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/a76867e4-78ae-4335-a08d-51a054c243fe

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

Reproduction

Analysis started2023-12-10 13:44:47.391501
Analysis finished2023-12-10 13:44:52.135109
Duration4.74 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
2023-12-10T22:44:52.250652image/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
2023-12-10T22:44:52.535364image/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

2023-12-10T22:44:52.809497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:44:52.978364image/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
2023-12-10T22:44:53.257126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2666667
Min length1

Characters and Unicode

Total characters68
Distinct characters43
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 row창업
3rd row지원
4th row기업
5th row사업
ValueCountFrequency (%)
경기도 1
 
3.3%
창업 1
 
3.3%
채용 1
 
3.3%
성남 1
 
3.3%
성공 1
 
3.3%
시장 1
 
3.3%
상권 1
 
3.3%
여성창업 1
 
3.3%
취업 1
 
3.3%
회사 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:44:53.771508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 8
 
11.8%
기 5
 
7.4%
성 4
 
5.9%
공 3
 
4.4%
소 2
 
2.9%
처 2
 
2.9%
벤 2
 
2.9%
장 2
 
2.9%
사 2
 
2.9%
원 2
 
2.9%
Other values (33) 36
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 8
 
11.8%
기 5
 
7.4%
성 4
 
5.9%
공 3
 
4.4%
소 2
 
2.9%
처 2
 
2.9%
벤 2
 
2.9%
장 2
 
2.9%
사 2
 
2.9%
원 2
 
2.9%
Other values (33) 36
52.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 8
 
11.8%
기 5
 
7.4%
성 4
 
5.9%
공 3
 
4.4%
소 2
 
2.9%
처 2
 
2.9%
벤 2
 
2.9%
장 2
 
2.9%
사 2
 
2.9%
원 2
 
2.9%
Other values (33) 36
52.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 8
 
11.8%
기 5
 
7.4%
성 4
 
5.9%
공 3
 
4.4%
소 2
 
2.9%
처 2
 
2.9%
벤 2
 
2.9%
장 2
 
2.9%
사 2
 
2.9%
원 2
 
2.9%
Other values (33) 36
52.9%

단어빈도
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.3
Minimum58
Maximum943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:44:53.982172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum58
5-th percentile58.45
Q164.5
median85.5
Q3123
95-th percentile585.75
Maximum943
Range885
Interquartile range (IQR)58.5

Descriptive statistics

Standard deviation205.56401
Coefficient of variation (CV)1.3676913
Kurtosis10.908823
Mean150.3
Median Absolute Deviation (MAD)24.5
Skewness3.3799693
Sum4509
Variance42256.562
MonotonicityDecreasing
2023-12-10T22:44:54.157058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
78 3
 
10.0%
58 2
 
6.7%
60 2
 
6.7%
943 1
 
3.3%
86 1
 
3.3%
59 1
 
3.3%
61 1
 
3.3%
63 1
 
3.3%
64 1
 
3.3%
66 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
58 2
6.7%
59 1
 
3.3%
60 2
6.7%
61 1
 
3.3%
63 1
 
3.3%
64 1
 
3.3%
66 1
 
3.3%
68 1
 
3.3%
78 3
10.0%
79 1
 
3.3%
ValueCountFrequency (%)
943 1
3.3%
822 1
3.3%
297 1
3.3%
192 1
3.3%
146 1
3.3%
129 1
3.3%
127 1
3.3%
124 1
3.3%
120 1
3.3%
110 1
3.3%

단어중요도
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028243333
Minimum0.0193
Maximum0.0581
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:44:54.388948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0193
5-th percentile0.022425
Q10.023975
median0.0261
Q30.030475
95-th percentile0.03901
Maximum0.0581
Range0.0388
Interquartile range (IQR)0.0065

Descriptive statistics

Standard deviation0.0073097801
Coefficient of variation (CV)0.25881435
Kurtosis9.0713251
Mean0.028243333
Median Absolute Deviation (MAD)0.00325
Skewness2.5863775
Sum0.8473
Variance5.3432885 × 10-5
MonotonicityNot monotonic
2023-12-10T22:44:54.641152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0227 2
 
6.7%
0.0239 1
 
3.3%
0.0306 1
 
3.3%
0.0247 1
 
3.3%
0.0391 1
 
3.3%
0.0389 1
 
3.3%
0.025 1
 
3.3%
0.0236 1
 
3.3%
0.0581 1
 
3.3%
0.0325 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0193 1
3.3%
0.0222 1
3.3%
0.0227 2
6.7%
0.0228 1
3.3%
0.0236 1
3.3%
0.0238 1
3.3%
0.0239 1
3.3%
0.0242 1
3.3%
0.0243 1
3.3%
0.0246 1
3.3%
ValueCountFrequency (%)
0.0581 1
3.3%
0.0391 1
3.3%
0.0389 1
3.3%
0.0325 1
3.3%
0.0324 1
3.3%
0.0322 1
3.3%
0.0307 1
3.3%
0.0306 1
3.3%
0.0301 1
3.3%
0.0299 1
3.3%

단어연결중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.044363333
Minimum0.0102
Maximum0.2032
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:44:54.873450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0102
5-th percentile0.012275
Q10.0203
median0.03265
Q30.0421
95-th percentile0.138965
Maximum0.2032
Range0.193
Interquartile range (IQR)0.0218

Descriptive statistics

Standard deviation0.044101822
Coefficient of variation (CV)0.99410523
Kurtosis7.8747526
Mean0.044363333
Median Absolute Deviation (MAD)0.01235
Skewness2.7811191
Sum1.3309
Variance0.0019449707
MonotonicityNot monotonic
2023-12-10T22:44:55.088138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0189 4
 
13.3%
0.0203 2
 
6.7%
0.029 2
 
6.7%
0.0348 2
 
6.7%
0.0421 2
 
6.7%
0.1814 1
 
3.3%
0.0116 1
 
3.3%
0.0232 1
 
3.3%
0.0218 1
 
3.3%
0.0102 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0.0102 1
 
3.3%
0.0116 1
 
3.3%
0.0131 1
 
3.3%
0.0189 4
13.3%
0.0203 2
6.7%
0.0218 1
 
3.3%
0.0232 1
 
3.3%
0.029 2
6.7%
0.0305 1
 
3.3%
0.0319 1
 
3.3%
ValueCountFrequency (%)
0.2032 1
3.3%
0.1814 1
3.3%
0.0871 1
3.3%
0.0726 1
3.3%
0.0668 1
3.3%
0.0522 1
3.3%
0.0493 1
3.3%
0.0421 2
6.7%
0.0406 1
3.3%
0.0377 1
3.3%

단어매개중심성
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.041036667
Minimum0.0014
Maximum0.3231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:44:55.329388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0014
5-th percentile0.004845
Q10.010325
median0.0214
Q30.032775
95-th percentile0.17722
Maximum0.3231
Range0.3217
Interquartile range (IQR)0.02245

Descriptive statistics

Standard deviation0.070343382
Coefficient of variation (CV)1.7141593
Kurtosis11.102241
Mean0.041036667
Median Absolute Deviation (MAD)0.01175
Skewness3.3415843
Sum1.2311
Variance0.0049481914
MonotonicityNot monotonic
2023-12-10T22:44:55.550617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.2521 1
 
3.3%
0.0334 1
 
3.3%
0.0086 1
 
3.3%
0.0136 1
 
3.3%
0.0087 1
 
3.3%
0.0073 1
 
3.3%
0.0169 1
 
3.3%
0.0071 1
 
3.3%
0.0099 1
 
3.3%
0.021 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.0014 1
3.3%
0.0039 1
3.3%
0.006 1
3.3%
0.0071 1
3.3%
0.0073 1
3.3%
0.0086 1
3.3%
0.0087 1
3.3%
0.0099 1
3.3%
0.0116 1
3.3%
0.0122 1
3.3%
ValueCountFrequency (%)
0.3231 1
3.3%
0.2521 1
3.3%
0.0857 1
3.3%
0.0718 1
3.3%
0.0583 1
3.3%
0.0363 1
3.3%
0.0356 1
3.3%
0.0334 1
3.3%
0.0309 1
3.3%
0.0254 1
3.3%

Interactions

2023-12-10T22:44:51.140776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:47.688281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:48.403363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:49.121315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:50.053230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:51.270926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:47.826394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:48.556407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:49.303533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:50.563331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:51.383369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:47.983569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:48.674156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:49.470341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:50.697787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:51.519298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:48.135682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:48.835930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:49.637174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:50.860251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:51.659597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:48.272672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:48.991502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:49.831719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:44:51.011129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:44:55.722923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스키워드명단어빈도단어중요도단어연결중심성단어매개중심성
분석인덱스1.0001.0000.4370.5290.4020.299
키워드명1.0001.0001.0001.0001.0001.000
단어빈도0.4371.0001.0000.0000.8970.988
단어중요도0.5291.0000.0001.0000.0000.000
단어연결중심성0.4021.0000.8970.0001.0001.000
단어매개중심성0.2991.0000.9880.0001.0001.000
2023-12-10T22:44:55.910611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스단어빈도단어중요도단어연결중심성단어매개중심성
분석인덱스1.000-0.9990.103-0.744-0.787
단어빈도-0.9991.000-0.1020.7430.784
단어중요도0.103-0.1021.0000.0740.008
단어연결중심성-0.7440.7430.0741.0000.970
단어매개중심성-0.7870.7840.0080.9701.000

Missing values

2023-12-10T22:44:51.888101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:44:52.071670image/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
012010-01경기도9430.02390.18140.2521
122010-01창업8220.02770.20320.3231
232010-01지원2970.03070.08710.0857
342010-01기업1920.0270.07260.0718
452010-01사업1460.02280.04930.0363
562010-01자금1290.02560.0290.0218
672010-01교육1270.02990.06680.0583
782010-01서울1240.02380.03770.0309
892010-01벤처기업1200.02270.04210.0239
9102010-01중소기업1100.02930.03340.0174
분석인덱스수집년월키워드명단어빈도단어중요도단어연결중심성단어매개중심성
20212010-01대학680.02660.03480.0226
21222010-01회사660.02220.01890.0122
22232010-01취업640.02430.03480.021
23242010-01여성창업630.03250.02030.0099
24252010-01상권610.05810.01890.0071
25262010-01시장600.02360.0290.0169
26272010-01성공600.0250.01890.0073
27282010-01성남590.03890.02180.0087
28292010-01채용580.03910.02030.0136
29302010-01공장580.02470.02320.0086