Overview

Dataset statistics

Number of variables4
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory36.3 B

Variable types

Categorical1
Numeric2
Text1

Alerts

rank_nm is highly overall correlated with fq_coHigh correlation
fq_co is highly overall correlated with rank_nmHigh correlation

Reproduction

Analysis started2023-12-10 09:47:15.667550
Analysis finished2023-12-10 09:47:17.082848
Duration1.42 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

anals_ym
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
202010
50 
202011
47 
201901
 
3

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202011
2nd row201901
3rd row202011
4th row202011
5th row202011

Common Values

ValueCountFrequency (%)
202010 50
50.0%
202011 47
47.0%
201901 3
 
3.0%

Length

2023-12-10T18:47:17.207403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:47:17.484870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202010 50
50.0%
202011 47
47.0%
201901 3
 
3.0%

rank_nm
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.55
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:47:17.714946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.95
Q114
median26.5
Q339.25
95-th percentile49
Maximum50
Range49
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation14.727405
Coefficient of variation (CV)0.55470453
Kurtosis-1.2166041
Mean26.55
Median Absolute Deviation (MAD)12.5
Skewness-0.034260165
Sum2655
Variance216.89646
MonotonicityNot monotonic
2023-12-10T18:47:18.009613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 3
 
3.0%
49 3
 
3.0%
48 3
 
3.0%
1 2
 
2.0%
37 2
 
2.0%
28 2
 
2.0%
29 2
 
2.0%
30 2
 
2.0%
31 2
 
2.0%
33 2
 
2.0%
Other values (40) 77
77.0%
ValueCountFrequency (%)
1 2
2.0%
2 1
1.0%
3 2
2.0%
4 2
2.0%
5 2
2.0%
6 2
2.0%
7 2
2.0%
8 1
1.0%
9 2
2.0%
10 2
2.0%
ValueCountFrequency (%)
50 3
3.0%
49 3
3.0%
48 3
3.0%
47 2
2.0%
46 2
2.0%
45 2
2.0%
44 2
2.0%
43 2
2.0%
42 2
2.0%
41 2
2.0%

association_nm
Text

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:47:18.554830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.42
Min length2

Characters and Unicode

Total characters242
Distinct characters133
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

Unique76 ?
Unique (%)76.0%

Sample

1st row인공지능
2nd row올해
3rd row학생
4th row캠프
5th row학교
ValueCountFrequency (%)
대상 2
 
2.0%
부분 2
 
2.0%
청소년 2
 
2.0%
겨울방학캠프 2
 
2.0%
진행 2
 
2.0%
프로그램 2
 
2.0%
이번 2
 
2.0%
캠프 2
 
2.0%
공부 2
 
2.0%
학교 2
 
2.0%
Other values (78) 80
80.0%
2023-12-10T18:47:19.250972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 12
 
5.0%
학 11
 
4.5%
프 6
 
2.5%
방 5
 
2.1%
부 4
 
1.7%
용 4
 
1.7%
계 4
 
1.7%
캠 4
 
1.7%
대 4
 
1.7%
생 4
 
1.7%
Other values (123) 184
76.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 242
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 12
 
5.0%
학 11
 
4.5%
프 6
 
2.5%
방 5
 
2.1%
부 4
 
1.7%
용 4
 
1.7%
계 4
 
1.7%
캠 4
 
1.7%
대 4
 
1.7%
생 4
 
1.7%
Other values (123) 184
76.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 242
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 12
 
5.0%
학 11
 
4.5%
프 6
 
2.5%
방 5
 
2.1%
부 4
 
1.7%
용 4
 
1.7%
계 4
 
1.7%
캠 4
 
1.7%
대 4
 
1.7%
생 4
 
1.7%
Other values (123) 184
76.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 242
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 12
 
5.0%
학 11
 
4.5%
프 6
 
2.5%
방 5
 
2.1%
부 4
 
1.7%
용 4
 
1.7%
계 4
 
1.7%
캠 4
 
1.7%
대 4
 
1.7%
생 4
 
1.7%
Other values (123) 184
76.0%

fq_co
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.21
Minimum8
Maximum331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:47:19.487599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12
Q114
median29
Q341.25
95-th percentile79.1
Maximum331
Range323
Interquartile range (IQR)27.25

Descriptive statistics

Standard deviation38.529877
Coefficient of variation (CV)1.0640673
Kurtosis35.443424
Mean36.21
Median Absolute Deviation (MAD)15
Skewness5.1339635
Sum3621
Variance1484.5514
MonotonicityNot monotonic
2023-12-10T18:47:19.750778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
12 11
 
11.0%
14 8
 
8.0%
13 5
 
5.0%
40 4
 
4.0%
29 4
 
4.0%
30 4
 
4.0%
38 4
 
4.0%
28 3
 
3.0%
61 3
 
3.0%
32 3
 
3.0%
Other values (34) 51
51.0%
ValueCountFrequency (%)
8 3
 
3.0%
12 11
11.0%
13 5
5.0%
14 8
8.0%
15 1
 
1.0%
16 2
 
2.0%
17 2
 
2.0%
18 2
 
2.0%
19 2
 
2.0%
20 1
 
1.0%
ValueCountFrequency (%)
331 1
 
1.0%
164 1
 
1.0%
125 1
 
1.0%
89 1
 
1.0%
81 1
 
1.0%
79 1
 
1.0%
74 2
2.0%
67 1
 
1.0%
66 1
 
1.0%
61 3
3.0%

Interactions

2023-12-10T18:47:16.437210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:16.072700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:16.589488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:47:16.259979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:47:19.936491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
anals_ymrank_nmassociation_nmfq_co
anals_ym1.0000.2090.4120.414
rank_nm0.2091.0000.8950.545
association_nm0.4120.8951.0000.000
fq_co0.4140.5450.0001.000
2023-12-10T18:47:20.115288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
rank_nmfq_coanals_ym
rank_nm1.000-0.6930.119
fq_co-0.6931.0000.184
anals_ym0.1190.1841.000

Missing values

2023-12-10T18:47:16.845294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:47:17.016463image/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

anals_ymrank_nmassociation_nmfq_co
02020111인공지능331
120190148올해8
22020113학생164
32020114캠프125
42020115학교81
52020116공부79
62020117청소년74
720190149스쿨링8
82020119대한74
920201110다양67
anals_ymrank_nmassociation_nmfq_co
9020201041디렉터12
9120201042보호자12
9220201043빈센트12
9320201044부분12
9420201045이용자12
9520201046이번12
9620201047사람들12
9720201048동아리12
9820201049동안12
9920201050학년12