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/394df779-6187-4594-9f63-273dc934aa51

Alerts

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

Reproduction

Analysis started2023-12-10 13:51:41.779029
Analysis finished2023-12-10 13:51:45.797347
Duration4.02 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
2023-12-10T22:51:45.912312image/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
2023-12-10T22:51:46.127739image/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

2023-12-10T22:51:46.332642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:51:46.488479image/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
2023-12-10T22:51:46.738037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2333333
Min length1

Characters and Unicode

Total characters67
Distinct characters44
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 row경기도
2nd row투자
3rd row산업
4th row지역
5th row사업
ValueCountFrequency (%)
경기도 1
 
3.3%
투자 1
 
3.3%
건설 1
 
3.3%
수도권 1
 
3.3%
용인 1
 
3.3%
산업단지 1
 
3.3%
구제역 1
 
3.3%
세종시 1
 
3.3%
정보 1
 
3.3%
수원 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:51:47.244524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 4
 
6.0%
경 3
 
4.5%
수 3
 
4.5%
도 3
 
4.5%
산 3
 
4.5%
지 3
 
4.5%
기 3
 
4.5%
시 3
 
4.5%
정 2
 
3.0%
부 2
 
3.0%
Other values (34) 38
56.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 67
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 4
 
6.0%
경 3
 
4.5%
수 3
 
4.5%
도 3
 
4.5%
산 3
 
4.5%
지 3
 
4.5%
기 3
 
4.5%
시 3
 
4.5%
정 2
 
3.0%
부 2
 
3.0%
Other values (34) 38
56.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 67
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 4
 
6.0%
경 3
 
4.5%
수 3
 
4.5%
도 3
 
4.5%
산 3
 
4.5%
지 3
 
4.5%
기 3
 
4.5%
시 3
 
4.5%
정 2
 
3.0%
부 2
 
3.0%
Other values (34) 38
56.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 67
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 4
 
6.0%
경 3
 
4.5%
수 3
 
4.5%
도 3
 
4.5%
산 3
 
4.5%
지 3
 
4.5%
기 3
 
4.5%
시 3
 
4.5%
정 2
 
3.0%
부 2
 
3.0%
Other values (34) 38
56.7%

단어빈도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1616.8667
Minimum657
Maximum11759
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:51:47.458300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum657
5-th percentile685.25
Q1805.5
median1060.5
Q31358
95-th percentile4053.8
Maximum11759
Range11102
Interquartile range (IQR)552.5

Descriptive statistics

Standard deviation2082.9194
Coefficient of variation (CV)1.2882444
Kurtosis20.693361
Mean1616.8667
Median Absolute Deviation (MAD)281.5
Skewness4.3500994
Sum48506
Variance4338553.2
MonotonicityStrictly decreasing
2023-12-10T22:51:47.632235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
11759 1
 
3.3%
968 1
 
3.3%
657 1
 
3.3%
674 1
 
3.3%
699 1
 
3.3%
711 1
 
3.3%
751 1
 
3.3%
775 1
 
3.3%
800 1
 
3.3%
805 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
657 1
3.3%
674 1
3.3%
699 1
3.3%
711 1
3.3%
751 1
3.3%
775 1
3.3%
800 1
3.3%
805 1
3.3%
807 1
3.3%
842 1
3.3%
ValueCountFrequency (%)
11759 1
3.3%
4376 1
3.3%
3660 1
3.3%
1770 1
3.3%
1662 1
3.3%
1621 1
3.3%
1608 1
3.3%
1359 1
3.3%
1355 1
3.3%
1338 1
3.3%

단어중요도
Real number (ℝ)

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.025666667
Minimum0.0213
Maximum0.0377
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:51:47.802635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0213
5-th percentile0.02238
Q10.023625
median0.02495
Q30.0268
95-th percentile0.030995
Maximum0.0377
Range0.0164
Interquartile range (IQR)0.003175

Descriptive statistics

Standard deviation0.0032387027
Coefficient of variation (CV)0.12618322
Kurtosis5.7261215
Mean0.025666667
Median Absolute Deviation (MAD)0.00155
Skewness2.0024725
Sum0.77
Variance1.0489195 × 10-5
MonotonicityNot monotonic
2023-12-10T22:51:47.995970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.025 2
 
6.7%
0.0245 2
 
6.7%
0.0249 2
 
6.7%
0.0234 2
 
6.7%
0.0285 1
 
3.3%
0.023 1
 
3.3%
0.0226 1
 
3.3%
0.027 1
 
3.3%
0.0377 1
 
3.3%
0.0305 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0.0213 1
3.3%
0.0222 1
3.3%
0.0226 1
3.3%
0.0229 1
3.3%
0.023 1
3.3%
0.0234 2
6.7%
0.0236 1
3.3%
0.0237 1
3.3%
0.0238 1
3.3%
0.0245 2
6.7%
ValueCountFrequency (%)
0.0377 1
3.3%
0.0314 1
3.3%
0.0305 1
3.3%
0.0285 1
3.3%
0.028 1
3.3%
0.0271 1
3.3%
0.027 1
3.3%
0.0269 1
3.3%
0.0265 1
3.3%
0.0263 1
3.3%

연결정도중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08759
Minimum0.0248
Maximum0.3135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:51:48.193634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0248
5-th percentile0.031595
Q10.052625
median0.0716
Q30.104175
95-th percentile0.193025
Maximum0.3135
Range0.2887
Interquartile range (IQR)0.05155

Descriptive statistics

Standard deviation0.058639126
Coefficient of variation (CV)0.66947284
Kurtosis7.223329
Mean0.08759
Median Absolute Deviation (MAD)0.02105
Skewness2.3891575
Sum2.6277
Variance0.0034385471
MonotonicityNot monotonic
2023-12-10T22:51:48.367918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.1053 2
 
6.7%
0.0582 2
 
6.7%
0.3135 1
 
3.3%
0.0542 1
 
3.3%
0.039 1
 
3.3%
0.0607 1
 
3.3%
0.0476 1
 
3.3%
0.0344 1
 
3.3%
0.0248 1
 
3.3%
0.0293 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0248 1
3.3%
0.0293 1
3.3%
0.0344 1
3.3%
0.039 1
3.3%
0.0476 1
3.3%
0.0511 1
3.3%
0.0516 1
3.3%
0.0521 1
3.3%
0.0542 1
3.3%
0.0582 2
6.7%
ValueCountFrequency (%)
0.3135 1
3.3%
0.2117 1
3.3%
0.1702 1
3.3%
0.1261 1
3.3%
0.1114 1
3.3%
0.1053 2
6.7%
0.1043 1
3.3%
0.1038 1
3.3%
0.0932 1
3.3%
0.0891 1
3.3%

매개중심성
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026326667
Minimum0.0016
Maximum0.2069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:51:48.949044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0016
5-th percentile0.00284
Q10.008325
median0.0147
Q30.02445
95-th percentile0.088035
Maximum0.2069
Range0.2053
Interquartile range (IQR)0.016125

Descriptive statistics

Standard deviation0.040007326
Coefficient of variation (CV)1.5196503
Kurtosis15.010897
Mean0.026326667
Median Absolute Deviation (MAD)0.00885
Skewness3.6625745
Sum0.7898
Variance0.0016005862
MonotonicityNot monotonic
2023-12-10T22:51:49.125082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.2069 1
 
3.3%
0.0093 1
 
3.3%
0.0035 1
 
3.3%
0.0105 1
 
3.3%
0.0045 1
 
3.3%
0.0016 1
 
3.3%
0.0066 1
 
3.3%
0.0036 1
 
3.3%
0.0023 1
 
3.3%
0.0141 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0016 1
3.3%
0.0023 1
3.3%
0.0035 1
3.3%
0.0036 1
3.3%
0.0045 1
3.3%
0.0059 1
3.3%
0.0066 1
3.3%
0.008 1
3.3%
0.0093 1
3.3%
0.0097 1
3.3%
ValueCountFrequency (%)
0.2069 1
3.3%
0.1059 1
3.3%
0.0662 1
3.3%
0.0404 1
3.3%
0.0308 1
3.3%
0.03 1
3.3%
0.0255 1
3.3%
0.0247 1
3.3%
0.0237 1
3.3%
0.0236 1
3.3%

Interactions

2023-12-10T22:51:44.754647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:42.043265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:42.708886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.306956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.039916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.883820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:42.197075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:42.822752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.480482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.180240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:45.017045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:42.313562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:42.936180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.645601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.347514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:45.214599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:42.457452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.059222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.776212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.491504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:45.347928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:42.591830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.190506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.922497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.628954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:51:49.234620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스키워드명단어빈도단어중요도연결정도중심성매개중심성
분석인덱스1.0001.0000.3530.1720.6410.725
키워드명1.0001.0001.0001.0001.0001.000
단어빈도0.3531.0001.0000.0001.0000.991
단어중요도0.1721.0000.0001.0000.0000.000
연결정도중심성0.6411.0001.0000.0001.0000.943
매개중심성0.7251.0000.9910.0000.9431.000
2023-12-10T22:51:49.395575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스단어빈도단어중요도연결정도중심성매개중심성
분석인덱스1.000-1.0000.254-0.885-0.907
단어빈도-1.0001.000-0.2540.8850.907
단어중요도0.254-0.2541.000-0.217-0.242
연결정도중심성-0.8850.885-0.2171.0000.975
매개중심성-0.9070.907-0.2420.9751.000

Missing values

2023-12-10T22:51:45.533752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:51:45.721627image/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
012010-01경기도117590.02290.31350.2069
122010-01투자43760.02340.17020.0662
232010-01산업36600.02460.21170.1059
342010-01지역17700.02450.12610.0404
452010-01사업16620.02690.11140.0237
562010-01자금16210.02220.08860.0236
672010-01서울16080.02450.09320.0247
782010-01수출13590.02370.10530.0308
892010-01경기13550.02380.10380.0205
9102010-01지원13380.03140.10430.0255
분석인덱스수집년월키워드명단어빈도단어중요도연결정도중심성매개중심성
20212010-01아파트8420.02540.06580.0151
21222010-01수원8070.02630.05160.008
22232010-01정보8050.02710.07590.0141
23242010-01세종시8000.03050.02930.0023
24252010-01구제역7750.03770.02480.0036
25262010-01산업단지7510.0270.05820.0066
26272010-01용인7110.0250.03440.0016
27282010-01수도권6990.02260.04760.0045
28292010-01건설6740.02490.06070.0105
29302010-01정부6570.0230.0390.0035