Overview

Dataset statistics

Number of variables15
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows220
Duplicate rows (%)2.2%
Total size in memory1.3 MiB
Average record size in memory138.0 B

Variable types

Categorical6
Numeric7
Text2

Dataset

Description남양주시의 과세년도별 법정동, 법정리, 특수지, 본번, 부번, 동, 호, 물건지에 따른 시가표준액, 연면적, 결정일자 데이터입니다.
Author경기도 남양주시
URLhttps://www.data.go.kr/data/15102956/fileData.do

Alerts

시군구명 has constant value ""Constant
자치단체 has constant value ""Constant
과세연도 has constant value ""Constant
결정일자 has constant value ""Constant
데이터기준일자 has constant value ""Constant
Dataset has 220 (2.2%) duplicate rowsDuplicates
법정동 is highly overall correlated with 법정리High correlation
법정리 is highly overall correlated with 법정동 and 1 other fieldsHigh correlation
본번 is highly overall correlated with 법정리High correlation
건물시가표준액 is highly overall correlated with 연면적High correlation
연면적 is highly overall correlated with 건물시가표준액High correlation
특수지 is highly imbalanced (94.5%)Imbalance
건물시가표준액 is highly skewed (γ1 = 62.74499563)Skewed
연면적 is highly skewed (γ1 = 30.5395072)Skewed
법정리 has 5041 (50.4%) zerosZeros
부번 has 3521 (35.2%) zerosZeros
건물동 has 562 (5.6%) zerosZeros
건물시가표준액 has 198 (2.0%) zerosZeros
연면적 has 198 (2.0%) zerosZeros

Reproduction

Analysis started2024-03-14 08:57:15.079573
Analysis finished2024-03-14 08:57:30.244760
Duration15.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구명
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남양주시 10000
100.0%

Length

2024-03-14T17:57:30.434686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:57:30.723621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남양주시 10000
100.0%

자치단체
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
41360
10000 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41360
2nd row41360
3rd row41360
4th row41360
5th row41360

Common Values

ValueCountFrequency (%)
41360 10000
100.0%

Length

2024-03-14T17:57:31.026441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:57:31.315149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41360 10000
100.0%

과세연도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 10000
100.0%

Length

2024-03-14T17:57:31.623714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:57:31.911278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 10000
100.0%

법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.3152
Minimum101
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:57:32.197314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile102
Q1112
median112
Q3256
95-th percentile340
Maximum360
Range259
Interquartile range (IQR)144

Descriptive statistics

Standard deviation84.723442
Coefficient of variation (CV)0.44752583
Kurtosis-1.427459
Mean189.3152
Median Absolute Deviation (MAD)11
Skewness0.30273696
Sum1893152
Variance7178.0617
MonotonicityNot monotonic
2024-03-14T17:57:32.565228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
112 3305
33.1%
256 2078
20.8%
250 1089
 
10.9%
259 894
 
8.9%
340 733
 
7.3%
101 469
 
4.7%
102 435
 
4.3%
103 350
 
3.5%
104 205
 
2.1%
360 165
 
1.7%
Other values (5) 277
 
2.8%
ValueCountFrequency (%)
101 469
 
4.7%
102 435
 
4.3%
103 350
 
3.5%
104 205
 
2.1%
105 87
 
0.9%
106 86
 
0.9%
108 92
 
0.9%
109 8
 
0.1%
110 4
 
< 0.1%
112 3305
33.1%
ValueCountFrequency (%)
360 165
 
1.7%
340 733
 
7.3%
259 894
 
8.9%
256 2078
20.8%
250 1089
 
10.9%
112 3305
33.1%
110 4
 
< 0.1%
109 8
 
0.1%
108 92
 
0.9%
106 86
 
0.9%

법정리
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.0125
Minimum0
Maximum30
Zeros5041
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:57:32.919657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q324
95-th percentile28
Maximum30
Range30
Interquartile range (IQR)24

Descriptive statistics

Standard deviation12.261957
Coefficient of variation (CV)1.0207664
Kurtosis-1.8900462
Mean12.0125
Median Absolute Deviation (MAD)0
Skewness0.091149303
Sum120125
Variance150.35558
MonotonicityNot monotonic
2024-03-14T17:57:33.271280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 5041
50.4%
21 1081
 
10.8%
25 705
 
7.0%
22 663
 
6.6%
23 534
 
5.3%
24 464
 
4.6%
26 443
 
4.4%
27 342
 
3.4%
28 293
 
2.9%
30 253
 
2.5%
ValueCountFrequency (%)
0 5041
50.4%
21 1081
 
10.8%
22 663
 
6.6%
23 534
 
5.3%
24 464
 
4.6%
25 705
 
7.0%
26 443
 
4.4%
27 342
 
3.4%
28 293
 
2.9%
29 181
 
1.8%
ValueCountFrequency (%)
30 253
 
2.5%
29 181
 
1.8%
28 293
 
2.9%
27 342
 
3.4%
26 443
4.4%
25 705
7.0%
24 464
4.6%
23 534
5.3%
22 663
6.6%
21 1081
10.8%

특수지
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9937 
2
 
63

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 9937
99.4%
2 63
 
0.6%

Length

2024-03-14T17:57:33.658425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:57:33.956272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9937
99.4%
2 63
 
0.6%

본번
Real number (ℝ)

HIGH CORRELATION 

Distinct1160
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2084.1732
Minimum1
Maximum6251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:57:34.281125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52.95
Q1283
median584
Q36052
95-th percentile6183
Maximum6251
Range6250
Interquartile range (IQR)5769

Descriptive statistics

Standard deviation2555.2307
Coefficient of variation (CV)1.2260165
Kurtosis-1.1440394
Mean2084.1732
Median Absolute Deviation (MAD)407.5
Skewness0.87879659
Sum20841732
Variance6529204.1
MonotonicityNot monotonic
2024-03-14T17:57:34.718035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6143 690
 
6.9%
6245 198
 
2.0%
6144 192
 
1.9%
6250 169
 
1.7%
6089 99
 
1.0%
579 85
 
0.9%
6028 80
 
0.8%
6073 76
 
0.8%
6052 75
 
0.8%
6058 72
 
0.7%
Other values (1150) 8264
82.6%
ValueCountFrequency (%)
1 14
0.1%
2 9
0.1%
3 12
0.1%
4 19
0.2%
5 7
 
0.1%
6 14
0.1%
7 10
0.1%
8 12
0.1%
9 3
 
< 0.1%
10 19
0.2%
ValueCountFrequency (%)
6251 4
 
< 0.1%
6250 169
1.7%
6246 3
 
< 0.1%
6245 198
2.0%
6243 2
 
< 0.1%
6240 3
 
< 0.1%
6239 2
 
< 0.1%
6235 3
 
< 0.1%
6234 4
 
< 0.1%
6225 2
 
< 0.1%

부번
Real number (ℝ)

ZEROS 

Distinct109
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0352
Minimum0
Maximum257
Zeros3521
Zeros (%)35.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:57:35.142984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile28
Maximum257
Range257
Interquartile range (IQR)5

Descriptive statistics

Standard deviation14.496799
Coefficient of variation (CV)2.4020412
Kurtosis49.169652
Mean6.0352
Median Absolute Deviation (MAD)2
Skewness5.807989
Sum60352
Variance210.15718
MonotonicityNot monotonic
2024-03-14T17:57:35.581232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3521
35.2%
1 1478
14.8%
2 977
 
9.8%
3 813
 
8.1%
4 500
 
5.0%
5 336
 
3.4%
6 272
 
2.7%
7 209
 
2.1%
10 178
 
1.8%
8 169
 
1.7%
Other values (99) 1547
15.5%
ValueCountFrequency (%)
0 3521
35.2%
1 1478
14.8%
2 977
 
9.8%
3 813
 
8.1%
4 500
 
5.0%
5 336
 
3.4%
6 272
 
2.7%
7 209
 
2.1%
8 169
 
1.7%
9 163
 
1.6%
ValueCountFrequency (%)
257 1
 
< 0.1%
203 5
0.1%
152 1
 
< 0.1%
151 4
< 0.1%
143 1
 
< 0.1%
139 1
 
< 0.1%
138 1
 
< 0.1%
131 2
 
< 0.1%
129 1
 
< 0.1%
127 1
 
< 0.1%

건물동
Real number (ℝ)

ZEROS 

Distinct83
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean671.5646
Minimum0
Maximum9999
Zeros562
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:57:36.000815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile9001
Maximum9999
Range9999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2337.9108
Coefficient of variation (CV)3.4812895
Kurtosis8.9357713
Mean671.5646
Median Absolute Deviation (MAD)0
Skewness3.2864115
Sum6715646
Variance5465826.9
MonotonicityNot monotonic
2024-03-14T17:57:36.611884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7461
74.6%
2 643
 
6.4%
0 562
 
5.6%
9001 430
 
4.3%
3 175
 
1.8%
101 153
 
1.5%
9991 62
 
0.6%
4 61
 
0.6%
9002 46
 
0.5%
7001 35
 
0.4%
Other values (73) 372
 
3.7%
ValueCountFrequency (%)
0 562
 
5.6%
1 7461
74.6%
2 643
 
6.4%
3 175
 
1.8%
4 61
 
0.6%
5 31
 
0.3%
6 14
 
0.1%
7 7
 
0.1%
8 10
 
0.1%
9 6
 
0.1%
ValueCountFrequency (%)
9999 25
0.2%
9997 3
 
< 0.1%
9996 4
 
< 0.1%
9995 1
 
< 0.1%
9994 3
 
< 0.1%
9993 5
 
0.1%
9992 18
 
0.2%
9991 62
0.6%
9202 1
 
< 0.1%
9018 1
 
< 0.1%
Distinct1632
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T17:57:37.890214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.1085
Min length1

Characters and Unicode

Total characters31085
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1323 ?
Unique (%)13.2%

Sample

1st row101
2nd rowCB9-75
3rd rowAA7-47
4th row808
5th row208
ValueCountFrequency (%)
101 2360
23.6%
0 1265
 
12.7%
201 614
 
6.1%
102 597
 
6.0%
301 272
 
2.7%
103 225
 
2.2%
8101 212
 
2.1%
202 150
 
1.5%
401 146
 
1.5%
9999 126
 
1.3%
Other values (1622) 4033
40.3%
2024-03-14T17:57:39.600071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9575
30.8%
0 8537
27.5%
2 2994
 
9.6%
3 1701
 
5.5%
4 1103
 
3.5%
9 984
 
3.2%
8 968
 
3.1%
5 928
 
3.0%
- 762
 
2.5%
A 753
 
2.4%
Other values (12) 2780
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28142
90.5%
Uppercase Letter 2179
 
7.0%
Dash Punctuation 762
 
2.5%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9575
34.0%
0 8537
30.3%
2 2994
 
10.6%
3 1701
 
6.0%
4 1103
 
3.9%
9 984
 
3.5%
8 968
 
3.4%
5 928
 
3.3%
6 731
 
2.6%
7 621
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
A 753
34.6%
C 389
17.9%
B 293
 
13.4%
F 279
 
12.8%
D 190
 
8.7%
S 137
 
6.3%
E 86
 
3.9%
R 29
 
1.3%
P 23
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
e 1
50.0%
b 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28904
93.0%
Latin 2181
 
7.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9575
33.1%
0 8537
29.5%
2 2994
 
10.4%
3 1701
 
5.9%
4 1103
 
3.8%
9 984
 
3.4%
8 968
 
3.3%
5 928
 
3.2%
- 762
 
2.6%
6 731
 
2.5%
Latin
ValueCountFrequency (%)
A 753
34.5%
C 389
17.8%
B 293
 
13.4%
F 279
 
12.8%
D 190
 
8.7%
S 137
 
6.3%
E 86
 
3.9%
R 29
 
1.3%
P 23
 
1.1%
e 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31085
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9575
30.8%
0 8537
27.5%
2 2994
 
9.6%
3 1701
 
5.5%
4 1103
 
3.5%
9 984
 
3.2%
8 968
 
3.1%
5 928
 
3.0%
- 762
 
2.5%
A 753
 
2.4%
Other values (12) 2780
 
8.9%
Distinct9369
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-03-14T17:57:41.216867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length33
Mean length27.5507
Min length16

Characters and Unicode

Total characters275507
Distinct characters89
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8831 ?
Unique (%)88.3%

Sample

1st row경기도 남양주시 화도읍 가곡리 247-25 1동 101호
2nd row경기도 남양주시 다산동 6143 1동 CB9-75호
3rd row경기도 남양주시 다산동 6143 1동 AA7-47호
4th row경기도 남양주시 평내동 579-2 1동 808호
5th row경기도 남양주시 다산동 667-4 1동 208호
ValueCountFrequency (%)
경기도 10000
15.8%
남양주시 10000
15.8%
1동 7461
 
11.8%
다산동 3305
 
5.2%
101호 2360
 
3.7%
화도읍 2078
 
3.3%
와부읍 1089
 
1.7%
진건읍 894
 
1.4%
수동면 733
 
1.2%
6143 690
 
1.1%
Other values (5767) 24585
38.9%
2024-03-14T17:57:43.237592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53195
19.3%
1 24663
 
9.0%
15212
 
5.5%
12324
 
4.5%
0 12181
 
4.4%
10155
 
3.7%
10126
 
3.7%
10011
 
3.6%
10000
 
3.6%
10000
 
3.6%
Other values (79) 107640
39.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133488
48.5%
Decimal Number 79405
28.8%
Space Separator 53195
 
19.3%
Dash Punctuation 7241
 
2.6%
Uppercase Letter 2178
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
15212
11.4%
12324
 
9.2%
10155
 
7.6%
10126
 
7.6%
10011
 
7.5%
10000
 
7.5%
10000
 
7.5%
10000
 
7.5%
9204
 
6.9%
4959
 
3.7%
Other values (58) 31497
23.6%
Decimal Number
ValueCountFrequency (%)
1 24663
31.1%
0 12181
15.3%
2 8256
 
10.4%
6 6808
 
8.6%
3 6429
 
8.1%
4 5931
 
7.5%
5 4598
 
5.8%
9 3937
 
5.0%
8 3471
 
4.4%
7 3131
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
A 753
34.6%
C 389
17.9%
B 293
 
13.5%
F 278
 
12.8%
D 190
 
8.7%
S 137
 
6.3%
E 86
 
3.9%
R 29
 
1.3%
P 23
 
1.1%
Space Separator
ValueCountFrequency (%)
53195
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 139841
50.8%
Hangul 133488
48.5%
Latin 2178
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
15212
11.4%
12324
 
9.2%
10155
 
7.6%
10126
 
7.6%
10011
 
7.5%
10000
 
7.5%
10000
 
7.5%
10000
 
7.5%
9204
 
6.9%
4959
 
3.7%
Other values (58) 31497
23.6%
Common
ValueCountFrequency (%)
53195
38.0%
1 24663
17.6%
0 12181
 
8.7%
2 8256
 
5.9%
- 7241
 
5.2%
6 6808
 
4.9%
3 6429
 
4.6%
4 5931
 
4.2%
5 4598
 
3.3%
9 3937
 
2.8%
Other values (2) 6602
 
4.7%
Latin
ValueCountFrequency (%)
A 753
34.6%
C 389
17.9%
B 293
 
13.5%
F 278
 
12.8%
D 190
 
8.7%
S 137
 
6.3%
E 86
 
3.9%
R 29
 
1.3%
P 23
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142019
51.5%
Hangul 133488
48.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53195
37.5%
1 24663
17.4%
0 12181
 
8.6%
2 8256
 
5.8%
- 7241
 
5.1%
6 6808
 
4.8%
3 6429
 
4.5%
4 5931
 
4.2%
5 4598
 
3.2%
9 3937
 
2.8%
Other values (11) 8780
 
6.2%
Hangul
ValueCountFrequency (%)
15212
11.4%
12324
 
9.2%
10155
 
7.6%
10126
 
7.6%
10011
 
7.5%
10000
 
7.5%
10000
 
7.5%
10000
 
7.5%
9204
 
6.9%
4959
 
3.7%
Other values (58) 31497
23.6%

건물시가표준액
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct7784
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78763270
Minimum0
Maximum2.953599 × 1010
Zeros198
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:57:43.654347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1295725
Q113296243
median41300820
Q388442931
95-th percentile2.1948445 × 108
Maximum2.953599 × 1010
Range2.953599 × 1010
Interquartile range (IQR)75146688

Descriptive statistics

Standard deviation3.505135 × 108
Coefficient of variation (CV)4.4502151
Kurtosis5053.7137
Mean78763270
Median Absolute Deviation (MAD)32762900
Skewness62.744996
Sum7.876327 × 1011
Variance1.2285971 × 1017
MonotonicityNot monotonic
2024-03-14T17:57:44.100327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 198
 
2.0%
9482938 71
 
0.7%
36612877 65
 
0.7%
6620828 41
 
0.4%
23161131 33
 
0.3%
61482960 32
 
0.3%
42517110 29
 
0.3%
16046561 28
 
0.3%
49248937 28
 
0.3%
55686262 26
 
0.3%
Other values (7774) 9449
94.5%
ValueCountFrequency (%)
0 198
2.0%
36540 6
 
0.1%
40600 4
 
< 0.1%
51480 1
 
< 0.1%
70560 1
 
< 0.1%
84600 1
 
< 0.1%
108000 1
 
< 0.1%
120000 1
 
< 0.1%
138600 1
 
< 0.1%
145800 1
 
< 0.1%
ValueCountFrequency (%)
29535990412 1
< 0.1%
9131504755 1
< 0.1%
5861392200 1
< 0.1%
5599392720 1
< 0.1%
4874538690 1
< 0.1%
3861721572 1
< 0.1%
2860634022 1
< 0.1%
2378167965 1
< 0.1%
2252020940 1
< 0.1%
2234422800 1
< 0.1%

연면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct6075
Distinct (%)60.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.4104
Minimum0
Maximum24233.665
Zeros198
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T17:57:44.527564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.0695
Q137.81
median83.9033
Q3185.20237
95-th percentile456.44084
Maximum24233.665
Range24233.665
Interquartile range (IQR)147.39237

Descriptive statistics

Standard deviation408.21113
Coefficient of variation (CV)2.6436764
Kurtosis1454.8996
Mean154.4104
Median Absolute Deviation (MAD)56.9033
Skewness30.539507
Sum1544104
Variance166636.33
MonotonicityNot monotonic
2024-03-14T17:57:44.968935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198.0 231
 
2.3%
0.0 198
 
2.0%
18.0 139
 
1.4%
14.4557 71
 
0.7%
40.1457 65
 
0.7%
99.0 59
 
0.6%
10.8007 41
 
0.4%
66.0 35
 
0.4%
249.6 33
 
0.3%
27.0 33
 
0.3%
Other values (6065) 9095
91.0%
ValueCountFrequency (%)
0.0 198
2.0%
0.1 10
 
0.1%
1.0 15
 
0.1%
1.08 1
 
< 0.1%
1.32 1
 
< 0.1%
1.33 1
 
< 0.1%
1.38 2
 
< 0.1%
1.44 1
 
< 0.1%
1.47 1
 
< 0.1%
1.5 1
 
< 0.1%
ValueCountFrequency (%)
24233.6646 1
< 0.1%
12279.37 1
< 0.1%
11683.69 1
< 0.1%
10854.43 1
< 0.1%
10619.91 1
< 0.1%
5990.23 1
< 0.1%
5690.72 1
< 0.1%
4960.95 1
< 0.1%
3576.0082 1
< 0.1%
3492.0 1
< 0.1%

결정일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-06-01
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-01
2nd row2023-06-01
3rd row2023-06-01
4th row2023-06-01
5th row2023-06-01

Common Values

ValueCountFrequency (%)
2023-06-01 10000
100.0%

Length

2024-03-14T17:57:45.380397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:57:45.681626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-01 10000
100.0%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-22
10000 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-01-22
2nd row2024-01-22
3rd row2024-01-22
4th row2024-01-22
5th row2024-01-22

Common Values

ValueCountFrequency (%)
2024-01-22 10000
100.0%

Length

2024-03-14T17:57:46.001371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T17:57:46.291646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-01-22 10000
100.0%

Interactions

2024-03-14T17:57:27.396034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:16.131667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:17.967290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:19.813889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:21.678486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:23.681317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:25.523143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:27.655275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:16.389085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:18.227366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:20.082123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:21.933869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:23.943000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:25.791987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:27.920710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:16.654847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:18.493088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:20.349663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:22.198681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:24.207546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:26.063006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:28.189714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:16.919529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:18.759954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:20.615846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:22.462243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:24.470850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:26.331140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:28.449354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:17.174252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:19.022074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:20.877537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:22.717850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:24.730439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:26.594767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:28.709280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:17.433724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:19.281386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:21.144399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:22.973696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:24.989514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:26.860133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:28.980737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:17.709116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:19.554741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:21.416908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:23.241686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:25.260815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T17:57:27.133876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T17:57:46.476491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리특수지본번부번건물동건물시가표준액연면적
법정동1.0000.9130.0890.7300.1280.1050.0310.000
법정리0.9131.0000.0920.7220.1110.0870.0310.027
특수지0.0890.0921.0000.0880.0800.0350.0890.137
본번0.7300.7220.0881.0000.5790.1330.0000.000
부번0.1280.1110.0800.5791.0000.0420.0360.000
건물동0.1050.0870.0350.1330.0421.0000.0000.000
건물시가표준액0.0310.0310.0890.0000.0360.0001.0000.925
연면적0.0000.0270.1370.0000.0000.0000.9251.000
2024-03-14T17:57:46.776937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
법정동법정리본번부번건물동건물시가표준액연면적특수지
법정동1.0000.851-0.4380.1860.071-0.0870.1700.059
법정리0.8511.000-0.5770.2470.075-0.0660.2600.068
본번-0.438-0.5771.000-0.401-0.0600.054-0.2920.066
부번0.1860.247-0.4011.000-0.0360.0050.1660.060
건물동0.0710.075-0.060-0.0361.000-0.043-0.0300.037
건물시가표준액-0.087-0.0660.0540.005-0.0431.0000.7180.059
연면적0.1700.260-0.2920.166-0.0300.7181.0000.099
특수지0.0590.0680.0660.0600.0370.0590.0991.000

Missing values

2024-03-14T17:57:29.365930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T17:57:29.977215image/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.

Sample

시군구명자치단체과세연도법정동법정리특수지본번부번건물동건물호물건지명건물시가표준액연면적결정일자데이터기준일자
30463남양주시413602023256231247251101경기도 남양주시 화도읍 가곡리 247-25 1동 101호7524000198.02023-06-012024-01-22
75370남양주시41360202311201614301CB9-75경기도 남양주시 다산동 6143 1동 CB9-75호792690512.08372023-06-012024-01-22
72218남양주시41360202311201614301AA7-47경기도 남양주시 다산동 6143 1동 AA7-47호2319012235.35082023-06-012024-01-22
6358남양주시4136020231020157921808경기도 남양주시 평내동 579-2 1동 808호2006392839.862023-06-012024-01-22
53793남양주시4136020231120166741208경기도 남양주시 다산동 667-4 1동 208호3242820033.092023-06-012024-01-22
8789남양주시41360202310301422110경기도 남양주시 금곡동 422-1 1동6281780102.982023-06-012024-01-22
58982남양주시4136020231050150731101경기도 남양주시 이패동 507-3 1동 101호00.02023-06-012024-01-22
27121남양주시41360202325621138361404경기도 남양주시 화도읍 마석우리 383-6 1동 404호1599540030.182023-06-012024-01-22
33172남양주시413602023256261175110경기도 남양주시 화도읍 답내리 175-1 1동839448015.722023-06-012024-01-22
72017남양주시41360202311201614301AA6-12경기도 남양주시 다산동 6143 1동 AA6-12호5568626261.05952023-06-012024-01-22
시군구명자치단체과세연도법정동법정리특수지본번부번건물동건물호물건지명건물시가표준액연면적결정일자데이터기준일자
60894남양주시41360202311201602809001175경기도 남양주시 다산동 6028 9001동 175호11561959892.87612023-06-012024-01-22
25009남양주시413602023250251798200경기도 남양주시 와부읍 율석리 798-298323700134.692023-06-012024-01-22
27274남양주시41360202325621139211110경기도 남양주시 화도읍 마석우리 392-1 1동 110호3882695849.392023-06-012024-01-22
68012남양주시41360202311201606111116경기도 남양주시 다산동 6061-1 1동 116호7725130464.32023-06-012024-01-22
26343남양주시413602023256211295311301경기도 남양주시 화도읍 마석우리 295-31 1동 301호49606020102.072023-06-012024-01-22
4125남양주시41360202310201140018106경기도 남양주시 평내동 140 1동 8106호357840011.362023-06-012024-01-22
75593남양주시41360202311201614301CC2-26경기도 남양주시 다산동 6143 1동 CC2-26호7558812073.0322023-06-012024-01-22
21550남양주시4136020232502211051718101경기도 남양주시 와부읍 도곡리 1051-7 1동 8101호98916185263.252023-06-012024-01-22
11369남양주시4136020231040182341101경기도 남양주시 일패동 823-4 1동 101호23531430603.372023-06-012024-01-22
764남양주시4136020231010140921101경기도 남양주시 호평동 409-2 1동 101호6658670867.83282023-06-012024-01-22

Duplicate rows

Most frequently occurring

시군구명자치단체과세연도법정동법정리특수지본번부번건물동건물호물건지명건물시가표준액연면적결정일자데이터기준일자# duplicates
39남양주시4136020231020161231101경기도 남양주시 평내동 612-3 1동 101호82753162119.882023-06-012024-01-2213
40남양주시4136020231020161231201경기도 남양주시 평내동 612-3 1동 201호78617500133.252023-06-012024-01-2211
35남양주시4136020231020160401301경기도 남양주시 평내동 604 1동 301호4060001.02023-06-012024-01-227
29남양주시4136020231020158011119경기도 남양주시 평내동 580-1 1동 119호365400.12023-06-012024-01-225
37남양주시41360202310201604018101경기도 남양주시 평내동 604 1동 8101호4060001.02023-06-012024-01-225
4남양주시41360202310101232118101경기도 남양주시 호평동 23-21 1동 8101호2847422011119.932023-06-012024-01-224
6남양주시413602023101012321100101경기도 남양주시 호평동 23-21 100동 101호268200018.02023-06-012024-01-224
30남양주시41360202310201580118201경기도 남양주시 평내동 580-1 1동 8201호406000.12023-06-012024-01-224
158남양주시41360202325625192210경기도 남양주시 화도읍 금남리 92-2 1동61992011.482023-06-012024-01-224
189남양주시413602023259231909219999경기도 남양주시 진건읍 진관리 909-2 1동 9999호00.02023-06-012024-01-224