Overview

Dataset statistics

Number of variables6
Number of observations400
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.1 KiB
Average record size in memory51.3 B

Variable types

Numeric3
Categorical1
DateTime1
Text1

Dataset

DescriptionSample
Author코난테크놀로지
URLhttps://www.bigdata-telecom.kr/invoke/SOKBP2603/?goodsCode=TPONATION

Alerts

"채널값" has constant value ""Constant
"차례값" is highly overall correlated with "건수값"High correlation
"건수값" is highly overall correlated with "차례값"High correlation
"기본키값" has unique valuesUnique

Reproduction

Analysis started2023-12-10 06:22:27.865864
Analysis finished2023-12-10 06:22:30.518602
Duration2.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

"기본키값"
Real number (ℝ)

UNIQUE 

Distinct400
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26846.86
Minimum15922
Maximum47861
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:22:30.656885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15922
5-th percentile15941.95
Q116021.75
median31895.5
Q331995.25
95-th percentile47841.05
Maximum47861
Range31939
Interquartile range (IQR)15973.5

Descriptive statistics

Standard deviation10786.31
Coefficient of variation (CV)0.40177174
Kurtosis-0.79218373
Mean26846.86
Median Absolute Deviation (MAD)15822
Skewness0.48860499
Sum10738744
Variance1.1634447 × 108
MonotonicityStrictly increasing
2023-12-10T15:22:30.907636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15922 1
 
0.2%
31960 1
 
0.2%
31970 1
 
0.2%
31969 1
 
0.2%
31968 1
 
0.2%
31967 1
 
0.2%
31966 1
 
0.2%
31965 1
 
0.2%
31964 1
 
0.2%
31963 1
 
0.2%
Other values (390) 390
97.5%
ValueCountFrequency (%)
15922 1
0.2%
15923 1
0.2%
15924 1
0.2%
15925 1
0.2%
15926 1
0.2%
15927 1
0.2%
15928 1
0.2%
15929 1
0.2%
15930 1
0.2%
15931 1
0.2%
ValueCountFrequency (%)
47861 1
0.2%
47860 1
0.2%
47859 1
0.2%
47858 1
0.2%
47857 1
0.2%
47856 1
0.2%
47855 1
0.2%
47854 1
0.2%
47853 1
0.2%
47852 1
0.2%

"채널값"
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
"블로그"
400 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"블로그"
2nd row"블로그"
3rd row"블로그"
4th row"블로그"
5th row"블로그"

Common Values

ValueCountFrequency (%)
"블로그" 400
100.0%

Length

2023-12-10T15:22:31.143509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T15:22:31.296469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
블로그 400
100.0%

"해당일자"
Date

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
Minimum2020-05-01 00:00:00
Maximum2020-05-03 00:00:00
2023-12-10T15:22:31.426262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:22:31.587485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=3)

"차례값"
Real number (ℝ)

HIGH CORRELATION 

Distinct176
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.82
Minimum1
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:22:31.796528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q134
median76.5
Q3126.25
95-th percentile166.05
Maximum176
Range175
Interquartile range (IQR)92.25

Descriptive statistics

Standard deviation52.286732
Coefficient of variation (CV)0.64695288
Kurtosis-1.2506889
Mean80.82
Median Absolute Deviation (MAD)45.5
Skewness0.19316505
Sum32328
Variance2733.9024
MonotonicityNot monotonic
2023-12-10T15:22:32.100615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3
 
0.8%
26 3
 
0.8%
28 3
 
0.8%
29 3
 
0.8%
30 3
 
0.8%
31 3
 
0.8%
32 3
 
0.8%
33 3
 
0.8%
34 3
 
0.8%
35 3
 
0.8%
Other values (166) 370
92.5%
ValueCountFrequency (%)
1 3
0.8%
2 3
0.8%
3 3
0.8%
4 3
0.8%
5 3
0.8%
6 3
0.8%
7 3
0.8%
8 3
0.8%
9 3
0.8%
10 3
0.8%
ValueCountFrequency (%)
176 2
0.5%
175 2
0.5%
174 2
0.5%
173 2
0.5%
172 2
0.5%
171 2
0.5%
170 2
0.5%
169 2
0.5%
168 2
0.5%
167 2
0.5%

"이슈어값"
Text

Distinct176
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Memory size3.3 KiB
2023-12-10T15:22:32.644215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length5.9075
Min length4

Characters and Unicode

Total characters2363
Distinct characters170
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row"미국"
2nd row"중국"
3rd row"일본"
4th row"영국"
5th row"독일"
ValueCountFrequency (%)
미국 3
 
0.8%
파키스탄 3
 
0.8%
스웨덴 3
 
0.8%
멕시코 3
 
0.8%
덴마크 3
 
0.8%
이집트 3
 
0.8%
노르웨이 3
 
0.8%
오스트리아 3
 
0.8%
벨기에 3
 
0.8%
포르투갈 3
 
0.8%
Other values (166) 370
92.5%
2023-12-10T15:22:33.413081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 800
33.9%
아 113
 
4.8%
스 63
 
2.7%
리 59
 
2.5%
이 45
 
1.9%
르 44
 
1.9%
라 44
 
1.9%
니 42
 
1.8%
나 30
 
1.3%
바 30
 
1.3%
Other values (160) 1093
46.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1563
66.1%
Other Punctuation 800
33.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 113
 
7.2%
스 63
 
4.0%
리 59
 
3.8%
이 45
 
2.9%
르 44
 
2.8%
라 44
 
2.8%
니 42
 
2.7%
나 30
 
1.9%
바 30
 
1.9%
트 29
 
1.9%
Other values (159) 1064
68.1%
Other Punctuation
ValueCountFrequency (%)
" 800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1563
66.1%
Common 800
33.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 113
 
7.2%
스 63
 
4.0%
리 59
 
3.8%
이 45
 
2.9%
르 44
 
2.8%
라 44
 
2.8%
니 42
 
2.7%
나 30
 
1.9%
바 30
 
1.9%
트 29
 
1.9%
Other values (159) 1064
68.1%
Common
ValueCountFrequency (%)
" 800
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1563
66.1%
ASCII 800
33.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
" 800
100.0%
Hangul
ValueCountFrequency (%)
아 113
 
7.2%
스 63
 
4.0%
리 59
 
3.8%
이 45
 
2.9%
르 44
 
2.8%
라 44
 
2.8%
니 42
 
2.7%
나 30
 
1.9%
바 30
 
1.9%
트 29
 
1.9%
Other values (159) 1064
68.1%

"건수값"
Real number (ℝ)

HIGH CORRELATION 

Distinct219
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean483.0675
Minimum1
Maximum12675
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.6 KiB
2023-12-10T15:22:33.662462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115.75
median52
Q3227.25
95-th percentile1853.5
Maximum12675
Range12674
Interquartile range (IQR)211.5

Descriptive statistics

Standard deviation1620.5171
Coefficient of variation (CV)3.3546391
Kurtosis37.615775
Mean483.0675
Median Absolute Deviation (MAD)44
Skewness5.9215354
Sum193227
Variance2626075.8
MonotonicityNot monotonic
2023-12-10T15:22:34.005527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 12
 
3.0%
16 11
 
2.8%
3 10
 
2.5%
7 9
 
2.2%
9 8
 
2.0%
10 8
 
2.0%
25 8
 
2.0%
6 8
 
2.0%
4 6
 
1.5%
8 6
 
1.5%
Other values (209) 314
78.5%
ValueCountFrequency (%)
1 2
 
0.5%
2 6
1.5%
3 10
2.5%
4 6
1.5%
5 4
 
1.0%
6 8
2.0%
7 9
2.2%
8 6
1.5%
9 8
2.0%
10 8
2.0%
ValueCountFrequency (%)
12675 1
0.2%
12092 1
0.2%
11986 1
0.2%
11898 1
0.2%
11753 1
0.2%
11454 1
0.2%
6919 1
0.2%
6389 1
0.2%
6333 1
0.2%
4244 1
0.2%

Interactions

2023-12-10T15:22:29.653658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:22:28.439327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:22:29.055084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:22:29.810935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:22:28.707310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:22:29.264703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:22:29.999580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:22:28.883387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T15:22:29.417947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T15:22:34.227767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"기본키값""해당일자""차례값""건수값"
"기본키값"1.0001.0000.5990.367
"해당일자"1.0001.0000.4470.135
"차례값"0.5990.4471.0000.472
"건수값"0.3670.1350.4721.000
2023-12-10T15:22:34.383751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
"기본키값""차례값""건수값"
"기본키값"1.0000.147-0.127
"차례값"0.1471.000-0.999
"건수값"-0.127-0.9991.000

Missing values

2023-12-10T15:22:30.235031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T15:22:30.442348image/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

"기본키값""채널값""해당일자""차례값""이슈어값""건수값"
015922"블로그"2020-05-011"미국"12675
115923"블로그"2020-05-012"중국"12092
215924"블로그"2020-05-013"일본"6919
315925"블로그"2020-05-014"영국"4142
415926"블로그"2020-05-015"독일"2686
515927"블로그"2020-05-016"프랑스"2268
615928"블로그"2020-05-017"이탈리아"2010
715929"블로그"2020-05-018"베트남"1831
815930"블로그"2020-05-019"캐나다"1762
915931"블로그"2020-05-0110"북한"1550
"기본키값""채널값""해당일자""차례값""이슈어값""건수값"
39047852"블로그"2020-05-0339"아르헨티나"166
39147853"블로그"2020-05-0340"핀란드"162
39247854"블로그"2020-05-0341"미얀마"147
39347855"블로그"2020-05-0342"방글라데시"136
39447856"블로그"2020-05-0343"칠레"135
39547857"블로그"2020-05-0344"사우디아라비아"126
39647858"블로그"2020-05-0345"헝가리"122
39747859"블로그"2020-05-0346"캄보디아"120
39847860"블로그"2020-05-0347"에티오피아"107
39947861"블로그"2020-05-0348"파키스탄"102