Overview

Dataset statistics

Number of variables7
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory61.3 B

Variable types

Numeric2
Categorical4
Text1

Dataset

Description샘플 데이터
Author마크로밀엠브레인
URLhttps://www.findatamall.or.kr/market/dataProdDetail?gdsSn=4551&gdsSeCd=GENERAL&gdsVer=1

Alerts

gender has constant value ""Constant
age_g has constant value ""Constant
package_name is highly overall correlated with APP_NAME_NHigh correlation
APP_NAME_N is highly overall correlated with package_nameHigh correlation
NO has unique valuesUnique
total_used_time has unique valuesUnique
last_used_time has unique valuesUnique

Reproduction

Analysis started2024-03-03 13:29:37.263226
Analysis finished2024-03-03 13:29:39.364806
Duration2.1 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

NO
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-03T22:29:39.636958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2024-03-03T22:29:40.212854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

package_name
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
com.samsungpop.android.mpop
24 
com.linkzen.app
22 
viva.republica.toss
21 
com.kbstar.kbbank
14 
net.ib.android.smcard
6 
Other values (7)
13 

Length

Max length27
Median length23
Mean length19.53
Min length11

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st rowcom.linkzen.app
2nd rowcom.samsungpop.android.mpop
3rd rowcom.kyobo.app
4th rowcom.samsungpop.android.mpop
5th rowviva.republica.toss

Common Values

ValueCountFrequency (%)
com.samsungpop.android.mpop 24
24.0%
com.linkzen.app 22
22.0%
viva.republica.toss 21
21.0%
com.kbstar.kbbank 14
14.0%
net.ib.android.smcard 6
 
6.0%
com.hmsec.mobile 3
 
3.0%
nh.smart.nhcok 3
 
3.0%
com.daishin 2
 
2.0%
com.shinhaninvest.nsmts 2
 
2.0%
com.kyobo.app 1
 
1.0%
Other values (2) 2
 
2.0%

Length

2024-03-03T22:29:40.994969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
com.samsungpop.android.mpop 24
24.0%
com.linkzen.app 22
22.0%
viva.republica.toss 21
21.0%
com.kbstar.kbbank 14
14.0%
net.ib.android.smcard 6
 
6.0%
com.hmsec.mobile 3
 
3.0%
nh.smart.nhcok 3
 
3.0%
com.daishin 2
 
2.0%
com.shinhaninvest.nsmts 2
 
2.0%
com.kyobo.app 1
 
1.0%
Other values (2) 2
 
2.0%

APP_NAME_N
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
삼성증권 mPOP
24 
키움증권 영웅문S
22 
토스
21 
KB국민은행 스타뱅킹
14 
삼성카드 마이홈
6 
Other values (7)
13 

Length

Max length22
Median length16
Mean length8.09
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row키움증권 영웅문S
2nd row삼성증권 mPOP
3rd row교보생명
4th row삼성증권 mPOP
5th row토스

Common Values

ValueCountFrequency (%)
삼성증권 mPOP 24
24.0%
키움증권 영웅문S 22
22.0%
토스 21
21.0%
KB국민은행 스타뱅킹 14
14.0%
삼성카드 마이홈 6
 
6.0%
현대차증권 The H Mobile (2) 3
 
3.0%
NH콕뱅크 3
 
3.0%
대신증권 CYBOS Touch 2
 
2.0%
신한금융투자 알파 2
 
2.0%
교보생명 1
 
1.0%
Other values (2) 2
 
2.0%

Length

2024-03-03T22:29:41.744051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
삼성증권 24
13.0%
mpop 24
13.0%
키움증권 22
11.9%
영웅문s 22
11.9%
토스 21
11.4%
kb국민은행 14
7.6%
스타뱅킹 14
7.6%
삼성카드 6
 
3.2%
마이홈 6
 
3.2%
mobile 3
 
1.6%
Other values (13) 29
15.7%

total_used_time
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean952182.4
Minimum461
Maximum7577308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-03-03T22:29:42.217352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum461
5-th percentile1444
Q193181
median404292.5
Q31497738.8
95-th percentile2635227.4
Maximum7577308
Range7576847
Interquartile range (IQR)1404557.8

Descriptive statistics

Standard deviation1197051.4
Coefficient of variation (CV)1.2571661
Kurtosis9.0381953
Mean952182.4
Median Absolute Deviation (MAD)402857.5
Skewness2.3828783
Sum95218240
Variance1.4329321 × 1012
MonotonicityNot monotonic
2024-03-03T22:29:42.687339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
913882 1
 
1.0%
2464619 1
 
1.0%
4539549 1
 
1.0%
45151 1
 
1.0%
373187 1
 
1.0%
2097924 1
 
1.0%
2932 1
 
1.0%
1673619 1
 
1.0%
2643119 1
 
1.0%
1600193 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
461 1
1.0%
981 1
1.0%
982 1
1.0%
1360 1
1.0%
1425 1
1.0%
1445 1
1.0%
1473 1
1.0%
1539 1
1.0%
1667 1
1.0%
1819 1
1.0%
ValueCountFrequency (%)
7577308 1
1.0%
4539549 1
1.0%
4111046 1
1.0%
3438479 1
1.0%
2643119 1
1.0%
2634812 1
1.0%
2599809 1
1.0%
2554214 1
1.0%
2512110 1
1.0%
2473504 1
1.0%

last_used_time
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
2024-03-03T22:29:44.046197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters700
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row29:50.8
2nd row51:55.2
3rd row47:28.1
4th row07:30.5
5th row59:30.5
ValueCountFrequency (%)
29:50.8 1
 
1.0%
33:07.2 1
 
1.0%
31:40.0 1
 
1.0%
54:20.9 1
 
1.0%
08:33.9 1
 
1.0%
47:39.5 1
 
1.0%
22:32.2 1
 
1.0%
52:11.5 1
 
1.0%
34:11.9 1
 
1.0%
58:57.7 1
 
1.0%
Other values (90) 90
90.0%
2024-03-03T22:29:45.745964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 100
14.3%
. 100
14.3%
2 80
11.4%
3 69
9.9%
5 67
9.6%
0 57
8.1%
1 49
7.0%
4 47
6.7%
9 35
 
5.0%
6 33
 
4.7%
Other values (2) 63
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
71.4%
Other Punctuation 200
 
28.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 80
16.0%
3 69
13.8%
5 67
13.4%
0 57
11.4%
1 49
9.8%
4 47
9.4%
9 35
7.0%
6 33
6.6%
7 32
 
6.4%
8 31
 
6.2%
Other Punctuation
ValueCountFrequency (%)
: 100
50.0%
. 100
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 100
14.3%
. 100
14.3%
2 80
11.4%
3 69
9.9%
5 67
9.6%
0 57
8.1%
1 49
7.0%
4 47
6.7%
9 35
 
5.0%
6 33
 
4.7%
Other values (2) 63
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 100
14.3%
. 100
14.3%
2 80
11.4%
3 69
9.9%
5 67
9.6%
0 57
8.1%
1 49
7.0%
4 47
6.7%
9 35
 
5.0%
6 33
 
4.7%
Other values (2) 63
9.0%

gender
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
2
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 100
100.0%

Length

2024-03-03T22:29:46.102287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T22:29:46.344621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2 100
100.0%

age_g
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
60
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row60
2nd row60
3rd row60
4th row60
5th row60

Common Values

ValueCountFrequency (%)
60 100
100.0%

Length

2024-03-03T22:29:46.962004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-03T22:29:47.215544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
60 100
100.0%

Interactions

2024-03-03T22:29:38.126000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-03T22:29:37.650461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-03T22:29:38.411984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-03T22:29:37.872660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-03T22:29:47.424403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NOpackage_nameAPP_NAME_Ntotal_used_timelast_used_time
NO1.0000.0000.0000.0001.000
package_name0.0001.0001.0000.2811.000
APP_NAME_N0.0001.0001.0000.2811.000
total_used_time0.0000.2810.2811.0001.000
last_used_time1.0001.0001.0001.0001.000
2024-03-03T22:29:47.691985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
package_nameAPP_NAME_N
package_name1.0001.000
APP_NAME_N1.0001.000
2024-03-03T22:29:47.972776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NOtotal_used_timepackage_nameAPP_NAME_N
NO1.000-0.0170.0000.000
total_used_time-0.0171.0000.1390.139
package_name0.0000.1391.0001.000
APP_NAME_N0.0000.1391.0001.000

Missing values

2024-03-03T22:29:38.769288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-03T22:29:39.163524image/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

NOpackage_nameAPP_NAME_Ntotal_used_timelast_used_timegenderage_g
01com.linkzen.app키움증권 영웅문S91388229:50.8260
12com.samsungpop.android.mpop삼성증권 mPOP180733951:55.2260
23com.kyobo.app교보생명96777947:28.1260
34com.samsungpop.android.mpop삼성증권 mPOP133649807:30.5260
45viva.republica.toss토스40037859:30.5260
56viva.republica.toss토스63686155:53.2260
67com.linkzen.app키움증권 영웅문S146490922:15.9260
78com.linkzen.app키움증권 영웅문S134092807:47.2260
89com.samsungpop.android.mpop삼성증권 mPOP247350450:52.9260
910com.kbstar.kbbankKB국민은행 스타뱅킹19616600:27.3260
NOpackage_nameAPP_NAME_Ntotal_used_timelast_used_timegenderage_g
9091com.linkzen.app키움증권 영웅문S259980937:35.1260
9192com.linkzen.app키움증권 영웅문S124741228:30.1260
9293net.ib.android.smcard삼성카드 마이홈9504036:00.4260
9394com.samsungpop.android.mpop삼성증권 mPOP196270957:43.5260
9495nh.smart.nhcokNH콕뱅크4004823:44.6260
9596com.samsungpop.android.mpop삼성증권 mPOP91990645:05.5260
9697viva.republica.toss토스23232636:10.1260
9798com.samsungpop.android.mpop삼성증권 mPOP76328211:55.1260
9899com.samsungpop.android.mpop삼성증권 mPOP214079127:09.0260
99100com.kbstar.kbbankKB국민은행 스타뱅킹19519237:46.0260