1
 
3.0%
득 1
 
3.0%
오 1
 
3.0%
족 1
 
3.0%
부 1
 
3.0%
Other values (9) 9
27.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 33
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 11
33.3%
용 2
 
6.1%
대 2
 
6.1%
기 2
 
6.1%
곡 2
 
6.1%
상 1
 
3.0%
득 1
 
3.0%
오 1
 
3.0%
족 1
 
3.0%
부 1
 
3.0%
Other values (9) 9
27.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 33
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 11
33.3%
용 2
 
6.1%
대 2
 
6.1%
기 2
 
6.1%
곡 2
 
6.1%
상 1
 
3.0%
득 1
 
3.0%
오 1
 
3.0%
족 1
 
3.0%
부 1
 
3.0%
Other values (9) 9
27.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 33
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 11
33.3%
용 2
 
6.1%
대 2
 
6.1%
기 2
 
6.1%
곡 2
 
6.1%
상 1
 
3.0%
득 1
 
3.0%
오 1
 
3.0%
족 1
 
3.0%
부 1
 
3.0%
Other values (9) 9
27.3%

Interactions

2023-12-12T17:58:16.736993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:11.363259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:12.499639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:13.897131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:14.723938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:15.376802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:16.879221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:11.540844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:12.716990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:14.042542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:14.826655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:15.520630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:17.012461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:11.706726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:13.002065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:14.179015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:14.925065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:16.019949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:17.244054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:11.879204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:13.269814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:14.340790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:15.043460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:16.200217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:17.457904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:12.074816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:13.517637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:14.483756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:15.136317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:16.348474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:17.596071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:12.277629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:13.746147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:14.618832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:15.255836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:16.564270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:58:24.104754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호행정동리구분본번부번Unnamed: 14Unnamed: 15
No1.0000.0000.0310.9840.2820.4910.244NaNNaN
일련번호0.0001.0000.5580.0000.0000.0090.0000.0001.000
행정동0.0310.5581.0000.0210.0000.0240.0000.0001.000
리0.9840.0000.0211.0000.2620.4450.275NaNNaN
구분0.2820.0000.0000.2621.0000.6770.023NaNNaN
본번0.4910.0090.0240.4450.6771.0000.246NaNNaN
부번0.2440.0000.0000.2750.0230.2461.000NaNNaN
Unnamed: 14NaN0.0000.000NaNNaNNaNNaN1.0001.000
Unnamed: 15NaN1.0001.000NaNNaNNaNNaN1.0001.000
2023-12-12T17:58:24.230237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동구분
행정동1.0000.000
구분0.0001.000
2023-12-12T17:58:24.314796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
No일련번호리본번부번Unnamed: 14행정동구분
No1.0000.9660.9970.128-0.1261.0000.0240.217
일련번호0.9661.0000.9630.121-0.1290.9910.3770.000
리0.9970.9631.0000.106-0.124NaN0.0160.201
본번0.1280.1210.1061.000-0.0880.9510.0190.527
부번-0.126-0.129-0.124-0.0881.000-0.1140.0000.017
Unnamed: 141.0000.991NaN0.951-0.1141.0000.0001.000
행정동0.0240.3770.0160.0190.0000.0001.0000.000
구분0.2170.0000.2010.5270.0171.0000.0001.000

Missing values

2023-12-12T17:58:17.868207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:58:18.246325image/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.
2023-12-12T17:58:18.515492image/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: 15
1133111332454333300471601021,90018,200<NA><NA><NA><NA><NA><NA>
1199711998460893300481709,5008,930<NA><NA><NA><NA><NA><NA>
129281292947003330048264829792<NA><NA><NA><NA><NA><NA>
647664774065933004316013,30012,900<NA><NA><NA><NA><NA><NA>
1063710638447513300462970529521<NA><NA><NA><NA><NA><NA>
5013501439226330041195118,00011,500<NA><NA><NA><NA><NA><NA>
95099510436403300452780545537<NA><NA><NA><NA><NA><NA>
64006401405853300422913,1202,710<NA><NA><NA><NA><NA><NA>
1601619999993303303511345177,000175,500<NA><NA><NA><NA><NA><NA>
2372237336626330037152515,5104,940<NA><NA><NA><NA><NA><NA>
No일련번호법정동행정동리구분본번부번20142013Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15
474847493896433004014681731,20029,300<NA><NA><NA><NA><NA><NA>
1229212293463793300481234154,80049,900<NA><NA><NA><NA><NA><NA>
47094710389263300401454331,20029,300<NA><NA><NA><NA><NA><NA>
11704117054580033004723902,0401,880<NA><NA><NA><NA><NA><NA>
22222223364783300371416071,40054,900<NA><NA><NA><NA><NA><NA>
38603861380863300401355833,90036,300<NA><NA><NA><NA><NA><NA>
13058130594713133004827711,0701,090<NA><NA><NA><NA><NA><NA>
727572764144433004318010447393<NA><NA><NA><NA><NA><NA>
1251312514465973300481449021,70017,800<NA><NA><NA><NA><NA><NA>
88688735167330035170106,2005,200<NA><NA><NA><NA><NA><NA>