Overview

Dataset statistics

Number of variables15
Number of observations9693
Missing cells4312
Missing cells (%)3.0%
Duplicate rows86
Duplicate rows (%)0.9%
Total size in memory1.2 MiB
Average record size in memory130.0 B

Variable types

Categorical7
Numeric6
Text2

Dataset

Description충청북도 증평군_지방세에 대한 자료입니다. 지방세에는 취득세, 재산세, 자동차세, 지방소득세, 등록면허세 등 다양한 자료가 있습니다.
URLhttps://www.data.go.kr/data/15080373/fileData.do

Alerts

Dataset has 86 (0.9%) duplicate rowsDuplicates
특수지 is highly overall correlated with 시도명 and 4 other fieldsHigh correlation
시군구명 is highly overall correlated with 법정리 and 11 other fieldsHigh correlation
법정동 is highly overall correlated with 법정리 and 5 other fieldsHigh correlation
과세년도 is highly overall correlated with 법정리 and 11 other fieldsHigh correlation
자치단체코드 is highly overall correlated with 법정리 and 11 other fieldsHigh correlation
시도명 is highly overall correlated with 법정리 and 11 other fieldsHigh correlation
기준일자 is highly overall correlated with 법정리 and 11 other fieldsHigh correlation
법정리 is highly overall correlated with 시도명 and 5 other fieldsHigh correlation
본번 is highly overall correlated with 시도명 and 4 other fieldsHigh correlation
부번 is highly overall correlated with 시도명 and 4 other fieldsHigh correlation
동 is highly overall correlated with 시도명 and 4 other fieldsHigh correlation
시가표준액 is highly overall correlated with 연면적 and 5 other fieldsHigh correlation
연면적 is highly overall correlated with 시가표준액 and 5 other fieldsHigh correlation
시도명 is highly imbalanced (69.0%)Imbalance
시군구명 is highly imbalanced (69.0%)Imbalance
자치단체코드 is highly imbalanced (69.0%)Imbalance
과세년도 is highly imbalanced (69.0%)Imbalance
특수지 is highly imbalanced (73.2%)Imbalance
기준일자 is highly imbalanced (69.0%)Imbalance
법정리 has 539 (5.6%) missing valuesMissing
본번 has 539 (5.6%) missing valuesMissing
부번 has 539 (5.6%) missing valuesMissing
동 has 539 (5.6%) missing valuesMissing
호 has 539 (5.6%) missing valuesMissing
물건지 has 539 (5.6%) missing valuesMissing
시가표준액 has 539 (5.6%) missing valuesMissing
연면적 has 539 (5.6%) missing valuesMissing
시가표준액 is highly skewed (γ1 = 26.54853073)Skewed
연면적 is highly skewed (γ1 = 20.48913313)Skewed
부번 has 4614 (47.6%) zerosZeros
동 has 3042 (31.4%) zerosZeros

Reproduction

Analysis started2023-12-12 18:39:20.562382
Analysis finished2023-12-12 18:39:32.661953
Duration12.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
충청북도
9154 
<NA>
 
539

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 9154
94.4%
<NA> 539
 
5.6%

Length

2023-12-13T03:39:33.302058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:39:33.487411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 9154
94.4%
na 539
 
5.6%

시군구명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
증평군
9154 
<NA>
 
539

Length

Max length4
Median length3
Mean length3.0556071
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
증평군 9154
94.4%
<NA> 539
 
5.6%

Length

2023-12-13T03:39:33.669924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:39:33.827858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
증평군 9154
94.4%
na 539
 
5.6%

자치단체코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
43745
9154 
<NA>
 
539

Length

Max length5
Median length5
Mean length4.9443929
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43745
2nd row43745
3rd row43745
4th row43745
5th row43745

Common Values

ValueCountFrequency (%)
43745 9154
94.4%
<NA> 539
 
5.6%

Length

2023-12-13T03:39:33.998279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:39:34.167169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43745 9154
94.4%
na 539
 
5.6%

과세년도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
2022
9154 
<NA>
 
539

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 9154
94.4%
<NA> 539
 
5.6%

Length

2023-12-13T03:39:34.319485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:39:34.499969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 9154
94.4%
na 539
 
5.6%

법정동
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
250
7169 
310
1985 
<NA>
 
539

Length

Max length4
Median length3
Mean length3.0556071
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
250 7169
74.0%
310 1985
 
20.5%
<NA> 539
 
5.6%

Length

2023-12-13T03:39:34.721423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:39:34.888271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
250 7169
74.0%
310 1985
 
20.5%
na 539
 
5.6%

법정리
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)0.2%
Missing539
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean28.422984
Minimum21
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.3 KiB
2023-12-13T03:39:35.073087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile22
Q124
median28
Q332
95-th percentile37
Maximum40
Range19
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.9726248
Coefficient of variation (CV)0.17495083
Kurtosis-0.79413802
Mean28.422984
Median Absolute Deviation (MAD)4
Skewness0.31726731
Sum260184
Variance24.726997
MonotonicityNot monotonic
2023-12-13T03:39:35.316331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
30 1060
10.9%
22 965
10.0%
31 804
 
8.3%
27 669
 
6.9%
26 666
 
6.9%
23 648
 
6.7%
25 611
 
6.3%
33 481
 
5.0%
32 474
 
4.9%
21 448
 
4.6%
Other values (10) 2328
24.0%
(Missing) 539
 
5.6%
ValueCountFrequency (%)
21 448
4.6%
22 965
10.0%
23 648
6.7%
24 362
 
3.7%
25 611
6.3%
26 666
6.9%
27 669
6.9%
28 351
 
3.6%
29 154
 
1.6%
30 1060
10.9%
ValueCountFrequency (%)
40 145
 
1.5%
39 108
 
1.1%
38 158
 
1.6%
37 250
 
2.6%
36 168
 
1.7%
35 439
4.5%
34 193
 
2.0%
33 481
5.0%
32 474
4.9%
31 804
8.3%

특수지
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
1
9002 
<NA>
 
539
2
 
152

Length

Max length4
Median length1
Mean length1.1668214
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9002
92.9%
<NA> 539
 
5.6%
2 152
 
1.6%

Length

2023-12-13T03:39:35.596516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:39:35.781708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9002
92.9%
na 539
 
5.6%
2 152
 
1.6%

본번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct936
Distinct (%)10.2%
Missing539
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean393.57352
Minimum1
Maximum1630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.3 KiB
2023-12-13T03:39:36.001605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q1108
median342
Q3600
95-th percentile936.7
Maximum1630
Range1629
Interquartile range (IQR)492

Descriptive statistics

Standard deviation311.27786
Coefficient of variation (CV)0.79090142
Kurtosis-0.154116
Mean393.57352
Median Absolute Deviation (MAD)243
Skewness0.68408688
Sum3602772
Variance96893.905
MonotonicityNot monotonic
2023-12-13T03:39:36.266904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1071 103
 
1.1%
602 97
 
1.0%
11 90
 
0.9%
24 83
 
0.9%
61 79
 
0.8%
582 67
 
0.7%
77 62
 
0.6%
84 55
 
0.6%
673 55
 
0.6%
532 54
 
0.6%
Other values (926) 8409
86.8%
(Missing) 539
 
5.6%
ValueCountFrequency (%)
1 18
0.2%
2 32
0.3%
3 27
0.3%
4 8
 
0.1%
5 36
0.4%
6 15
0.2%
7 34
0.4%
8 20
0.2%
9 16
0.2%
10 15
0.2%
ValueCountFrequency (%)
1630 16
0.2%
1629 1
 
< 0.1%
1515 1
 
< 0.1%
1482 2
 
< 0.1%
1473 1
 
< 0.1%
1420 12
0.1%
1362 1
 
< 0.1%
1359 1
 
< 0.1%
1357 2
 
< 0.1%
1353 3
 
< 0.1%

부번
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct67
Distinct (%)0.7%
Missing539
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean2.466135
Minimum0
Maximum148
Zeros4614
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size85.3 KiB
2023-12-13T03:39:36.576152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10
Maximum148
Range148
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.3591958
Coefficient of variation (CV)2.9841009
Kurtosis119.80673
Mean2.466135
Median Absolute Deviation (MAD)0
Skewness9.3902828
Sum22575
Variance54.157763
MonotonicityNot monotonic
2023-12-13T03:39:36.906310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4614
47.6%
1 1633
 
16.8%
2 777
 
8.0%
3 470
 
4.8%
4 360
 
3.7%
5 270
 
2.8%
6 167
 
1.7%
8 159
 
1.6%
7 148
 
1.5%
10 71
 
0.7%
Other values (57) 485
 
5.0%
(Missing) 539
 
5.6%
ValueCountFrequency (%)
0 4614
47.6%
1 1633
 
16.8%
2 777
 
8.0%
3 470
 
4.8%
4 360
 
3.7%
5 270
 
2.8%
6 167
 
1.7%
7 148
 
1.5%
8 159
 
1.6%
9 68
 
0.7%
ValueCountFrequency (%)
148 1
 
< 0.1%
135 1
 
< 0.1%
134 1
 
< 0.1%
132 1
 
< 0.1%
116 1
 
< 0.1%
115 1
 
< 0.1%
113 1
 
< 0.1%
103 1
 
< 0.1%
99 1
 
< 0.1%
98 3
< 0.1%

동
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct51
Distinct (%)0.6%
Missing539
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean832.26535
Minimum0
Maximum9999
Zeros3042
Zeros (%)31.4%
Negative0
Negative (%)0.0%
Memory size85.3 KiB
2023-12-13T03:39:37.253220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q310
95-th percentile9001
Maximum9999
Range9999
Interquartile range (IQR)10

Descriptive statistics

Standard deviation2581.7825
Coefficient of variation (CV)3.1021147
Kurtosis6.0343593
Mean832.26535
Median Absolute Deviation (MAD)1
Skewness2.8293902
Sum7618557
Variance6665601.1
MonotonicityNot monotonic
2023-12-13T03:39:37.533174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3155
32.5%
0 3042
31.4%
10 1452
15.0%
9001 701
 
7.2%
20 154
 
1.6%
101 129
 
1.3%
2 111
 
1.1%
9002 56
 
0.6%
3 53
 
0.5%
30 29
 
0.3%
Other values (41) 272
 
2.8%
(Missing) 539
 
5.6%
ValueCountFrequency (%)
0 3042
31.4%
1 3155
32.5%
2 111
 
1.1%
3 53
 
0.5%
4 25
 
0.3%
5 17
 
0.2%
6 9
 
0.1%
7 9
 
0.1%
8 5
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
9999 7
 
0.1%
9012 1
 
< 0.1%
9011 1
 
< 0.1%
9008 2
 
< 0.1%
9007 3
 
< 0.1%
9006 5
 
0.1%
9005 4
 
< 0.1%
9004 9
 
0.1%
9003 13
 
0.1%
9002 56
0.6%

호
Text

MISSING 

Distinct223
Distinct (%)2.4%
Missing539
Missing (%)5.6%
Memory size75.9 KiB
2023-12-13T03:39:37.867851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9542277
Min length1

Characters and Unicode

Total characters27043
Distinct characters11
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

Unique129 ?
Unique (%)1.4%

Sample

1st row202
2nd row101
3rd row201
4th row301
5th row8101
ValueCountFrequency (%)
101 4071
44.5%
102 1272
 
13.9%
201 906
 
9.9%
103 539
 
5.9%
301 306
 
3.3%
104 273
 
3.0%
0 260
 
2.8%
8101 239
 
2.6%
202 153
 
1.7%
105 153
 
1.7%
Other values (213) 982
 
10.7%
2023-12-13T03:39:38.509490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12813
47.4%
0 8887
32.9%
2 2804
 
10.4%
3 1051
 
3.9%
4 499
 
1.8%
8 377
 
1.4%
5 269
 
1.0%
6 139
 
0.5%
7 91
 
0.3%
9 67
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26997
99.8%
Other Letter 46
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12813
47.5%
0 8887
32.9%
2 2804
 
10.4%
3 1051
 
3.9%
4 499
 
1.8%
8 377
 
1.4%
5 269
 
1.0%
6 139
 
0.5%
7 91
 
0.3%
9 67
 
0.2%
Other Letter
ValueCountFrequency (%)
부 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26997
99.8%
Hangul 46
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12813
47.5%
0 8887
32.9%
2 2804
 
10.4%
3 1051
 
3.9%
4 499
 
1.8%
8 377
 
1.4%
5 269
 
1.0%
6 139
 
0.5%
7 91
 
0.3%
9 67
 
0.2%
Hangul
ValueCountFrequency (%)
부 46
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26997
99.8%
Hangul 46
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12813
47.5%
0 8887
32.9%
2 2804
 
10.4%
3 1051
 
3.9%
4 499
 
1.8%
8 377
 
1.4%
5 269
 
1.0%
6 139
 
0.5%
7 91
 
0.3%
9 67
 
0.2%
Hangul
ValueCountFrequency (%)
부 46
100.0%

물건지
Text

MISSING 

Distinct7910
Distinct (%)86.4%
Missing539
Missing (%)5.6%
Memory size75.9 KiB
2023-12-13T03:39:39.136304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length31
Mean length26.461438
Min length18

Characters and Unicode

Total characters242228
Distinct characters167
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7337 ?
Unique (%)80.2%

Sample

1st row충청북도 증평군 증평읍 신동리 651 10동 202호
2nd row[ 광장로 110-1 ] 0001동 0101호
3rd row[ 광장로 110-1 ] 0001동 0201호
4th row[ 광장로 110-1 ] 0001동 0301호
5th row[ 광장로 110-1 ] 0001동 8101호
ValueCountFrequency (%)
7504
 
12.7%
충청북도 5402
 
9.2%
증평군 5402
 
9.2%
증평읍 3888
 
6.6%
101호 2349
 
4.0%
1동 2201
 
3.7%
0101호 1722
 
2.9%
0000동 1632
 
2.8%
도안면 1514
 
2.6%
0001동 954
 
1.6%
Other values (2433) 26297
44.7%
2023-12-13T03:39:40.025823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49711
20.5%
0 29291
 
12.1%
1 24680
 
10.2%
증 9783
 
4.0%
평 9579
 
4.0%
호 8978
 
3.7%
동 8517
 
3.5%
도 7148
 
3.0%
2 7121
 
2.9%
청 5511
 
2.3%
Other values (157) 81909
33.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99780
41.2%
Decimal Number 81731
33.7%
Space Separator 49711
20.5%
Close Punctuation 3752
 
1.5%
Open Punctuation 3752
 
1.5%
Dash Punctuation 3502
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
증 9783
 
9.8%
평 9579
 
9.6%
호 8978
 
9.0%
동 8517
 
8.5%
도 7148
 
7.2%
청 5511
 
5.5%
충 5509
 
5.5%
리 5434
 
5.4%
북 5434
 
5.4%
군 5403
 
5.4%
Other values (143) 28484
28.5%
Decimal Number
ValueCountFrequency (%)
0 29291
35.8%
1 24680
30.2%
2 7121
 
8.7%
3 4180
 
5.1%
4 3210
 
3.9%
5 3042
 
3.7%
9 2752
 
3.4%
6 2607
 
3.2%
7 2459
 
3.0%
8 2389
 
2.9%
Space Separator
ValueCountFrequency (%)
49711
100.0%
Close Punctuation
ValueCountFrequency (%)
] 3752
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 3752
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3502
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 142448
58.8%
Hangul 99780
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
증 9783
 
9.8%
평 9579
 
9.6%
호 8978
 
9.0%
동 8517
 
8.5%
도 7148
 
7.2%
청 5511
 
5.5%
충 5509
 
5.5%
리 5434
 
5.4%
북 5434
 
5.4%
군 5403
 
5.4%
Other values (143) 28484
28.5%
Common
ValueCountFrequency (%)
49711
34.9%
0 29291
20.6%
1 24680
17.3%
2 7121
 
5.0%
3 4180
 
2.9%
] 3752
 
2.6%
[ 3752
 
2.6%
- 3502
 
2.5%
4 3210
 
2.3%
5 3042
 
2.1%
Other values (4) 10207
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142448
58.8%
Hangul 99780
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49711
34.9%
0 29291
20.6%
1 24680
17.3%
2 7121
 
5.0%
3 4180
 
2.9%
] 3752
 
2.6%
[ 3752
 
2.6%
- 3502
 
2.5%
4 3210
 
2.3%
5 3042
 
2.1%
Other values (4) 10207
 
7.2%
Hangul
ValueCountFrequency (%)
증 9783
 
9.8%
평 9579
 
9.6%
호 8978
 
9.0%
동 8517
 
8.5%
도 7148
 
7.2%
청 5511
 
5.5%
충 5509
 
5.5%
리 5434
 
5.4%
북 5434
 
5.4%
군 5403
 
5.4%
Other values (143) 28484
28.5%

시가표준액
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct7327
Distinct (%)80.0%
Missing539
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean80851036
Minimum17280
Maximum1.8388938 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.3 KiB
2023-12-13T03:39:40.275991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17280
5-th percentile570000
Q13168000
median17174800
Q363241178
95-th percentile2.950748 × 108
Maximum1.8388938 × 1010
Range1.838892 × 1010
Interquartile range (IQR)60073178

Descriptive statistics

Standard deviation3.7027533 × 108
Coefficient of variation (CV)4.5797228
Kurtosis1042.7788
Mean80851036
Median Absolute Deviation (MAD)16022800
Skewness26.548531
Sum7.4011039 × 1011
Variance1.3710382 × 1017
MonotonicityNot monotonic
2023-12-13T03:39:40.557847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5631240 34
 
0.4%
936000 24
 
0.2%
3968760 22
 
0.2%
846000 20
 
0.2%
756000 19
 
0.2%
6795000 19
 
0.2%
1026000 16
 
0.2%
2628000 14
 
0.1%
38734920 14
 
0.1%
1509820 14
 
0.1%
Other values (7317) 8958
92.4%
(Missing) 539
 
5.6%
ValueCountFrequency (%)
17280 1
< 0.1%
23400 1
< 0.1%
34560 1
< 0.1%
45600 1
< 0.1%
46000 1
< 0.1%
47520 1
< 0.1%
51300 1
< 0.1%
51840 1
< 0.1%
52500 1
< 0.1%
55000 1
< 0.1%
ValueCountFrequency (%)
18388937670 1
< 0.1%
15144452890 1
< 0.1%
7697112800 1
< 0.1%
7529236780 2
< 0.1%
6337757250 1
< 0.1%
5685796440 1
< 0.1%
5262517590 1
< 0.1%
4982998330 1
< 0.1%
4860211020 1
< 0.1%
4685683860 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct5049
Distinct (%)55.2%
Missing539
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean226.49302
Minimum0.54
Maximum33501.435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.3 KiB
2023-12-13T03:39:40.816857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.54
5-th percentile12.872
Q140
median95.5
Q3195
95-th percentile744.272
Maximum33501.435
Range33500.895
Interquartile range (IQR)155

Descriptive statistics

Standard deviation782.50292
Coefficient of variation (CV)3.4548655
Kurtosis632.57823
Mean226.49302
Median Absolute Deviation (MAD)67.5
Skewness20.489133
Sum2073317.1
Variance612310.82
MonotonicityNot monotonic
2023-12-13T03:39:41.092432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.0 526
 
5.4%
27.0 82
 
0.8%
198.0 40
 
0.4%
12.0 39
 
0.4%
33.72 35
 
0.4%
96.0 33
 
0.3%
36.0 32
 
0.3%
9.0 31
 
0.3%
15.0 31
 
0.3%
66.0 30
 
0.3%
Other values (5039) 8275
85.4%
(Missing) 539
 
5.6%
ValueCountFrequency (%)
0.54 1
 
< 0.1%
1.0 5
0.1%
1.2 3
< 0.1%
1.32 1
 
< 0.1%
1.4 1
 
< 0.1%
1.44 5
0.1%
1.56 1
 
< 0.1%
1.6 1
 
< 0.1%
1.8472 1
 
< 0.1%
1.95 1
 
< 0.1%
ValueCountFrequency (%)
33501.435 1
< 0.1%
27590.55 1
< 0.1%
16633.56 1
< 0.1%
15581.2 1
< 0.1%
15241.37 2
< 0.1%
13390.63 1
< 0.1%
13346.94 1
< 0.1%
12183.37 1
< 0.1%
11297.25 1
< 0.1%
10539.1 1
< 0.1%

기준일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size75.9 KiB
2022-06-01
9154 
<NA>
 
539

Length

Max length10
Median length10
Mean length9.6663572
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-06-01
2nd row2022-06-01
3rd row2022-06-01
4th row2022-06-01
5th row2022-06-01

Common Values

ValueCountFrequency (%)
2022-06-01 9154
94.4%
<NA> 539
 
5.6%

Length

2023-12-13T03:39:41.350003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:39:41.512155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-06-01 9154
94.4%
na 539
 
5.6%

Interactions

2023-12-13T03:39:30.146087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:24.020980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:25.090483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:26.411100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:27.649575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:28.960028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:30.387524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:24.193550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:25.332537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:26.613766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:27.876427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:29.169173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:30.610472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:24.369730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:25.600862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:26.828498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:28.091674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:29.361804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:30.812927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:24.570935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:25.791145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:27.019390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:28.288357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:29.554990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:30.994891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:24.733398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:25.985700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:27.223581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:28.500005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:29.741725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:31.214021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:24.921781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:26.195394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:27.430796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:28.741730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:39:29.933588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:39:41.638227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번동시가표준액연면적
법정동1.0000.6930.1820.3580.0840.0550.0360.035
법정리0.6931.0000.3400.6700.2220.2040.0950.106
특수지0.1820.3401.0000.2360.3000.0990.0000.000
본번0.3580.6700.2361.0000.1440.2060.1590.172
부번0.0840.2220.3000.1441.0000.0650.0000.000
동0.0550.2040.0990.2060.0651.0000.0000.000
시가표준액0.0360.0950.0000.1590.0000.0001.0000.988
연면적0.0350.1060.0000.1720.0000.0000.9881.000
2023-12-13T03:39:41.864493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특수지시군구명법정동과세년도자치단체코드시도명기준일자
특수지1.0001.0000.1171.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.0001.0001.000
법정동0.1171.0001.0001.0001.0001.0001.000
과세년도1.0001.0001.0001.0001.0001.0001.000
자치단체코드1.0001.0001.0001.0001.0001.0001.000
시도명1.0001.0001.0001.0001.0001.0001.000
기준일자1.0001.0001.0001.0001.0001.0001.000
2023-12-13T03:39:42.115620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정리본번부번동시가표준액연면적시도명시군구명자치단체코드과세년도법정동특수지기준일자
법정리1.0000.2600.108-0.070-0.0390.0031.0001.0001.0001.0000.5400.2611.000
본번0.2601.000-0.112-0.0720.0340.0291.0001.0001.0001.0000.2740.1811.000
부번0.108-0.1121.000-0.055-0.048-0.0881.0001.0001.0001.0000.0650.2301.000
동-0.070-0.072-0.0551.000-0.138-0.2101.0001.0001.0001.0000.0390.0711.000
시가표준액-0.0390.034-0.048-0.1381.0000.7021.0001.0001.0001.0000.0380.0001.000
연면적0.0030.029-0.088-0.2100.7021.0001.0001.0001.0001.0000.0380.0001.000
시도명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시군구명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
자치단체코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
과세년도1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
법정동0.5400.2740.0650.0390.0380.0381.0001.0001.0001.0001.0000.1171.000
특수지0.2610.1810.2300.0710.0000.0001.0001.0001.0001.0000.1171.0001.000
기준일자1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-13T03:39:31.486027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:39:31.881895image/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.
2023-12-13T03:39:32.308790image/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

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
0충청북도증평군437452022250251651010202충청북도 증평군 증평읍 신동리 651 10동 202호13892000173.652022-06-01
1충청북도증평군4374520222502318301101[ 광장로 110-1 ] 0001동 0101호3080215081.662022-06-01
2충청북도증평군4374520222502318301201[ 광장로 110-1 ] 0001동 0201호2678448081.662022-06-01
3충청북도증평군4374520222502318301301[ 광장로 110-1 ] 0001동 0301호2678448081.662022-06-01
4충청북도증평군43745202225023183018101[ 광장로 110-1 ] 0001동 8101호2511168095.72022-06-01
5충청북도증평군43745202225030157260101[ 초중8길 25-2 ] 0000동 0101호4177800099.02022-06-01
6충청북도증평군43745202225033185421105충청북도 증평군 증평읍 미암리 854-2 1동 105호348960043.622022-06-01
7충청북도증평군43745202225033185421102충청북도 증평군 증평읍 미암리 854-2 1동 102호552960069.122022-06-01
8충청북도증평군43745202225033185421103충청북도 증평군 증평읍 미암리 854-2 1동 103호8548800106.862022-06-01
9충청북도증평군43745202225033185421104충청북도 증평군 증평읍 미암리 854-2 1동 104호2304002.882022-06-01
시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자
9683<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9684<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9685<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9686<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9687<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9688<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9689<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9690<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9691<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9692<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

시도명시군구명자치단체코드과세년도법정동법정리특수지본번부번동호물건지시가표준액연면적기준일자# duplicates
85<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>539
19충청북도증평군43745202225031167301101충청북도 증평군 증평읍 연탄리 673 1동 101호679500090.62022-06-0114
51충청북도증평군43745202225037113101101충청북도 증평군 증평읍 남차리 131 1동 101호33264000264.02022-06-017
52충청북도증평군43745202225037113111101충청북도 증평군 증평읍 남차리 131-1 1동 101호33264000264.02022-06-017
72충청북도증평군43745202231026122201101[ 석곡길 94-20 ] 0001동 0101호18371220592.622022-06-016
16충청북도증평군43745202225031160211101충청북도 증평군 증평읍 연탄리 602-1 1동 101호648000086.42022-06-014
37충청북도증평군4374520222503413711101충청북도 증평군 증평읍 사곡리 37-1 1동 101호9408000224.02022-06-014
55충청북도증평군437452022250391901101충청북도 증평군 증평읍 죽리 9 1동 101호33473250230.852022-06-014
1충청북도증평군4374520222502811297010충청북도 증평군 증평읍 증천리 1297 1동24038910116.132022-06-013
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