Overview

Dataset statistics

Number of variables13
Number of observations620540
Missing cells38006
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.5 MiB
Average record size in memory104.0 B

Variable types

Numeric11
Categorical2

Alerts

categories has a high cardinality: 67004 distinct valuesHigh cardinality
citable_docs_3years is highly overall correlated with h_index and 4 other fieldsHigh correlation
h_index is highly overall correlated with citable_docs_3years and 7 other fieldsHigh correlation
journal_rating is highly overall correlated with h_index and 4 other fieldsHigh correlation
rank is highly overall correlated with h_index and 4 other fieldsHigh correlation
ref_per_doc is highly overall correlated with h_index and 2 other fieldsHigh correlation
total_cites_3years is highly overall correlated with citable_docs_3years and 6 other fieldsHigh correlation
total_docs is highly overall correlated with citable_docs_3years and 4 other fieldsHigh correlation
total_docs_3years is highly overall correlated with citable_docs_3years and 4 other fieldsHigh correlation
total_refs is highly overall correlated with citable_docs_3years and 7 other fieldsHigh correlation
sjr_best_quartile is highly overall correlated with rankHigh correlation
journal_rating has 38006 (6.1%) missing valuesMissing
citable_docs_3years is highly skewed (γ1 = 47.17653641)Skewed
total_cites_3years is highly skewed (γ1 = 33.27400829)Skewed
total_docs is highly skewed (γ1 = 44.54113692)Skewed
total_docs_3years is highly skewed (γ1 = 45.56996407)Skewed
total_refs is highly skewed (γ1 = 45.60770981)Skewed
citable_docs_3years has 29012 (4.7%) zerosZeros
h_index has 9095 (1.5%) zerosZeros
ref_per_doc has 153751 (24.8%) zerosZeros
total_cites_3years has 65410 (10.5%) zerosZeros
total_docs has 134190 (21.6%) zerosZeros
total_docs_3years has 27877 (4.5%) zerosZeros
total_refs has 153728 (24.8%) zerosZeros

Reproduction

Analysis started2023-05-04 15:11:05.642523
Analysis finished2023-05-04 15:11:45.790266
Duration40.15 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

journal__sourceid
Real number (ℝ)

Distinct70227
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1230085 × 109
Minimum12000
Maximum2.110106 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:45.870294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12000
5-th percentile14065
Q122618
median144819
Q31.9900192 × 1010
95-th percentile2.1100837 × 1010
Maximum2.110106 × 1010
Range2.1101048 × 1010
Interquartile range (IQR)1.9900169 × 1010

Descriptive statistics

Standard deviation9.4602373 × 109
Coefficient of variation (CV)1.1646224
Kurtosis-1.6611209
Mean8.1230085 × 109
Median Absolute Deviation (MAD)132149
Skewness0.46384165
Sum5.0406517 × 1015
Variance8.949609 × 1019
MonotonicityNot monotonic
2023-05-04T15:11:46.021640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16801 23
 
< 0.1%
5700168957 23
 
< 0.1%
30588 23
 
< 0.1%
1.970018816 × 101023
 
< 0.1%
26333 23
 
< 0.1%
16671 23
 
< 0.1%
29670 23
 
< 0.1%
13473 23
 
< 0.1%
25571 23
 
< 0.1%
23717 23
 
< 0.1%
Other values (70217) 620310
> 99.9%
ValueCountFrequency (%)
12000 13
< 0.1%
12001 23
< 0.1%
12002 23
< 0.1%
12004 22
< 0.1%
12005 23
< 0.1%
12006 23
< 0.1%
12007 4
 
< 0.1%
12008 6
 
< 0.1%
12009 20
< 0.1%
12010 23
< 0.1%
ValueCountFrequency (%)
2.110105979 × 10103
 
< 0.1%
2.110105978 × 101016
< 0.1%
2.110105978 × 10102
 
< 0.1%
2.110105949 × 10101
 
< 0.1%
2.11010593 × 10101
 
< 0.1%
2.11010593 × 10108
< 0.1%
2.110105901 × 10103
 
< 0.1%
2.110105901 × 10101
 
< 0.1%
2.110105897 × 10101
 
< 0.1%
2.110105896 × 10106
 
< 0.1%

year
Real number (ℝ)

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.3857
Minimum1999
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:46.164824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1999
5-th percentile2000
Q12007
median2012
Q32017
95-th percentile2020
Maximum2021
Range22
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2753051
Coefficient of variation (CV)0.0031198914
Kurtosis-1.0005721
Mean2011.3857
Median Absolute Deviation (MAD)5
Skewness-0.28788358
Sum1.2481453 × 109
Variance39.379454
MonotonicityIncreasing
2023-05-04T15:11:46.286521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2017 34766
 
5.6%
2016 34279
 
5.5%
2020 34169
 
5.5%
2018 33939
 
5.5%
2015 33651
 
5.4%
2014 32937
 
5.3%
2013 32470
 
5.2%
2012 31864
 
5.1%
2019 31861
 
5.1%
2011 30913
 
5.0%
Other values (13) 289691
46.7%
ValueCountFrequency (%)
1999 16987
2.7%
2000 17298
2.8%
2001 17972
2.9%
2002 19114
3.1%
2003 19638
3.2%
2004 20211
3.3%
2005 21092
3.4%
2006 22662
3.7%
2007 24195
3.9%
2008 26089
4.2%
ValueCountFrequency (%)
2021 27339
4.4%
2020 34169
5.5%
2019 31861
5.1%
2018 33939
5.5%
2017 34766
5.6%
2016 34279
5.5%
2015 33651
5.4%
2014 32937
5.3%
2013 32470
5.2%
2012 31864
5.1%

categories
Categorical

Distinct67004
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size4.7 MiB
Medicine (miscellaneous) (Q4)
 
12113
Engineering (miscellaneous)
 
7676
Medicine (miscellaneous) (Q3)
 
7648
Software
 
3593
Electrical and Electronic Engineering
 
3067
Other values (66999)
586443 

Length

Max length509
Median length301
Mean length65.562819
Min length3

Characters and Unicode

Total characters40684352
Distinct characters57
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25914 ?
Unique (%)4.2%

Sample

1st rowBiochemistry (Q1)
2nd rowBiochemistry, Genetics and Molecular Biology (miscellaneous) (Q1)
3rd rowImmunology (Q1); Immunology and Allergy (Q1)
4th rowCell Biology (Q1); Developmental Biology (Q1)
5th rowNeuroscience (miscellaneous) (Q1)

Common Values

ValueCountFrequency (%)
Medicine (miscellaneous) (Q4) 12113
 
2.0%
Engineering (miscellaneous) 7676
 
1.2%
Medicine (miscellaneous) (Q3) 7648
 
1.2%
Software 3593
 
0.6%
Electrical and Electronic Engineering 3067
 
0.5%
Computer Networks and Communications 2660
 
0.4%
Medicine (miscellaneous) (Q2) 2475
 
0.4%
Medicine (miscellaneous) 1459
 
0.2%
Literature and Literary Theory (Q4) 1458
 
0.2%
Electrical and Electronic Engineering; Hardware and Architecture 1450
 
0.2%
Other values (66994) 576941
93.0%

Length

2023-05-04T15:11:46.442398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 570125
 
11.8%
q1 267430
 
5.5%
q2 266021
 
5.5%
q3 264153
 
5.5%
q4 262127
 
5.4%
miscellaneous 217620
 
4.5%
science 153450
 
3.2%
engineering 147044
 
3.0%
medicine 108360
 
2.2%
computer 73497
 
1.5%
Other values (378) 2510647
51.9%

Most occurring characters

ValueCountFrequency (%)
4220950
 
10.4%
e 3116433
 
7.7%
n 2931015
 
7.2%
i 2807687
 
6.9%
a 2453829
 
6.0%
o 2189272
 
5.4%
c 1938240
 
4.8%
l 1836891
 
4.5%
t 1710166
 
4.2%
s 1553537
 
3.8%
Other values (47) 15926332
39.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27871190
68.5%
Space Separator 4220950
 
10.4%
Uppercase Letter 4004971
 
9.8%
Open Punctuation 1309111
 
3.2%
Close Punctuation 1309111
 
3.2%
Decimal Number 1059731
 
2.6%
Other Punctuation 890562
 
2.2%
Dash Punctuation 18726
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3116433
11.2%
n 2931015
10.5%
i 2807687
10.1%
a 2453829
8.8%
o 2189272
 
7.9%
c 1938240
 
7.0%
l 1836891
 
6.6%
t 1710166
 
6.1%
s 1553537
 
5.6%
r 1466024
 
5.3%
Other values (15) 5868096
21.1%
Uppercase Letter
ValueCountFrequency (%)
Q 1066611
26.6%
S 407734
 
10.2%
E 394214
 
9.8%
M 365109
 
9.1%
C 304612
 
7.6%
P 293668
 
7.3%
A 203207
 
5.1%
H 125352
 
3.1%
I 117754
 
2.9%
B 102816
 
2.6%
Other values (12) 623894
15.6%
Decimal Number
ValueCountFrequency (%)
1 267430
25.2%
2 266021
25.1%
3 264153
24.9%
4 262127
24.7%
Other Punctuation
ValueCountFrequency (%)
; 748349
84.0%
, 142213
 
16.0%
Space Separator
ValueCountFrequency (%)
4220950
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1309111
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1309111
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18726
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 31876161
78.3%
Common 8808191
 
21.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3116433
 
9.8%
n 2931015
 
9.2%
i 2807687
 
8.8%
a 2453829
 
7.7%
o 2189272
 
6.9%
c 1938240
 
6.1%
l 1836891
 
5.8%
t 1710166
 
5.4%
s 1553537
 
4.9%
r 1466024
 
4.6%
Other values (37) 9873067
31.0%
Common
ValueCountFrequency (%)
4220950
47.9%
( 1309111
 
14.9%
) 1309111
 
14.9%
; 748349
 
8.5%
1 267430
 
3.0%
2 266021
 
3.0%
3 264153
 
3.0%
4 262127
 
3.0%
, 142213
 
1.6%
- 18726
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40684352
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4220950
 
10.4%
e 3116433
 
7.7%
n 2931015
 
7.2%
i 2807687
 
6.9%
a 2453829
 
6.0%
o 2189272
 
5.4%
c 1938240
 
4.8%
l 1836891
 
4.5%
t 1710166
 
4.2%
s 1553537
 
3.8%
Other values (47) 15926332
39.1%

citable_docs_3years
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5239
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.02452
Minimum0
Maximum94370
Zeros29012
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:46.603117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q138
median86
Q3197
95-th percentile742
Maximum94370
Range94370
Interquartile range (IQR)159

Descriptive statistics

Standard deviation812.08001
Coefficient of variation (CV)3.7766856
Kurtosis3550.1556
Mean215.02452
Median Absolute Deviation (MAD)61
Skewness47.176536
Sum1.3343132 × 108
Variance659473.94
MonotonicityNot monotonic
2023-05-04T15:11:46.755620image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29012
 
4.7%
1 6135
 
1.0%
48 3649
 
0.6%
60 3639
 
0.6%
56 3636
 
0.6%
50 3635
 
0.6%
45 3589
 
0.6%
24 3572
 
0.6%
40 3545
 
0.6%
47 3542
 
0.6%
Other values (5229) 556586
89.7%
ValueCountFrequency (%)
0 29012
4.7%
1 6135
 
1.0%
2 3043
 
0.5%
3 2688
 
0.4%
4 2700
 
0.4%
5 2829
 
0.5%
6 3032
 
0.5%
7 3031
 
0.5%
8 3307
 
0.5%
9 3207
 
0.5%
ValueCountFrequency (%)
94370 1
< 0.1%
93640 1
< 0.1%
88176 1
< 0.1%
83873 1
< 0.1%
82993 1
< 0.1%
78745 1
< 0.1%
72423 1
< 0.1%
69798 1
< 0.1%
68541 1
< 0.1%
68277 1
< 0.1%

h_index
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct404
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.225832
Minimum0
Maximum1276
Zeros9095
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:46.908575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median20
Q350
95-th percentile137
Maximum1276
Range1276
Interquartile range (IQR)42

Descriptive statistics

Standard deviation53.600804
Coefficient of variation (CV)1.366467
Kurtosis44.797354
Mean39.225832
Median Absolute Deviation (MAD)15
Skewness4.4230824
Sum24341198
Variance2873.0462
MonotonicityNot monotonic
2023-05-04T15:11:47.070011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 22152
 
3.6%
4 21720
 
3.5%
6 20510
 
3.3%
3 20478
 
3.3%
7 19338
 
3.1%
8 18376
 
3.0%
2 18132
 
2.9%
9 17803
 
2.9%
10 16147
 
2.6%
11 15021
 
2.4%
Other values (394) 430863
69.4%
ValueCountFrequency (%)
0 9095
1.5%
1 13958
2.2%
2 18132
2.9%
3 20478
3.3%
4 21720
3.5%
5 22152
3.6%
6 20510
3.3%
7 19338
3.1%
8 18376
3.0%
9 17803
2.9%
ValueCountFrequency (%)
1276 23
< 0.1%
1229 23
< 0.1%
1079 23
< 0.1%
814 23
< 0.1%
807 23
< 0.1%
805 23
< 0.1%
745 23
< 0.1%
709 23
< 0.1%
647 23
< 0.1%
644 23
< 0.1%

journal_rating
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8524
Distinct (%)1.5%
Missing38006
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean0.55719131
Minimum0.1
Maximum88.192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:47.226532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.101
Q10.123
median0.231
Q30.594
95-th percentile1.834
Maximum88.192
Range88.092
Interquartile range (IQR)0.471

Descriptive statistics

Standard deviation1.1592403
Coefficient of variation (CV)2.0805067
Kurtosis350.0633
Mean0.55719131
Median Absolute Deviation (MAD)0.127
Skewness13.019474
Sum324582.88
Variance1.343838
MonotonicityNot monotonic
2023-05-04T15:11:47.372916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1 26231
 
4.2%
0.101 25271
 
4.1%
0.102 11394
 
1.8%
0.103 7718
 
1.2%
0.111 6423
 
1.0%
0.104 5988
 
1.0%
0.105 5281
 
0.9%
0.123 4622
 
0.7%
0.107 4592
 
0.7%
0.11 4469
 
0.7%
Other values (8514) 480545
77.4%
(Missing) 38006
 
6.1%
ValueCountFrequency (%)
0.1 26231
4.2%
0.101 25271
4.1%
0.102 11394
1.8%
0.103 7718
 
1.2%
0.104 5988
 
1.0%
0.105 5281
 
0.9%
0.106 4210
 
0.7%
0.107 4592
 
0.7%
0.108 3915
 
0.6%
0.109 3114
 
0.5%
ValueCountFrequency (%)
88.192 1
< 0.1%
72.576 1
< 0.1%
62.937 1
< 0.1%
61.786 1
< 0.1%
56.204 1
< 0.1%
50.518 1
< 0.1%
49.268 1
< 0.1%
48.894 1
< 0.1%
47.751 1
< 0.1%
47.288 1
< 0.1%

rank
Real number (ℝ)

Distinct34766
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14209.023
Minimum1
Maximum34766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:47.527234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1349.95
Q16745.75
median13490.5
Q320835
95-th percentile29983
Maximum34766
Range34765
Interquartile range (IQR)14089.25

Descriptive statistics

Standard deviation8870.0511
Coefficient of variation (CV)0.62425481
Kurtosis-0.90177875
Mean14209.023
Median Absolute Deviation (MAD)6999.5
Skewness0.29967013
Sum8.8172673 × 109
Variance78677807
MonotonicityNot monotonic
2023-05-04T15:11:47.678478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 23
 
< 0.1%
11333 23
 
< 0.1%
11319 23
 
< 0.1%
11320 23
 
< 0.1%
11321 23
 
< 0.1%
11322 23
 
< 0.1%
11323 23
 
< 0.1%
11324 23
 
< 0.1%
11325 23
 
< 0.1%
11326 23
 
< 0.1%
Other values (34756) 620310
> 99.9%
ValueCountFrequency (%)
1 23
< 0.1%
2 23
< 0.1%
3 23
< 0.1%
4 23
< 0.1%
5 23
< 0.1%
6 23
< 0.1%
7 23
< 0.1%
8 23
< 0.1%
9 23
< 0.1%
10 23
< 0.1%
ValueCountFrequency (%)
34766 1
< 0.1%
34765 1
< 0.1%
34764 1
< 0.1%
34763 1
< 0.1%
34762 1
< 0.1%
34761 1
< 0.1%
34760 1
< 0.1%
34759 1
< 0.1%
34758 1
< 0.1%
34757 1
< 0.1%

ref_per_doc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16545
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.429817
Minimum0
Maximum4841
Zeros153751
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:47.839454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.16
median23.9
Q339.15
95-th percentile69.13
Maximum4841
Range4841
Interquartile range (IQR)38.99

Descriptive statistics

Standard deviation35.475855
Coefficient of variation (CV)1.2933318
Kurtosis1376.9439
Mean27.429817
Median Absolute Deviation (MAD)17.67
Skewness17.911385
Sum17021299
Variance1258.5363
MonotonicityNot monotonic
2023-05-04T15:11:47.995500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 153751
 
24.8%
30 432
 
0.1%
28 432
 
0.1%
29 425
 
0.1%
26 418
 
0.1%
23 410
 
0.1%
25 403
 
0.1%
34 395
 
0.1%
27 395
 
0.1%
24 395
 
0.1%
Other values (16535) 463084
74.6%
ValueCountFrequency (%)
0 153751
24.8%
0.01 97
 
< 0.1%
0.02 117
 
< 0.1%
0.03 114
 
< 0.1%
0.04 97
 
< 0.1%
0.05 103
 
< 0.1%
0.06 79
 
< 0.1%
0.07 113
 
< 0.1%
0.08 87
 
< 0.1%
0.09 86
 
< 0.1%
ValueCountFrequency (%)
4841 1
< 0.1%
4500 1
< 0.1%
3261 1
< 0.1%
2753 1
< 0.1%
2633 1
< 0.1%
2310 1
< 0.1%
1992 1
< 0.1%
1927 1
< 0.1%
1751 1
< 0.1%
1719 1
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.7 MiB
Q1
139258 
-
138621 
Q2
117491 
Q3
114504 
Q4
110666 

Length

Max length2
Median length2
Mean length1.7766123
Min length1

Characters and Unicode

Total characters1102459
Distinct characters6
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
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 rowQ1
2nd rowQ1
3rd rowQ1
4th rowQ1
5th rowQ1

Common Values

ValueCountFrequency (%)
Q1 139258
22.4%
- 138621
22.3%
Q2 117491
18.9%
Q3 114504
18.5%
Q4 110666
17.8%

Length

2023-05-04T15:11:48.445009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-04T15:11:48.580465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
q1 139258
22.4%
138621
22.3%
q2 117491
18.9%
q3 114504
18.5%
q4 110666
17.8%

Most occurring characters

ValueCountFrequency (%)
Q 481919
43.7%
1 139258
 
12.6%
- 138621
 
12.6%
2 117491
 
10.7%
3 114504
 
10.4%
4 110666
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 481919
43.7%
Decimal Number 481919
43.7%
Dash Punctuation 138621
 
12.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 139258
28.9%
2 117491
24.4%
3 114504
23.8%
4 110666
23.0%
Uppercase Letter
ValueCountFrequency (%)
Q 481919
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 138621
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 620540
56.3%
Latin 481919
43.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 139258
22.4%
- 138621
22.3%
2 117491
18.9%
3 114504
18.5%
4 110666
17.8%
Latin
ValueCountFrequency (%)
Q 481919
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1102459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Q 481919
43.7%
1 139258
 
12.6%
- 138621
 
12.6%
2 117491
 
10.7%
3 114504
 
10.4%
4 110666
 
10.0%

total_cites_3years
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct11896
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean476.96202
Minimum0
Maximum321255
Zeros65410
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:48.726891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median42
Q3199
95-th percentile1797
Maximum321255
Range321255
Interquartile range (IQR)192

Descriptive statistics

Standard deviation2946.9901
Coefficient of variation (CV)6.1786683
Kurtosis1976.6331
Mean476.96202
Median Absolute Deviation (MAD)41
Skewness33.274008
Sum2.9597401 × 108
Variance8684750.9
MonotonicityNot monotonic
2023-05-04T15:11:48.878562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65410
 
10.5%
1 20124
 
3.2%
2 16098
 
2.6%
3 13688
 
2.2%
4 12125
 
2.0%
5 10747
 
1.7%
6 9917
 
1.6%
7 9246
 
1.5%
8 8579
 
1.4%
9 7924
 
1.3%
Other values (11886) 446682
72.0%
ValueCountFrequency (%)
0 65410
10.5%
1 20124
 
3.2%
2 16098
 
2.6%
3 13688
 
2.2%
4 12125
 
2.0%
5 10747
 
1.7%
6 9917
 
1.6%
7 9246
 
1.5%
8 8579
 
1.4%
9 7924
 
1.3%
ValueCountFrequency (%)
321255 1
< 0.1%
307069 1
< 0.1%
282400 1
< 0.1%
277415 1
< 0.1%
275478 1
< 0.1%
274206 1
< 0.1%
266827 1
< 0.1%
261696 1
< 0.1%
245865 1
< 0.1%
224506 1
< 0.1%

total_docs
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2921
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.936992
Minimum0
Maximum35329
Zeros134190
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:49.044222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median29
Q372
95-th percentile288
Maximum35329
Range35329
Interquartile range (IQR)65

Descriptive statistics

Standard deviation322.32011
Coefficient of variation (CV)4.0321771
Kurtosis3163.7742
Mean79.936992
Median Absolute Deviation (MAD)29
Skewness44.541137
Sum49604101
Variance103890.25
MonotonicityNot monotonic
2023-05-04T15:11:49.195701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 134190
 
21.6%
20 8677
 
1.4%
24 8258
 
1.3%
21 8104
 
1.3%
16 7876
 
1.3%
22 7873
 
1.3%
23 7695
 
1.2%
18 7642
 
1.2%
25 7639
 
1.2%
19 7597
 
1.2%
Other values (2911) 414989
66.9%
ValueCountFrequency (%)
0 134190
21.6%
1 4178
 
0.7%
2 2526
 
0.4%
3 2375
 
0.4%
4 2688
 
0.4%
5 3245
 
0.5%
6 3814
 
0.6%
7 3950
 
0.6%
8 4702
 
0.8%
9 5115
 
0.8%
ValueCountFrequency (%)
35329 1
< 0.1%
34849 1
< 0.1%
34154 1
< 0.1%
31198 1
< 0.1%
30978 1
< 0.1%
29385 1
< 0.1%
29351 1
< 0.1%
29296 1
< 0.1%
28799 1
< 0.1%
27974 1
< 0.1%

total_docs_3years
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5429
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.09512
Minimum0
Maximum95106
Zeros27877
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:49.360894image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q141
median92
Q3212
95-th percentile808
Maximum95106
Range95106
Interquartile range (IQR)171

Descriptive statistics

Standard deviation842.56359
Coefficient of variation (CV)3.6459601
Kurtosis3356.2104
Mean231.09512
Median Absolute Deviation (MAD)65
Skewness45.569964
Sum1.4340377 × 108
Variance709913.41
MonotonicityNot monotonic
2023-05-04T15:11:49.511131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27877
 
4.5%
1 5188
 
0.8%
47 3459
 
0.6%
58 3456
 
0.6%
48 3435
 
0.6%
60 3405
 
0.5%
64 3394
 
0.5%
54 3381
 
0.5%
36 3347
 
0.5%
45 3341
 
0.5%
Other values (5419) 560257
90.3%
ValueCountFrequency (%)
0 27877
4.5%
1 5188
 
0.8%
2 2835
 
0.5%
3 2280
 
0.4%
4 2080
 
0.3%
5 2262
 
0.4%
6 2482
 
0.4%
7 2707
 
0.4%
8 2909
 
0.5%
9 2958
 
0.5%
ValueCountFrequency (%)
95106 1
< 0.1%
94281 1
< 0.1%
88185 1
< 0.1%
83879 1
< 0.1%
83565 1
< 0.1%
78760 1
< 0.1%
75545 1
< 0.1%
71349 1
< 0.1%
71205 1
< 0.1%
69817 1
< 0.1%

total_refs
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct25931
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2459.6655
Minimum0
Maximum1469402
Zeros153728
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size4.7 MiB
2023-05-04T15:11:49.668256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median806
Q32065
95-th percentile8946
Maximum1469402
Range1469402
Interquartile range (IQR)2058

Descriptive statistics

Standard deviation10490.972
Coefficient of variation (CV)4.2652027
Kurtosis3952.8919
Mean2459.6655
Median Absolute Deviation (MAD)806
Skewness45.60771
Sum1.5263208 × 109
Variance1.100605 × 108
MonotonicityNot monotonic
2023-05-04T15:11:49.828113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 153728
 
24.8%
534 261
 
< 0.1%
558 258
 
< 0.1%
471 258
 
< 0.1%
759 257
 
< 0.1%
645 255
 
< 0.1%
575 255
 
< 0.1%
621 254
 
< 0.1%
470 254
 
< 0.1%
611 253
 
< 0.1%
Other values (25921) 464507
74.9%
ValueCountFrequency (%)
0 153728
24.8%
1 182
 
< 0.1%
2 221
 
< 0.1%
3 219
 
< 0.1%
4 239
 
< 0.1%
5 226
 
< 0.1%
6 203
 
< 0.1%
7 178
 
< 0.1%
8 180
 
< 0.1%
9 203
 
< 0.1%
ValueCountFrequency (%)
1469402 1
< 0.1%
1388656 1
< 0.1%
1317965 1
< 0.1%
1197093 1
< 0.1%
1155366 1
< 0.1%
1115013 1
< 0.1%
1104125 1
< 0.1%
1033652 1
< 0.1%
1030225 1
< 0.1%
948277 1
< 0.1%

Interactions

2023-05-04T15:11:41.333531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:19.111391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:21.296431image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:23.432756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:25.577277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:28.021010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:30.155969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:32.355879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:34.480884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:36.673024image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:38.880487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:41.530528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:19.305914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:21.480952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:23.623614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:25.776587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:28.212382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:30.352003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:32.545321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:34.678969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:36.869624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:39.372259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:41.728110image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:19.500272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:21.666529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:23.807778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:25.976178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:28.398581image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:30.548234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:32.730724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:34.868796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:37.064587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:39.567613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:41.924641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:19.698006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:21.856259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:23.998166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:26.172122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:28.588593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:30.742385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:32.917355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:35.063406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:37.257207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:39.758002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:42.133268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:19.900314image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:22.054040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:24.195232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:26.375561image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:28.782483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:30.944920image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:33.117166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:35.266020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:37.460141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:39.955727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:42.327880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:20.092724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:22.240481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:24.385196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:26.811054image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:28.969036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:31.132114image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:33.303906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:35.455379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:37.654259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:40.145326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:42.534812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:20.295295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:22.439354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:24.584151image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:27.013433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:29.163986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:31.336317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:33.500601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:35.658692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:37.857977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:40.345941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:42.736768image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:20.491807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:22.635107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:24.777035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:27.209892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:29.350735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:31.534722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:33.693340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:35.850045image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:38.057409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:40.541649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:42.946780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:20.694953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:22.833650image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:24.978069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:27.414442image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:29.552983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:31.743063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:33.887378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:36.054604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:38.258090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:40.741698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:43.156974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:20.900995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:23.034782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:25.182954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:27.621353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:29.760743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:31.951122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:34.090298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:36.266602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:38.467997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:40.942497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:43.351896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:21.092872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:23.226872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:25.369932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:27.814249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:29.948791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:32.145694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:34.277034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:36.464031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:38.665691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-04T15:11:41.130219image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-04T15:11:49.975637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
journal__sourceidyearcitable_docs_3yearsh_indexjournal_ratingrankref_per_doctotal_cites_3yearstotal_docstotal_docs_3yearstotal_refssjr_best_quartile
journal__sourceid1.0000.333-0.367-0.494-0.3140.487-0.202-0.313-0.411-0.369-0.3550.258
year0.3331.0000.033-0.1090.0710.2640.1500.163-0.0070.0400.0960.074
citable_docs_3years-0.3670.0331.0000.5910.429-0.4490.1670.7680.7260.9910.6130.011
h_index-0.494-0.1090.5911.0000.823-0.7850.5280.8240.6440.5870.7350.205
journal_rating-0.3140.0710.4290.8231.000-0.9110.5300.8440.4980.4230.6480.049
rank0.4870.264-0.449-0.785-0.9111.000-0.399-0.732-0.469-0.442-0.5560.516
ref_per_doc-0.2020.1500.1670.5280.530-0.3991.0000.4070.4290.1620.7360.004
total_cites_3years-0.3130.1630.7680.8240.844-0.7320.4071.0000.6270.7630.7070.029
total_docs-0.411-0.0070.7260.6440.498-0.4690.4290.6271.0000.7360.8580.010
total_docs_3years-0.3690.0400.9910.5870.423-0.4420.1620.7630.7361.0000.6130.011
total_refs-0.3550.0960.6130.7350.648-0.5560.7360.7070.8580.6131.0000.015
sjr_best_quartile0.2580.0740.0110.2050.0490.5160.0040.0290.0100.0110.0151.000

Missing values

2023-05-04T15:11:43.733079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-04T15:11:44.548122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

journal__sourceidyearcategoriescitable_docs_3yearsh_indexjournal_ratingrankref_per_docsjr_best_quartiletotal_cites_3yearstotal_docstotal_docs_3yearstotal_refs
0168011999Biochemistry (Q1)8030550.5181197.10Q1351330805913
1184341999Biochemistry, Genetics and Molecular Biology (miscellaneous) (Q1)133281443.449245.48Q148292351134015964
2206511999Immunology (Q1); Immunology and Allergy (Q1)8130943.0203180.55Q1411629815236
3183951999Cell Biology (Q1); Developmental Biology (Q1)6022635.0514165.36Q1181525614134
4141811999Neuroscience (miscellaneous) (Q1)6024825.7605162.81Q1163121603419
5221261999Developmental Biology (Q1); Genetics (Q1)88845325.272655.78Q11739129888916623
6207981999Immunology (Q1); Immunology and Allergy (Q1); Infectious Diseases (Q1)43841722.298751.46Q192071514387770
7185031999Cell Biology (Q1)29826721.691856.05Q166021043185829
895001541141999Mathematics (miscellaneous) (Q1); Physics and Astronomy (miscellaneous) (Q1)557520.965937.94Q1122848551821
9186061999Cell Biology (Q1); Molecular Biology (Q1)20241420.2821045.67Q136241932038814
journal__sourceidyearcategoriescitable_docs_3yearsh_indexjournal_ratingrankref_per_docsjr_best_quartiletotal_cites_3yearstotal_docstotal_docs_3yearstotal_refs
620530211010432362021Pediatrics, Perinatology and Child Health01NaN2733043.45-0200869
620531211008538912021Emergency Medicine04NaN2733120.66-08601777
620532211010429982021Internal Medicine01NaN2733249.75-080398
620533211010424902021Transplantation01NaN2733363.70-04002548
6205341448062021Electrical and Electronic Engineering017NaN2733422.32-0310692
6205351448072021Electrical and Electronic Engineering010NaN2733520.80-0200416
6205361448082021Computer Science Applications; Information Systems021NaN2733624.33-0210511
6205371448132021Computer Science Applications; Control and Systems Engineering027NaN2733722.82-0380867
620538211010466902021Arts and Humanities (miscellaneous); Communication; History; Library and Information Sciences00NaN2733840.63-0190772
620539209042021Medicine (miscellaneous)020NaN2733927.19-013403643