Overview

Dataset statistics

Number of variables18
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory165.1 B

Variable types

Categorical5
Text1
Numeric12

Dataset

Description연수구 내 외국인 부동산 거래 현황 통계에 대한 데이터로서 필지, 면적, 취득금액, 공시지가 금액 등의 데이터 항목 제공
Author인천광역시 연수구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15065556&srcSe=7661IVAWM27C61E190

Alerts

당해분기 처분-면적(제곱미터) is highly overall correlated with 전분기 누계 -필지 and 7 other fieldsHigh correlation
당해분기 처분-공시지가금액(백만원) is highly overall correlated with 전분기 누계 -필지 and 7 other fieldsHigh correlation
당해분기 처분-필지 is highly overall correlated with 전분기 누계 -필지 and 6 other fieldsHigh correlation
당해분기 처분-취득금액(백만원) is highly overall correlated with 당해분기 취득-필지 and 4 other fieldsHigh correlation
전분기 누계 -필지 is highly overall correlated with 전분기 누계 -면적(제곱미터) and 13 other fieldsHigh correlation
전분기 누계 -면적(제곱미터) is highly overall correlated with 전분기 누계 -필지 and 10 other fieldsHigh correlation
전분기 누계 -취득금액(백만원) is highly overall correlated with 전분기 누계 -필지 and 10 other fieldsHigh correlation
전분기 누계 -공시지가금액(백만원) is highly overall correlated with 전분기 누계 -필지 and 10 other fieldsHigh correlation
당해분기 취득-필지 is highly overall correlated with 전분기 누계 -필지 and 14 other fieldsHigh correlation
당해분기 취득-면적(제곱미터) is highly overall correlated with 전분기 누계 -필지 and 11 other fieldsHigh correlation
당해분기 취득-취득금액(백만원) is highly overall correlated with 전분기 누계 -필지 and 12 other fieldsHigh correlation
당해분기 취득-공시지가금액(백만원) is highly overall correlated with 전분기 누계 -필지 and 12 other fieldsHigh correlation
누계-필지 is highly overall correlated with 전분기 누계 -필지 and 13 other fieldsHigh correlation
누계-면적(제곱미터) is highly overall correlated with 전분기 누계 -필지 and 10 other fieldsHigh correlation
누계-취득금액(백만원) is highly overall correlated with 전분기 누계 -필지 and 11 other fieldsHigh correlation
누계-공시지가금액(백만원) is highly overall correlated with 전분기 누계 -필지 and 10 other fieldsHigh correlation
당해분기 처분-취득금액(백만원) is highly imbalanced (51.5%)Imbalance
구분 has unique valuesUnique
전분기 누계 -필지 has 3 (11.5%) zerosZeros
전분기 누계 -면적(제곱미터) has 3 (11.5%) zerosZeros
전분기 누계 -취득금액(백만원) has 4 (15.4%) zerosZeros
전분기 누계 -공시지가금액(백만원) has 3 (11.5%) zerosZeros
당해분기 취득-필지 has 15 (57.7%) zerosZeros
당해분기 취득-면적(제곱미터) has 15 (57.7%) zerosZeros
당해분기 취득-취득금액(백만원) has 15 (57.7%) zerosZeros
당해분기 취득-공시지가금액(백만원) has 15 (57.7%) zerosZeros
누계-필지 has 3 (11.5%) zerosZeros
누계-면적(제곱미터) has 3 (11.5%) zerosZeros
누계-취득금액(백만원) has 4 (15.4%) zerosZeros
누계-공시지가금액(백만원) has 3 (11.5%) zerosZeros

Reproduction

Analysis started2024-01-28 11:04:09.370744
Analysis finished2024-01-28 11:04:20.999350
Duration11.63 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
국적별
주체별
취득용도별
취득원인별

Length

Max length5
Median length3
Mean length3.8461538
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국적별 8
30.8%
주체별 7
26.9%
취득용도별 7
26.9%
취득원인별 4
15.4%

Length

2024-01-28T20:04:21.060895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:04:21.163437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국적별 8
30.8%
주체별 7
26.9%
취득용도별 7
26.9%
취득원인별 4
15.4%

구분
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-01-28T20:04:21.305133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length7
Mean length4.2692308
Min length2

Characters and Unicode

Total characters111
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row미국교포
2nd row기타교포
3rd row순수외국인
4th row미국합작법인
5th row기타합작법인
ValueCountFrequency (%)
미국교포 1
 
3.6%
기타교포 1
 
3.6%
공장용지 1
 
3.6%
상업용지 1
 
3.6%
레져용지 1
 
3.6%
주택 1
 
3.6%
밖의 1
 
3.6%
주택용지(그 1
 
3.6%
단독주택 1
 
3.6%
아파트 1
 
3.6%
Other values (18) 18
64.3%
2024-01-28T20:04:21.550550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8
 
7.2%
6
 
5.4%
6
 
5.4%
4
 
3.6%
4
 
3.6%
4
 
3.6%
4
 
3.6%
4
 
3.6%
4
 
3.6%
3
 
2.7%
Other values (44) 64
57.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107
96.4%
Space Separator 2
 
1.8%
Open Punctuation 1
 
0.9%
Close Punctuation 1
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8
 
7.5%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
3
 
2.8%
Other values (41) 60
56.1%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 107
96.4%
Common 4
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8
 
7.5%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
3
 
2.8%
Other values (41) 60
56.1%
Common
ValueCountFrequency (%)
2
50.0%
( 1
25.0%
) 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 107
96.4%
ASCII 4
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8
 
7.5%
6
 
5.6%
6
 
5.6%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
4
 
3.7%
3
 
2.8%
Other values (41) 60
56.1%
ASCII
ValueCountFrequency (%)
2
50.0%
( 1
25.0%
) 1
25.0%

전분기 누계 -필지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)80.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.538462
Minimum0
Maximum442
Zeros3
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:21.642181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5
median31
Q3106.75
95-th percentile306.75
Maximum442
Range442
Interquartile range (IQR)101.25

Descriptive statistics

Standard deviation112.76426
Coefficient of variation (CV)1.454301
Kurtosis3.8321405
Mean77.538462
Median Absolute Deviation (MAD)30
Skewness2.0162031
Sum2016
Variance12715.778
MonotonicityNot monotonic
2024-01-28T20:04:21.728346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3
 
11.5%
1 2
 
7.7%
36 2
 
7.7%
15 2
 
7.7%
63 1
 
3.8%
12 1
 
3.8%
45 1
 
3.8%
55 1
 
3.8%
131 1
 
3.8%
14 1
 
3.8%
Other values (11) 11
42.3%
ValueCountFrequency (%)
0 3
11.5%
1 2
7.7%
2 1
 
3.8%
5 1
 
3.8%
7 1
 
3.8%
12 1
 
3.8%
14 1
 
3.8%
15 2
7.7%
26 1
 
3.8%
36 2
7.7%
ValueCountFrequency (%)
442 1
3.8%
323 1
3.8%
258 1
3.8%
208 1
3.8%
131 1
3.8%
110 1
3.8%
108 1
3.8%
103 1
3.8%
63 1
3.8%
55 1
3.8%

전분기 누계 -면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9192.1538
Minimum0
Maximum26423
Zeros3
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:21.830373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1650
median5596.5
Q315910
95-th percentile23811.25
Maximum26423
Range26423
Interquartile range (IQR)15260

Descriptive statistics

Standard deviation9255.3617
Coefficient of variation (CV)1.0068763
Kurtosis-1.3703727
Mean9192.1538
Median Absolute Deviation (MAD)5596.5
Skewness0.46845836
Sum238996
Variance85661720
MonotonicityNot monotonic
2024-01-28T20:04:21.920380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 3
 
11.5%
23852 1
 
3.8%
5352 1
 
3.8%
19671 1
 
3.8%
15460 1
 
3.8%
306 1
 
3.8%
5841 1
 
3.8%
2411 1
 
3.8%
16060 1
 
3.8%
14434 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 3
11.5%
19 1
 
3.8%
306 1
 
3.8%
460 1
 
3.8%
554 1
 
3.8%
938 1
 
3.8%
1045 1
 
3.8%
1077 1
 
3.8%
1645 1
 
3.8%
2411 1
 
3.8%
ValueCountFrequency (%)
26423 1
3.8%
23852 1
3.8%
23689 1
3.8%
21626 1
3.8%
19671 1
3.8%
17942 1
3.8%
16060 1
3.8%
15460 1
3.8%
15246 1
3.8%
14681 1
3.8%

전분기 누계 -취득금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11247.423
Minimum-741
Maximum67389
Zeros4
Zeros (%)15.4%
Negative1
Negative (%)3.8%
Memory size366.0 B
2024-01-28T20:04:22.003616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-741
5-th percentile0
Q1387.25
median2953
Q314433.25
95-th percentile48652.5
Maximum67389
Range68130
Interquartile range (IQR)14046

Descriptive statistics

Standard deviation17422.498
Coefficient of variation (CV)1.5490213
Kurtosis4.3627582
Mean11247.423
Median Absolute Deviation (MAD)2953
Skewness2.1258528
Sum292433
Variance3.0354342 × 108
MonotonicityNot monotonic
2024-01-28T20:04:22.094563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4
 
15.4%
-741 1
 
3.8%
12229 1
 
3.8%
3202 1
 
3.8%
25275 1
 
3.8%
12081 1
 
3.8%
3333 1
 
3.8%
29217 1
 
3.8%
2760 1
 
3.8%
2959 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
-741 1
 
3.8%
0 4
15.4%
68 1
 
3.8%
338 1
 
3.8%
535 1
 
3.8%
2121 1
 
3.8%
2244 1
 
3.8%
2247 1
 
3.8%
2760 1
 
3.8%
2947 1
 
3.8%
ValueCountFrequency (%)
67389 1
3.8%
55131 1
3.8%
29217 1
3.8%
28784 1
3.8%
25275 1
3.8%
18587 1
3.8%
15168 1
3.8%
12229 1
3.8%
12081 1
3.8%
6559 1
3.8%

전분기 누계 -공시지가금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6831.2692
Minimum0
Maximum34583
Zeros3
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:22.183865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1684
median4604.5
Q39749.25
95-th percentile19285
Maximum34583
Range34583
Interquartile range (IQR)9065.25

Descriptive statistics

Standard deviation8582.3574
Coefficient of variation (CV)1.2563342
Kurtosis3.0066238
Mean6831.2692
Median Absolute Deviation (MAD)4002.5
Skewness1.6988505
Sum177613
Variance73656859
MonotonicityNot monotonic
2024-01-28T20:04:22.276181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 3
 
11.5%
4377 1
 
3.8%
5313 1
 
3.8%
1662 1
 
3.8%
15088 1
 
3.8%
444 1
 
3.8%
6306 1
 
3.8%
1231 1
 
3.8%
19672 1
 
3.8%
4832 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 3
11.5%
21 1
 
3.8%
444 1
 
3.8%
522 1
 
3.8%
682 1
 
3.8%
690 1
 
3.8%
1005 1
 
3.8%
1231 1
 
3.8%
1334 1
 
3.8%
1662 1
 
3.8%
ValueCountFrequency (%)
34583 1
3.8%
19672 1
3.8%
18124 1
3.8%
17836 1
3.8%
16333 1
3.8%
15088 1
3.8%
10791 1
3.8%
6624 1
3.8%
6306 1
3.8%
5313 1
3.8%

당해분기 취득-필지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7692308
Minimum0
Maximum18
Zeros15
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:22.357631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13.25
Maximum18
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4.9421266
Coefficient of variation (CV)1.7846568
Kurtosis3.1748086
Mean2.7692308
Median Absolute Deviation (MAD)0
Skewness1.9762506
Sum72
Variance24.424615
MonotonicityNot monotonic
2024-01-28T20:04:22.437778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 15
57.7%
2 3
 
11.5%
1 2
 
7.7%
14 1
 
3.8%
9 1
 
3.8%
7 1
 
3.8%
18 1
 
3.8%
11 1
 
3.8%
5 1
 
3.8%
ValueCountFrequency (%)
0 15
57.7%
1 2
 
7.7%
2 3
 
11.5%
5 1
 
3.8%
7 1
 
3.8%
9 1
 
3.8%
11 1
 
3.8%
14 1
 
3.8%
18 1
 
3.8%
ValueCountFrequency (%)
18 1
 
3.8%
14 1
 
3.8%
11 1
 
3.8%
9 1
 
3.8%
7 1
 
3.8%
5 1
 
3.8%
2 3
 
11.5%
1 2
 
7.7%
0 15
57.7%

당해분기 취득-면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.19231
Minimum0
Maximum1106
Zeros15
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:22.520334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3210.75
95-th percentile912
Maximum1106
Range1106
Interquartile range (IQR)210.75

Descriptive statistics

Standard deviation312.29166
Coefficient of variation (CV)1.8349341
Kurtosis3.5694213
Mean170.19231
Median Absolute Deviation (MAD)0
Skewness2.0955581
Sum4425
Variance97526.082
MonotonicityNot monotonic
2024-01-28T20:04:22.602789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15
57.7%
67 2
 
7.7%
79 1
 
3.8%
960 1
 
3.8%
272 1
 
3.8%
768 1
 
3.8%
1106 1
 
3.8%
438 1
 
3.8%
249 1
 
3.8%
96 1
 
3.8%
ValueCountFrequency (%)
0 15
57.7%
67 2
 
7.7%
79 1
 
3.8%
96 1
 
3.8%
249 1
 
3.8%
272 1
 
3.8%
323 1
 
3.8%
438 1
 
3.8%
768 1
 
3.8%
960 1
 
3.8%
ValueCountFrequency (%)
1106 1
3.8%
960 1
3.8%
768 1
3.8%
438 1
3.8%
323 1
3.8%
272 1
3.8%
249 1
3.8%
96 1
3.8%
79 1
3.8%
67 2
7.7%

당해분기 취득-취득금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean982.92308
Minimum0
Maximum6389
Zeros15
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:22.681153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3976
95-th percentile4711.25
Maximum6389
Range6389
Interquartile range (IQR)976

Descriptive statistics

Standard deviation1752.2199
Coefficient of variation (CV)1.7826623
Kurtosis3.316328
Mean982.92308
Median Absolute Deviation (MAD)0
Skewness2.0191639
Sum25556
Variance3070274.6
MonotonicityNot monotonic
2024-01-28T20:04:22.762458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15
57.7%
976 2
 
7.7%
435 1
 
3.8%
4978 1
 
3.8%
1502 1
 
3.8%
3911 1
 
3.8%
6389 1
 
3.8%
3600 1
 
3.8%
704 1
 
3.8%
335 1
 
3.8%
ValueCountFrequency (%)
0 15
57.7%
335 1
 
3.8%
435 1
 
3.8%
704 1
 
3.8%
976 2
 
7.7%
1502 1
 
3.8%
1750 1
 
3.8%
3600 1
 
3.8%
3911 1
 
3.8%
4978 1
 
3.8%
ValueCountFrequency (%)
6389 1
3.8%
4978 1
3.8%
3911 1
3.8%
3600 1
3.8%
1750 1
3.8%
1502 1
3.8%
976 2
7.7%
704 1
3.8%
435 1
3.8%
335 1
3.8%

당해분기 취득-공시지가금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)42.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.53846
Minimum0
Maximum1824
Zeros15
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:22.849388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3284.5
95-th percentile1521.5
Maximum1824
Range1824
Interquartile range (IQR)284.5

Descriptive statistics

Standard deviation520.45313
Coefficient of variation (CV)1.8551935
Kurtosis3.4346341
Mean280.53846
Median Absolute Deviation (MAD)0
Skewness2.0667158
Sum7294
Variance270871.46
MonotonicityNot monotonic
2024-01-28T20:04:22.958629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 15
57.7%
78 2
 
7.7%
127 1
 
3.8%
1619 1
 
3.8%
516 1
 
3.8%
1229 1
 
3.8%
1824 1
 
3.8%
742 1
 
3.8%
337 1
 
3.8%
123 1
 
3.8%
ValueCountFrequency (%)
0 15
57.7%
78 2
 
7.7%
123 1
 
3.8%
127 1
 
3.8%
337 1
 
3.8%
516 1
 
3.8%
621 1
 
3.8%
742 1
 
3.8%
1229 1
 
3.8%
1619 1
 
3.8%
ValueCountFrequency (%)
1824 1
3.8%
1619 1
3.8%
1229 1
3.8%
742 1
3.8%
621 1
3.8%
516 1
3.8%
337 1
3.8%
127 1
3.8%
123 1
3.8%
78 2
7.7%

당해분기 처분-필지
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size340.0 B
0
20 
3
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
76.9%
3 3
 
11.5%
1 3
 
11.5%

Length

2024-01-28T20:04:23.072050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:04:23.142974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
76.9%
3 3
 
11.5%
1 3
 
11.5%

당해분기 처분-면적(제곱미터)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
0
20 
101
42
 
1
24
 
1
34
 
1

Length

Max length3
Median length1
Mean length1.3461538
Min length1

Unique

Unique3 ?
Unique (%)11.5%

Sample

1st row0
2nd row101
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
76.9%
101 3
 
11.5%
42 1
 
3.8%
24 1
 
3.8%
34 1
 
3.8%

Length

2024-01-28T20:04:23.251224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:04:23.346658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
76.9%
101 3
 
11.5%
42 1
 
3.8%
24 1
 
3.8%
34 1
 
3.8%

당해분기 처분-취득금액(백만원)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size340.0 B
0
21 
296
160
 
1
136
 
1

Length

Max length3
Median length1
Mean length1.3846154
Min length1

Unique

Unique2 ?
Unique (%)7.7%

Sample

1st row0
2nd row296
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21
80.8%
296 3
 
11.5%
160 1
 
3.8%
136 1
 
3.8%

Length

2024-01-28T20:04:23.441311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:04:23.521561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21
80.8%
296 3
 
11.5%
160 1
 
3.8%
136 1
 
3.8%
Distinct5
Distinct (%)19.2%
Missing0
Missing (%)0.0%
Memory size340.0 B
0
20 
151
54
 
1
36
 
1
61
 
1

Length

Max length3
Median length1
Mean length1.3461538
Min length1

Unique

Unique3 ?
Unique (%)11.5%

Sample

1st row0
2nd row151
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
76.9%
151 3
 
11.5%
54 1
 
3.8%
36 1
 
3.8%
61 1
 
3.8%

Length

2024-01-28T20:04:23.838436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:04:23.919326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
76.9%
151 3
 
11.5%
54 1
 
3.8%
36 1
 
3.8%
61 1
 
3.8%

누계-필지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.846154
Minimum0
Maximum457
Zeros3
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:23.995179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.5
median31
Q3109
95-th percentile320
Maximum457
Range457
Interquartile range (IQR)103.5

Descriptive statistics

Standard deviation117.08619
Coefficient of variation (CV)1.4663973
Kurtosis3.8108583
Mean79.846154
Median Absolute Deviation (MAD)30
Skewness2.0209997
Sum2076
Variance13709.175
MonotonicityNot monotonic
2024-01-28T20:04:24.075662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 3
 
11.5%
1 2
 
7.7%
36 2
 
7.7%
15 2
 
7.7%
109 2
 
7.7%
65 1
 
3.8%
12 1
 
3.8%
46 1
 
3.8%
55 1
 
3.8%
133 1
 
3.8%
Other values (10) 10
38.5%
ValueCountFrequency (%)
0 3
11.5%
1 2
7.7%
2 1
 
3.8%
5 1
 
3.8%
7 1
 
3.8%
12 1
 
3.8%
14 1
 
3.8%
15 2
7.7%
26 1
 
3.8%
36 2
7.7%
ValueCountFrequency (%)
457 1
3.8%
337 1
3.8%
269 1
3.8%
216 1
3.8%
133 1
3.8%
110 1
3.8%
109 2
7.7%
65 1
3.8%
55 1
3.8%
46 1
3.8%

누계-면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9346.8846
Minimum0
Maximum26490
Zeros3
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:24.160291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1650
median5961
Q316425.25
95-th percentile23861.5
Maximum26490
Range26490
Interquartile range (IQR)15775.25

Descriptive statistics

Standard deviation9362.919
Coefficient of variation (CV)1.0017155
Kurtosis-1.406128
Mean9346.8846
Median Absolute Deviation (MAD)5961
Skewness0.45029285
Sum243019
Variance87664252
MonotonicityNot monotonic
2024-01-28T20:04:24.247340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 3
 
11.5%
23919 1
 
3.8%
6086 1
 
3.8%
19994 1
 
3.8%
15460 1
 
3.8%
306 1
 
3.8%
5836 1
 
3.8%
2660 1
 
3.8%
16498 1
 
3.8%
14434 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 3
11.5%
19 1
 
3.8%
306 1
 
3.8%
418 1
 
3.8%
554 1
 
3.8%
938 1
 
3.8%
1045 1
 
3.8%
1077 1
 
3.8%
1645 1
 
3.8%
2660 1
 
3.8%
ValueCountFrequency (%)
26490 1
3.8%
23919 1
3.8%
23689 1
3.8%
22631 1
3.8%
19994 1
3.8%
17921 1
3.8%
16498 1
3.8%
16207 1
3.8%
15460 1
3.8%
14928 1
3.8%

누계-취득금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12184.731
Minimum0
Maximum73482
Zeros4
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:24.332717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1387.25
median2953
Q315829.75
95-th percentile53285
Maximum73482
Range73482
Interquartile range (IQR)15442.5

Descriptive statistics

Standard deviation18898.317
Coefficient of variation (CV)1.5509836
Kurtosis4.5565806
Mean12184.731
Median Absolute Deviation (MAD)2953
Skewness2.1656655
Sum316803
Variance3.571464 × 108
MonotonicityNot monotonic
2024-01-28T20:04:24.422054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4
 
15.4%
235 1
 
3.8%
16004 1
 
3.8%
4952 1
 
3.8%
25275 1
 
3.8%
12120 1
 
3.8%
4037 1
 
3.8%
32816 1
 
3.8%
2760 1
 
3.8%
2959 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
0 4
15.4%
68 1
 
3.8%
235 1
 
3.8%
338 1
 
3.8%
535 1
 
3.8%
2087 1
 
3.8%
2121 1
 
3.8%
2244 1
 
3.8%
2760 1
 
3.8%
2947 1
 
3.8%
ValueCountFrequency (%)
73482 1
3.8%
60108 1
3.8%
32816 1
3.8%
30286 1
3.8%
25275 1
3.8%
19563 1
3.8%
16004 1
3.8%
15307 1
3.8%
12120 1
3.8%
6559 1
3.8%

누계-공시지가금액(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7088.6923
Minimum0
Maximum36256
Zeros3
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T20:04:24.521770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1647.5
median4643.5
Q39807.75
95-th percentile20175
Maximum36256
Range36256
Interquartile range (IQR)9160.25

Descriptive statistics

Standard deviation8928.4644
Coefficient of variation (CV)1.2595362
Kurtosis3.2401977
Mean7088.6923
Median Absolute Deviation (MAD)4064.5
Skewness1.7458977
Sum184306
Variance79717476
MonotonicityNot monotonic
2024-01-28T20:04:24.615603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 3
 
11.5%
4455 1
 
3.8%
6481 1
 
3.8%
2283 1
 
3.8%
15088 1
 
3.8%
444 1
 
3.8%
6278 1
 
3.8%
1569 1
 
3.8%
20415 1
 
3.8%
4832 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
0 3
11.5%
21 1
 
3.8%
444 1
 
3.8%
522 1
 
3.8%
636 1
 
3.8%
682 1
 
3.8%
1005 1
 
3.8%
1334 1
 
3.8%
1569 1
 
3.8%
2283 1
 
3.8%
ValueCountFrequency (%)
36256 1
3.8%
20415 1
3.8%
19455 1
3.8%
18605 1
3.8%
16309 1
3.8%
15088 1
3.8%
10869 1
3.8%
6624 1
3.8%
6481 1
3.8%
6278 1
3.8%

Interactions

2024-01-28T20:04:19.848265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.275019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.051897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.798360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.757925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.810311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.833732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.619526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.438330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.251109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.006350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.776571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.911712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.328490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.108464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.861533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.842754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.872931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.890943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.678995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.499743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.306906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.066070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.840911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.976005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.387350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.169190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.940740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.930685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.936412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.949895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.742804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.567020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.363976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.124072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.918085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:20.052665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.453553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.234580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.010299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.024264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.005176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.019288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.821096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.641925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.428169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.190176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.217046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:20.126026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.515844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.301287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.080487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.114021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.075332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.085642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.909839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.717607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.494112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.255107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.300210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:20.197379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.588223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.366176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.149930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.208090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.142112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.159321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.976709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.787989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.556716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.323952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.372646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:20.263288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.667276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.427864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.222136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.304512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.206443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.223860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.039706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.851390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.616182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.382393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.436387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:20.331140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.736606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.490644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.304815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.401290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.274409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.290956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.104264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.919165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.689064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.448260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.506218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:20.405868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.803703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.554906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.395484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.507467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.349187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.360193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.173175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.986533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.754629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.522099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.578762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:20.485600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.864607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.611800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.481133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.597454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.409167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.424142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.235960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.048256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.809852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.579622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.641034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:20.548883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.924978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.670184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.563200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.669651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.471155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.487752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.303232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.111862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.873942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.639255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.712108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:20.619644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:10.987398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:11.734243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:12.656371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:13.738075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:14.765325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:15.554076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:16.370482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.181622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:17.943000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:18.708069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:04:19.780319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T20:04:24.714841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대분류구분전분기 누계 -필지전분기 누계 -면적(제곱미터)전분기 누계 -취득금액(백만원)전분기 누계 -공시지가금액(백만원)당해분기 취득-필지당해분기 취득-면적(제곱미터)당해분기 취득-취득금액(백만원)당해분기 취득-공시지가금액(백만원)당해분기 처분-필지당해분기 처분-면적(제곱미터)당해분기 처분-취득금액(백만원)당해분기 처분-공시지가금액(백만원)누계-필지누계-면적(제곱미터)누계-취득금액(백만원)누계-공시지가금액(백만원)
대분류1.0001.0000.0000.2160.0000.0000.1750.2450.0000.1490.2560.0000.0000.0000.0000.3780.0000.000
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전분기 누계 -필지0.0001.0001.0000.7340.9820.7810.9470.9000.9740.9260.6910.7120.4210.7121.0000.6970.9830.781
전분기 누계 -면적(제곱미터)0.2161.0000.7341.0000.7770.7480.8950.6440.7550.8520.3890.3240.5800.3240.7340.9820.6130.748
전분기 누계 -취득금액(백만원)0.0001.0000.9820.7771.0000.9030.9310.8310.9390.8180.5510.5220.4000.5220.9820.7570.9950.903
전분기 누계 -공시지가금액(백만원)0.0001.0000.7810.7480.9031.0000.8000.8580.7180.6920.6330.3210.2440.3210.7810.7680.8521.000
당해분기 취득-필지0.1751.0000.9470.8950.9310.8001.0000.9610.9450.9820.8240.8620.9310.8620.9470.7260.9390.800
당해분기 취득-면적(제곱미터)0.2451.0000.9000.6440.8310.8580.9611.0000.9771.0000.7670.6550.7260.6550.9000.7370.8330.858
당해분기 취득-취득금액(백만원)0.0001.0000.9740.7550.9390.7180.9450.9771.0000.9660.5670.6930.6720.6930.9740.7830.9340.718
당해분기 취득-공시지가금액(백만원)0.1491.0000.9260.8520.8180.6920.9821.0000.9661.0000.6610.7970.8220.7970.9260.8260.8220.692
당해분기 처분-필지0.2561.0000.6910.3890.5510.6330.8240.7670.5670.6611.0001.0000.8241.0000.6910.0090.6690.633
당해분기 처분-면적(제곱미터)0.0001.0000.7120.3240.5220.3210.8620.6550.6930.7971.0001.0001.0001.0000.7120.0300.6160.321
당해분기 처분-취득금액(백만원)0.0001.0000.4210.5800.4000.2440.9310.7260.6720.8220.8241.0001.0001.0000.4210.1250.5560.244
당해분기 처분-공시지가금액(백만원)0.0001.0000.7120.3240.5220.3210.8620.6550.6930.7971.0001.0001.0001.0000.7120.0300.6160.321
누계-필지0.0001.0001.0000.7340.9820.7810.9470.9000.9740.9260.6910.7120.4210.7121.0000.6970.9830.781
누계-면적(제곱미터)0.3781.0000.6970.9820.7570.7680.7260.7370.7830.8260.0090.0300.1250.0300.6971.0000.5710.768
누계-취득금액(백만원)0.0001.0000.9830.6130.9950.8520.9390.8330.9340.8220.6690.6160.5560.6160.9830.5711.0000.852
누계-공시지가금액(백만원)0.0001.0000.7810.7480.9031.0000.8000.8580.7180.6920.6330.3210.2440.3210.7810.7680.8521.000
2024-01-28T20:04:24.855581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당해분기 처분-면적(제곱미터)당해분기 처분-공시지가금액(백만원)대분류당해분기 처분-필지당해분기 처분-취득금액(백만원)
당해분기 처분-면적(제곱미터)1.0001.0000.0000.9560.977
당해분기 처분-공시지가금액(백만원)1.0001.0000.0000.9560.977
대분류0.0000.0001.0000.2290.000
당해분기 처분-필지0.9560.9560.2291.0000.871
당해분기 처분-취득금액(백만원)0.9770.9770.0000.8711.000
2024-01-28T20:04:24.943309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전분기 누계 -필지전분기 누계 -면적(제곱미터)전분기 누계 -취득금액(백만원)전분기 누계 -공시지가금액(백만원)당해분기 취득-필지당해분기 취득-면적(제곱미터)당해분기 취득-취득금액(백만원)당해분기 취득-공시지가금액(백만원)누계-필지누계-면적(제곱미터)누계-취득금액(백만원)누계-공시지가금액(백만원)대분류당해분기 처분-필지당해분기 처분-면적(제곱미터)당해분기 처분-취득금액(백만원)당해분기 처분-공시지가금액(백만원)
전분기 누계 -필지1.0000.8240.8490.9240.8490.7950.8010.7950.9970.8280.9020.9190.0000.5420.5290.2630.529
전분기 누계 -면적(제곱미터)0.8241.0000.6550.8030.6170.5850.6350.5900.8310.9990.7360.8080.0000.2140.1540.2400.154
전분기 누계 -취득금액(백만원)0.8490.6551.0000.9150.6990.6950.6650.6970.8550.6580.9860.9190.0000.4030.3520.2660.352
전분기 누계 -공시지가금액(백만원)0.9240.8030.9151.0000.7260.6650.6670.6680.9230.8040.9500.9970.0000.2940.1980.1200.198
당해분기 취득-필지0.8490.6170.6990.7261.0000.9710.9700.9690.8600.6300.7430.7340.0000.6690.6980.5900.698
당해분기 취득-면적(제곱미터)0.7950.5850.6950.6650.9711.0000.9860.9990.8100.6010.7340.6810.1220.4080.4970.5280.497
당해분기 취득-취득금액(백만원)0.8010.6350.6650.6670.9700.9861.0000.9870.8170.6540.7170.6840.0000.4060.5070.4900.507
당해분기 취득-공시지가금액(백만원)0.7950.5900.6970.6680.9690.9990.9871.0000.8080.6070.7340.6850.0000.4670.6000.4330.600
누계-필지0.9970.8310.8550.9230.8600.8100.8170.8081.0000.8350.9100.9210.0000.5420.5290.2630.529
누계-면적(제곱미터)0.8280.9990.6580.8040.6300.6010.6540.6070.8351.0000.7400.8110.1880.0000.0000.0000.000
누계-취득금액(백만원)0.9020.7360.9860.9500.7430.7340.7170.7340.9100.7401.0000.9550.0000.5160.4240.3760.424
누계-공시지가금액(백만원)0.9190.8080.9190.9970.7340.6810.6840.6850.9210.8110.9551.0000.0000.2940.1980.1200.198
대분류0.0000.0000.0000.0000.0000.1220.0000.0000.0000.1880.0000.0001.0000.2290.0000.0000.000
당해분기 처분-필지0.5420.2140.4030.2940.6690.4080.4060.4670.5420.0000.5160.2940.2291.0000.9560.8710.956
당해분기 처분-면적(제곱미터)0.5290.1540.3520.1980.6980.4970.5070.6000.5290.0000.4240.1980.0000.9561.0000.9771.000
당해분기 처분-취득금액(백만원)0.2630.2400.2660.1200.5900.5280.4900.4330.2630.0000.3760.1200.0000.8710.9771.0000.977
당해분기 처분-공시지가금액(백만원)0.5290.1540.3520.1980.6980.4970.5070.6000.5290.0000.4240.1980.0000.9561.0000.9771.000

Missing values

2024-01-28T20:04:20.724042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T20:04:20.930781image/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

대분류구분전분기 누계 -필지전분기 누계 -면적(제곱미터)전분기 누계 -취득금액(백만원)전분기 누계 -공시지가금액(백만원)당해분기 취득-필지당해분기 취득-면적(제곱미터)당해분기 취득-취득금액(백만원)당해분기 취득-공시지가금액(백만원)당해분기 처분-필지당해분기 처분-면적(제곱미터)당해분기 처분-취득금액(백만원)당해분기 처분-공시지가금액(백만원)누계-필지누계-면적(제곱미터)누계-취득금액(백만원)누계-공시지가금액(백만원)
0주체별미국교포6323852-741437726797678000065239192354455
1주체별기타교포1101794215168163332794351273101296151109179211530716309
2주체별순수외국인32315246551311783614960497816190000337162076010819455
3주체별미국합작법인2164529475154000000002164529475154
4주체별기타합작법인510455356820000000051045535682
5주체별순수외국법인1196821000000001196821
6주체별정부단체등0000000000000000
7국적별미국108264231858710791267976780000110264901956310869
8국적별기타미주36102646559662400000000361026465596624
9국적별영국프랑스독일1593822441334000000001593822441334
대분류구분전분기 누계 -필지전분기 누계 -면적(제곱미터)전분기 누계 -취득금액(백만원)전분기 누계 -공시지가금액(백만원)당해분기 취득-필지당해분기 취득-면적(제곱미터)당해분기 취득-취득금액(백만원)당해분기 취득-공시지가금액(백만원)당해분기 처분-필지당해분기 처분-면적(제곱미터)당해분기 처분-취득금액(백만원)당해분기 처분-공시지가금액(백만원)누계-필지누계-면적(제곱미터)누계-취득금액(백만원)누계-공시지가금액(백만원)
16취득원인별계약외36236892959498900000000362368929594989
17취득원인별계속보유26144342760483200000000261443427604832
18취득원인별허가0000000000000000
19취득용도별아파트2581606029217196721143836007420000269164983281620415
20취득용도별단독주택142411333312311249704337000015266040371569
21취득용도별주택용지(그 밖의 주택)131584112081630659633512331012961511335836121206278
22취득용도별레져용지130604440000000013060444
23취득용도별상업용지551546025275150880000000055154602527515088
24취득용도별공장용지0000000000000000
25취득용도별기타451967132021662132317506210000461999449522283