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/285f43c8-94ad-4f8d-aafe-bd05a2042162

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 14:03:46.055696
Analysis finished2023-12-10 14:03:51.193324
Duration5.14 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
2023-12-10T23:03:51.287670image/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
2023-12-10T23:03:51.493597image/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

2023-12-10T23:03:51.709075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:03:51.873107image/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
2023-12-10T23:03:52.138501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.1333333
Min length2

Characters and Unicode

Total characters64
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 row경기도
2nd row축제
3rd row가평
4th row문화
5th row시장
ValueCountFrequency (%)
경기도 1
 
3.3%
축제 1
 
3.3%
인천 1
 
3.3%
영상 1
 
3.3%
도시 1
 
3.3%
사진 1
 
3.3%
광주 1
 
3.3%
세계 1
 
3.3%
뉴스 1
 
3.3%
전국 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:03:52.742267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 3
 
4.7%
도 3
 
4.7%
울 3
 
4.7%
원 2
 
3.1%
양 2
 
3.1%
주 2
 
3.1%
행 2
 
3.1%
시 2
 
3.1%
평 2
 
3.1%
겨 2
 
3.1%
Other values (38) 41
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 64
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 3
 
4.7%
도 3
 
4.7%
울 3
 
4.7%
원 2
 
3.1%
양 2
 
3.1%
주 2
 
3.1%
행 2
 
3.1%
시 2
 
3.1%
평 2
 
3.1%
겨 2
 
3.1%
Other values (38) 41
64.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 3
 
4.7%
도 3
 
4.7%
울 3
 
4.7%
원 2
 
3.1%
양 2
 
3.1%
주 2
 
3.1%
행 2
 
3.1%
시 2
 
3.1%
평 2
 
3.1%
겨 2
 
3.1%
Other values (38) 41
64.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 64
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 3
 
4.7%
도 3
 
4.7%
울 3
 
4.7%
원 2
 
3.1%
양 2
 
3.1%
주 2
 
3.1%
행 2
 
3.1%
시 2
 
3.1%
평 2
 
3.1%
겨 2
 
3.1%
Other values (38) 41
64.1%

단어빈도
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.83333
Minimum63
Maximum1367
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:03:53.006108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile65.9
Q182.25
median101
Q3136
95-th percentile728.05
Maximum1367
Range1304
Interquartile range (IQR)53.75

Descriptive statistics

Standard deviation295.36548
Coefficient of variation (CV)1.6067025
Kurtosis12.392562
Mean183.83333
Median Absolute Deviation (MAD)24
Skewness3.6277361
Sum5515
Variance87240.764
MonotonicityDecreasing
2023-12-10T23:03:53.256219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
98 2
 
6.7%
1147 1
 
3.3%
63 1
 
3.3%
65 1
 
3.3%
67 1
 
3.3%
69 1
 
3.3%
73 1
 
3.3%
78 1
 
3.3%
80 1
 
3.3%
82 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
63 1
3.3%
65 1
3.3%
67 1
3.3%
69 1
3.3%
73 1
3.3%
78 1
3.3%
80 1
3.3%
82 1
3.3%
83 1
3.3%
85 1
3.3%
ValueCountFrequency (%)
1367 1
3.3%
1147 1
3.3%
216 1
3.3%
191 1
3.3%
152 1
3.3%
145 1
3.3%
140 1
3.3%
138 1
3.3%
130 1
3.3%
126 1
3.3%

단어중요도
Real number (ℝ)

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.029373333
Minimum0.0215
Maximum0.0676
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:03:53.485652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0215
5-th percentile0.023405
Q10.024725
median0.0274
Q30.031475
95-th percentile0.038615
Maximum0.0676
Range0.0461
Interquartile range (IQR)0.00675

Descriptive statistics

Standard deviation0.0084077933
Coefficient of variation (CV)0.28623899
Kurtosis15.057312
Mean0.029373333
Median Absolute Deviation (MAD)0.003
Skewness3.4785794
Sum0.8812
Variance7.0690989 × 10-5
MonotonicityNot monotonic
2023-12-10T23:03:53.730093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0244 2
 
6.7%
0.0275 2
 
6.7%
0.0239 2
 
6.7%
0.0319 2
 
6.7%
0.0342 1
 
3.3%
0.0273 1
 
3.3%
0.0247 1
 
3.3%
0.0243 1
 
3.3%
0.023 1
 
3.3%
0.0311 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0.0215 1
3.3%
0.023 1
3.3%
0.0239 2
6.7%
0.0243 1
3.3%
0.0244 2
6.7%
0.0247 1
3.3%
0.0248 1
3.3%
0.0251 1
3.3%
0.0256 1
3.3%
0.0259 1
3.3%
ValueCountFrequency (%)
0.0676 1
3.3%
0.041 1
3.3%
0.0357 1
3.3%
0.0342 1
3.3%
0.0324 1
3.3%
0.0319 2
6.7%
0.0316 1
3.3%
0.0311 1
3.3%
0.0303 1
3.3%
0.0302 1
3.3%

연결정도중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063306667
Minimum0.0231
Maximum0.3333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:03:53.937356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0231
5-th percentile0.02571
Q10.0347
median0.04195
Q30.0521
95-th percentile0.22097
Maximum0.3333
Range0.3102
Interquartile range (IQR)0.0174

Descriptive statistics

Standard deviation0.073508915
Coefficient of variation (CV)1.161156
Kurtosis10.59939
Mean0.063306667
Median Absolute Deviation (MAD)0.01015
Skewness3.3428024
Sum1.8992
Variance0.0054035606
MonotonicityNot monotonic
2023-12-10T23:03:54.119130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0405 4
13.3%
0.0521 3
10.0%
0.0434 3
10.0%
0.0347 3
10.0%
0.0289 3
10.0%
0.0231 2
 
6.7%
0.0318 2
 
6.7%
0.0463 2
 
6.7%
0.0869 1
 
3.3%
0.0492 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
0.0231 2
6.7%
0.0289 3
10.0%
0.0318 2
6.7%
0.0347 3
10.0%
0.0376 1
 
3.3%
0.0405 4
13.3%
0.0434 3
10.0%
0.0463 2
6.7%
0.0492 1
 
3.3%
0.0521 3
10.0%
ValueCountFrequency (%)
0.3333 1
 
3.3%
0.3188 1
 
3.3%
0.1014 1
 
3.3%
0.0869 1
 
3.3%
0.0753 1
 
3.3%
0.055 1
 
3.3%
0.0521 3
10.0%
0.0492 1
 
3.3%
0.0463 2
6.7%
0.0434 3
10.0%

매개중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04229
Minimum0.0014
Maximum0.3883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:03:54.304385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0014
5-th percentile0.003825
Q10.0103
median0.01425
Q30.022825
95-th percentile0.237745
Maximum0.3883
Range0.3869
Interquartile range (IQR)0.012525

Descriptive statistics

Standard deviation0.092114709
Coefficient of variation (CV)2.1781676
Kurtosis11.340519
Mean0.04229
Median Absolute Deviation (MAD)0.00515
Skewness3.4855595
Sum1.2687
Variance0.0084851196
MonotonicityNot monotonic
2023-12-10T23:03:54.561169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0115 2
 
6.7%
0.3628 1
 
3.3%
0.0097 1
 
3.3%
0.0236 1
 
3.3%
0.0135 1
 
3.3%
0.0096 1
 
3.3%
0.0185 1
 
3.3%
0.0014 1
 
3.3%
0.0109 1
 
3.3%
0.0061 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0014 1
3.3%
0.0036 1
3.3%
0.0041 1
3.3%
0.0043 1
3.3%
0.0061 1
3.3%
0.0096 1
3.3%
0.0097 1
3.3%
0.0101 1
3.3%
0.0109 1
3.3%
0.0114 1
3.3%
ValueCountFrequency (%)
0.3883 1
3.3%
0.3628 1
3.3%
0.0849 1
3.3%
0.0505 1
3.3%
0.0413 1
3.3%
0.0295 1
3.3%
0.0254 1
3.3%
0.0236 1
3.3%
0.0205 1
3.3%
0.0199 1
3.3%

Interactions

2023-12-10T23:03:50.157435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:46.372643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:47.176077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:48.005018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:49.197521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:50.299386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:46.527054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:47.338499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:48.537829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:49.411605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:50.446293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:46.746966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:47.500252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:48.704687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:49.598024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:50.570950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:46.898354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:47.641854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:48.874457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:49.841193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:50.705655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:47.021579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:47.825210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:49.012356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:49.980582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:03:54.733512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스키워드명단어빈도단어중요도연결정도중심성매개중심성
분석인덱스1.0001.0000.3530.0000.6220.607
키워드명1.0001.0001.0001.0001.0001.000
단어빈도0.3531.0001.0000.6750.9350.854
단어중요도0.0001.0000.6751.0000.3280.000
연결정도중심성0.6221.0000.9350.3281.0000.976
매개중심성0.6071.0000.8540.0000.9761.000
2023-12-10T23:03:54.917446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스단어빈도단어중요도연결정도중심성매개중심성
분석인덱스1.000-1.000-0.231-0.531-0.340
단어빈도-1.0001.0000.2300.5320.337
단어중요도-0.2310.2301.0000.137-0.034
연결정도중심성-0.5310.5320.1371.0000.706
매개중심성-0.3400.337-0.0340.7061.000

Missing values

2023-12-10T23:03:50.924844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:03:51.121810image/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
012010-01경기도13670.02440.31880.3628
122010-01축제11470.02810.33330.3883
232010-01가평2160.0410.0550.0141
342010-01문화1910.02590.10140.0849
452010-01시장1520.03160.04050.0115
562010-01서울1450.02620.07530.0413
672010-01포천1400.03240.04050.0043
782010-01부천1380.03020.04340.0114
892010-01예술1300.02910.03470.0101
9102010-01행사1260.02510.05210.0295
분석인덱스수집년월키워드명단어빈도단어중요도연결정도중심성매개중심성
20212010-01공연850.03570.04630.0199
21222010-01전국830.02390.05210.0183
22232010-01뉴스820.02440.02890.0144
23242010-01세계800.02480.03180.0061
24252010-01광주780.03110.05210.0109
25262010-01사진730.0230.02310.0014
26272010-01도시690.02430.04340.0185
27282010-01영상670.02470.02310.0096
28292010-01인천650.02730.04050.0135
29302010-01양평630.03420.03470.0236