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/432d9660-ac1e-4714-9fdb-cd03c131cff1

Alerts

수집년월 has constant value ""Constant
분석인덱스 is highly overall correlated with 단어빈도 and 3 other fieldsHigh correlation
단어빈도 is highly overall correlated with 분석인덱스 and 3 other fieldsHigh correlation
단어중요도 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 분석인덱스 and 2 other fieldsHigh correlation
분석인덱스 has unique valuesUnique
키워드명 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:03:10.676577
Analysis finished2023-12-10 14:03:14.882132
Duration4.21 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
2023-12-10T23:03:14.979850image/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
2023-12-10T23:03:15.222695image/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

2023-12-10T23:03:15.520714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:03:16.006186image/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
2023-12-10T23:03:16.259139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.3333333
Min length1

Characters and Unicode

Total characters70
Distinct characters52
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 row편의점
2nd row서점
3rd row책
4th row슈퍼마켓
5th row구입
ValueCountFrequency (%)
편의점 1
 
3.3%
서점 1
 
3.3%
학교 1
 
3.3%
친구 1
 
3.3%
공부 1
 
3.3%
이용 1
 
3.3%
구매 1
 
3.3%
온라인 1
 
3.3%
여행 1
 
3.3%
근처 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:03:16.941449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 4
 
5.7%
점 4
 
5.7%
서 3
 
4.3%
인 3
 
4.3%
이 3
 
4.3%
아 2
 
2.9%
터 2
 
2.9%
넷 2
 
2.9%
마 2
 
2.9%
가 2
 
2.9%
Other values (42) 43
61.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 4
 
5.7%
점 4
 
5.7%
서 3
 
4.3%
인 3
 
4.3%
이 3
 
4.3%
아 2
 
2.9%
터 2
 
2.9%
넷 2
 
2.9%
마 2
 
2.9%
가 2
 
2.9%
Other values (42) 43
61.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 4
 
5.7%
점 4
 
5.7%
서 3
 
4.3%
인 3
 
4.3%
이 3
 
4.3%
아 2
 
2.9%
터 2
 
2.9%
넷 2
 
2.9%
마 2
 
2.9%
가 2
 
2.9%
Other values (42) 43
61.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 4
 
5.7%
점 4
 
5.7%
서 3
 
4.3%
인 3
 
4.3%
이 3
 
4.3%
아 2
 
2.9%
터 2
 
2.9%
넷 2
 
2.9%
마 2
 
2.9%
가 2
 
2.9%
Other values (42) 43
61.4%

단어빈도
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2292.8
Minimum758
Maximum13694
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:03:17.135785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum758
5-th percentile789.8
Q1930.5
median1114.5
Q31467.5
95-th percentile11174.9
Maximum13694
Range12936
Interquartile range (IQR)537

Descriptive statistics

Standard deviation3339.96
Coefficient of variation (CV)1.4567167
Kurtosis7.1344904
Mean2292.8
Median Absolute Deviation (MAD)252.5
Skewness2.8638163
Sum68784
Variance11155333
MonotonicityDecreasing
2023-12-10T23:03:17.322961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1043 2
 
6.7%
13694 1
 
3.3%
12551 1
 
3.3%
758 1
 
3.3%
761 1
 
3.3%
825 1
 
3.3%
855 1
 
3.3%
869 1
 
3.3%
882 1
 
3.3%
913 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
758 1
3.3%
761 1
3.3%
825 1
3.3%
855 1
3.3%
869 1
3.3%
882 1
3.3%
913 1
3.3%
929 1
3.3%
935 1
3.3%
951 1
3.3%
ValueCountFrequency (%)
13694 1
3.3%
12551 1
3.3%
9493 1
3.3%
2757 1
3.3%
2024 1
3.3%
1955 1
3.3%
1801 1
3.3%
1477 1
3.3%
1439 1
3.3%
1432 1
3.3%

단어중요도
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.026363333
Minimum0.0209
Maximum0.0371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:03:17.495283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0209
5-th percentile0.022445
Q10.023975
median0.02475
Q30.0282
95-th percentile0.033985
Maximum0.0371
Range0.0162
Interquartile range (IQR)0.004225

Descriptive statistics

Standard deviation0.0040046941
Coefficient of variation (CV)0.15190394
Kurtosis0.81087938
Mean0.026363333
Median Absolute Deviation (MAD)0.0015
Skewness1.2350555
Sum0.7909
Variance1.6037575 × 10-5
MonotonicityNot monotonic
2023-12-10T23:03:17.702673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0244 2
 
6.7%
0.0242 2
 
6.7%
0.0295 1
 
3.3%
0.0325 1
 
3.3%
0.0263 1
 
3.3%
0.0225 1
 
3.3%
0.0233 1
 
3.3%
0.0235 1
 
3.3%
0.0254 1
 
3.3%
0.0224 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.0209 1
3.3%
0.0224 1
3.3%
0.0225 1
3.3%
0.023 1
3.3%
0.0232 1
3.3%
0.0233 1
3.3%
0.0235 1
3.3%
0.0239 1
3.3%
0.0242 2
6.7%
0.0243 1
3.3%
ValueCountFrequency (%)
0.0371 1
3.3%
0.0352 1
3.3%
0.0325 1
3.3%
0.0323 1
3.3%
0.0317 1
3.3%
0.0304 1
3.3%
0.0295 1
3.3%
0.0287 1
3.3%
0.0267 1
3.3%
0.0263 1
3.3%

연결정도중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.071833333
Minimum0.0268
Maximum0.2848
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:03:17.893481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0268
5-th percentile0.02933
Q10.0435
median0.054
Q30.0719
95-th percentile0.195955
Maximum0.2848
Range0.258
Interquartile range (IQR)0.0284

Descriptive statistics

Standard deviation0.056906913
Coefficient of variation (CV)0.79220761
Kurtosis7.1722051
Mean0.071833333
Median Absolute Deviation (MAD)0.01195
Skewness2.6498933
Sum2.155
Variance0.0032383968
MonotonicityNot monotonic
2023-12-10T23:03:18.091703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0468 3
 
10.0%
0.0435 2
 
6.7%
0.054 2
 
6.7%
0.0612 2
 
6.7%
0.065 1
 
3.3%
0.0268 1
 
3.3%
0.0287 1
 
3.3%
0.0392 1
 
3.3%
0.0425 1
 
3.3%
0.0416 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
0.0268 1
 
3.3%
0.0287 1
 
3.3%
0.0301 1
 
3.3%
0.0382 1
 
3.3%
0.0392 1
 
3.3%
0.0416 1
 
3.3%
0.0425 1
 
3.3%
0.0435 2
6.7%
0.0459 1
 
3.3%
0.0468 3
10.0%
ValueCountFrequency (%)
0.2848 1
3.3%
0.214 1
3.3%
0.1739 1
3.3%
0.1218 1
3.3%
0.0827 1
3.3%
0.0736 1
3.3%
0.0731 1
3.3%
0.0726 1
3.3%
0.0698 1
3.3%
0.065 1
3.3%

매개중심성
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.025093333
Minimum0.005
Maximum0.2153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:03:18.276482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.005
5-th percentile0.0061
Q10.00745
median0.0117
Q30.0185
95-th percentile0.10042
Maximum0.2153
Range0.2103
Interquartile range (IQR)0.01105

Descriptive statistics

Standard deviation0.042782327
Coefficient of variation (CV)1.704928
Kurtosis14.144417
Mean0.025093333
Median Absolute Deviation (MAD)0.00505
Skewness3.6208492
Sum0.7528
Variance0.0018303275
MonotonicityNot monotonic
2023-12-10T23:03:18.491666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0061 2
 
6.7%
0.0073 2
 
6.7%
0.0145 1
 
3.3%
0.0087 1
 
3.3%
0.0082 1
 
3.3%
0.0071 1
 
3.3%
0.005 1
 
3.3%
0.0064 1
 
3.3%
0.0169 1
 
3.3%
0.0129 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
0.005 1
3.3%
0.0061 2
6.7%
0.0064 1
3.3%
0.0068 1
3.3%
0.0071 1
3.3%
0.0073 2
6.7%
0.0079 1
3.3%
0.0082 1
3.3%
0.0085 1
3.3%
0.0087 1
3.3%
ValueCountFrequency (%)
0.2153 1
3.3%
0.1114 1
3.3%
0.087 1
3.3%
0.0387 1
3.3%
0.0208 1
3.3%
0.0206 1
3.3%
0.0203 1
3.3%
0.0188 1
3.3%
0.0176 1
3.3%
0.0171 1
3.3%

Interactions

2023-12-10T23:03:13.923872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:11.000782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:11.730527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:12.595288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:13.270966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:14.060604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:11.136972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:11.887934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:12.747871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:13.403831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:14.179891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:11.265559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:12.045985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:12.881984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:13.545585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:14.324433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:11.424309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:12.303570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:13.017782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:13.685649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:14.435376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:11.599932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:12.455030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:13.145427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:03:13.799764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:03:18.636119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스키워드명단어빈도단어중요도연결정도중심성매개중심성
분석인덱스1.0001.0000.6240.1230.3770.437
키워드명1.0001.0001.0001.0001.0001.000
단어빈도0.6241.0001.0000.7911.0001.000
단어중요도0.1231.0000.7911.0000.6630.891
연결정도중심성0.3771.0001.0000.6631.0001.000
매개중심성0.4371.0001.0000.8911.0001.000
2023-12-10T23:03:18.821187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분석인덱스단어빈도단어중요도연결정도중심성매개중심성
분석인덱스1.000-1.000-0.540-0.649-0.653
단어빈도-1.0001.0000.5380.6530.654
단어중요도-0.5400.5381.0000.2880.245
연결정도중심성-0.6490.6530.2881.0000.890
매개중심성-0.6530.6540.2450.8901.000

Missing values

2023-12-10T23:03:14.628811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:03:14.814867image/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
012010-01편의점136940.02950.28480.2153
122010-01서점125510.02620.2140.1114
232010-01책94930.03170.17390.087
342010-01슈퍼마켓27570.02670.12180.0387
452010-01구입20240.02420.0540.0124
562010-01문구점19550.03040.06980.0206
672010-01아이18010.02490.05350.0136
782010-01가격14770.02510.05780.0171
892010-01택배14390.03520.03820.0068
9102010-01아르바이트14320.03230.04680.0079
분석인덱스수집년월키워드명단어빈도단어중요도연결정도중심성매개중심성
20212010-01가지9510.02090.04350.0093
21222010-01근처9350.02240.06160.0129
22232010-01여행9290.02420.06120.0169
23242010-01온라인9130.02540.04590.0061
24252010-01구매8820.02350.04160.0064
25262010-01이용8690.02330.04250.005
26272010-01공부8550.02250.03920.0073
27282010-01친구8250.02440.02870.0071
28292010-01학교7610.02630.04680.0082
29302010-01엄마7580.02440.02680.0087