Overview

Dataset statistics

Number of variables5
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 KiB
Average record size in memory44.3 B

Variable types

Numeric2
Categorical2
Text1

Alerts

seq_no is highly overall correlated with anals_ymHigh correlation
anals_ym is highly overall correlated with seq_noHigh correlation
seq_no has unique valuesUnique

Reproduction

Analysis started2023-12-10 10:04:50.307047
Analysis finished2023-12-10 10:04:51.755251
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

seq_no
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.95
Minimum1
Maximum630
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:51.952011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q127.75
median53.5
Q378.25
95-th percentile98.05
Maximum630
Range629
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation102.92553
Coefficient of variation (CV)1.492756
Kurtosis25.157555
Mean68.95
Median Absolute Deviation (MAD)25.5
Skewness4.937577
Sum6895
Variance10593.664
MonotonicityNot monotonic
2023-12-10T19:04:52.231957image/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%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
9 1
1.0%
10 1
1.0%
11 1
1.0%
12 1
1.0%
ValueCountFrequency (%)
630 1
1.0%
629 1
1.0%
628 1
1.0%
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%

anals_ym
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
201903
30 
201902
29 
201901
28 
201904
10 
202009
 
3

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row201901
2nd row202009
3rd row201901
4th row201901
5th row201901

Common Values

ValueCountFrequency (%)
201903 30
30.0%
201902 29
29.0%
201901 28
28.0%
201904 10
 
10.0%
202009 3
 
3.0%

Length

2023-12-10T19:04:52.480206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:04:52.684362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
201903 30
30.0%
201902 29
29.0%
201901 28
28.0%
201904 10
 
10.0%
202009 3
 
3.0%

search_rank_nm
Categorical

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1위
 
4
10위
 
4
7위
 
4
4위
 
4
5위
 
4
Other values (25)
80 

Length

Max length3
Median length3
Mean length2.67
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1위
2nd row28위
3rd row3위
4th row4위
5th row5위

Common Values

ValueCountFrequency (%)
1위 4
 
4.0%
10위 4
 
4.0%
7위 4
 
4.0%
4위 4
 
4.0%
5위 4
 
4.0%
6위 4
 
4.0%
3위 4
 
4.0%
29위 4
 
4.0%
9위 4
 
4.0%
30위 4
 
4.0%
Other values (20) 60
60.0%

Length

2023-12-10T19:04:52.921325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1위 4
 
4.0%
3위 4
 
4.0%
10위 4
 
4.0%
30위 4
 
4.0%
9위 4
 
4.0%
29위 4
 
4.0%
28위 4
 
4.0%
6위 4
 
4.0%
5위 4
 
4.0%
4위 4
 
4.0%
Other values (20) 60
60.0%

srchwrd_nm
Text

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:04:53.537404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length3.38
Min length1

Characters and Unicode

Total characters338
Distinct characters147
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)52.0%

Sample

1st row한국
2nd row부산
3rd row토트넘
4th row대한민국
5th row일본
ValueCountFrequency (%)
웹툰 4
 
3.8%
리버풀 4
 
3.8%
토트넘 4
 
3.8%
맨유 4
 
3.8%
학교 3
 
2.8%
바르셀로나 3
 
2.8%
맞춤법검사기 3
 
2.8%
메가스터디 3
 
2.8%
맨시티 3
 
2.8%
대학교 3
 
2.8%
Other values (64) 72
67.9%
2023-12-10T19:04:54.254573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 17
 
5.0%
트 8
 
2.4%
리 8
 
2.4%
이 7
 
2.1%
교 7
 
2.1%
학 7
 
2.1%
맨 7
 
2.1%
6
 
1.8%
르 6
 
1.8%
대 6
 
1.8%
Other values (137) 259
76.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 323
95.6%
Lowercase Letter 8
 
2.4%
Space Separator 6
 
1.8%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 17
 
5.3%
트 8
 
2.5%
리 8
 
2.5%
이 7
 
2.2%
교 7
 
2.2%
학 7
 
2.2%
맨 7
 
2.2%
르 6
 
1.9%
대 6
 
1.9%
아 5
 
1.5%
Other values (131) 245
75.9%
Lowercase Letter
ValueCountFrequency (%)
i 2
25.0%
e 2
25.0%
b 2
25.0%
s 2
25.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 323
95.6%
Latin 8
 
2.4%
Common 7
 
2.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 17
 
5.3%
트 8
 
2.5%
리 8
 
2.5%
이 7
 
2.2%
교 7
 
2.2%
학 7
 
2.2%
맨 7
 
2.2%
르 6
 
1.9%
대 6
 
1.9%
아 5
 
1.5%
Other values (131) 245
75.9%
Latin
ValueCountFrequency (%)
i 2
25.0%
e 2
25.0%
b 2
25.0%
s 2
25.0%
Common
ValueCountFrequency (%)
6
85.7%
2 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 323
95.6%
ASCII 15
 
4.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 17
 
5.3%
트 8
 
2.5%
리 8
 
2.5%
이 7
 
2.2%
교 7
 
2.2%
학 7
 
2.2%
맨 7
 
2.2%
르 6
 
1.9%
대 6
 
1.9%
아 5
 
1.5%
Other values (131) 245
75.9%
ASCII
ValueCountFrequency (%)
6
40.0%
i 2
 
13.3%
e 2
 
13.3%
b 2
 
13.3%
s 2
 
13.3%
2 1
 
6.7%

views_co
Real number (ℝ)

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean141.96
Minimum67
Maximum493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:04:54.656091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile73.95
Q194
median126
Q3166.25
95-th percentile270.45
Maximum493
Range426
Interquartile range (IQR)72.25

Descriptive statistics

Standard deviation68.593506
Coefficient of variation (CV)0.48318897
Kurtosis6.4654467
Mean141.96
Median Absolute Deviation (MAD)36
Skewness2.0437275
Sum14196
Variance4705.0691
MonotonicityNot monotonic
2023-12-10T19:04:54.965485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 3
 
3.0%
85 3
 
3.0%
126 3
 
3.0%
136 3
 
3.0%
107 2
 
2.0%
139 2
 
2.0%
98 2
 
2.0%
94 2
 
2.0%
92 2
 
2.0%
87 2
 
2.0%
Other values (63) 76
76.0%
ValueCountFrequency (%)
67 1
1.0%
69 2
2.0%
73 2
2.0%
74 1
1.0%
75 2
2.0%
76 1
1.0%
79 1
1.0%
82 1
1.0%
83 1
1.0%
84 2
2.0%
ValueCountFrequency (%)
493 1
1.0%
340 1
1.0%
305 1
1.0%
302 1
1.0%
279 1
1.0%
270 1
1.0%
248 1
1.0%
239 1
1.0%
232 1
1.0%
228 2
2.0%

Interactions

2023-12-10T19:04:51.131109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:50.738422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:51.277189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:04:50.943144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:04:55.121720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noanals_ymsearch_rank_nmsrchwrd_nmviews_co
seq_no1.0000.9190.0000.8240.113
anals_ym0.9191.0000.0000.0000.438
search_rank_nm0.0000.0001.0000.7490.589
srchwrd_nm0.8240.0000.7491.0000.000
views_co0.1130.4380.5890.0001.000
2023-12-10T19:04:55.318526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
anals_ymsearch_rank_nm
anals_ym1.0000.000
search_rank_nm0.0001.000
2023-12-10T19:04:56.041273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
seq_noviews_coanals_ymsearch_rank_nm
seq_no1.000-0.1460.9590.000
views_co-0.1461.0000.2820.235
anals_ym0.9590.2821.0000.000
search_rank_nm0.0000.2350.0001.000

Missing values

2023-12-10T19:04:51.437640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:04:51.663471image/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

seq_noanals_ymsearch_rank_nmsrchwrd_nmviews_co
012019011위한국493
162820200928위부산75
232019013위토트넘279
342019014위대한민국248
452019015위일본239
562019016위카타르228
672019017위맨유221
762920200929위노을73
892019019위스카이캐슬169
91020190110위바레인168
seq_noanals_ymsearch_rank_nmsrchwrd_nmviews_co
90912019041위토트넘340
91922019042위맨유302
92932019043위맨시티232
93942019044위산불228
94952019045위ebsi 고등215
95962019046위에이틴199
96972019047위웹툰183
97982019048위첼시182
98992019049위리버풀167
9910020190410위바르셀로나166