Overview

Dataset statistics

Number of variables5
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory46.0 B

Variable types

Text1
Numeric4

Dataset

Description한국부동산원(구.한국감정원)에서 제공하는 상업용부동산 임대동향조사 중 오피스의 분기별 지역별 임대가격지수 데이터입니다. - 기준시점 : 2021.4Q = 100 - (단위 : p) - 공표시기 : 계간(분기)
Author한국부동산원
URLhttps://www.data.go.kr/data/15069712/fileData.do

Alerts

2020_1분기 is highly overall correlated with 2020_2분기 and 2 other fieldsHigh correlation
2020_2분기 is highly overall correlated with 2020_1분기 and 2 other fieldsHigh correlation
2020_3분기 is highly overall correlated with 2020_1분기 and 2 other fieldsHigh correlation
2020_4분기 is highly overall correlated with 2020_1분기 and 2 other fieldsHigh correlation
지역 has unique valuesUnique
2020_1분기 has unique valuesUnique
2020_2분기 has unique valuesUnique
2020_3분기 has unique valuesUnique
2020_4분기 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:18:32.141458
Analysis finished2023-12-12 22:18:34.037012
Duration1.9 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-13T07:18:34.204245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length6.4477612
Min length2

Characters and Unicode

Total characters432
Distinct characters89
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique67 ?
Unique (%)100.0%

Sample

1st row서울
2nd row서울 도심
3rd row서울 도심 광화문
4th row서울 도심 남대문
5th row서울 도심 동대문
ValueCountFrequency (%)
서울 30
21.0%
도심 9
 
6.3%
기타 9
 
6.3%
강남 7
 
4.9%
부산 6
 
4.2%
경기 5
 
3.5%
대전 4
 
2.8%
여의도마포 4
 
2.8%
인천 4
 
2.8%
대구 4
 
2.8%
Other values (57) 61
42.7%
2023-12-13T07:18:34.554223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
80
18.5%
서 33
 
7.6%
울 33
 
7.6%
도 16
 
3.7%
산 16
 
3.7%
대 16
 
3.7%
남 14
 
3.2%
기 14
 
3.2%
동 11
 
2.5%
심 10
 
2.3%
Other values (79) 189
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 351
81.2%
Space Separator 80
 
18.5%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 33
 
9.4%
울 33
 
9.4%
도 16
 
4.6%
산 16
 
4.6%
대 16
 
4.6%
남 14
 
4.0%
기 14
 
4.0%
동 11
 
3.1%
심 10
 
2.8%
로 10
 
2.8%
Other values (77) 178
50.7%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 351
81.2%
Common 81
 
18.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 33
 
9.4%
울 33
 
9.4%
도 16
 
4.6%
산 16
 
4.6%
대 16
 
4.6%
남 14
 
4.0%
기 14
 
4.0%
동 11
 
3.1%
심 10
 
2.8%
로 10
 
2.8%
Other values (77) 178
50.7%
Common
ValueCountFrequency (%)
80
98.8%
/ 1
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 351
81.2%
ASCII 81
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
80
98.8%
/ 1
 
1.2%
Hangul
ValueCountFrequency (%)
서 33
 
9.4%
울 33
 
9.4%
도 16
 
4.6%
산 16
 
4.6%
대 16
 
4.6%
남 14
 
4.0%
기 14
 
4.0%
동 11
 
3.1%
심 10
 
2.8%
로 10
 
2.8%
Other values (77) 178
50.7%

2020_1분기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.01505
Minimum96.71125
Maximum99.972078
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:18:34.706921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum96.71125
5-th percentile97.656048
Q198.531857
median99.166353
Q399.607785
95-th percentile99.873393
Maximum99.972078
Range3.2608288
Interquartile range (IQR)1.075928

Descriptive statistics

Standard deviation0.74321311
Coefficient of variation (CV)0.007506062
Kurtosis0.29123922
Mean99.01505
Median Absolute Deviation (MAD)0.45926587
Skewness-0.89855998
Sum6634.0083
Variance0.55236572
MonotonicityNot monotonic
2023-12-13T07:18:34.876349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.50832901 1
 
1.5%
98.49833148 1
 
1.5%
98.7376925 1
 
1.5%
97.61298661 1
 
1.5%
98.28156225 1
 
1.5%
99.02623471 1
 
1.5%
98.11537984 1
 
1.5%
98.24934552 1
 
1.5%
99.118431 1
 
1.5%
99.16251013 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
96.71124969 1
1.5%
97.26986226 1
1.5%
97.47741835 1
1.5%
97.61298661 1
1.5%
97.75652501 1
1.5%
97.86760121 1
1.5%
98.02233763 1
1.5%
98.1014294 1
1.5%
98.11537984 1
1.5%
98.13715978 1
1.5%
ValueCountFrequency (%)
99.97207848 1
1.5%
99.96035873 1
1.5%
99.93301066 1
1.5%
99.87359024 1
1.5%
99.87293229 1
1.5%
99.87185165 1
1.5%
99.8142176 1
1.5%
99.77052081 1
1.5%
99.74143344 1
1.5%
99.71663785 1
1.5%

2020_2분기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.723652
Minimum95.726594
Maximum100.09094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:18:35.044067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95.726594
5-th percentile97.028423
Q197.883467
median98.929488
Q399.562116
95-th percentile99.877711
Maximum100.09094
Range4.3643428
Interquartile range (IQR)1.6786492

Descriptive statistics

Standard deviation0.98467153
Coefficient of variation (CV)0.0099740185
Kurtosis0.10080743
Mean98.723652
Median Absolute Deviation (MAD)0.67763607
Skewness-0.82194051
Sum6614.4847
Variance0.96957802
MonotonicityNot monotonic
2023-12-13T07:18:35.194312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.29251777 1
 
1.5%
97.16351502 1
 
1.5%
98.6872002 1
 
1.5%
96.97052707 1
 
1.5%
97.73689077 1
 
1.5%
98.6199279 1
 
1.5%
96.84389903 1
 
1.5%
97.68572969 1
 
1.5%
98.92948768 1
 
1.5%
98.56470693 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
95.72659391 1
1.5%
96.34789951 1
1.5%
96.84389903 1
1.5%
96.97052707 1
1.5%
97.16351502 1
1.5%
97.37046886 1
1.5%
97.39982842 1
1.5%
97.45370368 1
1.5%
97.48943449 1
1.5%
97.62983327 1
1.5%
ValueCountFrequency (%)
100.0909367 1
1.5%
99.93978652 1
1.5%
99.93301066 1
1.5%
99.88022218 1
1.5%
99.87185165 1
1.5%
99.78214007 1
1.5%
99.75194091 1
1.5%
99.72025394 1
1.5%
99.71616217 1
1.5%
99.67067202 1
1.5%

2020_3분기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.461255
Minimum95.317019
Maximum100.09094
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:18:35.329455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum95.317019
5-th percentile96.12078
Q197.502638
median98.929488
Q399.375537
95-th percentile99.852042
Maximum100.09094
Range4.7739178
Interquartile range (IQR)1.8728982

Descriptive statistics

Standard deviation1.212874
Coefficient of variation (CV)0.012318287
Kurtosis-0.15977207
Mean98.461255
Median Absolute Deviation (MAD)0.63884559
Skewness-0.85531885
Sum6596.9041
Variance1.4710633
MonotonicityNot monotonic
2023-12-13T07:18:35.465670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99.23344315 1
 
1.5%
95.31701891 1
 
1.5%
98.45998485 1
 
1.5%
96.08364536 1
 
1.5%
97.23101238 1
 
1.5%
98.07994598 1
 
1.5%
96.20742815 1
 
1.5%
96.47454247 1
 
1.5%
98.92948768 1
 
1.5%
98.46819607 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
95.31701891 1
1.5%
95.43119718 1
1.5%
95.80747308 1
1.5%
96.08364536 1
1.5%
96.20742815 1
1.5%
96.47454247 1
1.5%
96.65320178 1
1.5%
97.01216476 1
1.5%
97.06208618 1
1.5%
97.09112011 1
1.5%
ValueCountFrequency (%)
100.0909367 1
1.5%
99.93550906 1
1.5%
99.93301066 1
1.5%
99.85339715 1
1.5%
99.84887876 1
1.5%
99.78023211 1
1.5%
99.71205825 1
1.5%
99.62382176 1
1.5%
99.60712375 1
1.5%
99.56833327 1
1.5%

2020_4분기
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct67
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.031628
Minimum93.959956
Maximum99.935509
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-13T07:18:35.608851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum93.959956
5-th percentile95.530015
Q197.091189
median98.459985
Q399.132354
95-th percentile99.534843
Maximum99.935509
Range5.9755536
Interquartile range (IQR)2.0411649

Descriptive statistics

Standard deviation1.388513
Coefficient of variation (CV)0.014163929
Kurtosis0.38497788
Mean98.031628
Median Absolute Deviation (MAD)0.85361633
Skewness-0.97395168
Sum6568.119
Variance1.9279683
MonotonicityNot monotonic
2023-12-13T07:18:35.774922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.95017596 1
 
1.5%
93.95995551 1
 
1.5%
98.45998485 1
 
1.5%
96.05488981 1
 
1.5%
97.17175543 1
 
1.5%
97.15937276 1
 
1.5%
95.94657299 1
 
1.5%
95.83398834 1
 
1.5%
97.69373207 1
 
1.5%
98.08267079 1
 
1.5%
Other values (57) 57
85.1%
ValueCountFrequency (%)
93.95995551 1
1.5%
94.34397309 1
1.5%
94.87861516 1
1.5%
95.39974101 1
1.5%
95.83398834 1
1.5%
95.94657299 1
1.5%
96.05488981 1
1.5%
96.08365945 1
1.5%
96.30041534 1
1.5%
96.41560529 1
1.5%
ValueCountFrequency (%)
99.93550906 1
1.5%
99.72127428 1
1.5%
99.69184223 1
1.5%
99.55160281 1
1.5%
99.49573731 1
1.5%
99.48353224 1
1.5%
99.40932746 1
1.5%
99.39687742 1
1.5%
99.36998993 1
1.5%
99.31501533 1
1.5%

Interactions

2023-12-13T07:18:33.499116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:32.352465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:32.766106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:33.138482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:33.601368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:32.467451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:32.858819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:33.230765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:33.690355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:32.561731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:32.943595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:33.317681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:33.789248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:32.674326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:33.032967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:18:33.394909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:18:35.866771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역2020_1분기2020_2분기2020_3분기2020_4분기
지역1.0001.0001.0001.0001.000
2020_1분기1.0001.0000.9200.8570.863
2020_2분기1.0000.9201.0000.9530.921
2020_3분기1.0000.8570.9531.0000.953
2020_4분기1.0000.8630.9210.9531.000
2023-12-13T07:18:35.958713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2020_1분기2020_2분기2020_3분기2020_4분기
2020_1분기1.0000.9220.9070.772
2020_2분기0.9221.0000.9730.868
2020_3분기0.9070.9731.0000.912
2020_4분기0.7720.8680.9121.000

Missing values

2023-12-13T07:18:33.914108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:18:33.999851image/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

지역2020_1분기2020_2분기2020_3분기2020_4분기
0서울99.50832999.29251899.23344398.950176
1서울 도심99.1625198.56470798.46819698.082671
2서울 도심 광화문99.59009399.555999.38108398.868682
3서울 도심 남대문99.46855799.75194199.78023299.396877
4서울 도심 동대문97.47741897.37046997.20866696.083659
5서울 도심 명동98.78692997.94284197.61394597.372949
6서울 도심 시청99.1537697.39982897.39783197.049658
7서울 도심 을지로99.16635399.16635399.16635399.082988
8서울 도심 종로99.71616299.71616299.29094998.840943
9서울 도심 충무로99.35747799.08726699.08726698.977793
지역2020_1분기2020_2분기2020_3분기2020_4분기
57경기 일산동구99.31501599.31501599.31501599.315015
58경기 평촌범계99.09955999.09955999.09955999.075191
59강원98.84406398.57613198.55316598.419199
60충북98.80073897.73092597.0911296.415605
61충남98.1371697.48943497.38187697.244459
62전북99.42923799.1036198.9509898.610073
63전남98.77715698.58738697.57183897.470271
64경북97.75652597.78574797.34848797.093642
65경남98.10142997.62983397.06208696.598675
66제주98.79216598.78614898.70589898.23172