Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory70.3 B

Variable types

Numeric4
Categorical3
Text1

Alerts

기준년도 has constant value ""Constant
지점 has constant value ""Constant
주소 has constant value ""Constant
전년도개별공시지가(원) is highly overall correlated with 당해년도개별공시지가(원)High correlation
당해년도개별공시지가(원) is highly overall correlated with 전년도개별공시지가(원)High correlation
기본키아이디 has unique valuesUnique
지번명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:02:48.158395
Analysis finished2023-12-10 13:02:50.037258
Duration1.88 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키아이디
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:50.114406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2023-12-10T22:02:50.269753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

기준년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2022
100 

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 100
100.0%

Length

2023-12-10T22:02:50.397327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:50.486174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 100
100.0%

지점
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-1000-0239S-10
100 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-1000-0239S-10
2nd rowA-1000-0239S-10
3rd rowA-1000-0239S-10
4th rowA-1000-0239S-10
5th rowA-1000-0239S-10

Common Values

ValueCountFrequency (%)
A-1000-0239S-10 100
100.0%

Length

2023-12-10T22:02:50.581497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:50.677276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a-1000-0239s-10 100
100.0%

주소
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울 강동구 상일동
100 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울 강동구 상일동
2nd row서울 강동구 상일동
3rd row서울 강동구 상일동
4th row서울 강동구 상일동
5th row서울 강동구 상일동

Common Values

ValueCountFrequency (%)
서울 강동구 상일동 100
100.0%

Length

2023-12-10T22:02:50.788042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:02:50.895527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 100
33.3%
강동구 100
33.3%
상일동 100
33.3%

지번명
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T22:02:51.167008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.91
Min length3

Characters and Unicode

Total characters591
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row[1]
2nd row[2]
3rd row[2-2]
4th row[2-4]
5th row[2-6]
ValueCountFrequency (%)
1 1
 
1.0%
145-2 1
 
1.0%
148-2 1
 
1.0%
148-1 1
 
1.0%
148 1
 
1.0%
147 1
 
1.0%
146 1
 
1.0%
145-10 1
 
1.0%
145-8 1
 
1.0%
145-7 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T22:02:51.625462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 110
18.6%
[ 100
16.9%
] 100
16.9%
- 68
11.5%
2 50
8.5%
4 47
8.0%
6 24
 
4.1%
3 24
 
4.1%
5 23
 
3.9%
8 17
 
2.9%
Other values (3) 28
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 323
54.7%
Open Punctuation 100
 
16.9%
Close Punctuation 100
 
16.9%
Dash Punctuation 68
 
11.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 110
34.1%
2 50
15.5%
4 47
14.6%
6 24
 
7.4%
3 24
 
7.4%
5 23
 
7.1%
8 17
 
5.3%
7 11
 
3.4%
0 10
 
3.1%
9 7
 
2.2%
Open Punctuation
ValueCountFrequency (%)
[ 100
100.0%
Close Punctuation
ValueCountFrequency (%)
] 100
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 591
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 110
18.6%
[ 100
16.9%
] 100
16.9%
- 68
11.5%
2 50
8.5%
4 47
8.0%
6 24
 
4.1%
3 24
 
4.1%
5 23
 
3.9%
8 17
 
2.9%
Other values (3) 28
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 110
18.6%
[ 100
16.9%
] 100
16.9%
- 68
11.5%
2 50
8.5%
4 47
8.0%
6 24
 
4.1%
3 24
 
4.1%
5 23
 
3.9%
8 17
 
2.9%
Other values (3) 28
 
4.7%

전년도개별공시지가(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1441930
Minimum56500
Maximum9868000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:51.784238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum56500
5-th percentile161000
Q1230100
median587500
Q32235000
95-th percentile6525000
Maximum9868000
Range9811500
Interquartile range (IQR)2004900

Descriptive statistics

Standard deviation2015148.8
Coefficient of variation (CV)1.3975358
Kurtosis5.2079459
Mean1441930
Median Absolute Deviation (MAD)408500
Skewness2.3168227
Sum1.44193 × 108
Variance4.0608246 × 1012
MonotonicityNot monotonic
2023-12-10T22:02:51.925743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
2235000 17
 
17.0%
412500 12
 
12.0%
587500 6
 
6.0%
230100 6
 
6.0%
185000 3
 
3.0%
161000 3
 
3.0%
6525000 3
 
3.0%
1720000 2
 
2.0%
177200 2
 
2.0%
558100 2
 
2.0%
Other values (36) 44
44.0%
ValueCountFrequency (%)
56500 2
2.0%
151600 1
 
1.0%
157800 1
 
1.0%
161000 3
3.0%
162700 1
 
1.0%
164300 1
 
1.0%
173300 1
 
1.0%
174100 1
 
1.0%
177200 2
2.0%
179000 2
2.0%
ValueCountFrequency (%)
9868000 1
 
1.0%
8774000 1
 
1.0%
7422000 1
 
1.0%
6580000 1
 
1.0%
6525000 3
 
3.0%
5694000 1
 
1.0%
5215000 2
 
2.0%
2235000 17
17.0%
2102000 1
 
1.0%
2083000 1
 
1.0%

당해년도개별공시지가(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1611083
Minimum57000
Maximum10570000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:52.085334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57000
5-th percentile176030
Q1248400
median653100
Q32455000
95-th percentile7068000
Maximum10570000
Range10513000
Interquartile range (IQR)2206600

Descriptive statistics

Standard deviation2234793.3
Coefficient of variation (CV)1.3871373
Kurtosis5.0287206
Mean1611083
Median Absolute Deviation (MAD)450800
Skewness2.3041541
Sum1.611083 × 108
Variance4.994301 × 1012
MonotonicityNot monotonic
2023-12-10T22:02:52.249787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
2455000 17
 
17.0%
453700 12
 
12.0%
248400 6
 
6.0%
7068000 3
 
3.0%
653100 3
 
3.0%
919000 3
 
3.0%
202300 3
 
3.0%
178100 3
 
3.0%
2180000 2
 
2.0%
5795000 2
 
2.0%
Other values (38) 46
46.0%
ValueCountFrequency (%)
57000 1
 
1.0%
61800 2
2.0%
165700 1
 
1.0%
172800 1
 
1.0%
176200 1
 
1.0%
178100 3
3.0%
179900 1
 
1.0%
190600 1
 
1.0%
194100 2
2.0%
196000 1
 
1.0%
ValueCountFrequency (%)
10570000 1
 
1.0%
9699000 1
 
1.0%
8554000 1
 
1.0%
8204000 1
 
1.0%
7068000 3
 
3.0%
6328000 1
 
1.0%
5795000 2
 
2.0%
2455000 17
17.0%
2296000 1
 
1.0%
2273000 1
 
1.0%

전년대비변경율((%))
Real number (ℝ)

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1242
Minimum0.29
Maximum1.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:02:52.379888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.29
5-th percentile1.08
Q11.09
median1.1
Q31.11
95-th percentile1.56
Maximum1.59
Range1.3
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.15000458
Coefficient of variation (CV)0.13343229
Kurtosis13.095425
Mean1.1242
Median Absolute Deviation (MAD)0.01
Skewness0.12359627
Sum112.42
Variance0.022501374
MonotonicityNot monotonic
2023-12-10T22:02:52.489889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1.1 37
37.0%
1.09 18
18.0%
1.08 13
 
13.0%
1.11 13
 
13.0%
1.56 4
 
4.0%
1.12 2
 
2.0%
1.59 2
 
2.0%
1.14 2
 
2.0%
1.0 2
 
2.0%
1.57 1
 
1.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
0.29 1
 
1.0%
1.0 2
 
2.0%
1.07 1
 
1.0%
1.08 13
 
13.0%
1.09 18
18.0%
1.1 37
37.0%
1.11 13
 
13.0%
1.12 2
 
2.0%
1.13 1
 
1.0%
1.14 2
 
2.0%
ValueCountFrequency (%)
1.59 2
 
2.0%
1.57 1
 
1.0%
1.56 4
 
4.0%
1.3 1
 
1.0%
1.17 1
 
1.0%
1.16 1
 
1.0%
1.14 2
 
2.0%
1.13 1
 
1.0%
1.12 2
 
2.0%
1.11 13
13.0%

Interactions

2023-12-10T22:02:49.422717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:48.360285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:48.719392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:49.064439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:49.513314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:48.449383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:48.800960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:49.150076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:49.601316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:48.557940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:48.892939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:49.235928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:49.711550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:48.639786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:48.974939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:02:49.323146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:02:52.571024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키아이디지번명전년도개별공시지가(원)당해년도개별공시지가(원)전년대비변경율((%))
기본키아이디1.0001.0000.5700.5940.520
지번명1.0001.0001.0001.0001.000
전년도개별공시지가(원)0.5701.0001.0000.9930.417
당해년도개별공시지가(원)0.5941.0000.9931.0000.867
전년대비변경율((%))0.5201.0000.4170.8671.000
2023-12-10T22:02:52.667080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키아이디전년도개별공시지가(원)당해년도개별공시지가(원)전년대비변경율((%))
기본키아이디1.0000.0310.025-0.109
전년도개별공시지가(원)0.0311.0000.9900.179
당해년도개별공시지가(원)0.0250.9901.0000.243
전년대비변경율((%))-0.1090.1790.2431.000

Missing values

2023-12-10T22:02:49.858985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:02:49.979080image/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

기본키아이디기준년도지점주소지번명전년도개별공시지가(원)당해년도개별공시지가(원)전년대비변경율((%))
012022A-1000-0239S-10서울 강동구 상일동[1]199900021800001.09
122022A-1000-0239S-10서울 강동구 상일동[2]5581008860001.59
232022A-1000-0239S-10서울 강동구 상일동[2-2]199900021800001.09
342022A-1000-0239S-10서울 강동구 상일동[2-4]6946007656001.1
452022A-1000-0239S-10서울 강동구 상일동[2-6]5937009270001.56
562022A-1000-0239S-10서울 강동구 상일동[2-7]6946007656001.1
672022A-1000-0239S-10서울 강동구 상일동[2-8]5581008860001.59
782022A-1000-0239S-10서울 강동구 상일동[3]5343008400001.57
892022A-1000-0239S-10서울 강동구 상일동[4-2]210200022960001.09
9102022A-1000-0239S-10서울 강동구 상일동[4-4]5875006531001.11
기본키아이디기준년도지점주소지번명전년도개별공시지가(원)당해년도개별공시지가(원)전년대비변경율((%))
90912022A-1000-0239S-10서울 강동구 상일동[163-4]223500024550001.1
91922022A-1000-0239S-10서울 강동구 상일동[164-1]56500618001.09
92932022A-1000-0239S-10서울 강동구 상일동[165-1]1627001781001.09
93942022A-1000-0239S-10서울 강동구 상일동[166]569400063280001.11
94952022A-1000-0239S-10서울 강동구 상일동[166-1]172000019120001.11
95962022A-1000-0239S-10서울 강동구 상일동[167]223500024550001.1
96972022A-1000-0239S-10서울 강동구 상일동[168]223500024550001.1
97982022A-1000-0239S-10서울 강동구 상일동[172]521500057950001.11
98992022A-1000-0239S-10서울 강동구 상일동[173]521500057950001.11
991002022A-1000-0239S-10서울 강동구 상일동[173-1]2102002302001.1