Overview

Dataset statistics

Number of variables10
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory92.7 B

Variable types

Categorical2
Numeric8

Dataset

Description인천광역시 주택가격 현황(주택매매가격지수, 주택전세가격지수)등의 지수 정보(2022년 6월 기준)입니다.
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15055204&srcSe=7661IVAWM27C61E190

Alerts

매매(종합)지수 is highly overall correlated with 매매(아파트)지수 and 3 other fieldsHigh correlation
매매(종합)가격 변동률 is highly overall correlated with 매매(아파트)가격 변동률 and 2 other fieldsHigh correlation
매매(아파트)지수 is highly overall correlated with 매매(종합)지수 and 3 other fieldsHigh correlation
매매(아파트)가격 변동률 is highly overall correlated with 매매(종합)가격 변동률 and 2 other fieldsHigh correlation
전세(종합)지수 is highly overall correlated with 매매(종합)지수 and 3 other fieldsHigh correlation
전세(종합)가격 변동률 is highly overall correlated with 매매(종합)가격 변동률 and 2 other fieldsHigh correlation
전세(아파트)지수 is highly overall correlated with 매매(종합)지수 and 3 other fieldsHigh correlation
전세(아파트)가격 변동률 is highly overall correlated with 매매(종합)가격 변동률 and 2 other fieldsHigh correlation
시점 is highly overall correlated with 매매(종합)지수 and 3 other fieldsHigh correlation
전세(종합)지수 has unique valuesUnique
전세(아파트)지수 has unique valuesUnique
전세(종합)가격 변동률 has 1 (2.8%) zerosZeros

Reproduction

Analysis started2024-03-13 06:11:16.372661
Analysis finished2024-03-13 06:11:23.485001
Duration7.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시점
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
2022
2021
2020
2019

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 9
25.0%
2021 9
25.0%
2020 9
25.0%
2019 9
25.0%

Length

2024-03-13T15:11:23.555626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T15:11:23.646325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 9
25.0%
2021 9
25.0%
2020 9
25.0%
2019 9
25.0%
Distinct9
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
인천광역시
중구
동구
미추홀구
연수구
Other values (4)
16 

Length

Max length5
Median length4
Mean length3
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천광역시
2nd row중구
3rd row동구
4th row미추홀구
5th row연수구

Common Values

ValueCountFrequency (%)
인천광역시 4
11.1%
중구 4
11.1%
동구 4
11.1%
미추홀구 4
11.1%
연수구 4
11.1%
남동구 4
11.1%
부평구 4
11.1%
계양구 4
11.1%
서구 4
11.1%

Length

2024-03-13T15:11:23.749526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T15:11:23.884808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 4
11.1%
중구 4
11.1%
동구 4
11.1%
미추홀구 4
11.1%
연수구 4
11.1%
남동구 4
11.1%
부평구 4
11.1%
계양구 4
11.1%
서구 4
11.1%

매매(종합)지수
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.416667
Minimum73.6
Maximum112.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-13T15:11:24.019396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum73.6
5-th percentile84.85
Q191.75
median100.05
Q3107.05
95-th percentile109.725
Maximum112.1
Range38.5
Interquartile range (IQR)15.3

Descriptive statistics

Standard deviation9.71995
Coefficient of variation (CV)0.098763252
Kurtosis-0.66932325
Mean98.416667
Median Absolute Deviation (MAD)7.45
Skewness-0.49898973
Sum3543
Variance94.477429
MonotonicityNot monotonic
2024-03-13T15:11:24.148576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
107.2 2
 
5.6%
107.7 2
 
5.6%
91.3 1
 
2.8%
83.2 1
 
2.8%
92.4 1
 
2.8%
93.0 1
 
2.8%
93.7 1
 
2.8%
91.9 1
 
2.8%
86.3 1
 
2.8%
93.6 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
73.6 1
2.8%
83.2 1
2.8%
85.4 1
2.8%
86.0 1
2.8%
86.3 1
2.8%
86.6 1
2.8%
90.0 1
2.8%
90.6 1
2.8%
91.3 1
2.8%
91.9 1
2.8%
ValueCountFrequency (%)
112.1 1
2.8%
110.1 1
2.8%
109.6 1
2.8%
109.0 1
2.8%
107.7 2
5.6%
107.3 1
2.8%
107.2 2
5.6%
107.0 1
2.8%
106.6 1
2.8%
106.5 1
2.8%

매매(종합)가격 변동률
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2975
Minimum-0.6
Maximum1.26
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)27.8%
Memory size456.0 B
2024-03-13T15:11:24.296607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.6
5-th percentile-0.1475
Q1-0.015
median0.32
Q30.48
95-th percentile0.875
Maximum1.26
Range1.86
Interquartile range (IQR)0.495

Descriptive statistics

Standard deviation0.35897772
Coefficient of variation (CV)1.2066478
Kurtosis1.0536603
Mean0.2975
Median Absolute Deviation (MAD)0.165
Skewness0.20242153
Sum10.71
Variance0.128865
MonotonicityNot monotonic
2024-03-13T15:11:24.447647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.41 4
 
11.1%
-0.07 3
 
8.3%
0.48 3
 
8.3%
0.23 2
 
5.6%
0.49 2
 
5.6%
0.29 2
 
5.6%
-0.14 1
 
2.8%
0.34 1
 
2.8%
0.43 1
 
2.8%
0.75 1
 
2.8%
Other values (16) 16
44.4%
ValueCountFrequency (%)
-0.6 1
 
2.8%
-0.17 1
 
2.8%
-0.14 1
 
2.8%
-0.13 1
 
2.8%
-0.09 1
 
2.8%
-0.07 3
8.3%
-0.03 1
 
2.8%
-0.01 1
 
2.8%
0.17 1
 
2.8%
0.18 1
 
2.8%
ValueCountFrequency (%)
1.26 1
 
2.8%
1.01 1
 
2.8%
0.83 1
 
2.8%
0.75 1
 
2.8%
0.59 1
 
2.8%
0.5 1
 
2.8%
0.49 2
5.6%
0.48 3
8.3%
0.43 1
 
2.8%
0.42 1
 
2.8%

매매(아파트)지수
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.425
Minimum71.5
Maximum113.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-13T15:11:24.592549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71.5
5-th percentile80.15
Q187.525
median99.5
Q3108.75
95-th percentile111.45
Maximum113.2
Range41.7
Interquartile range (IQR)21.225

Descriptive statistics

Standard deviation12.47279
Coefficient of variation (CV)0.12802454
Kurtosis-1.4302072
Mean97.425
Median Absolute Deviation (MAD)10.25
Skewness-0.29361025
Sum3507.3
Variance155.5705
MonotonicityNot monotonic
2024-03-13T15:11:24.716780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
89.3 2
 
5.6%
109.2 1
 
2.8%
106.8 1
 
2.8%
93.6 1
 
2.8%
90.9 1
 
2.8%
81.8 1
 
2.8%
90.5 1
 
2.8%
87.8 1
 
2.8%
80.5 1
 
2.8%
86.7 1
 
2.8%
Other values (25) 25
69.4%
ValueCountFrequency (%)
71.5 1
2.8%
79.7 1
2.8%
80.3 1
2.8%
80.5 1
2.8%
81.4 1
2.8%
81.8 1
2.8%
85.2 1
2.8%
85.5 1
2.8%
86.7 1
2.8%
87.8 1
2.8%
ValueCountFrequency (%)
113.2 1
2.8%
111.6 1
2.8%
111.4 1
2.8%
111.0 1
2.8%
110.7 1
2.8%
110.3 1
2.8%
109.8 1
2.8%
109.3 1
2.8%
109.2 1
2.8%
108.6 1
2.8%

매매(아파트)가격 변동률
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33777778
Minimum-0.68
Maximum1.11
Zeros0
Zeros (%)0.0%
Negative10
Negative (%)27.8%
Memory size456.0 B
2024-03-13T15:11:24.846803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-0.68
5-th percentile-0.285
Q1-0.125
median0.425
Q30.6325
95-th percentile0.9875
Maximum1.11
Range1.79
Interquartile range (IQR)0.7575

Descriptive statistics

Standard deviation0.43248195
Coefficient of variation (CV)1.2803742
Kurtosis-0.46271659
Mean0.33777778
Median Absolute Deviation (MAD)0.275
Skewness-0.36021601
Sum12.16
Variance0.18704063
MonotonicityNot monotonic
2024-03-13T15:11:25.009865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.31 2
 
5.6%
0.7 2
 
5.6%
-0.15 2
 
5.6%
0.41 2
 
5.6%
-0.26 1
 
2.8%
0.75 1
 
2.8%
0.43 1
 
2.8%
1.11 1
 
2.8%
1.1 1
 
2.8%
0.62 1
 
2.8%
Other values (22) 22
61.1%
ValueCountFrequency (%)
-0.68 1
2.8%
-0.3 1
2.8%
-0.28 1
2.8%
-0.26 1
2.8%
-0.23 1
2.8%
-0.18 1
2.8%
-0.15 2
5.6%
-0.14 1
2.8%
-0.12 1
2.8%
0.12 1
2.8%
ValueCountFrequency (%)
1.11 1
2.8%
1.1 1
2.8%
0.95 1
2.8%
0.83 1
2.8%
0.75 1
2.8%
0.71 1
2.8%
0.7 2
5.6%
0.64 1
2.8%
0.63 1
2.8%
0.62 1
2.8%

전세(종합)지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.930389
Minimum77.871
Maximum111.077
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-13T15:11:25.156963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum77.871
5-th percentile87.88375
Q193.261
median98.888
Q3103.688
95-th percentile106.2695
Maximum111.077
Range33.206
Interquartile range (IQR)10.427

Descriptive statistics

Standard deviation7.2854016
Coefficient of variation (CV)0.074393676
Kurtosis0.02146899
Mean97.930389
Median Absolute Deviation (MAD)5.025
Skewness-0.57185406
Sum3525.494
Variance53.077077
MonotonicityNot monotonic
2024-03-13T15:11:25.286352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
103.633 1
 
2.8%
94.673 1
 
2.8%
97.701 1
 
2.8%
90.628 1
 
2.8%
93.886 1
 
2.8%
94.566 1
 
2.8%
95.33 1
 
2.8%
92.433 1
 
2.8%
88.513 1
 
2.8%
89.538 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
77.871 1
2.8%
85.996 1
2.8%
88.513 1
2.8%
89.029 1
2.8%
89.229 1
2.8%
89.538 1
2.8%
90.628 1
2.8%
90.911 1
2.8%
92.433 1
2.8%
93.537 1
2.8%
ValueCountFrequency (%)
111.077 1
2.8%
106.709 1
2.8%
106.123 1
2.8%
106.052 1
2.8%
105.207 1
2.8%
105.184 1
2.8%
104.628 1
2.8%
103.936 1
2.8%
103.766 1
2.8%
103.662 1
2.8%

전세(종합)가격 변동률
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37583333
Minimum-1.45
Maximum4.72
Zeros1
Zeros (%)2.8%
Negative7
Negative (%)19.4%
Memory size456.0 B
2024-03-13T15:11:25.399956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.45
5-th percentile-0.42
Q10.0175
median0.27
Q30.57
95-th percentile1.1175
Maximum4.72
Range6.17
Interquartile range (IQR)0.5525

Descriptive statistics

Standard deviation0.89246489
Coefficient of variation (CV)2.3746294
Kurtosis16.484868
Mean0.37583333
Median Absolute Deviation (MAD)0.265
Skewness3.217211
Sum13.53
Variance0.79649357
MonotonicityNot monotonic
2024-03-13T15:11:25.529269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0.1 2
 
5.6%
-0.42 2
 
5.6%
0.32 2
 
5.6%
0.33 2
 
5.6%
0.26 1
 
2.8%
1.04 1
 
2.8%
0.84 1
 
2.8%
0.63 1
 
2.8%
1.08 1
 
2.8%
-0.3 1
 
2.8%
Other values (22) 22
61.1%
ValueCountFrequency (%)
-1.45 1
2.8%
-0.42 2
5.6%
-0.3 1
2.8%
-0.1 1
2.8%
-0.06 1
2.8%
-0.05 1
2.8%
0.0 1
2.8%
0.01 1
2.8%
0.02 1
2.8%
0.05 1
2.8%
ValueCountFrequency (%)
4.72 1
2.8%
1.23 1
2.8%
1.08 1
2.8%
1.04 1
2.8%
0.86 1
2.8%
0.84 1
2.8%
0.74 1
2.8%
0.65 1
2.8%
0.63 1
2.8%
0.55 1
2.8%

전세(아파트)지수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.882722
Minimum76.103
Maximum112.225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2024-03-13T15:11:25.685556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76.103
5-th percentile82.6255
Q189.8145
median98.0225
Q3105.1795
95-th percentile108.41375
Maximum112.225
Range36.122
Interquartile range (IQR)15.365

Descriptive statistics

Standard deviation9.4937089
Coefficient of variation (CV)0.097991765
Kurtosis-1.0063528
Mean96.882722
Median Absolute Deviation (MAD)7.618
Skewness-0.35722979
Sum3487.778
Variance90.130509
MonotonicityNot monotonic
2024-03-13T15:11:25.823730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
104.403 1
 
2.8%
91.32 1
 
2.8%
95.436 1
 
2.8%
89.869 1
 
2.8%
91.411 1
 
2.8%
91.841 1
 
2.8%
92.974 1
 
2.8%
89.651 1
 
2.8%
83.394 1
 
2.8%
83.131 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
76.103 1
2.8%
81.109 1
2.8%
83.131 1
2.8%
83.394 1
2.8%
84.269 1
2.8%
85.125 1
2.8%
86.857 1
2.8%
88.84 1
2.8%
89.651 1
2.8%
89.869 1
2.8%
ValueCountFrequency (%)
112.225 1
2.8%
108.656 1
2.8%
108.333 1
2.8%
106.834 1
2.8%
106.7 1
2.8%
106.476 1
2.8%
106.174 1
2.8%
105.965 1
2.8%
105.316 1
2.8%
105.134 1
2.8%

전세(아파트)가격 변동률
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48694444
Minimum-1.66
Maximum5.24
Zeros0
Zeros (%)0.0%
Negative8
Negative (%)22.2%
Memory size456.0 B
2024-03-13T15:11:25.956666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.66
5-th percentile-0.625
Q10.02
median0.38
Q30.7525
95-th percentile1.725
Maximum5.24
Range6.9
Interquartile range (IQR)0.7325

Descriptive statistics

Standard deviation1.0549552
Coefficient of variation (CV)2.1664795
Kurtosis11.609121
Mean0.48694444
Median Absolute Deviation (MAD)0.36
Skewness2.5407694
Sum17.53
Variance1.1129304
MonotonicityNot monotonic
2024-03-13T15:11:26.080596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.82 2
 
5.6%
0.35 2
 
5.6%
0.49 2
 
5.6%
0.39 2
 
5.6%
0.02 2
 
5.6%
-0.13 2
 
5.6%
0.05 1
 
2.8%
0.19 1
 
2.8%
1.67 1
 
2.8%
1.89 1
 
2.8%
Other values (20) 20
55.6%
ValueCountFrequency (%)
-1.66 1
2.8%
-0.64 1
2.8%
-0.62 1
2.8%
-0.49 1
2.8%
-0.13 2
5.6%
-0.1 1
2.8%
-0.01 1
2.8%
0.02 2
5.6%
0.03 1
2.8%
0.05 1
2.8%
ValueCountFrequency (%)
5.24 1
2.8%
1.89 1
2.8%
1.67 1
2.8%
1.44 1
2.8%
1.25 1
2.8%
1.1 1
2.8%
0.93 1
2.8%
0.82 2
5.6%
0.73 1
2.8%
0.59 1
2.8%

Interactions

2024-03-13T15:11:22.529285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:16.810889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:17.855199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.529280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.202075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.978275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.756262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.567040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:22.615311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:17.211073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:17.944006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.611247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.282456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.085005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.857200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.654542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:22.698921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:17.295870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.025064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.684444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.366726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.199935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.944515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.738744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:22.772337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:17.374402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.098343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.756425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.434595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.292046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.070699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.825138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:22.855032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:17.476359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.179099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.858714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.516983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.382230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.199758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.928624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:22.957574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:17.561681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.277949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.958265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.611667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.476040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.323092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:22.009546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:23.040179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:17.657385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.357990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.045831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.722690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.566026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.401004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:22.089231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:23.141447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:17.766715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:18.444122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.119770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:19.854737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:20.661649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:21.481417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T15:11:22.173426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T15:11:26.182193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시점행정구역별(시군구)매매(종합)지수매매(종합)가격 변동률매매(아파트)지수매매(아파트)가격 변동률전세(종합)지수전세(종합)가격 변동률전세(아파트)지수전세(아파트)가격 변동률
시점1.0000.0000.7480.6980.9070.6500.7270.6250.8320.620
행정구역별(시군구)0.0001.0000.0000.0000.0000.0000.0000.0380.2140.000
매매(종합)지수0.7480.0001.0000.7420.8940.6740.8870.7580.8840.817
매매(종합)가격 변동률0.6980.0000.7421.0000.1390.9340.1330.8780.1010.790
매매(아파트)지수0.9070.0000.8940.1391.0000.6980.8960.0000.8810.000
매매(아파트)가격 변동률0.6500.0000.6740.9340.6981.0000.6780.7440.5100.675
전세(종합)지수0.7270.0000.8870.1330.8960.6781.0000.7140.9120.312
전세(종합)가격 변동률0.6250.0380.7580.8780.0000.7440.7141.0000.5230.944
전세(아파트)지수0.8320.2140.8840.1010.8810.5100.9120.5231.0000.374
전세(아파트)가격 변동률0.6200.0000.8170.7900.0000.6750.3120.9440.3741.000
2024-03-13T15:11:26.310328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구역별(시군구)시점
행정구역별(시군구)1.0000.000
시점0.0001.000
2024-03-13T15:11:26.400296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
매매(종합)지수매매(종합)가격 변동률매매(아파트)지수매매(아파트)가격 변동률전세(종합)지수전세(종합)가격 변동률전세(아파트)지수전세(아파트)가격 변동률시점행정구역별(시군구)
매매(종합)지수1.000-0.3580.991-0.3810.969-0.4070.946-0.4590.5940.000
매매(종합)가격 변동률-0.3581.000-0.3210.952-0.3040.724-0.2610.6940.4840.000
매매(아파트)지수0.991-0.3211.000-0.3540.966-0.3790.947-0.4320.5690.000
매매(아파트)가격 변동률-0.3810.952-0.3541.000-0.3500.741-0.3170.7260.4350.000
전세(종합)지수0.969-0.3040.966-0.3501.000-0.3440.991-0.4010.5160.000
전세(종합)가격 변동률-0.4070.724-0.3790.741-0.3441.000-0.3040.9860.4360.000
전세(아파트)지수0.946-0.2610.947-0.3170.991-0.3041.000-0.3630.6010.014
전세(아파트)가격 변동률-0.4590.694-0.4320.726-0.4010.986-0.3631.0000.4100.000
시점0.5940.4840.5690.4350.5160.4360.6010.4101.0000.000
행정구역별(시군구)0.0000.0000.0000.0000.0000.0000.0140.0000.0001.000

Missing values

2024-03-13T15:11:23.257757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T15:11:23.425191image/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

시점행정구역별(시군구)매매(종합)지수매매(종합)가격 변동률매매(아파트)지수매매(아파트)가격 변동률전세(종합)지수전세(종합)가격 변동률전세(아파트)지수전세(아파트)가격 변동률
02022인천광역시107.2-0.14109.2-0.26103.633-0.3104.403-0.49
12022중구105.1-0.07106.8-0.1899.844-0.4299.421-0.64
22022동구104.4-0.13105.4-0.3102.8890.02104.4930.03
32022미추홀구107.2-0.01108.2-0.12106.0520.0106.834-0.13
42022연수구110.1-0.6111.0-0.68105.207-1.45105.316-1.66
52022남동구105.5-0.09107.2-0.14103.432-0.1104.88-0.13
62022부평구107.7-0.07110.3-0.15103.936-0.05106.174-0.1
72022계양구109.6-0.07111.6-0.15106.709-0.06108.656-0.01
82022서구106.6-0.17108.6-0.23100.359-0.4299.319-0.62
92021인천광역시107.30.49109.80.52104.6280.33106.4760.37
시점행정구역별(시군구)매매(종합)지수매매(종합)가격 변동률매매(아파트)지수매매(아파트)가격 변동률전세(종합)지수전세(종합)가격 변동률전세(아파트)지수전세(아파트)가격 변동률
262020서구91.90.2987.80.4192.4331.0889.6511.67
272019인천광역시86.30.4180.50.4888.5130.2683.3940.39
282019중구91.30.4186.70.789.5380.1983.1310.35
292019동구93.6-0.0392.1-0.2896.5750.0195.0450.02
302019미추홀구90.60.4885.20.6393.5370.0588.840.05
312019연수구73.60.7571.50.8377.8710.7476.1030.82
322019남동구86.00.4381.40.4589.2290.185.1250.19
332019부평구86.60.4180.30.4689.0290.2484.2690.35
342019계양구90.00.2385.50.3190.9110.3586.8570.49
352019서구85.40.3479.70.3185.9960.3281.1090.42