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/9ac907cb-1f48-454c-9282-752d0d324c25

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
단어연결중심성 is highly overall correlated with 단어매개중심성High correlation
단어매개중심성 is highly overall correlated with 분석인덱스 and 2 other fieldsHigh correlation
분석인덱스 has unique valuesUnique
키워드명 has unique valuesUnique
단어빈도 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:07:02.588576
Analysis finished2023-12-10 14:07:07.665154
Duration5.08 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
2023-12-10T23:07:07.765940image/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
2023-12-10T23:07:07.976900image/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

2023-12-10T23:07:08.197180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:07:08.342839image/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
2023-12-10T23:07:08.645023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.1333333
Min length1

Characters and Unicode

Total characters64
Distinct characters47
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 row수원
2nd row서울
3rd row경기
4th row경기도
5th row정보
ValueCountFrequency (%)
수원 1
 
3.3%
서울 1
 
3.3%
부산 1
 
3.3%
지원 1
 
3.3%
수원화성 1
 
3.3%
채용 1
 
3.3%
대학 1
 
3.3%
이사 1
 
3.3%
아파트 1
 
3.3%
구입 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:07:09.218275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
성 4
 
6.2%
거 3
 
4.7%
원 3
 
4.7%
수 2
 
3.1%
화 2
 
3.1%
양 2
 
3.1%
산 2
 
3.1%
용 2
 
3.1%
인 2
 
3.1%
래 2
 
3.1%
Other values (37) 40
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 4
 
6.2%
거 3
 
4.7%
원 3
 
4.7%
수 2
 
3.1%
화 2
 
3.1%
양 2
 
3.1%
산 2
 
3.1%
용 2
 
3.1%
인 2
 
3.1%
래 2
 
3.1%
Other values (37) 40
62.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 4
 
6.2%
거 3
 
4.7%
원 3
 
4.7%
수 2
 
3.1%
화 2
 
3.1%
양 2
 
3.1%
산 2
 
3.1%
용 2
 
3.1%
인 2
 
3.1%
래 2
 
3.1%
Other values (37) 40
62.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
성 4
 
6.2%
거 3
 
4.7%
원 3
 
4.7%
수 2
 
3.1%
화 2
 
3.1%
양 2
 
3.1%
산 2
 
3.1%
용 2
 
3.1%
인 2
 
3.1%
래 2
 
3.1%
Other values (37) 40
62.5%

단어빈도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3551.5667
Minimum1095
Maximum46884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:07:09.434768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1095
5-th percentile1131.8
Q11311.75
median1652
Q32224.5
95-th percentile5224.6
Maximum46884
Range45789
Interquartile range (IQR)912.75

Descriptive statistics

Standard deviation8261.821
Coefficient of variation (CV)2.3262469
Kurtosis28.76724
Mean3551.5667
Median Absolute Deviation (MAD)423.5
Skewness5.3183342
Sum106547
Variance68257685
MonotonicityStrictly decreasing
2023-12-10T23:07:09.679461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
46884 1
 
3.3%
1592 1
 
3.3%
1095 1
 
3.3%
1121 1
 
3.3%
1145 1
 
3.3%
1215 1
 
3.3%
1230 1
 
3.3%
1280 1
 
3.3%
1289 1
 
3.3%
1300 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1095 1
3.3%
1121 1
3.3%
1145 1
3.3%
1215 1
3.3%
1230 1
3.3%
1280 1
3.3%
1289 1
3.3%
1300 1
3.3%
1347 1
3.3%
1421 1
3.3%
ValueCountFrequency (%)
46884 1
3.3%
5257 1
3.3%
5185 1
3.3%
4577 1
3.3%
3335 1
3.3%
2747 1
3.3%
2367 1
3.3%
2240 1
3.3%
2178 1
3.3%
2118 1
3.3%

단어중요도
Real number (ℝ)

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030116667
Minimum0.0218
Maximum0.0659
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:07:09.899518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0218
5-th percentile0.023425
Q10.0259
median0.02835
Q30.0318
95-th percentile0.040785
Maximum0.0659
Range0.0441
Interquartile range (IQR)0.0059

Descriptive statistics

Standard deviation0.0082837224
Coefficient of variation (CV)0.27505443
Kurtosis12.089939
Mean0.030116667
Median Absolute Deviation (MAD)0.00315
Skewness3.0921976
Sum0.9035
Variance6.8620057 × 10-5
MonotonicityNot monotonic
2023-12-10T23:07:10.119352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0241 2
 
6.7%
0.0259 2
 
6.7%
0.0349 1
 
3.3%
0.031 1
 
3.3%
0.0346 1
 
3.3%
0.0263 1
 
3.3%
0.026 1
 
3.3%
0.0659 1
 
3.3%
0.0315 1
 
3.3%
0.0323 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0218 1
3.3%
0.0232 1
3.3%
0.0237 1
3.3%
0.0241 2
6.7%
0.025 1
3.3%
0.0252 1
3.3%
0.0259 2
6.7%
0.026 1
3.3%
0.0263 1
3.3%
0.0266 1
3.3%
ValueCountFrequency (%)
0.0659 1
3.3%
0.0456 1
3.3%
0.0349 1
3.3%
0.0346 1
3.3%
0.034 1
3.3%
0.0331 1
3.3%
0.0323 1
3.3%
0.0319 1
3.3%
0.0315 1
3.3%
0.0314 1
3.3%

단어연결중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12185667
Minimum0.0404
Maximum0.6805
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:07:10.295074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0404
5-th percentile0.046185
Q10.078525
median0.0899
Q30.1199
95-th percentile0.218415
Maximum0.6805
Range0.6401
Interquartile range (IQR)0.041375

Descriptive statistics

Standard deviation0.11519501
Coefficient of variation (CV)0.945332
Kurtosis20.221054
Mean0.12185667
Median Absolute Deviation (MAD)0.01565
Skewness4.2137084
Sum3.6557
Variance0.013269889
MonotonicityNot monotonic
2023-12-10T23:07:10.488031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.1199 2
 
6.7%
0.0834 2
 
6.7%
0.0756 1
 
3.3%
0.0638 1
 
3.3%
0.0886 1
 
3.3%
0.099 1
 
3.3%
0.0808 1
 
3.3%
0.0873 1
 
3.3%
0.0821 1
 
3.3%
0.1003 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0404 1
3.3%
0.0456 1
3.3%
0.0469 1
3.3%
0.0638 1
3.3%
0.0677 1
3.3%
0.073 1
3.3%
0.0756 1
3.3%
0.0782 1
3.3%
0.0795 1
3.3%
0.0808 1
3.3%
ValueCountFrequency (%)
0.6805 1
3.3%
0.219 1
3.3%
0.2177 1
3.3%
0.2112 1
3.3%
0.1538 1
3.3%
0.1382 1
3.3%
0.1329 1
3.3%
0.1199 2
6.7%
0.1043 1
3.3%
0.1003 1
3.3%

단어매개중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028126667
Minimum0.0015
Maximum0.5605
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:07:10.688207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.002135
Q10.004975
median0.008
Q30.011275
95-th percentile0.032165
Maximum0.5605
Range0.559
Interquartile range (IQR)0.0063

Descriptive statistics

Standard deviation0.10087176
Coefficient of variation (CV)3.5863389
Kurtosis29.576721
Mean0.028126667
Median Absolute Deviation (MAD)0.00325
Skewness5.422132
Sum0.8438
Variance0.010175112
MonotonicityNot monotonic
2023-12-10T23:07:10.911030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0052 2
 
6.7%
0.5605 1
 
3.3%
0.0323 1
 
3.3%
0.0023 1
 
3.3%
0.0033 1
 
3.3%
0.007 1
 
3.3%
0.0112 1
 
3.3%
0.0049 1
 
3.3%
0.0047 1
 
3.3%
0.0072 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0015 1
3.3%
0.002 1
3.3%
0.0023 1
3.3%
0.0033 1
3.3%
0.0034 1
3.3%
0.0043 1
3.3%
0.0047 1
3.3%
0.0049 1
3.3%
0.0052 2
6.7%
0.0056 1
3.3%
ValueCountFrequency (%)
0.5605 1
3.3%
0.0323 1
3.3%
0.032 1
3.3%
0.0298 1
3.3%
0.0151 1
3.3%
0.0126 1
3.3%
0.0115 1
3.3%
0.0113 1
3.3%
0.0112 1
3.3%
0.0109 1
3.3%

Interactions

2023-12-10T23:07:06.535426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:02.962442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:03.699717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:04.607974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:05.833702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:06.690101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:03.167610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:03.956434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:04.758450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:05.971221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:06.846067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:03.298241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:04.115228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:05.414186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:06.109851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:06.988568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:03.427770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:04.335772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:05.558226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:06.248716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:07.136878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:03.576731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:04.478701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:05.706378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:07:06.380804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:07:11.059618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스키워드명단어빈도단어중요도단어연결중심성단어매개중심성
분석인덱스1.0001.0000.0000.0000.5900.159
키워드명1.0001.0001.0001.0001.0001.000
단어빈도0.0001.0001.0000.0001.0000.653
단어중요도0.0001.0000.0001.0000.0000.000
단어연결중심성0.5901.0001.0000.0001.0001.000
단어매개중심성0.1591.0000.6530.0001.0001.000
2023-12-10T23:07:11.244136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스단어빈도단어중요도단어연결중심성단어매개중심성
분석인덱스1.000-1.0000.198-0.423-0.706
단어빈도-1.0001.000-0.1980.4230.706
단어중요도0.198-0.1981.000-0.060-0.268
단어연결중심성-0.4230.423-0.0601.0000.601
단어매개중심성-0.7060.706-0.2680.6011.000

Missing values

2023-12-10T23:07:07.383155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:07:07.592222image/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

분석인덱스수집년월키워드명단어빈도단어중요도단어연결중심성단어매개중심성
012010-01수원468840.03490.68050.5605
122010-01서울52570.02590.21770.0323
232010-01경기51850.02830.21120.0298
342010-01경기도45770.02680.2190.032
452010-01정보33350.02590.15380.0151
562010-01판매27470.03190.09120.0115
672010-01거래23670.02690.06770.0077
782010-01인천22400.02410.13290.0087
892010-01가격21780.02860.07950.0102
9102010-01희망21180.03310.04560.0015
분석인덱스수집년월키워드명단어빈도단어중요도단어연결중심성단어매개중심성
20212010-01화성14210.02840.11990.0056
21222010-01구입13470.02370.04690.0084
22232010-01아파트13000.03060.10030.0072
23242010-01이사12890.04560.08210.0047
24252010-01대학12800.03230.08730.0049
25262010-01채용12300.03150.08340.0112
26272010-01수원화성12150.06590.08080.0052
27282010-01지원11450.0260.0990.007
28292010-01부산11210.02630.08860.0033
29302010-01취업10950.03460.06380.0023