Overview

Dataset statistics

Number of variables7
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory65.4 B

Variable types

Numeric5
Categorical1
Text1

Dataset

Description샘플 데이터
Author더아이엠씨
URLhttps://bigdata-region.kr/#/dataset/ba12aa77-caf3-4080-8b34-a3e26b658e2a

Alerts

수집년월 has constant value ""Constant
분석인덱스 is highly overall correlated with 단어빈도 and 1 other fieldsHigh correlation
단어빈도 is highly overall correlated with 분석인덱스 and 1 other fieldsHigh correlation
연결정도중심성 is highly overall correlated with 분석인덱스 and 2 other fieldsHigh correlation
매개중심성 is highly overall correlated with 연결정도중심성High correlation
분석인덱스 has unique valuesUnique
키워드명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:54:30.547224
Analysis finished2023-12-10 13:54:36.433279
Duration5.89 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분석인덱스
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:36.525038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-10T22:54:36.773354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

수집년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2010-01
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010-01
2nd row2010-01
3rd row2010-01
4th row2010-01
5th row2010-01

Common Values

ValueCountFrequency (%)
2010-01 30
100.0%

Length

2023-12-10T22:54:36.981956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:54:37.132489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2010-01 30
100.0%

키워드명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:54:37.420414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.2333333
Min length1

Characters and Unicode

Total characters67
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row성남
2nd row경기도
3rd row서울
4th row경기
5th row수원
ValueCountFrequency (%)
성남 1
 
3.3%
경기도 1
 
3.3%
차 1
 
3.3%
시간 1
 
3.3%
지원 1
 
3.3%
지만 1
 
3.3%
분양 1
 
3.3%
채용 1
 
3.3%
수도권 1
 
3.3%
안양 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:54:37.973998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
남 3
 
4.5%
거 3
 
4.5%
성 2
 
3.0%
래 2
 
3.0%
트 2
 
3.0%
아 2
 
3.0%
인 2
 
3.0%
용 2
 
3.0%
지 2
 
3.0%
주 2
 
3.0%
Other values (38) 45
67.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
남 3
 
4.5%
거 3
 
4.5%
성 2
 
3.0%
래 2
 
3.0%
트 2
 
3.0%
아 2
 
3.0%
인 2
 
3.0%
용 2
 
3.0%
지 2
 
3.0%
주 2
 
3.0%
Other values (38) 45
67.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
남 3
 
4.5%
거 3
 
4.5%
성 2
 
3.0%
래 2
 
3.0%
트 2
 
3.0%
아 2
 
3.0%
인 2
 
3.0%
용 2
 
3.0%
지 2
 
3.0%
주 2
 
3.0%
Other values (38) 45
67.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
남 3
 
4.5%
거 3
 
4.5%
성 2
 
3.0%
래 2
 
3.0%
트 2
 
3.0%
아 2
 
3.0%
인 2
 
3.0%
용 2
 
3.0%
지 2
 
3.0%
주 2
 
3.0%
Other values (38) 45
67.2%

단어빈도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1522.0667
Minimum542
Maximum17543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:38.162664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum542
5-th percentile551.5
Q1620.75
median754.5
Q31048
95-th percentile2550.8
Maximum17543
Range17001
Interquartile range (IQR)427.25

Descriptive statistics

Standard deviation3080.9102
Coefficient of variation (CV)2.0241625
Kurtosis27.686469
Mean1522.0667
Median Absolute Deviation (MAD)145
Skewness5.1819247
Sum45662
Variance9492007.8
MonotonicityDecreasing
2023-12-10T22:54:38.346360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
629 2
 
6.7%
843 2
 
6.7%
17543 1
 
3.3%
690 1
 
3.3%
542 1
 
3.3%
547 1
 
3.3%
557 1
 
3.3%
562 1
 
3.3%
586 1
 
3.3%
607 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
542 1
3.3%
547 1
3.3%
557 1
3.3%
562 1
3.3%
586 1
3.3%
607 1
3.3%
608 1
3.3%
618 1
3.3%
629 2
6.7%
651 1
3.3%
ValueCountFrequency (%)
17543 1
3.3%
2630 1
3.3%
2454 1
3.3%
2444 1
3.3%
1476 1
3.3%
1452 1
3.3%
1317 1
3.3%
1098 1
3.3%
898 1
3.3%
885 1
3.3%

단어중요도
Real number (ℝ)

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026816667
Minimum0.0199
Maximum0.0352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:38.533973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0199
5-th percentile0.022215
Q10.024225
median0.0254
Q30.029975
95-th percentile0.03327
Maximum0.0352
Range0.0153
Interquartile range (IQR)0.00575

Descriptive statistics

Standard deviation0.0039440119
Coefficient of variation (CV)0.14707316
Kurtosis-0.60710912
Mean0.026816667
Median Absolute Deviation (MAD)0.0016
Skewness0.63577004
Sum0.8045
Variance1.555523 × 10-5
MonotonicityNot monotonic
2023-12-10T22:54:38.747357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0254 2
 
6.7%
0.0251 2
 
6.7%
0.0246 2
 
6.7%
0.0239 2
 
6.7%
0.0339 1
 
3.3%
0.0325 1
 
3.3%
0.0226 1
 
3.3%
0.0243 1
 
3.3%
0.0271 1
 
3.3%
0.0199 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0.0199 1
3.3%
0.0219 1
3.3%
0.0226 1
3.3%
0.0234 1
3.3%
0.0236 1
3.3%
0.0239 2
6.7%
0.0242 1
3.3%
0.0243 1
3.3%
0.0246 2
6.7%
0.0251 2
6.7%
ValueCountFrequency (%)
0.0352 1
3.3%
0.0339 1
3.3%
0.0325 1
3.3%
0.0323 1
3.3%
0.0322 1
3.3%
0.032 1
3.3%
0.0319 1
3.3%
0.03 1
3.3%
0.0299 1
3.3%
0.0271 1
3.3%

연결정도중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.092486667
Minimum0.0146
Maximum0.5256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:39.064603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0146
5-th percentile0.02538
Q10.04275
median0.0717
Q30.10135
95-th percentile0.193065
Maximum0.5256
Range0.511
Interquartile range (IQR)0.0586

Descriptive statistics

Standard deviation0.094449748
Coefficient of variation (CV)1.0212256
Kurtosis15.626865
Mean0.092486667
Median Absolute Deviation (MAD)0.0308
Skewness3.5727661
Sum2.7746
Variance0.008920755
MonotonicityNot monotonic
2023-12-10T22:54:39.736547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0717 3
 
10.0%
0.1288 2
 
6.7%
0.0571 2
 
6.7%
0.0409 2
 
6.7%
0.5256 1
 
3.3%
0.0278 1
 
3.3%
0.0146 1
 
3.3%
0.0527 1
 
3.3%
0.0893 1
 
3.3%
0.0512 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
0.0146 1
3.3%
0.0234 1
3.3%
0.0278 1
3.3%
0.0307 1
3.3%
0.0336 1
3.3%
0.038 1
3.3%
0.0409 2
6.7%
0.0483 1
3.3%
0.0512 1
3.3%
0.0527 1
3.3%
ValueCountFrequency (%)
0.5256 1
3.3%
0.2049 1
3.3%
0.1786 1
3.3%
0.1639 1
3.3%
0.1317 1
3.3%
0.1288 2
6.7%
0.1039 1
3.3%
0.0937 1
3.3%
0.0893 1
3.3%
0.0863 1
3.3%

매개중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030993333
Minimum0.0006
Maximum0.4857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:54:39.943542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0006
5-th percentile0.001095
Q10.005325
median0.0116
Q30.018675
95-th percentile0.05446
Maximum0.4857
Range0.4851
Interquartile range (IQR)0.01335

Descriptive statistics

Standard deviation0.087197932
Coefficient of variation (CV)2.8134415
Kurtosis28.035383
Mean0.030993333
Median Absolute Deviation (MAD)0.00655
Skewness5.2238194
Sum0.9298
Variance0.0076034793
MonotonicityNot monotonic
2023-12-10T22:54:40.259220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0061 2
 
6.7%
0.0006 2
 
6.7%
0.4857 1
 
3.3%
0.032 1
 
3.3%
0.011 1
 
3.3%
0.0191 1
 
3.3%
0.0159 1
 
3.3%
0.0049 1
 
3.3%
0.0043 1
 
3.3%
0.007 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0006 2
6.7%
0.0017 1
3.3%
0.0023 1
3.3%
0.0038 1
3.3%
0.0043 1
3.3%
0.0049 1
3.3%
0.0052 1
3.3%
0.0057 1
3.3%
0.0061 2
6.7%
0.007 1
3.3%
ValueCountFrequency (%)
0.4857 1
3.3%
0.0604 1
3.3%
0.0472 1
3.3%
0.0446 1
3.3%
0.0415 1
3.3%
0.032 1
3.3%
0.0195 1
3.3%
0.0191 1
3.3%
0.0174 1
3.3%
0.0164 1
3.3%

Interactions

2023-12-10T22:54:35.336648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:30.887362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.047936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:33.158894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.516221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.500042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:31.123368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.204819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:33.462485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.661342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.649538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:31.312932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.412177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:33.729137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.819269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.796290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:31.594168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.651669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.000769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.055534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.927891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:31.807673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:32.882215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:34.270150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:54:35.193042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:54:40.416021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스키워드명단어빈도단어중요도연결정도중심성매개중심성
분석인덱스1.0001.0000.5750.0000.4490.281
키워드명1.0001.0001.0001.0001.0001.000
단어빈도0.5751.0001.0000.3390.8710.972
단어중요도0.0001.0000.3391.0000.3380.301
연결정도중심성0.4491.0000.8710.3381.0000.812
매개중심성0.2811.0000.9720.3010.8121.000
2023-12-10T22:54:40.698280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스단어빈도단어중요도연결정도중심성매개중심성
분석인덱스1.000-1.000-0.235-0.564-0.465
단어빈도-1.0001.0000.2400.5660.463
단어중요도-0.2350.2401.0000.048-0.016
연결정도중심성-0.5640.5660.0481.0000.791
매개중심성-0.4650.463-0.0160.7911.000

Missing values

2023-12-10T22:54:36.151550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:54:36.362244image/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

분석인덱스수집년월키워드명단어빈도단어중요도연결정도중심성매개중심성
012010-01성남175430.03390.52560.4857
122010-01경기도26300.02360.16390.0415
232010-01서울24540.02550.20490.0604
342010-01경기24440.02540.17860.0472
452010-01수원14760.02510.12880.0146
562010-01광주14520.02990.13170.0195
672010-01정보13170.02460.12880.0446
782010-01판매10980.0320.05710.0164
892010-01하남8980.02660.07610.0023
9102010-01용인8850.02530.10390.013
분석인덱스수집년월키워드명단어빈도단어중요도연결정도중심성매개중심성
20212010-01직거래6290.02390.04090.0052
21222010-01안양6290.02420.08630.007
22232010-01수도권6180.02340.05410.0043
23242010-01채용6080.03520.04830.0049
24252010-01분양6070.03190.05120.0061
25262010-01지만5860.01990.07170.0159
26272010-01지원5620.02710.08930.0191
27282010-01시간5570.02430.05270.011
28292010-01차5470.02460.07170.032
29302010-01구입5420.02260.01460.0006